MODEL PROFILE

Bonsai-Image-ternary-4B

litert-community · Bonsai-Image-ternary-4B

LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). PrismML Bonsai Image 4B — a ternary-weight diffusion transformer built on the FLUX.2-klein-4B architecture — converted to LiteRT for on-device text-to-image. The whole pipeline runs in three fixed-shape `.tflite` graphs; the host does only tokenization, the FlowMatch-Euler loop, and latent unpatchify (`generate.py` in this repo, ~150 lines, no torch and no diffusers). Smallest working set: **3.97 GiB** (int4 text encoder). Output is 512×512 with the default pair of graphs, or 256×256 with the `dit_256` / `vae_dec_256` pair (`generate.py --size 256`; `variants.256` in `pipeline_meta.json`) — the text encoder, tokenizer and latent constants are shared by both sizes; 256 prompt tokens and 4 sampling steps by default (the model is step-distilled; more steps also work).

Image generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Image
Model weightsNot verified
Profile updated2026/10/30 20:50:42
OVERVIEW

Model overview

LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). PrismML Bonsai Image 4B — a ternary-weight diffusion transformer built on the FLUX.2-klein-4B architecture — converted to LiteRT for on-device text-to-image. The whole pipeline runs in three fixed-shape `.tflite` graphs; the host does only tokenization, the FlowMatch-Euler loop, and latent unpatchify (`generate.py` in this repo, ~150 lines, no torch and no diffusers). Smallest working set: **3.97 GiB** (int4 text encoder). Output is 512×512 with the default pair of graphs, or 256×256 with the `dit_256` / `vae_dec_256` pair (`generate.py --size 256`; `variants.256` in `pipeline_meta.json`) — the text encoder, tokenizer and latent constants are shared by both sizes; 256 prompt tokens and 4 sampling steps by default (the model is step-distilled; more steps also work).

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。