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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?

AnimateDiff-Lightning

by ByteDance

Video generation model using progressive adversarial diffusion distillation. Distilled from various models.
Video
Limited
https://huggingface.co/ByteDance/AnimateDiff-Lightning
AnimateDiff-SD1.5-v2
AnimateDiff-Lightning
CreativeML-OpenRAIL-M
ByteDance, the parent company of TikTok.
https://www.bytedance.com/
March 2024
Availability
Training Code
No training code found.
Base Model Data
Paper mentions WebVid-10M as one base dataset. A great deal of synthetic data was used, which is not released.
https://arxiv.org/pdf/2307.04725
End User Model Data
Paper mentions WebVid-10M as one base dataset. A great deal of synthetic data was used, which is not released.
https://arxiv.org/pdf/2403.12706
Base Model Weights
Weights made available on HuggingFace.
https://huggingface.co/guoyww/animatediff
End User Model Weights
Weights made available on HuggingFace.
https://huggingface.co/ByteDance/AnimateDiff-Lightning
Documentation
Code Documentation
No training code, so no documentation.
Hardware Architecture
Hardware architecture described in paper.
https://arxiv.org/pdf/2403.12706
Preprint
Preprint available on arXiv.
https://arxiv.org/pdf/2403.12706
Paper
No peer-reviewed paper found.
Modelcard
Model card primarily contains information on usage and model architecture. No information provided on training and fine-tuning.
https://huggingface.co/ByteDance/AnimateDiff-Lightning
Datasheet
No datasheet found.
Access
Package
ComfyUI workflow for model available.
https://huggingface.co/ByteDance/AnimateDiff-Lightning#comfyui-usage
API and Meta Prompts
HuggingFace space containing model available.
https://huggingface.co/spaces/ByteDance/AnimateDiff-Lightning
Licenses
Model released under CreativeML-OpenRAIL-M, not an OSI-approved license.
https://huggingface.co/ByteDance/AnimateDiff-Lightning
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