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https://git.datalinker.icu/ali-vilab/TeaCache
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1483 lines
68 KiB
Python
1483 lines
68 KiB
Python
# Adapted from Latte
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# Latte: https://github.com/Vchitect/Latte
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# --------------------------------------------------------
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from dataclasses import dataclass
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from functools import partial
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from typing import Any, Dict, Optional, Tuple
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import torch
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import torch.nn.functional as F
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
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from diffusers.models.attention_processor import Attention
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from diffusers.models.embeddings import (
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ImagePositionalEmbeddings,
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PatchEmbed,
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PixArtAlphaCombinedTimestepSizeEmbeddings,
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PixArtAlphaTextProjection,
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SinusoidalPositionalEmbedding,
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get_1d_sincos_pos_embed_from_grid,
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)
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from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
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from diffusers.models.modeling_utils import ModelMixin
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from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero
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from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate
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from diffusers.utils.torch_utils import maybe_allow_in_graph
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from einops import rearrange, repeat
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from torch import nn
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from videosys.core.comm import all_to_all_with_pad, gather_sequence, get_pad, set_pad, split_sequence
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from videosys.core.pab_mgr import (
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enable_pab,
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get_mlp_output,
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if_broadcast_cross,
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if_broadcast_mlp,
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if_broadcast_spatial,
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if_broadcast_temporal,
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save_mlp_output,
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)
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from videosys.core.parallel_mgr import ParallelManager
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from videosys.utils.utils import batch_func
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@maybe_allow_in_graph
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class GatedSelfAttentionDense(nn.Module):
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r"""
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A gated self-attention dense layer that combines visual features and object features.
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Parameters:
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query_dim (`int`): The number of channels in the query.
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context_dim (`int`): The number of channels in the context.
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n_heads (`int`): The number of heads to use for attention.
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d_head (`int`): The number of channels in each head.
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"""
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def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
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super().__init__()
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# we need a linear projection since we need cat visual feature and obj feature
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self.linear = nn.Linear(context_dim, query_dim)
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self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
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self.ff = FeedForward(query_dim, activation_fn="geglu")
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self.norm1 = nn.LayerNorm(query_dim)
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self.norm2 = nn.LayerNorm(query_dim)
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self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
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self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
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self.enabled = True
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def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
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if not self.enabled:
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return x
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n_visual = x.shape[1]
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objs = self.linear(objs)
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x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
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x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
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return x
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class FeedForward(nn.Module):
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r"""
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A feed-forward layer.
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Parameters:
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dim (`int`): The number of channels in the input.
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dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
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mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
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final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
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"""
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def __init__(
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self,
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dim: int,
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dim_out: Optional[int] = None,
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mult: int = 4,
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dropout: float = 0.0,
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activation_fn: str = "geglu",
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final_dropout: bool = False,
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):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = dim_out if dim_out is not None else dim
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linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
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if activation_fn == "gelu":
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act_fn = GELU(dim, inner_dim)
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if activation_fn == "gelu-approximate":
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act_fn = GELU(dim, inner_dim, approximate="tanh")
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elif activation_fn == "geglu":
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act_fn = GEGLU(dim, inner_dim)
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elif activation_fn == "geglu-approximate":
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act_fn = ApproximateGELU(dim, inner_dim)
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self.net = nn.ModuleList([])
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# project in
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self.net.append(act_fn)
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# project dropout
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self.net.append(nn.Dropout(dropout))
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# project out
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self.net.append(linear_cls(inner_dim, dim_out))
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# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
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if final_dropout:
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self.net.append(nn.Dropout(dropout))
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def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
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compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
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for module in self.net:
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if isinstance(module, compatible_cls):
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hidden_states = module(hidden_states, scale)
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else:
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hidden_states = module(hidden_states)
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return hidden_states
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@maybe_allow_in_graph
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class BasicTransformerBlock(nn.Module):
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r"""
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A basic Transformer block.
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Parameters:
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dim (`int`): The number of channels in the input and output.
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num_attention_heads (`int`): The number of heads to use for multi-head attention.
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attention_head_dim (`int`): The number of channels in each head.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
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activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
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num_embeds_ada_norm (:
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obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
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attention_bias (:
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obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
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only_cross_attention (`bool`, *optional*):
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Whether to use only cross-attention layers. In this case two cross attention layers are used.
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double_self_attention (`bool`, *optional*):
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Whether to use two self-attention layers. In this case no cross attention layers are used.
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upcast_attention (`bool`, *optional*):
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Whether to upcast the attention computation to float32. This is useful for mixed precision training.
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norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
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Whether to use learnable elementwise affine parameters for normalization.
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norm_type (`str`, *optional*, defaults to `"layer_norm"`):
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The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
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final_dropout (`bool` *optional*, defaults to False):
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Whether to apply a final dropout after the last feed-forward layer.
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attention_type (`str`, *optional*, defaults to `"default"`):
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The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
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positional_embeddings (`str`, *optional*, defaults to `None`):
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The type of positional embeddings to apply to.
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num_positional_embeddings (`int`, *optional*, defaults to `None`):
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The maximum number of positional embeddings to apply.
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"""
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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dropout=0.0,
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cross_attention_dim: Optional[int] = None,
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activation_fn: str = "geglu",
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num_embeds_ada_norm: Optional[int] = None,
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attention_bias: bool = False,
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only_cross_attention: bool = False,
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double_self_attention: bool = False,
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upcast_attention: bool = False,
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norm_elementwise_affine: bool = True,
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norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
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norm_eps: float = 1e-5,
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final_dropout: bool = False,
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attention_type: str = "default",
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positional_embeddings: Optional[str] = None,
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num_positional_embeddings: Optional[int] = None,
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ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
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ada_norm_bias: Optional[int] = None,
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ff_inner_dim: Optional[int] = None,
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ff_bias: bool = True,
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attention_out_bias: bool = True,
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block_idx: Optional[int] = None,
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):
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super().__init__()
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self.only_cross_attention = only_cross_attention
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# We keep these boolean flags for backward-compatibility.
