| |
| import gc |
| import logging |
| import math |
| import os |
| import random |
| import sys |
| import types |
| from contextlib import contextmanager |
| from functools import partial |
|
|
| import torch |
| import torch.cuda.amp as amp |
| import torch.distributed as dist |
| import torchvision.transforms.functional as TF |
| from PIL import Image |
| from tqdm import tqdm |
|
|
| from .distributed.fsdp import shard_model |
| from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward |
| from .distributed.util import get_world_size |
| from .modules.model import WanModel |
| from .modules.t5 import T5EncoderModel |
| from .modules.vae2_2 import Wan2_2_VAE |
| from .utils.fm_solvers import ( |
| FlowDPMSolverMultistepScheduler, |
| get_sampling_sigmas, |
| retrieve_timesteps, |
| ) |
| from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler |
| from .utils.utils import best_output_size, masks_like |
|
|
|
|
| class WanTI2V: |
|
|
| def __init__( |
| self, |
| config, |
| checkpoint_dir, |
| device_id=0, |
| rank=0, |
| t5_fsdp=False, |
| dit_fsdp=False, |
| use_sp=False, |
| t5_cpu=False, |
| init_on_cpu=True, |
| convert_model_dtype=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_sp (`bool`, *optional*, defaults to False): |
| Enable distribution strategy of sequence parallel. |
| 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. |
| convert_model_dtype (`bool`, *optional*, defaults to False): |
| Convert DiT model parameters dtype to 'config.param_dtype'. |
| Only works without FSDP. |
| """ |
| self.device = torch.device(f"cuda:{device_id}") |
| self.config = config |
| self.rank = rank |
| self.t5_cpu = t5_cpu |
| self.init_on_cpu = init_on_cpu |
|
|
| self.num_train_timesteps = config.num_train_timesteps |
| self.param_dtype = config.param_dtype |
|
|
| if t5_fsdp or dit_fsdp or use_sp: |
| self.init_on_cpu = False |
|
|
| 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 = Wan2_2_VAE( |
| vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), |
| device=self.device) |
|
|
| logging.info(f"Creating WanModel from {checkpoint_dir}") |
| self.model = WanModel.from_pretrained(checkpoint_dir) |
| self.model = self._configure_model( |
| model=self.model, |
| use_sp=use_sp, |
| dit_fsdp=dit_fsdp, |
| shard_fn=shard_fn, |
| convert_model_dtype=convert_model_dtype) |
|
|
| if use_sp: |
| self.sp_size = get_world_size() |
| else: |
| self.sp_size = 1 |
|
|
| self.sample_neg_prompt = config.sample_neg_prompt |
|
|
| def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, |
| convert_model_dtype): |
| """ |
| Configures a model object. This includes setting evaluation modes, |
| applying distributed parallel strategy, and handling device placement. |
| |
| Args: |
| model (torch.nn.Module): |
| The model instance to configure. |
| use_sp (`bool`): |
| Enable distribution strategy of sequence parallel. |
| dit_fsdp (`bool`): |
| Enable FSDP sharding for DiT model. |
| shard_fn (callable): |
| The function to apply FSDP sharding. |
| convert_model_dtype (`bool`): |
| Convert DiT model parameters dtype to 'config.param_dtype'. |
| Only works without FSDP. |
| |
| Returns: |
| torch.nn.Module: |
| The configured model. |
| """ |
| model.eval().requires_grad_(False) |
|
|
| if use_sp: |
| for block in model.blocks: |
| block.self_attn.forward = types.MethodType( |
| sp_attn_forward, block.self_attn) |
| model.forward = types.MethodType(sp_dit_forward, model) |
|
|
| if dist.is_initialized(): |
| dist.barrier() |
|
|
| if dit_fsdp: |
| model = shard_fn(model) |
| else: |
| if convert_model_dtype: |
