---
license: apache-2.0
base_model: prism-ml/bonsai-image-ternary-4B-unpacked
tags:
- flux
- flux2
- text-to-image
- gguf
- stable-diffusion.cpp
- on-device
- android
library_name: gguf
pipeline_tag: text-to-image
---

# Bonsai Image (Ternary, FLUX.2 Klein 4B) — Q2_K GGUF

A GGUF conversion of **PrismML's Bonsai Image** ternary diffusion transformer
([`prism-ml/bonsai-image-ternary-4B-unpacked`](https://huggingface.co/prism-ml/bonsai-image-ternary-4B-unpacked)),
quantized to **Q2_K** so it runs on-device with
[stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) (and the
[llmedge](https://github.com/Aatricks/llmedge) Android library).

Bonsai Image is FLUX.2 Klein 4B fine-tuned with quantization-aware training (QAT) to ternary
weights. Its official releases ship only in MLX (Apple) and GemLite (CUDA) packings, neither of
which loads on Android. This is the **diffusion transformer only**, in the BFL tensor layout
sdcpp expects, quantized to Q2_K (~1.3 GB).

## Why Q2_K (not 1-bit)
ggml's literal ternary types (`tq1_0`/`tq2_0`) load and run, but their per-256-weight scale is too
coarse for Bonsai's per-128 trained scales and produce degraded output. **Q2_K**'s finer per-16
sub-block scales preserve the QAT quality at a comparable size.

## Usage (stable-diffusion.cpp)
Also needs a Qwen3-4B text encoder GGUF and the FLUX.2 VAE:
```bash
sd --diffusion-model bonsai-flux2-klein-ternary-q2_k.gguf \
   --vae flux2-vae.safetensors \
   --llm qwen_3_4b.gguf \
   -p "a red fox in snow, detailed, 8k" \
   --cfg-scale 1.0 --steps 4 --offload-to-cpu --diffusion-fa --vae-tiling
```

## How it was made
1. `scripts/convert_bonsai_flux2_to_bfl.py` (in llmedge): Bonsai diffusers transformer → BFL naming
   (renames + fuses the double-block `to_q/k/v` into `*_attn.qkv`).
2. `sd -M convert -m bonsai-flux2-bfl.safetensors --type q2_K -o <this file>`.

## License
Apache-2.0, inherited from the base model (PrismML / Deepgrove). All credit for the model and its
QAT training goes to PrismML; this repo only redistributes a GGUF quantization.