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| from controlnet_aux import OpenposeDetector | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| from diffusers import UniPCMultistepScheduler | |
| import gradio as gr | |
| import torch | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| # live conditioning | |
| canvas_html = "<pose-canvas id='canvas-root' style='display:flex;max-width: 500px;margin: 0 auto;'></pose-canvas>" | |
| load_js = """ | |
| async () => { | |
| const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/pose-gradio.js" | |
| fetch(url) | |
| .then(res => res.text()) | |
| .then(text => { | |
| const script = document.createElement('script'); | |
| script.type = "module" | |
| script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' })); | |
| document.head.appendChild(script); | |
| }); | |
| } | |
| """ | |
| get_js_image = """ | |
| async (image_in_img, prompt, image_file_live_opt, live_conditioning) => { | |
| const canvasEl = document.getElementById("canvas-root"); | |
| const data = canvasEl? canvasEl._data : null; | |
| return [image_in_img, prompt, image_file_live_opt, data] | |
| } | |
| """ | |
| # Constants | |
| low_threshold = 100 | |
| high_threshold = 200 | |
| # Models | |
| pose_model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 | |
| ) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 | |
| ) | |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| # This command loads the individual model components on GPU on-demand. So, we don't | |
| # need to explicitly call pipe.to("cuda"). | |
| pipe.enable_model_cpu_offload() | |
| # xformers | |
| pipe.enable_xformers_memory_efficient_attention() | |
| # Generator seed, | |
| generator = torch.manual_seed(0) | |
| def get_pose(image): | |
| return pose_model(image) | |
| def generate_images(image, prompt, image_file_live_opt='file', live_conditioning=None): | |
| if image is None and 'image' not in live_conditioning: | |
| raise gr.Error("Please provide an image") | |
| try: | |
| if image_file_live_opt == 'file': | |
| pose = get_pose(image) | |
| elif image_file_live_opt == 'webcam': | |
| base64_img = live_conditioning['image'] | |
| image_data = base64.b64decode(base64_img.split(',')[1]) | |
| pose = Image.open(BytesIO(image_data)).convert( | |
| 'RGB').resize((512, 512)) | |
| output = pipe( | |
| prompt, | |
| pose, | |
| generator=generator, | |
| num_images_per_prompt=3, | |
| num_inference_steps=20, | |
| ) | |
| all_outputs = [] | |
| all_outputs.append(pose) | |
| for image in output.images: | |
| all_outputs.append(image) | |
| return all_outputs | |
| except Exception as e: | |
| raise gr.Error(str(e)) | |
| def toggle(choice): | |
| if choice == "file": | |
| return gr.update(visible=True, value=None), gr.update(visible=False, value=None) | |
| elif choice == "webcam": | |
| return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html) | |
| with gr.Blocks() as blocks: | |
| gr.Markdown(""" | |
| ## Generate Uncanny Faces with ControlNet Stable Diffusion | |
| [Check out our blog to see how this was done (and train your own controlnet)](https://huggingface.co/blog/train-your-controlnet) | |
| """) | |
| with gr.Row(): | |
| live_conditioning = gr.JSON(value={}, visible=False) | |
| with gr.Column(): | |
| image_file_live_opt = gr.Radio(["file", "webcam"], value="file", | |
| label="How would you like to upload your image?") | |
| image_in_img = gr.Image(source="upload", visible=True, type="pil") | |
| canvas = gr.HTML(None, elem_id="canvas_html", visible=False) | |
| image_file_live_opt.change(fn=toggle, | |
| inputs=[image_file_live_opt], | |
| outputs=[image_in_img, canvas], | |
| queue=False) | |
| prompt = gr.Textbox( | |
| label="Enter your prompt", | |
| max_lines=1, | |
| placeholder="best quality, extremely detailed", | |
| ) | |
| run_button = gr.Button("Generate") | |
| with gr.Column(): | |
| gallery = gr.Gallery().style(grid=[2], height="auto") | |
| run_button.click(fn=generate_images, | |
| inputs=[image_in_img, prompt, | |
| image_file_live_opt, live_conditioning], | |
| outputs=[gallery], | |
| _js=get_js_image) | |
| blocks.load(None, None, None, _js=load_js) | |
| gr.Examples(fn=generate_images, | |
| examples=[ | |
| ["./yoga1.jpeg", | |
| "best quality, extremely detailed"] | |
| ], | |
| inputs=[image_in_img, prompt], | |
| outputs=[gallery], | |
| cache_examples=True) | |
| blocks.launch(debug=True) | |