## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No: n/a
## Description
At some point I typo'd this and set the max seed to signed int32 max. It
should be *un*signed int32 max.
This restored the seed range to what it was in v2.3.
Also fixed a bug in the Noise node which resulted in the max valid seed
being one less than intended.
## Related Tickets & Documents
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- Related Issues
#2843 is against v2.3 and increases the range of valid seeds
substantially. Maybe we can explore this in the future but as of v3.0,
we use numpy for a RNG in a few places, and it maxes out at the max
`uint32`. I will close this PR as this supersedes it.
- Closes#3866
## QA Instructions, Screenshots, Recordings
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You should be able to use seeds up to and including `4294967295`.
## Added/updated tests?
- [ ] Yes
- [x] No : don't think we have any relevant tests
## [optional] Are there any post deployment tasks we need to perform?
nope!
At some point I typo'd this and set the max seed to signed int32 max. It should be *un*signed int32 max.
This restored the seed range to what it was in v2.3.
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [x] Yes, we feel very passionate about this.
## Description
Uploading an incorrect JSON file to the Node Editor would crash the app.
While this is a much larger problem that we will tackle while refining
the Node Editor, this is a fix that should address 99% of the cases out
there.
When saving an InvokeAI node graph, there are three primary keys.
1. `nodes` - which has all the node related data.
2. `edges` - which has all the edges related data
3. `viewport` - which has all the viewport related data.
So when we load back the JSON, we now check if all three of these keys
exist in the retrieved JSON object. While the `viewport` itself is not a
mandatory key to repopulate the graph, checking for it will allow us to
treat it as an additional check to ensure that the graph was saved from
InvokeAI.
As a result ...
- If you upload an invalid JSON file, the app now warns you that the
JSON is invalid.
- If you upload a JSON of a graph editor that is not InvokeAI, it simply
warns you that you are uploading a non InvokeAI graph.
So effectively, you should not be able to load any graph that is not
generated by ReactFlow.
Here are the edge cases:
- What happens if a user maintains the above key structure but tampers
with the data inside them? Well tested it. Turns out because we validate
and build the graph based on the JSON data, if you tamper with any data
that is needed to rebuild that node, it simply will skip that and load
the rest of the graph with valid data.
- What happens if a user uploads a graph that was made by some other
random ReactFlow app? Well, same as above. Because we do not have to
parse that in our setup, it simply will skip it and only display what
are setup to do.
I think that just about covers 99% of the cases where this could go
wrong. If there's any other edges cases, can add checks if need be. But
can't think of any at the moment.
## Related Tickets & Documents
### Closes
- #3893
- #3881
## [optional] Are there any post deployment tasks we need to perform?
Yes. Making @psychedelicious a little bit happier. :P
- use the existing logic to determine if generation is txt2img, img2img, inpaint or outpaint
- technically `outpaint` and `inpaint` are the same, just display
"Inpaint" if its either
- debounce this by 1s to prevent jank
When a queue item is popped for processing, we need to retrieve its session from the DB. Pydantic serializes the graph at this stage.
It's possible for a graph to have been made invalid during the graph preparation stage (e.g. an ancestor node executes, and its output is not valid for its successor node's input field).
When this occurs, the session in the DB will fail validation, but we don't have a chance to find out until it is retrieved and parsed by pydantic.
This logic was previously not wrapped in any exception handling.
Just after retrieving a session, we retrieve the specific invocation to execute from the session. It's possible that this could also have some sort of error, though it should be impossible for it to be a pydantic validation error (that would have been caught during session validation). There was also no exception handling here.
When either of these processes fail, the processor gets soft-locked because the processor's cleanup logic is never run. (I didn't dig deeper into exactly what cleanup is not happening, because the fix is to just handle the exceptions.)
This PR adds exception handling to both the session retrieval and node retrieval and events for each: `session_retrieval_error` and `invocation_retrieval_error`.
These events are caught and displayed in the UI as toasts, along with the type of the python exception (e.g. `Validation Error`). The events are also logged to the browser console.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: n/a
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No n/a
## Description
Big cleanup:
- improve & simplify the app logging
- resolve all TS issues
- resolve all circular dependencies
- fix all lint/format issues
## QA Instructions, Screenshots, Recordings
`yarn lint` passes:
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## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
bask in the glory of what *should* be a fully-passing frontend lint on
this PR
Added the Ideal Size node
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: It's a community node addition
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Added a reference to my community node that calculates the ideal size
for initial latent generation that avoids duplication. This is the logic
that was present in 2.3.5's first pass of high-res optimization.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [X] No : This is a documentation change that references my community
node.
## [optional] Are there any post deployment tasks we need to perform?
Add Face Mask to communityNodes.md
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [x] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Add Face Mask to communituNodes.md list.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: just updated docs to try to help lead new users to
installs a little easier
## Have you updated relevant documentation?
- [x] Yes
- [ ] No
## Description
Some minor docs tweaks
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Revised boards logic and UI
## Related Tickets & Documents
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pull
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- Related Issue # discord convos
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
