Create app.py

app.py

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+import torch
+from diffusers import StableDiffusionXLPipeline
+import gradio as gr
+
+# Load model
+model_id = "OnomaAIResearch/Illustrious-XL-v1.1"
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    variant="fp16",
+    use_safetensors=True
+).to("cuda")
+
+def generate_image(prompt, negative_prompt="", steps=30, guidance=7.5, seed=None):
+    generator = torch.Generator(device="cuda").manual_seed(int(seed)) if seed else None
+    
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=guidance,
+        generator=generator,
+    ).images[0]
+    
+    return image
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎨 Illustrious-XL Image Generator")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(label="Negative Prompt")
+            steps = gr.Slider(10, 50, value=30, label="Steps")
+            guidance = gr.Slider(1.0, 15.0, value=7.5, label="Guidance Scale")
+            seed = gr.Number(label="Seed", value=None)
+            generate_btn = gr.Button("Generate")
+        with gr.Column():
+            output_image = gr.Image(label="Result", height=512)
+
+    generate_btn.click(
+        generate_image,
+        inputs=[prompt, negative_prompt, steps, guidance, seed],
+        outputs=output_image
+    )
+
+demo.launch()