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VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling

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arxiv 2408.01181 v1 pith:5R3RMP4F submitted 2024-08-02 cs.CV

Qian Zhang , Xiangzi Dai , Ninghua Yang , Xiang An , Ziyong Feng , Xingyu Ren This is my paper
classification cs.CV
keywords var-clipauto-regressivetextualvisualcaptionsclipextensivegeneration
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VAR is a new generation paradigm that employs 'next-scale prediction' as opposed to 'next-token prediction'. This innovative transformation enables auto-regressive (AR) transformers to rapidly learn visual distributions and achieve robust generalization. However, the original VAR model is constrained to class-conditioned synthesis, relying solely on textual captions for guidance. In this paper, we introduce VAR-CLIP, a novel text-to-image model that integrates Visual Auto-Regressive techniques with the capabilities of CLIP. The VAR-CLIP framework encodes captions into text embeddings, which are then utilized as textual conditions for image generation. To facilitate training on extensive datasets, such as ImageNet, we have constructed a substantial image-text dataset leveraging BLIP2. Furthermore, we delve into the significance of word positioning within CLIP for the purpose of caption guidance. Extensive experiments confirm VAR-CLIP's proficiency in generating fantasy images with high fidelity, textual congruence, and aesthetic excellence. Our project page are https://github.com/daixiangzi/VAR-CLIP

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Cited by 6 Pith papers

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

  1. SpectralAR: Spectral Autoregressive Visual Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    An autoregressive image generator that tokenizes images in the DCT frequency domain into nested 1D spectral sequences and generates them coarse-to-fine, reaching 3.02 gFID with 64 tokens on ImageNet-1K.

  2. Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance

    cs.CV 2025-09 conditional novelty 5.0 of 10

    IGG, an attention-based reweighting of classifier-free guidance, concentrates guidance on important tokens and modestly improves FID/IS in scale-wise autoregressive image generation.

  3. SATURN: Autoregressive Image Generation Guided by Scene Graphs

    cs.CV 2025-08 conditional novelty 5.0 of 10

    SATURN serializes scene graphs into salience-ordered captions, enabling a frozen CLIP encoder and a fine-tuned VAR transformer to generate images from graphs, with reported FID falling from 56.45 to 21.62 on Visual Genome.

  4. DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer

    cs.CV 2025-07 conditional novelty 5.0 of 10

    DC-AR generates 512x512 images in 12 masked autoregressive steps plus 20 diffusion refinement steps, using a 32x compressed 2D tokenizer, and reports gFID 5.49 on MJHQ-30K.

  5. CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    CycleVAR adapts a pretrained visual autoregressive model to unpaired image translation using softmax-relaxed quantization and source-token prefixes, achieving FID scores competitive with CycleGAN-Turbo.

  6. Marrying Autoregressive Transformer and Diffusion with Multi-Reference Autoregression

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A hybrid image generator that uses an autoregressive transformer as an encoder and a diffusion model as a decoder reaches FID 1.42 on ImageNet 256x256, with an iterative multi-reference mode that improves quality over...

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