Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+
+model_id = "runwayml/stable-diffusion-v1-5"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load once on startup
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to(device)
+pipe.enable_attention_slicing()
+
+def generate(prompt, steps, guidance, seed):
+    generator = None
+    if seed not in (None, "", "none"):
+        generator = torch.Generator(device).manual_seed(int(seed))
+    image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=float(guidance), generator=generator).images[0]
+    return image
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Stable Diffusion — Space")
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt", lines=2, value="A cinematic portrait of a Muslim scholar reading under a lamp, warm tones, detailed, realistic")
+    with gr.Row():
+        steps = gr.Slider(10, 50, value=30, step=1, label="Steps")
+        guidance = gr.Slider(1.0, 12.0, value=7.5, step=0.5, label="Guidance")
+        seed = gr.Textbox(label="Seed (optional)")
+    btn = gr.Button("Generate")
+    output = gr.Image(label="Generated image")
+
+    btn.click(generate, inputs=[prompt, steps, guidance, seed], outputs=[output])
+
+if __name__ == "__main__":
+    demo.launch()