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import inspect | |
import math | |
from dataclasses import dataclass | |
from typing import Any, Callable, Dict, List, Optional, Tuple, Union | |
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
import torchvision.transforms.functional as TF | |
from diffusers import FlowMatchEulerDiscreteScheduler | |
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback | |
from diffusers.image_processor import VaeImageProcessor | |
from diffusers.models.embeddings import get_1d_rotary_pos_embed | |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler | |
from diffusers.utils import BaseOutput, logging, replace_example_docstring | |
from diffusers.utils.torch_utils import randn_tensor | |
from diffusers.video_processor import VideoProcessor | |
from einops import rearrange | |
from PIL import Image | |
from transformers import T5Tokenizer | |
from ..models import (AutoencoderKLWan, AutoTokenizer, CLIPModel, | |
WanT5EncoderModel, WanTransformer3DModel) | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
EXAMPLE_DOC_STRING = """ | |
Examples: | |
```python | |
pass | |
``` | |
""" | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps | |
def retrieve_timesteps( | |
scheduler, | |
num_inference_steps: Optional[int] = None, | |
device: Optional[Union[str, torch.device]] = None, | |
timesteps: Optional[List[int]] = None, | |
sigmas: Optional[List[float]] = None, | |
**kwargs, | |
): | |
""" | |
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles | |
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. | |
Args: | |
scheduler (`SchedulerMixin`): | |
The scheduler to get timesteps from. | |
num_inference_steps (`int`): | |
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` | |
must be `None`. | |
device (`str` or `torch.device`, *optional*): | |
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | |
timesteps (`List[int]`, *optional*): | |
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, | |
`num_inference_steps` and `sigmas` must be `None`. | |
sigmas (`List[float]`, *optional*): | |
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, | |
`num_inference_steps` and `timesteps` must be `None`. | |
Returns: | |
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the | |
second element is the number of inference steps. | |
""" | |
if timesteps is not None and sigmas is not None: | |
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") | |
if timesteps is not None: | |
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) | |
if not accepts_timesteps: | |
raise ValueError( | |
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" | |
f" timestep schedules. Please check whether you are using the correct scheduler." | |
) | |
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) | |
timesteps = scheduler.timesteps | |
num_inference_steps = len(timesteps) | |
elif sigmas is not None: | |
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) | |
if not accept_sigmas: | |
raise ValueError( | |
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" | |
f" sigmas schedules. Please check whether you are using the correct scheduler." | |
) | |
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) | |
timesteps = scheduler.timesteps | |
num_inference_steps = len(timesteps) | |
else: | |
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) | |
timesteps = scheduler.timesteps | |
return timesteps, num_inference_steps | |
def resize_mask(mask, latent, process_first_frame_only=True): | |
latent_size = latent.size() | |
batch_size, channels, num_frames, height, width = mask.shape | |
if process_first_frame_only: | |
target_size = list(latent_size[2:]) | |
target_size[0] = 1 | |
first_frame_resized = F.interpolate( | |
mask[:, :, 0:1, :, :], | |
size=target_size, | |
mode='trilinear', | |
align_corners=False | |
) | |
target_size = list(latent_size[2:]) | |
target_size[0] = target_size[0] - 1 | |
if target_size[0] != 0: | |
remaining_frames_resized = F.interpolate( | |
mask[:, :, 1:, :, :], | |
size=target_size, | |
mode='trilinear', | |
align_corners=False | |
) | |
resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2) | |
else: | |
resized_mask = first_frame_resized | |
else: | |
target_size = list(latent_size[2:]) | |
resized_mask = F.interpolate( | |
mask, | |
size=target_size, | |
mode='trilinear', | |
align_corners=False | |
) | |
return resized_mask | |
class WanPipelineOutput(BaseOutput): | |
r""" | |
Output class for CogVideo pipelines. | |
Args: | |
video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): | |
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing | |
denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape | |
`(batch_size, num_frames, channels, height, width)`. | |
""" | |
