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from typing import Optional
import gradio as gr
import quantize
from huggingface_hub import HfApi, login
def run(model_id: str, model_version: str, additional_args: str, token: Optional[str] = None) -> str:
if model_id == "":
return "Please enter model_id."
login(token=token)
api = HfApi(token=token)
quantize.quantize(api=api, model_id=model_id, model_version=model_version, additional_args=additional_args)
DESCRIPTION = """
Simple utility tool to quantize diffusion models and convert them to CoreML.
"""
title="Quantize model and convert to CoreML"
with gr.Blocks(title=title) as demo:
description = gr.Markdown(f"""# {title}""")
description = gr.Markdown(DESCRIPTION)
with gr.Row() as r:
with gr.Column() as c:
model_id = gr.Text(max_lines=1, label="ID of output repo")
model_version = gr.Text(max_lines=1, label="Version of model to convert", value="stabilityai/sd-turbo")
additional_args = gr.Text(max_lines=1, label="Additional Args (optional)")
token = gr.Text(max_lines=1, label="Your HuggingFace write token")
with gr.Row() as c:
clean = gr.ClearButton()
submit = gr.Button("Submit", variant="primary")
with gr.Column() as d:
output = gr.Markdown()
submit.click(run, inputs=[model_id, model_version, additional_args, token], outputs=output, concurrency_limit=1)
demo.queue(max_size=10).launch(show_api=True) |