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self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
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self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
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self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
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self.use_layer_norm = norm_type == "layer_norm"
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self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"
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if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
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raise ValueError(
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f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
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f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
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)
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self.norm_type = norm_type
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self.num_embeds_ada_norm = num_embeds_ada_norm
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if positional_embeddings and (num_positional_embeddings is None):
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raise ValueError(
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"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
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)
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if positional_embeddings == "sinusoidal":
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self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
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else:
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self.pos_embed = None
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# Define 3 blocks. Each block has its own normalization layer.
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# 1. Self-Attn
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if norm_type == "ada_norm":
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self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
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elif norm_type == "ada_norm_zero":
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self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
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elif norm_type == "ada_norm_continuous":
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self.norm1 = AdaLayerNormContinuous(
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dim,
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ada_norm_continous_conditioning_embedding_dim,
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norm_elementwise_affine,
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norm_eps,
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ada_norm_bias,
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"rms_norm",
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)
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else:
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self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
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self.attn1 = Attention(
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query_dim=dim,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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cross_attention_dim=cross_attention_dim if only_cross_attention else None,
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upcast_attention=upcast_attention,
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out_bias=attention_out_bias,
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)
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# 2. Cross-Attn
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if cross_attention_dim is not None or double_self_attention:
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# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
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# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
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# the second cross attention block.
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if norm_type == "ada_norm":
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self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)
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elif norm_type == "ada_norm_continuous":
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self.norm2 = AdaLayerNormContinuous(
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dim,
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ada_norm_continous_conditioning_embedding_dim,
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norm_elementwise_affine,
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norm_eps,
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ada_norm_bias,
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"rms_norm",
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)
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else:
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self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
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self.attn2 = Attention(
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query_dim=dim,
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cross_attention_dim=cross_attention_dim if not double_self_attention else None,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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upcast_attention=upcast_attention,
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out_bias=attention_out_bias,
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) # is self-attn if encoder_hidden_states is none
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else:
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self.norm2 = None
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self.attn2 = None
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# 3. Feed-forward
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if norm_type == "ada_norm_continuous":
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self.norm3 = AdaLayerNormContinuous(
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dim,
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ada_norm_continous_conditioning_embedding_dim,
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norm_elementwise_affine,
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norm_eps,
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ada_norm_bias,
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"layer_norm",
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)
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elif norm_type in ["ada_norm_zero", "ada_norm", "layer_norm", "ada_norm_continuous"]:
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self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
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elif norm_type == "layer_norm_i2vgen":
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self.norm3 = None
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self.ff = FeedForward(
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dim,
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dropout=dropout,
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activation_fn=activation_fn,
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final_dropout=final_dropout,
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)
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# 4. Fuser
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if attention_type == "gated" or attention_type == "gated-text-image":
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self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
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# 5. Scale-shift for PixArt-Alpha.
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if norm_type == "ada_norm_single":
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self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
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# let chunk size default to None
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self._chunk_size = None
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self._chunk_dim = 0
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# pab
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self.cross_last = None
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self.cross_count = 0
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self.spatial_last = None
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self.spatial_count = 0
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self.block_idx = block_idx
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self.mlp_count = 0
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def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
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# Sets chunk feed-forward
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self._chunk_size = chunk_size
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self._chunk_dim = dim
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def set_cross_last(self, last_out: torch.Tensor):
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self.cross_last = last_out
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def set_spatial_last(self, last_out: torch.Tensor):
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self.spatial_last = last_out
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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timestep: Optional[torch.LongTensor] = None,
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cross_attention_kwargs: Dict[str, Any] = None,
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class_labels: Optional[torch.LongTensor] = None,
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added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
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org_timestep: Optional[torch.LongTensor] = None,
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all_timesteps=None,
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) -> torch.FloatTensor:
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# Notice that normalization is always applied before the real computation in the following blocks.
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# 0. Self-Attention
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batch_size = hidden_states.shape[0]
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# 1. Prepare GLIGEN inputs
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cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
