--- license: other license_name: mg-by-sa-2.0 license_link: https://ids.nus.edu.sg/docs/modelgo/v2/MG-BY-SA/LICENSE library_name: canter pipeline_tag: text-to-image tags: - flow-matching - text-to-image - photography - pytorch base_model: - HuggingFaceTB/SmolLM2-360M --- # 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 release:** [`v0001`](RELEASES.md#v0001) [Example gallery](GALLERY.md) · [Getting started](#getting-started) · [API and inference parameters](API.md) · [Technical report](TECHNICAL_REPORT.md) · [Releases](RELEASES.md) 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`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) weights, Python package, and Gradio interface. Image decoding uses [`data-archetype/dinac_ae_d2`](https://huggingface.co/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](https://pytorch.org/get-started/locally/) provides the appropriate command. ### Download and install Install the Hugging Face CLI, download the repository, and install the package from the downloaded directory: ```bash python -m pip install "huggingface-hub>=1.15,<2" hf download data-archetype/canter --revision v0001 --local-dir canter cd canter python -m pip install . ``` The default release stores most weights in bfloat16. Numerically sensitive parameters remain in float32. ### Start the Gradio interface Run the application from the downloaded repository: ```bash 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, and numbered model release as JSON metadata. The server listens on port 7860. To select the bind address explicitly: ```bash 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: ```bash 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 ```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](API.md) for configuration examples, guidance modes, solvers, schedules, output types, and loading options. ## Example gallery See the [example gallery](GALLERY.md). ## 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 Remote loading without a revision uses the package's pinned default release. It does not follow changes to `main`: ```python pipe = CanterPipeline.from_pretrained("data-archetype/canter") ``` Pin an immutable checkpoint tag for reproducible use: ```python pipe = CanterPipeline.from_pretrained( "data-archetype/canter", revision="v0001", ) ``` 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](RELEASES.md). ## Documentation - [Example gallery](GALLERY.md) - [API and inference parameters](API.md) - [Technical report](TECHNICAL_REPORT.md) - [Releases](RELEASES.md) - [Attribution](ATTRIBUTION.md) ## Citation ```bibtex @misc{canter, title = {Canter: An Efficient, Photography-Oriented Text-to-Image Model}, author = {data-archetype}, email = {data-archetype@proton.me}, 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](ATTRIBUTION.md). DINAC-AE-D2 remains under the license published in its own repository.