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app.py

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+import gradio as gr
+from diffusers import StableDiffusionPipeline
+import torch
+
+# Detect device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+# Load base model
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=dtype
+)
+
+# Load LoRA weights
+pipe.load_lora_weights("./lora")
+
+# Move to device
+pipe = pipe.to(device)
+
+# Extra CPU optimizations
+if device == "cpu":
+    pipe.enable_attention_slicing()
+    pipe.enable_sequential_cpu_offload()
+    pipe.enable_vae_tiling()
+
+# Define image generation function with quality toggle
+def generate(prompt, quality):
+    if quality == "Fast":
+        steps = 20
+        guidance_scale = 7.0
+    else:  # High Quality
+        steps = 40
+        guidance_scale = 8.5
+    
+    with torch.no_grad():
+        image = pipe(prompt, num_inference_steps=steps, guidance_scale=guidance_scale).images[0]
+    return image
+
+# Build Gradio UI
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(label="Enter your prompt"),
+        gr.Dropdown(["Fast", "High Quality"], value="Fast", label="Generation Mode")
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Fine-tuned Stable Diffusion with LoRA (CPU-Optimized)",
+    description="Choose 'Fast' for quicker generation or 'High Quality' for better details."
+)
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+transformers 
+diffusers>=0.21.0 
+gradio 
+peft 
+accelerate 
+safetensors