Spaces:
Running
Running
| import hashlib | |
| import os | |
| from io import BytesIO | |
| import gradio as gr | |
| import grpc | |
| from PIL import Image | |
| from cachetools import LRUCache | |
| from inference_pb2 import HairSwapRequest, HairSwapResponse | |
| from inference_pb2_grpc import HairSwapServiceStub | |
| from utils.shape_predictor import align_face | |
| def get_bytes(img): | |
| if img is None: | |
| return img | |
| buffered = BytesIO() | |
| img.save(buffered, format="JPEG") | |
| return buffered.getvalue() | |
| def bytes_to_image(image: bytes) -> Image.Image: | |
| image = Image.open(BytesIO(image)) | |
| return image | |
| def center_crop(img): | |
| width, height = img.size | |
| side = min(width, height) | |
| left = (width - side) / 2 | |
| top = (height - side) / 2 | |
| right = (width + side) / 2 | |
| bottom = (height + side) / 2 | |
| img = img.crop((left, top, right, bottom)) | |
| return img | |
| def resize(name): | |
| def resize_inner(img, align): | |
| global align_cache | |
| if name in align: | |
| img_hash = hashlib.md5(get_bytes(img)).hexdigest() | |
| if img_hash not in align_cache: | |
| img = align_face(img, return_tensors=False)[0] | |
| align_cache[img_hash] = img | |
| else: | |
| img = align_cache[img_hash] | |
| elif img.size != (1024, 1024): | |
| img = center_crop(img) | |
| img = img.resize((1024, 1024), Image.Resampling.LANCZOS) | |
| return img | |
| return resize_inner | |
| def swap_hair(face, shape, color, blending, poisson_iters, poisson_erosion): | |
| # Verificar que al menos uno de shape o color esté presente junto con face | |
| if not face and not (shape or color): | |
| return (gr.update(visible=False), | |
| gr.update(value="Need to upload a face and at least a shape or color ❗", visible=True)) | |
| elif not face: | |
| return (gr.update(visible=False), | |
| gr.update(value="Need to upload a face ❗", visible=True)) | |
| elif not (shape or color): | |
| return (gr.update(visible=False), | |
| gr.update(value="Need to upload at least a shape or color ❗", visible=True)) | |
| # Obtener los bytes de los blobs | |
| face_bytes, shape_bytes, color_bytes = map(lambda item: get_bytes(item) if item else None, (face, shape, color)) | |
| # Asignar valores por defecto si no están presentes | |
| if shape_bytes is None: | |
| shape_bytes = b'face' | |
| if color_bytes is None: | |
| color_bytes = b'shape' | |
| try: | |
| with grpc.insecure_channel(os.environ['SERVER']) as channel: | |
| stub = HairSwapServiceStub(channel) | |
| output: HairSwapResponse = stub.swap( | |
| HairSwapRequest(face=face_bytes, shape=shape_bytes, color=color_bytes, blending=blending, | |
| poisson_iters=poisson_iters, poisson_erosion=poisson_erosion, use_cache=True) | |
| ) | |
| output_image = bytes_to_image(output.image) | |
| return (gr.update(value=output_image, visible=True), | |
| gr.update(visible=False)) | |
| except grpc.RpcError as e: | |
| # Manejo de errores de gRPC | |
| error_message = f"gRPC error: {e.code()}: {e.details()}" | |
| return (gr.update(visible=False), | |
| gr.update(value=error_message, visible=True)) | |
| except Exception as e: | |
| # Manejo de cualquier otro error | |
| error_message = f"Unexpected error: {str(e)}" | |
| return (gr.update(visible=False), | |
| gr.update(value=error_message, visible=True)) | |
| def get_demo(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## HairFastGan") | |
| gr.Markdown( | |
| '<div style="display: flex; align-items: center; gap: 10px;">' | |
| '<span>Official HairFastGAN Gradio demo:</span>' | |
| '<a href="https://arxiv.org/abs/2404.01094"><img src="https://img.shields.io/badge/arXiv-2404.01094-b31b1b.svg" height=22.5></a>' | |
| '<a href="https://github.com/AIRI-Institute/HairFastGAN"><img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white" height=22.5></a>' | |
| '<a href="https://huggingface.co/AIRI-Institute/HairFastGAN"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-md.svg" height=22.5></a>' | |
| '<a href="https://colab.research.google.com/#fileId=https://huggingface.co/AIRI-Institute/HairFastGAN/blob/main/notebooks/HairFast_inference.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" height=22.5></a>' | |
| '</div>' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| source = gr.Image(label="Source photo to try on the hairstyle", type="pil") | |
| with gr.Row(): | |
| shape = gr.Image(label="Shape photo with desired hairstyle (optional)", type="pil") | |
| color = gr.Image(label="Color photo with desired hair color (optional)", type="pil") | |
| with gr.Accordion("Advanced Options", open=False): | |
| blending = gr.Radio(["Article", "Alternative_v1", "Alternative_v2"], value='Article', | |
| label="Color Encoder version", info="Selects a model for hair color transfer.") | |
| poisson_iters = gr.Slider(0, 2500, value=0, step=1, label="Poisson iters", | |
| info="The power of blending with the original image, helps to recover more details. Not included in the article, disabled by default.") | |
| poisson_erosion = gr.Slider(1, 100, value=15, step=1, label="Poisson erosion", | |
| info="Smooths out the blending area.") | |
| align = gr.CheckboxGroup(["Face", "Shape", "Color"], value=["Face", "Shape", "Color"], | |
| label="Image cropping [recommended]", | |
| info="Selects which images to crop by face") | |
| btn = gr.Button("Get the haircut") | |
| with gr.Column(): | |
| output = gr.Image(label="Your result") | |
| error_message = gr.Textbox(label="⚠️ Error ⚠️", visible=False, elem_classes="error-message") | |
| gr.Examples(examples=[["input/0.png", "input/1.png", "input/2.png"], ["input/6.png", "input/7.png", None], | |
| ["input/10.jpg", None, "input/11.jpg"]], | |
| inputs=[source, shape, color], outputs=output) | |
| source.upload(fn=resize('Face'), inputs=[source, align], outputs=source) | |
| shape.upload(fn=resize('Shape'), inputs=[shape, align], outputs=shape) | |
| color.upload(fn=resize('Color'), inputs=[color, align], outputs=color) | |
| btn.click(fn=swap_hair, inputs=[source, shape, color, blending, poisson_iters, poisson_erosion], | |
| outputs=[output, error_message]) | |
| gr.Markdown('''To cite the paper by the authors | |
| ``` | |
| @article{nikolaev2024hairfastgan, | |
| title={HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach}, | |
| author={Nikolaev, Maxim and Kuznetsov, Mikhail and Vetrov, Dmitry and Alanov, Aibek}, | |
| journal={arXiv preprint arXiv:2404.01094}, | |
| year={2024} | |
| } | |
| ``` | |
| ''') | |
| return demo | |
| if __name__ == '__main__': | |
| align_cache = LRUCache(maxsize=10) | |
| demo = get_demo() | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |