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| from utils.distributed import launch_distributed_job | |
| from utils.scheduler import FlowMatchScheduler | |
| from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder | |
| from utils.dataset import TextDataset | |
| import torch.distributed as dist | |
| from tqdm import tqdm | |
| import argparse | |
| import torch | |
| import math | |
| import os | |
| def init_model( | |
| device, | |
| ): | |
| model = WanDiffusionWrapper().to(device).to(torch.float32) | |
| encoder = WanTextEncoder().to(device).to(torch.float32) | |
| model.model.requires_grad_(False) | |
| scheduler = FlowMatchScheduler(shift=8.0, sigma_min=0.0, extra_one_step=True) | |
| scheduler.set_timesteps(num_inference_steps=48, denoising_strength=1.0) | |
| scheduler.sigmas = scheduler.sigmas.to(device) | |
| sample_neg_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" | |
| unconditional_dict = encoder(text_prompts=[sample_neg_prompt]) | |
| return model, encoder, scheduler, unconditional_dict | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--local_rank", type=int, default=-1) | |
| parser.add_argument("--output_folder", type=str) | |
| parser.add_argument("--caption_path", type=str) | |
| parser.add_argument("--guidance_scale", type=float, default=6.0) | |
| args = parser.parse_args() | |
| # launch_distributed_job() | |
| launch_distributed_job() | |
| device = torch.cuda.current_device() | |
| torch.set_grad_enabled(False) | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| torch.backends.cudnn.allow_tf32 = True | |
| model, encoder, scheduler, unconditional_dict = init_model(device=device) | |
| dataset = TextDataset(args.caption_path) | |
| # if global_rank == 0: | |
| os.makedirs(args.output_folder, exist_ok=True) | |
| for index in tqdm( | |
| range(int(math.ceil(len(dataset) / dist.get_world_size()))), | |
| disable=dist.get_rank() != 0, | |
| ): | |
| prompt_index = index * dist.get_world_size() + dist.get_rank() | |
| if prompt_index >= len(dataset): | |
| continue | |
| prompt = dataset[prompt_index] | |
| conditional_dict = encoder(text_prompts=prompt) | |
| latents = torch.randn([1, 21, 16, 60, 104], dtype=torch.float32, device=device) | |
| noisy_input = [] | |
| for progress_id, t in enumerate(tqdm(scheduler.timesteps)): | |
| timestep = t * torch.ones([1, 21], device=device, dtype=torch.float32) | |
| noisy_input.append(latents) | |
| _, x0_pred_cond = model(latents, conditional_dict, timestep) | |
| _, x0_pred_uncond = model(latents, unconditional_dict, timestep) | |
| x0_pred = x0_pred_uncond + args.guidance_scale * (x0_pred_cond - x0_pred_uncond) | |
| flow_pred = model._convert_x0_to_flow_pred( | |
| scheduler=scheduler, | |
| x0_pred=x0_pred.flatten(0, 1), | |
| xt=latents.flatten(0, 1), | |
| timestep=timestep.flatten(0, 1), | |
| ).unflatten(0, x0_pred.shape[:2]) | |
| latents = scheduler.step( | |
| flow_pred.flatten(0, 1), | |
| scheduler.timesteps[progress_id] | |
| * torch.ones([1, 21], device=device, dtype=torch.long).flatten(0, 1), | |
| latents.flatten(0, 1), | |
| ).unflatten(dim=0, sizes=flow_pred.shape[:2]) | |
| noisy_input.append(latents) | |
| noisy_inputs = torch.stack(noisy_input, dim=1) | |
| noisy_inputs = noisy_inputs[:, [0, 12, 24, 36, -1]] | |
| stored_data = noisy_inputs | |
| torch.save( | |
| {prompt: stored_data.cpu().detach()}, | |
| os.path.join(args.output_folder, f"{prompt_index:05d}.pt"), | |
| ) | |
| dist.barrier() | |
| if __name__ == "__main__": | |
| main() | |