app.py

import gradio as gr
from transformers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline, AutoTokenizer

def load_model(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    controlnet = FlaxControlNetModel.from_pretrained(model_name)
    pipeline = FlaxStableDiffusionControlNetPipeline.from_pretrained(model_name)
    return tokenizer, controlnet, pipeline

model_name = "Ryukijano/controlnet-fill-circle"
tokenizer, controlnet, pipeline = load_model(model_name)

def infer_fill_circle(prompt, image):
    # Your inference function for fill circle control
    inputs = tokenizer(prompt, return_tensors="jax")
    # Implement your image preprocessing here
    outputs = pipeline.generate(inputs, image)
    return outputs

with gr.Blocks(theme='gradio/soft') as demo:
    gr.Markdown("## Stable Diffusion with Fill Circle Control")
    gr.Markdown("In this app, you can find the ControlNet with Fill Circle control.")

    with gr.Tab("ControlNet Fill Circle"):
        prompt_input_fill_circle = gr.Textbox(label="Prompt")
        negative_prompt_fill_circle = gr.Textbox(label="Negative Prompt")
        fill_circle_input = gr.Image(label="Input Image")
        fill_circle_output = gr.Image(label="Output Image")
        submit_btn = gr.Button(value="Submit")
        fill_circle_inputs = [prompt_input_fill_circle, fill_circle_input]
        submit_btn.click(fn=infer_fill_circle, inputs=fill_circle_inputs, outputs=[fill_circle_output])

demo.launch()