import tempfile import ffmpegio import gradio as gr import numpy as np import omegaconf import tensorflow as tf from pyprojroot.pyprojroot import here from huggingface_hub import hf_hub_url, hf_hub_download from ganime.model.vqgan_clean.experimental.net2net_v3 import Net2Net IMAGE_SHAPE = (64, 128, 3) hf_hub_download(repo_id="Kurokabe/VQGAN_Kimetsu-no-yaiba_Tensorflow", filename="checkpoint.data-00000-of-00001", subfolder="vqgan_kny_image_full") hf_hub_download(repo_id="Kurokabe/VQGAN_Kimetsu-no-yaiba_Tensorflow", filename="checkpoint.index", subfolder="vqgan_kny_image_full") vqgan_path = hf_hub_download(repo_id="Kurokabe/VQGAN_Kimetsu-no-yaiba_Tensorflow", filename="checkpoint", subfolder="vqgan_kny_image_full") hf_hub_download(repo_id="Kurokabe/GANime_Kimetsu-no-yaiba_Tensorflow", filename="checkpoint.data-00000-of-00001", subfolder="ganime_kny_video_full") hf_hub_download(repo_id="Kurokabe/GANime_Kimetsu-no-yaiba_Tensorflow", filename="checkpoint.index", subfolder="ganime_kny_video_full") gpt_path = hf_hub_download(repo_id="Kurokabe/GANime_Kimetsu-no-yaiba_Tensorflow", filename="checkpoint", subfolder="ganime_kny_video_full") cfg = omegaconf.OmegaConf.load(here("configs/kny_video_gpt2_large_gradio.yaml")) cfg["model"]["first_stage_config"]["checkpoint_path"] = vqgan_path cfg["model"]["transformer_config"]["checkpoint_path"] = gpt_path model = Net2Net(**cfg["model"], trainer_config=cfg["train"], num_replicas=1) model.first_stage_model.build((20, *IMAGE_SHAPE)) # def save_video(video): # b, f, h, w, c = 1, 20, 500, 500, 3 # # filename = output_file.name # filename = "./test_video.mp4" # images = [] # for i in range(f): # # image = video[0][i].numpy() # # image = 255 * image # Now scale by 255 # # image = image.astype(np.uint8) # images.append(np.random.randint(0, 255, (h, w, c), dtype=np.uint8)) # ffmpegio.video.write(filename, 20, np.array(images), overwrite=True) # return filename def save_video(video): output_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) b, f, h, w, c = video.shape filename = output_file.name video = video.numpy() video = video * 255 video = video.astype(np.uint8) ffmpegio.video.write(filename, 20, video, overwrite=True) return filename def resize_if_necessary(image): if image.shape[0] != 64 and image.shape[1] != 128: image = tf.image.resize(image, (64, 128)) return image def normalize(image): image = (tf.cast(image, tf.float32) / 127.5) - 1 return image def generate(first, last, n_frames): # n_frames = 20 n_frames = int(n_frames) first = resize_if_necessary(first) last = resize_if_necessary(last) first = normalize(first) last = normalize(last) data = { "first_frame": np.expand_dims(first, axis=0), "last_frame": np.expand_dims(last, axis=0), "y": None, "n_frames": [n_frames], "remaining_frames": [list(reversed(range(n_frames)))], } generated = model.predict(data) return save_video(generated) gr.Interface( generate, inputs=[ gr.Image(label="Upload the first image"), gr.Image(label="Upload the last image"), gr.Slider( label="Number of frame to generate", minimum=15, maximum=100, value=15, step=1, ), ], outputs="video", title="Generate a video from the first and last frame", ).launch(share=True)