update
Browse files
app.py
CHANGED
@@ -13,7 +13,6 @@ import cv2
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from matplotlib import pyplot as plt
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from inpainting import StableDiffusionInpaintingPipeline
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from torchvision import transforms
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from clipseg.models.clipseg import CLIPDensePredT
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auth_token = os.environ.get("API_TOKEN") or True
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use_auth_token=auth_token,
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).to(device)
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model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
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model.eval()
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model.load_state_dict(torch.load('./clipseg/weights/rd64-uni.pth', map_location=torch.device('cuda')), strict=False)
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Resize((512, 512)),
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])
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def predict(
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if(radio == "draw a mask above"):
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with autocast("cuda"):
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init_image = dict["image"].convert("RGB").resize((512, 512))
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mask = dict["mask"].convert("RGB").resize((512, 512))
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else:
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img = transform(dict["image"]).unsqueeze(0)
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word_masks = [word_mask]
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with torch.no_grad():
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preds = model(img.repeat(len(word_masks),1,1,1), word_masks)[0]
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init_image = dict['image'].convert('RGB').resize((512, 512))
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filename = f"{uuid.uuid4()}.png"
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plt.imsave(filename,torch.sigmoid(preds[0][0]))
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img2 = cv2.imread(filename)
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gray_image = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
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(thresh, bw_image) = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY)
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cv2.cvtColor(bw_image, cv2.COLOR_BGR2RGB)
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mask = Image.fromarray(np.uint8(bw_image)).convert('RGB')
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os.remove(filename)
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with autocast("cuda"):
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images = pipe(prompt = prompt, init_image=init_image, mask_image=mask, strength=0.8)["sample"]
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return images[0]
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@@ -77,11 +57,6 @@ css = '''
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.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
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#image_upload .touch-none{display: flex}
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'''
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def swap_word_mask(radio_option):
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if(radio_option == "type what to mask below"):
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return gr.update(interactive=True, placeholder="A cat")
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else:
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return gr.update(interactive=False, placeholder="Disabled")
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image_blocks = gr.Blocks(css=css)
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with image_blocks as demo:
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font-size: 1.75rem;
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"
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>
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<svg
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width="0.65em"
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height="0.65em"
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viewBox="0 0 115 115"
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fill="none"
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xmlns="http://www.w3.org/2000/svg"
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>
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<rect width="23" height="23" fill="white"></rect>
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<rect y="69" width="23" height="23" fill="white"></rect>
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<rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
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<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
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<rect x="46" width="23" height="23" fill="white"></rect>
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<rect x="46" y="69" width="23" height="23" fill="white"></rect>
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<rect x="69" width="23" height="23" fill="black"></rect>
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<rect x="69" y="69" width="23" height="23" fill="black"></rect>
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<rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
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<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
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<rect x="115" y="46" width="23" height="23" fill="white"></rect>
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<rect x="115" y="115" width="23" height="23" fill="white"></rect>
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<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
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<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
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<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
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<rect x="92" y="69" width="23" height="23" fill="white"></rect>
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<rect x="69" y="46" width="23" height="23" fill="white"></rect>
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<rect x="69" y="115" width="23" height="23" fill="white"></rect>
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<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
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<rect x="46" y="46" width="23" height="23" fill="black"></rect>
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<rect x="46" y="115" width="23" height="23" fill="black"></rect>
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<rect x="46" y="69" width="23" height="23" fill="black"></rect>
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<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
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<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
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<rect x="23" y="69" width="23" height="23" fill="black"></rect>
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</svg>
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<h1 style="font-weight: 900; margin-bottom: 7px;">
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Stable Diffusion
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</h1>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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</p>
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</div>
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"""
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)
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with gr.
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with gr.
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image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
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demo.launch()
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from matplotlib import pyplot as plt
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from inpainting import StableDiffusionInpaintingPipeline
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from torchvision import transforms
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auth_token = os.environ.get("API_TOKEN") or True
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use_auth_token=auth_token,
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).to(device)
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Resize((512, 512)),
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])
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def predict(dict, prompt=""):
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with autocast("cuda"):
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init_image = dict["image"].convert("RGB").resize((512, 512))
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mask = dict["mask"].convert("RGB").resize((512, 512))
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images = pipe(prompt = prompt, init_image=init_image, mask_image=mask, strength=0.8)["sample"]
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return images[0]
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.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
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#image_upload .touch-none{display: flex}
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'''
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image_blocks = gr.Blocks(css=css)
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with image_blocks as demo:
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font-size: 1.75rem;
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"
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>
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<h1 style="font-weight: 900; margin-bottom: 7px;">
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Stable Diffusion Inpainting
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</h1>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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Stable Diffusion Inpainting by RunwayML, add a mask and text prompt for what you want to replace <br>For faster generation you can try
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<a
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href="https://app.runwayml.com/video-tools/teams/akhaliq/ai-tools/erase-and-replace"
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style="text-decoration: underline;"
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target="_blank"
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>erase and replace tool on Runway</a
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>
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</p>
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</div>
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"""
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)
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with gr.Group():
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with gr.Box():
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image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
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with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
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prompt = gr.Textbox(label = 'Your prompt (what you want to replace)')
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btn = gr.Button("Generate image").style(
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margin=False,
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rounded=(False, True, True, False),
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full_width=False,
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)
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btn.click(fn=predict, inputs=[image, prompt], outputs=image)
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gr.HTML(
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"""
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<div class="footer">
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<p>Model by <a href="https://huggingface.co/runwayml" style="text-decoration: underline;" target="_blank">RunwayML</a> - Gradio Demo by 🤗 Hugging Face
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</p>
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</div>
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<div class="acknowledgments">
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<p><h4>LICENSE</h4>
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The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
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<p><h4>Biases and content acknowledgment</h4>
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Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
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</div>
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"""
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)
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image_blocks.launch()
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