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gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
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if enable_pab():
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broadcast_spatial, self.spatial_count = if_broadcast_spatial(int(org_timestep[0]), self.spatial_count)
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if enable_pab() and broadcast_spatial:
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attn_output = self.spatial_last
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assert self.use_ada_layer_norm_single
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
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self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
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).chunk(6, dim=1)
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else:
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if self.norm_type == "ada_norm":
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norm_hidden_states = self.norm1(hidden_states, timestep)
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elif self.norm_type == "ada_norm_zero":
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norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
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hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
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)
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elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
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norm_hidden_states = self.norm1(hidden_states)
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elif self.norm_type == "ada_norm_continuous":
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norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
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elif self.norm_type == "ada_norm_single":
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
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self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
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).chunk(6, dim=1)
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norm_hidden_states = self.norm1(hidden_states)
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norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
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norm_hidden_states = norm_hidden_states.squeeze(1)
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else:
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raise ValueError("Incorrect norm used")
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if self.pos_embed is not None:
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norm_hidden_states = self.pos_embed(norm_hidden_states)
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attn_output = self.attn1(
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norm_hidden_states,
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encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
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attention_mask=attention_mask,
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**cross_attention_kwargs,
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)
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if self.norm_type == "ada_norm_zero":
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attn_output = gate_msa.unsqueeze(1) * attn_output
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elif self.norm_type == "ada_norm_single":
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attn_output = gate_msa * attn_output
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if enable_pab():
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self.set_spatial_last(attn_output)
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hidden_states = attn_output + hidden_states
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if hidden_states.ndim == 4:
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hidden_states = hidden_states.squeeze(1)
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# 1.2 GLIGEN Control
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if gligen_kwargs is not None:
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hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
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# 3. Cross-Attention
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if self.attn2 is not None:
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broadcast_cross, self.cross_count = if_broadcast_cross(int(org_timestep[0]), self.cross_count)
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if broadcast_cross:
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hidden_states = hidden_states + self.cross_last
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else:
|
|
if self.norm_type == "ada_norm":
|
|
norm_hidden_states = self.norm2(hidden_states, timestep)
|
|
elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
|
|
norm_hidden_states = self.norm2(hidden_states)
|
|
elif self.norm_type == "ada_norm_single":
|
|
# For PixArt norm2 isn't applied here:
|
|
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
|
|
norm_hidden_states = hidden_states
|
|
elif self.norm_type == "ada_norm_continuous":
|
|
norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
|
|
else:
|
|
raise ValueError("Incorrect norm")
|
|
|
|
if self.pos_embed is not None and self.norm_type != "ada_norm_single":
|
|
norm_hidden_states = self.pos_embed(norm_hidden_states)
|
|
|
|
attn_output = self.attn2(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
|
|
if enable_pab():
|
|
self.set_cross_last(attn_output)
|
|
|
|
hidden_states = attn_output + hidden_states
|
|
|
|
# 4. Feed-forward
|
|
# i2vgen doesn't have this norm 🤷♂️
|
|
if enable_pab():
|
|
broadcast_mlp, self.mlp_count, broadcast_next, broadcast_range = if_broadcast_mlp(
|
|
int(org_timestep[0]),
|
|
self.mlp_count,
|
|
self.block_idx,
|
|
all_timesteps.tolist(),
|
|
is_temporal=False,
|
|
)
|
|
|
|
if enable_pab() and broadcast_mlp:
|
|
ff_output = get_mlp_output(
|
|
broadcast_range,
|
|
timestep=int(org_timestep[0]),
|
|
block_idx=self.block_idx,
|
|
is_temporal=False,
|
|
)
|
|
else:
|
|
if self.norm_type == "ada_norm_continuous":
|
|
norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
|
|
elif not self.norm_type == "ada_norm_single":
|
|
norm_hidden_states = self.norm3(hidden_states)
|
|
|
|
if self.norm_type == "ada_norm_zero":
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
|
|
|
if self.norm_type == "ada_norm_single":
|
|
norm_hidden_states = self.norm2(hidden_states)
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
|
|
|
|
ff_output = self.ff(norm_hidden_states)
|
|
|
|
if self.norm_type == "ada_norm_zero":
|
|
ff_output = gate_mlp.unsqueeze(1) * ff_output
|
|
elif self.norm_type == "ada_norm_single":
|
|
ff_output = gate_mlp * ff_output
|
|
|
|
if enable_pab() and broadcast_next:
|
|
# spatial
|
|
save_mlp_output(
|
|
timestep=int(org_timestep[0]),
|
|
block_idx=self.block_idx,
|
|
ff_output=ff_output,
|
|
is_temporal=False,
|
|
)
|
|
|
|
hidden_states = ff_output + hidden_states
|
|
if hidden_states.ndim == 4:
|
|
hidden_states = hidden_states.squeeze(1)
|
|
|
|
return hidden_states
|
|
|
|
|
|
@maybe_allow_in_graph
|
|
class BasicTransformerBlock_(nn.Module):
|
|
r"""
|
|
A basic Transformer block.
|
|
|
|
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.
|
|
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
|
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
|
|
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
|
num_embeds_ada_norm (:
|
|
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
|
|
attention_bias (:
|
|
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
|
|
only_cross_attention (`bool`, *optional*):
|
|
Whether to use only cross-attention layers. In this case two cross attention layers are used.
|
|
double_self_attention (`bool`, *optional*):
|
|
Whether to use two self-attention layers. In this case no cross attention layers are used.
|
|
upcast_attention (`bool`, *optional*):
|
|
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
|
|
norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
|
|
Whether to use learnable elementwise affine parameters for normalization.
|
|
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
|
|
The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
|
|
final_dropout (`bool` *optional*, defaults to False):
|
|
Whether to apply a final dropout after the last feed-forward layer.
|
|
attention_type (`str`, *optional*, defaults to `"default"`):
|
|
The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
|
|
positional_embeddings (`str`, *optional*, defaults to `None`):
|
|
The type of positional embeddings to apply to.
|
|
num_positional_embeddings (`int`, *optional*, defaults to `None`):
|
|
The maximum number of positional embeddings to apply.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
num_attention_heads: int,
|
|
attention_head_dim: int,
|
|
dropout=0.0,
|
|
cross_attention_dim: Optional[int] = None,
|
|
activation_fn: str = "geglu",
|
|
num_embeds_ada_norm: Optional[int] = None,
|
|
attention_bias: bool = False,
|
|
only_cross_attention: bool = False,
|
|
double_self_attention: bool = False,
|
|
upcast_attention: bool = False,
|
|
norm_elementwise_affine: bool = True,
|
|
norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
|
|
norm_eps: float = 1e-5,
|
|
final_dropout: bool = False,
|
|
attention_type: str = "default",
|
|
positional_embeddings: Optional[str] = None,
|
|
num_positional_embeddings: Optional[int] = None,
|
|
block_idx: Optional[int] = None,
|
|
):
|
|
super().__init__()
|
|
self.only_cross_attention = only_cross_attention
|
|
|
|
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
|
|
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
|
|
self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
|
|
self.use_layer_norm = norm_type == "layer_norm"
|
|
|
|
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
|
|
raise ValueError(
|
|
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
|
|
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
|
|
)
|
|
|
|
if positional_embeddings and (num_positional_embeddings is None):
|
|
raise ValueError(
|
|
"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
|
|
)
|
|
|
|
if positional_embeddings == "sinusoidal":
|
|
self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
|
|
else:
|
|
self.pos_embed = None
|
|
|
|
# Define 3 blocks. Each block has its own normalization layer.
|
|
# 1. Self-Attn
|
|
if self.use_ada_layer_norm:
|
|
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
|
|
elif self.use_ada_layer_norm_zero:
|
|
self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
|
|
else:
