Update app.py

app.py

@@ -2,9 +2,7 @@ from diffusers import DiffusionPipeline
 import gradio as gr
 import torch
 import math
-
-orig_start_prompt = "a photograph of an adult lion"
-orig_end_prompt = "a photograph of a lion cub"
+import PIL
 
 if torch.cuda.is_available():
     device = "cuda"
@@ -16,21 +14,22 @@ else:
 pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=dtype, custom_pipeline='unclip_image_interpolation')
 pipe.to(device)
 
-def unclip_text_interpolation(
-  start_prompt,
-  end_prompt,
+def unclip_image_interpolation(
+  start_image,
+  end_image,
   steps,
   seed
 ):
     generator = torch.Generator()
     generator.manual_seed(seed)
 
-    output = pipe(start_prompt, end_prompt, steps, enable_sequential_cpu_offload=False, generator=generator)
+    images = [start_image, end_image]
+    output = pipe(images, steps, enable_sequential_cpu_offload=False, generator=generator)
     return output.images
 
 inputs = [
-  gr.Image(lines=2, default=orig_start_prompt, label="Start Prompt"),
-  gr.Image(lines=2, default=orig_end_prompt, label="End Prompt"),
+  gr.Image(type="pil"),
+  gr.Image(type="pil"),
   gr.Slider(minimum=2, maximum=12, default=5, step=1, label="Steps"),
   gr.Number(0, label="Seed", precision=0)
 ]
@@ -40,15 +39,15 @@ output = gr.Gallery(
         ).style(grid=[2], height="auto")
 
 examples = [
-  [orig_start_prompt, orig_end_prompt, 5, 42],
-  ["a photo of a landscape in winter","a photo of a landscape in fall", 5, 20],
-  ["a photo of a victorian house", "a photo of a modern house", 5, 20]
+  ["starry_night.jpg","dogs.jpg", 5, 20],
+  ["flowers.jpg", "dogs.jpg", 5, 42],
+  ["starry_night.jpg","flowers.jpg", 6, 9011]
 ]
 
 title = "UnClip Image Interpolation Pipeline"
 
 demo_app = gr.Interface(
-    fn=unclip_text_interpolation,
+    fn=unclip_image_interpolation,
     inputs=inputs,
     outputs=output,
     title=title,