* retain id through workflow state so that we correctly update or save new
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
Invoke 3.6.1 release
## QA Instructions, Screenshots, Recordings
[InvokeAI-installer-v3.6.1.zip](https://github.com/invoke-ai/InvokeAI/files/14041411/InvokeAI-installer-v3.6.1.zip)
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## Merge Plan
This PR can be merged when approved
## [optional] Are there any post deployment tasks we need to perform?
PyPI Release & GitHub Release
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Have you updated all relevant documentation?
- [x] No
## Description
- This adds the newly released Depth Anything to InvokeAI. A new node
`Depth Anything Processor` has been added to generate depth maps using
this new technique. https://depth-anything.github.io
- All related checkpoints will be downloaded automatically on first
boot. The `DinoV2` models will be loaded to your torch cache dir and the
checkpoints pertaining to Depth Anything will be downloaded to
`any/annotators/depth_anything`.
- Alternatively you can find the checkpoints here and download them to
that folder:
https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints
- This depth map can be used with any depth ControlNet model out there
but the folks at DepthAnything have also released a custom fine tuned
ControlNet model. From my limited testing, I still prefer the original
depth model because this one seems to be producing weird artifacts. Not
sure if that is a specific problem to Invoke or just the model itself.
I'll test more later. Place these in your controlnet folder like your
other ControlNets. You can get that here:
https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints_controlnet
- Also available in the LinearUI
- DepthAnything has three models `large`, `base` and `small` -- I've
defaulted the processor to small but a user can change to the large
model if they wish to do so. Small is way faster but obviously somewhat
of a lesser quality.
- DepthAnything is now the default processor for depth controlnet
models.
## Screenshots
## Merge Plan
DO NOT MERGE YET. Test it first and I'm sure the model caching can be
done better. Coz I don't think I've done that at all. I would appreciate
if @brandonrising or @lstein or anyone can take a look at that part of
it.
* feat: ✨ "Remix Image" option on images
Adds a new "remix image" option where applicable, recalls all metadata except the seed
* refactor: 🚨 lint code
* feat: ✨ "Remix Image" option on images
Adds a new "remix image" option where applicable, recalls all metadata except the seed
* refactor: 🚨 lint code
* feat: ✨ add new remix hotkey to hotkeys modal
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Currently translated at 60.0% (850 of 1415 strings)
Co-authored-by: Alexander Eichhorn <pfannkuchensack@einfach-doof.de>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
Remove `trim()` from model identifier schema, which prevented parsed model identifiers from matching.
The root issue here is that model names are identifiers. This will be resolved in the model manager refactor.
Closes#5556
- Bump `@invoke-ai/ui` for updated styles
- Update regex to parse prompts with newlines
- Update styling of overlay button when prompt has an error
- Fix bug where loading and error state sometimes weren't cleared
We had a one-behind issue with recalling metadata items that had a model.
For example, when recalling LoRAs, we check against the current main model to decide whether or not the requested LoRA is compatible and may be recalled.
When recalling all params, we are often also recalling the main model, but the compat logic didn't compare against this new main model.
The logic is updated to check against the new main model, if one is being set.
Closes#5512
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Description
Update UI README
## Merge Plan
This PR can be merged when approved
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The frontend docs should just be in the frontend. This is a standard practice for monorepos with developer information for specific packages within the monorepo.
The Ideal Size node is useful for High-Res Optimization as it gives the optimum size for creating an initial generation with minimal artifacts (duplication and other strangeness) from today's models.
After inclusion, front end graph generation can be simplified by offloading calculations for HRO initial generation to this node.
