By connecting the large language model with multimodal adapters and diffusion decoders, AnyGPT achieves the understanding of various modal inputs and the ability to generate outputs in any modality.
That is, you can process any combination of modal inputs (such as text, images, video, audio) and generate output for any modal…
Achieving true multimodal communication capabilities.
This project was previously called NExT-GPT:https://next-gpt.github.io, changed its name to AnyGPT, making a comeback!
AnyGPT uses discrete notation to process data in different modes, which means that whether it is speech, text, images, or music, it is converted into a unified form (i.e., discrete tokens) and then processed by the model. This approach allows models to easily add and process new modalities without changing their architecture or training methods.
AnyGPT main functions:
1. Any modal input and output: It can process any combination of modal inputs (such as text, images, video, audio) and generate output from any modal, achieving true multimodal communication capabilities.
2. Efficient multimodal understanding and generation: AnyGPT is capable of autoregressive multimodal understanding and generation, which means that it can receive input from one modality and generate output in another or more modalities. For example, it can generate images from text, music from speech, etc.
3. Any modal conversion: This model supports conversion between any modal, such as converting voice instructions into text and music responses, or converting image emotions into music, demonstrating a high degree of flexibility and creativity.
4. Multimodal dialogue generation: AnyGPT can generate multiple rounds of conversations containing different modal elements, for example, using voice, text and images simultaneously in one round of conversation. This provides a strong foundation for building complex interactive applications.
5. Lightweight alignment learning: By implementing LLM-centered alignment and instruction follow alignment on the encoding and decoding sides, AnyGPT only needs to adjust a small number of parameters (only 1%) to achieve effective cross-modal semantic alignment.
How AnyGPT works:
1. Multimodal input coding
Input adaptation: AnyGPT first accepts input from different modalities, such as text, images, audio or video. These inputs are converted into a unified format through a specific encoder so that LLM can process them. For example, images and video are converted by image and video encoders, and audio is converted by audio encoders.
Modality transformation: The transformed input is further processed to adapt to the way LLM works. This step typically involves converting the input data into a discrete representation (e.g., tokenization) so that LLM can understand and process the data.
2. LLM processing
Semantic understanding: Preprocessed multimodal input is sent to LLM for semantic understanding. LLM uses its extensive parameters and previously trained knowledge to understand the meaning of input content, whether they are text, images, audio or video.
Cross-modal reasoning: In addition to understanding the inputs of each modality, AnyGPT can also reason between modalities. For example, it can generate corresponding images from text descriptions, or generate descriptive text based on image content.
3. Multimodal output generation
Diffusion Decoder: After understanding and reasoning, the output produced by LLM needs to be transformed into content of a specific modality. AnyGPT uses a diffusion decoder to accomplish this step. Depending on the output and target modality of the LLM, the diffusion decoder can generate image, audio or video content.
Output adaptation: The generated content is adapted and optimized through post-processing steps to ensure that the output quality meets expectations. This may include adjusting the resolution, clarity of the image, or adjusting the quality of audio and video.
4. Modality switching and command adjustment
AnyGPT uses Modality Switching Command Adjustment (MosIT) technology to flexibly switch between different modalities according to user instructions, achieving complex cross-modal content generation.
This is supported by a manually created high-quality MosIT dataset that trains the model on how to generate precise content based on cross-modal user instructions.
AnyGPT collects and annotates 5000 high-quality samples of the MosIT dataset, helping MM-LLM achieve human-like cross-modal content understanding and command reasoning.
research significance
AnyGPT implements an end-to-end universal any-to-arbitrary MM-LLM for the first time by combining advanced LLM, multimodal adapters and diffusion decoders, capable of semantic understanding, reasoning, and generation of free input-output combinations.
It demonstrates the potential of building a unified AI agent that can simulate universal modalities, paving the way for more humane AI research.
Projects and demonstrations:https://junzhan2000.github.io/AnyGPT.github.io/
Thesis:https://arxiv.org/pdf/2309.05519.pdf
GitHub：https://github.com/NExT-GPT/NExT-GPT
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