DiT-XL/2 + Discriminator Guidance
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- Korea Advanced Institute of Science and Technology (KAIST),NAVER
- Organisation type
- Academia,Industry
- Country
- Korea (Republic of)
- Published
- 28 November 2022
- Authors
- Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon
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
- Base model
- DiT-XL/2
- Numerical format
- FP16
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
- 327,978,752 tokens
- Epochs
- 10
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A100
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
Attribution-NonCommercial-ShareAlike 4.0 International https://github.com/alsdudrla10/DG?tab=readme-ov-file I cannot see checkpoints / model weights in the repo
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 110
"Using our algorithm, we achive state-of-the-art results on ImageNet 256x256 with FID 1.83 and recall 0.64, similar to the validation data's FID (1.68) and recall (0.66)"
Sources
Where this record came from and when it was last checked.
- Reference
- Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DiT-XL/2 + Discriminator Guidance was published by Korea Advanced Institute of Science and Technology (KAIST),NAVER, in Korea (Republic of), in November 2022. The organisation is categorised as academia,Industry.
It works in Image generation, and is recorded as doing image generation, Text-to-image.
It builds on DiT-XL/2, which is why it shares that model's general shape and size.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Around 327,978,752 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
DiT-XL/2 + Discriminator Guidance — common questions
How many parameters does DiT-XL/2 + Discriminator Guidance have?
No parameter count has been published for DiT-XL/2 + Discriminator Guidance, which is why no memory or speed figure appears on this page.
Who created DiT-XL/2 + Discriminator Guidance?
DiT-XL/2 + Discriminator Guidance was published by Korea Advanced Institute of Science and Technology (KAIST),NAVER, based in Korea (Republic of), categorised as academia,Industry.
When was DiT-XL/2 + Discriminator Guidance released?
DiT-XL/2 + Discriminator Guidance was published in November 2022. 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 DiT-XL/2 + Discriminator Guidance used for?
DiT-XL/2 + Discriminator Guidance works in Image generation, and is recorded as handling image generation, Text-to-image. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run DiT-XL/2 + Discriminator Guidance?
None. DiT-XL/2 + Discriminator Guidance is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is DiT-XL/2 + Discriminator Guidance open source?
No. DiT-XL/2 + Discriminator Guidance has not had its weights published, so it exists only as a service controlled by its owner.
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.