| model.to(self.param_dtype) |
| if not self.init_on_cpu: |
| model.to(self.device) |
|
|
| return model |
|
|
| def generate(self, |
| input_prompt, |
| img=None, |
| size=(1280, 704), |
| max_area=704 * 1280, |
| frame_num=81, |
| 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 |
| img (PIL.Image.Image): |
| Input image tensor. Shape: [3, H, W] |
| size (`tuple[int]`, *optional*, defaults to (1280,704)): |
| Controls video resolution, (width,height). |
| max_area (`int`, *optional*, defaults to 704*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 |
| sample_solver (`str`, *optional*, defaults to 'unipc'): |
| Solver used to sample the video. |
| sampling_steps (`int`, *optional*, defaults to 50): |
| 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) |
| """ |
| |
| if img is not None: |
| return self.i2v( |
| input_prompt=input_prompt, |
| img=img, |
| max_area=max_area, |
| frame_num=frame_num, |
| shift=shift, |
| sample_solver=sample_solver, |
| sampling_steps=sampling_steps, |
| guide_scale=guide_scale, |
| n_prompt=n_prompt, |
| seed=seed, |
| offload_model=offload_model) |
| |
| return self.t2v( |
| input_prompt=input_prompt, |
| size=size, |
| frame_num=frame_num, |
| shift=shift, |
| sample_solver=sample_solver, |
| sampling_steps=sampling_steps, |
| guide_scale=guide_scale, |
| n_prompt=n_prompt, |
| seed=seed, |
| offload_model=offload_model) |
|
|
| def t2v(self, |
| input_prompt, |
| size=(1280, 704), |
| frame_num=121, |
| 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 (`tuple[int]`, *optional*, defaults to (1280,704)): |
| Controls video resolution, (width,height). |
| frame_num (`int`, *optional*, defaults to 121): |
| 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 50): |
| 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) |
| """ |
| |
| 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] |
|
|
| noise = [ |
| torch.randn( |
| target_shape[0], |
| target_shape[1], |
| target_shape[2], |
| target_shape[3], |
| dtype=torch.float32, |
| device=self.device, |
| generator=seed_g) |
| ] |
|
|
| @contextmanager |
| def noop_no_sync(): |
| yield |
|
|
| no_sync = getattr(self.model, 'no_sync', noop_no_sync) |
|
|
| |
| with ( |
| torch.amp.autocast('cuda', 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.") |
|
|
| |
| latents = noise |
| mask1, mask2 = masks_like(noise, zero=False) |
|
|
| arg_c = {'context': context, 'seq_len': seq_len} |
| arg_null = {'context': context_null, 'seq_len': seq_len} |
|
|
| if offload_model or self.init_on_cpu: |
| self.model.to(self.device) |
| torch.cuda.empty_cache() |
|
|
| for _, t in enumerate(tqdm(timesteps)): |
| latent_model_input = latents |
| timestep = [t] |
|
|
| timestep = torch.stack(timestep) |
|
|
| temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten() |
| temp_ts = torch.cat([ |
| temp_ts, |
| temp_ts.new_ones(seq_len - temp_ts.size(0)) * timestep |
| ]) |
| timestep = temp_ts.unsqueeze(0) |
|
|
| noise_pred_cond = self.model( |
| latent_model_input, t=timestep, **arg_c)[0] |
| noise_pred_uncond = self.model( |
| latent_model_input, t=timestep, **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.synchronize() |
| torch.cuda.empty_cache() |
| if self.rank == 0: |
| videos = self.vae.decode(x0) |
|
|
| 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 i2v(self, |
| input_prompt, |
| img, |
| max_area=704 * 1280, |
| frame_num=121, |
| shift=5.0, |
| sample_solver='unipc', |
| sampling_steps=40, |
| guide_scale=5.0, |
| n_prompt="", |
| seed=-1, |
| offload_model=True): |
| r""" |
