Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective

• URL: http://arxiv.org/abs/2410.12490v2
• Date: Thu, 31 Oct 2024 11:42:07 GMT
• Title: Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
• Authors: Yongxin Zhu, Bocheng Li, Hang Zhang, Xin Li, Linli Xu, Lidong Bing,
• Abstract summary: Latent-based image generative models have achieved notable success in image generation tasks. Despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. We propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling.
• Score: 52.778766190479374
• License:
• Abstract: Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencoders like VQGAN or VAE to encode pixels into a more compact latent space and learn the data distribution in the latent space instead of directly from pixels. However, this practice raises a pertinent question: Is it truly the optimal choice? In response, we begin with an intriguing observation: despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. This finding contrasts sharply with the field of NLP, where the autoregressive model GPT has established a commanding presence. To address this discrepancy, we introduce a unified perspective on the relationship between latent space and generative models, emphasizing the stability of latent space in image generative modeling. Furthermore, we propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling by applying K-Means on the latent features of self-supervised learning models. Experimental results show that image autoregressive modeling with our tokenizer (DiGIT) benefits both image understanding and image generation with the next token prediction principle, which is inherently straightforward for GPT models but challenging for other generative models. Remarkably, for the first time, a GPT-style autoregressive model for images outperforms LDMs, which also exhibits substantial improvement akin to GPT when scaling up model size. Our findings underscore the potential of an optimized latent space and the integration of discrete tokenization in advancing the capabilities of image generative models. The code is available at \url{https://github.com/DAMO-NLP-SG/DiGIT}.

Related papers

• Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens [53.99177152562075]
  Scaling up autoregressive models in vision has not proven as beneficial as in large language models. We focus on two critical factors: whether models use discrete or continuous tokens, and whether tokens are generated in a random or fixed order using BERT- or GPT-like transformer architectures. Our results show that while all models scale effectively in terms of validation loss, their evaluation performance -- measured by FID, GenEval score, and visual quality -- follows different trends.
  arXiv  Detail & Related papers  (2024-10-17T17:59:59Z)
• MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
  We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework. Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss. We show that MMAR demonstrates much more superior performance than other joint multi-modal models.
  arXiv  Detail & Related papers  (2024-10-14T17:57:18Z)
• Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis [62.06970466554273]
  We present Meissonic, which non-autoregressive masked image modeling (MIM) text-to-image elevates to a level comparable with state-of-the-art diffusion models like SDXL. We leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers to further enhance image fidelity and resolution. Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images.
  arXiv  Detail & Related papers  (2024-10-10T17:59:17Z)
• Towards Model-Agnostic Dataset Condensation by Heterogeneous Models [13.170099297210372]
  We develop a novel method to produce universally applicable condensed images through cross-model interactions. By balancing the contribution of each model and maintaining their semantic meaning closely, our approach overcomes the limitations associated with model-specific condensed images.
  arXiv  Detail & Related papers  (2024-09-22T17:13:07Z)
• DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception [66.88792390480343]
  We propose DEEM, a simple but effective approach that utilizes the generative feedback of diffusion models to align the semantic distributions of the image encoder. DEEM exhibits enhanced robustness and a superior capacity to alleviate model hallucinations while utilizing fewer trainable parameters, less pre-training data, and a smaller base model size.
  arXiv  Detail & Related papers  (2024-05-24T05:46:04Z)
• Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning [20.175586324567025]
  Mitigating catastrophic forgetting is a key hurdle in continual learning. A major issue is the deterioration in the quality of generated data compared to the original. We propose a GR-based approach for continual learning that enhances image quality in generators.
  arXiv  Detail & Related papers  (2023-12-10T17:39:42Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Semantic Image Synthesis with Semantically Coupled VQ-Model [42.19799555533789]
  We conditionally synthesize the latent space from a vector quantized model (VQ-model) pre-trained to autoencode images. We show that our model improves semantic image synthesis using autoregressive models on popular semantic image datasets ADE20k, Cityscapes and COCO-Stuff.
  arXiv  Detail & Related papers  (2022-09-06T14:37:01Z)
• Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation [19.156223720614186]
  The integration of Vector Quantised Variational AutoEncoder with autoregressive models as generation part has yielded high-quality results on image generation. We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context.
  arXiv  Detail & Related papers  (2021-12-03T09:09:34Z)

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.