TokenFlow-t2i
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- ByteDance
- Organisation type
- Industry
- Country
- China
- Published
- 4 December 2024
- Authors
- Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang, Yi Jiang, Yiming Gao, Hu Ye, Daniel K. Du, Zehuan Yuan, Xinglong Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation, Text-to-image
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
- Hugging Face
- ByteFlow-AI
Apache 2.0 https://github.com/ByteFlow-AI/TokenFlow/tree/main Apache 2.0 https://huggingface.co/ByteFlow-AI/TokenFlow-t2i
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
TokenFlow-t2i was published by ByteDance, in China, in December 2024. The organisation is categorised as industry.
It works in Image generation, and is recorded as doing image generation, Text-to-image.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the ByteFlow-AI organisation on Hugging Face.
Answers
TokenFlow-t2i — common questions
Where can I download TokenFlow-t2i?
Its weights are published under the ByteFlow-AI organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
What GPU do I need to run TokenFlow-t2i?
We cannot say. TokenFlow-t2i has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is TokenFlow-t2i open source?
Its weights are published, so TokenFlow-t2i can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does TokenFlow-t2i have?
No parameter count has been published for TokenFlow-t2i, which is why no memory or speed figure appears on this page.
Who created TokenFlow-t2i?
TokenFlow-t2i was published by ByteDance, based in China, categorised as industry.
When was TokenFlow-t2i released?
TokenFlow-t2i was published in December 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is TokenFlow-t2i used for?
TokenFlow-t2i works in Image generation, and is recorded as handling image generation, Text-to-image. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.