The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned.
## Results
This feature provides anywhere some significant to massive performance improvement.
The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal.
## Overview
A new `invocation_cache` service is added to handle the caching. There's not much to it.
All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching.
The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic.
To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key.
## In-Memory Implementation
An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts.
Max node cache size is added as `node_cache_size` under the `Generation` config category.
It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher.
Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them.
## Node Definition
The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`.
Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`.
The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something.
## One Gotcha
Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again.
If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit.
## Linear UI
The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs.
This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default.
This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`.
## Workflow Editor
All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user.
The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes.
Users should consider saving their workflows after loading them in and having them updated.
## Future Enhancements - Callback
A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not.
This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field.
## Future Enhancements - Persisted Cache
Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
A few Missed Translations From the Translation Update
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ X ] 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
## Description
Mask Edge was set to default, and producing poor results. I've updated
the default back to Unmasked.
The immutable and serializable checks for redux can cause substantial performance issues. The immutable check in particular is pretty heavy. It's only run in dev mode, but this and really slow down the already-slower performance of dev mode.
The most important one for us is serializable, which has far less of a performance impact.
The immutable check is largely redundant because we use immer-backed RTK for everything and immer gives us confidence there.
Disable the immutable check, leaving serializable in.
A few weeks back, we changed how the canvas scales in response to changes in window/panel size.
This introduced a bug where if we the user hadn't already clicked the canvas tab once to initialize the stage elements, the stage's dimensions were zero, then the calculation of the stage's scale ends up zero, then something is divided by that zero and Konva dies.
This is only a problem on Chromium browsers - somehow Firefox handles it gracefully.
Now, when calculating the stage scale, never return a 0 - if it's a zero, return 1 instead. This is enough to fix the crash, but the image ends up centered on the top-left corner of the stage (the origin of the canvas).
Because the canvas elements are not initialized at this point (we haven't switched tabs yet), the stage dimensions fall back to (0,0). This means the center of the stage is also (0,0) - so the image is centered on (0,0), the top-left corner of the stage.
To fix this, we need to ensure we:
- Change to the canvas tab before actually setting the image, so the stage elements are able to initialize
- Use `flushSync` to flush DOM updates for this tab change so we actually have DOM elements to work with
- Update the stage dimensions once on first load of it (so in the effect that sets up the resize observer, we update the stage dimensions)
The result now is the expected behaviour - images sent to canvas do not crash and end up in the center of the canvas.
JSX is not serializable, so it cannot be in redux. Non-serializable global state may be put into `nanostores`.
- Use `nanostores` for `customStarUI`
- Use `nanostores` for `headerComponent`
- Re-enable the serializable & immutable check redux middlewares
* Update collections.py
RangeOfSizeInvocation was not taking step into account when generating the end point of the range
* - updated the node description to refelect this mod
- added a gt=0 constraint to ensure only a positive size of the range
- moved the + 1 to be on the size. To ensure the range is the requested size in cases where the step is negative
- formatted with Black
* Removed +1 from the range calculation
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* New classes to support the PromptsFromFileInvocation Class
- PromptPosNegOutput
- PromptSplitNegInvocation
- PromptJoinInvocation
- PromptReplaceInvocation
* - Added PromptsToFileInvocation,
- PromptSplitNegInvocation
- now counts the bracket depth so ensures it cout the numbr of open and close brackets match.
- checks for escaped [ ] so ignores them if escaped e.g \[
- PromptReplaceInvocation - now has a user regex. and no regex in made caseinsesitive
* Update prompt.py
created class PromptsToFileInvocationOutput and use it in PromptsToFileInvocation instead of BaseInvocationOutput
* Update prompt.py
* Added schema_extra title and tags for PromptReplaceInvocation, PromptJoinInvocation, PromptSplitNegInvocation and PromptsToFileInvocation
* Added PTFileds Collect and Expand
* update to nodes v1
* added ui_type to file_path for PromptToFile
* update params for the primitive types used, remove the ui_type filepath, promptsToFile now only accepts collections until a fix is available
* updated the parameters for the StringOutput primitive
* moved the prompt tools nodes out of the prompt.py into prompt_tools.py
* more rework for v1
* added github link
* updated to use "@invocation"
* updated tags
* Adde new nodes PromptStrength and PromptStrengthsCombine
* chore: black
* feat(nodes): add version to prompt nodes
* renamed nodes from prompt related to string related. Also moved them into a strings.py file. Also moved and renamed the PromptsFromFileInvocation from prompt.py to strings.py. The PTfileds still remain in the Prompt_tool.py for now.
* added , version="1.0.0" to the invocations
* removed the PTField related nodes and the prompt-tools.py file all new nodes now live in the
* formatted prompt.py and strings.py with Black and fixed silly mistake in the new StringSplitInvocation
* - Revert Prompt.py back to original
- Update strings.py to be only StringJoin, StringJoinThre, StringReplace, StringSplitNeg, StringSplit
* applied isort to imports
* fix(nodes): typos in `strings.py`
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
This maps values to labels for multiple-choice fields.
This allows "enum" fields (i.e. `Literal["val1", "val2", ...]` fields) to use code-friendly string values for choices, but present this to the UI as human-friendly labels.
* Added crop option to ImagePasteInvocation
ImagePasteInvocation extended the image with transparency when pasting outside of the base image's bounds. This introduces a new option to crop the resulting image back to the original base image.
* Updated version for ImagePasteInvocation as 3.1.1 was released.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] 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
There was an issue with the responsiveness of the quick links buttons in
the documentation.
## Related Tickets & Documents
- Related Issue #4455
- Closes#4455
## QA Instructions, Screenshots, Recordings
• On the documentation website, go to the Home page, scroll down to the
quick-links section.
[Home - InvokeAI Stable Diffusion Toolkit
Docs.webm](https://github.com/invoke-ai/InvokeAI/assets/92071471/0a7095c1-9d78-47f2-8da7-9c1e796bea3d)
## Added/updated tests?
- [ ] Yes
- [x] No : _It is a minor change in the documentation website._
## [optional] Are there any post deployment tasks we need to perform? No
We need to parse the config before doing anything related to invocations to ensure that the invocations union picks up on denied nodes.
- Move that to the top of api_app and cli_app
- Wrap subsequent imports in `if True:`, as a hack to satisfy flake8 and not have to noqa every line or the whole file
- Add tests to ensure graph validation fails when using a denied node, and that the invocations union does not have denied nodes (this indirectly provides confidence that the generated OpenAPI schema will not include denied nodes)
This simply hides nodes from the workflow editor. The nodes will still work if an API request is made with them. For example, you could hide `iterate` nodes from the workflow editor, but if the Linear UI makes use of those nodes, they will still function.
- Update `AppConfig` with optional property `nodesDenylist: string[]`
- If provided, nodes are filtered out by `type` in the workflow editor
Allow denying and explicitly allowing nodes. When a not-allowed node is used, a pydantic `ValidationError` will be raised.
- When collecting all invocations, check against the allowlist and denylist first. When pydantic constructs any unions related to nodes, the denied nodes will be omitted
- Add `allow_nodes` and `deny_nodes` to `InvokeAIAppConfig`. These are `Union[list[str], None]`, and may be populated with the `type` of invocations.
- When `allow_nodes` is `None`, allow all nodes, else if it is `list[str]`, only allow nodes in the list
- When `deny_nodes` is `None`, deny no nodes, else if it is `list[str]`, deny nodes in the list
- `deny_nodes` overrides `allow_nodes`
## What type of PR is this? (check all applicable)
3.1.1 Release build & updates
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Adds a configuration option to fetch metadata and workflows from api
isntead of the image file. Needed for commercial.
Minor corrections to spell and grammar in the feature request template.
## 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:
This PR should be self explanatory.
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Minor corrections to spell and grammar in the feature request template.
No code or behavioural changes.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
N/A
## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
There are no tests for the issue template.
## [optional] Are there any post deployment tasks we need to perform?
I added extra steps to update the Cudnnn DLL found in the Torch package
because it wasn't optimised or didn't use the lastest version. So
manually updating it can speed up iteration but the result might differ
from each card. Exemple i passed from 3 it/s to a steady 20 it/s.
## 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?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [x] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(nodes): add version to iterate and collect
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
Scale Before Processing Dimensions now respect the Aspect Ratio that is
locked in. This makes it way easier to control the setting when using it
with locked ratios on the canvas.
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
Running the config script on Macs triggered an error due to absence of
VRAM on these machines! VRAM setting is now skipped.
## Added/updated tests?
- [ ] Yes
- [X] No : Will add this test in the near future.
I added extra steps to update the Cudnnn DLL found in the Torch package because it wasn't optimised or didn't use the lastest version. So manually updating it can speed up iteration but the result might differ from each card. Exemple i passed from 3 it/s to a steady 20 it/s.
@blessedcoolant Per discussion, have updated codeowners so that we're
not force merging things.
This will, however, necessitate a much more disciplined approval.
## 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?
- [ ] Yes
- [ ] No
## Description
Add textfontimage node to communityNodes.md
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
fix(ui): fix non-nodes validation logic being applied to nodes invoke
button
For example, if you had an invalid controlnet setup, it would prevent
you from invoking on nodes, when node validation was disabled.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes
https://discord.com/channels/1020123559063990373/1028661664519831552/1148431783289966603
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Optimization
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
# Coherence Mode
A new parameter called Coherence Mode has been added to Coherence Pass
settings. This parameter controls what kind of Coherence Pass is done
after Inpainting and Outpainting.
- Unmasked: This performs a complete unmasked image to image pass on the
entire generation.
- Mask: This performs a masked image to image pass using your input mask
as the coherence mask.
- Mask Edge [DEFAULT] - This performs as masked image to image pass on
the edges of your mask to try and clear out the seams.
# Why The Coherence Masked Modes?
One of the issues with unmasked coherence pass arises when the diffusion
process is trying to align detailed or organic objects. Because Image to
Image tends change the image a little bit even at lower strengths, this
ends up in the paste back process being slightly misaligned. By
providing the mask to the Coherence Pass, we can try to eliminate this
in those cases. While it will be impossible to address this for every
image out there, having these options will allow the user to automate a
lot of this. For everything else there's manual paint over with inpaint.
# Graph Improvements
The graphs have now been refined quite a bit. We no longer do manual
blurring of the masks anymore for outpainting. This is no longer needed
because we now dilate the mask depending on the blur size while pasting
back. As a result we got rid of quite a few nodes that were handling
this in the older graph.
The graphs are also a lot cleaner now because we now tackle Scaled
Dimensions & Coherence Mode completely independently.
Inpainting result seem very promising especially with the Mask Edge
mode.
---
# New Infill Methods [Experimental]
We are currently trying out various new infill methods to see which ones
might perform the best in outpainting. We may keep all of them or keep
none. This will be decided as we test more.
## LaMa Infill
- Renabled LaMA infill in the UI.
- We are trying to get this to work without a memory overhead.
In order to use LaMa, you need to manually download and place the LaMa
JIT model in `models/core/misc/lama/lama.pt`. You can download the JIT
model from Sanster
[here](https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt)
and rename it to `lama.pt` or you can use the script in the original
LaMA repo to convert the base model to a JIT model yourself.
## CV2 Infill
- Added a new infilling method using CV2's Inpaint.
## Patchmatch Rescaling
Patchmatch infill input image is now downscaled and infilled. Patchmatch
can be really slow at large resolutions and this is a pretty decent way
to get around that. Additionally, downscaling might also provide a
better patch match by avoiding larger areas to be infilled with
repeating patches. But that's just the theory. Still testing it out.
## [optional] Are there any post deployment tasks we need to perform?
- If we decide to keep LaMA infill, then we will need to host the model
and update the installer to download it as a core model.
Adds my (@dwringer's) released nodes to the community nodes page.
## 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?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Adds my released nodes -
Depth Map from Wavefront OBJ
Enhance Image
Generative Grammar-Based Prompt Nodes
Ideal Size Stepper
Image Compositor
Final Size & Orientation / Random Switch (Integers)
Text Mask (Simple 2D)
* Consolidated saturation/luminosity adjust.
Now allows increasing and inverting.
Accepts any color PIL format and channel designation.
* Updated docs/nodes/defaultNodes.md
* shortened tags list to channel types only
* fix typo in mode list
* split features into offset and multiply nodes
* Updated documentation
* Change invert to discrete boolean.
Previous math was unclear and had issues with 0 values.
* chore: black
* chore(ui): typegen
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Revised links to my node py files, replacing them with links to independent repos. Additionally I consolidated some nodes together (Image and Mask Composition Pack, Size Stepper nodes).
## 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?
- [x] Yes
- [ ] No
## Description
This PR is based on #4423 and should not be merged until it is merged.
[feat(nodes): add version to node
schemas](c179d4ccb7)
The `@invocation` decorator is extended with an optional `version` arg.
On execution of the decorator, the version string is parsed using the
`semver` package (this was an indirect dependency and has been added to
`pyproject.toml`).
All built-in nodes are set with `version="1.0.0"`.
The version is added to the OpenAPI Schema for consumption by the
client.
[feat(ui): handle node
versions](03de3e4f78)
- Node versions are now added to node templates
- Node data (including in workflows) include the version of the node
- On loading a workflow, we check to see if the node and template
versions match exactly. If not, a warning is logged to console.
- The node info icon (top-right corner of node, which you may click to
open the notes editor) now shows the version and mentions any issues.
- Some workflow validation logic has been shifted around and is now
executed in a redux listener.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes#4393
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Loading old workflows should prompt a warning, and the node status icon
should indicate some action is needed.
## [optional] Are there any post deployment tasks we need to perform?
I've updated the default workflows:
- Bump workflow versions from 1.0 to 1.0.1
- Add versions for all nodes in the workflows
- Test workflows
[Default
Workflows.zip](https://github.com/invoke-ai/InvokeAI/files/12511911/Default.Workflows.zip)
I'm not sure where these are being stored right now @Millu
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
### Polymorphic Fields
Initial support for polymorphic field types. Polymorphic types are a
single of or list of a specific type. For example, `Union[str,
list[str]]`.
Polymorphics do not yet have support for direct input in the UI (will
come in the future). They will be forcibly set as Connection-only
fields, in which case users will not be able to provide direct input to
the field.
If a polymorphic should present as a singleton type - which would allow
direct input - the node must provide an explicit type hint.
For example, `DenoiseLatents`' `CFG Scale` is polymorphic, but in the
node editor, we want to present this as a number input. In the node
definition, the field is given `ui_type=UIType.Float`, which tells the
UI to treat this as a `float` field.
The connection validation logic will prevent connecting a collection to
`CFG Scale` in this situation, because it is typed as `float`. The
workaround is to disable validation from the settings to make this
specific connection. A future improvement will resolve this.
### Collection Fields
This also introduces better support for collection field types. Like
polymorphics, collection types are parsed automatically by the client
and do not need any specific type hints.
Also like polymorphics, there is no support yet for direct input of
collection types in the UI.
### Other Changes
- Disabling validation in workflow editor now displays the visual hints
for valid connections, but lets you connect to anything.
- Added `ui_order: int` to `InputField` and `OutputField`. The UI will
use this, if present, to order fields in a node UI. See usage in
`DenoiseLatents` for an example.
- Updated the field colors - duplicate colors have just been lightened a
bit. It's not perfect but it was a quick fix.
- Field handles for collections are the same color as their single
counterparts, but have a dark dot in the center of them.
- Field handles for polymorphics are a rounded square with dot in the
middle.
- Removed all fields that just render `null` from `InputFieldRenderer`,
replaced with a single fallback
- Removed logic in `zValidatedWorkflow`, which checked for existence of
node templates for each node in a workflow. This logic introduced a
circular dependency, due to importing the global redux `store` in order
to get the node templates within a zod schema. It's actually fine to
just leave this out entirely; The case of a missing node template is
handled by the UI. Fixing it otherwise would introduce a substantial
headache.
- Fixed the `ControlNetInvocation.control_model` field default, which
was a string when it shouldn't have one.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes#4266
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Add this polymorphic float node to the end of your
`invokeai/app/invocations/primitives.py`:
```py
@invocation("float_poly", title="Float Poly Test", tags=["primitives", "float"], category="primitives")
class FloatPolyInvocation(BaseInvocation):
"""A float polymorphic primitive value"""
value: Union[float, list[float]] = InputField(default_factory=list, description="The float value")
def invoke(self, context: InvocationContext) -> FloatOutput:
return FloatOutput(value=self.value[0] if isinstance(self.value, list) else self.value)
``
Head over to nodes and try to connecting up some collection and polymorphic inputs.
- Node versions are now added to node templates
- Node data (including in workflows) include the version of the node
- On loading a workflow, we check to see if the node and template versions match exactly. If not, a warning is logged to console.
- The node info icon (top-right corner of node, which you may click to open the notes editor) now shows the version and mentions any issues.
- Some workflow validation logic has been shifted around and is now executed in a redux listener.
The `@invocation` decorator is extended with an optional `version` arg. On execution of the decorator, the version string is parsed using the `semver` package (this was an indirect dependency and has been added to `pyproject.toml`).
All built-in nodes are set with `version="1.0.0"`.
The version is added to the OpenAPI Schema for consumption by the client.
Initial support for polymorphic field types. Polymorphic types are a single of or list of a specific type. For example, `Union[str, list[str]]`.
Polymorphics do not yet have support for direct input in the UI (will come in the future). They will be forcibly set as Connection-only fields, in which case users will not be able to provide direct input to the field.
If a polymorphic should present as a singleton type - which would allow direct input - the node must provide an explicit type hint.
For example, `DenoiseLatents`' `CFG Scale` is polymorphic, but in the node editor, we want to present this as a number input. In the node definition, the field is given `ui_type=UIType.Float`, which tells the UI to treat this as a `float` field.
The connection validation logic will prevent connecting a collection to `CFG Scale` in this situation, because it is typed as `float`. The workaround is to disable validation from the settings to make this specific connection. A future improvement will resolve this.
This also introduces better support for collection field types. Like polymorphics, collection types are parsed automatically by the client and do not need any specific type hints.
Also like polymorphics, there is no support yet for direct input of collection types in the UI.
- Disabling validation in workflow editor now displays the visual hints for valid connections, but lets you connect to anything.
- Added `ui_order: int` to `InputField` and `OutputField`. The UI will use this, if present, to order fields in a node UI. See usage in `DenoiseLatents` for an example.
- Updated the field colors - duplicate colors have just been lightened a bit. It's not perfect but it was a quick fix.
- Field handles for collections are the same color as their single counterparts, but have a dark dot in the center of them.
- Field handles for polymorphics are a rounded square with dot in the middle.
- Removed all fields that just render `null` from `InputFieldRenderer`, replaced with a single fallback
- Removed logic in `zValidatedWorkflow`, which checked for existence of node templates for each node in a workflow. This logic introduced a circular dependency, due to importing the global redux `store` in order to get the node templates within a zod schema. It's actually fine to just leave this out entirely; The case of a missing node template is handled by the UI. Fixing it otherwise would introduce a substantial headache.
- Fixed the `ControlNetInvocation.control_model` field default, which was a string when it shouldn't have one.
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] No
## Description
Automatically infer the name of the model from the path supplied IF the
model name slot is empty. If the model name is not empty, we presume
that the user has entered a model name or made changes to it and we do
not touch it in order to not override user changes.
## Related Tickets & Documents
- Addresses: #4443
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(ui): clicking node collapse button does not bring node to front
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue
https://discord.com/channels/1020123559063990373/1130288930319761428/1147333454632071249
- Closes#4438
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
There is a call in `baseinvocation.invocation_output()` to
`cls.__annotations__`. However, in Python 3.9 not all objects have this
attribute. I have worked around the limitation in the way described in
https://docs.python.org/3/howto/annotations.html , which supposedly will
produce same results in 3.9, 3.10 and 3.11.
## Related Tickets & Documents
See
https://discord.com/channels/1020123559063990373/1146897072394608660/1146939182300799017
for first bug report.
## What type of PR is this? (check all applicable)
- [x] Cleanup
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Used https://github.com/albertas/deadcode to get rough overview of what
is not used, checked everything manually though. App still runs.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes#4424
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Ensure it doesn't explode when you run it.
* add StableDiffusionXLInpaintPipeline to probe list
* add StableDiffusionXLInpaintPipeline to probe list
* Blackified (?)
---------
Authored-by: Lincoln Stein <lstein@gmail.com>
Mucked about with to get it merged by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Add a click handler for node wrapper component that exclusively selects that node, IF no other modifier keys are held.
Technically I believe this means we are doubling up on the selection logic, as reactflow handles this internally also. But this is by far the most reliable way to fix the UX.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
The logic that introduced a circular import was actually extraneous. I
have entirely removed it.
This fixes the frontend lint test.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
This is the 3.1.0 release candidate. Minor bugfixes will be applied here
during testing and then merged into main upon release.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
- Workflows are saved to image files directly
- Image-outputting nodes have an `Embed Workflow` checkbox which, if
enabled, saves the workflow
- `BaseInvocation` now has an `workflow: Optional[str]` field, so all
nodes automatically have the field (but again only image-outputting
nodes display this in UI)
- If this field is enabled, when the graph is created, the workflow is
stringified and set in this field
- Nodes should add `workflow=self.workflow` when they save their output
image to have the workflow written to the image
- Uploads now have their metadata retained so that you can upload
somebody else's image and have access to that workflow
- Graphs are no longer saved to images, workflows replace them
### TODO
- Images created in the linear UI do not have a workflow saved yet. Need
to write a function to build a workflow around the linear UI graph when
using linear tabs. Unfortunately it will not have the nice positioning
and size data the node editor gives you when you save a workflow...
we'll have to figure out how to handle this.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
All invocation metadata (type, title, tags and category) are now defined in decorators.
The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.
Category is a new invocation metadata, but it is not used by the frontend just yet.
- `@invocation()` decorator for invocations
```py
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
...
```
- `@invocation_output()` decorator for invocation outputs
```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
...
```
- update invocation docs
- add category to decorator
- regen frontend types
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
- Keep Boards Modal open by default.
- Combine Coherence and Mask settings under Compositing
- Auto Change Dimensions based on model type (option)
- Size resets are now model dependent
- Add Set Control Image Height & Width to Width and Height option.
- Fix numerous color & spacing issues (especially those pertaining to
sliders being too close to the bottom)
- Add Lock Ratio Option
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] 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
- [ ] No
## Description
## Related Tickets & Documents
## QA Instructions, Screenshots, Recordings
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
In current main, long prompts and support for [Compel's `.and()`
syntax](https://github.com/damian0815/compel/blob/main/doc/syntax.md#conjunction)
is missing. This PR adds it back.
### needs Compel>=2.0.2.dev1
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
Send stuff directly from canvas to ControlNet
## Usage
- Two new buttons available on canvas Controlnet to import image and
mask.
- Click them.
## 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
- [ ] No
## Description
Adds Next and Prev Buttons to the current image node
As usual you don't have to use 😄
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] 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
## Description
Adds Seamless back into the options for Denoising.
## Related Tickets & Documents
- Related Issue #3975
## QA Instructions, Screenshots, Recordings
- Should test X, Y, and XY seamless tiling for all model architectures.
## Added/updated tests?
- [ ] Yes
- [ X ] No : Will need some guidance on automating this.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Allow an image and action to be passed into the app for starting state
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x] 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
## Description
Fix masked generation with inpaint models
## Related Tickets & Documents
- Closes#4295
## Added/updated tests?
- [ ] Yes
- [x] No
Added a node to prompt Oobabooga Text-Generation-Webui
## 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
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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?
Adds loading workflows with exhaustive validation via `zod`.
There is a load button but no dedicated save/load UI yet. Also need to add versioning to the workflow format itself.
## What type of PR is this? (check all applicable)
- [X Refactor
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
### Refactoring
This PR refactors `invokeai.app.services.config` to be easier to
maintain by splitting off the argument, environment and init file
parsing code from the InvokeAIAppConfig object. This will hopefully make
it easier for people to find the place where the various settings are
defined.
### New Features
In collaboration with @StAlKeR7779 , I have renamed and reorganized the
settings controlling image generation and model management to be more
intuitive. The relevant portion of the init file now looks like this:
```
Model Cache:
ram: 14.5
vram: 0.5
lazy_offload: true
Device:
precision: auto
device: auto
Generation:
sequential_guidance: false
attention_type: auto
attention_slice_size: auto
force_tiled_decode: false
```
Key differences are:
1. Split `Performance/Memory` into `Device`, `Generation` and `Model
Cache`
2. Added the ability to force the `device`. The value of this option is
one of {`auto`, `cpu`, `cuda`, `cuda:1`, `mps`}
3. Added the ability to force the `attention_type`. Possible values are
{`auto`, `normal`, `xformers`, `sliced`, `torch-sdp`}
4. Added the ability to force the `attention_slice_size` when `sliced`
attention is in use. The value of this option is one of {`auto`, `max`}
or an integer between 1 and 8.
@StAlKeR7779 Please confirm that I wired the `attention_type` and
`attention_slice_size` configuration options to the diffusers backend
correctly.
In addition, I have exposed the generation-related configuration options
to the TUI:

### Backward Compatibility
This refactor should be backward compatible with earlier versions of
`invokeai.yaml`. If the user re-runs the `invokeai-configure` script,
`invokeai.yaml` will be upgraded to the current format. Several
configuration attributes had to be changed in order to preserve backward
compatibility. These attributes been changed in the code where
appropriate. For the record:
| Old Name | Preferred New Name | Comment |
| ------------| ---------------|------------|
| `max_cache_size` | `ram_cache_size` |
| `max_vram_cache` | `vram_cache_size` |
| `always_use_cpu` | `use_cpu` | Better to check conf.device == "cpu" |
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
[fix(stats): fix fail case when previous graph is
invalid](d1d2d5a47d)
When retrieving a graph, it is parsed through pydantic. It is possible
that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
[fix(stats): fix InvocationStatsService
types](1b70bd1380)
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances
of the class. if they should not be on the ABC, then maybe there needs
to be some restructuring
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
On `main` (not this PR), create a situation in which an graph is valid
but will be rendered invalid on invoke. Easy way in node editor:
- create an `Integer Primitive` node, set value to 3
- create a `Resize Image` node and add an image to it
- route the output of `Integer Primitive` to the `width` of `Resize
Image`
- Invoke - this will cause first a `Validation Error` (expected), and if
you inspect the error in the JS console, you'll see it is a "session
retrieval error"
- Invoke again - this will also cause a `Validation Error`, but if you
inspect the error you should see it originates in the stats module (this
is the error this PR fixes)
- Fix the graph by setting the `Integer Primitive` to 512
- Invoke again - you get the same `Validation Error` originating from
stats, even tho there are no issues
Switch to this PR, and then you should only ever get the `Validation
Error` that that is classified as a "session retrieval error".
It is `"invocation"` for invocations and `"output"` for outputs. Clients may use this to confidently and positively identify if an OpenAPI schema object is an invocation or output, instead of using a potentially fragile heuristic.
Doing this via `BaseInvocation`'s `Config.schema_extra()` means all clients get an accurate OpenAPI schema.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
Doing this via these classes' `Config.schema_extra()` method makes it unintrusive and clients will get the correct types for these properties.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
The `type` property is required on all of them, but because this is defined in pydantic as a Literal, it is not required in the OpenAPI schema. Easier to fix this by changing the generated types than fiddling around with pydantic.
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances of the class. if they should not be on the ABC, then maybe there needs to be some restructuring
When retrieving a graph, it is parsed through pydantic. It is possible that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
Previously if an image was used in nodes and you deleted it, it would reset all of node editor. Same for controlnet.
