diff --git a/.gitignore b/.gitignore
index da24bdf..d75870d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,5 @@ master_ip
logs/
*.DS_Store
.idea
-*.pt
\ No newline at end of file
+*.pt
+tools/
\ No newline at end of file
diff --git a/cogvideox_fun/autoencoder_magvit.py b/cogvideox_fun/autoencoder_magvit.py
deleted file mode 100644
index 9c2b906..0000000
--- a/cogvideox_fun/autoencoder_magvit.py
+++ /dev/null
@@ -1,1296 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders.single_file_model import FromOriginalModelMixin
-from diffusers.utils import logging
-from diffusers.utils.accelerate_utils import apply_forward_hook
-from diffusers.models.activations import get_activation
-from diffusers.models.downsampling import CogVideoXDownsample3D
-from diffusers.models.modeling_outputs import AutoencoderKLOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.upsampling import CogVideoXUpsample3D
-from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-class CogVideoXSafeConv3d(nn.Conv3d):
- r"""
- A 3D convolution layer that splits the input tensor into smaller parts to avoid OOM in CogVideoX Model.
- """
-
- def forward(self, input: torch.Tensor) -> torch.Tensor:
- memory_count = torch.prod(torch.tensor(input.shape)).item() * 2 / 1024**3
-
- # Set to 2GB, suitable for CuDNN
- if memory_count > 2:
- kernel_size = self.kernel_size[0]
- part_num = int(memory_count / 2) + 1
- input_chunks = torch.chunk(input, part_num, dim=2)
-
- if kernel_size > 1:
- input_chunks = [input_chunks[0]] + [
- torch.cat((input_chunks[i - 1][:, :, -kernel_size + 1 :], input_chunks[i]), dim=2)
- for i in range(1, len(input_chunks))
- ]
-
- output_chunks = []
- for input_chunk in input_chunks:
- output_chunks.append(super().forward(input_chunk))
- output = torch.cat(output_chunks, dim=2)
- return output
- else:
- return super().forward(input)
-
-
-class CogVideoXCausalConv3d(nn.Module):
- r"""A 3D causal convolution layer that pads the input tensor to ensure causality in CogVideoX Model.
-
- Args:
- in_channels (`int`): Number of channels in the input tensor.
- out_channels (`int`): Number of output channels produced by the convolution.
- kernel_size (`int` or `Tuple[int, int, int]`): Kernel size of the convolutional kernel.
- stride (`int`, defaults to `1`): Stride of the convolution.
- dilation (`int`, defaults to `1`): Dilation rate of the convolution.
- pad_mode (`str`, defaults to `"constant"`): Padding mode.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: Union[int, Tuple[int, int, int]],
- stride: int = 1,
- dilation: int = 1,
- pad_mode: str = "constant",
- ):
- super().__init__()
-
- if isinstance(kernel_size, int):
- kernel_size = (kernel_size,) * 3
-
- time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
-
- self.pad_mode = pad_mode
- time_pad = dilation * (time_kernel_size - 1) + (1 - stride)
- height_pad = height_kernel_size // 2
- width_pad = width_kernel_size // 2
-
- self.height_pad = height_pad
- self.width_pad = width_pad
- self.time_pad = time_pad
- self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0)
-
- self.temporal_dim = 2
- self.time_kernel_size = time_kernel_size
-
- stride = (stride, 1, 1)
- dilation = (dilation, 1, 1)
- self.conv = CogVideoXSafeConv3d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- )
-
- self.conv_cache = None
-
- def fake_context_parallel_forward(self, inputs: torch.Tensor) -> torch.Tensor:
- kernel_size = self.time_kernel_size
- if kernel_size > 1:
- cached_inputs = (
- [self.conv_cache] if self.conv_cache is not None else [inputs[:, :, :1]] * (kernel_size - 1)
- )
- inputs = torch.cat(cached_inputs + [inputs], dim=2)
- return inputs
-
- def _clear_fake_context_parallel_cache(self):
- del self.conv_cache
- self.conv_cache = None
-
- def forward(self, inputs: torch.Tensor) -> torch.Tensor:
- inputs = self.fake_context_parallel_forward(inputs)
-
- self._clear_fake_context_parallel_cache()
- # Note: we could move these to the cpu for a lower maximum memory usage but its only a few
- # hundred megabytes and so let's not do it for now
- self.conv_cache = inputs[:, :, -self.time_kernel_size + 1 :].clone()
-
- padding_2d = (self.width_pad, self.width_pad, self.height_pad, self.height_pad)
- inputs = F.pad(inputs, padding_2d, mode="constant", value=0)
-
- output = self.conv(inputs)
- return output
-
-
-class CogVideoXSpatialNorm3D(nn.Module):
- r"""
- Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. This implementation is specific
- to 3D-video like data.
-
- CogVideoXSafeConv3d is used instead of nn.Conv3d to avoid OOM in CogVideoX Model.
-
- Args:
- f_channels (`int`):
- The number of channels for input to group normalization layer, and output of the spatial norm layer.
- zq_channels (`int`):
- The number of channels for the quantized vector as described in the paper.
- groups (`int`):
- Number of groups to separate the channels into for group normalization.
- """
-
- def __init__(
- self,
- f_channels: int,
- zq_channels: int,
- groups: int = 32,
- ):
- super().__init__()
- self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
- self.conv_y = CogVideoXCausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1)
- self.conv_b = CogVideoXCausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1)
-
- def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor:
- if f.shape[2] > 1 and f.shape[2] % 2 == 1:
- f_first, f_rest = f[:, :, :1], f[:, :, 1:]
- f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
- z_first, z_rest = zq[:, :, :1], zq[:, :, 1:]
- z_first = F.interpolate(z_first, size=f_first_size)
- z_rest = F.interpolate(z_rest, size=f_rest_size)
- zq = torch.cat([z_first, z_rest], dim=2)
- else:
- zq = F.interpolate(zq, size=f.shape[-3:])
-
- norm_f = self.norm_layer(f)
- new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
- return new_f
-
-
-class CogVideoXResnetBlock3D(nn.Module):
- r"""
- A 3D ResNet block used in the CogVideoX model.
-
- Args:
- in_channels (`int`):
- Number of input channels.
- out_channels (`int`, *optional*):
- Number of output channels. If None, defaults to `in_channels`.
- dropout (`float`, defaults to `0.0`):
- Dropout rate.
- temb_channels (`int`, defaults to `512`):
- Number of time embedding channels.
- groups (`int`, defaults to `32`):
- Number of groups to separate the channels into for group normalization.
- eps (`float`, defaults to `1e-6`):
- Epsilon value for normalization layers.
- non_linearity (`str`, defaults to `"swish"`):
- Activation function to use.
- conv_shortcut (bool, defaults to `False`):
- Whether or not to use a convolution shortcut.
- spatial_norm_dim (`int`, *optional*):
- The dimension to use for spatial norm if it is to be used instead of group norm.
- pad_mode (str, defaults to `"first"`):
- Padding mode.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: Optional[int] = None,
- dropout: float = 0.0,
- temb_channels: int = 512,
- groups: int = 32,
- eps: float = 1e-6,
- non_linearity: str = "swish",
- conv_shortcut: bool = False,
- spatial_norm_dim: Optional[int] = None,
- pad_mode: str = "first",
- ):
- super().__init__()
-
- out_channels = out_channels or in_channels
-
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.nonlinearity = get_activation(non_linearity)
- self.use_conv_shortcut = conv_shortcut
-
- if spatial_norm_dim is None:
- self.norm1 = nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
- self.norm2 = nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
- else:
- self.norm1 = CogVideoXSpatialNorm3D(
- f_channels=in_channels,
- zq_channels=spatial_norm_dim,
- groups=groups,
- )
- self.norm2 = CogVideoXSpatialNorm3D(
- f_channels=out_channels,
- zq_channels=spatial_norm_dim,
- groups=groups,
- )
-
- self.conv1 = CogVideoXCausalConv3d(
- in_channels=in_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
- )
-
- if temb_channels > 0:
- self.temb_proj = nn.Linear(in_features=temb_channels, out_features=out_channels)
-
- self.dropout = nn.Dropout(dropout)
- self.conv2 = CogVideoXCausalConv3d(
- in_channels=out_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
- )
-
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- self.conv_shortcut = CogVideoXCausalConv3d(
- in_channels=in_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
- )
- else:
- self.conv_shortcut = CogVideoXSafeConv3d(
- in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0
- )
-
- def forward(
- self,
- inputs: torch.Tensor,
- temb: Optional[torch.Tensor] = None,
- zq: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- hidden_states = inputs
-
- if zq is not None:
- hidden_states = self.norm1(hidden_states, zq)
- else:
- hidden_states = self.norm1(hidden_states)
-
- hidden_states = self.nonlinearity(hidden_states)
- hidden_states = self.conv1(hidden_states)
-
- if temb is not None:
- hidden_states = hidden_states + self.temb_proj(self.nonlinearity(temb))[:, :, None, None, None]
-
- if zq is not None:
- hidden_states = self.norm2(hidden_states, zq)
- else:
- hidden_states = self.norm2(hidden_states)
-
- hidden_states = self.nonlinearity(hidden_states)
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.conv2(hidden_states)
-
- if self.in_channels != self.out_channels:
- inputs = self.conv_shortcut(inputs)
-
- hidden_states = hidden_states + inputs
- return hidden_states
-
-
-class CogVideoXDownBlock3D(nn.Module):
- r"""
- A downsampling block used in the CogVideoX model.
-
- Args:
- in_channels (`int`):
- Number of input channels.
- out_channels (`int`, *optional*):
- Number of output channels. If None, defaults to `in_channels`.
- temb_channels (`int`, defaults to `512`):
- Number of time embedding channels.
- num_layers (`int`, defaults to `1`):
- Number of resnet layers.
- dropout (`float`, defaults to `0.0`):
- Dropout rate.
- resnet_eps (`float`, defaults to `1e-6`):
- Epsilon value for normalization layers.
- resnet_act_fn (`str`, defaults to `"swish"`):
- Activation function to use.
- resnet_groups (`int`, defaults to `32`):
- Number of groups to separate the channels into for group normalization.
- add_downsample (`bool`, defaults to `True`):
- Whether or not to use a downsampling layer. If not used, output dimension would be same as input dimension.
- compress_time (`bool`, defaults to `False`):
- Whether or not to downsample across temporal dimension.
- pad_mode (str, defaults to `"first"`):
- Padding mode.
- """
-
- _supports_gradient_checkpointing = True
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- temb_channels: int,
- dropout: float = 0.0,
- num_layers: int = 1,
- resnet_eps: float = 1e-6,
- resnet_act_fn: str = "swish",
- resnet_groups: int = 32,
- add_downsample: bool = True,
- downsample_padding: int = 0,
- compress_time: bool = False,
- pad_mode: str = "first",
- ):
- super().__init__()
-
- resnets = []
- for i in range(num_layers):
- in_channel = in_channels if i == 0 else out_channels
- resnets.append(
- CogVideoXResnetBlock3D(
- in_channels=in_channel,
- out_channels=out_channels,
- dropout=dropout,
- temb_channels=temb_channels,
- groups=resnet_groups,
- eps=resnet_eps,
- non_linearity=resnet_act_fn,
- pad_mode=pad_mode,
- )
- )
-
- self.resnets = nn.ModuleList(resnets)
- self.downsamplers = None
-
- if add_downsample:
- self.downsamplers = nn.ModuleList(
- [
- CogVideoXDownsample3D(
- out_channels, out_channels, padding=downsample_padding, compress_time=compress_time
- )
- ]
- )
-
- self.gradient_checkpointing = False
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- temb: Optional[torch.Tensor] = None,
- zq: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- for resnet in self.resnets:
- if self.training and self.gradient_checkpointing:
-
- def create_custom_forward(module):
- def create_forward(*inputs):
- return module(*inputs)
-
- return create_forward
-
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(resnet), hidden_states, temb, zq
- )
- else:
- hidden_states = resnet(hidden_states, temb, zq)
-
- if self.downsamplers is not None:
- for downsampler in self.downsamplers:
- hidden_states = downsampler(hidden_states)
-
- return hidden_states
-
-
-class CogVideoXMidBlock3D(nn.Module):
- r"""
- A middle block used in the CogVideoX model.
-
- Args:
- in_channels (`int`):
- Number of input channels.
- temb_channels (`int`, defaults to `512`):
- Number of time embedding channels.
- dropout (`float`, defaults to `0.0`):
- Dropout rate.
- num_layers (`int`, defaults to `1`):
- Number of resnet layers.
- resnet_eps (`float`, defaults to `1e-6`):
- Epsilon value for normalization layers.
- resnet_act_fn (`str`, defaults to `"swish"`):
- Activation function to use.
- resnet_groups (`int`, defaults to `32`):
- Number of groups to separate the channels into for group normalization.
- spatial_norm_dim (`int`, *optional*):
- The dimension to use for spatial norm if it is to be used instead of group norm.
- pad_mode (str, defaults to `"first"`):
- Padding mode.
- """
-
- _supports_gradient_checkpointing = True
-
- def __init__(
- self,
- in_channels: int,
- temb_channels: int,
- dropout: float = 0.0,
- num_layers: int = 1,
- resnet_eps: float = 1e-6,
- resnet_act_fn: str = "swish",
- resnet_groups: int = 32,
- spatial_norm_dim: Optional[int] = None,
- pad_mode: str = "first",
- ):
- super().__init__()
-
- resnets = []
- for _ in range(num_layers):
- resnets.append(
- CogVideoXResnetBlock3D(
- in_channels=in_channels,
- out_channels=in_channels,
- dropout=dropout,
- temb_channels=temb_channels,
- groups=resnet_groups,
- eps=resnet_eps,
- spatial_norm_dim=spatial_norm_dim,
- non_linearity=resnet_act_fn,
- pad_mode=pad_mode,
- )
- )
- self.resnets = nn.ModuleList(resnets)
-
- self.gradient_checkpointing = False
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- temb: Optional[torch.Tensor] = None,
- zq: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- for resnet in self.resnets:
- if self.training and self.gradient_checkpointing:
-
- def create_custom_forward(module):
- def create_forward(*inputs):
- return module(*inputs)
-
- return create_forward
-
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(resnet), hidden_states, temb, zq
- )
- else:
- hidden_states = resnet(hidden_states, temb, zq)
-
- return hidden_states
-
-
-class CogVideoXUpBlock3D(nn.Module):
- r"""
- An upsampling block used in the CogVideoX model.
-
- Args:
- in_channels (`int`):
- Number of input channels.
- out_channels (`int`, *optional*):
- Number of output channels. If None, defaults to `in_channels`.
- temb_channels (`int`, defaults to `512`):
- Number of time embedding channels.
- dropout (`float`, defaults to `0.0`):
- Dropout rate.
- num_layers (`int`, defaults to `1`):
- Number of resnet layers.
- resnet_eps (`float`, defaults to `1e-6`):
- Epsilon value for normalization layers.
- resnet_act_fn (`str`, defaults to `"swish"`):
- Activation function to use.
- resnet_groups (`int`, defaults to `32`):
- Number of groups to separate the channels into for group normalization.
- spatial_norm_dim (`int`, defaults to `16`):
- The dimension to use for spatial norm if it is to be used instead of group norm.
- add_upsample (`bool`, defaults to `True`):
- Whether or not to use a upsampling layer. If not used, output dimension would be same as input dimension.
- compress_time (`bool`, defaults to `False`):
- Whether or not to downsample across temporal dimension.
- pad_mode (str, defaults to `"first"`):
- Padding mode.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- temb_channels: int,
- dropout: float = 0.0,
- num_layers: int = 1,
- resnet_eps: float = 1e-6,
- resnet_act_fn: str = "swish",
- resnet_groups: int = 32,
- spatial_norm_dim: int = 16,
- add_upsample: bool = True,
- upsample_padding: int = 1,
- compress_time: bool = False,
- pad_mode: str = "first",
- ):
- super().__init__()
-
- resnets = []
- for i in range(num_layers):
- in_channel = in_channels if i == 0 else out_channels
- resnets.append(
- CogVideoXResnetBlock3D(
- in_channels=in_channel,
- out_channels=out_channels,
- dropout=dropout,
- temb_channels=temb_channels,
- groups=resnet_groups,
- eps=resnet_eps,
- non_linearity=resnet_act_fn,
- spatial_norm_dim=spatial_norm_dim,
- pad_mode=pad_mode,
- )
- )
-
- self.resnets = nn.ModuleList(resnets)
- self.upsamplers = None
-
- if add_upsample:
- self.upsamplers = nn.ModuleList(
- [
- CogVideoXUpsample3D(
- out_channels, out_channels, padding=upsample_padding, compress_time=compress_time
- )
- ]
- )
-
- self.gradient_checkpointing = False
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- temb: Optional[torch.Tensor] = None,
- zq: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- r"""Forward method of the `CogVideoXUpBlock3D` class."""
- for resnet in self.resnets:
- if self.training and self.gradient_checkpointing:
-
- def create_custom_forward(module):
- def create_forward(*inputs):
- return module(*inputs)
-
- return create_forward
-
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(resnet), hidden_states, temb, zq
- )
- else:
- hidden_states = resnet(hidden_states, temb, zq)
-
- if self.upsamplers is not None:
- for upsampler in self.upsamplers:
- hidden_states = upsampler(hidden_states)
-
- return hidden_states
-
-
-class CogVideoXEncoder3D(nn.Module):
- r"""
- The `CogVideoXEncoder3D` layer of a variational autoencoder that encodes its input into a latent representation.
-
- Args:
- in_channels (`int`, *optional*, defaults to 3):
- The number of input channels.
- out_channels (`int`, *optional*, defaults to 3):
- The number of output channels.
- down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
- The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available
- options.
- block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
- The number of output channels for each block.
- act_fn (`str`, *optional*, defaults to `"silu"`):
- The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
- layers_per_block (`int`, *optional*, defaults to 2):
- The number of layers per block.
- norm_num_groups (`int`, *optional*, defaults to 32):
- The number of groups for normalization.
- """
-
- _supports_gradient_checkpointing = True
-
- def __init__(
- self,
- in_channels: int = 3,
- out_channels: int = 16,
- down_block_types: Tuple[str, ...] = (
- "CogVideoXDownBlock3D",
- "CogVideoXDownBlock3D",
- "CogVideoXDownBlock3D",
- "CogVideoXDownBlock3D",
- ),
- block_out_channels: Tuple[int, ...] = (128, 256, 256, 512),
- layers_per_block: int = 3,
- act_fn: str = "silu",
- norm_eps: float = 1e-6,
- norm_num_groups: int = 32,
- dropout: float = 0.0,
- pad_mode: str = "first",
- temporal_compression_ratio: float = 4,
- ):
- super().__init__()
-
- # log2 of temporal_compress_times
- temporal_compress_level = int(np.log2(temporal_compression_ratio))
-
- self.conv_in = CogVideoXCausalConv3d(in_channels, block_out_channels[0], kernel_size=3, pad_mode=pad_mode)
- self.down_blocks = nn.ModuleList([])
-
- # down blocks
- output_channel = block_out_channels[0]
- for i, down_block_type in enumerate(down_block_types):
- input_channel = output_channel
- output_channel = block_out_channels[i]
- is_final_block = i == len(block_out_channels) - 1
- compress_time = i < temporal_compress_level
-
- if down_block_type == "CogVideoXDownBlock3D":
- down_block = CogVideoXDownBlock3D(
- in_channels=input_channel,
- out_channels=output_channel,
- temb_channels=0,
- dropout=dropout,
- num_layers=layers_per_block,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- add_downsample=not is_final_block,
- compress_time=compress_time,
- )
- else:
- raise ValueError("Invalid `down_block_type` encountered. Must be `CogVideoXDownBlock3D`")
-
- self.down_blocks.append(down_block)
-
- # mid block
- self.mid_block = CogVideoXMidBlock3D(
- in_channels=block_out_channels[-1],
- temb_channels=0,
- dropout=dropout,
- num_layers=2,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- pad_mode=pad_mode,
- )
-
- self.norm_out = nn.GroupNorm(norm_num_groups, block_out_channels[-1], eps=1e-6)
- self.conv_act = nn.SiLU()
- self.conv_out = CogVideoXCausalConv3d(
- block_out_channels[-1], 2 * out_channels, kernel_size=3, pad_mode=pad_mode
- )
-
- self.gradient_checkpointing = False
-
- def forward(self, sample: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
- r"""The forward method of the `CogVideoXEncoder3D` class."""
- hidden_states = self.conv_in(sample)
-
- if self.training and self.gradient_checkpointing:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return module(*inputs)
-
- return custom_forward
-
- # 1. Down
- for down_block in self.down_blocks:
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(down_block), hidden_states, temb, None
- )
-
- # 2. Mid
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(self.mid_block), hidden_states, temb, None
- )
- else:
- # 1. Down
- for down_block in self.down_blocks:
- hidden_states = down_block(hidden_states, temb, None)
-
- # 2. Mid
- hidden_states = self.mid_block(hidden_states, temb, None)
-
- # 3. Post-process
- hidden_states = self.norm_out(hidden_states)
- hidden_states = self.conv_act(hidden_states)
- hidden_states = self.conv_out(hidden_states)
- return hidden_states
-
-
-class CogVideoXDecoder3D(nn.Module):
- r"""
- The `CogVideoXDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output
- sample.
-
- Args:
- in_channels (`int`, *optional*, defaults to 3):
- The number of input channels.
- out_channels (`int`, *optional*, defaults to 3):
- The number of output channels.
- up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
- The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
- block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
- The number of output channels for each block.
- act_fn (`str`, *optional*, defaults to `"silu"`):
- The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
- layers_per_block (`int`, *optional*, defaults to 2):
- The number of layers per block.
- norm_num_groups (`int`, *optional*, defaults to 32):
- The number of groups for normalization.
- """
-
- _supports_gradient_checkpointing = True
-
- def __init__(
- self,
- in_channels: int = 16,
- out_channels: int = 3,
- up_block_types: Tuple[str, ...] = (
- "CogVideoXUpBlock3D",
- "CogVideoXUpBlock3D",
- "CogVideoXUpBlock3D",
- "CogVideoXUpBlock3D",
- ),
- block_out_channels: Tuple[int, ...] = (128, 256, 256, 512),
- layers_per_block: int = 3,
- act_fn: str = "silu",
- norm_eps: float = 1e-6,
- norm_num_groups: int = 32,
- dropout: float = 0.0,
- pad_mode: str = "first",
- temporal_compression_ratio: float = 4,
- ):
- super().__init__()
-
- reversed_block_out_channels = list(reversed(block_out_channels))
-
- self.conv_in = CogVideoXCausalConv3d(
- in_channels, reversed_block_out_channels[0], kernel_size=3, pad_mode=pad_mode
- )
-
- # mid block
- self.mid_block = CogVideoXMidBlock3D(
- in_channels=reversed_block_out_channels[0],
- temb_channels=0,
- num_layers=2,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- spatial_norm_dim=in_channels,
- pad_mode=pad_mode,
- )
-
- # up blocks
- self.up_blocks = nn.ModuleList([])
-
- output_channel = reversed_block_out_channels[0]
- temporal_compress_level = int(np.log2(temporal_compression_ratio))
-
- for i, up_block_type in enumerate(up_block_types):
- prev_output_channel = output_channel
- output_channel = reversed_block_out_channels[i]
- is_final_block = i == len(block_out_channels) - 1
- compress_time = i < temporal_compress_level
-
- if up_block_type == "CogVideoXUpBlock3D":
- up_block = CogVideoXUpBlock3D(
- in_channels=prev_output_channel,
- out_channels=output_channel,
- temb_channels=0,
- dropout=dropout,
- num_layers=layers_per_block + 1,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- spatial_norm_dim=in_channels,
- add_upsample=not is_final_block,
- compress_time=compress_time,
- pad_mode=pad_mode,
- )
- prev_output_channel = output_channel
- else:
- raise ValueError("Invalid `up_block_type` encountered. Must be `CogVideoXUpBlock3D`")
-
- self.up_blocks.append(up_block)
-
- self.norm_out = CogVideoXSpatialNorm3D(reversed_block_out_channels[-1], in_channels, groups=norm_num_groups)
- self.conv_act = nn.SiLU()
- self.conv_out = CogVideoXCausalConv3d(
- reversed_block_out_channels[-1], out_channels, kernel_size=3, pad_mode=pad_mode
- )
-
- self.gradient_checkpointing = False
-
- def forward(self, sample: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
- r"""The forward method of the `CogVideoXDecoder3D` class."""
- hidden_states = self.conv_in(sample)
-
- if self.training and self.gradient_checkpointing:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return module(*inputs)
-
- return custom_forward
-
- # 1. Mid
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(self.mid_block), hidden_states, temb, sample
- )
-
- # 2. Up
- for up_block in self.up_blocks:
- hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(up_block), hidden_states, temb, sample
- )
- else:
- # 1. Mid
- hidden_states = self.mid_block(hidden_states, temb, sample)
-
- # 2. Up
- for up_block in self.up_blocks:
- hidden_states = up_block(hidden_states, temb, sample)
-
- # 3. Post-process
- hidden_states = self.norm_out(hidden_states, sample)
- hidden_states = self.conv_act(hidden_states)
- hidden_states = self.conv_out(hidden_states)
- return hidden_states
-
-
-class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
- r"""
- A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
- [CogVideoX](https://github.com/THUDM/CogVideo).
-
- This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
- for all models (such as downloading or saving).
-
- Parameters:
- in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
- out_channels (int, *optional*, defaults to 3): Number of channels in the output.
- down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
- Tuple of downsample block types.
- up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
- Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
- Tuple of block output channels.
- act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
- sample_size (`int`, *optional*, defaults to `32`): Sample input size.
- scaling_factor (`float`, *optional*, defaults to `1.15258426`):
- The component-wise standard deviation of the trained latent space computed using the first batch of the
- training set. This is used to scale the latent space to have unit variance when training the diffusion
- model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
- diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
- / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
- Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
- force_upcast (`bool`, *optional*, default to `True`):
- If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
- can be fine-tuned / trained to a lower range without loosing too much precision in which case
- `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
- """
-
- _supports_gradient_checkpointing = True
- _no_split_modules = ["CogVideoXResnetBlock3D"]
-
- @register_to_config
- def __init__(
- self,
- in_channels: int = 3,
- out_channels: int = 3,
- down_block_types: Tuple[str] = (
- "CogVideoXDownBlock3D",
- "CogVideoXDownBlock3D",
- "CogVideoXDownBlock3D",
- "CogVideoXDownBlock3D",
- ),
- up_block_types: Tuple[str] = (
- "CogVideoXUpBlock3D",
- "CogVideoXUpBlock3D",
- "CogVideoXUpBlock3D",
- "CogVideoXUpBlock3D",
- ),
- block_out_channels: Tuple[int] = (128, 256, 256, 512),
- latent_channels: int = 16,
- layers_per_block: int = 3,
- act_fn: str = "silu",
- norm_eps: float = 1e-6,
- norm_num_groups: int = 32,
- temporal_compression_ratio: float = 4,
- sample_height: int = 480,
- sample_width: int = 720,
- scaling_factor: float = 1.15258426,
- shift_factor: Optional[float] = None,
- latents_mean: Optional[Tuple[float]] = None,
- latents_std: Optional[Tuple[float]] = None,
- force_upcast: float = True,
- use_quant_conv: bool = False,
- use_post_quant_conv: bool = False,
- ):
- super().__init__()
-
- self.encoder = CogVideoXEncoder3D(
- in_channels=in_channels,
- out_channels=latent_channels,
- down_block_types=down_block_types,
- block_out_channels=block_out_channels,
- layers_per_block=layers_per_block,
- act_fn=act_fn,
- norm_eps=norm_eps,
- norm_num_groups=norm_num_groups,
- temporal_compression_ratio=temporal_compression_ratio,
- )
- self.decoder = CogVideoXDecoder3D(
- in_channels=latent_channels,
- out_channels=out_channels,
- up_block_types=up_block_types,
- block_out_channels=block_out_channels,
- layers_per_block=layers_per_block,
- act_fn=act_fn,
- norm_eps=norm_eps,
- norm_num_groups=norm_num_groups,
- temporal_compression_ratio=temporal_compression_ratio,
- )
- self.quant_conv = CogVideoXSafeConv3d(2 * out_channels, 2 * out_channels, 1) if use_quant_conv else None
- self.post_quant_conv = CogVideoXSafeConv3d(out_channels, out_channels, 1) if use_post_quant_conv else None
-
- self.use_slicing = False
- self.use_tiling = False
-
- # Can be increased to decode more latent frames at once, but comes at a reasonable memory cost and it is not
- # recommended because the temporal parts of the VAE, here, are tricky to understand.
- # If you decode X latent frames together, the number of output frames is:
- # (X + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) => X + 6 frames
- #
- # Example with num_latent_frames_batch_size = 2:
- # - 12 latent frames: (0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (10, 11) are processed together
- # => (12 // 2 frame slices) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
- # => 6 * 8 = 48 frames
- # - 13 latent frames: (0, 1, 2) (special case), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12) are processed together
- # => (1 frame slice) * ((3 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) +
- # ((13 - 3) // 2) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
- # => 1 * 9 + 5 * 8 = 49 frames
- # It has been implemented this way so as to not have "magic values" in the code base that would be hard to explain. Note that
- # setting it to anything other than 2 would give poor results because the VAE hasn't been trained to be adaptive with different
- # number of temporal frames.
- self.num_latent_frames_batch_size = 2
-
- # We make the minimum height and width of sample for tiling half that of the generally supported
- self.tile_sample_min_height = sample_height // 2
- self.tile_sample_min_width = sample_width // 2
- self.tile_latent_min_height = int(
- self.tile_sample_min_height / (2 ** (len(self.config.block_out_channels) - 1))
- )
- self.tile_latent_min_width = int(self.tile_sample_min_width / (2 ** (len(self.config.block_out_channels) - 1)))
-
- # These are experimental overlap factors that were chosen based on experimentation and seem to work best for
- # 720x480 (WxH) resolution. The above resolution is the strongly recommended generation resolution in CogVideoX
- # and so the tiling implementation has only been tested on those specific resolutions.
- self.tile_overlap_factor_height = 1 / 6
- self.tile_overlap_factor_width = 1 / 5
-
- def _set_gradient_checkpointing(self, module, value=False):
- if isinstance(module, (CogVideoXEncoder3D, CogVideoXDecoder3D)):
- module.gradient_checkpointing = value
-
- def _clear_fake_context_parallel_cache(self):
- for name, module in self.named_modules():
- if isinstance(module, CogVideoXCausalConv3d):
- logger.debug(f"Clearing fake Context Parallel cache for layer: {name}")
- module._clear_fake_context_parallel_cache()
-
- def enable_tiling(
- self,
- tile_sample_min_height: Optional[int] = None,
- tile_sample_min_width: Optional[int] = None,
- tile_overlap_factor_height: Optional[float] = None,
- tile_overlap_factor_width: Optional[float] = None,
- ) -> None:
- r"""
- Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
- compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
- processing larger images.
-
- Args:
- tile_sample_min_height (`int`, *optional*):
- The minimum height required for a sample to be separated into tiles across the height dimension.
- tile_sample_min_width (`int`, *optional*):
- The minimum width required for a sample to be separated into tiles across the width dimension.
- tile_overlap_factor_height (`int`, *optional*):
- The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
- no tiling artifacts produced across the height dimension. Must be between 0 and 1. Setting a higher
- value might cause more tiles to be processed leading to slow down of the decoding process.
- tile_overlap_factor_width (`int`, *optional*):
- The minimum amount of overlap between two consecutive horizontal tiles. This is to ensure that there
- are no tiling artifacts produced across the width dimension. Must be between 0 and 1. Setting a higher
- value might cause more tiles to be processed leading to slow down of the decoding process.
- """
- self.use_tiling = True
- self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
- self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
- self.tile_latent_min_height = int(
- self.tile_sample_min_height / (2 ** (len(self.config.block_out_channels) - 1))
- )
- self.tile_latent_min_width = int(self.tile_sample_min_width / (2 ** (len(self.config.block_out_channels) - 1)))
- self.tile_overlap_factor_height = tile_overlap_factor_height or self.tile_overlap_factor_height
- self.tile_overlap_factor_width = tile_overlap_factor_width or self.tile_overlap_factor_width
-
- def disable_tiling(self) -> None:
- r"""
- Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
- decoding in one step.
- """
- self.use_tiling = False
-
- def enable_slicing(self) -> None:
- r"""
- Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
- compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
- """
- self.use_slicing = True
-
- def disable_slicing(self) -> None:
- r"""
- Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
- decoding in one step.
- """
- self.use_slicing = False
-
- @apply_forward_hook
- def encode(
- self, x: torch.Tensor, return_dict: bool = True
- ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
- """
- Encode a batch of images into latents.
-
- Args:
- x (`torch.Tensor`): Input batch of images.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
-
- Returns:
- The latent representations of the encoded images. If `return_dict` is True, a
- [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
- """
- batch_size, num_channels, num_frames, height, width = x.shape
- if num_frames == 1:
- h = self.encoder(x)
- if self.quant_conv is not None:
- h = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(h)
- else:
- frame_batch_size = 4
- h = []
- for i in range(num_frames // frame_batch_size):
- remaining_frames = num_frames % frame_batch_size
- start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
- end_frame = frame_batch_size * (i + 1) + remaining_frames
- z_intermediate = x[:, :, start_frame:end_frame]
- z_intermediate = self.encoder(z_intermediate)
- if self.quant_conv is not None:
- z_intermediate = self.quant_conv(z_intermediate)
- h.append(z_intermediate)
- self._clear_fake_context_parallel_cache()
- h = torch.cat(h, dim=2)
- posterior = DiagonalGaussianDistribution(h)
- if not return_dict:
- return (posterior,)
- return AutoencoderKLOutput(latent_dist=posterior)
-
- def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
- batch_size, num_channels, num_frames, height, width = z.shape
-
- if self.use_tiling and (width > self.tile_latent_min_width or height > self.tile_latent_min_height):
- return self.tiled_decode(z, return_dict=return_dict)
-
- if num_frames == 1:
- dec = []
- z_intermediate = z
- if self.post_quant_conv is not None:
- z_intermediate = self.post_quant_conv(z_intermediate)
- z_intermediate = self.decoder(z_intermediate)
- dec.append(z_intermediate)
- else:
- frame_batch_size = self.num_latent_frames_batch_size
- dec = []
- for i in range(num_frames // frame_batch_size):
- remaining_frames = num_frames % frame_batch_size
- start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
- end_frame = frame_batch_size * (i + 1) + remaining_frames
- z_intermediate = z[:, :, start_frame:end_frame]
- if self.post_quant_conv is not None:
- z_intermediate = self.post_quant_conv(z_intermediate)
- z_intermediate = self.decoder(z_intermediate)
- dec.append(z_intermediate)
-
- self._clear_fake_context_parallel_cache()
- dec = torch.cat(dec, dim=2)
-
- if not return_dict:
- return (dec,)
-
- return DecoderOutput(sample=dec)
-
- @apply_forward_hook
- def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
- """
- Decode a batch of images.
-
- Args:
- z (`torch.Tensor`): Input batch of latent vectors.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
- Returns:
- [`~models.vae.DecoderOutput`] or `tuple`:
- If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
- returned.
- """
- if self.use_slicing and z.shape[0] > 1:
- decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
- decoded = torch.cat(decoded_slices)
- else:
- decoded = self._decode(z).sample
-
- if not return_dict:
- return (decoded,)
- return DecoderOutput(sample=decoded)
-
- def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
- blend_extent = min(a.shape[3], b.shape[3], blend_extent)
- for y in range(blend_extent):
- b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
- y / blend_extent
- )
- return b
-
- def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
- blend_extent = min(a.shape[4], b.shape[4], blend_extent)
- for x in range(blend_extent):
- b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
- x / blend_extent
- )
- return b
-
- def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
- r"""
- Decode a batch of images using a tiled decoder.
-
- Args:
- z (`torch.Tensor`): Input batch of latent vectors.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
- Returns:
- [`~models.vae.DecoderOutput`] or `tuple`:
- If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
- returned.
- """
- # Rough memory assessment:
- # - In CogVideoX-2B, there are a total of 24 CausalConv3d layers.
- # - The biggest intermediate dimensions are: [1, 128, 9, 480, 720].
- # - Assume fp16 (2 bytes per value).
- # Memory required: 1 * 128 * 9 * 480 * 720 * 24 * 2 / 1024**3 = 17.8 GB
- #
- # Memory assessment when using tiling:
- # - Assume everything as above but now HxW is 240x360 by tiling in half
- # Memory required: 1 * 128 * 9 * 240 * 360 * 24 * 2 / 1024**3 = 4.5 GB
-
- batch_size, num_channels, num_frames, height, width = z.shape
-
- overlap_height = int(self.tile_latent_min_height * (1 - self.tile_overlap_factor_height))
- overlap_width = int(self.tile_latent_min_width * (1 - self.tile_overlap_factor_width))
- blend_extent_height = int(self.tile_sample_min_height * self.tile_overlap_factor_height)
- blend_extent_width = int(self.tile_sample_min_width * self.tile_overlap_factor_width)
- row_limit_height = self.tile_sample_min_height - blend_extent_height
- row_limit_width = self.tile_sample_min_width - blend_extent_width
- frame_batch_size = self.num_latent_frames_batch_size
-
- # Split z into overlapping tiles and decode them separately.
- # The tiles have an overlap to avoid seams between tiles.
- rows = []
- for i in range(0, height, overlap_height):
- row = []
- for j in range(0, width, overlap_width):
- time = []
- for k in range(num_frames // frame_batch_size):
- remaining_frames = num_frames % frame_batch_size
- start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
- end_frame = frame_batch_size * (k + 1) + remaining_frames
- tile = z[
- :,
- :,
- start_frame:end_frame,
- i : i + self.tile_latent_min_height,
- j : j + self.tile_latent_min_width,
- ]
- if self.post_quant_conv is not None:
- tile = self.post_quant_conv(tile)
- tile = self.decoder(tile)
- time.append(tile)
- self._clear_fake_context_parallel_cache()
- row.append(torch.cat(time, dim=2))
- rows.append(row)
-
- result_rows = []
- for i, row in enumerate(rows):
- result_row = []
- for j, tile in enumerate(row):
- # blend the above tile and the left tile
- # to the current tile and add the current tile to the result row
- if i > 0:
- tile = self.blend_v(rows[i - 1][j], tile, blend_extent_height)
- if j > 0:
- tile = self.blend_h(row[j - 1], tile, blend_extent_width)
- result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
- result_rows.append(torch.cat(result_row, dim=4))
-
- dec = torch.cat(result_rows, dim=3)
-
- if not return_dict:
- return (dec,)
-
- return DecoderOutput(sample=dec)
-
- def forward(
- self,
- sample: torch.Tensor,
- sample_posterior: bool = False,
- return_dict: bool = True,
- generator: Optional[torch.Generator] = None,
- ) -> Union[torch.Tensor, torch.Tensor]:
- x = sample
- posterior = self.encode(x).latent_dist
- if sample_posterior:
- z = posterior.sample(generator=generator)
- else:
- z = posterior.mode()
- dec = self.decode(z)
- if not return_dict:
- return (dec,)
- return dec
diff --git a/cogvideox_fun/fun_pab_transformer_3d.py b/cogvideox_fun/fun_pab_transformer_3d.py
deleted file mode 100644
index 25a3934..0000000
--- a/cogvideox_fun/fun_pab_transformer_3d.py
+++ /dev/null
@@ -1,741 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any, Dict, Optional, Tuple, Union
-
-import os
-import json
-import torch
-import glob
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import is_torch_version, logging
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from diffusers.models.attention import Attention, FeedForward
-#from diffusers.models.attention_processor import AttentionProcessor
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-#from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
-
-from ..videosys.modules.normalization import AdaLayerNorm, CogVideoXLayerNormZero
-from ..videosys.modules.embeddings import apply_rotary_emb
-from ..videosys.core.pab_mgr import enable_pab, if_broadcast_spatial
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-try:
- from sageattention import sageattn
- SAGEATTN_IS_AVAVILABLE = True
- logger.info("Using sageattn")
-except:
- logger.info("sageattn not found, using sdpa")
- SAGEATTN_IS_AVAVILABLE = False
-
-class CogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- query = attn.to_q(hidden_states)
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
-
- # if attn.parallel_manager.sp_size > 1:
- # assert (
- # attn.heads % attn.parallel_manager.sp_size == 0
- # ), f"Number of heads {attn.heads} must be divisible by sequence parallel size {attn.parallel_manager.sp_size}"
- # attn_heads = attn.heads // attn.parallel_manager.sp_size
- # query, key, value = map(
- # lambda x: all_to_all_comm(x, attn.parallel_manager.sp_group, scatter_dim=2, gather_dim=1),
- # [query, key, value],
- # )
-
- attn_heads = attn.heads
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn_heads
-
- query = query.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- emb_len = image_rotary_emb[0].shape[0]
- query[:, :, text_seq_length : emb_len + text_seq_length] = apply_rotary_emb(
- query[:, :, text_seq_length : emb_len + text_seq_length], image_rotary_emb
- )
- if not attn.is_cross_attention:
- key[:, :, text_seq_length : emb_len + text_seq_length] = apply_rotary_emb(
- key[:, :, text_seq_length : emb_len + text_seq_length], image_rotary_emb
- )
-
- if SAGEATTN_IS_AVAVILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn_heads * head_dim)
-
- #if attn.parallel_manager.sp_size > 1:
- # hidden_states = all_to_all_comm(hidden_states, attn.parallel_manager.sp_group, scatter_dim=1, gather_dim=2)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
-
-class FusedCogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- qkv = attn.to_qkv(hidden_states)
- split_size = qkv.shape[-1] // 3
- query, key, value = torch.split(qkv, split_size, dim=-1)
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn.heads
-
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
- if not attn.is_cross_attention:
- key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
- if SAGEATTN_IS_AVAVILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
-class CogVideoXPatchEmbed(nn.Module):
- def __init__(
- self,
- patch_size: int = 2,
- in_channels: int = 16,
- embed_dim: int = 1920,
- text_embed_dim: int = 4096,
- bias: bool = True,
- ) -> None:
- super().__init__()
- self.patch_size = patch_size
-
- self.proj = nn.Conv2d(
- in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
- )
- self.text_proj = nn.Linear(text_embed_dim, embed_dim)
-
- def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
- r"""
- Args:
- text_embeds (`torch.Tensor`):
- Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
- image_embeds (`torch.Tensor`):
- Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
- """
- text_embeds = self.text_proj(text_embeds)
-
- batch, num_frames, channels, height, width = image_embeds.shape
- image_embeds = image_embeds.reshape(-1, channels, height, width)
- image_embeds = self.proj(image_embeds)
- image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
- image_embeds = image_embeds.flatten(3).transpose(2, 3) # [batch, num_frames, height x width, channels]
- image_embeds = image_embeds.flatten(1, 2) # [batch, num_frames x height x width, channels]
-
- embeds = torch.cat(
- [text_embeds, image_embeds], dim=1
- ).contiguous() # [batch, seq_length + num_frames x height x width, channels]
- return embeds
-
-@maybe_allow_in_graph
-class CogVideoXBlock(nn.Module):
- r"""
- Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
-
- Parameters:
- dim (`int`):
- The number of channels in the input and output.
- num_attention_heads (`int`):
- The number of heads to use for multi-head attention.
- attention_head_dim (`int`):
- The number of channels in each head.
- time_embed_dim (`int`):
- The number of channels in timestep embedding.
- dropout (`float`, defaults to `0.0`):
- The dropout probability to use.
- activation_fn (`str`, defaults to `"gelu-approximate"`):
- Activation function to be used in feed-forward.
- attention_bias (`bool`, defaults to `False`):
- Whether or not to use bias in attention projection layers.
- qk_norm (`bool`, defaults to `True`):
- Whether or not to use normalization after query and key projections in Attention.
- norm_elementwise_affine (`bool`, defaults to `True`):
- Whether to use learnable elementwise affine parameters for normalization.
- norm_eps (`float`, defaults to `1e-5`):
- Epsilon value for normalization layers.
- final_dropout (`bool` defaults to `False`):
- Whether to apply a final dropout after the last feed-forward layer.
- ff_inner_dim (`int`, *optional*, defaults to `None`):
- Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
- ff_bias (`bool`, defaults to `True`):
- Whether or not to use bias in Feed-forward layer.
- attention_out_bias (`bool`, defaults to `True`):
- Whether or not to use bias in Attention output projection layer.
- """
-
- def __init__(
- self,
- dim: int,
- num_attention_heads: int,
- attention_head_dim: int,
- time_embed_dim: int,
- dropout: float = 0.0,
- activation_fn: str = "gelu-approximate",
- attention_bias: bool = False,
- qk_norm: bool = True,
- norm_elementwise_affine: bool = True,
- norm_eps: float = 1e-5,
- final_dropout: bool = True,
- ff_inner_dim: Optional[int] = None,
- ff_bias: bool = True,
- attention_out_bias: bool = True,
- block_idx: int = 0,
- ):
- super().__init__()
-
- # 1. Self Attention
- self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
- self.attn1 = Attention(
- query_dim=dim,
- dim_head=attention_head_dim,
- heads=num_attention_heads,
- qk_norm="layer_norm" if qk_norm else None,
- eps=1e-6,
- bias=attention_bias,
- out_bias=attention_out_bias,
- processor=CogVideoXAttnProcessor2_0(),
- )
-
- # parallel
- #self.attn1.parallel_manager = None
-
- # 2. Feed Forward
- self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
- self.ff = FeedForward(
- dim,
- dropout=dropout,
- activation_fn=activation_fn,
- final_dropout=final_dropout,
- inner_dim=ff_inner_dim,
- bias=ff_bias,
- )
-
- # pab
- self.attn_count = 0
- self.last_attn = None
- self.block_idx = block_idx
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- temb: torch.Tensor,
- image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- timestep=None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- # norm & modulate
- norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
- hidden_states, encoder_hidden_states, temb
- )
-
- # attention
- if enable_pab():
- broadcast_attn, self.attn_count = if_broadcast_spatial(int(timestep[0]), self.attn_count, self.block_idx)
- if enable_pab() and broadcast_attn:
- attn_hidden_states, attn_encoder_hidden_states = self.last_attn
- else:
- attn_hidden_states, attn_encoder_hidden_states = self.attn1(
- hidden_states=norm_hidden_states,
- encoder_hidden_states=norm_encoder_hidden_states,
- image_rotary_emb=image_rotary_emb,
- )
- if enable_pab():
- self.last_attn = (attn_hidden_states, attn_encoder_hidden_states)
-
- hidden_states = hidden_states + gate_msa * attn_hidden_states
- encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
-
- # norm & modulate
- norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
- hidden_states, encoder_hidden_states, temb
- )
-
- # feed-forward
- norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
- ff_output = self.ff(norm_hidden_states)
-
- hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
- encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
-
- return hidden_states, encoder_hidden_states
-
-
-class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
- """
- A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
-
- Parameters:
- num_attention_heads (`int`, defaults to `30`):
- The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `64`):
- The number of channels in each head.
- in_channels (`int`, defaults to `16`):
- The number of channels in the input.
- out_channels (`int`, *optional*, defaults to `16`):
- The number of channels in the output.
- flip_sin_to_cos (`bool`, defaults to `True`):
- Whether to flip the sin to cos in the time embedding.
- time_embed_dim (`int`, defaults to `512`):
- Output dimension of timestep embeddings.
- text_embed_dim (`int`, defaults to `4096`):
- Input dimension of text embeddings from the text encoder.
- num_layers (`int`, defaults to `30`):
- The number of layers of Transformer blocks to use.
- dropout (`float`, defaults to `0.0`):
- The dropout probability to use.
- attention_bias (`bool`, defaults to `True`):
- Whether or not to use bias in the attention projection layers.
- sample_width (`int`, defaults to `90`):
- The width of the input latents.
- sample_height (`int`, defaults to `60`):
- The height of the input latents.
- sample_frames (`int`, defaults to `49`):
- The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
- instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
- but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
- K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
- patch_size (`int`, defaults to `2`):
- The size of the patches to use in the patch embedding layer.
- temporal_compression_ratio (`int`, defaults to `4`):
- The compression ratio across the temporal dimension. See documentation for `sample_frames`.
- max_text_seq_length (`int`, defaults to `226`):
- The maximum sequence length of the input text embeddings.
- activation_fn (`str`, defaults to `"gelu-approximate"`):
- Activation function to use in feed-forward.
- timestep_activation_fn (`str`, defaults to `"silu"`):
- Activation function to use when generating the timestep embeddings.
- norm_elementwise_affine (`bool`, defaults to `True`):
- Whether or not to use elementwise affine in normalization layers.
- norm_eps (`float`, defaults to `1e-5`):
- The epsilon value to use in normalization layers.
- spatial_interpolation_scale (`float`, defaults to `1.875`):
- Scaling factor to apply in 3D positional embeddings across spatial dimensions.
- temporal_interpolation_scale (`float`, defaults to `1.0`):
- Scaling factor to apply in 3D positional embeddings across temporal dimensions.
- """
-
- _supports_gradient_checkpointing = True
-
- @register_to_config
- def __init__(
- self,
- num_attention_heads: int = 30,
- attention_head_dim: int = 64,
- in_channels: int = 16,
- out_channels: Optional[int] = 16,
- flip_sin_to_cos: bool = True,
- freq_shift: int = 0,
- time_embed_dim: int = 512,
- text_embed_dim: int = 4096,
- num_layers: int = 30,
- dropout: float = 0.0,
- attention_bias: bool = True,
- sample_width: int = 90,
- sample_height: int = 60,
- sample_frames: int = 49,
- patch_size: int = 2,
- temporal_compression_ratio: int = 4,
- max_text_seq_length: int = 226,
- activation_fn: str = "gelu-approximate",
- timestep_activation_fn: str = "silu",
- norm_elementwise_affine: bool = True,
- norm_eps: float = 1e-5,
- spatial_interpolation_scale: float = 1.875,
- temporal_interpolation_scale: float = 1.0,
- use_rotary_positional_embeddings: bool = False,
- add_noise_in_inpaint_model: bool = False,
- ):
- super().__init__()
- inner_dim = num_attention_heads * attention_head_dim
-
- post_patch_height = sample_height // patch_size
- post_patch_width = sample_width // patch_size
- post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1
- self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames
- self.post_patch_height = post_patch_height
- self.post_patch_width = post_patch_width
- self.post_time_compression_frames = post_time_compression_frames
- self.patch_size = patch_size
-
- # 1. Patch embedding
- self.patch_embed = CogVideoXPatchEmbed(patch_size, in_channels, inner_dim, text_embed_dim, bias=True)
- self.embedding_dropout = nn.Dropout(dropout)
-
- # 2. 3D positional embeddings
- spatial_pos_embedding = get_3d_sincos_pos_embed(
- inner_dim,
- (post_patch_width, post_patch_height),
- post_time_compression_frames,
- spatial_interpolation_scale,
- temporal_interpolation_scale,
- )
- spatial_pos_embedding = torch.from_numpy(spatial_pos_embedding).flatten(0, 1)
- pos_embedding = torch.zeros(1, max_text_seq_length + self.num_patches, inner_dim, requires_grad=False)
- pos_embedding.data[:, max_text_seq_length:].copy_(spatial_pos_embedding)
- self.register_buffer("pos_embedding", pos_embedding, persistent=False)
-
- # 3. Time embeddings
- self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
- self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
-
- # 4. Define spatio-temporal transformers blocks
- self.transformer_blocks = nn.ModuleList(
- [
- CogVideoXBlock(
- dim=inner_dim,
- num_attention_heads=num_attention_heads,
- attention_head_dim=attention_head_dim,
- time_embed_dim=time_embed_dim,
- dropout=dropout,
- activation_fn=activation_fn,
- attention_bias=attention_bias,
- norm_elementwise_affine=norm_elementwise_affine,
- norm_eps=norm_eps,
- )
- for _ in range(num_layers)
- ]
- )
- self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
-
- # 5. Output blocks
- self.norm_out = AdaLayerNorm(
- embedding_dim=time_embed_dim,
- output_dim=2 * inner_dim,
- norm_elementwise_affine=norm_elementwise_affine,
- norm_eps=norm_eps,
- chunk_dim=1,
- )
- self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
-
- self.gradient_checkpointing = False
-
- def _set_gradient_checkpointing(self, module, value=False):
- self.gradient_checkpointing = value
-
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
- def fuse_qkv_projections(self):
- """
- Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
- are fused. For cross-attention modules, key and value projection matrices are fused.
-
-
-
- This API is 🧪 experimental.
-
-
- """
- self.original_attn_processors = None
-
- for _, attn_processor in self.attn_processors.items():
- if "Added" in str(attn_processor.__class__.__name__):
- raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
-
- self.original_attn_processors = self.attn_processors
-
- for module in self.modules():
- if isinstance(module, Attention):
- module.fuse_projections(fuse=True)
-
- self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())
-
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
- def unfuse_qkv_projections(self):
- """Disables the fused QKV projection if enabled.
-
-
-
- This API is 🧪 experimental.
-
-
-
- """
- if self.original_attn_processors is not None:
- self.set_attn_processor(self.original_attn_processors)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- timestep: Union[int, float, torch.LongTensor],
- timestep_cond: Optional[torch.Tensor] = None,
- inpaint_latents: Optional[torch.Tensor] = None,
- control_latents: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- return_dict: bool = True,
- ):
- batch_size, num_frames, channels, height, width = hidden_states.shape
-
- # 1. Time embedding
- timesteps = timestep
- t_emb = self.time_proj(timesteps)
-
- # timesteps does not contain any weights and will always return f32 tensors
- # but time_embedding might actually be running in fp16. so we need to cast here.
- # there might be better ways to encapsulate this.
- t_emb = t_emb.to(dtype=hidden_states.dtype)
- emb = self.time_embedding(t_emb, timestep_cond)
-
- # 2. Patch embedding
- if inpaint_latents is not None:
- hidden_states = torch.concat([hidden_states, inpaint_latents], 2)
- if control_latents is not None:
- hidden_states = torch.concat([hidden_states, control_latents], 2)
- hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
-
- # 3. Position embedding
- text_seq_length = encoder_hidden_states.shape[1]
- if not self.config.use_rotary_positional_embeddings:
- seq_length = height * width * num_frames // (self.config.patch_size**2)
- # pos_embeds = self.pos_embedding[:, : text_seq_length + seq_length]
- pos_embeds = self.pos_embedding
- emb_size = hidden_states.size()[-1]
- pos_embeds_without_text = pos_embeds[:, text_seq_length: ].view(1, self.post_time_compression_frames, self.post_patch_height, self.post_patch_width, emb_size)
- pos_embeds_without_text = pos_embeds_without_text.permute([0, 4, 1, 2, 3])
- pos_embeds_without_text = F.interpolate(pos_embeds_without_text,size=[self.post_time_compression_frames, height // self.config.patch_size, width // self.config.patch_size],mode='trilinear',align_corners=False)
- pos_embeds_without_text = pos_embeds_without_text.permute([0, 2, 3, 4, 1]).view(1, -1, emb_size)
- pos_embeds = torch.cat([pos_embeds[:, :text_seq_length], pos_embeds_without_text], dim = 1)
- pos_embeds = pos_embeds[:, : text_seq_length + seq_length]
- hidden_states = hidden_states + pos_embeds
- hidden_states = self.embedding_dropout(hidden_states)
-
- encoder_hidden_states = hidden_states[:, :text_seq_length]
- hidden_states = hidden_states[:, text_seq_length:]
-
- # 4. Transformer blocks
-
- for i, block in enumerate(self.transformer_blocks):
- if self.training and self.gradient_checkpointing:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return module(*inputs)
-
- return custom_forward
-
- ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
- hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
- create_custom_forward(block),
- hidden_states,
- encoder_hidden_states,
- emb,
- image_rotary_emb,
- **ckpt_kwargs,
- )
- else:
- hidden_states, encoder_hidden_states = block(
- hidden_states=hidden_states,
- encoder_hidden_states=encoder_hidden_states,
- temb=emb,
- image_rotary_emb=image_rotary_emb,
- timestep=timestep,
- )
-
- if not self.config.use_rotary_positional_embeddings:
- # CogVideoX-2B
- hidden_states = self.norm_final(hidden_states)
- else:
- # CogVideoX-5B
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
- hidden_states = self.norm_final(hidden_states)
- hidden_states = hidden_states[:, text_seq_length:]
-
- # 5. Final block
- hidden_states = self.norm_out(hidden_states, temb=emb)
- hidden_states = self.proj_out(hidden_states)
-
- # 6. Unpatchify
- p = self.config.patch_size
- output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, channels, p, p)
- output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
-
- if not return_dict:
- return (output,)
- return Transformer2DModelOutput(sample=output)
-
- @classmethod
- def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs={}):
- if subfolder is not None:
- pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
- print(f"loaded 3D transformer's pretrained weights from {pretrained_model_path} ...")
