mirror of
https://github.com/Wan-Video/Wan2.1.git
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520 lines
22 KiB
Python
520 lines
22 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import gc
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import logging
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import math
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import os
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import random
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import sys
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import types
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from contextlib import contextmanager
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from functools import partial
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from mmgp import offload
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import torch
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import torch.cuda.amp as amp
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import torch.distributed as dist
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from tqdm import tqdm
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from PIL import Image
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import torchvision.transforms.functional as TF
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import torch.nn.functional as F
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from .distributed.fsdp import shard_model
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from .modules.model import WanModel
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from .modules.t5 import T5EncoderModel
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from .modules.vae import WanVAE
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from .utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
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get_sampling_sigmas, retrieve_timesteps)
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from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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from wan.modules.posemb_layers import get_rotary_pos_embed
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from .utils.vace_preprocessor import VaceVideoProcessor
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def optimized_scale(positive_flat, negative_flat):
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# Calculate dot production
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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# Squared norm of uncondition
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squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
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# st_star = v_cond^T * v_uncond / ||v_uncond||^2
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st_star = dot_product / squared_norm
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return st_star
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class WanT2V:
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def __init__(
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self,
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config,
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checkpoint_dir,
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device_id=0,
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rank=0,
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t5_fsdp=False,
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dit_fsdp=False,
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use_usp=False,
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t5_cpu=False,
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model_filename = None,
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text_encoder_filename = None
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):
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r"""
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Initializes the Wan text-to-video generation model components.
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Args:
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config (EasyDict):
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Object containing model parameters initialized from config.py
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checkpoint_dir (`str`):
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Path to directory containing model checkpoints
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device_id (`int`, *optional*, defaults to 0):
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Id of target GPU device
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rank (`int`, *optional*, defaults to 0):
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Process rank for distributed training
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t5_fsdp (`bool`, *optional*, defaults to False):
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Enable FSDP sharding for T5 model
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dit_fsdp (`bool`, *optional*, defaults to False):
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Enable FSDP sharding for DiT model
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use_usp (`bool`, *optional*, defaults to False):
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Enable distribution strategy of USP.
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t5_cpu (`bool`, *optional*, defaults to False):
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Whether to place T5 model on CPU. Only works without t5_fsdp.
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"""
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self.device = torch.device(f"cuda:{device_id}")
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self.config = config
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self.rank = rank
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self.t5_cpu = t5_cpu
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self.num_train_timesteps = config.num_train_timesteps
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self.param_dtype = config.param_dtype
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shard_fn = partial(shard_model, device_id=device_id)
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self.text_encoder = T5EncoderModel(
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text_len=config.text_len,
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dtype=config.t5_dtype,
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device=torch.device('cpu'),
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checkpoint_path=text_encoder_filename,
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tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
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shard_fn=shard_fn if t5_fsdp else None)
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self.vae_stride = config.vae_stride
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self.patch_size = config.patch_size
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self.vae = WanVAE(
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vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
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device=self.device)
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logging.info(f"Creating WanModel from {model_filename}")
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from mmgp import offload
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self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel, writable_tensors= False)
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self.model.eval().requires_grad_(False)
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if use_usp:
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from xfuser.core.distributed import \
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get_sequence_parallel_world_size
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from .distributed.xdit_context_parallel import (usp_attn_forward,
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usp_dit_forward)
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for block in self.model.blocks:
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block.self_attn.forward = types.MethodType(
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usp_attn_forward, block.self_attn)