Now it only resets the specific nodes or controlnets that used that image.
Add "nodrag", "nowheel" and "nopan" class names in interactable elements, as neeeded. This fixes the mouse interactions and also makes the node draggable from anywhere without needing shift.
Also fixes ctrl/cmd multi-select to support deselecting.
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances of the class. if they should not be on the ABC, then maybe there needs to be some restructuring
When retrieving a graph, it is parsed through pydantic. It is possible that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
## What type of PR is this? (check all applicable)
- [X] Feature
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
Follow symbolic links when auto importing from a directory. Previously
links to files worked, but links to directories weren’t entered during
the scanning/import process.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
Should be removed when added in diffusers
https://github.com/huggingface/diffusers/pull/4599
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
PR to add Seam Painting back to the Canvas.
## TODO Later
While the graph works as intended, it has become extremely large and
complex. I don't know if there's a simpler way to do this. Maybe there
is but there's soo many connections and visualizing the graph in my head
is extremely difficult. We might need to create some kind of tooling for
this. Coz it's going going to get crazier.
But well works for now.
## 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] Yes
## Description
This PR enhances the logging of performance statistics to include RAM
and model cache information. After each generation, the following will
be logged. The new information follows TOTAL GRAPH EXECUTION TIME.
```
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> Graph stats: 2408dbec-50d0-44a3-bbc4-427037e3f7d4
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> main_model_loader 1 0.004s 0.000G
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> clip_skip 1 0.002s 0.000G
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> compel 2 2.706s 0.246G
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> rand_int 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> range_of_size 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> iterate 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> noise 1 0.003s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> denoise_latents 1 2.429s 2.022G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> l2i 1 1.020s 1.858G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 6.171s
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> RAM used by InvokeAI process: 4.50G (delta=0.10G)
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> RAM used to load models: 1.99G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> VRAM in use: 0.303G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> RAM cache statistics:
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Model cache hits: 2
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Model cache misses: 5
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Models cached: 5
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Models cleared from cache: 0
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Cache high water mark: 1.99/7.50G
```
There may be a memory leak in InvokeAI. I'm seeing the process memory
usage increasing by about 100 MB with each generation as shown in the
example above.
Previously the editor was using prop-drilling node data and templates to get values deep into nodes. This ended up causing very noticeable performance degradation. For example, any text entry fields were super laggy.
Refactor the whole thing to use memoized selectors via hooks. The hooks are mostly very narrow, returning only the data needed.
Data objects are never passed down, only node id and field name - sometimes the field kind ('input' or 'output').
The end result is a *much* smoother node editor with very minimal rerenders.
There is a tricky mouse event interaction between chakra's `useOutsideClick()` hook (used by chakra `<Menu />`) and reactflow. The hook doesn't work when you click the main reactflow area.
To get around this, I've used a dirty hack, copy-pasting the simple context menu component we use, and extending it slightly to respond to a global `contextMenusClosed` redux action.
- also implement pessimistic updates for starring, only changing the images that were successfully updated by backend
- some autoformat changes crept in
If `reactflow` initializes before the node templates are parsed, edges may not be rendered and the viewport may get reset.
- Add `isReady` state to `NodesState`. This is false when we are loading or parsing node templates and true when that is finished.
- Conditionally render `reactflow` based on `isReady`.
- Add `viewport` to `NodesState` & handlers to keep it synced. This allows `reactflow` to mount and unmount freely and not lose viewport.
Refine concept of "parameter" nodes to "primitives":
- integer
- float
- string
- boolean
- image
- latents
- conditioning
- color
Each primitive has:
- A field definition, if it is not already python primitive value. The field is how this primitive value is passed between nodes. Collections are lists of the field in node definitions. ex: `ImageField` & `list[ImageField]`
- A single output class. ex: `ImageOutput`
- A collection output class. ex: `ImageCollectionOutput`
- A node, which functions to load or pass on the primitive value. ex: `ImageInvocation` (in this case, `ImageInvocation` replaces `LoadImage`)
Plus a number of related changes:
- Reorganize these into `primitives.py`
- Update all nodes and logic to use primitives
- Consolidate "prompt" outputs into "string" & "mask" into "image" (there's no reason for these to be different, the function identically)
- Update default graphs & tests
- Regen frontend types & minor frontend tidy related to changes
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
On Windows systems, model merging was crashing at the very last step
with an error related to not being able to serialize a WindowsPath
object. I have converted the path that is passed to `save_pretrained`
into a string, which I believe will solve the problem.
Note that I had to rebuild the web frontend and add it to the PR in
order to test on my Windows VM which does not have the full node stack
installed due to space limitations.
## Related Tickets & Documents
https://discord.com/channels/1020123559063990373/1042475531079262378/1140680788954861698
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: it's smol
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
docker_entrypoint.sh does not quote variable expansion to prevent word
splitting, causing paths with spaces to fail as in #3913
## Related Tickets & Documents
#3913
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #3913
- Closes#3913
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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
- [x] Feature
- [x] Bug Fix
- [x] 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
## Description
- Remove SDXL raw prompt nodes
- SDXL and SD1/2 generation merged to same nodes - t2l/l2l
- Fixed - if no xformers installed we trying to enable attention
slicing, ignoring torch-sdp availability
- Fixed - In SDXL negative prompt now creating zeroed tensor(according
to official code)
- Added mask field to l2l node
- Removed inpaint node and all legacy code related to this node
- Pass info about seed in latents, so we can use it to initialize
ancestral/sde schedulers
- t2l and l2l nodes moved from strength to denoising_start/end
- Removed code for noise threshold(@hipsterusername said that there no
plans to restore this feature)
- Fixed - first preview image now not gray
- Fixed - report correct total step count in progress, added scheduler
order in progress event
- Added MaskEdge and ColorCorrect nodes (@hipsterusername)
## Added/updated tests?
- [ ] Yes
- [x] No
This is probably better done on the backend or in a different way. This can cause steps to go above 1000 which is more than the set number for the model.
This fixes an import issue introduced in commit 1bfe983. The change made
'invokeai_configure' into a module but this line still tries to call it
as if it's a function. This will result in a `'module' not callable`
error.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] 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
## Description
imic from discord ask that I submit a PR to fix this bug.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
This fixes an import issue introduced in commit 1bfe983.
The change made 'invokeai_configure' into a module but this line still tries to call it as if it's a function. This will result in a `'module' not callable` error.
Seam options are now removed. They are replaced by two options --Mask Blur and Mask Blur Method .. which control the softness of the mask that is being painted.
During install testing I discovered two small problems in the
command-line scripts. These are fixed.
## What type of PR is this? (check all applicable)
- [X Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
-
## Have you updated all relevant documentation?
- [X] Yes
## Description
- installer - use correct entry point for invokeai-configure
- model merge script - prevent error when `--root` not provided
## 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
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Add support for LyCORIS IA3 format
## Related Tickets & Documents
- Closes#4229
## Added/updated tests?
- [ ] Yes
- [x] No
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] No - minor fix
## Have you updated all relevant documentation?
- [X] Yes
## Description
It turns out that some LoRAs do not have the text encoder model, and
this was causing the code that distinguishes the model base type during
model import to reject them as having an unknown base model. This PR
enables detection of these cases.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [s] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
was sorting with disabled at top of list instead of bottom
fixes#4217
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes#4217
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->

## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
There was no check at all to see if the canvas had a valid model already
selected. The first model in the list was selected every time.
Now, we check if its valid. If not, we go through the logic to try and
pick the first valid model.
If there are no valid models, or there was a problem listing models, the
model selection is cleared.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes#4125
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
- Go to Canvas tab
- Select a model other than the first one in the list
- Go to a different tab
- Go back to Canvas tab
- The model should be the same as you selected
There was no check at all to see if the canvas had a valid model already selected. The first model in the list was selected every time.
Now, we check if its valid. If not, we go through the logic to try and pick the first valid model.
If there are no valid models, or there was a problem listing models, the model selection is cleared.
## 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] Yes
## Description
This PR adds the `invokeai-import-images` script, which imports a
directory of 2.*.* -generated images into the current InvokeAI root
directory, preserving and converting their metadata. The script also
handles 3.* images.
Many thanks to @techjedi for writing this. This version differs from the
original in two minor respects:
1. It is installed as an `invokeai-import-images` command.
2. The prompts for image and database paths use file completion provided
by the `prompt_toolkit` library.
## To Test
1. Activate the virtual environment for the destination root to import
INTO
2. Run `invokeai-import-images`
3. Follow the prompts
## Related Tickets & Documents
This is a frequently-requested feature on Discord, but I couldn't find
an Issue.
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [X] No : but should in the future
## What type of PR is this? (check all applicable)
- [X ] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No - will be in release notes
## Description
On CUDA systems, this PR adds a new slider to the install-time configure
script for adjusting the VRAM cache and suggests a good starting value
based on the user's max VRAM (this is subject to verification).
On non-CUDA systems this slider is suppressed.
Please test on both CUDA and non-CUDA systems using:
```
invokeai-configure --root ~/invokeai-main/ --skip-sd --skip-support
```
To see and test the default values, move `invokeai.yaml` out of the way
before running.
**Note added 8 August 2023**
This PR also fixes the configure and model install scripts so that if
the window is too small to fit the user interface, the user will be
prompted to interactively resize the window and/or change font size
(with the option to give up). This will prevent `npyscreen` from
generating its horrible tracebacks.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X Yes
- [ ] No
## Description
If `models.yaml` is cleared out for some reason, the model manager will
repopulate it by scanning `models`. However, this would fail with a
pydantic validation error if any SDXL checkpoint models were present
because the lack of logic to pick the correct configuration file. This
has now been added.
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X No, because small fix
## Have you updated all relevant documentation?
- [X] Yes
## Description
A logic bug was introduced in PR #4109 that caused Web-based model
updates to fail with a pydantic validation error. This corrects the
problem.
## Related Tickets & Documents
PR #4109
* Fix hue adjustment
Hue adjustment wasn't working correctly because color channels got swapped. This has now been fixed and we're using PIL rather than cv2 to do the RGBA->HSV->RGBA conversion. The range of hue adjustment is also the more typical 0..360 degrees.
orphaned since #3550 removed the LazilyLoadedModelGroup code, probably unused since ModelCache took over responsibility for sequential offload somewhere around #3335.
ApiDependencies.invoker` provides typing for the API's services layer. Marking it `Optional` results in all the routes seeing it as optional, which is not good.
Instead of marking it optional to satisfy the initial assignment to `None`, we can just skip the initial assignment. This preserves the IDE hinting in API layer and is types-legal.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] 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?
- [X] Yes
- [ ] No
## Description
At install time, when the user's config specified "auto" precision, the
installer was downloading the fp32 models even when an fp16 model would
be appropriate for the OS.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes#4127
## 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
## Description
Add lora loading for sdxl.
NOT TESTED - I run only 2 loras, please check more(including lycoris if
they already exists).
## QA Instructions, Screenshots, Recordings
https://civitai.com/models/118536/voxel-xl

## Added/updated tests?
- [ ] Yes
- [x] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] 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
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR adds execution time and VRAM usage reporting to each graph
invocation. The log output will look like this:
```
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
```
On systems without CUDA, the VRAM stats are not printed.
The current implementation keeps track of graph ids separately so will
not be confused when several graphs are executing in parallel. It
handles exceptions, and it is integrated into the app framework by
defining an abstract base class and storing an implementation instance
in `InvocationServices`.
multi-select actions include:
- drag to board to move all to that board
- right click to add all to board or delete all
backend changes:
- add routes for changing board for list of image names, deleting list of images
- change image-specific routes to `images/i/{image_name}` to not clobber other routes (like `images/upload`, `images/delete`)
- subclass pydantic `BaseModel` as `BaseModelExcludeNull`, which excludes null values when calling `dict()` on the model. this fixes inconsistent types related to JSON parsing null values into `null` instead of `undefined`
- remove `board_id` from `remove_image_from_board`
frontend changes:
- multi-selection stuff uses `ImageDTO[]` as payloads, for dnd and other mutations. this gives us access to image `board_id`s when hitting routes, and enables efficient cache updates.
- consolidate change board and delete image modals to handle single and multiples
- board totals are now re-fetched on mutation and not kept in sync manually - was way too tedious to do this
- fixed warning about nested `<p>` elements
- closes#4088 , need to handle case when `autoAddBoardId` is `"none"`
- add option to show gallery image delete button on every gallery image
frontend refactors/organisation:
- make typegen script js instead of ts
- enable `noUncheckedIndexedAccess` to help avoid bugs when indexing into arrays, many small changes needed to satisfy TS after this
- move all image-related endpoints into `endpoints/images.ts`, its a big file now, but this fixes a number of circular dependency issues that were otherwise felt impossible to resolve
Currently we use some workflow trigger conditionals to run either a real test workflow (installing the app and running it) or a fake workflow, disguised as the real one, that just auto-passes.
This change refactors the workflow to use a single workflow that can be skipped, using another github action to determine which things to run depending on the paths changed.
## What type of PR is this? (check all applicable)
- [x] Refactor
## Have you discussed this change with the InvokeAI team?
- [x] No, because it's pretty minor
## Have you updated all relevant documentation?
- [x] No
## Description
This PR just moves the PR template to within the `.github/` directory
leading to a overall minimal project structure.
## Added/updated tests?
- [x] No : because this change doesn't affect or need a separate test
- Create abstract base class InvocationStatsServiceBase
- Store InvocationStatsService in the InvocationServices object
- Collect and report stats on simultaneous graph execution
independently for each graph id
- Track VRAM usage for each node
- Handle cancellations and other exceptions gracefully
## 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: invisible change
## Have you updated all relevant documentation?
- [ X] Yes
- [ ] No
## Description
There was a problem in 3.0.1 with root resolution. If INVOKEAI_ROOT were
set to "." (or any relative path), then the location of root would
change if the code did an os.chdir() after config initialization. I
fixed this in a quick and dirty way for 3.0.1.post3.
This PR cleans up the code with a little refactoring.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [ ]X Bug Fix
- [ ] Optimization
- [ X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ X] No, because: obvious problem
## Have you updated all relevant documentation?
- [ X] Yes
- [ ] No
## Description
The manual installation documentation in both README.md and
020_MANUAL_INSTALL give an incomplete `invokeai-configure` command which
leaves out the path to the root directory to create. As a result, the
invokeai root directory gets created in the user’s home directory, even
if they intended it to be placed somewhere else.
This is a fairly important issue.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [x] Feature
- [x] Bug Fix
- [?] Optimization
## Have you discussed this change with the InvokeAI team?
- [x] No
## Description
- Fixed filter type select using `images` instead of `all` -- Probably
some merge conflict.
- Added loading state for model lists. Handy when the model list takes
longer than a second for any reason to fetch. Better to show this than
an empty screen.
- Refactored the render model list function so we modify the display
component in one area rather than have repeated code.
### Other Issues
- The list can get a bit laggy on initial load when you have a hundreds
of models / loras. This needs to be fixed. Will make another PR for
this.
## What type of PR is this? (check all applicable)
- [x] Refactor
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: trivial
## Description
Adds a few obviously missing `Optional` on fields that default to
`None`.
## 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 a documentation update
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Updated documentation with a getting started guide & a glossary of terms
needed to get started
Updated the landing page flow for users
<img width="1430" alt="Screenshot 2023-07-27 at 9 53 25 PM"
src="https://github.com/invoke-ai/InvokeAI/assets/7254508/d0006ba7-2ed4-4044-a1bc-ca9a99df9397">
## Related Tickets & Documents
<!--
For pull requests that relate or
close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
This is a relatively stable release that corrects the urgent windows
install and model manager problems in 3.0.1. It still has two known
bugs:
1. Many inpainting models are not loading correctly.
2. The merge script is failing to start.
- Remove FaceMask and add link FaceTools repository, which includes FaceMask, FaceOff, and FacePlace
- Move Ideal Size up from under the template entry
## What type of PR is this? (check all applicable)
- [ X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes - bug discovered by jpphoto
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] Not needed
## Description
The user can customize the location of the models directory by setting
configuration variable `models_dir`. However, the model manager and the
TUI installer were all treating model relative paths as relative to the
invokeai root rather than the designated models directory. This has been
fixed by changing path resolution calls from using `config.root_path` to
`config.models_path`
Unfortunately there were many instances that needed replacement, so this
needs a bit of functional testing - try adding models, removing models,
renaming them, converting checkpoints, etc.
## What type of PR is this? (check all applicable)
- [ X] Optimization
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X ] Yes
- [ ] No
## Description
This PR does two things:
1. if the environment variable INVOKEAI_ROOT is defined at install time,
the zipfile installer will default to its value when asking the user
where to install the software
2. If the user has more than 72 models of any type installed, then the
list will be truncated in the TUI and the user given a warning. Anything
larger than this number of models causes the vertical space to overflow.
The only effect of truncation is that the user will not be able to see
and delete the models that were truncated. The message advises the user
to go to the Web Model Manager interface in this event.
## What type of PR is this? (check all applicable)
- [X ] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] No
## Description
This PR fixes several issues with the 3.0.0 conversion script:
- Handles checkpoint variants that don't put dots between fields in the
long state dict key names
- Handles ema, non-ema, pruned and non-pruned ckpts
- Does not add safety checker to converted checkpoints
- Respects precision of original checkpoint file
## What type of PR is this? (check all applicable)
- [ X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] Not needed
## Description
Windows users have been getting a lot of OSErrors while installing 3.0.1
during the pip dependency installation phase. Generally the errors have
involved just two packages, pydantic and numpy. Looking at the install
logs, I see that both of these packages are first installed under one
version number by a dependency, and then uninstalled and replaced by a
slightly different version specified in invoke's `pyproject.toml`. I
think this is the problem - maybe the earlier package is not completely
closed before it is uninstalled and reinstalled.
This PR relaxes pinning of numpy and pydantic in `pyproject.toml`.
Everything seems to install and run properly. Hopefully it will address
the windows install bug as well.
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
- SDXL Metadata was not being retrieved. This PR fixes it.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
not yet, making pr to show
## Have you updated relevant documentation?
- [ ] Yes
- [ ] No
## Description
Temp Change Node String input to a textbox, to allow easier input of
prompts and larger strings, it works for me but please tell me if I did
it wrong and if the size is ok
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: minor fix, let me know your thoughts
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue # https://github.com/invoke-ai/InvokeAI/issues/4017
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : Requires mps device
## [optional] Are there any post deployment tasks we need to perform?
Please test on an MPS (M1/M2) device.
Relevant code causing the error in #401701b6ec21fa/src/diffusers/schedulers/scheduling_euler_discrete.py (L263C3-L268C75)
```
self.sigmas = torch.from_numpy(sigmas).to(device=device)
if str(device).startswith("mps"):
# mps does not support float64
self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
else:
self.timesteps = torch.from_numpy(timesteps).to(device=device)
```
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Description
- Fix SDXL Concat Link animation not considering the fact that prompt
boxes can be resized.
- Also fixed a minor issue where the overlaying animation box would
block the prompt input resize slightly. Should be good now.
## What type of PR is this? (check all applicable)
- [X ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
## Have you updated all relevant documentation?
- [X ] Yes
## Description
Added solutions for installation issues related to large SDXL files and
Windows dependency glitches.
## 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
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Making the prompt area styling match across all tabs / models and move
all prompt related components into a parent components for quick add.
Cherry picked stuff from the Styles PR coz we ain't gonna merge that.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
- make the `SDXLConcatLink` icon match existing colors in light mode
- make the link toggle button accent color when active (its not super obvious but at least there is *some* visual difference for the button)
## 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] Yes - this makes invokeai behave the way it is described in
LOGGING.md
## Description
Prior to this PR, the uvicorn embedded web server did all its logging
independently of the InvokeAI logging infrastructure, and none of the
command-line or `invokeai.yaml` configuration directives, such as
`log_level` had any effect on its output. This PR replaces the uvicorn
logger with InvokeAI's, simultaneously creating a more uniform output
experience, as well as a unified way to control the amount and
destination of the logs.
Here's what we used to see at startup:
```
[2023-07-27 07:29:48,027]::[InvokeAI]::INFO --> InvokeAI version 3.0.1rc2
[2023-07-27 07:29:48,027]::[InvokeAI]::INFO --> Root directory = /home/lstein/invokeai-main
[2023-07-27 07:29:48,028]::[InvokeAI]::INFO --> GPU device = cuda NVIDIA GeForce RTX 4070
[2023-07-27 07:29:48,040]::[InvokeAI]::INFO --> Scanning /home/lstein/invokeai-main/models for new models
[2023-07-27 07:29:49,263]::[InvokeAI]::INFO --> Scanned 22 files and directories, imported 10 models
[2023-07-27 07:29:49,271]::[InvokeAI]::INFO --> Model manager service initialized
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:9090 (Press CTRL+C to quit)
INFO: 127.0.0.1:44404 - "GET /socket.io/?EIO=4&transport=polling&t=OcN7Pvd HTTP/1.1" 200 OK
INFO: 127.0.0.1:44404 - "POST /socket.io/?EIO=4&transport=polling&t=OcN7Pw6&sid=SB-NsBKLSrW7YtM0AAAA HTTP/1.1" 200 OK
INFO: ('127.0.0.1', 44418) - "WebSocket /socket.io/?EIO=4&transport=websocket&sid=SB-NsBKLSrW7YtM0AAAA" [accepted]
INFO: connection open
INFO: 127.0.0.1:44430 - "GET /socket.io/?EIO=4&transport=polling&t=OcN7Pw9&sid=SB-NsBKLSrW7YtM0AAAA HTTP/1.1" 200 OK
INFO: 127.0.0.1:44404 - "GET /socket.io/?EIO=4&transport=polling&t=OcN7PwU&sid=SB-NsBKLSrW7YtM0AAAA HTTP/1.1" 200 OK
INFO: 127.0.0.1:44404 - "GET /api/v1/images/?is_intermediate=true HTTP/1.1" 200 OK
INFO: 127.0.0.1:43448 - "GET / HTTP/1.1" 200 OK
INFO: connection closed
INFO: 127.0.0.1:43448 - "GET /assets/index-5a784cdd.js HTTP/1.1" 200 OK
INFO: 127.0.0.1:43458 - "GET /assets/favicon-0d253ced.ico HTTP/1.1" 304 Not Modified
INFO: 127.0.0.1:43448 - "GET /locales/en.json HTTP/1.1" 200 OK
```
And here's what we see with the new implementation:
```
[2023-07-27 12:05:28,810]::[uvicorn.error]::INFO --> Started server process [875161]
[2023-07-27 12:05:28,810]::[uvicorn.error]::INFO --> Waiting for application startup.
[2023-07-27 12:05:28,810]::[InvokeAI]::INFO --> InvokeAI version 3.0.1rc2
[2023-07-27 12:05:28,810]::[InvokeAI]::INFO --> Root directory = /home/lstein/invokeai-main
[2023-07-27 12:05:28,811]::[InvokeAI]::INFO --> GPU device = cuda NVIDIA GeForce RTX 4070
[2023-07-27 12:05:28,823]::[InvokeAI]::INFO --> Scanning /home/lstein/invokeai-main/models for new models
[2023-07-27 12:05:29,970]::[InvokeAI]::INFO --> Scanned 22 files and directories, imported 10 models
[2023-07-27 12:05:29,977]::[InvokeAI]::INFO --> Model manager service initialized
[2023-07-27 12:05:29,980]::[uvicorn.error]::INFO --> Application startup complete.
[2023-07-27 12:05:29,981]::[uvicorn.error]::INFO --> Uvicorn running on http://127.0.0.1:9090 (Press CTRL+C to quit)
[2023-07-27 12:05:32,140]::[uvicorn.access]::INFO --> 127.0.0.1:45236 - "GET /socket.io/?EIO=4&transport=polling&t=OcO6ILb HTTP/1.1" 200
[2023-07-27 12:05:32,142]::[uvicorn.access]::INFO --> 127.0.0.1:45248 - "GET /socket.io/?EIO=4&transport=polling&t=OcO6ILb HTTP/1.1" 200
[2023-07-27 12:05:32,154]::[uvicorn.access]::INFO --> 127.0.0.1:45236 - "POST /socket.io/?EIO=4&transport=polling&t=OcO6ILr&sid=13O4-5uLx5eP-NuqAAAA HTTP/1.1" 200
[2023-07-27 12:05:32,168]::[uvicorn.access]::INFO --> 127.0.0.1:45252 - "POST /socket.io/?EIO=4&transport=polling&t=OcO6IM0&sid=0KRqxmh7JLc8t7wZAAAB HTTP/1.1" 200
[2023-07-27 12:05:32,171]::[uvicorn.error]::INFO --> ('127.0.0.1', 45264) - "WebSocket /socket.io/?EIO=4&transport=websocket&sid=0KRqxmh7JLc8t7wZAAAB" [accepted]
[2023-07-27 12:05:32,172]::[uvicorn.error]::INFO --> connection open
[2023-07-27 12:05:32,174]::[uvicorn.access]::INFO --> 127.0.0.1:45276 - "GET /socket.io/?EIO=4&transport=polling&t=OcO6IM3&sid=0KRqxmh7JLc8t7wZAAAB HTTP/1.1" 200
```
You can also divert everything to a file with a `invokeai.yaml` entry
like this:
```
Logging:
log_handlers:
- file=/home/lstein/invokeai/logs/access_log
log_format: plain
log_level: info
```
This works with syslog and other log handlers as well.
## 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
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
https://github.com/huggingface/diffusers/releases/tag/v0.19.0
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] 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?
- [X ] Yes
- [ ] No
## Description
This updates InvokeAI's pyproject.toml to the minimum library versions
needed to support Python 3.11. It updates the installer to find and
allow for 3.11, and the documentation.
Between 3.10 and 3.11 there was a change to the handling of `enum`
interpolation into strings that caused the model manager to break. I
think I have fixed the places where this was a problem, but there may be
other instances in which this will cause problems. Please be alert for
errors involving `ModelType` or `BaseModelType`.
I also took the opportunity to add a `SilenceWarnings()` context to the
t2i and i2i invocations. This quenches nags from diffusers about the
HuggingFace NSFW library.
I have tested basic functionality (t2i, i2i, inpaint, lora, controlnet,
nodes) on 3.10 and 3.11 and all seems good. Please test more
extensively!
## Added/updated tests?
- [ X ] Yes - existing tests run to completion
- [ ] No
## [optional] Are there any post deployment tasks we need to perform?
Should be a drop-in replacement.
* add upper bound for minWidth to prevent crash with cypress
* add fallback so UI doesnt crash when backend isnt running
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
when multiple python versions are installed with `pyenv`, the executable
(shim) exists, but returns an error when trying to run it
unless activated with `pyenv`. This commit ensures the python
executable is usable.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature (dev feature and reformatting)
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Introducing black to the code base as a first step towards this:
https://github.com/invoke-ai/InvokeAI/discussions/3721
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : Not applicable
## [optional] Are there any post deployment tasks we need to perform?
All active branches will be affected by this and will need to be
updated.
This PR adds a new github workflow for black as well as config for
pre-commit hooks to those who wish to use it
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X] 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 ] Not needed
## Description
This bugfix enables InvokeAI to convert sd-1, sd-2 and sdxl base model
checkpoints (.safetensors) to diffusers.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ X] 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
## Description
This PR causes the installer to install, by default, the fine-tuned
SDXL-1.0 VAE located at
https://huggingface.co/madebyollin/sdxl-vae-fp16-fix.
