diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml index 9de458b1e..f7d30a9a4 100644 --- a/.github/workflows/stable-release.yml +++ b/.github/workflows/stable-release.yml @@ -22,7 +22,7 @@ on: description: 'Python patch version' required: true type: string - default: "8" + default: "9" jobs: diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml index afbbb7afe..7a8ec5782 100644 --- a/.github/workflows/windows_release_dependencies.yml +++ b/.github/workflows/windows_release_dependencies.yml @@ -29,7 +29,7 @@ on: description: 'python patch version' required: true type: string - default: "8" + default: "9" # push: # branches: # - master diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index b68400177..416544f71 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -19,7 +19,7 @@ on: description: 'python patch version' required: true type: string - default: "8" + default: "9" # push: # branches: # - master diff --git a/app/frontend_management.py b/app/frontend_management.py index 20345faf1..e4d589209 100644 --- a/app/frontend_management.py +++ b/app/frontend_management.py @@ -22,7 +22,8 @@ try: import comfyui_frontend_package except ImportError: # TODO: Remove the check after roll out of 0.3.16 - logging.error(f"\n\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed. Please install the updated requirements.txt file by running:\n{sys.executable} -m pip install -r requirements.txt\n\nThis error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.\n********** ERROR **********\n") + req_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'requirements.txt')) + logging.error(f"\n\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed. Please install the updated requirements.txt file by running:\n{sys.executable} -m pip install -r {req_path}\n\nThis error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.\n\nIf you are on the portable package you can run: update\\update_comfyui.bat to solve this problem\n********** ERROR **********\n") exit(-1) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index c99c9e65e..a864205be 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -1,7 +1,6 @@ import argparse import enum import os -from typing import Optional import comfy.options @@ -166,13 +165,14 @@ parser.add_argument( """, ) -def is_valid_directory(path: Optional[str]) -> Optional[str]: - """Validate if the given path is a directory.""" - if path is None: - return None - +def is_valid_directory(path: str) -> str: + """Validate if the given path is a directory, and check permissions.""" + if not os.path.exists(path): + raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.") if not os.path.isdir(path): - raise argparse.ArgumentTypeError(f"{path} is not a valid directory.") + raise argparse.ArgumentTypeError(f"'{path}' is not a directory.") + if not os.access(path, os.R_OK): + raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.") return path parser.add_argument( diff --git a/comfy/clip_model.py b/comfy/clip_model.py index cf5b58b62..c8294d483 100644 --- a/comfy/clip_model.py +++ b/comfy/clip_model.py @@ -97,8 +97,12 @@ class CLIPTextModel_(torch.nn.Module): self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32): - x = self.embeddings(input_tokens, dtype=dtype) + def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32): + if embeds is not None: + x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device) + else: + x = self.embeddings(input_tokens, dtype=dtype) + mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) @@ -116,7 +120,10 @@ class CLIPTextModel_(torch.nn.Module): if i is not None and final_layer_norm_intermediate: i = self.final_layer_norm(i) - pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] + if num_tokens is not None: + pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))] + else: + pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] return x, i, pooled_output class CLIPTextModel(torch.nn.Module): @@ -204,6 +211,15 @@ class CLIPVision(torch.nn.Module): pooled_output = self.post_layernorm(x[:, 0, :]) return x, i, pooled_output +class LlavaProjector(torch.nn.Module): + def __init__(self, in_dim, out_dim, dtype, device, operations): + super().__init__() + self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype) + self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype) + + def forward(self, x): + return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:]))) + class CLIPVisionModelProjection(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() @@ -213,7 +229,16 @@ class CLIPVisionModelProjection(torch.nn.Module): else: self.visual_projection = lambda a: a + if "llava3" == config_dict.get("projector_type", None): + self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations) + else: + self.multi_modal_projector = None + def forward(self, *args, **kwargs): x = self.vision_model(*args, **kwargs) out = self.visual_projection(x[2]) - return (x[0], x[1], out) + projected = None + if self.multi_modal_projector is not None: + projected = self.multi_modal_projector(x[1]) + + return (x[0], x[1], out, projected) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index c9c82e9ad..297b3bca3 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -65,6 +65,7 @@ class ClipVisionModel(): outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) + outputs["mm_projected"] = out[3] return outputs def convert_to_transformers(sd, prefix): @@ -104,7 +105,10 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False): if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json") elif sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") + if "multi_modal_projector.linear_1.bias" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json") + else: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") else: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") else: diff --git a/comfy/clip_vision_config_vitl_336_llava.json b/comfy/clip_vision_config_vitl_336_llava.json new file mode 100644 index 000000000..f23a50d8b --- /dev/null +++ b/comfy/clip_vision_config_vitl_336_llava.json @@ -0,0 +1,19 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 336, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-5, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "projector_type": "llava3", + "torch_dtype": "float32" +} diff --git a/comfy/comfy_types/__init__.py b/comfy/comfy_types/__init__.py index 19ec33f98..7640fbe3f 100644 --- a/comfy/comfy_types/__init__.py +++ b/comfy/comfy_types/__init__.py @@ -1,6 +1,6 @@ import torch from typing import Callable, Protocol, TypedDict, Optional, List -from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin +from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator class UnetApplyFunction(Protocol): @@ -42,4 +42,5 @@ __all__ = [ InputTypeDict.__name__, ComfyNodeABC.__name__, CheckLazyMixin.__name__, + FileLocator.__name__, ] diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index 0696dbe5e..fe130567d 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -134,6 +134,8 @@ class InputTypeOptions(TypedDict): """ remote: RemoteInputOptions """Specifies the configuration for a remote input.""" + control_after_generate: bool + """Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types.""" class HiddenInputTypeDict(TypedDict): @@ -293,3 +295,14 @@ class CheckLazyMixin: need = [name for name in kwargs if kwargs[name] is None] return need + + +class FileLocator(TypedDict): + """Provides type hinting for the file location""" + + filename: str + """The filename of the file.""" + subfolder: str + """The subfolder of the file.""" + type: Literal["input", "output", "temp"] + """The root folder of the file.""" diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index 2a02acd65..6e8e06181 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -7,7 +7,7 @@ from einops import rearrange import math from typing import Dict, Optional, Tuple -from .symmetric_patchifier import SymmetricPatchifier +from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords def get_timestep_embedding( @@ -377,12 +377,16 @@ class LTXVModel(torch.nn.Module): positional_embedding_theta=10000.0, positional_embedding_max_pos=[20, 2048, 2048], + causal_temporal_positioning=False, + vae_scale_factors=(8, 32, 32), dtype=None, device=None, operations=None, **kwargs): super().