WeiQing Chen
83fe7d005b
Signed-off-by: David Chen <530634352@qq.com> Signed-off-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>
706 lines
31 KiB
Python
706 lines
31 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from __future__ import annotations
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import copy
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from typing import Any
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import numpy as np
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import PIL.Image
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import torch
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg, retrieve_timesteps
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_latents
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.video_processor import VideoProcessor
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from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
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from vllm_omni.diffusion.distributed.parallel_state import get_cfg_group, get_classifier_free_guidance_rank
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from .pipeline_ltx2 import (
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LTX2Pipeline,
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_get_prompt_field,
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calculate_shift,
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)
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from .pipeline_ltx2 import (
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get_ltx2_post_process_func as _get_ltx2_post_process_func,
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)
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def get_ltx2_post_process_func(od_config: OmniDiffusionConfig):
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return _get_ltx2_post_process_func(od_config)
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class LTX2ImageToVideoPipeline(LTX2Pipeline):
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support_image_input = True
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def __init__(
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self,
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*,
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od_config: OmniDiffusionConfig,
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prefix: str = "",
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):
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super().__init__(od_config=od_config, prefix=prefix)
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio, resample="bilinear")
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@staticmethod
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def _normalize_latents(
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latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
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) -> torch.Tensor:
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latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
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latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
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latents = (latents - latents_mean) * scaling_factor / latents_std
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return latents
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def prepare_latents(
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self,
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image: torch.Tensor | None = None,
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batch_size: int = 1,
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num_channels_latents: int = 128,
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height: int = 512,
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width: int = 768,
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num_frames: int = 121,
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dtype: torch.dtype | None = None,
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device: torch.device | None = None,
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generator: torch.Generator | list[torch.Generator] | None = None,
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latents: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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height = height // self.vae_spatial_compression_ratio
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width = width // self.vae_spatial_compression_ratio
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num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
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shape = (batch_size, num_channels_latents, num_frames, height, width)
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mask_shape = (batch_size, 1, num_frames, height, width)
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if latents is not None:
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conditioning_mask = latents.new_zeros(mask_shape)
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conditioning_mask[:, :, 0] = 1.0
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conditioning_mask = self._pack_latents(
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conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
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).squeeze(-1)
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if latents.ndim != 3 or latents.shape[:2] != conditioning_mask.shape:
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raise ValueError(
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"Provided `latents` tensor has shape"
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f" {latents.shape}, but the expected shape is {conditioning_mask.shape + (num_channels_latents,)}."
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)
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return latents.to(device=device, dtype=dtype), conditioning_mask
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if image is None:
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raise ValueError("`image` must be provided when `latents` is None.")
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image_batch_size = image.shape[0]
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if image_batch_size == 0:
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raise ValueError("`image` batch is empty.")
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if batch_size % image_batch_size != 0:
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raise ValueError(
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f"`batch_size` ({batch_size}) must be divisible by image batch size ({image_batch_size}) "
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"for image-to-video outputs."
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)
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num_videos_per_prompt = batch_size // image_batch_size
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if isinstance(generator, list):
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if len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective"
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f" batch size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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image_generators = [generator[i * num_videos_per_prompt] for i in range(image_batch_size)]
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init_latents = [
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retrieve_latents(self.vae.encode(image[i].unsqueeze(0).unsqueeze(2)), image_generators[i], "argmax")
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for i in range(image_batch_size)
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]
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else:
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init_latents = [
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retrieve_latents(self.vae.encode(img.unsqueeze(0).unsqueeze(2)), generator, "argmax") for img in image
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]
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init_latents = torch.cat(init_latents, dim=0).to(dtype)
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if num_videos_per_prompt > 1:
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init_latents = init_latents.repeat_interleave(num_videos_per_prompt, dim=0)
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init_latents = self._normalize_latents(init_latents, self.vae.latents_mean, self.vae.latents_std)
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init_latents = init_latents.repeat(1, 1, num_frames, 1, 1)
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conditioning_mask = torch.zeros(mask_shape, device=device, dtype=dtype)
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conditioning_mask[:, :, 0] = 1.0
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noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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latents = init_latents * conditioning_mask + noise * (1 - conditioning_mask)
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conditioning_mask = self._pack_latents(
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conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
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).squeeze(-1)
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latents = self._pack_latents(latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size)
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return latents, conditioning_mask
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def check_inputs(
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self,
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image,
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height,
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width,
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prompt,
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latents=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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prompt_attention_mask=None,
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negative_prompt_attention_mask=None,
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):
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if height % 32 != 0 or width % 32 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
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if image is None and latents is None:
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raise ValueError("Provide either `image` or `latents`. Cannot leave both undefined.")
