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ease
features
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support

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Description

The Bonsai Image Ternary 4B MLX 2-bit is a text-to-image diffusion transformer specifically designed for deployment on Apple Silicon, emphasizing quality in its Bonsai Image variant. This model utilizes ternary weights of {−1, 0, +1} along with FP16 group-wise scaling in its transformer layers, which encompass Q/K/V projections, output projections, and MLP weights. Notably, it reduces the size of the FLUX.2 Klein 4B transformer from 7.75 GB FP16 to just 1.21 GB, achieving a remarkable 6.4× smaller footprint while maintaining visual quality and fidelity to prompts akin to the original model. The deployment package for Apple Silicon is 3.88 GB, which includes the MLX 2-bit diffusion transformer, a 4-bit Qwen3-4B text encoder, and an FP16 Flux2 VAE. After the text encoder handles prompt encoding, it is offloaded to ensure that only the compact transformer and VAE remain in memory during the denoising loop. Furthermore, the model employs a 4-step FlowMatchEuler sampler with guidance set at 1.0 and a shift of 3.0, eliminating the need for CFG and negative prompts, thus streamlining the generation process for enhanced user experience. Overall, this innovation represents a significant advancement in efficient and effective image generation technology.

Description

Karlo serves as an innovative model designed to create images from textual descriptions. It enhances the impressive unCLIP architecture developed by OpenAI by improving the conventional super-resolution model, enabling it to capture complex details at an impressive resolution of 256px, while effectively reducing noise through a limited number of denoising iterations. In developing Karlo, we undertook a comprehensive training regimen that began from the ground up, leveraging a substantial dataset of 115 million image-text pairs, which included COYO-100M, CC3M, and CC12M. For the Prior and Decoder sections, we utilized the advanced ViT-L/14 text encoder sourced from OpenAI's CLIP library. To boost performance, we implemented a notable alteration to the original unCLIP design; rather than using a trainable transformer in the decoder, we opted to incorporate the text encoder from ViT-L/14, thereby enhancing the model's capability. This strategic choice not only streamlined the architecture but also contributed to improved image quality and fidelity.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

B^ DISCOVER
B^ EDIT

Integrations

B^ DISCOVER
B^ EDIT

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

PrismML

Founded

2026

Country

United States

Website

prismml.com

Vendor Details

Company Name

Kakao Brain

Founded

2017

Country

South Korea

Website

github.com/kakaobrain/karlo

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