vq-diffusion / app.py
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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_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq", torch_dtype=torch.float16, revision="fp16").to("cuda")
else:
pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
title = "VQ Diffusion vs. Stable Diffusion 1-5"
description = "[VQ-Diffusion-ITHQ](https://huggingface.co/microsoft/vq-diffusion-ithq) for text to image generation."
def inference(text):
output_vq_diffusion = pipe_vq(text, truncation_rate=0.86).images[0]
return output_vq_diffusion
io = gr.Interface(
inference,
gr.Textbox(lines=3),
outputs=[
gr.Image(type="pil", label="VQ-Diffusion"),
],
title=title,
description=description
)
io.launch()