|
|
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) # go here
|
|
|
|
self.attn1 = Attention(
|
|
query_dim=dim,
|
|
heads=num_attention_heads,
|
|
dim_head=attention_head_dim,
|
|
dropout=dropout,
|
|
bias=attention_bias,
|
|
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
|
|
# # 2. Cross-Attn
|
|
# if cross_attention_dim is not None or double_self_attention:
|
|
# # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
|
|
# # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
|
|
# # the second cross attention block.
|
|
# self.norm2 = (
|
|
# AdaLayerNorm(dim, num_embeds_ada_norm)
|
|
# if self.use_ada_layer_norm
|
|
# else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
|
|
# )
|
|
# self.attn2 = Attention(
|
|
# query_dim=dim,
|
|
# cross_attention_dim=cross_attention_dim if not double_self_attention else None,
|
|
# heads=num_attention_heads,
|
|
# dim_head=attention_head_dim,
|
|
# dropout=dropout,
|
|
# bias=attention_bias,
|
|
# upcast_attention=upcast_attention,
|
|
# ) # is self-attn if encoder_hidden_states is none
|
|
# else:
|
|
# self.norm2 = None
|
|
# self.attn2 = None
|
|
|
|
# 3. Feed-forward
|
|
# if not self.use_ada_layer_norm_single:
|
|
# self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
|
|
self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
|
|
|
|
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
|
|
|
|
# 4. Fuser
|
|
if attention_type == "gated" or attention_type == "gated-text-image":
|
|
self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
|
|
|
|
# 5. Scale-shift for PixArt-Alpha.
|
|
if self.use_ada_layer_norm_single:
|
|
self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
|
|
|
|
# let chunk size default to None
|
|
self._chunk_size = None
|
|
self._chunk_dim = 0
|
|
|
|
# pab
|
|
self.last_out = None
|
|
self.mlp_count = 0
|
|
self.block_idx = block_idx
|
|
self.count = 0
|
|
|
|
# parallel
|
|
self.parallel_manager: ParallelManager = None
|
|
|
|
def set_last_out(self, last_out: torch.Tensor):
|
|
self.last_out = last_out
|
|
|
|
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
|
|
# Sets chunk feed-forward
|
|
self._chunk_size = chunk_size
|
|
self._chunk_dim = dim
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
timestep: Optional[torch.LongTensor] = None,
|
|
cross_attention_kwargs: Dict[str, Any] = None,
|
|
class_labels: Optional[torch.LongTensor] = None,
|
|
org_timestep: Optional[torch.LongTensor] = None,
|
|
all_timesteps=None,
|
|
) -> torch.FloatTensor:
|
|
# Notice that normalization is always applied before the real computation in the following blocks.
|
|
# 0. Self-Attention
|
|
batch_size = hidden_states.shape[0]
|
|
|
|
# 1. Retrieve lora scale.
|
|
lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
|
|
|
|
# 2. Prepare GLIGEN inputs
|
|
cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
|
|
gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
|
|
|
|
if enable_pab():
|
|
broadcast_temporal, self.count = if_broadcast_temporal(int(org_timestep[0]), self.count)
|
|
|
|
if enable_pab() and broadcast_temporal:
|
|
attn_output = self.last_out
|
|
assert self.use_ada_layer_norm_single
|
|
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
|
self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
|
|
).chunk(6, dim=1)
|
|
else:
|
|
if self.use_ada_layer_norm:
|
|
norm_hidden_states = self.norm1(hidden_states, timestep)
|
|
elif self.use_ada_layer_norm_zero:
|
|
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
|
|
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
|
|
)
|
|
elif self.use_layer_norm:
|
|
norm_hidden_states = self.norm1(hidden_states)
|
|
elif self.use_ada_layer_norm_single: # go here
|
|
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
|
self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
|
|
).chunk(6, dim=1)
|
|
norm_hidden_states = self.norm1(hidden_states)
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
|
|
# norm_hidden_states = norm_hidden_states.squeeze(1)
|
|
else:
|
|
raise ValueError("Incorrect norm used")
|
|
|
|
if self.pos_embed is not None:
|
|
norm_hidden_states = self.pos_embed(norm_hidden_states)
|
|
|
|
if self.parallel_manager.sp_size > 1:
|
|
norm_hidden_states = self.dynamic_switch(norm_hidden_states, to_spatial_shard=True)
|
|
|
|
attn_output = self.attn1(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
|
attention_mask=attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
|
|
if self.parallel_manager.sp_size > 1:
|
|
attn_output = self.dynamic_switch(attn_output, to_spatial_shard=False)
|
|
|
|
if self.use_ada_layer_norm_zero:
|
|
attn_output = gate_msa.unsqueeze(1) * attn_output
|
|
elif self.use_ada_layer_norm_single:
|
|
attn_output = gate_msa * attn_output
|
|
|
|
if enable_pab():
|
|
self.last_out = attn_output
|
|
|
|
hidden_states = attn_output + hidden_states
|
|
|
|
if enable_pab():
|
|
broadcast_mlp, self.mlp_count, broadcast_next, broadcast_range = if_broadcast_mlp(
|
|
int(org_timestep[0]),
|
|
self.mlp_count,
|
|
self.block_idx,
|
|
all_timesteps.tolist(),
|
|
is_temporal=True,
|
|
)
|
|
|
|
if enable_pab() and broadcast_mlp:
|
|
ff_output = get_mlp_output(
|
|
broadcast_range,
|
|
timestep=int(org_timestep[0]),
|
|
block_idx=self.block_idx,
|
|
is_temporal=True,
|
|
)
|
|
else:
|
|
if hidden_states.ndim == 4:
|
|
hidden_states = hidden_states.squeeze(1)
|
|
|
|
# 2.5 GLIGEN Control
|
|
if gligen_kwargs is not None:
|
|
hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
|
|
|
|
if self.use_ada_layer_norm_zero:
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
|
|
|
if self.use_ada_layer_norm_single:
|
|
# norm_hidden_states = self.norm2(hidden_states)
|
|
norm_hidden_states = self.norm3(hidden_states)
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
|
|
|
|
if self._chunk_size is not None:
|
|
# "feed_forward_chunk_size" can be used to save memory
|
|
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
|
|
raise ValueError(
|
|
f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
|
|
)
|
|
|
|
num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
|
|
ff_output = torch.cat(
|
|
[
|
|
self.ff(hid_slice, scale=lora_scale)
|
|
for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
|
|
],
|
|
dim=self._chunk_dim,
|
|
)
|
|
else:
|
|
ff_output = self.ff(norm_hidden_states, scale=lora_scale)
|
|
|
|
if self.use_ada_layer_norm_zero:
|
|
ff_output = gate_mlp.unsqueeze(1) * ff_output
|
|
elif self.use_ada_layer_norm_single:
|
|
ff_output = gate_mlp * ff_output
|
|
|
|
if enable_pab() and broadcast_next:
|
|
save_mlp_output(
|
|
timestep=int(org_timestep[0]),
|
|
block_idx=self.block_idx,
|
|
ff_output=ff_output,
|
|
is_temporal=True,
|
|
)
|
|
|
|
hidden_states = ff_output + hidden_states
|
|
if hidden_states.ndim == 4:
|
|
hidden_states = hidden_states.squeeze(1)
|
|
|
|
return hidden_states
|
|
|
|
def dynamic_switch(self, x, to_spatial_shard: bool):
|
|
if to_spatial_shard:
|
|
scatter_dim, gather_dim = 0, 1
|
|
scatter_pad = get_pad("spatial")
|
|
gather_pad = get_pad("temporal")
|
|
else:
|
|
scatter_dim, gather_dim = 1, 0
|
|
scatter_pad = get_pad("temporal")
|
|
gather_pad = get_pad("spatial")
|
|
x = all_to_all_with_pad(
|
|
x,
|
|
self.parallel_manager.sp_group,
|
|
scatter_dim=scatter_dim,
|
|
gather_dim=gather_dim,
|
|
scatter_pad=scatter_pad,
|
|
gather_pad=gather_pad,
|
|
)
|
|
return x
|
|
|
|
|
|
class AdaLayerNormSingle(nn.Module):
|
|
r"""
|
|
Norm layer adaptive layer norm single (adaLN-single).