Although this VAE is supposed to run at fp16 resolution, currently it
only works in InvokeAI at fp32. However, because it is a fine tune, it
may have fewer of the watermark-related artifacts that we see with the
SDXL-1.0 VAE.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X] 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] Not necessary
## Description
When adding new core models to a 3.0.0 root directory needed to support
SDXL, the configure script was (under some conditions) overwriting
models.yaml. This PR corrects the problem.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X ] 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?
- [X ] Yes
- [ ] No
## Description
I have reworked the console TUIs for the configure and model install
scripts to require much less vertical space. In the event that the
"NEXT" button is still missing and "page 1/2" is displayed, scrolling
beyond the last checkbox will now automatically move to page 2 where the
buttons are displayed. This is not ideal, but will no longer block user
completely.
If users continue to have problems after this, I'll get rid of the TUI
altogether and replace with a web form.
## Added/updated tests?
- [ ] Yes
- [X ] No : not needed
## [optional] Are there any post deployment tasks we need to perform?
## 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 they trust me
## Have you updated all relevant documentation?
- [ X] Yes
- [ ] No
## Description
* Added the RAIL++ license for SDXL
* Updated configure script with URLs for both the original RAIL-M and
RAIL++ licenses
* Added invisible watermark documentation and renamed doc file
* Updated documentation for installation
* Updated documentation on settings in invokeai.yaml
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Metadata was not getting saved coz the accumulator was not plugged in if
watermark or nsfw nodes were turned off.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ x] No, because there was no time!
## Have you updated all relevant documentation?
- [ ] Yes
- [X ] No
## Description
Hotfix for issue of SD1 and SD2 legacy safetensors models not converting
in 3.0.1rc1.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] 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?
- [X ] Yes
- [] No
## Description
This PR adds NSFW checker and invisible watermark fields. The NSFW
checker takes an image input and produces an image output. If NSFW
content is detected, the output image will be blurred and a "caution"
icon pasted into its upper left corner. A boolean `active` field
controls whether the checker is active. If turned off it simply returns
a copy of the image.
The invisible watermark node adds an invisible text to the image,
defaulting to "InvokeAI". To decode the watermark use the
`invisible-watermark` command, which is part of the
`invisible-watermark` library:
```
$ invisible-watermark -v -a decode -t bytes -m dwtDct -l 64 ./bluebird-watermark.png
decode time ms: 14.129877090454102
InvokeAI
```
Note that the `-l` (length) argument is mandatory. It is set to 64 here
because the watermark `InvokeAI` is 8 bytes/64 bits long. The length
must match in order for the watermark to be decoded correctly.
Both nodes are now incorporated into the linear Text2Image and
Image2Image UIs, including the canvas. They are not implemented for
inpaint currently.
The nodes can be disabled with configuration options:
```
invisible_watermark: false
nsfw_checker: false
```
or at launch time with `--no-invisible_watermark` and
`--no-nsfw_checker`.
feat(ui) use `as` for menuitem links
I had requested this be done with the chakra `Link` component, but actually using `as` is correct according to the docs. For other components, you are supposed to use `Link` but looks like `MenuItem` has this built in.
Fixed in all places where we use it.
Also:
- fix github icon
- give menu hamburger button padding
- add menu motion props so it animates the same as other menus
feat(ui): restore ColorModeButton
@maryhipp
chore(ui): lint
feat(ui): remove colormodebutton again
sry
## 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 - not yet WIP
## Description
This PR adds support for loading and converting checkpoint-format
ControlNet and SDXL models. The SDXL and SDXL-refiner model conversions
are working; however saving the unet in safetensors format leads to
corrupted model files, so currently is saving in .bin format (after
scanning the input model).
ControlNet conversion seems to be working but needs further testing.
To use this PR, you will need to copy the files
`invokeai/configs/stable-diffusion/sd_xl_base.yaml` and
`invokeai/configs/stable-diffusion/sd_xl_refiner.yaml` into
`INVOKEAI/configs/stable-diffusion`. You will also need to run
`invokeai-configure --yes --skip-sd` in order to install additional core
model files needed by the converter.
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
- Update the Aspect Ratio tags to show the aspect ratio values rather
than Wide / Square and etc.
- Updated Lora Input to take values between -50 and 50 coz I found some
LoRA that are actually trained to work until -25 and +15 too. So these
input caps should mostly suffice. If there's ever a LoRA that goes
bonkers on that, we can change it.
- Fixed LoRA's being sorted the wrong way in Lora Select.
- Fixed Embeddings being sorted the wrong way in Embedding Select.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
- add `addNSFWCheckerToGraph` and `addWatermarkerToGraph` functions
- use them in all linear graph creation
- add state & toggles to settings modal to enable these
- trigger queries for app config on socket connect
- disable the nsfw/watermark booleans if we get the app config and they are not available
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
This PR adds support for SDXL Models in the Linear UI
### DONE
- SDXL Base Text To Image Support
- SDXL Base Image To Image Support
- SDXL Refiner Support
- SDXL Relevant UI
## [optional] Are there any post deployment tasks we need to perform?
Double check to ensure nothing major changed with 1.0 -- In any case
those changes would be backend related mostly. If Refiner is scrapped
for 1.0 models, then we simply disable the Refiner Graph.
Rolled back the earlier split of the refiner model query.
Now, when you use `useGetMainModelsQuery()`, you must provide it an array of base model types.
They are provided as constants for simplicity:
- ALL_BASE_MODELS
- NON_REFINER_BASE_MODELS
- REFINER_BASE_MODELS
Opted to just use args for the hook instead of wrapping the hook in another hook, we can tidy this up later if desired.
We can derive `isRefinerAvailable` from the query result (eg are there any refiner models installed). This is a piece of server state, so by using the list models response directly, we can avoid needing to manually keep the client in sync with the server.
Created a `useIsRefinerAvailable()` hook to return this boolean wherever it is needed.
Also updated the main models & refiner models endpoints to only return the appropriate models. Now we don't need to filter the data on these endpoints.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] 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
## Description
Updated script to close stale issues with the newest version of the
actions/stale
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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?
Not sure how this script gets kicked off
## 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: This is a minor fix that I happened upon while
reading
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Within the `mkdocs.yml` file, there's a typo where `Model Merging` is
spelled as `Model Mergeing`. I also found some unnecessary white space
that I removed.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : Not big enough of a change to require tests (unless it is)
## [optional] Are there any post deployment tasks we need to perform?
Might need to re-run the yml file for docs to regenerate, but I'm hardly
familiar with the codebase so 🤷
## 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: n/a
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No n/a
## Description
Add a generation mode indicator to canvas.
- 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
I was going to disable controlnet conditionally when the mode is inpaint
but that involves a lot of fiddly changes to the controlnet UI
components. Instead, I'm hoping we can get inpaint moved over to latents
by next release, at which point controlnet will work.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
https://github.com/invoke-ai/InvokeAI/assets/4822129/87464ae9-4136-4367-b992-e243ff0d05b4
## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
n/a
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] 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
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.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
Closes#3860 , #3412
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Create an valid graph that will become invalid during execution. Here's
an example:

This is valid before execution, but the `width` field of the `Noise`
node will end up with an invalid value (`0`). Previously, this would
soft-lock the app and you'd have to restart it.
Now, with this graph, you will get an error toast, and the app will not
get locked up.
## Added/updated tests?
- [x] Yes (ish)
- [ ] No
@Kyle0654 @brandonrising
It seems because the processor runs in its own thread, `pytest` cannot
catch exceptions raised in the processor.
I added a test that does work, insofar as it does recreate the issue.
But, because the exception occurs in a separate thread, the test doesn't
see it. The result is that the test passes even without the fix.
So when running the test, we see the exception:
```py
Exception in thread invoker_processor:
Traceback (most recent call last):
File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/usr/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/home/bat/Documents/Code/InvokeAI/invokeai/app/services/processor.py", line 50, in __process
self.__invoker.services.graph_execution_manager.get(
File "/home/bat/Documents/Code/InvokeAI/invokeai/app/services/sqlite.py", line 79, in get
return self._parse_item(result[0])
File "/home/bat/Documents/Code/InvokeAI/invokeai/app/services/sqlite.py", line 52, in _parse_item
return parse_raw_as(item_type, item)
File "pydantic/tools.py", line 82, in pydantic.tools.parse_raw_as
File "pydantic/tools.py", line 38, in pydantic.tools.parse_obj_as
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
```
But `pytest` doesn't actually see it as an exception. Not sure how to
fix this, it's a bit beyond me.
## [optional] Are there any post deployment tasks we need to perform?
nope don't think so
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
`search_for_models` is explicitly typed as taking a singular `Path` but
was given a list because some later function in the stack expects a
list. Fixed that to be compatible with the paths. This is the only use
of that function.
The `list()` call is unrelated but removes a type warning since it's
supposed to return a list, not a set. I can revert it if requested.
This was found through pylance type errors. Go types!
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
This import is missing and used later in the file.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] 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
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- 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
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
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:

<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue # discord convos
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
On mps generating images with resolution above ~1536x1536 results in
"fried" output. Main problem that such resolution results in tensors in
size more then 4gb. Looks like that some of mps internals can't handle
properly this, so to mitigate it I break attention calculation in
chunks.
## QA Instructions, Screenshots, Recordings
Example of bad output:

## What type of PR is this? (check all applicable)
- [ X] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Description
This is a WIP to collect documentation enhancements and other polish
prior to final 3.0.0 release. Minor bug fixes may go in here if
non-controversial. It should be merged into main prior to the final
release.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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] Bug Fix
## Desc
Fixes a bug where the board name is not displayed in the header if there
are no images in it.
## 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:
## Description
Add progress preview for sdxl generation nodes
## 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 relevant documentation?
- [ X] Yes (swagger)
- [ ] No
## Description
This add new routes for getting and setting the command line console
logging level.
## 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 Discussed with @hipsterusername yesterday
- [ ] No, because:
## Have you updated relevant documentation?
- [ ] Yes
- [X] No Not yet (but change to default ControlNet resizing doesn't
require any user documentation)
## Description
This PR adds resize modes (just_resize, crop_resize, fill_resize) to
InvokeAI's ControlNet node. The implementation is largely based on
lllyasviel's, which includes a high quality resizer specifically
intended to handle common ControlNet preprocessor outputs, such as
binary (black/white) images, grayscale images, and binary or grayscale
thin lines. Previously the InvokeAI ControlNet implementation only did a
simple resize with independent x/y scaling to match noise latent.
### "just_resize" mode (the default setting)
With the new implementation, using the default "just_resize" mode,
ControlNet images are still resized with independent x/y scaling to
match the noise latent resolution, but with the high quality resizer. As
a result, images generated in InvokeAI now look much closer to
counterparts generated via sd-webui-controlnet. See example below. All
inference runs are using prompt="old man", same ControlNet canny edge
detection preprocessor and model and control image, identical other
parameters except for control_mode. The top row is previous simple
resize implementation, the bottom row is with new high quality resizer
and "just_resize" mode. Control_mode is: left="balanced", middle="more
prompt", right="more control". The high quality resize images are
identical (at least by eye) to output from sd-webui-controlnet with same
settings.

## "crop_resize" and "fill_resize" modes
The other two resize modes are "crop_resize" and "fill_resize". Whereas
"just_resize" ignores any aspect ratio mismatch between the ControlNet
image and the noise latent, these other modes preserve the aspect ratio
of the ControlNet image. The "crop_resize" mode does this by cropping
the image, and the "fill_resize" option does this by expanding the image
(adding fill pixels). See example below. In this case all inference runs
are using prompt="old man", the ControlNet Midas depth detection
preprocessor and depth model, same control image of size 512x512,
control_mode="balanced", and identical other parameters except for
resize_mode and noise latent dimensions. For top row noise latent size
is 768x512, and for bottom row noise latent size is 512x768. Resize_mode
is: left="just_resize", middle="crop_resize", right="fill_resize"

## Are there any post deployment tasks we need to perform?
To use "just_resize" mode in linear UI, no post deployment work is
needed. The default is switched from old resizer to new high quality
resizer.
To use "just_resize", "crop_resize", and "fill_resize" modes in node UI,
no post deployment work is needed. There is also an additional option
"just_resize_simple" that uses old resizer, mainly left in for testing
and for anyone curious to see the difference.
To use "crop_resize" and "fill_resize" in linear UI, there will need to
be some work to incorporate choice of three modes in ControlNet UI
(probably best to not expose "just_resize_simple" in linear UI, it just
confuses things).
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Description
This changes the "sync" route from a GET to POST method, in keeping with
the Representational Existential(?) State Transfer (REST) protocol.
* feat(ui): enhance clear intermediates feature
- retrieve the # of intermediates using a new query (just uses list images endpoint w/ limit of 0)
- display the count in the UI
- add types for clearIntermediates mutation
- minor styling and verbiage changes
* feat(ui): remove unused settings option for guides
* feat(ui): use solid badge variant
consistent with the rest of the usage of badges
* feat(ui): update board ctx menu, add board auto-add
- add context menu to system boards - only open is select board. did this so that you dont think its broken when you click it
- add auto-add board. you can right click a user board to enable it for auto-add, or use the gallery settings popover to select it. the invoke button has a tooltip on a short delay to remind you that you have auto-add enabled
- made useBoardName hook, provide it a board id and it gets your the board name
- removed `boardIdToAdTo` state & logic, updated workflows to auto-switch and auto-add on image generation
* fix(ui): clear controlnet when clearing intermediates
* feat: Make Add Board icon a button
* feat(db, api): clear intermediates now clears all of them
* feat(ui): make reset webui text subtext style
* feat(ui): board name change submits on blur
---------
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: documentation update that needs review from the team
before going live
## Description
I updated the contribution guidelines, adding more structure and a
getting started guide. Also re-organized the tabs to be in the order of
most commonly used.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
run `mkdocs serve` to check it out
## 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)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Description
ImageToLatentsInvocation defaulted to float16 rather than detect the
requested precision from configs.
This caused an exception to be raised on systems that don't support
float16 (e.g. CPU).
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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?
* feat(ui): migrate listImages to RTK query using createEntityAdapter
- see comments in `endpoints/images.ts` for explanation of the caching
- so far, only manually updating `all` images when new image is generated. no other manual cache updates are implemented, but will be needed.
- fixed some weirdness with loading state components (like the spinners in gallery)
- added `useThumbnailFallback` for `IAIDndImage`, this displays the tiny webp thumbnail while the full-size images load
- comment out some old thunk related stuff in gallerySlice, which is no longer needed
* feat(ui): add manual cache updates for board changes (wip)
- update RTK Query caches when adding/removing single image to/from board
- work more on migrating all image-related operations to RTK Query
* update AddImagesToBoardContext so that it works when user uses context menu + modal
* handle case where no image is selected
* get assets working for main list and boards - dnd only
* feat(ui): migrate image uploads to RTK Query
- minor refactor of `ImageUploader` and `useImageUploadButton` hooks, simplify some logic
- style filesystem upload overlay to match existing UI
- replace all old `imageUploaded` thunks with `uploadImage` RTK Query calls, update associated logic including canvas related uploads
- simplify `PostUploadAction`s that only need to display user input
* feat(ui): remove `receivedPageOfImages` thunks
* feat(ui): remove `receivedImageUrls` thunk
* feat(ui): finish removing all images thunks
stuff now broken:
- image usage
- delete board images
- on first load, no image selected
* feat(ui): simplify `updateImage` cache manipulation
- we don't actually ever change categories, so we can remove a lot of logic
* feat(ui): simplify canvas autosave
- instead of using a network request to set the canvas generation as not intermediate, we can just do that in the graph
* feat(ui): simplify & handle edge cases in cache updates
* feat(db, api): support `board_id='none'` for `get_many` images queries
This allows us to get all images that are not on a board.
* chore(ui): regen types
* feat(ui): add `All Assets`, `No Board` boards
Restructure boards:
- `all images` is all images
- `all assets` is all assets
- `no board` is all images/assets without a board set
- user boards may have images and assets
Update caching logic
- much simpler without every board having sub-views of images and assets
- update drag and drop operations for all possible interactions
* chore(ui): regen types
* feat(ui): move download to top of context menu
* feat(ui): improve drop overlay styles
* fix(ui): fix image not selected on first load
- listen for first load of all images board, then select the first image
* feat(ui): refactor board deletion
api changes:
- add route to list all image names for a board. this is required to handle board + image deletion. we need to know every image in the board to determine the image usage across the app. this is fetched only when the delete board and images modal is opened so it's as efficient as it can be.
- update the delete board route to respond with a list of deleted `board_images` and `images`, as image names. this is needed to perform accurate clientside state & cache updates after deleting.
db changes:
- remove unused `board_images` service method to get paginated images dtos for a board. this is now done thru the list images endpoint & images service. needs a small logic change on `images.delete_images_on_board`
ui changes:
- simplify the delete board modal - no context, just minor prop drilling. this is feasible for boards only because the components that need to trigger and manipulate the modal are very close together in the tree
- add cache updates for `deleteBoard` & `deleteBoardAndImages` mutations
- the only thing we cannot do directly is on `deleteBoardAndImages`, update the `No Board` board. we'd need to insert image dtos that we may not have loaded. instead, i am just invalidating the tags for that `listImages` cache. so when you `deleteBoardAndImages`, the `No Board` will re-fetch the initial image limit. i think this is more efficient than e.g. fetching all image dtos to insert then inserting them.
- handle image usage for `deleteBoardAndImages`
- update all (i think/hope) the little bits and pieces in the UI to accomodate these changes
* fix(ui): fix board selection logic
* feat(ui): add delete board modal loading state
* fix(ui): use thumbnails for board cover images
* fix(ui): fix race condition with board selection
when selecting a board that doesn't have any images loaded, we need to wait until the images haveloaded before selecting the first image.
this logic is debounced to ~1000ms.
* feat(ui): name 'No Board' correctly, change icon
* fix(ui): do not cache listAllImageNames query
if we cache it, we can end up with stale image usage during deletion.
we could of course manually update the cache as we are doing elsewhere. but because this is a relatively infrequent network request, i'd like to trade increased cache mgmt complexity here for increased resource usage.
* feat(ui): reduce drag preview opacity, remove border
* fix(ui): fix incorrect queryArg used in `deleteImage` and `updateImage` cache updates
* fix(ui): fix doubled open in new tab
* fix(ui): fix new generations not getting added to 'No Board'
* fix(ui): fix board id not changing on new image when autosave enabled
* fix(ui): context menu when selection is 0
need to revise how context menu is triggered later, when we approach multi select
* fix(ui): fix deleting does not update counts for all images and all assets
* fix(ui): fix all assets board name in boards list collapse button
* fix(ui): ensure we never go under 0 for total board count
* fix(ui): fix text overflow on board names
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* new route to clear intermediates
* UI to clear intermediates from settings modal
* cleanup
* PR feedback
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Description
In transformers 4.31.0 `text_model.embeddings.position_ids` no longer
part of state_dict.
Fix untested as can't run right now but should be correct. Also need to
check how transformers 4.30.2 works with this fix.
## Related Tickets & Documents
8e5d1619b3 (diff-7f53db5caa73a4cbeb0dca3b396e3d52f30f025b8c48d4daf51eb7abb6e2b949R191)https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_buffer
## QA Instructions, Screenshots, Recordings
```
File "C:\Users\artis\Documents\invokeai\.venv\lib\site-packages\invokeai\backend\model_management\convert_ckpt_to_diffusers.py", line 844, in convert_ldm_clip_checkpoint
text_model.load_state_dict(text_model_dict)
File "C:\Users\artis\Documents\invokeai\.venv\lib\site-packages\torch\nn\modules\module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for CLIPTextModel:
Unexpected key(s) in state_dict: "text_model.embeddings.position_ids".
```
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
## Description
Fix for
```
File "/home/invokeuser/InvokeAI/invokeai/app/services/processor.py",
line 70, in __process
outputs = invocation.invoke(
File "/home/invokeuser/InvokeAI/invokeai/app/invocations/latent.py",
line 660, in invoke
device=choose_torch_device()
NameError: name 'choose_torch_device' is not defined
```
when using scale latents node
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Description
This PR points mkdocs to the `main` branch again, so that the 3.0.0
documentation appears in gh-pages.
It also makes a minor tweak to the tooltip for model imports, so that
users know that URLs are accepted.
Also rebuilds frontend for use in beta testing.
I've opted to leave out any additional upscaling parameters like scale
and denoising strength, which, from my review of the ESRGAN code, don't
do much:
- scale just resizes the image using CV2 after the AI upscaling, so
that's not particularly useful
- denoising strength is only valid for one class of model, which we are
no longer supporting
If there is demand, we can implement output size/scale UI and handle it
by passing the upscaled image to that a resize/scale node.
I also understand we previously had some functionality to blend the
upscaled image with the original. If that is desired, we would need to
implement that as a node that we can pass the upscaled image to.
Demo:
https://github.com/invoke-ai/InvokeAI/assets/4822129/32eee615-62a1-40ce-a183-87e7d935fbf1
---
[feat(nodes): add RealESRGAN_x2plus.pth, update upscale
nodes](dbc256c5b4)
- add `RealESRGAN_x2plus.pth` model to installer @lstein
- add `RealESRGAN_x2plus.pth` to `realesrgan` node
- rename `RealESRGAN` to `ESRGAN` in nodes
- make `scale_factor` optional in `img_scale` node
[feat(ui): restore ad-hoc
upscaling](b3fd29e5ad)
- remove face restoration entirely
- add dropdown for ESRGAN model select
- add ad-hoc upscaling graph and workflow
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
There no vram cleanup on models offload which leads to filling vram and
slow generation speed.
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Optimization
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [x] Optimization
- [ ] Documentation Update
## Description
Various fixes to consume less memory and make run sdxl on 8gb vram.
Most changes due to moving all output tensors to cpu, so that cached
tensors not consume vram.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Fixes a bug in the `inpaint` node introduced by the new version of
`compel`. The other nodes were updated, but this one was missed. Fixed
by @StAlKeR7779 ty
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue # discord reports
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : n/a, bugfix
This contains minor fixes to the beta as well as the version bump to
3.0.0.
Fixes include:
- Warning user when the installer window size is inadequate for the TUI.
- Selection of the most frequently downloaded controlnet models for
default installation.
- Adding the LowRA LoRA for dark image enhancement
- Documentation
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Making some final style fixes before we push the next 3.0 version
tomorrow.
- Fixed light mode colors in Settings Modal.
- Double checked other light mode colors. Nothing seems off.
- Added Base Model badge to the model list item. Makes it visually
better and also serves as a quick glance feature for the user.
- Some minor styling updates to the Node Editor.
- Fixed hotkeys 'G' and 'O', 'Shift+G' and 'Shift+O' used to toggle the
panels not resizing canvas. #3780
- Fixed hotkey 'N' not working for Snap To Grid on Canvas.
- Fixed brush opacity hotkeys not working.
- Cleaned up hotkeys modal of hotkeys that are no longer used.
- Updated compel requirement to `2.0.0`
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes#3780
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## 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)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
hides sdxl models from linear ui model select. just a hold-me-over
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
- add `RealESRGAN_x2plus.pth` model to installer
- add `RealESRGAN_x2plus.pth` to `realesrgan` node
- rename `RealESRGAN` to `ESRGAN` in nodes
- make `scale_factor` optional in `img_scale` node
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:
If its not useful, they do not have to use it 😄
## Description
While I was still in the viewportcontrols.tsx
added Option to toggle off the minimap with default being on(true)
added Tooltips to the buttons in viewportcontrols.tsx
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
This is a WIP to add SDXL support.
Tasks:
- [x] SDXL model loading support
- [x] SDXL model installation
- [x] SDXL model loader
- [x] SDXL base invocations for text2latent and latent2latent
- [ ] SDXL refiner invocations for text2latent and latent2latent
- [x] Compel support / pooled embeddings
- [ ] Linear UI graph for SDXL
- [ ] Documentation
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] 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?
fix json formatting to not have big red comment blocks
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: simple docs fix
## Description
Fix LOCAL_DEVELOPMENT.md json comment highlighting
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue # n/a
- Closes # n/a
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : simple docs change
This PR completely ports over the Model Manager to 3.0 -- all of the
functionality has now been restored in addition to the following
changes.
- Model Manager now has been moved to its own tab on the left hand side.
- Model Manager has three tabs - Model Manager, Import Models and Merge
Models
- The edit forms for the Models now allow the users to update the model
name and the base model too along with other details.
- Checkpoint Edit form now displays the available config files from
InvokeAI and also allows users to supply their own custom config file.
- Under Import Models you can directly add models or a scan a folder for
your checkpoint files.
- Adding models has two modes -- Simple and Advanced.
- In Simple Mode, you just simply need to pass a path and InvokeAI will
try to determine kind of model it is and fill up the rest of the details
accordingly. This input lets you supply local paths to diffusers / local
paths to checkpoints / huggingface repo ID's to download models /
CivitAI links.
- Simple Mode also allows you to download different models types like
VAE's and Controlnet models and etc. Not just main models.
- In cases where the auto detection system of InvokeAI fails to read a
model correctly, you can take the manual approach and go to Advanced
where you can configure your model while adding it exactly the way you
want it. Both Diffusers and Checkpoint models now have their own custom
forms.
- Scan Models has been cleaned up. It will now only display the models
that are not already installed to InvokeAI. And each item will have two
options - Quick Add and Advanced .. replicating the Add Model behavior
from above.
- Scan Models now has a search bar for you to search through your
scanned models.
- Merge Models functionality has been restored.
This is a wrap for this PR.
**TODO: (Probably for 3.1)**
- Add model management for model types such as VAE's and ControlNet
Models
- Replace the VAE slot on the edit forms with the installed VAE drop
down + custom option
[feat(nodes): emit model loading
events](7b6159f8d6)
- remove dependency on having access to a `node` during emits, would
need a bit of additional args passed through the system and I don't
think its necessary at this point. this also allowed us to drop an
extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the
events are able to be emitted
- test all model loading events in the app and confirm socket events are
received
[feat(ui): add listeners for model load
events](c487166d9c)
- currently only exposed as DEBUG-level logs
---
One change I missed in the commit messages is the `ModelInfo` class is
not serializable, so I split out the pieces of information we didn't
already have (hash, location, precision) and added them to the event
payload directly.
This small patch improves the stability of `invokeai-*` scripts by
avoiding crashes in the model manager while scanning the models
directory for new and removed models.
Both support the same actions:
- Open in new tab
- Copy image (if supported by browser)
- Use prompt
- Use seed
- Use all
- Send to img2img
- Send to canvas
- Change board
- Download image
- Delete
- restore copy image functionality* in image context menu, current image buttons
- give IAIDndImage the same context menu
* copying image to clipboard is not possible on Firefox unless the user enables a setting which is disabled by default. if the browser does not support copying an image, the copy functionality is disabled.
- filename -> file_path
- pre and post prompt changed to optional
- clearer pre and post prompt descriptions
- handle pre and post prompt passed as None
- max_prompts defaults to 1 isted of 0 to avoid accidentally processing large prompt files with it set to 0 when adding a new node.
This PR adds several default models to the ones selected at install
time. It also removes the GFPGAN and text2clip models, which should
shave a little time off the install process.
