Kelix-DiT

Paper | Citation

Introduction

Kelix-DiT is the pretraining-stage checkpoint of the diffusion-based image de-tokenizer of Kelix, a fully discrete autoregressive unified multimodal model. It renders high-fidelity 1024×1024 images from the semantic hidden states produced by the Kelix unified LLM, closing the long-standing understanding gap between discrete and continuous visual representations.

Kelix is built on a modular Tokenizer → LLM → Detokenizer pipeline:

• Kelix-Tok — a multi-token vision tokenizer that decomposes each patch embedding into N parallel discrete codes, expanding the coding capacity exponentially while keeping the LLM context length unchanged via sum pooling on the encoder side.
• Kelix-LLM — a unified Qwen3-8B backbone trained with a Next-Block Prediction (NBP) paradigm.
• Kelix-DiT (this checkpoint) — a diffusion-based image de-tokenizer that turns the LLM's hidden states into high-resolution images.

Kelix achieves state-of-the-art results among comparable-scale unified models on both understanding and generation benchmarks; notably, it reaches 86.7 on OCRBench, matching continuous-feature VLMs and surpassing the previous best discrete model by +23%.

Model Overview

Training (Pretraining Stage)

This checkpoint corresponds to the Pretraining Stage of Kelix-DiT:

• Data: inverted large-scale image-caption pairs.
• Condition image: resized to 504×504 for Kelix-LLM hidden-state extraction.
• Target image: resized to 1024×1024 and encoded into a 32×32 latent via the frozen DC-AE.
• Aspect ratio: source images constrained to 0.67–1.5 to preserve native composition.
• Optimization: all DiT parameters are updated; the Kelix LLM, DC-AE encoder, and DC-AE decoder are frozen.

This stage equips the de-tokenizer with robust semantic–image alignment and strong generalization to diverse, unseen scenarios. The companion SFT-stage checkpoint is released as OpenOneRec/Kelix-SFT, which further enhances instruction-following and fine-grained control.

Usage

Kelix-DiT is designed to be driven by the hidden states of the Kelix unified LLM. A typical generation pipeline is:

1. Feed a text prompt (and optional images) into the Kelix LLM, which autoregressively produces last hidden states {h_*} for the image blocks.
2. Use {h_*} as the semantic condition (y-embedder input) for Kelix-DiT.
3. Run flow-matching denoising in the DC-AE latent space (32×32) and decode with DC-AE to obtain a 1024×1024 image.

⚠️ This checkpoint is a component of the Kelix pipeline, not a standalone text-to-image model. To generate images end-to-end you also need the Kelix unified LLM and the frozen DC-AE-F32C32 VAE.

Key Results

Kelix (8B, with Kelix-DiT) image-generation results:

Highlights (see the technical report for full tables):

• GenEval 87.6 — SOTA among discrete-tokenization unified models, +0.6 over the 27B Qwen-Image.
• WISE 57.0 — 2nd only to Nextflow (7B, 59.0), beating all continuous-tokenization unified models and larger dedicated T2I models (e.g., FLUX.1-dev 12B, 50.0).
• DPG-Bench 85.5 — competitive with the SOTA X-Omni (7B, 87.7) without using reinforcement learning.

Citation

If you find Kelix useful, please cite our technical report.

@article{kelix2026,
  title   = {Kelix Technique Report: Closing the Understanding Gap of Discrete Tokens in Unified Multimodal Models},
  author  = {Kuaishou Technology},
  journal = {arXiv preprint arXiv:2602.09843},
  year    = {2026},
  url     = {https://arxiv.org/abs/2602.09843}
}

License

Please contact the OneRec Team for the license of the Kelix series. The base SANA-DiT and DC-AE components are subject to their respective original licenses.