The intention for this issue is to provide a comprehensive outline of all the core features and capabilities other distributions of Stable Diffusion (primarily A1111) provide. It's a big list, but not all are nearly as high priority as others. Some items in this outline will be turned into GitHub issues for discussing and tracking progress on implementation. Please comment on this issue to suggest additions, clarifications, and sub-features and I'll aim to keep the outline up to date.
Generation methods
Generation parameters
Model support
Stylization
ControlNet
Some features are described at https://github.com/Mikubill/sd-webui-controlnet but I don't currently have time to make a list of them. Help with such a list would be appreciated.
Optimizations
VRAM reduction strategies, things like xformers and floating point precision? I don't understand this stuff enough to really get it. Also other methods will remove certain parts of the pipeline from VRAM after that stage has been completed which trades time for VRAM requirements. I'll need help creating a list of out this.
Upscaling
Some upscalers are entirely separate models and are thus likely out of scope. Other upscalers, I think, are part of the SD pipeline. Some are scripts, but I think others are actual models which require being implemented in the actual pipeline? Those ones should probably be included here, but I need help creating a list.
Sampling methods
Other models
Did I miss something? Probably! Hopefully the community can help me keep this list updated so it's as comprehensive as possible. Thanks ❤️.
Ideally these capabilities would be modular, allowing for composability and opting in and out of specific features at will for any desired image generation pipeline. In our use case with Graphite, we want to put different options into nodes within a node graph so they are user-configurable. (I should also mention that keeping the MIT/Apache 2.0 license is important for Graphite, since our project is also Apache 2.0, so I'd humbly request that some care be taken to not copy from copyleft code which would force this library to change its license, thanks 😃).
The intention for this issue is to provide a comprehensive outline of all the core features and capabilities other distributions of Stable Diffusion (primarily A1111) provide. It's a big list, but not all are nearly as high priority as others. Some items in this outline will be turned into GitHub issues for discussing and tracking progress on implementation. Please comment on this issue to suggest additions, clarifications, and sub-features and I'll aim to keep the outline up to date.
Generation methods
Generation parameters
space ship(sci-fi) vs.space AND ship(sailing ship in space))(beautiful:1.5) tree (with autumn leaves:0.8))Model support
Stylization
ControlNet
Some features are described at https://github.com/Mikubill/sd-webui-controlnet but I don't currently have time to make a list of them. Help with such a list would be appreciated.
Optimizations
VRAM reduction strategies, things like xformers and floating point precision? I don't understand this stuff enough to really get it. Also other methods will remove certain parts of the pipeline from VRAM after that stage has been completed which trades time for VRAM requirements. I'll need help creating a list of out this.
Upscaling
Some upscalers are entirely separate models and are thus likely out of scope. Other upscalers, I think, are part of the SD pipeline. Some are scripts, but I think others are actual models which require being implemented in the actual pipeline? Those ones should probably be included here, but I need help creating a list.
Sampling methods
Other models
Did I miss something? Probably! Hopefully the community can help me keep this list updated so it's as comprehensive as possible. Thanks ❤️.
Ideally these capabilities would be modular, allowing for composability and opting in and out of specific features at will for any desired image generation pipeline. In our use case with Graphite, we want to put different options into nodes within a node graph so they are user-configurable. (I should also mention that keeping the MIT/Apache 2.0 license is important for Graphite, since our project is also Apache 2.0, so I'd humbly request that some care be taken to not copy from copyleft code which would force this library to change its license, thanks 😃).