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import gradio as gr
import torch
import numpy as np
from PIL import Image
import os
from vslerp import UnCLIPImageInterpolationPipeline # your pipeline + vSLERP
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load pipeline once
pipe = UnCLIPImageInterpolationPipeline.from_pretrained(
"kakaobrain/karlo-v1-alpha-image-variations",
torch_dtype=torch.float16
).to(device)
# Put your own images in a local "bank" folder
IMAGE_BANK = {
"Example 1": "lj.png",
"Example 2": "kd.png",
"Example 3": "vase.png",
"Example 4": "lamp.jpeg"
}
def run_vslerp(img0, img1, bank0, bank1, slerp_num_steps, vslerp_start_idx, vslerp_end_idx, vslerp_num_steps):
# Decide input images: uploaded takes precedence, else from bank
if img0 is None and bank0 != "None":
img0 = Image.open(IMAGE_BANK[bank0])
if img1 is None and bank1 != "None":
img1 = Image.open(IMAGE_BANK[bank1])
if img0 is None or img1 is None:
raise ValueError("Please provide two images (either upload or select from bank).")
images = [img0, img1]
generator = torch.Generator(device=device).manual_seed(42)
# Prepare a 2D list for the gallery
gallery_matrix = []
vslerp_values = np.linspace(vslerp_start_idx, vslerp_end_idx, vslerp_num_steps)
for m_val in vslerp_values:
row = []
for step in range(slerp_num_steps):
out = pipe(
image=images,
generator=generator,
steps=slerp_num_steps,
decoder_guidance_scale=1,
mean_val=m_val
)
row.append(out.images[0]) # assuming pipe returns a list with one image per call
gallery_matrix.append(row)
return gallery_matrix
with gr.Blocks() as demo:
gr.Markdown("## vSLERP Demo")
gr.Markdown("Note: The run may take a while, please be patient 🙏")
with gr.Row():
with gr.Column():
img0 = gr.Image(label="Upload Image 0", type="pil")
bank0 = gr.Dropdown(choices=["None"] + list(IMAGE_BANK.keys()), value="None", label="Or choose from bank")
with gr.Column():
img1 = gr.Image(label="Upload Image 1", type="pil")
bank1 = gr.Dropdown(choices=["None"] + list(IMAGE_BANK.keys()), value="None", label="Or choose from bank")
with gr.Row():
slerp_num_steps = gr.Slider(3, 6, value=6, step=1, label="slerp_num_steps")
vslerp_start_idx = gr.Slider(-2, 0, value=-1, step=1, label="vslerp_start_idx")
vslerp_end_idx = gr.Slider(1, 3, value=3, step=1, label="vslerp_end_idx")
vslerp_num_steps = gr.Slider(3, 6, value=6, step=1, label="vslerp_num_steps")
run_btn = gr.Button("Run vSLERP")
gallery = gr.Gallery(label="Generated Interpolations").style(grid=[4], height="auto")
run_btn.click(
run_vslerp,
inputs=[img0, img1, bank0, bank1, slerp_num_steps, vslerp_start_idx, vslerp_end_idx, vslerp_num_steps],
outputs=[gallery]
)
if __name__ == "__main__":
demo.launch()
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