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import gradio as gr |
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from PIL import Image |
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from torchvision.transforms import Compose, ToTensor, Resize, Normalize |
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import numpy as np |
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import imageio |
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import tempfile |
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from utils.utils import denorm |
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from model.hub import MultiInputResShiftHub |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = MultiInputResShiftHub.from_pretrained("vfontech/Multiple-Input-Resshift-VFI") |
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model.requires_grad_(False).to(device).eval() |
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transform = Compose([ |
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Resize((256, 448)), |
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ToTensor(), |
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Normalize(mean=[0.5]*3, std=[0.5]*3), |
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]) |
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def to_numpy(img_tensor): |
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img_np = denorm(img_tensor, mean=[0.5]*3, std=[0.5]*3).squeeze().permute(1, 2, 0).cpu().numpy() |
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img_np = np.clip(img_np, 0, 1) |
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return (img_np * 255).astype(np.uint8) |
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def interpolate(img0_pil, img2_pil, tau, num_samples): |
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img0 = transform(img0_pil.convert("RGB")).unsqueeze(0).to(device) |
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img2 = transform(img2_pil.convert("RGB")).unsqueeze(0).to(device) |
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if num_samples == 1: |
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img1 = model.reverse_process([img0, img2], tau) |
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return Image.fromarray(to_numpy(img1)), None |
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else: |
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frames = [to_numpy(img0)] |
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for t in np.linspace(0, 1, num_samples): |
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img = model.reverse_process([img0, img2], float(t)) |
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frames.append(to_numpy(img)) |
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frames.append(to_numpy(img2)) |
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temp_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name |
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imageio.mimsave(temp_path, frames, fps=8) |
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return None, temp_path |
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demo = gr.Interface( |
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fn=interpolate, |
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inputs=[ |
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gr.Image(type="pil", label="Initial Image (frame1)"), |
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gr.Image(type="pil", label="Final Image (frame3)"), |
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gr.Slider(0.0, 1.0, step=0.05, value=0.5, label="Tau Value (only if Num Samples = 1)"), |
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gr.Slider(1, 15, step=1, value=1, label="Number of Samples"), |
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], |
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outputs=[ |
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gr.Image(label="Interpolated Image (if num_samples = 1)"), |
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gr.Video(label="Interpolation in video (if num_samples > 1)"), |
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], |
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title="Multi-Input ResShift Diffusion VFI", |
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description=( |
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"📄 [arXiv Paper](https://arxiv.org/pdf/2504.05402) • " |
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"🤗 [Model](https://huggingface.co./vfontech/Multiple-Input-Resshift-VFI) • " |
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"🧪 [Colab](https://colab.research.google.com/drive/1MGYycbNMW6Mxu5MUqw_RW_xxiVeHK5Aa#scrollTo=EKaYCioiP3tQ) • " |
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"🌐 [GitHub](https://github.com/VicFonch/Multi-Input-Resshift-Diffusion-VFI)\n\n" |
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"Video interpolation using Conditional Residual Diffusion.\n" |
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"- All images are resized to 256x448.\n" |
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"- If `Number of Samples` = 1, generates only one intermediate image with the given Tau value.\n" |
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"- If `Number of Samples` > 1, ignores Tau and generates a sequence of interpolated images." |
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), |
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examples=[ |
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["_data/example_images/frame1.png", "_data/example_images/frame3.png", 0.5], |
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], |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=12) |
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demo.launch(max_threads=1) |
