# -*- coding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import os import sys import gc import math import random import types import logging from contextlib import contextmanager from functools import partial from PIL import Image import torchvision.transforms.functional as TF import torch import torch.nn.functional as F import torch.cuda.amp as amp import torch.distributed as dist from tqdm import tqdm from wan.text2video import (WanT2V, T5EncoderModel, WanVAE, shard_model, FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, FlowUniPCMultistepScheduler) from .modules.model import VaceWanModel from ..utils.preprocessor import VaceVideoProcessor class WanVace(WanT2V): def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_usp=False, t5_cpu=False, ): r""" Initializes the Wan text-to-video generation model components. Args: config (EasyDict): Object containing model parameters initialized from config.py checkpoint_dir (`str`): Path to directory containing model checkpoints device_id (`int`, *optional*, defaults to 0): Id of target GPU device rank (`int`, *optional*, defaults to 0): Process rank for distributed training t5_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for T5 model dit_fsdp (`bool`, *optional*, defaults to False): Enable FSDP sharding for DiT model use_usp (`bool`, *optional*, defaults to False): Enable distribution strategy of USP. t5_cpu (`bool`, *optional*, defaults to False): Whether to place T5 model on CPU. Only works without t5_fsdp. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.t5_cpu = t5_cpu self.num_train_timesteps = config.num_train_timesteps self.param_dtype = config.param_dtype shard_fn = partial(shard_model, device_id=device_id) self.text_encoder = T5EncoderModel( text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), shard_fn=shard_fn if t5_fsdp else None) self.vae_stride = config.vae_stride self.patch_size = config.patch_size self.vae = WanVAE( vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), device=self.device) logging.info(f"Creating VaceWanModel from {checkpoint_dir}") self.model = VaceWanModel.from_pretrained(checkpoint_dir) self.model.eval().requires_grad_(False) if use_usp: from xfuser.core.distributed import \ get_sequence_parallel_world_size from .distributed.xdit_context_parallel import (usp_attn_forward, usp_dit_forward, usp_dit_forward_vace) for block in self.model.blocks: block.self_attn.forward = types.MethodType( usp_attn_forward, block.self_attn) for block in self.model.vace_blocks: block.self_attn.forward = types.MethodType( usp_attn_forward, block.self_attn) self.model.forward = types.MethodType(usp_dit_forward, self.model) self.model.forward_vace = types.MethodType(usp_dit_forward_vace, self.model) self.sp_size = get_sequence_parallel_world_size() else: self.sp_size = 1 if dist.is_initialized(): dist.barrier() if dit_fsdp: self.model = shard_fn(self.model) else: self.model.to(self.device) self.sample_neg_prompt = config.sample_neg_prompt self.vid_proc = VaceVideoProcessor(downsample=tuple([x * y for x, y in zip(config.vae_stride, self.patch_size)]), min_area=480*832, max_area=480*832, min_fps=config.sample_fps, max_fps=config.sample_fps, zero_start=True, seq_len=32760, keep_last=True) def vace_encode_frames(self, frames, ref_images, masks=None): if ref_images is None: ref_images = [None] * len(frames) else: assert len(frames) == len(ref_images) if masks is None: latents = self.vae.encode(frames) else: inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)] reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)] inactive = self.vae.encode(inactive) reactive = self.vae.encode(reactive) latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)] cat_latents = [] for latent, refs in zip(latents, ref_images): if refs is not None: if masks is None: ref_latent = self.vae.encode(refs) else: ref_latent = self.vae.encode(refs) ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent] assert all([x.shape[1] == 1 for x in ref_latent]) latent = torch.cat([*ref_latent, latent], dim=1) cat_latents.append(latent) return cat_latents def vace_encode_masks(self, masks, ref_images=None): if ref_images is None: ref_images = [None] * len(masks) else: assert len(masks) == len(ref_images) result_masks = [] for mask, refs in zip(masks, ref_images): c, depth, height, width = mask.shape new_depth = int((depth + 3) // self.vae_stride[0]) height = 2 * (int(height) // (self.vae_stride[1] * 2)) width = 2 * (int(width) // (self.vae_stride[2] * 2)) # reshape mask = mask[0, :, :, :] mask = mask.view( depth, height, self.vae_stride[1], width, self.vae_stride[1] ) # depth, height, 8, width, 8 mask = mask.permute(2, 4, 0, 1, 3) # 8, 8, depth, height, width mask = mask.reshape( self.vae_stride[1] * self.vae_stride[2], depth, height, width ) # 8*8, depth, height, width # interpolation mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0) if refs is not None: length = len(refs) mask_pad = torch.zeros_like(mask[:, :length, :, :]) mask = torch.cat((mask_pad, mask), dim=1) result_masks.append(mask) return result_masks def vace_latent(self, z, m): return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)] def prepare_source(self, src_video, src_mask, src_ref_images, num_frames, image_size, device): image_sizes = [] for i, (sub_src_video, sub_src_mask) in enumerate(zip(src_video, src_mask)): if sub_src_mask is not None and sub_src_video is not None: src_video[i], src_mask[i], _, _, _ = self.vid_proc.load_video_pair(sub_src_video, sub_src_mask) src_video[i] = src_video[i].to(device) src_mask[i] = src_mask[i].to(device) src_mask[i] = torch.clamp((src_mask[i][:1, :, :, :] + 1) / 2, min=0, max=1) image_sizes.append(src_video[i].shape[2:]) elif sub_src_video is None: src_video[i] = torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device) src_mask[i] = torch.ones_like(src_video[i], device=device) image_sizes.append(image_size) else: src_video[i], _, _, _ = self.vid_proc.load_video(sub_src_video) src_video[i] = src_video[i].to(device) src_mask[i] = torch.ones_like(src_video[i], device=device) image_sizes.append(src_video[i].shape[2:]) for i, ref_images in enumerate(src_ref_images): if ref_images is not None: image_size = image_sizes[i] for j, ref_img in enumerate(ref_images): if ref_img is not None: ref_img = Image.open(ref_img).convert("RGB") ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1) if ref_img.shape[-2:] != image_size: canvas_height, canvas_width = image_size ref_height, ref_width = ref_img.shape[-2:] white_canvas = torch.ones((3, 1, canvas_height, canvas_width), device=device) # [-1, 1] scale = min(canvas_height / ref_height, canvas_width / ref_width) new_height = int(ref_height * scale) new_width = int(ref_width * scale) resized_image = F.interpolate(ref_img.squeeze(1).unsqueeze(0), size=(new_height, new_width), mode='bilinear', align_corners=False).squeeze(0).unsqueeze(1) top = (canvas_height - new_height) // 2 left = (canvas_width - new_width) // 2 white_canvas[:, :, top:top + new_height, left:left + new_width] = resized_image ref_img = white_canvas src_ref_images[i][j] = ref_img.to(device) return src_video, src_mask, src_ref_images def decode_latent(self, zs, ref_images=None): if ref_images is None: ref_images = [None] * len(zs) else: assert len(zs) == len(ref_images) trimed_zs = [] for z, refs in zip(zs, ref_images): if refs is not None: z = z[:, len(refs):, :, :] trimed_zs.append(z) return self.vae.decode(trimed_zs) def generate(self, input_prompt, input_frames, input_masks, input_ref_images, size=(1280, 720), frame_num=81, context_scale=1.0, shift=5.0, sample_solver='unipc', sampling_steps=50, guide_scale=5.0, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation size (tupele[`int`], *optional*, defaults to (1280,720)): Controls video resolution, (width,height). frame_num (`int`, *optional*, defaults to 81): How many frames to sample from a video. The number should be 4n+1 shift (`float`, *optional*, defaults to 5.0): Noise schedule shift parameter. Affects temporal dynamics