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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() | |