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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and freely 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 enduser interacts with comprehensively documented and freely 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 enduser interacts with made freely available?
Training Code
Is the source code of datasource processing, model training and tuining comprehensively and freely 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 in standardized format available that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation are available?
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?

PixArt

by PixArt-Alpha

Weak-to-strong-trained model (higher quality data incorporated later) for text-to-image generation.
Image
Full
https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
PixArt-Sigma-XL-2-1024-MS
PixArt-Sigma-XL-2-1024-MS
CreativeML Open RAIL++-M License
Collaboration between Huawei's Noah's Ark Lab and two Chinese universities.
https://huggingface.co/PixArt-alpha
April 2024
Availability
Training Code
Training code made available on GitHub.
https://github.com/PixArt-alpha/PixArt-sigma
Base Model Data
Internal dataset, apparantly undisclosed.
End User Model Data
Base Model Weights
https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
End User Model Weights
Weights available through HuggingFace.
Documentation
Code Documentation
Training code usage documented in README, no documentation in training code itself.
https://github.com/PixArt-alpha/PixArt-sigma
Hardware Architecture
Architecture described in paper.
https://arxiv.org/pdf/2403.04692
Preprint
Preprint made available on arXiv.
https://arxiv.org/pdf/2403.04692
Paper
Paper published as conference paper.
https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/04633.pdf
Modelcard
Model card provides surface-level information, refers to GitHub and paper for further information.
https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
Datasheet
Access
Package
No package found.
API and Meta Prompts
Inference API available through HuggingFace.
https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
Licenses
CreativeML Open RAIL++-M License. Not an OSI-approved license.
https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS#model-description
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