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""" |
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Generate a large batch of samples from a super resolution model, given a batch |
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of samples from a regular model from image_sample.py. |
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""" |
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import argparse |
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import os |
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import blobfile as bf |
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import numpy as np |
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import torch as th |
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import torch.distributed as dist |
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from guided_diffusion import dist_util, logger |
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from guided_diffusion.script_util import ( |
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sr_model_and_diffusion_defaults, |
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sr_create_model_and_diffusion, |
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args_to_dict, |
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add_dict_to_argparser, |
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) |
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def main(): |
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args = create_argparser().parse_args() |
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dist_util.setup_dist() |
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logger.configure() |
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logger.log("creating model...") |
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model, diffusion = sr_create_model_and_diffusion( |
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**args_to_dict(args, sr_model_and_diffusion_defaults().keys()) |
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) |
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model.load_state_dict( |
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dist_util.load_state_dict(args.model_path, map_location="cpu") |
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) |
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model.to(dist_util.dev()) |
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if args.use_fp16: |
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model.convert_to_fp16() |
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model.eval() |
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logger.log("loading data...") |
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data = load_data_for_worker(args.base_samples, args.batch_size, args.class_cond) |
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logger.log("creating samples...") |
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all_images = [] |
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while len(all_images) * args.batch_size < args.num_samples: |
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model_kwargs = next(data) |
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model_kwargs = {k: v.to(dist_util.dev()) for k, v in model_kwargs.items()} |
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sample = diffusion.p_sample_loop( |
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model, |
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(args.batch_size, 3, args.large_size, args.large_size), |
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clip_denoised=args.clip_denoised, |
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model_kwargs=model_kwargs, |
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) |
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sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8) |
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sample = sample.permute(0, 2, 3, 1) |
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sample = sample.contiguous() |
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all_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())] |
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dist.all_gather(all_samples, sample) |
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for sample in all_samples: |
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all_images.append(sample.cpu().numpy()) |
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logger.log(f"created {len(all_images) * args.batch_size} samples") |
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arr = np.concatenate(all_images, axis=0) |
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arr = arr[: args.num_samples] |
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if dist.get_rank() == 0: |
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shape_str = "x".join([str(x) for x in arr.shape]) |
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out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz") |
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logger.log(f"saving to {out_path}") |
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np.savez(out_path, arr) |
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dist.barrier() |
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logger.log("sampling complete") |
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def load_data_for_worker(base_samples, batch_size, class_cond): |
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with bf.BlobFile(base_samples, "rb") as f: |
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obj = np.load(f) |
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image_arr = obj["arr_0"] |
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if class_cond: |
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label_arr = obj["arr_1"] |
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rank = dist.get_rank() |
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num_ranks = dist.get_world_size() |
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buffer = [] |
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label_buffer = [] |
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while True: |
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for i in range(rank, len(image_arr), num_ranks): |
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buffer.append(image_arr[i]) |
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if class_cond: |
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label_buffer.append(label_arr[i]) |
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if len(buffer) == batch_size: |
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batch = th.from_numpy(np.stack(buffer)).float() |
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batch = batch / 127.5 - 1.0 |
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batch = batch.permute(0, 3, 1, 2) |
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res = dict(low_res=batch) |
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if class_cond: |
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res["y"] = th.from_numpy(np.stack(label_buffer)) |
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yield res |
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buffer, label_buffer = [], [] |
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def create_argparser(): |
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defaults = dict( |
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clip_denoised=True, |
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num_samples=10000, |
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batch_size=16, |
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use_ddim=False, |
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base_samples="", |
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model_path="", |
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) |
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defaults.update(sr_model_and_diffusion_defaults()) |
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parser = argparse.ArgumentParser() |
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add_dict_to_argparser(parser, defaults) |
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return parser |
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if __name__ == "__main__": |
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main() |
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