""" Thanks to nateraw for making this scape happen! This code has been mostly taken from https://huggingface.co/spaces/nateraw/animegan-v2-for-videos/tree/main """ import os os.system("wget https://github.com/Sxela/ArcaneGAN/releases/download/v0.3/ArcaneGANv0.3.jit") import sys from subprocess import call def run_cmd(command): try: print(command) call(command, shell=True) except KeyboardInterrupt: print("Process interrupted") sys.exit(1) print("⬇️ Installing latest gradio==2.4.7b9") run_cmd("pip install --upgrade pip") run_cmd('pip install gradio==2.4.7b9') import gc import math import gradio as gr import numpy as np import torch from encoded_video import EncodedVideo, write_video from PIL import Image from torchvision.transforms.functional import center_crop, to_tensor print("🧠 Loading Model...") model = torch.jit.load('./ArcaneGANv0.3.jit').cuda().eval().half() # This function is taken from pytorchvideo! 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) # This function is taken from pytorchvideo! def short_side_scale( x: torch.Tensor, size: int, interpolation: str = "bilinear", ) -> torch.Tensor: """ Determines the shorter spatial dim of the video (i.e. width or height) and scales it to the given size. To maintain aspect ratio, the longer side is then scaled accordingly. Args: x (torch.Tensor): A video tensor of shape (C, T, H, W) and type torch.float32. size (int): The size the shorter side is scaled to. interpolation (str): Algorithm used for upsampling, options: nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' Returns: An x-like Tensor with scaled spatial dims. """ assert len(x.shape) == 4 assert x.dtype == torch.float32 c, t, h, w = x.shape if w < h: new_h = int(math.floor((float(h) / w) * size)) new_w = size else: new_h = size new_w = int(math.floor((float(w) / h) * size)) return torch.nn.functional.interpolate(x, size=(new_h, new_w), mode=interpolation, align_corners=False) means = [0.485, 0.456, 0.406] stds = [0.229, 0.224, 0.225] from torchvision import transforms norm = transforms.Normalize(means,stds) norms = torch.tensor(means)[None,:,None,None].cuda() stds = torch.tensor(stds)[None,:,None,None].cuda() def inference_step(vid, start_sec, duration, out_fps, interpolate): clip = vid.get_clip(start_sec, start_sec + duration) video_arr = torch.from_numpy(clip['video']).permute(3, 0, 1, 2) audio_arr = np.expand_dims(clip['audio'], 0) audio_fps = None if not vid._has_audio else vid._container.streams.audio[0].sample_rate x = uniform_temporal_subsample(video_arr, duration * out_fps) x = center_crop(short_side_scale(x, 512), 512) x /= 255. x = x.permute(1, 0, 2, 3) x = norm(x) with torch.no_grad(): output = model(x.to('cuda').half()) output = (output * stds + norms).clip(0, 1) * 255. output_video = output.permute(0, 2, 3, 1).float().detach().cpu().numpy() if interpolate == 'Yes': output_video[1:] = output_video[1:]*(0.5) + output_video[:-1]*(0.5) return output_video, audio_arr, out_fps, audio_fps def predict_fn(filepath, start_sec, duration, out_fps, interpolate): # out_fps=12 vid = EncodedVideo.from_path(filepath) for i in range(duration): video, audio, fps, audio_fps = inference_step( vid = vid, start_sec = i + start_sec, duration = 1, out_fps = out_fps, interpolate = interpolate ) gc.collect() if i == 0: video_all = video audio_all = audio else: video_all = np.concatenate((video_all, video)) audio_all = np.hstack((audio_all, audio)) write_video( 'out.mp4', video_all, fps=fps, audio_array=audio_all, audio_fps=audio_fps, audio_codec='aac' ) del video_all del audio_all return 'out.mp4' title = "ArcaneGAN" description = "Gradio demo for ArcaneGAN, video to Arcane style. To use it, simply upload your video, or click on an example below. Follow me on twitter for more info and updates." article = "
ArcaneGan by Alex Spirin | Github Repo |
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" gr.Interface( predict_fn, inputs=[gr.inputs.Video(), gr.inputs.Slider(minimum=0, maximum=300, step=1, default=0), gr.inputs.Slider(minimum=1, maximum=10, step=1, default=2), gr.inputs.Slider(minimum=12, maximum=30, step=6, default=24), gr.inputs.Radio(choices=['Yes','No'], type="value", default='Yes', label='Remove flickering')], outputs=gr.outputs.Video(), title='ArcaneGAN On Videos', description = description, article = article, enable_queue=True, examples=[ ['obama.webm', 23, 10, 30], ], allow_flagging=False ).launch()