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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?

DeepFloyd

by DeepFloyd, StabilityAI, LAION (major contributors)

Pixel-based triple-cascaded diffusion model.
Image
Full
https://huggingface.co/DeepFloyd/IF-I-XL-v1.0
IF-I-XL-V1.0
IF-I-XL-V1.0
DeepFloyd IF Research License Agreement
Collaboration between various organizations.
[ "https://huggingface.co/DeepFloyd", "https://stability.ai/", "https://laion.ai/" ]
April 2023
Availability
Training Code
No training code found.
https://github.com/deep-floyd/IF
Base Model Data
Model training partially based on LAION-A, however also uses internal datasets.
https://huggingface.co/DeepFloyd/IF-I-XL-v1.0#training
End User Model Data
Base Model Weights
Gated model made available through HuggingFace.
https://huggingface.co/DeepFloyd/IF-I-XL-v1.0
End User Model Weights
Documentation
Code Documentation
No training code, so no documentation.
Hardware Architecture
Hardware information provided.
https://huggingface.co/DeepFloyd/IF-I-XL-v1.0
Preprint
Model inspired by paper, however does not have a paper of its own.
https://arxiv.org/pdf/2205.11487
Paper
No paper found.
Modelcard
Model card provides the desired information.
https://huggingface.co/DeepFloyd/IF-I-XL-v1.0
Datasheet
No datasheet found.
Access
Package
Package available on PyPi.
https://pypi.org/project/deepfloyd-if/
API and Meta Prompts
No API found.
Licenses
DeepFloyd IF Research License Agreement, not an OSI-approved license.
https://huggingface.co/DeepFloyd/IF-I-XL-v1.0#model-details
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