---
license: apache-2.0
pipeline_tag: text-to-image
tags:
- ternary
- 1.58-bit
- gemlite
- hqq
- cuda
- text-to-image
- diffusion
- flux
- prismml
- bonsai
base_model:
- prism-ml/bonsai-image-ternary-4B-unpacked
---
Prism ML Website |
White Paper |
Demo & Examples |
Discord
# bonsai-image-ternary-4B-gemlite-2bit
Ternary weight (1.58-bit) text-to-image diffusion transformer deployment for NVIDIA GPUs
> **1.21 GB transformer** | **6.4×** smaller than FP16 | **4.5 s / 1024²** on RTX 3080 | **2.8 s / 1024²** on A100 | runs natively on Linux and Windows
## Highlights
- **1.21 GB** diffusion transformer, down from **7.75 GB** for the FP16 FLUX.2 Klein 4B transformer
- Ternary {-1, 0, +1} transformer weights with FP16 group-wise scaling in the matrix-heavy transformer layers (Q/K/V projections, output projections, MLP weights)
- Quality-oriented Bonsai Image variant: the additional zero state improves visual quality and prompt fidelity while keeping the transformer compact
- 4.55 GB CUDA deployment payload including the 4-bit text encoder and FP16 VAE — text encoder is offloaded after prompt encode, so the denoising loop only keeps the compact transformer and VAE resident
- 4-step FlowMatch-Euler sampler with guidance = 1.0 and shift = 3.0 — no CFG, no negative prompts needed
- Gemlite low-bit GEMM path for NVIDIA GPUs, with HQQ used for the compressed text encoder
- Runs on Linux and Windows natively through the same CUDA / Gemlite deployment stack
- Cross-platform companion: also available as [MLX 2-bit](https://huggingface.co/prism-ml/bonsai-image-ternary-4B-mlx-2bit) for Apple Silicon
## Resources
- **[White Paper](https://github.com/PrismML-Eng/Bonsai-Image-Demo/blob/main/bonsai-image-4b-whitepaper.pdf)** — full benchmarks, kernels, and memory analysis
- **[Demo repo](https://github.com/PrismML-Eng/Bonsai-Image-Demo)** — one-command setup for Mac / Linux / Windows
- **[Discord](https://discord.gg/prismml)** — community + support
- **Kernels**: [gemlite](https://github.com/mobiusml/gemlite) (fused low-bit GEMM) · [HQQ](https://github.com/mobiusml/hqq) (low-bit quantization runtime) · [triton-windows](https://github.com/triton-lang/triton-windows) (Windows path)
## Model Overview
| Item | Specification |
| :-------------------- | :----------------------------------------------------------------------------------------------|
| Base architecture | FLUX.2 Klein 4B (MMDiT diffusion transformer) |
| Parameters | ~4.0B (transformer trunk) |
| Blocks | 25 MMDiT blocks: 5 double-stream + 20 single-stream |
| Sampler | FlowMatchEuler, **4 steps**, guidance = 1.0, shift = 3.0 |
| Text encoder | Qwen3-4B at 4-bit HQQ (≈ 2.84 GB CUDA payload, offloaded after prompt encode) |
| VAE | Flux2 32-channel latent, tiled decode (128 px tiles) |
| Native resolution | 1024×1024 (also supports 512×512 and arbitrary multiples of 32) |
| Weight format | Gemlite INT2 pack, ternary values + FP16 group-wise scales |
| **Transformer size** | **1.21 GB** model-level Bonsai representation; **1.54 GB** CUDA packed deployment size |
| Total payload | **4.55 GB** CUDA deployment payload (transformer + 4-bit text encoder + FP16 VAE) |
| Ternary coverage | All 100 matmul-heavy linears in the 25 MMDiT blocks |
| Platforms | Linux x86_64 + Windows native on NVIDIA GPUs |
| License | Apache 2.0 |
## Ternary Weight Representation: 1.58-bit g128
Each ternary weight takes a value from {−1, 0, +1} with one shared FP16 scale per group of 128 weights:
```text
w_i = scale_g * t_i, t_i in {−1, 0, +1}
```
Ternary values carry log₂(3) ≈ 1.585 bits of information per weight. With one FP16 scale per group of 128, the effective storage is:
```text
b_eff ≈ log2(3) + 16/128 ≈ 1.585 + 0.125 ≈ 1.71 bits/weight
```
This gives an idealized **9.4× reduction** relative to FP16 for the ternary transformer layers. A small set of precision-sensitive supporting tensors remains in FP16, so the final Ternary Bonsai Image 4B diffusion transformer is **1.21 GB**, a 6.4x reduction from the 7.75 GB FP16 FLUX.2 Klein 4B transformer.
