Today we’re releasing Bonsai Image 4B, a family of compact image-generation models designed to run high-quality diffusion inference on local hardware: from laptops to phones.

Bonsai Image 4B comes in two variants:

• 1-bit Bonsai Image 4B uses binary {−1, +1} transformer weights with an FP16 group-wise scaling factor, giving 1.125 effective bits per weight. It targets maximum compression and is the right fit when memory pressure, bandwidth, and the deployment footprint are the primary constraints.
• Ternary Bonsai Image 4B uses {−1, 0, +1} transformer weights with an FP16 group-wise scaling factor, giving 1.71 effective bits per weight. The additional zero state gives the model more representational flexibility, improving visual quality and prompt fidelity while remaining extremely compact.

The result is a new deployment regime for image generation: capable outputs, open weights, and practical local inference on devices that were previously out of reach for this class of model. To our knowledge, Bonsai Image 4B is the first image model in its parameter class to run directly on an iPhone.

Local image generation starts with a hard constraint: the model has to fit within the device’s memory budget.

For a 4B-class image model, the diffusion transformer is the largest part of the model and the part that runs repeatedly during generation. Each denoising step invokes the transformer again, so transformer size directly shapes memory pressure, bandwidth demand, and local inference speed.

Bonsai Image 4B is built from the FLUX.2 Klein 4B. It keeps the architecture intact but changes how the transformer weights are represented. By moving those weights into binary and ternary form, Bonsai reduces the part of the image pipeline that matters most for local deployment.

The binary layers provide roughly a 14x reduction relative to full-precision transformer weights. A small set of precision-sensitive supporting tensors (~5%), called the projection layers, remains in FP16 so the final 1-bit Bonsai Image 4B transformer is 0.93 GB: an 8.3x reduction from the 7.75 GB full-precision FLUX.2 Klein 4B. 

The ternary variant follows the same structure. Its ternary layers provide roughly a 10x reduction and the final Ternary Bonsai Image 4B transformer is 1.21 GB, a 6.4x reduction from the full-precision transformer. It is slightly larger than the 1-bit model, but the additional zero state improves visual quality and prompt fidelity.

Related:
#1472773
#1463199