|
|
|
|
As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
|
|
|
|
Parameters:
|
|
embedding_dim (`int`): The size of each embedding vector.
|
|
use_additional_conditions (`bool`): To use additional conditions for normalization or not.
|
|
"""
|
|
|
|
def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):
|
|
super().__init__()
|
|
|
|
self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
|
|
embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions
|
|
)
|
|
|
|
self.silu = nn.SiLU()
|
|
self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
|
|
|
|
def forward(
|
|
self,
|
|
timestep: torch.Tensor,
|
|
added_cond_kwargs: Dict[str, torch.Tensor] = None,
|
|
batch_size: int = None,
|
|
hidden_dtype: Optional[torch.dtype] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
# No modulation happening here.
|
|
embedded_timestep = self.emb(
|
|
timestep, batch_size=batch_size, hidden_dtype=hidden_dtype, resolution=None, aspect_ratio=None
|
|
)
|
|
return self.linear(self.silu(embedded_timestep)), embedded_timestep
|
|
|
|
|
|
@dataclass
|
|
class Transformer3DModelOutput(BaseOutput):
|
|
"""
|
|
The output of [`Transformer2DModel`].
|
|
|
|
Args:
|
|
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
|
|
The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
|
|
distributions for the unnoised latent pixels.
|
|
"""
|
|
|
|
sample: torch.FloatTensor
|
|
|
|
|
|
class LatteT2V(ModelMixin, ConfigMixin):
|
|
_supports_gradient_checkpointing = True
|
|
|
|
"""
|
|
A 2D Transformer model for image-like data.
|
|
|
|
Parameters:
|
|
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
|
|
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
|
|
in_channels (`int`, *optional*):
|
|
The number of channels in the input and output (specify if the input is **continuous**).
|
|
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
|
|
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
|
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
|
|
sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
|
|
This is fixed during training since it is used to learn a number of position embeddings.
|
|
num_vector_embeds (`int`, *optional*):
|
|
The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
|
|
Includes the class for the masked latent pixel.
|
|
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
|
|
num_embeds_ada_norm ( `int`, *optional*):
|
|
The number of diffusion steps used during training. Pass if at least one of the norm_layers is
|
|
`AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
|
|
added to the hidden states.
|
|
|
|
During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
|
|
attention_bias (`bool`, *optional*):
|
|
Configure if the `TransformerBlocks` attention should contain a bias parameter.
|
|
"""
|
|
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
num_attention_heads: int = 16,
|
|
attention_head_dim: int = 88,
|
|
in_channels: Optional[int] = None,
|
|
out_channels: Optional[int] = None,
|
|
num_layers: int = 1,
|
|
dropout: float = 0.0,
|
|
norm_num_groups: int = 32,
|
|
cross_attention_dim: Optional[int] = None,
|
|
attention_bias: bool = False,
|
|
sample_size: Optional[int] = None,
|
|
num_vector_embeds: Optional[int] = None,
|
|
patch_size: Optional[int] = None,
|
|
activation_fn: str = "geglu",
|
|
num_embeds_ada_norm: Optional[int] = None,
|
|
use_linear_projection: bool = False,
|
|
only_cross_attention: bool = False,
|
|
double_self_attention: bool = False,
|
|
upcast_attention: bool = False,
|
|
norm_type: str = "layer_norm",
|
|
norm_elementwise_affine: bool = True,
|
|
norm_eps: float = 1e-5,
|
|
attention_type: str = "default",
|
|
caption_channels: int = None,
|
|
video_length: int = 16,
|
|
):
|
|
super().__init__()
|
|
self.use_linear_projection = use_linear_projection
|
|
self.num_attention_heads = num_attention_heads
|
|
self.attention_head_dim = attention_head_dim
|
|
inner_dim = num_attention_heads * attention_head_dim
|
|
self.video_length = video_length
|
|
|
|
conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
|
|
linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
|
|
|
|
# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
|
|
# Define whether input is continuous or discrete depending on configuration
|
|
self.is_input_continuous = (in_channels is not None) and (patch_size is None)
|
|
self.is_input_vectorized = num_vector_embeds is not None
|
|
self.is_input_patches = in_channels is not None and patch_size is not None
|
|
|
|
if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
|
|
deprecation_message = (
|
|
f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
|
|
" incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
|
|
" Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
|
|
" results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
|
|
" would be very nice if you could open a Pull request for the `transformer/config.json` file"
|
|
)
|
|
deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
|
|
norm_type = "ada_norm"
|
|
|
|
if self.is_input_continuous and self.is_input_vectorized:
|
|
raise ValueError(
|
|
f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
|
|
" sure that either `in_channels` or `num_vector_embeds` is None."
