Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
+
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+
+model_id = "SimianLuo/LCM_Dreamshaper_v7"
+
+pipe = DiffusionPipeline.from_pretrained(model_id)
+pipe.to(device)
+
+def generate_image(prompt, width, height, steps):
+    result = pipe(
+        prompt=prompt,
+        width=int(width),
+        height=int(height),
+        num_inference_steps=int(steps),
+        guidance_scale=8.0,
+        lcm_origin_steps=50,
+        output_type="pil"
+    ).images[0]
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("## LCM Image Generator (CPU Optimized)")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", placeholder="A futuristic city with neon lights...")
+            
+            with gr.Row():
+                width_slider = gr.Slider(minimum=256, maximum=768, step=64, value=512, label="Width")
+                height_slider = gr.Slider(minimum=256, maximum=768, step=64, value=512, label="Height")
+            
+            steps_slider = gr.Slider(minimum=1, maximum=15, step=1, value=4, label="Inference Steps (Quality)")
+            
+            generate_btn = gr.Button("Generate Image")
+        
+        with gr.Column():
+            image_output = gr.Image(label="Generated Image")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt_input, width_slider, height_slider, steps_slider],
+        outputs=image_output
+    )
+
+demo.launch()