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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 + "/checkpoint"
cfg["model"]["transformer_config"]["checkpoint_path"] = gpt_path + "/checkpoint"
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)
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