sample_solver (`str`, *optional*, defaults to 'unipc'): Solver used to sample the video. sampling_steps (`int`, *optional*, defaults to 40): Number of diffusion sampling steps. Higher values improve quality but slow generation guide_scale (`float`, *optional*, defaults 5.0): Classifier-free guidance scale. Controls prompt adherence vs. creativity n_prompt (`str`, *optional*, defaults to ""): Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` seed (`int`, *optional*, defaults to -1): Random seed for noise generation. If -1, use random seed. offload_model (`bool`, *optional*, defaults to True): If True, offloads models to CPU during generation to save VRAM Returns: torch.Tensor: Generated video frames tensor. Dimensions: (C, N H, W) where: - C: Color channels (3 for RGB) - N: Number of frames (81) - H: Frame height (from size) - W: Frame width from size) """ # preprocess # F = frame_num # target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, # size[1] // self.vae_stride[1], # size[0] // self.vae_stride[2]) # # seq_len = math.ceil((target_shape[2] * target_shape[3]) / # (self.patch_size[1] * self.patch_size[2]) * # target_shape[1] / self.sp_size) * self.sp_size if n_prompt == "": n_prompt = self.sample_neg_prompt seed = seed if seed >= 0 else random.randint(0, sys.maxsize) seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) if not self.t5_cpu: self.text_encoder.model.to(self.device) context = self.text_encoder([input_prompt], self.device) context_null = self.text_encoder([n_prompt], self.device) if offload_model: self.text_encoder.model.cpu() else: context = self.text_encoder([input_prompt], torch.device('cpu')) context_null = self.text_encoder([n_prompt], torch.device('cpu')) context = [t.to(self.device) for t in context] context_null = [t.to(self.device) for t in context_null] # vace context encode z0 = self.vace_encode_frames(input_frames, input_ref_images, masks=input_masks) m0 = self.vace_encode_masks(input_masks, input_ref_images) z = self.vace_latent(z0, m0) target_shape = list(z0[0].shape) target_shape[0] = int(target_shape[0] / 2) noise = [ torch.randn( target_shape[0], target_shape[1], target_shape[2], target_shape[3], dtype=torch.float32, device=self.device, generator=seed_g) ] seq_len = math.ceil((target_shape[2] * target_shape[3]) / (self.patch_size[1] * self.patch_size[2]) * target_shape[1] / self.sp_size) * self.sp_size @contextmanager def noop_no_sync(): yield no_sync = getattr(self.model, 'no_sync', noop_no_sync) # evaluation mode with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync(): if sample_solver == 'unipc': sample_scheduler = FlowUniPCMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sample_scheduler.set_timesteps( sampling_steps, device=self.device, shift=shift) timesteps = sample_scheduler.timesteps elif sample_solver == 'dpm++': sample_scheduler = FlowDPMSolverMultistepScheduler( num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False) sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) timesteps, _ = retrieve_timesteps( sample_scheduler, device=self.device, sigmas=sampling_sigmas) else: raise NotImplementedError("Unsupported solver.") # sample videos latents = noise arg_c = {'context': context, 'seq_len': seq_len} arg_null = {'context': context_null, 'seq_len': seq_len} for _, t in enumerate(tqdm(timesteps)): latent_model_input = latents timestep = [t] timestep = torch.stack(timestep) self.model.to(self.device) noise_pred_cond = self.model( latent_model_input, t=timestep, vace_context=z, vace_context_scale=context_scale, **arg_c)[0] noise_pred_uncond = self.model( latent_model_input, t=timestep, vace_context=z, vace_context_scale=context_scale,**arg_null)[0] noise_pred = noise_pred_uncond + guide_scale * ( noise_pred_cond - 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)] x0 = latents if offload_model: self.model.cpu() torch.cuda.empty_cache() if self.rank == 0: videos = self.decode_latent(x0, input_ref_images) 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