app.py

import gradio as gr
from gradio_client import Client

client = Client("multimodalart/FLUX.1-merged")

def infer(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, api_name):
    
    result = client.predict(
		prompt=prompt,
		seed=seed,
		randomize_seed=True,
		width=width,
		height=height,
		guidance_scale=guidance_scale,
		num_inference_steps=num_inference_steps,
		api_name="/infer"
    )

    return result 

    
css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""


with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
       # gr.Markdown(f"""
       # FallnAI: DiffusionLab Beta 
       # """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Create", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=999999,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=2048,
                    step=32,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=2024,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.1,
                    maximum=10.0,
                    step=0.1,
                    value=1.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=10,
                )
        

    run_button.click(
        fn = infer,
        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

demo.queue().launch()