Create app.py

app.py

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+import gradio as gr
+from diffusers import StableDiffusionPipeline
+
+def apply_lora(pipeline, lora_path):
+    """
+    Dummy function to simulate the application of LoRA weights.
+    Replace this with your actual code to load and integrate LoRA weights.
+    """
+    if lora_path:
+        print(f"Applying LoRA weights from {lora_path}")
+        # Insert your LoRA integration code here.
+    return pipeline
+
+def generate_image(model_name, lora_path, width, height, inference_steps, prompt):
+    # Use the provided model name or fall back to a default model
+    model_id = model_name.strip() if model_name.strip() else "CompVis/stable-diffusion-v1-5"
+    
+    # Load the diffusion pipeline from Hugging Face
+    pipeline = StableDiffusionPipeline.from_pretrained(model_id)
+    device = "cuda" if gr.get_config().get("device") == "gpu" else "cpu"
+    pipeline = pipeline.to(device)
+    
+    # Apply LoRA if a path is provided
+    if lora_path.strip():
+        pipeline = apply_lora(pipeline, lora_path.strip())
+    
+    # Generate the image using the specified parameters
+    result = pipeline(prompt, width=width, height=height, num_inference_steps=inference_steps)
+    return result.images[0]
+
+# Build the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Generator with Custom Model & LoRA")
+    
+    model_name_box = gr.Textbox(
+        label="Enter Model Name/ID (e.g., CompVis/stable-diffusion-v1-5)",
+        value="CompVis/stable-diffusion-v1-5",
+        lines=1
+    )
+    lora_path_box = gr.Textbox(
+        label="Enter LoRA Path (leave empty if not using)",
+        value="",
+        lines=1
+    )
+    
+    width_slider = gr.Slider(minimum=256, maximum=1024, value=512, step=64, label="Image Width")
+    height_slider = gr.Slider(minimum=256, maximum=1024, value=512, step=64, label="Image Height")
+    steps_slider = gr.Slider(minimum=10, maximum=100, value=50, step=1, label="Inference Steps")
+    prompt_box = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
+    
+    generate_button = gr.Button("Generate Image")
+    output_image = gr.Image(label="Generated Image")
+    
+    generate_button.click(
+        fn=generate_image, 
+        inputs=[model_name_box, lora_path_box, width_slider, height_slider, steps_slider, prompt_box], 
+        outputs=output_image
+    )
+
+demo.launch()