app.py

import gradio as gr

from convert import convert

DESCRIPTION = """
The steps are the following:

- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
- Input a model id from the Hub
- Input the filename from the root dir of the repo that you would like to convert, e.g. 'v2-1_768-ema-pruned.ckpt' or 'v1-5-pruned.safetensors'
- Chose which Stable Diffusion version, image size, scheduler type the model has and whether you want the "ema", or "non-ema" weights.
- Click "Submit"
- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥

⚠️ If you encounter weird error messages, please have a look into the Logs and feel free to open a PR to correct the error messages.
"""

demo = gr.Interface(
    title="Convert any Stable Diffusion checkpoint to Diffusers and open a PR",
    description=DESCRIPTION,
    allow_flagging="never",
    article="Check out the [Diffusers repo on GitHub](https://github.com/huggingface/diffusers)",
    inputs=[
        gr.Text(max_lines=1, label="your_hf_token"),
        gr.Text(max_lines=1, label="model_id"),
        gr.Text(max_lines=1, label="filename"),
        gr.Radio(label="Model type", choices=["v1", "v2", "ControlNet"]),
        gr.Radio(label="Sample size (px)", choices=[512, 768]),
        gr.Radio(label="Scheduler type", choices=["pndm", "heun", "euler", "dpm", "ddim"], value="dpm"),
        gr.Radio(label="Extract EMA or non-EMA?", choices=["ema", "non-ema"], value="ema"),
    ],
    outputs=[gr.Markdown(label="output")],
    fn=convert,
).queue(max_size=10, concurrency_count=1)

demo.launch(show_api=True)