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Running
on
Zero
| import os | |
| import gradio as gr | |
| from gradio_imageslider import ImageSlider | |
| from loadimg import load_img | |
| import spaces | |
| from transformers import AutoModelForImageSegmentation | |
| import torch | |
| from torchvision import transforms | |
| torch.set_float32_matmul_precision(["high", "highest"][0]) | |
| birefnet = AutoModelForImageSegmentation.from_pretrained( | |
| "briaai/RMBG-2.0", trust_remote_code=True | |
| ) | |
| birefnet.to("cuda") | |
| transform_image = transforms.Compose( | |
| [ | |
| transforms.Resize((1024, 1024)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), | |
| ] | |
| ) | |
| output_folder = 'output_images' | |
| if not os.path.exists(output_folder): | |
| os.makedirs(output_folder) | |
| def fn(image): | |
| im = load_img(image, output_type="pil") | |
| im = im.convert("RGB") | |
| origin = im.copy() | |
| image = process(im) | |
| image_path = os.path.join(output_folder, "no_bg_image.png") | |
| image.save(image_path) | |
| return (image, origin), image_path | |
| def process(image): | |
| image_size = image.size | |
| input_images = transform_image(image).unsqueeze(0).to("cuda") | |
| # Prediction | |
| with torch.no_grad(): | |
| preds = birefnet(input_images)[-1].sigmoid().cpu() | |
| pred = preds[0].squeeze() | |
| pred_pil = transforms.ToPILImage()(pred) | |
| mask = pred_pil.resize(image_size) | |
| image.putalpha(mask) | |
| return image | |
| def process_file(f): | |
| name_path = f.rsplit(".",1)[0]+".png" | |
| im = load_img(f, output_type="pil") | |
| im = im.convert("RGB") | |
| transparent = process(im) | |
| transparent.save(name_path) | |
| return name_path | |
| slider1 = ImageSlider(label="RMBG-2.0", type="pil") | |
| slider2 = ImageSlider(label="RMBG-2.0", type="pil") | |
| image = gr.Image(label="Upload an image") | |
| image2 = gr.Image(label="Upload an image",type="filepath") | |
| text = gr.Textbox(label="Paste an image URL") | |
| png_file = gr.File(label="output png file") | |
| chameleon = load_img("giraffe.jpg", output_type="pil") | |
| url = "http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg" | |
| tab1 = gr.Interface( | |
| fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[chameleon], api_name="image" | |
| ) | |
| tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=[url], api_name="text") | |
| tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png") | |
| demo = gr.TabbedInterface( | |
| [tab1, tab2], ["input image", "input url"], title = ( | |
| "RMBG-2.0 for background removal <br>" | |
| "<span style='font-size:16px; font-weight:300;'>" | |
| "Background removal model developed by " | |
| "<a href='https://bria.ai' target='_blank'>BRIA.AI</a>, trained on a carefully selected dataset,<br> " | |
| "and is available as an open-source model for non-commercial use.</span><br>" | |
| "<span style='font-size:16px; font-weight:500;'> For testing upload your image and wait.<br>" | |
| "<a href='https://huggingface.co/briaai/RMBG-2.0' target='_blank'>Model card</a> | " | |
| "<a href='https://blog.bria.ai/brias-new-state-of-the-art-remove-background-2.0-outperforms-the-competition' target='_blank'>Blog</a>" | |
| "</span><br>" | |
| "<span style='font-size:16px; font-weight:300;'>" | |
| "API Endpoint available on: " | |
| "<a href='https://platform.bria.ai/console/api/image-editing' target='_blank'>Bria.ai</a>, " | |
| "<a href='https://fal.ai/models/fal-ai/bria/background/remove' target='_blank'>fal.ai</a><br>" | |
| "ComfyUI node is available here: " | |
| "<a href='https://github.com/Bria-AI/ComfyUI-BRIA-API' target='_blank'>ComfyUI Node</a><br>" | |
| "Purchase weigths for commercial use: " | |
| "<a href='https://bria.ai/contact-us' target='_blank'>here</a>" | |
| "</span>" | |
| ) | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(show_error=True) | |