Delete app.py

app.py

@@ -1,108 +0,0 @@
-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)