| import gradio as gr | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler | |
| import torch | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16 | |
| ) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", | |
| controlnet=controlnet, | |
| torch_dtype=torch.float16, | |
| safety_checker=None | |
| ) | |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe.to("cuda" if torch.cuda.is_available() else "cpu") | |
| def generate(image, prompt="a person posing"): | |
| result = pipe(prompt=prompt, image=image, num_inference_steps=20).images[0] | |
| return result | |
| demo = gr.Interface( | |
| fn=generate, | |
| inputs=[gr.Image(type="pil"), gr.Textbox(label="Prompt")], | |
| outputs="image", | |
| title="Pose Generator", | |
| description="Upload an image and enter a prompt to generate a ControlNet-based pose output." | |
| ) | |
| demo.launch() | |