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, 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] 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") 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()