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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if prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if prompt_embeds is not None and prompt_attention_mask is None:
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raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
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if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
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raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
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raise ValueError(
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"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when"
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" passed directly, but"
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f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
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f" {negative_prompt_attention_mask.shape}."
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)
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def _step_video_latents_i2v(
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self,
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noise_pred_video: torch.Tensor,
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latents: torch.Tensor,
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t: torch.Tensor,
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latent_num_frames: int,
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latent_height: int,
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latent_width: int,
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) -> torch.Tensor:
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noise_pred_video = self._unpack_latents(
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noise_pred_video,
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latent_num_frames,
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latent_height,
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latent_width,
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self.transformer_spatial_patch_size,
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self.transformer_temporal_patch_size,
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)
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latents_unpacked = self._unpack_latents(
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latents,
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latent_num_frames,
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latent_height,
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latent_width,
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self.transformer_spatial_patch_size,
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self.transformer_temporal_patch_size,
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)
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noise_pred_video = noise_pred_video[:, :, 1:]
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noise_latents = latents_unpacked[:, :, 1:]
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pred_latents = self.scheduler.step(noise_pred_video, t, noise_latents, return_dict=False)[0]
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latents_unpacked = torch.cat([latents_unpacked[:, :, :1], pred_latents], dim=2)
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latents = self._pack_latents(
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latents_unpacked, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
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)
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return latents
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@torch.no_grad()
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def forward(
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self,
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req: OmniDiffusionRequest,
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image: PIL.Image.Image | torch.Tensor | None = None,
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prompt: str | list[str] | None = None,
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negative_prompt: str | list[str] | None = None,
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height: int | None = None,
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width: int | None = None,
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num_frames: int | None = None,
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frame_rate: float | None = None,
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num_inference_steps: int | None = None,
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timesteps: list[int] | None = None,
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guidance_scale: float = 4.0,
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guidance_rescale: float = 0.0,
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num_videos_per_prompt: int | None = 1,
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generator: torch.Generator | list[torch.Generator] | None = None,
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latents: torch.Tensor | None = None,
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audio_latents: torch.Tensor | None = None,
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prompt_embeds: torch.Tensor | None = None,
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negative_prompt_embeds: torch.Tensor | None = None,
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prompt_attention_mask: torch.Tensor | None = None,
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negative_prompt_attention_mask: torch.Tensor | None = None,
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decode_timestep: float | list[float] = 0.0,
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decode_noise_scale: float | list[float] | None = None,
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output_type: str = "np",
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return_dict: bool = True,
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attention_kwargs: dict[str, Any] | None = None,
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max_sequence_length: int | None = None,
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) -> DiffusionOutput:
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prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt
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if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts):
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negative_prompt = None
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elif req.prompts:
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negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]
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height = req.sampling_params.height or height or 512
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width = req.sampling_params.width or width or 768
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num_frames = req.sampling_params.num_frames or num_frames or 121
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frame_rate = req.sampling_params.resolved_frame_rate or frame_rate or 24.0
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num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps or 40
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if timesteps is None:
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num_inference_steps = max(int(num_inference_steps), 2)
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elif len(timesteps) < 2:
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raise ValueError("`timesteps` must contain at least 2 values for FlowMatchEulerDiscreteScheduler.")