|
|
)
|
|
elif self.is_input_vectorized and self.is_input_patches:
|
|
raise ValueError(
|
|
f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
|
|
" sure that either `num_vector_embeds` or `num_patches` is None."
|
|
)
|
|
elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
|
|
raise ValueError(
|
|
f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
|
|
f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
|
|
)
|
|
|
|
# 2. Define input layers
|
|
if self.is_input_continuous:
|
|
self.in_channels = in_channels
|
|
|
|
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
|
if use_linear_projection:
|
|
self.proj_in = linear_cls(in_channels, inner_dim)
|
|
else:
|
|
self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
|
|
elif self.is_input_vectorized:
|
|
assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
|
|
assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
|
|
|
|
self.height = sample_size
|
|
self.width = sample_size
|
|
self.num_vector_embeds = num_vector_embeds
|
|
self.num_latent_pixels = self.height * self.width
|
|
|
|
self.latent_image_embedding = ImagePositionalEmbeddings(
|
|
num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
|
|
)
|
|
elif self.is_input_patches:
|
|
assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
|
|
|
|
self.height = sample_size
|
|
self.width = sample_size
|
|
|
|
self.patch_size = patch_size
|
|
interpolation_scale = self.config.sample_size // 64 # => 64 (= 512 pixart) has interpolation scale 1
|
|
interpolation_scale = max(interpolation_scale, 1)
|
|
self.pos_embed = PatchEmbed(
|
|
height=sample_size,
|
|
width=sample_size,
|
|
patch_size=patch_size,
|
|
in_channels=in_channels,
|
|
embed_dim=inner_dim,
|
|
interpolation_scale=interpolation_scale,
|
|
)
|
|
|
|
# 3. Define transformers blocks
|
|
self.transformer_blocks = nn.ModuleList(
|
|
[
|
|
BasicTransformerBlock(
|
|
inner_dim,
|
|
num_attention_heads,
|
|
attention_head_dim,
|
|
dropout=dropout,
|
|
cross_attention_dim=cross_attention_dim,
|
|
activation_fn=activation_fn,
|
|
num_embeds_ada_norm=num_embeds_ada_norm,
|
|
attention_bias=attention_bias,
|
|
only_cross_attention=only_cross_attention,
|
|
double_self_attention=double_self_attention,
|
|
upcast_attention=upcast_attention,
|
|
norm_type=norm_type,
|
|
norm_elementwise_affine=norm_elementwise_affine,
|
|
norm_eps=norm_eps,
|
|
attention_type=attention_type,
|
|
block_idx=d,
|
|
)
|
|
for d in range(num_layers)
|
|
]
|
|
)
|
|
|
|
# Define temporal transformers blocks
|
|
self.temporal_transformer_blocks = nn.ModuleList(
|
|
[
|
|
BasicTransformerBlock_( # one attention
|
|
inner_dim,
|
|
num_attention_heads, # num_attention_heads
|
|
attention_head_dim, # attention_head_dim 72
|
|
dropout=dropout,
|
|
cross_attention_dim=None,
|
|
activation_fn=activation_fn,
|
|
num_embeds_ada_norm=num_embeds_ada_norm,
|
|
attention_bias=attention_bias,
|
|
only_cross_attention=only_cross_attention,
|
|
double_self_attention=False,
|
|
upcast_attention=upcast_attention,
|
|
norm_type=norm_type,
|
|
norm_elementwise_affine=norm_elementwise_affine,
|
|
norm_eps=norm_eps,
|
|
attention_type=attention_type,
|
|
block_idx=d,
|
|
)
|
|
for d in range(num_layers)
|
|
]
|
|
)
|
|
|
|
# 4. Define output layers
|
|
self.out_channels = in_channels if out_channels is None else out_channels
|
|
if self.is_input_continuous:
|
|
# TODO: should use out_channels for continuous projections
|
|
if use_linear_projection:
|
|
self.proj_out = linear_cls(inner_dim, in_channels)
|
|
else:
|
|
self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
|
|
elif self.is_input_vectorized:
|
|
self.norm_out = nn.LayerNorm(inner_dim)
|
|
self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
|
|
elif self.is_input_patches and norm_type != "ada_norm_single":
|
|
self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
|
|
self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
|
|
self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
|
|
elif self.is_input_patches and norm_type == "ada_norm_single":
|
|
self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
|
|
self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
|
|
self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
|
|
|
|
# 5. PixArt-Alpha blocks.
|
|
self.adaln_single = None
|
|
self.use_additional_conditions = False
|
|
if norm_type == "ada_norm_single":
|
|
self.use_additional_conditions = self.config.sample_size == 128 # False, 128 -> 1024
|
|
# TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
|
|
# additional conditions until we find better name
|
|
self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)
|
|
|
|
self.caption_projection = None
|
|
if caption_channels is not None:
|
|
self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
# define temporal positional embedding
|
|
temp_pos_embed = self.get_1d_sincos_temp_embed(inner_dim, video_length) # 1152 hidden size
|
|
self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False)
|
|
|
|
# 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(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,
|
|
timestep: Optional[torch.LongTensor] = None,
|
|
all_timesteps=None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
added_cond_kwargs: Dict[str, torch.Tensor] = None,
|
|
class_labels: Optional[torch.LongTensor] = None,
|
|
cross_attention_kwargs: Dict[str, Any] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
use_image_num: int = 0,
|
|
enable_temporal_attentions: bool = True,
|
|
return_dict: bool = True,
|
|
):
|
|
"""
|
|
The [`Transformer2DModel`] forward method.
|
|
|
|
Args:
|
|
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous):
|
|
Input `hidden_states`.
|
|
encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
|
|
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
|
|
self-attention.
|
|
timestep ( `torch.LongTensor`, *optional*):
|
|
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
|
|
class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
|
|
Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
|
|
`AdaLayerZeroNorm`.
