REVIEW 9 cited by
ImageFolder: Autoregressive Image Generation with Folded Tokens
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Image tokenizers are crucial for visual generative models, e.g., diffusion models (DMs) and autoregressive (AR) models, as they construct the latent representation for modeling. Increasing token length is a common approach to improve the image reconstruction quality. However, tokenizers with longer token lengths are not guaranteed to achieve better generation quality. There exists a trade-off between reconstruction and generation quality regarding token length. In this paper, we investigate the impact of token length on both image reconstruction and generation and provide a flexible solution to the tradeoff. We propose ImageFolder, a semantic tokenizer that provides spatially aligned image tokens that can be folded during autoregressive modeling to improve both generation efficiency and quality. To enhance the representative capability without increasing token length, we leverage dual-branch product quantization to capture different contexts of images. Specifically, semantic regularization is introduced in one branch to encourage compacted semantic information while another branch is designed to capture the remaining pixel-level details. Extensive experiments demonstrate the superior quality of image generation and shorter token length with ImageFolder tokenizer.
Forward citations
Cited by 9 Pith papers
-
UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling
An MLLM-conditioned next-scale VAR decoder handles 15+ unified visual generation tasks with competitive quality and substantially lower latency than diffusion baselines.
-
Twins: Learn to Predict Unified Representations with Focal Loss
Channel-wise concatenation of SigLIP2 and Flux VAE features into one token, trained with a focal-style flow-matching loss, yields a unified representation with 1.59 gFID on ImageNet 256 and VAE-level reconstruction.
-
Orbis 2: A Hierarchical World Model for Driving
A hierarchical driving world model — planning in compressed DINO space at 2 Hz and rendering detailed frames at 10 Hz — achieves state-of-the-art long-horizon stability, steering response, and representation quality.
-
Language-Guided Transformer Tokenizer for Human Motion Generation
Injecting language into the motion tokenizer yields more compact semantic tokens and state-of-the-art generation scores on HumanML3D and Motion-X.
-
Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective
A new linear attention with spatial-aware decay at row boundaries lowers FID for autoregressive image generation on ImageNet relative to the softmax LlamaGen baseline, but the description of the core mask is internall...
-
HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation
HMAR is an image generator that builds each resolution scale from the previous scale and refines it with masked prediction, matching or improving ImageNet FID/IS versus VAR with faster training and inference.
-
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
A compact unified model that reuses a frozen VLM encoder and hybrid continuous/discrete tokens reaches competitive image understanding and generation with 15.6M training images and about $2,000 in compute.
-
EVEv2: Improved Baselines for Encoder-Free Vision-Language Models
An encoder-free vision-language model using separate attention, normalization, and feed-forward weights for image versus text tokens outperforms earlier encoder-free models and narrows the gap to encoder-based VLMs wi...
-
High-Fidelity Functional Ultrasound Reconstruction via A Visual Auto-Regressive Framework
UltraVAR, a visual auto-regressive augmenter for functional ultrasound, reports downstream classification gains that are not proven to come from generation quality.
Discussion (0). Continue with ORCID to comment.