【ソニー社内講演】拡散モデルと基盤モデル（2023年研究動向）

nnabla ディープラーニングチャンネル

16 Nov 202318:40

Summary

TLDRThe speaker from Sony Research introduces the relationship between diffusion models and foundation models in AI, focusing on recent trends in 2023. They discuss how diffusion models, used for generating images from text, can be enhanced by foundation models like GPT. The presentation covers four main topics: using foundation models to improve diffusion model performance, incorporating diffusion models into AI agents like chatbots, efficient fine-tuning methods for foundation models, and multimodal data generation across different domains like images, text, and audio. Examples include systems like DALL-E 3 for detailed text-to-image generation and Visual Chat GPT for image manipulation through natural language. The talk concludes with an exploration of unified and composable approaches to multimodal data generation, highlighting the flexibility and efficiency of these advanced AI techniques.

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Outlines

00:00

📚 Introduction to Diffusion and Foundation Models

The speaker from Sony Research introduces the topic of diffusion models and foundation models, highlighting their interrelation and recent research trends. Diffusion models, a type of generative model often used for text-to-image generation like Stable Diffusion or DALL-E 3, are explained as systems that generate high-fidelity images by reversing the diffusion process. Foundation models, such as GPT, are versatile models trained on diverse data and can be applied to various tasks. The talk will cover trends in 2023 regarding the use of foundation models to enhance diffusion models' performance and the concept of diffusion models as foundation models, capable of solving diverse tasks without fine-tuning. The speaker also briefly explains how diffusion models work, starting from pure noise and gradually removing noise to create clear images using deep neural networks.

05:02

🤖 Enhancing Diffusion Models with Foundation Models

The speaker discusses techniques to enhance the performance of diffusion models by actively utilizing foundation models. One simple method involves using a foundation model as a text encoder to efficiently extract semantic information from text. An example given is the DALL-E 3 system, which specializes in generating detailed images based on text prompts. It uses a large language model to upscale vague text prompts into detailed descriptions, which are then used to guide the diffusion model in creating images. The importance of learning from detailed descriptions to faithfully generate images is emphasized, contrasting with previous models trained on noisy image-caption pairs from the internet.

10:05

🖼️ Integrating Diffusion Models into AI Agents

The presentation introduces Visual Chat GPT, a system that integrates diffusion models into AI agents, allowing for tasks like image editing and answering questions about images using natural language. The system works by calling upon various visual foundation models as needed, based on prompts that define the modules' names, usage, and input/output specifications. However, the system has limitations, such as the number of visual foundation models that can be called is restricted by the prompt book, and it is not optimized for cross-modal generation. Recent proposals include search-based approaches to overcome prompt length limitations and parameter-efficient fine-tuning to specialize in cross-modal generation.

15:08

🎨 Efficient Fine-Tuning and Multimodal Data Generation

The speaker presents methods for efficient and effective fine-tuning of diffusion models as foundation models, mentioning ControlNet, LOLA, and AnimateDiff as popular techniques in 2023. These methods involve fixing the original text-to-image model and adding small new modules that are fine-tuned for specific tasks, such as pose or style transfer, or generating animations. The strategies allow for maximizing the performance of pre-trained models while applying them to new tasks. Additionally, multimodal data generation using diffusion models is explored, with approaches like learning a large unified multimodal diffusion model or a composer approach that collaborates single-modal diffusion models to generate coherent multimodal data. The composer approach is highlighted for its flexibility in combining different modalities and generating coherent results.

Mindmap

Keywords

💡Diffusion Models

Diffusion models are a type of generative model that has gained popularity for tasks such as text-to-image generation. In the script, diffusion models are discussed as a foundation for generating high-fidelity images and are also mentioned in the context of various applications beyond images, such as in audio generation and robotics for motion planning.

💡Foundation Models

Foundation models refer to models that are trained on diverse data and can be used for a variety of tasks, serving as an infrastructural backbone. They are not limited to generative models but can include models like GPT, which are capable of understanding and generating human-like text, as referenced in the script.