|
|
cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
attention_mask ( `torch.Tensor`, *optional*):
|
|
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
|
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
|
negative values to the attention scores corresponding to "discard" tokens.
|
|
encoder_attention_mask ( `torch.Tensor`, *optional*):
|
|
Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
|
|
|
|
* Mask `(batch, sequence_length)` True = keep, False = discard.
|
|
* Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
|
|
|
|
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
|
|
above. This bias will be added to the cross-attention scores.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
|
|
tuple.
|
|
|
|
Returns:
|
|
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
|
|
`tuple` where the first element is the sample tensor.
|
|
"""
|
|
|
|
# 0. Split batch for data parallelism
|
|
if self.parallel_manager.cp_size > 1:
|
|
(
|
|
hidden_states,
|
|
timestep,
|
|
encoder_hidden_states,
|
|
added_cond_kwargs,
|
|
class_labels,
|
|
attention_mask,
|
|
encoder_attention_mask,
|
|
) = batch_func(
|
|
partial(split_sequence, process_group=self.parallel_manager.cp_group, dim=0),
|
|
hidden_states,
|
|
timestep,
|
|
encoder_hidden_states,
|
|
added_cond_kwargs,
|
|
class_labels,
|
|
attention_mask,
|
|
encoder_attention_mask,
|
|
)
|
|
|
|
input_batch_size, c, frame, h, w = hidden_states.shape
|
|
frame = frame - use_image_num
|
|
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w").contiguous()
|
|
org_timestep = timestep
|
|
|
|
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
|
|
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
|
|
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
|
|
# expects mask of shape:
|
|
# [batch, key_tokens]
|
|
# adds singleton query_tokens dimension:
|
|
# [batch, 1, key_tokens]
|
|
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
|
|
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
|
|
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
|
|
if attention_mask is not None and attention_mask.ndim == 2:
|
|
# assume that mask is expressed as:
|
|
# (1 = keep, 0 = discard)
|
|
# convert mask into a bias that can be added to attention scores:
|
|
# (keep = +0, discard = -10000.0)
|
|
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
|
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: # ndim == 2 means no image joint
|
|
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
|
|
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
|
encoder_attention_mask = repeat(encoder_attention_mask, "b 1 l -> (b f) 1 l", f=frame).contiguous()
|
|
elif encoder_attention_mask is not None and encoder_attention_mask.ndim == 3: # ndim == 3 means image joint
|
|
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
|
|
encoder_attention_mask_video = encoder_attention_mask[:, :1, ...]
|
|
encoder_attention_mask_video = repeat(
|
|
encoder_attention_mask_video, "b 1 l -> b (1 f) l", f=frame
|
|
).contiguous()
|
|
encoder_attention_mask_image = encoder_attention_mask[:, 1:, ...]
|
|
encoder_attention_mask = torch.cat([encoder_attention_mask_video, encoder_attention_mask_image], dim=1)
|
|
encoder_attention_mask = rearrange(encoder_attention_mask, "b n l -> (b n) l").contiguous().unsqueeze(1)
|
|
|
|
# Retrieve lora scale.
|
|
cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
|
|
|
|
# 1. Input
|
|
if self.is_input_patches: # here
|
|
height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
|
|
num_patches = height * width
|
|
|
|
hidden_states = self.pos_embed(hidden_states) # alrady add positional embeddings
|
|
|
|
if self.adaln_single is not None:
|
|
if self.use_additional_conditions and added_cond_kwargs is None:
|
|
raise ValueError(
|
|
"`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
|
|
)
|
|
# batch_size = hidden_states.shape[0]
|
|
batch_size = input_batch_size
|
|
timestep, embedded_timestep = self.adaln_single(
|
|
timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
|
|
)
|
|
|
|
# 2. Blocks
|
|
if self.caption_projection is not None:
|
|
batch_size = hidden_states.shape[0]
|
|
encoder_hidden_states = self.caption_projection(encoder_hidden_states) # 3 120 1152
|
|
|
|
if use_image_num != 0 and self.training:
|
|
encoder_hidden_states_video = encoder_hidden_states[:, :1, ...]
|
|
encoder_hidden_states_video = repeat(
|
|
encoder_hidden_states_video, "b 1 t d -> b (1 f) t d", f=frame
|
|
).contiguous()
|
|
encoder_hidden_states_image = encoder_hidden_states[:, 1:, ...]
|
|
encoder_hidden_states = torch.cat([encoder_hidden_states_video, encoder_hidden_states_image], dim=1)
|
|
encoder_hidden_states_spatial = rearrange(encoder_hidden_states, "b f t d -> (b f) t d").contiguous()
|
|
else:
|
|
encoder_hidden_states_spatial = repeat(
|
|
encoder_hidden_states, "b t d -> (b f) t d", f=frame
|
|
).contiguous()
|
|
|
|
# prepare timesteps for spatial and temporal block
|
|
timestep_spatial = repeat(timestep, "b d -> (b f) d", f=frame + use_image_num).contiguous()
|
|
timestep_temp = repeat(timestep, "b d -> (b p) d", p=num_patches).contiguous()
|
|
|
|
if self.parallel_manager.sp_size > 1:
|
|
set_pad("temporal", frame + use_image_num, self.parallel_manager.sp_group)
|
|
set_pad("spatial", num_patches, self.parallel_manager.sp_group)
|
|
hidden_states = self.split_from_second_dim(hidden_states, input_batch_size)
|
|
encoder_hidden_states_spatial = self.split_from_second_dim(encoder_hidden_states_spatial, input_batch_size)
|
|
timestep_spatial = self.split_from_second_dim(timestep_spatial, input_batch_size)
|
|
temp_pos_embed = split_sequence(
|
|
self.temp_pos_embed, self.parallel_manager.sp_group, dim=1, grad_scale="down", pad=get_pad("temporal")
|
|
)
|
|
else:
|
|
temp_pos_embed = self.temp_pos_embed
|
|
|
|
for i, (spatial_block, temp_block) in enumerate(zip(self.transformer_blocks, self.temporal_transformer_blocks)):
|
|
if self.training and self.gradient_checkpointing:
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
spatial_block,
|
|
hidden_states,
|
|
attention_mask,
|
|
encoder_hidden_states_spatial,
|
|
encoder_attention_mask,
|
|
timestep_spatial,
|
|
cross_attention_kwargs,
|
|
class_labels,
|
|
use_reentrant=False,
|
|
)
|
|
|
|
if enable_temporal_attentions:
|
|
hidden_states = rearrange(hidden_states, "(b f) t d -> (b t) f d", b=input_batch_size).contiguous()
|
|
|
|
if use_image_num != 0: # image-video joitn training
|
|
hidden_states_video = hidden_states[:, :frame, ...]