-
- config_file = os.path.join(pretrained_model_path, 'config.json')
- if not os.path.isfile(config_file):
- raise RuntimeError(f"{config_file} does not exist")
- with open(config_file, "r") as f:
- config = json.load(f)
-
- from diffusers.utils import WEIGHTS_NAME
- model = cls.from_config(config, **transformer_additional_kwargs)
- model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
- model_file_safetensors = model_file.replace(".bin", ".safetensors")
- if os.path.exists(model_file):
- state_dict = torch.load(model_file, map_location="cpu")
- elif os.path.exists(model_file_safetensors):
- from safetensors.torch import load_file, safe_open
- state_dict = load_file(model_file_safetensors)
- else:
- from safetensors.torch import load_file, safe_open
- model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
- state_dict = {}
- for model_file_safetensors in model_files_safetensors:
- _state_dict = load_file(model_file_safetensors)
- for key in _state_dict:
- state_dict[key] = _state_dict[key]
-
- if model.state_dict()['patch_embed.proj.weight'].size() != state_dict['patch_embed.proj.weight'].size():
- new_shape = model.state_dict()['patch_embed.proj.weight'].size()
- if len(new_shape) == 5:
- state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).expand(new_shape).clone()
- state_dict['patch_embed.proj.weight'][:, :, :-1] = 0
- else:
- if model.state_dict()['patch_embed.proj.weight'].size()[1] > state_dict['patch_embed.proj.weight'].size()[1]:
- model.state_dict()['patch_embed.proj.weight'][:, :state_dict['patch_embed.proj.weight'].size()[1], :, :] = state_dict['patch_embed.proj.weight']
- model.state_dict()['patch_embed.proj.weight'][:, state_dict['patch_embed.proj.weight'].size()[1]:, :, :] = 0
- state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
- else:
- model.state_dict()['patch_embed.proj.weight'][:, :, :, :] = state_dict['patch_embed.proj.weight'][:, :model.state_dict()['patch_embed.proj.weight'].size()[1], :, :]
- state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
-
- tmp_state_dict = {}
- for key in state_dict:
- if key in model.state_dict().keys() and model.state_dict()[key].size() == state_dict[key].size():
- tmp_state_dict[key] = state_dict[key]
- else:
- print(key, "Size don't match, skip")
- state_dict = tmp_state_dict
-
- m, u = model.load_state_dict(state_dict, strict=False)
- print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
- print(m)
-
- params = [p.numel() if "mamba" in n else 0 for n, p in model.named_parameters()]
- print(f"### Mamba Parameters: {sum(params) / 1e6} M")
-
- params = [p.numel() if "attn1." in n else 0 for n, p in model.named_parameters()]
- print(f"### attn1 Parameters: {sum(params) / 1e6} M")
-
- return model
\ No newline at end of file
diff --git a/cogvideox_fun/pipeline_cogvideox_control.py b/cogvideox_fun/pipeline_cogvideox_control.py
deleted file mode 100644
index 85687fe..0000000
--- a/cogvideox_fun/pipeline_cogvideox_control.py
+++ /dev/null
@@ -1,974 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import math
-from dataclasses import dataclass
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-
-from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
-from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
-from diffusers.models.embeddings import get_3d_rotary_pos_embed
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
-from diffusers.utils import BaseOutput, logging, replace_example_docstring
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.video_processor import VideoProcessor
-from diffusers.image_processor import VaeImageProcessor
-from einops import rearrange
-
-from ..videosys.core.pipeline import VideoSysPipeline
-from ..videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
-from ..videosys.core.pab_mgr import set_pab_manager
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```python
- >>> import torch
- >>> from diffusers import CogVideoX_Fun_Pipeline
- >>> from diffusers.utils import export_to_video
-
- >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
- >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
- >>> prompt = (
- ... "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
- ... "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
- ... "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
- ... "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
- ... "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
- ... "atmosphere of this unique musical performance."
- ... )
- >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
- >>> export_to_video(video, "output.mp4", fps=8)
- ```
-"""
-
-
-# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
-def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
- tw = tgt_width
- th = tgt_height
- h, w = src
- r = h / w
- if r > (th / tw):
- resize_height = th
- resize_width = int(round(th / h * w))
- else:
- resize_width = tw
- resize_height = int(round(tw / w * h))
-
- crop_top = int(round((th - resize_height) / 2.0))
- crop_left = int(round((tw - resize_width) / 2.0))
-
- return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
- scheduler,
- num_inference_steps: Optional[int] = None,
- device: Optional[Union[str, torch.device]] = None,
- timesteps: Optional[List[int]] = None,
- sigmas: Optional[List[float]] = None,
- **kwargs,
-):
- """
- Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
- custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
- Args:
- scheduler (`SchedulerMixin`):
- The scheduler to get timesteps from.
- num_inference_steps (`int`):
- The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
- must be `None`.
- device (`str` or `torch.device`, *optional*):
- The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
- timesteps (`List[int]`, *optional*):
- Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
- `num_inference_steps` and `sigmas` must be `None`.
- sigmas (`List[float]`, *optional*):
- Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
- `num_inference_steps` and `timesteps` must be `None`.
-
- Returns:
- `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
- second element is the number of inference steps.
- """
- if timesteps is not None and sigmas is not None:
- raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
- if timesteps is not None:
- accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
- if not accepts_timesteps:
- raise ValueError(
- f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
- f" timestep schedules. Please check whether you are using the correct scheduler."
- )
- scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
- timesteps = scheduler.timesteps
- num_inference_steps = len(timesteps)
- elif sigmas is not None:
- accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
- if not accept_sigmas:
- raise ValueError(
- f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
- f" sigmas schedules. Please check whether you are using the correct scheduler."
- )
- scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
- timesteps = scheduler.timesteps
- num_inference_steps = len(timesteps)
- else:
- scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
- timesteps = scheduler.timesteps
- return timesteps, num_inference_steps
-
-
-@dataclass
-class CogVideoX_Fun_PipelineOutput(BaseOutput):
- r"""
- Output class for CogVideo pipelines.
-
- Args:
- video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
- List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
- denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
- `(batch_size, num_frames, channels, height, width)`.
- """
-
- videos: torch.Tensor
-
-
-class CogVideoX_Fun_Pipeline_Control(VideoSysPipeline):
- r"""
- Pipeline for text-to-video generation using CogVideoX.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
- transformer ([`CogVideoXTransformer3DModel`]):
- A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
- """
-
- _optional_components = []
- model_cpu_offload_seq = "vae->transformer->vae"
-
- _callback_tensor_inputs = [
- "latents",
- "prompt_embeds",
- "negative_prompt_embeds",
- ]
-
- def __init__(
- self,
- vae: AutoencoderKLCogVideoX,
- transformer: CogVideoXTransformer3DModel,
- scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
- pab_config = None
- ):
- super().__init__()
-
- self.register_modules(
- vae=vae, transformer=transformer, scheduler=scheduler
- )
- self.vae_scale_factor_spatial = (
- 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
- )
- self.vae_scale_factor_temporal = (
- self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
- )
-
- self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
-
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
- self.mask_processor = VaeImageProcessor(
- vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
- )
-
- if pab_config is not None:
- set_pab_manager(pab_config)
-
- def prepare_latents(
- self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, timesteps, denoise_strength, num_inference_steps,
- latents=None, freenoise=True, context_size=None, context_overlap=None
- ):
- shape = (
- batch_size,
- (num_frames - 1) // self.vae_scale_factor_temporal + 1,
- num_channels_latents,
- height // self.vae_scale_factor_spatial,
- width // self.vae_scale_factor_spatial,
- )
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
- noise = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=self.vae.dtype)
- if freenoise:
- print("Applying FreeNoise")
- # code and comments from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
- video_length = num_frames // 4
- delta = context_size - context_overlap
- for start_idx in range(0, video_length-context_size, delta):
- # start_idx corresponds to the beginning of a context window
- # goal: place shuffled in the delta region right after the end of the context window
- # if space after context window is not enough to place the noise, adjust and finish
- place_idx = start_idx + context_size
- # if place_idx is outside the valid indexes, we are already finished
- if place_idx >= video_length:
- break
- end_idx = place_idx - 1
- #print("video_length:", video_length, "start_idx:", start_idx, "end_idx:", end_idx, "place_idx:", place_idx, "delta:", delta)
-
- # if there is not enough room to copy delta amount of indexes, copy limited amount and finish
- if end_idx + delta >= video_length:
- final_delta = video_length - place_idx
- # generate list of indexes in final delta region
- list_idx = torch.tensor(list(range(start_idx,start_idx+final_delta)), device=torch.device("cpu"), dtype=torch.long)
- # shuffle list
- list_idx = list_idx[torch.randperm(final_delta, generator=generator)]
- # apply shuffled indexes
- noise[:, place_idx:place_idx + final_delta, :, :, :] = noise[:, list_idx, :, :, :]
- break
- # otherwise, do normal behavior
- # generate list of indexes in delta region
- list_idx = torch.tensor(list(range(start_idx,start_idx+delta)), device=torch.device("cpu"), dtype=torch.long)
- # shuffle list
- list_idx = list_idx[torch.randperm(delta, generator=generator)]
- # apply shuffled indexes
- #print("place_idx:", place_idx, "delta:", delta, "list_idx:", list_idx)
- noise[:, place_idx:place_idx + delta, :, :, :] = noise[:, list_idx, :, :, :]
- if latents is None:
- latents = noise.to(device)
- else:
- latents = latents.to(device)
- timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, denoise_strength, device)
- latent_timestep = timesteps[:1]
-
- noise = randn_tensor(shape, generator=generator, device=device, dtype=self.vae.dtype)
- frames_needed = noise.shape[1]
- current_frames = latents.shape[1]
-
- if frames_needed > current_frames:
- repeat_factor = frames_needed // current_frames
- additional_frame = torch.randn((latents.size(0), repeat_factor, latents.size(2), latents.size(3), latents.size(4)), dtype=latents.dtype, device=latents.device)
- latents = torch.cat((latents, additional_frame), dim=1)
- elif frames_needed < current_frames:
- latents = latents[:, :frames_needed, :, :, :]
-
- latents = self.scheduler.add_noise(latents, noise, latent_timestep)
- latents = latents * self.scheduler.init_noise_sigma # scale the initial noise by the standard deviation required by the scheduler
- return latents, timesteps, noise
-
- def prepare_control_latents(
- self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
- ):
- # resize the mask to latents shape as we concatenate the mask to the latents
- # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
- # and half precision
-
- if mask is not None:
- mask = mask.to(device=device, dtype=self.vae.dtype)
- bs = 1
- new_mask = []
- for i in range(0, mask.shape[0], bs):
- mask_bs = mask[i : i + bs]
- mask_bs = self.vae.encode(mask_bs)[0]
- mask_bs = mask_bs.mode()
- new_mask.append(mask_bs)
- mask = torch.cat(new_mask, dim = 0)
- mask = mask * self.vae.config.scaling_factor
-
- if masked_image is not None:
- masked_image = masked_image.to(device=device, dtype=self.vae.dtype)
- bs = 1
- new_mask_pixel_values = []
- for i in range(0, masked_image.shape[0], bs):
- mask_pixel_values_bs = masked_image[i : i + bs]
- mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0]
- mask_pixel_values_bs = mask_pixel_values_bs.mode()
- new_mask_pixel_values.append(mask_pixel_values_bs)
- masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
- masked_image_latents = masked_image_latents * self.vae.config.scaling_factor
- else:
- masked_image_latents = None
-
- return mask, masked_image_latents
-
- def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
- latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
- latents = 1 / self.vae.config.scaling_factor * latents
-
- frames = self.vae.decode(latents).sample
- frames = (frames / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
- frames = frames.cpu().float().numpy()
- return frames
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- def _gaussian_weights(self, t_tile_length, t_batch_size):
- from numpy import pi, exp, sqrt
-
- var = 0.01
- midpoint = (t_tile_length - 1) / 2 # -1 because index goes from 0 to latent_width - 1
- t_probs = [exp(-(t-midpoint)*(t-midpoint)/(t_tile_length*t_tile_length)/(2*var)) / sqrt(2*pi*var) for t in range(t_tile_length)]
- weights = torch.tensor(t_probs)
- weights = weights.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(4).repeat(1, t_batch_size,1, 1, 1)
- return weights
-
- # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
- def check_inputs(
- self,
- prompt,
- height,
- width,
- negative_prompt,
- callback_on_step_end_tensor_inputs,
- prompt_embeds=None,
- negative_prompt_embeds=None,
- ):
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- if callback_on_step_end_tensor_inputs is not None and not all(
- k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
- ):
- raise ValueError(
- f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
- )
- if prompt is not None and prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
- " only forward one of the two."
- )
- elif prompt is None and prompt_embeds is None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
- )
- elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if negative_prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if prompt_embeds is not None and negative_prompt_embeds is not None:
- if prompt_embeds.shape != negative_prompt_embeds.shape:
- raise ValueError(
- "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
- f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
- f" {negative_prompt_embeds.shape}."
- )
-
- def fuse_qkv_projections(self) -> None:
- r"""Enables fused QKV projections."""
- self.fusing_transformer = True
- self.transformer.fuse_qkv_projections()
-
- def unfuse_qkv_projections(self) -> None:
- r"""Disable QKV projection fusion if enabled."""
- if not self.fusing_transformer:
- logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
- else:
- self.transformer.unfuse_qkv_projections()
- self.fusing_transformer = False
-
- def _prepare_rotary_positional_embeddings(
- self,
- height: int,
- width: int,
- num_frames: int,
- device: torch.device,
- start_frame: Optional[int] = None,
- end_frame: Optional[int] = None,
- context_frames: Optional[int] = None,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-
- grid_crops_coords = get_resize_crop_region_for_grid(
- (grid_height, grid_width), base_size_width, base_size_height
- )
- freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
- embed_dim=self.transformer.config.attention_head_dim,
- crops_coords=grid_crops_coords,
- grid_size=(grid_height, grid_width),
- temporal_size=num_frames,
- use_real=True,
- )
-
- if start_frame is not None or context_frames is not None:
- freqs_cos = freqs_cos.view(num_frames, grid_height * grid_width, -1)
- freqs_sin = freqs_sin.view(num_frames, grid_height * grid_width, -1)
- if context_frames is not None:
- freqs_cos = freqs_cos[context_frames]
- freqs_sin = freqs_sin[context_frames]
- else:
- freqs_cos = freqs_cos[start_frame:end_frame]
- freqs_sin = freqs_sin[start_frame:end_frame]
-
- freqs_cos = freqs_cos.view(-1, freqs_cos.shape[-1])
- freqs_sin = freqs_sin.view(-1, freqs_sin.shape[-1])
-
- freqs_cos = freqs_cos.to(device=device)
- freqs_sin = freqs_sin.to(device=device)
- return freqs_cos, freqs_sin
-
- @property
- def guidance_scale(self):
- return self._guidance_scale
-
- @property
- def num_timesteps(self):
- return self._num_timesteps
-
- @property
- def interrupt(self):
- return self._interrupt
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
- def get_timesteps(self, num_inference_steps, strength, device):
- # get the original timestep using init_timestep
- init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
- t_start = max(num_inference_steps - init_timestep, 0)
- timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
- return timesteps, num_inference_steps - t_start
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_DOC_STRING)
- def __call__(
- self,
- prompt: Optional[Union[str, List[str]]] = None,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- height: int = 480,
- width: int = 720,
- video: Union[torch.FloatTensor] = None,
- control_video: Union[torch.FloatTensor] = None,
- num_frames: int = 49,
- num_inference_steps: int = 50,
- timesteps: Optional[List[int]] = None,
- guidance_scale: float = 6,
- use_dynamic_cfg: bool = False,
- denoise_strength: float = 1.0,
- num_videos_per_prompt: int = 1,
- eta: float = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- output_type: str = "numpy",
- return_dict: bool = False,
- callback_on_step_end: Optional[
- Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
- ] = None,
- callback_on_step_end_tensor_inputs: List[str] = ["latents"],
- max_sequence_length: int = 226,
- comfyui_progressbar: bool = False,
- control_strength: float = 1.0,
- control_start_percent: float = 0.0,
- control_end_percent: float = 1.0,
- scheduler_name: str = "DPM",
- context_schedule: Optional[str] = None,
- context_frames: Optional[int] = None,
- context_stride: Optional[int] = None,
- context_overlap: Optional[int] = None,
- freenoise: Optional[bool] = True,
- tora: Optional[dict] = None,
- ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
- """
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
- instead.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image. This is set to 1024 by default for the best results.
- width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The width in pixels of the generated image. This is set to 1024 by default for the best results.
- num_frames (`int`, defaults to `48`):
- Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
- contain 1 extra frame because CogVideoX_Fun is conditioned with (num_seconds * fps + 1) frames where
- num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
- needs to be satisfied is that of divisibility mentioned above.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- timesteps (`List[int]`, *optional*):
- Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
- in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
- passed will be used. Must be in descending order.
- guidance_scale (`float`, *optional*, defaults to 7.0):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- num_videos_per_prompt (`int`, *optional*, defaults to 1):
- The number of videos to generate per prompt.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
- to make generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
- of a plain tuple.
- callback_on_step_end (`Callable`, *optional*):
- A function that calls at the end of each denoising steps during the inference. The function is called
- with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
- callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
- `callback_on_step_end_tensor_inputs`.
- callback_on_step_end_tensor_inputs (`List`, *optional*):
- The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
- will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
- `._callback_tensor_inputs` attribute of your pipeline class.
- max_sequence_length (`int`, defaults to `226`):
- Maximum sequence length in encoded prompt. Must be consistent with
- `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
-
- Examples:
-
- Returns:
- [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`:
- [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a
- `tuple`. When returning a tuple, the first element is a list with the generated images.
- """
-
- # if num_frames > 49:
- # raise ValueError(
- # "The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation."
- # )
-
- if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
- callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
- height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
- width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
- num_videos_per_prompt = 1
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(
- prompt,
- height,
- width,
- negative_prompt,
- callback_on_step_end_tensor_inputs,
- prompt_embeds,
- negative_prompt_embeds,
- )
- self._guidance_scale = guidance_scale
- self._interrupt = False
-
- # 2. Default call parameters
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- device = self._execution_device
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- if do_classifier_free_guidance:
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-
- # 4. Prepare timesteps
- timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
- self._num_timesteps = len(timesteps)
- if comfyui_progressbar:
- from comfy.utils import ProgressBar
- pbar = ProgressBar(num_inference_steps + 2)
-
- # 5. Prepare latents.
- latent_channels = self.vae.config.latent_channels
- latents, timesteps, noise = self.prepare_latents(
- batch_size * num_videos_per_prompt,
- latent_channels,
- num_frames,
- height,
- width,
- self.vae.dtype,
- device,
- generator,
- timesteps,
- denoise_strength,
- num_inference_steps,
- latents,
- context_size=context_frames,
- context_overlap=context_overlap,
- freenoise=freenoise,
- )
- if comfyui_progressbar:
- pbar.update(1)
-
-
- control_video_latents_input = (
- torch.cat([control_video] * 2) if do_classifier_free_guidance else control_video
- )
- control_latents = rearrange(control_video_latents_input, "b c f h w -> b f c h w")
-
- control_latents = control_latents * control_strength
-
- if comfyui_progressbar:
- pbar.update(1)
-
- # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-
-
- # 8. Denoising loop
- num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
- # 8.5. Temporal tiling prep
- if context_schedule is not None and context_schedule == "temporal_tiling":
- t_tile_length = context_frames
- t_tile_overlap = context_overlap
- t_tile_weights = self._gaussian_weights(t_tile_length=t_tile_length, t_batch_size=1).to(latents.device).to(self.vae.dtype)
- use_temporal_tiling = True
- print("Temporal tiling enabled")
- elif context_schedule is not None:
- print(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap")
- use_temporal_tiling = False
- use_context_schedule = True
- from .context import get_context_scheduler
- context = get_context_scheduler(context_schedule)
-
- else:
- use_temporal_tiling = False
- use_context_schedule = False
- print("Temporal tiling and context schedule disabled")
- # 7. Create rotary embeds if required
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
- if tora is not None and do_classifier_free_guidance:
- video_flow_features = tora["video_flow_features"].repeat(1, 2, 1, 1, 1).contiguous()
-
- if tora is not None:
- for module in self.transformer.fuser_list:
- for param in module.parameters():
- param.data = param.data.to(device)
-
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- # for DPM-solver++
- old_pred_original_sample = None
- for i, t in enumerate(timesteps):
- if self.interrupt:
- continue
-
- if use_temporal_tiling and isinstance(self.scheduler, CogVideoXDDIMScheduler):
- #temporal tiling code based on https://github.com/mayuelala/FollowYourEmoji/blob/main/models/video_pipeline.py
- # =====================================================
- grid_ts = 0
- cur_t = 0
- while cur_t < latents.shape[1]:
- cur_t = max(grid_ts * t_tile_length - t_tile_overlap * grid_ts, 0) + t_tile_length
- grid_ts += 1
-
- all_t = latents.shape[1]
- latents_all_list = []
- # =====================================================
-
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, context_frames, device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
-
- for t_i in range(grid_ts):
- if t_i < grid_ts - 1:
- ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
- if t_i == grid_ts - 1:
- ofs_t = all_t - t_tile_length
-
- input_start_t = ofs_t
- input_end_t = ofs_t + t_tile_length
-
- latents_tile = latents[:, input_start_t:input_end_t,:, :, :]
- control_latents_tile = control_latents[:, input_start_t:input_end_t, :, :, :]
-
- latent_model_input_tile = torch.cat([latents_tile] * 2) if do_classifier_free_guidance else latents_tile
- latent_model_input_tile = self.scheduler.scale_model_input(latent_model_input_tile, t)
-
- #t_input = t[None].to(device)
- t_input = t.expand(latent_model_input_tile.shape[0]) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-
- # predict noise model_output
- noise_pred = self.transformer(
- hidden_states=latent_model_input_tile,
- encoder_hidden_states=prompt_embeds,
- timestep=t_input,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- control_latents=control_latents_tile,
- )[0]
- noise_pred = noise_pred.float()
-
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents_tile = self.scheduler.step(noise_pred, t, latents_tile.to(self.vae.dtype), **extra_step_kwargs, return_dict=False)[0]
- latents_all_list.append(latents_tile)
-
- # ==========================================
- latents_all = torch.zeros(latents.shape, device=latents.device, dtype=self.vae.dtype)
- contributors = torch.zeros(latents.shape, device=latents.device, dtype=self.vae.dtype)
- # Add each tile contribution to overall latents
- for t_i in range(grid_ts):
- if t_i < grid_ts - 1:
- ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
- if t_i == grid_ts - 1:
- ofs_t = all_t - t_tile_length
-
- input_start_t = ofs_t
- input_end_t = ofs_t + t_tile_length
-
- latents_all[:, input_start_t:input_end_t,:, :, :] += latents_all_list[t_i] * t_tile_weights
- contributors[:, input_start_t:input_end_t,:, :, :] += t_tile_weights
-
- latents_all /= contributors
-
- latents = latents_all
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- pbar.update(1)
- # ==========================================
- elif use_context_schedule:
-
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # Calculate the current step percentage
- current_step_percentage = i / num_inference_steps
-
- # Determine if control_latents should be applied
- apply_control = control_start_percent <= current_step_percentage <= control_end_percent
- current_control_latents = control_latents if apply_control else torch.zeros_like(control_latents)
-
- # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
- timestep = t.expand(latent_model_input.shape[0])
-
- context_queue = list(context(
- i, num_inference_steps, latents.shape[1], context_frames, context_stride, context_overlap,
- ))
- counter = torch.zeros_like(latent_model_input)
- noise_pred = torch.zeros_like(latent_model_input)
-
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, context_frames, device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
-
- for c in context_queue:
- partial_latent_model_input = latent_model_input[:, c, :, :, :]
- partial_control_latents = current_control_latents[:, c, :, :, :]
-
- # predict noise model_output
- noise_pred[:, c, :, :, :] += self.transformer(
- hidden_states=partial_latent_model_input,
- encoder_hidden_states=prompt_embeds,
- timestep=timestep,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- control_latents=partial_control_latents,
- )[0]
-
- counter[:, c, :, :, :] += 1
- noise_pred = noise_pred.float()
-
- noise_pred /= counter
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- if not isinstance(self.scheduler, CogVideoXDPMScheduler):
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
- else:
- latents, old_pred_original_sample = self.scheduler.step(
- noise_pred,
- old_pred_original_sample,
- t,
- timesteps[i - 1] if i > 0 else None,
- latents,
- **extra_step_kwargs,
- return_dict=False,
- )
- latents = latents.to(prompt_embeds.dtype)
-
- # call the callback, if provided
- if callback_on_step_end is not None:
- callback_kwargs = {}
- for k in callback_on_step_end_tensor_inputs:
- callback_kwargs[k] = locals()[k]
- callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
- latents = callback_outputs.pop("latents", latents)
- prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
- negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if comfyui_progressbar:
- pbar.update(1)
- else:
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # Calculate the current step percentage
- current_step_percentage = i / num_inference_steps
-
- # Determine if control_latents should be applied
- apply_control = control_start_percent <= current_step_percentage <= control_end_percent
- current_control_latents = control_latents if apply_control else torch.zeros_like(control_latents)
-
- # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
- timestep = t.expand(latent_model_input.shape[0])
-
- # predict noise model_output
- noise_pred = self.transformer(
- hidden_states=latent_model_input,
- encoder_hidden_states=prompt_embeds,
- timestep=timestep,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- control_latents=current_control_latents,
- video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
-
- )[0]
- noise_pred = noise_pred.float()
-
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- if not isinstance(self.scheduler, CogVideoXDPMScheduler):
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
- else:
- latents, old_pred_original_sample = self.scheduler.step(
- noise_pred,
- old_pred_original_sample,
- t,
- timesteps[i - 1] if i > 0 else None,
- latents,
- **extra_step_kwargs,
- return_dict=False,
- )
- latents = latents.to(prompt_embeds.dtype)
-
- # call the callback, if provided
- if callback_on_step_end is not None:
- callback_kwargs = {}
- for k in callback_on_step_end_tensor_inputs:
- callback_kwargs[k] = locals()[k]
- callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
- latents = callback_outputs.pop("latents", latents)
- prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
- negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if comfyui_progressbar:
- pbar.update(1)
-
- # if output_type == "numpy":
- # video = self.decode_latents(latents)
- # elif not output_type == "latent":
- # video = self.decode_latents(latents)
- # video = self.video_processor.postprocess_video(video=video, output_type=output_type)
- # else:
- # video = latents
-
- # Offload all models
- self.maybe_free_model_hooks()
-
- # if not return_dict:
- # video = torch.from_numpy(video)
-
- return latents
\ No newline at end of file
diff --git a/cogvideox_fun/pipeline_cogvideox_inpaint.py b/cogvideox_fun/pipeline_cogvideox_inpaint.py
deleted file mode 100644
index 4b3b4f3..0000000
--- a/cogvideox_fun/pipeline_cogvideox_inpaint.py
+++ /dev/null
@@ -1,1150 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import math
-from dataclasses import dataclass
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-
-from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
-from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
-from diffusers.models.embeddings import get_3d_rotary_pos_embed
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
-from diffusers.utils import BaseOutput, logging, replace_example_docstring
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.video_processor import VideoProcessor
-from diffusers.image_processor import VaeImageProcessor
-from einops import rearrange
-
-from ..videosys.core.pipeline import VideoSysPipeline
-from ..videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
-from ..videosys.core.pab_mgr import set_pab_manager
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```python
- >>> import torch
- >>> from diffusers import CogVideoX_Fun_Pipeline
- >>> from diffusers.utils import export_to_video
-
- >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
- >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
- >>> prompt = (
- ... "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
- ... "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
- ... "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
- ... "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
- ... "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
- ... "atmosphere of this unique musical performance."
- ... )
- >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
- >>> export_to_video(video, "output.mp4", fps=8)
- ```
-"""
-
-
-# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
-def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
- tw = tgt_width
- th = tgt_height
- h, w = src
- r = h / w
- if r > (th / tw):
- resize_height = th
- resize_width = int(round(th / h * w))
- else:
- resize_width = tw
- resize_height = int(round(tw / w * h))
-
- crop_top = int(round((th - resize_height) / 2.0))
- crop_left = int(round((tw - resize_width) / 2.0))
-
- return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
- scheduler,
- num_inference_steps: Optional[int] = None,
- device: Optional[Union[str, torch.device]] = None,
- timesteps: Optional[List[int]] = None,
- sigmas: Optional[List[float]] = None,
- **kwargs,
-):
- """
- Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
- custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
- Args:
- scheduler (`SchedulerMixin`):
- The scheduler to get timesteps from.
- num_inference_steps (`int`):
- The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
- must be `None`.
- device (`str` or `torch.device`, *optional*):
- The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
- timesteps (`List[int]`, *optional*):
- Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
- `num_inference_steps` and `sigmas` must be `None`.
- sigmas (`List[float]`, *optional*):
- Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
- `num_inference_steps` and `timesteps` must be `None`.
-
- Returns:
- `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
- second element is the number of inference steps.
- """
- if timesteps is not None and sigmas is not None:
- raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
- if timesteps is not None:
- accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
- if not accepts_timesteps:
- raise ValueError(
- f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
- f" timestep schedules. Please check whether you are using the correct scheduler."
- )
- scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
- timesteps = scheduler.timesteps
- num_inference_steps = len(timesteps)
- elif sigmas is not None:
- accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
- if not accept_sigmas:
- raise ValueError(
- f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
- f" sigmas schedules. Please check whether you are using the correct scheduler."
- )
- scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
- timesteps = scheduler.timesteps
- num_inference_steps = len(timesteps)
- else:
- scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
- timesteps = scheduler.timesteps
- return timesteps, num_inference_steps
-
-
-def resize_mask(mask, latent, process_first_frame_only=True):
- latent_size = latent.size()
- batch_size, channels, num_frames, height, width = mask.shape
-
- if process_first_frame_only:
- target_size = list(latent_size[2:])
- target_size[0] = 1
- first_frame_resized = F.interpolate(
- mask[:, :, 0:1, :, :],
- size=target_size,
- mode='trilinear',
- align_corners=False
- )
-
- target_size = list(latent_size[2:])
- target_size[0] = target_size[0] - 1
- if target_size[0] != 0:
- remaining_frames_resized = F.interpolate(
- mask[:, :, 1:, :, :],
- size=target_size,
- mode='trilinear',
- align_corners=False
- )
- resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2)
- else:
- resized_mask = first_frame_resized
- else:
- target_size = list(latent_size[2:])
- resized_mask = F.interpolate(
- mask,
- size=target_size,
- mode='trilinear',
- align_corners=False
- )
- return resized_mask
-
-def add_noise_to_reference_video(image, ratio=None):
- if ratio is None:
- sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
- sigma = torch.exp(sigma).to(image.dtype)
- else:
- sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
-
- image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
- image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
- image = image + image_noise
- return image
-
-@dataclass
-class CogVideoX_Fun_PipelineOutput(BaseOutput):
- r"""
- Output class for CogVideo pipelines.
-
- Args:
- video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
- List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
- denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
- `(batch_size, num_frames, channels, height, width)`.
- """
-
- videos: torch.Tensor
-
-
-class CogVideoX_Fun_Pipeline_Inpaint(VideoSysPipeline):
- r"""
- Pipeline for text-to-video generation using CogVideoX.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
- transformer ([`CogVideoXTransformer3DModel`]):
- A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
- """
-
- _optional_components = []
- model_cpu_offload_seq = "vae->transformer->vae"
-
- _callback_tensor_inputs = [
- "latents",
- "prompt_embeds",
- "negative_prompt_embeds",
- ]
-
- def __init__(
- self,
- vae: AutoencoderKLCogVideoX,
- transformer: CogVideoXTransformer3DModel,
- scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
- pab_config = None
- ):
- super().__init__()
-
- self.register_modules(
- vae=vae, transformer=transformer, scheduler=scheduler
- )
- self.vae_scale_factor_spatial = (
- 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
- )
- self.vae_scale_factor_temporal = (
- self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
- )
-
- self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
-
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
- self.mask_processor = VaeImageProcessor(
- vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
- )
-
- if pab_config is not None:
- set_pab_manager(pab_config)
-
- def prepare_latents(
- self,
- batch_size,
- num_channels_latents,
- height,
- width,
- video_length,
- dtype,
- device,
- generator,
- latents=None,
- video=None,
- timestep=None,
- is_strength_max=True,
- return_noise=False,
- return_video_latents=False,
- context_size=None,
- context_overlap=None,
- freenoise=False,
- ):
- shape = (
- batch_size,
- (video_length - 1) // self.vae_scale_factor_temporal + 1,
- num_channels_latents,
- height // self.vae_scale_factor_spatial,
- width // self.vae_scale_factor_spatial,
- )
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if return_video_latents or (latents is None and not is_strength_max):
- video = video.to(device=device, dtype=self.vae.dtype)
-
- bs = 1
- new_video = []
- for i in range(0, video.shape[0], bs):
- video_bs = video[i : i + bs]
- video_bs = self.vae.encode(video_bs)[0]
- video_bs = video_bs.sample()
- new_video.append(video_bs)
- video = torch.cat(new_video, dim = 0)
- video = video * self.vae.config.scaling_factor
-
- video_latents = video.repeat(batch_size // video.shape[0], 1, 1, 1, 1)
- video_latents = video_latents.to(device=device, dtype=dtype)
- video_latents = rearrange(video_latents, "b c f h w -> b f c h w")
-
- if latents is None:
- noise = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=dtype)
- if freenoise:
- print("Applying FreeNoise")
- # code and comments from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
- video_length = video_length // 4
- delta = context_size - context_overlap
- for start_idx in range(0, video_length-context_size, delta):
- # start_idx corresponds to the beginning of a context window
- # goal: place shuffled in the delta region right after the end of the context window
- # if space after context window is not enough to place the noise, adjust and finish
- place_idx = start_idx + context_size
- # if place_idx is outside the valid indexes, we are already finished
- if place_idx >= video_length:
- break
- end_idx = place_idx - 1
- #print("video_length:", video_length, "start_idx:", start_idx, "end_idx:", end_idx, "place_idx:", place_idx, "delta:", delta)
-
- # if there is not enough room to copy delta amount of indexes, copy limited amount and finish
- if end_idx + delta >= video_length:
- final_delta = video_length - place_idx
- # generate list of indexes in final delta region
- list_idx = torch.tensor(list(range(start_idx,start_idx+final_delta)), device=torch.device("cpu"), dtype=torch.long)
- # shuffle list
- list_idx = list_idx[torch.randperm(final_delta, generator=generator)]
- # apply shuffled indexes
- noise[:, place_idx:place_idx + final_delta, :, :, :] = noise[:, list_idx, :, :, :]
- break
- # otherwise, do normal behavior
- # generate list of indexes in delta region
- list_idx = torch.tensor(list(range(start_idx,start_idx+delta)), device=torch.device("cpu"), dtype=torch.long)
- # shuffle list
- list_idx = list_idx[torch.randperm(delta, generator=generator)]
- # apply shuffled indexes
- #print("place_idx:", place_idx, "delta:", delta, "list_idx:", list_idx)
- noise[:, place_idx:place_idx + delta, :, :, :] = noise[:, list_idx, :, :, :]
-
- # if strength is 1. then initialise the latents to noise, else initial to image + noise
- latents = noise if is_strength_max else self.scheduler.add_noise(video_latents.to(noise), noise, timestep)
- # if pure noise then scale the initial latents by the Scheduler's init sigma
- latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
- latents = latents.to(device)
- else:
- noise = latents.to(device)
- latents = noise * self.scheduler.init_noise_sigma
-
- # scale the initial noise by the standard deviation required by the scheduler
- outputs = (latents,)
-
- if return_noise:
- outputs += (noise,)
-
- if return_video_latents:
- outputs += (video_latents,)
-
- return outputs
-
- def prepare_mask_latents(
- self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance, noise_aug_strength
- ):
- # resize the mask to latents shape as we concatenate the mask to the latents
- # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
- # and half precision
-
- if mask is not None:
- mask = mask.to(device=device, dtype=self.vae.dtype)
- bs = 1
- new_mask = []
- for i in range(0, mask.shape[0], bs):
- mask_bs = mask[i : i + bs]
- mask_bs = self.vae.encode(mask_bs)[0]
- mask_bs = mask_bs.mode()
- new_mask.append(mask_bs)
- mask = torch.cat(new_mask, dim = 0)
- mask = mask * self.vae.config.scaling_factor
-
- if masked_image is not None:
- if self.transformer.config.add_noise_in_inpaint_model:
- masked_image = add_noise_to_reference_video(masked_image, ratio=noise_aug_strength)
- masked_image = masked_image.to(device=device, dtype=self.vae.dtype)
- bs = 1
- new_mask_pixel_values = []
- for i in range(0, masked_image.shape[0], bs):
- mask_pixel_values_bs = masked_image[i : i + bs]
- mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0]
- mask_pixel_values_bs = mask_pixel_values_bs.mode()
- new_mask_pixel_values.append(mask_pixel_values_bs)
- masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
- masked_image_latents = masked_image_latents * self.vae.config.scaling_factor
- else:
- masked_image_latents = None
-
- return mask, masked_image_latents
-
- def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
- latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
- latents = 1 / self.vae.config.scaling_factor * latents
-
- frames = self.vae.decode(latents).sample
- frames = (frames / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
- frames = frames.cpu().float().numpy()
- return frames
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- def _gaussian_weights(self, t_tile_length, t_batch_size):
- from numpy import pi, exp, sqrt
-
- var = 0.01
- midpoint = (t_tile_length - 1) / 2 # -1 because index goes from 0 to latent_width - 1
- t_probs = [exp(-(t-midpoint)*(t-midpoint)/(t_tile_length*t_tile_length)/(2*var)) / sqrt(2*pi*var) for t in range(t_tile_length)]
- weights = torch.tensor(t_probs)
- weights = weights.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(4).repeat(1, t_batch_size,1, 1, 1)
- return weights
-
- # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
- def check_inputs(
- self,
- prompt,
- height,
- width,
- negative_prompt,
- callback_on_step_end_tensor_inputs,
- prompt_embeds=None,
- negative_prompt_embeds=None,
- ):
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- if callback_on_step_end_tensor_inputs is not None and not all(
- k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
- ):
- raise ValueError(
- f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
- )
- if prompt is not None and prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
- " only forward one of the two."
- )
- elif prompt is None and prompt_embeds is None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
- )
- elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if negative_prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if prompt_embeds is not None and negative_prompt_embeds is not None:
- if prompt_embeds.shape != negative_prompt_embeds.shape:
- raise ValueError(
- "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
- f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
- f" {negative_prompt_embeds.shape}."
- )
-
- def fuse_qkv_projections(self) -> None:
- r"""Enables fused QKV projections."""
- self.fusing_transformer = True
- self.transformer.fuse_qkv_projections()
-
- def unfuse_qkv_projections(self) -> None:
- r"""Disable QKV projection fusion if enabled."""
- if not self.fusing_transformer:
- logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
- else:
- self.transformer.unfuse_qkv_projections()
- self.fusing_transformer = False
-
- def _prepare_rotary_positional_embeddings(
- self,
- height: int,
- width: int,
- num_frames: int,
- device: torch.device,
- start_frame: Optional[int] = None,
- end_frame: Optional[int] = None,
- context_frames: Optional[int] = None,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-
- grid_crops_coords = get_resize_crop_region_for_grid(
- (grid_height, grid_width), base_size_width, base_size_height
- )
- freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
- embed_dim=self.transformer.config.attention_head_dim,
- crops_coords=grid_crops_coords,
- grid_size=(grid_height, grid_width),
- temporal_size=num_frames,
- use_real=True,
- )
-
- if start_frame is not None or context_frames is not None:
- freqs_cos = freqs_cos.view(num_frames, grid_height * grid_width, -1)
- freqs_sin = freqs_sin.view(num_frames, grid_height * grid_width, -1)
- if context_frames is not None:
- freqs_cos = freqs_cos[context_frames]
- freqs_sin = freqs_sin[context_frames]
- else:
- freqs_cos = freqs_cos[start_frame:end_frame]
- freqs_sin = freqs_sin[start_frame:end_frame]
-
- freqs_cos = freqs_cos.view(-1, freqs_cos.shape[-1])
- freqs_sin = freqs_sin.view(-1, freqs_sin.shape[-1])
-
- freqs_cos = freqs_cos.to(device=device)
- freqs_sin = freqs_sin.to(device=device)
- return freqs_cos, freqs_sin
-
- @property
- def guidance_scale(self):
- return self._guidance_scale
-
- @property
- def num_timesteps(self):
- return self._num_timesteps
-
- @property
- def interrupt(self):
- return self._interrupt
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
- def get_timesteps(self, num_inference_steps, strength, device):
- # get the original timestep using init_timestep
- init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
- t_start = max(num_inference_steps - init_timestep, 0)
- timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
- return timesteps, num_inference_steps - t_start
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_DOC_STRING)
- def __call__(
- self,
- prompt: Optional[Union[str, List[str]]] = None,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- height: int = 480,
- width: int = 720,
- video: Union[torch.FloatTensor] = None,
- mask_video: Union[torch.FloatTensor] = None,
- masked_video_latents: Union[torch.FloatTensor] = None,
- num_frames: int = 49,
- num_inference_steps: int = 50,
- timesteps: Optional[List[int]] = None,
- guidance_scale: float = 6,
- use_dynamic_cfg: bool = False,
- num_videos_per_prompt: int = 1,
- eta: float = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- output_type: str = "numpy",
- return_dict: bool = False,
- callback_on_step_end: Optional[
- Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
- ] = None,
- callback_on_step_end_tensor_inputs: List[str] = ["latents"],
- max_sequence_length: int = 226,
- strength: float = 1,
- noise_aug_strength: float = 0.0563,
- comfyui_progressbar: bool = False,
- context_schedule: Optional[str] = None,
- context_frames: Optional[int] = None,
- context_stride: Optional[int] = None,
- context_overlap: Optional[int] = None,
- freenoise: Optional[bool] = True,
- tora: Optional[dict] = None,
- ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
- """
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
- instead.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image. This is set to 1024 by default for the best results.
- width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The width in pixels of the generated image. This is set to 1024 by default for the best results.
- num_frames (`int`, defaults to `48`):
- Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
- contain 1 extra frame because CogVideoX_Fun is conditioned with (num_seconds * fps + 1) frames where
- num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
- needs to be satisfied is that of divisibility mentioned above.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- timesteps (`List[int]`, *optional*):
- Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
- in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
- passed will be used. Must be in descending order.
- guidance_scale (`float`, *optional*, defaults to 7.0):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- num_videos_per_prompt (`int`, *optional*, defaults to 1):
- The number of videos to generate per prompt.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
- to make generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
- of a plain tuple.
- callback_on_step_end (`Callable`, *optional*):
- A function that calls at the end of each denoising steps during the inference. The function is called
- with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
- callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
- `callback_on_step_end_tensor_inputs`.
- callback_on_step_end_tensor_inputs (`List`, *optional*):
- The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
- will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
- `._callback_tensor_inputs` attribute of your pipeline class.
- max_sequence_length (`int`, defaults to `226`):
- Maximum sequence length in encoded prompt. Must be consistent with
- `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
-
- Examples:
-
- Returns:
- [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`:
- [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a
- `tuple`. When returning a tuple, the first element is a list with the generated images.
- """
-
- # if num_frames > 49:
- # raise ValueError(
- # "The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation."
- # )
-
- if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
- callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
- height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
- width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
- num_videos_per_prompt = 1
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(
- prompt,
- height,
- width,
- negative_prompt,
- callback_on_step_end_tensor_inputs,
- prompt_embeds,
- negative_prompt_embeds,
- )
- self._guidance_scale = guidance_scale
- self._interrupt = False
-
- # 2. Default call parameters
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- device = self._execution_device
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- if do_classifier_free_guidance:
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-
- # 4. set timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps, num_inference_steps = self.get_timesteps(
- num_inference_steps=num_inference_steps, strength=strength, device=device
- )
- self._num_timesteps = len(timesteps)
- if comfyui_progressbar:
- from comfy.utils import ProgressBar
- pbar = ProgressBar(num_inference_steps + 2)
- # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
- latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
- # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
- is_strength_max = strength == 1.0
-
- # 5. Prepare latents.
- if video is not None:
- video_length = video.shape[2]
- init_video = self.image_processor.preprocess(rearrange(video, "b c f h w -> (b f) c h w"), height=height, width=width)
- init_video = init_video.to(dtype=torch.float32)
- init_video = rearrange(init_video, "(b f) c h w -> b c f h w", f=video_length)
- else:
- init_video = None
-
- num_channels_latents = self.vae.config.latent_channels
- num_channels_transformer = self.transformer.config.in_channels
- return_image_latents = num_channels_transformer == num_channels_latents
-
- latents_outputs = self.prepare_latents(
- batch_size * num_videos_per_prompt,
- num_channels_latents,
- height,
- width,
- video_length,
- self.vae.dtype,
- device,
- generator,
- latents,
- video=init_video,
- timestep=latent_timestep,
- is_strength_max=is_strength_max,
- return_noise=True,
- return_video_latents=return_image_latents,
- context_size=context_frames,
- context_overlap=context_overlap,
- freenoise=freenoise,
- )
- if return_image_latents:
- latents, noise, image_latents = latents_outputs
- else:
- latents, noise = latents_outputs
- if comfyui_progressbar:
- pbar.update(1)
-
- if mask_video is not None:
- if (mask_video == 255).all():
- mask_latents = torch.zeros_like(latents)[:, :, :1].to(latents.device, latents.dtype)
- masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype)
-
- mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents
- masked_video_latents_input = (
- torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
- )
- inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype)
- else:
- # Prepare mask latent variables
- video_length = video.shape[2]
- mask_condition = self.mask_processor.preprocess(rearrange(mask_video, "b c f h w -> (b f) c h w"), height=height, width=width)
- mask_condition = mask_condition.to(dtype=torch.float32)
- mask_condition = rearrange(mask_condition, "(b f) c h w -> b c f h w", f=video_length)
-
- if num_channels_transformer != num_channels_latents:
- mask_condition_tile = torch.tile(mask_condition, [1, 3, 1, 1, 1])
- if masked_video_latents is None:
- masked_video = init_video * (mask_condition_tile < 0.5) + torch.ones_like(init_video) * (mask_condition_tile > 0.5) * -1
- else:
- masked_video = masked_video_latents
-
- _, masked_video_latents = self.prepare_mask_latents(
- None,
- masked_video,
- batch_size,
- height,
- width,
- self.vae.dtype,
- device,
- generator,
- do_classifier_free_guidance,
- noise_aug_strength=noise_aug_strength,
- )
- mask_latents = resize_mask(1 - mask_condition, masked_video_latents)
- mask_latents = mask_latents.to(masked_video_latents.device) * self.vae.config.scaling_factor
-
- mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1])
- mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype)
-
- mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents
- masked_video_latents_input = (
- torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
- )
-
- mask = rearrange(mask, "b c f h w -> b f c h w")
- mask_input = rearrange(mask_input, "b c f h w -> b f c h w")
- masked_video_latents_input = rearrange(masked_video_latents_input, "b c f h w -> b f c h w")
-
- inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype)
- else:
- mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1])
- mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype)
- mask = rearrange(mask, "b c f h w -> b f c h w")
-
- inpaint_latents = None
- else:
- if num_channels_transformer != num_channels_latents:
- mask = torch.zeros_like(latents).to(latents.device, latents.dtype)
- masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype)
-
- mask_input = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
- masked_video_latents_input = (
- torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
- )
- inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype)
- else:
- mask = torch.zeros_like(init_video[:, :1])
- mask = torch.tile(mask, [1, num_channels_latents, 1, 1, 1])
- mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype)
- mask = rearrange(mask, "b c f h w -> b f c h w")
-
- inpaint_latents = None
- if comfyui_progressbar:
- pbar.update(1)
-
- # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 7. Create rotary embeds if required
- if context_schedule is not None and context_schedule == "temporal_tiling":
- t_tile_length = context_frames
- t_tile_overlap = context_overlap
- t_tile_weights = self._gaussian_weights(t_tile_length=t_tile_length, t_batch_size=1).to(latents.device).to(self.vae.dtype)
- use_temporal_tiling = True
- print("Temporal tiling enabled")
- elif context_schedule is not None:
- print(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap")
- use_temporal_tiling = False
- use_context_schedule = True
- from .context import get_context_scheduler
- context = get_context_scheduler(context_schedule)
- else:
- use_temporal_tiling = False
- use_context_schedule = False
- print("Temporal tiling and context schedule disabled")
- # 7. Create rotary embeds if required
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
- if tora is not None and do_classifier_free_guidance:
- video_flow_features = tora["video_flow_features"].repeat(1, 2, 1, 1, 1).contiguous()
-
- if tora is not None:
- trajectory_length = tora["video_flow_features"].shape[1]
- logger.info(f"Tora trajectory length: {trajectory_length}")
- logger.info(f"Tora trajectory shape: {tora['video_flow_features'].shape}")
- logger.info(f"latents shape: {latents.shape}")
- if trajectory_length != latents.shape[1]:
- raise ValueError(f"Tora trajectory length {trajectory_length} does not match latent count {latents.shape[2]}")
- for module in self.transformer.fuser_list:
- for param in module.parameters():
- param.data = param.data.to(device)
-
- # 8. Denoising loop
- num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
- from ..latent_preview import prepare_callback
- callback = prepare_callback(self.transformer, num_inference_steps)
-
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- # for DPM-solver++
- old_pred_original_sample = None
- for i, t in enumerate(timesteps):
- if self.interrupt:
- continue
-
- if use_temporal_tiling and isinstance(self.scheduler, CogVideoXDDIMScheduler):
- #temporal tiling code based on https://github.com/mayuelala/FollowYourEmoji/blob/main/models/video_pipeline.py
- # =====================================================
- grid_ts = 0
- cur_t = 0
- while cur_t < latents.shape[1]:
- cur_t = max(grid_ts * t_tile_length - t_tile_overlap * grid_ts, 0) + t_tile_length
- grid_ts += 1
-
- all_t = latents.shape[1]
- latents_all_list = []
- # =====================================================
-
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, t_tile_length, device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
-
- for t_i in range(grid_ts):
- if t_i < grid_ts - 1:
- ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
- if t_i == grid_ts - 1:
- ofs_t = all_t - t_tile_length
-
- input_start_t = ofs_t
- input_end_t = ofs_t + t_tile_length
-
- latents_tile = latents[:, input_start_t:input_end_t,:, :, :]
- inpaint_latents_tile = inpaint_latents[:, input_start_t:input_end_t, :, :, :]
-
- latent_model_input_tile = torch.cat([latents_tile] * 2) if do_classifier_free_guidance else latents_tile
- latent_model_input_tile = self.scheduler.scale_model_input(latent_model_input_tile, t)
-
- #t_input = t[None].to(device)
- t_input = t.expand(latent_model_input_tile.shape[0]) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-
- # predict noise model_output
- noise_pred = self.transformer(
- hidden_states=latent_model_input_tile,
- encoder_hidden_states=prompt_embeds,
- timestep=t_input,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- inpaint_latents=inpaint_latents_tile,
- )[0]
- noise_pred = noise_pred.float()
-
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents_tile = self.scheduler.step(noise_pred, t, latents_tile.to(self.vae.dtype), **extra_step_kwargs, return_dict=False)[0]
- latents_all_list.append(latents_tile)
-
- # ==========================================
- latents_all = torch.zeros(latents.shape, device=latents.device, dtype=self.vae.dtype)
- contributors = torch.zeros(latents.shape, device=latents.device, dtype=self.vae.dtype)
- # Add each tile contribution to overall latents
- for t_i in range(grid_ts):
- if t_i < grid_ts - 1:
- ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
- if t_i == grid_ts - 1:
- ofs_t = all_t - t_tile_length
-
- input_start_t = ofs_t
- input_end_t = ofs_t + t_tile_length
-
- latents_all[:, input_start_t:input_end_t,:, :, :] += latents_all_list[t_i] * t_tile_weights
- contributors[:, input_start_t:input_end_t,:, :, :] += t_tile_weights
-
- latents_all /= contributors
-
- latents = latents_all
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- pbar.update(1)
- # ==========================================
- elif use_context_schedule:
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
- timestep = t.expand(latent_model_input.shape[0])
-
- context_queue = list(context(
- i, num_inference_steps, latents.shape[1], context_frames, context_stride, context_overlap,
- ))
- counter = torch.zeros_like(latent_model_input)
- noise_pred = torch.zeros_like(latent_model_input)
-
- current_step_percentage = i / num_inference_steps
-
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, context_frames, device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
-
- for c in context_queue:
- partial_latent_model_input = latent_model_input[:, c, :, :, :]
- partial_inpaint_latents = inpaint_latents[:, c, :, :, :]
- partial_inpaint_latents[:, 0, :, :, :] = inpaint_latents[:, 0, :, :, :]
- if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]):
- if do_classifier_free_guidance:
- partial_video_flow_features = tora["video_flow_features"][:, c, :, :, :].repeat(1, 2, 1, 1, 1).contiguous()
- else:
- partial_video_flow_features = tora["video_flow_features"][:, c, :, :, :]
- else:
- partial_video_flow_features = None
-
- # predict noise model_output
- noise_pred[:, c, :, :, :] += self.transformer(
- hidden_states=partial_latent_model_input,
- encoder_hidden_states=prompt_embeds,
- timestep=timestep,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- inpaint_latents=partial_inpaint_latents,
- video_flow_features=partial_video_flow_features
- )[0]
-
- counter[:, c, :, :, :] += 1
-
- noise_pred = noise_pred.float()
-
- noise_pred /= counter
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- if not isinstance(self.scheduler, CogVideoXDPMScheduler):
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
- else:
- latents, old_pred_original_sample = self.scheduler.step(
- noise_pred,
- old_pred_original_sample,
- t,
- timesteps[i - 1] if i > 0 else None,
- latents,
- **extra_step_kwargs,
- return_dict=False,
- )
- latents = latents.to(prompt_embeds.dtype)
-
- # call the callback, if provided
- if callback_on_step_end is not None:
- callback_kwargs = {}
- for k in callback_on_step_end_tensor_inputs:
- callback_kwargs[k] = locals()[k]
- callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
- latents = callback_outputs.pop("latents", latents)
- prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
- negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if comfyui_progressbar:
- pbar.update(1)
-
- else:
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
- timestep = t.expand(latent_model_input.shape[0])
-
- current_step_percentage = i / num_inference_steps
-
- # predict noise model_output
- noise_pred = self.transformer(
- hidden_states=latent_model_input,
- encoder_hidden_states=prompt_embeds,
- timestep=timestep,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- inpaint_latents=inpaint_latents,
- video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
-
- )[0]
- noise_pred = noise_pred.float()
-
- # perform guidance
- if use_dynamic_cfg:
- self._guidance_scale = 1 + guidance_scale * (
- (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
- )
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- if not isinstance(self.scheduler, CogVideoXDPMScheduler):
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
- else:
- latents, old_pred_original_sample = self.scheduler.step(
- noise_pred,
- old_pred_original_sample,
- t,
- timesteps[i - 1] if i > 0 else None,
- latents,
- **extra_step_kwargs,
- return_dict=False,
- )
- latents = latents.to(prompt_embeds.dtype)
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if comfyui_progressbar:
- if callback is not None:
- callback(i, latents.detach()[-1], None, num_inference_steps)
- else:
- pbar.update(1)
-
- # if output_type == "numpy":
- # video = self.decode_latents(latents)
- # elif not output_type == "latent":
- # video = self.decode_latents(latents)
- # video = self.video_processor.postprocess_video(video=video, output_type=output_type)
- # else:
- # video = latents
-
- # Offload all models
- self.maybe_free_model_hooks()
-
- # if not return_dict:
- # video = torch.from_numpy(video)
-
- return latents
\ No newline at end of file
diff --git a/cogvideox_fun/transformer_3d.py b/cogvideox_fun/transformer_3d.py
deleted file mode 100644
index 83614e2..0000000
--- a/cogvideox_fun/transformer_3d.py
+++ /dev/null
@@ -1,873 +0,0 @@
-# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any, Dict, Optional, Tuple, Union
-
-import os
-import json
-import torch
-import glob
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import is_torch_version, logging
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from diffusers.models.attention import Attention, FeedForward
-from diffusers.models.attention_processor import AttentionProcessor#, CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-from einops import rearrange
-try:
- from sageattention import sageattn
- SAGEATTN_IS_AVAVILABLE = True
- logger.info("Using sageattn")
-except:
- logger.info("sageattn not found, using sdpa")
- SAGEATTN_IS_AVAVILABLE = False
-
-def fft(tensor):
- tensor_fft = torch.fft.fft2(tensor)
- tensor_fft_shifted = torch.fft.fftshift(tensor_fft)
- B, C, H, W = tensor.size()
- radius = min(H, W) // 5
-
- Y, X = torch.meshgrid(torch.arange(H), torch.arange(W))
- center_x, center_y = W // 2, H // 2
- mask = (X - center_x) ** 2 + (Y - center_y) ** 2 <= radius ** 2
- low_freq_mask = mask.unsqueeze(0).unsqueeze(0).to(tensor.device)
- high_freq_mask = ~low_freq_mask
-
- low_freq_fft = tensor_fft_shifted * low_freq_mask
- high_freq_fft = tensor_fft_shifted * high_freq_mask
-
- return low_freq_fft, high_freq_fft
-
-class CogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- query = attn.to_q(hidden_states)
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn.heads
-
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- from diffusers.models.embeddings import apply_rotary_emb
-
- query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
- if not attn.is_cross_attention:
- key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
- if SAGEATTN_IS_AVAVILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
-
-class FusedCogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- qkv = attn.to_qkv(hidden_states)
- split_size = qkv.shape[-1] // 3
- query, key, value = torch.split(qkv, split_size, dim=-1)
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn.heads
-
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- from diffusers.models.embeddings import apply_rotary_emb
-
- query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
- if not attn.is_cross_attention:
- key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
- if SAGEATTN_IS_AVAVILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
-class CogVideoXPatchEmbed(nn.Module):
- def __init__(
- self,
- patch_size: int = 2,
- in_channels: int = 16,
- embed_dim: int = 1920,
- text_embed_dim: int = 4096,
- bias: bool = True,
- ) -> None:
- super().__init__()
- self.patch_size = patch_size
-
- self.proj = nn.Conv2d(
- in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
- )
- self.text_proj = nn.Linear(text_embed_dim, embed_dim)
-
- def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
- r"""
- Args:
- text_embeds (`torch.Tensor`):
- Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
- image_embeds (`torch.Tensor`):
- Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
- """
- text_embeds = self.text_proj(text_embeds)
-
- batch, num_frames, channels, height, width = image_embeds.shape
- image_embeds = image_embeds.reshape(-1, channels, height, width)
- image_embeds = self.proj(image_embeds)
- image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
- image_embeds = image_embeds.flatten(3).transpose(2, 3) # [batch, num_frames, height x width, channels]
- image_embeds = image_embeds.flatten(1, 2) # [batch, num_frames x height x width, channels]
-
- embeds = torch.cat(
- [text_embeds, image_embeds], dim=1
- ).contiguous() # [batch, seq_length + num_frames x height x width, channels]
- return embeds
-
-@maybe_allow_in_graph
-class CogVideoXBlock(nn.Module):
- r"""
- Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
-
- Parameters:
- dim (`int`):
- The number of channels in the input and output.
