AR-LDM
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
- Alibaba,University of Waterloo,Vector Institute
- Organisation type
- Industry,Academia,Academia
- Country
- China, Canada
- Published
- 20 November 2022
- Authors
- Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue, Wenhu Chen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Text-to-image
- Base model
- Stable Diffusion (LDM-KL-8-G)
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.
- Parameters
- 1.5B
- Training data
- tokens
- Epochs
- 50
Table 1
PororoSV, FlintstonesSV and VIST. All storytelling datasets, sizes would be possible to look up.
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
- 5.1 × 10²⁰ FLOP
- How it was established
- Hardware
8 NVIDIA A100 GPUs for 8 days
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
- Wall-clock time
- 194 hours (8.1 days)
- Compute cost
- $746
8 NVIDIA A100 GPUs for 8 days
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)
no weights, no license. training and inference code here: https://github.com/xichenpan/ARLDM
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
- 86
The first latent diffusion model for coherent visual story synthesizing. "Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV, FlintstonesSV, and the newly introduced challenging dataset VIST containing natural images"
Sources
Where this record came from and when it was last checked.
- Reference
- Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
- Last updated
- 25 May 2026
What the numbers mean
Background
AR-LDM was published by Alibaba,University of Waterloo,Vector Institute, in the country recorded as China, during November 2022. The category the publisher falls under is industry,Academia,Academia.
It works in the domain of Image generation, and is recorded as performing the task of text-to-image.
Rather than being trained from scratch, it is derived from Stable Diffusion (LDM-KL-8-G). Most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required arithmetic totalling around 5.1 × 10²⁰ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Its inclusion criterion: sOTA improvement.
Answers
AR-LDM — common questions
AR-LDM— who created it?
It was published by Alibaba,University of Waterloo,Vector Institute, based in China, an organisation categorised as industry,Academia,Academia.
AR-LDM— 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.
AR-LDM— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of text-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.
AR-LDM— how much compute was used to train it?
Training consumed around 5.1 × 10²⁰ FLOP, on hardware recorded as NVIDIA A100. 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.
AR-LDM— 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.
AR-LDM— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AR-LDM— how many parameters does it have?
It has a parameter count of 1.5B. Table 1. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.