Add local models via Git LFS (23 models, ~143 GB)

2026-06-28 17:25:21 +00:00 · 2026-06-28 17:25:21 +00:00 · 9b381fb3de
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--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/LICENSE
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--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/NOTICE.md
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/NOTICE.md
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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
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/README.md
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+---
+license: apache-2.0
+pipeline_tag: text-to-image
+tags:
+- 1-bit
+- gemlite
+- hqq
+- cuda
+- text-to-image
+- diffusion
+- flux
+- prismml
+- bonsai
+base_model:
+- prism-ml/bonsai-image-binary-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>Whitepaper</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-binary-4B-gemlite-1bit
+
+Binary weight (1-bit) text-to-image diffusion transformer deployment for NVIDIA GPUs
+
+> **0.93 GB transformer** | **8.3×** smaller than FP16 | **4.5 s / 1024²** on RTX 3080 | **2.7 s / 1024²** on A100 | runs natively on Linux and Windows
+
+## Highlights
+
+- **0.93 GB** diffusion transformer, down from **7.75 GB** for the FP16 FLUX.2 Klein 4B transformer
+- Binary {-1, +1} transformer weights with FP16 group-wise scaling in the matrix-heavy transformer layers (Q/K/V projections, output projections, MLP weights)
+- 4.09 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 1-bit](https://huggingface.co/prism-ml/bonsai-image-binary-4B-mlx-1bit) for Apple Silicon
+
+## Resources
+
+- **[Whitepaper](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 INT1 pack, binary values + FP16 group-wise scales                                      |
+| **Transformer size**  | **0.93 GB** model-level Bonsai representation; **1.08 GB** CUDA packed deployment size         |
+| Total payload         | **4.09 GB** CUDA deployment payload (transformer + 4-bit text encoder + FP16 VAE)              |
+| 1-bit coverage        | All 100 matmul-heavy linears in the 25 MMDiT blocks                                            |
+| Platforms             | Linux x86_64 + Windows native on NVIDIA GPUs                                                   |
+| License               | Apache 2.0                                                                                     |
+
+## Binary Weight Representation: 1-bit g128
+
+Each binary weight takes a value from {−1, +1} with one shared FP16 scale per group of 128 weights:
+
+```text
+w_i = scale_g * b_i,    b_i in {−1, +1}
+```
+
+Binary values carry exactly 1 bit of information per weight. With one FP16 scale per group of 128, the effective storage is
+
+```text
+b_eff ≈ 1 + 16/128 ≈ 1.125 bits/weight
+```
+
+This gives an idealized **14.2× reduction** relative to FP16 for the binary transformer layers. A small set of precision-sensitive supporting tensors remains in FP16, so the final 1-bit Bonsai Image 4B diffusion transformer is **0.93 GB**, an 8.3x reduction from the 7.75 GB FP16 FLUX.2 Klein 4B transformer.
+
+The binary 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 INT1 packed format. The model-level Bonsai representation is **0.93 GB**; the deployed CUDA pack is **1.08 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×     |
+| **1-bit Bonsai Image 4B**       | **0.93 GB**    | **88.0%** | **8.3×** |
+
+CUDA deployment:
+
+| Component                       | Size    |
+| :------------------------------ | ------: |
+| Gemlite INT1 diffusion transformer | 1.08 GB |
+| HQQ 4-bit text encoder          | 2.84 GB |
+| FP16 VAE                        | 0.17 GB |
+| **Total payload**    | **4.09 GB** |
+
+At runtime, the text encoder is offloaded after prompt encoding. During denoising, the repeated image-generation loop is dominated by the compact binary diffusion transformer and active image-generation components rather than the full payload.
+
+Peak HBM at 1024² on RTX 3080 is ~6.4 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
+BONSAI_VARIANT=binary ./scripts/download_model.sh
+BONSAI_VARIANT=binary ./scripts/serve.sh
+```
+
+On Windows (PowerShell):
+
+```powershell
+Set-ExecutionPolicy -Scope CurrentUser RemoteSigned   # one-time
+.\setup.ps1
+$env:BONSAI_VARIANT = 'binary'
+.\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-binary-4B-gemlite-1bit")
+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 sampler steps, guidance = 1.0, same prompts as the Mac and iPhone measurements. Linux + locally built gemlite kernels except where noted.
