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| import argparse | |
| import requests | |
| import json | |
| import os | |
| import shutil | |
| from collections import defaultdict | |
| from inspect import signature | |
| from tempfile import TemporaryDirectory | |
| from typing import Dict, List, Optional, Set | |
| import torch | |
| from io import BytesIO | |
| from huggingface_hub import CommitInfo, Discussion, HfApi, hf_hub_download | |
| from huggingface_hub.file_download import repo_folder_name | |
| from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt | |
| from transformers import CONFIG_MAPPING | |
| COMMIT_MESSAGE = " This PR adds the both fp32 and fp16 in PyTorch and safetensors format to {}" | |
| def convert_single(model_id: str, filename: str, model_type: str, sample_size: int, scheduler_type: str, extract_ema: bool, folder: str): | |
| from_safetensors = filename.endswith(".safetensors") | |
| local_file = os.path.join(model_id, filename) | |
| ckpt_file = local_file if os.path.isfile(local_file) else hf_hub_download(repo_id=model_id, filename=filename) | |
| if model_type == "v1": | |
| config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" | |
| elif model_type == ["v2.0", "v2.1"] and sample_size == 512: | |
| config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference.yaml" | |
| elif model_type in ["v2.0", "v2.1"]: | |
| config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml" | |
| config_file = BytesIO(requests.get(config_url).content) | |
| pipeline = download_from_original_stable_diffusion_ckpt(ckpt_file, config_file, image_size=sample_size, scheduler_type=scheduler_type, from_safetensors=from_safetensors, extract_ema=extract_ema) | |
| pipeline.save_pretrained(folder) | |
| pipeline.save_pretrained(folder, safe_serialization=True) | |
| pipeline = pipeline.to(torch_dtype=torch.float16) | |
| pipeline.save_pretrained(folder, variant="fp16") | |
| pipeline.save_pretrained(folder, safe_serialization=True, variant="fp16") | |
| return folder | |
| def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]: | |
| try: | |
| discussions = api.get_repo_discussions(repo_id=model_id) | |
| except Exception: | |
| return None | |
| for discussion in discussions: | |
| if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title: | |
| details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num) | |
| if details.target_branch == "refs/heads/main": | |
| return discussion | |
| def convert(token: str, model_id: str, filename: str, model_type: str, sample_size: int = 512, scheduler_type: str = "pndm", extract_ema: bool = True): | |
| api = HfApi() | |
| pr_title = "Adding `diffusers` weights of this model" | |
| with TemporaryDirectory() as d: | |
| folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models")) | |
| os.makedirs(folder) | |
| new_pr = None | |
| try: | |
| folder = convert_single(model_id, filename, model_type, sample_size, scheduler_type, extract_ema, folder) | |
| new_pr = api.upload_folder(folder_path=folder, path_in_repo="./", repo_id=model_id, repo_type="model", token=token, commit_description=COMMIT_MESSAGE.format(model_id), create_pr=True) | |
| pr_number = new_pr.split("%2F")[-1].split("/")[0] | |
| print(f"Pr created at: {'https://huggingface.co/' + os.path.join(model_id, 'discussions', pr_number)}") | |
| finally: | |
| shutil.rmtree(folder) | |
| return new_pr | |