## ESRGAN:
* models/core/upscaling/realesrgan/RealESRGAN_x4plus.pth
* models/core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth
*
models/core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth
## ControlNet
* models/sd-1/controlnet/canny
* models/sd-1/controlnet/depth
* models/sd-1/controlnet/lineart
* models/sd-1/controlnet/openpose
## Embedding (textual inversion)
* models/sd-1/embedding/EasyNegative.safetensors
- remove dependency on having access to a `node` during emits, would need a bit of additional args passed through the system and I don't think its necessary at this point. this also allowed us to drop an extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the events are able to be emitted
- test all model loading events in the app and confirm socket events are received
- update controlnet state to use object format for model
- update model-parsing helper functions to log errors
- update nodes components, types and state
- remove controlnets from state when models are loaded and the controlnet's model is not available
# Multiple enhancements to model manager REACT API
1. add a `/sync` route for synchronizing the in-memory model lists to
models.yaml, the models directory, and the autoimport directories.
2. added optional destination directories to convert_model and
merge_model operations.
3. added a `/ckpt_confs` route for retrieving known legacy checkpoint
configuration files.
4. added a `/search` route for finding all models in a directory located
in the server filesystem
5. added a `/add` route for manual addition of a local models
6. added a `/rename` route for renaming and/or rebasing models
7. changed the path of the `import_model` route to `/import`
# Slightly annoying detail:
When adding a model manually using `/add`, the body JSON must exactly
match one of the model configurations returned by `list_models` (i.e.
there is no defaulting of fields). This includes the `error` field,
which should be set to "null".
1. add a /sync route for synchronizing the in-memory model lists to
models.yaml, the models directory, and the autoimport directories.
2. add optional destination_directories to convert_model and merge_model
operations.
3. add /ckpt_confs route for retrieving known legacy checkpoint configuration
files.
4. add /search route for finding all models in a directory located in the server
filesystem
DONE:
- Restore Update Model functionality
- Restore Delete Model functionality
- Restore Model Convert functionality
- Restore Model Merge functionality
- Refine UX (fine tweaks when everything is done - TODO)
TODO
- Add Model (will be finished in a future PR once the backend work is
done)
IAIMantineSelect and IAIMantineMultiSelect have a bit of extra logic that prevents simple select functionality from working as expected.
- extract the styles into hooks
- rename those two components to IAIMantineSearchableSelect and IAIMantineSearchableMultiSelect
- Create IAIMantineSelect (which is just a dropdown) and use it in model manager and a few other places
When we only have a few options to present and searching is not efficient, we should use this instead.
Image files are immutable and we expect deletion to result in no further
requests for a given image, so we can set the max-age to something
thicc.
Resolves#3426
@ebr @brandonrising @maryhipp
- simplify UI logic in `ModelManagerPanel` components
- fix up the types a bit to make it easier to select models
- remove `openModel` state, just make it a useState since it is very local to model manager
similar to the previous commit, update the node editor to not just store models as strings - instead, store the model object.
the model select components in nodes are now just kinda copy-pastes over the linear UI versions of the same components, but they were different enough that we can't just share them.
i explored adding some props to override the linear ui components' logic, but it was too brittle. so just copy/paste.
We were storing all types of models by their model ID, which is a format like `sd-1/main/deliberate`.
This meant we had to do a lot of extra parsing, because nodes actually wants something like `{base_model: 'sd-1', model_name: 'deliberate'}`.
Some of this parsing was done with zod's error-throwing `parse()` method, and in other places it was done with brittle string parsing.
This commit refactors the state to use the object form of models.
There is still a bit of string parsing done in the to construct the ID from the object form, but it's far less complicated.
Also, the zod parsing is now done using `safeParse()`, which does not throw. This requires a few more conditional checks, but should prevent further crashes.
* feat(ui): salvaged gallery UI enhancements
* restore boardimage functionality, load boardimages and remove some cachine optimizations in the name of data integrity
* fix assets, fix load more params
* jk NOW fix assets, fix load more params
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: Mary Hipp Rogers <maryhipp@gmail.com>
- available infill methods is server state - remove it from client state, use the query to populate the dropdown
- add listener to ensure the selected infill method is an available one
As it said in comment to this branch we want to use conditioning run:
```python
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
```
But in code used unconditioning
embeddings(`conditioning_data.unconditioned_embeddings`).
Later in code confirms that we want to run conditioning generation by
comment and tensor concatenation order(as all code expect to get [uc, c]
tensor):
```python
if cfg_injection:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
```
Adds a Clear Nodes Button with Confirmation Dialog, I think I Did it
right 😃
I am sure there is a way to make the Confirmation look better and have
Yes/No instead of OK/Cancel
- Restore recall functionality to `CurrentImageButtons` and `ImageContextMenu`.
- Debounce metadata requests for `ImageMetadataViewer` and `CurrentImageButtons` by 500ms. It's possible to scroll through these really fast, so we want to debounce the network requests.
- `ImageContextMenu` is lazy-mounted so it does not need to be debounced; it makes the metadata request as soon as you click it.
- Move next/prev image selection logic into hook and add the hotkeys for this to `CurrentImageButtons`. The hotkeys now work when metadata viewer is open.
I will follow up with improved loading state during the debounced calls in the future
- Update for new routes
- Update model storage in state to be `MainModelField` type instead of `string`, simplifies a lot of model handling
- Update model-related stuff for model `name` --> `model_name`
- Update linear graphs to use `MetadataAccumulator`
- Update `ImageMetadataViewer` UI
- Ensure all `recall` functions work (well, the ones that are active anyways)
Metadata for the Linear UI is now sneakily provided via a `MetadataAccumulator` node, which the client populates / hooks up while building the graph.
Additionally, we provide the unexpanded graph with the metadata API response.
Both of these are embedded into the PNGs.
- Remove `metadata` from `ImageDTO`
- Split up the `images/` routes to accomodate this; metadata is only retrieved per-image
- `images/{image_name}` now gets the DTO
- `images/{image_name}/metadata` gets the new metadata
- `images/{image_name}/full` gets the full-sized image file
- Remove old metadata service
- Add `MetadataAccumulator` node, `CoreMetadataField`, hook up to `LatentsToImage` node
- Add `get_raw()` method to `ItemStorage`, retrieves the row from DB as a string, no pydantic parsing
- Update `images`related services to handle storing and retrieving the new metadata
- Add `get_metadata_graph_from_raw_session` which extracts the `graph` from `session` without needing to hydrate the session in pydantic, in preparation for providing it as metadata; also removes all references to the `MetadataAccumulator` node
Our model fields use `model_name`, but the API response uses `name`. Some places use `model_type` but the API response used `type`.
Changed the API response to provide `model_name` and `model_type`, which simplifies how we manage models on the client substantially.
- rewrite Dockerfile
- add a stage to build the UI
- add docker-compose.yml
- add docker-entrypoint.sh such that any command may be used at runtime
- docker-compose adds .env support - add a sample .env file
* fix the test of the config system
* Add torchmetrics==0.11.4 to installer
- Closes#3700
- Closes#3658
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
To be consistent with max_cache_size, the amount of memory to hold in
VRAM for model caching is now controlled by the max_vram_cache_size
configuration parameter.
[feat(ui): memoize ImageContextMenu
selector](265996d230)
Without the selector itself being memoized, the gallery was rerendering
on every progress image.
[feat(ui): memoize NextPrevImageButtons
component](a7b8109ac2)
This was rerendering on every progress image, now it doesn't
[fix(ui): correctly set disabled on invoke button during
generation](1c45d18e6d)
It wasn't disabled when it should have been, looked clickable during
generation.
[fix(nodes): remove board_id column from images
table](00e26ffa9a)
This is extraneous; the `board_images` table holds image-board
relationships. @maryhipp
Image files are immutable and we expect deletion to result in no further requests for a given image, so we can set the max-age to something thicc.
Resolves#3426
Just a small thing now, as nodes are all still wip, but since
@psychedelicious was nice enough to add the progress image node for me,
what I noticed was missing now is the cancel button on nodes tab
@psychedelicious @blessedcoolant Somehow i deleted the branch the other
version of this pull request was on. 🤭
Just an idea, if you think its worth while please make changes ( I did
what I could)
I added a load more to the right arrow to avoid having to open gallery
to load more images,
I am not sure about the icon i used, maybe it should just be the normal
arrow, so you don't even need to show its loading more images.
there is an issue with it not disappearing once all images have been
loaded, (I did play around for a while to try and fix that)
Some users want the model select to take full width coz their model
names might be long. As this is a more frequently used feature,
rearrange it to do that.
Followed by VAE (as it is related to the model) and the Sampler next to
it.
I made a recent change to the function that finds the default root
directory locatoin that broke it when run under Conda (where VIRTUAL_ENV
is not set). This revision fixes the issue.
Mantine's multiselect does not let you edit the search box with mouse, paste into it, etc. Normal select is fine.
I can't remember why I made Lora etc multiselects, but everything seems to work with normal selects, so I've change to that.
- `isLoading` - now `true` *only* on first load
- added `isFetching` - `true` whenever gallery images are fetching
- on first load, show a spinner instead of skeletons. this prevents an awkward flash of skeletons into empty gallery when the gallery doesn't have enough images to fill it.
- removed `imageCategoriesChanged` listener, bc now on app start, both images and assets will be populated. leaving this in caused jank flashes of skeletons when switching gallery tabs when gallery doesn't have images to load
taking the coward's way out on this and just fetching 100 images & 100 assets on app start...
- add `appStarted` action, dispatched once on mount in App.tsx. listener fetches 100 images & 100 assets
- fix bug with selectedBoardId & assets tab
The shift key listener didn't catch pressed when focused in a textarea
or input field, causing jank on slider number inputs.
Add keydown and keyup listeners to all such fields, which ensures that
the `shift` state is always correct.
Also add the action tracking it to `actionsDenylist` to not clutter up
devtools.
The shift key listener didn't catch pressed when focused in a textarea or input field, causing jank on slider number inputs.
Add keydown and keyup listeners to all such fields, which ensures that the `shift` state is always correct.
Also add the action tracking it to `actionsDenylist` to not clutter up devtools.
There was a props on IAISlider to make the input component readonly - I
didn't know this existed and at some point used a component with that
prop as a template for other sliders, copying the flag over.
It's not actually used anywhere, so I removed the prop entirely,
enabling the number inputs everywhere.
There was a props on IAISlider to make the input component readonly - I didn't know this existed and at some point used a component with that prop as a template for other sliders, copying the flag over.
It's not actually used anywhere, so I removed the prop entirely, enabling the number inputs everywhere.
I'm not sure if this was just my local install, but even after a fresh
`yarn install` my upload network request was failing because no file was
passed in. I don't think the `bodySerializer` part is getting run
I'm not sure if this was just my local install, but even after a fresh
`yarn install` my upload network request was failing because no file was
passed in. I don't think the `bodySerializer` part is getting run
This PR is to allow FP16 precision to work on Macs with MPS. In
addition, it centralizes the torch fixes/workarounds required for MPS
into a new backend utility `mps_fixes.py`. This is conditionally
imported in `api_app.py`/`cli_app.py`.
Many MANY thanks to @StAlKeR7779 for patiently working to debug and fix
these issues.
- No longer fail root directory probing if invokeai.yaml is missing
(test is now whether a `models/core` directory exists).
- Migrate script does not overwrite previously-installed models.
- Can run migrate script on an existing 2.3 version directory
with --from and --to pointing to same 2.3 root.
Clip Skip breaks when you supply a number greater than the number of
layers for the model type. So capping this out based on the model on the
frontend
- `sd-1` at 12
- `sd-2` at 24
- Will update later to whatever SDXL needs if it is different.
- Also fixes LoRA's breaking with Clip Skip.
My PR to fix an issue with the handling of formdata in `openapi-fetch` is released. This means we no longer need to patch the package (no patches at all now!).
This PR bumps its version and adds a transformer to our typegen script to handle typing binary form fields correctly as `Blob`.
Also regens types.
This is PR adds the following API methods for managing models:
* list_models (GET)
* update_model (PATCH)
* import_model (POST)
* delete_model (DELETE)
* convert_model (PUT)
* merge_models (PUT)
* load images on gallery render
* wait for models to be loaded before you can invoke
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
This PR enables model manager importation of diffusers-style .bin LoRAs.
However, since there is no backend support for this type of LoRA yet,
attempts to use them will result in an unimplemented error.
It closes#3636 and #3637
The list models route should just be the base route path, and should use query parameters as opposed to path parameters (which cannot be optional)
Removed defaults for update model route - for the purposes of the API, we should always be explicit with this
This PR fixes the migrate script so that it uses the same directory for
both the tokenizer and text encoder CLIP models. This will fix a crash
that occurred during checkpoint->diffusers conversions
This PR also removes the check for an existing models directory in the
target root directory when `invokeai-migrate3` is run.
* close modal when user clicks cancel
* close modal when delete image context cleared
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
A user discovered that 2.3 models whose symbolic names contain the "/"
character are not imported properly by the `migrate-models-3` script.
This fixes the issue by changing "/" to underscore at import time.
- Accordions now may be opened or closed regardless of whether or not
their contents are enabled or active
- Accordions have a short text indicator alerting the user if their
contents are enabled, either a simple `Enabled` or, for accordions like
LoRA or ControlNet, `X Active` if any are active
https://github.com/invoke-ai/InvokeAI/assets/4822129/43db63bd-7ef3-43f2-8dad-59fc7200af2e
- Accordions now may be opened or closed regardless of whether or not their contents are enabled or active
- Accordions have a short text indicator alerting the user if their contents are enabled, either a simple `Enabled` or, for accordions like LoRA or ControlNet, `X Active` if any are active
This caused a lot of re-rendering whenever the selection changed, which caused a huge performance hit. It also made changing the current image lag a bit.
Instead of providing an array of image names as a multi-select dnd payload, there is now no multi-select dnd payload at all - instead, the payload types are used by the `imageDropped` listener to pull the selection out of redux.
Now, the only big re-renders are when the selectionCount changes. In the future I'll figure out a good way to do image names as payload without incurring re-renders.
Every `GalleryImage` was rerendering any time the app rerendered bc the selector function itself was not memoized. This resulted in the memoization cache inside the selector constantly being reset.
Same for `BatchImage`.
Also updated memoization for a few other selectors.
Eg `useGetMainModelsQuery()`, `useGetLoRAModelsQuery()` instead of `useListModelsQuery({base_type})`.
Add specific adapters for each model type. Just more organised and easier to consume models now.
Also updated LoRA UI to use the model name.
This PR is to allow FP16 precision to work on Macs with MPS. In addition, it centralizes the torch fixes/workarounds
required for MPS into a new backend utility file `mps_fixes.py`. This is conditionally imported in `api_app.py`/`cli_app.py`.
Many MANY thanks to StAlKeR7779 for patiently working to debug and fix these issues.
This PR is for adjusting the unit tests in the `tests` directory so that
they no longer throw errors.
I've removed two tests that were obsoleted by the shift to latent nodes,
but `test_graph_execution_state.py` and `test_invoker.py` are throwing
this validation error:
```
TypeError: InvocationServices.__init__() missing 2 required positional arguments: 'boards' and 'board_images'
```
The `invokeai-configure` script migrates the old invokeai.init file to
the new invokeai.yaml format. However, the parser for the invokeai.init
file was missing the names of the k* samplers and was giving a parser
error on any invokeai.init file that referred to one of these samplers.
This PR fixes the problem.
Ironically, there is no longer the concept of the preferred scheduler in
3.0, and so these sampler names are simply ignored and not written into
`invokeai.yaml`
This introduces the core functionality for batch operations on images and multiple selection in the gallery/batch manager.
A number of other substantial changes are included:
- `imagesSlice` is consolidated into `gallerySlice`, allowing for simpler selection of filtered images
- `batchSlice` is added to manage the batch
- The wonky context pattern for image deletion has been changed, much simpler now using a `imageDeletionSlice` and redux listeners; this needs to be implemented still for the other image modals
- Minimum gallery size in px implemented as a hook
- Many style fixes & several bug fixes
TODO:
- The UI and UX need to be figured out, especially for controlnet
- Batch processing is not hooked up; generation does not do anything with batch
- Routes to support batch image operations, specifically delete and add/remove to/from boards
@blessedcoolant it looks like with the new theme buttons not being
transparent the progress bar was completely hidden, I moved to be on
top, however it was not transparent so it hid the invoke text, after
trying for a while couldn't get it to be transparent, so I just made the
height 15%,
- Set min size for floating gallery panel
- Correct the default pinned width (it cannot be less than the min width
and this was sometimes happening during window resize)
- Set min size for floating gallery panel
- Correct the default pinned width (it cannot be less than the min width and this was sometimes happening during window resize)
Add `useMinimumPanelSize()` hook to provide minimum resizable panel sizes (in pixels).
The library we are using for the gallery panel uses percentages only. To provide a minimum size in pixels, we need to do some math to calculate the percentage of window size that corresponds to the desired min width in pixels.
The node polyfills needed to run the `swagger-parser` library (used to
dereference the OpenAPI schema) cause the canvas tab to immediately
crash when the package build is used in another react application.
I'm sure this is fixable but it's not clear what is causing the issue
and troubleshooting is very time consuming.
Selectively rolling back the implementation of `swagger-parser`.
The node polyfills needed to run the `swagger-parser` library (used to dereference the OpenAPI schema) cause the canvas tab to immediately crash when the package build is used in another react application.
I'm sure this is fixable but it's not clear what is causing the issue and troubleshooting is very time consuming.
Selectively rolling back the implementation of `swagger-parser`.
[feat(ui): remove themes, add hand-crafted dark and light
modes](032c7e68d0)
[032c7e6](032c7e68d0)
Themes are very fun but due to the differences in perceived saturation
and lightness across the
the color spectrum, it's impossible to have have multiple themes that
look great without hand-
crafting *every* shade for *every* theme. We've ended up with 4 OK
themes (well, 3, because the
light theme was pretty bad).
I've removed the themes and added color mode support. There is now a
single dark and light mode,
each with their own color palette and the classic grey / purple / yellow
invoke colors that
@blessedcoolant first designed.
I've re-styled almost everything except the model manager and lightbox,
which I keep forgetting
to work on.
One new concept is the Chakra `layerStyle`. This lets us define "layers"
- think body, first layer,
second layer, etc - that can be applied on various components. By
defining layers, we can be more
consistent about the z-axis and its relationship to color and lightness.
Themes are very fun but due to the differences in perceived saturation and lightness across the
the color spectrum, it's impossible to have have multiple themes that look great without hand-
crafting *every* shade for *every* theme. We've ended up with 4 OK themes (well, 3, because the
light theme was pretty bad).
I've removed the themes and added color mode support. There is now a single dark and light mode,
each with their own color palette and the classic grey / purple / yellow invoke colors that
@blessedcoolant first designed.
I've re-styled almost everything except the model manager and lightbox, which I keep forgetting
to work on.
One new concept is the Chakra `layerStyle`. This lets us define "layers" - think body, first layer,
second layer, etc - that can be applied on various components. By defining layers, we can be more
consistent about the z-axis and its relationship to color and lightness.
The TS Language Server slows down immensely with our translation JSON, which is used to provide kinda-type-safe translation keys. I say "kinda", because you don't get autocomplete - you only get red squigglies when the key is incorrect.
To improve the performance, we can opt out of this process entirely, at the cost of no red squigglies for translation keys. Hopefully we can resolve this in the future.
It's not clear why this became an issue only recently (like past couple weeks). We've tried rolling back the app dependencies, VSCode extensions, VSCode itself, and the TS version to before the time when the issue started, but nothing seems to improve the performance.
1. Disable `resolveJsonModule` in `tsconfig.json`
2. Ignore TS in `i18n.ts` when importing the JSON
3. Comment out the custom types in `i18.d.ts` entirely
It's possible that only `3` is needed to fix the issue.
I've tested building the app and running the build - it works fine, and translation works fine.
Rewrite lora to be applied by model patching as it gives us benefits:
1) On model execution calculates result only on model weight, while with
hooks we need to calculate on model and each lora
2) As lora now patched in model weights, there no need to store lora in
vram
Results:
Speed:
| loras count | hook | patch |
| --- | --- | --- |
| 0 | ~4.92 it/s | ~4.92 it/s |
| 1 | ~3.51 it/s | ~4.89 it/s |
| 2 | ~2.76 it/s | ~4.92 it/s |
VRAM:
| loras count | hook | patch |
| --- | --- | --- |
| 0 | ~3.6 gb | ~3.6 gb |
| 1 | ~4.0 gb | ~3.6 gb |
| 2 | ~4.4 gb | ~3.7 gb |
As based on #3547 wait to merge.
# Restore invokeai-configure and invokeai-model-install
This PR updates invokeai-configure and invokeai-model-install to work
with the new model manager file layout. It addresses a naming issue for
`ModelType.Main` (was `ModelType.Pipeline`) requested by
@blessedcoolant, and adds back the feature that allows users to dump
models into an `autoimport` directory for discovery at startup time.
Trying to get a few ControlNet extras in before 3.0 release:
- SegmentAnything ControlNet preprocessor node
- LeResDepth ControlNet preprocessor node (but commented out till
controlnet_aux v0.0.6 is released & required by InvokeAI)
- TileResampler ControlNet preprocessor node (should be equivalent to
Mikubill/sd-webui-controlnet extension tile_resampler)
- fix for Midas ControlNet preprocessor error with images that have
alpha channel
Example usage of SegmentAnything preprocessor node:

The installer TUI requires a minimum window width and height to provide
a satisfactory user experience. If, after trying and exhausting all
means of enlarging the window (on Linux, Mac and Windows) the window is
still too small, this PR generates a message telling the user to enlarge
the window and pausing until they do so. If the user fails to enlarge
the window the program will proceed, and either issue an error message
that it can't continue (on Windows), or show a clipped display that the
user can remedy by enlarging the window.
"Fixes" the test suite generally so it doesn't fail CI, but some tests
needed to be skipped/xfailed due to recent refactor.
- ignore three test suites that broke following the model manager
refactor
- move `InvocationServices` fixture to `conftest.py`
- add `boards` items to the `InvocationServices` fixture
This PR makes the unit tests work, but end-to-end tests are temporarily
commented out due to `invokeai-configure` being broken in `main` -
pending #3547
Looks like a lot of the tests need to be rewritten as they reference
`TextToImageInvocation` / `ImageToImageInvocation`
fixes the test suite generally, but some tests needed to be
skipped/xfailed due to recent refactor
- ignore three test suites that broke following the model manager
refactor
- move InvocationServices fixture to conftest.py
- add `boards` InvocationServices to the fixture
This PR adds the "control_mode" option to ControlNet implementation.
Possible control_mode options are:
- balanced -- this is the default, same as previous implementation
without control_mode
- more_prompt -- pays more attention to the prompt
- more _control -- pays more attention to the ControlNet (in earlier
implementations this was called "guess_mode")
- unbalanced -- pays even more attention to the ControlNet
balanced, more_prompt, and more_control should be nearly identical to
the equivalent options in the [auto1111 sd-webui-controlnet
extension](https://github.com/Mikubill/sd-webui-controlnet#more-control-modes-previously-called-guess-mode)
The changes to enable balanced, more_prompt, and more_control are
managed deeper in the code by two booleans, "soft_injection" and
"cfg_injection". The three control mode options in sd-webui-controlnet
map to these booleans like:
!soft_injection && !cfg_injection ⇒ BALANCED
soft_injection && cfg_injection ⇒ MORE_CONTROL
soft_injection && !cfg_injection ⇒ MORE_PROMPT
The "unbalanced" option simply exposes the fourth possible combination
of these two booleans:
!soft_injection && cfg_injection ⇒ UNBALANCED
With "unbalanced" mode it is very easy to overdrive the controlnet
inputs. It's recommended to use a cfg_scale between 2 and 4 to mitigate
this, along with lowering controlnet weight and possibly lowering "end
step percent". With those caveats, "unbalanced" can yield interesting
results.
Example of all four modes using Canny edge detection ControlNet with
prompt "old man", identical params except for control_mode:

Top middle: BALANCED
Top right: MORE_CONTROL
Bottom middle: MORE_PROMPT
Bottom right : UNBALANCED
I kind of chose this seed because it shows pretty rough results with
BALANCED (the default), but in my opinion better results with both
MORE_CONTROL and MORE_PROMPT. And you can definitely see how MORE_PROMPT
pays more attention to the prompt, and MORE_CONTROL pays more attention
to the control image. And shows that UNBALANCED with default cfg_scale
etc is unusable.
But here are four examples from same series (same seed etc), all have
control_mode = UNBALANCED but now cfg_scale is set to 3.

And param differences are:
Top middle: prompt="old man", control_weight=0.3, end_step_percent=0.5
Top right: prompt="old man", control_weight=0.4, end_step_percent=1.0
Bottom middle: prompt=None, control_weight=0.3, end_step_percent=0.5
Bottom right: prompt=None, control_weight=0.4, end_step_percent=1.0
So with the right settings UNBALANCED seems useful.
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a
similar one
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a similar one
Only "real" conflicts were in:
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
- Reset and Upload buttons along top of initial image
- Also had to mess around with the control net & DnD image stuff after changing the styles
- Abstract image upload logic into hook - does not handle native HTML drag and drop upload - only the button click upload
`openapi-fetch` does not handle non-JSON `body`s, always stringifying them, and sets the `content-type` to `application/json`.
The patch here does two things:
- Do not stringify `body` if it is one of the types that should not be stringified (https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch#body)
- Do not add `content-type: application/json` unless it really is stringified JSON.
Upstream issue: https://github.com/drwpow/openapi-typescript/issues/1123
I'm not a bit lost on fixing the types and adding tests, so not raising a PR upstream.
*migrate from `openapi-typescript-codegen` to `openapi-typescript` and `openapi-fetch`*
`openapi-typescript-codegen` is not very actively maintained - it's been over a year since the last update.
`openapi-typescript` and `openapi-fetch` are part of the actively maintained repo. key differences:
- provides a `fetch` client instead of `axios`, which means we need to be a bit more verbose with typing thunks
- fetch client is created at runtime and has a very nice typescript DX
- generates a single file with all types in it, from which we then extract individual types. i don't like how verbose this is, but i do like how it is more explicit.
- removed npm api generation scripts - now we have a single `typegen` script
overall i have more confidence in this new library.
*use nanostores for api base and token*
very simple reactive store for api base url and token. this was suggested in the `openapi-fetch` docs and i quite like the strategy.
*organise rtk-query api*
split out each endpoint (models, images, boards, boardImages) into their own api extensions. tidy!
Unsure at which moment it broke, but now I can't convert vae(and model
as vae it's part) without this fix.
Need further research - maybe it's breaking change in `transformers`?
Changes:
* Linux `install.sh` now prints the maximum python version to use in
case no installed python version matches
Commits:
fix(linux): installer script prints maximum python version usable
PR for the Model Manager UI work related to 3.0
[DONE]
- Update ModelType Config names to be specific so that the front end can
parse them correctly.
- Rebuild frontend schema to reflect these changes.
- Update Linear UI Text To Image and Image to Image to work with the new
model loader.
- Updated the ModelInput component in the Node Editor to work with the
new changes.
[TODO REMEMBER]
- Add proper types for ModelLoaderType in `ModelSelect.tsx`
[TODO]
- Everything else.
Basically updated all slices to be more descriptive in their names. Did so in order to make sure theres good naming scheme available for secondary models.
To determine whether the Load More button should work, we need to keep track of how many images are left to load for a given board or category.
The Assets tab doesn't work, though. Need to figure out a better way to handle this.
We need to access the initial image dimensions during the creation of the `ImageToImage` graph to determine if we need to resize the image.
Because the `initialImage` is now just an image name, we need to either store (easy) or dynamically retrieve its dimensions during graph creation (a bit less easy).
Took the easiest path. May need to revise this in the future.
Images that are used as parameters (e.g. init image, canvas images) are stored as full `ImageDTO` objects in state, separate from and duplicating any object representing those same objects in the `imagesSlice`.
