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app.py

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+import gradio as gr
+import torch
+from diffusers import AutoPipelineForImage2Image
+from PIL import Image
+
+# ---------------------------------------------------------------------------
+# Why SD-Turbo?
+# Hugging Face Free Spaces only have 2 vCPUs. Standard Image models take 
+# 3-5 minutes per image on CPU because they require 30-50 steps.
+# SD-Turbo only requires 1 to 3 steps! It is incredibly fast and perfect 
+# for a free deployment.
+# ---------------------------------------------------------------------------
+
+print("Loading SD-Turbo Model... (This may take a minute on boot)")
+pipe = AutoPipelineForImage2Image.from_pretrained(
+    "stabilityai/sd-turbo", 
+    torch_dtype=torch.float32 # Use float32 for CPU compatibility
+)
+
+def process_image(init_image, prompt, strength, steps):
+    if init_image is None:
+        return None
+        
+    print(f"Received request: '{prompt}'")
+    
+    # Resize image to SD-Turbo's preferred 512x512 resolution
+    # Maintaining aspect ratio by cropping or padding would be better, 
+    # but exact 512x512 prevents memory spikes on the free CPU tier.
+    init_image = init_image.convert("RGB")
+    init_image = init_image.resize((512, 512))
+    
+    # Run the pipeline
+    image = pipe(
+        prompt=prompt, 
+        image=init_image, 
+        num_inference_steps=int(steps), 
+        strength=float(strength), 
+        guidance_scale=0.0 # Turbo mathematically requires guidance_scale=0.0
+    ).images[0]
+    
+    return image
+
+# Define the Gradio Interface
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("# 🪄 WiggleAgent // Free Img2Img Backend")
+    gr.Markdown("Powered by SD-Turbo (Optimized for Free CPU Tiers)")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Input Image (Your Screenshot)")
+            prompt = gr.Textbox(label="Prompt", value="cyberpunk style, dark neon city, glowing interface")
+            
+            # Strength determines how much of the original image is preserved.
+            # 0.1 = Almost no change. 1.0 = Completely new image.
+            strength = gr.Slider(minimum=0.1, maximum=1.0, value=0.6, step=0.05, label="Transformation Strength")
+            
+            # Steps determines quality. 2 is the sweet spot for SD-Turbo.
+            steps = gr.Slider(minimum=1, maximum=4, value=2, step=1, label="Inference Steps (Keep low for CPU)")
+            
+            btn = gr.Button("Generate", variant="primary")
+        
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Output Image")
+            
+    btn.click(
+        fn=process_image, 
+        inputs=[input_image, prompt, strength, steps], 
+        outputs=output_image,
+        api_name="predict" # Exposes this function to our gradio_client in WiggleAgent!
+    )
+
+demo.launch()

requirements.txt

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+gradio==4.31.5
+torch==2.3.0
+diffusers==0.27.2
+transformers==4.41.1
+accelerate==0.30.1