Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import AutoPipelineForText2Image
+
+# List of available models
+MODEL_OPTIONS = {
+    "Stable Diffusion 1.5": "runwayml/stable-diffusion-v1-5",
+    "Stable Diffusion 2.1": "stabilityai/stable-diffusion-2-1", 
+    "Stable Diffusion XL": "stabilityai/stable-diffusion-xl-base-1.0"
+}
+
+def generate_image(
+    model_choice,
+    lora_url,
+    prompt,
+    negative_prompt,
+    steps,
+    width,
+    height,
+    guidance_scale,
+    seed
+):
+    # Get the selected model ID
+    model_id = MODEL_OPTIONS[model_choice]
+    
+    # Initialize the pipeline
+    pipe = AutoPipelineForText2Image.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        use_safetensors=True
+    ).to("cuda")
+
+    # Load LoRA weights if provided
+    if lora_url:
+        pipe.load_lora_weights(lora_url)
+        pipe.fuse_lora()
+
+    # Set up generator with seed if provided
+    generator = None
+    if seed:
+        generator = torch.Generator(device="cuda").manual_seed(int(seed))
+
+    # Generate image
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(steps),
+        width=int(width),
+        height=int(height),
+        guidance_scale=guidance_scale,
+        generator=generator
+    ).images[0]
+
+    return image
+
+# Gradio UI components
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎨 Text-to-Image Generation with LoRA")
+    
+    with gr.Row():
+        with gr.Column():
+            model_choice = gr.Dropdown(
+                label="Select Base Model",
+                choices=list(MODEL_OPTIONS.keys()),
+                value="Stable Diffusion XL"
+            )
+            lora_url = gr.Textbox(
+                label="LoRA Repository ID (e.g., 'username/lora-name')",
+                placeholder="Optional Hugging Face repository ID"
+            )
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt here..."
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="Enter what to exclude from the image..."
+            )
+            
+            with gr.Row():
+                steps = gr.Number(
+                    label="Inference Steps",
+                    value=25,
+                    precision=0
+                )
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1.0,
+                    maximum=20.0,
+                    value=7.5
+                )
+                
+            with gr.Row():
+                width = gr.Number(
+                    label="Width",
+                    value=1024,
+                    precision=0
+                )
+                height = gr.Number(
+                    label="Height", 
+                    value=1024,
+                    precision=0
+                )
+                seed = gr.Number(
+                    label="Seed (optional)",
+                    precision=0
+                )
+            
+            generate_btn = gr.Button("Generate Image", variant="primary")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image", height=600)
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[
+            model_choice,
+            lora_url,
+            prompt,
+            negative_prompt,
+            steps,
+            width,
+            height,
+            guidance_scale,
+            seed
+        ],
+        outputs=output_image
+    )
+
+demo.launch()