- num_attention_heads (`int`):
- The number of heads to use for multi-head attention.
- attention_head_dim (`int`):
- The number of channels in each head.
- time_embed_dim (`int`):
- The number of channels in timestep embedding.
- dropout (`float`, defaults to `0.0`):
- The dropout probability to use.
- activation_fn (`str`, defaults to `"gelu-approximate"`):
- Activation function to be used in feed-forward.
- attention_bias (`bool`, defaults to `False`):
- Whether or not to use bias in attention projection layers.
- qk_norm (`bool`, defaults to `True`):
- Whether or not to use normalization after query and key projections in Attention.
- norm_elementwise_affine (`bool`, defaults to `True`):
- Whether to use learnable elementwise affine parameters for normalization.
- norm_eps (`float`, defaults to `1e-5`):
- Epsilon value for normalization layers.
- final_dropout (`bool` defaults to `False`):
- Whether to apply a final dropout after the last feed-forward layer.
- ff_inner_dim (`int`, *optional*, defaults to `None`):
- Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
- ff_bias (`bool`, defaults to `True`):
- Whether or not to use bias in Feed-forward layer.
- attention_out_bias (`bool`, defaults to `True`):
- Whether or not to use bias in Attention output projection layer.
- """
-
- def __init__(
- self,
- dim: int,
- num_attention_heads: int,
- attention_head_dim: int,
- time_embed_dim: int,
- dropout: float = 0.0,
- activation_fn: str = "gelu-approximate",
- attention_bias: bool = False,
- qk_norm: bool = True,
- norm_elementwise_affine: bool = True,
- norm_eps: float = 1e-5,
- final_dropout: bool = True,
- ff_inner_dim: Optional[int] = None,
- ff_bias: bool = True,
- attention_out_bias: bool = True,
- ):
- super().__init__()
-
- # 1. Self Attention
- self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
- self.attn1 = Attention(
- query_dim=dim,
- dim_head=attention_head_dim,
- heads=num_attention_heads,
- qk_norm="layer_norm" if qk_norm else None,
- eps=1e-6,
- bias=attention_bias,
- out_bias=attention_out_bias,
- processor=CogVideoXAttnProcessor2_0(),
- )
-
- # 2. Feed Forward
- self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
- self.ff = FeedForward(
- dim,
- dropout=dropout,
- activation_fn=activation_fn,
- final_dropout=final_dropout,
- inner_dim=ff_inner_dim,
- bias=ff_bias,
- )
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- temb: torch.Tensor,
- image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- video_flow_feature: Optional[torch.Tensor] = None,
- fuser=None,
- fastercache_counter=0,
- fastercache_start_step=15,
- fastercache_device="cuda:0",
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- # norm & modulate
- norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
- hidden_states, encoder_hidden_states, temb
- )
- # Tora Motion-guidance Fuser
- if video_flow_feature is not None:
- H, W = video_flow_feature.shape[-2:]
- T = norm_hidden_states.shape[1] // H // W
- h = rearrange(norm_hidden_states, "B (T H W) C -> (B T) C H W", H=H, W=W)
- h = fuser(h, video_flow_feature.to(h), T=T)
- norm_hidden_states = rearrange(h, "(B T) C H W -> B (T H W) C", T=T)
- del h, fuser
- #fastercache
- B = norm_hidden_states.shape[0]
- if fastercache_counter >= fastercache_start_step + 3 and fastercache_counter%3!=0 and self.cached_hidden_states[-1].shape[0] >= B:
- attn_hidden_states = (
- self.cached_hidden_states[1][:B] +
- (self.cached_hidden_states[1][:B] - self.cached_hidden_states[0][:B])
- * 0.3
- ).to(norm_hidden_states.device, non_blocking=True)
- attn_encoder_hidden_states = (
- self.cached_encoder_hidden_states[1][:B] +
- (self.cached_encoder_hidden_states[1][:B] - self.cached_encoder_hidden_states[0][:B])
- * 0.3
- ).to(norm_hidden_states.device, non_blocking=True)
- else:
- attn_hidden_states, attn_encoder_hidden_states = self.attn1(
- hidden_states=norm_hidden_states,
- encoder_hidden_states=norm_encoder_hidden_states,
- image_rotary_emb=image_rotary_emb,
- )
- if fastercache_counter == fastercache_start_step:
- self.cached_hidden_states = [attn_hidden_states.to(fastercache_device), attn_hidden_states.to(fastercache_device)]
- self.cached_encoder_hidden_states = [attn_encoder_hidden_states.to(fastercache_device), attn_encoder_hidden_states.to(fastercache_device)]
- elif fastercache_counter > fastercache_start_step:
- self.cached_hidden_states[-1].copy_(attn_hidden_states.to(fastercache_device))
- self.cached_encoder_hidden_states[-1].copy_(attn_encoder_hidden_states.to(fastercache_device))
-
- hidden_states = hidden_states + gate_msa * attn_hidden_states
- encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
-
- # norm & modulate
- norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
- hidden_states, encoder_hidden_states, temb
- )
-
- # feed-forward
- norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
- ff_output = self.ff(norm_hidden_states)
-
- hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
- encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
-
- return hidden_states, encoder_hidden_states
-
-
-class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
- """
- A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
-
- Parameters:
- num_attention_heads (`int`, defaults to `30`):
- The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `64`):
- The number of channels in each head.
- in_channels (`int`, defaults to `16`):
- The number of channels in the input.
- out_channels (`int`, *optional*, defaults to `16`):
- The number of channels in the output.
- flip_sin_to_cos (`bool`, defaults to `True`):
- Whether to flip the sin to cos in the time embedding.
- time_embed_dim (`int`, defaults to `512`):
- Output dimension of timestep embeddings.
- text_embed_dim (`int`, defaults to `4096`):
- Input dimension of text embeddings from the text encoder.
- num_layers (`int`, defaults to `30`):
- The number of layers of Transformer blocks to use.
- dropout (`float`, defaults to `0.0`):
- The dropout probability to use.
- attention_bias (`bool`, defaults to `True`):
- Whether or not to use bias in the attention projection layers.
- sample_width (`int`, defaults to `90`):
- The width of the input latents.
- sample_height (`int`, defaults to `60`):
- The height of the input latents.
- sample_frames (`int`, defaults to `49`):
- The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
- instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
- but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
- K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
- patch_size (`int`, defaults to `2`):
- The size of the patches to use in the patch embedding layer.
- temporal_compression_ratio (`int`, defaults to `4`):
- The compression ratio across the temporal dimension. See documentation for `sample_frames`.
- max_text_seq_length (`int`, defaults to `226`):
- The maximum sequence length of the input text embeddings.
- activation_fn (`str`, defaults to `"gelu-approximate"`):
- Activation function to use in feed-forward.
- timestep_activation_fn (`str`, defaults to `"silu"`):
- Activation function to use when generating the timestep embeddings.
- norm_elementwise_affine (`bool`, defaults to `True`):
- Whether or not to use elementwise affine in normalization layers.
- norm_eps (`float`, defaults to `1e-5`):
- The epsilon value to use in normalization layers.
- spatial_interpolation_scale (`float`, defaults to `1.875`):
- Scaling factor to apply in 3D positional embeddings across spatial dimensions.
- temporal_interpolation_scale (`float`, defaults to `1.0`):
- Scaling factor to apply in 3D positional embeddings across temporal dimensions.
- """
-
- _supports_gradient_checkpointing = True
-
- @register_to_config
- def __init__(
- self,
- num_attention_heads: int = 30,
- attention_head_dim: int = 64,
- in_channels: int = 16,
- out_channels: Optional[int] = 16,
- flip_sin_to_cos: bool = True,
- freq_shift: int = 0,
- time_embed_dim: int = 512,
- text_embed_dim: int = 4096,
- num_layers: int = 30,
- dropout: float = 0.0,
- attention_bias: bool = True,
- sample_width: int = 90,
- sample_height: int = 60,
- sample_frames: int = 49,
- patch_size: int = 2,
- temporal_compression_ratio: int = 4,
- max_text_seq_length: int = 226,
- activation_fn: str = "gelu-approximate",
- timestep_activation_fn: str = "silu",
- norm_elementwise_affine: bool = True,
- norm_eps: float = 1e-5,
- spatial_interpolation_scale: float = 1.875,
- temporal_interpolation_scale: float = 1.0,
- use_rotary_positional_embeddings: bool = False,
- add_noise_in_inpaint_model: bool = False,
- ):
- super().__init__()
- inner_dim = num_attention_heads * attention_head_dim
-
- post_patch_height = sample_height // patch_size
- post_patch_width = sample_width // patch_size
- post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1
- self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames
- self.post_patch_height = post_patch_height
- self.post_patch_width = post_patch_width
- self.post_time_compression_frames = post_time_compression_frames
- self.patch_size = patch_size
-
- # 1. Patch embedding
- self.patch_embed = CogVideoXPatchEmbed(patch_size, in_channels, inner_dim, text_embed_dim, bias=True)
- self.embedding_dropout = nn.Dropout(dropout)
-
- # 2. 3D positional embeddings
- spatial_pos_embedding = get_3d_sincos_pos_embed(
- inner_dim,
- (post_patch_width, post_patch_height),
- post_time_compression_frames,
- spatial_interpolation_scale,
- temporal_interpolation_scale,
- )
- spatial_pos_embedding = torch.from_numpy(spatial_pos_embedding).flatten(0, 1)
- pos_embedding = torch.zeros(1, max_text_seq_length + self.num_patches, inner_dim, requires_grad=False)
- pos_embedding.data[:, max_text_seq_length:].copy_(spatial_pos_embedding)
- self.register_buffer("pos_embedding", pos_embedding, persistent=False)
-
- # 3. Time embeddings
- self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
- self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
-
- # 4. Define spatio-temporal transformers blocks
- self.transformer_blocks = nn.ModuleList(
- [
- CogVideoXBlock(
- dim=inner_dim,
- num_attention_heads=num_attention_heads,
- attention_head_dim=attention_head_dim,
- time_embed_dim=time_embed_dim,
- dropout=dropout,
- activation_fn=activation_fn,
- attention_bias=attention_bias,
- norm_elementwise_affine=norm_elementwise_affine,
- norm_eps=norm_eps,
- )
- for _ in range(num_layers)
- ]
- )
- self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
-
- # 5. Output blocks
- self.norm_out = AdaLayerNorm(
- embedding_dim=time_embed_dim,
- output_dim=2 * inner_dim,
- norm_elementwise_affine=norm_elementwise_affine,
- norm_eps=norm_eps,
- chunk_dim=1,
- )
- self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
-
- self.gradient_checkpointing = False
-
- self.fuser_list = None
-
- self.use_fastercache = False
- self.fastercache_counter = 0
- self.fastercache_start_step = 15
- self.fastercache_lf_step = 40
- self.fastercache_hf_step = 30
- self.fastercache_device = "cuda"
-
- def _set_gradient_checkpointing(self, module, value=False):
- self.gradient_checkpointing = value
-
- @property
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
- def attn_processors(self) -> Dict[str, AttentionProcessor]:
- r"""
- Returns:
- `dict` of attention processors: A dictionary containing all attention processors used in the model with
- indexed by its weight name.
- """
- # set recursively
- processors = {}
-
- def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
- if hasattr(module, "get_processor"):
- processors[f"{name}.processor"] = module.get_processor()
-
- for sub_name, child in module.named_children():
- fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
- return processors
-
- for name, module in self.named_children():
- fn_recursive_add_processors(name, module, processors)
-
- return processors
-
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
- def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
- r"""
- Sets the attention processor to use to compute attention.
-
- Parameters:
- processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
- The instantiated processor class or a dictionary of processor classes that will be set as the processor
- for **all** `Attention` layers.
-
- If `processor` is a dict, the key needs to define the path to the corresponding cross attention
- processor. This is strongly recommended when setting trainable attention processors.
-
- """
- count = len(self.attn_processors.keys())
-
- if isinstance(processor, dict) and len(processor) != count:
- raise ValueError(
- f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
- f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
- )
-
- def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
- if hasattr(module, "set_processor"):
- if not isinstance(processor, dict):
- module.set_processor(processor)
- else:
- module.set_processor(processor.pop(f"{name}.processor"))
-
- for sub_name, child in module.named_children():
- fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
- for name, module in self.named_children():
- fn_recursive_attn_processor(name, module, processor)
-
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
- def fuse_qkv_projections(self):
- """
- Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
- are fused. For cross-attention modules, key and value projection matrices are fused.
-
-
-
- This API is 🧪 experimental.
-
-
- """
- self.original_attn_processors = None
-
- for _, attn_processor in self.attn_processors.items():
- if "Added" in str(attn_processor.__class__.__name__):
- raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
-
- self.original_attn_processors = self.attn_processors
-
- for module in self.modules():
- if isinstance(module, Attention):
- module.fuse_projections(fuse=True)
-
- self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())
-
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
- def unfuse_qkv_projections(self):
- """Disables the fused QKV projection if enabled.
-
-
-
- This API is 🧪 experimental.
-
-
-
- """
- if self.original_attn_processors is not None:
- self.set_attn_processor(self.original_attn_processors)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- timestep: Union[int, float, torch.LongTensor],
- timestep_cond: Optional[torch.Tensor] = None,
- inpaint_latents: Optional[torch.Tensor] = None,
- control_latents: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- video_flow_features: Optional[torch.Tensor] = None,
- return_dict: bool = True,
- ):
- batch_size, num_frames, channels, height, width = hidden_states.shape
-
- # 1. Time embedding
- timesteps = timestep
- t_emb = self.time_proj(timesteps)
-
- # timesteps does not contain any weights and will always return f32 tensors
- # but time_embedding might actually be running in fp16. so we need to cast here.
- # there might be better ways to encapsulate this.
- t_emb = t_emb.to(dtype=hidden_states.dtype)
- emb = self.time_embedding(t_emb, timestep_cond)
-
- # 2. Patch embedding
- if inpaint_latents is not None:
- hidden_states = torch.concat([hidden_states, inpaint_latents], 2)
- if control_latents is not None:
- hidden_states = torch.concat([hidden_states, control_latents], 2)
- hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
-
- # 3. Position embedding
- text_seq_length = encoder_hidden_states.shape[1]
- if not self.config.use_rotary_positional_embeddings:
- seq_length = height * width * num_frames // (self.config.patch_size**2)
- # pos_embeds = self.pos_embedding[:, : text_seq_length + seq_length]
- pos_embeds = self.pos_embedding
- emb_size = hidden_states.size()[-1]
- pos_embeds_without_text = pos_embeds[:, text_seq_length: ].view(1, self.post_time_compression_frames, self.post_patch_height, self.post_patch_width, emb_size)
- pos_embeds_without_text = pos_embeds_without_text.permute([0, 4, 1, 2, 3])
- pos_embeds_without_text = F.interpolate(pos_embeds_without_text,size=[self.post_time_compression_frames, height // self.config.patch_size, width // self.config.patch_size],mode='trilinear',align_corners=False)
- pos_embeds_without_text = pos_embeds_without_text.permute([0, 2, 3, 4, 1]).view(1, -1, emb_size)
- pos_embeds = torch.cat([pos_embeds[:, :text_seq_length], pos_embeds_without_text], dim = 1)
- pos_embeds = pos_embeds[:, : text_seq_length + seq_length]
- hidden_states = hidden_states + pos_embeds
- hidden_states = self.embedding_dropout(hidden_states)
-
- encoder_hidden_states = hidden_states[:, :text_seq_length]
- hidden_states = hidden_states[:, text_seq_length:]
-
- if self.use_fastercache:
- self.fastercache_counter+=1
- if self.fastercache_counter >= self.fastercache_start_step + 3 and self.fastercache_counter % 5 !=0:
- # 4. Transformer blocks
- for i, block in enumerate(self.transformer_blocks):
- hidden_states, encoder_hidden_states = block(
- hidden_states=hidden_states[:1],
- encoder_hidden_states=encoder_hidden_states[:1],
- temb=emb[:1],
- image_rotary_emb=image_rotary_emb,
- video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
- fuser = self.fuser_list[i] if self.fuser_list is not None else None,
- fastercache_counter = self.fastercache_counter,
- fastercache_device = self.fastercache_device
- )
-
- if not self.config.use_rotary_positional_embeddings:
- # CogVideoX-2B
- hidden_states = self.norm_final(hidden_states)
- else:
- # CogVideoX-5B
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
- hidden_states = self.norm_final(hidden_states)
- hidden_states = hidden_states[:, text_seq_length:]
-
- # 5. Final block
- hidden_states = self.norm_out(hidden_states, temb=emb[:1])
- hidden_states = self.proj_out(hidden_states)
-
- # 6. Unpatchify
- p = self.config.patch_size
- output = hidden_states.reshape(1, num_frames, height // p, width // p, channels, p, p)
- output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
-
- (bb, tt, cc, hh, ww) = output.shape
- cond = rearrange(output, "B T C H W -> (B T) C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
- lf_c, hf_c = fft(cond.float())
- #lf_step = 40
- #hf_step = 30
- if self.fastercache_counter <= self.fastercache_lf_step:
- self.delta_lf = self.delta_lf * 1.1
- if self.fastercache_counter >= self.fastercache_hf_step:
- self.delta_hf = self.delta_hf * 1.1
-
- new_hf_uc = self.delta_hf + hf_c
- new_lf_uc = self.delta_lf + lf_c
-
- combine_uc = new_lf_uc + new_hf_uc
- combined_fft = torch.fft.ifftshift(combine_uc)
- recovered_uncond = torch.fft.ifft2(combined_fft).real
- recovered_uncond = rearrange(recovered_uncond.to(output.dtype), "(B T) C H W -> B T C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
- output = torch.cat([output, recovered_uncond])
- else:
- # 4. Transformer blocks
- for i, block in enumerate(self.transformer_blocks):
- hidden_states, encoder_hidden_states = block(
- hidden_states=hidden_states,
- encoder_hidden_states=encoder_hidden_states,
- temb=emb,
- image_rotary_emb=image_rotary_emb,
- video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
- fuser = self.fuser_list[i] if self.fuser_list is not None else None,
- fastercache_counter = self.fastercache_counter,
- fastercache_device = self.fastercache_device
- )
-
- if not self.config.use_rotary_positional_embeddings:
- # CogVideoX-2B
- hidden_states = self.norm_final(hidden_states)
- else:
- # CogVideoX-5B
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
- hidden_states = self.norm_final(hidden_states)
- hidden_states = hidden_states[:, text_seq_length:]
-
- # 5. Final block
- hidden_states = self.norm_out(hidden_states, temb=emb)
- hidden_states = self.proj_out(hidden_states)
-
- # 6. Unpatchify
- p = self.config.patch_size
- output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, channels, p, p)
- output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
-
- if self.fastercache_counter >= self.fastercache_start_step + 1:
- (bb, tt, cc, hh, ww) = output.shape
- cond = rearrange(output[0:1].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
- uncond = rearrange(output[1:2].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
-
- lf_c, hf_c = fft(cond)
- lf_uc, hf_uc = fft(uncond)
-
- self.delta_lf = lf_uc - lf_c
- self.delta_hf = hf_uc - hf_c
-
-
- if not return_dict:
- return (output,)
- return Transformer2DModelOutput(sample=output)
-
- @classmethod
- def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs={}):
- if subfolder is not None:
- pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
- print(f"loaded 3D transformer's pretrained weights from {pretrained_model_path} ...")
-
- config_file = os.path.join(pretrained_model_path, 'config.json')
- if not os.path.isfile(config_file):
- raise RuntimeError(f"{config_file} does not exist")
- with open(config_file, "r") as f:
- config = json.load(f)
-
- from diffusers.utils import WEIGHTS_NAME
- model = cls.from_config(config, **transformer_additional_kwargs)
- model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
- model_file_safetensors = model_file.replace(".bin", ".safetensors")
- if os.path.exists(model_file):
- state_dict = torch.load(model_file, map_location="cpu")
- elif os.path.exists(model_file_safetensors):
- from safetensors.torch import load_file, safe_open
- state_dict = load_file(model_file_safetensors)
- else:
- from safetensors.torch import load_file, safe_open
- model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
- state_dict = {}
- for model_file_safetensors in model_files_safetensors:
- _state_dict = load_file(model_file_safetensors)
- for key in _state_dict:
- state_dict[key] = _state_dict[key]
-
- if model.state_dict()['patch_embed.proj.weight'].size() != state_dict['patch_embed.proj.weight'].size():
- new_shape = model.state_dict()['patch_embed.proj.weight'].size()
- if len(new_shape) == 5:
- state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).expand(new_shape).clone()
- state_dict['patch_embed.proj.weight'][:, :, :-1] = 0
- else:
- if model.state_dict()['patch_embed.proj.weight'].size()[1] > state_dict['patch_embed.proj.weight'].size()[1]:
- model.state_dict()['patch_embed.proj.weight'][:, :state_dict['patch_embed.proj.weight'].size()[1], :, :] = state_dict['patch_embed.proj.weight']
- model.state_dict()['patch_embed.proj.weight'][:, state_dict['patch_embed.proj.weight'].size()[1]:, :, :] = 0
- state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
- else:
- model.state_dict()['patch_embed.proj.weight'][:, :, :, :] = state_dict['patch_embed.proj.weight'][:, :model.state_dict()['patch_embed.proj.weight'].size()[1], :, :]
- state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
-
- tmp_state_dict = {}
- for key in state_dict:
- if key in model.state_dict().keys() and model.state_dict()[key].size() == state_dict[key].size():
- tmp_state_dict[key] = state_dict[key]
- else:
- print(key, "Size don't match, skip")
- state_dict = tmp_state_dict
-
- m, u = model.load_state_dict(state_dict, strict=False)
- print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
- print(m)
-
- params = [p.numel() if "mamba" in n else 0 for n, p in model.named_parameters()]
- print(f"### Mamba Parameters: {sum(params) / 1e6} M")
-
- params = [p.numel() if "attn1." in n else 0 for n, p in model.named_parameters()]
- print(f"### attn1 Parameters: {sum(params) / 1e6} M")
-
- return model
\ No newline at end of file
diff --git a/cogvideox_fun/utils.py b/cogvideox_fun/utils.py
index f790161..9f670ec 100644
--- a/cogvideox_fun/utils.py
+++ b/cogvideox_fun/utils.py
@@ -1,26 +1,6 @@
-import os
-import gc
import numpy as np
-import torch
from PIL import Image
-# Copyright (c) OpenMMLab. All rights reserved.
-
-def tensor2pil(image):
- return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
-
-def numpy2pil(image):
- return Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8))
-
-def to_pil(image):
- if isinstance(image, Image.Image):
- return image
- if isinstance(image, torch.Tensor):
- return tensor2pil(image)
- if isinstance(image, np.ndarray):
- return numpy2pil(image)
- raise ValueError(f"Cannot convert {type(image)} to PIL.Image")
-
ASPECT_RATIO_512 = {
'0.25': [256.0, 1024.0], '0.26': [256.0, 992.0], '0.27': [256.0, 960.0], '0.28': [256.0, 928.0],
'0.32': [288.0, 896.0], '0.33': [288.0, 864.0], '0.35': [288.0, 832.0], '0.4': [320.0, 800.0],
@@ -54,126 +34,10 @@ def get_closest_ratio(height: float, width: float, ratios: dict = ASPECT_RATIO_5
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
return ratios[closest_ratio], float(closest_ratio)
-
def get_width_and_height_from_image_and_base_resolution(image, base_resolution):
target_pixels = int(base_resolution) * int(base_resolution)
original_width, original_height = Image.open(image).size
ratio = (target_pixels / (original_width * original_height)) ** 0.5
width_slider = round(original_width * ratio)
height_slider = round(original_height * ratio)
- return height_slider, width_slider
-
-def get_image_to_video_latent(validation_image_start, validation_image_end, video_length, sample_size):
- if validation_image_start is not None and validation_image_end is not None:
- if type(validation_image_start) is str and os.path.isfile(validation_image_start):
- image_start = clip_image = Image.open(validation_image_start).convert("RGB")
- image_start = image_start.resize([sample_size[1], sample_size[0]])
- clip_image = clip_image.resize([sample_size[1], sample_size[0]])
- else:
- image_start = clip_image = validation_image_start
- image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
- clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
-
- if type(validation_image_end) is str and os.path.isfile(validation_image_end):
- image_end = Image.open(validation_image_end).convert("RGB")
- image_end = image_end.resize([sample_size[1], sample_size[0]])
- else:
- image_end = validation_image_end
- image_end = [_image_end.resize([sample_size[1], sample_size[0]]) for _image_end in image_end]
-
- if type(image_start) is list:
- clip_image = clip_image[0]
- start_video = torch.cat(
- [torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start],
- dim=2
- )
- input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
- input_video[:, :, :len(image_start)] = start_video
-
- input_video_mask = torch.zeros_like(input_video[:, :1])
- input_video_mask[:, :, len(image_start):] = 255
- else:
- input_video = torch.tile(
- torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
- [1, 1, video_length, 1, 1]
- )
- input_video_mask = torch.zeros_like(input_video[:, :1])
- input_video_mask[:, :, 1:] = 255
-
- if type(image_end) is list:
- image_end = [_image_end.resize(image_start[0].size if type(image_start) is list else image_start.size) for _image_end in image_end]
- end_video = torch.cat(
- [torch.from_numpy(np.array(_image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_end in image_end],
- dim=2
- )
- input_video[:, :, -len(end_video):] = end_video
-
- input_video_mask[:, :, -len(image_end):] = 0
- else:
- image_end = image_end.resize(image_start[0].size if type(image_start) is list else image_start.size)
- input_video[:, :, -1:] = torch.from_numpy(np.array(image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0)
- input_video_mask[:, :, -1:] = 0
-
- input_video = input_video / 255
-
- elif validation_image_start is not None:
- if type(validation_image_start) is str and os.path.isfile(validation_image_start):
- image_start = clip_image = Image.open(validation_image_start).convert("RGB")
- image_start = image_start.resize([sample_size[1], sample_size[0]])
- clip_image = clip_image.resize([sample_size[1], sample_size[0]])
- else:
- image_start = clip_image = validation_image_start
- image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
- clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
- image_end = None
-
- if type(image_start) is list:
- clip_image = clip_image[0]
- start_video = torch.cat(
- [torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start],
- dim=2
- )
- input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
- input_video[:, :, :len(image_start)] = start_video
- input_video = input_video / 255
-
- input_video_mask = torch.zeros_like(input_video[:, :1])
- input_video_mask[:, :, len(image_start):] = 255
- else:
- input_video = torch.tile(
- torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
- [1, 1, video_length, 1, 1]
- ) / 255
- input_video_mask = torch.zeros_like(input_video[:, :1])
- input_video_mask[:, :, 1:, ] = 255
- else:
- image_start = None
- image_end = None
- input_video = torch.zeros([1, 3, video_length, sample_size[0], sample_size[1]])
- input_video_mask = torch.ones([1, 1, video_length, sample_size[0], sample_size[1]]) * 255
- clip_image = None
-
- del image_start
- del image_end
- gc.collect()
-
- return input_video, input_video_mask, clip_image
-
-def get_video_to_video_latent(input_video_path, video_length, sample_size, validation_video_mask=None):
- input_video = input_video_path
-
- input_video = torch.from_numpy(np.array(input_video))[:video_length]
- input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255
-
- if validation_video_mask is not None:
- validation_video_mask = Image.open(validation_video_mask).convert('L').resize((sample_size[1], sample_size[0]))
- input_video_mask = np.where(np.array(validation_video_mask) < 240, 0, 255)
-
- input_video_mask = torch.from_numpy(np.array(input_video_mask)).unsqueeze(0).unsqueeze(-1).permute([3, 0, 1, 2]).unsqueeze(0)
- input_video_mask = torch.tile(input_video_mask, [1, 1, input_video.size()[2], 1, 1])
- input_video_mask = input_video_mask.to(input_video.device, input_video.dtype)
- else:
- input_video_mask = torch.zeros_like(input_video[:, :1])
- input_video_mask[:, :, :] = 255
-
- return input_video, input_video_mask, None
\ No newline at end of file
+ return height_slider, width_slider
\ No newline at end of file
diff --git a/cogvideox_fun/context.py b/context.py
similarity index 100%
rename from cogvideox_fun/context.py
rename to context.py
diff --git a/custom_cogvideox_transformer_3d.py b/custom_cogvideox_transformer_3d.py
index 09e6771..f3d0d94 100644
--- a/custom_cogvideox_transformer_3d.py
+++ b/custom_cogvideox_transformer_3d.py
@@ -27,23 +27,28 @@ from diffusers.utils import logging
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.attention import Attention, FeedForward
from diffusers.models.attention_processor import AttentionProcessor
-from diffusers.models.embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.embeddings import apply_rotary_emb
+from .embeddings import CogVideoXPatchEmbed
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
try:
from sageattention import sageattn
+
SAGEATTN_IS_AVAILABLE = True
- logger.info("Using sageattn")
except:
- logger.info("sageattn not found, using sdpa")
SAGEATTN_IS_AVAILABLE = False
+@torch.compiler.disable()
+def sageattn_func(query, key, value, attn_mask=None, dropout_p=0.0,is_causal=False):
+ return sageattn(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p,is_causal=is_causal)
+
def fft(tensor):
tensor_fft = torch.fft.fft2(tensor)
tensor_fft_shifted = torch.fft.fftshift(tensor_fft)
@@ -70,7 +75,7 @@ class CogVideoXAttnProcessor2_0:
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
- @torch.compiler.disable()
+
def __call__(
self,
attn: Attention,
@@ -78,6 +83,7 @@ class CogVideoXAttnProcessor2_0:
encoder_hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
+ attention_mode: Optional[str] = None,
) -> torch.Tensor:
text_seq_length = encoder_hidden_states.size(1)
@@ -91,9 +97,14 @@ class CogVideoXAttnProcessor2_0:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
- query = attn.to_q(hidden_states)
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
+ if attention_mode != "fused_sdpa" or attention_mode != "fused_sageattn":
+ query = attn.to_q(hidden_states)
+ key = attn.to_k(hidden_states)
+ value = attn.to_v(hidden_states)
+ else:
+ qkv = attn.to_qkv(hidden_states)
+ split_size = qkv.shape[-1] // 3
+ query, key, value = torch.split(qkv, split_size, dim=-1)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
@@ -109,18 +120,18 @@ class CogVideoXAttnProcessor2_0:
# Apply RoPE if needed
if image_rotary_emb is not None:
- from diffusers.models.embeddings import apply_rotary_emb
-
query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
if not attn.is_cross_attention:
key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
- if SAGEATTN_IS_AVAILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
+
+ if attention_mode == "sageattn" or attention_mode == "fused_sageattn":
+ hidden_states = sageattn_func(query, key, value, attn_mask=attention_mask, dropout_p=0.0,is_causal=False)
else:
hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
+ query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+ )
+ #if torch.isinf(hidden_states).any():
+ # raise ValueError(f"hidden_states after dot product has inf")
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
@@ -134,80 +145,7 @@ class CogVideoXAttnProcessor2_0:
)
return hidden_states, encoder_hidden_states
-
-class FusedCogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
- @torch.compiler.disable()
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- qkv = attn.to_qkv(hidden_states)
- split_size = qkv.shape[-1] // 3
- query, key, value = torch.split(qkv, split_size, dim=-1)
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn.heads
-
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- from diffusers.models.embeddings import apply_rotary_emb
-
- query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
- if not attn.is_cross_attention:
- key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
- if SAGEATTN_IS_AVAILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
+#region Blocks
@maybe_allow_in_graph
class CogVideoXBlock(nn.Module):
@@ -266,6 +204,7 @@ class CogVideoXBlock(nn.Module):
# 1. Self Attention
self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
self.attn1 = Attention(
query_dim=dim,
@@ -300,15 +239,21 @@ class CogVideoXBlock(nn.Module):
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
video_flow_feature: Optional[torch.Tensor] = None,
fuser=None,
+ block_use_fastercache=False,
fastercache_counter=0,
fastercache_start_step=15,
fastercache_device="cuda:0",
+ attention_mode="sdpa",
) -> torch.Tensor:
+ #print("hidden_states in block: ", hidden_states.shape) #1.5: torch.Size([2, 3200, 3072]) 10.: torch.Size([2, 6400, 3072])
text_seq_length = encoder_hidden_states.size(1)
+
# norm & modulate
norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
hidden_states, encoder_hidden_states, temb
)
+ #print("norm_hidden_states in block: ", norm_hidden_states.shape) #torch.Size([2, 3200, 3072])
+
# Tora Motion-guidance Fuser
if video_flow_feature is not None:
H, W = video_flow_feature.shape[-2:]
@@ -318,36 +263,46 @@ class CogVideoXBlock(nn.Module):
norm_hidden_states = rearrange(h, "(B T) C H W -> B (T H W) C", T=T)
del h, fuser
- #fastercache
- B = norm_hidden_states.shape[0]
- if fastercache_counter >= fastercache_start_step + 3 and fastercache_counter%3!=0 and self.cached_hidden_states[-1].shape[0] >= B:
- attn_hidden_states = (
- self.cached_hidden_states[1][:B] +
- (self.cached_hidden_states[1][:B] - self.cached_hidden_states[0][:B])
- * 0.3
- ).to(norm_hidden_states.device, non_blocking=True)
- attn_encoder_hidden_states = (
- self.cached_encoder_hidden_states[1][:B] +
- (self.cached_encoder_hidden_states[1][:B] - self.cached_encoder_hidden_states[0][:B])
- * 0.3
- ).to(norm_hidden_states.device, non_blocking=True)
+ #region fastercache
+ if block_use_fastercache:
+ B = norm_hidden_states.shape[0]
+ if fastercache_counter >= fastercache_start_step + 3 and fastercache_counter%3!=0 and self.cached_hidden_states[-1].shape[0] >= B:
+ attn_hidden_states = (
+ self.cached_hidden_states[1][:B] +
+ (self.cached_hidden_states[1][:B] - self.cached_hidden_states[0][:B])
+ * 0.3
+ ).to(norm_hidden_states.device, non_blocking=True)
+ attn_encoder_hidden_states = (
+ self.cached_encoder_hidden_states[1][:B] +
+ (self.cached_encoder_hidden_states[1][:B] - self.cached_encoder_hidden_states[0][:B])
+ * 0.3
+ ).to(norm_hidden_states.device, non_blocking=True)
+ else:
+ attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+ hidden_states=norm_hidden_states,
+ encoder_hidden_states=norm_encoder_hidden_states,
+ image_rotary_emb=image_rotary_emb,
+ attention_mode=attention_mode,
+ )
+ if fastercache_counter == fastercache_start_step:
+ self.cached_hidden_states = [attn_hidden_states.to(fastercache_device), attn_hidden_states.to(fastercache_device)]
+ self.cached_encoder_hidden_states = [attn_encoder_hidden_states.to(fastercache_device), attn_encoder_hidden_states.to(fastercache_device)]
+ elif fastercache_counter > fastercache_start_step:
+ self.cached_hidden_states[-1].copy_(attn_hidden_states.to(fastercache_device))
+ self.cached_encoder_hidden_states[-1].copy_(attn_encoder_hidden_states.to(fastercache_device))
else:
attn_hidden_states, attn_encoder_hidden_states = self.attn1(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
+ attention_mode=attention_mode,
)
- if fastercache_counter == fastercache_start_step:
- self.cached_hidden_states = [attn_hidden_states.to(fastercache_device), attn_hidden_states.to(fastercache_device)]
- self.cached_encoder_hidden_states = [attn_encoder_hidden_states.to(fastercache_device), attn_encoder_hidden_states.to(fastercache_device)]
- elif fastercache_counter > fastercache_start_step:
- self.cached_hidden_states[-1].copy_(attn_hidden_states.to(fastercache_device))
- self.cached_encoder_hidden_states[-1].copy_(attn_encoder_hidden_states.to(fastercache_device))
-
+
hidden_states = hidden_states + gate_msa * attn_hidden_states
encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
# norm & modulate
+
norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
hidden_states, encoder_hidden_states, temb
)
@@ -361,7 +316,7 @@ class CogVideoXBlock(nn.Module):
return hidden_states, encoder_hidden_states
-
+#region Transformer
class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
"""
A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
@@ -428,6 +383,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
time_embed_dim: int = 512,
+ ofs_embed_dim: Optional[int] = None,
text_embed_dim: int = 4096,
num_layers: int = 30,
dropout: float = 0.0,
@@ -436,6 +392,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
sample_height: int = 60,
sample_frames: int = 49,
patch_size: int = 2,
+ patch_size_t: int = None,
temporal_compression_ratio: int = 4,
max_text_seq_length: int = 226,
activation_fn: str = "gelu-approximate",
@@ -446,6 +403,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
temporal_interpolation_scale: float = 1.0,
use_rotary_positional_embeddings: bool = False,
use_learned_positional_embeddings: bool = False,
+ patch_bias: bool = True,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
@@ -460,10 +418,11 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
# 1. Patch embedding
self.patch_embed = CogVideoXPatchEmbed(
patch_size=patch_size,
+ patch_size_t=patch_size_t,
in_channels=in_channels,
embed_dim=inner_dim,
text_embed_dim=text_embed_dim,
- bias=True,
+ bias=patch_bias,
sample_width=sample_width,
sample_height=sample_height,
sample_frames=sample_frames,
@@ -480,6 +439,13 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
+ self.ofs_proj = None
+ self.ofs_embedding = None
+
+ if ofs_embed_dim:
+ self.ofs_proj = Timesteps(ofs_embed_dim, flip_sin_to_cos, freq_shift)
+ self.ofs_embedding = TimestepEmbedding(ofs_embed_dim, ofs_embed_dim, timestep_activation_fn) # same as time embeddings, for ofs
+
# 3. Define spatio-temporal transformers blocks
self.transformer_blocks = nn.ModuleList(
[
@@ -507,7 +473,14 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
norm_eps=norm_eps,
chunk_dim=1,
)
- self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
+ if patch_size_t is None:
+ # For CogVideox 1.0
+ output_dim = patch_size * patch_size * out_channels
+ else:
+ # For CogVideoX 1.5
+ output_dim = patch_size * patch_size * patch_size_t * out_channels
+
+ self.proj_out = nn.Linear(inner_dim, output_dim)
self.gradient_checkpointing = False
@@ -518,6 +491,9 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
self.fastercache_lf_step = 40
self.fastercache_hf_step = 30
self.fastercache_device = "cuda"
+ self.fastercache_num_blocks_to_cache = len(self.transformer_blocks)
+ self.attention_mode = "sdpa"
+
def _set_gradient_checkpointing(self, module, value=False):
self.gradient_checkpointing = value
@@ -582,45 +558,6 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
- def fuse_qkv_projections(self):
- """
- Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
- are fused. For cross-attention modules, key and value projection matrices are fused.
-
-
-
- This API is 🧪 experimental.
-
-
- """
- self.original_attn_processors = None
-
- for _, attn_processor in self.attn_processors.items():
- if "Added" in str(attn_processor.__class__.__name__):
- raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
-
- self.original_attn_processors = self.attn_processors
-
- for module in self.modules():
- if isinstance(module, Attention):
- module.fuse_projections(fuse=True)
-
- self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())
-
- # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
- def unfuse_qkv_projections(self):
- """Disables the fused QKV projection if enabled.
-
-
-
- This API is 🧪 experimental.
-
-
-
- """
- if self.original_attn_processors is not None:
- self.set_attn_processor(self.original_attn_processors)
def forward(
self,
@@ -628,6 +565,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
encoder_hidden_states: torch.Tensor,
timestep: Union[int, float, torch.LongTensor],
timestep_cond: Optional[torch.Tensor] = None,
+ ofs: Optional[Union[int, float, torch.LongTensor]] = None,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
controlnet_states: torch.Tensor = None,
controlnet_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
@@ -635,7 +573,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
return_dict: bool = True,
):
batch_size, num_frames, channels, height, width = hidden_states.shape
-
+
# 1. Time embedding
timesteps = timestep
t_emb = self.time_proj(timesteps)
@@ -644,40 +582,57 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=hidden_states.dtype)
+
emb = self.time_embedding(t_emb, timestep_cond)
+ if self.ofs_embedding is not None: #1.5 I2V
+ ofs_emb = self.ofs_proj(ofs)
+ ofs_emb = ofs_emb.to(dtype=hidden_states.dtype)
+ ofs_emb = self.ofs_embedding(ofs_emb)
+ emb = emb + ofs_emb
# 2. Patch embedding
+ p = self.config.patch_size
+ p_t = self.config.patch_size_t
+
+ #print("hidden_states before patch_embedding", hidden_states.shape) #torch.Size([2, 4, 16, 60, 90])
+
hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
+ #print("hidden_states after patch_embedding", hidden_states.shape) #1.5: torch.Size([2, 2926, 3072]) #1.0: torch.Size([2, 5626, 3072])
hidden_states = self.embedding_dropout(hidden_states)
text_seq_length = encoder_hidden_states.shape[1]
encoder_hidden_states = hidden_states[:, :text_seq_length]
hidden_states = hidden_states[:, text_seq_length:]
+ #print("hidden_states after split", hidden_states.shape) #1.5: torch.Size([2, 2700, 3072]) #1.0: torch.Size([2, 5400, 3072])
+
if self.use_fastercache:
self.fastercache_counter+=1
if self.fastercache_counter >= self.fastercache_start_step + 3 and self.fastercache_counter % 5 !=0:
# 3. Transformer blocks
for i, block in enumerate(self.transformer_blocks):
- hidden_states, encoder_hidden_states = block(
- hidden_states=hidden_states[:1],
- encoder_hidden_states=encoder_hidden_states[:1],
- temb=emb[:1],
- image_rotary_emb=image_rotary_emb,
- video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
- fuser = self.fuser_list[i] if self.fuser_list is not None else None,
- fastercache_counter = self.fastercache_counter,
- fastercache_device = self.fastercache_device
- )
+ hidden_states, encoder_hidden_states = block(
+ hidden_states=hidden_states[:1],
+ encoder_hidden_states=encoder_hidden_states[:1],
+ temb=emb[:1],
+ image_rotary_emb=image_rotary_emb,
+ video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
+ fuser = self.fuser_list[i] if self.fuser_list is not None else None,
+ block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
+ fastercache_counter = self.fastercache_counter,
+ fastercache_start_step = self.fastercache_start_step,
+ fastercache_device = self.fastercache_device,
+ attention_mode = self.attention_mode
+ )
- if (controlnet_states is not None) and (i < len(controlnet_states)):
- controlnet_states_block = controlnet_states[i]
- controlnet_block_weight = 1.0
- if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
- controlnet_block_weight = controlnet_weights[i]
- elif isinstance(controlnet_weights, (float, int)):
- controlnet_block_weight = controlnet_weights
-
- hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
+ if (controlnet_states is not None) and (i < len(controlnet_states)):
+ controlnet_states_block = controlnet_states[i]
+ controlnet_block_weight = 1.0
+ if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
+ controlnet_block_weight = controlnet_weights[i]
+ elif isinstance(controlnet_weights, (float, int)):
+ controlnet_block_weight = controlnet_weights
+
+ hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
if not self.config.use_rotary_positional_embeddings:
# CogVideoX-2B
@@ -696,9 +651,15 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
# Note: we use `-1` instead of `channels`:
# - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
# - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
- p = self.config.patch_size
- output = hidden_states.reshape(1, num_frames, height // p, width // p, -1, p, p)
- output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+
+ if p_t is None:
+ output = hidden_states.reshape(1, num_frames, height // p, width // p, -1, p, p)
+ output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+ else:
+ output = hidden_states.reshape(
+ 1, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+ )
+ output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
(bb, tt, cc, hh, ww) = output.shape
cond = rearrange(output, "B T C H W -> (B T) C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
@@ -727,19 +688,26 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
image_rotary_emb=image_rotary_emb,
video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
fuser = self.fuser_list[i] if self.fuser_list is not None else None,
+ block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
fastercache_counter = self.fastercache_counter,
- fastercache_device = self.fastercache_device
+ fastercache_start_step = self.fastercache_start_step,
+ fastercache_device = self.fastercache_device,
+ attention_mode = self.attention_mode
)
+ #has_nan = torch.isnan(hidden_states).any()
+ #if has_nan:
+ # raise ValueError(f"block output hidden_states has nan: {has_nan}")
- if (controlnet_states is not None) and (i < len(controlnet_states)):
- controlnet_states_block = controlnet_states[i]
- controlnet_block_weight = 1.0
- if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
- controlnet_block_weight = controlnet_weights[i]
- elif isinstance(controlnet_weights, (float, int)):
- controlnet_block_weight = controlnet_weights
-
- hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
+ #controlnet
+ if (controlnet_states is not None) and (i < len(controlnet_states)):
+ controlnet_states_block = controlnet_states[i]
+ controlnet_block_weight = 1.0
+ if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
+ controlnet_block_weight = controlnet_weights[i]
+ print(controlnet_block_weight)
+ elif isinstance(controlnet_weights, (float, int)):
+ controlnet_block_weight = controlnet_weights
+ hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
if not self.config.use_rotary_positional_embeddings:
# CogVideoX-2B
@@ -758,9 +726,15 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
# Note: we use `-1` instead of `channels`:
# - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
# - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
- p = self.config.patch_size
- output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
- output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+
+ if p_t is None:
+ output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
+ output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+ else:
+ output = hidden_states.reshape(
+ batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+ )
+ output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
if self.fastercache_counter >= self.fastercache_start_step + 1:
(bb, tt, cc, hh, ww) = output.shape
diff --git a/embeddings.py b/embeddings.py
new file mode 100644
index 0000000..111ba04
--- /dev/null
+++ b/embeddings.py
@@ -0,0 +1,226 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from typing import Tuple, Union, Optional
+from diffusers.models.embeddings import get_3d_sincos_pos_embed, get_1d_rotary_pos_embed
+
+
+class CogVideoXPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ patch_size: int = 2,
+ patch_size_t: Optional[int] = None,
+ in_channels: int = 16,
+ embed_dim: int = 1920,
+ text_embed_dim: int = 4096,
+ bias: bool = True,
+ sample_width: int = 90,
+ sample_height: int = 60,
+ sample_frames: int = 49,
+ temporal_compression_ratio: int = 4,
+ max_text_seq_length: int = 226,
+ spatial_interpolation_scale: float = 1.875,
+ temporal_interpolation_scale: float = 1.0,
+ use_positional_embeddings: bool = True,
+ use_learned_positional_embeddings: bool = True,
+ ) -> None:
+ super().__init__()
+
+ self.patch_size = patch_size
+ self.patch_size_t = patch_size_t
+ self.embed_dim = embed_dim
+ self.sample_height = sample_height
+ self.sample_width = sample_width
+ self.sample_frames = sample_frames
+ self.temporal_compression_ratio = temporal_compression_ratio
+ self.max_text_seq_length = max_text_seq_length
+ self.spatial_interpolation_scale = spatial_interpolation_scale
+ self.temporal_interpolation_scale = temporal_interpolation_scale
+ self.use_positional_embeddings = use_positional_embeddings
+ self.use_learned_positional_embeddings = use_learned_positional_embeddings
+
+ if patch_size_t is None:
+ # CogVideoX 1.0 checkpoints
+ self.proj = nn.Conv2d(
+ in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+ )
+ else:
+ # CogVideoX 1.5 checkpoints
+ self.proj = nn.Linear(in_channels * patch_size * patch_size * patch_size_t, embed_dim)
+
+ self.text_proj = nn.Linear(text_embed_dim, embed_dim)
+
+ if use_positional_embeddings or use_learned_positional_embeddings:
+ persistent = use_learned_positional_embeddings
+ pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
+ self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)
+
+ def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
+ post_patch_height = sample_height // self.patch_size
+ post_patch_width = sample_width // self.patch_size
+ post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
+ num_patches = post_patch_height * post_patch_width * post_time_compression_frames
+
+ pos_embedding = get_3d_sincos_pos_embed(
+ self.embed_dim,
+ (post_patch_width, post_patch_height),
+ post_time_compression_frames,
+ self.spatial_interpolation_scale,
+ self.temporal_interpolation_scale,
+ )
+ pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
+ joint_pos_embedding = torch.zeros(
+ 1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
+ )
+ joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)
+
+ return joint_pos_embedding
+
+ def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
+ r"""
+ Args:
+ text_embeds (`torch.Tensor`):
+ Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
+ image_embeds (`torch.Tensor`):
+ Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
+ """
+ text_embeds = self.text_proj(text_embeds)
+
+ batch_size, num_frames, channels, height, width = image_embeds.shape
+
+ if self.patch_size_t is None:
+ image_embeds = image_embeds.reshape(-1, channels, height, width)
+ image_embeds = self.proj(image_embeds)
+ image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
+ image_embeds = image_embeds.flatten(3).transpose(2, 3) # [batch, num_frames, height x width, channels]
+ image_embeds = image_embeds.flatten(1, 2) # [batch, num_frames x height x width, channels]
+ else:
+ p = self.patch_size
+ p_t = self.patch_size_t
+
+ image_embeds = image_embeds.permute(0, 1, 3, 4, 2)
+ image_embeds = image_embeds.reshape(
+ batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
+ )
+ image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3)
+ image_embeds = self.proj(image_embeds)
+
+ embeds = torch.cat(
+ [text_embeds, image_embeds], dim=1
+ ).contiguous() # [batch, seq_length + num_frames x height x width, channels]
+
+ if self.use_positional_embeddings or self.use_learned_positional_embeddings:
+ if self.use_learned_positional_embeddings and (self.sample_width != width or self.sample_height != height):
+ raise ValueError(
+ "It is currently not possible to generate videos at a different resolution that the defaults. This should only be the case with 'THUDM/CogVideoX-5b-I2V'."
+ "If you think this is incorrect, please open an issue at https://github.com/huggingface/diffusers/issues."
+ )
+
+ pre_time_compression_frames = (num_frames - 1) * self.temporal_compression_ratio + 1
+
+ if (
+ self.sample_height != height
+ or self.sample_width != width
+ or self.sample_frames != pre_time_compression_frames
+ ):
+ pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
+ pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
+ else:
+ pos_embedding = self.pos_embedding
+
+ embeds = embeds + pos_embedding
+
+ return embeds
+
+def get_3d_rotary_pos_embed(
+ embed_dim,
+ crops_coords,
+ grid_size,
+ temporal_size,
+ theta: int = 10000,
+ use_real: bool = True,
+ grid_type: str = "linspace",
+ max_size: Optional[Tuple[int, int]] = None,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+ """
+ RoPE for video tokens with 3D structure.
+
+ Args:
+ embed_dim: (`int`):
+ The embedding dimension size, corresponding to hidden_size_head.
+ crops_coords (`Tuple[int]`):
+ The top-left and bottom-right coordinates of the crop.
+ grid_size (`Tuple[int]`):
+ The grid size of the spatial positional embedding (height, width).
+ temporal_size (`int`):
+ The size of the temporal dimension.
+ theta (`float`):
+ Scaling factor for frequency computation.
+ grid_type (`str`):
+ Whether to use "linspace" or "slice" to compute grids.
+
+ Returns:
+ `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+ """
+ if use_real is not True:
+ raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
+
+ if grid_type == "linspace":
+ start, stop = crops_coords
+ grid_size_h, grid_size_w = grid_size
+ grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
+ grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
+ grid_t = np.arange(temporal_size, dtype=np.float32)
+ grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+ elif grid_type == "slice":
+ max_h, max_w = max_size
+ grid_size_h, grid_size_w = grid_size
+ grid_h = np.arange(max_h, dtype=np.float32)
+ grid_w = np.arange(max_w, dtype=np.float32)
+ grid_t = np.arange(temporal_size, dtype=np.float32)
+ else:
+ raise ValueError("Invalid value passed for `grid_type`.")