We cannot store only image names as parameters, then pull the full `ImageDTO` from `imagesSlice`, because if an image is not on a loaded page, it doesn't exist in `imagesSlice`. For example, if you scroll down a few pages in the gallery and send that image to canvas, on reloading the app, the canvas will be unable to load that image.
We solved this temporarily by storing the full `ImageDTO` object wherever it was needed, but this is both inefficient and allows for stale `ImageDTO`s across the app.
One other possible solution was to just fetch the `ImageDTO` for all images at startup, and insert them into the `imagesSlice`, but then we run into an issue where we are displaying images in the gallery totally out of context.
For example, if an image from several pages into the gallery was sent to canvas, and the user refreshes, we'd display the first 20 images in gallery. Then to populate the canvas, we'd fetch that image we sent to canvas and add it to `imagesSlice`. Now we'd have 21 images in the gallery: 1 to 20 and whichever image we sent to canvas. Weird.
Using `rtk-query` solves this by allowing us to very easily fetch individual images in the components that need them, and not directly interact with `imagesSlice`.
This commit changes all references to images-as-parameters to store only the name of the image, and not the full `ImageDTO` object. Then, we use an `rtk-query` generated `useGetImageDTOQuery()` hook in each of those components to fetch the image.
We can use cache invalidation when we mutate any image to trigger automated re-running of the query and all the images are automatically kept up to date.
This also obviates the need for the convoluted URL fetching scheme for images that are used as parameters. The `imagesSlice` still need this handling unfortunately.
Added sde schedulers.
Problem - they add random on each step, to get consistent image we need
to provide seed or generator.
I done it, but if you think that it better do in other way - feel free
to change.
Also made ancestral schedulers reproducible, this done same way as for
sde scheduler.
- Add graph builders for canvas txt2img & img2img - they are mostly copy and paste from the linear graph builders but different in a few ways that are very tricky to work around. Just made totally new functions for them.
- Canvas txt2img and img2img support ControlNet (not inpaint/outpaint). There's no way to determine in real-time which mode the canvas is in just yet, so we cannot disable the ControlNet UI when the mode will be inpaint/outpaint - it will always display. It's possible to determine this in near-real-time, will add this at some point.
- Canvas inpaint/outpaint migrated to use model loader, though inpaint/outpaint are still using the non-latents nodes.
Instead of manually creating every node and edge, we can simply copy/paste the base graph from node editor, then sub in parameters.
This is a much more intelligible process. We still need to handle seed, img2img fit and controlnet separately.
- Ports Schedulers to use IAIMantineSelect.
- Adds ability to favorite schedulers in Settings. Favorited schedulers
show up at the top of the list.
- Adds IAIMantineMultiSelect component.
- Change SettingsSchedulers component to use IAIMantineMultiSelect
instead of Chakra Menus.
- remove UI-specific state (the enabled schedulers) from redux, instead derive it in a selector
- simplify logic by putting schedulers in an object instead of an array
- rename `activeSchedulers` to `enabledSchedulers`
- remove need for `useEffect()` when `enabledSchedulers` changes by adding a listener for the `enabledSchedulersChanged` action/event to `generationSlice`
- increase type safety by making `enabledSchedulers` an array of `SchedulerParam`, which is created by the zod schema for scheduler
Basically updated all slices to be more descriptive in their names. Did so in order to make sure theres good naming scheme available for secondary models.
Update the text to imaeg and image to image graphs to work with the new model loader. Currently only supports 1.x models. Will update this soon to make it work with all models.
- `DiskImageStorage` and `DiskLatentsStorage` have now both been updated
to exclusively work with `Path` objects and not rely on the `os` lib to
handle pathing related functions.
- We now also validate the existence of the required image output
folders and latent output folders to ensure that the app does not break
in case the required folders get tampered with mid-session.
- Just overall general cleanup.
Tested it. Don't seem to be any thing breaking.
- remove `image_origin` from most places where we interact with images
- consolidate image file storage into a single `images/` dir
Images have an `image_origin` attribute but it is not actually used when retrieving images, nor will it ever be. It is still used when creating images and helps to differentiate between internally generated images and uploads.
It was included in eg API routes and image service methods as a holdover from the previous app implementation where images were not managed in a database. Now that we have images in a db, we can do away with this and simplify basically everything that touches images.
The one potentially controversial change is to no longer separate internal and external images on disk. If we retain this separation, we have to keep `image_origin` around in a number of spots and it getting image paths on disk painful.
So, I am have gotten rid of this organisation. Images are now all stored in `images`, regardless of their origin. As we improve the image management features, this change will hopefully become transparent.
Diffusers is due for an update soon. #3512
Opening up a PR now with the required changes for when the new version
is live.
I've tested it out on Windows and nothing has broken from what I could
tell. I'd like someone to run some tests on Linux / Mac just to make
sure. Refer to the PR above on how to test it or install the release
branch.
```
pip install diffusers[torch]==0.17.0
```
Feel free to push any other changes to this PR you see fit.
There are some bugs with it that I cannot figure out related to `floating-ui` and `downshift`'s handling of refs.
Will need to revisit this component in the future.
* Testing change to LatentsToText to allow setting different cfg_scale values per diffusion step.
* Adding first attempt at float param easing node, using Penner easing functions.
* Core implementation of ControlNet and MultiControlNet.
* Added support for ControlNet and MultiControlNet to legacy non-nodal Txt2Img in backend/generator. Although backend/generator will likely disappear by v3.x, right now they are very useful for testing core ControlNet and MultiControlNet functionality while node codebase is rapidly evolving.
* Added example of using ControlNet with legacy Txt2Img generator
* Resolving rebase conflict
* Added first controlnet preprocessor node for canny edge detection.
* Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node
* Switching to ControlField for output from controlnet nodes.
* Resolving conflicts in rebase to origin/main
* Refactored ControlNet nodes so they subclass from PreprocessedControlInvocation, and only need to override run_processor(image) (instead of reimplementing invoke())
* changes to base class for controlnet nodes
* Added HED, LineArt, and OpenPose ControlNet nodes
* Added an additional "raw_processed_image" output port to controlnets, mainly so could route ImageField to a ShowImage node
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* More rebase repair.
* Added support for using multiple control nets. Unfortunately this breaks direct usage of Control node output port ==> TextToLatent control input port -- passing through a Collect node is now required. Working on fixing this...
* Fixed use of ControlNet control_weight parameter
* Fixed lint-ish formatting error
* Core implementation of ControlNet and MultiControlNet.
* Added first controlnet preprocessor node for canny edge detection.
* Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node
* Switching to ControlField for output from controlnet nodes.
* Refactored controlnet node to output ControlField that bundles control info.
* changes to base class for controlnet nodes
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* Cleaning up TextToLatent arg testing
* Cleaning up mistakes after rebase.
* Removed last bits of dtype and and device hardwiring from controlnet section
* Refactored ControNet support to consolidate multiple parameters into data struct. Also redid how multiple controlnets are handled.
* Added support for specifying which step iteration to start using
each ControlNet, and which step to end using each controlnet (specified as fraction of total steps)
* Cleaning up prior to submitting ControlNet PR. Mostly turning off diagnostic printing. Also fixed error when there is no controlnet input.
* Added dependency on controlnet-aux v0.0.3
* Commented out ZoeDetector. Will re-instate once there's a controlnet-aux release that supports it.
* Switched CotrolNet node modelname input from free text to default list of popular ControlNet model names.
* Fix to work with current stable release of controlnet_aux (v0.0.3). Turned of pre-processor params that were added post v0.0.3. Also change defaults for shuffle.
* Refactored most of controlnet code into its own method to declutter TextToLatents.invoke(), and make upcoming integration with LatentsToLatents easier.
* Cleaning up after ControlNet refactor in TextToLatentsInvocation
* Extended node-based ControlNet support to LatentsToLatentsInvocation.
* chore(ui): regen api client
* fix(ui): add value to conditioning field
* fix(ui): add control field type
* fix(ui): fix node ui type hints
* fix(nodes): controlnet input accepts list or single controlnet
* Moved to controlnet_aux v0.0.4, reinstated Zoe controlnet preprocessor. Also in pyproject.toml had to specify downgrade of timm to 0.6.13 _after_ controlnet-aux installs timm >= 0.9.2, because timm >0.6.13 breaks Zoe preprocessor.
* Core implementation of ControlNet and MultiControlNet.
* Added first controlnet preprocessor node for canny edge detection.
* Switching to ControlField for output from controlnet nodes.
* Resolving conflicts in rebase to origin/main
* Refactored ControlNet nodes so they subclass from PreprocessedControlInvocation, and only need to override run_processor(image) (instead of reimplementing invoke())
* changes to base class for controlnet nodes
* Added HED, LineArt, and OpenPose ControlNet nodes
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* Added support for using multiple control nets. Unfortunately this breaks direct usage of Control node output port ==> TextToLatent control input port -- passing through a Collect node is now required. Working on fixing this...
* Fixed use of ControlNet control_weight parameter
* Core implementation of ControlNet and MultiControlNet.
* Added first controlnet preprocessor node for canny edge detection.
* Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node
* Switching to ControlField for output from controlnet nodes.
* Refactored controlnet node to output ControlField that bundles control info.
* changes to base class for controlnet nodes
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* Cleaning up TextToLatent arg testing
* Cleaning up mistakes after rebase.
* Removed last bits of dtype and and device hardwiring from controlnet section
* Refactored ControNet support to consolidate multiple parameters into data struct. Also redid how multiple controlnets are handled.
* Added support for specifying which step iteration to start using
each ControlNet, and which step to end using each controlnet (specified as fraction of total steps)
* Cleaning up prior to submitting ControlNet PR. Mostly turning off diagnostic printing. Also fixed error when there is no controlnet input.
* Commented out ZoeDetector. Will re-instate once there's a controlnet-aux release that supports it.
* Switched CotrolNet node modelname input from free text to default list of popular ControlNet model names.
* Fix to work with current stable release of controlnet_aux (v0.0.3). Turned of pre-processor params that were added post v0.0.3. Also change defaults for shuffle.
* Refactored most of controlnet code into its own method to declutter TextToLatents.invoke(), and make upcoming integration with LatentsToLatents easier.
* Cleaning up after ControlNet refactor in TextToLatentsInvocation
* Extended node-based ControlNet support to LatentsToLatentsInvocation.
* chore(ui): regen api client
* fix(ui): fix node ui type hints
* fix(nodes): controlnet input accepts list or single controlnet
* Added Mediapipe image processor for use as ControlNet preprocessor.
Also hacked in ability to specify HF subfolder when loading ControlNet models from string.
* Fixed bug where MediapipFaceProcessorInvocation was ignoring max_faces and min_confidence params.
* Added nodes for float params: ParamFloatInvocation and FloatCollectionOutput. Also added FloatOutput.
* Added mediapipe install requirement. Should be able to remove once controlnet_aux package adds mediapipe to its requirements.
* Added float to FIELD_TYPE_MAP ins constants.ts
* Progress toward improvement in fieldTemplateBuilder.ts getFieldType()
* Fixed controlnet preprocessors and controlnet handling in TextToLatents to work with revised Image services.
* Cleaning up from merge, re-adding cfg_scale to FIELD_TYPE_MAP
* Making sure cfg_scale of type list[float] can be used in image metadata, to support param easing for cfg_scale
* Fixed math for per-step param easing.
* Added option to show plot of param value at each step
* Just cleaning up after adding param easing plot option, removing vestigial code.
* Modified control_weight ControlNet param to be polistmorphic --
can now be either a single float weight applied for all steps, or a list of floats of size total_steps, that specifies weight for each step.
* Added more informative error message when _validat_edge() throws an error.
* Just improving parm easing bar chart title to include easing type.
* Added requirement for easing-functions package
* Taking out some diagnostic prints.
* Added option to use both easing function and mirror of easing function together.
* Fixed recently introduced problem (when pulled in main), triggered by num_steps in StepParamEasingInvocation not having a default value -- just added default.
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
In some cases the command-line was getting parsed before the logger was
initialized, causing the logger not to pick up custom logging
instructions from `--log_handlers`. This PR fixes the issue.
[fix(ui): blur tab on
click](93f3658a4a)
Fixes issue where after clicking a tab, using the arrow keys changes tab
instead of changing selected image
[fix(ui): fix canvas not filling screen on first
load](68be95acbb)
[feat(ui): remove clear temp folder canvas
button](813f79f0f9)
This button is nonfunctional.
Soon we will introduce a different way to handle clearing out
intermediate images (likely automated).
There was an issue where for graphs w/ iterations, your images were output all at once, at the very end of processing. So if you canceled halfway through an execution of 10 nodes, you wouldn't get any images - even though you'd completed 5 images' worth of inference.
## Cause
Because graphs executed breadth-first (i.e. depth-by-depth), leaf nodes were necessarily processed last. For image generation graphs, your `LatentsToImage` will be leaf nodes, and be the last depth to be executed.
For example, a `TextToLatents` graph w/ 3 iterations would execute all 3 `TextToLatents` nodes fully before moving to the next depth, where the `LatentsToImage` nodes produce output images, resulting in a node execution order like this:
1. TextToLatents
2. TextToLatents
3. TextToLatents
4. LatentsToImage
5. LatentsToImage
6. LatentsToImage
## Solution
This PR makes a two changes to graph execution to execute as deeply as it can along each branch of the graph.
### Eager node preparation
We now prepare as many nodes as possible, instead of just a single node at a time.
We also need to change the conditions in which nodes are prepared. Previously, nodes were prepared only when all of their direct ancestors were executed.
The updated logic prepares nodes that:
- are *not* `Iterate` nodes whose inputs have *not* been executed
- do *not* have any unexecuted `Iterate` ancestor nodes
This results in graphs always being maximally prepared.
### Always execute the deepest prepared node
We now choose the next node to execute by traversing from the bottom of the graph instead of the top, choosing the first node whose inputs are all executed.
This means we always execute the deepest node possible.
## Result
Graphs now execute depth-first, so instead of an execution order like this:
1. TextToLatents
2. TextToLatents
3. TextToLatents
4. LatentsToImage
5. LatentsToImage
6. LatentsToImage
... we get an execution order like this:
1. TextToLatents
2. LatentsToImage
3. TextToLatents
4. LatentsToImage
5. TextToLatents
6. LatentsToImage
Immediately after inference, the image is decoded and sent to the gallery.
fixes#3400
This PR creates the databases directory at app startup time. It also
removes a couple of debugging statements that were inadvertently left in
the model manager.
# Make InvokeAI package installable by mere mortals
This commit makes InvokeAI 3.0 to be installable via PyPi.org and/or the
installer script. The install process is now pretty much identical to
the 2.3 process, including creating launcher scripts `invoke.sh` and
`invoke.bat`.
Main changes:
1. Moved static web pages into `invokeai/frontend/web` and modified the
API to look for them there. This allows pip to copy the files into the
distribution directory so that user no longer has to be in repo root to
launch, and enables PyPi installations with `pip install invokeai`
2. Update invoke.sh and invoke.bat to launch the new web application
properly. This also changes the wording for launching the CLI from
"generate images" to "explore the InvokeAI node system," since I would
not recommend using the CLI to generate images routinely.
3. Fix a bug in the checkpoint converter script that was identified
during testing.
4. Better error reporting when checkpoint converter fails.
5. Rebuild front end.
# Major improvements to the model installer.
1. The text user interface for `invokeai-model-install` has been
expanded to allow the user to install controlnet, LoRA, textual
inversion, diffusers and checkpoint models. The user can install
interactively (without leaving the TUI), or in batch mode after exiting
the application.

2. The `invokeai-model-install` command now lets you list, add and
delete models from the command line:
## Listing models
```
$ invokeai-model-install --list diffusers
Diffuser models:
analog-diffusion-1.0 not loaded diffusers An SD-1.5 model trained on diverse analog photographs (2.13 GB)
d&d-diffusion-1.0 not loaded diffusers Dungeons & Dragons characters (2.13 GB)
deliberate-1.0 not loaded diffusers Versatile model that produces detailed images up to 768px (4.27 GB)
DreamShaper not loaded diffusers Imported diffusers model DreamShaper
sd-inpainting-1.5 not loaded diffusers RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
sd-inpainting-2.0 not loaded diffusers Stable Diffusion version 2.0 inpainting model (5.21 GB)
stable-diffusion-1.5 not loaded diffusers Stable Diffusion version 1.5 diffusers model (4.27 GB)
stable-diffusion-2.1 not loaded diffusers Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
```
```
$ invokeai-model-install --list tis
Loading Python libraries...
Installed Textual Inversion Embeddings:
EasyNegative
ahx-beta-453407d
```
## Installing models
(this example shows correct handling of a server side error at Civitai)
```
$ invokeai-model-install --diffusers https://civitai.com/api/download/models/46259 Linaqruf/anything-v3.0
Loading Python libraries...
[2023-06-05 22:17:23,556]::[InvokeAI]::INFO --> INSTALLING EXTERNAL MODELS
[2023-06-05 22:17:23,557]::[InvokeAI]::INFO --> Probing https://civitai.com/api/download/models/46259 for import
[2023-06-05 22:17:23,557]::[InvokeAI]::INFO --> https://civitai.com/api/download/models/46259 appears to be a URL
[2023-06-05 22:17:23,763]::[InvokeAI]::ERROR --> An error occurred during downloading /home/lstein/invokeai-test/models/ldm/stable-diffusion-v1/46259: Internal Server Error
[2023-06-05 22:17:23,763]::[InvokeAI]::ERROR --> ERROR DOWNLOADING https://civitai.com/api/download/models/46259: {"error":"Invalid database operation","cause":{"clientVersion":"4.12.0"}}
[2023-06-05 22:17:23,764]::[InvokeAI]::INFO --> Probing Linaqruf/anything-v3.0 for import
[2023-06-05 22:17:23,764]::[InvokeAI]::DEBUG --> Linaqruf/anything-v3.0 appears to be a HuggingFace diffusers repo_id
[2023-06-05 22:17:23,768]::[InvokeAI]::INFO --> Loading diffusers model from Linaqruf/anything-v3.0
[2023-06-05 22:17:23,769]::[InvokeAI]::DEBUG --> Using faster float16 precision
[2023-06-05 22:17:23,883]::[InvokeAI]::ERROR --> An unexpected error occurred while downloading the model: 404 Client Error. (Request ID: Root=1-647e9733-1b0ee3af67d6ac3456b1ebfc)
Revision Not Found for url: https://huggingface.co/Linaqruf/anything-v3.0/resolve/fp16/model_index.json.
Invalid rev id: fp16)
Downloading (…)ain/model_index.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 511/511 [00:00<00:00, 2.57MB/s]
Downloading (…)cial_tokens_map.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 472/472 [00:00<00:00, 6.13MB/s]
Downloading (…)cheduler_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 341/341 [00:00<00:00, 3.30MB/s]
Downloading (…)okenizer_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 807/807 [00:00<00:00, 11.3MB/s]
```
## Deleting models
```
invokeai-model-install --delete --diffusers anything-v3
Loading Python libraries...
[2023-06-05 22:19:45,927]::[InvokeAI]::INFO --> Processing requested deletions
[2023-06-05 22:19:45,927]::[InvokeAI]::INFO --> anything-v3...
[2023-06-05 22:19:45,927]::[InvokeAI]::INFO --> Deleting the cached model directory for Linaqruf/anything-v3.0
[2023-06-05 22:19:45,948]::[InvokeAI]::WARNING --> Deletion of this model is expected to free 4.3G
```
1. Contents of autoscan directory field are restored after doing an installation.
2. Activate dialogue to choose V2 parameterization when importing from a directory.
3. Remove autoscan directory from init file when its checkbox is unselected.
4. Add widget cycling behavior to install models form.
The processor is automatically selected when model is changed.
But if the user manually changes the processor, processor settings, or disables the new `Auto configure processor` switch, auto processing is disabled.
The user can enable auto configure by turning the switch back on.
When auto configure is enabled, a small dot is overlaid on the expand button to remind the user that the system is not auto configuring the processor for them.
If auto configure is enabled, the processor settings are reset to the default for the selected model.
Add uploading to IAIDndImage
- add `postUploadAction` arg to `imageUploaded` thunk, with several current valid options (set control image, set init, set nodes image, set canvas, or toast)
- updated IAIDndImage to optionally allow click to upload
- when the controlnet model is changed, if there is a default processor for the model set, the processor is changed.
- once a control image is selected (and processed), changing the model does not change the processor - must be manually changed
- Also fixed up order in which logger is created in invokeai-web
so that handlers are installed after command-line options are
parsed (and not before!)
This handles the case when an image is deleted but is still in use in as eg an init image on canvas, or a control image. If we just delete the image, canvas/controlnet/etc may break (the image would just fail to load).
When an image is deleted, the app checks to see if it is in use in:
- Image to Image
- ControlNet
- Unified Canvas
- Node Editor
The delete dialog will always open if the image is in use anywhere, and the user is advised that deleting the image will reset the feature(s).
Even if the user has ticked the box to not confirm on delete, the dialog will still show if the image is in use somewhere.
- fix "bounding box region only" not being respected when saving
- add toasts for each action
- improve workflow `take()` predicates to use the requestId
- responsive changes were causing a lot of weird layout issues, had to remove the rest of them
- canvas (non-beta) toolbar now wraps
- reduces minH for prompt boxes a bit
1. Model installer works correctly under Windows 11 Terminal
2. Fixed crash when configure script hands control off to installer
3. Kill install subprocess on keyboard interrupt
4. Command-line functionality for --yes configuration and model installation
restored.
5. New command-line features:
- install/delete lists of diffusers, LoRAS, controlnets and textual inversions
using repo ids, paths or URLs.
Help:
```
usage: invokeai-model-install [-h] [--diffusers [DIFFUSERS ...]] [--loras [LORAS ...]] [--controlnets [CONTROLNETS ...]] [--textual-inversions [TEXTUAL_INVERSIONS ...]] [--delete] [--full-precision | --no-full-precision]
[--yes] [--default_only] [--list-models {diffusers,loras,controlnets,tis}] [--config_file CONFIG_FILE] [--root_dir ROOT]
InvokeAI model downloader
options:
-h, --help show this help message and exit
--diffusers [DIFFUSERS ...]
List of URLs or repo_ids of diffusers to install/delete
--loras [LORAS ...] List of URLs or repo_ids of LoRA/LyCORIS models to install/delete
--controlnets [CONTROLNETS ...]
List of URLs or repo_ids of controlnet models to install/delete
--textual-inversions [TEXTUAL_INVERSIONS ...]
List of URLs or repo_ids of textual inversion embeddings to install/delete
--delete Delete models listed on command line rather than installing them
--full-precision, --no-full-precision
use 32-bit weights instead of faster 16-bit weights (default: False)
--yes, -y answer "yes" to all prompts
--default_only only install the default model
--list-models {diffusers,loras,controlnets,tis}
list installed models
--config_file CONFIG_FILE, -c CONFIG_FILE
path to configuration file to create
--root_dir ROOT path to root of install directory
```
There was a potential gotcha in the config system that was previously
merged with main. The `InvokeAIAppConfig` object was configuring itself
from the command line and configuration file within its initialization
routine. However, this could cause it to read `argv` from the command
line at unexpected times. This PR fixes the object so that it only reads
from the init file and command line when its `parse_args()` method is
explicitly called, which should be done at startup time in any top level
script that uses it.
In addition, using the `get_invokeai_config()` function to get a global
version of the config object didn't feel pythonic to me, so I have
changed this to `InvokeAIAppConfig.get_config()` throughout.
## Updated Usage
In the main script, at startup time, do the following:
```
from invokeai.app.services.config import InvokeAIAppConfig
config = InvokeAIAppConfig.get_config()
config.parse_args()
```
In non-main scripts, it is not necessary (or recommended) to call
`parse_args()`:
```
from invokeai.app.services.config import InvokeAIAppConfig
config = InvokeAIAppConfig.get_config()
```
The configuration object properties can be overridden when
`get_config()` is called by passing initialization values in the usual
way. If a property is set this way, then it will not be changed by
subsequent calls to `parse_args()`, but can only be changed by
explicitly setting the property.
```
config = InvokeAIAppConfig.get_config(nsfw_checker=True)
config.parse_args(argv=['--no-nsfw_checker'])
config.nsfw_checker
# True
```
You may specify alternative argv lists and configuration files in
`parse_args()`:
```
config.parse_args(argv=['--no-nsfw_checker'],
conf = OmegaConf.load('/tmp/test.yaml')
)
```
For backward compatibility, the `get_invokeai_config()` function is
still available from the module, but has been removed from the rest of
the source tree.
this PR adds long prompt support and enables compel's new `.and()`
concatenation feature which improves image quality especially with SD2.1
example of a long prompt:
> a moist sloppy pindlesackboy sloppy hamblin' bogomadong, Clem Fandango
is pissed-off, Wario's Woods in background, making a noise like
ga-woink-a

the same prompt broken into fragments and concatenated using `.and()`
(syntax works like `.blend()`):
```
("a moist sloppy pindlesackboy sloppy hamblin' bogomadong",
"Clem Fandango is pissed-off",
"Wario's Woods in background",
"making a noise like ga-woink-a").and()
```

and a less silly example:
> A dream of a distant galaxy, by Caspar David Friedrich, matte
painting, trending on artstation, HQ

the same prompt broken into two fragments and concatenated:
```
("A dream of a distant galaxy, by Caspar David Friedrich, matte painting",
"trending on artstation, HQ").and()
```

as with `.blend()` you can also weight the parts eg `("a man eating an
apple", "sitting on the roof of a car", "high quality, trending on
artstation, 8K UHD").and(1, 0.5, 0.5)` which will assign weight `1` to
`a man eating an apple` and `0.5` to `sitting on the roof of a car` and
`high quality, trending on artstation, 8K UHD`.
Implement `dnd-kit` for image drag and drop
- vastly simplifies logic bc we can drag and drop non-serializable data (like an `ImageDTO`)
- also much prettier
- also will fix conflicts with file upload via OS drag and drop, bc `dnd-kit` does not use native HTML drag and drop API
- Implemented for Init image, controlnet, and node editor so far
More progress on the ControlNet UI
- The invokeai.db database file has now been moved into
`INVOKEAIROOT/databases`. Using plural here for possible
future with more than one database file.
- Removed a few dangling debug messages that appeared during
testing.
- Rebuilt frontend to test web.
This PR provides a number of options for controlling how InvokeAI logs
messages, including options to log to a file, syslog and a web server.
Several logging handlers can be configured simultaneously.
## Controlling How InvokeAI Logs Status Messages
InvokeAI logs status messages using a configurable logging system. You
can log to the terminal window, to a designated file on the local
machine, to the syslog facility on a Linux or Mac, or to a properly
configured web server. You can configure several logs at the same time,
and control the level of message logged and the logging format (to a
limited extent).
Three command-line options control logging:
### `--log_handlers <handler1> <handler2> ...`
This option activates one or more log handlers. Options are "console",
"file", "syslog" and "http". To specify more than one, separate them by
spaces:
```bash
invokeai-web --log_handlers console syslog=/dev/log file=C:\Users\fred\invokeai.log
```
The format of these options is described below.
### `--log_format {plain|color|legacy|syslog}`
This controls the format of log messages written to the console. Only
the "console" log handler is currently affected by this setting.
