Canter

An efficient, photography-oriented text-to-image model

Preview release

The model is still training. Checkpoints and behavior may change during the preview period, and generation quality is still quite variable.

Current checkpoint: v0001

Example gallery · Getting started · API and inference parameters · Technical report · Releases

This is a 2 billion parameter indie model trained on a single GPU. It is designed for efficient text-to-image generation with a strong focus on photography, natural scenes, people, objects, and places.

The repository bundles the flow-matching denoiser, text tokenizer with a copy of the required SmolLM2-360M weights, Python package, and Gradio interface. Image decoding uses data-archetype/dinac_ae_d2 VAE, which is downloaded automatically.

Getting started

Requirements

The release requires:

• Python 3.10 to 3.13
• PyTorch 2.12 (>=2.12,<2.13) with a compatible CUDA build
• an NVIDIA GPU with CUDA and bfloat16 support
• 8 GB VRAM for 1024 by 1024 generation with the default bfloat16 release
• Linux or Windows

Install a CUDA-enabled PyTorch build for your system first. The PyTorch installation selector provides the appropriate command.

Install the latest code and checkpoint

Install the Hugging Face CLI, download the moving main revision, and install the package in editable mode:

python -m pip install "huggingface-hub>=1.15,<2"
hf download data-archetype/canter --revision main --local-dir canter
cd canter
python -m pip install -e .

main contains the latest Canter code and the current default checkpoint. Editable installation means that refreshing the same directory updates the code used by the installed canter-web command.

The default checkpoint stores most weights in bfloat16. Numerically sensitive parameters remain in float32.

Update an existing download

Refresh a directory created with hf download by running:

hf download data-archetype/canter --revision main --local-dir canter

If the package was installed without -e, reinstall it afterwards with python -m pip install --upgrade ./canter. A Git clone on the main branch can instead be updated with git pull; an editable installation immediately uses the updated checkout.

Start the Gradio interface

Run the application from the downloaded repository:

python app.py --in-browser

app.py loads the weights from its own repository directory and downloads the latest compatible DINAC-AE-D2 VAE. The interface appears immediately and reports model loading and pytorch dynamo compilation progress. Downloaded PNG files contain the prompt, effective per-image settings, Canter code version, and numbered checkpoint release as JSON metadata.

The server listens on port 7860. To select the bind address explicitly:

python app.py --server-name 0.0.0.0 --server-port 7860

Use --server-name 127.0.0.1 to restrict access to the local machine.

After package installation, the interface can also download and run the model directly from Hugging Face:

canter-web --model data-archetype/canter --in-browser

Run python app.py --help or canter-web --help for model revision, weight dtype, text backend, device, cache, and server options.

Generate an image with Python

from canter import CanterPipeline

pipe = CanterPipeline.from_pretrained("data-archetype/canter")
result = pipe(
    "A weathered wooden boardwalk descending toward a rugged coastline "
    "under a stormy sky"
)
result.image.save("canter.png")

The default configuration generates a 1216 by 832 image with seed 42, 50 ABM2 updates, a Beta(0.6, 0.6) schedule, PDG 2.5, and image self-attention gain -0.03. The selected text backend is compiled during model loading.

See API and inference parameters for configuration examples, guidance modes, solvers, schedules, output types, and loading options.

Example gallery

See the example gallery.

Limitations

The model has more limited knowledge than larger models. Some concepts may be unknown or undertrained, especially uncommon subjects and specialist domains.

Text rendering is currently undertrained and unreliable.

The model has been trained almost exclusively on photographs. It has seen limited artwork outside a few thousand classical paintings, so results for illustration and other non-photographic styles may be weak or inconsistent.

Responsible use

The model and its outputs are provided without guarantees of accuracy, suitability, or safety. Users are responsible for reviewing generated content and complying with applicable laws, privacy obligations, and third-party rights.

Releases

The installed package supplies the inference code. Remote loading without a revision uses the checkpoint pinned by that package:

pipe = CanterPipeline.from_pretrained("data-archetype/canter")

Select an immutable older checkpoint while retaining the installed code:

pipe = CanterPipeline.from_pretrained(
    "data-archetype/canter",
    revision="v0001",
)

The web interface supports the same separation:

canter-web \
  --model data-archetype/canter \
  --revision v0001 \
  --in-browser

Running app.py from a tagged standalone download intentionally uses the code bundled with that historical snapshot. Use the installed canter-web command as above when testing old weights with current code.

Release tags follow the v0001, v0002, and later numbering scheme. Optional full-float32 releases use tags such as v0001-fp32.

See the release table and update instructions.

Documentation

• Example gallery
• API and inference parameters
• Technical report
• Releases
• Attribution

Citation

@misc{canter,
  title   = {Canter: An Efficient, Photography-Oriented Text-to-Image Model},
  author  = {data-archetype},
  email   = {[email protected]},
  year    = {2026},
  month   = jul,
  url     = {https://huggingface.co/data-archetype/canter},
}

License

The original weights, architecture, model-specific code, and documentation are licensed under the ModelGo Attribution-ShareAlike License 2.0 (MG-BY-SA-2.0). Commercial use, modification, redistribution, and hosted use are permitted subject to its attribution, source-disclosure, and share-alike conditions. Distributions must retain NOTICE.

The bundled SmolLM2 subset remains under Apache License 2.0. See LICENSE-APACHE-2.0 and Attribution. DINAC-AE-D2 remains under the license published in its own repository.