+
+ # Compute dimensions for each axis
+ dim_t = embed_dim // 4
+ dim_h = embed_dim // 8 * 3
+ dim_w = embed_dim // 8 * 3
+
+ # Temporal frequencies
+ freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
+ # Spatial frequencies for height and width
+ freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
+ freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
+
+ # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
+ def combine_time_height_width(freqs_t, freqs_h, freqs_w):
+ freqs_t = freqs_t[:, None, None, :].expand(
+ -1, grid_size_h, grid_size_w, -1
+ ) # temporal_size, grid_size_h, grid_size_w, dim_t
+ freqs_h = freqs_h[None, :, None, :].expand(
+ temporal_size, -1, grid_size_w, -1
+ ) # temporal_size, grid_size_h, grid_size_2, dim_h
+ freqs_w = freqs_w[None, None, :, :].expand(
+ temporal_size, grid_size_h, -1, -1
+ ) # temporal_size, grid_size_h, grid_size_2, dim_w
+
+ freqs = torch.cat(
+ [freqs_t, freqs_h, freqs_w], dim=-1
+ ) # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
+ freqs = freqs.view(
+ temporal_size * grid_size_h * grid_size_w, -1
+ ) # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
+ return freqs
+
+ t_cos, t_sin = freqs_t # both t_cos and t_sin has shape: temporal_size, dim_t
+ h_cos, h_sin = freqs_h # both h_cos and h_sin has shape: grid_size_h, dim_h
+ w_cos, w_sin = freqs_w # both w_cos and w_sin has shape: grid_size_w, dim_w
+
+ if grid_type == "slice":
+ t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
+ h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
+ w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]
+
+ cos = combine_time_height_width(t_cos, h_cos, w_cos)
+ sin = combine_time_height_width(t_sin, h_sin, w_sin)
+ return cos, sin
\ No newline at end of file
diff --git a/examples/cogvideo_2b_context_schedule_test_01.json b/examples/cogvideo_2b_context_schedule_test_01.json
deleted file mode 100644
index ed7b6fa..0000000
--- a/examples/cogvideo_2b_context_schedule_test_01.json
+++ /dev/null
@@ -1,561 +0,0 @@
-{
- "last_node_id": 34,
- "last_link_id": 61,
- "nodes": [
- {
- "id": 33,
- "type": "GetImageSizeAndCount",
- "pos": {
- "0": 1176,
- "1": 122
- },
- "size": {
- "0": 210,
- "1": 86
- },
- "flags": {},
- "order": 7,
- "mode": 0,
- "inputs": [
- {
- "name": "image",
- "type": "IMAGE",
- "link": 59
- }
- ],
- "outputs": [
- {
- "name": "image",
- "type": "IMAGE",
- "links": [
- 60
- ],
- "slot_index": 0,
- "shape": 3
- },
- {
- "name": "720 width",
- "type": "INT",
- "links": null,
- "shape": 3
- },
- {
- "name": "480 height",
- "type": "INT",
- "links": null,
- "shape": 3
- },
- {
- "name": "104 count",
- "type": "INT",
- "links": null,
- "shape": 3
- }
- ],
- "properties": {
- "Node name for S&R": "GetImageSizeAndCount"
- },
- "widgets_values": []
- },
- {
- "id": 30,
- "type": "CogVideoTextEncode",
- "pos": {
- "0": 500,
- "1": 308
- },
- "size": [
- 474.8035864085422,
- 211.10369504535595
- ],
- "flags": {},
- "order": 3,
- "mode": 0,
- "inputs": [
- {
- "name": "clip",
- "type": "CLIP",
- "link": 54
- }
- ],
- "outputs": [
- {
- "name": "conditioning",
- "type": "CONDITIONING",
- "links": [
- 55
- ],
- "slot_index": 0,
- "shape": 3
- }
- ],
- "properties": {
- "Node name for S&R": "CogVideoTextEncode"
- },
- "widgets_values": [
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diff --git a/examples/cogvideo_5b_vid2vid_example_01.json b/examples/cogvideox_1.0_5b_vid2vid_02.json
similarity index 84%
rename from examples/cogvideo_5b_vid2vid_example_01.json
rename to examples/cogvideox_1.0_5b_vid2vid_02.json
index 5c545e6..a45ff0b 100644
--- a/examples/cogvideo_5b_vid2vid_example_01.json
+++ b/examples/cogvideox_1.0_5b_vid2vid_02.json
@@ -1,6 +1,6 @@
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@@ -22,8 +22,7 @@
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+ {
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+ "type": "TORAFEATURES",
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+ {
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+ "widget": {
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+ 216
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+ "1": -79
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+ }
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"type": "DownloadAndLoadCogVideoModel",
"pos": {
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@@ -313,18 +764,12 @@
},
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{
"name": "block_edit",
"type": "TRANSFORMERBLOCKS",
@@ -336,18 +781,29 @@
"type": "COGLORA",
"link": null,
"shape": 7
+ },
+ {
+ "name": "compile_args",
+ "type": "COMPILEARGS",
+ "link": null,
+ "shape": 7
}
],
"outputs": [
{
- "name": "cogvideo_pipe",
- "type": "COGVIDEOPIPE",
+ "name": "model",
+ "type": "COGVIDEOMODEL",
"links": [
- 83,
- 192
- ],
- "slot_index": 0,
- "shape": 3
+ 212
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+ },
+ {
+ "name": "vae",
+ "type": "VAE",
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}
],
"properties": {
@@ -355,75 +811,23 @@
},
"widgets_values": [
"THUDM/CogVideoX-5b",
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"disabled",
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- 8,
- true
+ false,
+ "sdpa",
+ "main_device"
]
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],
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"order": 14,
@@ -478,7 +882,7 @@
"hidden": false,
"paused": false,
"params": {
- "filename": "CogVideoX_vid2vid_00001.mp4",
+ "filename": "CogVideoX_vid2vid_00003.mp4",
"subfolder": "",
"type": "temp",
"format": "video/h264-mp4",
@@ -486,421 +890,6 @@
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}
}
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similarity index 80%
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{
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+ "shape": 7
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}
],
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- "shape": 3
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{
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{
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"loop_count": 0,
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"crf": 19,
@@ -748,7 +823,7 @@
"hidden": false,
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"params": {
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+ "filename": "CogVideoX2B_controlnet_00003.mp4",
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diff --git a/examples/cogvideox_I2V_example_01.json b/examples/cogvideox_1_0_5b_I2V_02.json
similarity index 75%
rename from examples/cogvideox_I2V_example_01.json
rename to examples/cogvideox_1_0_5b_I2V_02.json
index b6cefa3..f1265d6 100644
--- a/examples/cogvideox_I2V_example_01.json
+++ b/examples/cogvideox_1_0_5b_I2V_02.json
@@ -1,42 +1,7 @@
{
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@@ -46,16 +11,16 @@
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@@ -63,10 +28,15 @@
"name": "conditioning",
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- 123
+ 146
],
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}
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@@ -78,6 +48,208 @@
true
]
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+ "bf16",
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+ false,
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+ "main_device"
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{
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@@ -90,7 +262,7 @@
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@@ -104,10 +276,18 @@
"name": "conditioning",
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+ {
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@@ -116,22 +296,22 @@
"widgets_values": [
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1,
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},
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- "order": 5,
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@@ -142,7 +322,8 @@
{
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@@ -166,7 +347,7 @@
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@@ -199,64 +380,88 @@
]
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{
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+ "inputs": [],
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{
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+ "widgets_values": [
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- "Node name for S&R": "CogVideoImageEncode"
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@@ -264,14 +469,14 @@
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@@ -279,17 +484,15 @@
"name": "images",
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@@ -367,180 +573,6 @@
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mraeMdAfH+n7T6NC4/pXlFHFPnYvYQPYE8R6K4yup230LY/nVmPVtOkxt1C1Oen75f8a8WwKMUe0YvYR6M9zRlkGY2Dj1U5p2Mda8MjkkiOUdlP8AsnFWY9Tv4T+6vblP92Vh/Wn7QX1fzPacUYryODxRrlvnZqU5z/z0w/8A6EDVyPxzrsagGeGT3eEZ/TFNTQnQken4pF4H4n+ZrzyP4gamMeZbWjDvhWB/nV6H4iLwJ9N+rRzf0I/rT5kS6Mztu9HauWh8e6VIwEkVzD7lQR+hrQj8XaDL0vwp/wBuNx/SjmRDpyXQ2aPxqjFrmkzECPUrUk9jKAT+dX1xIgZGDKe6nNMlpoaaKdijFAhDSU40lACfjRzS0YoABRRRQAhqJ7eCT78MbfVRUtHSmm1sJpPcz5NGs5eiMhPdT/jVOXw8Cf3VwQPRlzW4KK0VepHZmMsNSlvE5iXQ7tPuBZB7HH86qSWVzF9+CQD/AHTXYntRW0cZNbnPPAU3s7HD4oxXZvbQSA+ZCjH3UGqkmi2b52qyH2b/ABraOMi9zmll0ujOXorcl8PnrFOD7Mv9aoyaVeR5/clh6qc1vGvCWzOaeEqx3RQxmlxUjxPEcOjKfQjFNFbJpnO4tDce1GKdRii4rDMUmKfjFGKAsMIoxTiKTFNEjcUU6kIpgNwKTFPpMUhDaMU7FLTEMIpKeaTFADcUU7FFADTSYp9JikMbikp+KQincQwilpaMUEs8woFIKXvXgH3Y7NKDTKcKQhwpaQUtIAoopR0oGKD60ozTRS0gHd6Wmc+tLmgB4NLmmfjS596VgHg07NR5pc0WHcfmjNNDe9KGpWC48GnCq88siQO0Sh5AOFJxms9dYi8tR9tg88H5o2XA/PPFUoN7DubGaM1FHKJEDDHTnBp26psA/NLmo93vS7qLBcfml3e9R7qXd70rDH7qXNR7qN1FguSZo3YqPd707cKLASBqM1Hu96XdRYLkmaX8ai3e9LupWGS5pQai3UBqLAS5pc1Hupd1Fh3H5ozTN1G6iw7kmaKYGpc+9ILj88UAim596QdOvc/zoAk4oxTPxpQT60rDHUqSSQtuikaNvVDg03dRmgRfh1zVoMeXqN0MeshP860bfxrrsDAtcpMB/DLGP5jB/WufzRVJsTjF7o7CH4h3yt+/sbd19ELJ/PNaEPxDtHH76wmjP+w4cf0rz/8AGlwKOeRLpQfQ9Pg8b6JKcPNLCf8AppEf6ZrTh1vSrjAi1G2Ynt5gB/I145jmlxT5yXQie4IySLuRldfVTkUteK295d2mfs1zNDnr5blf5Vfg8Ta5btlNSnPtIQ4/8ezVc6M3h30Z6170YrzqLx9qiACWG3l99pU/oa07b4hW7H/StPlT3ikDfocU+ZEOjNHZUGsW38XaJcYH2zymIziVCuPxxj9a0oL6zuuILuCUnskgNO6IcWt0TelFKVxxRimSJRmlpOfWgVgoowaOaAsNZVcFWUEehFVpdMtJRzCFPqvFWjmiqjNx2JlCMt0Y82gDrFOR7OM/qKpTaPdxDIUOP9g5rpqK2jiqi3OaWCpS2VjjJIZYj86Mv1GKZXaOiyKVdQynqCM1Vl0uzlHMQX3TiuiOMX2kcs8uf2WcrikxW9LoKnJimIPowzVGXSbyL/lnvHqhzW8cRCWzOSeDqx3Rn4oxUrxvGcOrKfQjFR1smc7g0NI4pMU7FGKdyXETFJin4oxTuTYZijFOxRii4WGYoxTse9G3mmFhmKXFKR70EUh2G0YpcUY96YhhBopxoxQKx5UDTs1CGHrS78d68Cx9yTDFOFQCUU7zR60rBYmHWlFQ+co7003kS9XGfY0WY7Fmioo51k6BqmJAUk8YHepCzEpcgUxmGOtM8z3phYmzSZqHzfc0eZmiwE+73o3e9Qb+epxS7x60WCxNuGKUPVfcT3pd9FgJ99LuqDdRvosFifd70yFRbRXcULNHHdqVnRTgSA8EEVHupd1VqthDoI47aIRxKQg4AyTUu+oN9HmUrDLAajfVYuTShjipsBY30u+q+6l3Uxk+/Bo3iod3FAb60WAn30u+oNx96Nx96LAWNwo3VBuNKGJpWGTBqcHqvk0uTRYCffS76g3GgPilYZY3Uu6oN/1o8z60coXJw1LuFQeZ9aPM+tKwFjeKXcKreZ9aXzKLAWS+Kgubs28G9V3HJzk4AHqabvpj7Jo2R1DIwwQehppdxjRqbrbCWQx7T0YKwQ+mG6fzq9HLvjViMEgEj0NZzWweyjsnmna0iYMkDTMUUjpgE1aVsCnNLoJXLO6nBqrb6cHqLDuT5pc1BvpQ1KwE2aUGog1AeiwE2aM1Hv8AelDUrBckzS5pgajNFgH5pabmjdQFx1NKgnkA0uaKQXLtvrGp2iqsF9cIo6L5hIH4dK17Lxtq1s3+kNHdJ6OgUj8RiucJparmYnFPc7q2+IMTuBc6e6LjlopA3P0IH861rfxhok7BTdNEx7SoQPzGRXl4ApQKamzN0ons8N1bXQBguYZQenlyBv5VNivE0Z4pA8bsjjkMpwRWlB4j1q3xs1Kc+0hDj/x7NVzmbodmeskUmK4C38f36DFxa282B1XKE/zH6VsWfjzTZyFuoZ7Zj3wHX8xz+lPmRm6UkdPRVS31jS7v/U39uxPRS4DfkeavbeM9jVENNDKDT8UmKBDKWlIpMUDGMiOu11DKexGapT6TaTAgIY27FeP0q/SE1UZyjszOVOMviRhS6FIOYpQ3+8MVSl0+4gHzxNj1HIrqcUoHFdEcVNb6nNPBU5baHGlfrSY+tdbJaW8xJkhQk98c1Rl0OJuYpGU+h5FdEcXF76HHPL5r4dTBIpMVfm0q5iPCeYPVKqNGyHDKQfQjFdMakZbM5J0ZR0kiKjFPxRiruZcozFJipMUhFAWGYoxT8Um2mKwzFIRUm2grQFjxbcaaXI71gP4lH8Fsfxf/AOtUD+I7hvuQRKP9rJrx1Skfb88TozIfU0hd/WuXbXL9j8rIvsEz/PNNGrak3SU/hGP8KpUWT7SJ0z727mo4YXE4JbIx0rmpLzUZOTLL+HH8qvaE10+qJ57yMu08MxNEqbSbuNTV9jtLSPgVPeKEtJWZgFCHJJwBS2qfKKTXF/4kt2B3iIri+0VzHNjVbaDj7XGR6Bs07+3rHk/aV/I1y32L1f8ASlFiP7/6V3+yiZuUux03/CQ6eOsz/ghpp8S6eP4pW+if/XrnRYp3Y04WcQ65/On7OAryN4+KLEdEnP8AwEf40xvFMAPyW8h+pArG+yw/3T+dL9nhCn5P1NHs4CvI1f8AhK17Wjf9/P8A61J/wlef+XM/9/f/AK1c0DU9qqvcoGGR6fhVeyh2FzM3v+ErYf8ALmP+/v8A9aj/AISuTtZp+LmqYji/55L+QpQkY6Iv5VPLDsVqTv4ouz923iH5n+tRHxJqR6eWv0SlG0dAB+FLuHpRaPYdn3Iz4h1Q/wAaf9+xR/bmrHpJ+UY/wqTcPSkzTSj2Cz7jDq2rn/lq4+iD/Ck/tbV/+e0v/fA/wqTPtRmnZdhW8xn9p6uf+W83/fP/ANak+36v/wA/E/51KGoBpadgt5kP27Vv+fmf/vql+36sP+Xqf/vqps0Zo07BbzIf7Q1b/n6n/wC+qP7Q1b/n6n/76qXNGaenYLFc6vqqPt+2TbvTNSDVtaHS4m/FR/hVWbH2z8RV8UNJdBLUjGsa0P8AlvKf+AD/AAp39t6yOsz/AIxj/CpM0bjS93sOw0eINYX/AJag/WIf4U4eI9VHJaP/AL9CjNGaLR7BZ9yVPFl8vDwwv+BH9amHi6X+KxXPtIf8KplUP8IP4UnlR/8APNP++RScYdh69y+PFx/isfyl/wDrU8eL4+9m4+kmf6VlmCE9Y1/Dimm1g/ufqaXs4dg1NxPFtiR88Nwp9lU/1qVfFOnE8+cv1T/A1z32SD+4f++jSGyhP94fQ0vZUx3kdQPE2lngTMPrGami1rTZMAXsI4/iO3+dcAyhZio7EimsOlDw8Re0Z6ZHcwTDMU8Tj/ZcGpQx+tebx2LPGrhhyKlVL63/ANTPKo/2JCKh0F0ZXO+x6JvpQ9cEmqaxCu0XEpH+0ob9SKeviLVoz8zq/s0YH8sUvYS6MXOd2GpweuMi8XXaj97aRN7qSv8AjVuPxhD/AMtLORfUq4P9BUOhPsPnR1Qel31gweKNMmOGeSH/AK6J/hmr0eq2Ev8Aq7yA+mXAP5Gs3Tkt0PmTNAPTw9VVkD/dIb6HNODGpsMtB6XfVcNijd70rBcsB6dvqtu96cHosBZD5pwb3qsr08NSsBPu96A2Rmod2aUNilYLk4NLuqIPShsmiwEoNBPNMBpc0gHUcd6SlHWmAYFWrPUb7Tyfsl3NCCckI5AP4dKrGlFAM6Sy8capbv8A6T5d0nowCn8wK3bTx7Yy5F1azQHsUIcf0P6V59S1Sk0ZunF9D1q017Sr4gQX0O48BHO1vyNaGM9ORXiu2r1lrOpadxa3kqL/AHSdy/keKfOQ6PZnrZWmkV5/aeOtTgwtxFDcr3JGxv04/St2x8babdOEuY5LUn+JvmX8x/hVKSZm6ckdHiiobe+s7xd1tdQyj/YcGp9pqiLCUlOpvekApqN4Y5VKugYHsafRTTsJq+5nTaRBJyhMZ9uRVGbSJ48lMOPbrW9iito4ipHqc88LTn0OUkheI4dGU+4pmK6x40kG10DD0IqnJpVs4JQFD7GuqGLT+JHHPANfCzn8UYrSm0maMZQiQe3Bqi8TxnDqyn3GK6I1Yy2ZyzoTh8SI8UhFPINJitLmLifMCxRj+EU8Kg6KPyqMNkUu73rh1PsNCXj0oyKi3UbqQyQmtHRBu1Jf901lbq1/DmG1UD0Q1M/hYrnd2cfyiovECbdFuT/sVoWcXyD6VV8TDboN1/1zNcNveQKR5nmlyfSoCxBpRIDXfYLk26kLVCT3o3U7Bcl3Uhb5T9Ki3UFuD9KdiSitWLU4ul/H+VRIKliG24X6/wBKt7ELc0N9G6ot1JurOxoTbqUNUG6l3UWAm30b/eod1G+iwE2+l31Buo3UWC5Nv96XfUG40bqLBcn8yl31X3H1p2/iiwE2+gPUO+jdRYCGU/6WD7ir26qL8zZ9xVjdTaJTJ91G6qrfMQdxGPSlAKHdubB9ehpWKuWd1LuqDfRuosFybdzTt1QB+aN3vSsMn3Ck3Cot3vRuosBLuFLuqHdShqLAUJObhv8AeNNYdPrUm3dM3+8aJEwB9a0MzRt+IE+lP3VDCcQp9Kdmsmaku7igtUefejNIBxVG+8in8KY1vA38A/Cl3UuaYiJrGEj5dw/WojYej/0q2WpU+dgq9ScCndhZFT+zbuOISoSFJ4IfFTRX+sWv3bifHoTuH65rSvZAm22ThYwAfc1U3VKk3ugcUOi8ValCw81Y5FHUFcE/iK0Y/GNu2PNtZU/3WDf4VlkBxhgCPeo2tYG6p+RxScYPdCszpofE2mSgZmaMns6n+nFaFve21z/qLiKT2RwTXCPYRH7pYfXmoWsZByrAn64qXRg9mNNnpQbmnh/rXnMGoatZ/cnmCjjDfMP1zV6DxZfxECZIpV78bT+n+FZvDy6D5kdzvpfMrlIvGNu2BLayoe+xg3+Fa1rrVhdKvl3KKx/gc7SPzrN05R3Q00zXD04Pk1WByM9qerVFgLO+nBhVfdTg9JoCwDUFxqEdsVBUsSeTkAD6k0oeqs+mWd1dR3M0StNGMKTyPxB4P4iiKV9RNltdRhknWNFLq3R1IK1aBFZllp1rYSSPbxhXlOWPQfgBwPwFXg9ErX0AmpajDZpwNSA+lpuaXNABiijIpc0AIMg5HBFatn4j1ayAWO8dkH8Mp3j9elZYpe9MnRna2fjtPLVb2zbeOC8J4P4Hp+dbdr4l0e7IC3axsf4ZRs/U8V5gKXFVzMhwiz2NSrqGUhlPQg5BpcV5NZ6lfaeQbS5kiGc7QflP4Hit2z8b30LYu4Y7hMclfkbP8v0qudEOmzuxRWJYeLdMviVd2tnA6TYAP0PStpGSVA8bBlPQqcimjNprcMUYpSKQimISkZFcEMoI9CKcKKAKE+lW8mSgMbe3T8qoy6TOnK7XHt1rcpe9bQxE49TCphqc+h8U7qN1R5o3e9dVj0OYl3Ubqi3e9KGosFyXNb3hIb9ax/0zP8xXO7q6TwUN2tn/AK5H+YqJL3WKT0PULSL92KzPFa7dAvD/ANM/6ity1XEYrH8Xj/inbz/rn/UVx21RFOWp5CzZpm6kY88Uw13pGrJA9O3ZqDNKGosK5Juo3cGo80ZpiJIkzmnAbZ/8+lPt1+99aHGJj9f6U+hK3HE0maQ0VBoLmlzTcDNLigLhmlzRtpQhJwBmgLiZoqUW8zfdic/RSaeLK6PS2m/74NFmLmRXpasjTr09LWb/AL4NO/sq/P8Ay5z/APfBp2YuZdypRV0aRqBP/HpL+IxT10TUGP8Ax7MPqRRZhzx7lDNGa0x4f1AjPkqPq4pf+Ee1DH+rX/vsU+Vi9pHuZbKcZ9x/WjmrRtJRP9mIHm71TGe/NXB4evj/AAIP+BinysXPFbsyc4o71rf8I5ff3Y/++xR/wjt9/wBMv++6XKx+0j3MrNLnNah8O3+OPK/77ph0C/X/AJZo30cUuVh7SPczgSDS7jVw6Rfqf+PVz9OaadMvh/y6Tf8AfJo5WNTj3KuTS5NTmwvF62sw/wCAGm/ZLn/n3l/74NKzDmXci3UobkUphkXrGw+opoU5osVcZEuZj9TT7lNsa/Wlt1zKfxqS8H7tOP4v6VdjO42M/u1+lO3UxBhF+lFZtGtx+6jdTDRnilYCTNG6osmjNFguTbqvaaFBkuJPuRL+tZgPNXpj5FlHD0Z/3j/0qJLoUiKSQu7Mx5JyaQNxUW6jcc1SQmyYGlzUW7mnbqTQEmacKh3ZpwakMlzUM4VoXyASAacGzTZf9S/+6aEDMpaU8EUkYp7CtjJbFmE3tuA8LyJkZBRv8K07XxVfwECdUmUdcjDH8RTYP+PaL/cH8qHhSX76A1jKz3Roo9jct/Fmny4EvmQHvkbh+Y/wrWt7+1u8/Z7iOQjqFbn8q4V9PibOMiq7afKnMZzjpg4NZulB7B7x6ZmlVq8+t/EGrWXytJ5i4wFmXP69a1bTxmpAF5alT/eiOR+R/wAazdCS2C6OvDU7dWZaarZXqgw3KEn+AnDflV7cRWTi1uBOGpwaq4fmnhqmwyffTg1V91ODUWEWAacDUAenB6LAT5paiDU8NxSJHilFN3Zp1AC0UlLQFxRUsM01u2+CaSJvVGKn9KizTs+1AHQ2PjLULZgt0FuowMc/K35gfzFdDaeL9KuRiSR7dsdJF4/MV5517Uu3IpqTJcUz1yCeG5jEkEqSoejIwIp5FeS21xPZy+bbSvE+MFlOM1v2vjPUIdqzxwzqOpIKsfxHH6VSkQ6fY7rFFY1h4p029bY7NbvjpLgA/j0raUrIoZGDKehByKozaaPiGjNGB60nA716ZqLRSbhRvAHA5oFckHSup8CjdrrD/pif5iuULkEjA/A5rq/h9l/EEntbsf8Ax5aiWwpS0PXLZMRjisXxiMeG73/c/qK37df3Y+lYPjQ48MX3+5/UVy21RnTep4ue/IphZR3FNfrUZ6HPWuw2cidFLhmUZCjJIHSpUgLmIKykydMducc1UeVmjSPChUGOB15zk1dsruXR74yCNWlUbSG7Z/rVIhyfQZdwy2c7xSoQy9+gNOtpbU8To2cNyGwM4+X9abfalcX0sjyNhXP3R0A7Cqi/eH1o0C7e5q24HzDHerulWUd/rCW8udrbicHHRc/0qvbJzJ/vVo+H+PEcJH/TT/0E00hSdk2bv/CO6an3rct9Xb/GpU0nTk6WcX/Ahn+dbHyzDbjDenrVdk2Ng1aSOR1Jdyn/AGbY/wDPnb/9+x/hTlsLNelpAP8AtmP8KsUVVkRzy7jUjjThI1X2UYqeOUr8rZMZ6rn9R71FmjNFkHMxZVaPBDb0PRvX/Cmj5hxTkk2gqy7kPVc/r9aZInlEMp3I3Q/0PvSKbuL0pc01TmlxTJA0lKaSmAZpaTvS4pDOUcf8VD/29p/M11QzXMOMeIB/19p/M11JpI0qdBh5pMU40lVczExQBS/hQTkk4HXtQAdOlFFApAFA5opRQAoJ7Gn5zjPP1qMU8dRRYdziLhQNZuwOglf+ZplyPlj/AN7+lTXHOtXZHTzX/maZdrhY/wDf/pWR2LY6y1hilsLbfFG2Yk+8oPYUNYWZP/HrD/3wKfZf8g+2/wCuKfyFSmtLI5XJplF9JsZOtso/3SR/Kqz6BZNnb5i/Rv8AGtY00jNLlTGqkl1MJ/DJO4w3YPorp/XP9KrN4cvV6PC30Y/1Feg+HJ9HtZ531bawKhY0e3Min1PDDB/xro21LwaYyRb2ZbHANnKM/wDjxrGbUXblZtGpJ63PG4NCvVnUvCDGp3MQwPA56ZzVa7trx7h5Ht5BuPA2ngV27gBHmCbBMSETP3Vzn/634GoPeqVJPUHiZLQ4Mgg4I5o6V3UsUc67ZY1cf7QzVOXRrCUH9zsPqjEUOl2KWJXVHJZpc10L+G4SfkuJF/3gD/hVSXw9cKf3UqOPf5al02aKvB9TJDUuanm067gYh4HwP4lXI/MVXKlTyMVm423NVJPYfmhzmJx/smmUN/q2HtRYdynCuafKuAKdbrz+FSTrgL9a0sQti7EcQRj/AGB/Kn5qGNsRJz/CKcDWDWpqSdQRntSr8oxkn6nNMDU8GlYYpAYYYAj0NQyWcL/w7fpU2aKLtCM2TTnXPlsCPTOKmh1XVrEjFxKVH8LncP1q5SEZ7Zp819xcvY0bLxhGQEvYWRu7pyPyrftNWsL3/UXSM3908H8jXDyWkTj7u0+oqpLZGNSyMCBzjpUOnB+Qao9QyaVWrzix8R6jZbVWXzIl/gkGRj69a37PxlbyHbdW7RH+8h3D/H+dZyoSQrpnVBqcGqpb3dvdxh7eZJFP908/iO1TZxWLVgLAb3qRXqtupwaiwiyGp4aqwfmnh6VgLAYU7IqANUgakIkpRTFNSCkAuBS0etOxQAgpcUUUgCp7e8urQk21xLFk5OxyM/WocUtMD56kieJtsiMpwDgjseRTM4BFOkleQAMeFzgemajr2DAKUCkpwoAUV2Pw4GfEU3/Xs3/oS1xwrtPhqM+Ibj/r1b/0JamWwS2PYIF/d1zvjcf8Uxff7g/mK6WEfu/wrmvHRK+Fb3H90f8AoQrne6MqT1PEnB64NRnpWlaaiLOG6TyEkaeIx7yeUBHOKoRRPPIsUYy7cAetdRuyOinyKF2jBDY+bPr/AJxTKYgpyfeH1ptOT74+tAG3bj5pPrWhoA/4qGL6Sf8AoJqlbj5pP96tHw+P+KiX/df+Rqo7kVPhZ17DNSBw42yDns3+NIRzTCK1aucN7Ehtz25pv2dj6imYpMVNmO6Jfs596T7M2e9R7RRgUWY7of8AZzTljKcEZU9VPQ1FSYoswuiTyhEQ33o+57j61Lsi/vR/mKgjcxk8BlYYZT0IqQRxh1kKs0JIDAHBHtn+tS7opNMGSMDO5SO+0jNRvFsPYg8gg9a6VbTwe0QLX+oq2MkeUpwfyrCmFtFdyQQytLbZ+SVk2n64qVK5TViuqKfT86eI19qf5Ef/AD2j/wC+qBbx5/1kf/fQqrhY4+TjxAB6XafzNdPXMuufEarkY+2rznj7xrsGtk/57Rf99ihOxU1exUIpMVb+zp/z2i/77FI9vGIyRNGSOQAw5p8xnylTFFKRSYqiQoFO3EIU4wSD0+v+NNoGFKKKKBB3p46iminDqKYzi251W6/66v8A+hGm3f3I/wDf/pT8f8TO5/66P/M0l4MJH/v/ANDWR2o62y/5B9t/1xT+QqTFMsh/xLrX/rin8hUhFaHHLcb+FFLijFAjUg1LT0hSOfRLaYqoBfzZFJ9+GqhIEubs/Z4VhR2wkYYkL+J5qPFWYcQwyTYBYjYmexPU/gP51Fraoq5DdsGnZVbdGnyIfUDgGoKcRSYq1oiW9QxRS4ooEJS0UCgAzTJIYphiWNH/AN5QakpKNx3sUZdFspuREYz6ocfpWbdeH3WN2hmVgAThxiuiFNm/1En+6f5VLhFmsas11OBthnt/DT7gfKn1othz/wABqS6H7tfr/SszrT0Bf9Wv0FLXR2+gWt1pltKGeOR4UYkcgkgHpVW48OXMXMLrMuP90/l/9es3TY414PS5jg04OQaJIZIXKSRsjDswxTKixtcmDg04NVfNKGNS0BZoqJZegqVSDUlCFajmX9zJ/umrGKjmH7iT/dP8qAMnT0V51DAEYPB+lXZrCNxlPkNVdOH+kD6GtbFaSbTIiroyPJubVw6FlKnIZD0rTsvFF/auBNJ56dxJ1/Pr/OnkVDLaxS8suD6ipbT+JD5Ox01n4o0+6AEjmBz2ccfmOK2I5UdA6OGU9CpyDXmkmmuuTGwb2PFNtr690yYtC7Rk8EY4P4dKh0U/hYtVuenh/eniT3rjLTxgSFW6twT/ABOhx+h/xrdttUt7tN0Mgb26EfhWMqclug3NkS8df1qRZR61jm6x3pPthHSs+ULG6sq+oqZZk7sPzrl5NS2nHU+1RG8nm4B2j260OI+U7JHVjgMCR2BqcDNYHh+Hif1+XJ/Ot0bl75HvWT0YrIo3+pR2eVVd8vYE7R+Z/pTbPUGuCvyxyA43GJidh7g5Azj1FaOULZaMbh0OM1IuwDCAAe1CkhWG7aXbT6Xbmi4rHzveQeTINuNrDIHpVatXUlPlRnJwGPH1/wD1VnKwR1YDOOcNzmvYhLmjcmtDkm0N2kYyCPSipZ5vPZTyMDAUnIUeg9qjAqjMceeQAAewrtfhqP8Aiop/+vRv/Q0rjGTY23IJHociu1+Ga58QXB9LVv8A0NKmWwp/Cz2CIfIPpXMeOxnwte/Qf+hCupiH7v8ACuW8eHHhW89wB/48Kwe6MaW54pIgVVbepLDOB2+tRhmXocfSnkZ54HNXLqw+wWqPO0LvOm6NY5QWj5HJA9Rmuk6CpcTRyiIJEI9qAHnO4+tQ9sYoABzlguBnnvUyBp0IATESFuTgkZ/XrTERMhU4OOPQ5oj++v1FSAIBl13ZyMA4xU1vatJGJVwx3Y2Lywxjkj0xmhBc1rcff+taPhznX8+ivVC2+6+fWtHw2M642OyP/OqW5FT4Wdg1MNSGmkVqcAzFGKfiqct9DFqMNif9bKCQew4P+FK40m9iwRSYqTFG2mIZjijFP20EUAMxT4yUbI/HI60KKo6veS2FmbiMD5CCcng+xqWXFNuw/UtQgsJLfP3ZXIIPGz8fyp0U8V0vmRSK6eoNchrus2+qx2zRKyOgYOrdBnHQ1L4WvCl81ux+WReB7j/JqFLWxu6XuX6nXbaUCn7aAK0MDlH/AOQ8P+vlf5muoxXLt/yH1/6+l/ma6k9aEaVOgzFGKcaMUGY3FJinYoxQAwikxUmKQigBtHpS4B5FLigBMU9aKUDFAHF4zqdz/wBdH/maS8+5H/v/ANDT1/5CFwf9t/8A0KmXmcRf7/8AQ1kdvQ66z/5B9t/1yT+QqQjmmWgxp9t/1yT+QqQ1qcb3GnrSUp5oANAkKBk4AyTU91iNlgGP3QwxHdj1/wAPwotfkdpj/wAshuH16D9SKgOaXUb2GmjFLijbTJEoxTiKMUANxRinAfzpdtAxoFNYqhXcwG44GT1NNN3BHceTI+184G7gHjPWsfVb5WleDeuwMCpHVTgZqJTSRrCk5M0b28FttKEPtyZEGMgY606O7iubNnDKpKnKlhmua81pNrONwGRu5z+dIsmwmRGVcrwuOvrWXtnc3+rqxTthz+FPuVyi/U0lqvT/AHf61LcjCqe2T/KqNDs9OkRdIsyzqP3CdT/sirakPGHUjaRmuat5QLaFD2jA/Sri3hWExADB79wKXtTF0OpYv7mPhGCSL0YMoIrGawt53ZR+6PY54/KrM6qT95XGB839KjVwGBPJ9azcrs2hHlWhQn0a7iXcieanUMnP6daoMjKcMCD6EYrrG1HMZCpg8fhUcj299KIp4lIA69CD7Gm1HoNTmviRy1KGI6Gti90WNMvbTgrnAVv8aypYJIX2uu0/zqGrGsZJ7CrcEdelPeVHgkAPJU8H6VX2ijZwaXKXch01czA+qmtcrWZpg/er9DWrSnuENhmKQgU8kCo2kVRyQKgoMVHIqMuHCke9RSXYHCjntnvURZ25JJqkmIhns4W/1Z2n9Kpss1udwJGOjA1o4qG6GIGq03sQ0SWWtXomiidxIjMF+ccjn1reWV3+834CuRtP+PyD/rov8666JazqxSY4PQmiT2q5ElQxLVyNawsU2dD4cjBW44/u/wBa2jF7VmeGUyLn/gP9a3zHWU46mDlqZ5iphjrQaKmGKs3EamUsOO9KJCOoP4VZMWaY0WKmw+ZM8Gv1zanpgMKy5ImjfY6kNwcfUZravJ3sRDLER5ivkZAPT2NZMkzzMrSNkgY6ds5r16PwmuK/iBcW4gKASxyErk7Odvtmoe9dP9nsx4TllRP9ICpuYrjOX4xn6Gsi7t4YbCweMHzZY2eQ5/22A/QVs1Y5FK5FcQxJDbvEwYsn7z5s4bJ4x24xXZfDFc61dH/p3I/8eWuGr0D4at5ut3jhFT/RwMKMDqP8KiWwqnws9WT7n4Vy3j3aPDFzvzt3LnHXG4V1SfdrkviB/wAird/Vf/QhWL3RhS3PFXxuOOnamHnmpCON3HXHWmVujrsNxxSqSpyKcQoXBB359eMYppA4wfrxTJZotd/2nqKz6hIsasu0ukQ4444FdDD4Tktbh5jcRS2qRlg8bfe+U9vTNYFhol9fxl7eHcoI5JA69/pXXaNok2mWF5LcMdzRuoUHjAzzj14q4q5jOVtmYUAwG+uK0fDA/wCJ3IfRH/mKzoM4f61qeFhnW5v+ub/+hChbjn8LOuI5pMU8jmkx3rQ4TO1W+FjaFtwDN8qk+tcPc3091f8A2iR8OTjI4wMVt+ItQjun+zIcNGcgk9DyCCK5jdiQeuawlK7PQo07R1PTYJkmVQrqW2gsoPTIqXFcjoGqR2STecSSVL8+2AB+OTWzoF5LeWsjSkHEhC/z/rWqlc5Z0nG7NWsnX3u4bAyWrABfvYB3fhitikYAqQwBBGCPWmyIuzucfo3id1nWG/fdEf8AloRyv19RV7xJMsMDNBqTbZBgxYDgAj8xXL6wunx3LLYrOhVirpIBgH2Oaqw3kkUckWFaKT76kdfx7Vk30O1U03zIgPHTpViyupLS5SaIgOvIzVZsbjjOO2aAccUkzW11Y9G1fUxp+nK24GZgBgH25/nU2lXYnsLVpZB5siZAJ5bnGcV55c3kl04aRs4AAHYYAH9KW2u5Le4imQgtGcru5AqufUwdBctjo2H/ABPkPrdD+ZrqiK5GzZptQsXc5Z5lY/XrXYd6tGFXdDMUlPIopmZGRRinkUgoATFGKd+FH4UAMPWlxTutFFwGjrTsc0YFLimgOKT/AI/Z/wDeb+dNvekX+/8A0pYebuY+pb+dF6OIv9/+lZnb0OutB/oFt/1yT+QqUim2o/0OAf8ATNf5VIQau5xvcfa3EtnOs8JUSLkAsgYc8dCCKlvdQnvlQTLANp48uBIz+O0DNV8cdaTbzU6XuCJXIjtkjxhn+Zj7dh/M/jVfFP20baaYMbigCn4o207isNxRinbaMUrjsNxWZq7wmII1wYpF5GMjP4itbHrWfqV3awlFnjLZBIZRnHalJqxdNPmOXeWTILN5h7YbJxS+Yybnl5GenekdjI5eEBFJwq4xjHemIXkcfNwx6LwQK5GegkSFiVHl4OB1PGPwpWw0WT5ZI6ELjimsy+X06nHy0ZbYVzuKgjp0FJDZDa/wf7v9amn+4v1P8qjtR8qH/ZP86mnHCfU/yrp6GQ+G6IAVgGCpwR1wKuQzxyABHBOM47iqLiKNVTGJGUZ5yDQxGG3FMlcHP8Nc7eptY0iT2bPtTS2OSBWc128GU8xWKjOCP60sepqVBkUrz25oQWLxc888YpA5HfnFQrPFNzG4z6U/nnnNMLEwlwOv1qQSJJGUfDc9DVTdjPP6UqvkdOKBWJBp9vKX+dYmLYUDmq0mnSxMQAJB6pzVjdgD0qaOUqvynnt7UXDVHP2ZEZB6cYq0bgAE5PX0pmnwrPchH3bST93rVufRZsZjkRv9npVOF9RKajoyhJckn5cVXd2IJJzT5reWBtsiFTUZHyN9KOWxV7j4eUzgZzUwXFMtVzCfrVkJWctzREW2obsf6M5q5sqter/okn0/rQnqDWhnWYze2/8A10X+ddjEtcnZL/plt/10X+ddzBZStyQFHqxxRWZnDYWNDgVZjXmpIreGP/WSZPooq5Fc20GSijPqeTXM5DaNnw0BGlwXIUNtxu4z1reMkX/PRP8AvoVxh1QetJ/ag9ah3Zm6d3c7LfH/AM9E/wC+hRmP++v51xn9qL60f2mvrSsxezOyIQ9x+dNKL6j864t9aRJFTZIxY9VXgfU1J/aq+v6UuVlch5Jqk4mutqHKR8D3PeqI4p2KbXqRXKrBOTlJyZMbmVofKMr+WP4cnFR7icAkkDpTe1KKZJIBXoPwuTOo3xHXylGPxrgApGM9xmvaP2f7JJtV1i6cZMEUSqD6sW5/8dqJuyJmrxZ2Rs7mOHzHglVMfeKECuL8fc+Fbv6r/wChCvd7nBiIPIxXiPxOiWDRL6NBhdyED0+YVgndmVONpHh7DFDRBYVfIyRnAOfz9KV+tMNdSOhkkkUflGWORcFsCMnLAep4xUcSgyLubaM8nGcVNJHHFBFJHOryODvUA/L7c+1N83MKxBEGDuL45P1pkbnpeiXVrLp8TRzRnIAxwpHsR61oah/yDbn/AK5N/KuP8H2CNNJPMjb48bQy8YPeui127MGmuAwDP8mD3zWqehxOHv2RyEH8f1/pWn4VH/E6l943/wDQhWZCRl+v3v6VCLp7SXzIyQ24j9ai51OPMmj0l9qKWYgKBkk9qp6jdRQ6XJNvG10+Qg9cjjFRSTQ6toBJl27xt3dAGH/1645tTmFodOmI2xudpHbrxn0pylZGFOjd+hmyszylt2SSST60xSBKCxxj2pCcMQfSkJyQSOeuaxO+xIzk4Y9TwK6Pwom68LGYfKpxGBn6nPauTMnzcH860rDVZtPiYW5IeQEEn+lVHRmdSN42R6WOtQ3ySG0cxT+Q6jcHbG3j14PFcff+JpXtYUgcpKo/eB1wc+tZ6+JdRTyNsv8Aqk2EHkOM9xWjmcsaEr3KWoo7TNMTA5PLPA2QeeuO1UM+tTyvHICwUq2eg6VBxWZ2RjZC0UCjvQULSg0hpB1oE0dVpv8Ax/ab670/lXaMmCSPyrjNPwL/AE3P/PSP+VdszpIDhgcHGRz+Fao4ay1IjSYp4p4XNFzEgNAGaseWfw+tKI29P1ouFivikq15bf3ahkXDZ9aLjI6XtRjNGKYhBwafjNM5z0p4oQHDwDE7++f50Xn/ACx/3/6GlhH75j9f50XIz5Xs39Kk7Tsrcf6LD/uL/KpMVJZW0k8UUca5OwfyrXi8OXhVXcKinoTnBrGviqVFXqSsZUsPUqv3FcxcUYrp4vDUZxvlYn2q5F4XtiM7JH/GvNeeYb7N38jtWV1vtWXzOMxk0u2u6Xw3bIM/ZXP1U1J/Y9sn/Lqv/fNZvO49Kci1ld95o4LbRt9q7v8As21H/Luv/fNKNMtz0tV/74qf7c/6dP8Ar5Ff2Uv+fi/r5nBbaiuY5zARbsqv6tXoZ02FRzaLj/rnR/ZluRk2KEf9c6Tz1dab/r5FLKrP41/XzPE5ri8MjKZ8sDyQeDVYmVnGJACT36GvbW0fSSTusEz9SP61Xl8M6DP9/Tl/Bm/xrP8AtinfWLR0LANLRniwcybVLAdQOMZpSHOSGXA444IxXr7+CfDjf8uci/SU1Wl8A6C5ypuoz/suD/MVazag+4fUZnkcshMmxhlccYHeiNPLQqWyx644/CvUW+GuksSyahco56EoCBVab4ZyFT9m1SCQnHEkZX+RNbwzHDy+0ZSwlRdDzm3HyRj/AGf61LLyF+p/lXYf8Kz1uLaIpLSUKMfLLjPPuKq3XgPxFHgiwDYz92VP8a7o4ujJaSRg8PNdDk5CpTAbBAGT1qt9oclk3Ag8ZxWpe6Nq9lE6z2FxGc8tsJGPqKyljKybmUhh0B4oUovVMpwkt0WHZVQgYywyT1FVHRW+593+IelWiCilsncVxkDiq2wo6nI9TxTTRNhEt5POzn5fXOOKuG6aFMo/AOPn5prxuwLueNvY0ySFVT5WII5ye34UXuFi3HfRuo3kpnjmpw2eUYY9RWSQphIJGWbAzxinRxhFKLvZieccYoEaoLf3gf1pSzEEEqB64qi8nloIwxJP3sn+tRq0GCGL7+2KAJ9LkEVwrtnHPQZroTOhXIP4YrlbYlVGCR9K1EuY1iGS39a3TMJRvqaDyxsMFciqM1lbTpIyoUIGTg9eRT9wK7h0NOjYbJf93+orRx0M1o9CrDp5SM7XBGeh4pTA6dUNXInAQ5OOaUzr7muacdTqjPQo+Wx/hb8qqX6FbSXIxgf1rV85mIAGB+dUdRGba4/z3qLWaL5roxrZzHJDIOqsCPzrqpruWK1Fwb22cEj92kylxkenWuTUfulPpVY3DVc4czIjJROp/tiQ9G/Oj+1XP8Vcr9pNL9pNT7JD50dR/arf3qT+1G/vVy/2k0faTR7JBzo6j+1H/vUv9pt/erlvtJ9aX7UfWj2SFzo6f+0z/eNH9qN/eNcv9qPrS/aGo9kh86IB2x1pCpBIIII7GrAtZAc5XP1pzW0juWO3JOeK3MisFz065pQOas/ZX9B+dAtZM9BSAjQV7v8AAKR5rnxFLIQWZbckgAd5OwrxJLVx1H617f8AAJCj6/nutv8AzkrKpsOWx7Fc/wCrP0rxX4pn/iWXg/3P/QhXtVyfkP0rxT4n4eyu1/3P/QhWC3RjHc8RkHJqIirr2r5OAMfWojaSe3511o3KtOQ7WB61P9kk/wBn86kFm+3tn61RJt+HtSvmnEKSbkJGd7dB7UuuX73OqNEsrNCjjCnscYNV9Elk06Z3Yrg4O3aDk/XtVfyZXuN7YJLZJz1p3IUVzXLUX8f+9/SqN30/4Ef51fUBck8bm4/KqN5xt+p/nQ2Ulqa9nqBXw5cWqsRJG+4496wJJC7ZZuR7dauW06qs0ZPEqY6dD2qhIhD471F7lxjZsPMOzJPOfSgrhd2eewpqAE4Y80+U5xjPTpmkWVuuOTU8jtM4baFG0AKowAAKYiqWO7OMdvWrMMBYktnYgy59P89KolkE00lxJvldmYADJOSahNXr2XzHBW2WKMDCgD+vc1ToBCUmeaO9KAKADNLijFLSGGKTFSIMuo9TU0y8jFFwaNgxvILOOM7XZkVTnGD0612CyWel20cDyqnHAIOWPc1x12r/AGa3KjnIx9cVatdNVLoPqlzsAG/aDuLY7Z7VdzmnDmOyjIkjV1PysAQfapUz0P4Vjw+JLCWWdVykMSKUJXr1z/SoT4rtfsssgRvND7UQ/wAXoSaLnP7KXY6LB9KMf7IqnZ3UlxaxyFslhnp/9YVP5j+v6U7Gb0JlI/u0y4xheKarNnkn16USuWC5NCAhopcUYNMQmPelA5oqvcX0NuvXc3YChsaTexydpC0kpCgk4qzLFGMAqZJFOQA2F/PvWlb6dc3p2wxCG3/vngf4k12Wh+DYoVSe6LZPIyBvb8P4R+tc1bEU6MeabsjuhCU5csVdnK6Z4e1HX5le4j8uFByxU4A9BuyR+FegWljo/hPSJXM/kWmAzyTNjew7Knc/QE1D4j8Wad4WgMO0S3uP3doh4XPdj2+nU15XrOqah4ovzeagOnEdvFnZGvoB/Pua4lGtjVyzXLB9Or9eyOn3KDutZfgjoNW+JOqX7yW+gQiztweJyP3pHuTwv0HPvXO3Oo+JL4lrjXrls/wtdSEflUASdVCrbuAOwQ0uy5P/ACwk/wC+DXoUsNClHlgrIxlV5neTuVWsrx23PeKWPfk05bK5Uf8AH6R9Mj+tWPLuv+eMn/fJpDHcj/ljL/3ya05GLnRH5F4P+YhIPxP+NN8i9z/yEGP1ZqeVuM/6iX/vg0hEo6xsPqpo5GPnQJ/akZymouv+7Iwqymo6/GP3et3a/S6kFUy7DrSeaaPZD5zUTxD4qTga7d497lj/ADp//CU+LgQf7Vkkx/fKN/6EKyDMfSmmc0nRi90NTZ0MXj/xXAwErJOB2NvGR+aqKtn4p60G/eaVp+B2Ecq/+z1yfnmk88+prKWBoy3ivuLVecdmd1Y/FWMsRqOmke8Eo4/Bv8a14viP4fmxuN1Dn+/GDj/vkmvLTNkYPIPY0wrA/wB6JPwGP5VyzybCy+zY1jjqq6nttl4l0W/GbfVLZj/dd9h/JsVqrMwAKSHB6YPFeH+H9Ej1PVFgSUxKiSTMxOQAiliPxxj8a9xcgW9sgABWFQQBjHf+teDmuX08JBTpyZ34TFyrT5JIX7TKP4s/UVWubaxvR/pen2kxPUtEM/nT6SvFhiqsdpHounF7oybjwl4dueVsmtnxjdA5GPwPH6VhX/w1tZIiNO1FgxPK3IGPzArsjTa66ea14dbmcsLTl0PMNR8C+IrYF4rdblFGAbZwxP8AwHr+lctcQywO8VwHWUcEOMEfhXvKyMhBViCPSuU16ytv7Rme7QNHMd0bmMMAO49sGvfy7H/Wrxa1R5mMoKglLozypI2VwVcsPpUzzBIwuTgEDArrJPDVrcljZzlfYHI/I4NYl14b1GJm2wiUKM4Q4P5HBr1L9zhUk9jGkHnOVUtnOST3qQtGqHO7IOMU8WzBz5gcY6hh0/CmyhHLYJViemOtUgsNjYJGpOce1SJLhhk4BOcdQajU4iHAPtQ0bNjavOegP9K1urk20NCO4Mqk7h+FBZvU1FbqRCMjB6HNS4roi7o55KzJYf8AVn61KBTYB+6P1qZRxWMtzWOwgXLD61W1FP8ARrj6GroHzD61DqKj7LcfQ1jLdGkdjmgP3C1mmtYD9wPrWSa1IY2iiimSIelJQelJQIWkNGaKYgqUGoqkFJlRPdR4d0n/AKB1t/36FOHhvR886dbf9+xWmKcK4uZnPzMzP+Eb0f8A6B1v/wB8Cl/4RrR/+gdb/wDfArVFKKnnY7syv+Eb0cD/AJB8H/fArtPh3ptnYSakbW3SIuI920Yzjdj+dYOOK6nwRw999E/9mqeZ3LTOmufuH6V5br9pb3+sTW9zGJYioJVuhr1K6OEP0rzPUv8AkYp/9wU2yVuYbeF9G/6B8P5VXl8NaMP+YfD+VdCe9VpgMUcz7lJsxo/DWjE/8g+H8qtx+F9F/wCgdD+VWoqvR0cz7jbM9PCWhnrp0P61YXwhoI/5hsX61px1YFHO+5F2eUePNMs9L1S0SygWFGjBIXucnmuJveCn416B8Sz/AMTuxHbyf/ZjXn98OUrrpO8dTojsischcg/jSsVLBuc96iJKkihWxnPcVRqLgbyR0prMD1qRSuAew6g0m1Tg4zzQFiewihecGdmWNQWO0ZzjtSvMzL5ca7VOCQO5qeOzLWsUkb4MpIYlsAAdjV62tY7fTHumA81mKoDzkd/wpmdzJuI5Ys7jt3KDtJ6g+lUzVmbG87TkHoPT2qEigtLQixzTsU/bRii4WG44pQKdtzTttK47CIMSJ9asyDLCq6KfOT61cZfnFS2O2h23i+wtrTQLdoYwpFztyPTyxXCyzyy7d7khRtA9BXoPjY58NW59bz/2mK87Ip3M6auhuferMZS3YGVPMYEHy84/P/Cq2KcASc00y3E7TR9Ya6YI45J4AwqIPqetdCoBGR0riNAsmkuFle2MqZxuY4Vf8a7VGLIGVht7YFaJnnV4JS0EuB+5NPI4HsKikLlDk8Z9KQyleWbjPpRcy5dCXFRSzJCDuIyB09PrVd7p5spbjAH3mJxj8e386sabos+oEzHAhU4aaQYVfoO5/X6VMp22LVNWvJ2RRM1xeyLDAp+bgYHJ+gra0/w7HHIDd757hjxEDxn3Pf6Vs6ZpYy0Gmxc4/eXL9cfXt9K6FYrDQNOe6upljgQEyXMnVj/dUevsK48RiFTfLvJ7I2owlVfuaR7kGnaKluElmCvKq5CYASIDv6f0rkvE/wARUtpnsPDzJPOB+8vTyqn0T/4r8qwvFXja88UKbGyR7LSc5z/y0m/3sHp7dPrWn4J8GadfWy3d/LIqknyY0x26sx+tZ0MJKc/a13eX4L0OmdWNKHLDRfizlLPSJLmU3N3JIxbks5yzE9evStqO3hgKJEgVeSfU8VvarpEVnHJNDNvReV+YMCM+3SsCScRsDgnrXrKKijznUlUZMRSEA02KdJhkH8KkNO5nZrcbtBqvPMbeVWAyCCOaW5u1gyvIYjINZst4ZYwrdR0PcVnKpbQ6KVJvV7GtDI7rl+/IA61LsHesYah5SlQDu45q5FqkbEB1Kj1ojNEzpSvoi2yKRggEe9QtZ2zD5oIj9UFOju4pIw+duSQM0l3II42TcVkI49605kZcsr2KM9rpyk7oEznHyjHNY8tvb7uEI/HrVp53djuAJ6E460ht5FhW4dD5e8puxwTjOP1rNzZ1wjyrUz2tY8A7mBP6Uz7FuUFZO+PmFX5F3OMHANNK4wExnNNTZqZz2kqReYcFR1welQc1oXTFbULnl25+gqhitE9AR1ngW2M11dOQ+CiwKQONzuox/wB8hq9ZlYPKzAYBOQPSuI+HlkIbK3mlVh5sk1yAehEaeWp/77c/lXZmvkOJKl5Qh6s9bKo/FP5BTc80Zppr5c9pCk5ppoJpDTKQlRyxxzIUkQOp6hhkU880lXCUoPmi7MbipKzMW58NWkxLQO8D+3I/Ks2TT9ZtB8yrdxDsPm/Tr+VdXS17GHzvEU9KnvLz/wAzzq2VUJ6x91+RxjXFlc/ur62Knphl3Y/PkfnVKfwvpd8+60laNvRDu/8AHTz+tdzPawXClZokcf7S5rIuPDVux3W0jwn0PzCvYoZvhKnxXg/vR59XLMTT+B8y+44Wbwrf22WiCzqpzhD82P8AdPNYk6vatsIZXU8hlwa9N+x6xZMApW4jHY4b9DUU9xZ3B8vULBkJ4IaPePybkfga9KM1UV6clJeRxvmp6VItHmqXBYgEnGeQT+tWo97LuYYyePpXVTeEbG6lL6dchCckIOf/AB08/wA6ypPD2o2au0kRkAONyZOB7jqPyrSFWzs9BSUZLQrwD9yfrUyjimwphGHfPSpQtaNkpAq/MPrUWor/AKJcH2NWVHI+tQaiP9EuB/smspbouJzK824/Gsg81sIv+jD6msgjFbEtDKKdikIpkjOtGKU0lBNhKKdRigBtSgYqLvU1JjifRA6U8UxaetcRyodThyKSnAcVIxK6rwT9+++if+zVyxHFdT4J+/f/AET/ANmpItHSXf3D9K801D/kYJ/9z/CvS7v/AFZ+leaXxzr9z7J/hQ2CWo0iqs1WzVSbrUjSGR9aux9KpxirsYNHMkU02Wo6nFQRj0qyqn+6fyrJ1qa3kvvGqNR7RZ5j8ShnW7H/AK4/+zGuCvRllH+zXq3jTwzqmtatazWVr5kSRbWYuq4OT2Jz6Vgt8MtcudpzaxYGDvkP9Aa3hjcPGNnNfedVOhU00POzGc0nlGvSE+E+sHh7qzH0Zj/Spx8JL/8Ai1G2H0VjUPM8Mvto6Pq8ux5iI29KcIzjGK9Vt/hI+T9o1RQP+mcWf5mtKH4U6OgHn3d/Ie5Qoo/9BNQ81w6+0P6vLsePRF4iCvbseRV2C88q1aJwXznaD0GetevL8MvDidRfP/vTqP5LUyfDzw0ow2nzP7m6b+gqP7Yw/f8AAHhZPoeFvEu75M496aIj6V7wPh/4YHTSm/G6f/GpY/BHh6E/JpEB/wB+SRv/AGaoec0FtctYWR4KLcntTvspNfQaeG9Hj6aJph/3oSf5mpF0PTFPy6Npi/S2H+NZvO6PRMpYVnzwLfFO+zs3RSfwr6LGnWaLhdM08f8AbuKcLWJTlbKxB9oBWbzyn2KWFPnJLdhKnynr6Vb+zuz9K+hRGynIgtR9Iv8A69O3Tdo7cf8AAP8A69Q89h/L/X3D+qeZ5h4ttZp/DdskcTuwvCSFUkj5BXFDQ9Tf7mnXbfSFj/SvoMvc/wB2H/vk/wCNHm3fYQ/981Dz3tEVPAqKtc+fv+Ed1c9NKvf/AAHf/CoLjSb+xjD3VlcwITtDSxMoJ9MkV9DGS7P8UY/CsrWtIl1q3itrpw9uj7ygbGT0Bzj3P51dPPE5WktC3gl0Z4/b2l/BFDLO0/kPgrGrn5l/pW6byfVrf7FZWxg3AhnZ87gB0UhRz2xyea7RfCFmgjzEXEa7VDyEgDJOMemSa0YtOMKqqRIqr0C4AFa1M9gv4abJhlies5I4WG11e2s5Hv4J2fcWZ5EPAPTPHJ9hSpY3dwymSKbJ5EYjIJHuewr0FCqkqMkqcHapOD6cU8swAxA7jPsMfmayWfS6w/EyllUekjkLPS44wGuxu2/dgQ8D6kfyrptP0i51FElm/cWK8LgYBHoo71ejMjZ87bt/hQDge/vVoXMqqqh2AUYUA9BWk+IKcU1GGpyf2PNyvKV0Q6zrOk+FNIW4uwY4zn7Nax/fnYdefTpk+9eP6trV/wCL9US61JngtVOIrePOyJfXHf8Ar+levXUUN8ytdxR3BUYUzKHx9M9KqyaRpsv37G2/CJR/SufD5xh6bvKLbe70udUsDNq0Wl5Hks9ukcreRl4gflYjGR9K6rw1bRNolzeTsEW3mVXLjIZGHKr/ALXGa7ZvC2nPas7abAygcLGg3n6beayfE2jy/wDCP2VnpemSRxI7M+UK7XPRuDzxxk56178cRHEU/gaXmcHI6U/iTKevQaXbafvt3XzpVAjjVh93qSQO3FcfKNzDIyOc4ppFzZyNbyQRq8ZKuoHIPfpWtpj6PczrHepdQuejiVdpP4rxUU8RSw9Plcm/xNamDr1ZcyjYqRxrAu9jgY5z2quNUt9xVgw98V283hPTJkK+bdhT6SLz/wCO1TPgLSTnE15n/fX/AOJrnee4Xo/wIjlVV6zX4nD3t2J8qMFB0YVQZ8ZPPvXobeANP/gurgf7wB/wqJvAFsel/IP+2Q/xqP7YwsteY6I4GrFWSOBJ39zx6+lITtBBzjPFd4fAEXQX7fjF/wDXpv8AwgQU5W+z9Y//AK9XHNsL/ODwVbscUrsI+pApXnkdBuJO08Z7V2X/AAgkvA+2xfihpp8CXCvuS9gB9drf4Vqszwv/AD8Rk8HW/lOPUo6gYw2cnB61t6jZSQ+G9MlEbBJHkZ2PQscbePoP1rQHw+ui4P222A7nL5/9BrQ1Dw1q+oKiy3dsVTou5sZ6Z+76YraOZYS38RfeYzwdfmVos4Eggc9PWmkEkAZOelda/gXUz0ltT/wNv8KI/AWobstcWq46ZLH+lNZjhP8An4vvK+rVv5WcHcn97t/ujFRou5uld9H8NZS5M+pxjPOY4y388VsWPgHR7Vlec3Fyw6hnCqfwAz+tRUznBwXx39DWGCrv7Jd8LweXpoyjL9mgjtUz05HnSfjucD8K2Cailt1WECBShjZpFAY8seTnPrU6hXUMpyrDII7ivkM0xcMVW9pDbY9nBUXQpcstyM0lTeWKXy1rzbnXzIrGkq15a0mwUcw+dFbmkwc1a2rSFVp3HzlXBpMVa2LSFV9Kdx85WpM1ZKKaaY0JppjU0V+tDKrjDKGHoRmp/KX1pPKHrVxm4u6Y24vczpNNsnOTbqD7EinLbKihVkk2joGbdj8+n4VdMPuKb5J7V2Qx+JjopswlhcPLeKMy40uK6j/fxQSydm2lCR7sMk1nN4SSQjypGjJHch1B+vB/Suk8l/7ppu2RTwCK7qebYhKzs/kYSy3DyemnzOLufDOp2gDeR5q9cxc4/DrWDqSMlvcqwIIB616vHcXEXTke4pl3b2GqQtFf6fGyuMMyjDfn1r0KWaqVudHNUy1r4GeDon+jD6msgrXtV78NtNuedNv3t1x/q5xuGfr1/nXEar8OPEOmb2+xm6iUZ8y2O8Y9cdf0r1aeKpVPhZwTw1SG6OJK00ir0trJGxDoykdQRjFQNHXQpI53Blc0mKlK00rVXJcSOinbaMUXJaGYqUUypMUMcUfQ4GKlWmCniuJnEhwHNOpAKcKhspIQ9K6nwT1vvon/ALNXMbSxwoyTW7ov2qxjm8sqhmxknkgDPT865q2Jp0dZnRSoyqbHV30iRxEu6qMdziuAl0+efV7idAvlMAFOeT0ro/LjZt80pdvUnJqZZrSLopNedPMJyfu2S8zsjhYR3uzFt9BeZgCWJJ7cVYk8MpG+HjJ+rGty31e3iIAhA96dd63G3+rUfUiqdSDpc0q2vkNQkp2jT0MWPRYox8sCD6jNTLpoH8I/Knyaq7dwPoKgbUWP8VebKpTe8mzsjCr2SJhZIvak+zxjsKrG+J70n2vPWseamWoVOpZMaDtTSgqNJC5471citZJDwCa0hDn+FCk+T4mV9lGz2q7JbrCMuwHtVKWdFJC80qkFD4hRlzbCEY7U0iomufaozOT2rmc4myhIlOKbkVCZs00yVHMjRQZPkUuRVXzaUTD1ppofIy9DC08gSNcsegpt/H/Z+43JVAi72JPAHrUEV68DB43KsO4qpfy/2jFNFcsXWZCj89QRg1rejyWd+a/yt/mQqdTn8jMfxv4bQkHU049I3P8ASnx+L9ClXcl5lfXy2/wrwi+ge0vp7dxh4pGQ/UHFdn4NtbfXL4RS7hHFFvMYOM4IGM+nNexiMroU6ftE3b+vIVKSlJxkej/8JTo//P3/AOQ2/wAKnh1yxuBmF5ZB6rC5/pSQafZ22PJtYUx0IQZ/Or8Yya8OTo/ZT+83aiiOO5SX7qyfjGw/mKlzUzsI4duOW/lVepcEiE7i0hoppNLlKQHpVSaRpZfs0LEMRmR1P3F/xPb86luJvIiL7S7cBVXGWJ4A5pLaIxQgMQZGO5yO7H/OB7CtYxsuZj8iSONIowkahVHQCloorCTuxhiigmkzUNDFzRmkzSZpWCwpAPUD8qY0UToUaNSp6jHFOzSZrSNWpHRSf3i5E+hRfRtOfO60jJJySc5P41UufC+m3DqwR4iv/PNuv55rZzSVSxFVO6kygiRYokjQfKoCjJ7U7PtTaKx1eoWHZpOKTNNzQOw/ijimZozQFh/FHHpTM0ZoCw/ijNMzRmgLD80vWo84qvf6lBp9uHdJmcngRxFxj8Oa2oUZVp8kXr5kVJci5mW8UYrlR490jJBuFUjs0cg/9lq9p3iax1Sbyra8ty2MtuWQAD3JXA/Gu95RiV2+8w+t013+42jxUds+5ZEIP7t9oJ7jAb+uPwpsc6zxCRPuknH4HH9KdDxFIcctMfy2r/8AXrz3Bx5oy3R0pqUU11Js00tTSaQmsbFJDi1ITTM0madirDiaQmkzSUJDsLuo3UYpNppgGaM0u00m007j0G5o3UFTTcUxodmkzSUmaY7Dg1O3kdGP51HmlzVJtA0SCZx3B+ozS+bnqiH/AIDUWaM1oqs11Fyol8xe8Y/AmpEnVOQ0i/Q5qtmjNWsTNCcEyW9tNM1aExahbQXKkYDSx/MPow5H4GuP1P4YaPdIzaddy2svZZCHQ/jww/WurozXXTzStA554SnI8c1b4e69phJ+x/aogM+ZbfvB+XUfiK5WSBo3KMpDDggjBFfRwYqcgkH2qG5tbW9B+120NxkYPmxhv516NLPrfxI/ccs8tT+FnzmYj6U0p7V7heeBfD14zN9jaBiOsEhXH4HI/SuevfhbEWLWWpsFPRJ4s4/4ED/SvQp5xhp7u3qcs8vqrZXPLdvNPxXX3/w61y0+aKKK7Qd4H5/I4Nc5d2E9lM0M8MkUi9VdSDXoU8RTq/BJM5JUZw+JWPomDS7uYxBYWBlGUDDBYYzxmtODwvevzIFj9iea4G8129vvGGn6ikc0k6yKDb79xUjBIUD159a7zwt4ing1YaFf2riaQySmVjtIyxPKknAxjofwrhdRt2OP2RpweE4/+WszH6Vd/sDTrSNpZR8qjJLnirun6lb3lm8yXCSLG7K7hSgGD71yeta62oTeXGcWyH5f9r3NcmJxcaMLvfodWFwcq8+VbLcmu76Fn8u1RY4AewwW9zVY3hxgGsvzSaXzK+Yq1KlWXPJn0lPCQpx5Yo0DdsepppuCe9UDMBSfaAPWs7M0VHyNDzz60hnPrVD7SPekNyvvRaQ/Y+ReMx9aTzjVL7UnvSfao/U0csh+xfYueec1LC7yuABVKNhKfl5rVgCWsRlkIAA6mjW9jOpaKtbU19Ojjhw8xH0q1ea3DCpHmRwJ79a878ReOLTSowryNGWHygLmRvcDsPc15pqvxAv7vzUtUWBWbIlOWkx9en6V7uCoYmdPlgrLueZVo01LmrPXse36jr1nFGJPOLjuzfIo/E1zNz440yEsGvrNMdhJvP6V4bdahdXkhe4nklY93Ymq5ya7lksJO9SX3f8ABuJYuEFaMfvPZ5viNpI6aiG9kgb+oqD/AIWRpROPts3/AH4rx7BpcGtVkmGXf+vkH9oy6RX3HsifEbSCfmv2H+9A39BV2DxzpE/3dStjns2U/mK8NxSc1Msiw72b/D/IFmUusUfRVrq1veLuhkjkX1icMP0qx5yt91hXzhDczWzh4ZXjcdGRiDXUaV4/1WxUR3Gy7jz1lzvA9AR/XNcVfIpx1pSv6nRTx9KTtJWPZ/NI60u7Ncxoniuw1sGOBys4GTDJ97Ht61trPtPXivDrYedOXLNWZ6EVGa5oO5xPjvwzNNdDU7C3aQMMTLGMnP8AexWJ4PkudM8T2geOSMSuI2VlIyDxz+h/CvXEZHHD4P1qUIM58zJ/CvQpZnKND2NSN1axx1MOufmTsycDAyWX8CKtRRknpkDk49KpgsP+Wn6CpBNKAR5xweDzivLio31HKMnsPkcu5NNzTOvej8avcaVhc0hNIT70ySQIjMeQoJwKaKSIOJ7373ywYOAf4iD1+g/nVvNVbOPyYPnIMjku5HqTn9On4VYzVVN7LoJIdmkJpuaM1jYdhaDTSaN1Kw7BmjNJmjNTYdgzRmjNJmiwC0ZpM0maLDFJopM0ZpWAM0GjNJRYYtGaTNGaVgFzSZozSZosFhc0ZpM0ZosFhc0lLmkNMY1o43Pzxo3+8Aah8uyLlIooiSPmKR8H8cYNT00r827JzjHJNdFOpBU5Kd79O3zM5RlzJxtbqNCrGgRFCqOgAwKkRybWEE8/MfwLsR+mKikOyNnx90E1JHH5UMUWd3loqZPfAAz+lZp+42+pbWqFzmkNLSGsikNooqe2t2ncBRVJXCTUVdkaoWOAKlW2buK3f7L+yW/mygBcZJPasW81a2gVmU7gvVjwo/E1rOhOLSktTmhXdV/uxwgAoMY9K4/UfiJpdszILpXYdoELfr0rn7n4oQjiG1uZf9+UJ/IGuinleJqbRG5wj8U0enFRTSFHcV5DN8S7pmylhGB6PKzfyxUX/Cyb8f8ALhafm/8A8VXSsjxPb8SfrNBfa/A9gIU9CKaUryH/AIWXfZ50+1x7F/8AGrUXxPIAEmmN7lLj+hFN5JilsvxGsXQ/m/A9QKVGwxXE2nxK0uQgSfaYf99AwH5c10djr9hqQzbXMM2BkiNuR9Qea5qmBr0tZxZ006kJ/DJM0c0oNRiRXGVNOU5rmcbGzQ6ilpagkbRS4FIetABmkzRSGqGG40u6m0UWHYdmjNNpKLBYfmopooriMxzxpLGeqyKGB/A06msaqLcXdByp6M5Pw5dS2cc+qQxW8V3GWaFjIFEsQyWGzsSOc8dOKs2dvfeItXlvBHviY7/KhkG446KMsPxOe9cRp/2MaHczXEdy90JVCsqfu4o8ZJJ7kttXHpmvUrPVrTW/Bq2NtDdNd29pvR7e1kI81cbRkDAP6V9tKifHxaRu317DHax6fpcItrNF+ZEGNx75rKKt6GsdNbnsBENSaS2uHTeVKlSOSOfy/WrsXiiN/uag3/fRr5bE068qjdRH1GGhCFJKjaxcEbnopP0FNk3R/eBX61YtNeJkXfqRSPuwbOPwzUF3qjXMjD7Z52DjIfNYulZXNoym52aX9fIgaTnrQMt90E1A8hY8sT+NV5L3UrWeM6dcSQllYSGM4J6Y/rVUaKnPlNqsnCHMi8dwppJ9aj8zxlJB5sdxeumcHjn8sU118aAKHe+G7pjH8x0rq+pLu/uOVYt/3fv/AOASk+9IoLMBWLe6jrK3Vpb6jdXTKblAUl7Ehv6A10VlFvkFYYmj7FLXc3o4jni2+ho2UKwwmV8ADnJrivG3jcWEb2lpIGuzwF6iIep9/atfxd4hi0TTySNxXCoo/ifsPoK8LuLiS4nkmlcvJIxZmPUk125Tl6rP21RadPM8vGYn2a0+J/gv8yS5vbi8maa4meWRurO2TUOSTT7W2mvLhIIELyOcACvSfDnhKx063Goagwdk5LsAQp9EHf6n/wCtX1SSWiPCnVe7OV0fwVr2tRmW2sWWHGfMmIjU/TPX8K6AeA9MsbcNqeuKs+OYoIt2D6ZJ/pWpq/jK6v7hbHSY2AJwAp5Pbk/lWJPHZWFzG2o3rTTLc/vYogGUxhQwYHPOSQOcd6UppEJTn5EL6ToKMEiN1M/puHP4AVUmtdKjYobdkYdQzkEVZstVuJ713sNHWaaSLy3WNCc/NnO1QAOwI6HFSXXhTxRqtwbs6NKNygAABcAAAcE+goU3fVFcqW7Mk2WmueGkH0Yf4UxtFt5FzFdc+jLTr3QdX0zJvNOuYQO7RnH5jiqsc7KQa0ugt2ZXm0m5iBYKrAd1YVSdGjOGUqfQjFdHFc7xhuabMsUqFJY1bd0buo9qGCbRz8NxNbzLLBK8cinKsjEEfjXpvg/xeNSRdP1GQC8HEch/5aj39/515vd6fLagSfehJwHA7+lV43ZHDKxDA5BHauPF4SniIcst+/Y7MNip0ZXWx7/uKtj8qduPqa5/w1ry6/pKyMNt1BhZRnqcfe/GttHBFfHVaMqc3CS1R9TTnGpBTj1Jdx9TSbm/vH86bupC1ZWNLD/Mb+8fzp80wt9Pe5ZizbtqRh8Fj37Hp9KrlhmqmoQm5hj8v5ZomLI4OCpOOh/AV04RUvar22xzYtVPZP2W5bjuzMu5d6A5wrn5sA46UTO7QOoPJUgZNVIBcNNHJO5JRCpLOWLk45Pp90cVYdgRRiI041X7LYrD+0dNe1Wo+KUyQRyK5IdQ2c+op4dv7x/OqdpIdksZ3ZSQjnuDyMe3OPwqfdWVSNpM2VrE3mN/eP50b2/vH86h3Uu6osOyJfMb+8fzo8xv7x/Ood1G6jlCyJvNf++350vmyf32/OoN1Luo5QsibzX/AL7fnR5r/wB9vzqHdRupWCyJ/Nk/vt+dJ5sn99vzqHdS7hRYLIl86Qf8tG/Ol8+X/no3/fRqLIoyKLBZEvnS/wDPRvzNHnS/89H/AO+jUW6l3UrByol86X/no350edL/AM9G/Oot1GaLByol86X/AJ6N+dHnS/8APRvzqLNLmiwuVdiTz5f+ejfnR58399vzqPNGaLByrsSfaJv+eho+0Tf89GqLcKQmjlQcsexN9pm/56Gj7TN/fNQk0m6jlQ+WPYdcXcohI3t85VOO2SBn9an+1Tf3zVNiDNFx0Jb9CP61LmrlFcqRPKr7E32qb++aQ3U3979KizSdTUcqK5I9i3byTTShQc/hXS2txDpsPmygFv4V/vGsWxRLeIyydhmuD8e+L3hZ9OtWIuHH7xwf9Wp/hHua1wlCdaso00ebjHBp82kV+Js+M/ii6F7W0lSeXoyqT5cf/wAUa8m1HXNR1Rybu7lkHULnCj6DpVBnLHrmk2k19jQwcKer1l3e54k67tyx0XZCE5pMVcW0YJl/lHv1rUttBmPzT7LaNWAdpWAZf+Ak5/yK63yx3MNWYAQnsaDGfSui+y6NEreffTyEOQFijC5XsQeR6+n41inrQpJ7Cd0VChHakK1c2g9Sad5MBGCGz65qtBGdzT4bme3lEkMrxuvRkYgj8RVx7OP+FyPrzVWW2kjG7G5fUdqGkJSaeh1/h/x5c2rpBqbNNDn/AF38a/X1H616Xa3yTxJLG6ujDIZTkMPavn0Eius8GeITp18LO4c/ZZzgEniN+x+nrXi5hlkZxdSkrPt3PXwOYNNU6ruu56y2ohWwFz+NJ/af/TP9azLh9lwAeN/8+9M3cda+d9jE9tqJqHVcf8s/1pP7VH/PL9ayi9ML0/Yx7BaJr/2sv/PI/nR/ayf88z+dYpemmSmqEewtDb/taP8A55n86P7Yh7o1YXmU1pBVLDxFdHQjV7Y9Q/5Uf2vag4xJn/drDsbuG3v4JZyBGjhmyMjANcxPdalNKXfU23HqRM/9K6qGX06iu3Ywq11B2sehNq9qOvmD/gFNOsWX99v++TXnO7VM5GoSvj/pvJz+YrSs5J1t2NzIWIyctnI/E9a0qZZTirpkwxKbtY2b3R/EyrCljYIvmL5k8nyE7+4AGdoHQYGfetLTdBvDHjUr2+LnqoJCj9c0y3sI42WRri5ZhyrGY5H0I5FXDql9plpJM04u4Y1LlZx85x2Djp+INfQQraWaPjqkNfdZoQ+GdCLKZ1nkYdyCR+prpIND8HJApNpH5gHUqf5CvPIPGV/rC2r6RbWStITvtbnLSYH3mQhgHx3XAI9x0t6h4j1OxYH7NZvE33X8tx+B+eipOEdZIujGtJ8sH+J6XLZ+GDprReVZBWXB/dcn6jrXn+o+GdAeR/IuI4fTybWVf1FYr+M9QI/49rP/AL4f/wCKqBvFt+T/AMe1l/3w/wD8XWf1mjtb8DqWDxd7p/iMniudEm8x79prQuE2zRydCf4WZQMjrjNaZcMuQxwR2OK5fU7641SRGuCpCZKIowq59BWlpl0ZLfy2+9GADn07V42OjByVSkrH0OA9pGHJWdzVDPji4uVHos7j+tDB2XBurwj/AK+X/wAar+ZxR5prh56v8zO32dP+VfcSfZlllhaaa4m8lt8ayTMwU+oya6G2YQW7SkfdXNc/DJucZ9a1tQl8nTv94gf1/pXPWc6koqTuEoxUbRVjyrx/qbXOsrbCQssKZcdt55P6YrkURpJFRAWZjgAdyas6ndm91K5uW6ySFq1PDFk0txJeEgLAMLkZyx9PoMmvtcNSVKlGC6I+TxVXnqyl5nT+HdJi06zczeX5h5mcjJH+yD/n1pbm8udbvY9Ot5lhg3KrOxwkalgoJ/EgfU1Dql39nt1gA6jJI9abe2seg+HILue3uYtTu8NBIwBjaM87lI46EcHkHBrSTa0W7OaK6sgubgaLHb21hDJFq486C7J+cOC2F+U9CMfoD1re0XwZBBFDqPiCb5nbc0D9B357sfYfjR4W0UaXaf23qYbzpOUVh86g89ezMO/Ye54ztZ1e61LUpLSBMysdrsDwo/uj0/z3qYx6IUp9jfu/Gmk6LB5VhDbRNvI2RoOnvgYH61lL8Sbm5u1gh0+3mBOFL7gc/T/61MsfhLq+q3ETxSgW0qgiZ0xg9wRn9e9dhpvwu0Tw7qNrcXmvRvcRnJheMbScfXOK2UEtzCUooxrfxrOjAajZeXEx567cfgAf1NTz6J4e8UWwaDyLW7f7rRfLk/XofocGup1DwzA8Hn20lpcx87zCmAPwya8p8SXkGiaxtsAPMCgy4+4QedvvxjmplTW8WKnUu7GVqmj3+h3hgvIiuc7HA+Vx6ioVkLLg16Jp99B460JtPumAuVXfFL1YEcc+4yM+oNefXFpNZXc1rOu2WJyjD3FEZX0Z1RdyNSCTHIqsjcENWNdwfZ5ygJKHlCRjI7VrvwRVK9UyxgjGByAB3psoveENXOla7CXk228x8uUdsHofwOK9aJ8t8Z4PIrwUHmvYtB1E6p4YtblsebEPLf6rxn8Rg/jXg5xh/hqr0f6HuZTX3pP1Nnfz1oL+9VBLQZa8NwPa5iyW96bvquZaTzKOQXMWd/0pC4qv5lJ5lPkGpEiSBLsocDemRz3B/wDr/pU5es+aQIUlPRGBJ9B0P6GrBbBqpQ0TFfUn30m+oN9LvqOUdyffSb6h30b6OULk2+l3e9Qb6XzKOULlgNRuNQB6N9LlHcn3Uu6oQ9L5lLlBMm3Ubqi8wUb6XKO5Lupd1Q76N9HKFybdS7qg8ylD0uULk2+jdUO8Ub6OUVybdRuqHfRv5o5QuTbqTf71Fvppenyhcm3Uhaod9LvyaaiO45WUzu38SqFznp3P/stSB6rIy7Ayj75LE+vPH6AUvmVU462Ii+pZ3+9SW43yge9UfMzWlpowWkPQDNZTVolSlaNyLxRrC6PpEswxlF+VT/E56D+teGTzyXM7zSuXkc7mY9Sa7P4j6q019DYKTtQea/PVj0/Ifzrh0BdwqglicADvX1GUYVUqHO95fkfN5hVvPkWy/MsWttLdTrFDGzuxwAoroZNNt9MRU+W5u3wOOin0Hr9a0LW1i8P6QzMEa8mHzP8A3f8AZH+f5VFB5UGjXmpXsYaSeMLZ55IYPy3Xjp+NerKXKjzE3J+QsMa6aZ47iN21cMFgiSNZR/gDn6nj8a2bPwZe30X2zxJetapkFYmYb8erE9P1NaukWsXhjT5Nd1o+bqtzH543DJiQ9/ryB9SB71UgN545ka4RtkCNtCBs7T7+p/8A1VMYuTIqVbbbD5P+EQ03CRWdrOq8eY53Z9+Q2f0qBta8MSMETT9JI9oVB/MpWvb+C2ViwiWRkOGZIixz+ANcbr3w+1EXV3c2yOYy5fY0LLgE9uvStnSsjmjVUnqzpv7D8LaxEVEUVtMfutE+cfipwPxWuW8QeB73R4xc2zG7tTkl0XlPrj+Y/Ss/R9J1uCfcsnlwxNySc/gtd3omvPHOLedFViQJAeAw9SP6jke44rnalF3Rup22Z5Ypp2CD7V3vi7wojJJrOmR7YutxBjBjI6ke3+Oe9cPsyK1jJSRqndFG7tB5YmhA4HzqB096oqea2OUcH86ypU8qVlHQdPpVAz0nRNSk1PwxDMxzPZv5bc8sAAQT+Bx+BrZEgZAwPBGRXBeCr4RahPYsfku4yBzxuGSP6iustJiqtC3VDx9K+bxmH5KjS23+/wD4J9Hg6/tKSb32L7Se9RtJ71A0lRmSuRQOvmJzJ70wy+9QGSmF81agTzExlNNMlQ7jRk1fKTzD2fNRFVJ+6KXmnKpqkrCbuIqqB90UllZT+IdXTS7ZvLgX57q4AyEQdR9T0Hv+NQyi6vb2PS9OjMl5L1x0Re5PpxXpOi6NDoGlpYW53tndLLtwZXPfH6AV34Wh9uXyPJzHGqmvZw3PK/8AhIrPaAdMlwOxvZKjGu6djB0ptvcG8lP8zWeI7LjFxN16eSP/AIqrUVpDMAytcEL1Itxx+tehyQ/q543MyaDW9JgdXj0VY3XkMk75U5zleeD9K7PRvFFh4iD2N7GEkPQyMAZfcEYAYe3X69eZtdNWWVYmeRRggs1kP51oDwpC+HXUYw+OhhAIx9DWU3T2YrtO6LGraRJp85ABaFvuPjr/APXrLZTnkV2tgWitFtru4iuk2hTuGM//AF/eqd1o1rIS1tOoB6JIMH6Z6V59SOt4nsYbGwkuWo7M5M8VJa3Bt59w6Hg/Sto6DcMG2RGTHXZ82PyqlLpckfDIVPoRWEmtmenCXVEhux6ik+1j1qqbSQcYpn2d896y9lE19qzTguxvU571p+IbvOh7kPOx249lNcyIZAeKmu5JX0yWJicCNsflWboJzi+zKdRuLPLm5Ndzpy/YtFtIFAG9fNkOOrNyP021wx612l26phBjCRomR7KBX1h8jIksbS51rV9kNqboRfvZIQ4UugYAgEkcnIHrzTnaDxF46trSAyzafbqiRLJkHaiAYI92wDU3hGDVF1F9T0y6it2tpIo3MgLbvMYrjGORwT+FWvhsif8ACR3l07qMLtBYerbv/ZaybvJ+QS0gdD4zuhpsP2aKRSLckq3ZmbHJ9x1/EGq/w/0SK5uUubsDk+Y2/PIHPNc/8Q75JpnMY4dmJPr85H8lH5VZ+Gt48djcmSV2DM67SxIAWMngfjWkPdRzzTcdDuvEviy7ubgabpj/AGeNOSo7DsTjqT1x0A61x9zPNIxJluJWzn5V3YFRR3Id55nA3Sykq38QJ5x716Bpt7d6fpyw6dbrgqGeZ0GXyAc5/lVPzM72MDT7qO1sBdQ6h9mk/iSRSNw9GAHI9wfwrL8RaZB4j0lb6EYkcE7VAJD9sexIwR6kEd6b4rkPnWs5CxrdFhtTnkHBPFXPDyy/2bdWzkEtGZQ2OQVOB+mKL2DzOO8DpqthqyyRwSrGcglkIUMAeCTxyMj8a6rxFs07xTpeuRWsdxFKql0kUFHYcYOf9kr/ADrfukdhDNMN6iReD7kD+tZPiuAHwbavnmOVMfipH9KxnqzWE22jmfHlsYtca6XS302K5QSJC0iPnsSNnA5HSuMnc+ScYFelfE6FXbSr5II0Se2zvhfdGx4JwMDaeeR715nL901VOV4nWUGG1j6V3fw+v/3V5p7sNpxKg/Q/0rhpxiT6itTwvcG38Q2pzgOTGfxGB+uKxxlL2tCUTfCVPZ14v+tT0oTYJGehxS+d71UKsGOe5pOa+b5EfS85c873o873qnzSZPrR7NC5y753vS+aPWqOTS5NLkHzluVlkiZG5Vhgj1FPhnZ4EZ8bsYYj1HBqluOKW3ZhJJH1z849ux/p+dPkvFopS1L3m+9Hm+9VsN3oAao5B8xZ80+tKJPcVV+alG70o5A5y15nuKPM96rfN6UDPpS5B85a8z3pwk96pndSjdR7MOcueb70ebVPJoBal7MOcu+ZS+bVLLe9GT70vZj5y55lL5lUsn3pQze9L2Ycxd8yjzKp7m96C7e9Hsw5i7vo8yqQc0nmH3o9mHMXvMpN/vVLzDR5ho9mHMXfMpPMql5hpPMNP2Ycxd8ymSzFEJUjd/D7ntVXzTSeaPMXeAVBJwe/Bx+uKqNPUHPQuM6oojVvlQbR9BTPN96pNKSc0wymj2dyVI0BL83Wtu1OLAnoW4rlYnJcc1v3NyLXRDKzYEcbyE/QVlVp3aiupalpc8f8R3v27xBez5yDKVX6Dgfyqx4atle7N04yIvu59awXYtIzHqTk11emJ9msoo2IyyiTgevP8sV9pTgoRUV0Pjq03OTl3L12smrapb6fE2N7hQfT1NbWn6dY6v41s7CDYdLsoFeQbiV7tzk8dRkfWsPw+Fl8TwPJ5xEYklBhBLBlRmU8c8EA1s+CI3mXxBMXHnyBE3j/AGid386znfmIbtCxl+PPEFxqGqGySUkHaXRfX+BP+AgjP+0T7V1vw70RtItrrUby9NtbBQs+4DDH0HsOmepORVPTvDGm6osupzowuUlYhkPUjPUHitXxJcwpo+naRC5iiD/vj64Ax/M10QtFHHKfNoS6x4w13WTL/ZxNrpsZCjy1AYj19fyrGWbU44pLmHWt6oQGEjMeTnjac+nJHSruiW8eq6lPFMxhsrYDcsY+Zh0AHufWr2u6PZR2zzaX5kUUR3TQM2S49R7ilcSSKmnaimqQNbzokd4BkEdJfcGsm+tTE4uIyVkQfKQ2AR/nv2NVoZ4bS+txFKHEkoO0jBU4yKyvH91cfbYzHII7aRm/dx8DcArEn2w44+tJ6lxjroejeH7lbyyRJUR1m/dTg8deAcfofTOO1eb69pDaTrdxaAHy1bMZx1U9P8PwrvPB2JdOUAgzT2gaPI6N6/mM/jVL4kQRtdWd0nB2mNvcEBh/M1nHRmkJWlY80uU21lXfO1seor0Hxf4bstK0fTL+zvjN9rT5o2wSpCqSRjtkkfhXn9wv7o+xrSMk1dGzF0y4NrqdtODjZKrfhnmvSbiPybxiOn868sTlwB3NeshTc28Mo/iX+XFeXmK1i/U9TLZe7JehVzmjBParQtmp/wBkfGT09cV5tj0+Yo7T6UoQmrRWFfvTxj/gQpjTWqZ/ehvYA07BqQbKeIiR0py3tuW2pHIzHoMAUy71EWQHnpHbkjIErYOPoapQk9kTKSjq3YesBPaodRmXTrCS4bqOEHq3aq0GtQXkzRDUIoCBlXlyiE+mf/rVJeaFY6nta88Y2Kbeka7cD835rro4STknPRHFiMfThFqDuzoPAttZ2NmLozx3etagCWjhYPIq5+7gdOgJ6ds9K9QtdP8As6q8uxpsevC/T1+teK2mnW2mxqll4+Nuq9Fhuwg564UPXqHg3Tr60tp57zWbzUVuAhjNyzHbjOSA3IzkflXpSjofOVJc0nI44aNpZYN9mhxg9V6086TpwXAtIgAORyDmp/OjEoVMZBKjI6D1pFuVbZiQE8qcDnp3rzuaXcLsWOzSABI4QoAO45NPaFFZMpGA33TtzxUK3LDkEY5znpSm5dNpaQKQpGQO3NKw7lgeX0GwLt4+XrSGVVG7bjavTHGTUCTsI0zIQGXAOMCgzgqAZduB/wB9GpsNMsDYWOWVjwR8veuC1O48barqDQJDPbrCWEaxt5a7c9SxPzdu9dqXIDbmKtjccj3oE+12ZnTpkew960pz5HtcpTa2ZzNhpnifyx9q1hI8DlDEsh/Mj/GtY6ddmIj7erMBnLQr178DFajMSjBZUYgY3YpN7MHZJI+eFXHT60p2l0X3GscXWjtJnMXln4ht03QLZ3XcquUOPxODWBqGt6xaxSR3Wm+RkFdzxsBz79K9HcuefNQDgnaO4/8A11I7ArKrPGy8URVNbxRusfXtbmPAsc9a62+lEsrOARuVXwfQqD6e9d7daLpV87LPZWxywO4RgE/iOa5HxLYx2d8Y4QUi8tdi88ADGMnnHFehTrKeiOdS5mange2hu9P1iGXU/wCz22xPHKHCkOC2OvbJAP1rH8HSFbu+iU/vNisMd/nCn/0OpPCet2uialcSX0cr289tJAxhVWdC2MMAxAJBAqrJqFiPG0+oW/mpZXEzkBlVSobOCVBxw2DgHtVJPmY57MXxLZktcRquCSSHlbjg7sL9Qw611ngnwzLp+kOLq5QtK4lCRLnaChVhu7kq3b061U8RafueK5h+T5mRdy5wR/C3t1q94N1lYQmnXGQCSsLHofVD9P5Vs0+U5nK6IZNLaO9kSYZy5IyMc9McdulbOn+I2spoLCePfKMIsg5GDwBjvWrf/YFAhnmQ5H7tSfnHsCeo9OahsdJtmvUluPKdQQM3Ecquo9MjANCaktTJ3M3xRomo/bLa6kiWQZKwoo5Vic9KsQwjTdKuLmR/mC+XyOSoUFz7/dwPUkV6Jqt5o1np8dzEkEkiqEG0Y49CTzjivDPiDr95PdNaCNlSdQQ+MAr/AHV9s4z6nFS10RcdXY1YvHMfiFVsobI29xHiV5FffHhTnjjPXAqXxTdKvhmztmYeZJIrAewQHP8A4+Kz/A3h6SyLRzvsuZiHlCjPloP4TnqSeoHp61uzWN74p8cn+zYYJItOwxWQ4R9hA29MZYjFKSSWhUfj0Mb4ix2ljp+iWFpcNIvkGaQeYXXJwoYZJwDt6DivMpDwT7103jG80+48QXj6VZtaWm4KsLYypAAboSBznpxXMN0A7mlTjyxsdlyrPzIPpSwSNBcRzIcNGwYfUHNIWR5Gyec4FPEQPRs/Sra7iT1uj0pL15VSQ4ZWUEcY4NWk2yKGXkfyrjtK1hbe2S3ut/y8K+M8ehrYg1e0Vw6XKficZrwKuGnCTVj6WlWp1YJp6m35ftSGL2NQp4h0rA3SgN3AII/nTv8AhINJP/LY/kP8aw9lPsPmXcf5fsaXy/Y00a5pJP8Ar8fl/jTv7a0r/n5X8SP8al059hqce4eX7Ux4iGR/MaMggZHcEgY/l+VOOt6SP+XlD9CP8abJrGkyxtG04wwwen+NOEJp7MblF6XJcOP+Wjfp/hS4k/56P+AX/CmRa9pRiQvP8+Pm4A5796X+3tH/AOex/T/Gm41L7fgF13HbSessv4bf/iaXb/00n/NP/iaZ/b+kjpL/AC/xo/t/Sf8Anp+q/wCNLlqdvwD3e4/aP+etx+af/EUmwf8APW5/NP8A4im/29pH/PX9V/xpf7e0j/nr+q/40ctTt+Ae73HbfSa4/NP/AIik2d/OuPzT/wCJo/t3SP8Anr/6D/jS/wBu6Of+W/5lf8aVqvb8Cvc7/iJtP/PWf/xz/wCJo2HP+um/8c/+Jpf7c0j/AJ7j8x/jR/bWlH/lsP0/xo/e/wAv4f8AAC0O/wCIm1v+es3/AI5/8TRtb/nrL/45/wDE1INX0k/8t/5U7+1NKP8Ay3/8dNF6n8v4f8AfLH+mRbZP+esn/jv/AMTTdsv/AD2f/vlf8Ks/2jpn/Pc/98H/AApf7Q03HE//AI43+FLmn/L+C/yDlRV2T/8APdv++F/wppS4I/4+PzjFXRe6ex/16/ip/wAKd9psSf8Aj4j/ACP+FTzS7fgPlRm7Lrtcx/jD/wDZUeXd/wDPxD/35P8A8VWl59j/AM/Ef5H/AApfMsj/AMvEf60ueXb8A5EZgjvP+fmH/vwf/i6Xy7zvcQ/9+D/8XWmHsv8An4j/AFpc2Z6TpT9o+34BymV5d3/z8Q/9+D/8XR5d3/z8Qf8Afg//ABdapFt2mj/Om4g/56x/99Cl7V9vwDlMspd4/wCPiD/vwf8A4umxrP8AaP3skbgIeFjKYyR7n0rUZIT/AMtov++hTY4EcyMrKQDtz9P/AK5NUqmj0/ATVigQf7v5GmkHH+rJ/EVqfZV9RSG1A7ip5kMzUOxgSjAe2DVnxdcG28HzHIy8aoAf9o/4U+VYoULO6gDnGeTXG+NNae6t7ezJJAO8j0wMD+tbYai6teFtk7meIqqnQk/I5CNPMlRM43EDJrs7p9t6VBOAgAyMcDj0FcXFJsmjf0YGux1QGO+LOwZiATgYxX073Plrk/hdbuXxHDHYSpHdPHMI96bgx8tjtx79PxrY8IvPCNfhZPLulkXMeOhBbI/PFcvY6ncaLq9vqNoV8+3cOm4ZGfcd62vD3iBbvxjLNLFFZDUVMTCAbUWTqDjPGSP1rJxblcU/hOz8MHfpOoCWaK3CXDgtIwULk5HX2o8aWMZtLe+s1SaLem8qxIcc8gjtyBn2rz7xxpzpcC5jA8vaCRjBH/6jx+VbPgHxWj2jaFqCmQSDbEQB6Yxz37f55uztc5nHqjXtrtNGu1LOrRzjMixrjZjpg9TjvW8NStrqCV4bm3klYfKiEkntyOw+tYWp6JK7OLctNCihcIMsn+yQcfTkio/D2mXVjq8cs0Gy3zhi5AZR643c1Voy1M3ojSXwFLHLZ3zGNEmmjk29TgHP4cZrB8b+GrvUbCPULKJTFA8jyIWAIUhACM9eF5r1ee6W5hFnYh3Ljb5rDC474/lWdqcUAiXSIgsyxFZr3J4UZyqfVmA49B7ik3d6BGUtzi/DjSWA01UY74wsLDHQkAEfgTVDxVcySWttHKSW3ZBPoEUf1rvLHSo7YJNO2JcnaMdO5J+nJ/KuE1q2Oq+KLXSrf5Sg8t2c8IfvOT7KOv8Au0pKyNKUrzuReN7LToPDmk3Vu9u166qkvlSZJXykPzLng5J5715tN/qX6f5Nd98StTS91O1tnslt72zh8i5KfcbBJTae67SCM+tcdbaZe6mWhsraa4k4JSGMscepwOmcfnUU9InU3oUtNgSW+iErqkYOWLHHTtXZrr8VvCkS3SBVzjbz1NUbP4b+Lbv/AFWiTr7yssf/AKERW9afBnxLMitPLYW+eqPKWZf++VI/Ws69GFVpyZ0YfG+wi0ktTIl8TgcJNM49uKpv4ikkJAhdx/tS/wD1q9H0/wCCFusatqOsyu5HzJbwhQD9STn8hXa6J4C8P6B5clrp6yXKDH2if52J9cHgH6AVksPRXS455nVezt8jwqBdevQhs9JuHRxlWS3dgfxxita08HeO70Ax6XJCCcEzGOLHvhjmvoJmO371Rl/9oVSjBbRRyzx1aXU8p0f4Y6+rONS1G3COmADNK2w+oClcn8cVrQ/CSy+1l77Vpbq1PLQLbJEW9jIPmx+vvXemT/aH4im+YT3Wq5uxzSqzerZix+BvC8TRFdEtP3Qwu5M/nnr+OasS+H9DQADRbBnY4Vfsyc/p0rReXapPJ9MDrSQI6Au5QzN95gPyA9h/9fvRzMhyZFa6Nplngw2FoknXekCqc+2BxV0p/tGmeZIvp+VBmfPQH60mTds8im8P+Iw26WyuJGyT8ig4HbBqE6R4oSFCNKumkThdqAZHPXnrivXgx/2acHY8cVn7NGvtF2PI49F8SBY2Gk3eVzlCAOT/AEoOkeJS4X+y7oqM7iY8E/TmvXstjtS5fsRS9nEftV2PHptO8RxWnmvpd+zEj90sW45yPT2zTfsHiI/8we9KhAwBhPX0r2Vd3fFL83bFHs4h7RdjxZrbxJGQJdJvmEvZYWOwZ9cU0W3iJMu+m3CiRQoURHK9ueK9sAPc/rSgkd6XJEftF2PGTba2AYjZTruP9xuBn8v8/hUaxaztkL2sw2/KoMDZz9MV7Vz/AJNHP+TR7ND512PE2GsICwtXJ9RG3P4Yz3qRJdR2MWsrlWXB5hYZ5+le05b1pwLDrS9kg9oux4oLnWNzbbByM7gfLP8ALrWfr8N7e2E8txaSDycNG5BGQeDkfrXvjSiNd0jBF9WOBVW5vdMuraa2nv7MxyoUdTOnQjB71cKfLK6Gqi7HytISpIzyPSo8k9a2/FeiHQdZksxcRXMY+aOWJg29D0Jxnn1FYitjBFd8Wmi9zu/C+rDVrV9OvyZZEAwN3zyKO6/7Q9O4x6VNf6NJbyl0dsnDAqOvow9/cc+orh4zuIZThgcgg45rt9I8WIsAtdWj3DvMF3B/99fXH8Qwau9jCcGndHNazba2bw3kNzJc7xtKqOQPTb3HuOK1PCmu6zJqNvp9w00MB3DcqFADgkZxgcniu0tU0jVFY2l9GhLfcEynj6Ptb9D9auwaLMswbe23++I8nH/fOKLJkOelmi7pWkS6rKCDJMyZ3EnIX8TwP51X1rwtP9uj+1WzyQryrtgJG2OD6sf/ANWK7fQL3TdLscS3qB84PmMob8FFUvEuuSXkBg0qN/MdSv2hl+6Mc7VPOfcj86yk1EIq5wOszR6Bp8dvBzqdwpX5h8yqerbex6Yz9cDFQNnwj4Ha+i1J7fVLhlKRIwwwO5ee5Iw+fQke1XbGLSvB5tta1WVLgXgdFYN5snQ5Yc5PIAJOOteW+JNfute1N7m4ldlHyxKxHyJk4HHGeeT3OTWWs5W6HTTikjGlfcx5qrLIUVmHpgcVI5ydufqaoTyb2AByqjH/ANeuhItsYOtdB4a0J9WvNzq4tYiDIyg8/wCzn3wau+EvBE/iC4Sa8vLfTtNB+e4nkUM3sik5J9+n8q+hNLv/AAjoOmQadZahp0UEK4A85cse7H1J9azqydrR3IvY8mk8I6LKSsaXUYA/glOT/wB9ZqI+B9JVyUuL3HUKzLnH/fNezN4l8Lt97UtOb/gamk/4SPwvuDfb9OyOMgrXHar/ADFe08jxaTwTp5LFJ7xSCRlowwGPwFRt4HtghYX0wHZvKBGfzr27/hI/DB66hp/5ikHiDwp/z/6Z+a/4Ufvf5vwGqnkeLP8AD+BU3jVX2nHP2fp/49VKTwLcCSQR38O2PktKhX+RNe7tr3hKQfPqGlN9Shp39v8AhPcW/tLSsngnenNNOquv4D9p5HgEngjUUGVmgf6ZHHr0pD4N1JAdpifGMYbr/n3r6BOu+FGHOoaUf+BpUg1fw24+W+005H99KfNV7/gL2vkfPK+D9UbHESgvsyzYGfyqNvCGqLtLCIZYr9//AOtX0X9r8Osf9dphPrmOnG58OtjL6WQOmTHRzVe/4B7XyPm+TwpqsZAMSEnphqYvhnVJFDCNNp6HdX0j9o8OBdofSguc4zHjNHmeHe7aX+cdHPU/pC9r5HzanhfVpArJbhg/TDAfzxUT6DqaY3Wj89MYPfH86+mRJ4dbjdpZHpmOgDw9nj+y/wAPLo56ge18j5pk8N6tGAzWpwe+9f8AGoX0XUoyAbKfJzwEJx9a+nPK8PNkbNMbPUfuzThDoWc+Xp5PrhKaqVB+18j5kk8P6tGqsbCYqwzlRuA+pHApg0HVD5hFhOfLOGwmfy9fwr6eW30MfdgsQPZE4pTa6LsGbay2qd2Si8e9HtJh7U+Xf7I1DdtFnPnGceWc4oGk6iWKrZXBYdQIicc4r6YSbwvPmNJtLfZ1XfHxSm38MnqNKOOR88f+NHtJdh+2PmZtN1Jd3+i3I2gMfkPAPelXTdWJ4tbrOccIf896+nE07w/cA7LewkH+ztP8qVtE0I8mxsz7bRT9pLsL2x8wCz1R1LCC7YDrhG4pf7O1UDd9lu8euxv89xX1B/ZWjAHbZ2wyMH5BTv7L0lzlrS2fH+wDR7SXYftj5dey1OMZe3ulHqUamSJfQ/6wXCc4+bcK+pv7J0ncG+xwbgcg+WOKH0vSZE2PY2rqTu2vApGfXkdafP3Qe3fmfKvnXA/5aS/maX7Rc/8APWb/AL6NfUh0PQyf+QXYf+Aqf4Un9h6F30rTz/26p/hT512D6zLufLn2i4/57yf99mlF3c9riT6bic19Rf2JofbSdP8A/AVP8KX+xdEPXSdO/wDAVP8AClzrsP6zNdT5eF7eZ+WeQdzzT0vtRK4hnnIzwFr6ujaONQke1VAwFUYAHpT959TReP8AKH1up3Z8oSXuqRY8yW4TPTdkZqxDD4hu5AkFtqcrMMgRwu2fyFfUrfvFwVJ/ClXzY1wAzgdFPUfQ1N4/yi+t1O7PlTVl1/QLmOLUYbm2lkjEiLOOqn/9XSucuriW5naWViznqa+svFvhWw8ZaLJYXOI7lPmgmKfNC/8Ageh9fwFfMfifwnrHhW/+zaraNCW+5IPmSQeqsOD/ADrai4PVKzCVec42kzCDV2Ly/btLgvE5GAsihOjAcnj6da4w1saHeKvm2cr7UlwUJGQrDvj6ZrdozTsXZfm59qq4KOCpKkHIPcVZIKllbscGo2HNCNHqeh2N1F4o0Uu+wXSfLPG3OTjG7Hoe/p+FZekaemgalLcwQeZNu2eW/wB9Af7nZs/nXL2N7Pp90lzbOUlQ8eh9j7V3dv4g0rWrYRzubK6IwVbGwn2P+f61VkzmknH0N2LV9PuWAm2JMOCsqlHHtg4NR3TXj3Df2bNpkMKRbpZb+ZgqZJAwoPPQ9c1nyaVcG2dIpmaFgCudwT2Pdf0qvYaRNazbmvLTB5IURA/otT7HqmZOaM7SL/xGPFy3dlqMtwFkEf2nZshdOmAp42+gr3vRND057ANEG8x38yWXdlpJD1Zj3/pXnVhYRo3ms7Pk581YyVH1Y/KPxIro7PxZDZKkVv8Av2j6RxOGB/33xgf7q59zSfKhKfMzT8SxRaZazyNKhwm2KNl+8euB/X2GO9ea6PayQx6t4ia7iSeyRpGjmXcLhWyHQ9/myBx611OpNLfkapqT+WkhYK+07IwoJ5IGFHHU/wA6848e+M011ILGzlme0jZZm8xQB5mxVwoAyFGD165zWLfM7I6acEjk9Z1K41fVbi+uWDTTuWbHQegHsBgfhXrfwb0gw6bd6rJGwMxEMRPQqOWI/HA/CvJNG0q71zV4LCziMk0rYAxwB3J9AK+oNOsItM0y2sYB+7gjCLwBnHf8etVPRWCrLSxazjsKAw9P1pCD6UmD2WsDAdv9v1o8z2pu0/3aPLPpSAGlyOVH60wlT1UfmaUo3pSFH/u/nQBGyoevX86aI1JOCp/SpCjE/dB/Gk8pj/CB+NAiMx/MDsY46YBPNKZQDg7gad5Lj+DOKCjkYIPuDTQCB165el3Kf4j+NJgjqin07fypCueMEfrQIn2D0pdg9D+VHngdf5Un2pR/CTWZQ7aB3I/CkJX+/wDpTftS+hppuU96Vxqw8lf79JuHHz1H56n+E0hkU87T+VTdjsibeo/i5qG6vfs6Bktri5JONkIUkD1O5gMUocHscVkeJLPWb+xWDRr2Gzcn9475DEezDp+X404u7KsN1LxbZaTGHv5prDd91JrMsx/J8VgS/FTSlBEV5Ox7E6fwf/Iormrz4ZeIriZpZb20nkbks0rkn8StUH+F/iUE7IYHHtMBn866oqC3Y9C/qfxX1aWd0sBbxQcbZDBhz74LMB+tYT/ELxPyBrNyM/3SF/kKs/8ACsvFP/PjH/4EJ/jTT8L/ABSf+XGP/wACE/xrRciKTic9fa/qOoHde3k9yf8AptIXx+ZqkdQkA4OB7V1p+Fnik/8ALin/AIEJ/jTf+FUeKT/y4xD63Cf40+aPcfMjjpb55F2uSy9wTVfcA+A25T37iu2Pwm8Vk8WUP/gQn+NNPwn8WLk/YYcDrm5QD+dPmj3KUkccjlDwa0bW+RcK4BHoazb23+wXT27zQyOpwxhkEij/AIEOD+BqJZB2aquDsztbR9KmUeaCv0rorJvDqIPNugB6Fa8rE2OjEfjSidv+eh/OhszdK/U95GpeG9I0BL9by3zNHIYYvMG5nUkAbAM4ODzmsDUPivBp2pRvotos0CQFGa4GC7tgk8c4GMe/NeSGQ9S1NMgPesnC71LjBJF6+1a7vhGs8zOIwVRSeFBJJA9OSTWa8nYcn1pHJPSo/KZh0IrRJbFNkEsmflU8dz60yJ2icOuNw6ZANWhaMR92lFkx7VVhXH/2tfMctOxJ7mnjVLnHMhNN+xGpE06V/uoT+FKyGmA1O4/vmnDUrj++alTRbthlbeU/RDU6+HdRYgLZXBJ9Im/wpOw1Iqf2jP8A36X+0Z/79akfg3XZRlNHv2HqLZv8K0Ifhp4quIjLHo8oQdfMdEP/AHySCfypXiPmOb/tGf8AvUv9ozf3j+dXpPC+rRMVl066Rh1DQsD/ACpo8Paj/wA+U/8A37P+FF0HMU/7Sm/vfrQNSmH8X61dPhz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+ "color": "#432",
+ "bgcolor": "#653"
+ },
+ {
+ "id": 85,
+ "type": "SplineEditor",
+ "pos": {
+ "0": 232,
+ "1": 1226
+ },
+ "size": [
+ 765,
+ 910
+ ],
+ "flags": {},
+ "order": 12,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "bg_image",
+ "type": "IMAGE",
+ "link": 216,
+ "shape": 7
+ }
+ ],
+ "outputs": [
+ {
+ "name": "mask",
+ "type": "MASK",
+ "links": [],
+ "slot_index": 0
+ },
+ {
+ "name": "coord_str",
+ "type": "STRING",
+ "links": [
+ 218
+ ],
+ "slot_index": 1
+ },
+ {
+ "name": "float",
+ "type": "FLOAT",
+ "links": null
+ },
+ {
+ "name": "count",
+ "type": "INT",
+ "links": null
+ },
+ {
+ "name": "normalized_str",
+ "type": "STRING",
+ "links": null
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "SplineEditor",
+ "points": "SplineEditor",
+ "imgData": {
+ "name": "bg_image",
+ "base64": [
+ 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diff --git a/examples/cogvideox_5b_example_01.json b/examples/cogvideox_1_0_5b_T2V_02.json
similarity index 68%
rename from examples/cogvideox_5b_example_01.json
rename to examples/cogvideox_1_0_5b_T2V_02.json
index af7522a..01c3669 100644
--- a/examples/cogvideox_5b_example_01.json
+++ b/examples/cogvideox_1_0_5b_T2V_02.json
@@ -1,48 +1,7 @@
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diff --git a/examples/cogvideox_interpolation_example_01.json b/examples/cogvideox_1_0_5b_interpolation_02.json
similarity index 78%
rename from examples/cogvideox_interpolation_example_01.json
rename to examples/cogvideox_1_0_5b_interpolation_02.json
index 8198543..6bea1e8 100644
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+++ b/examples/cogvideox_1_0_5b_interpolation_02.json
@@ -1,42 +1,7 @@
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diff --git a/examples/cogvidex_fun_5b_GGUF_10GB_VRAM_example_02.json b/examples/cogvideox_1_5_5b_I2V_01.json
similarity index 66%
rename from examples/cogvidex_fun_5b_GGUF_10GB_VRAM_example_02.json
rename to examples/cogvideox_1_5_5b_I2V_01.json
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+ "shape": 7
+ },
+ {
+ "name": "image_cond_latents",
+ "type": "LATENT",
+ "link": 147,
+ "shape": 7
+ },
+ {
+ "name": "context_options",
+ "type": "COGCONTEXT",
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "controlnet",
+ "type": "COGVIDECONTROLNET",
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "tora_trajectory",
+ "type": "TORAFEATURES",
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "fastercache",
+ "type": "FASTERCACHEARGS",
+ "link": null,
+ "shape": 7
+ }
+ ],
+ "outputs": [
+ {
+ "name": "samples",
+ "type": "LATENT",
+ "links": [
+ 148
+ ]
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "CogVideoSampler"
+ },
+ "widgets_values": [
+ 49,
+ 25,
+ 6,
+ 0,
+ "fixed",
+ "CogVideoXDDIM",
+ 1
+ ]
+ },
+ {
+ "id": 62,
+ "type": "CogVideoImageEncode",
+ "pos": {
+ "0": 1149,
+ "1": 711
+ },
+ "size": {
+ "0": 315,
+ "1": 122
+ },
+ "flags": {},
+ "order": 6,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "vae",
+ "type": "VAE",
+ "link": 141
+ },
+ {
+ "name": "start_image",
+ "type": "IMAGE",
+ "link": 142
+ },
+ {
+ "name": "end_image",
+ "type": "IMAGE",
+ "link": null,
+ "shape": 7
+ }
+ ],
+ "outputs": [
+ {
+ "name": "samples",
+ "type": "LATENT",
+ "links": [
+ 147
+ ]
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "CogVideoImageEncode"
+ },
+ "widgets_values": [
+ false,
+ 0
+ ]
+ },
+ {
+ "id": 30,
+ "type": "CogVideoTextEncode",
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+ "1": 168.08047485351562
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+ "inputs": [
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+ "type": "CLIP",
+ "link": 54
+ }
+ ],
+ "outputs": [
+ {
+ "name": "conditioning",
+ "type": "CONDITIONING",
+ "links": [
+ 145
+ ],
+ "slot_index": 0,
+ "shape": 3
+ },
+ {
+ "name": "clip",
+ "type": "CLIP",
+ "links": [
+ 149
+ ],
+ "slot_index": 1
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "CogVideoTextEncode"
+ },
+ "widgets_values": [
+ "a majestic stag is grazing in an enhanced forest, basking in the setting sun filtered by the trees",
+ 1,
+ false
+ ]
+ },
+ {
+ "id": 36,
+ "type": "LoadImage",
+ "pos": {
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+ "1": 731
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+ "size": {
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+ "mode": 0,
+ "inputs": [],
+ "outputs": [
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+ "links": [
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+ "slot_index": 0,
+ "shape": 3
+ },
+ {
+ "name": "MASK",
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+ "links": null,
+ "shape": 3
+ }
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+ "properties": {
+ "Node name for S&R": "LoadImage"
+ },
+ "widgets_values": [
+ "sd3stag.png",
+ "image"
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{
"id": 20,
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"pos": {
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+ "0": -2,
+ "1": 304
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@@ -37,48 +264,49 @@
]
},
{
- "id": 31,
- "type": "CogVideoTextEncode",
+ "id": 60,
+ "type": "CogVideoDecode",
"pos": {
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},
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+ "0": 315,
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"inputs": [
{
- "name": "clip",
- "type": "CLIP",
- "link": 108
+ "name": "vae",
+ "type": "VAE",
+ "link": 132
+ },
+ {
+ "name": "samples",
+ "type": "LATENT",
+ "link": 148
}
],
"outputs": [
{
- "name": "conditioning",
- "type": "CONDITIONING",
+ "name": "images",
+ "type": "IMAGE",
"links": [
- 111
- ],
- "slot_index": 0,
- "shape": 3
- },
- {
- "name": "clip",
- "type": "CLIP",
- "links": null
+ 134
+ ]
}
],
"properties": {
- "Node name for S&R": "CogVideoTextEncode"
+ "Node name for S&R": "CogVideoDecode"
},
"widgets_values": [
- "The video is not of a high quality, it has a low resolution. Watermark present in each frame. Strange motion trajectory. ",
- 1,
+ true,
+ 240,
+ 360,
+ 0.2,
+ 0.2,
true
]
},
@@ -86,21 +314,21 @@
"id": 44,
"type": "VHS_VideoCombine",
"pos": {
- "0": 1842,
- "1": 345
+ "0": 1884,
+ "1": -6
},
"size": [
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],
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"inputs": [
{
"name": "images",
"type": "IMAGE",
- "link": 97
+ "link": 134
},
{
"name": "audio",
@@ -133,9 +361,9 @@
"Node name for S&R": "VHS_VideoCombine"
},
"widgets_values": {
- "frame_rate": 16,
+ "frame_rate": 8,
"loop_count": 0,
- "filename_prefix": "CogVideoX_Fun",
+ "filename_prefix": "CogVideoX-I2V",
"format": "video/h264-mp4",
"pix_fmt": "yuv420p",
"crf": 19,
@@ -146,62 +374,22 @@
"hidden": false,
"paused": false,
"params": {
- "filename": "CogVideoX_Fun_00003.mp4",
+ "filename": "CogVideoX-I2V_00004.mp4",
"subfolder": "",
"type": "temp",
"format": "video/h264-mp4",
- "frame_rate": 16
+ "frame_rate": 8
},
"muted": false
}
}
},
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{
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"type": "ImageResizeKJ",
"pos": {
- "0": 688,
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+ "1": 731
},
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@@ -244,7 +432,7 @@
"name": "IMAGE",
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"links": [
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],
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"shape": 3
@@ -266,10 +454,10 @@
"Node name for S&R": "ImageResizeKJ"
},
"widgets_values": [
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+ 1360,
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0,
0,
@@ -277,73 +465,24 @@
]
},
{
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+ "id": 31,
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- "slot_index": 0,
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],
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@@ -351,7 +490,7 @@
"name": "conditioning",
"type": "CONDITIONING",
"links": [
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],
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@@ -359,169 +498,128 @@
{
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"type": "CLIP",
- "links": [
- 108
- ],
- "slot_index": 1
+ "links": null
}
],
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"Node name for S&R": "CogVideoTextEncode"
},
"widgets_values": [
- "majestic stag grazing in a forest and basking in the setting sun",
+ "",
1,
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]
},
{
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+ "id": 59,
+ "type": "DownloadAndLoadCogVideoModel",
"pos": {
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+ "name": "block_edit",
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},
{
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+ "name": "lora",
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+ "shape": 7
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],
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{
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+ "name": "model",
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],
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],
"properties": {
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+ "Node name for S&R": "DownloadAndLoadCogVideoModel"
},
"widgets_values": [
- "CogVideoX_5b_fun_1_1_GGUF_Q4_0.safetensors",
+ "kijai/CogVideoX-5b-1.5-I2V",
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+ "disabled",
false,
- "offload_device",
+ "sdpa",
+ "main_device"
+ ]
+ },
+ {
+ "id": 64,
+ "type": "CogVideoImageEncodeFunInP",
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+ {
+ "name": "start_image",
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+ "link": null
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+ {
+ "name": "end_image",
+ "type": "IMAGE",
+ "link": null,
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+ "outputs": [
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+ "links": null
+ }
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+ "properties": {
+ "Node name for S&R": "CogVideoImageEncodeFunInP"
+ },
+ "widgets_values": [
+ 49,
false,
- "disabled"
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]
}
],
@@ -543,78 +641,94 @@
"IMAGE"
],
[
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[
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+ [
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- "LATENT"
]
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+ "scale": 0.8390545288825803,
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diff --git a/examples/cogvideox_5b_Tora_I2V_testing_01.json b/examples/cogvideox_5b_Tora_I2V_testing_01.json
deleted file mode 100644
index 604e0fe..0000000
--- a/examples/cogvideox_5b_Tora_I2V_testing_01.json
+++ /dev/null
@@ -1,1337 +0,0 @@
-{
- "last_node_id": 77,
- "last_link_id": 186,
- "nodes": [
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- 56
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- "slot_index": 0,
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- "properties": {
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- "widgets_values": [
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- "sd3"
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diff --git a/examples/cogvideox_5b_tora_trajectory_example_01.json b/examples/cogvideox_5b_tora_trajectory_example_01.json
deleted file mode 100644
index bad2570..0000000
--- a/examples/cogvideox_5b_tora_trajectory_example_01.json
+++ /dev/null
@@ -1,1119 +0,0 @@
-{
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diff --git a/examples/cogvidex_fun_i2v_example_02.json b/examples/cogvideox_Fun_I2V_02.json
similarity index 75%
rename from examples/cogvidex_fun_i2v_example_02.json
rename to examples/cogvideox_Fun_I2V_02.json
index d7023d1..0a107f8 100644
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+ "color": "#432",
+ "bgcolor": "#653"
+ },
+ {
+ "id": 85,
+ "type": "SplineEditor",
+ "pos": {
+ "0": 232,
+ "1": 1226
+ },
+ "size": [
+ 765,
+ 910
+ ],
+ "flags": {},
+ "order": 11,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "bg_image",
+ "type": "IMAGE",
+ "link": 216,
+ "shape": 7
+ }
+ ],
+ "outputs": [
+ {
+ "name": "mask",
+ "type": "MASK",
+ "links": [],
+ "slot_index": 0
+ },
+ {
+ "name": "coord_str",
+ "type": "STRING",
+ "links": [
+ 218
+ ],
+ "slot_index": 1
+ },
+ {
+ "name": "float",
+ "type": "FLOAT",
+ "links": null
+ },
+ {
+ "name": "count",
+ "type": "INT",
+ "links": null
+ },
+ {
+ "name": "normalized_str",
+ "type": "STRING",
+ "links": null
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "SplineEditor",
+ "points": "SplineEditor",
+ "imgData": {
+ "name": "bg_image",
+ "base64": [
+ 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diff --git a/examples/cogvideox_fun_pose_example_01.json b/examples/cogvideox_Fun_pose_02.json
similarity index 78%
rename from examples/cogvideox_fun_pose_example_01.json
rename to examples/cogvideox_Fun_pose_02.json
index da8750d..9d85910 100644
--- a/examples/cogvideox_fun_pose_example_01.json
+++ b/examples/cogvideox_Fun_pose_02.json
@@ -1,48 +1,7 @@
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- "Node name for S&R": "CogVideoControlImageEncode"
+ "Node name for S&R": "CogVideoImageEncode"
},
"widgets_values": [
- 512,
false,
0
]
},
{
- "id": 82,
- "type": "CogVideoXFunControlSampler",
+ "id": 83,
+ "type": "CogVideoSampler",
"pos": {
- "0": 1085,
- "1": 312
- },
- "size": {
- "0": 311.2205810546875,
- "1": 350
+ "0": 1089,
+ "1": 316
},
+ "size": [
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+ 570
+ ],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
- "name": "pipeline",
- "type": "COGVIDEOPIPE",
- "link": 177
+ "name": "model",
+ "type": "COGVIDEOMODEL",
+ "link": 186
},
{
"name": "positive",
"type": "CONDITIONING",
- "link": 178
+ "link": 183
},
{
"name": "negative",
"type": "CONDITIONING",
- "link": 179
- },
- {
- "name": "control_latents",
- "type": "COGCONTROL_LATENTS",
- "link": 182
+ "link": 184
},
{
"name": "samples",
"type": "LATENT",
- "link": null
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "image_cond_latents",
+ "type": "LATENT",
+ "link": 193,
+ "shape": 7
},
{
"name": "context_options",
"type": "COGCONTEXT",
- "link": null
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "controlnet",
+ "type": "COGVIDECONTROLNET",
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "tora_trajectory",
+ "type": "TORAFEATURES",
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "fastercache",
+ "type": "FASTERCACHEARGS",
+ "link": null,
+ "shape": 7
+ },
+ {
+ "name": "num_frames",
+ "type": "INT",
+ "link": 195,
+ "widget": {
+ "name": "num_frames"
+ }
}
],
"outputs": [
- {
- "name": "cogvideo_pipe",
- "type": "COGVIDEOPIPE",
- "links": [
- 180
- ],
- "shape": 3
- },
{
"name": "samples",
"type": "LATENT",
"links": [
- 181
- ],
- "shape": 3
+ 189
+ ]
}
],
"properties": {
- "Node name for S&R": "CogVideoXFunControlSampler"
+ "Node name for S&R": "CogVideoSampler"
},
"widgets_values": [
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- "fixed",
- 25,
+ 49,
+ 50,
6,
- "CogVideoXDPMScheduler",
- 0.7000000000000001,
0,
- 1,
+ "fixed",
+ "CogVideoXDDIM",
1
]
},
@@ -814,8 +894,8 @@
"id": 58,
"type": "ImageConcatMulti",
"pos": {
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- "1": 649
+ "0": 1545,
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},
"size": {
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@@ -833,7 +913,7 @@
{
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"type": "IMAGE",
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+ "link": 190
}
],
"outputs": [
@@ -854,42 +934,6 @@
true,
null
]
- },
- {
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- "type": "INTConstant",
- "pos": {
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- "1": 260
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- "size": {
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- "flags": {},
- "order": 2,
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- "outputs": [
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- "links": [
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- ],
- "slot_index": 0,
- "shape": 3
- }
- ],
- "title": "Frames",
- "properties": {
- "Node name for S&R": "INTConstant"
- },
- "widgets_values": [
- 49
- ],
- "color": "#1b4669",
- "bgcolor": "#29699c"
}
],
"links": [
@@ -901,22 +945,6 @@
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[
126,
58,
@@ -990,78 +1018,102 @@
"IMAGE"
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[
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- [
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- 0,
- 82,
- 0,
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- [
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+ 183,
30,
0,
- 82,
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1,
"CONDITIONING"
],
[
- 179,
+ 184,
31,
0,
- 82,
+ 83,
2,
"CONDITIONING"
],
[
- 180,
- 82,
+ 186,
+ 84,
0,
- 11,
+ 83,
0,
- "COGVIDEOPIPE"
+ "COGVIDEOMODEL"
],
[
- 181,
- 82,
+ 188,
+ 84,
1,
- 11,
+ 85,
+ 0,
+ "VAE"
+ ],
+ [
+ 189,
+ 83,
+ 0,
+ 85,
1,
"LATENT"
],
[
- 182,
- 81,
+ 190,
+ 85,
0,
- 82,
- 3,
- "COGCONTROL_LATENTS"
+ 58,
+ 1,
+ "IMAGE"
+ ],
+ [
+ 191,
+ 84,
+ 1,
+ 86,
+ 0,
+ "VAE"
+ ],
+ [
+ 192,
+ 61,
+ 0,
+ 86,
+ 1,
+ "IMAGE"
+ ],
+ [
+ 193,
+ 86,
+ 0,
+ 83,
+ 4,
+ "LATENT"
+ ],
+ [
+ 194,
+ 30,
+ 1,
+ 31,
+ 0,
+ "CLIP"
+ ],
+ [
+ 195,
+ 72,
+ 0,
+ 83,
+ 9,
+ "INT"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
- "scale": 0.6934334949442492,
+ "scale": 0.6303940863129809,
"offset": [
- 39.55130702561554,
- 104.54407751572876
+ 814.9475817376318,
+ 180.21736528457424
]
}
},
diff --git a/examples/cogvideox_fun_img2vid_tora_01.json b/examples/cogvideox_fun_img2vid_tora_01.json
deleted file mode 100644
index 6df7f35..0000000
--- a/examples/cogvideox_fun_img2vid_tora_01.json
+++ /dev/null
@@ -1,1315 +0,0 @@
-{
- "last_node_id": 83,
- "last_link_id": 209,
- "nodes": [
- {
- "id": 72,
- "type": "LoadImage",
- "pos": {
- "0": -820,
- "1": 531
- },
- "size": {
- "0": 315,
- "1": 314
- },
- "flags": {},
- "order": 0,
- "mode": 0,
- "inputs": [],
- "outputs": [
- {
- "name": "IMAGE",
- "type": "IMAGE",
- "links": [
- 166
- ],
- "slot_index": 0
- },
- {
- "name": "MASK",
- "type": "MASK",
- "links": null
- }
- ],
- "properties": {
- "Node name for S&R": "LoadImage"
- },
- "widgets_values": [
- "6e1a7befce6daa63fc01cb66c1a22ed0.jpg",
- "image"
- ]
- },
- {
- "id": 60,
- "type": "SplineEditor",
- "pos": {
- "0": -307,
- "1": 868
- },
- "size": {
- "0": 557,
- "1": 942
- },
- "flags": {},
- "order": 7,
- "mode": 0,
- "inputs": [
- {
- "name": "bg_image",
- "type": "IMAGE",
- "link": 188,
- "shape": 7
- }
- ],
- "outputs": [
- {
- "name": "mask",
- "type": "MASK",
- "links": [
- 146
- ],
- "slot_index": 0
- },
- {
- "name": "coord_str",
- "type": "STRING",
- "links": [
- 145,
- 176
- ],
- "slot_index": 1
- },
- {
- "name": "float",
- "type": "FLOAT",
- "links": null
- },
- {
- "name": "count",
- "type": "INT",
- "links": null
- },
- {
- "name": "normalized_str",
- "type": "STRING",
- "links": null
- }
- ],
- "properties": {
- "Node name for S&R": "SplineEditor",
- "points": "SplineEditor",
- "imgData": {
- "name": "bg_image",
- "base64": [
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diff --git a/fp8_optimization.py b/fp8_optimization.py
index b01ac91..09f026d 100644
--- a/fp8_optimization.py
+++ b/fp8_optimization.py
@@ -36,10 +36,12 @@ def fp8_linear_forward(cls, original_dtype, input):
else:
return cls.original_forward(input)
-def convert_fp8_linear(module, original_dtype):
+def convert_fp8_linear(module, original_dtype, params_to_keep={}):
setattr(module, "fp8_matmul_enabled", True)
+
for name, module in module.named_modules():
- if isinstance(module, nn.Linear):
- original_forward = module.forward
- setattr(module, "original_forward", original_forward)
- setattr(module, "forward", lambda input, m=module: fp8_linear_forward(m, original_dtype, input))
+ if not any(keyword in name for keyword in params_to_keep):
+ if isinstance(module, nn.Linear):
+ original_forward = module.forward
+ setattr(module, "original_forward", original_forward)
+ setattr(module, "forward", lambda input, m=module: fp8_linear_forward(m, original_dtype, input))
diff --git a/model_loading.py b/model_loading.py
index 6a2cf43..780295f 100644
--- a/model_loading.py
+++ b/model_loading.py
@@ -1,9 +1,41 @@
import os
-import torch
-import torch.nn as nn
import json
import folder_paths
import comfy.model_management as mm
+from typing import Union
+
+def patched_write_atomic(
+ path_: str,
+ content: Union[str, bytes],
+ make_dirs: bool = False,
+ encode_utf_8: bool = False,
+) -> None:
+ # Write into temporary file first to avoid conflicts between threads
+ # Avoid using a named temporary file, as those have restricted permissions
+ from pathlib import Path
+ import os
+ import shutil
+ import threading
+ assert isinstance(
+ content, (str, bytes)
+ ), "Only strings and byte arrays can be saved in the cache"
+ path = Path(path_)
+ if make_dirs:
+ path.parent.mkdir(parents=True, exist_ok=True)
+ tmp_path = path.parent / f".{os.getpid()}.{threading.get_ident()}.tmp"
+ write_mode = "w" if isinstance(content, str) else "wb"
+ with tmp_path.open(write_mode, encoding="utf-8" if encode_utf_8 else None) as f:
+ f.write(content)
+ shutil.copy2(src=tmp_path, dst=path) #changed to allow overwriting cache files
+ os.remove(tmp_path)
+try:
+ import torch._inductor.codecache
+ torch._inductor.codecache.write_atomic = patched_write_atomic
+except:
+ pass
+
+import torch
+import torch.nn as nn
from .utils import check_diffusers_version, remove_specific_blocks, log
check_diffusers_version()
@@ -22,6 +54,10 @@ from .cogvideox_fun.pipeline_cogvideox_control import CogVideoX_Fun_Pipeline_Con
from .videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
+from accelerate import init_empty_weights
+from accelerate.utils import set_module_tensor_to_device
+
+from .utils import remove_specific_blocks, log
from comfy.utils import load_torch_file
script_directory = os.path.dirname(os.path.abspath(__file__))
@@ -61,7 +97,8 @@ class CogVideoLoraSelect:
cog_loras_list.append(cog_lora)
print(cog_loras_list)
return (cog_loras_list,)
-
+
+#region DownloadAndLoadCogVideoModel
class DownloadAndLoadCogVideoModel:
@classmethod
def INPUT_TYPES(s):
@@ -72,6 +109,8 @@ class DownloadAndLoadCogVideoModel:
"THUDM/CogVideoX-2b",
"THUDM/CogVideoX-5b",
"THUDM/CogVideoX-5b-I2V",
+ "kijai/CogVideoX-5b-1.5-T2V",
+ "kijai/CogVideoX-5b-1.5-I2V",
"bertjiazheng/KoolCogVideoX-5b",
"kijai/CogVideoX-Fun-2b",
"kijai/CogVideoX-Fun-5b",
@@ -90,28 +129,33 @@ class DownloadAndLoadCogVideoModel:
"precision": (["fp16", "fp32", "bf16"],
{"default": "bf16", "tooltip": "official recommendation is that 2b model should be fp16, 5b model should be bf16"}
