app.py

import gradio as gr
from gradio_client import Client, handle_file
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048


flux_1_schell_spaces = ["https://black-forest-labs-flux-1-schnell.hf.space", "ChristianHappy/FLUX.1-schnell", "innoai/FLUX.1-schnell", "tuan2308/FLUX.1-schnell", "FiditeNemini/FLUX.1-schnell"]
flux_1_schnell_space = "https://black-forest-labs-flux-1-schnell.hf.space"

client = None
job = None

selected_space = gr.State(flux_1_schell_spaces[0]);

def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
    global job
    global client
    if client is None:
        try:
            client = Client(flux_1_schnell_space)
            print(f"Loaded custom model from {flux_1_schnell_space}")
        except ValueError as e:
            print(f"Failed to load custom model: {e}")
            client = None
            raise gr.Error("Failed to load client for " + flux_1_schnell_space)
        
    try:
        job = client.submit(
            prompt=prompt,
            seed=seed,
            randomize_seed=randomize_seed,
            width=width,
            height=height,
            num_inference_steps=num_inference_steps,
            api_name="/infer"
        )
        result = job.result()
    except ValueError as e:
        raise gr.Error(e)
    
    return result
 
examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

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"""# FLUX.1 [schnell]
[black-forest-labs/FLUX.1-schnell](https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell)
12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
[[blog](https://blackforestlabs.ai/2024/07/31/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", 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=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=4,
                )
        
        gr.Examples(
            examples = examples,
            fn = infer,
            inputs = [prompt],
            outputs = [result, seed],
            cache_examples="lazy"
        )

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

demo.launch()