# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import gc import logging import math import os import random import sys import types from contextlib import contextmanager from functools import partial import numpy as np import torch import torch.cuda.amp as amp import torch.distributed as dist import torchvision.transforms.functional as TF from tqdm import tqdm from .distributed.fsdp import shard_model from .modules.clip import CLIPModel from .modules.model import WanModel from .modules.t5 import T5EncoderModel from .modules.vae import WanVAE from .utils.fm_solvers import ( FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps, ) from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler class WanFLF2V: def __init__( self, config, checkpoint_dir, device_id=0, rank=0, t5_fsdp=False, dit_fsdp=False, use_usp=False, t5_cpu=False, init_on_cpu=True, ): r""" Initializes the image-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. init_on_cpu (`bool`, *optional*, defaults to True): Enable initializing Transformer Model on CPU. Only works without FSDP or USP. """ self.device = torch.device(f"cuda:{device_id}") self.config = config self.rank = rank self.use_usp = use_usp 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) self.clip = CLIPModel( dtype=config.clip_dtype, device=self.device, checkpoint_path=os.path.join(checkpoint_dir, config.clip_checkpoint), tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer)) logging.info(f"Creating WanModel from {checkpoint_dir}") self.model = WanModel.from_pretrained(checkpoint_dir) self.model.eval().requires_grad_(False) if t5_fsdp or dit_fsdp or use_usp: init_on_cpu = 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, ) for block in self.model.blocks: block.self_attn.forward = types.MethodType( usp_attn_forward, block.self_attn) self.model.forward = types.MethodType(usp_dit_forward, 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: if not init_on_cpu: self.model.to(self.device) self.sample_neg_prompt = config.sample_neg_prompt def generate(self, input_prompt, first_frame, last_frame, max_area=720 * 1280, frame_num=81, shift=16, sample_solver='unipc', sampling_steps=50, guide_scale=5.5, n_prompt="", seed=-1, offload_model=True): r""" Generates video frames from input first-last frame and text prompt using diffusion process. Args: input_prompt (`str`): Text prompt for content generation. first_frame (PIL.Image.Image): Input image tensor. Shape: [3, H, W] last_frame (PIL.Image.Image): Input image tensor. Shape: [3, H, W] [NOTE] If the sizes of first_frame and last_frame are mismatched, last_frame will be cropped & resized to match first_frame. max_area (`int`, *optional*, defaults to 720*1280): Maximum pixel area for latent space calculation. Controls video resolution scaling 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 [NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0. 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 max_area) - W: Frame width from max_area) """ first_frame_size = first_frame.size last_frame_size = last_frame.size first_frame = TF.to_tensor(first_frame).sub_(0.5).div_(0.5).to( self.device) last_frame = TF.to_tensor(last_frame).sub_(0.5).div_(0.5).to( self.device) F = frame_num first_frame_h, first_frame_w = first_frame.shape[1:] aspect_ratio = first_frame_h / first_frame_w lat_h = round( np.sqrt(max_area * aspect_ratio) // self.vae_stride[1] // self.patch_size[1] * self.patch_size[1]) lat_w = round( np.sqrt(max_area / aspect_ratio) // self.vae_stride[2] // self.patch_size[2] * self.patch_size[2]) first_frame_h = lat_h * self.vae_stride[1] first_frame_w = lat_w * self.vae_stride[2] if first_frame_size != last_frame_size: # 1. resize last_frame_resize_ratio = max( first_frame_size[0] / last_frame_size[0], first_frame_size[1] / last_frame_size[1]) last_frame_size = [ round(last_frame_size[0] * last_frame_resize_ratio), round(last_frame_size[1] * last_frame_resize_ratio), ] # 2. center crop last_frame = TF.center_crop(last_frame, last_frame_size) max_seq_len = ((F - 1) // self.vae_stride[0] + 1) * lat_h * lat_w // ( self.patch_size[1] * self.patch_size[2]) max_seq_len = int(math.ceil(max_seq_len / self.sp_size)) * self.sp_size seed = seed if seed >= 0 else random.randint(0, sys.maxsize) seed_g = torch.Generator(device=self.device) seed_g.manual_seed(seed) noise = torch.randn( 16, (F - 1) // 4 + 1, lat_h, lat_w, dtype=torch.float32, generator=seed_g, device=self.device) msk = torch.ones(1, 81, lat_h, lat_w, device=self.device) msk[:, 1:-1] = 0 msk = torch.concat([ torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:] ], dim=1) msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w) msk = msk.transpose(1, 2)[0] if n_prompt == "": n_prompt = self.sample_neg_prompt # preprocess 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] self.clip.model.to(self.device) clip_context = self.clip.visual( [first_frame[:, None, :, :], last_frame[:, None, :, :]]) if offload_model: self.clip.model.cpu() y = self.vae.encode([ torch.concat([ torch.nn.functional.interpolate( first_frame[None].cpu(), size=(first_frame_h, first_frame_w), mode='bicubic').transpose(0, 1), torch.zeros(3, F - 2, first_frame_h, first_frame_w), torch.nn.functional.interpolate( last_frame[None].cpu(), size=(first_frame_h, first_frame_w), mode='bicubic').transpose(0, 1), ], dim=1).to(self.device) ])[0] y = torch.concat([msk, y]) @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 latent = noise arg_c = { 'context': [context[0]], 'clip_fea': clip_context, 'seq_len': max_seq_len, 'y': [y], } arg_null = { 'context': context_null, 'clip_fea': clip_context, 'seq_len': max_seq_len, 'y': [y], } if offload_model: torch.cuda.empty_cache() self.model.to(self.device) for _, t in enumerate(tqdm(timesteps)): latent_model_input = [latent.to(self.device)] timestep = [t] timestep = torch.stack(timestep).to(self.device) noise_pred_cond = self.model( latent_model_input, t=timestep, **arg_c)[0].to( torch.device('cpu') if offload_model else self.device) if offload_model: torch.cuda.empty_cache() noise_pred_uncond = self.model( latent_model_input, t=timestep, **arg_null)[0].to( torch.device('cpu') if offload_model else self.device) if offload_model: torch.cuda.empty_cache() noise_pred = noise_pred_uncond + guide_scale * ( noise_pred_cond - noise_pred_uncond) latent = latent.to( torch.device('cpu') if offload_model else self.device) temp_x0 = sample_scheduler.step( noise_pred.unsqueeze(0), t, latent.unsqueeze(0), return_dict=False, generator=seed_g)[0] latent = temp_x0.squeeze(0) x0 = [latent.to(self.device)] del latent_model_input, timestep if offload_model: self.model.cpu() torch.cuda.empty_cache() if self.rank == 0: videos = self.vae.decode(x0) del noise, latent 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