Upload OFASmallmodel.py

OFASmallmodel.py

@@ -0,0 +1,44 @@
+import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
+
+# 1. USE A SMALLER MODEL (CPU-FRIENDLY)
+model_id = "OFA-Sys/small-stable-diffusion-v0"  # Lightweight model
+
+# 2. SIMPLIFIED PIPELINE FOR CPU
+pipe = DiffusionPipeline.from_pretrained(model_id)
+pipe = pipe.to("cpu")  # Force CPU usage
+
+# 3. FASTER GENERATION SETTINGS
+def generate_image(prompt, negative_prompt="", steps=13):
+    return pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=steps,
+        guidance_scale=7.5
+    ).images[0]
+
+# 4. STREAMLINED UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Lightweight CPU Image Generator using OFA Small model")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Your Prompt", value="a beautiful flower")
+            negative = gr.Textbox(label="Avoid (Optional)", value="low-resolution")
+            steps = gr.Slider(1, 30, value=13, label="Quality Steps")
+            btn = gr.Button("Generate →")
+        
+        output = gr.Image(label="Result", height=400)
+    
+    btn.click(fn=generate_image, inputs=[prompt, negative, steps], outputs=output)
+    
+    gr.Examples(
+        examples=[
+            ["cityscape at night, red lights", "people", 12],
+            ["watercolor painting of a flower", "photorealistic", 8]
+        ],
+        inputs=[prompt, negative, steps]
+    )
+
+demo.launch()