💡Stable Diffusion

Stable Diffusion is a specific diffusion model mentioned in the script that is known for its ability to generate images from text descriptions. It represents an advancement in the field of generative models, allowing for the creation of detailed and coherent images based on textual prompts.

💡DALL-E 3

DALL-E 3 is a system specialized in generating detailed images based on text prompts, as mentioned in the script. It is designed to handle vague or less descriptive text inputs by using a large language model to translate them into more detailed descriptions, which are then used to guide the image generation process.

💡AI Agents

AI agents, such as chatbots like Chat GPT mentioned in the script, are systems that can interact with users in a conversational manner. They are being integrated with diffusion models to serve as creative tools, allowing for natural language instructions to guide the generation of images or other content.

💡Fine-tuning

Fine-tuning is a technique used to adapt a pre-trained model to a specific task by making minor adjustments to its parameters. In the context of the script, fine-tuning is discussed as a method to efficiently apply diffusion models to new tasks without the need for extensive retraining.

💡Control Nets

Control Nets, as discussed in the script, are a method for efficiently fine-tuning diffusion models for tasks such as generating images under specific conditions or poses. They involve adding a small module to the existing model that can take in additional information, like pose data, to guide the image generation process.

💡Multimodal Data Generation

Multimodal data generation refers to the creation of data that involves multiple types of content, such as images, text, and audio, simultaneously. The script mentions this concept in the context of diffusion models that can generate coherent outputs across different modalities, although ensuring synchronization between them can be challenging.

💡Composer Approach

The Composer approach, mentioned in the script, is a method for multimodal data generation where single-modal diffusion models collaborate to produce outputs that are coherent across modalities. This approach allows for the flexible combination of different types of input and output modalities, such as generating text from images and audio.

💡Cross-Modal Generation

Cross-modal generation is the process of creating content in one modality based on input from another. For example, generating an image from text or audio from an image. The script discusses the challenges and approaches to optimize systems for cross-modal generation, such as using parameter-efficient fine-tuning.

💡LLM (Large Language Models)

LLMs, or large language models, are AI models that have been trained on vast amounts of text data and can generate human-like text. In the script, LLMs are used in the context of DALL-E 3 to upsample vague text prompts into more detailed descriptions, which are crucial for guiding the generation of detailed images.

Highlights

Introduction to diffusion models and foundation models, their relationship, and recent research trends.

Diffusion models as a type of generative model, particularly for text-to-image generation like Stable Diffusion and DALL-E 3.

Foundation models as versatile models trained on diverse data and applicable to various tasks, such as GPT.

Improving diffusion model performance by using foundation models to understand human instructions.

Diffusion models can solve diverse tasks without fine-tuning or prompting, showcased as foundation models.

Trends in 2023 for using foundation models more actively and integrating diffusion models into AI agents like chatbots.

Proposals for efficient and effective foundation tuning methods that have become widely used.

Introduction to multimodal data generation models that create data spanning across images, audio, and text.

Explanation of diffusion models based on the diffusion process, which involves gradually adding and then removing noise to generate data.

Utilizing deep neural networks to estimate and remove noise at each stage of the diffusion process.

Controlling the type of image generated through text by attaching an encoder module to extract meaning from text.

Integrating foundation models to efficiently extract meaning from text for improved performance in diffusion models.

The DALI-3 system, specialized in generating detailed images from broad text descriptions using foundation models.

Visual Chat GPT, a system that allows image editing and questions about images through natural language.

Limitations of Visual Chat GPT regarding the number of visual foundation models it can call upon and its lack of cross-modal optimization.

Proposals for parameter-efficient fine-tuning and specialized systems for cross-modal generation.

Techniques like ControlNet, LOLA, and Animating Diffusion for efficient fine-tuning and generation of specific objects or styles.

Strategies for multimodal diffusion models that generate coherent data across modalities like text, images, and audio.

Approaches like Composer for collaborative single-modal diffusion models to achieve multimodal data generation.

Examples of multimodal data generation, such as generating video and audio based on text input.

The flexibility of Composer to freely determine input and output modalities for multimodal data generation.

Summary of the presented topics and trends in foundation models and diffusion models for AI.