videos: torch.Tensor | |
class WanFunInpaintPipeline(DiffusionPipeline): | |
r""" | |
Pipeline for text-to-video generation using Wan. | |
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the | |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | |
""" | |
_optional_components = [] | |
model_cpu_offload_seq = "text_encoder->clip_image_encoder->transformer->vae" | |
_callback_tensor_inputs = [ | |
"latents", | |
"prompt_embeds", | |
"negative_prompt_embeds", | |
] | |
def __init__( | |
self, | |
tokenizer: AutoTokenizer, | |
text_encoder: WanT5EncoderModel, | |
vae: AutoencoderKLWan, | |
transformer: WanTransformer3DModel, | |
clip_image_encoder: CLIPModel, | |
scheduler: FlowMatchEulerDiscreteScheduler, | |
): | |
super().__init__() | |
self.register_modules( | |
tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, clip_image_encoder=clip_image_encoder, scheduler=scheduler | |
) | |
self.video_processor = VideoProcessor(vae_scale_factor=self.vae.spacial_compression_ratio) | |
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae.spacial_compression_ratio) | |
self.mask_processor = VaeImageProcessor( | |
vae_scale_factor=self.vae.spacial_compression_ratio, do_normalize=False, do_binarize=True, do_convert_grayscale=True | |
) | |
def _get_t5_prompt_embeds( | |
self, | |
prompt: Union[str, List[str]] = None, | |
num_videos_per_prompt: int = 1, | |
max_sequence_length: int = 512, | |
device: Optional[torch.device] = None, | |
dtype: Optional[torch.dtype] = None, | |
): | |
device = device or self._execution_device | |
dtype = dtype or self.text_encoder.dtype | |
prompt = [prompt] if isinstance(prompt, str) else prompt | |
batch_size = len(prompt) | |
text_inputs = self.tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=max_sequence_length, | |
truncation=True, | |
add_special_tokens=True, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
prompt_attention_mask = text_inputs.attention_mask | |
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): | |
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) | |
logger.warning( | |
"The following part of your input was truncated because `max_sequence_length` is set to " | |
f" {max_sequence_length} tokens: {removed_text}" | |
) | |
seq_lens = prompt_attention_mask.gt(0).sum(dim=1).long() | |
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask.to(device))[0] | |
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) | |
# duplicate text embeddings for each generation per prompt, using mps friendly method | |
_, seq_len, _ = prompt_embeds.shape | |
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) | |
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) | |
return [u[:v] for u, v in zip(prompt_embeds, seq_lens)] | |
def encode_prompt( | |
self, | |
prompt: Union[str, List[str]], | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
do_classifier_free_guidance: bool = True, | |
num_videos_per_prompt: int = 1, | |
prompt_embeds: Optional[torch.Tensor] = None, | |
negative_prompt_embeds: Optional[torch.Tensor] = None, | |
max_sequence_length: int = 512, | |
device: Optional[torch.device] = None, | |
dtype: Optional[torch.dtype] = None, | |
): | |
r""" | |
Encodes the prompt into text encoder hidden states. | |
Args: | |
prompt (`str` or `List[str]`, *optional*): | |
prompt to be encoded | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the image generation. If not defined, one has to pass | |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is | |
less than `1`). | |
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): | |
Whether to use classifier free guidance or not. | |
num_videos_per_prompt (`int`, *optional*, defaults to 1): | |
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on | |
prompt_embeds (`torch.Tensor`, *optional*): | |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | |
provided, text embeddings will be generated from `prompt` input argument. | |
negative_prompt_embeds (`torch.Tensor`, *optional*): | |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input | |
argument. | |
device: (`torch.device`, *optional*): | |
torch device | |
dtype: (`torch.dtype`, *optional*): | |
torch dtype | |
""" | |
device = device or self._execution_device | |
prompt = [prompt] if isinstance(prompt, str) else prompt | |
if prompt is not None: | |
batch_size = len(prompt) | |
else: | |
batch_size = prompt_embeds.shape[0] | |
if prompt_embeds is None: | |
prompt_embeds = self._get_t5_prompt_embeds( | |
prompt=prompt, | |
num_videos_per_prompt=num_videos_per_prompt, | |
max_sequence_length=max_sequence_length, | |
device=device, | |
dtype=dtype, | |
) | |
if do_classifier_free_guidance and negative_prompt_embeds is None: | |
negative_prompt = negative_prompt or "" | |
