Update README model card (Kelix technique report)

README.md

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+---
+language:
+  - en
+library_name: "muse"
+tags:
+  - text-to-image
+  - image-generation
+  - diffusion
+  - diffusion-transformer
+  - DiT
+  - flow-matching
+  - unified-multimodal
+  - discrete-token
+  - kelix
+base_model: "Efficient-Large-Model/Sana_1600M_1024px_diffusers"
+pipeline_tag: text-to-image
+arxiv: "https://arxiv.org/pdf/2602.09843"
+---
+
+# Kelix-DiT
+
+**Kelix-DiT** is the **pretraining-stage** checkpoint of the diffusion-based image de-tokenizer of **Kelix**, a fully discrete autoregressive unified multimodal model proposed by the OneRec Team. 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.
+
+> 📄 **Technical report**: <https://arxiv.org/pdf/2602.09843>
+
+## Background
+
+Most vision–language models (VLMs) rely on a hybrid interface — discrete text tokens paired with continuous ViT features — and are biased toward understanding. Fully autoregressive unified models that use **discrete** visual tokens, on the other hand, have historically suffered from an information bottleneck: a single discrete code carries far less information than the continuous embedding it replaces, which degrades multimodal understanding (especially on text-rich tasks such as OCRBench).
+
+Kelix addresses this with a **multi-token vision tokenizer** (Kelix-Tok) 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. The unified LLM (Qwen3-8B) is trained with a **Next-Block Prediction (NBP)** paradigm, and a diffusion-based **image de-tokenizer (Kelix-DiT)** turns the LLM's hidden states into high-resolution images — forming a modular *Tokenizer → LLM → Detokenizer* pipeline that unifies understanding and generation under a single autoregressive objective.
+
+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
+
+| Item | Value |
+|---|---|
+| Role | Diffusion-based image de-tokenizer (pretraining stage) |
+| Base architecture | SANA-DiT (customized) |
+| Training objective | Flow-matching |
+| Latent VAE | DC-AE-F32C32 (32× spatial downsampling → 32×32 latent) |
+| Condition | Last hidden states from the Kelix LLM (between vision start/end tokens) |
+| Output resolution | **1024 × 1024** |
+| Training stage | **Pretraining** (Stage 1 of 2) |
+
+## 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`](https://huggingface.co/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:
+
+| Benchmark | Score |
+|---|---|
+| GenEval (Overall) | **87.6** |
+| WISE (Overall) | **57.0** |
+| DPG-Bench (Overall) | **85.5** |
+
+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
+
+```bibtex
+@techreport{kelix2026,
+  title   = {Kelix Technique Report: Closing the Understanding Gap of Discrete Tokens in Unified Multimodal Models},
+  author  = {OneRec Team},
+  year    = {2026},
+  url     = {https://arxiv.org/pdf/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.