import sys import subprocess from safetensors.torch import load_file from diffusers import AutoPipelineForText2Image from datasets import load_dataset from huggingface_hub.repocard import RepoCard from huggingface_hub import HfApi import torch import re import argparse import os import zipfile def do_preprocess(class_data_dir): print("Unzipping dataset") zip_file_path = f"{class_data_dir}/class_images.zip" with zipfile.ZipFile(zip_file_path, 'r') as zip_ref: zip_ref.extractall(class_data_dir) os.remove(zip_file_path) def do_train(script_args): # Pass all arguments to trainer.py print("Starting training...") result = subprocess.run(['python', 'trainer.py'] + script_args) if result.returncode != 0: raise Exception("Training failed.") def replace_output_dir(text, output_dir, replacement): # Define a pattern that matches the output_dir followed by whitespace, '/', new line, or "'" # Add system name from HF only in the correct spots pattern = rf"{output_dir}(?=[\s/'\n])" return re.sub(pattern, replacement, text) def do_inference(dataset_name, output_dir, num_tokens): widget_content = [] try: print("Starting inference to generate example images...") dataset = load_dataset(dataset_name) pipe = AutoPipelineForText2Image.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") pipe.load_lora_weights(f'{output_dir}/pytorch_lora_weights.safetensors') prompts = dataset["train"]["prompt"] if(num_tokens > 0): tokens_sequence = ''.join(f'' for i in range(num_tokens)) tokens_list = [f'' for i in range(num_tokens)] state_dict = load_file(f"{output_dir}/{output_dir}_emb.safetensors") pipe.load_textual_inversion(state_dict["clip_l"], token=tokens_list, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer) pipe.load_textual_inversion(state_dict["clip_g"], token=tokens_list, text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2) prompts = [prompt.replace("TOK", tokens_sequence) for prompt in prompts] for i, prompt in enumerate(prompts): image = pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0] filename = f"image-{i}.png" image.save(f"{output_dir}/{filename}") card_dict = { "text": prompt, "output": { "url": filename } } widget_content.append(card_dict) except Exception as e: print("Something went wrong with generating images, specifically: ", e) try: api = HfApi() username = api.whoami()["name"] repo_id = api.create_repo(f"{username}/{output_dir}", exist_ok=True, private=True).repo_id with open(f'{output_dir}/README.md', 'r') as file: readme_content = file.read() readme_content = replace_output_dir(readme_content, output_dir, f"{username}/{output_dir}") card = RepoCard(readme_content) if widget_content: card.data["widget"] = widget_content card.save(f'{output_dir}/README.md') print("Starting upload...") api.upload_folder( folder_path=output_dir, repo_id=f"{username}/{output_dir}", repo_type="model", ) except Exception as e: print("Something went wrong with uploading your model, specificaly: ", e) else: print("Upload finished!") import sys import argparse def main(): # Capture all arguments except the script name script_args = sys.argv[1:] # Create the argument parser parser = argparse.ArgumentParser() parser.add_argument('--dataset_name', required=True) parser.add_argument('--output_dir', required=True) parser.add_argument('--num_new_tokens_per_abstraction', type=int, default=0) parser.add_argument('--train_text_encoder_ti', action='store_true') parser.add_argument('--class_data_dir', help="Name of the class images dataset") # Parse known arguments args, _ = parser.parse_known_args(script_args) # Set num_tokens to 0 if '--train_text_encoder_ti' is not present if not args.train_text_encoder_ti: args.num_new_tokens_per_abstraction = 0 # Proceed with training and inference if args.class_data_dir: do_preprocess(args.class_data_dir) print("Pre-processing finished!") do_train(script_args) print("Training finished!") do_inference(args.dataset_name, args.output_dir, args.num_new_tokens_per_abstraction) print("All finished!") if __name__ == "__main__": main()