),
- "fp8_transformer": (['disabled', 'enabled', 'fastmode'], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs and requires torch 2.4.0 and cu124 minimum"}),
- "compile": (["disabled","onediff","torch"], {"tooltip": "compile the model for faster inference, these are advanced options only available on Linux, see readme for more info"}),
+ "quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fastmode', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs and requires torch 2.4.0 and cu124 minimum"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
- "pab_config": ("PAB_CONFIG", {"default": None}),
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
"lora": ("COGLORA", {"default": None}),
"compile_args":("COMPILEARGS", ),
+ "attention_mode": (["sdpa", "sageattn", "fused_sdpa", "fused_sageattn"], {"default": "sdpa"}),
+ "load_device": (["main_device", "offload_device"], {"default": "main_device"}),
}
}
- RETURN_TYPES = ("COGVIDEOPIPE",)
- RETURN_NAMES = ("cogvideo_pipe", )
+ RETURN_TYPES = ("COGVIDEOMODEL", "VAE",)
+ RETURN_NAMES = ("model", "vae", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Downloads and loads the selected CogVideo model from Huggingface to 'ComfyUI/models/CogVideo'"
- def loadmodel(self, model, precision, fp8_transformer="disabled", compile="disabled", enable_sequential_cpu_offload=False, pab_config=None, block_edit=None, lora=None, compile_args=None):
+ def loadmodel(self, model, precision, quantization="disabled", compile="disabled",
+ enable_sequential_cpu_offload=False, block_edit=None, lora=None, compile_args=None,
+ attention_mode="sdpa", load_device="main_device"):
- check_diffusers_version()
+ if precision == "fp16" and "1.5" in model:
+ raise ValueError("1.5 models do not currently work in fp16")
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
+ manual_offloading = True
+ transformer_load_device = device if load_device == "main_device" else offload_device
mm.soft_empty_cache()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
@@ -134,6 +178,8 @@ class DownloadAndLoadCogVideoModel:
if not os.path.exists(base_path):
base_path = os.path.join(download_path, (model.split("/")[-1]))
download_path = base_path
+ subfolder = "transformer"
+ allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
elif "2b" in model:
if 'img2vid' in model:
@@ -144,41 +190,44 @@ class DownloadAndLoadCogVideoModel:
base_path = os.path.join(download_path, "CogVideo2B")
download_path = base_path
repo_id = model
+ subfolder = "transformer"
+ allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
+ elif "1.5-T2V" in model or "1.5-I2V" in model:
+ base_path = os.path.join(download_path, "CogVideoX-5b-1.5")
+ download_path = base_path
+ subfolder = "transformer_T2V" if "1.5-T2V" in model else "transformer_I2V"
+ allow_patterns = [f"*{subfolder}*", "*vae*", "*scheduler*"]
+ repo_id = "kijai/CogVideoX-5b-1.5"
else:
base_path = os.path.join(download_path, (model.split("/")[-1]))
download_path = base_path
repo_id = model
-
+ subfolder = "transformer"
+ allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
if "2b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
else:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
- if not os.path.exists(base_path) or not os.path.exists(os.path.join(base_path, "transformer")):
+ if not os.path.exists(base_path) or not os.path.exists(os.path.join(base_path, subfolder)):
log.info(f"Downloading model to: {base_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=repo_id,
+ allow_patterns=allow_patterns,
ignore_patterns=["*text_encoder*", "*tokenizer*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
- # transformer
- if "Fun" in model:
- if pab_config is not None:
- transformer = CogVideoXTransformer3DModelFunPAB.from_pretrained(base_path, subfolder="transformer")
- else:
- transformer = CogVideoXTransformer3DModelFun.from_pretrained(base_path, subfolder="transformer")
- else:
- if pab_config is not None:
- transformer = CogVideoXTransformer3DModelPAB.from_pretrained(base_path, subfolder="transformer")
- else:
- transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder="transformer")
-
- transformer = transformer.to(dtype).to(offload_device)
+ transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder=subfolder)
+ transformer = transformer.to(dtype).to(transformer_load_device)
+
+ if "1.5" in model:
+ transformer.config.sample_height = 300
+ transformer.config.sample_width = 300
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
@@ -190,26 +239,21 @@ class DownloadAndLoadCogVideoModel:
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config)
# VAE
- if "Fun" in model:
- vae = AutoencoderKLCogVideoXFun.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
- if "Pose" in model:
- pipe = CogVideoX_Fun_Pipeline_Control(vae, transformer, scheduler, pab_config=pab_config)
- else:
- pipe = CogVideoX_Fun_Pipeline_Inpaint(vae, transformer, scheduler, pab_config=pab_config)
- else:
- vae = AutoencoderKLCogVideoX.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
- pipe = CogVideoXPipeline(vae, transformer, scheduler, pab_config=pab_config)
- if "cogvideox-2b-img2vid" in model:
- pipe.input_with_padding = False
+ vae = AutoencoderKLCogVideoX.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
+
+ #pipeline
+ pipe = CogVideoXPipeline(
+ transformer,
+ scheduler,
+ dtype=dtype,
+ is_fun_inpaint=True if "fun" in model.lower() and "pose" not in model.lower() else False
+ )
+ if "cogvideox-2b-img2vid" in model:
+ pipe.input_with_padding = False
#LoRAs
if lora is not None:
- from .lora_utils import merge_lora#, load_lora_into_transformer
- if "fun" in model.lower():
- for l in lora:
- log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
- transformer = merge_lora(transformer, l["path"], l["strength"])
- else:
+ try:
adapter_list = []
adapter_weights = []
for l in lora:
@@ -246,46 +290,146 @@ class DownloadAndLoadCogVideoModel:
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
+ lora_scale = 1
+ dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
+ if any(item in lora[-1]["path"].lower() for item in dimension_loras):
+ lora_scale = lora_scale / lora_rank
+ pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
+ except: #Fun trainer LoRAs are loaded differently
+ from .lora_utils import merge_lora
+ for l in lora:
+ log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
+ transformer = merge_lora(transformer, l["path"], l["strength"])
+
+ if "fused" in attention_mode:
+ from diffusers.models.attention import Attention
+ transformer.fuse_qkv_projections = True
+ for module in transformer.modules():
+ if isinstance(module, Attention):
+ module.fuse_projections(fuse=True)
+ transformer.attention_mode = attention_mode
+
+ if compile_args is not None:
+ pipe.transformer.to(memory_format=torch.channels_last)
+
+ #fp8
+ if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fastmode":
+ params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm_k", "norm_q", "to_k.bias", "to_q.bias", "to_v.bias"}
+ if "1.5" in model:
+ params_to_keep.update({"norm1.linear.weight", "ofs_embedding", "norm_final", "norm_out", "proj_out"})
+ for name, param in pipe.transformer.named_parameters():
+ if not any(keyword in name for keyword in params_to_keep):
+ param.data = param.data.to(torch.float8_e4m3fn)
+
+ if quantization == "fp8_e4m3fn_fastmode":
+ from .fp8_optimization import convert_fp8_linear
+ if "1.5" in model:
+ params_to_keep.update({"ff"}) #otherwise NaNs
+ convert_fp8_linear(pipe.transformer, dtype, params_to_keep=params_to_keep)
# compilation
- if compile == "torch":
- pipe.transformer.to(memory_format=torch.channels_last)
- if compile_args is not None:
- torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
- for i, block in enumerate(pipe.transformer.transformer_blocks):
- if "CogVideoXBlock" in str(block):
- pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
- else:
- for i, block in enumerate(pipe.transformer.transformer_blocks):
- if "CogVideoXBlock" in str(block):
- pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=False, dynamic=False, backend="inductor")
-
-
-
- elif compile == "onediff":
- from onediffx import compile_pipe
- os.environ['NEXFORT_FX_FORCE_TRITON_SDPA'] = '1'
-
- pipe = compile_pipe(
- pipe,
- backend="nexfort",
- options= {"mode": "max-optimize:max-autotune:max-autotune", "memory_format": "channels_last", "options": {"inductor.optimize_linear_epilogue": False, "triton.fuse_attention_allow_fp16_reduction": False}},
- ignores=["vae"],
- fuse_qkv_projections=True if pab_config is None else False,
- )
+ if compile_args is not None:
+ torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
+ for i, block in enumerate(pipe.transformer.transformer_blocks):
+ if "CogVideoXBlock" in str(block):
+ pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
+ if "torchao" in quantization:
+ try:
+ from torchao.quantization import (
+ quantize_,
+ fpx_weight_only,
+ float8_dynamic_activation_float8_weight,
+ int8_dynamic_activation_int8_weight
+ )
+ except:
+ raise ImportError("torchao is not installed, please install torchao to use fp8dq")
+
+ def filter_fn(module: nn.Module, fqn: str) -> bool:
+ target_submodules = {'attn1', 'ff'} # avoid norm layers, 1.5 at least won't work with quantized norm1 #todo: test other models
+ if any(sub in fqn for sub in target_submodules):
+ return isinstance(module, nn.Linear)
+ return False
+
+ if "fp6" in quantization: #slower for some reason on 4090
+ quant_func = fpx_weight_only(3, 2)
+ elif "fp8dq" in quantization: #very fast on 4090 when compiled
+ quant_func = float8_dynamic_activation_float8_weight()
+ elif 'fp8dqrow' in quantization:
+ from torchao.quantization.quant_api import PerRow
+ quant_func = float8_dynamic_activation_float8_weight(granularity=PerRow())
+ elif 'int8dq' in quantization:
+ quant_func = int8_dynamic_activation_int8_weight()
+
+ for i, block in enumerate(pipe.transformer.transformer_blocks):
+ if "CogVideoXBlock" in str(block):
+ quantize_(block, quant_func, filter_fn=filter_fn)
+
+ manual_offloading = False # to disable manual .to(device) calls
+
+ if enable_sequential_cpu_offload:
+ pipe.enable_sequential_cpu_offload()
+ manual_offloading = False
+
+ # CogVideoXBlock(
+ # (norm1): CogVideoXLayerNormZero(
+ # (silu): SiLU()
+ # (linear): Linear(in_features=512, out_features=18432, bias=True)
+ # (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
+ # )
+ # (attn1): Attention(
+ # (norm_q): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+ # (norm_k): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+ # (to_q): Linear(in_features=3072, out_features=3072, bias=True)
+ # (to_k): Linear(in_features=3072, out_features=3072, bias=True)
+ # (to_v): Linear(in_features=3072, out_features=3072, bias=True)
+ # (to_out): ModuleList(
+ # (0): Linear(in_features=3072, out_features=3072, bias=True)
+ # (1): Dropout(p=0.0, inplace=False)
+ # )
+ # )
+ # (norm2): CogVideoXLayerNormZero(
+ # (silu): SiLU()
+ # (linear): Linear(in_features=512, out_features=18432, bias=True)
+ # (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
+ # )
+ # (ff): FeedForward(
+ # (net): ModuleList(
+ # (0): GELU(
+ # (proj): Linear(in_features=3072, out_features=12288, bias=True)
+ # )
+ # (1): Dropout(p=0.0, inplace=False)
+ # (2): Linear(in_features=12288, out_features=3072, bias=True)
+ # (3): Dropout(p=0.0, inplace=False)
+ # )
+ # )
+ # )
+
+ # if compile == "onediff":
+ # from onediffx import compile_pipe
+ # os.environ['NEXFORT_FX_FORCE_TRITON_SDPA'] = '1'
+
+ # pipe = compile_pipe(
+ # pipe,
+ # backend="nexfort",
+ # options= {"mode": "max-optimize:max-autotune:max-autotune", "memory_format": "channels_last", "options": {"inductor.optimize_linear_epilogue": False, "triton.fuse_attention_allow_fp16_reduction": False}},
+ # ignores=["vae"],
+ # fuse_qkv_projections= False,
+ # )
+
pipeline = {
"pipe": pipe,
"dtype": dtype,
"base_path": base_path,
"onediff": True if compile == "onediff" else False,
"cpu_offloading": enable_sequential_cpu_offload,
+ "manual_offloading": manual_offloading,
"scheduler_config": scheduler_config,
- "model_name": model
+ "model_name": model,
}
- return (pipeline,)
-
+ return (pipeline, vae)
+#region GGUF
class DownloadAndLoadCogVideoGGUFModel:
@classmethod
def INPUT_TYPES(s):
@@ -295,12 +439,12 @@ class DownloadAndLoadCogVideoGGUFModel:
[
"CogVideoX_5b_GGUF_Q4_0.safetensors",
"CogVideoX_5b_I2V_GGUF_Q4_0.safetensors",
+ "CogVideoX_5b_1_5_I2V_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_1_1_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_1_1_Pose_GGUF_Q4_0.safetensors",
"CogVideoX_5b_Interpolation_GGUF_Q4_0.safetensors",
"CogVideoX_5b_Tora_GGUF_Q4_0.safetensors",
-
],
),
"vae_precision": (["fp16", "fp32", "bf16"], {"default": "bf16", "tooltip": "VAE dtype"}),
@@ -309,20 +453,20 @@ class DownloadAndLoadCogVideoGGUFModel:
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
},
"optional": {
- "pab_config": ("PAB_CONFIG", {"default": None}),
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
+ #"lora": ("COGLORA", {"default": None}),
"compile": (["disabled","torch"], {"tooltip": "compile the model for faster inference, these are advanced options only available on Linux, see readme for more info"}),
+ "attention_mode": (["sdpa", "sageattn"], {"default": "sdpa"}),
}
}
- RETURN_TYPES = ("COGVIDEOPIPE",)
- RETURN_NAMES = ("cogvideo_pipe", )
+ RETURN_TYPES = ("COGVIDEOMODEL", "VAE",)
+ RETURN_NAMES = ("model", "vae",)
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
- def loadmodel(self, model, vae_precision, fp8_fastmode, load_device, enable_sequential_cpu_offload, pab_config=None, block_edit=None, compile="disabled"):
-
- check_diffusers_version()
+ def loadmodel(self, model, vae_precision, fp8_fastmode, load_device, enable_sequential_cpu_offload,
+ block_edit=None, compile="disabled", attention_mode="sdpa"):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
@@ -358,7 +502,6 @@ class DownloadAndLoadCogVideoGGUFModel:
with open(transformer_path) as f:
transformer_config = json.load(f)
- sd = load_torch_file(gguf_path)
from . import mz_gguf_loader
import importlib
@@ -370,45 +513,45 @@ class DownloadAndLoadCogVideoGGUFModel:
transformer_config["in_channels"] = 32
else:
transformer_config["in_channels"] = 33
- if pab_config is not None:
- transformer = CogVideoXTransformer3DModelFunPAB.from_config(transformer_config)
- else:
- transformer = CogVideoXTransformer3DModelFun.from_config(transformer_config)
elif "I2V" in model or "Interpolation" in model:
transformer_config["in_channels"] = 32
- if pab_config is not None:
- transformer = CogVideoXTransformer3DModelPAB.from_config(transformer_config)
- else:
- transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
+ if "1_5" in model:
+ transformer_config["ofs_embed_dim"] = 512
+ transformer_config["use_learned_positional_embeddings"] = False
+ transformer_config["patch_size_t"] = 2
+ transformer_config["patch_bias"] = False
+ transformer_config["sample_height"] = 300
+ transformer_config["sample_width"] = 300
else:
transformer_config["in_channels"] = 16
- if pab_config is not None:
- transformer = CogVideoXTransformer3DModelPAB.from_config(transformer_config)
- else:
- transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
+ transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
+
+ params_to_keep = {"patch_embed", "pos_embedding", "time_embedding"}
if "2b" in model:
- for name, param in transformer.named_parameters():
- if name != "pos_embedding":
- param.data = param.data.to(torch.float8_e4m3fn)
- else:
- param.data = param.data.to(torch.float16)
- else:
- transformer.to(torch.float8_e4m3fn)
-
+ cast_dtype = torch.float16
+ elif "1_5" in model:
+ params_to_keep = {"norm1.linear.weight", "patch_embed", "time_embedding", "ofs_embedding", "norm_final", "norm_out", "proj_out"}
+ cast_dtype = torch.bfloat16
+ for name, param in transformer.named_parameters():
+ if not any(keyword in name for keyword in params_to_keep):
+ param.data = param.data.to(torch.float8_e4m3fn)
+ else:
+ param.data = param.data.to(cast_dtype)
+ #for name, param in transformer.named_parameters():
+ # print(name, param.data.dtype)
+
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
- transformer = mz_gguf_loader.quantize_load_state_dict(transformer, sd, device="cpu")
- if load_device == "offload_device":
- transformer.to(offload_device)
- else:
- transformer.to(device)
-
+ transformer.attention_mode = attention_mode
if fp8_fastmode:
+ params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
+ if "1.5" in model:
+ params_to_keep.update({"ff","norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"})
from .fp8_optimization import convert_fp8_linear
- convert_fp8_linear(transformer, vae_dtype)
+ convert_fp8_linear(transformer, vae_dtype, params_to_keep=params_to_keep)
if compile == "torch":
# compilation
@@ -435,22 +578,25 @@ class DownloadAndLoadCogVideoGGUFModel:
with open(os.path.join(script_directory, 'configs', 'vae_config.json')) as f:
vae_config = json.load(f)
+ #VAE
vae_sd = load_torch_file(vae_path)
- if "fun" in model:
- vae = AutoencoderKLCogVideoXFun.from_config(vae_config).to(vae_dtype).to(offload_device)
- vae.load_state_dict(vae_sd)
- if "Pose" in model:
- pipe = CogVideoX_Fun_Pipeline_Control(vae, transformer, scheduler, pab_config=pab_config)
- else:
- pipe = CogVideoX_Fun_Pipeline_Inpaint(vae, transformer, scheduler, pab_config=pab_config)
- else:
- vae = AutoencoderKLCogVideoX.from_config(vae_config).to(vae_dtype).to(offload_device)
- vae.load_state_dict(vae_sd)
- pipe = CogVideoXPipeline(vae, transformer, scheduler, pab_config=pab_config)
+ vae = AutoencoderKLCogVideoX.from_config(vae_config).to(vae_dtype).to(offload_device)
+ vae.load_state_dict(vae_sd)
+ del vae_sd
+ pipe = CogVideoXPipeline(transformer, scheduler, dtype=vae_dtype)
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
+ sd = load_torch_file(gguf_path)
+ pipe.transformer = mz_gguf_loader.quantize_load_state_dict(pipe.transformer, sd, device="cpu")
+ del sd
+
+ if load_device == "offload_device":
+ pipe.transformer.to(offload_device)
+ else:
+ pipe.transformer.to(device)
+
pipeline = {
"pipe": pipe,
"dtype": vae_dtype,
@@ -458,11 +604,289 @@ class DownloadAndLoadCogVideoGGUFModel:
"onediff": False,
"cpu_offloading": enable_sequential_cpu_offload,
"scheduler_config": scheduler_config,
- "model_name": model
+ "model_name": model,
+ "manual_offloading": True,
+ }
+
+ return (pipeline, vae)
+
+#region ModelLoader
+class CogVideoXModelLoader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load.",}),
+
+ "base_precision": (["fp16", "fp32", "bf16"], {"default": "bf16"}),
+ "quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "optional quantization method"}),
+ "load_device": (["main_device", "offload_device"], {"default": "main_device"}),
+ "enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
+ },
+ "optional": {
+ "block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
+ "lora": ("COGLORA", {"default": None}),
+ "compile_args":("COMPILEARGS", ),
+ "attention_mode": (["sdpa", "sageattn", "fused_sdpa", "fused_sageattn"], {"default": "sdpa"}),
+ }
+ }
+
+ RETURN_TYPES = ("COGVIDEOMODEL",)
+ RETURN_NAMES = ("model", )
+ FUNCTION = "loadmodel"
+ CATEGORY = "CogVideoWrapper"
+
+ def loadmodel(self, model, base_precision, load_device, enable_sequential_cpu_offload,
+ block_edit=None, compile_args=None, lora=None, attention_mode="sdpa", quantization="disabled"):
+
+ device = mm.get_torch_device()
+ offload_device = mm.unet_offload_device()
+ manual_offloading = True
+ transformer_load_device = device if load_device == "main_device" else offload_device
+ mm.soft_empty_cache()
+
+ base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[base_precision]
+
+ model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
+ sd = load_torch_file(model_path, device=transformer_load_device)
+
+ model_type = ""
+ if sd["patch_embed.proj.weight"].shape == (3072, 33, 2, 2):
+ model_type = "fun_5b"
+ elif sd["patch_embed.proj.weight"].shape == (3072, 16, 2, 2):
+ model_type = "5b"
+ elif sd["patch_embed.proj.weight"].shape == (3072, 128):
+ model_type = "5b_1_5"
+ elif sd["patch_embed.proj.weight"].shape == (3072, 256):
+ model_type = "5b_I2V_1_5"
+ elif sd["patch_embed.proj.weight"].shape == (1920, 33, 2, 2):
+ model_type = "fun_2b"
+ elif sd["patch_embed.proj.weight"].shape == (1920, 16, 2, 2):
+ model_type = "2b"
+ elif sd["patch_embed.proj.weight"].shape == (3072, 32, 2, 2):
+ if "pos_embedding" in sd:
+ model_type = "fun_5b_pose"
+ else:
+ model_type = "I2V_5b"
+ else:
+ raise Exception("Selected model is not recognized")
+ log.info(f"Detected CogVideoX model type: {model_type}")
+
+ if "5b" in model_type:
+ scheduler_config_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
+ transformer_config_path = os.path.join(script_directory, 'configs', 'transformer_config_5b.json')
+ elif "2b" in model_type:
+ scheduler_config_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
+ transformer_config_path = os.path.join(script_directory, 'configs', 'transformer_config_2b.json')
+
+ with open(transformer_config_path) as f:
+ transformer_config = json.load(f)
+
+ if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5"]:
+ transformer_config["in_channels"] = 32
+ if "1_5" in model_type:
+ transformer_config["ofs_embed_dim"] = 512
+ elif "fun" in model_type:
+ transformer_config["in_channels"] = 33
+ else:
+ transformer_config["in_channels"] = 16
+ if "1_5" in model_type:
+ transformer_config["use_learned_positional_embeddings"] = False
+ transformer_config["patch_size_t"] = 2
+ transformer_config["patch_bias"] = False
+ transformer_config["sample_height"] = 300
+ transformer_config["sample_width"] = 300
+
+ with init_empty_weights():
+ transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
+
+ #load weights
+ #params_to_keep = {}
+ log.info("Using accelerate to load and assign model weights to device...")
+
+ for name, param in transformer.named_parameters():
+ #dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
+ set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=base_dtype, value=sd[name])
+ del sd
+
+
+ #scheduler
+ with open(scheduler_config_path) as f:
+ scheduler_config = json.load(f)
+ scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config, subfolder="scheduler")
+
+ if block_edit is not None:
+ transformer = remove_specific_blocks(transformer, block_edit)
+
+ if "fused" in attention_mode:
+ from diffusers.models.attention import Attention
+ transformer.fuse_qkv_projections = True
+ for module in transformer.modules():
+ if isinstance(module, Attention):
+ module.fuse_projections(fuse=True)
+ transformer.attention_mode = attention_mode
+
+ if "fun" in model_type:
+ if not "pose" in model_type:
+ raise NotImplementedError("Fun models besides pose are not supported with this loader yet")
+ pipe = CogVideoX_Fun_Pipeline_Inpaint(vae, transformer, scheduler)
+ else:
+ pipe = CogVideoXPipeline(transformer, scheduler, dtype=base_dtype)
+ else:
+ pipe = CogVideoXPipeline(transformer, scheduler, dtype=base_dtype)
+
+ if enable_sequential_cpu_offload:
+ pipe.enable_sequential_cpu_offload()
+
+ #LoRAs
+ if lora is not None:
+ from .lora_utils import merge_lora#, load_lora_into_transformer
+ if "fun" in model.lower():
+ for l in lora:
+ log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
+ transformer = merge_lora(transformer, l["path"], l["strength"])
+ else:
+ adapter_list = []
+ adapter_weights = []
+ for l in lora:
+ fuse = True if l["fuse_lora"] else False
+ lora_sd = load_torch_file(l["path"])
+ for key, val in lora_sd.items():
+ if "lora_B" in key:
+ lora_rank = val.shape[1]
+ break
+ log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
+ adapter_name = l['path'].split("/")[-1].split(".")[0]
+ adapter_weight = l['strength']
+ pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
+
+ #transformer = load_lora_into_transformer(lora, transformer)
+ adapter_list.append(adapter_name)
+ adapter_weights.append(adapter_weight)
+ for l in lora:
+ pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
+ if fuse:
+ lora_scale = 1
+ dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
+ if any(item in lora[-1]["path"].lower() for item in dimension_loras):
+ lora_scale = lora_scale / lora_rank
+ pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
+
+ if compile_args is not None:
+ pipe.transformer.to(memory_format=torch.channels_last)
+
+ #quantization
+ if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fast":
+ params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm_k", "norm_q", "to_k.bias", "to_q.bias", "to_v.bias"}
+ if "1.5" in model:
+ params_to_keep.update({"norm1.linear.weight", "ofs_embedding", "norm_final", "norm_out", "proj_out"})
+ for name, param in pipe.transformer.named_parameters():
+ if not any(keyword in name for keyword in params_to_keep):
+ param.data = param.data.to(torch.float8_e4m3fn)
+
+ if quantization == "fp8_e4m3fn_fast":
+ from .fp8_optimization import convert_fp8_linear
+ if "1.5" in model:
+ params_to_keep.update({"ff"}) #otherwise NaNs
+ convert_fp8_linear(pipe.transformer, base_dtype, params_to_keep=params_to_keep)
+
+ #compile
+ if compile_args is not None:
+ torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
+ for i, block in enumerate(pipe.transformer.transformer_blocks):
+ if "CogVideoXBlock" in str(block):
+ pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
+
+ if "torchao" in quantization:
+ try:
+ from torchao.quantization import (
+ quantize_,
+ fpx_weight_only,
+ float8_dynamic_activation_float8_weight,
+ int8_dynamic_activation_int8_weight
+ )
+ except:
+ raise ImportError("torchao is not installed, please install torchao to use fp8dq")
+
+ def filter_fn(module: nn.Module, fqn: str) -> bool:
+ target_submodules = {'attn1', 'ff'} # avoid norm layers, 1.5 at least won't work with quantized norm1 #todo: test other models
+ if any(sub in fqn for sub in target_submodules):
+ return isinstance(module, nn.Linear)
+ return False
+
+ if "fp6" in quantization: #slower for some reason on 4090
+ quant_func = fpx_weight_only(3, 2)
+ elif "fp8dq" in quantization: #very fast on 4090 when compiled
+ quant_func = float8_dynamic_activation_float8_weight()
+ elif 'fp8dqrow' in quantization:
+ from torchao.quantization.quant_api import PerRow
+ quant_func = float8_dynamic_activation_float8_weight(granularity=PerRow())
+ elif 'int8dq' in quantization:
+ quant_func = int8_dynamic_activation_int8_weight()
+
+ for i, block in enumerate(pipe.transformer.transformer_blocks):
+ if "CogVideoXBlock" in str(block):
+ quantize_(block, quant_func, filter_fn=filter_fn)
+
+ manual_offloading = False # to disable manual .to(device) calls
+ log.info(f"Quantized transformer blocks to {quantization}")
+
+ # if load_device == "offload_device":
+ # pipe.transformer.to(offload_device)
+ # else:
+ # pipe.transformer.to(device)
+
+ pipeline = {
+ "pipe": pipe,
+ "dtype": base_dtype,
+ "base_path": model,
+ "onediff": False,
+ "cpu_offloading": enable_sequential_cpu_offload,
+ "scheduler_config": scheduler_config,
+ "model_name": model,
+ "manual_offloading": manual_offloading,
}
return (pipeline,)
+
+#region VAE
+class CogVideoXVAELoader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "The name of the checkpoint (vae) to load."}),
+ },
+ "optional": {
+ "precision": (["fp16", "fp32", "bf16"],
+ {"default": "bf16"}
+ ),
+ }
+ }
+
+ RETURN_TYPES = ("VAE",)
+ RETURN_NAMES = ("vae", )
+ FUNCTION = "loadmodel"
+ CATEGORY = "CogVideoWrapper"
+ DESCRIPTION = "Loads CogVideoX VAE model from 'ComfyUI/models/vae'"
+
+ def loadmodel(self, model_name, precision):
+ device = mm.get_torch_device()
+ offload_device = mm.unet_offload_device()
+
+ dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
+ with open(os.path.join(script_directory, 'configs', 'vae_config.json')) as f:
+ vae_config = json.load(f)
+ model_path = folder_paths.get_full_path("vae", model_name)
+ vae_sd = load_torch_file(model_path)
+
+ vae = AutoencoderKLCogVideoX.from_config(vae_config).to(dtype).to(offload_device)
+ vae.load_state_dict(vae_sd)
+
+ return (vae,)
+
+#region Tora
class DownloadAndLoadToraModel:
@classmethod
def INPUT_TYPES(s):
@@ -483,9 +907,6 @@ class DownloadAndLoadToraModel:
DESCRIPTION = "Downloads and loads the the Tora model from Huggingface to 'ComfyUI/models/CogVideo/CogVideoX-5b-Tora'"
def loadmodel(self, model):
-
- check_diffusers_version()
-
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
@@ -570,7 +991,7 @@ class DownloadAndLoadToraModel:
}
return (toramodel,)
-
+#region controlnet
class DownloadAndLoadCogVideoControlNet:
@classmethod
def INPUT_TYPES(s):
@@ -625,6 +1046,8 @@ NODE_CLASS_MAPPINGS = {
"DownloadAndLoadCogVideoControlNet": DownloadAndLoadCogVideoControlNet,
"DownloadAndLoadToraModel": DownloadAndLoadToraModel,
"CogVideoLoraSelect": CogVideoLoraSelect,
+ "CogVideoXVAELoader": CogVideoXVAELoader,
+ "CogVideoXModelLoader": CogVideoXModelLoader,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DownloadAndLoadCogVideoModel": "(Down)load CogVideo Model",
@@ -632,4 +1055,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"DownloadAndLoadCogVideoControlNet": "(Down)load CogVideo ControlNet",
"DownloadAndLoadToraModel": "(Down)load Tora Model",
"CogVideoLoraSelect": "CogVideo LoraSelect",
+ "CogVideoXVAELoader": "CogVideoX VAE Loader",
+ "CogVideoXModelLoader": "CogVideoX Model Loader",
}
diff --git a/mz_gguf_loader.py b/mz_gguf_loader.py
index f5a6059..fd8c640 100644
--- a/mz_gguf_loader.py
+++ b/mz_gguf_loader.py
@@ -19,17 +19,21 @@ class quantize_lazy_load():
def quantize_load_state_dict(model, state_dict, device="cpu"):
- Q4_0_qkey = []
+ quant_keys = []
for key in state_dict.keys():
if key.endswith(".Q4_0_qweight"):
- Q4_0_qkey.append(key.replace(".Q4_0_qweight", ""))
+ quant_keys.append(key.replace(".Q4_0_qweight", ""))
+ qtype = "Q4_0"
+ elif key.endswith(".Q8_0_qweight"):
+ quant_keys.append(key.replace(".Q8_0_qweight", ""))
+ qtype = "Q8_0"
for name, module in model.named_modules():
- if name in Q4_0_qkey:
+ if name in quant_keys:
q_linear = WQLinear_GGUF.from_linear(
linear=module,
device=device,
- qtype="Q4_0",
+ qtype=qtype,
)
set_op_by_name(model, name, q_linear)
@@ -117,14 +121,14 @@ class WQLinear_GGUF(nn.Module):
@torch.no_grad()
def forward(self, x):
- # x = torch.matmul(x, dequantize_blocks_Q4_0(self.qweight))
if self.qtype == "Q4_0":
- x = F.linear(x, dequantize_blocks_Q4_0(
- self.Q4_0_qweight, x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
+ dequant = dequantize_blocks_Q4_0(self.Q4_0_qweight, x.dtype)
+ elif self.qtype == "Q8_0":
+ dequant = dequantize_blocks_Q8_0(self.Q8_0_qweight, x.dtype)
else:
raise ValueError(f"Unknown qtype: {self.qtype}")
-
- return x
+
+ return F.linear(x, dequant, bias=self.bias.to(x.dtype) if self.bias is not None else None)
def split_block_dims(blocks, *args):
@@ -153,6 +157,7 @@ def quant_shape_from_byte_shape(shape, qtype) -> tuple[int, ...]:
GGML_QUANT_SIZES = {
"Q4_0": (32, 2 + 16),
+ "Q8_0": (32, 2 + 32),
}
@@ -186,3 +191,31 @@ def dequantize_blocks_Q4_0(data, dtype=torch.float16):
)).to(dtype)
return out
+def dequantize_blocks_Q8_0(data, dtype=torch.float16):
+ block_size, type_size = GGML_QUANT_SIZES["Q8_0"]
+
+ data = data.to(torch.uint8)
+ shape = data.shape
+
+ rows = data.reshape(
+ (-1, data.shape[-1])
+ ).view(torch.uint8)
+
+ n_blocks = rows.numel() // type_size
+ blocks = data.reshape((n_blocks, type_size))
+
+ n_blocks = blocks.shape[0]
+
+ d, qs = split_block_dims(blocks, 2)
+ d = d.view(torch.float16).to(torch.float32)
+
+ qs = qs.view(torch.int8).to(torch.float32)
+
+ out = (d * qs)
+
+ out = out.reshape(quant_shape_from_byte_shape(
+ shape,
+ qtype="Q8_0",
+ )).to(dtype)
+ return out
+
diff --git a/nodes.py b/nodes.py
index 4e4ce6f..6af6b6c 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1,10 +1,11 @@
import os
import torch
-import folder_paths
-import comfy.model_management as mm
+import json
from einops import rearrange
from contextlib import nullcontext
+from .utils import log, check_diffusers_version, print_memory
+check_diffusers_version()
from diffusers.schedulers import (
CogVideoXDDIMScheduler,
CogVideoXDPMScheduler,
@@ -36,13 +37,10 @@ scheduler_mapping = {
}
available_schedulers = list(scheduler_mapping.keys())
-from .cogvideox_fun.utils import get_image_to_video_latent, get_video_to_video_latent, ASPECT_RATIO_512, get_closest_ratio, to_pil
+from diffusers.video_processor import VideoProcessor
-from PIL import Image
-import numpy as np
-import json
-
-from .utils import log, check_diffusers_version
+import folder_paths
+import comfy.model_management as mm
script_directory = os.path.dirname(os.path.abspath(__file__))
@@ -51,61 +49,11 @@ if not "CogVideo" in folder_paths.folder_names_and_paths:
if not "cogvideox_loras" in folder_paths.folder_names_and_paths:
folder_paths.add_model_folder_path("cogvideox_loras", os.path.join(folder_paths.models_dir, "CogVideo", "loras"))
-#PAB
-from .videosys.pab import CogVideoXPABConfig
-
-class CogVideoPABConfig:
- @classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "spatial_broadcast": ("BOOLEAN", {"default": True, "tooltip": "Enable Spatial PAB, highest impact"}),
- "spatial_threshold_start": ("INT", {"default": 850, "min": 0, "max": 1000, "tooltip": "PAB Start Timestep"} ),
- "spatial_threshold_end": ("INT", {"default": 100, "min": 0, "max": 1000, "tooltip": "PAB End Timestep"} ),
- "spatial_range": ("INT", {"default": 2, "min": 0, "max": 10, "tooltip": "Broadcast timesteps range, higher values are faster but quality may suffer"} ),
- "temporal_broadcast": ("BOOLEAN", {"default": False, "tooltip": "Enable Temporal PAB, medium impact"}),
- "temporal_threshold_start": ("INT", {"default": 850, "min": 0, "max": 1000, "tooltip": "PAB Start Timestep"} ),
- "temporal_threshold_end": ("INT", {"default": 100, "min": 0, "max": 1000, "tooltip": "PAB End Timestep"} ),
- "temporal_range": ("INT", {"default": 4, "min": 0, "max": 10, "tooltip": "Broadcast timesteps range, higher values are faster but quality may suffer"} ),
- "cross_broadcast": ("BOOLEAN", {"default": False, "tooltip": "Enable Cross Attention PAB, low impact"}),
- "cross_threshold_start": ("INT", {"default": 850, "min": 0, "max": 1000, "tooltip": "PAB Start Timestep"} ),
- "cross_threshold_end": ("INT", {"default": 100, "min": 0, "max": 1000, "tooltip": "PAB End Timestep"} ),
- "cross_range": ("INT", {"default": 6, "min": 0, "max": 10, "tooltip": "Broadcast timesteps range, higher values are faster but quality may suffer"} ),
-
- "steps": ("INT", {"default": 50, "min": 0, "max": 1000, "tooltip": "Should match the sampling steps"} ),
- }
- }
-
- RETURN_TYPES = ("PAB_CONFIG",)
- RETURN_NAMES = ("pab_config", )
- FUNCTION = "config"
- CATEGORY = "CogVideoWrapper"
- DESCRIPTION = "EXPERIMENTAL:Pyramid Attention Broadcast (PAB) speeds up inference by mitigating redundant attention computation. Increases memory use"
-
- def config(self, spatial_broadcast, spatial_threshold_start, spatial_threshold_end, spatial_range,
- temporal_broadcast, temporal_threshold_start, temporal_threshold_end, temporal_range,
- cross_broadcast, cross_threshold_start, cross_threshold_end, cross_range, steps):
-
- os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
- pab_config = CogVideoXPABConfig(
- steps=steps,
- spatial_broadcast=spatial_broadcast,
- spatial_threshold=[spatial_threshold_end, spatial_threshold_start],
- spatial_range=spatial_range,
- temporal_broadcast=temporal_broadcast,
- temporal_threshold=[temporal_threshold_end, temporal_threshold_start],
- temporal_range=temporal_range,
- cross_broadcast=cross_broadcast,
- cross_threshold=[cross_threshold_end, cross_threshold_start],
- cross_range=cross_range
- )
-
- return (pab_config, )
-
class CogVideoContextOptions:
@classmethod
def INPUT_TYPES(s):
return {"required": {
- "context_schedule": (["uniform_standard", "uniform_looped", "static_standard", "temporal_tiling"],),
+ "context_schedule": (["uniform_standard", "uniform_looped", "static_standard"],),
"context_frames": ("INT", {"default": 48, "min": 2, "max": 100, "step": 1, "tooltip": "Number of pixel frames in the context, NOTE: the latent space has 4 frames in 1"} ),
"context_stride": ("INT", {"default": 4, "min": 4, "max": 100, "step": 1, "tooltip": "Context stride as pixel frames, NOTE: the latent space has 4 frames in 1"} ),
"context_overlap": ("INT", {"default": 4, "min": 4, "max": 100, "step": 1, "tooltip": "Context overlap as pixel frames, NOTE: the latent space has 4 frames in 1"} ),
@@ -179,94 +127,6 @@ class CogVideoXTorchCompileSettings:
return (compile_args, )
#region TextEncode
-class CogVideoEncodePrompt:
- @classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
- "prompt": ("STRING", {"default": "", "multiline": True} ),
- "negative_prompt": ("STRING", {"default": "", "multiline": True} ),
- }
- }
-
- RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
- RETURN_NAMES = ("positive", "negative")
- FUNCTION = "process"
- CATEGORY = "CogVideoWrapper"
-
- def process(self, pipeline, prompt, negative_prompt):
- device = mm.get_torch_device()
- offload_device = mm.unet_offload_device()
- pipe = pipeline["pipe"]
- dtype = pipeline["dtype"]
-
- pipe.text_encoder.to(device)
- pipe.transformer.to(offload_device)
-
- positive, negative = pipe.encode_prompt(
- prompt=prompt,
- negative_prompt=negative_prompt,
- do_classifier_free_guidance=True,
- num_videos_per_prompt=1,
- max_sequence_length=226,
- device=device,
- dtype=dtype,
- )
- pipe.text_encoder.to(offload_device)
-
- return (positive, negative)
-
-# Inject clip_l and t5xxl w/ individual strength adjustments for ComfyUI's DualCLIPLoader node for CogVideoX. Use CLIPSave node from any SDXL model then load in a custom clip_l model.
-# For some reason seems to give a lot more movement and consistency on new CogVideoXFun img2vid? set 'type' to flux / DualClipLoader.
-class CogVideoDualTextEncode_311:
- @classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "clip": ("CLIP",),
- "clip_l": ("STRING", {"default": "", "multiline": True}),
- "t5xxl": ("STRING", {"default": "", "multiline": True}),
- "clip_l_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), # excessive max for testing, have found intesting results up to 20 max?
- "t5xxl_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), # setting this to 0.0001 or level as high as 18 seems to work.