__init__() self.generator = None + self.vae_scale_factors = vae_scale_factors self.dtype = dtype self.out_channels = in_channels self.inner_dim = num_attention_heads * attention_head_dim + self.causal_temporal_positioning = causal_temporal_positioning self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device) @@ -416,42 +420,23 @@ class LTXVModel(torch.nn.Module): self.patchifier = SymmetricPatchifier(1) - def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, guiding_latent_noise_scale=0, transformer_options={}, **kwargs): + def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs): patches_replace = transformer_options.get("patches_replace", {}) - indices_grid = self.patchifier.get_grid( - orig_num_frames=x.shape[2], - orig_height=x.shape[3], - orig_width=x.shape[4], - batch_size=x.shape[0], - scale_grid=((1 / frame_rate) * 8, 32, 32), - device=x.device, - ) - - if guiding_latent is not None: - ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype) - input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1)) - ts *= input_ts - ts[:, :, 0] = guiding_latent_noise_scale * (input_ts[:, :, 0] ** 2) - timestep = self.patchifier.patchify(ts) - input_x = x.clone() - x[:, :, 0] = guiding_latent[:, :, 0] - if guiding_latent_noise_scale > 0: - if self.generator is None: - self.generator = torch.Generator(device=x.device).manual_seed(42) - elif self.generator.device != x.device: - self.generator = torch.Generator(device=x.device).set_state(self.generator.get_state()) - - noise_shape = [guiding_latent.shape[0], guiding_latent.shape[1], 1, guiding_latent.shape[3], guiding_latent.shape[4]] - scale = guiding_latent_noise_scale * (input_ts ** 2) - guiding_noise = scale * torch.randn(size=noise_shape, device=x.device, generator=self.generator) - - x[:, :, 0] = guiding_noise[:, :, 0] + x[:, :, 0] * (1.0 - scale[:, :, 0]) - - orig_shape = list(x.shape) - x = self.patchifier.patchify(x) + x, latent_coords = self.patchifier.patchify(x) + pixel_coords = latent_to_pixel_coords( + latent_coords=latent_coords, + scale_factors=self.vae_scale_factors, + causal_fix=self.causal_temporal_positioning, + ) + + if keyframe_idxs is not None: + pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs + + fractional_coords = pixel_coords.to(torch.float32) + fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate) x = self.patchify_proj(x) timestep = timestep * 1000.0 @@ -459,7 +444,7 @@ class LTXVModel(torch.nn.Module): if attention_mask is not None and not torch.is_floating_point(attention_mask): attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max - pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype) + pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype) batch_size = x.shape[0] timestep, embedded_timestep = self.adaln_single( @@ -519,8 +504,4 @@ class LTXVModel(torch.nn.Module): out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size), ) - if guiding_latent is not None: - x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0] - - # print("res", x) return x diff --git a/comfy/ldm/lightricks/symmetric_patchifier.py b/comfy/ldm/lightricks/symmetric_patchifier.py index c58dfb20b..4b9972b9f 100644 --- a/comfy/ldm/lightricks/symmetric_patchifier.py +++ b/comfy/ldm/lightricks/symmetric_patchifier.py @@ -6,16 +6,29 @@ from einops import rearrange from torch import Tensor -def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor: - """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError( - f"input has {x.ndim} dims but target_dims is {target_dims}, which is less" - ) - elif dims_to_append == 0: - return x - return x[(...,) + (None,) * dims_to_append] +def latent_to_pixel_coords( + latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False +) -> Tensor: + """ + Converts latent coordinates to pixel coordinates by scaling them according to the VAE's + configuration. + Args: + latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents] + containing the latent corner coordinates of each token. + scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space. + causal_fix (bool): Whether to take into account the different temporal scale + of the first frame. Default = False for backwards compatibility. + Returns: + Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates. + """ + pixel_coords = ( + latent_coords + * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None] + ) + if causal_fix: + # Fix temporal scale for first frame to 1 due to causality + pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0) + return pixel_coords class Patchifier(ABC): @@ -44,29 +57,26 @@ class Patchifier(ABC): def patch_size(self): return self._patch_size - def get_grid( - self, orig_num_frames, orig_height, orig_width, batch_size, scale_grid, device + def get_latent_coords( + self, latent_num_frames, latent_height, latent_width, batch_size, device ): - f = orig_num_frames // self._patch_size[0] - h = orig_height // self._patch_size[1] - w = orig_width // self._patch_size[2] - grid_h = torch.arange(h, dtype=torch.float32, device=device) - grid_w = torch.arange(w, dtype=torch.float32, device=device) - grid_f = torch.arange(f, dtype=torch.float32, device=device) - grid = torch.meshgrid(grid_f, grid_h, grid_w, indexing='ij') - grid = torch.stack(grid, dim=0) - grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) - - if scale_grid is not None: - for i in range(3): - if isinstance(scale_grid[i], Tensor): - scale = append_dims(scale_grid[i], grid.ndim - 1) - else: - scale = scale_grid[i] - grid[:, i, ...] = grid[:, i, ...] * scale * self._patch_size[i] - - grid = rearrange(grid, "b c f h w -> b c (f h w)", b=batch_size) - return grid + """ + Return a tensor of shape [batch_size, 3, num_patches] containing the + top-left corner latent coordinates of each latent patch. + The tensor is repeated for each batch element. + """ + latent_sample_coords = torch.meshgrid( + torch.arange(0, latent_num_frames, self._patch_size[0], device=device), + torch.arange(0, latent_height, self._patch_size[1], device=device), + torch.arange(0, latent_width, self._patch_size[2], device=device), + indexing="ij", + ) + latent_sample_coords = torch.stack(latent_sample_coords, dim=0) + latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) + latent_coords = rearrange( + latent_coords, "b c f h w -> b c (f h w)", b=batch_size + ) + return latent_coords class SymmetricPatchifier(Patchifier): @@ -74,6 +84,8 @@ class SymmetricPatchifier(Patchifier): self, latents: Tensor, ) -> Tuple[Tensor, Tensor]: + b, _, f, h, w = latents.shape + latent_coords = self.get_latent_coords(f, h, w, b, latents.device) latents = rearrange( latents, "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)", @@ -81,7 +93,7 @@ class SymmetricPatchifier(Patchifier): p2=self._patch_size[1], p3=self._patch_size[2], ) - return latents + return latents, latent_coords def unpatchify( self, diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py index c572e7e86..70d612e86 100644 --- a/comfy/ldm/lightricks/vae/causal_conv3d.py +++ b/comfy/ldm/lightricks/vae/causal_conv3d.py @@ -15,6 +15,7 @@ class CausalConv3d(nn.Module): stride: Union[int, Tuple[int]] = 1, dilation: int = 1, groups: int = 1, + spatial_padding_mode: str = "zeros", **kwargs, ): super().__init__() @@ -38,7 +39,7 @@ class CausalConv3d(nn.Module): stride=stride, dilation=dilation, padding=padding, - padding_mode="zeros", + padding_mode=spatial_padding_mode, groups=groups, ) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index e0344deec..f91870d71 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -1,13 +1,15 @@ +from __future__ import annotations import torch from torch import nn from functools import partial import math from einops import rearrange -from typing import Optional, Tuple, Union +from typing import List, Optional, Tuple, Union from .conv_nd_factory import make_conv_nd, make_linear_nd from .pixel_norm import PixelNorm from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings import comfy.ops + ops = comfy.ops.disable_weight_init class Encoder(nn.Module): @@ -32,7 +34,7 @@ class Encoder(nn.Module): norm_layer (`str`, *optional*, defaults to `group_norm`): The normalization layer to use. Can be either `group_norm` or `pixel_norm`. latent_log_var (`str`, *optional*, defaults to `per_channel`): - The number of channels for the log variance. Can be either `per_channel`, `uniform`, or `none`. + The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`. """ def __init__( @@ -40,12 +42,13 @@ class Encoder(nn.Module): dims: Union[int, Tuple[int, int]] = 3, in_channels: int = 3, out_channels: int = 3, - blocks=[("res_x", 1)], + blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], base_channels: int = 128, norm_num_groups: int = 32, patch_size: Union[int, Tuple[int]] = 1, norm_layer: str = "group_norm", # group_norm, pixel_norm latent_log_var: str = "per_channel", + spatial_padding_mode: str = "zeros", ): super().