1 Bit Has Only Two States — Is It a Joke to Use It for Drawing?

title: "1 Bit Has Only Two States — Is It a Joke to Use It for Drawing?" description: "Bonsai Image 4B compresses the FLUX.2 drawing model from 7.75GB to 0.93GB, generating images in 9 seconds on iPhone, but the damage of 1-bit quantization to the latent space of diffusion models is an unavoidable cost" tags: ["Bonsai", "On-Device AI", "1-bit Quantization", "FLUX", "PrismML"]

1 bit, only two states: -1 and +1.

Using this precision to represent a 4-billion-parameter diffusion Transformer and then making it draw — it sounds like doing weather forecasting with an abacus.

But the PrismML team at Caltech made it happen. Bonsai Image 4B, based on FLUX.2 Klein 4B, has a model size of 0.93GB after 1-bit quantization, an 8.3x compression. Loaded on iPhone, it generates 512×512 images in 9 seconds. Licensed under Apache 2.0, commercially usable.

This is the first 4B-level drawing model that can run directly on iPhone.

But "can run" and "draws well" are two different things. First, let's see how it actually achieves it, then see if the cost is acceptable to you.

From 7.75GB to 0.93GB: Not Quantization, but Retraining

Most people understand model compression as taking a trained FP16 model, quantizing it with INT8 or INT4, reducing size, and losing precision.

Bonsai Image 4B doesn't do that.

PrismML's approach is to train the entire network from scratch at 1-bit precision. Weights only have two states {-1, +1}, combined with FP16 group-wise scaling factors, resulting in an effective precision of about 1.125 bits/weight.

Key difference: post-hoc quantization "cuts precision" on a full-precision model, while 1-bit native training lets the model adapt to low-precision survival from the start. Like a person learning to write with their left hand from childhood vs forced to switch after breaking their right hand — the former is more natural.

But not all parameters are 1-bit. About 5% of precision-sensitive tensors (projection layers) remain in FP16. This 5% is the lifeline for image quality — if all were 1-bit, the image would likely collapse.

Two Versions: 1-bit Extreme Compression vs Ternary Quality Balance

PrismML officially recommends the Ternary version — 0.28GB more, significantly better quality, yet the size is still only 1/6.4 of the original.

The 1-bit version is a technical extreme demonstration — "Look, 1-bit can also draw." The Ternary version is the choice for daily use.

Hands-on Test: It Can Draw, But How Well?

GIGAZINE conducted a comparative test with the same prompt, all three models generating images from the same prompt:

• 1-bit Bonsai Image 4B: Main structure correct, details feel smudged, colors flat
• Ternary Bonsai Image 4B: Close to original quality, slight loss in detail texture, good color reproduction
• FP16 FLUX.2 Klein 4B: Full precision, richest details

The Ternary version shows little visual difference from the original in most scenes, but significantly degrades in the following scenarios:

• High-frequency details: hair, fabric textures, small text — these require high-precision weights to accurately reproduce
• Color transitions: gradient sky, skin tone transitions — the discreteness of 1-bit results in insufficiently smooth color steps
• Complex compositions: multiple people, occlusions, fine hands — high information density, low-precision weights can't handle

In simple terms: Drawing stickers, avatars, pixel art — sufficient. Drawing commercial illustrations, product images, fine portraits — not enough.

On-Device Drawing Landscape Comparison

Bonsai's positioning is clear: the highest quality among on-device drawing, the most private among cloud drawing.

SD Turbo Mobile can also run on phone, but its image quality is not on the same level as the FLUX series. FLUX.1 Schnell has good quality but must run on cloud. Bonsai Ternary at the intersection of "local + FLUX-level quality" currently has no competitor.

Cost Awareness: 1-bit Is Not a Free Lunch

1. The latent space of diffusion models is damaged.

This is the sharpest criticism from the Reddit r/LocalLLaMA community: 1-bit extreme compression destroys the continuity of the latent space of diffusion models. The working principle of diffusion models is to gradually denoise in a continuous latent space; 1-bit quantization turns this continuous space into a discrete one — like turning a smooth curve into a series of steps.