|
|
hidden_states_image = hidden_states[:, frame:, ...]
|
|
|
|
if i == 0:
|
|
hidden_states_video = hidden_states_video + temp_pos_embed
|
|
|
|
hidden_states_video = torch.utils.checkpoint.checkpoint(
|
|
temp_block,
|
|
hidden_states_video,
|
|
None, # attention_mask
|
|
None, # encoder_hidden_states
|
|
None, # encoder_attention_mask
|
|
timestep_temp,
|
|
cross_attention_kwargs,
|
|
class_labels,
|
|
use_reentrant=False,
|
|
)
|
|
|
|
hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1)
|
|
hidden_states = rearrange(
|
|
hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size
|
|
).contiguous()
|
|
|
|
else:
|
|
if i == 0:
|
|
hidden_states = hidden_states + temp_pos_embed
|
|
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
temp_block,
|
|
hidden_states,
|
|
None, # attention_mask
|
|
None, # encoder_hidden_states
|
|
None, # encoder_attention_mask
|
|
timestep_temp,
|
|
cross_attention_kwargs,
|
|
class_labels,
|
|
use_reentrant=False,
|
|
)
|
|
|
|
hidden_states = rearrange(
|
|
hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size
|
|
).contiguous()
|
|
else:
|
|
hidden_states = spatial_block(
|
|
hidden_states,
|
|
attention_mask,
|
|
encoder_hidden_states_spatial,
|
|
encoder_attention_mask,
|
|
timestep_spatial,
|
|
cross_attention_kwargs,
|
|
class_labels,
|
|
None,
|
|
org_timestep,
|
|
all_timesteps=all_timesteps,
|
|
)
|
|
|
|
if enable_temporal_attentions:
|
|
hidden_states = rearrange(hidden_states, "(b f) t d -> (b t) f d", b=input_batch_size).contiguous()
|
|
|
|
if use_image_num != 0 and self.training:
|
|
hidden_states_video = hidden_states[:, :frame, ...]
|
|
hidden_states_image = hidden_states[:, frame:, ...]
|
|
|
|
hidden_states_video = temp_block(
|
|
hidden_states_video,
|
|
None, # attention_mask
|
|
None, # encoder_hidden_states
|
|
None, # encoder_attention_mask
|
|
timestep_temp,
|
|
cross_attention_kwargs,
|
|
class_labels,
|
|
org_timestep,
|
|
)
|
|
|
|
hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1)
|
|
hidden_states = rearrange(
|
|
hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size
|
|
).contiguous()
|
|
|
|
else:
|
|
if i == 0 and frame > 1:
|
|
hidden_states = hidden_states + temp_pos_embed
|
|
hidden_states = temp_block(
|
|
hidden_states,
|
|
None, # attention_mask
|
|
None, # encoder_hidden_states
|
|
None, # encoder_attention_mask
|
|
timestep_temp,
|
|
cross_attention_kwargs,
|
|
class_labels,
|
|
org_timestep,
|
|
all_timesteps=all_timesteps,
|
|
)
|
|
|
|
hidden_states = rearrange(
|
|
hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size
|
|
).contiguous()
|
|
|
|
if self.parallel_manager.sp_size > 1:
|
|
hidden_states = self.gather_from_second_dim(hidden_states, input_batch_size)
|
|
|
|
if self.is_input_patches:
|
|
if self.config.norm_type != "ada_norm_single":
|
|
conditioning = self.transformer_blocks[0].norm1.emb(
|
|
timestep, class_labels, hidden_dtype=hidden_states.dtype
|
|
)
|
|
shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
|
|
hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
|
|
hidden_states = self.proj_out_2(hidden_states)
|
|
elif self.config.norm_type == "ada_norm_single":
|
|
embedded_timestep = repeat(embedded_timestep, "b d -> (b f) d", f=frame + use_image_num).contiguous()
|
|
shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
|
|
hidden_states = self.norm_out(hidden_states)
|
|
# Modulation
|
|
hidden_states = hidden_states * (1 + scale) + shift
|
|
hidden_states = self.proj_out(hidden_states)
|
|
|
|
# unpatchify
|
|
if self.adaln_single is None:
|
|
height = width = int(hidden_states.shape[1] ** 0.5)
|
|
hidden_states = hidden_states.reshape(
|
|
shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
|
|
)
|
|
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
|
|
output = hidden_states.reshape(
|
|
shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
|
|
)
|
|
output = rearrange(output, "(b f) c h w -> b c f h w", b=input_batch_size).contiguous()
|
|
|
|
# 3. Gather batch for data parallelism
|
|
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 Transformer3DModelOutput(sample=output)
|
|
|
|
def get_1d_sincos_temp_embed(self, embed_dim, length):
|
|
pos = torch.arange(0, length).unsqueeze(1)
|
|
return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
|
|
|
|
def split_from_second_dim(self, x, batch_size):
|
|
x = x.view(batch_size, -1, *x.shape[1:])
|
|
x = split_sequence(x, self.parallel_manager.sp_group, dim=1, grad_scale="down", pad=get_pad("temporal"))
|
|
x = x.reshape(-1, *x.shape[2:])
|
|
return x
|
|
|
|
def gather_from_second_dim(self, x, batch_size):
|
|
x = x.view(batch_size, -1, *x.shape[1:])
|
|
x = gather_sequence(x, self.parallel_manager.sp_group, dim=1, grad_scale="up", pad=get_pad("temporal"))
|
|
x = x.reshape(-1, *x.shape[2:])
|
|
return x
|