__init__() self.patch_size = patch_size @@ -65,6 +68,7 @@ class Encoder(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.down_blocks = nn.ModuleList([]) @@ -82,6 +86,7 @@ class Encoder(nn.Module): resnet_eps=1e-6, resnet_groups=norm_num_groups, norm_layer=norm_layer, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "res_x_y": output_channel = block_params.get("multiplier", 2) * output_channel @@ -92,6 +97,7 @@ class Encoder(nn.Module): eps=1e-6, groups=norm_num_groups, norm_layer=norm_layer, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time": block = make_conv_nd( @@ -101,6 +107,7 @@ class Encoder(nn.Module): kernel_size=3, stride=(2, 1, 1), causal=True, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space": block = make_conv_nd( @@ -110,6 +117,7 @@ class Encoder(nn.Module): kernel_size=3, stride=(1, 2, 2), causal=True, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all": block = make_conv_nd( @@ -119,6 +127,7 @@ class Encoder(nn.Module): kernel_size=3, stride=(2, 2, 2), causal=True, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all_x_y": output_channel = block_params.get("multiplier", 2) * output_channel @@ -129,6 +138,34 @@ class Encoder(nn.Module): kernel_size=3, stride=(2, 2, 2), causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_all_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(2, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_space_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(1, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_time_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(2, 1, 1), + spatial_padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unknown block: {block_name}") @@ -152,10 +189,18 @@ class Encoder(nn.Module): conv_out_channels *= 2 elif latent_log_var == "uniform": conv_out_channels += 1 + elif latent_log_var == "constant": + conv_out_channels += 1 elif latent_log_var != "none": raise ValueError(f"Invalid latent_log_var: {latent_log_var}") self.conv_out = make_conv_nd( - dims, output_channel, conv_out_channels, 3, padding=1, causal=True + dims, + output_channel, + conv_out_channels, + 3, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.gradient_checkpointing = False @@ -197,6 +242,15 @@ class Encoder(nn.Module): sample = torch.cat([sample, repeated_last_channel], dim=1) else: raise ValueError(f"Invalid input shape: {sample.shape}") + elif self.latent_log_var == "constant": + sample = sample[:, :-1, ...] + approx_ln_0 = ( + -30 + ) # this is the minimal clamp value in DiagonalGaussianDistribution objects + sample = torch.cat( + [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0], + dim=1, + ) return sample @@ -231,7 +285,7 @@ class Decoder(nn.Module): dims, in_channels: int = 3, out_channels: int = 3, - blocks=[("res_x", 1)], + blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], base_channels: int = 128, layers_per_block: int = 2, norm_num_groups: int = 32, @@ -239,6 +293,7 @@ class Decoder(nn.Module): norm_layer: str = "group_norm", causal: bool = True, timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", ): super().__init__() self.patch_size = patch_size @@ -264,6 +319,7 @@ class Decoder(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.up_blocks = nn.ModuleList([]) @@ -283,6 +339,7 @@ class Decoder(nn.Module): norm_layer=norm_layer, inject_noise=block_params.get("inject_noise", False), timestep_conditioning=timestep_conditioning, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "attn_res_x": block = UNetMidBlock3D( @@ -294,6 +351,7 @@ class Decoder(nn.Module): inject_noise=block_params.get("inject_noise", False), timestep_conditioning=timestep_conditioning, attention_head_dim=block_params["attention_head_dim"], + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "res_x_y": output_channel = output_channel // block_params.get("multiplier", 2) @@ -306,14 +364,21 @@ class Decoder(nn.Module): norm_layer=norm_layer, inject_noise=block_params.get("inject_noise", False), timestep_conditioning=False, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time": block = DepthToSpaceUpsample( - dims=dims, in_channels=input_channel, stride=(2, 1, 1) + dims=dims, + in_channels=input_channel, + stride=(2, 1, 1), + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space": block = DepthToSpaceUpsample( - dims=dims, in_channels=input_channel, stride=(1, 2, 2) + dims=dims, + in_channels=input_channel, + stride=(1, 2, 2), + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all": output_channel = output_channel // block_params.get("multiplier", 1) @@ -323,6 +388,7 @@ class Decoder(nn.Module): stride=(2, 2, 2), residual=block_params.get("residual", False), out_channels_reduction_factor=block_params.get("multiplier", 1), + spatial_padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unknown layer: {block_name}") @@ -340,7 +406,13 @@ class Decoder(nn.Module): self.conv_act = nn.SiLU() self.conv_out = make_conv_nd( - dims, output_channel, out_channels, 3, padding=1, causal=True + dims, + output_channel, + out_channels, + 3, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.gradient_checkpointing = False @@ -433,6 +505,12 @@ class UNetMidBlock3D(nn.Module): resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. resnet_groups (`int`, *optional*, defaults to 32): The number of groups to use in the group normalization layers of the resnet blocks. + norm_layer (`str`, *optional*, defaults to `group_norm`): + The normalization layer to use. Can be either `group_norm` or `pixel_norm`. + inject_noise (`bool`, *optional*, defaults to `False`): + Whether to inject noise into the hidden states. + timestep_conditioning (`bool`, *optional*, defaults to `False`): + Whether to condition the hidden states on the timestep. Returns: `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, @@ -451,6 +529,7 @@ class UNetMidBlock3D(nn.Module): norm_layer: str = "group_norm", inject_noise: bool = False, timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", ): super().__init__() resnet_groups = ( @@ -476,13 +555,17 @@ class UNetMidBlock3D(nn.Module): norm_layer=norm_layer, inject_noise=inject_noise, timestep_conditioning=timestep_conditioning, + spatial_padding_mode=spatial_padding_mode, ) for _ in range(num_layers) ] ) def forward( - self, hidden_states: torch.FloatTensor, causal: bool = True, timestep: Optional[torch.Tensor] = None + self, + hidden_states: torch.FloatTensor, + causal: bool = True, + timestep: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: timestep_embed = None if self.timestep_conditioning: @@ -507,9 +590,62 @@ class UNetMidBlock3D(nn.Module): return hidden_states +class SpaceToDepthDownsample(nn.Module): + def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode): + super().__init__() + self.stride = stride + self.group_size = in_channels * math.prod(stride) // out_channels + self.conv = make_conv_nd( + dims=dims, + in_channels=in_channels, + out_channels=out_channels // math.prod(stride), + kernel_size=3, + stride=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + def forward(self, x, causal: bool = True): + if self.stride[0] == 2: + x = torch.cat( + [x[:, :, :1, :, :], x], dim=2 + ) # duplicate first frames for padding + + # skip connection + x_in = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + x_in = rearrange(x_in, "b (c g) d h w -> b c g d h w", g=self.group_size) + x_in = x_in.mean(dim=2) + + # conv + x = self.conv(x, causal=causal) + x = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + + x = x + x_in + + return x + + class DepthToSpaceUpsample(nn.Module): def __init__( - self, dims, in_channels, stride, residual=False, out_channels_reduction_factor=1 + self, + dims, + in_channels, + stride, + residual=False, + out_channels_reduction_factor=1, + spatial_padding_mode="zeros", ): super().__init__() self.stride = stride @@ -523,6 +659,7 @@ class DepthToSpaceUpsample(nn.Module): kernel_size=3, stride=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.residual = residual self.out_channels_reduction_factor = out_channels_reduction_factor @@ -558,7 +695,7 @@ class DepthToSpaceUpsample(nn.Module): class LayerNorm(nn.Module): def __init__(self, dim, eps, elementwise_affine=True) -> None: super().__init__() - self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) + self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) def forward(self, x): x = rearrange(x, "b c d h w -> b d h w c") @@ -591,6 +728,7 @@ class ResnetBlock3D(nn.Module): norm_layer: str = "group_norm", inject_noise: bool = False, timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", ): super().