+
+| Platform                  | 512² (s) | 1024² (s) | Notes                                       |
+| :------------------------ | -------: | --------: | :------------------------------------------ |
+| **A100** (Colab)          | 1.0      | **2.7**   | Ampere datacenter (40 GB)                   |
+| **RTX PRO 6000 Blackwell** (Colab) | 1.0 | **1.8** | NVIDIA Blackwell, 96 GB VRAM                |
+| **RTX 3080** 10 GB        | 1.5      | **4.5**   | Ampere consumer; 6.4 GiB peak HBM at 1024²  |
+| **RTX 3060** 6 GB (laptop)| 4.4      | 24.8      | Ampere mobile; memory-bound at 1024²        |
+
+The sub-2-bit pack is what keeps generation viable on commodity GPUs at 1024² — the consumer RTX 3080 reaches 4.5 s/image while the 6 GB laptop 3060 is the slow tail (memory-pressure limited).
+
+## 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 · Binary 4B** | **0.93**        | **0.671** | **11.15** | **0.822** |
+| **Bonsai Image · Ternary 4B**| **1.21**        | **0.723** | **12.22** | **0.851** |
+| 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. 1-bit Bonsai Image 4B is the footprint-oriented variant: it reduces the diffusion transformer below 1 GB while still delivering strong GenEval, HPSv3, and DPG-Bench results. The ternary companion is the quality-oriented variant, using a slightly larger representation to achieve very close visual quality and prompt fidelity to the original FLUX.2 Klein 4B model.
+
+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
+
+- 1-bit Bonsai Image 4B is not bit-identical to the FP16 FLUX.2 Klein 4B model; it is a compact binary-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 binary 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**
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/assets/bonsai-logo.svg
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/assets/bonsai-logo.svg
@ -0,0 +1 @@
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--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/manifest.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/manifest.json
@ -0,0 +1,101 @@
+{
+  "model_version": "binary g128 (gemlite-int1 deployment for CUDA inference)",
+  "total_bytes": 4087241554,
+  "files": [
+    {
+      "remote_path": "model_index.json",
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+      "sha256": "ecb4735e37691a8733f62957fd6c548f841af40624905bb24fa639756153c8a1"
+    },
+    {
+      "remote_path": "LICENSE",
+      "size": 10174,
+      "sha256": "69849221bfb90053de2134ef5e6d540287b4b98062326492f1f96f5da685524b"
+    },
+    {
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+    },
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+    },
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+    },
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+      "size": 168120878,
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+    }
+  ]
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/model_index.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/model_index.json
@ -0,0 +1,4 @@
+{
+  "_class_name": "Flux2KleinPipeline",
+  "_diffusers_version": "0.37.0.dev0"
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/config.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/config.json
@ -0,0 +1,68 @@
+{
+  "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,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/qmodel.pt
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/qmodel.pt
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/added_tokens.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/added_tokens.json
@ -0,0 +1,28 @@
+{
+  "</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
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/chat_template.jinja
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/chat_template.jinja
@ -0,0 +1,89 @@
+{%- 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 %}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/merges.txt
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/merges.txt
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/special_tokens_map.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/special_tokens_map.json
@ -0,0 +1,31 @@
+{
+  "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
+  }
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/tokenizer.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/tokenizer.json
