| 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 |
|
|
| @spaces.GPU |
| def process(image): |
| image_size = image.size |
| input_images = transform_image(image).unsqueeze(0).to("cuda") |
| |
| 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://go.bria.ai/3ZCBTLH' target='_blank'>Commercial use license</a> | " |
| "<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 commercial weigths for commercial use: " |
| "<a href='https://go.bria.ai/3D5EGp0' target='_blank'>here</a>" |
| "</span>" |
| ) |
|
|
|
|
|
|
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(show_error=True) |
|
|