__init__() self.in_channels = in_channels @@ -617,6 +755,7 @@ class ResnetBlock3D(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) if inject_noise: @@ -641,6 +780,7 @@ class ResnetBlock3D(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) if inject_noise: @@ -801,9 +941,44 @@ class processor(nn.Module): return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x) class VideoVAE(nn.Module): - def __init__(self, version=0): + def __init__(self, version=0, config=None): super().__init__() + if config is None: + config = self.guess_config(version) + + self.timestep_conditioning = config.get("timestep_conditioning", False) + double_z = config.get("double_z", True) + latent_log_var = config.get( + "latent_log_var", "per_channel" if double_z else "none" + ) + + self.encoder = Encoder( + dims=config["dims"], + in_channels=config.get("in_channels", 3), + out_channels=config["latent_channels"], + blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), + patch_size=config.get("patch_size", 1), + latent_log_var=latent_log_var, + norm_layer=config.get("norm_layer", "group_norm"), + spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), + ) + + self.decoder = Decoder( + dims=config["dims"], + in_channels=config["latent_channels"], + out_channels=config.get("out_channels", 3), + blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), + patch_size=config.get("patch_size", 1), + norm_layer=config.get("norm_layer", "group_norm"), + causal=config.get("causal_decoder", False), + timestep_conditioning=self.timestep_conditioning, + spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), + ) + + self.per_channel_statistics = processor() + + def guess_config(self, version): if version == 0: config = { "_class_name": "CausalVideoAutoencoder", @@ -830,7 +1005,7 @@ class VideoVAE(nn.Module): "use_quant_conv": False, "causal_decoder": False, } - else: + elif version == 1: config = { "_class_name": "CausalVideoAutoencoder", "dims": 3, @@ -866,37 +1041,47 @@ class VideoVAE(nn.Module): "causal_decoder": False, "timestep_conditioning": True, } - - double_z = config.get("double_z", True) - latent_log_var = config.get( - "latent_log_var", "per_channel" if double_z else "none" - ) - - self.encoder = Encoder( - dims=config["dims"], - in_channels=config.get("in_channels", 3), - out_channels=config["latent_channels"], - blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), - patch_size=config.get("patch_size", 1), - latent_log_var=latent_log_var, - norm_layer=config.get("norm_layer", "group_norm"), - ) - - self.decoder = Decoder( - dims=config["dims"], - in_channels=config["latent_channels"], - out_channels=config.get("out_channels", 3), - blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), - patch_size=config.get("patch_size", 1), - norm_layer=config.get("norm_layer", "group_norm"), - causal=config.get("causal_decoder", False), - timestep_conditioning=config.get("timestep_conditioning", False), - ) - - self.timestep_conditioning = config.get("timestep_conditioning", False) - self.per_channel_statistics = processor() + else: + config = { + "_class_name": "CausalVideoAutoencoder", + "dims": 3, + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "encoder_blocks": [ + ["res_x", {"num_layers": 4}], + ["compress_space_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_time_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}] + ], + "decoder_blocks": [ + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}] + ], + "scaling_factor": 1.0, + "norm_layer": "pixel_norm", + "patch_size": 4, + "latent_log_var": "uniform", + "use_quant_conv": False, + "causal_decoder": False, + "timestep_conditioning": True + } + return config def encode(self, x): + frames_count = x.shape[2] + if ((frames_count - 1) % 8) != 0: + raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.") means, logvar = torch.chunk(self.encoder(x), 2, dim=1) return self.per_channel_statistics.normalize(means) diff --git a/comfy/ldm/lightricks/vae/conv_nd_factory.py b/comfy/ldm/lightricks/vae/conv_nd_factory.py index 52df4ee22..b4026b14f 100644 --- a/comfy/ldm/lightricks/vae/conv_nd_factory.py +++ b/comfy/ldm/lightricks/vae/conv_nd_factory.py @@ -17,7 +17,11 @@ def make_conv_nd( groups=1, bias=True, causal=False, + spatial_padding_mode="zeros", + temporal_padding_mode="zeros", ): + if not (spatial_padding_mode == temporal_padding_mode or causal): + raise NotImplementedError("spatial and temporal padding modes must be equal") if dims == 2: return ops.Conv2d( in_channels=in_channels, @@ -28,6 +32,7 @@ def make_conv_nd( dilation=dilation, groups=groups, bias=bias, + padding_mode=spatial_padding_mode, ) elif dims == 3: if causal: @@ -40,6 +45,7 @@ def make_conv_nd( dilation=dilation, groups=groups, bias=bias, + spatial_padding_mode=spatial_padding_mode, ) return ops.Conv3d( in_channels=in_channels, @@ -50,6 +56,7 @@ def make_conv_nd( dilation=dilation, groups=groups, bias=bias, + padding_mode=spatial_padding_mode, ) elif dims == (2, 1): return DualConv3d( @@ -59,6 +66,7 @@ def make_conv_nd( stride=stride, padding=padding, bias=bias, + padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unsupported dimensions: {dims}") diff --git a/comfy/ldm/lightricks/vae/dual_conv3d.py b/comfy/ldm/lightricks/vae/dual_conv3d.py index 6bd54c0a6..dcf889296 100644 --- a/comfy/ldm/lightricks/vae/dual_conv3d.py +++ b/comfy/ldm/lightricks/vae/dual_conv3d.py @@ -18,11 +18,13 @@ class DualConv3d(nn.Module): dilation: Union[int, Tuple[int, int, int]] = 1, groups=1, bias=True, + padding_mode="zeros", ): super(DualConv3d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels + self.padding_mode = padding_mode # Ensure kernel_size, stride, padding, and dilation are tuples of length 3 if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size, kernel_size) @@ -108,6 +110,7 @@ class DualConv3d(nn.Module): self.padding1, self.dilation1, self.groups, + padding_mode=self.padding_mode, ) if skip_time_conv: @@ -122,6 +125,7 @@ class DualConv3d(nn.Module): self.padding2, self.dilation2, self.groups, + padding_mode=self.padding_mode, ) return x @@ -137,7 +141,16 @@ class DualConv3d(nn.Module): stride1 = (self.stride1[1], self.stride1[2]) padding1 = (self.padding1[1], self.padding1[2]) dilation1 = (self.dilation1[1], self.dilation1[2]) - x = F.conv2d(x, weight1, self.bias1, stride1, padding1, dilation1, self.groups) + x = F.conv2d( + x, + weight1, + self.bias1, + stride1, + padding1, + dilation1, + self.groups, + padding_mode=self.padding_mode, + ) _, _, h, w = x.shape @@ -154,7 +167,16 @@ class DualConv3d(nn.Module): stride2 = self.stride2[0] padding2 = self.padding2[0] dilation2 = self.dilation2[0] - x = F.conv1d(x, weight2, self.bias2, stride2, padding2, dilation2, self.groups) + x = F.conv1d( + x, + weight2, + self.bias2, + stride2, + padding2, + dilation2, + self.groups, + padding_mode=self.padding_mode, + ) x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w) return x diff --git a/comfy/model_base.py b/comfy/model_base.py index 66cd0ded1..a304c58bd 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -161,9 +161,13 @@ class BaseModel(torch.nn.Module): extra = extra.to(dtype) extra_conds[o] = extra + t = self.process_timestep(t, x=x, **extra_conds) model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() return self.model_sampling.calculate_denoised(sigma, model_output, x) + def process_timestep(self, timestep, **kwargs): + return timestep + def get_dtype(self): return self.diffusion_model.dtype @@ -185,6 +189,11 @@ class BaseModel(torch.nn.Module): if concat_latent_image.shape[1:] != noise.shape[1:]: concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + if noise.ndim == 5: + if concat_latent_image.shape[-3] < noise.shape[-3]: + concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), "constant", 0) + else: + concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]] concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) @@ -213,6 +222,11 @@ class BaseModel(torch.nn.Module): cond_concat.append(self.blank_inpaint_image_like(noise)) elif ck == "mask_inverted": cond_concat.append(torch.zeros_like(noise)[:, :1]) + if ck == "concat_image": + if concat_latent_image is not None: + cond_concat.append(concat_latent_image.to(device)) + else: + cond_concat.append(torch.zeros_like(noise)) data = torch.cat(cond_concat, dim=1) return data return None @@ -845,17 +859,26 @@ class LTXV(BaseModel): if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - guiding_latent = kwargs.get("guiding_latent", None) - if guiding_latent is not None: - out['guiding_latent'] = comfy.conds.CONDRegular(guiding_latent) - - guiding_latent_noise_scale = kwargs.get("guiding_latent_noise_scale", None) - if guiding_latent_noise_scale is not None: - out["guiding_latent_noise_scale"] = comfy.conds.CONDConstant(guiding_latent_noise_scale) - out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25)) + + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if denoise_mask is not None: + out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) + + keyframe_idxs = kwargs.get("keyframe_idxs", None) + if keyframe_idxs is not None: + out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs) + return out + def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): + if denoise_mask is None: + return timestep + return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0] + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + return latent_image + class HunyuanVideo(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) @@ -872,20 +895,24 @@ class HunyuanVideo(BaseModel): if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - image = kwargs.get("concat_latent_image", None) - noise = kwargs.get("noise", None) - - if image is not None: - padding_shape = (noise.shape[0], 16, noise.shape[2] - 1, noise.shape[3], noise.shape[4]) - latent_padding = torch.zeros(padding_shape, device=noise.device, dtype=noise.dtype) - image_latents = torch.cat([image.to(noise), latent_padding], dim=2) - out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_latents)) - guidance = kwargs.get("guidance", 6.0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out + +class HunyuanVideoI2V(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + self.concat_keys = ("concat_image", "mask_inverted") + + +class HunyuanVideoSkyreelsI2V(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + self.concat_keys = ("concat_image",) + + class CosmosVideo(BaseModel): def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index f149a4bf7..1aef549f4 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -1,3 +1,4 @@ +import json import comfy.supported_models import comfy.supported_models_base import comfy.utils @@ -33,7 +34,7 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict): return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross return None -def detect_unet_config(state_dict, key_prefix): +def detect_unet_config(state_dict, key_prefix, metadata=None): state_dict_keys = list(state_dict.keys()) if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model @@ -210,6 +211,8 @@ def detect_unet_config(state_dict, key_prefix): if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv dit_config = {} dit_config["image_model"] = "ltxv" + if metadata is not None and "config" in metadata: + dit_config.update(json.loads(metadata["config"]).get("transformer", {})) return dit_config if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt @@ -454,8 +457,8 @@ def model_config_from_unet_config(unet_config, state_dict=None): logging.error("no match {}".format(unet_config)) return None -def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): - unet_config = detect_unet_config(state_dict, unet_key_prefix) +def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None): + unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata) if unet_config is None: return None model_config = model_config_from_unet_config(unet_config, state_dict) diff --git a/comfy/sd.py b/comfy/sd.py index 21913cf3e..fd98585a1 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1,4 +1,5 @@ from __future__ import annotations +import json import torch from enum import Enum import logging @@ -134,8 +135,8 @@ class CLIP: def clip_layer(self, layer_idx): self.layer_idx = layer_idx - def tokenize(self, text, return_word_ids=False): - return self.tokenizer.tokenize_with_weights(text, return_word_ids) + def tokenize(self, text, return_word_ids=False, **kwargs): + return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) def add_hooks_to_dict(self, pooled_dict: dict[str]): if self.apply_hooks_to_conds: @@ -249,7 +250,7 @@ class CLIP: return self.patcher.get_key_patches() class VAE: - def __init__(self, sd=None, device=None, config=None, dtype=None): + def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) @@ -357,7 +358,12 @@ class VAE: version = 0 elif tensor_conv1.shape[0] == 1024: version = 1 - self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version) + if "encoder.down_blocks.1.conv.conv.bias" in sd: + version = 2 + vae_config = None + if metadata is not None and "config" in metadata: + vae_config = json.loads(metadata["config"]).get("vae", None) + self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config) self.latent_channels = 128 self.latent_dim = 3 self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) @@ -873,13 +879,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl return (model, clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): - sd = comfy.utils.load_torch_file(ckpt_path) - out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options) + sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) + out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata) if out is None: raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) return out -def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): +def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None): clip = None clipvision = None vae = None @@ -891,7 +897,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) load_device = model_management.get_torch_device() - model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix) + model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) if model_config is None: return None @@ -920,7 +926,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c if output_vae: vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) - vae = VAE(sd=vae_sd) + vae = VAE(sd=vae_sd, metadata=metadata) if output_clip: clip_target = model_config.clip_target(state_dict=sd) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index d2457731d..22adcbac9 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -158,71 +158,92 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.layer_idx = self.options_default[1] self.return_projected_pooled = self.options_default[2] - def set_up_textual_embeddings(self, tokens, current_embeds): - out_tokens = [] - next_new_token = token_dict_size = current_embeds.weight.shape[0] - embedding_weights = [] + def process_tokens(self, tokens, device): + end_token = self.special_tokens.get("end", None) + if end_token is None: + cmp_token = self.special_tokens.get("pad", -1) + else: + cmp_token = end_token + + embeds_out = [] + attention_masks = [] + num_tokens = [] for x in tokens: + attention_mask = [] tokens_temp = [] + other_embeds = [] + eos = False + index = 0 for y in x: if isinstance(y, numbers.Integral): - tokens_temp += [int(y)] - else: - if y.shape[0] == current_embeds.weight.shape[1]: - embedding_weights += [y] - tokens_temp += [next_new_token] - next_new_token += 1 + if eos: + attention_mask.append(0) else: - logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1])) - while len(tokens_temp) < len(x): - tokens_temp += [self.special_tokens["pad"]] - out_tokens += [tokens_temp] + attention_mask.append(1) + token = int(y) + tokens_temp += [token] + if not eos and token == cmp_token: + if end_token is None: + attention_mask[-1] = 0 + eos = True + else: + other_embeds.append((index, y)) + index += 1 - n = token_dict_size - if len(embedding_weights) > 0: - new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) - new_embedding.weight[:token_dict_size] = current_embeds.weight - for x in embedding_weights: - new_embedding.weight[n] = x - n += 1 - self.transformer.set_input_embeddings(new_embedding) + tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) + tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32) + index = 0 + pad_extra = 0 + for o in other_embeds: + emb = o[1] + if torch.is_tensor(emb): + emb = {"type": "embedding", "data": emb} - processed_tokens = [] - for x in out_tokens: - processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one + emb_type = emb.get("type", None) + if emb_type == "embedding": + emb = emb.get("data", None) + else: + if hasattr(self.transformer, "preprocess_embed"): + emb = self.transformer.preprocess_embed(emb, device=device) + else: + emb = None - return processed_tokens + if emb is None: + index += -1 + continue + + ind = index + o[0] + emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) + emb_shape = emb.shape[1] + if emb.shape[-1] == tokens_embed.shape[-1]: + tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) + attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] + index += emb_shape - 1 + else: + index += -1 + pad_extra += emb_shape + logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1])) + + if pad_extra > 0: + padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32) + tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1) + + embeds_out.append(tokens_embed) + attention_masks.append(attention_mask) + num_tokens.append(sum(attention_mask)) + + return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens def forward(self, tokens): - backup_embeds = self.transformer.get_input_embeddings() - device = backup_embeds.weight.device - tokens = self.set_up_textual_embeddings(tokens, backup_embeds) - tokens = torch.LongTensor(tokens).to(device) - - attention_mask = None - if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks: - attention_mask = torch.zeros_like(tokens) - end_token = self.special_tokens.get("end", None) - if end_token is None: - cmp_token = self.special_tokens.get("pad", -1) - else: - cmp_token = end_token - - for x in range(attention_mask.shape[0]): - for y in range(attention_mask.shape[1]): - attention_mask[x, y] = 1 - if tokens[x, y] == cmp_token: - if end_token is None: - attention_mask[x, y] = 0 - break + device = self.transformer.get_input_embeddings().weight.device + embeds, attention_mask, num_tokens = self.process_tokens(tokens, device) attention_mask_model = None if self.enable_attention_masks: attention_mask_model = attention_mask - outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32) - self.transformer.set_input_embeddings(backup_embeds) + outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32) if self.layer == "last": z = outputs[0].float() @@ -482,7 +503,7 @@ class SDTokenizer: return (embed, leftover) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. @@ -596,7 +617,7 @@ class SD1Tokenizer: tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) return out diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index 4d0a4e8e7..5b7c8a412 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -26,7 +26,7 @@ class SDXLTokenizer: self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index a8212c1fa..7157a15f2 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -762,7 +762,7 @@ class LTXV(supported_models_base.BASE): unet_extra_config = {} latent_format = latent_formats.LTXV - memory_usage_factor = 2.7 + memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid supported_inference_dtypes = [torch.bfloat16, torch.float32] @@ -826,6 +826,26 @@ class HunyuanVideo(supported_models_base.BASE): hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect)) +class HunyuanVideoI2V(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "in_channels": 33, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideoI2V(self, device=device) + return out + +class HunyuanVideoSkyreelsI2V(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "in_channels": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideoSkyreelsI2V(self, device=device) + return out + class CosmosT2V(supported_models_base.BASE): unet_config = { "image_model": "cosmos", @@ -939,6 +959,6 @@ class WAN21_I2V(WAN21_T2V): out = model_base.WAN21(self, image_to_video=True, device=device) return out -models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V] +models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V] models += [SVD_img2vid] diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py index d4edd5aa5..551b03162 100644 --- a/comfy/text_encoders/bert.py +++ b/comfy/text_encoders/bert.py @@ -93,8 +93,11 @@ class BertEmbeddings(torch.nn.Module): self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device) - def forward(self, input_tokens, token_type_ids=None, dtype=None): - x = self.word_embeddings(input_tokens, out_dtype=dtype) + def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None): + if embeds is not None: + x = embeds + else: + x = self.word_embeddings(input_tokens, out_dtype=dtype) x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x) if token_type_ids is not None: x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype) @@ -113,8 +116,8 @@ class BertModel_(torch.nn.Module): self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations) self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations) - def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): - x = self.embeddings(input_tokens, dtype=dtype) + def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py index b945b1aaa..a12995ec0 100644 --- a/comfy/text_encoders/flux.py +++ b/comfy/text_encoders/flux.py @@ -18,7 +18,7 @@ class FluxTokenizer: self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py index 7149d6878..1d814aadd 100644 --- a/comfy/text_encoders/hunyuan_video.py +++ b/comfy/text_encoders/hunyuan_video.py @@ -4,6 +4,7 @@ import comfy.text_encoders.llama from transformers import LlamaTokenizerFast import torch import os +import numbers def llama_detect(state_dict, prefix=""): @@ -22,7 +23,7 @@ def llama_detect(state_dict, prefix=""): class LLAMA3Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "llama_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=128258, end_token=128009, min_length=min_length) + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=128258, min_length=min_length) class LLAMAModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}): @@ -38,15 +39,26 @@ class HunyuanVideoTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) - self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n""" # 95 tokens + self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, **kwargs): out = {} out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - llama_text = "{}{}".format(self.llama_template, text) - out["llama"] = self.llama.tokenize_with_weights(llama_text, return_word_ids) + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids) + embed_count = 0 + for r in llama_text_tokens: + for i in range(len(r)): + if r[i][0] == 128257: + if image_embeds is not None and embed_count < image_embeds.shape[0]: + r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:] + embed_count += 1 + out["llama"] = llama_text_tokens return out def untokenize(self, token_weight_pair): @@ -80,20 +92,45 @@ class HunyuanVideoClipModel(torch.nn.Module): llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama) template_end = 0 - for i, v in enumerate(token_weight_pairs_llama[0]): - if v[0] == 128007: # <|end_header_id|> - template_end = i + image_start = None + image_end = None + extra_sizes = 0 + user_end = 9999999999999 + + tok_pairs = token_weight_pairs_llama[0] + for i, v in enumerate(tok_pairs): + elem = v[0] + if not torch.is_tensor(elem): + if isinstance(elem, numbers.Integral): + if elem == 128006: + if tok_pairs[i + 1][0] == 882: + if tok_pairs[i + 2][0] == 128007: + template_end = i + 2 + user_end = -1 + if elem == 128009 and user_end == -1: + user_end = i + 1 + else: + if elem.get("original_type") == "image": + elem_size = elem.get("data").shape[0] + if image_start is None: + image_start = i + extra_sizes + image_end = i + elem_size + extra_sizes + extra_sizes += elem_size - 1 if llama_out.shape[1] > (template_end + 2): - if token_weight_pairs_llama[0][template_end + 1][0] == 271: + if tok_pairs[template_end + 1][0] == 271: template_end += 2 - llama_out = llama_out[:, template_end:] - llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end:] + llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes] + llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes] if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]): llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements + if image_start is not None: + image_output = llama_out[:, image_start: image_end] + llama_output = torch.cat([image_output[:, ::2], llama_output], dim=1) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - return llama_out, l_pooled, llama_extra_out + return llama_output, l_pooled, llama_extra_out def load_sd(self, sd): if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py index 7cb790f45..7da3e9fc5 100644 --- a/comfy/text_encoders/hydit.py +++ b/comfy/text_encoders/hydit.py @@ -37,7 +37,7 @@ class HyditTokenizer: self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory) self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids) out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids) diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index 3f234015a..58710b2bf 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -241,8 +241,11 @@ class Llama2_(nn.Module): self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) - def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): - x = self.embed_tokens(x, out_dtype=dtype) + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + if embeds is not None: + x = embeds + else: + x = self.embed_tokens(x, out_dtype=dtype) if self.normalize_in: x *= self.config.hidden_size ** 0.5 diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py index 00d7e31ad..3ad2ed93a 100644 --- a/comfy/text_encoders/sd3_clip.py +++ b/comfy/text_encoders/sd3_clip.py @@ -43,7 +43,7 @@ class SD3Tokenizer: self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory) self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py index df2b5b5cd..49f0ba4fe 100644 --- a/comfy/text_encoders/t5.py +++ b/comfy/text_encoders/t5.py @@ -239,8 +239,11 @@ class T5(torch.nn.Module): def set_input_embeddings(self, embeddings): self.shared = embeddings - def forward(self, input_ids, *args, **kwargs): - x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) + def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs): + if input_ids is None: + x = embeds + else: + x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: x = torch.nan_to_num(x) #Fix for fp8 T5 base - return self.encoder(x, *args, **kwargs) + return self.encoder(x, attention_mask=attention_mask, **kwargs) diff --git a/comfy/utils.py b/comfy/utils.py index df7057c6a..a826e41bf 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -46,12 +46,18 @@ if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in else: logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.") -def load_torch_file(ckpt, safe_load=False, device=None): +def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False): if device is None: device = torch.device("cpu") + metadata = None if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): try: - sd = safetensors.torch.load_file(ckpt, device=device.type) + with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f: + sd = {} + for k in f.keys(): + sd[k] = f.get_tensor(k) + if return_metadata: + metadata = f.metadata() except Exception as e: if len(e.args) > 0: message = e.args[0] @@ -77,7 +83,7 @@ def load_torch_file(ckpt, safe_load=False, device=None): sd = pl_sd else: sd = pl_sd - return sd + return (sd, metadata) if return_metadata else sd def save_torch_file(sd, ckpt, metadata=None): if metadata is not None: diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 3cb918e09..136ad6159 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import torchaudio import torch import comfy.model_management @@ -10,6 +12,7 @@ import random import hashlib import node_helpers from comfy.cli_args import args +from comfy.comfy_types import FileLocator class EmptyLatentAudio: def __init__(self): @@ -164,7 +167,7 @@ class SaveAudio: def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results = list() + results: list[FileLocator] = [] metadata = {} if not args.disable_metadata: diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 576fc3b2c..c9689b745 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -454,7 +454,7 @@ class SamplerCustom: return {"required": {"model": ("MODEL",), "add_noise": ("BOOLEAN", {"default": True}), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), @@ -605,10 +605,16 @@ class DisableNoise: class RandomNoise(DisableNoise): @classmethod def INPUT_TYPES(s): - return {"required":{ - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), - } - } + return { + "required": { + "noise_seed": ("INT", { + "default": 0, + "min": 0, + "max": 0xffffffffffffffff, + "control_after_generate": True, + }), + } + } def get_noise(self, noise_seed): return (Noise_RandomNoise(noise_seed),) diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index d6408269f..4f700bbe6 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -1,4 +1,5 @@ import nodes +import node_helpers import torch import comfy.model_management @@ -38,7 +39,73 @@ class EmptyHunyuanLatentVideo: latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) return ({"samples":latent}, ) +PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +class TextEncodeHunyuanVideo_ImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, clip_vision_output, prompt): + tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected) + return (clip.encode_from_tokens_scheduled(tokens), ) + + +class HunyuanImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"start_image": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, vae, width, height, length, batch_size, start_image=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + concat_latent_image = vae.encode(start_image) + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, out_latent) + + NODE_CLASS_MAPPINGS = { "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT, + "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo, "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo, + "HunyuanImageToVideo": HunyuanImageToVideo, } diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index af37666b2..e11a4583a 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import nodes import folder_paths from comfy.cli_args import args @@ -9,6 +11,8 @@ import numpy as np import json import os +from comfy.comfy_types import FileLocator + MAX_RESOLUTION = nodes.MAX_RESOLUTION class ImageCrop: @@ -99,7 +103,7 @@ class SaveAnimatedWEBP: method = self.methods.get(method) filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - results = list() + results: list[FileLocator] = [] pil_images = [] for image in images: i = 255. * image.cpu().numpy() diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index dec912416..b608b9407 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -1,9 +1,14 @@ +import io import nodes import node_helpers import torch import comfy.model_management import comfy.model_sampling +import comfy.utils import math +import numpy as np +import av +from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords class EmptyLTXVLatentVideo: @classmethod @@ -33,7 +38,6 @@ class LTXVImgToVideo: "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "image_noise_scale": ("FLOAT", {"default": 0.15, "min": 0, "max": 1.0, "step": 0.01, "tooltip": "Amount of noise to apply on conditioning image latent."}) }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @@ -42,16 +46,217 @@ class LTXVImgToVideo: CATEGORY = "conditioning/video_models" FUNCTION = "generate" - def generate(self, positive, negative, image, vae, width, height, length, batch_size, image_noise_scale): + def generate(self, positive, negative, image, vae, width, height, length, batch_size): pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) encode_pixels = pixels[:, :, :, :3] t = vae.encode(encode_pixels) - positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t, "guiding_latent_noise_scale": image_noise_scale}) - negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t, "guiding_latent_noise_scale": image_noise_scale}) latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) latent[:, :, :t.shape[2]] = t - return (positive, negative, {"samples": latent}, ) + + conditioning_latent_frames_mask = torch.ones( + (batch_size, 1, latent.shape[2], 1, 1), + dtype=torch.float32, + device=latent.device, + ) + conditioning_latent_frames_mask[:, :, :t.shape[2]] = 0 + + return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, ) + + +def conditioning_get_any_value(conditioning, key, default=None): + for t in conditioning: + if key in t[1]: + return t[1][key] + return default + + +def get_noise_mask(latent): + noise_mask = latent.get("noise_mask", None) + latent_image = latent["samples"] + if noise_mask is None: + batch_size, _, latent_length, _, _ = latent_image.shape + noise_mask = torch.ones( + (batch_size, 1, latent_length, 1, 1), + dtype=torch.float32, + device=latent_image.device, + ) + else: + noise_mask = noise_mask.clone() + return noise_mask + +def get_keyframe_idxs(cond): + keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None) + if keyframe_idxs is None: + return None, 0 + num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0] + return keyframe_idxs, num_keyframes + +class LTXVAddGuide: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE",), + "latent": ("LATENT",), + "image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames." \ + "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames."}), + "frame_idx": ("INT", {"default": 0, "min": -9999, "max": 9999, + "tooltip": "Frame index to start the conditioning at. Must be divisible by 8. " \ + "If a frame is not divisible by 8, it will be rounded down to the nearest multiple of 8. " \ + "Negative values are counted from the end of the video."