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num_videos_per_prompt = (
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req.sampling_params.num_outputs_per_prompt
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if req.sampling_params.num_outputs_per_prompt > 0
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else num_videos_per_prompt or 1
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)
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max_sequence_length = (
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req.sampling_params.max_sequence_length or max_sequence_length or self.tokenizer_max_length
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)
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if req.sampling_params.guidance_scale_provided:
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guidance_scale = req.sampling_params.guidance_scale
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if req.sampling_params.guidance_rescale is not None:
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guidance_rescale = req.sampling_params.guidance_rescale
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if generator is None:
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generator = req.sampling_params.generator
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if generator is None and req.sampling_params.seed is not None:
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generator = torch.Generator(device=self.device).manual_seed(req.sampling_params.seed)
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latents = req.sampling_params.latents if req.sampling_params.latents is not None else latents
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audio_latents = (
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req.sampling_params.audio_latents
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if req.sampling_params.audio_latents is not None
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else req.sampling_params.extra_args.get("audio_latents", audio_latents)
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)
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req_prompt_embeds = [_get_prompt_field(p, "prompt_embeds") for p in req.prompts]
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if any(p is not None for p in req_prompt_embeds):
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prompt_embeds = torch.stack(req_prompt_embeds) # type: ignore[arg-type]
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req_negative_prompt_embeds = [_get_prompt_field(p, "negative_prompt_embeds") for p in req.prompts]
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if any(p is not None for p in req_negative_prompt_embeds):
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negative_prompt_embeds = torch.stack(req_negative_prompt_embeds) # type: ignore[arg-type]
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req_prompt_attention_masks = [
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_get_prompt_field(p, "prompt_attention_mask") or _get_prompt_field(p, "attention_mask") for p in req.prompts
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]
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if any(m is not None for m in req_prompt_attention_masks):
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prompt_attention_mask = torch.stack(req_prompt_attention_masks) # type: ignore[arg-type]
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req_negative_attention_masks = [
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_get_prompt_field(p, "negative_prompt_attention_mask") or _get_prompt_field(p, "negative_attention_mask")
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for p in req.prompts
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]
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if any(m is not None for m in req_negative_attention_masks):
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negative_prompt_attention_mask = torch.stack(req_negative_attention_masks) # type: ignore[arg-type]
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if req.sampling_params.decode_timestep is not None:
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decode_timestep = req.sampling_params.decode_timestep
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if req.sampling_params.decode_noise_scale is not None:
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decode_noise_scale = req.sampling_params.decode_noise_scale
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if req.sampling_params.output_type is not None:
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output_type = req.sampling_params.output_type
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if image is None and req.prompts:
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raw_images = []
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for prompt_item in req.prompts:
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if isinstance(prompt_item, str):
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raw_image = None
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else:
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multi_modal_data = prompt_item.get("multi_modal_data") or {}
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raw_image = multi_modal_data.get("image")
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if raw_image is None:
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additional = prompt_item.get("additional_information") or {}
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raw_image = (
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additional.get("preprocessed_image")
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or additional.get("pixel_values")
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or additional.get("image")
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)
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if isinstance(raw_image, list):
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raw_image = raw_image[0] if raw_image else None
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if isinstance(raw_image, str):
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raw_image = PIL.Image.open(raw_image).convert("RGB")
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raw_images.append(raw_image)
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if any(img is None for img in raw_images):
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if latents is None:
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raise ValueError("Image is required for LTX2 I2V generation.")
