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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?

AMD Nitro Diffusion

by AMD

More efficient implementation of Stable Diffusion 2.1.
Image
Full
https://huggingface.co/amd/SD2.1-Nitro
Stable-Diffusion-2-Base
SD2.1-Nitro
(undefined)
AMD, a major chip manufacturer.
https://www.amd.com/en.html
November 2024
Availability
Training Code
Training code made available through GitHub.
https://github.com/AMD-AIG-AIMA/AMD-Diffusion-Distillation
Base Model Data
Trained on a subset of LAION-5B, an openly available dataset.
https://laion.ai/blog/laion-5b/
End User Model Data
Distilled model, so not applicable.
Base Model Weights
Weights available through HuggingFace.
https://huggingface.co/stabilityai/stable-diffusion-2-base
End User Model Weights
Weights available through HuggingFace.
https://huggingface.co/amd/SD2.1-Nitro
Documentation
Code Documentation
Model code well-documented.
https://github.com/AMD-AIG-AIMA/AMD-LLM
Hardware Architecture
Hardware outlined in model card.
https://huggingface.co/amd/SD2.1-Nitro
Preprint
No preprint available.
Paper
Modelcard
Model card provides superficial knowledge about architecture, training, the fine-tuning approach used, and evaluation.
https://huggingface.co/amd/SD2.1-Nitro
Datasheet
Data collection procedures for LAION are outlined in a corresponding paper.
https://arxiv.org/pdf/2210.08402
Access
Package
No package found.
API and Meta Prompts
No API found.
Licenses
Model licensed under Apache-2.0, an OSI-approved license.
https://huggingface.co/amd/SD2.1-Nitro#license
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