Elucidating the design space of language models for image generation (2410.16257v1)

Abstract: The success of autoregressive (AR) LLMs in text generation has inspired the computer vision community to adopt LLMs for image generation. However, considering the essential differences between text and image modalities, the design space of LLMs for image generation remains underexplored. We observe that image tokens exhibit greater randomness compared to text tokens, which presents challenges when training with token prediction. Nevertheless, AR models demonstrate their potential by effectively learning patterns even from a seemingly suboptimal optimization problem. Our analysis also reveals that while all models successfully grasp the importance of local information in image generation, smaller models struggle to capture the global context. In contrast, larger models showcase improved capabilities in this area, helping to explain the performance gains achieved when scaling up model size. We further elucidate the design space of LLMs for vision generation, including tokenizer choice, model choice, model scalability, vocabulary design, and sampling strategy through extensive comparative experiments. Our work is the first to analyze the optimization behavior of LLMs in vision generation, and we believe it can inspire more effective designs when applying LMs to other domains. Finally, our elucidated LLM for image generation, termed as ELM, achieves state-of-the-art performance on the ImageNet 256*256 benchmark. The code is available at https://github.com/Pepperlll/LMforImageGeneration.git.

Elucidating the Design Space of LLMs for Image Generation

The paper, "Elucidating the Design Space of LLMs for Image Generation," addresses the increasingly relevant task of transferring autoregressive (AR) LLMs to the domain of image generation. The work highlights the foundational differences between text and image token distributions and explores several design choices on model architecture and training strategy. Given the intrinsic complexities of image versus text data, this paper provides valuable insights and proposals to enhance image generation using LLMs.

Key Contributions

1. Token Distribution Analysis: The authors provide a quantitative analysis that reveals image tokens exhibit a near-uniform distribution compared to text tokens. This insight implies that image generation allows for greater flexibility in modeling due to the inherent randomness, which impacts the optimization and training behavior of LLMs. This perspective sheds light on why models can produce high-quality images despite less precise token predictions.
2. Tokenizer Evaluation: The paper contrasts vector quantized generative adversarial network (VQGAN) and binary autoencoders (BAE) as image tokenizers. It finds that BAE achieves superior performance, particularly due to its 100% code utilization and reduced reconstruction error, denoting it as a more effective tool for future applications of LLMs in image tasks.
3. Model Architecture and Scalability: An analysis of both AR models and masked LLMs (MLM) strongly favors AR approaches for image generation, emphasizing their superior ability to manage larger model scales and capture global contextual information. Detailed attention score analyses demonstrate how AR models effectively incorporate local and global information, indicating their robust scaling behavior.
4. Tokenization Strategy: The paper presents a sophisticated token decomposition approach, where high-dimensional binary codes (from BAE) are split into multiple sub-codes. This strategy optimizes model performance by balancing vocabulary complexity and model capacity. The results illustrate that a decomposed vocabulary yields better generation performance and is more computationally efficient.
5. Sampling Methodology: Exploring sampling strategies such as classifier-free guidance and introducing randomness, the authors emphasize the importance of randomness in achieving high-quality and diverse image outputs. Their experiments underline how larger AR models intrinsically require less randomness to generate realistic images, yet benefit from careful configuration of sampling parameters.

Implications and Future Directions

The findings of this research showcase effective techniques in bridging LLMs from text to vision, opening possibilities for broader applications across modalities. The potential impact extends to developing unified models that seamlessly handle both language and image tasks, pushing closer toward more generalized AI systems.

Future research could further refine these models by exploring alternative objective functions that are more suitable for image generation tasks, acknowledging the differences between text and image data structures. Additionally, leveraging larger and more diverse training datasets could reveal further strengths and limitations of LLMs in handling complex multimodal tasks.

In summary, this research provides a thorough exploration of using LLMs for image generation, identifying challenges and proposing insights that will inform both current applications and future developments in artificial intelligence frameworks.