Create app.py

app.py

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+from PIL import Image
+import requests
+
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+from diffusers import DiffusionPipeline
+from torch import autocast
+
+url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+image
+
+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
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = pipe.to(device)
+
+
+def process_image(image, text, prompt):
+  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="text"), gr.Textbox(label="prompt")], 
+                     outputs=gr.Image(type="pil"),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+                     
+interface.launch(debug=True)