- }
- }
-
- RETURN_TYPES = ("CONDITIONING",)
- RETURN_NAMES = ("conditioning",)
- FUNCTION = "process"
- CATEGORY = "CogVideoWrapper"
-
- def process(self, clip, clip_l, t5xxl, clip_l_strength, t5xxl_strength):
- load_device = mm.text_encoder_device()
- offload_device = mm.text_encoder_offload_device()
-
- # setup tokenizer for clip_l and t5xxl
- clip.tokenizer.t5xxl.pad_to_max_length = True
- clip.tokenizer.t5xxl.max_length = 226
- clip.cond_stage_model.to(load_device)
-
- # tokenize clip_l and t5xxl
- tokens_l = clip.tokenize(clip_l, return_word_ids=True)
- tokens_t5 = clip.tokenize(t5xxl, return_word_ids=True)
-
- # encode the tokens separately
- embeds_l = clip.encode_from_tokens(tokens_l, return_pooled=False, return_dict=False)
- embeds_t5 = clip.encode_from_tokens(tokens_t5, return_pooled=False, return_dict=False)
-
- # apply strength adjustments to each embedding
- if embeds_l.dim() == 3:
- embeds_l *= clip_l_strength
- if embeds_t5.dim() == 3:
- embeds_t5 *= t5xxl_strength
-
- # combine the embeddings by summing them
- combined_embeds = embeds_l + embeds_t5
-
- # offload the model to save memory
- clip.cond_stage_model.to(offload_device)
-
- return (combined_embeds,)
-
class CogVideoTextEncode:
@classmethod
def INPUT_TYPES(s):
@@ -296,7 +156,7 @@ class CogVideoTextEncode:
embeds = clip.encode_from_tokens(tokens, return_pooled=False, return_dict=False)
- if embeds.shape[1] > 226:
+ if embeds.shape[1] > max_tokens:
raise ValueError(f"Prompt is too long, max tokens supported is {max_tokens} or less, got {embeds.shape[1]}")
embeds *= strength
if force_offload:
@@ -335,18 +195,31 @@ class CogVideoTextEncodeCombine:
return (embeds, )
-#region ImageEncode
+#region ImageEncode
+
+def add_noise_to_reference_video(image, ratio=None):
+ if ratio is None:
+ sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
+ sigma = torch.exp(sigma).to(image.dtype)
+ else:
+ sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
+
+ image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
+ image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
+ image = image + image_noise
+ return image
+
class CogVideoImageEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
- "image": ("IMAGE", ),
+ "vae": ("VAE",),
+ "start_image": ("IMAGE", ),
},
"optional": {
- "chunk_size": ("INT", {"default": 16, "min": 4}),
+ "end_image": ("IMAGE", ),
"enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}),
- "mask": ("MASK", ),
+ "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Augment image with noise"}),
},
}
@@ -355,148 +228,146 @@ class CogVideoImageEncode:
FUNCTION = "encode"
CATEGORY = "CogVideoWrapper"
- def encode(self, pipeline, image, chunk_size=8, enable_tiling=False, mask=None):
+ def encode(self, vae, start_image, end_image=None, enable_tiling=False, noise_aug_strength=0.0):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0)
-
- B, H, W, C = image.shape
-
- vae = pipeline["pipe"].vae
- vae.enable_slicing()
+
+ try:
+ vae.enable_slicing()
+ except:
+ pass
+
+ vae_scaling_factor = vae.config.scaling_factor
if enable_tiling:
from .mz_enable_vae_encode_tiling import enable_vae_encode_tiling
enable_vae_encode_tiling(vae)
- if not pipeline["cpu_offloading"]:
- vae.to(device)
+ vae.to(device)
- check_diffusers_version()
try:
vae._clear_fake_context_parallel_cache()
except:
pass
- input_image = image.clone()
- if mask is not None:
- pipeline["pipe"].original_mask = mask
- # print(mask.shape)
- # mask = mask.repeat(B, 1, 1) # Shape: [B, H, W]
- # mask = mask.unsqueeze(-1).repeat(1, 1, 1, C)
- # print(mask.shape)
- # input_image = input_image * (1 -mask)
- else:
- pipeline["pipe"].original_mask = None
-
- input_image = input_image * 2.0 - 1.0
- input_image = input_image.to(vae.dtype).to(device)
- input_image = input_image.unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
- B, C, T, H, W = input_image.shape
-
+ if noise_aug_strength > 0:
+ start_image = add_noise_to_reference_video(start_image, ratio=noise_aug_strength)
+ if end_image is not None:
+ end_image = add_noise_to_reference_video(end_image, ratio=noise_aug_strength)
+
latents_list = []
- # Loop through the temporal dimension in chunks of 16
- for i in range(0, T, chunk_size):
- # Get the chunk of 16 frames (or remaining frames if less than 16 are left)
- end_index = min(i + chunk_size, T)
- image_chunk = input_image[:, :, i:end_index, :, :] # Shape: [B, C, chunk_size, H, W]
- # Encode the chunk of images
- latents = vae.encode(image_chunk)
+ start_image = (start_image * 2.0 - 1.0).to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
+ start_latents = vae.encode(start_image).latent_dist.sample(generator)
+ start_latents = start_latents.permute(0, 2, 1, 3, 4) # B, T, C, H, W
- sample_mode = "sample"
- if hasattr(latents, "latent_dist") and sample_mode == "sample":
- latents = latents.latent_dist.sample(generator)
- elif hasattr(latents, "latent_dist") and sample_mode == "argmax":
- latents = latents.latent_dist.mode()
- elif hasattr(latents, "latents"):
- latents = latents.latents
+ if end_image is not None:
+ end_image = (end_image * 2.0 - 1.0).to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3)
+ end_latents = vae.encode(end_image).latent_dist.sample(generator)
+ end_latents = end_latents.permute(0, 2, 1, 3, 4) # B, T, C, H, W
+ latents_list = [start_latents, end_latents]
+ final_latents = torch.cat(latents_list, dim=1)
+ else:
+ final_latents = start_latents
- latents = vae.config.scaling_factor * latents
- latents = latents.permute(0, 2, 1, 3, 4) # B, T_chunk, C, H, W
- latents_list.append(latents)
-
- # Concatenate all the chunks along the temporal dimension
- final_latents = torch.cat(latents_list, dim=1)
+ final_latents = final_latents * vae_scaling_factor
+
log.info(f"Encoded latents shape: {final_latents.shape}")
- if not pipeline["cpu_offloading"]:
- vae.to(offload_device)
+ vae.to(offload_device)
return ({"samples": final_latents}, )
-class CogVideoImageInterpolationEncode:
+class CogVideoImageEncodeFunInP:
@classmethod
def INPUT_TYPES(s):
return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
+ "vae": ("VAE",),
"start_image": ("IMAGE", ),
- "end_image": ("IMAGE", ),
+ "num_frames": ("INT", {"default": 49, "min": 2, "max": 1024, "step": 1}),
},
"optional": {
+ "end_image": ("IMAGE", ),
"enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}),
- "mask": ("MASK", ),
+ "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Augment image with noise"}),
},
}
RETURN_TYPES = ("LATENT",)
- RETURN_NAMES = ("samples",)
+ RETURN_NAMES = ("image_cond_latents",)
FUNCTION = "encode"
CATEGORY = "CogVideoWrapper"
- def encode(self, pipeline, start_image, end_image, chunk_size=8, enable_tiling=False, mask=None):
+ def encode(self, vae, start_image, num_frames, end_image=None, enable_tiling=False, noise_aug_strength=0.0):
+
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0)
-
- B, H, W, C = start_image.shape
-
- vae = pipeline["pipe"].vae
- vae.enable_slicing()
+
+ try:
+ vae.enable_slicing()
+ except:
+ pass
+
+ vae_scaling_factor = vae.config.scaling_factor
if enable_tiling:
from .mz_enable_vae_encode_tiling import enable_vae_encode_tiling
enable_vae_encode_tiling(vae)
- if not pipeline["cpu_offloading"]:
- vae.to(device)
+ vae.to(device)
- check_diffusers_version()
try:
vae._clear_fake_context_parallel_cache()
except:
pass
- if mask is not None:
- pipeline["pipe"].original_mask = mask
- # print(mask.shape)
- # mask = mask.repeat(B, 1, 1) # Shape: [B, H, W]
- # mask = mask.unsqueeze(-1).repeat(1, 1, 1, C)
- # print(mask.shape)
- # input_image = input_image * (1 -mask)
+ if end_image is not None:
+ # Create a tensor of zeros for padding
+ padding = torch.zeros((num_frames - 2, start_image.shape[1], start_image.shape[2], 3), device=end_image.device, dtype=end_image.dtype) * -1
+ # Concatenate start_image, padding, and end_image
+ input_image = torch.cat([start_image, padding, end_image], dim=0)
else:
- pipeline["pipe"].original_mask = None
-
- start_image = (start_image * 2.0 - 1.0).to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
- end_image = (end_image * 2.0 - 1.0).to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3)
- B, T, C, H, W = start_image.shape
-
- latents_list = []
-
- # Encode the chunk of images
- start_latents = vae.encode(start_image).latent_dist.sample(generator) * vae.config.scaling_factor
- end_latents = vae.encode(end_image).latent_dist.sample(generator) * vae.config.scaling_factor
-
- start_latents = start_latents.permute(0, 2, 1, 3, 4) # B, T, C, H, W
- end_latents = end_latents.permute(0, 2, 1, 3, 4) # B, T, C, H, W
- latents_list = [start_latents, end_latents]
-
- # Concatenate all the chunks along the temporal dimension
- final_latents = torch.cat(latents_list, dim=1)
- log.info(f"Encoded latents shape: {final_latents.shape}")
- if not pipeline["cpu_offloading"]:
- vae.to(offload_device)
+ # Create a tensor of zeros for padding
+ padding = torch.zeros((num_frames - 1, start_image.shape[1], start_image.shape[2], 3), device=start_image.device, dtype=start_image.dtype) * -1
+ # Concatenate start_image and padding
+ input_image = torch.cat([start_image, padding], dim=0)
- return ({"samples": final_latents}, )
+ input_image = input_image * 2.0 - 1.0
+ input_image = input_image.to(vae.dtype).to(device)
+ input_image = input_image.unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
+
+ B, C, T, H, W = input_image.shape
+ if noise_aug_strength > 0:
+ input_image = add_noise_to_reference_video(input_image, ratio=noise_aug_strength)
+
+ bs = 1
+ new_mask_pixel_values = []
+ print("input_image shape: ",input_image.shape)
+ for i in range(0, input_image.shape[0], bs):
+ mask_pixel_values_bs = input_image[i : i + bs]
+ mask_pixel_values_bs = vae.encode(mask_pixel_values_bs)[0]
+ print("mask_pixel_values_bs: ",mask_pixel_values_bs.parameters.shape)
+ mask_pixel_values_bs = mask_pixel_values_bs.mode()
+ print("mask_pixel_values_bs: ",mask_pixel_values_bs.shape, mask_pixel_values_bs.min(), mask_pixel_values_bs.max())
+ new_mask_pixel_values.append(mask_pixel_values_bs)
+ masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
+ masked_image_latents = masked_image_latents.permute(0, 2, 1, 3, 4) # B, T, C, H, W
+
+ mask = torch.zeros_like(masked_image_latents[:, :, :1, :, :])
+ if end_image is not None:
+ mask[:, -1, :, :, :] = vae_scaling_factor
+ mask[:, 0, :, :, :] = vae_scaling_factor
+
+ final_latents = masked_image_latents * vae_scaling_factor
+
+ log.info(f"Encoded latents shape: {final_latents.shape}")
+ vae.to(offload_device)
+
+ return ({
+ "samples": final_latents,
+ "mask": mask
+ },)
#region Tora
from .tora.traj_utils import process_traj, scale_traj_list_to_256
@@ -506,8 +377,8 @@ class ToraEncodeTrajectory:
@classmethod
def INPUT_TYPES(s):
return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
"tora_model": ("TORAMODEL",),
+ "vae": ("VAE",),
"coordinates": ("STRING", {"forceInput": True}),
"width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 8}),
"height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 8}),
@@ -517,7 +388,7 @@ class ToraEncodeTrajectory:
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
- "enable_tiling": ("BOOLEAN", {"default": False}),
+ "enable_tiling": ("BOOLEAN", {"default": True}),
}
}
@@ -526,14 +397,16 @@ class ToraEncodeTrajectory:
FUNCTION = "encode"
CATEGORY = "CogVideoWrapper"
- def encode(self, pipeline, width, height, num_frames, coordinates, strength, start_percent, end_percent, tora_model, enable_tiling=False):
+ def encode(self, vae, width, height, num_frames, coordinates, strength, start_percent, end_percent, tora_model, enable_tiling=False):
check_diffusers_version()
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0)
- vae = pipeline["pipe"].vae
- vae.enable_slicing()
+ try:
+ vae.enable_slicing()
+ except:
+ pass
try:
vae._clear_fake_context_parallel_cache()
except:
@@ -559,31 +432,26 @@ class ToraEncodeTrajectory:
video_flow, points = process_traj(coords_list, num_frames, (height,width), device=device)
video_flow = rearrange(video_flow, "T H W C -> T C H W")
video_flow = flow_to_image(video_flow).unsqueeze_(0).to(device) # [1 T C H W]
-
-
- video_flow = (
- rearrange(video_flow / 255.0 * 2 - 1, "B T C H W -> B C T H W").contiguous().to(vae.dtype)
- )
+ video_flow = (rearrange(video_flow / 255.0 * 2 - 1, "B T C H W -> B C T H W").contiguous().to(vae.dtype))
video_flow_image = rearrange(video_flow, "B C T H W -> (B T) H W C")
- print(video_flow_image.shape)
+ #print(video_flow_image.shape)
mm.soft_empty_cache()
# VAE encode
- if not pipeline["cpu_offloading"]:
- vae.to(device)
-
+ vae.to(device)
video_flow = vae.encode(video_flow).latent_dist.sample(generator) * vae.config.scaling_factor
+ log.info(f"video_flow shape after encoding: {video_flow.shape}") #torch.Size([1, 16, 4, 80, 80])
+ vae.to(offload_device)
- if not pipeline["cpu_offloading"]:
- vae.to(offload_device)
-
+ tora_model["traj_extractor"].to(device)
+ #print("video_flow shape before traj_extractor: ", video_flow.shape) #torch.Size([1, 16, 4, 80, 80])
video_flow_features = tora_model["traj_extractor"](video_flow.to(torch.float32))
+ tora_model["traj_extractor"].to(offload_device)
video_flow_features = torch.stack(video_flow_features)
+ #print("video_flow_features after traj_extractor: ", video_flow_features.shape) #torch.Size([42, 4, 128, 40, 40])
video_flow_features = video_flow_features * strength
- log.info(f"video_flow shape: {video_flow.shape}")
-
tora = {
"video_flow_features" : video_flow_features,
"start_percent" : start_percent,
@@ -598,7 +466,7 @@ class ToraEncodeOpticalFlow:
@classmethod
def INPUT_TYPES(s):
return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
+ "vae": ("VAE",),
"tora_model": ("TORAMODEL",),
"optical_flow": ("IMAGE", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
@@ -613,15 +481,18 @@ class ToraEncodeOpticalFlow:
FUNCTION = "encode"
CATEGORY = "CogVideoWrapper"
- def encode(self, pipeline, optical_flow, strength, tora_model, start_percent, end_percent):
+ def encode(self, vae, optical_flow, strength, tora_model, start_percent, end_percent):
check_diffusers_version()
B, H, W, C = optical_flow.shape
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0)
- vae = pipeline["pipe"].vae
- vae.enable_slicing()
+ try:
+ vae.enable_slicing()
+ except:
+ pass
+
try:
vae._clear_fake_context_parallel_cache()
except:
@@ -633,15 +504,14 @@ class ToraEncodeOpticalFlow:
mm.soft_empty_cache()
# VAE encode
- if not pipeline["cpu_offloading"]:
- vae.to(device)
+
+ vae.to(device)
video_flow = video_flow.to(vae.dtype).to(vae.device)
video_flow = vae.encode(video_flow).latent_dist.sample(generator) * vae.config.scaling_factor
vae.to(offload_device)
video_flow_features = tora_model["traj_extractor"](video_flow.to(torch.float32))
video_flow_features = torch.stack(video_flow_features)
-
video_flow_features = video_flow_features * strength
log.info(f"video_flow shape: {video_flow.shape}")
@@ -656,91 +526,7 @@ class ToraEncodeOpticalFlow:
return (tora, )
-def add_noise_to_reference_video(image, ratio=None):
- if ratio is None:
- sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
- sigma = torch.exp(sigma).to(image.dtype)
- else:
- sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
-
- image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
- image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
- image = image + image_noise
- return image
-class CogVideoControlImageEncode:
- @classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
- "control_video": ("IMAGE", ),
- "base_resolution": ("INT", {"min": 64, "max": 1280, "step": 64, "default": 512, "tooltip": "Base resolution, closest training data bucket resolution is chosen based on the selection."}),
- "enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}),
- "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
- },
- }
-
- RETURN_TYPES = ("COGCONTROL_LATENTS", "INT", "INT",)
- RETURN_NAMES = ("control_latents", "width", "height")
- FUNCTION = "encode"
- CATEGORY = "CogVideoWrapper"
-
- def encode(self, pipeline, control_video, base_resolution, enable_tiling, noise_aug_strength=0.0563):
- device = mm.get_torch_device()
- offload_device = mm.unet_offload_device()
-
- B, H, W, C = control_video.shape
-
- vae = pipeline["pipe"].vae
- vae.enable_slicing()
-
- if enable_tiling:
- from .mz_enable_vae_encode_tiling import enable_vae_encode_tiling
- enable_vae_encode_tiling(vae)
-
- if not pipeline["cpu_offloading"]:
- vae.to(device)
-
- # Count most suitable height and width
- aspect_ratio_sample_size = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
-
- control_video = np.array(control_video.cpu().numpy() * 255, np.uint8)
- original_width, original_height = Image.fromarray(control_video[0]).size
-
- closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
- height, width = [int(x / 16) * 16 for x in closest_size]
- log.info(f"Closest bucket size: {width}x{height}")
-
- video_length = int((B - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if B != 1 else 1
- input_video, input_video_mask, clip_image = get_video_to_video_latent(control_video, video_length=video_length, sample_size=(height, width))
-
- control_video = pipeline["pipe"].image_processor.preprocess(rearrange(input_video, "b c f h w -> (b f) c h w"), height=height, width=width)
- control_video = control_video.to(dtype=torch.float32)
- control_video = rearrange(control_video, "(b f) c h w -> b c f h w", f=video_length)
-
- masked_image = control_video.to(device=device, dtype=vae.dtype)
- if noise_aug_strength > 0:
- masked_image = add_noise_to_reference_video(masked_image, ratio=noise_aug_strength)
- bs = 1
- new_mask_pixel_values = []
- for i in range(0, masked_image.shape[0], bs):
- mask_pixel_values_bs = masked_image[i : i + bs]
- mask_pixel_values_bs = vae.encode(mask_pixel_values_bs)[0]
- mask_pixel_values_bs = mask_pixel_values_bs.mode()
- new_mask_pixel_values.append(mask_pixel_values_bs)
- masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
- masked_image_latents = masked_image_latents * vae.config.scaling_factor
-
- vae.to(offload_device)
-
- control_latents = {
- "latents": masked_image_latents,
- "num_frames" : B,
- "height" : height,
- "width" : width,
- }
-
- return (control_latents, width, height)
#region FasterCache
class CogVideoXFasterCache:
@@ -752,6 +538,7 @@ class CogVideoXFasterCache:
"hf_step": ("INT", {"default": 30, "min": 0, "max": 1024, "step": 1}),
"lf_step": ("INT", {"default": 40, "min": 0, "max": 1024, "step": 1}),
"cache_device": (["main_device", "offload_device", "cuda:1"], {"default": "main_device", "tooltip": "The device to use for the cache, main_device is on GPU and uses a lot of VRAM"}),
+ "num_blocks_to_cache": ("INT", {"default": 42, "min": 0, "max": 1024, "step": 1, "tooltip": "Number of transformer blocks to cache, 5b model has 42 blocks, tradeoff between speed and memory"}),
},
}
@@ -760,7 +547,7 @@ class CogVideoXFasterCache:
FUNCTION = "args"
CATEGORY = "CogVideoWrapper"
- def args(self, start_step, hf_step, lf_step, cache_device):
+ def args(self, start_step, hf_step, lf_step, cache_device, num_blocks_to_cache):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
if cache_device == "cuda:1":
@@ -769,7 +556,8 @@ class CogVideoXFasterCache:
"start_step" : start_step,
"hf_step" : hf_step,
"lf_step" : lf_step,
- "cache_device" : device if cache_device != "offload_device" else offload_device
+ "cache_device" : device if cache_device != "offload_device" else offload_device,
+ "num_blocks_to_cache" : num_blocks_to_cache,
}
return (fastercache,)
@@ -779,12 +567,10 @@ class CogVideoSampler:
def INPUT_TYPES(s):
return {
"required": {
- "pipeline": ("COGVIDEOPIPE",),
+ "model": ("COGVIDEOMODEL",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
- "height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 8}),
- "width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 8}),
- "num_frames": ("INT", {"default": 49, "min": 16, "max": 1024, "step": 1}),
+ "num_frames": ("INT", {"default": 49, "min": 1, "max": 1024, "step": 1}),
"steps": ("INT", {"default": 50, "min": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
@@ -794,9 +580,9 @@ class CogVideoSampler:
}),
},
"optional": {
- "samples": ("LATENT", ),
+ "samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ),
+ "image_cond_latents": ("LATENT",{"tooltip": "Latent to use for image2video conditioning"} ),
"denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
- "image_cond_latents": ("LATENT", ),
"context_options": ("COGCONTEXT", ),
"controlnet": ("COGVIDECONTROLNET",),
"tora_trajectory": ("TORAFEATURES", ),
@@ -804,27 +590,53 @@ class CogVideoSampler:
}
}
- RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
- RETURN_NAMES = ("cogvideo_pipe", "samples",)
+ RETURN_TYPES = ("LATENT",)
+ RETURN_NAMES = ("samples",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
- def process(self, pipeline, positive, negative, steps, cfg, seed, height, width, num_frames, scheduler, samples=None,
+ def process(self, model, positive, negative, steps, cfg, seed, scheduler, num_frames, samples=None,
denoise_strength=1.0, image_cond_latents=None, context_options=None, controlnet=None, tora_trajectory=None, fastercache=None):
mm.soft_empty_cache()
- base_path = pipeline["base_path"]
+ model_name = model.get("model_name", "")
+ supports_image_conds = True if "I2V" in model_name or "interpolation" in model_name.lower() or "fun" in model_name.lower() else False
- assert "fun" not in base_path.lower(), "'Fun' models not supported in 'CogVideoSampler', use the 'CogVideoXFunSampler'"
- assert ("I2V" not in pipeline.get("model_name","") or num_frames == 49 or context_options is not None), "I2V model can only do 49 frames"
+ if "fun" in model_name.lower() and "pose" not in model_name.lower() and image_cond_latents is not None:
+ assert image_cond_latents["mask"] is not None, "For fun inpaint models use CogVideoImageEncodeFunInP"
+ fun_mask = image_cond_latents["mask"]
+ else:
+ fun_mask = None
+
+ if image_cond_latents is not None:
+ assert supports_image_conds, "Image condition latents only supported for I2V and Interpolation models"
+ image_conds = image_cond_latents["samples"]
+ if "1.5" in model_name or "1_5" in model_name:
+ image_conds = image_conds / 0.7 # needed for 1.5 models
+ else:
+ if not "fun" in model_name.lower():
+ assert not supports_image_conds, "Image condition latents required for I2V models"
+ image_conds = None
+
+ if samples is not None:
+ if len(samples["samples"].shape) == 5:
+ B, T, C, H, W = samples["samples"].shape
+ latents = samples["samples"]
+ if len(samples["samples"].shape) == 4:
+ B, C, H, W = samples["samples"].shape
+ latents = None
+ if image_cond_latents is not None:
+ B, T, C, H, W = image_cond_latents["samples"].shape
+ height = H * 8
+ width = W * 8
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
- pipe = pipeline["pipe"]
- dtype = pipeline["dtype"]
- scheduler_config = pipeline["scheduler_config"]
+ pipe = model["pipe"]
+ dtype = model["dtype"]
+ scheduler_config = model["scheduler_config"]
- if not pipeline["cpu_offloading"]:
+ if not model["cpu_offloading"] and model["manual_offloading"]:
pipe.transformer.to(device)
generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
@@ -856,21 +668,33 @@ class CogVideoSampler:
pipe.transformer.fastercache_lf_step = fastercache["lf_step"]
pipe.transformer.fastercache_hf_step = fastercache["hf_step"]
pipe.transformer.fastercache_device = fastercache["cache_device"]
+ pipe.transformer.fastercache_num_blocks_to_cache = fastercache["num_blocks_to_cache"]
+ log.info(f"FasterCache enabled for {pipe.transformer.fastercache_num_blocks_to_cache} blocks out of {len(pipe.transformer.transformer_blocks)}")
else:
pipe.transformer.use_fastercache = False
pipe.transformer.fastercache_counter = 0
- autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
- autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
+ if not isinstance(cfg, list):
+ cfg = [cfg for _ in range(steps)]
+ else:
+ assert len(cfg) == steps, "Length of cfg list must match number of steps"
+ try:
+ torch.cuda.reset_peak_memory_stats(device)
+ except:
+ pass
+
+ autocastcondition = not model["onediff"] or not dtype == torch.float32
+ autocast_context = torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocastcondition else nullcontext()
with autocast_context:
- latents = pipeline["pipe"](
+ latents = model["pipe"](
num_inference_steps=steps,
height = height,
width = width,
num_frames = num_frames,
guidance_scale=cfg,
- latents=samples["samples"] if samples is not None else None,
- image_cond_latents=image_cond_latents["samples"] if image_cond_latents is not None else None,
+ latents=latents if samples is not None else None,
+ fun_mask = fun_mask,
+ image_cond_latents=image_conds,
denoise_strength=denoise_strength,
prompt_embeds=positive.to(dtype).to(device),
negative_prompt_embeds=negative.to(dtype).to(device),
@@ -884,7 +708,7 @@ class CogVideoSampler:
controlnet=controlnet,
tora=tora_trajectory if tora_trajectory is not None else None,
)
- if not pipeline["cpu_offloading"]:
+ if not model["cpu_offloading"] and model["manual_offloading"]:
pipe.transformer.to(offload_device)
if fastercache is not None:
@@ -892,10 +716,19 @@ class CogVideoSampler:
if (hasattr, block, "cached_hidden_states") and block.cached_hidden_states is not None:
block.cached_hidden_states = None
block.cached_encoder_hidden_states = None
-
- mm.soft_empty_cache()
- return (pipeline, {"samples": latents})
+ print_memory(device)
+ mm.soft_empty_cache()
+ try:
+ torch.cuda.reset_peak_memory_stats(device)
+ except:
+ pass
+
+ additional_frames = getattr(pipe, "additional_frames", 0)
+ return ({
+ "samples": latents,
+ "additional_frames": additional_frames,
+ },)
class CogVideoControlNet:
@classmethod
@@ -915,13 +748,7 @@ class CogVideoControlNet:
CATEGORY = "CogVideoWrapper"
def encode(self, controlnet, images, control_strength, control_start_percent, control_end_percent):
- device = mm.get_torch_device()
- offload_device = mm.unet_offload_device()
-
- B, H, W, C = images.shape
-
control_frames = images.permute(0, 3, 1, 2).unsqueeze(0) * 2 - 1
-
controlnet = {
"control_model": controlnet,
"control_frames": control_frames,
@@ -929,7 +756,6 @@ class CogVideoControlNet:
"control_start": control_start_percent,
"control_end": control_end_percent,
}
-
return (controlnet,)
#region VideoDecode
@@ -937,34 +763,36 @@ class CogVideoDecode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
- "pipeline": ("COGVIDEOPIPE",),
- "samples": ("LATENT", ),
- "enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": "Drastically reduces memory use but may introduce seams"}),
- },
- "optional": {
- "tile_sample_min_height": ("INT", {"default": 240, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile height, default is half the height"}),
- "tile_sample_min_width": ("INT", {"default": 360, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile width, default is half the width"}),
- "tile_overlap_factor_height": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
- "tile_overlap_factor_width": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
- "auto_tile_size": ("BOOLEAN", {"default": True, "tooltip": "Auto size based on height and width, default is half the size"}),
- }
- }
+ "vae": ("VAE",),
+ "samples": ("LATENT",),
+ "enable_vae_tiling": ("BOOLEAN", {"default": True, "tooltip": "Drastically reduces memory use but may introduce seams"}),
+ "tile_sample_min_height": ("INT", {"default": 240, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile height, default is half the height"}),
+ "tile_sample_min_width": ("INT", {"default": 360, "min": 16, "max": 2048, "step": 8, "tooltip": "Minimum tile width, default is half the width"}),
+ "tile_overlap_factor_height": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
+ "tile_overlap_factor_width": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
+ "auto_tile_size": ("BOOLEAN", {"default": True, "tooltip": "Auto size based on height and width, default is half the size"}),
+ },
+ }
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "decode"
CATEGORY = "CogVideoWrapper"
- def decode(self, pipeline, samples, enable_vae_tiling, tile_sample_min_height, tile_sample_min_width, tile_overlap_factor_height, tile_overlap_factor_width, auto_tile_size=True):
+ def decode(self, vae, samples, enable_vae_tiling, tile_sample_min_height, tile_sample_min_width, tile_overlap_factor_height, tile_overlap_factor_width,
+ auto_tile_size=True, pipeline=None):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
latents = samples["samples"]
- vae = pipeline["pipe"].vae
+
+ additional_frames = samples.get("additional_frames", 0)
- vae.enable_slicing()
+ try:
+ vae.enable_slicing()
+ except:
+ pass
- if not pipeline["cpu_offloading"]:
- vae.to(device)
+ vae.to(device)
if enable_vae_tiling:
if auto_tile_size:
vae.enable_tiling()
@@ -980,17 +808,27 @@ class CogVideoDecode:
latents = latents.to(vae.dtype).to(device)
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
latents = 1 / vae.config.scaling_factor * latents
+
try:
vae._clear_fake_context_parallel_cache()
except:
pass
- frames = vae.decode(latents).sample
+ try:
+ frames = vae.decode(latents[:, :, additional_frames:]).sample
+ except:
+ mm.soft_empty_cache()
+ log.warning("Failed to decode, retrying with tiling")
+ vae.enable_tiling()
+ frames = vae.decode(latents[:, :, additional_frames:]).sample
+
vae.disable_tiling()
- if not pipeline["cpu_offloading"]:
- vae.to(offload_device)
+ vae.to(offload_device)
mm.soft_empty_cache()
- video = pipeline["pipe"].video_processor.postprocess_video(video=frames, output_type="pt")
+ video_processor = VideoProcessor(vae_scale_factor=8)
+ video_processor.config.do_resize = False
+
+ video = video_processor.postprocess_video(video=frames, output_type="pt")
video = video[0].permute(0, 2, 3, 1).cpu().float()
return (video,)
@@ -1014,6 +852,7 @@ class CogVideoXFunResizeToClosestBucket:
def resize(self, images, base_resolution, upscale_method, crop):
from comfy.utils import common_upscale
+ from .cogvideox_fun.utils import ASPECT_RATIO_512, get_closest_ratio
B, H, W, C = images.shape
# Count most suitable height and width
@@ -1029,264 +868,6 @@ class CogVideoXFunResizeToClosestBucket:
return (resized_images, width, height)
-#region FunSamplers
-class CogVideoXFunSampler:
- @classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "pipeline": ("COGVIDEOPIPE",),
- "positive": ("CONDITIONING", ),
- "negative": ("CONDITIONING", ),
- "video_length": ("INT", {"default": 49, "min": 5, "max": 2048, "step": 4}),
- "width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 8}),
- "height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 8}),
- "seed": ("INT", {"default": 43, "min": 0, "max": 0xffffffffffffffff}),
- "steps": ("INT", {"default": 50, "min": 1, "max": 200, "step": 1}),
- "cfg": ("FLOAT", {"default": 6.0, "min": 1.0, "max": 20.0, "step": 0.01}),
- "scheduler": (available_schedulers, {"default": 'DDIM'})
- },
- "optional":{
- "start_img": ("IMAGE",),
- "end_img": ("IMAGE",),
- "noise_aug_strength": ("FLOAT", {"default": 0.0563, "min": 0.0, "max": 1.0, "step": 0.001}),
- "context_options": ("COGCONTEXT", ),
- "tora_trajectory": ("TORAFEATURES", ),
- "fastercache": ("FASTERCACHEARGS",),
- "vid2vid_images": ("IMAGE",),
- "vid2vid_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
- },
- }
-
- RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
- RETURN_NAMES = ("cogvideo_pipe", "samples",)
- FUNCTION = "process"
- CATEGORY = "CogVideoWrapper"
-
- def process(self, pipeline, positive, negative, video_length, width, height, seed, steps, cfg, scheduler,
- start_img=None, end_img=None, noise_aug_strength=0.0563, context_options=None, fastercache=None,
- tora_trajectory=None, vid2vid_images=None, vid2vid_denoise=1.0):
- device = mm.get_torch_device()
- offload_device = mm.unet_offload_device()
- pipe = pipeline["pipe"]
- dtype = pipeline["dtype"]
- base_path = pipeline["base_path"]
- assert "fun" in base_path.lower(), "'Unfun' models not supported in 'CogVideoXFunSampler', use the 'CogVideoSampler'"
- assert "pose" not in base_path.lower(), "'Pose' models not supported in 'CogVideoXFunSampler', use the 'CogVideoXFunControlSampler'"
-
-
- if not pipeline["cpu_offloading"]:
- pipe.enable_model_cpu_offload(device=device)
-
- mm.soft_empty_cache()
-
- #vid2vid
- if vid2vid_images is not None:
- validation_video = np.array(vid2vid_images.cpu().numpy() * 255, np.uint8)
- #img2vid
- elif start_img is not None:
- start_img = [to_pil(_start_img) for _start_img in start_img] if start_img is not None else None
- end_img = [to_pil(_end_img) for _end_img in end_img] if end_img is not None else None
-
- # Load Sampler
- if context_options is not None and context_options["context_schedule"] == "temporal_tiling":
- log.info("Temporal tiling enabled, changing scheduler to CogVideoXDDIM")
- scheduler="CogVideoXDDIM"
- scheduler_config = pipeline["scheduler_config"]
- if scheduler in scheduler_mapping:
- noise_scheduler = scheduler_mapping[scheduler].from_config(scheduler_config)
- pipe.scheduler = noise_scheduler
- else:
- raise ValueError(f"Unknown scheduler: {scheduler}")
-
- #if not pipeline["cpu_offloading"]:
- # pipe.transformer.to(device)
-
- if context_options is not None:
- context_frames = context_options["context_frames"] // 4
- context_stride = context_options["context_stride"] // 4
- context_overlap = context_options["context_overlap"] // 4
- else:
- context_frames, context_stride, context_overlap = None, None, None
-
- if tora_trajectory is not None:
- pipe.transformer.fuser_list = tora_trajectory["fuser_list"]
-
- if fastercache is not None:
- pipe.transformer.use_fastercache = True
- pipe.transformer.fastercache_counter = 0
- pipe.transformer.fastercache_start_step = fastercache["start_step"]
- pipe.transformer.fastercache_lf_step = fastercache["lf_step"]
- pipe.transformer.fastercache_hf_step = fastercache["hf_step"]
- pipe.transformer.fastercache_device = fastercache["cache_device"]
- else:
- pipe.transformer.use_fastercache = False
- pipe.transformer.fastercache_counter = 0
-
- generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
-
- autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
- autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
- with autocast_context:
- video_length = int((video_length - 1) // pipe.vae.config.temporal_compression_ratio * pipe.vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
- if vid2vid_images is not None:
- input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, video_length=video_length, sample_size=(height, width))
- else:
- input_video, input_video_mask, clip_image = get_image_to_video_latent(start_img, end_img, video_length=video_length, sample_size=(height, width))
-
- common_params = {
- "prompt_embeds": positive.to(dtype).to(device),
- "negative_prompt_embeds": negative.to(dtype).to(device),
- "num_frames": video_length,
- "height": height,
- "width": width,
- "generator": generator,
- "guidance_scale": cfg,
- "num_inference_steps": steps,
- "comfyui_progressbar": True,
- "context_schedule":context_options["context_schedule"] if context_options is not None else None,
- "context_frames":context_frames,
- "context_stride": context_stride,
- "context_overlap": context_overlap,
- "freenoise":context_options["freenoise"] if context_options is not None else None,
- "tora":tora_trajectory if tora_trajectory is not None else None,
- }
- latents = pipe(
- **common_params,
- video = input_video,
- mask_video = input_video_mask,
- noise_aug_strength = noise_aug_strength,
- strength = vid2vid_denoise,
- )
- #if not pipeline["cpu_offloading"]:
- # pipe.transformer.to(offload_device)
- #clear FasterCache
- if fastercache is not None:
- for block in pipe.transformer.transformer_blocks:
- if (hasattr, block, "cached_hidden_states") and block.cached_hidden_states is not None:
- block.cached_hidden_states = None
- block.cached_encoder_hidden_states = None
-
- mm.soft_empty_cache()
-
- return (pipeline, {"samples": latents})
-
-class CogVideoXFunVid2VidSampler:
- @classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "note": ("STRING", {"default": "This node is deprecated, functionality moved to 'CogVideoXFunSampler' node instead.", "multiline": True}),
- },
- }
-
- RETURN_TYPES = ()
- FUNCTION = "process"
- CATEGORY = "CogVideoWrapper"
- DEPRECATED = True
- def process(self):
- return ()
-
-class CogVideoXFunControlSampler:
- @classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "pipeline": ("COGVIDEOPIPE",),
- "positive": ("CONDITIONING", ),
- "negative": ("CONDITIONING", ),
- "control_latents": ("COGCONTROL_LATENTS",),
- "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}),
- "steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
- "cfg": ("FLOAT", {"default": 6.0, "min": 1.0, "max": 20.0, "step": 0.01}),
- "scheduler": (available_schedulers, {"default": 'DDIM'}),
- "control_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
- "control_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
- "control_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
- },
- "optional": {
- "samples": ("LATENT", ),
- "denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
- "context_options": ("COGCONTEXT", ),
- },
- }
-
- RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
- RETURN_NAMES = ("cogvideo_pipe", "samples",)
- FUNCTION = "process"
- CATEGORY = "CogVideoWrapper"
-
- def process(self, pipeline, positive, negative, seed, steps, cfg, scheduler, control_latents,
- control_strength=1.0, control_start_percent=0.0, control_end_percent=1.0, t_tile_length=16, t_tile_overlap=8,
- samples=None, denoise_strength=1.0, context_options=None):
- device = mm.get_torch_device()
- offload_device = mm.unet_offload_device()
- pipe = pipeline["pipe"]
- dtype = pipeline["dtype"]
- base_path = pipeline["base_path"]
-
- assert "fun" in base_path.lower(), "'Unfun' models not supported in 'CogVideoXFunSampler', use the 'CogVideoSampler'"
-
- if not pipeline["cpu_offloading"]:
- pipe.enable_model_cpu_offload(device=device)
-
- mm.soft_empty_cache()
-
- if context_options is not None:
- context_frames = context_options["context_frames"] // 4
- context_stride = context_options["context_stride"] // 4
- context_overlap = context_options["context_overlap"] // 4
- else:
- context_frames, context_stride, context_overlap = None, None, None
-
- # Load Sampler
- scheduler_config = pipeline["scheduler_config"]
- if context_options is not None and context_options["context_schedule"] == "temporal_tiling":
- log.info("Temporal tiling enabled, changing scheduler to CogVideoXDDIM")
- scheduler="CogVideoXDDIM"
- if scheduler in scheduler_mapping:
- noise_scheduler = scheduler_mapping[scheduler].from_config(scheduler_config)
- pipe.scheduler = noise_scheduler
- else:
- raise ValueError(f"Unknown scheduler: {scheduler}")
-
- generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
-
- autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
- autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
- with autocast_context:
-
- common_params = {
- "prompt_embeds": positive.to(dtype).to(device),
- "negative_prompt_embeds": negative.to(dtype).to(device),
- "num_frames": control_latents["num_frames"],
- "height": control_latents["height"],
- "width": control_latents["width"],
- "generator": generator,
- "guidance_scale": cfg,
- "num_inference_steps": steps,
- "comfyui_progressbar": True,
- }
-
- latents = pipe(
- **common_params,
- control_video=control_latents["latents"],
- control_strength=control_strength,
- control_start_percent=control_start_percent,
- control_end_percent=control_end_percent,
- scheduler_name=scheduler,
- latents=samples["samples"] if samples is not None else None,
- denoise_strength=denoise_strength,
- context_schedule=context_options["context_schedule"] if context_options is not None else None,
- context_frames=context_frames,
- context_stride= context_stride,
- context_overlap= context_overlap,
- freenoise=context_options["freenoise"] if context_options is not None else None
-
- )
-
- return (pipeline, {"samples": latents})
-
class CogVideoLatentPreview:
@classmethod
def INPUT_TYPES(s):
@@ -1313,9 +894,6 @@ class CogVideoLatentPreview:
latents = samples["samples"].clone()
print("in sample", latents.shape)
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
-
- device = mm.get_torch_device()
- offload_device = mm.unet_offload_device()
#[[0.0658900170023352, 0.04687556512203313, -0.056971557475649186], [-0.01265770449940036, -0.02814809569100843, -0.0768912512529372], [0.061456544746314665, 0.0005511617552452358, -0.0652574975291287], [-0.09020669168815276, -0.004755440180558637, -0.023763970904494294], [0.031766964513999865, -0.030959599938418375, 0.08654669098083616], [-0.005981764690055846, -0.08809119252349802, -0.06439852368217663], [-0.0212114426433989, 0.08894281999597677, 0.05155629477559985], [-0.013947446911030725, -0.08987475069900677, -0.08923124751217484], [-0.08235967967978511, 0.07268025379974379, 0.08830486164536037], [-0.08052049179735378, -0.050116143175332195, 0.02023752569687405], [-0.07607527759162447, 0.06827156419895981, 0.08678111754261035], [-0.04689089232553825, 0.017294986041038893, -0.10280492336438908], [-0.06105783150270304, 0.07311850680875913, 0.019995735372550075], [-0.09232589996527711, -0.012869815059053047, -0.04355587834255975], [-0.06679931010802251, 0.018399815879067458, 0.06802404982033876], [-0.013062632927118165, -0.04292991477896661, 0.07476243356192845]]
latent_rgb_factors =[[0.11945946736445662, 0.09919175788574555, -0.004832707433877734], [-0.0011977028264356232, 0.05496505130267682, 0.021321622433638193], [-0.014088548986590666, -0.008701477861945644, -0.020991313281459367], [0.03063921972519621, 0.12186477097625073, 0.0139593690235148], [0.0927403067854673, 0.030293187650929136, 0.05083134241694003], [0.0379112441305742, 0.04935199882777209, 0.058562766246777774], [0.017749911959153715, 0.008839453404921545, 0.036005638019226294], [0.10610119248526109, 0.02339855688237826, 0.057154257614084596], [0.1273639464837117, -0.010959856130713416, 0.043268631260428896], [-0.01873510946881321, 0.08220930648486932, 0.10613256772247093], [0.008429116376722327, 0.07623856561000408, 0.09295712117576727], [0.12938137079617007, 0.12360403483892413, 0.04478930933220116], [0.04565908794779364, 0.041064156741596365, -0.017695041535528512], [0.00019003240570281826, -0.013965147883381978, 0.05329669529635849], [0.08082391586738358, 0.11548306825496074, -0.021464170006615893], [-0.01517932393230994, -0.0057985555313003236, 0.07216646476618871]]
@@ -1355,16 +933,9 @@ NODE_CLASS_MAPPINGS = {
"CogVideoSampler": CogVideoSampler,
"CogVideoDecode": CogVideoDecode,
"CogVideoTextEncode": CogVideoTextEncode,
- "CogVideoDualTextEncode_311": CogVideoDualTextEncode_311,
"CogVideoImageEncode": CogVideoImageEncode,
- "CogVideoImageInterpolationEncode": CogVideoImageInterpolationEncode,
- "CogVideoXFunSampler": CogVideoXFunSampler,
- "CogVideoXFunVid2VidSampler": CogVideoXFunVid2VidSampler,
- "CogVideoXFunControlSampler": CogVideoXFunControlSampler,
"CogVideoTextEncodeCombine": CogVideoTextEncodeCombine,
- "CogVideoPABConfig": CogVideoPABConfig,
"CogVideoTransformerEdit": CogVideoTransformerEdit,
- "CogVideoControlImageEncode": CogVideoControlImageEncode,
"CogVideoContextOptions": CogVideoContextOptions,
"CogVideoControlNet": CogVideoControlNet,
"ToraEncodeTrajectory": ToraEncodeTrajectory,
@@ -1372,22 +943,16 @@ NODE_CLASS_MAPPINGS = {
"CogVideoXFasterCache": CogVideoXFasterCache,
"CogVideoXFunResizeToClosestBucket": CogVideoXFunResizeToClosestBucket,
"CogVideoLatentPreview": CogVideoLatentPreview,
- "CogVideoXTorchCompileSettings": CogVideoXTorchCompileSettings
+ "CogVideoXTorchCompileSettings": CogVideoXTorchCompileSettings,
+ "CogVideoImageEncodeFunInP": CogVideoImageEncodeFunInP,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"CogVideoSampler": "CogVideo Sampler",
"CogVideoDecode": "CogVideo Decode",
"CogVideoTextEncode": "CogVideo TextEncode",
- "CogVideoDualTextEncode_311": "CogVideo DualTextEncode",
"CogVideoImageEncode": "CogVideo ImageEncode",
- "CogVideoImageInterpolationEncode": "CogVideo ImageInterpolation Encode",
- "CogVideoXFunSampler": "CogVideoXFun Sampler",
- "CogVideoXFunVid2VidSampler": "CogVideoXFun Vid2Vid Sampler",
- "CogVideoXFunControlSampler": "CogVideoXFun Control Sampler",
"CogVideoTextEncodeCombine": "CogVideo TextEncode Combine",
- "CogVideoPABConfig": "CogVideo PABConfig",
"CogVideoTransformerEdit": "CogVideo TransformerEdit",
- "CogVideoControlImageEncode": "CogVideo Control ImageEncode",
"CogVideoContextOptions": "CogVideo Context Options",
"ToraEncodeTrajectory": "Tora Encode Trajectory",
"ToraEncodeOpticalFlow": "Tora Encode OpticalFlow",
@@ -1395,4 +960,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"CogVideoXFunResizeToClosestBucket": "CogVideoXFun ResizeToClosestBucket",
"CogVideoLatentPreview": "CogVideo LatentPreview",
"CogVideoXTorchCompileSettings": "CogVideo TorchCompileSettings",
+ "CogVideoImageEncodeFunInP": "CogVideo ImageEncode FunInP",
}
\ No newline at end of file
diff --git a/pipeline_cogvideox.py b/pipeline_cogvideox.py
index 9e38ea8..f869ce4 100644
--- a/pipeline_cogvideox.py
+++ b/pipeline_cogvideox.py
@@ -17,27 +17,23 @@ import inspect
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
-import torch.nn.functional as F
import math
-from diffusers.models import AutoencoderKLCogVideoX#, CogVideoXTransformer3DModel
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
from diffusers.utils import logging
from diffusers.utils.torch_utils import randn_tensor
-from diffusers.video_processor import VideoProcessor
-from diffusers.models.embeddings import get_3d_rotary_pos_embed
+
+#from diffusers.models.embeddings import get_3d_rotary_pos_embed
from diffusers.loaders import CogVideoXLoraLoaderMixin
+from .embeddings import get_3d_rotary_pos_embed
from .custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
from comfy.utils import ProgressBar
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-from .videosys.core.pipeline import VideoSysPipeline
-from .videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
-from .videosys.core.pab_mgr import set_pab_manager
-
def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
tw = tgt_width
th = tgt_height
@@ -114,7 +110,7 @@ def retrieve_timesteps(
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
-class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
+class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for text-to-video generation using CogVideoX.
@@ -122,15 +118,6 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
- text_encoder ([`T5EncoderModel`]):
- Frozen text-encoder. CogVideoX uses
- [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
- [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
- tokenizer (`T5Tokenizer`):
- Tokenizer of class
- [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
transformer ([`CogVideoXTransformer3DModel`]):
A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
@@ -142,33 +129,25 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
def __init__(
self,
- vae: AutoencoderKLCogVideoX,
- transformer: Union[CogVideoXTransformer3DModel, CogVideoXTransformer3DModelPAB],
+ transformer: CogVideoXTransformer3DModel,
scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
- original_mask = None,
- pab_config = None
+ dtype: torch.dtype = torch.bfloat16,
+ is_fun_inpaint: bool = False,
):
super().__init__()
- self.register_modules(
- vae=vae, transformer=transformer, scheduler=scheduler
- )
- self.vae_scale_factor_spatial = (
- 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
- )
- self.vae_scale_factor_temporal = (
- self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
- )
- self.original_mask = original_mask
- self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
-
- if pab_config is not None:
- set_pab_manager(pab_config)
+ self.register_modules(transformer=transformer, scheduler=scheduler)
+ self.vae_scale_factor_spatial = 8
+ self.vae_scale_factor_temporal = 4
+ self.vae_latent_channels = 16
+ self.vae_dtype = dtype
+ self.is_fun_inpaint = is_fun_inpaint
self.input_with_padding = True
+
def prepare_latents(
- self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, timesteps, denoise_strength,
+ self, batch_size, num_channels_latents, num_frames, height, width, device, generator, timesteps, denoise_strength,
num_inference_steps, latents=None, freenoise=True, context_size=None, context_overlap=None
):
shape = (
@@ -178,14 +157,10 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
- noise = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=self.vae.dtype)
+
+ noise = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=self.vae_dtype)
if freenoise:
- print("Applying FreeNoise")
+ logger.info("Applying FreeNoise")
# code and comments from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
video_length = num_frames // 4
delta = context_size - context_overlap
@@ -225,20 +200,20 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, denoise_strength, device)
latent_timestep = timesteps[:1]
- noise = randn_tensor(shape, generator=generator, device=device, dtype=self.vae.dtype)
frames_needed = noise.shape[1]
current_frames = latents.shape[1]
if frames_needed > current_frames:
- repeat_factor = frames_needed // current_frames
+ repeat_factor = frames_needed - current_frames
additional_frame = torch.randn((latents.size(0), repeat_factor, latents.size(2), latents.size(3), latents.size(4)), dtype=latents.dtype, device=latents.device)
- latents = torch.cat((latents, additional_frame), dim=1)
+ latents = torch.cat((additional_frame, latents), dim=1)
+ self.additional_frames = repeat_factor
elif frames_needed < current_frames:
latents = latents[:, :frames_needed, :, :, :]
- latents = self.scheduler.add_noise(latents, noise, latent_timestep)
+ latents = self.scheduler.add_noise(latents, noise.to(device), latent_timestep)
latents = latents * self.scheduler.init_noise_sigma # scale the initial noise by the standard deviation required by the scheduler
- return latents, timesteps, noise
+ return latents, timesteps
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
@@ -286,29 +261,6 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps.to(device), num_inference_steps - t_start
-
- def _gaussian_weights(self, t_tile_length, t_batch_size):
- from numpy import pi, exp, sqrt
-
- var = 0.01
- midpoint = (t_tile_length - 1) / 2 # -1 because index goes from 0 to latent_width - 1
- t_probs = [exp(-(t-midpoint)*(t-midpoint)/(t_tile_length*t_tile_length)/(2*var)) / sqrt(2*pi*var) for t in range(t_tile_length)]
- weights = torch.tensor(t_probs)
- weights = weights.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(4).repeat(1, t_batch_size,1, 1, 1)
- return weights
-
- def fuse_qkv_projections(self) -> None:
- r"""Enables fused QKV projections."""
- self.fusing_transformer = True
- self.transformer.fuse_qkv_projections()
-
- def unfuse_qkv_projections(self) -> None:
- r"""Disable QKV projection fusion if enabled."""
- if not self.fusing_transformer:
- logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
- else:
- self.transformer.unfuse_qkv_projections()
- self.fusing_transformer = False
def _prepare_rotary_positional_embeddings(
self,
@@ -316,34 +268,41 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
width: int,
num_frames: int,
device: torch.device,
- start_frame: int = None,
- end_frame: int = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
- grid_crops_coords = get_resize_crop_region_for_grid(
- (grid_height, grid_width), base_size_width, base_size_height
- )
- freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
- embed_dim=self.transformer.config.attention_head_dim,
- crops_coords=grid_crops_coords,
- grid_size=(grid_height, grid_width),
- temporal_size=num_frames,
- use_real=True,
- )
-
- if start_frame is not None:
- freqs_cos = freqs_cos.view(num_frames, grid_height * grid_width, -1)
- freqs_sin = freqs_sin.view(num_frames, grid_height * grid_width, -1)
+ p = self.transformer.config.patch_size
+ p_t = self.transformer.config.patch_size_t
- freqs_cos = freqs_cos[start_frame:end_frame]
- freqs_sin = freqs_sin[start_frame:end_frame]
+ if p_t is None:
+ # CogVideoX 1.0 I2V
+ base_size_width = self.transformer.config.sample_width // p
+ base_size_height = self.transformer.config.sample_height // p
- freqs_cos = freqs_cos.view(-1, freqs_cos.shape[-1])
- freqs_sin = freqs_sin.view(-1, freqs_sin.shape[-1])
+ grid_crops_coords = get_resize_crop_region_for_grid(
+ (grid_height, grid_width), base_size_width, base_size_height
+ )
+ freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+ embed_dim=self.transformer.config.attention_head_dim,
+ crops_coords=grid_crops_coords,
+ grid_size=(grid_height, grid_width),
+ temporal_size=num_frames,
+ )
+ else:
+ # CogVideoX 1.5 I2V
+ base_size_width = self.transformer.config.sample_width // p
+ base_size_height = self.transformer.config.sample_height // p
+ base_num_frames = (num_frames + p_t - 1) // p_t
+
+ freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+ embed_dim=self.transformer.config.attention_head_dim,
+ crops_coords=None,
+ grid_size=(grid_height, grid_width),
+ temporal_size=base_num_frames,
+ grid_type="slice",
+ max_size=(base_size_height, base_size_width),
+ )
freqs_cos = freqs_cos.to(device=device)
freqs_sin = freqs_sin.to(device=device)
@@ -370,16 +329,15 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
height: int = 480,
width: int = 720,
num_frames: int = 48,
- t_tile_length: int = 12,
- t_tile_overlap: int = 4,
num_inference_steps: int = 50,
timesteps: Optional[List[int]] = None,
guidance_scale: float = 6,
denoise_strength: float = 1.0,
- num_videos_per_prompt: int = 1,
+ sigmas: Optional[List[float]] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
+ fun_mask: Optional[torch.Tensor] = None,
image_cond_latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
@@ -419,8 +377,6 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
- num_videos_per_prompt (`int`, *optional*, defaults to 1):
- The number of videos to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
@@ -436,15 +392,13 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
-
- #assert (
- # num_frames <= 48 and num_frames % fps == 0 and fps == 8
- #), f"The number of frames must be divisible by {fps=} and less than 48 frames (for now). Other values are not supported in CogVideoX."
-
+
height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
num_videos_per_prompt = 1
+ self.num_frames = num_frames
+
# 1. Check inputs. Raise error if not correct
self.check_inputs(
height,
@@ -462,33 +416,42 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
+ do_classifier_free_guidance = guidance_scale[0] > 1.0
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
- prompt_embeds = prompt_embeds.to(self.vae.dtype)
+ prompt_embeds = prompt_embeds.to(self.vae_dtype)
# 4. Prepare timesteps
- timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+ if sigmas is None:
+ timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+ else:
+ timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, sigmas=sigmas, device=device)
self._num_timesteps = len(timesteps)
# 5. Prepare latents.
- latent_channels = self.vae.config.latent_channels
+ latent_channels = self.vae_latent_channels
+ latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
- if latents is None and num_frames == t_tile_length:
- num_frames += 1
+ # For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t
+ patch_size_t = getattr(self.transformer.config, "patch_size_t", None)
+ if patch_size_t is None:
+ self.transformer.config.patch_size_t = None
+ ofs_embed_dim = getattr(self.transformer.config, "ofs_embed_dim", None)
+ if ofs_embed_dim is None:
+ self.transformer.config.ofs_embed_dim = None
- if self.original_mask is not None:
- image_latents = latents
- original_image_latents = image_latents
+ self.additional_frames = 0
+ if patch_size_t is not None and latent_frames % patch_size_t != 0:
+ self.additional_frames = patch_size_t - latent_frames % patch_size_t
+ num_frames += self.additional_frames * self.vae_scale_factor_temporal
- latents, timesteps, noise = self.prepare_latents(
- batch_size * num_videos_per_prompt,
+ latents, timesteps = self.prepare_latents(
+ batch_size,
latent_channels,
num_frames,
height,
width,
- self.vae.dtype,
device,
generator,
timesteps,
@@ -499,95 +462,82 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
context_overlap=context_overlap,
freenoise=freenoise,
)
- latents = latents.to(self.vae.dtype)
- #print("latents", latents.shape)
+ latents = latents.to(self.vae_dtype)
+
+ if self.is_fun_inpaint and fun_mask is None: # For FUN inpaint vid2vid, we need to mask all the latents
+ fun_mask = torch.zeros_like(latents[:, :, :1, :, :], device=latents.device, dtype=latents.dtype)
+ fun_masked_video_latents = torch.zeros_like(latents, device=latents.device, dtype=latents.dtype)
# 5.5.
if image_cond_latents is not None:
- if image_cond_latents.shape[1] > 1:
+ if image_cond_latents.shape[1] == 2:
logger.info("More than one image conditioning frame received, interpolating")
padding_shape = (
- batch_size,
- (latents.shape[1] - 2),
- self.vae.config.latent_channels,
- height // self.vae_scale_factor_spatial,
- width // self.vae_scale_factor_spatial,
+ batch_size,
+ (latents.shape[1] - 2),
+ self.vae_latent_channels,
+ height // self.vae_scale_factor_spatial,
+ width // self.vae_scale_factor_spatial,
)
- latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae.dtype)
-
+ latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
image_cond_latents = torch.cat([image_cond_latents[:, 0, :, :, :].unsqueeze(1), latent_padding, image_cond_latents[:, -1, :, :, :].unsqueeze(1)], dim=1)
+ if self.transformer.config.patch_size_t is not None:
+ first_frame = image_cond_latents[:, : image_cond_latents.size(1) % self.transformer.config.patch_size_t, ...]
+ image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
+
logger.info(f"image cond latents shape: {image_cond_latents.shape}")
- else:
+ elif image_cond_latents.shape[1] == 1:
logger.info("Only one image conditioning frame received, img2vid")
if self.input_with_padding:
padding_shape = (
batch_size,
(latents.shape[1] - 1),
- self.vae.config.latent_channels,
+ self.vae_latent_channels,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
- latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae.dtype)
+ latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
image_cond_latents = torch.cat([image_cond_latents, latent_padding], dim=1)
+ # Select the first frame along the second dimension
+ if self.transformer.config.patch_size_t is not None:
+ first_frame = image_cond_latents[:, : image_cond_latents.size(1) % self.transformer.config.patch_size_t, ...]
