| | |
| | 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 |
| |
|