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/tokenizer_config.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/tokenizer_config.json
@ -0,0 +1,239 @@
+{
+  "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|>",
+      "lstrip": false,
+      "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|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "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
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "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,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "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>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "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|>"
+  ],
+  "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
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/vocab.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/text_encoder-hqq-4bit/tokenizer/vocab.json
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/config.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/config.json
@ -0,0 +1,27 @@
+{
+  "_class_name": "Flux2Transformer2DModel",
+  "_diffusers_version": "0.37.1",
+  "_name_or_path": "black-forest-labs/FLUX.2-klein-4B",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    32,
+    32,
+    32,
+    32
+  ],
+  "enable_time_sign_embed": false,
+  "eps": 1e-06,
+  "guidance_embeds": false,
+  "in_channels": 128,
+  "joint_attention_dim": 7680,
+  "mlp_ratio": 3.0,
+  "musubi_block_swap_device": "cpu",
+  "musubi_blocks_to_swap": 0,
+  "num_attention_heads": 24,
+  "num_layers": 5,
+  "num_single_layers": 20,
+  "out_channels": null,
+  "patch_size": 1,
+  "rope_theta": 2000,
+  "timestep_guidance_channels": 256
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/gemlite_autotune.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/gemlite_autotune.json
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/quantization_config.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/quantization_config.json
@ -0,0 +1,162 @@
+{
+  "format": "gemlite-int1-g128",
+  "bits": 1,
+  "group_size": 128,
+  "packing_bitwidth": 8,
+  "input_dtype": "fp16",
+  "output_dtype": "fp16",
+  "skip_patterns": [
+    "proj_out",
+    "x_embedder",
+    "context_embedder",
+    "time_text_embed",
+    "time_guidance_embed",
+    "norm_out",
+    "double_stream_modulation_img",
+    "double_stream_modulation_txt",
+    "single_stream_modulation"
+  ],
+  "quantized_count": 100,
+  "skipped_count": 9,
+  "quantized_fqns": [
+    "transformer_blocks.0.attn.to_q",
+    "transformer_blocks.0.attn.to_k",
+    "transformer_blocks.0.attn.to_v",
+    "transformer_blocks.0.attn.add_q_proj",
+    "transformer_blocks.0.attn.add_k_proj",
+    "transformer_blocks.0.attn.add_v_proj",
+    "transformer_blocks.0.attn.to_add_out",
+    "transformer_blocks.0.attn.to_out.0",
+    "transformer_blocks.0.ff.linear_in",
+    "transformer_blocks.0.ff.linear_out",
+    "transformer_blocks.0.ff_context.linear_in",
+    "transformer_blocks.0.ff_context.linear_out",
+    "transformer_blocks.1.attn.to_q",
+    "transformer_blocks.1.attn.to_k",
+    "transformer_blocks.1.attn.to_v",
+    "transformer_blocks.1.attn.add_q_proj",
+    "transformer_blocks.1.attn.add_k_proj",
+    "transformer_blocks.1.attn.add_v_proj",
+    "transformer_blocks.1.attn.to_add_out",
+    "transformer_blocks.1.attn.to_out.0",
+    "transformer_blocks.1.ff.linear_in",
+    "transformer_blocks.1.ff.linear_out",
+    "transformer_blocks.1.ff_context.linear_in",
+    "transformer_blocks.1.ff_context.linear_out",
+    "transformer_blocks.2.attn.to_q",
+    "transformer_blocks.2.attn.to_k",
+    "transformer_blocks.2.attn.to_v",
+    "transformer_blocks.2.attn.add_q_proj",
+    "transformer_blocks.2.attn.add_k_proj",
+    "transformer_blocks.2.attn.add_v_proj",
+    "transformer_blocks.2.attn.to_add_out",
+    "transformer_blocks.2.attn.to_out.0",
+    "transformer_blocks.2.ff.linear_in",
+    "transformer_blocks.2.ff.linear_out",
+    "transformer_blocks.2.ff_context.linear_in",
+    "transformer_blocks.2.ff_context.linear_out",
+    "transformer_blocks.3.attn.to_q",
+    "transformer_blocks.3.attn.to_k",
+    "transformer_blocks.3.attn.to_v",
+    "transformer_blocks.3.attn.add_q_proj",
+    "transformer_blocks.3.attn.add_k_proj",