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self.model.forward = types.MethodType(usp_dit_forward, self.model)
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self.sp_size = get_sequence_parallel_world_size()
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else:
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self.sp_size = 1
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# if dist.is_initialized():
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# dist.barrier()
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# if dit_fsdp:
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# self.model = shard_fn(self.model)
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# else:
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# self.model.to(self.device)
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self.sample_neg_prompt = config.sample_neg_prompt
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if "Vace" in model_filename:
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self.vid_proc = VaceVideoProcessor(downsample=tuple([x * y for x, y in zip(config.vae_stride, self.patch_size)]),
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min_area=480*832,
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max_area=480*832,
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min_fps=config.sample_fps,
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max_fps=config.sample_fps,
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zero_start=True,
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seq_len=32760,
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keep_last=True)
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self.adapt_vace_model()
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def vace_encode_frames(self, frames, ref_images, masks=None, tile_size = 0):
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if ref_images is None:
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ref_images = [None] * len(frames)
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else:
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assert len(frames) == len(ref_images)
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if masks is None:
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latents = self.vae.encode(frames, tile_size = tile_size)
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else:
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inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)]
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reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)]
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inactive = self.vae.encode(inactive, tile_size = tile_size)
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reactive = self.vae.encode(reactive, tile_size = tile_size)
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latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)]
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cat_latents = []
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for latent, refs in zip(latents, ref_images):
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if refs is not None:
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if masks is None:
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ref_latent = self.vae.encode(refs, tile_size = tile_size)
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else:
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ref_latent = self.vae.encode(refs, tile_size = tile_size)
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ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent]
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assert all([x.shape[1] == 1 for x in ref_latent])
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latent = torch.cat([*ref_latent, latent], dim=1)
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cat_latents.append(latent)
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return cat_latents
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def vace_encode_masks(self, masks, ref_images=None):
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if ref_images is None:
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ref_images = [None] * len(masks)
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else:
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assert len(masks) == len(ref_images)
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result_masks = []
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for mask, refs in zip(masks, ref_images):
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c, depth, height, width = mask.shape
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new_depth = int((depth + 3) // self.vae_stride[0])
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height = 2 * (int(height) // (self.vae_stride[1] * 2))
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width = 2 * (int(width) // (self.vae_stride[2] * 2))
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# reshape
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mask = mask[0, :, :, :]
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mask = mask.view(
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depth, height, self.vae_stride[1], width, self.vae_stride[1]
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) # depth, height, 8, width, 8
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mask = mask.permute(2, 4, 0, 1, 3) # 8, 8, depth, height, width
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mask = mask.reshape(
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self.vae_stride[1] * self.vae_stride[2], depth, height, width
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) # 8*8, depth, height, width
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# interpolation
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mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0)
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if refs is not None:
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length = len(refs)
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mask_pad = torch.zeros_like(mask[:, :length, :, :])
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mask = torch.cat((mask_pad, mask), dim=1)
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result_masks.append(mask)
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return result_masks
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def vace_latent(self, z, m):
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return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)]
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def prepare_source(self, src_video, src_mask, src_ref_images, num_frames, image_size, device, original_video = False, trim_video= 0):
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image_sizes = []
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for i, (sub_src_video, sub_src_mask) in enumerate(zip(src_video, src_mask)):
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if sub_src_mask is not None and sub_src_video is not None:
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src_video[i], src_mask[i], _, _, _ = self.vid_proc.load_video_pair(sub_src_video, sub_src_mask, max_frames= num_frames, trim_video = trim_video)
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# src_video is [-1, 1], 0 = inpainting area (in fact 127 in [0, 255])
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# src_mask is [-1, 1], 0 = preserve original video (in fact 127 in [0, 255]) and 1 = Inpainting (in fact 255 in [0, 255])
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src_video[i] = src_video[i].to(device)
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src_mask[i] = src_mask[i].to(device)
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src_video_shape = src_video[i].shape
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if src_video_shape[1] != num_frames:
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src_video[i] = torch.cat( [src_video[i], src_video[i].new_zeros(src_video_shape[0], num_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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src_mask[i] = torch.cat( [src_mask[i], src_mask[i].new_ones(src_video_shape[0], num_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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src_mask[i] = torch.clamp((src_mask[i][:1, :, :, :] + 1) / 2, min=0, max=1)
