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About Tiamat

Tiamat uses generative AI to automatically create new digital images by combining known text or image files, breaking through the efficiency constraints faced by professional illustrators, designers, and digital artists. Oasis Capital has been paying attention to the fundamental changes brought by AIGC to the industry since the first half of last year. We believe that Tiamat, with its accumulated specific style pipelines and widespread influence among community users, will definitely provide users with a new generation of creative design tools. Tiamat is gradually building a 'AIGC-based' workflow around the vertical design scene, and we believe this will bring about significant changes to the entire creative industry. ”

Tiamat official website:https://www.tiamat.world/

Origin and development of Tiamat

Tiamat was founded in 2021 and is a domestic AI image generation technology service provider.Its independently developed MorpherVLM is the first domestic cross-modal generation model based on the concept fusion paradigm, with nearly 10 billion parameters.By using a heterogeneous visual encoding-decoding network structure and introducing reinforcement learning (RLHF) based on user feedback and fine-grained prompt-variable alignment technology, it improves the model's ability to model image multi-scale information and also achieves progress in understanding user prompts.

How is Tiamat?

Q: Starting from large models, what are the differences between AI painting and conversational AI like ChatGPT?

Qinggan:Different tracks in AIGC actually have many details of differences. Firstly, it involves whether these models are single-modal or multi-modal. Our model connects multiple modalities, which is reflected in users being able to guide the generated images with text or get new generation results with a sketch. While ChatGPT, as a model focused on text understanding and generation, only has text as its modality. However, at the same time, it has a very large number of parameters, requiring a significant amount of time and data to clean harmful information, so the time, effort, and computing power required is actually much greater than that of our AI Art field. From a more intuitive perspective, if there are logical errors or distortions in the text, users can quickly feel it, unlike the better tolerance for minor flaws in images.

What we are doing now Tiamat Text-to-image model, with approximatelytens of billions of parameters, which is actually about 100 times lower than the parameter volume of ChatGPT's text model. In terms of their development at the time, it is roughly between GPT1-2. So, although both are called large models, there is still a significant difference in data and parameter scale. On this basis, for us, continuously expanding the scale of model parameters is not the most important indicator.

Eric:In addition to the differences in modality and input types, the generated task is another dimension for reference. When we talk about AI, there are actually many areas of explanation, such as facial recognition, big data text analysis, etc., which are more about understanding data. But when it comes to the AIGC field, more is about understanding and generation. ChatGPT can first do a good job of understanding your input content and calling on the knowledge hidden in its parameters to respond to human input. In this aspect, we can make an analogy with ChatGPT. We will put the user's input into a more natural space for AI using multiple different networks and then find the appropriate answers from this space. This is different from the previous generation of AI that focuses on understanding.