* dont show duplicate toasts if workflow actions fail due to auth
* dynamic order by options based on projectId
* add endpointName to authtoast to makeit unique per endpoint
* lint
* update toast logic to check based on endpoint name w type safety
* fix save as endpoit name
* lint
* fix type
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Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* retain id through workflow state so that we correctly update or save new
* lint
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Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* 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
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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
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.
