|
|
| 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 .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_1 import Wan2_1_VAE
|
| from .utils.fm_solvers import (
|
| FlowDPMSolverMultistepScheduler,
|
| get_sampling_sigmas,
|
| retrieve_timesteps,
|
| )
|
| from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
|
|
|
|
|
| class WanT2V:
|
|
|
| def __init__(
|
| self,
|
| config,
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| checkpoint_dir,
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| device_id=0,
|
| rank=0,
|
| t5_fsdp=False,
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| 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}")
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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.init_on_cpu = init_on_cpu
|
|
|
| self.num_train_timesteps = config.num_train_timesteps
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| self.boundary = config.boundary
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| 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(
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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)
|
|
|
| self.vae_stride = config.vae_stride
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| self.patch_size = config.patch_size
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| self.vae = Wan2_1_VAE(
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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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|
|
| logging.info(f"Creating WanModel from {checkpoint_dir}")
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| self.low_noise_model = WanModel.from_pretrained(
|
| checkpoint_dir, subfolder=config.low_noise_checkpoint)
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| self.low_noise_model = self._configure_model(
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| model=self.low_noise_model,
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| use_sp=use_sp,
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| dit_fsdp=dit_fsdp,
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| shard_fn=shard_fn,
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| convert_model_dtype=convert_model_dtype)
|
|
|
| self.high_noise_model = WanModel.from_pretrained(
|
| checkpoint_dir, subfolder=config.high_noise_checkpoint)
|
| self.high_noise_model = self._configure_model(
|
| model=self.high_noise_model,
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| use_sp=use_sp,
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| dit_fsdp=dit_fsdp,
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| shard_fn=shard_fn,
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| 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 _prepare_model_for_timestep(self, t, boundary, offload_model):
|
| r"""
|
| Prepares and returns the required model for the current timestep.
|
|
|
| Args:
|
| t (torch.Tensor):
|
| current timestep.
|
| boundary (`int`):
|
| The timestep threshold. If `t` is at or above this value,
|
| the `high_noise_model` is considered as the required model.
|
| offload_model (`bool`):
|
| A flag intended to control the offloading behavior.
|
|
|
| Returns:
|
| torch.nn.Module:
|
| The active model on the target device for the current timestep.
|
| """
|
| if t.item() >= boundary:
|
| required_model_name = 'high_noise_model'
|
| offload_model_name = 'low_noise_model'
|
| else:
|
| required_model_name = 'low_noise_model'
|
| offload_model_name = 'high_noise_model'
|
| if offload_model or self.init_on_cpu:
|
| if next(getattr(
|
| self,
|
| offload_model_name).parameters()).device.type == 'cuda':
|
| getattr(self, offload_model_name).to('cpu')
|
| if next(getattr(
|
| self,
|
| required_model_name).parameters()).device.type == 'cpu':
|
| getattr(self, required_model_name).to(self.device)
|
| return getattr(self, required_model_name)
|
|
|
| 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 (`tuple[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 50):
|
| Number of diffusion sampling steps. Higher values improve quality but slow generation
|
| guide_scale (`float` or tuple[`float`], *optional*, defaults 5.0):
|
| Classifier-free guidance scale. Controls prompt adherence vs. creativity.
|
| If tuple, the first guide_scale will be used for low noise model and
|
| the second guide_scale will be used for high noise model.
|
| 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)
|
| """
|
|
|
| guide_scale = (guide_scale, guide_scale) if isinstance(
|
| guide_scale, float) else guide_scale
|
| F = frame_num
|
| target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1,
|
| size[1] // self.vae_stride[1],
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| 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_low_noise = getattr(self.low_noise_model, 'no_sync',
|
| noop_no_sync)
|
| no_sync_high_noise = getattr(self.high_noise_model, 'no_sync',
|
| noop_no_sync)
|
|
|
|
|
| with (
|
| torch.amp.autocast('cuda', dtype=self.param_dtype),
|
| torch.no_grad(),
|
| no_sync_low_noise(),
|
| no_sync_high_noise(),
|
| ):
|
| boundary = self.boundary * self.num_train_timesteps
|
|
|
| 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)
|
|
|
| model = self._prepare_model_for_timestep(
|
| t, boundary, offload_model)
|
| sample_guide_scale = guide_scale[1] if t.item(
|
| ) >= boundary else guide_scale[0]
|
|
|
| noise_pred_cond = model(
|
| latent_model_input, t=timestep, **arg_c)[0]
|
| noise_pred_uncond = model(
|
| latent_model_input, t=timestep, **arg_null)[0]
|
|
|
| noise_pred = noise_pred_uncond + sample_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.low_noise_model.cpu()
|
| self.high_noise_model.cpu()
|
| 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
|
|
|