The ternary representation is applied to the matrix-heavy transformer layers, including Q / K / V projections, output projections, MLP linears, and the double-stream add-K / Q / V linears. Supporting tensors (less than 5% of the total parameters) such as modulation streams, embedders, output norm, and output projection remain FP16 for image quality and stability.
The CUDA deployment uses a Gemlite INT2 packed format. Ternary values are stored in 2-bit slots, with the fourth code unused. The model-level Bonsai representation is **1.21 GB**; the deployed CUDA pack is **1.54 GB** on disk due to runtime packing and alignment overhead in the current Gemlite path.
### Memory
| Format | Transformer size | Reduction | Ratio |
| :------------------------------ | ---------------: | --------: | -------: |
| FP16 FLUX.2 Klein 4B | 7.75 GB | — | 1.0× |
| **Ternary Bonsai Image 4B** | **1.21 GB** | **84.4%** | **6.4×** |
CUDA deployment:
| Component | Size |
| :--------------------------------- | ------: |
| Gemlite INT2 diffusion transformer | 1.54 GB |
| HQQ 4-bit text encoder | 2.84 GB |
| FP16 VAE | 0.17 GB |
| **Total payload** | **4.55 GB** |
At runtime, the text encoder is offloaded after prompt encoding. During denoising, the repeated image-generation loop is dominated by the compact ternary diffusion transformer and active image-generation components rather than the full payload.
Peak HBM at 1024² on RTX 3080 is ~6.8 GiB end-to-end (transformer + VAE + activation memory).
## Best Practices
- Sampler: FlowMatchEuler-discrete with 4 steps, guidance = 1.0, shift = 3.0. The model is designed for 4 steps; running more steps does not improve quality significantly and can introduce artifacts.
- Resolution: native 1024² is the design target. 512² works for quick previews.
- Aspect ratios: multiples of 32 are supported, including 832x1248 and 1248x832.
- Prompting: natural-language prompts. Negative prompts are not required.
- Runtime memory: the text encoder is offloaded after prompt encoding, so the denoising loop is memory-light.
## Quickstart
### Bonsai Studio (Linux / Windows)
The simplest path is the [Bonsai Image Demo repo](https://github.com/PrismML-Eng/Bonsai-Image-Demo), which sets up the full Bonsai Studio (FastAPI backend + Next.js frontend) and selects gemlite automatically on Linux / Windows:
```bash
git clone https://github.com/PrismML-Eng/Bonsai-Image-Demo.git
cd Bonsai-Image-Demo
./setup.sh
./scripts/download_model.sh # ternary is the default
./scripts/serve.sh
```
On Windows (PowerShell):
```powershell
Set-ExecutionPolicy -Scope CurrentUser RemoteSigned # one-time
.\setup.ps1
.\scripts\download_model.ps1
.\scripts\serve.ps1
```
### Python API (backend_gpu)
For inference without the studio frontend:
```python
from backend_gpu.server import build_pipeline
pipe = build_pipeline(model_id="prism-ml/bonsai-image-ternary-4B-gemlite-2bit")
image = pipe(
prompt="A bonsai tree in a quiet ceramic studio, soft morning light",
num_inference_steps=4,
guidance_scale=1.0,
height=1024,
width=1024,
).images[0]
image.save("bonsai.png")
```
## Throughput (CUDA / gemlite)
Warmed wall-clock per image, 4 denoising steps, guidance = 1.0, matched prompts and sampler settings.
