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Update app.py
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app.py
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@@ -1,11 +1,12 @@
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from diffusers import
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import torch
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import PIL.Image
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import gradio as gr
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import random
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import numpy as np
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pipeline =
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def predict(steps, seed):
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generator = torch.manual_seed(seed)
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@@ -19,7 +20,7 @@ gr.Interface(
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gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
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gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
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],
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outputs=gr.Image(shape=[
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css="#output_image{width: 256px}",
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title="Unconditional butterflies",
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description="A DDPM scheduler and UNet model trained on a subset of the <a href=\"https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset\">Smithsonian Butterflies</a> dataset for unconditional image generation.",
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from diffusers import DiffusionPipeline
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import torch
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import PIL.Image
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import gradio as gr
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import random
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import numpy as np
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pipeline = DiffusionPipeline.from_pretrained("johnowhitaker/ddpm-butterflies-32px")
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pipeline.to('cuda')
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def predict(steps, seed):
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generator = torch.manual_seed(seed)
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gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
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gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
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],
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outputs=gr.Image(shape=[128,128], type="pil", elem_id="output_image"),
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css="#output_image{width: 256px}",
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title="Unconditional butterflies",
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description="A DDPM scheduler and UNet model trained on a subset of the <a href=\"https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset\">Smithsonian Butterflies</a> dataset for unconditional image generation.",
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