+    "transformer_blocks.3.attn.add_v_proj",
+    "transformer_blocks.3.attn.to_add_out",
+    "transformer_blocks.3.attn.to_out.0",
+    "transformer_blocks.3.ff.linear_in",
+    "transformer_blocks.3.ff.linear_out",
+    "transformer_blocks.3.ff_context.linear_in",
+    "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",
+    "transformer_blocks.4.attn.add_v_proj",
+    "transformer_blocks.4.attn.to_add_out",
+    "transformer_blocks.4.attn.to_out.0",
+    "transformer_blocks.4.ff.linear_in",
+    "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",
+    "single_transformer_blocks.7.attn.to_out",
+    "single_transformer_blocks.8.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.8.attn.to_out",
+    "single_transformer_blocks.9.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.9.attn.to_out",
+    "single_transformer_blocks.10.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.10.attn.to_out",
+    "single_transformer_blocks.11.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.11.attn.to_out",
+    "single_transformer_blocks.12.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.12.attn.to_out",
+    "single_transformer_blocks.13.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.13.attn.to_out",
+    "single_transformer_blocks.14.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.14.attn.to_out",
+    "single_transformer_blocks.15.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.15.attn.to_out",
+    "single_transformer_blocks.16.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.16.attn.to_out",
+    "single_transformer_blocks.17.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.17.attn.to_out",
+    "single_transformer_blocks.18.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.18.attn.to_out",
+    "single_transformer_blocks.19.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.19.attn.to_out"
+  ],
+  "skipped": [
+    {
+      "fqn": "x_embedder",
+      "reason": "x_embedder"
+    },
+    {
+      "fqn": "context_embedder",
+      "reason": "context_embedder"
+    },
+    {
+      "fqn": "proj_out",
+      "reason": "proj_out"
+    },
+    {
+      "fqn": "time_guidance_embed.timestep_embedder.linear_1",
+      "reason": "time_guidance_embed"
+    },
+    {
+      "fqn": "time_guidance_embed.timestep_embedder.linear_2",
+      "reason": "time_guidance_embed"
+    },
+    {
+      "fqn": "double_stream_modulation_img.linear",
+      "reason": "double_stream_modulation_img"
+    },
+    {
+      "fqn": "double_stream_modulation_txt.linear",
+      "reason": "double_stream_modulation_txt"
+    },
+    {
+      "fqn": "single_stream_modulation.linear",
+      "reason": "single_stream_modulation"
+    },
+    {
+      "fqn": "norm_out.linear",
+      "reason": "norm_out"
+    }
+  ],
+  "pack_seconds": 128.37
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/state_dict.pt
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/transformer-gemlite-int1/state_dict.pt
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/vae/config.json
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/vae/config.json
@ -0,0 +1,41 @@
+{
+  "_class_name": "AutoencoderKLFlux2",
+  "_diffusers_version": "0.37.1",
+  "_name_or_path": "black-forest-labs/FLUX.2-klein-base-4B",
+  "act_fn": "silu",
+  "batch_norm_eps": 0.0001,
+  "batch_norm_momentum": 0.1,
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "decoder_block_out_channels": null,
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 32,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "patch_size": [
+    2,
+    2
+  ],
+  "sample_size": 1024,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}
--- a/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/vae/diffusion_pytorch_model.safetensors
+++ b/image/gemlite/prism-ml/bonsai-image-binary-4B-gemlite-1bit/vae/diffusion_pytorch_model.safetensors
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/.gitattributes
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/.gitattributes
@ -0,0 +1,36 @@
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+text_encoder-hqq-4bit/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/LICENSE
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/LICENSE
@ -0,0 +1,177 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+      outstanding shares, or (iii) beneficial ownership of such entity.