| Generates video frames from input image and text prompt using diffusion process. |
| |
| Args: |
| input_prompt (`str`): |
| Text prompt for content generation. |
| img (PIL.Image.Image): |
| Input image tensor. Shape: [3, H, W] |
| max_area (`int`, *optional*, defaults to 704*1280): |
| Maximum pixel area for latent space calculation. Controls video resolution scaling |
| frame_num (`int`, *optional*, defaults to 121): |
| 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 (121) |
| - H: Frame height (from max_area) |
| - W: Frame width (from max_area) |
| """ |
| |
| ih, iw = img.height, img.width |
| dh, dw = self.patch_size[1] * self.vae_stride[1], self.patch_size[ |
| 2] * self.vae_stride[2] |
| ow, oh = best_output_size(iw, ih, dw, dh, max_area) |
|
|
| scale = max(ow / iw, oh / ih) |
| img = img.resize((round(iw * scale), round(ih * scale)), Image.LANCZOS) |
|
|
| |
| x1 = (img.width - ow) // 2 |
| y1 = (img.height - oh) // 2 |
| img = img.crop((x1, y1, x1 + ow, y1 + oh)) |
| assert img.width == ow and img.height == oh |
|
|
| |
| img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device).unsqueeze(1) |
|
|
| F = frame_num |
| seq_len = ((F - 1) // self.vae_stride[0] + 1) * ( |
| oh // self.vae_stride[1]) * (ow // self.vae_stride[2]) // ( |
| self.patch_size[1] * self.patch_size[2]) |
| seq_len = int(math.ceil(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( |
| self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, |
| oh // self.vae_stride[1], |
| ow // self.vae_stride[2], |
| dtype=torch.float32, |
| generator=seed_g, |
| device=self.device) |
|
|
| if n_prompt == "": |
| n_prompt = self.sample_neg_prompt |
|
|
| |
| 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] |
|
|
| z = self.vae.encode([img]) |
|
|
| @contextmanager |
| def noop_no_sync(): |
| yield |
|
|
| no_sync = getattr(self.model, 'no_sync', noop_no_sync) |
|
|
| |
| with ( |
| torch.amp.autocast('cuda', 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.") |
|
|
| |
| latent = noise |
| mask1, mask2 = masks_like([noise], zero=True) |
| latent = (1. - mask2[0]) * z[0] + mask2[0] * latent |
|
|
| arg_c = { |
| 'context': [context[0]], |
| 'seq_len': seq_len, |
| } |
|
|
| arg_null = { |
| 'context': context_null, |
| 'seq_len': seq_len, |
| } |
|
|
| if offload_model or self.init_on_cpu: |
| self.model.to(self.device) |
| torch.cuda.empty_cache() |
|
|
| for _, t in enumerate(tqdm(timesteps)): |
| latent_model_input = [latent.to(self.device)] |
| timestep = [t] |
|
|
| timestep = torch.stack(timestep).to(self.device) |
|
|
| temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten() |
| temp_ts = torch.cat([ |
| temp_ts, |
| temp_ts.new_ones(seq_len - temp_ts.size(0)) * timestep |
| ]) |
| timestep = temp_ts.unsqueeze(0) |
|
|
| noise_pred_cond = self.model( |
| latent_model_input, t=timestep, **arg_c)[0] |
| if offload_model: |
| torch.cuda.empty_cache() |
| noise_pred_uncond = self.model( |
| latent_model_input, t=timestep, **arg_null)[0] |
| if offload_model: |
| torch.cuda.empty_cache() |
| noise_pred = noise_pred_uncond + guide_scale * ( |
| noise_pred_cond - noise_pred_uncond) |
|
|
| 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) |
| latent = (1. - mask2[0]) * z[0] + mask2[0] * latent |
|
|
| x0 = [latent] |
| del latent_model_input, timestep |
|
|
| if offload_model: |
| self.model.cpu() |
| torch.cuda.synchronize() |
| torch.cuda.empty_cache() |
|
|
| if self.rank == 0: |
| videos = self.vae.decode(x0) |
|
|
| del noise, latent, x0 |
| 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 |
|
|
