Update app.py

app.py

@@ -1,29 +1,35 @@
+import gradio as gr
+import numpy as np
 import torch
 from diffusers import DiffusionPipeline
-import gradio as gr
+from PIL import Image
 
 multi_view_diffusion_pipeline = DiffusionPipeline.from_pretrained(
-    "Akash887/My-Multi-View-Diffusion",
+    "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)
+    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]
 
-  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))
 
-  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))
 
-  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
 
-  return grid_img
 
 demo = gr.Interface(fn=run, inputs="image", outputs="image")
 demo.launch()