FLUX.2 [klein] 9B KV is an optimized variant of FLUX.2 [klein] 9B with KV cache support for accelerated multi reference editing . This variant caches key value pairs from reference images during the first denoising step, eliminating redundant computation in subsequent steps for significantly faster multi image editing workflows. For more information about FLUX.2 [klein], please read our blog post. Key Features 1. KV Cache Optimization : Reference image KV pairs are computed once and cached, reducing computation and speeding up inference by up to 2.5 times for multi reference editing tasks. 2. All capabilities of FLUX.2 [klein] 9B: sub second generation, text to image, and multi reference editing in a single unified model. 3. Ideal for interactive applications and real time editing pipelines where the same reference images are used across multiple generations. 4. 9B flow model with 8B Qwen3 text embedder, step distilled to 4 inference steps. 5. Available for non commercial use. How KV Caching Works In standard image editing, reference image tokens are processed at every denoising step. With KV caching: Step 0 : Full forward pass processes reference tokens and extracts their key valu…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy