FLUX.2
FLUX.2 is built for real production workflows, delivering high-quality visuals while maintaining character, product, and style consistency across multiple reference images. It handles structured prompts, brand-safe layouts, complex text rendering, and detailed logos with precision. The model supports multi-reference inputs, editing at up to 4 megapixels, and generates both photorealistic scenes and highly stylized compositions. With a focus on reliability, FLUX.2 processes real-world creative tasks—such as infographics, product shots, and UI mockups—with exceptional stability. It represents Black Forest Labs’ open-core approach, pairing frontier-level capability with open-weight models that invite experimentation. Across its variants, FLUX.2 provides flexible options for studios, developers, and researchers who need scalable, customizable visual intelligence.
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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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FLUX.1 Krea
FLUX.1 Krea is an open source, guidance-distilled 12 billion-parameter diffusion transformer released by Krea in collaboration with Black Forest Labs, engineered to deliver superior aesthetic control and photorealism while eschewing the generic “AI look.” Fully compatible with the FLUX.1-dev ecosystem, it starts from a raw, untainted base model (flux-dev-raw) rich in world knowledge and employs a two-phase post-training pipeline, supervised fine-tuning on a hand-curated mix of high-quality and synthetic samples, followed by reinforcement learning from human feedback using opinionated preference data, to bias outputs toward a distinct style. By leveraging negative prompts during pre-training, custom loss functions for classifier-free guidance, and targeted preference labels, it achieves significant quality improvements with under one million examples, all without extensive prompting or additional LoRA modules.
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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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