()
| 21 | |
| 22 | |
| 23 | def main(): |
| 24 | args = create_argparser().parse_args() |
| 25 | |
| 26 | dist_util.setup_dist() |
| 27 | logger.configure() |
| 28 | |
| 29 | logger.log("creating model and diffusion...") |
| 30 | model, diffusion = create_model_and_diffusion( |
| 31 | **args_to_dict(args, model_and_diffusion_defaults().keys()) |
| 32 | ) |
| 33 | model.load_state_dict( |
| 34 | dist_util.load_state_dict(args.model_path, map_location="cpu") |
| 35 | ) |
| 36 | model.to(dist_util.dev()) |
| 37 | if args.use_fp16: |
| 38 | model.convert_to_fp16() |
| 39 | model.eval() |
| 40 | |
| 41 | logger.log("sampling...") |
| 42 | all_images = [] |
| 43 | all_labels = [] |
| 44 | while len(all_images) * args.batch_size < args.num_samples: |
| 45 | model_kwargs = {} |
| 46 | if args.class_cond: |
| 47 | classes = th.randint( |
| 48 | low=0, high=NUM_CLASSES, size=(args.batch_size,), device=dist_util.dev() |
| 49 | ) |
| 50 | model_kwargs["y"] = classes |
| 51 | sample_fn = ( |
| 52 | diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop |
| 53 | ) |
| 54 | sample = sample_fn( |
| 55 | model, |
| 56 | (args.batch_size, 3, args.image_size, args.image_size), |
| 57 | clip_denoised=args.clip_denoised, |
| 58 | model_kwargs=model_kwargs, |
| 59 | ) |
| 60 | sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8) |
| 61 | sample = sample.permute(0, 2, 3, 1) |
| 62 | sample = sample.contiguous() |
| 63 | |
| 64 | gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())] |
| 65 | dist.all_gather(gathered_samples, sample) # gather not supported with NCCL |
| 66 | all_images.extend([sample.cpu().numpy() for sample in gathered_samples]) |
| 67 | if args.class_cond: |
| 68 | gathered_labels = [ |
| 69 | th.zeros_like(classes) for _ in range(dist.get_world_size()) |
| 70 | ] |
| 71 | dist.all_gather(gathered_labels, classes) |
| 72 | all_labels.extend([labels.cpu().numpy() for labels in gathered_labels]) |
| 73 | logger.log(f"created {len(all_images) * args.batch_size} samples") |
| 74 | |
| 75 | arr = np.concatenate(all_images, axis=0) |
| 76 | arr = arr[: args.num_samples] |
| 77 | if args.class_cond: |
| 78 | label_arr = np.concatenate(all_labels, axis=0) |
| 79 | label_arr = label_arr[: args.num_samples] |
| 80 | if dist.get_rank() == 0: |
no test coverage detected