| Platform | 512² (s) | 1024² (s) | Notes |
| :------------------------ | -------: | --------: | :------------------------------------------ |
| **A100** (Colab) | 1.1 | **2.8** | Ampere datacenter (40 GB) |
| **RTX PRO 6000 Blackwell** (Colab) | 1.0 | **2.1** | NVIDIA Blackwell, 96 GB VRAM |
| **RTX 3080** 10 GB | 1.4 | **4.5** | Ampere consumer; 6.8 GiB peak HBM at 1024² |
| **RTX 3060** 6 GB (laptop)| 3.3 | 17.5 | Ampere mobile; memory-bound at 1024² |
The sub-2-bit pack keeps generation viable on commodity GPUs at 1024². The RTX 3080 10 GB reaches 4.5 s/image, while the 6 GB laptop RTX 3060 is the memory-constrained tail.
## Benchmarks
Evaluated with matched generation settings across the comparison set on H100. GenEval uses the official 512x512 protocol. For HPSv3 and DPG-Bench, larger-backbone rows are evaluated at 1024x1024, while smaller-backbone rows are evaluated at their native 512x512 setting. Higher is better for all three benchmarks.
| Model | Transformer (GB) | GenEval | HPSv3 | DPG-Bench |
| :--------------------------- | ---------------: | ------: | -----: | --------: |
| **Bonsai Image · Ternary 4B**| **1.21** | **0.723** | **12.22** | **0.851** |
| **Bonsai Image · Binary 4B** | **0.93** | **0.671** | **11.15** | **0.822** |
| FLUX.2 Klein 4B | 7.75 | 0.819 | 12.84 | 0.853 |
| FLUX.1-schnell | 23.8 | 0.716 | 12.67 | 0.848 |
| SDXL | 5.14 | 0.300 | 10.05 | 0.740 |
| PixArt-Σ XL 2 | 1.20 | 0.541 | 11.93 | 0.769 |
| Stable Diffusion 1.5 | 1.72 | 0.396 | 4.20 | 0.601 |
| BK-SDM-Small | 0.98 | 0.297 | 3.05 | 0.559 |
The benchmark results show the intended quality-footprint trade-off. Ternary Bonsai Image 4B is the quality-oriented variant: at 1.21 GB, it sits very close to FLUX.2 Klein 4B across GenEval, HPSv3, and DPG-Bench while reducing the diffusion transformer footprint by 6.4x. The binary companion is the footprint-oriented variant, reducing the diffusion transformer below 1 GB while still delivering strong benchmark results.
Together, the Bonsai Image variants move the quality-footprint frontier: they bring modern diffusion-transformer behavior into a memory range previously occupied by much smaller, lower-capability models.
## Use Cases
- **Local creative tooling**: image generation directly on CUDA-equipped workstations and consumer GPUs
- **Private generation**: prompts and generated assets can remain in local or controlled environments
- **Rapid iteration**: lower local latency and no remote queue for iterative creative workflows
- **Commodity-GPU serving**: lower transformer footprint and reduced memory pressure for serving on NVIDIA GPUs
- **Windows and Linux deployment**: native paths through the same Gemlite deployment stack
- **Enterprise and controlled inference**: local or private environments for data residency and compliance-sensitive workflows
## Limitations
- Ternary Bonsai Image 4B is not bit-identical to the FP16 FLUX.2 Klein 4B model; it is a compact ternary-weight deployment designed to deliver similar practical behavior at much smaller size.
- Image-generation quality remains prompt- and workflow-dependent. Small text, fine details, object counts, and strict compositional constraints should be evaluated for the target use case.
- Current commodity inference stacks do not yet expose fully native ternary execution as a standard hardware path. This release uses practical Gemlite low-bit GEMM kernels on CUDA.
- After the diffusion transformer is made compact, other components such as the VAE can become more visible memory bottlenecks. The runtime mitigates this with text-encoder offload and tiled VAE decoding.
## Citation
```bibtex
@techreport{bonsaiimage4b,
title = {Bonsai Image 4B: Low-Bit Diffusion on Apple Silicon and Consumer GPUs},
author = {Prism ML},
year = {2026},
month = {May},
url = {https://prismml.com}
}
```
## Contact
For questions, feedback, or collaboration inquiries: **contact@prismml.com**