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Browse files- README.md +5 -6
- app.py +47 -0
- requirements.txt +4 -0
README.md
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---
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title:
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emoji:
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colorFrom:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: I Like Flan
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emoji: 🍮
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colorFrom: gray
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colorTo: purple
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sdk: gradio
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sdk_version: 3.6
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import os
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import gradio as gr
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import torch
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from diffusers import DiffusionPipeline
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print(f"Is CUDA available: {torch.cuda.is_available()}")
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print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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if torch.cuda.is_available():
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pipe_sd = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, revision="fp16", use_auth_token=os.getenv("HUGGING_FACE_HUB_TOKEN"))
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pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq", torch_dtype=torch.float16, revision="fp16")
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else:
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pipe_sd = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=os.getenv("HUGGING_FACE_HUB_TOKEN"))
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pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
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examples = [
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["An astronaut riding a horse."],
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["A teddy bear playing in the water."],
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["A simple wedding cake with lego bride and groom topper and cake pops."],
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["A realistic tree using a mixture of different colored pencils."],
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["Muscular Santa Claus."],
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["A man with a pineapple head."],
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["Pebble tower standing on the left on the sea beach."],
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]
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title = "VQ Diffusion vs. Stable Diffusion 1-5"
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description = "This demo compares [VQ-Diffusion-ITHQ](https://huggingface.co/microsoft/vq-diffusion-ithq) and [Stable-Diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) for text to image generation."
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def inference(text):
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output_sd = pipe_sd(text).images[0]
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output_vq_diffusion = pipe_vq(text, truncation_rate=0.86).images[0]
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return [output_vq_diffusion, output_sd]
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io = gr.Interface(
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inference,
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gr.Textbox(lines=3),
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outputs=[
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gr.Image(type="pil", label="VQ-Diffusion"),
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gr.Image(type="pil", label="Stable Diffusion"),
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],
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title=title,
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description=description,
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examples=examples
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)
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io.launch()
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requirements.txt
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--extra-index-url https://download.pytorch.org/whl/cu113
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torch
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transformers
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diffusers[torch]
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