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image_sizes.append(src_video[i].shape[2:])
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elif sub_src_video is None:
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src_video[i] = torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device)
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src_mask[i] = torch.ones_like(src_video[i], device=device)
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image_sizes.append(image_size)
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else:
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src_video[i], _, _, _ = self.vid_proc.load_video(sub_src_video, max_frames= num_frames, trim_video = trim_video)
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src_video[i] = src_video[i].to(device)
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src_mask[i] = torch.zeros_like(src_video[i], device=device) if original_video else torch.ones_like(src_video[i], device=device)
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src_video_shape = src_video[i].shape
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if src_video_shape[1] != num_frames:
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src_video[i] = torch.cat( [src_video[i], src_video[i].new_zeros(src_video_shape[0], num_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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src_mask[i] = torch.cat( [src_mask[i], src_mask[i].new_ones(src_video_shape[0], num_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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image_sizes.append(src_video[i].shape[2:])
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for i, ref_images in enumerate(src_ref_images):
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if ref_images is not None:
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image_size = image_sizes[i]
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for j, ref_img in enumerate(ref_images):
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if ref_img is not None:
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ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1)
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if ref_img.shape[-2:] != image_size:
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canvas_height, canvas_width = image_size
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ref_height, ref_width = ref_img.shape[-2:]
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white_canvas = torch.ones((3, 1, canvas_height, canvas_width), device=device) # [-1, 1]
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scale = min(canvas_height / ref_height, canvas_width / ref_width)
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new_height = int(ref_height * scale)
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new_width = int(ref_width * scale)
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resized_image = F.interpolate(ref_img.squeeze(1).unsqueeze(0), size=(new_height, new_width), mode='bilinear', align_corners=False).squeeze(0).unsqueeze(1)
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top = (canvas_height - new_height) // 2
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left = (canvas_width - new_width) // 2
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white_canvas[:, :, top:top + new_height, left:left + new_width] = resized_image
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ref_img = white_canvas
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src_ref_images[i][j] = ref_img.to(device)
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return src_video, src_mask, src_ref_images
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def decode_latent(self, zs, ref_images=None, tile_size= 0 ):
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if ref_images is None:
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ref_images = [None] * len(zs)
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else:
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assert len(zs) == len(ref_images)
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trimed_zs = []
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for z, refs in zip(zs, ref_images):
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if refs is not None:
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z = z[:, len(refs):, :, :]
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trimed_zs.append(z)
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return self.vae.decode(trimed_zs, tile_size= tile_size)
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def generate(self,
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input_prompt,
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input_frames= None,
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input_masks = None,
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input_ref_images = None,
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context_scale=1.0,
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size=(1280, 720),
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frame_num=81,
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shift=5.0,
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sample_solver='unipc',
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sampling_steps=50,
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guide_scale=5.0,
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n_prompt="",
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seed=-1,
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offload_model=True,
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callback = None,
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enable_RIFLEx = None,
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VAE_tile_size = 0,
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joint_pass = False,
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slg_layers = None,
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slg_start = 0.0,
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slg_end = 1.0,
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cfg_star_switch = True,
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cfg_zero_step = 5,
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):
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r"""
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Generates video frames from text prompt using diffusion process.
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Args:
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input_prompt (`str`):
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Text prompt for content generation
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size (tupele[`int`], *optional*, defaults to (1280,720)):
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Controls video resolution, (width,height).
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frame_num (`int`, *optional*, defaults to 81):
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How many frames to sample from a video. The number should be 4n+1
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shift (`float`, *optional*, defaults to 5.0):
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Noise schedule shift parameter. Affects temporal dynamics
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sample_solver (`str`, *optional*, defaults to 'unipc'):
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Solver used to sample the video.
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sampling_steps (`int`, *optional*, defaults to 40):
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Number of diffusion sampling steps. Higher values improve quality but slow generation
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guide_scale (`float`, *optional*, defaults 5.0):
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Classifier-free guidance scale. Controls prompt adherence vs. creativity
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n_prompt (`str`, *optional*, defaults to ""):
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Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt`
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seed (`int`, *optional*, defaults to -1):
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Random seed for noise generation. If -1, use random seed.