* "plain" provides formatted messages like this:
```bash
[2023-05-24 23:18:2[2023-05-24 23:18:50,352]::[InvokeAI]::DEBUG --> this is a debug message
[2023-05-24 23:18:50,352]::[InvokeAI]::INFO --> this is an informational messages
[2023-05-24 23:18:50,352]::[InvokeAI]::WARNING --> this is a warning
[2023-05-24 23:18:50,352]::[InvokeAI]::ERROR --> this is an error
[2023-05-24 23:18:50,352]::[InvokeAI]::CRITICAL --> this is a critical error
```
* "color" produces similar output, but the text will be color coded to
indicate the severity of the message.
* "legacy" produces output similar to InvokeAI versions 2.3 and earlier:
```bash
### this is a critical error
*** this is an error
** this is a warning
>> this is an informational messages
| this is a debug message
```
* "syslog" produces messages suitable for syslog entries:
```bash
InvokeAI [2691178] <CRITICAL> this is a critical error
InvokeAI [2691178] <ERROR> this is an error
InvokeAI [2691178] <WARNING> this is a warning
InvokeAI [2691178] <INFO> this is an informational messages
InvokeAI [2691178] <DEBUG> this is a debug message
```
(note that the date, time and hostname will be added by the syslog
system)
### `--log_level {debug|info|warning|error|critical}`
Providing this command-line option will cause only messages at the
specified level or above to be emitted.
## Console logging
When "console" is provided to `--log_handlers`, messages will be written
to the command line window in which InvokeAI was launched. By default,
the color formatter will be used unless overridden by `--log_format`.
## File logging
When "file" is provided to `--log_handlers`, entries will be written to
the file indicated in the path argument. By default, the "plain" format
will be used:
```bash
invokeai-web --log_handlers file=/var/log/invokeai.log
```
## Syslog logging
When "syslog" is requested, entries will be sent to the syslog system.
There are a variety of ways to control where the log message is sent:
* Send to the local machine using the `/dev/log` socket:
```
invokeai-web --log_handlers syslog=/dev/log
```
* Send to the local machine using a UDP message:
```
invokeai-web --log_handlers syslog=localhost
```
* Send to the local machine using a UDP message on a nonstandard port:
```
invokeai-web --log_handlers syslog=localhost:512
```
* Send to a remote machine named "loghost" on the local LAN using
facility LOG_USER and UDP packets:
```
invokeai-web --log_handlers syslog=loghost,facility=LOG_USER,socktype=SOCK_DGRAM
```
This can be abbreviated `syslog=loghost`, as LOG_USER and SOCK_DGRAM are
defaults.
* Send to a remote machine named "loghost" using the facility LOCAL0 and
using a TCP socket:
```
invokeai-web --log_handlers syslog=loghost,facility=LOG_LOCAL0,socktype=SOCK_STREAM
```
If no arguments are specified (just a bare "syslog"), then the logging
system will look for a UNIX socket named `/dev/log`, and if not found
try to send a UDP message to `localhost`. The Macintosh OS used to
support logging to a socket named `/var/run/syslog`, but this feature
has since been disabled.
## Web logging
If you have access to a web server that is configured to log messages
when a particular URL is requested, you can log using the "http" method:
```
invokeai-web --log_handlers http=http://my.server/path/to/logger,method=POST
```
The optional [,method=] part can be used to specify whether the URL
accepts GET (default) or POST messages.
Currently password authentication and SSL are not supported.
## Using the configuration file
You can set and forget logging options by adding a "Logging" section to
`invokeai.yaml`:
```
InvokeAI:
[... other settings...]
Logging:
log_handlers:
- console
- syslog=/dev/log
log_level: info
log_format: color
```
1. Separated the "starter models" and "more models" sections. This
gives us room to list all installed diffuserse models, not just
those that are on the starter list.
2. Support mouse-based paste into the textboxes with either middle
or right mouse buttons.
3. Support terminal-style cursor movement:
^A to move to beginning of line
^E to move to end of line
^K kill text to right and put in killring
^Y yank text back
4. Internal code cleanup.
The gallery could get in a state where it thought it had just reached the end of the list and endlessly fetches more images, if there are no more images to fetch (weird I know).
Add some logic to remove the `end reached` handler when there are no more images to load.
it doesn't work for the img2img pipelines, but the implemented conditional display could break the scheduler selection dropdown.
simple fix until diffusers merges the fix - never use this scheduler.
Inputs with explicit values are validated by pydantic even if they also
have a connection (which is the actual value that is used).
Fix this by omitting explicit values for inputs that have a connection.
Problem was that controlnet support involved adding **kwargs to method calls down in denoising loop, and AddsMaskLatents didn't accept **kwarg arg. So just changed to accept and pass on **kwargs.
This may cause minor gallery jumpiness at the very end of processing, but is necessary to prevent the progress image from sticking around if the last node in a session did not have an image output.
Some socket events should not be handled by the slice reducers. For example generation progress should not be handled for a canceled session.
Added another layer of socket actions.
Example:
- `socketGeneratorProgress` is dispatched when the actual socket event is received
- Listener middleware exclusively handles this event and determines if the application should also handle it
- If so, it dispatches `appSocketGeneratorProgress`, which the slices can handle
Needed to fix issues related to canceling invocations.
Now that images are in a database and we can make filtered queries, we can do away with the cumbersome `resultsSlice` and `uploadsSlice`.
- Remove `resultsSlice` and `uploadsSlice` entirely
- Add `imagesSlice` fills the same role
- Convert the application to use `imagesSlice`, reducing a lot of messy logic where we had to check which category was selected
- Add a simple filter popover to the gallery, which lets you select any number of image categories
Because we dynamically insert images into the DB and UI's images state, `page`/`per_page` pagination makes loading the images awkward.
Using `offset`/`limit` pagination lets us query for images with an offset equal to the number of images already loaded (which match the query parameters).
The result is that we always get the correct next page of images when loading more.
- Update all thunks & network related things
- Update gallery
What I have not done yet is rename the gallery tabs and the relevant slices, but I believe the functionality is all there.
Also I fixed several bugs along the way but couldn't really commit them separately bc I was refactoring. Can't remember what they were, but related to the gallery image switching.
- Remove `ImageType` entirely, it is confusing
- Create `ResourceOrigin`, may be `internal` or `external`
- Revamp `ImageCategory`, may be `general`, `mask`, `control`, `user`, `other`. Expect to add more as time goes on
- Update images `list` route to accept `include_categories` OR `exclude_categories` query parameters to afford finer-grained querying. All services are updated to accomodate this change.
The new setup should account for our types of images, including the combinations we couldn't really handle until now:
- Canvas init and masks
- Canvas when saved-to-gallery or merged
Currenly only used to make names for images, but when latents, conditioning, etc are managed in DB, will do the same for them.
Intended to eventually support custom naming schemes.
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
- Update the canvas graph generation to flag its uploaded init and mask images as `intermediate`.
- During canvas setup, hit the update route to associate the uploaded images with the session id.
- Organize the socketio and RTK listener middlware better. Needed to facilitate the updated canvas logic.
- Add a new action `sessionReadyToInvoke`. The `sessionInvoked` action is *only* ever run in response to this event. This lets us do whatever complicated setup (eg canvas) and explicitly invoking. Previously, invoking was tied to the socket subscribe events.
- Some minor tidying.
- `ImageType` is now restricted to `results` and `uploads`.
- Add a reserved `meta` field to nodes to hold the `is_intermediate` boolean. We can extend it in the future to support other node `meta`.
- Add a `is_intermediate` column to the `images` table to hold this. (When `latents`, `conditioning` etc are added to the DB, they will also have this column.)
- All nodes default to `*not* intermediate`. Nodes must explicitly be marked `intermediate` for their outputs to be `intermediate`.
- When building a graph, you can set `node.meta.is_intermediate=True` and it will be handled as an intermediate.
- Add a new `update()` method to the `ImageService`, and a route to call it. Updates have a strict model, currently only `session_id` and `image_category` may be updated.
- Add a new `update()` method to the `ImageRecordStorageService` to update the image record using the model.
The `RangeInvocation` is a simple wrapper around `range()`, but you must provide `stop > start`.
`RangeOfSizeInvocation` replaces the `stop` parameter with `size`, so that you can just provide the `start` and `step` and get a range of `size` length.
When returning a `FileResponse`, we must provide a valid path, else an exception is raised outside the route handler.
Add the `validate_path` method back to the service so we can validate paths before returning the file.
I don't like this but apparently this is just how `starlette` and `fastapi` works with `FileResponse`.
- Address database feedback:
- Remove all the extraneous tables. Only an `images` table now:
- `image_type` and `image_category` are unrestricted strings. When creating images, the provided values are checked to ensure they are a valid type and category.
- Add `updated_at` and `deleted_at` columns. `deleted_at` is currently unused.
- Use SQLite's built-in timestamp features to populate these. Add a trigger to update `updated_at` when the row is updated. Currently no way to update a row.
- Rename the `id` column in `images` to `image_name`
- Rename `ImageCategory.IMAGE` to `ImageCategory.GENERAL`
- Move all exceptions outside their base classes to make them more portable.
- Add `width` and `height` columns to the database. These store the actual dimensions of the image file, whereas the metadata's `width` and `height` refer to the respective generation parameters and are nullable.
- Make `deserialize_image_record` take a `dict` instead of `sqlite3.Row`
- Improve comments throughout
- Tidy up unused code/files and some minor organisation
feat(nodes): add ResultsServiceABC & SqliteResultsService
**Doesn't actually work bc of circular imports. Can't even test it.**
- add a base class for ResultsService and SQLite implementation
- use `graph_execution_manager` `on_changed` callback to keep `results` table in sync
fix(nodes): fix results service bugs
chore(ui): regen api
fix(ui): fix type guards
feat(nodes): add `result_type` to results table, fix types
fix(nodes): do not shadow `list` builtin
feat(nodes): add results router
It doesn't work due to circular imports still
fix(nodes): Result class should use outputs classes, not fields
feat(ui): crude results router
fix(ui): send to canvas in currentimagebuttons not working
feat(nodes): add core metadata builder
feat(nodes): add design doc
feat(nodes): wip latents db stuff
feat(nodes): images_db_service and resources router
feat(nodes): wip images db & router
feat(nodes): update image related names
feat(nodes): update urlservice
feat(nodes): add high-level images service
The problem was the same seed was getting used for the seam painting pass, causing the fried look.
Same issue as if you do img2img on a txt2img with the same seed/prompt.
Thanks to @hipsterusername for teaming up to debug this. We got pretty deep into the weeds.
This commit makes InvokeAI 3.0 to be installable via PyPi.org and the
installer script.
Main changes.
1. Move static web pages into `invokeai/frontend/web` and modify the
API to look for them there. This allows pip to copy the files into the
distribution directory so that user no longer has to be in repo root
to launch.
2. Update invoke.sh and invoke.bat to launch the new web application
properly. This also changes the wording for launching the CLI from
"generate images" to "explore the InvokeAI node system," since I would
not recommend using the CLI to generate images routinely.
3. Fix a bug in the checkpoint converter script that was identified
during testing.
4. Better error reporting when checkpoint converter fails.
5. Rebuild front end.
* added optional middleware prop and new actions needed
* accidental import
* make middleware an array
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
# Application-wide configuration service
This PR creates a new `InvokeAIAppConfig` object that reads
application-wide settings from an init file, the environment, and the
command line.
Arguments and fields are taken from the pydantic definition of the
model. Defaults can be set by creating a yaml configuration file that
has a top-level key of "InvokeAI" and subheadings for each of the
categories returned by `invokeai --help`.
The file looks like this:
[file: invokeai.yaml]
```
InvokeAI:
Paths:
root: /home/lstein/invokeai-main
conf_path: configs/models.yaml
legacy_conf_dir: configs/stable-diffusion
outdir: outputs
embedding_dir: embeddings
lora_dir: loras
autoconvert_dir: null
gfpgan_model_dir: models/gfpgan/GFPGANv1.4.pth
Models:
model: stable-diffusion-1.5
embeddings: true
Memory/Performance:
xformers_enabled: false
sequential_guidance: false
precision: float16
max_loaded_models: 4
always_use_cpu: false
free_gpu_mem: false
Features:
nsfw_checker: true
restore: true
esrgan: true
patchmatch: true
internet_available: true
log_tokenization: false
Cross-Origin Resource Sharing:
allow_origins: []
allow_credentials: true
allow_methods:
- '*'
allow_headers:
- '*'
Web Server:
host: 127.0.0.1
port: 8081
```
The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can use any OmegaConf dictionary by passing it to
the config object at initialization time:
```
omegaconf = OmegaConf.load('/tmp/init.yaml')
conf = InvokeAIAppConfig(conf=omegaconf)
```
The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can replace supersede this by providing
anyOmegaConf dictionary object initialization time:
```
omegaconf = OmegaConf.load('/tmp/init.yaml')
conf = InvokeAIAppConfig(conf=omegaconf)
```
By default, InvokeAIAppConfig will parse the contents of `sys.argv` at
initialization time. You may pass a list of strings in the optional
`argv` argument to use instead of the system argv:
```
conf = InvokeAIAppConfig(arg=['--xformers_enabled'])
```
It is also possible to set a value at initialization time. This value
has highest priority.
```
conf = InvokeAIAppConfig(xformers_enabled=True)
```
Any setting can be overwritten by setting an environment variable of
form: "INVOKEAI_<setting>", as in:
```
export INVOKEAI_port=8080
```
Order of precedence (from highest):
1) initialization options
2) command line options
3) environment variable options
4) config file options
5) pydantic defaults
Typical usage:
```
from invokeai.app.services.config import InvokeAIAppConfig
# get global configuration and print its nsfw_checker value
conf = InvokeAIAppConfig()
print(conf.nsfw_checker)
```
Finally, the configuration object is able to recreate its (modified)
yaml file, by calling its `to_yaml()` method:
```
conf = InvokeAIAppConfig(outdir='/tmp', port=8080)
print(conf.to_yaml())
```
# Legacy code removal and porting
This PR replaces Globals with the InvokeAIAppConfig system throughout,
and therefore removes the `globals.py` and `args.py` modules. It also
removes `generate` and the legacy CLI. ***The old CLI and web servers
are now gone.***
I have ported the functionality of the configuration script, the model
installer, and the merge and textual inversion scripts. The `invokeai`
command will now launch `invokeai-node-cli`, and `invokeai-web` will
launch the web server.
I have changed the continuous invocation tests to accommodate the new
command syntax in `invokeai-node-cli`. As a convenience function, you
can also pass invocations to `invokeai-node-cli` (or its alias
`invokeai`) on the command line as as standard input:
```
invokeai-node-cli "t2i --positive_prompt 'banana sushi' --seed 42"
invokeai < invocation_commands.txt
```
- Make environment variable settings case InSenSiTive:
INVOKEAI_MAX_LOADED_MODELS and InvokeAI_Max_Loaded_Models
environment variables will both set `max_loaded_models`
- Updated realesrgan to use new config system.
- Updated textual_inversion_training to use new config system.
- Discovered a race condition when InvokeAIAppConfig is created
at module load time, which makes it impossible to customize
or replace the help message produced with --help on the command
line. To fix this, moved all instances of get_invokeai_config()
from module load time to object initialization time. Makes code
cleaner, too.
- Added `--from_file` argument to `invokeai-node-cli` and changed
github action to match. CI tests will hopefully work now.
- invokeai-configure updated to work with new config system
- migrate invokeai.init to invokeai.yaml during configure
- replace legacy invokeai with invokeai-node-cli
- add ability to run an invocation directly from invokeai-node-cli command line
- update CI tests to work with new invokeai syntax
* refetch images list if error loading
* tell user to refresh instead of refetching
* unused import
* feat(ui): use `useAppToaster` to make toast
* fix(ui): clear selected/initial image on error
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
The `ModelsList` OpenAPI schema is generated as being keyed by plain strings. This means that API consumers do not know the shape of the dict. It _should_ be keyed by the `SDModelType` enum.
Unfortunately, `fastapi` does not actually handle this correctly yet; it still generates the schema with plain string keys.
Adding this anyways though in hopes that it will be resolved upstream and we can get the correct schema. Until then, I'll implement the (simple but annoying) logic on the frontend.
https://github.com/pydantic/pydantic/issues/4393
1. If an external VAE is specified in config file, then
get_model(submodel=vae) will return the external VAE, not the one
burnt into the parent diffusers pipeline.
2. The mechanism in (1) is generalized such that you can now have
"unet:", "text_encoder:" and similar stanzas in the config file.
Valid formats of these subsections:
unet:
repo_id: foo/bar
unet:
path: /path/to/local/folder
unet:
repo_id: foo/bar
subfolder: unet
In the near future, these will also be used to attach external
parts to the pipeline, generalizing VAE behavior.
3. Accommodate callers (i.e. the WebUI) that are passing the
model key ("diffusers/stable-diffusion-1.5") to get_model()
instead of the tuple of model_name and model_type.
4. Fixed bug in VAE model attaching code.
5. Rebuilt web front end.
This PR improves the logging module a tad bit along with the
documentation.
**New Look:**

## Usage
**General Logger**
InvokeAI has a module level logger. You can call it this way.
In this below example, you will use the default logger `InvokeAI` and
all your messages will be logged under that name.
```python
from invokeai.backend.util.logging import logger
logger.critical("CriticalMessage") // In Bold Red
logger.error("Info Message") // In Red
logger.warning("Info Message") // In Yellow
logger.info("Info Message") // In Grey
logger.debug("Debug Message") // In Grey
```
Results:
```
[12-05-2023 20]::[InvokeAI]::CRITICAL --> This is an info message [In Bold Red]
[12-05-2023 20]::[InvokeAI]::ERROR --> This is an info message [In Red]
[12-05-2023 20]::[InvokeAI]::WARNING --> This is an info message [In Yellow]
[12-05-2023 20]::[InvokeAI]::INFO --> This is an info message [In Grey]
[12-05-2023 20]::[InvokeAI]::DEBUG --> This is an info message [In Grey]
```
**Custom Logger**
If you want to use a custom logger for your module, you can import it
the following way.
```python
from invokeai.backend.util.logging import logging
logger = logging.getLogger(name='Model Manager')
logger.critical("CriticalMessage") // In Bold Red
logger.error("Info Message") // In Red
logger.warning("Info Message") // In Yellow
logger.info("Info Message") // In Grey
logger.debug("Debug Message") // In Grey
```
Results:
```
[12-05-2023 20]::[Model Manager]::CRITICAL --> This is an info message [In Bold Red]
[12-05-2023 20]::[Model Manager]::ERROR --> This is an info message [In Red]
[12-05-2023 20]::[Model Manager]::WARNING --> This is an info message [In Yellow]
[12-05-2023 20]::[Model Manager]::INFO --> This is an info message [In Grey]
[12-05-2023 20]::[Model Manager]::DEBUG --> This is an info message [In Grey]
```
**When to use custom logger?**
It is recommended to use a custom logger if your module is not a part of
base InvokeAI. For example: custom extensions / nodes.
1. if retrieving an item from the queue raises an exception, the
InvocationProcessor thread crashes, but the API continues running in
a non-functional state. This fixes the issue
2. when there are no items in the queue, sleep 1 second before checking
again.
3. Also ensures the thread isn't crashed if an exception is raised from
invoker, and emits the error event
Intentionally using base Exceptions because for now we don't know which
specific exception to expect.
Fixes (sort of)? #3222
- do not show canvas intermediates in gallery
- do not show progress image in uploads gallery category
- use custom dark mode `localStorage` key (prevents collision with
commercial)
- use variable font (reduce bundle size by factor of 10)
- change how custom headers are used
- use style injection for building package
- fix tab icon sizes
when building for package, CSS is all in JS files. when used as a package, it is then injected into the page. bit of a hack to missing CSS in commercial product
**Features:**
- Add UniPC Scheduler
- Add Euler Karras Scheduler
- Add DPMPP_2 Karras Scheduler
- Add DEIS Scheduler
- Add DDPM Scheduler
**Other:**
- Renamed schedulers to their accurate names: _a = Ancestral, _k =
Karras
- Fix scheduler not defaulting correctly to DDIM.
- Code split SCHEDULER_MAP so its consistently loaded from the same
place.
**Known Bugs:**
- dpmpp_2s not working in img2img for denoising values < 0.8 ==> // This
seems to be an upstream bug. I've disabled it in img2img and canvas
until the upstream bug is fixed.
https://github.com/huggingface/diffusers/issues/1866
This PR updates to `xformers ~= 0.0.19` and `torch ~= 2.0.0`, which
together seem to solve the non-deterministic image generation issue that
was previously seen with earlier versions of `xformers`.
Update the push trigger with the branch which should deploy the docs,
also bring over the updates to the workflow from the v2.3 branch and:
- remove main and development branch from trigger
- they would fail without the updated toml
- cache pip environment
- update install method (`pip install ".[docs]"`)
hi there, love the project! i noticed a small typo when going over the
install process.
when copying the automated install instructions from the docs into a
terminal, the line to install the python packages failed as it was
missing the `-y` flag.
when copying the automated install instructions from the docs into a terminal, the line to install the python packages failed as it was missing the `-y` flag.
Seems like this is the only change needed for the existing inpaint code
to work as a node. Kyle said on Discord that inpaint shouldn't be a
node, so feel free to just reject this if this code is going to be gone
soon.
# Intro
This commit adds invokeai.backend.util.logging, which provides support
for formatted console and logfile messages that follow the status
reporting conventions of earlier InvokeAI versions:
```
### A critical error
*** A non-fatal error
** A warning
>> Informational message
| Debugging message
```
Internally, the invokeai logging module creates a new default logger
named "invokeai" so that its logging does not interfere with other
module's use of the vanilla logging module. So `logging.error("foo")`
will go through the regular logging path and not add InvokeAI's
informational message decorations, while `ialog.error("foo")` will add
the decorations.
# Usage:
This is a thin wrapper around the standard Python logging module. It can
be used in several ways:
## Module-level logging style
This style logs everything through a single default logging object and
is identical to using Python's `logging` module. The commonly-used
module-level logging functions are implemented as simple pass-thrus to
logging:
```
import invokeai.backend.util.logging as logger
logger.debug('this is a debugging message')
logger.info('this is a informational message')
logger.log(level=logging.CRITICAL, 'get out of dodge')
logger.disable(level=logging.INFO)
logger.basicConfig(filename='/var/log/invokeai.log')
logger.error('this will be logged to console and to invokeai.log')
```
Internally these functions all go through a custom logging object named
"invokeai". You can access it to perform additional customization in
either of these ways:
```
logger = logger.getLogger()
logger = logger.getLogger('invokeai')
```
## Object-oriented style
For more control, the logging module's object-oriented logging style is
also supported. The API is identical to the vanilla logging usage. In
fact, the only thing that has changed is that the getLogger() method
adds a custom formatter to the log messages.
```
import logging
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.getLogger(__name__)
fh = logging.FileHandler('/var/invokeai.log')
logger.addHandler(fh)
logger.critical('this will be logged to both the console and the log file')
```
## Within the nodes API
From within the nodes API, the logger module is stored in the `logger`
slot of InvocationServices during dependency initialization. For
example, in a router, the idiom is:
```
from ..dependencies import ApiDependencies
logger = ApiDependencies.invoker.services.logger
logger.warning('uh oh')
```
Currently, to change the logger used by the API, one must change the
logging module passed to `ApiDependencies.initialize()` in `api_app.py`.
However, this will eventually be replaced with a method to select the
preferred logging module using the configuration file (dependent on
merging of PR #3221)
- I've sorted out the issues that make *not* persisting troublesome, these will be rolled out with canvas
- Also realized that persisting gallery images very quickly fills up localStorage, so we can't really do it anyways
vastly improves the gallery performance when many images are loaded.
- `react-virtuoso` to do the virtualized list
- `overlayscrollbars` for a scrollbar
On hyperthreaded CPUs we get two threads operating on the queue by
default on each core. This cases two threads to process queue items.
This results in pytorch errors and sometimes generates garbage.
Locking this to single thread makes sense because we are bound by the
number of GPUs in the system, not by CPU cores. And to parallelize
across GPUs we should just start multiple processors (and use async
instead of threading)
Fixes#3289
- `disabledParametersPanels` -> `disabledFeatures`
- handle disabling `faceRestore`, `upscaling`, `lightbox`, `modelManager` and OSS header links/buttons
- wait until models are loaded to hide loading screen
- also wait until schema is parsed if `nodes` is an enabled tab
When gallery was empty (and there is therefore no selected image), no
progress images were displayed.
- fix by correcting the logic in CurrentImageDisplay
- also fix app crash introduced by fixing the first bug
Prevent legacy CLI crash caused by removal of convert option
- Compensatory change to the CLI that prevents it from crashing when it
tries to import a model.
- Bug introduced when the "convert" option removed from the model
manager.
- Fix the update script to work again and fixes the ambiguity between
when a user wants to update to a tag vs updating to a branch, by making
these two operations explicitly separate.
- Remove dangling functions and arguments related to legacy checkpoint
conversion. These are no longer needed now that all legacy models are
either converted at import time, or on-the-fly in RAM.
I noticed that the current invokeai-new.py was using almost all of a CPU
core. After a bit of profileing I noticed that there were many thousands
of calls to epoll() which suggested to me that something wasn't sleeping
properly in asyncio's loop.
A bit of further investigation with Python profiling revealed that the
__dispatch_from_queue() method in FastAPIEventService
(app/api/events.py:33) was also being called thousands of times.
I believe the asyncio.sleep(0.001) in that method is too aggressive (it
means that the queue will be polled every 1ms) and that 0.1 (100ms) is
still entirely reasonable.
Currently translated at 100.0% (512 of 512 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (512 of 512 strings)
translationBot(ui): update translation (English)
Currently translated at 100.0% (512 of 512 strings)
translationBot(ui): update translation (Ukrainian)
Currently translated at 100.0% (506 of 506 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (506 of 506 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (506 of 506 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/en/
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/uk/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (512 of 512 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (511 of 511 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (506 of 506 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (512 of 512 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (511 of 511 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (506 of 506 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
* if `shouldFetchImages` is passed in, UI will make an additional
request to get valid image URL when an invocation is complete
* this is necessary in order to have optional authorization for images
- Style the Minimap
- Made the Node UI Legend Responsive
- Set Min Width for nodes on Spawn so resize doesn't snap.
- Initial Implementation of Node Search
- Added FuseJS to handle the node filtering
The first draft for a Responsive Mobile Layout for InvokeAI. Some basic
documentation to help contributors. // Notes from: @blessedcoolant
---
The whole rework needs to be done using the `mobile first` concept where
the base design will be catered to mobile and we add responsive changes
as we grow to larger screens.
**Added**
- Basic breakpoints have been added to the `theme.ts` file that indicate
at which values Chakra makes the responsive changes.
- A basic `useResolution` hook has been added that either returns
`mobile`, `tablet` or `desktop` based on the breakpoint. We can
customize this hook further to do more complex checks for us if need be.
**Syntax**
- Any Chakra component is directly capable of taking different values
for the different breakpoints set in our `theme.ts` file. These can be
passed in a few ways with the most descriptive being an object. For
example:
`flexDir={{ base: 'column', xl: 'row' }}` - This would set the `0em and
above` to be column for the flex direction but change to row
automatically when we hit `xl` and above resolutions which in our case
is `80em or 1280px`. This same format is applicable for any element in
Chakra.
`flexDir={['column', null, null, 'row', null]}` - The above syntax can
also be passed as an array to the property with each value in the array
corresponding to each breakpoint we have. Setting `null` just bypasses
it. This is a good short hand but I think we stick to the above syntax
for readability.
**Note**: I've modified a few elements here and there to give an idea on
how the responsive syntax works for reference.