}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + CATEGORY = "conditioning/video_models" + FUNCTION = "generate" + + def __init__(self): + self._num_prefix_frames = 2 + self._patchifier = SymmetricPatchifier(1) + + def encode(self, vae, latent_width, latent_height, images, scale_factors): + time_scale_factor, width_scale_factor, height_scale_factor = scale_factors + images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1] + pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1) + encode_pixels = pixels[:, :, :, :3] + t = vae.encode(encode_pixels) + return encode_pixels, t + + def get_latent_index(self, cond, latent_length, frame_idx, scale_factors): + time_scale_factor, _, _ = scale_factors + _, num_keyframes = get_keyframe_idxs(cond) + latent_count = latent_length - num_keyframes + frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * 8 + 1 + frame_idx, 0) + frame_idx = frame_idx // time_scale_factor * time_scale_factor # frame index must be divisible by 8 + + latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor + + return frame_idx, latent_idx + + def add_keyframe_index(self, cond, frame_idx, guiding_latent, scale_factors): + keyframe_idxs, _ = get_keyframe_idxs(cond) + _, latent_coords = self._patchifier.patchify(guiding_latent) + pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, True) + pixel_coords[:, 0] += frame_idx + if keyframe_idxs is None: + keyframe_idxs = pixel_coords + else: + keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2) + return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs}) + + def append_keyframe(self, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors): + positive = self.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors) + negative = self.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors) + + mask = torch.full( + (noise_mask.shape[0], 1, guiding_latent.shape[2], 1, 1), + 1.0 - strength, + dtype=noise_mask.dtype, + device=noise_mask.device, + ) + + latent_image = torch.cat([latent_image, guiding_latent], dim=2) + noise_mask = torch.cat([noise_mask, mask], dim=2) + return positive, negative, latent_image, noise_mask + + def replace_latent_frames(self, latent_image, noise_mask, guiding_latent, latent_idx, strength): + cond_length = guiding_latent.shape[2] + assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence." + + mask = torch.full( + (noise_mask.shape[0], 1, cond_length, 1, 1), + 1.0 - strength, + dtype=noise_mask.dtype, + device=noise_mask.device, + ) + + latent_image = latent_image.clone() + noise_mask = noise_mask.clone() + + latent_image[:, :, latent_idx : latent_idx + cond_length] = guiding_latent + noise_mask[:, :, latent_idx : latent_idx + cond_length] = mask + + return latent_image, noise_mask + + def generate(self, positive, negative, vae, latent, image, frame_idx, strength): + scale_factors = vae.downscale_index_formula + latent_image = latent["samples"] + noise_mask = get_noise_mask(latent) + + _, _, latent_length, latent_height, latent_width = latent_image.shape + image, t = self.encode(vae, latent_width, latent_height, image, scale_factors) + + frame_idx, latent_idx = self.get_latent_index(positive, latent_length, frame_idx, scale_factors) + assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence." + + num_prefix_frames = min(self._num_prefix_frames, t.shape[2]) + + positive, negative, latent_image, noise_mask = self.append_keyframe( + positive, + negative, + frame_idx, + latent_image, + noise_mask, + t[:, :, :num_prefix_frames], + strength, + scale_factors, + ) + + latent_idx += num_prefix_frames + + t = t[:, :, num_prefix_frames:] + if t.shape[2] == 0: + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + latent_image, noise_mask = self.replace_latent_frames( + latent_image, + noise_mask, + t, + latent_idx, + strength, + ) + + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + +class LTXVCropGuides: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent": ("LATENT",), + } + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + CATEGORY = "conditioning/video_models" + FUNCTION = "crop" + + def __init__(self): + self._patchifier = SymmetricPatchifier(1) + + def crop(self, positive, negative, latent): + latent_image = latent["samples"].clone() + noise_mask = get_noise_mask(latent) + + _, num_keyframes = get_keyframe_idxs(positive) + if num_keyframes == 0: + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + latent_image = latent_image[:, :, :-num_keyframes] + noise_mask = noise_mask[:, :, :-num_keyframes] + + positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None}) + negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None}) + + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) class LTXVConditioning: @@ -174,6 +379,77 @@ class LTXVScheduler: return (sigmas,) +def encode_single_frame(output_file, image_array: np.ndarray, crf): + container = av.open(output_file, "w", format="mp4") + try: + stream = container.add_stream( + "h264", rate=1, options={"crf": str(crf), "preset": "veryfast"} + ) + stream.height = image_array.shape[0] + stream.width = image_array.shape[1] + av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat( + format="yuv420p" + ) + container.mux(stream.encode(av_frame)) + container.mux(stream.encode()) + finally: + container.close() + + +def decode_single_frame(video_file): + container = av.open(video_file) + try: + stream = next(s for s in container.streams if s.type == "video") + frame = next(container.decode(stream)) + finally: + container.close() + return frame.to_ndarray(format="rgb24") + + +def preprocess(image: torch.Tensor, crf=29): + if crf == 0: + return image + + image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy() + with io.BytesIO() as output_file: + encode_single_frame(output_file, image_array, crf) + video_bytes = output_file.getvalue() + with io.BytesIO(video_bytes) as video_file: + image_array = decode_single_frame(video_file) + tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0 + return tensor + + +class LTXVPreprocess: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "img_compression": ( + "INT", + { + "default": 35, + "min": 0, + "max": 100, + "tooltip": "Amount of compression to apply on image.", + }, + ), + } + } + + FUNCTION = "preprocess" + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("output_image",) + CATEGORY = "image" + + def preprocess(self, image, img_compression): + if img_compression > 0: + output_images = [] + for i in range(image.shape[0]): + output_images.append(preprocess(image[i], img_compression)) + return (torch.stack(output_images),) + NODE_CLASS_MAPPINGS = { "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo, @@ -181,4 +457,7 @@ NODE_CLASS_MAPPINGS = { "ModelSamplingLTXV": ModelSamplingLTXV, "LTXVConditioning": LTXVConditioning, "LTXVScheduler": LTXVScheduler, + "LTXVAddGuide": LTXVAddGuide, + "LTXVPreprocess": LTXVPreprocess, + "LTXVCropGuides": LTXVCropGuides, } diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py index 53920ba18..97ca513d8 100644 --- a/comfy_extras/nodes_video.py +++ b/comfy_extras/nodes_video.py @@ -1,9 +1,12 @@ +from __future__ import annotations + import os import av import torch import folder_paths import json from fractions import Fraction +from comfy.comfy_types import FileLocator class SaveWEBM: @@ -62,7 +65,7 @@ class SaveWEBM: container.mux(stream.encode()) container.close() - results = [{ + results: list[FileLocator] = [{ "filename": file, "subfolder": subfolder, "type": self.type diff --git a/comfyui_version.py b/comfyui_version.py index 9d69edfc1..ac257abf8 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.18" +__version__ = "0.3.23" diff --git a/nodes.py b/nodes.py index f7f6cb156..bbf49915c 100644 --- a/nodes.py +++ b/nodes.py @@ -25,7 +25,7 @@ import comfy.sample import comfy.sd import comfy.utils import comfy.controlnet -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator import comfy.clip_vision @@ -479,7 +479,7 @@ class SaveLatent: file = f"{filename}_{counter:05}_.latent" - results = list() + results: list[FileLocator] = [] results.append({ "filename": file, "subfolder": subfolder, @@ -1519,7 +1519,7 @@ class KSampler: return { "required": { "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}), @@ -1547,7 +1547,7 @@ class KSamplerAdvanced: return {"required": {"model": ("MODEL",), "add_noise": (["enable", "disable"], ), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), diff --git a/pyproject.toml b/pyproject.toml index be52c6028..824887a94 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.18" +version = "0.3.23" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9"