| import torch |
| import json |
| import copy |
| import os |
| from torch.utils.data import Dataset |
| from PIL import Image |
|
|
| def dpg_save_fn(image, metadata, root_path): |
| image_path = os.path.join(root_path, str(metadata['filename'])+"_"+str(metadata['seed'])+".png") |
| Image.fromarray(image).save(image_path) |
|
|
| class DPGDataset(Dataset): |
| def __init__(self, prompt_path, num_samples_per_instance, latent_shape): |
| self.latent_shape = latent_shape |
| self.prompt_path = prompt_path |
| prompt_files = os.listdir(self.prompt_path) |
| self.prompts = [] |
| self.filenames = [] |
| for prompt_file in prompt_files: |
| with open(os.path.join(self.prompt_path, prompt_file)) as fp: |
| self.prompts.append(fp.readline().strip()) |
| self.filenames.append(prompt_file.replace('.txt', '')) |
| self.num_instances = len(self.prompts) |
| self.num_samples_per_instance = num_samples_per_instance |
| self.num_samples = self.num_instances * self.num_samples_per_instance |
|
|
| def __len__(self): |
| return self.num_samples |
|
|
| def __getitem__(self, idx): |
| instance_idx = idx // self.num_samples_per_instance |
| sample_idx = idx % self.num_samples_per_instance |
| generator = torch.Generator().manual_seed(sample_idx) |
| metadata = dict( |
| prompt=self.prompts[instance_idx], |
| filename=self.filenames[instance_idx], |
| seed=sample_idx, |
| save_fn=dpg_save_fn, |
| ) |
| condition = metadata["prompt"] |
| latent = torch.randn(self.latent_shape, generator=generator, dtype=torch.float32) |
| return latent, condition, metadata |
