Update app.py
Browse filesUpdated to 2.1
app.py
CHANGED
@@ -6,7 +6,7 @@ from datasets import load_dataset
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from diffusers import StableDiffusionImg2ImgPipeline
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2")
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pipe = pipe.to(device)
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def resize(value,img):
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@@ -26,4 +26,4 @@ gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label=
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gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'),
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gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
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gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
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outputs='image', title = "Stable Diffusion 2.
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from diffusers import StableDiffusionImg2ImgPipeline
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
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pipe = pipe.to(device)
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def resize(value,img):
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gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'),
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gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
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gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
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outputs='image', title = "Stable Diffusion 2.1 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion 2.1 see https://github.com/Stability-AI/stablediffusion <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()
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