Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline, LCMScheduler
+import time
+
+# Load the lightweight SD 1.5 model
+model_id = "runwayml/stable-diffusion-v1-5"
+adapter_id = "latent-consistency/lcm-lora-sdv1-5"
+
+# Load pipeline and move to CPU
+pipe = DiffusionPipeline.from_pretrained(model_id)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# Load the LCM LoRA to allow 4-step generation
+pipe.load_lora_weights(adapter_id)
+pipe.to("cpu")
+
+# Function to generate image and time it
+def generate(prompt):
+    start_time = time.time()
+    
+    # We use 4 steps for the best speed/quality balance on 2 vCPUs
+    # guidance_scale is low (1.0 - 2.0) for LCM models
+    image = pipe(
+        prompt=prompt, 
+        num_inference_steps=4, 
+        guidance_scale=1.0,
+        width=512,
+        height=512
+    ).images[0]
+    
+    end_time = time.time()
+    duration = round(end_time - start_time, 2)
+    
+    timer_output = f"✅ Image generated in {duration} seconds"
+    return image, timer_output
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# ⚡ Fast CPU Image Generator (SD 1.5 + LCM)")
+    gr.Markdown("Optimized for 2 vCPUs. Expect ~20-40 seconds per image.")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Enter your prompt", placeholder="A cinematic forest...")
+            generate_btn = gr.Button("Generate Image")
+        
+        with gr.Column():
+            timer_text = gr.Markdown("Timer: Ready")
+            image_output = gr.Image(label="Generated Image")
+
+    generate_btn.click(
+        fn=generate, 
+        inputs=prompt_input, 
+        outputs=[image_output, timer_text]
+    )
+
+demo.launch()