Upload folder using huggingface_hub

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 shibatch
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,6 +1,7 @@
 ---
 library_name: pytorch
 pipeline_tag: text-to-image
+license: mit
 datasets:
   - ylecun/mnist
 tags:
@@ -54,6 +55,10 @@ Generate an eight-digit image:
 python generate_tiny_digits.py 31415926 --output 31415926.png
 ```
 
+On the first run, the script downloads `model/config.json` and
+`model/model.safetensors` from `shibatch/tinydigitdiffusion3m` on Hugging
+Face. Later runs reuse the local Hugging Face cache.
+
 The script accepts the following useful options:
 
 ```bash
@@ -65,19 +70,42 @@ python generate_tiny_digits.py 2026 \
   --output 2026.png
 ```
 
+Use a different Hub revision with `--revision`, or override the repository
+with `--repo-id`. To use model files already stored locally (including the
+`model/` directory included in this distribution package), pass
+`--model-dir`:
+
+```bash
+python generate_tiny_digits.py 2026 --model-dir ./model --output 2026.png
+```
+
 The output is a fixed `32 x 256` grayscale PNG. Short prompts are centered on
 the eight available 32-pixel slots.
 
 ## Python example
 
 ```python
+import sys
+from pathlib import Path
+
 import torch
+from huggingface_hub import snapshot_download
 from PIL import Image
 
+repo_dir = Path(snapshot_download(
+    repo_id="shibatch/tinydigitdiffusion3m",
+    allow_patterns=[
+        "model/config.json",
+        "model/model.safetensors",
+        "tiny_digit_diffusion.py",
+    ],
+))
+sys.path.insert(0, str(repo_dir))
+
 from tiny_digit_diffusion import ddim_sample, load_model
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = load_model("model", device)
+model = load_model(repo_dir / "model", device)
 
 image = ddim_sample(
     model,
@@ -228,3 +256,13 @@ This checkpoint is intended for education, architecture experiments, tests,
 and demonstrations of a complete conditional image generator at a very small
 parameter count. It should not be interpreted as an official MNIST model or as
 an image-generation counterpart of any production diffusion system.
+
+## License
+
+The source code and model checkpoint in this package are released under the
+MIT License. See `LICENSE` for the complete terms.
+
+The MNIST images are not redistributed in this package. Training used MNIST
+through `torchvision`; the [MNIST dataset card](https://huggingface.co/datasets/ylecun/mnist)
+identifies its license as MIT and credits Yann LeCun, Corinna Cortes, and
+Christopher J. C. Burges as the dataset curators.

SHA256SUMS

@@ -1,11 +1,12 @@
 32c3d5d9a15f591030c3a116ba7d068f88aa3e59f3f50edfa173e9174fa8d627  .gitattributes
-1f1c500038095b00eef5c89dac5fa260f7f3b132052b4788281874e08329dbf3  README.md
+cdf09a0b07d7cdb2ea535ac3127541ea55d6a996787a8e1b9266015d69f708a2  LICENSE
+ae30ff06819481a2d607ae9e294960ecbe9f41b1d4d2b32467cf0b7f79ba935f  README.md
 334a86682c1450ba57900e4ac690557b5ccab31feb67e780b0d8c56eeaabe1ab  artifact_metadata.json
-3cf8877a074fc77f0e50197a800b3614a532dbba1386ed0b30ae5252004747cc  generate_tiny_digits.py
+15751648e2d970f57428314a159319154a38bf24a37b1db0643e397c174e3336  generate_tiny_digits.py
 421b80e7ba8f6b89125b7d6f65bb2356a1df2c7f39b7c125001a093a09d20795  model/config.json
 035ccfb2876e5d78a637e282288e6bfaff227d7a18b3e9fe4562ff2a441458c2  model/model.safetensors
 e18e17c2ae10c8fa2a3947abc31ea4a0eac86fc5c2ecad39578b9fe027999aca  requirements-train.txt
-7db726c6b928d639520fbeb88c6146fb7b33cb6960dc8a01e2cc8380822c51dc  requirements.txt
+974c4ced7ad83a8821cbdf74c6d5c40051f0ed61e65fb95feaa79bfecd59a046  requirements.txt
 d382d9dae68f91c93c9da353ee49a746428ab0c64a4b3e43ac88024765bd4a79  sample_grid.png
 38e6cb6b1daa5b27a6663a6c29455cfdd0255d43ae1b4f487d48417334d211bb  tiny_digit_diffusion.py
 8c70ce74440d2a4930ddef0199d68c191de598fca7220241ac75d46e9b72fb89  train_tiny_digit_diffusion.py

generate_tiny_digits.py

@@ -11,14 +11,19 @@ from PIL import Image
 
 from tiny_digit_diffusion import ddim_sample, load_model
 
+DEFAULT_REPO_ID = "shibatch/tinydigitdiffusion3m"
+
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     parser.add_argument("prompt", help="A numeric prompt containing 1-8 digits, for example 2026.")
     parser.add_argument(
         "--model-dir",
-        default=str(Path(__file__).resolve().parent / "model"),
+        default=None,
+        help="Local model directory. If omitted, download the model from Hugging Face.",
     )
+    parser.add_argument("--repo-id", default=DEFAULT_REPO_ID)
+    parser.add_argument("--revision", default="main")
     parser.add_argument("--output", default="generated_digits.png")
     parser.add_argument("--steps", type=int, default=50)
     parser.add_argument("--guidance-scale", type=float, default=1.0)
@@ -27,13 +32,32 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
+def resolve_model_dir(
+    model_dir: str | None,
+    repo_id: str,
+    revision: str,
+) -> Path:
+    if model_dir is not None:
+        return Path(model_dir).expanduser().resolve()
+
+    from huggingface_hub import snapshot_download
+
+    snapshot_dir = snapshot_download(
+        repo_id=repo_id,
+        revision=revision,
+        allow_patterns=["model/config.json", "model/model.safetensors"],
+    )
+    return Path(snapshot_dir) / "model"
+
+
 def main() -> None:
     args = parse_args()
     device = torch.device(
         "cuda" if args.device == "auto" and torch.cuda.is_available() else
         "cpu" if args.device == "auto" else args.device
     )
-    model = load_model(args.model_dir, device)
+    model_dir = resolve_model_dir(args.model_dir, args.repo_id, args.revision)
+    model = load_model(model_dir, device)
     image = ddim_sample(
         model,
         [args.prompt],

requirements.txt

@@ -1,3 +1,4 @@
 torch>=2.3
 safetensors>=0.4
 Pillow>=10.0
+huggingface_hub>=0.25