Create app.py

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")
+
+examples = [
+  ["An astronaut riding a horse."],
+  ["A teddy bear playing in the water."],
+  ["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 = "[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,
+  examples=examples
+)
+io.launch()