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt | |
if prompt is not None and type(prompt) is not type(negative_prompt): | |
raise TypeError( | |
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" | |
f" {type(prompt)}." | |
) | |
elif batch_size != len(negative_prompt): | |
raise ValueError( | |
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" | |
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | |
" the batch size of `prompt`." | |
) | |
negative_prompt_embeds = self._get_t5_prompt_embeds( | |
prompt=negative_prompt, | |
num_videos_per_prompt=num_videos_per_prompt, | |
max_sequence_length=max_sequence_length, | |
device=device, | |
dtype=dtype, | |
) | |
return prompt_embeds, negative_prompt_embeds | |
def prepare_latents( | |
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None | |
): | |
if isinstance(generator, list) and len(generator) != batch_size: | |
raise ValueError( | |
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" | |
f" size of {batch_size}. Make sure the batch size matches the length of the generators." | |
) | |
shape = ( | |
batch_size, | |
num_channels_latents, | |
(num_frames - 1) // self.vae.temporal_compression_ratio + 1, | |
height // self.vae.spacial_compression_ratio, | |
width // self.vae.spacial_compression_ratio, | |
) | |
if latents is None: | |
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) | |
else: | |
latents = latents.to(device) | |
# scale the initial noise by the standard deviation required by the scheduler | |
if hasattr(self.scheduler, "init_noise_sigma"): | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
def prepare_mask_latents( | |
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance, noise_aug_strength | |
): | |
# resize the mask to latents shape as we concatenate the mask to the latents | |
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload | |
# and half precision | |
if mask is not None: | |
mask = mask.to(device=device, dtype=self.vae.dtype) | |
bs = 1 | |
new_mask = [] | |
for i in range(0, mask.shape[0], bs): | |
mask_bs = mask[i : i + bs] | |
mask_bs = self.vae.encode(mask_bs)[0] | |
mask_bs = mask_bs.mode() | |
new_mask.append(mask_bs) | |
mask = torch.cat(new_mask, dim = 0) | |
# mask = mask * self.vae.config.scaling_factor | |
if masked_image is not None: | |
masked_image = masked_image.to(device=device, dtype=self.vae.dtype) | |
bs = 1 | |
new_mask_pixel_values = [] | |
for i in range(0, masked_image.shape[0], bs): | |
mask_pixel_values_bs = masked_image[i : i + bs] | |
mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0] | |
mask_pixel_values_bs = mask_pixel_values_bs.mode() | |
new_mask_pixel_values.append(mask_pixel_values_bs) | |
masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0) | |
# masked_image_latents = masked_image_latents * self.vae.config.scaling_factor | |
else: | |
masked_image_latents = None | |
return mask, masked_image_latents | |
def decode_latents(self, latents: torch.Tensor) -> torch.Tensor: | |
frames = self.vae.decode(latents.to(self.vae.dtype)).sample | |
frames = (frames / 2 + 0.5).clamp(0, 1) | |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 | |
frames = frames.cpu().float().numpy() | |
return frames | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs | |
def prepare_extra_step_kwargs(self, generator, eta): | |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 | |
# and should be between [0, 1] | |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
extra_step_kwargs = {} | |
if accepts_eta: | |
extra_step_kwargs["eta"] = eta | |
# check if the scheduler accepts generator | |
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
if accepts_generator: | |
extra_step_kwargs["generator"] = generator | |
return extra_step_kwargs | |
# Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs | |
def check_inputs( | |
self, | |
prompt, | |
height, | |
width, | |
negative_prompt, | |
callback_on_step_end_tensor_inputs, | |
prompt_embeds=None, | |
negative_prompt_embeds=None, | |
): | |
if height % 8 != 0 or width % 8 != 0: | |
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | |
if callback_on_step_end_tensor_inputs is not None and not all( | |
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs | |
): | |
raise ValueError( | |
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" | |
) | |
if prompt is not None and prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" | |
" only forward one of the two." | |
) | |
elif prompt is None and prompt_embeds is None: | |
raise ValueError( | |
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." | |
) | |
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
if prompt is not None and negative_prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" | |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
