NExT-GPT is a research system designed to understand and generate combinations of text, images, video, and audio. Rather than replacing every modality-specific model with one new model, it connects a language model to multimodal input adaptors and separate generation models. The project was presented at ICML 2024.
What NExT-GPT is—and what “any-to-any” means
In the authors’ description, “any-to-any” means the system can take in and produce different combinations of the modalities it supports: text, image, video, and audio. It does not mean that this implementation handles every conceivable media type or that one model processes and generates all modalities by itself.
The paper, by Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua, appeared in the Proceedings of the 41st International Conference on Machine Learning in 2024 (PMLR volume 235, pages 53366–53397). The authors describe NExT-GPT as an end-to-end, general-purpose multimodal LLM. In the paper abstract, they say they “connect an LLM with multimodal adaptors and different diffusion decoders,” allowing it to perceive and generate combinations of text, image, video, and audio. Read the ICML 2024 paper.
The practical distinction is that NExT-GPT coordinates pretrained components: an input encoder and a language model are paired with modality-specific decoders. The supported set and combinations should be understood in terms of those documented components and demonstrations, not as a claim of universal modality support.
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How the system handles input and generates media
The project describes a three-stage flow. An encoder turns non-text input into representations; projection layers make those representations usable by the language model; then the language model produces text and, when needed, special signal tokens that route generation to a modality-specific decoder.
- Encode inputs: ImageBind is the project’s named unified input encoder. Projection layers map incoming modality features into a form the language model can use.
- Reason and select outputs: Vicuna is the LLM core. It processes the conversation and can emit ordinary text alongside special modality signal tokens.
- Decode requested media: Output projection layers prepare signals for the relevant generation model: Stable Diffusion for images, ZeroScope for video, and AudioLDM for audio.
The signal tokens act as routing cues. In the project’s inference description, a decoder is activated when the language model emits a token for its modality; a modality without a corresponding token is not generated. This lets a conversation produce text, media, or a mixture rather than requiring every response to use every output type.
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The project page illustrates requests such as asking what time appears in a picture, what a person is doing in a video, or asking for a celebratory song. These are examples from the authors’ demonstrations, not independent performance evaluations. See the NExT-GPT project page.
How NExT-GPT is trained
The training approach addresses both directions of the system. On the input side, multimodal features are aligned with the language model’s text feature space. On the output side, signal representations are aligned with conditioning representations used by the diffusion decoders.
The authors also introduce modality-switching instruction tuning, or MosIT. It uses conversations that combine multimodal inputs and outputs to train cross-modal interaction and controllability. The project authors say they manually curated a dataset for MosIT; this describes their training approach, not a guarantee that every possible modality switch will work reliably.
The paper reports tuning 1% of certain projection-layer parameters. That figure applies to those projection layers; it is not a claim that only 1% of the whole system’s parameters were trained, nor does it quantify total compute, training cost, or inference cost.
Code, checkpoints, and running the implementation
The official repository provides code, data, model weights, environment instructions, and prediction steps. Its example environment uses Python 3.8 and a CUDA-enabled PyTorch installation. The documented setup loads the relevant pretrained component checkpoints as well as NExT-GPT’s tunable parameters before prediction. Check the official repository for current setup instructions.
Those instructions document a research setup, not a current compatibility guarantee or a minimum hardware specification. The repository does not establish a minimum GPU model or memory requirement. A CUDA-capable GPU is relevant to the documented setup, but choosing hardware requires checking the current dependencies, checkpoints, and intended workload; the project’s documentation does not provide a benchmark-based recommendation.
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The repository’s dated updates list a model checkpoint release in October 2023 and a data and construction-method release in October 2024. It also says a newer codebase supersedes its legacy directory for training and tuning procedures. Since dependencies and model links may change, use the repository’s current guidance rather than relying on copied setup commands.
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The repository references a BSD 3-Clause license for the code and separately describes NExT-GPT as a research project intended for non-commercial use. It says potential commercial use of the code should be approved by the authors. These statements should be considered together; the code’s license label alone does not settle the project’s commercial-use terms. Third-party models, data, and weights may carry their own terms.
The available official descriptions establish the system’s architecture, components, and demonstrations, but not an independently verified comparative benchmark or quantified cost comparison. Treat claims about capability as the authors’ reported design and examples, rather than evidence that NExT-GPT outperforms another multimodal system. For a useful comparison, look at supported input and output combinations, component architecture, which layers are tuned or frozen, availability of code and weights, and the compute and usage terms for the workload you care about.
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