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if len(raw_images) == 1:
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image = raw_images[0]
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elif raw_images:
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image = raw_images
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self.check_inputs(
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image=image,
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height=height,
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width=width,
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prompt=prompt,
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latents=latents,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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prompt_attention_mask=prompt_attention_mask,
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negative_prompt_attention_mask=negative_prompt_attention_mask,
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)
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self._guidance_scale = guidance_scale
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self._guidance_rescale = guidance_rescale
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self._attention_kwargs = attention_kwargs
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self._interrupt = False
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self._current_timestep = None
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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device = self.device
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(
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prompt_embeds,
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prompt_attention_mask,
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negative_prompt_embeds,
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negative_prompt_attention_mask,
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) = self.encode_prompt(
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prompt=prompt,
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negative_prompt=negative_prompt,
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do_classifier_free_guidance=self.do_classifier_free_guidance,
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num_videos_per_prompt=num_videos_per_prompt,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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prompt_attention_mask=prompt_attention_mask,
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negative_prompt_attention_mask=negative_prompt_attention_mask,
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max_sequence_length=max_sequence_length,
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device=device,
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)
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cfg_parallel_ready = self._is_cfg_parallel_enabled(self.do_classifier_free_guidance)
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if self.do_classifier_free_guidance and not cfg_parallel_ready:
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
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additive_attention_mask = (1 - prompt_attention_mask.to(prompt_embeds.dtype)) * -1000000.0
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connector_prompt_embeds, connector_audio_prompt_embeds, connector_attention_mask = self.connectors(
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prompt_embeds, additive_attention_mask, additive_mask=True
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)
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negative_connector_prompt_embeds = None
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negative_connector_audio_prompt_embeds = None
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negative_connector_attention_mask = None
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if cfg_parallel_ready:
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negative_additive_attention_mask = (
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1 - negative_prompt_attention_mask.to(negative_prompt_embeds.dtype)
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) * -1000000.0
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(
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negative_connector_prompt_embeds,
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negative_connector_audio_prompt_embeds,
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negative_connector_attention_mask,
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) = self.connectors(
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negative_prompt_embeds,
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negative_additive_attention_mask,
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additive_mask=True,
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)
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if latents is None:
