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This software is copyright 2026-present Prism ML, Inc. It is available under the Apache 2.0 license.
If you publicly deploy or redistribute this software, we would appreciate attribution such as: "Created using Bonsai Image by Prism ML."
This software is built from FLUX.2 [klein] 4B, Copyright 2026 Black Forest Labs, which is available under the Apache 2.0 License: https://huggingface.co/black-forest-labs/FLUX.2-klein-4B/blob/main/LICENSE.md
The text encoder is built from Qwen3-4B, Copyright 2024 Alibaba Cloud, which is available under the Apache 2.0 License: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE

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---
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
---
<p align="center">
<img src="./assets/bonsai-logo.svg" width="280" alt="Bonsai Image">
</p>
<p align="center">
<a href="https://prismml.com"><b>Prism ML Website</b></a> &nbsp;|&nbsp;
<a href="https://github.com/PrismML-Eng/Bonsai-Image-Demo/blob/main/bonsai-image-4b-whitepaper.pdf"><b>White Paper</b></a> &nbsp;|&nbsp;
<a href="https://github.com/PrismML-Eng/Bonsai-Image-Demo"><b>Demo &amp; Examples</b></a> &nbsp;|&nbsp;
<a href="https://discord.gg/prismml"><b>Discord</b></a>
</p>
# 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**

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{
"_class_name": "Flux2KleinPipeline",
"_diffusers_version": "0.37.0.dev0"
}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "float16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
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"layer_types": [
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"max_position_embeddings": 40960,
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{
"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
"<think>": 151667,
"<tool_call>": 151657,
"<tool_response>": 151665,
"<|box_end|>": 151649,
"<|box_start|>": 151648,
"<|endoftext|>": 151643,
"<|file_sep|>": 151664,
"<|fim_middle|>": 151660,
"<|fim_pad|>": 151662,
"<|fim_prefix|>": 151659,
"<|fim_suffix|>": 151661,
"<|im_end|>": 151645,
"<|im_start|>": 151644,
"<|image_pad|>": 151655,
"<|object_ref_end|>": 151647,
"<|object_ref_start|>": 151646,
"<|quad_end|>": 151651,
"<|quad_start|>": 151650,
"<|repo_name|>": 151663,
"<|video_pad|>": 151656,
"<|vision_end|>": 151653,
"<|vision_pad|>": 151654,
"<|vision_start|>": 151652
}

89
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151645": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151646": {
"content": "<|object_ref_start|>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151647": {
"content": "<|object_ref_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151648": {
"content": "<|box_start|>",
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"normalized": false,
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"single_word": false,
"special": true
},
"151649": {
"content": "<|box_end|>",
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"rstrip": false,
"single_word": false,
"special": true
},
"151650": {
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"special": true
},
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151652": {
"content": "<|vision_start|>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151653": {
"content": "<|vision_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151654": {
"content": "<|vision_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
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"content": "<tool_call>",
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},
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"content": "</tool_call>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
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"special": false
},
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"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151665": {
"content": "<tool_response>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
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"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": false
},
"151667": {
"content": "<think>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151668": {
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
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"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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{
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"_name_or_path": "black-forest-labs/FLUX.2-klein-4B",
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32
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{
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"skipped_count": 9,
"quantized_fqns": [
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"transformer_blocks.0.attn.to_k",
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"transformer_blocks.3.ff_context.linear_out",
"transformer_blocks.4.attn.to_q",
"transformer_blocks.4.attn.to_k",
"transformer_blocks.4.attn.to_v",
"transformer_blocks.4.attn.add_q_proj",
"transformer_blocks.4.attn.add_k_proj",
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"transformer_blocks.4.ff.linear_out",
"transformer_blocks.4.ff_context.linear_in",
"transformer_blocks.4.ff_context.linear_out",
"single_transformer_blocks.0.attn.to_qkv_mlp_proj",
"single_transformer_blocks.0.attn.to_out",
"single_transformer_blocks.1.attn.to_qkv_mlp_proj",
"single_transformer_blocks.1.attn.to_out",
"single_transformer_blocks.2.attn.to_qkv_mlp_proj",
"single_transformer_blocks.2.attn.to_out",
"single_transformer_blocks.3.attn.to_qkv_mlp_proj",
"single_transformer_blocks.3.attn.to_out",
"single_transformer_blocks.4.attn.to_qkv_mlp_proj",
"single_transformer_blocks.4.attn.to_out",
"single_transformer_blocks.5.attn.to_qkv_mlp_proj",
"single_transformer_blocks.5.attn.to_out",
"single_transformer_blocks.6.attn.to_qkv_mlp_proj",
"single_transformer_blocks.6.attn.to_out",
"single_transformer_blocks.7.attn.to_qkv_mlp_proj",
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