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DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation

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arxiv 2410.08159 v2 pith:2O74O6U5 submitted 2024-10-10 cs.CV cs.LG

Jiatao Gu , Yuyang Wang , Yizhe Zhang , Qihang Zhang , Dinghuai Zhang , Navdeep Jaitly , Josh Susskind , Shuangfei Zhai This is my paper
classification cs.CVcs.LG
keywords dartimagegenerationmodeldiffusionmodelsscalableapproach
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Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model's ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. DART does not rely on image quantization, which enables more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. Our approach demonstrates competitive performance on class-conditioned and text-to-image generation tasks, offering a scalable, efficient alternative to traditional diffusion models. Through this unified framework, DART sets a new benchmark for scalable, high-quality image synthesis.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Distilling frozen Motion-JEPA features into a compact 32-D latent whose geometry is coupled to the decoder lets a standard non-autoregressive flow-matching DiT reach state-of-the-art text-to-motion quality on HumanML3...

  2. EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Iterative latent thought refinement plus terminal text grounding lets diffusion models solve multi-step visual reasoning tasks at 92.1% average accuracy, beating DiffThinker by 8.3 points.

  3. STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A latent-space transformer autoregressive flow with one deep block plus shallow refiners, tuned noise injection, and score-based guidance reaches competitive FID in high-resolution image synthesis, the first at this s...

  4. SRDiffusion: Accelerate Video Diffusion Inference via Sketching-Rendering Cooperation

    cs.GR 2025-05 conditional novelty 6.0 of 10

    SRDiffusion accelerates video diffusion by switching from a large model to a smaller sibling model after early high-noise steps, using an adaptive threshold for the switch.

  5. Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV Cache Compression

    cs.LG 2025-05 conditional novelty 5.0 of 10

    ScaleKV cuts KV cache memory for Visual Autoregressive text-to-image generation to 10% by classifying layers as drafters or refiners per scale and pruning low-attention tokens while keeping benchmark scores nearly unchanged.

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