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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() | |