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==3.0.4") run_cmd("pip install --upgrade pip") run_cmd('pip install gradio==3.0.4') 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.hub.load( "AK391/animegan2-pytorch:main", "generator", pretrained=True, device="cuda", progress=True, ) def face2paint(model: torch.nn.Module, img: Image.Image, size: int = 512, device: str = 'cuda'): w, h = img.size s = min(w, h) img = img.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2)) img = img.resize((size, size), Image.LANCZOS) with torch.no_grad(): input = to_tensor(img).unsqueeze(0) * 2 - 1 output = model(input.to(device)).cpu()[0] output = (output * 0.5 + 0.5).clip(0, 1) * 255.0 return output # 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) def inference_step(vid, start_sec, duration, out_fps): 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 /= 255.0 x = x.permute(1, 0, 2, 3) with torch.no_grad(): output = model(x.to('cuda')).detach().cpu() output = (output * 0.5 + 0.5).clip(0, 1) * 255.0 output_video = output.permute(0, 2, 3, 1).numpy() return output_video, audio_arr, out_fps, audio_fps def predict_fn(filepath, start_sec, duration): out_fps = 12 vid = EncodedVideo.from_path(filepath) for i in range(duration): print(f"🖼️ Processing step {i + 1}/{duration}...") video, audio, fps, audio_fps = inference_step(vid=vid, start_sec=i + start_sec, duration=1, out_fps=out_fps) 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)) print(f"💾 Writing output video...") try: write_video('out.mp4', video_all, fps=fps, audio_array=audio_all, audio_fps=audio_fps, audio_codec='aac') except: print("❌ Error when writing with audio...trying without audio") write_video('out.mp4', video_all, fps=fps) print(f"✅ Done!") del video_all del audio_all return 'out.mp4' article = """

Github Repo Pytorch

""" iface_webcam = gr.Interface( predict_fn, inputs=[ gr.inputs.Video(source="webcam"), gr.inputs.Slider(minimum=0, maximum=300, step=1, default=0), gr.inputs.Slider(minimum=1, maximum=4, step=1, default=2), # gr.inputs.Slider(minimum=6, maximum=18, step=6, default=12), # Leaving manual fps out for now ], outputs=gr.outputs.Video(), title='AnimeGANV2 On Videos', description="Applying AnimeGAN-V2 to frames from video clips", article=article, enable_queue=True, # examples=[ # ['obama.webm', 0, 4], # ], allow_flagging=False, ) iface_file = gr.Interface( predict_fn, inputs=[ gr.inputs.Video(source="upload"), gr.inputs.Slider(minimum=0, maximum=300, step=1, default=0), gr.inputs.Slider(minimum=1, maximum=4, step=1, default=2), ], outputs=gr.outputs.Video(), title='AnimeGANV2 On Videos', description="Applying AnimeGAN-V2 to frames from video clips", article=article, enable_queue=True, examples=[ ['obama.webm', 0, 4], ], allow_flagging=False, ) if __name__ == '__main__': gr.TabbedInterface( interface_list=[iface_file, iface_webcam], tab_names=["From a File!", "From your Webcam!"] ).launch()