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if isinstance(image, torch.Tensor):
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if image.ndim == 3:
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image = image.unsqueeze(0)
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elif isinstance(image, list) and image and isinstance(image[0], torch.Tensor):
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image = torch.stack(image, dim=0)
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else:
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image = self.video_processor.preprocess(image, height=height, width=width)
|
|
image = image.to(device=device, dtype=prompt_embeds.dtype)
|
|
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
latents, conditioning_mask = self.prepare_latents(
|
|
image,
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
num_frames,
|
|
torch.float32,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
if self.do_classifier_free_guidance and not cfg_parallel_ready:
|
|
conditioning_mask = torch.cat([conditioning_mask, conditioning_mask])
|
|
|
|
num_mel_bins = self.audio_vae.config.mel_bins if getattr(self, "audio_vae", None) is not None else 64
|
|
latent_mel_bins = num_mel_bins // self.audio_vae_mel_compression_ratio
|
|
|
|
num_channels_latents_audio = (
|
|
self.audio_vae.config.latent_channels if getattr(self, "audio_vae", None) is not None else 8
|
|
)
|
|
audio_latents, audio_num_frames = self.prepare_audio_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents=num_channels_latents_audio,
|
|
num_mel_bins=num_mel_bins,
|
|
num_frames=num_frames,
|
|
frame_rate=frame_rate,
|
|
sampling_rate=self.audio_sampling_rate,
|
|
hop_length=self.audio_hop_length,
|
|
dtype=torch.float32,
|
|
device=device,
|
|
generator=generator,
|
|
latents=audio_latents,
|
|
)
|
|
|
|
latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
|
|
latent_height = height // self.vae_spatial_compression_ratio
|
|
latent_width = width // self.vae_spatial_compression_ratio
|
|
video_sequence_length = latent_num_frames * latent_height * latent_width
|
|
|
|
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
|
|
mu = calculate_shift(
|
|
video_sequence_length,
|
|
self.scheduler.config.get("base_image_seq_len", 1024),
|
|
self.scheduler.config.get("max_image_seq_len", 4096),
|
|
self.scheduler.config.get("base_shift", 0.95),
|
|
self.scheduler.config.get("max_shift", 2.05),
|
|
)
|
|
audio_scheduler = copy.deepcopy(self.scheduler)
|
|
_ = retrieve_timesteps(
|
|
audio_scheduler,
|
|
num_inference_steps,
|
|
device,
|
|
timesteps,
|
|
sigmas=sigmas,
|
|
mu=mu,
|
|
)
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler,
|
|
num_inference_steps,
|
|
device,
|
|
timesteps,
|
|
sigmas=sigmas,
|
|
mu=mu,
|
|
)
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
video_coords = self.transformer.rope.prepare_video_coords(
|
|
latents.shape[0], latent_num_frames, latent_height, latent_width, latents.device, fps=frame_rate
|
|
)
|
|
audio_coords = self.transformer.audio_rope.prepare_audio_coords(
|
|
audio_latents.shape[0], audio_num_frames, audio_latents.device
|
|
)
|
|
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
self._current_timestep = t
|
|
|
|
if cfg_parallel_ready:
|
|
latent_model_input = latents.to(prompt_embeds.dtype)
|
|
audio_latent_model_input = audio_latents.to(prompt_embeds.dtype)
|
|
|
|
timestep = t.expand(latent_model_input.shape[0])
|
|
video_timestep = timestep.unsqueeze(-1) * (1 - conditioning_mask)
|
|
|
|
positive_kwargs = {
|
|
"hidden_states": latent_model_input,
|
|
"audio_hidden_states": audio_latent_model_input,
|
|
"encoder_hidden_states": connector_prompt_embeds,
|
|
"audio_encoder_hidden_states": connector_audio_prompt_embeds,
|
|
"timestep": video_timestep,
|
|
"audio_timestep": timestep,
|
|
"encoder_attention_mask": connector_attention_mask,
|
|
"audio_encoder_attention_mask": connector_attention_mask,
|
|
"num_frames": latent_num_frames,
|
|
"height": latent_height,
|
|
"width": latent_width,
|
|
"fps": frame_rate,
|
|
"audio_num_frames": audio_num_frames,
|
|
"video_coords": video_coords,
|
|
"audio_coords": audio_coords,
|
|
"attention_kwargs": attention_kwargs,
|
|
"return_dict": False,
|
|
}
|
|
negative_kwargs = {
|
|
"hidden_states": latent_model_input,
|
|
"audio_hidden_states": audio_latent_model_input,
|
|
"encoder_hidden_states": negative_connector_prompt_embeds,
|
|
"audio_encoder_hidden_states": negative_connector_audio_prompt_embeds,
|
|
"timestep": video_timestep,
|
|
"audio_timestep": timestep,
|
|
"encoder_attention_mask": negative_connector_attention_mask,
|
|
"audio_encoder_attention_mask": negative_connector_attention_mask,
|
|
"num_frames": latent_num_frames,
|
|
"height": latent_height,
|
|
"width": latent_width,
|
|
"fps": frame_rate,
|
|
"audio_num_frames": audio_num_frames,
|
|
"video_coords": video_coords,
|
|
"audio_coords": audio_coords,
|
|
"attention_kwargs": attention_kwargs,
|
|
"return_dict": False,
|
|
}
|
|
|
|
