CVPR 2026  |  Paper  |  Project Page  |  Checkpoints

We introduce an efficient, resolution-agnostic autoregressive (AR) image synthesis approach that generalizes to arbitrary resolutions and aspect ratios, narrowing the gap to diffusion models at scale. At its core is VibeToken, a novel resolution-agnostic 1D Transformer-based image tokenizer that encodes images into a dynamic, user-controllable sequence of 32--256 tokens, achieving state-of-the-art efficiency and performance trade-off. Building on VibeToken, we present VibeToken-Gen, a class-conditioned AR generator with out-of-the-box support for arbitrary resolutions while requiring significantly fewer compute resources.

• [Feb 2026] 🎉 VibeToken is accepted at CVPR 2026!
• [Feb 2026] Training scripts released.
• [Feb 2026] Inference code and checkpoints released.

# 1. Clone and setup git clone https://github.com/<your-org>/VibeToken.git cd VibeToken uv venv --python=3.11.6 source .venv/bin/activate uv pip install -r requirements.txt # 2. Download a checkpoint (see Checkpoints section below) mkdir -p checkpoints wget https://huggingface.co/mpatel57/VibeToken/resolve/main/VibeToken_LL.bin -O ./checkpoints/VibeToken_LL.bin # 3. Reconstruct an image python reconstruct.py --auto \ --config configs/vibetoken_ll.yaml \ --checkpoint ./checkpoints/VibeToken_LL.bin \ --image ./assets/example_1.png \ --output ./assets/reconstructed.png

All checkpoints are hosted on Hugging Face.

uv venv --python=3.11.6 source .venv/bin/activate uv pip install -r requirements.txt

Tip: If you don't have uv, install it via pip install uv or see uv docs. Alternatively, use python -m venv .venv && pip install -r requirements.txt.

Download the VibeToken-LL checkpoint (see Checkpoints), then:

# Auto mode (recommended) -- automatically determines optimal patch sizes python reconstruct.py --auto \ --config configs/vibetoken_ll.yaml \ --checkpoint ./checkpoints/VibeToken_LL.bin \ --image ./assets/example_1.png \ --output ./assets/reconstructed.png # Manual mode -- specify patch sizes explicitly python reconstruct.py \ --config configs/vibetoken_ll.yaml \ --checkpoint ./checkpoints/VibeToken_LL.bin \ --image ./assets/example_1.png \ --output ./assets/reconstructed.png \ --encoder_patch_size 16 \ --decoder_patch_size 16

Note: For best performance, the input image resolution should be a multiple of 32. Images with other resolutions are automatically rescaled to the nearest multiple of 32.

Download both the VibeToken-LL and VibeToken-Gen-XXL checkpoints (see Checkpoints), then:

python generate.py \ --gpt-ckpt ./checkpoints/VibeTokenGen-xxl-dynamic-65_750k.pt \ --gpt-model GPT-XXL --num-output-layer 4 \ --num-codebooks 8 --codebook-size 32768 \ --image-size 256 --cfg-scale 4.0 --top-k 500 --temperature 1.0 \ --class-dropout-prob 0.1 \ --extra-layers "QKV" \ --latent-size 65 \ --config ./configs/vibetoken_ll.yaml \ --vq-ckpt ./checkpoints/VibeToken_LL.bin \ --sample-dir ./assets/ \ --skip-folder-creation \ --compile \ --decoder-patch-size 32,32 \ --target-resolution 1024,1024 \ --llamagen-target-resolution 256,256 \ --precision bf16 \ --global-seed 156464151

The --target-resolution controls the tokenizer output resolution, while --llamagen-target-resolution controls the generator's internal resolution (max 512×512; for higher resolutions, the tokenizer handles upscaling).

To train the VibeToken tokenizer from scratch, please refer to TRAIN.md for detailed instructions.

We would like to acknowledge the following repositories that inspired our work and upon which we directly build: 1d-tokenizer, LlamaGen, and UniTok.

If you find VibeToken useful in your research, please consider citing:

@inproceedings{vibetoken2026, title = {VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations}, author = {Patel, Maitreya and Li, Jingtao and Zhuang, Weiming and Yang, Yezhou and Lyu, Lingjuan}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2026} }

If you have any questions, feel free to open an issue or reach out!