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+   END OF TERMS AND CONDITIONS
+
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/NOTICE.md
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/NOTICE.md
@ -0,0 +1,6 @@
+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
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/README.md
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/README.md
@ -0,0 +1,224 @@
+---
+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**
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/assets/bonsai-logo.svg
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--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/config.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/config.json
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--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/added_tokens.json
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+}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/chat_template.jinja
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/chat_template.jinja
@ -0,0 +1,89 @@
+{%- 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 %}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/merges.txt
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/merges.txt
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/special_tokens_map.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/special_tokens_map.json
@ -0,0 +1,31 @@
+{
+  "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
+  }
+}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/tokenizer.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/tokenizer.json
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/tokenizer_config.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/tokenizer_config.json
@ -0,0 +1,239 @@
+{
+  "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|>",
+      "lstrip": false,
+      "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|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "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
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "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,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "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>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "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|>"
+  ],
+  "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
+}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/vocab.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/text_encoder-hqq-4bit/tokenizer/vocab.json
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/config.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/config.json
@ -0,0 +1,27 @@
+{
+  "_class_name": "Flux2Transformer2DModel",
+  "_diffusers_version": "0.37.1",
+  "_name_or_path": "black-forest-labs/FLUX.2-klein-4B",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    32,
+    32,
+    32,
+    32
+  ],
+  "enable_time_sign_embed": false,
+  "eps": 1e-06,
+  "guidance_embeds": false,
+  "in_channels": 128,
+  "joint_attention_dim": 7680,
+  "mlp_ratio": 3.0,
+  "musubi_block_swap_device": "cpu",
+  "musubi_blocks_to_swap": 0,
+  "num_attention_heads": 24,
+  "num_layers": 5,
+  "num_single_layers": 20,
+  "out_channels": null,
+  "patch_size": 1,
+  "rope_theta": 2000,
+  "timestep_guidance_channels": 256
+}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/gemlite_autotune.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/gemlite_autotune.json
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/quantization_config.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/quantization_config.json
@ -0,0 +1,163 @@
+{
+  "format": "gemlite-int2-ternary-g128",
+  "bits": 2,
+  "group_size": 128,
+  "packing_bitwidth": 8,
+  "solver": "ternary",
+  "input_dtype": "fp16",
+  "output_dtype": "fp16",
+  "skip_patterns": [
+    "proj_out",
+    "x_embedder",
+    "context_embedder",
+    "time_text_embed",
+    "time_guidance_embed",
+    "norm_out",
+    "double_stream_modulation_img",
+    "double_stream_modulation_txt",
+    "single_stream_modulation"
+  ],
+  "quantized_count": 100,
+  "skipped_count": 9,
+  "quantized_fqns": [
+    "transformer_blocks.0.attn.to_q",
+    "transformer_blocks.0.attn.to_k",
+    "transformer_blocks.0.attn.to_v",
+    "transformer_blocks.0.attn.add_q_proj",
+    "transformer_blocks.0.attn.add_k_proj",
+    "transformer_blocks.0.attn.add_v_proj",
+    "transformer_blocks.0.attn.to_add_out",
+    "transformer_blocks.0.attn.to_out.0",
+    "transformer_blocks.0.ff.linear_in",
+    "transformer_blocks.0.ff.linear_out",
+    "transformer_blocks.0.ff_context.linear_in",
+    "transformer_blocks.0.ff_context.linear_out",
+    "transformer_blocks.1.attn.to_q",
+    "transformer_blocks.1.attn.to_k",
+    "transformer_blocks.1.attn.to_v",
+    "transformer_blocks.1.attn.add_q_proj",
+    "transformer_blocks.1.attn.add_k_proj",
