Add gradio ui

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers.utils import load_image
+from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
+from diffusers.models.controlnet_flux import FluxControlNetModel
+
+base_model = 'black-forest-labs/FLUX.1-dev'
+controlnet_model = 'promeai/FLUX.1-controlnet-lineart-promeai'
+controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
+pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+def generate_image(prompt, control_image, controlnet_conditioning_scale, num_inference_steps, guidance_scale):
+    control_image = load_image(control_image) if isinstance(control_image, str) else control_image
+
+    result = pipe(
+        prompt,
+        control_image=control_image,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+    ).images[0]
+    
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("# FLUX ControlNet Pipeline Interface")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", lines=3, placeholder="Enter your prompt here...")
+            control_image = gr.Image(source="upload", type="filepath", label="Control Image")
+            
+            controlnet_conditioning_scale = gr.Slider(0.0, 1.0, value=0.6, label="ControlNet Conditioning Scale")
+            num_inference_steps = gr.Slider(1, 100, value=28, step=1, label="Number of Inference Steps")
+            guidance_scale = gr.Slider(1.0, 10.0, value=3.5, label="Guidance Scale")
+            
+            generate_button = gr.Button("Generate Image")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+    generate_button.click(
+        generate_image, 
+        inputs=[prompt, control_image, controlnet_conditioning_scale, num_inference_steps, guidance_scale],
+        outputs=output_image
+    )
+
+demo.launch()