FLUX 3
FLUX 3 is a multimodal foundation model that jointly learns from images, video, and audio within one unified architecture, building a representation of how objects hold together, how things move, and how events sound. Built on the Self-Flow approach, it aligns multimodal generation and understanding in the same backbone so each modality constrains the others, sound matches impact, motion follows physical properties, and future events follow from the past. FLUX 3 can mix modalities and jointly generate images, video, and native audio from text prompts or references such as images, video, and audio. Its video capabilities include text-to-video, image-to-video animation, video-to-video transformation, generative video-and-audio continuation, keyframe-controlled transitions, multilingual dialogue, animated typography, diverse styles and aspect ratios, and agentic chaining into longer multi-shot sequences.
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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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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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Qwen-Image
Qwen-Image is a multimodal diffusion transformer (MMDiT) foundation model offering state-of-the-art image generation, text rendering, editing, and understanding. It excels at complex text integration, seamlessly embedding alphabetic and logographic scripts into visuals with typographic fidelity, and supports diverse artistic styles from photorealism to impressionism, anime, and minimalist design. Beyond creation, it enables advanced image editing operations such as style transfer, object insertion or removal, detail enhancement, in-image text editing, and human pose manipulation through intuitive prompts. Its built-in vision understanding tasks, including object detection, semantic segmentation, depth and edge estimation, novel view synthesis, and super-resolution, extend its capabilities into intelligent visual comprehension. Qwen-Image is accessible via popular libraries like Hugging Face Diffusers and integrates prompt-enhancement tools for multilingual support.
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