ImageFolder: Autoregressive Image Generation with Folded Tokens

• URL: http://arxiv.org/abs/2410.01756v2
• Date: Tue, 15 Oct 2024 17:07:45 GMT
• Title: ImageFolder: Autoregressive Image Generation with Folded Tokens
• Authors: Xiang Li, Kai Qiu, Hao Chen, Jason Kuen, Jiuxiang Gu, Bhiksha Raj, Zhe Lin,
• Abstract summary: Increasing token length is a common approach to improve the image reconstruction quality. There exists a trade-off between reconstruction and generation quality regarding token length. We propose Image, a semantic tokenizer that provides spatially aligned image tokens that can be folded during autoregressive modeling.
• Score: 51.815319504939396
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Adaptive Length Image Tokenization via Recurrent Allocation [81.10081670396956]
  Current vision systems assign fixed-length representations to images, regardless of the information content. Inspired by this, we propose an approach to learn variable-length token representations for 2D images.
  arXiv  Detail & Related papers  (2024-11-04T18:58:01Z)
• Unlocking Pre-trained Image Backbones for Semantic Image Synthesis [29.688029979801577]
  We propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes.
  arXiv  Detail & Related papers  (2023-12-20T09:39:19Z)
• Cross-Image Attention for Zero-Shot Appearance Transfer [68.43651329067393]
  We introduce a cross-image attention mechanism that implicitly establishes semantic correspondences across images. We harness three mechanisms that either manipulate the noisy latent codes or the model's internal representations throughout the denoising process. Experiments show that our method is effective across a wide range of object categories and is robust to variations in shape, size, and viewpoint.
  arXiv  Detail & Related papers  (2023-11-06T18:33:24Z)
• DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation [73.54038780856554]
  Class Incremental Semantic (CISS) extends the traditional segmentation task by incrementally learning newly added classes. Previous work has introduced generative replay, which involves replaying old class samples generated from a pre-trained GAN. We propose DiffusePast, a novel framework featuring a diffusion-based generative replay module that generates semantically accurate images with more reliable masks guided by different instructions.
  arXiv  Detail & Related papers  (2023-08-02T13:13:18Z)
• Progressive Text-to-Image Generation [40.09326229583334]
  We present a progressive model for high-fidelity text-to-image generation. The proposed method takes effect by creating new image tokens from coarse to fine based on the existing context. The resulting coarse-to-fine hierarchy makes the image generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2022-10-05T14:27:20Z)
• Re-Imagen: Retrieval-Augmented Text-to-Image Generator [58.60472701831404]
  Retrieval-Augmented Text-to-Image Generator (Re-Imagen) Retrieval-Augmented Text-to-Image Generator (Re-Imagen)
  arXiv  Detail & Related papers  (2022-09-29T00:57:28Z)
• Improved Masked Image Generation with Token-Critic [16.749458173904934]
  We introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. A state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity.
  arXiv  Detail & Related papers  (2022-09-09T17:57:21Z)
• Hierarchical Text-Conditional Image Generation with CLIP Latents [20.476720970770128]
  We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style.
  arXiv  Detail & Related papers  (2022-04-13T01:10:33Z)
• Length-Controllable Image Captioning [67.2079793803317]
  We propose to use a simple length level embedding to endow them with this ability. Due to their autoregressive nature, the computational complexity of existing models increases linearly as the length of the generated captions grows. We further devise a non-autoregressive image captioning approach that can generate captions in a length-irrelevant complexity.
  arXiv  Detail & Related papers  (2020-07-19T03:40:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.