+ image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
else:
image_cond_latents = image_cond_latents.repeat(1, latents.shape[1], 1, 1, 1)
+ else:
+ logger.info(f"Received {image_cond_latents.shape[1]} image conditioning frames")
+
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 6.5. Create rotary embeds if required
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
-
- # masks
- if self.original_mask is not None:
- mask = self.original_mask.to(device)
- logger.info(f"self.original_mask: {self.original_mask.shape}")
-
- mask = F.interpolate(self.original_mask.unsqueeze(1), size=(latents.shape[-2], latents.shape[-1]), mode='bilinear', align_corners=False)
- if mask.shape[0] != latents.shape[1]:
- mask = mask.unsqueeze(1).repeat(1, latents.shape[1], 16, 1, 1)
- else:
- mask = mask.unsqueeze(0).repeat(1, 1, 16, 1, 1)
- logger.info(f"latents: {latents.shape}")
- logger.info(f"mask: {mask.shape}")
-
- # 7. Denoising loop
+
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
- comfy_pbar = ProgressBar(num_inference_steps)
- # 8. context schedule and temporal tiling
- if context_schedule is not None and context_schedule == "temporal_tiling":
- t_tile_length = context_frames
- t_tile_overlap = context_overlap
- t_tile_weights = self._gaussian_weights(t_tile_length=t_tile_length, t_batch_size=1).to(latents.device).to(self.vae.dtype)
- use_temporal_tiling = True
- logger.info("Temporal tiling enabled")
- elif context_schedule is not None:
+ # 7. context schedule
+ if context_schedule is not None:
if image_cond_latents is not None:
raise NotImplementedError("Context schedule not currently supported with image conditioning")
logger.info(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap")
- use_temporal_tiling = False
use_context_schedule = True
- from .cogvideox_fun.context import get_context_scheduler
+ from .context import get_context_scheduler
context = get_context_scheduler(context_schedule)
+ #todo ofs embeds?
else:
- use_temporal_tiling = False
use_context_schedule = False
- logger.info("Temporal tiling and context schedule disabled")
- # 7. Create rotary embeds if required
+ logger.info("Context schedule disabled")
+ # 7.5. Create rotary embeds if required
image_rotary_emb = (
self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
if self.transformer.config.use_rotary_positional_embeddings
else None
)
+ # 7.6. Create ofs embeds if required
+ ofs_emb = None if self.transformer.config.ofs_embed_dim is None else latents.new_full((1,), fill_value=2.0)
+
if tora is not None and do_classifier_free_guidance:
video_flow_features = tora["video_flow_features"].repeat(1, 2, 1, 1, 1).contiguous()
- # 9. Controlnet
+ #8. Controlnet
if controlnet is not None:
self.controlnet = controlnet["control_model"].to(device)
if self.transformer.dtype == torch.float8_e4m3fn:
@@ -616,112 +566,26 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
if tora is not None:
trajectory_length = tora["video_flow_features"].shape[1]
logger.info(f"Tora trajectory length: {trajectory_length}")
- if trajectory_length != latents.shape[1]:
- raise ValueError(f"Tora trajectory length {trajectory_length} does not match inpaint_latents count {latents.shape[2]}")
+ #if trajectory_length != latents.shape[1]:
+ # raise ValueError(f"Tora trajectory length {trajectory_length} does not match inpaint_latents count {latents.shape[2]}")
for module in self.transformer.fuser_list:
for param in module.parameters():
param.data = param.data.to(device)
- # 10. Denoising loop
- with self.progress_bar(total=num_inference_steps) as progress_bar:
+ logger.info(f"Sampling {num_frames} frames in {latent_frames} latent frames at {width}x{height} with {num_inference_steps} inference steps")
+
+ from .latent_preview import prepare_callback
+ callback = prepare_callback(self.transformer, num_inference_steps)
+
+ # 9. Denoising loop
+ comfy_pbar = ProgressBar(len(timesteps))
+ with self.progress_bar(total=len(timesteps)) as progress_bar:
old_pred_original_sample = None # for DPM-solver++
for i, t in enumerate(timesteps):
if self.interrupt:
continue
- if use_temporal_tiling and isinstance(self.scheduler, CogVideoXDDIMScheduler):
- #temporal tiling code based on https://github.com/mayuelala/FollowYourEmoji/blob/main/models/video_pipeline.py
- # =====================================================
- grid_ts = 0
- cur_t = 0
- while cur_t < latents.shape[1]:
- cur_t = max(grid_ts * t_tile_length - t_tile_overlap * grid_ts, 0) + t_tile_length
- grid_ts += 1
-
- all_t = latents.shape[1]
- latents_all_list = []
- # =====================================================
-
- for t_i in range(grid_ts):
- if t_i < grid_ts - 1:
- ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
- if t_i == grid_ts - 1:
- ofs_t = all_t - t_tile_length
-
- input_start_t = ofs_t
- input_end_t = ofs_t + t_tile_length
-
- #latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
- #latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- image_rotary_emb = (
- self._prepare_rotary_positional_embeddings(height, width, t_tile_length, device)
- if self.transformer.config.use_rotary_positional_embeddings
- else None
- )
-
- latents_tile = latents[:, input_start_t:input_end_t,:, :, :]
- latent_model_input_tile = torch.cat([latents_tile] * 2) if do_classifier_free_guidance else latents_tile
- latent_model_input_tile = self.scheduler.scale_model_input(latent_model_input_tile, t)
-
- #t_input = t[None].to(device)
- t_input = t.expand(latent_model_input_tile.shape[0]) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-
- # predict noise model_output
- noise_pred = self.transformer(
- hidden_states=latent_model_input_tile,
- encoder_hidden_states=prompt_embeds,
- timestep=t_input,
- image_rotary_emb=image_rotary_emb,
- return_dict=False,
- )[0]
- noise_pred = noise_pred.float()
-
- if self.do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents_tile = self.scheduler.step(noise_pred, t, latents_tile.to(self.vae.dtype), **extra_step_kwargs, return_dict=False)[0]
- latents_all_list.append(latents_tile)
-
- # ==========================================
- latents_all = torch.zeros(latents.shape, device=latents.device, dtype=self.vae.dtype)
- contributors = torch.zeros(latents.shape, device=latents.device, dtype=self.vae.dtype)
- # Add each tile contribution to overall latents
- for t_i in range(grid_ts):
- if t_i < grid_ts - 1:
- ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
- if t_i == grid_ts - 1:
- ofs_t = all_t - t_tile_length
-
- input_start_t = ofs_t
- input_end_t = ofs_t + t_tile_length
-
- latents_all[:, input_start_t:input_end_t,:, :, :] += latents_all_list[t_i] * t_tile_weights
- contributors[:, input_start_t:input_end_t,:, :, :] += t_tile_weights
-
- latents_all /= contributors
-
- latents = latents_all
- #print("latents",latents.shape)
- # start diff diff
- if i < len(timesteps) - 1 and self.original_mask is not None:
- noise_timestep = timesteps[i + 1]
- image_latent = self.scheduler.add_noise(original_image_latents, noise, torch.tensor([noise_timestep])
- )
- mask = mask.to(latents)
- ts_from = timesteps[0]
- ts_to = timesteps[-1]
- threshold = (t - ts_to) / (ts_from - ts_to)
- mask = torch.where(mask >= threshold, mask, torch.zeros_like(mask))
- latents = image_latent * mask + latents * (1 - mask)
- # end diff diff
-
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- comfy_pbar.update(1)
- # ==========================================
- elif use_context_schedule:
+ # region context schedule sampling
+ if use_context_schedule:
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
counter = torch.zeros_like(latent_model_input)
@@ -819,7 +683,7 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
noise_pred /= counter
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond)
+ noise_pred = noise_pred_uncond + self._guidance_scale[i] * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler):
@@ -839,14 +703,26 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
comfy_pbar.update(1)
-
+
+ # region sampling
else:
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
if image_cond_latents is not None:
latent_image_input = torch.cat([image_cond_latents] * 2) if do_classifier_free_guidance else image_cond_latents
- latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
+ if fun_mask is not None: #for fun img2vid and interpolation
+ fun_inpaint_mask = torch.cat([fun_mask] * 2) if do_classifier_free_guidance else fun_mask
+ masks_input = torch.cat([fun_inpaint_mask, latent_image_input], dim=2)
+ latent_model_input = torch.cat([latent_model_input, masks_input], dim=2)
+ else:
+ latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
+ else: # for Fun inpaint vid2vid
+ if fun_mask is not None:
+ fun_inpaint_mask = torch.cat([fun_mask] * 2) if do_classifier_free_guidance else fun_mask
+ fun_inpaint_masked_video_latents = torch.cat([fun_masked_video_latents] * 2) if do_classifier_free_guidance else fun_masked_video_latents
+ fun_inpaint_latents = torch.cat([fun_inpaint_mask, fun_inpaint_masked_video_latents], dim=2).to(latents.dtype)
+ latent_model_input = torch.cat([latent_model_input, fun_inpaint_latents], dim=2)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latent_model_input.shape[0])
@@ -866,9 +742,9 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
return_dict=False,
)[0]
if isinstance(controlnet_states, (tuple, list)):
- controlnet_states = [x.to(dtype=self.vae.dtype) for x in controlnet_states]
+ controlnet_states = [x.to(dtype=self.vae_dtype) for x in controlnet_states]
else:
- controlnet_states = controlnet_states.to(dtype=self.vae.dtype)
+ controlnet_states = controlnet_states.to(dtype=self.vae_dtype)
# predict noise model_output
@@ -877,53 +753,43 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
+ ofs=ofs_emb,
return_dict=False,
controlnet_states=controlnet_states,
controlnet_weights=control_weights,
video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
-
)[0]
noise_pred = noise_pred.float()
-
if isinstance(self.scheduler, CogVideoXDPMScheduler):
- self._guidance_scale = 1 + guidance_scale * (
+ self._guidance_scale[i] = 1 + guidance_scale[i] * (
(1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
)
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond)
+ noise_pred = noise_pred_uncond + self._guidance_scale[i] * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler):
- latents = self.scheduler.step(noise_pred, t, latents.to(self.vae.dtype), **extra_step_kwargs, return_dict=False)[0]
+ latents = self.scheduler.step(noise_pred, t, latents.to(self.vae_dtype), **extra_step_kwargs, return_dict=False)[0]
else:
latents, old_pred_original_sample = self.scheduler.step(
noise_pred,
old_pred_original_sample,
t,
timesteps[i - 1] if i > 0 else None,
- latents.to(self.vae.dtype),
+ latents.to(self.vae_dtype),
**extra_step_kwargs,
return_dict=False,
)
latents = latents.to(prompt_embeds.dtype)
- # start diff diff
- if i < len(timesteps) - 1 and self.original_mask is not None:
- noise_timestep = timesteps[i + 1]
- image_latent = self.scheduler.add_noise(original_image_latents, noise, torch.tensor([noise_timestep])
- )
- mask = mask.to(latents)
- ts_from = timesteps[0]
- ts_to = timesteps[-1]
- threshold = (t - ts_to) / (ts_from - ts_to)
- mask = torch.where(mask >= threshold, mask, torch.zeros_like(mask))
- latents = image_latent * mask + latents * (1 - mask)
- # end diff diff
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
- comfy_pbar.update(1)
+ if callback is not None:
+ callback(i, latents.detach()[-1], None, num_inference_steps)
+ else:
+ comfy_pbar.update(1)
# Offload all models
diff --git a/pyproject.toml b/pyproject.toml
index 78b9ed8..1ca05f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,9 @@
[project]
name = "comfyui-cogvideoxwrapper"
description = "Diffusers wrapper for CogVideoX -models: [a/https://github.com/THUDM/CogVideo](https://github.com/THUDM/CogVideo)"
-version = "1.1.0"
+version = "1.5.0"
license = {file = "LICENSE"}
-dependencies = ["huggingface_hub", "diffusers>=0.30.1", "accelerate>=0.33.0"]
+dependencies = ["huggingface_hub", "diffusers>=0.31.0", "accelerate>=0.33.0"]
[project.urls]
Repository = "https://github.com/kijai/ComfyUI-CogVideoXWrapper"
diff --git a/readme.md b/readme.md
index 3639341..97c72c1 100644
--- a/readme.md
+++ b/readme.md
@@ -1,5 +1,32 @@
# WORK IN PROGRESS
+## BREAKING Update8
+
+This is big one, and unfortunately to do the necessary cleanup and refactoring this will break every old workflow as they are.
+I apologize for the inconvenience, if I don't do this now I'll keep making it worse until maintaining becomes too much of a chore, so from my pov there was no choice.
+
+*Please either use the new workflows or fix the nodes in your old ones before posting issue reports!*
+
+Old version will be kept in a legacy branch, but not maintained
+
+- Support CogVideoX 1.5 models
+- Major code cleanup (it was bad, still isn't great, wip)
+- Merge Fun -model functionality into main pipeline:
+ - All Fun specific nodes, besides image encode node for Fun -InP models are gone
+ - Main CogVideo Sampler works with Fun models
+ - DimensionX LoRAs now work with Fun models as well
+
+- Remove width/height from the sampler widgets and detect from input instead, this meanst text2vid now requires using empty latents
+- Separate VAE from the model, allow using fp32 VAE
+- Add ability to load some of the non-GGUF models as single files (only few available for now: https://huggingface.co/Kijai/CogVideoX-comfy)
+- Add some torchao quantizations as options
+- Add interpolation as option for the main encode node, old interpolation specific node is gone
+- torch.compile optimizations
+- Remove PAB in favor of FasterCache and cleaner code
+- other smaller things I forgot about at this point
+
+For Fun -model based workflows it's more drastic change, for others migrating generally means re-setting many of the nodes.
+
## Update7
- Refactored the Fun version's sampler to accept any resolution, this should make it lot simpler to use with Tora. **BREAKS OLD WORKFLOWS**, old FunSampler nodes need to be remade.
diff --git a/requirements.txt b/requirements.txt
index c0ac679..8ab8109 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ diffusers>=0.31.0
accelerate>=0.33.0
einops
peft
+opencv-python
\ No newline at end of file
diff --git a/utils.py b/utils.py
index e3c6fd4..d667097 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,5 @@
import importlib.metadata
-
+import torch
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
log = logging.getLogger(__name__)
@@ -19,4 +19,12 @@ def remove_specific_blocks(model, block_indices_to_remove):
new_blocks = [block for i, block in enumerate(transformer_blocks) if i not in block_indices_to_remove]
model.transformer_blocks = nn.ModuleList(new_blocks)
- return model
\ No newline at end of file
+ return model
+
+def print_memory(device):
+ memory = torch.cuda.memory_allocated(device) / 1024**3
+ max_memory = torch.cuda.max_memory_allocated(device) / 1024**3
+ max_reserved = torch.cuda.max_memory_reserved(device) / 1024**3
+ log.info(f"Allocated memory: {memory=:.3f} GB")
+ log.info(f"Max allocated memory: {max_memory=:.3f} GB")
+ log.info(f"Max reserved memory: {max_reserved=:.3f} GB")
\ No newline at end of file
diff --git a/videosys/cogvideox_transformer_3d.py b/videosys/cogvideox_transformer_3d.py
deleted file mode 100644
index 26550a2..0000000
--- a/videosys/cogvideox_transformer_3d.py
+++ /dev/null
@@ -1,621 +0,0 @@
-# Adapted from CogVideo
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# References:
-# CogVideo: https://github.com/THUDM/CogVideo
-# diffusers: https://github.com/huggingface/diffusers
-# --------------------------------------------------------
-
-from typing import Any, Dict, Optional, Tuple, Union
-from einops import rearrange
-import torch
-import torch.nn.functional as F
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.attention import Attention, FeedForward
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed, CogVideoXPatchEmbed
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils import is_torch_version
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from torch import nn
-
-from .core.pab_mgr import enable_pab, if_broadcast_spatial
-from .modules.embeddings import apply_rotary_emb
-
-#from .modules.embeddings import CogVideoXPatchEmbed
-
-from .modules.normalization import AdaLayerNorm, CogVideoXLayerNormZero
-try:
- from sageattention import sageattn
- SAGEATTN_IS_AVAVILABLE = True
-except:
- SAGEATTN_IS_AVAVILABLE = False
-
-class CogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
- @torch.compiler.disable()
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- query = attn.to_q(hidden_states)
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
-
- attn_heads = attn.heads
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn_heads
-
- query = query.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- emb_len = image_rotary_emb[0].shape[0]
- query[:, :, text_seq_length : emb_len + text_seq_length] = apply_rotary_emb(
- query[:, :, text_seq_length : emb_len + text_seq_length], image_rotary_emb
- )
- if not attn.is_cross_attention:
- key[:, :, text_seq_length : emb_len + text_seq_length] = apply_rotary_emb(
- key[:, :, text_seq_length : emb_len + text_seq_length], image_rotary_emb
- )
-
- if SAGEATTN_IS_AVAVILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn_heads * head_dim)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
-
-class FusedCogVideoXAttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
- query and key vectors, but does not include spatial normalization.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
- @torch.compiler.disable()
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- qkv = attn.to_qkv(hidden_states)
- split_size = qkv.shape[-1] // 3
- query, key, value = torch.split(qkv, split_size, dim=-1)
-
- inner_dim = key.shape[-1]
- head_dim = inner_dim // attn.heads
-
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- if attn.norm_q is not None:
- query = attn.norm_q(query)
- if attn.norm_k is not None:
- key = attn.norm_k(key)
-
- # Apply RoPE if needed
- if image_rotary_emb is not None:
- query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
- if not attn.is_cross_attention:
- key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
- if SAGEATTN_IS_AVAVILABLE:
- hidden_states = sageattn(query, key, value, is_causal=False)
- else:
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- encoder_hidden_states, hidden_states = hidden_states.split(
- [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
- )
- return hidden_states, encoder_hidden_states
-
-
-@maybe_allow_in_graph
-class CogVideoXBlock(nn.Module):
- r"""
- Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
-
- Parameters:
- dim (`int`):
- The number of channels in the input and output.
- num_attention_heads (`int`):
- The number of heads to use for multi-head attention.
- attention_head_dim (`int`):
- The number of channels in each head.
- time_embed_dim (`int`):
- The number of channels in timestep embedding.
- dropout (`float`, defaults to `0.0`):
- The dropout probability to use.
- activation_fn (`str`, defaults to `"gelu-approximate"`):
- Activation function to be used in feed-forward.
- attention_bias (`bool`, defaults to `False`):
- Whether or not to use bias in attention projection layers.
- qk_norm (`bool`, defaults to `True`):
- Whether or not to use normalization after query and key projections in Attention.
- norm_elementwise_affine (`bool`, defaults to `True`):
- Whether to use learnable elementwise affine parameters for normalization.
- norm_eps (`float`, defaults to `1e-5`):
- Epsilon value for normalization layers.
- final_dropout (`bool` defaults to `False`):
- Whether to apply a final dropout after the last feed-forward layer.
- ff_inner_dim (`int`, *optional*, defaults to `None`):
- Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
- ff_bias (`bool`, defaults to `True`):
- Whether or not to use bias in Feed-forward layer.
- attention_out_bias (`bool`, defaults to `True`):
- Whether or not to use bias in Attention output projection layer.
- """
-
- def __init__(
- self,
- dim: int,
- num_attention_heads: int,
- attention_head_dim: int,
- time_embed_dim: int,
- dropout: float = 0.0,
- activation_fn: str = "gelu-approximate",
- attention_bias: bool = False,
- qk_norm: bool = True,
- norm_elementwise_affine: bool = True,
- norm_eps: float = 1e-5,
- final_dropout: bool = True,
- ff_inner_dim: Optional[int] = None,
- ff_bias: bool = True,
- attention_out_bias: bool = True,
- block_idx: int = 0,
- ):
- super().__init__()
-
- # 1. Self Attention
- self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
- self.attn1 = Attention(
- query_dim=dim,
- dim_head=attention_head_dim,
- heads=num_attention_heads,
- qk_norm="layer_norm" if qk_norm else None,
- eps=1e-6,
- bias=attention_bias,
- out_bias=attention_out_bias,
- processor=CogVideoXAttnProcessor2_0(),
- )
-
- # parallel
- #self.attn1.parallel_manager = None
-
- # 2. Feed Forward
- self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
-
- self.ff = FeedForward(
- dim,
- dropout=dropout,
- activation_fn=activation_fn,
- final_dropout=final_dropout,
- inner_dim=ff_inner_dim,
- bias=ff_bias,
- )
-
- # pab
- self.attn_count = 0
- self.last_attn = None
- self.block_idx = block_idx
- #@torch.compiler.disable()
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- temb: torch.Tensor,
- image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- timestep=None,
- video_flow_feature: Optional[torch.Tensor] = None,
- fuser=None,
- ) -> torch.Tensor:
- text_seq_length = encoder_hidden_states.size(1)
-
- # norm & modulate
- norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
- hidden_states, encoder_hidden_states, temb
- )
- # Tora Motion-guidance Fuser
- if video_flow_feature is not None:
- H, W = video_flow_feature.shape[-2:]
- T = norm_hidden_states.shape[1] // H // W
- h = rearrange(norm_hidden_states, "B (T H W) C -> (B T) C H W", H=H, W=W)
- h = fuser(h, video_flow_feature.to(h), T=T)
- norm_hidden_states = rearrange(h, "(B T) C H W -> B (T H W) C", T=T)
- del h, fuser
- # attention
- if enable_pab():
- broadcast_attn, self.attn_count = if_broadcast_spatial(int(timestep[0]), self.attn_count, self.block_idx)
- if enable_pab() and broadcast_attn:
- attn_hidden_states, attn_encoder_hidden_states = self.last_attn
- else:
- attn_hidden_states, attn_encoder_hidden_states = self.attn1(
- hidden_states=norm_hidden_states,
- encoder_hidden_states=norm_encoder_hidden_states,
- image_rotary_emb=image_rotary_emb,
- )
- if enable_pab():
- self.last_attn = (attn_hidden_states, attn_encoder_hidden_states)
-
- hidden_states = hidden_states + gate_msa * attn_hidden_states
- encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
-
- # norm & modulate
- norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
- hidden_states, encoder_hidden_states, temb
- )
-
- # feed-forward
- norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
- ff_output = self.ff(norm_hidden_states)
-
- hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
- encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
-
- return hidden_states, encoder_hidden_states
-
-
-class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
- """
- A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
-
- Parameters:
- num_attention_heads (`int`, defaults to `30`):
- The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `64`):
- The number of channels in each head.
- in_channels (`int`, defaults to `16`):
- The number of channels in the input.
- out_channels (`int`, *optional*, defaults to `16`):
- The number of channels in the output.
- flip_sin_to_cos (`bool`, defaults to `True`):
- Whether to flip the sin to cos in the time embedding.
- time_embed_dim (`int`, defaults to `512`):
- Output dimension of timestep embeddings.
- text_embed_dim (`int`, defaults to `4096`):
- Input dimension of text embeddings from the text encoder.
- num_layers (`int`, defaults to `30`):
- The number of layers of Transformer blocks to use.
- dropout (`float`, defaults to `0.0`):
- The dropout probability to use.
- attention_bias (`bool`, defaults to `True`):
- Whether or not to use bias in the attention projection layers.
- sample_width (`int`, defaults to `90`):
- The width of the input latents.
- sample_height (`int`, defaults to `60`):
- The height of the input latents.
- sample_frames (`int`, defaults to `49`):
- The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
- instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
- but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
- K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
- patch_size (`int`, defaults to `2`):
- The size of the patches to use in the patch embedding layer.
- temporal_compression_ratio (`int`, defaults to `4`):
- The compression ratio across the temporal dimension. See documentation for `sample_frames`.
- max_text_seq_length (`int`, defaults to `226`):
- The maximum sequence length of the input text embeddings.
- activation_fn (`str`, defaults to `"gelu-approximate"`):
- Activation function to use in feed-forward.
- timestep_activation_fn (`str`, defaults to `"silu"`):
- Activation function to use when generating the timestep embeddings.
- norm_elementwise_affine (`bool`, defaults to `True`):
- Whether or not to use elementwise affine in normalization layers.
- norm_eps (`float`, defaults to `1e-5`):
- The epsilon value to use in normalization layers.
- spatial_interpolation_scale (`float`, defaults to `1.875`):
- Scaling factor to apply in 3D positional embeddings across spatial dimensions.
- temporal_interpolation_scale (`float`, defaults to `1.0`):
- Scaling factor to apply in 3D positional embeddings across temporal dimensions.
- """
-
- _supports_gradient_checkpointing = True
-
- @register_to_config
- def __init__(
- self,
- num_attention_heads: int = 30,
- attention_head_dim: int = 64,
- in_channels: int = 16,
- out_channels: Optional[int] = 16,
- flip_sin_to_cos: bool = True,
- freq_shift: int = 0,
- time_embed_dim: int = 512,
- text_embed_dim: int = 4096,
- num_layers: int = 30,
- dropout: float = 0.0,
- attention_bias: bool = True,
- sample_width: int = 90,
- sample_height: int = 60,
- sample_frames: int = 49,
- patch_size: int = 2,
- temporal_compression_ratio: int = 4,
- max_text_seq_length: int = 226,
- activation_fn: str = "gelu-approximate",
- timestep_activation_fn: str = "silu",
- norm_elementwise_affine: bool = True,
- norm_eps: float = 1e-5,
- spatial_interpolation_scale: float = 1.875,
- temporal_interpolation_scale: float = 1.0,
- use_rotary_positional_embeddings: bool = False,
- use_learned_positional_embeddings: bool = False,
- ):
- super().__init__()
- inner_dim = num_attention_heads * attention_head_dim
-
- post_patch_height = sample_height // patch_size
- post_patch_width = sample_width // patch_size
- post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1
- self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames
-
- # 1. Patch embedding
- self.patch_embed = CogVideoXPatchEmbed(
- patch_size=patch_size,
- in_channels=in_channels,
- embed_dim=inner_dim,
- text_embed_dim=text_embed_dim,
- bias=True,
- sample_width=sample_width,
- sample_height=sample_height,
- sample_frames=sample_frames,
- temporal_compression_ratio=temporal_compression_ratio,
- max_text_seq_length=max_text_seq_length,
- spatial_interpolation_scale=spatial_interpolation_scale,
- temporal_interpolation_scale=temporal_interpolation_scale,
- use_positional_embeddings=not use_rotary_positional_embeddings,
- use_learned_positional_embeddings=use_learned_positional_embeddings,
- )
- self.embedding_dropout = nn.Dropout(dropout)
-
- # 2. 3D positional embeddings
- spatial_pos_embedding = get_3d_sincos_pos_embed(
- inner_dim,
- (post_patch_width, post_patch_height),
- post_time_compression_frames,
- spatial_interpolation_scale,
- temporal_interpolation_scale,
- )
- spatial_pos_embedding = torch.from_numpy(spatial_pos_embedding).flatten(0, 1)
- pos_embedding = torch.zeros(1, max_text_seq_length + self.num_patches, inner_dim, requires_grad=False)
- pos_embedding.data[:, max_text_seq_length:].copy_(spatial_pos_embedding)
- self.register_buffer("pos_embedding", pos_embedding, persistent=False)
-
- # 3. Time embeddings
- self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
- self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
-
- # 4. Define spatio-temporal transformers blocks
- self.transformer_blocks = nn.ModuleList(
- [
- CogVideoXBlock(
- dim=inner_dim,
- num_attention_heads=num_attention_heads,
- attention_head_dim=attention_head_dim,
- time_embed_dim=time_embed_dim,
- dropout=dropout,
- activation_fn=activation_fn,
- attention_bias=attention_bias,
- norm_elementwise_affine=norm_elementwise_affine,
- norm_eps=norm_eps,
- )
- for _ in range(num_layers)
- ]
- )
- self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
-
- # 5. Output blocks
- self.norm_out = AdaLayerNorm(
- embedding_dim=time_embed_dim,
- output_dim=2 * inner_dim,
- norm_elementwise_affine=norm_elementwise_affine,
- norm_eps=norm_eps,
- chunk_dim=1,
- )
- self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
-
- self.gradient_checkpointing = False
-
- self.fuser_list = None
-
- # parallel
- #self.parallel_manager = None
-
- # def enable_parallel(self, dp_size, sp_size, enable_cp):
- # # update cfg parallel
- # if enable_cp and sp_size % 2 == 0:
- # sp_size = sp_size // 2
- # cp_size = 2
- # else:
- # cp_size = 1
-
- # self.parallel_manager: ParallelManager = ParallelManager(dp_size, cp_size, sp_size)
-
- # for _, module in self.named_modules():
- # if hasattr(module, "parallel_manager"):
- # module.parallel_manager = self.parallel_manager
-
- def _set_gradient_checkpointing(self, module, value=False):
- self.gradient_checkpointing = value
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- timestep: Union[int, float, torch.LongTensor],
- timestep_cond: Optional[torch.Tensor] = None,
- image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
- return_dict: bool = True,
- controlnet_states: torch.Tensor = None,
- controlnet_weights: Optional[Union[float, int, list, torch.FloatTensor]] = 1.0,
- video_flow_features: Optional[torch.Tensor] = None,
- ):
- # if self.parallel_manager.cp_size > 1:
- # (
- # hidden_states,
- # encoder_hidden_states,
- # timestep,
- # timestep_cond,
- # image_rotary_emb,
- # ) = batch_func(
- # partial(split_sequence, process_group=self.parallel_manager.cp_group, dim=0),
- # hidden_states,
- # encoder_hidden_states,
- # timestep,
- # timestep_cond,
- # image_rotary_emb,
- # )
-
- batch_size, num_frames, channels, height, width = hidden_states.shape
-
- # 1. Time embedding
- timesteps = timestep
- t_emb = self.time_proj(timesteps)
-
- # timesteps does not contain any weights and will always return f32 tensors
- # but time_embedding might actually be running in fp16. so we need to cast here.
- # there might be better ways to encapsulate this.
- t_emb = t_emb.to(dtype=hidden_states.dtype)
- emb = self.time_embedding(t_emb, timestep_cond)
-
- # 2. Patch embedding
- hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
-
- # 3. Position embedding
- text_seq_length = encoder_hidden_states.shape[1]
- if not self.config.use_rotary_positional_embeddings:
- seq_length = height * width * num_frames // (self.config.patch_size**2)
-
- pos_embeds = self.pos_embedding[:, : text_seq_length + seq_length]
- hidden_states = hidden_states + pos_embeds
- hidden_states = self.embedding_dropout(hidden_states)
-
- encoder_hidden_states = hidden_states[:, :text_seq_length]
- hidden_states = hidden_states[:, text_seq_length:]
-
- # if self.parallel_manager.sp_size > 1:
- # set_pad("pad", hidden_states.shape[1], self.parallel_manager.sp_group)
- # hidden_states = split_sequence(hidden_states, self.parallel_manager.sp_group, dim=1, pad=get_pad("pad"))
-
- # 4. Transformer blocks
- for i, block in enumerate(self.transformer_blocks):
- hidden_states, encoder_hidden_states = block(
- hidden_states=hidden_states,
- encoder_hidden_states=encoder_hidden_states,
- temb=emb,
- image_rotary_emb=image_rotary_emb,
- timestep=timesteps if enable_pab() else None,
- video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
- fuser = self.fuser_list[i] if self.fuser_list is not None else None,
- )
- if (controlnet_states is not None) and (i < len(controlnet_states)):
- controlnet_states_block = controlnet_states[i]
- controlnet_block_weight = 1.0
- if isinstance(controlnet_weights, (list)) or torch.is_tensor(controlnet_weights):
- controlnet_block_weight = controlnet_weights[i]
- elif isinstance(controlnet_weights, (float, int)):
- controlnet_block_weight = controlnet_weights
-
- hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
-
- #if self.parallel_manager.sp_size > 1:
- # hidden_states = gather_sequence(hidden_states, self.parallel_manager.sp_group, dim=1, pad=get_pad("pad"))
-
- if not self.config.use_rotary_positional_embeddings:
- # CogVideoX-2B
- hidden_states = self.norm_final(hidden_states)
- else:
- # CogVideoX-5B
- hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
- hidden_states = self.norm_final(hidden_states)
- hidden_states = hidden_states[:, text_seq_length:]
-
- # 5. Final block
- hidden_states = self.norm_out(hidden_states, temb=emb)
- hidden_states = self.proj_out(hidden_states)
-
- # 6. Unpatchify
- p = self.config.patch_size
- output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
- output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
-
- #if self.parallel_manager.cp_size > 1:
- # output = gather_sequence(output, self.parallel_manager.cp_group, dim=0)
-
- if not return_dict:
- return (output,)
- return Transformer2DModelOutput(sample=output)
diff --git a/videosys/core/__init__.py b/videosys/core/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/videosys/core/pab_mgr.py b/videosys/core/pab_mgr.py
deleted file mode 100644
index 6f19a50..0000000
--- a/videosys/core/pab_mgr.py
+++ /dev/null
@@ -1,232 +0,0 @@
-
-PAB_MANAGER = None
-
-
-class PABConfig:
- def __init__(
- self,
- steps: int,
- cross_broadcast: bool = False,
- cross_threshold: list = None,
- cross_range: int = None,
- spatial_broadcast: bool = False,
- spatial_threshold: list = None,
- spatial_range: int = None,
- temporal_broadcast: bool = False,
- temporal_threshold: list = None,
- temporal_range: int = None,
- mlp_broadcast: bool = False,
- mlp_spatial_broadcast_config: dict = None,
- mlp_temporal_broadcast_config: dict = None,
- ):
- self.steps = steps
-
- self.cross_broadcast = cross_broadcast
- self.cross_threshold = cross_threshold
- self.cross_range = cross_range
-
- self.spatial_broadcast = spatial_broadcast
- self.spatial_threshold = spatial_threshold
- self.spatial_range = spatial_range
-
- self.temporal_broadcast = temporal_broadcast
- self.temporal_threshold = temporal_threshold
- self.temporal_range = temporal_range
-
- self.mlp_broadcast = mlp_broadcast
- self.mlp_spatial_broadcast_config = mlp_spatial_broadcast_config
- self.mlp_temporal_broadcast_config = mlp_temporal_broadcast_config
- self.mlp_temporal_outputs = {}
- self.mlp_spatial_outputs = {}
-
-
-class PABManager:
- def __init__(self, config: PABConfig):
- self.config: PABConfig = config
-
- init_prompt = f"Init Pyramid Attention Broadcast. steps: {config.steps}."
- init_prompt += f" spatial broadcast: {config.spatial_broadcast}, spatial range: {config.spatial_range}, spatial threshold: {config.spatial_threshold}."
- init_prompt += f" temporal broadcast: {config.temporal_broadcast}, temporal range: {config.temporal_range}, temporal_threshold: {config.temporal_threshold}."
- init_prompt += f" cross broadcast: {config.cross_broadcast}, cross range: {config.cross_range}, cross threshold: {config.cross_threshold}."
- init_prompt += f" mlp broadcast: {config.mlp_broadcast}."
- print(init_prompt)
-
- def if_broadcast_cross(self, timestep: int, count: int):
- if (
- self.config.cross_broadcast
- and (timestep is not None)
- and (count % self.config.cross_range != 0)
- and (self.config.cross_threshold[0] < timestep < self.config.cross_threshold[1])
- ):
- flag = True
- else:
- flag = False
- count = (count + 1) % self.config.steps
- return flag, count
-
- def if_broadcast_temporal(self, timestep: int, count: int):
- if (
- self.config.temporal_broadcast
- and (timestep is not None)
- and (count % self.config.temporal_range != 0)
- and (self.config.temporal_threshold[0] < timestep < self.config.temporal_threshold[1])
- ):
- flag = True
- else:
- flag = False
- count = (count + 1) % self.config.steps
- return flag, count
-
- def if_broadcast_spatial(self, timestep: int, count: int, block_idx: int):
- if (
- self.config.spatial_broadcast
- and (timestep is not None)
- and (count % self.config.spatial_range != 0)
- and (self.config.spatial_threshold[0] < timestep < self.config.spatial_threshold[1])
- ):
- flag = True
- else:
- flag = False
- count = (count + 1) % self.config.steps
- return flag, count
-
- @staticmethod
- def _is_t_in_skip_config(all_timesteps, timestep, config):
- is_t_in_skip_config = False
- skip_range = None
- for key in config:
- if key not in all_timesteps:
- continue
- index = all_timesteps.index(key)
- skip_range = all_timesteps[index : index + 1 + int(config[key]["skip_count"])]
- if timestep in skip_range:
- is_t_in_skip_config = True
- skip_range = [all_timesteps[index], all_timesteps[index + int(config[key]["skip_count"])]]
- break
- return is_t_in_skip_config, skip_range
-
- def if_skip_mlp(self, timestep: int, count: int, block_idx: int, all_timesteps, is_temporal=False):
- if not self.config.mlp_broadcast:
- return False, None, False, None
-
- if is_temporal:
- cur_config = self.config.mlp_temporal_broadcast_config
- else:
- cur_config = self.config.mlp_spatial_broadcast_config
-
- is_t_in_skip_config, skip_range = self._is_t_in_skip_config(all_timesteps, timestep, cur_config)
- next_flag = False
- if (
- self.config.mlp_broadcast
- and (timestep is not None)
- and (timestep in cur_config)
- and (block_idx in cur_config[timestep]["block"])
- ):
- flag = False
- next_flag = True
- count = count + 1
- elif (
- self.config.mlp_broadcast
- and (timestep is not None)
- and (is_t_in_skip_config)
- and (block_idx in cur_config[skip_range[0]]["block"])
- ):
- flag = True
- count = 0
- else:
- flag = False
-
- return flag, count, next_flag, skip_range
-
- def save_skip_output(self, timestep, block_idx, ff_output, is_temporal=False):
- if is_temporal:
- self.config.mlp_temporal_outputs[(timestep, block_idx)] = ff_output
- else:
- self.config.mlp_spatial_outputs[(timestep, block_idx)] = ff_output
-
- def get_mlp_output(self, skip_range, timestep, block_idx, is_temporal=False):
- skip_start_t = skip_range[0]
- if is_temporal:
- skip_output = (
- self.config.mlp_temporal_outputs.get((skip_start_t, block_idx), None)
- if self.config.mlp_temporal_outputs is not None
- else None
- )
- else:
- skip_output = (
- self.config.mlp_spatial_outputs.get((skip_start_t, block_idx), None)
- if self.config.mlp_spatial_outputs is not None
- else None
- )
-
- if skip_output is not None:
- if timestep == skip_range[-1]:
- # TODO: save memory
- if is_temporal:
- del self.config.mlp_temporal_outputs[(skip_start_t, block_idx)]
- else:
- del self.config.mlp_spatial_outputs[(skip_start_t, block_idx)]
- else:
- raise ValueError(
- f"No stored MLP output found | t {timestep} |[{skip_range[0]}, {skip_range[-1]}] | block {block_idx}"
- )
-
- return skip_output
-
- def get_spatial_mlp_outputs(self):
- return self.config.mlp_spatial_outputs
-
- def get_temporal_mlp_outputs(self):
- return self.config.mlp_temporal_outputs
-
-
-def set_pab_manager(config: PABConfig):
- global PAB_MANAGER
- PAB_MANAGER = PABManager(config)
-
-
-def enable_pab():
- if PAB_MANAGER is None:
- return False
- return (
- PAB_MANAGER.config.cross_broadcast
- or PAB_MANAGER.config.spatial_broadcast
- or PAB_MANAGER.config.temporal_broadcast
- )
-
-
-def update_steps(steps: int):
- if PAB_MANAGER is not None:
- PAB_MANAGER.config.steps = steps
-
-
-def if_broadcast_cross(timestep: int, count: int):
- if not enable_pab():
- return False, count
- return PAB_MANAGER.if_broadcast_cross(timestep, count)
-
-
-def if_broadcast_temporal(timestep: int, count: int):
- if not enable_pab():
- return False, count
- return PAB_MANAGER.if_broadcast_temporal(timestep, count)
-
-
-def if_broadcast_spatial(timestep: int, count: int, block_idx: int):
- if not enable_pab():
- return False, count
- return PAB_MANAGER.if_broadcast_spatial(timestep, count, block_idx)
-
-
-def if_broadcast_mlp(timestep: int, count: int, block_idx: int, all_timesteps, is_temporal=False):
- if not enable_pab():
- return False, count
- return PAB_MANAGER.if_skip_mlp(timestep, count, block_idx, all_timesteps, is_temporal)
-
-
-def save_mlp_output(timestep: int, block_idx: int, ff_output, is_temporal=False):
- return PAB_MANAGER.save_skip_output(timestep, block_idx, ff_output, is_temporal)
-
-
-def get_mlp_output(skip_range, timestep, block_idx: int, is_temporal=False):
- return PAB_MANAGER.get_mlp_output(skip_range, timestep, block_idx, is_temporal)
diff --git a/videosys/core/pipeline.py b/videosys/core/pipeline.py
deleted file mode 100644
index 3244749..0000000
--- a/videosys/core/pipeline.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import inspect
-from abc import abstractmethod
-
-import torch
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-
-class VideoSysPipeline(DiffusionPipeline):
- def __init__(self):
- super().__init__()
-
- @staticmethod
- def set_eval_and_device(device: torch.device, *modules):
- for module in modules:
- module.eval()
- module.to(device)
-
- @abstractmethod
- def generate(self, *args, **kwargs):
- pass
-
- def __call__(self, *args, **kwargs):
- """
- In diffusers, it is a convention to call the pipeline object.
- But in VideoSys, we will use the generate method for better prompt.
- This is a wrapper for the generate method to support the diffusers usage.
- """
- return self.generate(*args, **kwargs)
-
- @classmethod
- def _get_signature_keys(cls, obj):
- parameters = inspect.signature(obj.__init__).parameters
- required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
- optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
- expected_modules = set(required_parameters.keys()) - {"self"}
- # modify: remove the config module from the expected modules
- expected_modules = expected_modules - {"config"}
-
- optional_names = list(optional_parameters)
- for name in optional_names:
- if name in cls._optional_components:
- expected_modules.add(name)
- optional_parameters.remove(name)
-
- return expected_modules, optional_parameters
diff --git a/videosys/modules/__init__.py b/videosys/modules/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/videosys/modules/activations.py b/videosys/modules/activations.py
deleted file mode 100644
index cf24149..0000000
--- a/videosys/modules/activations.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import torch.nn as nn
-
-approx_gelu = lambda: nn.GELU(approximate="tanh")
diff --git a/videosys/modules/downsampling.py b/videosys/modules/downsampling.py
deleted file mode 100644
index 9455a32..0000000
--- a/videosys/modules/downsampling.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class CogVideoXDownsample3D(nn.Module):
- # Todo: Wait for paper relase.
- r"""
- A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI
-
- Args:
- in_channels (`int`):
- Number of channels in the input image.
- out_channels (`int`):
- Number of channels produced by the convolution.
- kernel_size (`int`, defaults to `3`):
- Size of the convolving kernel.
- stride (`int`, defaults to `2`):
- Stride of the convolution.
- padding (`int`, defaults to `0`):
- Padding added to all four sides of the input.
- compress_time (`bool`, defaults to `False`):
- Whether or not to compress the time dimension.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int = 3,
- stride: int = 2,
- padding: int = 0,
- compress_time: bool = False,
- ):
- super().__init__()
-
- self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
- self.compress_time = compress_time
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- if self.compress_time:
- batch_size, channels, frames, height, width = x.shape
-
- # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames)
- x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames)
-
- if x.shape[-1] % 2 == 1:
- x_first, x_rest = x[..., 0], x[..., 1:]
- if x_rest.shape[-1] > 0:
- # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
- x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2)
-
- x = torch.cat([x_first[..., None], x_rest], dim=-1)
- # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width)
- x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
- else:
- # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2)
- x = F.avg_pool1d(x, kernel_size=2, stride=2)
- # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
- x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
-
- # Pad the tensor
- pad = (0, 1, 0, 1)
- x = F.pad(x, pad, mode="constant", value=0)
- batch_size, channels, frames, height, width = x.shape
- # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
- x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width)
- x = self.conv(x)
- # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width)
- x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
- return x
diff --git a/videosys/modules/embeddings.py b/videosys/modules/embeddings.py
deleted file mode 100644
index 04eba82..0000000
--- a/videosys/modules/embeddings.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import math
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from einops import rearrange
-
-class CogVideoXPatchEmbed(nn.Module):
- def __init__(
- self,
- patch_size: int = 2,
- in_channels: int = 16,
- embed_dim: int = 1920,
- text_embed_dim: int = 4096,
- bias: bool = True,
- ) -> None:
- super().__init__()
- self.patch_size = patch_size
-
- self.proj = nn.Conv2d(
- in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
- )
- self.text_proj = nn.Linear(text_embed_dim, embed_dim)
-
- def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
- r"""
- Args:
- text_embeds (`torch.Tensor`):
- Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
- image_embeds (`torch.Tensor`):
- Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
- """
- text_embeds = self.text_proj(text_embeds)
-
- batch, num_frames, channels, height, width = image_embeds.shape
- image_embeds = image_embeds.reshape(-1, channels, height, width)
- image_embeds = self.proj(image_embeds)
- image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
- image_embeds = image_embeds.flatten(3).transpose(2, 3) # [batch, num_frames, height x width, channels]
- image_embeds = image_embeds.flatten(1, 2) # [batch, num_frames x height x width, channels]
-
- embeds = torch.cat(
- [text_embeds, image_embeds], dim=1
- ).contiguous() # [batch, seq_length + num_frames x height x width, channels]
- return embeds
-
-
-class OpenSoraPatchEmbed3D(nn.Module):
- """Video to Patch Embedding.
-
- Args:
- patch_size (int): Patch token size. Default: (2,4,4).
- in_chans (int): Number of input video channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(
- self,
- patch_size=(2, 4, 4),
- in_chans=3,
- embed_dim=96,
- norm_layer=None,
- flatten=True,
- ):
- super().__init__()
- self.patch_size = patch_size
- self.flatten = flatten
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- """Forward function."""
- # padding
- _, _, D, H, W = x.size()
- if W % self.patch_size[2] != 0:
- x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
- if H % self.patch_size[1] != 0:
- x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
- if D % self.patch_size[0] != 0:
- x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))
-
- x = self.proj(x) # (B C T H W)
- if self.norm is not None:
- D, Wh, Ww = x.size(2), x.size(3), x.size(4)
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)
- if self.flatten:
- x = x.flatten(2).transpose(1, 2) # BCTHW -> BNC
- return x
-
-
-class TimestepEmbedder(nn.Module):
- """
- Embeds scalar timesteps into vector representations.
- """
-
- def __init__(self, hidden_size, frequency_embedding_size=256):
- super().__init__()
- self.mlp = nn.Sequential(
- nn.Linear(frequency_embedding_size, hidden_size, bias=True),
- nn.SiLU(),
- nn.Linear(hidden_size, hidden_size, bias=True),
- )
- self.frequency_embedding_size = frequency_embedding_size
-
- @staticmethod
- def timestep_embedding(t, dim, max_period=10000):
- """
- Create sinusoidal timestep embeddings.
- :param t: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- :param dim: the dimension of the output.
- :param max_period: controls the minimum frequency of the embeddings.
- :return: an (N, D) Tensor of positional embeddings.
- """
- # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
- half = dim // 2
- freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half)
- freqs = freqs.to(device=t.device)
- args = t[:, None].float() * freqs[None]
- embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
- if dim % 2:
- embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
- return embedding
-
- def forward(self, t, dtype):
- t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
- if t_freq.dtype != dtype:
- t_freq = t_freq.to(dtype)
- t_emb = self.mlp(t_freq)
- return t_emb
-
-
-class SizeEmbedder(TimestepEmbedder):
- """
- Embeds scalar timesteps into vector representations.
- """
-
- def __init__(self, hidden_size, frequency_embedding_size=256):
- super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size)
- self.mlp = nn.Sequential(
- nn.Linear(frequency_embedding_size, hidden_size, bias=True),
- nn.SiLU(),
- nn.Linear(hidden_size, hidden_size, bias=True),
- )
- self.frequency_embedding_size = frequency_embedding_size
- self.outdim = hidden_size
-
- def forward(self, s, bs):
- if s.ndim == 1:
- s = s[:, None]
- assert s.ndim == 2
- if s.shape[0] != bs:
- s = s.repeat(bs // s.shape[0], 1)
- assert s.shape[0] == bs
- b, dims = s.shape[0], s.shape[1]
- s = rearrange(s, "b d -> (b d)")
- s_freq = self.timestep_embedding(s, self.frequency_embedding_size).to(self.dtype)
- s_emb = self.mlp(s_freq)
- s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
- return s_emb
-
- @property
- def dtype(self):
- return next(self.parameters()).dtype
-
-def get_3d_rotary_pos_embed(
- embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
-) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
- """
- RoPE for video tokens with 3D structure.
-
- Args:
- embed_dim: (`int`):
- The embedding dimension size, corresponding to hidden_size_head.
- crops_coords (`Tuple[int]`):
- The top-left and bottom-right coordinates of the crop.
- grid_size (`Tuple[int]`):
- The grid size of the spatial positional embedding (height, width).
- temporal_size (`int`):
- The size of the temporal dimension.
- theta (`float`):
- Scaling factor for frequency computation.
- use_real (`bool`):
- If True, return real part and imaginary part separately. Otherwise, return complex numbers.
-
- Returns:
- `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
- """
- start, stop = crops_coords
- grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
- grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
- grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
-
- # Compute dimensions for each axis
- dim_t = embed_dim // 4
- dim_h = embed_dim // 8 * 3
- dim_w = embed_dim // 8 * 3
-
- # Temporal frequencies
- freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
- grid_t = torch.from_numpy(grid_t).float()
- freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
- freqs_t = freqs_t.repeat_interleave(2, dim=-1)
-
- # Spatial frequencies for height and width
- freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
- freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
- grid_h = torch.from_numpy(grid_h).float()
- grid_w = torch.from_numpy(grid_w).float()
- freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
- freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
- freqs_h = freqs_h.repeat_interleave(2, dim=-1)
- freqs_w = freqs_w.repeat_interleave(2, dim=-1)
-
- # Broadcast and concatenate tensors along specified dimension
- def broadcast(tensors, dim=-1):
- num_tensors = len(tensors)
- shape_lens = {len(t.shape) for t in tensors}
- assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
- shape_len = list(shape_lens)[0]
- dim = (dim + shape_len) if dim < 0 else dim
- dims = list(zip(*(list(t.shape) for t in tensors)))
- expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
- assert all(
- [*(len(set(t[1])) <= 2 for t in expandable_dims)]
- ), "invalid dimensions for broadcastable concatenation"
- max_dims = [(t[0], max(t[1])) for t in expandable_dims]
- expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
- expanded_dims.insert(dim, (dim, dims[dim]))
- expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
- tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
- return torch.cat(tensors, dim=dim)
-
- freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
-
- t, h, w, d = freqs.shape
- freqs = freqs.view(t * h * w, d)
-
- # Generate sine and cosine components
- sin = freqs.sin()
- cos = freqs.cos()
-
- if use_real:
- return cos, sin
- else:
- freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
- return freqs_cis
-
-
-def apply_rotary_emb(
- x: torch.Tensor,
- freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
- use_real: bool = True,
- use_real_unbind_dim: int = -1,
-) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
- to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
- reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
- tensors contain rotary embeddings and are returned as real tensors.
-
- Args:
- x (`torch.Tensor`):
- Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
- freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
-
- Returns:
- Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
- """
- if use_real:
- cos, sin = freqs_cis # [S, D]
- cos = cos[None, None]
- sin = sin[None, None]
- cos, sin = cos.to(x.device), sin.to(x.device)
-
- if use_real_unbind_dim == -1:
- # Use for example in Lumina
- x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
- x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
- elif use_real_unbind_dim == -2:
- # Use for example in Stable Audio
- x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2) # [B, S, H, D//2]
- x_rotated = torch.cat([-x_imag, x_real], dim=-1)
- else:
- raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.")
-
- out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
-
- return out
- else:
- x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
- freqs_cis = freqs_cis.unsqueeze(2)
- x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
-
- return x_out.type_as(x)
diff --git a/videosys/modules/normalization.py b/videosys/modules/normalization.py
deleted file mode 100644
index 216d0cc..0000000
--- a/videosys/modules/normalization.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-
-
-class CogVideoXLayerNormZero(nn.Module):
- def __init__(
- self,
- conditioning_dim: int,
- embedding_dim: int,
- elementwise_affine: bool = True,
- eps: float = 1e-5,
- bias: bool = True,
- ) -> None:
- super().__init__()
-
- self.silu = nn.SiLU()
- self.linear = nn.Linear(conditioning_dim, 6 * embedding_dim, bias=bias)
- self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)
-
- def forward(
- self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1)
- hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
- encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale)[:, None, :] + enc_shift[:, None, :]
- return hidden_states, encoder_hidden_states, gate[:, None, :], enc_gate[:, None, :]
-
-
-class AdaLayerNorm(nn.Module):
- r"""
- Norm layer modified to incorporate timestep embeddings.
-
- Parameters:
- embedding_dim (`int`): The size of each embedding vector.
- num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
- output_dim (`int`, *optional*):
- norm_elementwise_affine (`bool`, defaults to `False):
- norm_eps (`bool`, defaults to `False`):
- chunk_dim (`int`, defaults to `0`):
- """
-
- def __init__(
- self,
- embedding_dim: int,
- num_embeddings: Optional[int] = None,
- output_dim: Optional[int] = None,
- norm_elementwise_affine: bool = False,
- norm_eps: float = 1e-5,
- chunk_dim: int = 0,
- ):
- super().__init__()
-
- self.chunk_dim = chunk_dim
- output_dim = output_dim or embedding_dim * 2
-
- if num_embeddings is not None:
- self.emb = nn.Embedding(num_embeddings, embedding_dim)
- else:
- self.emb = None
-
- self.silu = nn.SiLU()
- self.linear = nn.Linear(embedding_dim, output_dim)
- self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
-
- def forward(
- self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None
- ) -> torch.Tensor:
- if self.emb is not None:
- temb = self.emb(timestep)
-
- temb = self.linear(self.silu(temb))
-
- if self.chunk_dim == 1:
- # This is a bit weird why we have the order of "shift, scale" here and "scale, shift" in the
- # other if-branch. This branch is specific to CogVideoX for now.
- shift, scale = temb.chunk(2, dim=1)
- shift = shift[:, None, :]
- scale = scale[:, None, :]
- else:
- scale, shift = temb.chunk(2, dim=0)
-
- x = self.norm(x) * (1 + scale) + shift
- return x
diff --git a/videosys/modules/upsampling.py b/videosys/modules/upsampling.py
deleted file mode 100644
index f9a61b7..0000000
--- a/videosys/modules/upsampling.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class CogVideoXUpsample3D(nn.Module):
- r"""
- A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase.
-
- Args:
- in_channels (`int`):
- Number of channels in the input image.
- out_channels (`int`):
- Number of channels produced by the convolution.
- kernel_size (`int`, defaults to `3`):
- Size of the convolving kernel.
- stride (`int`, defaults to `1`):
- Stride of the convolution.
- padding (`int`, defaults to `1`):
- Padding added to all four sides of the input.
- compress_time (`bool`, defaults to `False`):
- Whether or not to compress the time dimension.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int = 3,
- stride: int = 1,
- padding: int = 1,
- compress_time: bool = False,
- ) -> None:
- super().__init__()
-
- self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
- self.compress_time = compress_time
-
- def forward(self, inputs: torch.Tensor) -> torch.Tensor:
- if self.compress_time:
- if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
- # split first frame
- x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
-
- x_first = F.interpolate(x_first, scale_factor=2.0)
- x_rest = F.interpolate(x_rest, scale_factor=2.0)
- x_first = x_first[:, :, None, :, :]
- inputs = torch.cat([x_first, x_rest], dim=2)
- elif inputs.shape[2] > 1:
- inputs = F.interpolate(inputs, scale_factor=2.0)
- else:
- inputs = inputs.squeeze(2)
- inputs = F.interpolate(inputs, scale_factor=2.0)
- inputs = inputs[:, :, None, :, :]
- else:
- # only interpolate 2D
- b, c, t, h, w = inputs.shape
- inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
- inputs = F.interpolate(inputs, scale_factor=2.0)
- inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
-
- b, c, t, h, w = inputs.shape
- inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
- inputs = self.conv(inputs)
- inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
-
- return inputs
diff --git a/videosys/pab.py b/videosys/pab.py
deleted file mode 100644
index 007e1b3..0000000
--- a/videosys/pab.py
+++ /dev/null
@@ -1,64 +0,0 @@
-class PABConfig:
- def __init__(
- self,
- steps: int,
- cross_broadcast: bool = False,
- cross_threshold: list = None,
- cross_range: int = None,
- spatial_broadcast: bool = False,
- spatial_threshold: list = None,
- spatial_range: int = None,
- temporal_broadcast: bool = False,
- temporal_threshold: list = None,
- temporal_range: int = None,
- mlp_broadcast: bool = False,
- mlp_spatial_broadcast_config: dict = None,
- mlp_temporal_broadcast_config: dict = None,
- ):
- self.steps = steps
-
- self.cross_broadcast = cross_broadcast
- self.cross_threshold = cross_threshold
- self.cross_range = cross_range
-
- self.spatial_broadcast = spatial_broadcast
- self.spatial_threshold = spatial_threshold
- self.spatial_range = spatial_range
-
- self.temporal_broadcast = temporal_broadcast
- self.temporal_threshold = temporal_threshold
- self.temporal_range = temporal_range
-
- self.mlp_broadcast = mlp_broadcast
- self.mlp_spatial_broadcast_config = mlp_spatial_broadcast_config
- self.mlp_temporal_broadcast_config = mlp_temporal_broadcast_config
- self.mlp_temporal_outputs = {}
- self.mlp_spatial_outputs = {}
-
-class CogVideoXPABConfig(PABConfig):
- def __init__(
- self,
- steps: int = 50,
- spatial_broadcast: bool = True,
- spatial_threshold: list = [100, 850],
- spatial_range: int = 2,
- temporal_broadcast: bool = False,
- temporal_threshold: list = [100, 850],
- temporal_range: int = 4,
- cross_broadcast: bool = False,
- cross_threshold: list = [100, 850],
- cross_range: int = 6,
- ):
- super().__init__(
- steps=steps,
- spatial_broadcast=spatial_broadcast,
- spatial_threshold=spatial_threshold,
- spatial_range=spatial_range,
- temporal_broadcast=temporal_broadcast,
- temporal_threshold=temporal_threshold,
- temporal_range=temporal_range,
- cross_broadcast=cross_broadcast,
- cross_threshold=cross_threshold,
- cross_range=cross_range
-
- )
\ No newline at end of file