WIP

app.py

@@ -0,0 +1,59 @@
+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"
+
+if torch.cuda.is_available():
+    device = "cuda"
+    dtype = torch.float16
+else:
+    device = "cpu"
+    dtype = torch.bfloat16
+
+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,
+  steps,
+  seed
+):
+    generator = torch.Generator()
+    generator.manual_seed(seed)
+
+    output = pipe(start_prompt, end_prompt, 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.Slider(minimum=2, maximum=12, default=5, step=1, label="Steps"),
+  gr.Number(0, label="Seed", precision=0)
+]
+
+output = gr.Gallery(
+            label="Generated images", show_label=False, elem_id="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]
+]
+
+title = "UnClip Image Interpolation Pipeline"
+
+demo_app = gr.Interface(
+    fn=unclip_text_interpolation,
+    inputs=inputs,
+    outputs=output,
+    title=title,
+    theme='huggingface',
+    examples=examples,
+    cache_examples=True
+)
+demo_app.launch(debug=True, enable_queue=True)