FLUX.2 [klein]
FLUX.2 [klein] is the fastest member of the FLUX.2 family of AI image models, designed to unify text-to-image generation, image editing, and multi-reference composition into a single compact architecture that delivers state-of-the-art visual quality at sub-second inference times on modern GPUs, making it suitable for real-time and latency-critical applications. It supports both generation from prompts and editing existing images with references, combining high diversity and photorealistic outputs with extremely low latency so users can iterate quickly in interactive workflows; distilled versions can produce or edit images in under 0.5 seconds on capable hardware, and even compact 4 B variants run on consumer GPUs with about 8–13 GB of VRAM. The FLUX.2 [klein] family comes in different variants, including distilled and base versions at 9 B and 4 B parameter scales, giving developers options for local deployment, fine-tuning, research, and production integration.
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Bonsai 27B
Bonsai 27B is the new multimodal flagship of the Bonsai family and the first 27B-class model to run on a phone. Based on Qwen3.6 27B, it brings a new capability tier to local devices; multi-step reasoning, structured tool calls, vision tasks, and computer-use agentic loops that stay coherent across many steps. Bonsai 27B comes in two variants. Ternary Bonsai 27B uses ternary weights with FP16 group-wise scaling, giving 1.71 effective bits per weight and a 5.9 GB footprint for the quality-oriented laptop-class version. 1-bit Bonsai 27B uses binary weights with the same group-wise scaling, giving 1.125 effective bits per weight and a 3.9 GB footprint that fits within the memory budget of an iPhone 17 Pro. Both variants run end-to-end across the language network, embeddings, attention, MLPs, and LM head with no higher-precision escape hatches. They are multimodal, with a compact 4-bit vision tower, so on-device workflows can understand screenshots, documents, and camera input.
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ERNIE-Image
ERNIE-Image is an open text-to-image generation model developed by Baidu, designed to deliver high-quality visuals with strong instruction accuracy and controllability. It is built on a single-stream Diffusion Transformer (DiT) architecture with around 8 billion parameters, allowing it to achieve state-of-the-art performance among open-weight image models while remaining relatively efficient. The model includes a built-in prompt enhancement system that expands simple user inputs into richer, structured descriptions, improving the quality and consistency of generated images. ERNIE-Image is optimized for complex instruction following, enabling accurate rendering of text within images, structured layouts, and multi-element compositions, making it particularly suitable for use cases like posters, comics, and multi-panel designs. It supports multilingual prompts, including English, Chinese, and Japanese, broadening accessibility and usability across regions.
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FLUX 3 Image Klein
FLUX 3 Image Klein is an image-generation model from Black Forest Labs for developers and creative teams. It creates images from text prompts and supports instruction-based image editing, reference-image guidance with up to 10 images, and output at resolutions up to 4K. The 5.6-billion-parameter model is available through the Black Forest Labs API and as downloadable weights for local inference. Base and step-distilled variants support different workflows, with additional quantized formats for local deployment. Use it for creative production, design exploration, prototyping, and image-generation features in applications. Local and commercial use are subject to the model license. API usage is charged per generated image, with pricing varying by resolution.
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