Instruct-Imagen: Image Generation with Multi-modal Instruction (AI summary)

Key Points

1. The research focuses on various image generation tasks including subject-driven image generation, style transfer, in-context face generation, and instruction template generation.

2. The backbone U-Network model architecture is used for the text-to-image network without DBlock-4 and UBlock-4.

3. Evaluation datasets and samples are drawn from DreamBench v1 and v2, Celeb-A, Celeb-HQ, WikiArt, and CustomConcept101 datasets for different image generation tasks.

4. Zero-shot compositional evaluation datasets are employed for style and subject conditioned image generation, multi-subject conditioned image generation, subject and control conditioned image generation, and style and control conditioned image generation.

5. The study involves additional qualitative evaluations for in-domain tasks, data for retrieval-augmented training, and image generation based on specific captions and artworks.

6. Evaluations include the generation of images based on captions such as "British short hair cat and golden retriever" and "The Triumph of Hope, an allegorical painting by Erasmus Quellinus The Younger in the Baroque style."

7. The research also involves the generation of images based on specific descriptions such as "A black and white puppy in a sunflower field" and "A stuffed animal on a beach blanket."

8. Control-related data for instruction-tuning is presented for producing facial images with specific references and descriptions.

9. Style-related data for instruction-tuning is also provided for generating images in specific visual styles.

Summary

The paper introduces Instruct-Imagen, a model that comprehends multi-modal instruction to accomplish a variety of visual generative tasks. It outlines the challenges with existing text-to-image models and presents the development of Instruct-Imagen through retrieval-augmented training and multi-modal instruction-tuning. The key contributions include the introduction of multi-modal instruction, adaptation of the pre-trained text-to-image model to handle additional multi-modal inputs, and the building of Instruct-Imagen, a unified model that surpasses several state-of-the-art models in their domains and generalizes to unseen and complex tasks. The paper details the experimental setup, human evaluation protocol, and results, demonstrating the superior performance of Instruct-Imagen in both in-domain and zero-shot tasks. Additionally, it provides comprehensive supplementary information and datasets used for evaluation, along with architectural details of the model and the specifics of the training process. The paper emphasizes the need for responsible use of generative AI models and aims to address social biases in AI systems.

Development and Training Approach of Instruct-Imagen
The research paper introduces the concept of multi-modal instruction in image generation to address challenges with existing models in comprehending complex instructions involving multiple modalities. The main contribution of the paper is the development of the Instruct-Imagen model, which is trained using a multi-modal approach to understand and generalize to unseen and complex image generation tasks. The Instruct-Imagen model is demonstrated to have capabilities in understanding multi-modal instructions and generalizing to complex image generation tasks. The paper also outlines the datasets used for zero-shot compositional evaluation of the model, including style transfer, style and subject conditioned image generation, multi-subject conditioned image generation, subject and control conditioned image generation, and style and control conditioned image generation.

Additionally, the paper includes qualitative evaluations on Instruct-Imagen for in-domain tasks and presents training situations where multi-modal context is presented to the model during image generation, as well as situations where multi-modal context is dropped during training. The paper also provides examples of text-to-image data for instruction-tuning, control-related data for instruction-tuning, and style-related data for instruction-tuning, showcasing the diverse application of the Instruct-Imagen model. Overall, the paper presents the development of the Instruct-Imagen model, its training approach, and its demonstrated capabilities in understanding multi-modal instructions and generalizing to complex image generation tasks, as well as its diverse applications in instruction-tuning.

Reference: https://arxiv.org/abs/2401.01952