Create app.py

app.py

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+import gradio as gr
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+import torch
+
+
+model_id = "runwayml/stable-diffusion-v1-5"
+controlnet_id = "lllyasviel/control_v11p_sd15_openpose"
+
+
+controlnet = ControlNetModel.from_pretrained(controlnet_id, torch_dtype=torch.float32)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    model_id,
+    controlnet=controlnet,
+    safety_checker=None,  # Disable safety checker for demo purposes
+    torch_dtype=torch.float32
+)
+pipe = pipe.to("cpu")
+
+
+pipe.enable_attention_slicing()
+
+def generate_image(prompt, control_image, num_inference_steps=25, guidance_scale=7.5, controlnet_conditioning_scale=1.0):
+    """
+    Generate an image using the ControlNet pipeline.
+    
+    Args:
+      prompt (str): Your text prompt for image generation.
+      control_image (PIL.Image): A control image to guide generation.
+      num_inference_steps (int): Number of denoising steps.
+      guidance_scale (float): Classifier-free guidance scale.
+      controlnet_conditioning_scale (float): How strongly to condition on the control image.
+      
+    Returns:
+      PIL.Image: The generated image.
+    """
+    result = pipe(
+        prompt=prompt,
+        image=control_image,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        controlnet_conditioning_scale=controlnet_conditioning_scale
+    )
+    return result.images[0]
+
+# Create the Gradio interface.
+with gr.Blocks() as demo:
+    gr.Markdown("# ControlNet Image Generator on CPU\nThis demo uses a ControlNet pipeline (openpose variant) with Stable Diffusion to generate images guided by a control image. Note: Running on CPU can be slow!")
+    
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your image prompt here", value="A futuristic cityscape at dusk")
+    
+    with gr.Row():
+        control_image_input = gr.Image(label="Control Image", type="pil", source="upload")
+        output_image = gr.Image(label="Generated Image", type="pil")
+    
+    with gr.Row():
+        num_steps = gr.Slider(minimum=10, maximum=50, value=25, step=1, label="Inference Steps")
+        guidance = gr.Slider(minimum=1.0, maximum=15.0, value=7.5, step=0.5, label="Guidance Scale")
+        control_scale = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="ControlNet Conditioning Scale")
+    
+    generate_btn = gr.Button("Generate Image")
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt_input, control_image_input, num_steps, guidance, control_scale],
+        outputs=output_image
+    )
+
+demo.launch()