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offload_model (`bool`, *optional*, defaults to True):
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If True, offloads models to CPU during generation to save VRAM
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Returns:
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torch.Tensor:
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Generated video frames tensor. Dimensions: (C, N H, W) where:
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- C: Color channels (3 for RGB)
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- N: Number of frames (81)
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- H: Frame height (from size)
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- W: Frame width from size)
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"""
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# preprocess
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if n_prompt == "":
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n_prompt = self.sample_neg_prompt
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seed = seed if seed >= 0 else random.randint(0, sys.maxsize)
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seed_g = torch.Generator(device=self.device)
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seed_g.manual_seed(seed)
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if not self.t5_cpu:
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# self.text_encoder.model.to(self.device)
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context = self.text_encoder([input_prompt], self.device)
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context_null = self.text_encoder([n_prompt], self.device)
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if offload_model:
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self.text_encoder.model.cpu()
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else:
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context = self.text_encoder([input_prompt], torch.device('cpu'))
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context_null = self.text_encoder([n_prompt], torch.device('cpu'))
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context = [t.to(self.device) for t in context]
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context_null = [t.to(self.device) for t in context_null]
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if input_frames != None:
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# vace context encode
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input_frames = [u.to(self.device) for u in input_frames]
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input_ref_images = [ None if u == None else [v.to(self.device) for v in u] for u in input_ref_images]
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input_masks = [u.to(self.device) for u in input_masks]
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z0 = self.vace_encode_frames(input_frames, input_ref_images, masks=input_masks, tile_size = VAE_tile_size)
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m0 = self.vace_encode_masks(input_masks, input_ref_images)
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z = self.vace_latent(z0, m0)
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target_shape = list(z0[0].shape)
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target_shape[0] = int(target_shape[0] / 2)
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else:
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F = frame_num
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target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1,
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size[1] // self.vae_stride[1],
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size[0] // self.vae_stride[2])
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seq_len = math.ceil((target_shape[2] * target_shape[3]) /
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(self.patch_size[1] * self.patch_size[2]) *
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target_shape[1] / self.sp_size) * self.sp_size
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noise = [
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torch.randn(
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target_shape[0],
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target_shape[1],
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target_shape[2],
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target_shape[3],
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dtype=torch.float32,
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device=self.device,
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generator=seed_g)
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]
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@contextmanager
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def noop_no_sync():
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yield
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no_sync = getattr(self.model, 'no_sync', noop_no_sync)
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# evaluation mode
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if sample_solver == 'unipc':
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sample_scheduler = FlowUniPCMultistepScheduler(
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num_train_timesteps=self.num_train_timesteps,
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shift=1,
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use_dynamic_shifting=False)
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sample_scheduler.set_timesteps(
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sampling_steps, device=self.device, shift=shift)
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timesteps = sample_scheduler.timesteps
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elif sample_solver == 'dpm++':
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sample_scheduler = FlowDPMSolverMultistepScheduler(
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num_train_timesteps=self.num_train_timesteps,
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shift=1,
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use_dynamic_shifting=False)
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sampling_sigmas = get_sampling_sigmas(sampling_steps, shift)
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timesteps, _ = retrieve_timesteps(
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sample_scheduler,
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device=self.device,
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sigmas=sampling_sigmas)
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else:
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raise NotImplementedError("Unsupported solver.")
