Discriminator Guidance
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
- 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
- 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
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.2 × 10²⁰ FLOP
- How it was established
- Hardware
481 hours * 312 TFLOPS (A100) * 40% utilization
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 PCIe
- Wall-clock time
- 481 hours (20 days)
- Compute cost
- $338
Table 6
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 (non-commercial)
- Training code
- Open (non-commercial)
https://github.com/alsdudrla10/DG Attribution-NonCommercial-ShareAlike 4.0 International checkpoints and train code here: https://github.com/alsdudrla10/DG/blob/main/README.md
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
- Confident
- 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)." https://paperswithcode.com/paper/refining-generative-process-with
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
About this model
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.
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.
Training and provenance
The training run consumed about 2.2 × 10²⁰ FLOP, on hardware recorded as NVIDIA A100 PCIe. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 327,978,752 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
Discriminator Guidance — common questions
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. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Discriminator Guidance— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Discriminator Guidance— how much compute was used to train it?
Training consumed around 2.2 × 10²⁰ FLOP, on hardware recorded as NVIDIA A100 PCIe. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Discriminator Guidance— what GPU do I need to run it?
We cannot say. It 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.
Discriminator Guidance— is it open source?
Its weights are published, so it 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.
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.
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.
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.
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.