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#!/usr/bin/env python
from __future__ import annotations
import io
import pathlib
import tarfile
import gradio as gr
import numpy as np
import PIL.Image
from huggingface_hub import hf_hub_download
TITLE = "TADNE (This Anime Does Not Exist) Image Viewer"
DESCRIPTION = """The original TADNE site is https://thisanimedoesnotexist.ai/.
You can view images generated by the TADNE model with seed 0-99999.
The original images are 512x512 in size, but they are resized to 128x128 here.
Expected execution time on Hugging Face Spaces: 4s
Related Apps:
- [TADNE](https://huggingface.co/spaces/hysts/TADNE)
- [TADNE Image Viewer](https://huggingface.co/spaces/hysts/TADNE-image-viewer)
- [TADNE Image Selector](https://huggingface.co/spaces/hysts/TADNE-image-selector)
- [TADNE Interpolation](https://huggingface.co/spaces/hysts/TADNE-interpolation)
- [TADNE Image Search with DeepDanbooru](https://huggingface.co/spaces/hysts/TADNE-image-search-with-DeepDanbooru)
"""
image_size = 128
min_seed = 0
max_seed = 99999
dirname = "0-99999"
tarball_path = hf_hub_download("hysts/TADNE-sample-images", f"{image_size}/{dirname}.tar", repo_type="dataset")
def run(
start_seed: int,
nrows: int,
ncols: int,
) -> np.ndarray:
start_seed = int(start_seed)
num = nrows * ncols
images = []
dummy = np.ones((image_size, image_size, 3), dtype=np.uint8) * 255
with tarfile.TarFile(tarball_path) as tar_file:
for seed in range(start_seed, start_seed + num):
if not min_seed <= seed <= max_seed:
images.append(dummy)
continue
member = tar_file.getmember(f"{dirname}/{seed:07d}.jpg")
with tar_file.extractfile(member) as f: # type: ignore
data = io.BytesIO(f.read())
image = PIL.Image.open(data)
image = np.asarray(image)
images.append(image)
res = (
np.asarray(images)
.reshape(nrows, ncols, image_size, image_size, 3)
.transpose(0, 2, 1, 3, 4)
.reshape(nrows * image_size, ncols * image_size, 3)
)
return res
demo = gr.Interface(
fn=run,
inputs=[
gr.Number(label="Start Seed", value=0),
gr.Slider(label="Number of Rows", minimum=1, maximum=10, step=1, value=2),
gr.Slider(label="Number of Columns", minimum=1, maximum=10, step=1, value=5),
],
outputs=gr.Image(label="Output"),
title=TITLE,
description=DESCRIPTION,
)
if __name__ == "__main__":
demo.queue().launch()
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