129 lines
5.2 KiB
Python
129 lines
5.2 KiB
Python
# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Sample new images from a pre-trained Latte.
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"""
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import os
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import sys
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from accelerate import Accelerator
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from tqdm import tqdm
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from opensora1.dataset import ae_denorm
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from opensora1.models.ae import ae_channel_config, getae, ae_stride_config
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from opensora1.models.ae.videobase import CausalVQVAEModelWrapper
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from opensora1.models.diffusion import Diffusion_models
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from opensora1.models.diffusion.diffusion import create_diffusion_T as create_diffusion
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from opensora1.models.diffusion.latte.modeling_latte import Latte
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from opensora1.utils.utils import find_model
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import torch
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import argparse
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from einops import rearrange
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import imageio
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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def main(args):
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# Setup PyTorch:
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# torch.manual_seed(args.seed)
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torch.set_grad_enabled(False)
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assert torch.cuda.is_available(), "Training currently requires at least one GPU."
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# Setup accelerator:
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accelerator = Accelerator(mixed_precision=args.mixed_precision)
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device = accelerator.device
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using_cfg = args.cfg_scale > 1.0
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# Load model:
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latent_size = (args.image_size // ae_stride_config[args.ae][1], args.image_size // ae_stride_config[args.ae][2])
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args.latent_size = latent_size
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model = Latte.from_pretrained(args.ckpt, subfolder="model").to(device)
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model.eval() # important!
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model = accelerator.prepare(model)
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diffusion = create_diffusion(str(args.num_sampling_steps))
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ae = getae(args).to(device)
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if isinstance(ae, CausalVQVAEModelWrapper):
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video_length = args.num_frames // ae_stride_config[args.ae][0] + 1
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else:
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video_length = args.num_frames // ae_stride_config[args.ae][0]
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bar = tqdm(range(args.num_sample))
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for i in bar:
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# Create sampling noise:
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z = torch.randn(1, model.module.in_channels, video_length, latent_size[0], latent_size[1], device=device)
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# Setup classifier-free guidance:
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if using_cfg and args.train_classcondition:
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z = torch.cat([z, z], 0)
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y = torch.randint(0, args.num_classes, (1,), device=device)
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cls_id = str(int(y.detach().cpu()))
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y_null = torch.tensor([args.num_classes] * 1, device=device)
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y = torch.cat([y, y_null], dim=0)
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model_kwargs = dict(class_labels=y, cfg_scale=args.cfg_scale)
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sample_fn = model.module.forward_with_cfg
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else:
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if args.train_classcondition:
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sample_fn = model.forward
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y = torch.randint(0, args.num_classes, (1,), device=device)
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cls_id = str(int(y.detach().cpu()))
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model_kwargs = dict(class_labels=y)
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else:
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sample_fn = model.forward
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model_kwargs = dict(class_labels=None)
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# Sample images:
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if args.sample_method == 'ddim':
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samples = diffusion.ddim_sample_loop(
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sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
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)
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elif args.sample_method == 'ddpm':
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samples = diffusion.p_sample_loop(
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sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
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)
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with torch.no_grad():
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samples = ae.decode(samples)
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# Save and display images:
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if not os.path.exists(args.save_video_path):
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os.makedirs(args.save_video_path)
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video_ = (ae_denorm[args.ae](samples[0]) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().permute(0, 2, 3, 1).contiguous()
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if args.train_classcondition:
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video_save_path = os.path.join(args.save_video_path, f"sample_{i:03d}_cls" + str(cls_id) + '.mp4')
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else:
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video_save_path = os.path.join(args.save_video_path, f"sample_{i:03d}" + '.mp4')
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print(video_save_path)
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imageio.mimwrite(video_save_path, video_, fps=args.fps, quality=9)
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print('save path {}'.format(args.save_video_path))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--ckpt", type=str, default="")
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parser.add_argument("--model", type=str, default='Latte-XL/122')
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parser.add_argument("--ae", type=str, default='stabilityai/sd-vae-ft-mse')
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parser.add_argument("--save_video_path", type=str, default="./sample_videos/")
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parser.add_argument("--fps", type=int, default=10)
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parser.add_argument("--num_classes", type=int, default=101)
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parser.add_argument("--num_frames", type=int, default=16)
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parser.add_argument("--image_size", type=int, default=256)
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parser.add_argument("--train_classcondition", action="store_true")
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parser.add_argument("--num_sampling_steps", type=int, default=250)
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parser.add_argument("--num_sample", type=int, default=1)
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parser.add_argument("--cfg_scale", type=float, default=1.0)
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parser.add_argument("--sample_method", type=str, default='ddpm')
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parser.add_argument("--mixed_precision", type=str, default=None, choices=[None, "fp16", "bf16"])
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parser.add_argument("--attention_mode", type=str, choices=['xformers', 'math', 'flash'], default="math")
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args = parser.parse_args()
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main(args)
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