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# sample videos
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latents = noise
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batch_size =len(latents)
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freqs = get_rotary_pos_embed(latents[0].shape[1:], enable_RIFLEx= enable_RIFLEx)
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arg_c = {'context': context, 'seq_len': seq_len, 'freqs': freqs, 'pipeline': self}
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arg_null = {'context': context_null, 'seq_len': seq_len, 'freqs': freqs, 'pipeline': self}
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arg_both = {'context': context, 'context2': context_null, 'seq_len': seq_len, 'freqs': freqs, 'pipeline': self}
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if input_frames != None:
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vace_dict = {'vace_context' : z, 'vace_context_scale' : context_scale}
|
|
arg_c.update(vace_dict)
|
|
arg_null.update(vace_dict)
|
|
arg_both.update(vace_dict)
|
|
|
|
if self.model.enable_teacache:
|
|
self.model.compute_teacache_threshold(self.model.teacache_start_step, timesteps, self.model.teacache_multiplier)
|
|
if callback != None:
|
|
callback(-1, True)
|
|
for i, t in enumerate(tqdm(timesteps)):
|
|
latent_model_input = latents
|
|
slg_layers_local = None
|
|
if int(slg_start * sampling_steps) <= i < int(slg_end * sampling_steps):
|
|
slg_layers_local = slg_layers
|
|
timestep = [t]
|
|
offload.set_step_no_for_lora(self.model, i)
|
|
timestep = torch.stack(timestep)
|
|
|
|
# self.model.to(self.device)
|
|
if joint_pass:
|
|
noise_pred_cond, noise_pred_uncond = self.model(
|
|
latent_model_input, t=timestep, current_step=i, slg_layers=slg_layers_local, **arg_both)
|
|
if self._interrupt:
|
|
return None
|
|
else:
|
|
noise_pred_cond = self.model(
|
|
latent_model_input, t=timestep,current_step=i, is_uncond = False, **arg_c)[0]
|
|
if self._interrupt:
|
|
return None
|
|
noise_pred_uncond = self.model(
|
|
latent_model_input, t=timestep,current_step=i, is_uncond = True, slg_layers=slg_layers_local, **arg_null)[0]
|
|
if self._interrupt:
|
|
return None
|
|
|
|
del latent_model_input
|
|
|
|
# CFG Zero *. Thanks to https://github.com/WeichenFan/CFG-Zero-star/
|
|
noise_pred_text = noise_pred_cond
|
|
if cfg_star_switch:
|
|
positive_flat = noise_pred_text.view(batch_size, -1)
|
|
negative_flat = noise_pred_uncond.view(batch_size, -1)
|
|
|
|
alpha = optimized_scale(positive_flat,negative_flat)
|
|
alpha = alpha.view(batch_size, 1, 1, 1)
|
|
|
|
if (i <= cfg_zero_step):
|
|
noise_pred = noise_pred_text*0. # it would be faster not to compute noise_pred...
|
|
else:
|
|
noise_pred_uncond *= alpha
|
|
noise_pred = noise_pred_uncond + guide_scale * (noise_pred_text - noise_pred_uncond)
|
|
del noise_pred_uncond
|
|
|
|
temp_x0 = sample_scheduler.step(
|
|
noise_pred.unsqueeze(0),
|
|
t,
|
|
latents[0].unsqueeze(0),
|
|
return_dict=False,
|
|
generator=seed_g)[0]
|
|
latents = [temp_x0.squeeze(0)]
|
|
del temp_x0
|
|
|
|
if callback is not None:
|
|
callback(i, False)
|
|
|
|
x0 = latents
|
|
if offload_model:
|
|
self.model.cpu()
|
|
torch.cuda.empty_cache()
|
|
if self.rank == 0:
|
|
|
|
if input_frames == None:
|
|
videos = self.vae.decode(x0, VAE_tile_size)
|
|
else:
|
|
videos = self.decode_latent(x0, input_ref_images, VAE_tile_size)
|
|
|
|
|
|
del noise, latents
|
|
del sample_scheduler
|
|
if offload_model:
|
|
gc.collect()
|
|
torch.cuda.synchronize()
|
|
if dist.is_initialized():
|
|
dist.barrier()
|
|
|
|
return videos[0] if self.rank == 0 else None
|
|
|
|
def adapt_vace_model(self):
|
|
model = self.model
|
|
modules_dict= { k: m for k, m in model.named_modules()}
|
|
for model_layer, vace_layer in model.vace_layers_mapping.items():
|
|
module = modules_dict[f"vace_blocks.{vace_layer}"]
|
|
target = modules_dict[f"blocks.{model_layer}"]
|
|
setattr(target, "vace", module )
|
|
delattr(model, "vace_blocks")
|
|
|
|
|