:tada: init

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.github/workflows/check_size.yaml

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+name: Check file size
+
+on:
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/lfs-warning@v2.0
+        with:
+          filesizelimit: 10485760 # = 10MB, so we can sync to HF spaces

.github/workflows/sync_to_hub.yaml

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+name: Sync to Hugging Face hub
+
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://nateraw:$HF_TOKEN@huggingface.co/spaces/nateraw/animegan-v2-for-videos main --force

README.md

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+---
+title: AnimeGAN-v2 For Videos
+emoji: 🔥
+colorFrom: blue
+colorTo: red
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+# AnimeGAN-v2 For Videos
+
+[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/nateraw/animegan-v2-for-videos)
+
+Apply AnimeGAN-v2 across frames of a video
+
+---
+
+Autogenerated using [this template](https://github.com/nateraw/spaces-template)

app.py

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+from PIL import Image
+import torch
+import gradio as gr
+import numpy as np
+from encoded_video import EncodedVideo, write_video
+from io import BytesIO
+
+model2 = torch.hub.load(
+    "AK391/animegan2-pytorch:main",
+    "generator",
+    pretrained=True,
+    device="cuda",
+    progress=True,
+    force_reload=True,
+)
+face2paint = torch.hub.load(
+    'AK391/animegan2-pytorch:main', 'face2paint', 
+    size=512, device="cuda",side_by_side=False
+)
+
+def uniform_temporal_subsample(
+    x: torch.Tensor, num_samples: int, temporal_dim: int = -3
+) -> torch.Tensor:
+    """
+    Uniformly subsamples num_samples indices from the temporal dimension of the video.
+    When num_samples is larger than the size of temporal dimension of the video, it
+    will sample frames based on nearest neighbor interpolation.
+    Args:
+        x (torch.Tensor): A video tensor with dimension larger than one with torch
+            tensor type includes int, long, float, complex, etc.
+        num_samples (int): The number of equispaced samples to be selected
+        temporal_dim (int): dimension of temporal to perform temporal subsample.
+    Returns:
+        An x-like Tensor with subsampled temporal dimension.
+    """
+    t = x.shape[temporal_dim]
+    assert num_samples > 0 and t > 0
+    # Sample by nearest neighbor interpolation if num_samples > t.
+    indices = torch.linspace(0, t - 1, num_samples)
+    indices = torch.clamp(indices, 0, t - 1).long()
+    return torch.index_select(x, temporal_dim, indices)
+
+
+def inference_video(video_file):
+    out_fps = 12
+    start_sec = 0
+    duration = 2
+    vid = EncodedVideo.from_path(video_file)
+    clip = vid.get_clip(start_sec, start_sec + duration)
+    video_arr = clip['video']
+    audio_arr = np.expand_dims(clip['audio'], 0)
+    audio_fps = None if not vid._has_audio else vid._container.streams.audio[0].sample_rate
+
+    frames = uniform_temporal_subsample(torch.from_numpy(video_arr), duration * out_fps, 0).to(torch.uint8).numpy()
+
+    out_frames = []
+    for frame in frames:
+        im = Image.fromarray(frame)
+        out = face2paint(model2, im)
+        out_frames.append(np.array(out))
+
+
+    out_frames = np.array(out_frames)
+
+    bytes_mp4 = bytes()
+    out_file = BytesIO(bytes_mp4)
+
+    # Add dummy file name to stream, as write_video will be looking for it
+    out_file.name = "out.mp4"
+
+    write_video(
+        'out.mp4',
+        out_frames,
+        fps=out_fps,
+        audio_array=audio_arr,
+        audio_fps=audio_fps,
+        audio_codec='aac'
+    )
+    return 'out.mp4'
+
+gr.Interface(
+    inference_video,
+    inputs=gr.inputs.Video(),
+    outputs=gr.outputs.Video(),
+    title='AnimeGANV2 On Videos',
+    description="Applying AnimeGAN-V2 to frame from video clips",
+    article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p><p style='text-align: center'>samples from repo: <img src='https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg' alt='animation'/><img src='https://user-images.githubusercontent.com/26464535/127134790-93595da2-4f8b-4aca-a9d7-98699c5e6914.jpg' alt='animation'/></p>",
+    enable_queue=True,
+    # examples=examples,
+    allow_flagging=False
+).launch(debug=True)

requirements.txt

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+gradio
+torch
+torchvision
+Pillow
+gdown
+numpy
+scipy
+cmake
+onnxruntime-gpu
+opencv-python-headless