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Publish Canter 0.5.0 contrastive PDG support
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Canter releases

Model card · Getting started · API and inference parameters

Checkpoint tags are immutable snapshots. The installed package supplies the inference code, so current code can load any compatible checkpoint tag without executing the Python files stored in that historical snapshot. The current package also supplies the small latent-RGB preview projection; preview behavior therefore follows the installed code rather than changing an older denoiser checkpoint tag.

Canter 0.5.0 adds contrastive PDG. Its middle-skipped guidance path uses an explicit negative prompt when supplied and learned unconditional text when the negative prompt is blank or absent. The Python API, Gradio interface, and ComfyUI nodes use the same branch-selection rule.

Canter 0.4.1 makes the dense backend apply to the bundled text encoder as well as denoiser text attention. Dense text encoding now uses padded PyTorch scaled dot-product attention and does not require the explicit CUDA FlashAttention operator. The jagged backend retains its packed FlashAttention implementation.

Canter 0.4.0 supports PyTorch 2.13 and adds public APIs for conditioning, guidance configuration, guided velocity, schedules, solvers, the latent-RGB projection, and DINAC VAE encoding. Euler-Maruyama and ER-SDE now use one shared sde_noise_multiplier inference control. Gradio is provided by the optional webui extra and is not an inference-package dependency.

Canter 0.3.0 added asynchronous native-resolution latent previews and the responsive preview grid. The browser scales previews for display; final decoded images remain full resolution.

Release Date Weight storage Status
v0002 July 2026 bfloat16 with float32 precision islands Preview
v0001 July 2026 bfloat16 with float32 precision islands Preview

Updating code

Refresh the moving main directory and use an editable installation:

hf download data-archetype/canter --revision main --local-dir canter
python -m pip install -e "./canter[webui]"

For a Git clone on main, use git pull. If the checkout was installed without -e, reinstall it after updating.

Selecting a checkpoint

Remote API and web UI loading can select any compatible checkpoint explicitly:

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

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

These commands use the currently installed code and download only the selected checkpoint artifacts. Running app.py inside a tagged standalone directory instead uses the historical code bundled with that snapshot.

For exact reproduction, pin both the Canter package version and checkpoint tag. Passing revision="main" explicitly selects the moving repository head and is not an immutable checkpoint reference.

v0002

Preview candidate exported from a 50/50 EMA blend of training steps 1,010,000 and 1,090,000.

v0001

Initial preview release.