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 the country recorded as Korea (Republic of), during November 2022. The publishing organisation is categorised as academia,Industry.
It works in the domain of Image generation, and is recorded as performing the task of image generation, Text-to-image.
It builds on DiT-XL/2. That is the usual way a specialised model is produced.
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
Training consumed a corpus of around 327,978,752 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
DiT-XL/2 + Discriminator Guidance — common questions
DiT-XL/2 + Discriminator Guidance— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
DiT-XL/2 + Discriminator Guidance— who created it?
It was published by Korea Advanced Institute of Science and Technology (KAIST),NAVER, based in Korea (Republic of), an organisation categorised as academia,Industry.
DiT-XL/2 + Discriminator Guidance— when was it released?
It 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.
DiT-XL/2 + Discriminator Guidance— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation, Text-to-image. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DiT-XL/2 + Discriminator Guidance— what GPU do I need to run it?
None. This 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.
DiT-XL/2 + Discriminator Guidance— is it open source?
No. Its weights have not been 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.