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import gradio as gr
from gradio_client import Client
# Connect to the existing FLUX.1-dev space
flux_client = Client("black-forest-labs/FLUX.1-dev")
def flux_dev_generate(
prompt: str,
seed: int = 0,
randomize_seed: bool = True,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 3.5,
num_inference_steps: int = 28
) -> str:
"""Generate high-quality images using FLUX.1-dev model.
Args:
prompt: Text description of the image to generate
seed: Random seed for reproducibility
randomize_seed: Whether to randomize the seed
width: Image width in pixels
height: Image height in pixels
guidance_scale: How closely to follow the prompt (1.0-20.0)
num_inference_steps: Quality vs speed tradeoff (1-50)
Returns:
Generated image file
"""
result = flux_client.predict(
prompt=prompt,
seed=seed,
randomize_seed=randomize_seed,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
api_name="/infer"
)
return result
demo = gr.Interface(
fn=flux_dev_generate,
inputs=[
gr.Textbox(label="Prompt", placeholder="Describe your image..."),
gr.Number(label="Seed", value=0),
gr.Checkbox(label="Randomize Seed", value=True),
gr.Slider(256, 2048, value=1024, label="Width"),
gr.Slider(256, 2048, value=1024, label="Height"),
gr.Slider(1.0, 20.0, value=3.5, label="Guidance Scale"),
gr.Slider(1, 50, value=28, label="Inference Steps")
],
outputs="image",
title="FLUX.1-dev MCP Wrapper"
)
# This makes it accessible to MCP clients!
demo.launch(mcp_server=True) |