For LLMs, the impact of 1-bit quantization is relatively controllable (language is discrete). But for diffusion models, the continuity of the latent space is a core assumption — breaking it destroys the model's ability to gradually transition "from noise to clear image."

1. Prompt adherence decreases.

Low-precision weights have limited understanding of complex prompts. "An orange cat wearing a red bow tie sitting on a blue sofa" — the subject (cat) is fine, but attributes (orange, red, blue) may be confused. The longer and more complex the prompt, the more adherence drops.

1. Only supports text-to-image.

No image-to-image (img2img), no ControlNet, no Inpainting. These features require additional model components and higher precision, which is difficult to guarantee quality under 1-bit quantization.

1. Not available on Android for now.

Bonsai Studio App currently only has an iOS version, relying on Apple Silicon's MLX framework and Neural Engine acceleration. Android on-device deployment requires adapting to different NPU architectures (Snapdragon Hexagon, MediaTek APU), which is a significant workload.

1. 1024×1024 takes 34 seconds.

Native resolution 1024×1024 takes 34 seconds on iPhone 17 Pro Max. For "casual drawing for fun," 9 seconds (512²) is acceptable, but 34 seconds tests patience. Moreover, at 1024², active memory is 1.95GB, putting considerable pressure on iPhone's memory.

Related Technology: Bonsai 8B LLM

PrismML didn't just make a drawing model. Their 1-bit Bonsai 8B LLM was released earlier:

• 8.2B parameters, 1.15 GB size, 14× compression
• 44 tokens/s on iPhone 17 Pro Max
• In LLM benchmarks at the same parameter level, matches or beats competitors 14x its size

The quality loss of 1-bit LLM is much smaller than that of drawing models — because language itself is discrete, the damage of 1-bit quantization to LLMs is far less than to diffusion models. This also confirms from the side: 1-bit quantization is more suitable for LLMs, and is a more aggressive bet for drawing models.

Who Should Use It, Who Shouldn't

Should use:

• iOS developers who want to integrate offline drawing functionality into their apps
• Creators making stickers, avatars, pixel art, etc., with low precision requirements
• Scenarios with strict privacy requirements where data cannot go to the cloud
• Tech enthusiasts who want to experience on-device AI drawing

Should not use:

• Commercial illustration or product image scenarios with professional-level quality requirements
• Workflows requiring image-to-image, ControlNet, Inpainting
• Android users (for now)
• Scenarios needing high resolution (1024²+) and sensitive to generation speed

The True Meaning of On-Device AI Is Not to Replace the Cloud

The value of Bonsai Image 4B is not that "it draws better than the cloud" — it doesn't. It lies in:

Some images you simply don't want the cloud to see.

Concept images of trade secrets, personal private photo edits, internal product prototype designs — these content risk leakage when uploaded to the cloud. Bonsai lets you complete these tasks on your phone, with data never leaving the device.

This is the true positioning of on-device AI: not a replacement for cloud AI, but a supplement for scenarios that cloud AI dares not touch.

1-bit quantization is a means to achieve this goal, not the end. The Ternary version finds a better balance between quality and size — if you're not chasing the technological limit, choose Ternary.

PrismML's core insight is worth remembering: intelligence density is more important than sheer parameter count. A 1.21GB model that covers 80% of daily drawing needs is more practically valuable than a 7.75GB model that must run on the cloud.

But don't forget that 20% — high-frequency details, color transitions, complex compositions — these are the dead zones of 1-bit quantization, not a "future improvement" issue, but a structural limitation of discrete weights in a continuous latent space.

Related Links:

• PrismML Official: https://prismml.com
• Bonsai Image 4B Release: https://prismml.com/news/bonsai-image-4b
• GitHub: https://github.com/PrismML-Eng
• Bonsai Studio App: Search "Bonsai Studio" on App Store
• HuggingFace (Ternary): https://huggingface.co/prism-ml/bonsai-image-ternary-4B-mlx-2bit