The painter half of mindview, packed for WebGPU in the browser. It is what neovand.github.io/mindview downloads. These are not new weights for general use; they are a browser layout of existing ones, plus two small parts trained for mindview.
| File | What it holds |
|---|---|
manifest.json | Every tensor: shape, file and byte offsets; the model config and the default 4-step schedule. |
dit_0.bin … dit_4.bin, dit_misc.bin | The diffusion transformer of Bonsai Image 4B (FLUX.2 [klein] 4B with ternary weights). Ternary tensors are 16 two-bit codes per u32 plus one f32 scale per 128 inputs; the rest is dense. |
adapter.bin | A ternary linear adapter trained for mindview: it maps Ternary Bonsai 1.7B's states at layers 7, 14 and 21 to the DiT's 7,680-wide text conditioning, in place of the original text encoder. |
taef2.bin | TAEF2, the tiny latent decoder. |
viz.bin, viz.json | A per-block lens fitted for mindview (a linear readout of the picture the DiT has in mind after each block) and colour probes for the decoder's stages. For display only. |
schedules.bin, schedules.json | Noise levels and the timestep modulation for other step counts. |
export_report.json | How the export was checked. |
The files are written by research/scripts/export_painter.py (with export_painter_schedules.py and
export_painter_viz2.py) in the mindview repository. The browser runtime that reads them matches the PyTorch reference
block for block.
prism-ml/bonsai-image-ternary-4B-unpacked@30bbc03 with every ternary weight exact (export_report.json). The
timestep embedding is not included: its outputs, the modulation for each step of the schedules, are precomputed
(mod.*), checked against float64.The painter half of mindview, packed for WebGPU in the browser. It is what neovand.github.io/mindview downloads. These are not new weights for general use; they are a browser layout of existing ones, plus two small parts trained for mindview.
| File | What it holds |
|---|---|
manifest.json | Every tensor: shape, file and byte offsets; the model config and the default 4-step schedule. |
dit_0.bin … dit_4.bin, dit_misc.bin | The diffusion transformer of Bonsai Image 4B (FLUX.2 [klein] 4B with ternary weights). Ternary tensors are 16 two-bit codes per u32 plus one f32 scale per 128 inputs; the rest is dense. |
adapter.bin | A ternary linear adapter trained for mindview: it maps Ternary Bonsai 1.7B's states at layers 7, 14 and 21 to the DiT's 7,680-wide text conditioning, in place of the original text encoder. |
taef2.bin | TAEF2, the tiny latent decoder. |
viz.bin, viz.json | A per-block lens fitted for mindview (a linear readout of the picture the DiT has in mind after each block) and colour probes for the decoder's stages. For display only. |
schedules.bin, schedules.json | Noise levels and the timestep modulation for other step counts. |
export_report.json | How the export was checked. |
The files are written by research/scripts/export_painter.py (with export_painter_schedules.py and
export_painter_viz2.py) in the mindview repository. The browser runtime that reads them matches the PyTorch reference
block for block.
prism-ml/bonsai-image-ternary-4B-unpacked@30bbc03 with every ternary weight exact (export_report.json). The
timestep embedding is not included: its outputs, the modulation for each step of the schedules, are precomputed
(mod.*), checked against float64.