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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and made available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Data
Are datasources for training the model that the end user interacts with comprehensively documented and made available?
Base Model Weights
Are the weights of the base models made freely available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Weights
Are the weights of the model that the end user interacts with made freely available?
Training Code
Is the source code of dataset processing, model training and tuning comprehensively made available?
Code Documentation
Is the source code of datasource processing, model training and tuning comprehensively documented?
Hardware Architecture
Is the hardware architecture used for datasource processing and model training comprehensively documented?
Preprint
Are archived preprint(s) are available that detail all major parts of the system including datasource processing, model training and tuning steps?
Paper
Are peer-reviewed scientific publications available that detail all major parts of the system including datasource processing, model training and tuning steps?
Modelcard
Is a model card available in standardized format that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation?
Datasheet
Is a datasheet as defined in "Datasheets for Datasets" (Gebru et al. 2021) available?
Package
Is a packaged release of the model available on a software repository (e.g. a Python Package Index, Homebrew)?
API and Meta Prompts
Is an API available that provides unrestricted access to the model (other than security and CDN restrictions)? If applicable, this entry also collects information on the use and availability of meta prompts.
Licenses
Is the project fully covered by Open Source Initiative (OSI)-approved licenses, including all data sources and training pipeline code?

SDXL-Lightning

by ByteDance

The model is distilled from Stable Diffusion XL
Image
Full
https://huggingface.co/ByteDance/SDXL-Lightning
Stable Diffusion XL Base 1.0
SDXL-Lightning
CreativeML Open RAIL++-M License
Chinese technology company that owns Tiktok
https://www.bytedance.com/
February 2024
Availability
Base Model Data
Trained on subsets of ImageNET and COCO, but specific dataset details are not disclosed.
https://arxiv.org/pdf/2307.01952
End User Model Data
Trained on subsets of LAION and COYO, but specific dataset details are not disclosed.
https://arxiv.org/pdf/2402.13929
Base Model Weights
Available through HuggingFace in the form of SafeTensors
https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
End User Model Weights
Available through HuggingFace in the form of SafeTensors
https://huggingface.co/ByteDance/SDXL-Lightning
Training Code
The training code for SDXL-Lightning has not been publicly released.
Documentation
Code Documentation
On HuggingFace: 'We are currently not planning on releasing the training code.'
https://huggingface.co/ByteDance/SDXL-Lightning/discussions/1
Hardware Architecture
Specific hardware details for training are not disclosed.
Preprint
https://arxiv.org/pdf/2402.13929
Paper
No peer-reviewed paper found
Modelcard
HuggingFace model card omits many details on datasets, training, fine-tuning etc..
https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/README.md
Datasheet
A formal datasheet has not been provided for SDXL-Lightning
Access
Package
Available through Hugging Face spaces and ComfyUI
https://huggingface.co/ByteDance/SDXL-Lightning/tree/main
API and Meta Prompts
Licenses
The model is released under the CreativeML Open RAIL++-M License, not an OSI-approved license
https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/LICENSE.md
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