|
|
| 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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|
|
| 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 .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 (
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| FlowDPMSolverMultistepScheduler,
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| get_sampling_sigmas,
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| retrieve_timesteps,
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| )
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| from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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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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| ):
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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=os.path.join(checkpoint_dir, config.t5_checkpoint),
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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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|
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| logging.info(f"Creating WanModel from {checkpoint_dir}")
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| self.model = WanModel.from_pretrained(checkpoint_dir)
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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 get_sequence_parallel_world_size
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|
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| from .distributed.xdit_context_parallel import (
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| usp_attn_forward,
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| usp_dit_forward,
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| )
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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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|
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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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|
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| self.sample_neg_prompt = config.sample_neg_prompt
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|
|
| def generate(self,
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| input_prompt,
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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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| r"""
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| Generates video frames from text prompt using diffusion process.
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|
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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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|
|
| 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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|
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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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|
|
| 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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|
|
| 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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|
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| no_sync = getattr(self.model, 'no_sync', noop_no_sync)
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|
|
|
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| with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync():
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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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|
|
|
|
| latents = noise
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|
|
| arg_c = {'context': context, 'seq_len': seq_len}
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| arg_null = {'context': context_null, 'seq_len': seq_len}
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|
|
| for _, t in enumerate(tqdm(timesteps)):
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| latent_model_input = latents
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| timestep = [t]
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|
|
| timestep = torch.stack(timestep)
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|
|
| self.model.to(self.device)
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| noise_pred_cond = self.model(
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| latent_model_input, t=timestep, **arg_c)[0]
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| noise_pred_uncond = self.model(
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| latent_model_input, t=timestep, **arg_null)[0]
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|
|
| noise_pred = noise_pred_uncond + guide_scale * (
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| noise_pred_cond - noise_pred_uncond)
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|
|
| temp_x0 = sample_scheduler.step(
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| noise_pred.unsqueeze(0),
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| t,
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| latents[0].unsqueeze(0),
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| return_dict=False,
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| generator=seed_g)[0]
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| latents = [temp_x0.squeeze(0)]
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|
|
| x0 = latents
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| if offload_model:
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| self.model.cpu()
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| torch.cuda.empty_cache()
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| if self.rank == 0:
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| videos = self.vae.decode(x0)
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|
|
| del noise, latents
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| del sample_scheduler
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| if offload_model:
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| gc.collect()
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| torch.cuda.synchronize()
|
| if dist.is_initialized():
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| dist.barrier()
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|
|
| return videos[0] if self.rank == 0 else None
|
|
|