---
**Problems to be solved** @SammCheese
- Some issues you might run into are with the Resizable components.
We've decided we will get not use resizable components for smaller
resolutions. Doesn't make sense. So you'll need to make conditional
renderings around these.
- Some components that need custom layouts for different screens might
be better if ported over to `Grid` and use `gridTemplateAreas` to swap
out the design layout. I've demonstrated an example of this in a commit
I've made. I'll let you be the judge of where we might need this.
- The header will probably need to be converted to a burger menu of some
sort with the model changing being handled correctly UX wise. We'll
discuss this on discord.
---
Anyone willing to contribute to this PR can feel free to join the
discussion on discord.
https://discord.com/channels/1020123559063990373/1020839344170348605/threads/1097323866780606615
* feat(ui): add axios client generator and simple example
* fix(ui): update client & nodes test code w/ new Edge type
* chore(ui): organize generated files
* chore(ui): update .eslintignore, .prettierignore
* chore(ui): update openapi.json
* feat(backend): fixes for nodes/generator
* feat(ui): generate object args for api client
* feat(ui): more nodes api prototyping
* feat(ui): nodes cancel
* chore(ui): regenerate api client
* fix(ui): disable OG web server socket connection
* fix(ui): fix scrollbar styles typing and prop
just noticed the typo, and made the types stronger.
* feat(ui): add socketio types
* feat(ui): wip nodes
- extract api client method arg types instead of manually declaring them
- update example to display images
- general tidy up
* start building out node translations from frontend state and add notes about missing features
* use reference to sampler_name
* use reference to sampler_name
* add optional apiUrl prop
* feat(ui): start hooking up dynamic txt2img node generation, create middleware for session invocation
* feat(ui): write separate nodes socket layer, txt2img generating and rendering w single node
* feat(ui): img2img implementation
* feat(ui): get intermediate images working but types are stubbed out
* chore(ui): add support for package mode
* feat(ui): add nodes mode script
* feat(ui): handle random seeds
* fix(ui): fix middleware types
* feat(ui): add rtk action type guard
* feat(ui): disable NodeAPITest
This was polluting the network/socket logs.
* feat(ui): fix parameters panel border color
This commit should be elsewhere but I don't want to break my flow
* feat(ui): make thunk types more consistent
* feat(ui): add type guards for outputs
* feat(ui): load images on socket connect
Rudimentary
* chore(ui): bump redux-toolkit
* docs(ui): update readme
* chore(ui): regenerate api client
* chore(ui): add typescript as dev dependency
I am having trouble with TS versions after vscode updated and now uses TS 5. `madge` has installed 3.9.10 and for whatever reason my vscode wants to use that. Manually specifying 4.9.5 and then setting vscode to use that as the workspace TS fixes the issue.
* feat(ui): begin migrating gallery to nodes
Along the way, migrate to use RTK `createEntityAdapter` for gallery images, and separate `results` and `uploads` into separate slices. Much cleaner this way.
* feat(ui): clean up & comment results slice
* fix(ui): separate thunk for initial gallery load so it properly gets index 0
* feat(ui): POST upload working
* fix(ui): restore removed type
* feat(ui): patch api generation for headers access
* chore(ui): regenerate api
* feat(ui): wip gallery migration
* feat(ui): wip gallery migration
* chore(ui): regenerate api
* feat(ui): wip refactor socket events
* feat(ui): disable panels based on app props
* feat(ui): invert logic to be disabled
* disable panels when app mounts
* feat(ui): add support to disableTabs
* docs(ui): organise and update docs
* lang(ui): add toast strings
* feat(ui): wip events, comments, and general refactoring
* feat(ui): add optional token for auth
* feat(ui): export StatusIndicator and ModelSelect for header use
* feat(ui) working on making socket URL dynamic
* feat(ui): dynamic middleware loading
* feat(ui): prep for socket jwt
* feat(ui): migrate cancelation
also updated action names to be event-like instead of declaration-like
sorry, i was scattered and this commit has a lot of unrelated stuff in it.
* fix(ui): fix img2img type
* chore(ui): regenerate api client
* feat(ui): improve InvocationCompleteEvent types
* feat(ui): increase StatusIndicator font size
* fix(ui): fix middleware order for multi-node graphs
* feat(ui): add exampleGraphs object w/ iterations example
* feat(ui): generate iterations graph
* feat(ui): update ModelSelect for nodes API
* feat(ui): add hi-res functionality for txt2img generations
* feat(ui): "subscribe" to particular nodes
feels like a dirty hack but oh well it works
* feat(ui): first steps to node editor ui
* fix(ui): disable event subscription
it is not fully baked just yet
* feat(ui): wip node editor
* feat(ui): remove extraneous field types
* feat(ui): nodes before deleting stuff
* feat(ui): cleanup nodes ui stuff
* feat(ui): hook up nodes to redux
* fix(ui): fix handle
* fix(ui): add basic node edges & connection validation
* feat(ui): add connection validation styling
* feat(ui): increase edge width
* feat(ui): it blends
* feat(ui): wip model handling and graph topology validation
* feat(ui): validation connections w/ graphlib
* docs(ui): update nodes doc
* feat(ui): wip node editor
* chore(ui): rebuild api, update types
* add redux-dynamic-middlewares as a dependency
* feat(ui): add url host transformation
* feat(ui): handle already-connected fields
* feat(ui): rewrite SqliteItemStore in sqlalchemy
* fix(ui): fix sqlalchemy dynamic model instantiation
* feat(ui, nodes): metadata wip
* feat(ui, nodes): models
* feat(ui, nodes): more metadata wip
* feat(ui): wip range/iterate
* fix(nodes): fix sqlite typing
* feat(ui): export new type for invoke component
* tests(nodes): fix test instantiation of ImageField
* feat(nodes): fix LoadImageInvocation
* feat(nodes): add `title` ui hint
* feat(nodes): make ImageField attrs optional
* feat(ui): wip nodes etc
* feat(nodes): roll back sqlalchemy
* fix(nodes): partially address feedback
* fix(backend): roll back changes to pngwriter
* feat(nodes): wip address metadata feedback
* feat(nodes): add seeded rng to RandomRange
* feat(nodes): address feedback
* feat(nodes): move GET images error handling to DiskImageStorage
* feat(nodes): move GET images error handling to DiskImageStorage
* fix(nodes): fix image output schema customization
* feat(ui): img2img/txt2img -> linear
- remove txt2img and img2img tabs
- add linear tab
- add initial image selection to linear parameters accordion
* feat(ui): tidy graph builders
* feat(ui): tidy misc
* feat(ui): improve invocation union types
* feat(ui): wip metadata viewer recall
* feat(ui): move fonts to normal deps
* feat(nodes): fix broken upload
* feat(nodes): add metadata module + tests, thumbnails
- `MetadataModule` is stateless and needed in places where the `InvocationContext` is not available, so have not made it a `service`
- Handles loading/parsing/building metadata, and creating png info objects
- added tests for MetadataModule
- Lifted thumbnail stuff to util
* fix(nodes): revert change to RandomRangeInvocation
* feat(nodes): address feedback
- make metadata a service
- rip out pydantic validation, implement metadata parsing as simple functions
- update tests
- address other minor feedback items
* fix(nodes): fix other tests
* fix(nodes): add metadata service to cli
* fix(nodes): fix latents/image field parsing
* feat(nodes): customise LatentsField schema
* feat(nodes): move metadata parsing to frontend
* fix(nodes): fix metadata test
---------
Co-authored-by: maryhipp <maryhipp@gmail.com>
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Since the change itself is quite straight-forward, I'll just describe
the context. Tried using automatic installer on my laptop, kept erroring
out on line 140-something of installer.py, "ERROR: Can not perform a
'--user' install. User site-packages are not visible in this
virtualenv."
Got tired of of fighting with pip so moved on to command line install.
Worked immediately, but at the time lacked instruction for CPU, so
instead of opening any helpful hyperlinks in the readme, took a few
minutes to grab the link from installer.py - thus this pr.
- Fixed a bunch of padding and margin issues across the app
- Fixed the Invoke logo compressing
- Disabled the visibility of the options panel pin button in tablet and mobile views
- Refined the header menu options in mobile and tablet views
- Refined other site header elements in mobile and tablet views
- Aligned Tab Icons to center in mobile and tablet views
Made some basic responsive changes to demonstrate how to go about making changes.
There are a bunch of problems not addressed yet. Like dealing with the resizeable component and etc.
This component just classifies `base` and `sm` as mobile, `md` and `lg` as tablet and `xl` and `2xl` as desktop.
This is a basic hook for quicker work with resolutions. Can be modified and adjusted to our needs. All resolution related work can go into this hook.
This commit adds invokeai.backend.util.logging, which provides support
for formatted console and logfile messages that follow the status
reporting conventions of earlier InvokeAI versions.
Examples:
### A critical error (logging.CRITICAL)
*** A non-fatal error (logging.ERROR)
** A warning (logging.WARNING)
>> Informational message (logging.INFO)
| Debugging message (logging.DEBUG)
This style logs everything through a single logging object and is
identical to using Python's `logging` module. The commonly-used
module-level logging functions are implemented as simple pass-thrus
to logging:
import invokeai.backend.util.logging as ialog
ialog.debug('this is a debugging message')
ialog.info('this is a informational message')
ialog.log(level=logging.CRITICAL, 'get out of dodge')
ialog.disable(level=logging.INFO)
ialog.basicConfig(filename='/var/log/invokeai.log')
Internally, the invokeai logging module creates a new default logger
named "invokeai" so that its logging does not interfere with other
module's use of the vanilla logging module. So `logging.error("foo")`
will go through the regular logging path and not add the additional
message decorations.
For more control, the logging module's object-oriented logging style
is also supported. The API is identical to the vanilla logging
usage. In fact, the only thing that has changed is that the
getLogger() method adds a custom formatter to the log messages.
import logging
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.getLogger(__name__)
fh = logging.FileHandler('/var/invokeai.log')
logger.addHandler(fh)
logger.critical('this will be logged to both the console and the log file')
This commit adds invokeai.backend.util.logging, which provides support
for formatted console and logfile messages that follow the status
reporting conventions of earlier InvokeAI versions.
Examples:
### A critical error (logging.CRITICAL)
*** A non-fatal error (logging.ERROR)
** A warning (logging.WARNING)
>> Informational message (logging.INFO)
| Debugging message (logging.DEBUG)
- add invocation schema customisation
done via fastapi's `Config` class and `schema_extra`. when using `Config`, inherit from `InvocationConfig` to get type hints.
where it makes sense - like for all math invocations - define a `MathInvocationConfig` class and have all invocations inherit from it.
this customisation can provide any arbitrary additional data to the UI. currently it provides tags and field type hints.
this is necessary for `model` type fields, which are actually string fields. without something like this, we can't reliably differentiate `model` fields from normal `string` fields.
can also be used for future field types.
all invocations now have tags, and all `model` fields have ui type hints.
- fix model handling for invocations
added a helper to fall back to the default model if an invalid model name is chosen. model names in graphs now work.
- fix latents progress callback
noticed this wasn't correct while working on everything else.
When running this app first time in WSL2 environment, which is
notoriously slow when it comes to IO, computing the SHAs of the models
takes an eternity.
Computing shas for sd2.1
```
| Calculating sha256 hash of model files
| sha256 = 1e4ce085102fe6590d41ec1ab6623a18c07127e2eca3e94a34736b36b57b9c5e (49 files hashed in 510.87s)
```
I increased the chunk size to 16MB reduce the number of round trips for
loading the data. New results:
```
| Calculating sha256 hash of model files
| sha256 = 1e4ce085102fe6590d41ec1ab6623a18c07127e2eca3e94a34736b36b57b9c5e (49 files hashed in 59.89s)
```
Higher values don't seem to make an impact.
- add `list_images` endpoint at `GET api/v1/images`
- extend `ImageStorageBase` with `list()` method, implemented it for `DiskImageStorage`
- add `ImageReponse` class to for image responses, which includes urls, metadata
- add `ImageMetadata` class (basically a stub at the moment)
- uploaded images now named `"{uuid}_{timestamp}.png"`
- add `models` modules. besides separating concerns more clearly, this helps to mitigate circular dependencies
- improve thumbnail handling
- the functionality to automatically import and run legacy checkpoint
files in a designated folder has been removed from the backend but there
are vestiges of the code remaining in the frontend that are causing
crashes.
- This fixes the problem.
- Closes#3075
This PR introduces a new set of ModelManager methods that enables you to
retrieve the individual parts of a stable diffusion pipeline model,
including the vae, text_encoder, unet, tokenizer, etc.
To use:
```
from invokeai.backend import ModelManager
manager = ModelManager('/path/to/models.yaml')
# get the VAE
vae = manager.get_model_vae('stable-diffusion-1.5')
# get the unet
unet = manager.get_model_unet('stable-diffusion-1.5')
# get the tokenizer
tokenizer = manager.get_model_tokenizer('stable-diffusion-1.5')
# etc etc
feature_extractor = manager.get_model_feature_extractor('stable-diffusion-1.5')
scheduler = manager.get_model_scheduler('stable-diffusion-1.5')
text_encoder = manager.get_model_text_encoder('stable-diffusion-1.5')
# if no model provided, then defaults to the one currently in GPU, if any
vae = manager.get_model_vae()
```
- Compensatory change to the CLI that prevents it from crashing
when it tries to import a model.
- Bug introduced when the "convert" option removed from the model
manager.
* Add latents nodes.
* Fix iteration expansion.
* Add collection generator nodes, math nodes.
* Add noise node.
* Add some graph debug commands to the CLI.
* Fix negative id linking in CLI.
* Fix a CLI bug with multiple links per node.
- New method is ModelManager.get_sub_model(model_name:str,model_part:SDModelComponent)
To use:
```
from invokeai.backend import ModelManager, SDModelComponent as sdmc
manager = ModelManager('/path/to/models.yaml')
vae = manager.get_sub_model('stable-diffusion-1.5', sdmc.vae)
```
The typo accidentally did not affect functionality; when `query==""`, it
`search()`ed but found everything due to empty query, then paginated
results, so it worked the same as `list()`.
Still fix it
currently if users input eg `happy (camper:0.3)` it gets parsed
incorrectly, which causes crashes if it's in the negative prompt. bump
to compel 1.0.5 fixes the parser to avoid this (note the weight is
parsed as plain text, it's not converted to proper invoke syntax)
- This PR adds support for embedding files that contain a single key
"emb_params". The only example I know of this format is the
"EasyNegative" embedding on HuggingFace, but there are certainly others.
- This PR also adds support for loading embedding files that have been
saved in safetensors format.
- It also cleans up the code so that the logic of probing for and
selecting the right format parser is clear.
- This is the same as #3045, which is on the 2.3 branch.
- Commands, invocations and their parameters will now autocomplete using
introspection.
- Two types of parameter *arguments* will also autocomplete:
- --sampler_name will autocomplete the scheduler name
- --model will autocomplete the model name
- There don't seem to be commands for reading/writing image files yet,
so path autocompletion is not implemented
A long-standing issue with importing legacy checkpoints (both ckpt and
safetensors) is that the user has to identify the correct config file,
either by providing its path or by selecting which type of model the
checkpoint is (e.g. "v1 inpainting"). In addition, some users wish to
provide custom VAEs for use with the model. Currently this is done in
the WebUI by importing the model, editing it, and then typing in the
path to the VAE.
## Model configuration file selection
To improve the user experience, the model manager's `heuristic_import()`
method has been enhanced as follows:
1. When initially called, the caller can pass a config file path, in
which case it will be used.
2. If no config file provided, the method looks for a .yaml file in the
same directory as the model which bears the same basename. e.g.
```
my-new-model.safetensors
my-new-model.yaml
```
The yaml file is then used as the configuration file for importation and
conversion.
3. If no such file is found, then the method opens up the checkpoint and
probes it to determine whether it is V1, V1-inpaint or V2. If it is a V1
format, then the appropriate v1-inference.yaml config file is used.
Unfortunately there are two V2 variants that cannot be distinguished by
introspection.
4. If the probe algorithm is unable to determine the model type, then
its last-ditch effort is to execute an optional callback function that
can be provided by the caller. This callback, named
`config_file_callback` receives the path to the legacy checkpoint and
returns the path to the config file to use. The CLI uses to put up a
multiple choice prompt to the user. The WebUI **could** use this to
prompt the user to choose from a radio-button selection.
5. If the config file cannot be determined, then the import is
abandoned.
## Custom VAE Selection
The user can attach a custom VAE to the imported and converted model by
copying the desired VAE into the same directory as the file to be
imported, and giving it the same basename. E.g.:
```
my-new-model.safetensors
my-new-model.vae.pt
```
For this to work, the VAE must end with ".vae.pt", ".vae.ckpt", or
".vae.safetensors". The indicated VAE will be converted into diffusers
format and stored with the converted models file, so the ".pt" file can
be deleted after conversion.
No facility is currently provided to swap a diffusers VAE at import
time, but this can be done after the fact using the WebUI and CLI's
model editing functions.
Note that this is the same fix that was applied to the 2.3 branch in
#3043 . This applies to `main`.
## Enable the on-the-fly conversion of models based on SD 2.0/2.1 into
diffusers
This commit fixes bugs related to the on-the-fly conversion and loading
of legacy checkpoint models built on SD-2.0 base.
- When legacy checkpoints built on SD-2.0 models were converted
on-the-fly using --ckpt_convert, generation would crash with a precision
incompatibility error. This problem has been found and fixed.
This commit fixes bugs related to the on-the-fly conversion and loading of
legacy checkpoint models built on SD-2.0 base.
- When legacy checkpoints built on SD-2.0 models were converted
on-the-fly using --ckpt_convert, generation would crash with a
precision incompatibility error.
The Pytorch ROCm version in the documentation in outdated (`rocm5.2`)
which leads to errors during the installation of InvokeAI.
This PR updates the documentation with the latest Pytorch ROCm `5.4.2`
version.
A long-standing issue with importing legacy checkpoints (both ckpt and
safetensors) is that the user has to identify the correct config file,
either by providing its path or by selecting which type of model the
checkpoint is (e.g. "v1 inpainting"). In addition, some users wish to
provide custom VAEs for use with the model. Currently this is done in
the WebUI by importing the model, editing it, and then typing in the
path to the VAE.
To improve the user experience, the model manager's
`heuristic_import()` method has been enhanced as follows:
1. When initially called, the caller can pass a config file path, in
which case it will be used.
2. If no config file provided, the method looks for a .yaml file in the
same directory as the model which bears the same basename. e.g.
```
my-new-model.safetensors
my-new-model.yaml
```
The yaml file is then used as the configuration file for
importation and conversion.
3. If no such file is found, then the method opens up the checkpoint
and probes it to determine whether it is V1, V1-inpaint or V2.
If it is a V1 format, then the appropriate v1-inference.yaml config
file is used. Unfortunately there are two V2 variants that cannot be
distinguished by introspection.
4. If the probe algorithm is unable to determine the model type, then its
last-ditch effort is to execute an optional callback function that can
be provided by the caller. This callback, named `config_file_callback`
receives the path to the legacy checkpoint and returns the path to the
config file to use. The CLI uses to put up a multiple choice prompt to
the user. The WebUI **could** use this to prompt the user to choose
from a radio-button selection.
5. If the config file cannot be determined, then the import is abandoned.
The user can attach a custom VAE to the imported and converted model
by copying the desired VAE into the same directory as the file to be
imported, and giving it the same basename. E.g.:
```
my-new-model.safetensors
my-new-model.vae.pt
```
For this to work, the VAE must end with ".vae.pt", ".vae.ckpt", or
".vae.safetensors". The indicated VAE will be converted into diffusers
format and stored with the converted models file, so the ".pt" file
can be deleted after conversion.
No facility is currently provided to swap a diffusers VAE at import
time, but this can be done after the fact using the WebUI and CLI's
model editing functions.
- This PR adds support for embedding files that contain a single key
"emb_params". The only example I know of this format is the
"EasyNegative" embedding on HuggingFace, but there are certainly
others.
- This PR also adds support for loading embedding files that have been
saved in safetensors format.
- It also cleans up the code so that the logic of probing for and
selecting the right format parser is clear.
keeping `main` up to date with my api nodes branch:
- bd7e515290: [nodes] Add cancelation to
the API @Kyle0654
- 5fe38f7: fix(backend): simple typing fixes
- just picking some low-hanging fruit to improve IDE hinting
- c34ac91: fix(nodes): fix cancel; fix callback for img2img, inpaint
- makes nodes cancel immediate, use fix progress images on nodes, fix
callbacks for img2img/inpaint
- 4221cf7: fix(nodes): fix schema generation for output classes
- did this previously for some other class; needed to not have node
outputs be optional
Some schedulers report not only the noisy latents at the current
timestep, but also their estimate so far of what the de-noised latents
will be.
It makes for a more legible preview than the noisy latents do.
I think this is a huge improvement, but there are a few considerations:
- Need to not spook @JPPhoto by changing how previews look.
- Some schedulers (most notably **DPM Solver++**) don't provide this
data, and it falls back to the current behavior there. That's not
terrible, but seeing such a big difference in how _previews_ look from
one scheduler to the next might mislead people into thinking there's a
bigger difference in their overall effectiveness than there really is.
My fear of configuration-option-overwhelm leaves me inclined to _not_
add a configuration option for this, but we could.
- Commands, invocations and their parameters will now autocomplete
using introspection.
- Two types of parameter *arguments* will also autocomplete:
- --sampler_name will autocomplete the scheduler name
- --model will autocomplete the model name
- There don't seem to be commands for reading/writing image files yet, so
path autocompletion is not implemented
- resolve conflicts with generate.py invocation
- remove unused symbols that pyflakes complains about
- add **untested** code for passing intermediate latent image to the
step callback in the format expected.
This PR fixes#2951 and restores the step_callback argument in the
refactored generate() method. Note that this issue states that
"something is still wrong because steps and step are zero." However,
I think this is confusion over the call signature of the callback, which
since the diffusers merge has been `callback(state:PipelineIntermediateState)`
This is the test script that I used to determine that `step` is being passed
correctly:
```
from pathlib import Path
from invokeai.backend import ModelManager, PipelineIntermediateState
from invokeai.backend.globals import global_config_dir
from invokeai.backend.generator import Txt2Img
def my_callback(state:PipelineIntermediateState, total_steps:int):
print(f'callback(step={state.step}/{total_steps})')
def main():
manager = ModelManager(Path(global_config_dir()) / "models.yaml")
model = manager.get_model('stable-diffusion-1.5')
print ('=== TXT2IMG TEST ===')
steps=30
output = next(Txt2Img(model).generate(prompt='banana sushi',
iterations=None,
steps=steps,
step_callback=lambda x: my_callback(x,steps)
)
)
print(f'image={output.image}, seed={output.seed}, steps={output.params.steps}')
if __name__=='__main__':
main()
```
- When a legacy checkpoint model is loaded via --convert_ckpt and its
models.yaml stanza refers to a custom VAE path (using the 'vae:' key),
the custom VAE will be converted and used within the diffusers model.
Otherwise the VAE contained within the legacy model will be used.
- Note that the checkpoint import functions in the CLI or Web UIs
continue to default to the standard stabilityai/sd-vae-ft-mse VAE. This
can be fixed after the fact by editing VAE key using either the CLI or
Web UI.
- Fixes issue #2917
The mkdocs-workflow has been failing over the past week due to
permission denied errors. I *think* this is the result of not passing
the GitHub API token to the workflow, and this is a speculative fix for
the issue.
- This PR turns on pickle scanning before a legacy checkpoint file is
loaded from disk within the checkpoint_to_diffusers module.
- Also miscellaneous diagnostic message cleanup.
- See also #3011 for a similar patch to the 2.3 branch.
Currently translated at 100.0% (504 of 504 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (501 of 501 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (504 of 504 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (501 of 501 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (500 of 500 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
This PR fixes#2951 and restores the step_callback argument in the
refactored generate() method. Note that this issue states that
"something is still wrong because steps and step are zero." However,
I think this is confusion over the call signature of the callback, which
since the diffusers merge has been `callback(state:PipelineIntermediateState)`
This is the test script that I used to determine that `step` is being passed
correctly:
```
from pathlib import Path
from invokeai.backend import ModelManager, PipelineIntermediateState
from invokeai.backend.globals import global_config_dir
from invokeai.backend.generator import Txt2Img
def my_callback(state:PipelineIntermediateState, total_steps:int):
print(f'callback(step={state.step}/{total_steps})')
def main():
manager = ModelManager(Path(global_config_dir()) / "models.yaml")
model = manager.get_model('stable-diffusion-1.5')
print ('=== TXT2IMG TEST ===')
steps=30
output = next(Txt2Img(model).generate(prompt='banana sushi',
iterations=None,
steps=steps,
step_callback=lambda x: my_callback(x,steps)
)
)
print(f'image={output.image}, seed={output.seed}, steps={output.params.steps}')
if __name__=='__main__':
main()
```
This PR corrects a bug in which embeddings were not being applied when a
non-diffusers model was loaded.
- Fixes#2954
- Also improves diagnostic reporting during embedding loading.
- This PR turns on pickle scanning before a legacy checkpoint file
is loaded from disk within the checkpoint_to_diffusers module.
- Also miscellaneous diagnostic message cleanup.
- When a legacy checkpoint model is loaded via --convert_ckpt and its
models.yaml stanza refers to a custom VAE path (using the 'vae:'
key), the custom VAE will be converted and used within the diffusers
model. Otherwise the VAE contained within the legacy model will be
used.
- Note that the heuristic_import() method, which imports arbitrary
legacy files on disk and URLs, will continue to default to the
the standard stabilityai/sd-vae-ft-mse VAE. This can be fixed after
the fact by editing the models.yaml stanza using the Web or CLI
UIs.
- Fixes issue #2917
- 86932469e76f1315ee18bfa2fc52b588241dace1 add image_to_dataURL util
- 0c2611059711b45bb6142d30b1d1343ac24268f3 make fast latents method
static
- this method doesn't really need `self` and should be able to be called
without instantiating `Generator`
- 2360bfb6558ea511e9c9576f3d4b5535870d84b4 fix schema gen for
GraphExecutionState
- `GraphExecutionState` uses `default_factory` in its fields; the result
is the OpenAPI schema marks those fields as optional, which propagates
to the generated API client, which means we need a lot of unnecessary
type guards to use this data type. the [simple
fix](https://github.com/pydantic/pydantic/discussions/4577) is to add
config to explicitly say all class properties are required. looks this
this will be resolved in a future pydantic release
- 3cd7319cfdb0f07c6bb12d62d7d02efe1ab12675 fix step callback and fast
latent generation on nodes. have this working in UI. depends on the
small change in #2957
Update `compel` to 1.0.0.
This fixes#2832.
It also changes the way downweighting is applied. In particular,
downweighting should now be much better and more controllable.
From the [compel
changelog](https://github.com/damian0815/compel#changelog):
> Downweighting now works by applying an attention mask to remove the
downweighted tokens, rather than literally removing them from the
sequence. This behaviour is the default, but the old behaviour can be
re-enabled by passing `downweight_mode=DownweightMode.REMOVE` on init of
the `Compel` instance.
>
> Formerly, downweighting a token worked by both multiplying the
weighting of the token's embedding, and doing an inverse-weighted blend
with a copy of the token sequence that had the downweighted tokens
removed. The intuition is that as weight approaches zero, the tokens
being downweighted should be actually removed from the sequence.
However, removing the tokens resulted in the positioning of all
downstream tokens becoming messed up. The blend ended up blending a lot
more than just the tokens in question.
