| | |
| | 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 |
| | from tqdm import tqdm |
| |
|
| | from .distributed.fsdp import shard_model |
| | 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 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 WanModel from {checkpoint_dir}") |
| | self.model = WanModel.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) |
| | 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: |
| | self.model.to(self.device) |
| |
|
| | self.sample_neg_prompt = config.sample_neg_prompt |
| |
|
| | def generate(self, |
| | input_prompt, |
| | size=(1280, 720), |
| | 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 |
| | 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) |
| | """ |
| | |
| | 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 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.") |
| |
|
| | |
| | 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, **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() |
| | 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 |
| |
|
