| import gradio as gr |
|
|
| from PIL import Image |
| from torchvision.transforms import Compose, ToTensor, Resize, Normalize |
| import numpy as np |
| import imageio |
| import tempfile |
|
|
| from utils.utils import denorm |
| from model.hub import MultiInputResShiftHub |
|
|
| import torch |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| model = MultiInputResShiftHub.from_pretrained("vfontech/Multiple-Input-Resshift-VFI") |
| model.requires_grad_(False).to(device).eval() |
|
|
| transform = Compose([ |
| Resize((256, 448)), |
| ToTensor(), |
| Normalize(mean=[0.5]*3, std=[0.5]*3), |
| ]) |
|
|
| def to_numpy(img_tensor: torch.Tensor) -> np.ndarray: |
| img_np = denorm(img_tensor, mean=[0.5]*3, std=[0.5]*3).squeeze().permute(1, 2, 0).cpu().numpy() |
| img_np = np.clip(img_np, 0, 1) |
| return (img_np * 255).astype(np.uint8) |
|
|
| def interpolate(img0_pil: Image.Image, |
| img2_pil: Image.Image, |
| tau: float=0.5, |
| num_samples: int=1) -> tuple: |
| img0 = transform(img0_pil.convert("RGB")).unsqueeze(0).to(device) |
| img2 = transform(img2_pil.convert("RGB")).unsqueeze(0).to(device) |
|
|
| try: |
| if num_samples == 1: |
| |
| img1 = model.reverse_process([img0, img2], tau) |
| return Image.fromarray(to_numpy(img1)), None |
| else: |
| |
| frames = [to_numpy(img0)] |
| for t in np.linspace(0, 1, num_samples): |
| img = model.reverse_process([img0, img2], float(t)) |
| frames.append(to_numpy(img)) |
| frames.append(to_numpy(img2)) |
|
|
| temp_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name |
| imageio.mimsave(temp_path, frames, fps=8) |
| return None, temp_path |
| except Exception as e: |
| print(f"Error during interpolation: {e}") |
| return None, None |
| |
|
|
| |
| def build_demo() -> gr.Blocks: |
| header = """ |
| <div style="text-align: center; padding: 1rem 0;"> |
| <h1 style="font-size: 2.2rem; margin-bottom: 0.4rem;">🎞️ Multi-Input ResShift Diffusion VFI</h1> |
| <p style="font-size: 1.1rem; color: #555; margin-bottom: 1rem;"> |
| Efficient and stochastic video frame interpolation for hand-drawn animation |
| </p> |
| <div style="display: flex; justify-content: center; flex-wrap: wrap; gap: 10px;"> |
| <a href="https://arxiv.org/pdf/2504.05402"> |
| <img src="https://img.shields.io/badge/arXiv-Paper-A42C25.svg" alt="arXiv"> |
| </a> |
| <a href="https://huggingface.co/vfontech/Multiple-Input-Resshift-VFI"> |
| <img src="https://img.shields.io/badge/🤗-Model-ffbd45.svg" alt="HF"> |
| </a> |
| <a href="https://colab.research.google.com/drive/1MGYycbNMW6Mxu5MUqw_RW_xxiVeHK5Aa#scrollTo=EKaYCioiP3tQ"> |
| <img src="https://img.shields.io/badge/Colab-Demo-green.svg" alt="Colab"> |
| </a> |
| <a href="https://github.com/VicFonch/Multi-Input-Resshift-Diffusion-VFI"> |
| <img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github" alt="GitHub"> |
| </a> |
| </div> |
| </div> |
| """ |
| with gr.Blocks() as demo: |
| gr.HTML(header) |
| gr.Interface( |
| fn=interpolate, |
| inputs=[ |
| gr.Image(type="pil", label="Initial Image (frame1)"), |
| gr.Image(type="pil", label="Final Image (frame3)"), |
| gr.Slider(0.0, 1.0, step=0.05, value=0.5, label="Tau Value (only if Num Samples = 1)"), |
| gr.Slider(1, 15, step=1, value=1, label="Number of Samples"), |
| ], |
| outputs=[ |
| gr.Image(label="Interpolated Image (if num_samples = 1)"), |
| gr.Video(label="Interpolation in video (if num_samples > 1)"), |
| ], |
| |
| description=( |
| "Video interpolation using Conditional Residual Diffusion.\n" |
| "- All images are resized to 256x448.\n" |
| "- If `Number of Samples = 1`, generates only one intermediate image with the given Tau value.\n" |
| "- If `Number of Samples > 1`, ignores Tau and generates a sequence of interpolated images." |
| ), |
| examples=[ |
| ["_data/example_images/frame1.png", "_data/example_images/frame3.png", 0.5, 1], |
| ], |
| ) |
| return demo |
|
|
| if __name__ == "__main__": |
| demo = build_demo() |
| demo.launch(server_name="0.0.0.0", ssr_mode=False) |
| |
