Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation (2311.16201v2)

Published 27 Nov 2023 in cs.CV, cs.AI, cs.CL, and cs.LG

Abstract: Recent advances in image tokenizers, such as VQ-VAE, have enabled text-to-image generation using auto-regressive methods, similar to LLMing. However, these methods have yet to leverage pre-trained LLMs, despite their adaptability to various downstream tasks. In this work, we explore this gap by adapting a pre-trained LLM for auto-regressive text-to-image generation, and find that pre-trained LLMs offer limited help. We provide a two-fold explanation by analyzing tokens from each modality. First, we demonstrate that image tokens possess significantly different semantics compared to text tokens, rendering pre-trained LLMs no more effective in modeling them than randomly initialized ones. Second, the text tokens in the image-text datasets are too simple compared to normal LLM pre-training data, which causes the catastrophic degradation of LLMs' capability.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. Scaling laws for generative mixed-modal language models. In ICML, 2023.
  2. Pythia: A suite for analyzing large language models across training and scaling. In ICML, 2023.
  3. GPT-NeoX-20B: An open-source autoregressive language model. In ACL Workshop, 2022.
  4. Re-imagen: Retrieval-augmented text-to-image generator. In ICLR, 2022.
  5. T. Computer. Redpajama: An open source recipe to reproduce llama training dataset, 2023.
  6. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019.
  7. Taming transformers for high-resolution image synthesis. In CVPR, 2021.
  8. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020.
  9. Planting a seed of vision in large language model. arXiv preprint arXiv:2307.08041, 2023.
  10. open_lm: a minimal but performative language modeling (lm) repository, 2023. GitHub repository.
  11. Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset. In NeurIPS, 2022.
  12. Measuring massive multitask language understanding. In ICLR, 2021.
  13. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, 2017.
  14. Opt-iml: Scaling language model instruction meta learning through the lens of generalization. arXiv preprint arXiv:2212.12017, 2022.
  15. Microsoft coco: Common objects in context. In ECCV, 2014.
  16. S2ORC: The semantic scholar open research corpus. In ACL, 2020.
  17. I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In ICLR, 2019.
  18. Learning transferable visual models from natural language supervision. In ICML, 2021.
  19. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020.
  20. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
  21. Zero-shot text-to-image generation. In ICML, 2021.
  22. Perceptual grouping in contrastive vision-language models. In ICCV, 2023.
  23. Generating diverse high-fidelity images with VQ-VAE-2. In NeurIPS, 2019.
  24. High-resolution image synthesis with latent diffusion models. In CVPR, 2022.
  25. Photorealistic text-to-image diffusion models with deep language understanding. In NeurIPS, 2022.
  26. Analysing mathematical reasoning abilities of neural models. In ICLR, 2019.
  27. Neural discrete representation learning. In NeurIPS, 2017.
  28. Attention is all you need. In NeurIPS, 2017.
  29. Vector-quantized image modeling with improved VQGAN. In ICLR, 2022.
  30. Scaling autoregressive models for content-rich text-to-image generation. TMLR, 2022.
  31. Spae: Semantic pyramid autoencoder for multimodal generation with frozen llms. arXiv preprint arXiv:2306.17842, 2023.
  32. Scaling autoregressive multi-modal models: Pretraining and instruction tuning. arXiv preprint arXiv:2309.02591, 2023.
  33. HellaSwag: Can a machine really finish your sentence? In ACL, 2019.
  34. Defending against neural fake news. In NeurIPS, 2019.
  35. Opt: Open pre-trained transformer language models, 2022.
  36. Movq: Modulating quantized vectors for high-fidelity image generation. In NeurIPS, 2022.
Citations (1)
View on Semantic Scholar

Summary

  • The paper shows that pre-trained language models do not enhance performance in auto-regressive text-to-image generation.
  • Methodology reveals that semantic discrepancies between image and text tokens hinder effective image synthesis.
  • Experiments on large-scale datasets indicate that token imbalances lead to similar outcomes as using randomly initialized models.

Analysis of "Pre-trained LLMs Do Not Help Auto-regressive Text-to-Image Generation"

The paper "Pre-trained LLMs Do Not Help Auto-regressive Text-to-Image Generation" presents an insightful paper demonstrating the limitations of pre-trained LLMs when applied to auto-regressive text-to-image generation tasks. The authors rigorously explore the hypothesis that leveraging the pre-trained LLMs, which have been robust performers across numerous tasks, might enhance the efficiency and output quality of auto-regressive text-to-image models. However, the findings contest the assumption of their utility and propose fundamental reasons behind this dissonance.

Key Findings and Methodological Rigor

The investigation is premised on adapting a pre-trained LLM for text-to-image generation tasks, using auto-regressive methods for tokenizing images via VQ-VAE and similar technologies. Despite the logical presuppositions that pre-trained models would benefit from their linguistic proficiency, the evidence contradicts. The work comprehensively outlines two primary reasons for this outcome:

  1. Semantic Discrepancies: Image tokens demonstrate semantic configurations significantly different from those of text tokens. This disparity results in pre-trained models not being more effective in processing image tokens than their randomly initialized counterparts.
  2. Text Complexity and Overrepresentation: Text tokens in image-caption datasets are less complex and are overshadowed by the quantity of image tokens (with a typical ratio of 30:1). This imbalance leads to deteriorated LLM capabilities.

The experimental design is commendable, utilizing a pre-trained LLM structured through a comprehensive dataset to validate and dissect loss across both text and image tokens. The model was trained on a sizable data corpus, incorporating 100 billion tokens with diverse batch sizes and configurations, ensuring robust conclusions. Results established an equivalence in both loss and image generation quality between pre-trained and randomly initialized models, further evidenced by the loss breakdown figures.

Implications of Research

The findings of the paper carry profound implications for the trajectory of research in image generation and the adaptable use of LLM architectures. Although pre-trained LLMs achieve remarkable feats in text domains, their extension into mixed-modal domains such as text-to-image must be approached with considerations of semantic coherence and modality-specific adaptations.

Despite the impressive scaling and performance of LLMs like T5 when fused into diffusion models for image generation tasks, this research delineates clear constraints when analogous methods are implemented in auto-regressive setups. It underscores the necessity for innovative strategies that bridge the semantic gaps between modalities, potentially through advanced tokenization techniques such as SEED or SPAE.

Future Prospects

This investigation opens avenues for further exploration of modality congruence and tokenization refinement in the sphere of multi-modal AI. Given the demonstrated challenges of direct adaptation of LLMs, upcoming research could divert towards developing hybrid models or novel architectures inherently cognizant of such modality distinctions. It also hints at the value of experimenting with different pre-training datasets that offer a more balanced semantic representation conducive to text-image synthesis.

Overall, the paper contributes significantly to understanding the constraints of contemporary auto-regressive text-to-image generation technologies, inviting the research community to reassess strategies for future innovations in image generation using LLM architectures.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube