app.py

import os
import subprocess
from huggingface_hub import HfApi, upload_folder
import gradio as gr
import hf_utils
import utils
from safetensors import safe_open
import torch

subprocess.run(["git", "clone", "https://github.com/huggingface/diffusers", "diffs"])

def error_str(error, title="Error"):
    return f"""#### {title}
            {error}"""  if error else ""

def on_token_change(token):
    model_names, error = hf_utils.get_my_model_names(token)
    if model_names:
        model_names.append("Other")

    return gr.update(visible=bool(model_names)), gr.update(choices=model_names, value=model_names[0] if model_names else None), gr.update(visible=bool(model_names)), gr.update(value=error_str(error))

def url_to_model_id(model_id_str):
    return model_id_str.split("/")[-2] + "/" + model_id_str.split("/")[-1] if model_id_str.startswith("https://huggingface.co/") else model_id_str
    
def get_ckpt_names(token, radio_model_names, input_model):
    
    model_id = url_to_model_id(input_model) if radio_model_names == "Other" else radio_model_names

    if token == "" or model_id == "":
        return error_str("Please enter both a token and a model name.", title="Invalid input"), gr.update(choices=[]), gr.update(visible=False)

    try:
        api = HfApi(token=token)
        ckpt_files = [f for f in api.list_repo_files(repo_id=model_id) if f.endswith(".ckpt")]
        
        if not ckpt_files:
            return error_str("No checkpoint files found in the model repo."), gr.update(choices=[]), gr.update(visible=False)
        
        return None, gr.update(choices=ckpt_files, value=ckpt_files[0], visible=True), gr.update(visible=True)
        
    except Exception as e:
        return error_str(e), gr.update(choices=[]), None

def convert_and_push(radio_model_names, input_model, ckpt_name, sd_version, token, path_in_repo, ema, safetensors):
    extract_ema = ema == "ema"
    
    if sd_version == None:
        return error_str("You must select a stable diffusion version.", title="Invalid input")

    model_id = url_to_model_id(input_model) if radio_model_names == "Other" else radio_model_names

    try:
        model_id = url_to_model_id(model_id)

        # 1. Download the checkpoint file
        ckpt_path, revision = hf_utils.download_file(repo_id=model_id, filename=ckpt_name, token=token)

        if safetensors == "yes":
            tensors = {}
            with safe_open(ckpt_path, framework="pt", device="cpu") as f:
               for key in f.keys():
                   tensors[key] = f.get_tensor(key)

            new_checkpoint_path = "/".join(ckpt_path.split("/")[:-1] + ["model_safe.ckpt"])
            torch.save(tensors, new_checkpoint_path)
            ckpt_path = new_checkpoint_path
            print("Converting ckpt_path", ckpt_path)

        print(ckpt_path)

        # 2. Run the conversion script
        os.makedirs(model_id, exist_ok=True)
        run_command = [
            "python3",
            "./diffs/scripts/convert_original_stable_diffusion_to_diffusers.py",
            "--checkpoint_path",
            ckpt_path,
            "--dump_path" ,
            model_id,
        ]
        if extract_ema:
            run_command.append("--extract_ema")
        subprocess.run(run_command)

        # 3. Push to the model repo
        commit_message="Add Diffusers weights"
        upload_folder(
            folder_path=model_id,
            repo_id=model_id,
            path_in_repo=path_in_repo,
            token=token,
            create_pr=True,
            commit_message=commit_message,
            commit_description=f"Add Diffusers weights converted from checkpoint `{ckpt_name}` in revision {revision}",
        )

        # # 4. Delete the downloaded checkpoint file, yaml files, and the converted model folder
        hf_utils.delete_file(revision)
        subprocess.run(["rm", "-rf", model_id.split('/')[0]])
        import glob
        for f in glob.glob("*.yaml*"):
            subprocess.run(["rm", "-rf", f])

        return f"""Successfully converted the checkpoint and opened a PR to add the weights to the model repo.
                You can view and merge the PR [here]({hf_utils.get_pr_url(HfApi(token=token), model_id, commit_message)})."""

        return "Done"
    
    except Exception as e:
        return error_str(e)


DESCRIPTION = """### Convert a stable diffusion checkpoint to Diffusers🧨
                With this space, you can easily convert a CompVis stable diffusion checkpoint to Diffusers and automatically create a pull request to the model repo.
                You can choose to convert a checkpoint from one of your own models, or from any other model on the Hub.
                You can skip the queue by running the app in the colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/qunash/f0f3152c5851c0c477b68b7b98d547fe/convert-sd-to-diffusers.ipynb)"""

with gr.Blocks() as demo:

    gr.Markdown(DESCRIPTION)
    with gr.Row():

        with gr.Column(scale=11):
            with gr.Column():
                gr.Markdown("## 1. Load model info")
                input_token = gr.Textbox(
                    max_lines=1,
                    type="password",
                    label="Enter your Hugging Face token",
                    placeholder="READ permission is sufficient"
                )
                gr.Markdown("You can get a token [here](https://huggingface.co/settings/tokens)")
                with gr.Group(visible=False) as group_model:
                    radio_model_names = gr.Radio(label="Choose a model")
                    input_model = gr.Textbox(
                        max_lines=1,
                        label="Model name or URL",
                        placeholder="username/model_name",
                        visible=False,
                    )

            btn_get_ckpts = gr.Button("Load", visible=False)

        with gr.Column(scale=10):
            with gr.Column(visible=False) as group_convert:
                gr.Markdown("## 2. Convert to Diffusers🧨")
                radio_ckpts = gr.Radio(label="Choose the checkpoint to convert", visible=False)
                path_in_repo = gr.Textbox(label="Path where the weights will be saved", placeholder="Leave empty for root folder")
                ema = gr.Radio(label="Extract EMA or non-EMA?", choices=["ema", "non-ema"])
                safetensors = gr.Radio(label="Extract from safetensors", choices=["yes", "no"], value="no")
                radio_sd_version = gr.Radio(label="Choose the model version", choices=["v1", "v2", "v2.1"])
                gr.Markdown("Conversion may take a few minutes.")
                btn_convert = gr.Button("Convert & Push")

    error_output = gr.Markdown(label="Output")

    input_token.change(
        fn=on_token_change,
        inputs=input_token,
        outputs=[group_model, radio_model_names, btn_get_ckpts, error_output],
        queue=False,
        scroll_to_output=True)
    
    radio_model_names.change(
        lambda x: gr.update(visible=x == "Other"),
        inputs=radio_model_names,
        outputs=input_model,
        queue=False,
        scroll_to_output=True)
    
    btn_get_ckpts.click(
        fn=get_ckpt_names,
        inputs=[input_token, radio_model_names, input_model],
        outputs=[error_output, radio_ckpts, group_convert],
        scroll_to_output=True,
        queue=False
    )

    btn_convert.click(
        fn=convert_and_push,
        inputs=[radio_model_names, input_model, radio_ckpts, radio_sd_version, input_token, path_in_repo, ema, safetensors],
        outputs=error_output,
        scroll_to_output=True
    )

    # gr.Markdown("""<img src="https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/imgs/diffusers_library.jpg" width="150"/>""")
    gr.HTML("""
    <div style="border-top: 1px solid #303030;">
      <br>
      <p>Space by: <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a></p><br>
      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.sd-to-diffusers" alt="visitors"></p>
    </div>
    """)
    
demo.queue()
demo.launch(debug=True, share=utils.is_google_colab())