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| from PIL import Image | |
| import requests | |
| import os | |
| from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| from torch import autocast | |
| import gradio as gr | |
| auth_token = os.environ.get("API_TOKEN") or True | |
| url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") | |
| model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", | |
| custom_pipeline="text_inpainting", | |
| segmentation_model=model, | |
| segmentation_processor=processor, | |
| use_auth_token=auth_token, | |
| ) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = pipe.to(device) | |
| def pad_image(image): | |
| w, h = image.size | |
| if w == h: | |
| return image | |
| elif w > h: | |
| new_image = Image.new(image.mode, (w, w), (0, 0, 0)) | |
| new_image.paste(image, (0, (w - h) // 2)) | |
| return new_image | |
| else: | |
| new_image = Image.new(image.mode, (h, h), (0, 0, 0)) | |
| new_image.paste(image, ((h - w) // 2, 0)) | |
| return new_image | |
| def process_image(image, text, prompt): | |
| image = pad_image(image) | |
| image = image.resize((512, 512)) | |
| with autocast("cuda"): | |
| inpainted_image = pipe(image=image, text=text, prompt=prompt).images[0] | |
| return inpainted_image | |
| title = "Interactive demo: Text-based inpainting with CLIPSeg x Stable Diffusion" | |
| description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. This model can be used to segment things in an image based on text. This way, one can use it to provide a binary mask for Stable Diffusion, which the latter needs to inpaint. To use it, simply upload an image and add a text to mask as well as a text which indicates what to replace, or use one of the examples below and click 'submit'. Results will show up in a few seconds." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>" | |
| examples = [["example_image.png", "a glass", "a cup"]] | |
| interface = gr.Interface(fn=process_image, | |
| inputs=[gr.Image(type="pil"), gr.Textbox(label="What's the thing you want to replace?"), gr.Textbox(label="What do you want as replacement?")], | |
| outputs=gr.Image(type="pil"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| interface.launch(debug=True) |