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import os
import gradio as gr
import torch
from diffusers import DiffusionPipeline
print(f"Is CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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")).to("cuda")
pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq", torch_dtype=torch.float16).to("cuda")
else:
pipe_sd = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=os.getenv("HUGGING_FACE_HUB_TOKEN"))
pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
examples = [
["A sketch of a palm tree."],
["A teddy bear playing in the pool."],
["A simple wedding cake with lego bride and groom topper and cake pops."],
["A realistic tree using a mixture of different colored pencils."],
["Muscular Santa Claus."],
["A man with a pineapple head."],
["Pebble tower standing on the left on the sea beach."],
]
title = "VQ Diffusion vs. Stable Diffusion 1-5"
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."
def inference(text):
output_sd = pipe_sd(text).images[0]
output_vq_diffusion = pipe_vq(text, truncation_rate=1.0).images[0]
return [output_vq_diffusion, output_sd]
io = gr.Interface(
inference,
gr.Textbox(lines=3),
outputs=[
gr.Image(type="pil", label="VQ-Diffusion"),
gr.Image(type="pil", label="Stable Diffusion"),
],
title=title,
description=description,
examples=examples
)
io.launch()
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