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ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation

Ethan Chern, Jiadi Su, Yan Ma, Pengfei Liu

arXiv:2407.06135v1cs.CLcs.AIcs.CV

TL;DR

Open-source LMMs often lack native multimodal integration or multimodal generation, while some that generate images rely on separate diffusion models. ANOLE builds on Chameleon and selectively fine-tunes it to facilitate image and multimodal generation using fewer than 40M parameters and about 6,000 samples. ANOLE-7b-v0.1 demonstrated image and multimodal generation capabilities after fine-tuning on 5,859 LAION-5B art images with fewer than 40M parameters.

  • Problem

    Open-source LMMs often lack native multimodal integration or multimodal generation, while some that generate images rely on separate diffusion models.

    Source: §1 · p. 2

  • Method

    ANOLE builds on Chameleon and selectively fine-tunes it to facilitate image and multimodal generation using fewer than 40M parameters and about 6,000 samples.

    Source: §1 · p. 2

  • Results

    ANOLE-7b-v0.1 demonstrated image and multimodal generation capabilities after fine-tuning on 5,859 LAION-5B art images with fewer than 40M parameters.

    Source: §3.2 · p. 4

  • Takeaways & Limitations

    The authors release ANOLE, its training and multimodal-inference framework, and instruction-tuning data for researchers and developers.

    Source: §1 · p. 2, §5 · p. 7

    ANOLE’s image-generation capabilities have not been aligned for safety and harmlessness, and the model remains under development.

    Source: §6 · p. 7

Abstract

from arXiv · show

Previous open-source large multimodal models (LMMs) have faced several limitations: (1) they often lack native integration, requiring adapters to align visual representations with pre-trained large language models (LLMs); (2) many are restricted to single-modal generation; (3) while some support multimodal generation, they rely on separate diffusion models for visual modeling and generation. To mitigate these limitations, we present Anole, an open, autoregressive, native large multimodal model for interleaved image-text generation. We build Anole from Meta AI's Chameleon, adopting an innovative fine-tuning strategy that is both data-efficient and parameter-efficient. Anole demonstrates high-quality, coherent multimodal generation capabilities. We have open-sourced our model, training framework, and instruction tuning data.

1 Introduction

Open-source LMMs often lack native multimodal training, multimodal generation, or diffusion-free visual generation. ANOLE addresses this gap by extending Chameleon and providing efficient fine-tuning, training and inference resources, and data.

1 Introduction

  • Open-source LMMs often lack multimodal generation, native multimodal pretraining, or vision generation without an additional diffusion model.The paper notes that extra mechanisms can increase training and inference complexity.Source: §1 · p. 2
  • ANOLE builds on Chameleon to facilitate image and multimodal generation while aiming to preserve its text and multimodal-comprehension strengths.The open-source Chameleon release supported those strengths but not image or multimodal generation.Source: §1 · p. 2
  • ANOLE’s fine-tuning uses fewer than 40M parameters and about 6,000 samples to facilitate image and multimodal generation.Source: §1 · p. 2
  • The authors provide a training and multimodal-inference framework and qualitative analysis of autoregressive LMMs.They also offer data resources and tutorials for onboarding and experimentation.Source: §1 · p. 2
  • The paper presents ANOLE as an open model intended to broaden access to advanced multimodal AI research.It describes the release as building on Chameleon and supporting more inclusive, collaborative research.Source: §1 · p. 2

2 Related Works

Related multimodal systems combine pretrained language models with visual encoders or diffusion models, while token-based approaches model image and text sequences together. ANOLE extends Chameleon’s token-based approach to generation.

2 Related Works

  • Combining CLIP vision encoders, diffusion decoders, and pretrained LLMs can increase architectural complexity, training overhead, and reduce inference efficiency.Source: §2 · p. 3
  • Token-based methods encode images as discrete tokens and model multimodal sequences autoregressively, without requiring CLIP or diffusion components.Unified token-based modeling supports interleaved image-text generation and can reduce model complexity.Source: §2 · p. 3
  • ANOLE extends Chameleon’s token-based architecture to image and multimodal generation, with the paper describing it as diffusion-free, native, and efficient.Table 1 compares related works by diffusion use, native status, token-based modeling, and open-source interleaved generation.Source: §2 · p. 3, Table 1 · p. 3

3 ANOLE

ANOLE uses Chameleon’s early-fusion, token-based autoregressive architecture to model image-text sequences. It facilitates image and multimodal generation by selectively fine-tuning the output head, with reported qualitative generation capabilities after a small-data, parameter-efficient training run.

3.1 Overview

  • ANOLE uses early-fusion tokenization: modality-specific token sequences are concatenated and modeled by an autoregressive transformer.The approach uses Chameleon’s architecture and does not use diffusion models.Source: §3.1 · p. 3

3.2 Facilitating the image generation and multimodal generation capabilities from Chameleon

  • Fine-tuning freezes most Chameleon parameters and adjusts only output-head logits for image-token IDs to facilitate image and multimodal generation.Figure 2 depicts the fine-tuning process.Source: §3.2 · p. 4, Figure 2 · p. 4
  • ANOLE-7b-v0.1 showed image and multimodal generation capabilities after fine-tuning on 5,859 LAION-5B art images and fewer than 40M parameters.The run took around 30 minutes on 8 A100 GPUs; the authors describe its capabilities as impressive despite these limited resources.Source: §3.2 · p. 4

4 Evaluation

ANOLE is qualitatively evaluated for text-to-image generation and interleaved image-text generation. The examples are described as instruction-adherent and varied, with organized text paired seamlessly with relevant images.

4 Evaluation

  • The evaluation conducts qualitative analysis of ANOLE’s image-generation and interleaved image-text-generation capabilities.Table 2 and Table 3 provide text-to-image examples.Source: §4 · p. 4, Table 2 · p. 5, Table 3 · p. 6

4.1 Image Generation

  • ANOLE’s generated images are described as high-quality, closely instruction-adherent, and versatile across realistic and imaginative scenes.Examples include a coffee-and-croissant scene and a dinosaur in Times Square.Source: §4.1 · p. 4

4.2 Interleaved Image-Text Generation

  • ANOLE’s interleaved outputs pair organized, detailed text with relevant images, including introductions to traditional Chinese dishes and Gyumri.The paper describes the dish images as complementary to the text and the Gyumri images as capturing architectural and cultural highlights.Source: §4.2 · p. 7, Figure 3 · p. 7

5 Conclusion & Future Directions

ANOLE is an open, autoregressive, native LMM for interleaved image-text generation, fine-tuned from Chameleon with 6,000 samples and 40M parameters. The paper identifies instruction following, context length, multimodal understanding, and downstream applications as future directions.

5 Conclusion & Future Directions

  • ANOLE is an open, autoregressive, native LMM for interleaved image-text generation, fine-tuned from Chameleon with 6,000 samples and 40M parameters.The paper describes its multimodal generation abilities as advanced.Source: §5 · p. 7
  • Future work targets precise instruction following, longer context, improved multimodal understanding, and downstream multimodal-generation tasks.Source: §5 · p. 7

6 Limitations & Disclaimer

ANOLE is intended for research use, and its image-generation capabilities have not been aligned for safety and harmlessness.

6 Limitations & Disclaimer

  • ANOLE’s image-generation capabilities have not been aligned for safety and harmlessness, and users are encouraged to interact with caution.The model is intended for research use only and remains under development.Source: §6 · p. 7

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