adapt_config.py

import argparse
import json
import os
import shutil
from tempfile import TemporaryDirectory
from typing import List, Optional

from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download
from huggingface_hub.file_download import repo_folder_name


class AlreadyExists(Exception):
    pass


def is_index_stable_diffusion_like(config_dict):
    if "_class_name" not in config_dict:
        return False

    compatible_classes = [
        "AltDiffusionImg2ImgPipeline",
        "AltDiffusionPipeline",
        "CycleDiffusionPipeline",
        "StableDiffusionImageVariationPipeline",
        "StableDiffusionImg2ImgPipeline",
        "StableDiffusionInpaintPipeline",
        "StableDiffusionInpaintPipelineLegacy",
        "StableDiffusionPipeline",
        "StableDiffusionPipelineSafe",
        "StableDiffusionUpscalePipeline",
        "VersatileDiffusionDualGuidedPipeline",
        "VersatileDiffusionImageVariationPipeline",
        "VersatileDiffusionPipeline",
        "VersatileDiffusionTextToImagePipeline",
        "OnnxStableDiffusionImg2ImgPipeline",
        "OnnxStableDiffusionInpaintPipeline",
        "OnnxStableDiffusionInpaintPipelineLegacy",
        "OnnxStableDiffusionPipeline",
        "StableDiffusionOnnxPipeline",
        "FlaxStableDiffusionPipeline",
    ]
    return config_dict["_class_name"] in compatible_classes


def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]:
    config_file = "scheduler/scheduler_config.json"
    os.makedirs(os.path.join(folder, "scheduler"), exist_ok=True)
    model_index_file = hf_hub_download(repo_id=model_id, filename="model_index.json")

    with open(model_index_file, "r") as f:
        index_dict = json.load(f)
        if not is_index_stable_diffusion_like(index_dict):
            print(f"{model_id} is not of type stable diffusion.")
            return False, False

    old_config_file = hf_hub_download(repo_id=model_id, filename=config_file)

    new_config_file = os.path.join(folder, config_file)
    success = convert_file(old_config_file, new_config_file)
    if success:
        operations = [CommitOperationAdd(path_in_repo=config_file, path_or_fileobj=new_config_file)]
        model_type = success
        return operations, model_type
    else:
        return False, False


def convert_file(
    old_config: str,
    new_config: str,
):
    with open(old_config, "r") as f:
        old_dict = json.load(f)

    if "clip_sample" not in old_dict:
        print("Make scheduler DDIM compatible")
        old_dict["clip_sample"] = False
    else:
        print("No matching config")
        return False

#    is_stable_diffusion = "down_block_types" in old_dict and list(old_dict["down_block_types"]) == ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"]
#
#    is_stable_diffusion_1 = is_stable_diffusion and ("use_linear_projection" not in old_dict or old_dict["use_linear_projection"] is False)
#    is_stable_diffusion_2 = is_stable_diffusion and ("use_linear_projection" in old_dict and old_dict["use_linear_projection"] is True)
#
#    if not is_stable_diffusion_1 and not is_stable_diffusion_2:
#        print("No matching config")
#        return False
#
#    if is_stable_diffusion_1:
#        if old_dict["sample_size"] == 64:
#            print("Dict correct")
#            return False
#
#        print("Correct stable diffusion 1")
#        old_dict["sample_size"] = 64
#
#    if is_stable_diffusion_2:
#        if old_dict["sample_size"] == 96:
#            print("Dict correct")
#            return False
#
#        print("Correct stable diffusion 2")
#        old_dict["sample_size"] = 96
#
    with open(new_config, 'w') as f:
        json_str = json.dumps(old_dict, indent=2, sort_keys=True) + "\n"
        f.write(json_str)

#
#    return "Stable Diffusion 1" if is_stable_diffusion_1 else "Stable Diffusion 2"

    return "Stable Diffusion"


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:
            return discussion


def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["CommitInfo"]:
#    pr_title = "Correct `sample_size` of {}'s unet to have correct width and height default"
    pr_title = "Add `clip_sample=False` to scheduler to make model compatible with DDIM." 
    info = api.model_info(model_id)
    filenames = set(s.rfilename for s in info.siblings)

    if "unet/config.json" not in filenames:
        print(f"Model: {model_id} has no 'unet/config.json' file to change")
        return

    if "vae/config.json" not in filenames:
        print(f"Model: {model_id} has no 'vae/config.json' file to change")
        return

    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:
            operations = None
            pr = previous_pr(api, model_id, pr_title)
            if pr is not None and not force:
                url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
                new_pr = pr
                raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
            else:
                operations, model_type = convert_single(model_id, folder)

            if operations:
                pr_title = pr_title.format(model_type)
#                if model_type == "Stable Diffusion 1":
#                    sample_size = 64
#                    image_size = 512
#                elif model_type == "Stable Diffusion 2":
#                    sample_size = 96
#                    image_size = 768

#                pr_description = (
#                        f"Since `diffusers==0.9.0` the width and height is automatically inferred from the `sample_size` attribute of your unet's config. It seems like your diffusion model has the same architecture as {model_type} which means that when using this model, by default an image size of {image_size}x{image_size} should be generated. This in turn means the unet's sample size should be **{sample_size}**. \n\n In order to suppress to update your configuration on the fly and to suppress the deprecation warning added in this PR: https://github.com/huggingface/diffusers/pull/1406/files#r1035703505 it is strongly recommended to merge this PR."
#                )
                contributor = model_id.split("/")[0]
                pr_description = (
                    f"Hey {contributor} 👋, \n\n Your model repository seems to contain a stable diffusion checkpoint. We have noticed that your scheduler config currently does not correctly work with the [DDIMScheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers#diffusers.DDIMScheduler) because `clip_sample` is not set to False and will therefore [incorrectly default to True](https://github.com/huggingface/diffusers/blob/3ce6380d3a2ec5c3e3f4f48889d380d657b151bc/src/diffusers/schedulers/scheduling_ddim.py#L127). \n The official stable diffusion checkpoints have `clip_sample=False` so that the scheduler config works will **all** schedulers, see: https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/scheduler/scheduler_config.json#L7. \n\n We strongly recommend that you merge this PR to make sure your model works correctly with DDIM. \n\n Diffusingly, \n Patrick."
                )
                new_pr = api.create_commit(
                    repo_id=model_id,
                    operations=operations,
                    commit_message=pr_title,
                    commit_description=pr_description,
                    create_pr=True,
                )
                print(f"Pr created at {new_pr.pr_url}")
            else:
                print(f"No files to convert for {model_id}")
        finally:
            shutil.rmtree(folder)
        return new_pr


if __name__ == "__main__":
    DESCRIPTION = """
    Simple utility tool to convert automatically some weights on the hub to `safetensors` format.
    It is PyTorch exclusive for now.
    It works by downloading the weights (PT), converting them locally, and uploading them back
    as a PR on the hub.
    """
    parser = argparse.ArgumentParser(description=DESCRIPTION)
    parser.add_argument(
        "model_id",
        type=str,
        help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`",
    )
    parser.add_argument(
        "--force",
        action="store_true",
        help="Create the PR even if it already exists of if the model was already converted.",
    )
    args = parser.parse_args()
    model_id = args.model_id
    api = HfApi()
    convert(api, model_id, force=args.force)