+    "transformer_blocks.1.attn.add_v_proj",
+    "transformer_blocks.1.attn.to_add_out",
+    "transformer_blocks.1.attn.to_out.0",
+    "transformer_blocks.1.ff.linear_in",
+    "transformer_blocks.1.ff.linear_out",
+    "transformer_blocks.1.ff_context.linear_in",
+    "transformer_blocks.1.ff_context.linear_out",
+    "transformer_blocks.2.attn.to_q",
+    "transformer_blocks.2.attn.to_k",
+    "transformer_blocks.2.attn.to_v",
+    "transformer_blocks.2.attn.add_q_proj",
+    "transformer_blocks.2.attn.add_k_proj",
+    "transformer_blocks.2.attn.add_v_proj",
+    "transformer_blocks.2.attn.to_add_out",
+    "transformer_blocks.2.attn.to_out.0",
+    "transformer_blocks.2.ff.linear_in",
+    "transformer_blocks.2.ff.linear_out",
+    "transformer_blocks.2.ff_context.linear_in",
+    "transformer_blocks.2.ff_context.linear_out",
+    "transformer_blocks.3.attn.to_q",
+    "transformer_blocks.3.attn.to_k",
+    "transformer_blocks.3.attn.to_v",
+    "transformer_blocks.3.attn.add_q_proj",
+    "transformer_blocks.3.attn.add_k_proj",
+    "transformer_blocks.3.attn.add_v_proj",
+    "transformer_blocks.3.attn.to_add_out",
+    "transformer_blocks.3.attn.to_out.0",
+    "transformer_blocks.3.ff.linear_in",
+    "transformer_blocks.3.ff.linear_out",
+    "transformer_blocks.3.ff_context.linear_in",
+    "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",
+    "transformer_blocks.4.attn.add_v_proj",
+    "transformer_blocks.4.attn.to_add_out",
+    "transformer_blocks.4.attn.to_out.0",
+    "transformer_blocks.4.ff.linear_in",
+    "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",
+    "single_transformer_blocks.7.attn.to_out",
+    "single_transformer_blocks.8.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.8.attn.to_out",
+    "single_transformer_blocks.9.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.9.attn.to_out",
+    "single_transformer_blocks.10.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.10.attn.to_out",
+    "single_transformer_blocks.11.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.11.attn.to_out",
+    "single_transformer_blocks.12.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.12.attn.to_out",
+    "single_transformer_blocks.13.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.13.attn.to_out",
+    "single_transformer_blocks.14.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.14.attn.to_out",
+    "single_transformer_blocks.15.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.15.attn.to_out",
+    "single_transformer_blocks.16.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.16.attn.to_out",
+    "single_transformer_blocks.17.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.17.attn.to_out",
+    "single_transformer_blocks.18.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.18.attn.to_out",
+    "single_transformer_blocks.19.attn.to_qkv_mlp_proj",
+    "single_transformer_blocks.19.attn.to_out"
+  ],
+  "skipped": [
+    {
+      "fqn": "x_embedder",
+      "reason": "x_embedder"
+    },
+    {
+      "fqn": "context_embedder",
+      "reason": "context_embedder"
+    },
+    {
+      "fqn": "proj_out",
+      "reason": "proj_out"
+    },
+    {
+      "fqn": "time_guidance_embed.timestep_embedder.linear_1",
+      "reason": "time_guidance_embed"
+    },
+    {
+      "fqn": "time_guidance_embed.timestep_embedder.linear_2",
+      "reason": "time_guidance_embed"
+    },
+    {
+      "fqn": "double_stream_modulation_img.linear",
+      "reason": "double_stream_modulation_img"
+    },
+    {
+      "fqn": "double_stream_modulation_txt.linear",
+      "reason": "double_stream_modulation_txt"
+    },
+    {
+      "fqn": "single_stream_modulation.linear",
+      "reason": "single_stream_modulation"
+    },
+    {
+      "fqn": "norm_out.linear",
+      "reason": "norm_out"
+    }
+  ],
+  "pack_seconds": 150.48
+}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/state_dict.pt
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/transformer-gemlite-int2/state_dict.pt
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/vae/config.json
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/vae/config.json
@ -0,0 +1,41 @@
+{
+  "_class_name": "AutoencoderKLFlux2",
+  "_diffusers_version": "0.37.1",
+  "_name_or_path": "black-forest-labs/FLUX.2-klein-base-4B",
+  "act_fn": "silu",
+  "batch_norm_eps": 0.0001,
+  "batch_norm_momentum": 0.1,
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "decoder_block_out_channels": null,
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 32,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "patch_size": [
+    2,
+    2
+  ],
+  "sample_size": 1024,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}
--- a/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/vae/diffusion_pytorch_model.safetensors
+++ b/image/gemlite/prism-ml/bonsai-image-ternary-4B-gemlite-2bit/vae/diffusion_pytorch_model.safetensors
				`@ -0,0 +1 @@`
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