Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image
+
+multi_view_diffusion_pipeline = DiffusionPipeline.from_pretrained(
+    "dylanebert/multi-view-diffusion",
+    custom_pipeline="dylanebert/multi-view-diffusion",
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+).to("cuda")
+
+
+def run(image):
+    image = np.array(image, dtype=np.float32) / 255.0
+    images = multi_view_diffusion_pipeline(
+        "", image, guidance_scale=5, num_inference_steps=30, elevation=0
+    )
+
+    images = [Image.fromarray((img * 255).astype("uint8")) for img in images]
+
+    width, height = images[0].size
+    grid_img = Image.new("RGB", (2 * width, 2 * height))
+
+    grid_img.paste(images[0], (0, 0))
+    grid_img.paste(images[1], (width, 0))
+    grid_img.paste(images[2], (0, height))
+    grid_img.paste(images[3], (width, height))
+
+    return grid_img
+
+
+demo = gr.Interface(fn=run, inputs="image", outputs="image")
+demo.launch()