) | |
if negative_prompt is not None and negative_prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
) | |
if prompt_embeds is not None and negative_prompt_embeds is not None: | |
if prompt_embeds.shape != negative_prompt_embeds.shape: | |
raise ValueError( | |
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" | |
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" | |
f" {negative_prompt_embeds.shape}." | |
) | |
def guidance_scale(self): | |
return self._guidance_scale | |
def num_timesteps(self): | |
return self._num_timesteps | |
def attention_kwargs(self): | |
return self._attention_kwargs | |
def interrupt(self): | |
return self._interrupt | |
def __call__( | |
self, | |
prompt: Optional[Union[str, List[str]]] = None, | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
height: int = 480, | |
width: int = 720, | |
video: Union[torch.FloatTensor] = None, | |
mask_video: Union[torch.FloatTensor] = None, | |
num_frames: int = 49, | |
num_inference_steps: int = 50, | |
timesteps: Optional[List[int]] = None, | |
guidance_scale: float = 6, | |
num_videos_per_prompt: int = 1, | |
eta: float = 0.0, | |
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
latents: Optional[torch.FloatTensor] = None, | |
prompt_embeds: Optional[torch.FloatTensor] = None, | |
negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
output_type: str = "numpy", | |
return_dict: bool = False, | |
callback_on_step_end: Optional[ | |
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] | |
] = None, | |
attention_kwargs: Optional[Dict[str, Any]] = None, | |
callback_on_step_end_tensor_inputs: List[str] = ["latents"], | |
clip_image: Image = None, | |
max_sequence_length: int = 512, | |
comfyui_progressbar: bool = False, | |
) -> Union[WanPipelineOutput, Tuple]: | |
""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
Examples: | |
Returns: | |
""" | |
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): | |
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs | |
num_videos_per_prompt = 1 | |
# 1. Check inputs. Raise error if not correct | |
self.check_inputs( | |
prompt, | |
height, | |
width, | |
negative_prompt, | |
callback_on_step_end_tensor_inputs, | |
prompt_embeds, | |
negative_prompt_embeds, | |
) | |
self._guidance_scale = guidance_scale | |
self._attention_kwargs = attention_kwargs | |
self._interrupt = False | |
# 2. Default call parameters | |
if prompt is not None and isinstance(prompt, str): | |
batch_size = 1 | |
elif prompt is not None and isinstance(prompt, list): | |
batch_size = len(prompt) | |
else: | |
batch_size = prompt_embeds.shape[0] | |
device = self._execution_device | |
weight_dtype = self.text_encoder.dtype | |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
# corresponds to doing no classifier free guidance. | |
do_classifier_free_guidance = guidance_scale > 1.0 | |
# 3. Encode input prompt | |
prompt_embeds, negative_prompt_embeds = self.encode_prompt( | |
prompt, | |
negative_prompt, | |
do_classifier_free_guidance, | |
num_videos_per_prompt=num_videos_per_prompt, | |
prompt_embeds=prompt_embeds, | |
negative_prompt_embeds=negative_prompt_embeds, | |
max_sequence_length=max_sequence_length, | |
device=device, | |
) | |
if do_classifier_free_guidance: | |
prompt_embeds = negative_prompt_embeds + prompt_embeds | |
# 4. Prepare timesteps | |
if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler): | |
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps, mu=1) | |
else: | |
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) | |
self._num_timesteps = len(timesteps) | |
if comfyui_progressbar: | |
from comfy.utils import ProgressBar | |
pbar = ProgressBar(num_inference_steps + 2) | |
# 5. Prepare latents. | |
if video is not None: | |
video_length = video.shape[2] | |
init_video = self.image_processor.preprocess(rearrange(video, "b c f h w -> (b f) c h w"), height=height, width=width) | |
init_video = init_video.to(dtype=torch.float32) | |
init_video = rearrange(init_video, "(b f) c h w -> b c f h w", f=video_length) | |
else: | |
init_video = None | |
latent_channels = self.vae.config.latent_channels | |
latents = self.prepare_latents( | |
batch_size * num_videos_per_prompt, | |
latent_channels, | |
num_frames, | |
height, | |
width, | |
weight_dtype, | |
device, | |
generator, | |
latents, | |
) | |
if comfyui_progressbar: | |
pbar.update(1) | |
# Prepare mask latent variables | |
if init_video is not None: | |
if (mask_video == 255).all(): | |
mask_latents = torch.tile( | |
torch.zeros_like(latents)[:, :1].to(device, weight_dtype), [1, 4, 1, 1, 1] | |
) | |
masked_video_latents = torch.zeros_like(latents).to(device, weight_dtype) | |
mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents | |
masked_video_latents_input = ( | |
torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents | |
) | |
y = torch.cat([mask_input, masked_video_latents_input], dim=1).to(device, weight_dtype) | |
else: | |
bs, _, video_length, height, width = video.size() | |
mask_condition = self.mask_processor.preprocess(rearrange(mask_video, "b c f h w -> (b f) c h w"), height=height, width=width) | |
mask_condition = mask_condition.to(dtype=torch.float32) | |
mask_condition = rearrange(mask_condition, "(b f) c h w -> b c f h w", f=video_length) | |
masked_video = init_video * (torch.tile(mask_condition, [1, 3, 1, 1, 1]) < 0.5) | |
_, masked_video_latents = self.prepare_mask_latents( | |
None, | |
masked_video, | |
batch_size, | |
height, | |
width, | |
weight_dtype, | |
device, | |
generator, | |
do_classifier_free_guidance, | |
noise_aug_strength=None, | |
) | |
mask_condition = torch.concat( | |
[ | |
torch.repeat_interleave(mask_condition[:, :, 0:1], repeats=4, dim=2), | |
mask_condition[:, :, 1:] | |
], dim=2 | |
) | |
mask_condition = mask_condition.view(bs, mask_condition.shape[2] // 4, 4, height, width) | |
mask_condition = mask_condition.transpose(1, 2) | |
mask_latents = resize_mask(1 - mask_condition, masked_video_latents, True).to(device, weight_dtype) | |
mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents | |
masked_video_latents_input = ( | |
torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents | |
) | |
y = torch.cat([mask_input, masked_video_latents_input], dim=1).to(device, weight_dtype) | |
# Prepare clip latent variables | |
if clip_image is not None: | |
clip_image = TF.to_tensor(clip_image).sub_(0.5).div_(0.5).to(device, weight_dtype) | |
clip_context = self.clip_image_encoder([clip_image[:, None, :, :]]) | |
clip_context = ( | |
torch.cat([clip_context] * 2) if do_classifier_free_guidance else clip_context | |
) | |
else: | |
clip_image = Image.new("RGB", (512, 512), color=(0, 0, 0)) | |
clip_image = TF.to_tensor(clip_image).sub_(0.5).div_(0.5).to(device, weight_dtype) | |
clip_context = self.clip_image_encoder([clip_image[:, None, :, :]]) | |
clip_context = ( | |
torch.cat([clip_context] * 2) if do_classifier_free_guidance else clip_context | |
) | |
clip_context = torch.zeros_like(clip_context) | |
if comfyui_progressbar: | |
pbar.update(1) | |
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
target_shape = (self.vae.latent_channels, (num_frames - 1) // self.vae.temporal_compression_ratio + 1, width // self.vae.spacial_compression_ratio, height // self.vae.spacial_compression_ratio) | |
seq_len = math.ceil((target_shape[2] * target_shape[3]) / (self.transformer.config.patch_size[1] * self.transformer.config.patch_size[2]) * target_shape[1]) | |
# 7. Denoising loop | |
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) | |
with self.progress_bar(total=num_inference_steps) as progress_bar: | |
for i, t in enumerate(timesteps): | |
if self.interrupt: | |
continue | |
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
if hasattr(self.scheduler, "scale_model_input"): | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML | |
timestep = t.expand(latent_model_input.shape[0]) | |
# predict noise model_output | |
with torch.cuda.amp.autocast(dtype=weight_dtype): | |
noise_pred = self.transformer( | |
x=latent_model_input, | |
context=prompt_embeds, | |
t=timestep, | |
seq_len=seq_len, | |
y=y, | |
clip_fea=clip_context, | |
) | |
# perform guidance | |
if do_classifier_free_guidance: | |
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) | |
# compute the previous noisy sample x_t -> x_t-1 | |
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] | |
if callback_on_step_end is not None: | |
callback_kwargs = {} | |
for k in callback_on_step_end_tensor_inputs: | |
callback_kwargs[k] = locals()[k] | |
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | |
latents = callback_outputs.pop("latents", latents) | |
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) | |
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) | |
prompt_embeds_2 = callback_outputs.pop("prompt_embeds_2", prompt_embeds_2) | |
negative_prompt_embeds_2 = callback_outputs.pop( | |
"negative_prompt_embeds_2", negative_prompt_embeds_2 | |
) | |
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): | |
progress_bar.update() | |
if comfyui_progressbar: | |
pbar.update(1) | |
if output_type == "numpy": | |
video = self.decode_latents(latents) | |
elif not output_type == "latent": | |
video = self.decode_latents(latents) | |
video = self.video_processor.postprocess_video(video=video, output_type=output_type) | |
else: | |
video = latents | |
# Offload all models | |
self.maybe_free_model_hooks() | |
if not return_dict: | |
video = torch.from_numpy(video) | |
return WanPipelineOutput(videos=video) | |