noise_pred_video, noise_pred_audio = self.predict_noise_av_maybe_with_cfg(
|
|
do_true_cfg=True,
|
|
true_cfg_scale=guidance_scale,
|
|
positive_kwargs=positive_kwargs,
|
|
negative_kwargs=negative_kwargs,
|
|
guidance_rescale=guidance_rescale,
|
|
cfg_normalize=False,
|
|
)
|
|
|
|
if get_classifier_free_guidance_rank() == 0:
|
|
latents = self._step_video_latents_i2v(
|
|
noise_pred_video,
|
|
latents,
|
|
t,
|
|
latent_num_frames,
|
|
latent_height,
|
|
latent_width,
|
|
)
|
|
audio_latents = audio_scheduler.step(noise_pred_audio, t, audio_latents, return_dict=False)[0]
|
|
|
|
cfg_group = get_cfg_group()
|
|
latents = latents.contiguous()
|
|
audio_latents = audio_latents.contiguous()
|
|
cfg_group.broadcast(latents, src=0)
|
|
cfg_group.broadcast(audio_latents, src=0)
|
|
else:
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
latent_model_input = latent_model_input.to(prompt_embeds.dtype)
|
|
audio_latent_model_input = (
|
|
torch.cat([audio_latents] * 2) if self.do_classifier_free_guidance else audio_latents
|
|
)
|
|
audio_latent_model_input = audio_latent_model_input.to(prompt_embeds.dtype)
|
|
|
|
timestep = t.expand(latent_model_input.shape[0])
|
|
video_timestep = timestep.unsqueeze(-1) * (1 - conditioning_mask)
|
|
|
|
with self._transformer_cache_context("cond_uncond"):
|
|
noise_pred_video, noise_pred_audio = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
audio_hidden_states=audio_latent_model_input,
|
|
encoder_hidden_states=connector_prompt_embeds,
|
|
audio_encoder_hidden_states=connector_audio_prompt_embeds,
|
|
timestep=video_timestep,
|
|
audio_timestep=timestep,
|
|
encoder_attention_mask=connector_attention_mask,
|
|
audio_encoder_attention_mask=connector_attention_mask,
|
|
num_frames=latent_num_frames,
|
|
height=latent_height,
|
|
width=latent_width,
|
|
fps=frame_rate,
|
|
audio_num_frames=audio_num_frames,
|
|
video_coords=video_coords,
|
|
audio_coords=audio_coords,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)
|
|
noise_pred_video = noise_pred_video.float()
|
|
noise_pred_audio = noise_pred_audio.float()
|
|
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_video_uncond, noise_pred_video_text = noise_pred_video.chunk(2)
|
|
noise_pred_video = noise_pred_video_uncond + guidance_scale * (
|
|
noise_pred_video_text - noise_pred_video_uncond
|
|
)
|
|
|
|
noise_pred_audio_uncond, noise_pred_audio_text = noise_pred_audio.chunk(2)
|
|
noise_pred_audio = noise_pred_audio_uncond + guidance_scale * (
|
|
noise_pred_audio_text - noise_pred_audio_uncond
|
|
)
|
|
|
|
if guidance_rescale > 0:
|
|
noise_pred_video = rescale_noise_cfg(
|
|
noise_pred_video, noise_pred_video_text, guidance_rescale=guidance_rescale
|
|
)
|
|
noise_pred_audio = rescale_noise_cfg(
|
|
noise_pred_audio, noise_pred_audio_text, guidance_rescale=guidance_rescale
|
|
)
|
|
|
|
latents = self._step_video_latents_i2v(
|
|
noise_pred_video,
|
|
latents,
|
|
t,
|
|
latent_num_frames,
|
|
latent_height,
|
|
latent_width,
|
|
)
|
|
|
|
audio_latents = audio_scheduler.step(noise_pred_audio, t, audio_latents, return_dict=False)[0]
|
|
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
pass
|
|
|
|
latents = self._unpack_latents(
|
|
latents,
|
|
latent_num_frames,
|
|
latent_height,
|
|
latent_width,
|
|
self.transformer_spatial_patch_size,
|
|
self.transformer_temporal_patch_size,
|
|
)
|
|
latents = self._denormalize_latents(
|
|
latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
|
|
)
|
|
|
|
audio_latents = self._denormalize_audio_latents(
|
|
audio_latents, self.audio_vae.latents_mean, self.audio_vae.latents_std
|
|
)
|
|
audio_latents = self._unpack_audio_latents(audio_latents, audio_num_frames, num_mel_bins=latent_mel_bins)
|
|
|
|
if output_type == "latent":
|
|
video = latents
|
|
audio = audio_latents
|
|
else:
|
|
latents = latents.to(prompt_embeds.dtype)
|
|
|
|
if not self.vae.config.timestep_conditioning:
|
|
timestep = None
|
|
else:
|
|
noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
|
|
if not isinstance(decode_timestep, list):
|
|
decode_timestep = [decode_timestep] * batch_size
|
|
if decode_noise_scale is None:
|
|
decode_noise_scale = decode_timestep
|
|
elif not isinstance(decode_noise_scale, list):
|
|
decode_noise_scale = [decode_noise_scale] * batch_size
|
|
|
|
timestep = torch.tensor(decode_timestep, device=device, dtype=latents.dtype)
|
|
decode_noise_scale = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[
|
|
:, None, None, None, None
|
|
]
|
|
latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
|
|
|
|
latents = latents.to(self.vae.dtype)
|
|
video = self.vae.decode(latents, timestep, return_dict=False)[0]
|
|
video = self.video_processor.postprocess_video(video, output_type=output_type)
|
|
|
|
audio_latents = audio_latents.to(self.audio_vae.dtype)
|
|
generated_mel_spectrograms = self.audio_vae.decode(audio_latents, return_dict=False)[0]
|
|
audio = self.vocoder(generated_mel_spectrograms)
|
|
|
|
if not return_dict:
|
|
return DiffusionOutput(output=(video, audio))
|
|
|
|
return DiffusionOutput(output=(video, audio))
|