>
> As of v1.0.0, taking advice from @keturn and @bonlime
(https://github.com/damian0815/compel/issues/7) the procedure is by
default different. Downweighting still involves a blend but what is
blended is a version of the token sequence with the downweighted tokens
masked out, rather than removed. This correctly preserves positioning
embeddings of the other tokens.
* Update root component to allow optional children that will render as
dynamic header of UI
* Export additional components (logo & themeChanger) for use in said
dynamic header (more to come here)
# The Problem
Pickle files (.pkl, .ckpt, etc) are extremely unsafe as they can be
trivially crafted to execute arbitrary code when parsed using
`torch.load`
Right now the conventional wisdom among ML researchers and users is to
simply `not run untrusted pickle files ever` and instead only use
Safetensor files, which cannot be injected with arbitrary code. This is
very good advice.
Unfortunately, **I have discovered a vulnerability inside of InvokeAI
that allows an attacker to disguise a pickle file as a safetensor and
have the payload execute within InvokeAI.**
# How It Works
Within `model_manager.py` and `convert_ckpt_to_diffusers.py` there are
if-statements that decide which `load` method to use based on the file
extension of the model file. The logic (written in a slightly more
readable format than it exists in the codebase) is as follows:
```
if Path(file).suffix == '.safetensors':
safetensor_load(file)
else:
unsafe_pickle_load(file)
```
A malicious actor would only need to create an infected .ckpt file, and
then rename the extension to something that does not pass the `==
'.safetensors'` check, but still appears to a user to be a safetensors
file.
For example, this might be something like `.Safetensors`,
`.SAFETENSORS`, `SafeTensors`, etc.
InvokeAI will happily import the file in the Model Manager and execute
the payload.
# Proof of Concept
1. Create a malicious pickle file.
(https://gist.github.com/CodeZombie/27baa20710d976f45fb93928cbcfe368)
2. Rename the `.ckpt` extension to some variation of `.Safetensors`,
ensuring there is a capital letter anywhere in the extension (eg.
`malicious_pickle.SAFETENSORS`)
3. Import the 'model' like you would normally with any other safetensors
file with the Model Manager.
4. Upon trying to select the model in the web ui, it will be loaded (or
attempt to be converted to a Diffuser) with `torch.load` and the payload
will execute.

# The Fix
This pull request changes the logic InvokeAI uses to decide which model
loader to use so that the safe behavior is the default. Instead of
loading as a pickle if the extension is not exactly `.safetensors`, it
will now **always** load as a safetensors file unless the extension is
**exactly** `.ckpt`.
# Notes:
I think support for pickle files should be totally dropped ASAP as a
matter of security, but I understand that there are reasons this would
be difficult.
In the meantime, I think `RestrictedUnpickler` or something similar
should be implemented as a replacement for `torch.load`, as this
significantly reduces the amount of Python methods that an attacker has
to work with when crafting malicious payloads
inside a pickle file.
Automatic1111 already uses this with some success.
(https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/safe.py)
- The value of png_compression was always 6, despite the value provided
to the --png_compression argument. This fixes the bug.
- It also fixes an inconsistency between the maximum range of
png_compression and the help text.
- Closes#2945
- The value of png_compression was always 6, despite the value provided to the
--png_compression argument. This fixes the bug.
- It also fixes an inconsistency between the maximum range of png_compression
and the help text.
- Closes#2945
Prior to this commit, all models would be loaded with the extremely unsafe `torch.load` method, except those with the exact extension `.safetensors`. Even a change in casing (eg. `saFetensors`, `Safetensors`, etc) would cause the file to be loaded with torch.load instead of the much safer `safetensors.toch.load_file`.
If a malicious actor renamed an infected `.ckpt` to something like `.SafeTensors` or `.SAFETENSORS` an unsuspecting user would think they are loading a safe .safetensor, but would in fact be parsing an unsafe pickle file, and executing an attacker's payload. This commit fixes this vulnerability by reversing the loading-method decision logic to only use the unsafe `torch.load` when the file extension is exactly `.ckpt`.
#2931 was caused by new code that held onto the PRNG in `get_make_image`
and used it in `make_image` for img2img and inpainting. This
functionality has been moved elsewhere so that we can generate multiple
images again.
fix(ui): remove old scrollbar css
fix(ui): make guidepopover lazy
feat(ui): wip resizable drawer
feat(ui): wip resizable drawer
feat(ui): add scroll-linked shadow
feat(ui): organize files
Align Scrollbar next to content
Move resizable drawer underneath the progress bar
Add InvokeLogo to unpinned & align
Adds Invoke Logo to Unpinned Parameters panel and aligns to make it feel seamless.
# Remove node dependencies on generate.py
This is a draft PR in which I am replacing `generate.py` with a cleaner,
more structured interface to the underlying image generation routines.
The basic code pattern to generate an image using the new API is this:
```
from invokeai.backend import ModelManager, Txt2Img, Img2Img
manager = ModelManager('/data/lstein/invokeai-main/configs/models.yaml')
model = manager.get_model('stable-diffusion-1.5')
txt2img = Txt2Img(model)
outputs = txt2img.generate(prompt='banana sushi', steps=12, scheduler='k_euler_a', iterations=5)
# generate() returns an iterator
for next_output in outputs:
print(next_output.image, next_output.seed)
outputs = Img2Img(model).generate(prompt='strawberry` sushi', init_img='./banana_sushi.png')
output = next(outputs)
output.image.save('strawberries.png')
```
### model management
The `ModelManager` handles model selection and initialization. Its
`get_model()` method will return a `dict` with the following keys:
`model`, `model_name`,`hash`, `width`, and `height`, where `model` is
the actual StableDiffusionGeneratorPIpeline. If `get_model()` is called
without a model name, it will return whatever is defined as the default
in `models.yaml`, or the first entry if no default is designated.
### InvokeAIGenerator
The abstract base class `InvokeAIGenerator` is subclassed into into
`Txt2Img`, `Img2Img`, `Inpaint` and `Embiggen`. The constructor for
these classes takes the model dict returned by
`model_manager.get_model()` and optionally an
`InvokeAIGeneratorBasicParams` object, which encapsulates all the
parameters in common among `Txt2Img`, `Img2Img` etc. If you don't
provide the basic params, a reasonable set of defaults will be chosen.
Any of these parameters can be overridden at `generate()` time.
These classes are defined in `invokeai.backend.generator`, but they are
also exported by `invokeai.backend` as shown in the example below.
```
from invokeai.backend import InvokeAIGeneratorBasicParams, Img2Img
params = InvokeAIGeneratorBasicParams(
perlin = 0.15
steps = 30
scheduler = 'k_lms'
)
img2img = Img2Img(model, params)
outputs = img2img.generate(scheduler='k_heun')
```
Note that we were able to override the basic params in the call to
`generate()`
The `generate()` method will returns an iterator over a series of
`InvokeAIGeneratorOutput` objects. These objects contain the PIL image,
the seed, the model name and hash, and attributes for all the parameters
used to generate the object (you can also get these as a dict). The
`iterations` argument controls how many objects will be returned,
defaulting to 1. Pass `None` to get an infinite iterator.
Given the proposed use of `compel` to generate a templated series of
prompts, I thought the API would benefit from a style that lets you loop
over the output results indefinitely. I did consider returning a single
`InvokeAIGeneratorOutput` object in the event that `iterations=1`, but I
think it's dangerous for a method to return different types of result
under different circumstances.
Changing the model is as easy as this:
```
model = manager.get_model('inkspot-2.0`)
txt2img = Txt2Img(model)
```
### Node and legacy support
With respect to `Nodes`, I have written `model_manager_initializer` and
`restoration_services` modules that return `model_manager` and
`restoration` services respectively. The latter is used by the face
reconstruction and upscaling nodes. There is no longer any reference to
`Generate` in the `app` tree.
I have confirmed that `txt2img` and `img2img` work in the nodes client.
I have not tested `embiggen` or `inpaint` yet. pytests are passing, with
some warnings that I don't think are related to what I did.
The legacy WebUI and CLI are still working off `Generate` (which has not
yet been removed from the source tree) and fully functional.
I've finished all the tasks on my TODO list:
- [x] Update the pytests, which are failing due to dangling references
to `generate`
- [x] Rewrite the `reconstruct.py` and `upscale.py` nodes to call
directly into the postprocessing modules rather than going through
`Generate`
- [x] Update the pytests, which are failing due to dangling references
to `generate`
Prior to the folder restructure, the `paths` for `test-invoke-pip` did
not include the UI's path `invokeai/frontend/`:
```yaml
paths:
- 'pyproject.toml'
- 'ldm/**'
- 'invokeai/backend/**'
- 'invokeai/configs/**'
- 'invokeai/frontend/dist/**'
```
After the restructure, more code was moved into the `invokeai/frontend/`
folder, and `paths` was updated:
```yaml
paths:
- 'pyproject.toml'
- 'invokeai/**'
- 'invokeai/backend/**'
- 'invokeai/configs/**'
- 'invokeai/frontend/web/dist/**'
```
Now, the second path includes the UI. The UI now needs to be excluded,
and must be excluded prior to `invokeai/frontend/web/dist/**` being
included.
On `test-invoke-pip-skip`, we need to do a bit of logic juggling to
invert the folder selection. First, include the web folder, then exclude
everying around it and finally exclude the `dist/` folder
Currently translated at 100.0% (500 of 500 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (500 of 500 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (482 of 482 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (480 of 480 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (500 of 500 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (482 of 482 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (480 of 480 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Cause of the problem was inadvertent activation of the safety checker.
When conversion occurs on disk, the safety checker is disabled during loading.
However, when converting in RAM, the safety checker was not removed, resulting
in it activating even when user specified --no-nsfw_checker.
This PR fixes the problem by detecting when the caller has requested the InvokeAi
StableDiffusionGeneratorPipeline class to be returned and setting safety checker
to None. Do not do this with diffusers models destined for disk because then they
will be incompatible with the merge script!!
Closes#2836
Some schedulers report not only the noisy latents at the current timestep,
but also their estimate so far of what the de-noised latents will be.
It makes for a more legible preview than the noisy latents do.
Reverts invoke-ai/InvokeAI#2903
@mauwii has a point here. It looks like triggering on a comment results
in an action for each of the stale issues, even ones that have been
previously dealt with. I'd like to revert this back to the original
behavior of running once every time the cron job executes.
What's the original motivation for having more frequent labeling of the
issues?
I found it to be a chore to remove labels manually in order to
"un-stale" issues. This is contrary to the bot message which says
commenting should remove "stale" status. On the current `cron` schedule,
there may be a delay of up to 24 hours before the label is removed. This
PR will trigger the workflow on issue comments in addition to the
schedule.
Also adds a condition to not run this job on PRs (Github treats issues
and PRs equivalently in this respect), and rewords the messages for
clarity.
This ought to be working but i don't know how it's supposed to behave so
i haven't been able to verify. At least, I know the numbers are getting
pushed all the way to the SD unet, i just have been unable to verify if
what's coming out is what is expected. Please test.
You'll `need to pip install -e .` after switching to the branch, because
it's currently pulling from a non-main `compel` branch. Once it's
verified as working as intended i'll promote the compel branch to pypi.
# Overview
Adding a few accessibility items (I think 9 total items). Mostly
`aria-label`, but also a `<VisuallyHidden>` to the left-side nav tab
icons. Tried to match existing copy that was being used. Feedback
welcome
* Fix img2img and inpainting code so a strength of 1 behaves the same as txt2img.
* Make generated images identical to their txt2img counterparts when strength is 1.
Updates the CLI to define CLI commands as Pydantic objects, similar to
how Invocations (nodes) work. For example:
```py
class HelpCommand(BaseCommand):
"""Shows help"""
type: Literal['help'] = 'help'
def run(self, context: CliContext) -> None:
context.parser.print_help()
```
*looks like this #2814 was reverted accidentally. instead of trying to
revert the revert, this PR can simply be re-accepted and will fix the
ui.*
- Migrate UI from SCSS to Chakra's CSS-in-JS system
- better dx
- more capable theming
- full RTL language support (we now have Arabic and Hebrew)
- general cleanup of the whole UI's styling
- Tidy npm packages and update scripts, necessitates update to github
actions
To test this PR in dev mode, you will need to do a `yarn install` as a
lot has changed.
thanks to @blessedcoolant for helping out on this, it was a big effort.
There are actually two Stable Diffusion v2 legacy checkpoint
configurations:
1. "epsilon" prediction type for Stable Diffusion v2 Base
2. "v-prediction" type for Stable Diffusion v2-768
This commit adds the configuration file needed for epsilon prediction
type models as well as the UI that prompts the user to select the
appropriate configuration file when the code can't do so automatically.
To avoid `git blame` recording all the autoformatting changes under the
name 'lstein', this PR adds a `.git-blame-ignore-revs` that will ignore
any provenance changes that occurred during the recent refactor merge.
This fixes the crash that was occurring when trying to load a legacy
checkpoint file.
Note that this PR includes commits from #2867 to avoid diffusers files
from re-downloading at startup time.
There are actually two Stable Diffusion v2 legacy checkpoint
configurations:
1) "epsilon" prediction type for Stable Diffusion v2 Base
2) "v-prediction" type for Stable Diffusion v2-768
This commit adds the configuration file needed for epsilon prediction
type models as well as the UI that prompts the user to select the
appropriate configuration file when the code can't do so
automatically.
# Migrate to new HF diffusers cache location
This PR adjusts the model cache directory to use the layout of
`diffusers 0.14`. This will automatically migrate any diffusers models
located in `INVOKEAI_ROOT/models/diffusers` to
`INVOKEAI_ROOT/models/hub`, and cache new downloaded diffusers files
into the same location.
As before, if environment variable `HF_HOME` is set, then both
HuggingFace `from_pretrained()` calls as well as all InvokeAI methods
will use `HF_HOME/hub` as their cache.
- Migrate UI from SCSS to Chakra's CSS-in-JS system
- better dx
- more capable theming
- full RTL language support (we now have Arabic and Hebrew)
- general cleanup of the whole UI's styling
- Tidy npm packages and update scripts, necessitates update to github
actions
To test this PR in dev mode, you will need to do a `yarn install` as a
lot has changed.
thanks to @blessedcoolant for helping out on this, it was a big effort.
This removes modules that appear to be no longer used by any code under
the `invokeai` package now that the `ckpt_generator` is gone.
There are a few small changes in here to code that was referencing code
in a conditional branch for ckpt, or to swap out a ⚡ function for a
🤗 one, but only as much was strictly necessary to get things to
run. We'll follow with more clean-up to get lingering `if isinstance` or
`except AttributeError` branches later.
build(ui): fix husky path
build(ui): fix hmr issue, remove emotion cache
build(ui): clean up package.json
build(ui): update gh action and npm scripts
feat(ui): wip port lightbox to chakra theme
feat(ui): wip use chakra theme tokens
feat(ui): Add status text to main loading spinner
feat(ui): wip chakra theme tweaking
feat(ui): simply iaisimplemenu button
feat(ui): wip chakra theming
feat(ui): Theme Management
feat(ui): Add Ocean Blue Theme
feat(ui): wip lightbox
fix(ui): fix lightbox mouse
feat(ui): set default theme variants
feat(ui): model manager chakra theme
chore(ui): lint
feat(ui): remove last scss
feat(ui): fix switch theme
feat(ui): Theme Cleanup
feat(ui): Stylize Search Models Found List
feat(ui): hide scrollbars
feat(ui): fix floating button position
feat(ui): Scrollbar Styling
fix broken scripts
This PR fixes the following scripts:
1) Scripts that can be executed within the repo's scripts directory.
Note that these are for development testing and are not intended
to be exposed to the user.
configure_invokeai.py - configuration
dream.py - the legacy CLI
images2prompt.py - legacy "dream prompt" retriever
invoke-new.py - new nodes-based CLI
invoke.py - the legacy CLI under another name
make_models_markdown_table.py - a utility used during the release/doc process
pypi_helper.py - another utility used during the release process
sd-metadata.py - retrieve JSON-formatted metadata from a PNG file
2) Scripts that are installed by pip install. They get placed into the venv's
PATH and are intended to be the official entry points:
invokeai-node-cli - new nodes-based CLI
invokeai-node-web - new nodes-based web server
invokeai - legacy CLI
invokeai-configure - install time configuration script
invokeai-merge - model merging script
invokeai-ti - textual inversion script
invokeai-model-install - model installer
invokeai-update - update script
invokeai-metadata" - retrieve JSON-formatted metadata from PNG files
protect invocations against black autoformatting
deps: upgrade to diffusers 0.14, safetensors 0.3, transformers 4.26, accelerate 0.16
Things to check for in this version:
- `diffusers` cache location is now more consistent with other
huggingface-hub using code (i.e. `transformers`) as of
https://github.com/huggingface/diffusers/pull/2005. I think ultimately
this should make @damian0815 (and other folks with multiple
diffusers-using projects) happier, but it's worth taking a look to make
sure the way @lstein set things up to respect `HF_HOME` is still
functioning as intended.
- I've gone ahead and updated `transformers` to the current version
(4.26), but I have a vague memory that we were holding it back at some
point? Need to look that up and see if that's the case and why.
This PR fixes the following scripts:
1) Scripts that can be executed within the repo's scripts directory.
Note that these are for development testing and are not intended
to be exposed to the user.
```
configure_invokeai.py - configuration
dream.py - the legacy CLI
images2prompt.py - legacy "dream prompt" retriever
invoke-new.py - new nodes-based CLI
invoke.py - the legacy CLI under another name
make_models_markdown_table.py - a utility used during the release/doc process
pypi_helper.py - another utility used during the release process
sd-metadata.py - retrieve JSON-formatted metadata from a PNG file
```
2) Scripts that are installed by pip install. They get placed into the
venv's
PATH and are intended to be the official entry points:
```
invokeai-node-cli - new nodes-based CLI
invokeai-node-web - new nodes-based web server
invokeai - legacy CLI
invokeai-configure - install time configuration script
invokeai-merge - model merging script
invokeai-ti - textual inversion script
invokeai-model-install - model installer
invokeai-update - update script
invokeai-metadata" - retrieve JSON-formatted metadata from PNG files
```
Fix error when using txt2img
ModuleNotFoundError: No module named 'invokeai.backend.models'
and
ModuleNotFoundError: No module named
'invokeai.backend.generator.diffusers_pipeline'
This PR fixes the following scripts:
1) Scripts that can be executed within the repo's scripts directory.
Note that these are for development testing and are not intended
to be exposed to the user.
configure_invokeai.py - configuration
dream.py - the legacy CLI
images2prompt.py - legacy "dream prompt" retriever
invoke-new.py - new nodes-based CLI
invoke.py - the legacy CLI under another name
make_models_markdown_table.py - a utility used during the release/doc process
pypi_helper.py - another utility used during the release process
sd-metadata.py - retrieve JSON-formatted metadata from a PNG file
2) Scripts that are installed by pip install. They get placed into the venv's
PATH and are intended to be the official entry points:
invokeai-node-cli - new nodes-based CLI
invokeai-node-web - new nodes-based web server
invokeai - legacy CLI
invokeai-configure - install time configuration script
invokeai-merge - model merging script
invokeai-ti - textual inversion script
invokeai-model-install - model installer
invokeai-update - update script
invokeai-metadata" - retrieve JSON-formatted metadata from PNG files
To avoid `git blame` recording all the autoformatting changes
under the name 'lstein', this PR adds a `.git-blame-ignore-revs`
that will ignore any provenance changes that occurred during the
recent refactor merge.
# All python code has been moved under `invokeai`. All vestiges of `ldm`
and `ldm.invoke` are now gone.
***You will need to run `pip install -e .` before the code will work
again!***
Everything seems to be functional, but extensive testing is advised.
A guide to where the files have gone is forthcoming.
This is the first phase of a big shifting of files and directories
in the source tree.
You will need to run `pip install -e .` before the code will work again!
Here's what's in the current commit:
1) Remove a lot of dead code that dealt with checkpoint and safetensor loading.
2) Entire ckpt_generator hierarchy is now gone!
3) ldm.invoke.generator.* => invokeai.generator.*
4) ldm.model.* => invokeai.model.*
5) ldm.invoke.model_manager => invokeai.model.model_manager
6) In addition, a number of frequently-accessed classes can be imported
from the invokeai.model and invokeai.generator modules:
from invokeai.generator import ( Generator, PipelineIntermediateState,
StableDiffusionGeneratorPipeline, infill_methods)
from invokeai.models import ( ModelManager, SDLegacyType
InvokeAIDiffuserComponent, AttentionMapSaver,
DDIMSampler, KSampler, PLMSSampler,
PostprocessingSettings )
* [nodes] Add better error handling to processor and CLI
* [nodes] Use more explicit name for marking node execution error
* [nodes] Update the processor call to error
This should make caching way easier and therefore speed up the image
(re-)creation a lot.
Other small improvements:
- reorder .dockerignore
- rename amd flavor to rocm to align with cuda flavor
- use `user:group` for definitions
- add `--platform=${TARGETPLATFORM}` to base
This PR adds the core of the node-based invocation system first
discussed in https://github.com/invoke-ai/InvokeAI/discussions/597 and
implements it through a basic CLI and API. This supersedes #1047, which
was too far behind to rebase.
## Architecture
### Invocations
The core of the new system is **invocations**, found in
`/ldm/invoke/app/invocations`. These represent individual nodes of
execution, each with inputs and outputs. Core invocations are already
implemented (`txt2img`, `img2img`, `upscale`, `face_restore`) as well as
a debug invocation (`show_image`). To implement a new invocation, all
that is required is to add a new implementation in this folder (there is
a markdown document describing the specifics, though it is slightly
out-of-date).
### Sessions
Invocations and links between them are maintained in a **session**.
These can be queued for invocation (either the next ready node, or all
nodes). Some notes:
* Sessions may be added to at any time (including after invocation), but
may not be modified.
* Links are always added with a node, and are always links from existing
nodes to the new node. These links can be relative "history" links, e.g.
`-1` to link from a previously executed node, and can link either
specific outputs, or can opportunistically link all matching outputs by
name and type by using `*`.
* There are no iteration/looping constructs. Most needs for this could
be solved by either duplicating nodes or cloning sessions. This is open
for discussion, but is a difficult problem to solve in a way that
doesn't make the code even more complex/confusing (especially regarding
node ids and history).
### Services
These make up the core the invocation system, found in
`/ldm/invoke/app/services`. One of the key design philosophies here is
that most components should be replaceable when possible. For example,
if someone wants to use cloud storage for their images, they should be
able to replace the image storage service easily.
The services are broken down as follows (several of these are
intentionally implemented with an initial simple/naïve approach):
* Invoker: Responsible for creating and executing **sessions** and
managing services used to do so.
* Session Manager: Manages session history. An on-disk implementation is
provided, which stores sessions as json files on disk, and caches
recently used sessions for quick access.
* Image Storage: Stores images of multiple types. An on-disk
implementation is provided, which stores images on disk and retains
recently used images in an in-memory cache.
* Invocation Queue: Used to queue invocations for execution. An
in-memory implementation is provided.
* Events: An event system, primarily used with socket.io to support
future web UI integration.
## Apps
Apps are available through the `/scripts/invoke-new.py` script (to-be
integrated/renamed).
### CLI
```
python scripts/invoke-new.py
```
Implements a simple CLI. The CLI creates a single session, and
automatically links all inputs to the previous node's output. Commands
are automatically generated from all invocations, with command options
being automatically generated from invocation inputs. Help is also
available for the cli and for each command, and is very verbose.
Additionally, the CLI supports command piping for single-line entry of
multiple commands. Example:
```
> txt2img --prompt "a cat eating sushi" --steps 20 --seed 1234 | upscale | show_image
```
### API
```
python scripts/invoke-new.py --api --host 0.0.0.0
```
Implements an API using FastAPI with Socket.io support for signaling.
API documentation is available at `http://localhost:9090/docs` or
`http://localhost:9090/redoc`. This includes OpenAPI schema for all
available invocations, session interaction APIs, and image APIs.
Socket.io signals are per-session, and can be subscribed to by session
id. These aren't currently auto-documented, though the code for event
emission is centralized in `/ldm/invoke/app/services/events.py`.
A very simple test html and script are available at
`http://localhost:9090/static/test.html` This demonstrates creating a
session from a graph, invoking it, and receiving signals from Socket.io.
## What's left?
* There are a number of features not currently covered by invocations. I
kept the set of invocations small during core development in order to
simplify refactoring as I went. Now that the invocation code has
stabilized, I'd love some help filling those out!
* There's no image metadata generated. It would be fairly
straightforward (and would make good sense) to serialize either a
session and node reference into an image, or the entire node into the
image. There are a lot of questions to answer around source images,
linked images, etc. though. This history is all stored in the session as
well, and with complex sessions, the metadata in an image may lose its
value. This needs some further discussion.
* We need a list of features (both current and future) that would be
difficult to implement without looping constructs so we can have a good
conversation around it. I'm really hoping we can avoid needing
looping/iteration in the graph execution, since it'll necessitate
separating an execution of a graph into its own concept/system, and will
further complicate the system.
* The API likely needs further filling out to support the UI. I think
using the new API for the current UI is possible, and potentially
interesting, since it could work like the new/demo CLI in a "single
operation at a time" workflow. I don't know how compatible that will be
with our UI goals though. It would be nice to support only a single API
though.
* Deeper separation of systems. I intentionally tried to not touch
Generate or other systems too much, but a lot could be gained by
breaking those apart. Even breaking apart Args into two pieces (command
line arguments and the parser for the current CLI) would make it easier
to maintain. This is probably in the future though.
author Kyle Schouviller <kyle0654@hotmail.com> 1669872800 -0800
committer Kyle Schouviller <kyle0654@hotmail.com> 1676240900 -0800
Adding base node architecture
Fix type annotation errors
Runs and generates, but breaks in saving session
Fix default model value setting. Fix deprecation warning.
Fixed node api
Adding markdown docs
Simplifying Generate construction in apps
[nodes] A few minor changes (#2510)
* Pin api-related requirements
* Remove confusing extra CORS origins list
* Adds response models for HTTP 200
[nodes] Adding graph_execution_state to soon replace session. Adding tests with pytest.
Minor typing fixes
[nodes] Fix some small output query hookups
[node] Fixing some additional typing issues
[nodes] Move and expand graph code. Add base item storage and sqlite implementation.
Update startup to match new code
[nodes] Add callbacks to item storage
[nodes] Adding an InvocationContext object to use for invocations to provide easier extensibility
[nodes] New execution model that handles iteration
[nodes] Fixing the CLI
[nodes] Adding a note to the CLI
[nodes] Split processing thread into separate service
[node] Add error message on node processing failure
Removing old files and duplicated packages
Adding python-multipart
- Add curated set of starter models based on team discussion. The final
list of starter models can be found in
`invokeai/configs/INITIAL_MODELS.yaml`
- To test model installation, I selected and installed all the models on
the list. This led to my discovering that when there are no more starter
models to display, the console front end crashes. So I made a fix to
this in which the entire starter model selection is no longer shown.
- Update model table in 050_INSTALL_MODELS.md
- Add guide to dealing with low-memory situations
- Version is now `v2.3.1`
- add new script `scripts/make_models_markdown_table.py` that parses
INITIAL_MODELS.yaml and creates markdown table for the model installation
documentation file
- update 050_INSTALLING_MODELS.md with above table, and add a warning
about additional license terms that apply to some of the models.
- Final list can be found in invokeai/configs/INITIAL_MODELS.yaml
- After installing all the models, I discovered a bug in the file
selection form that caused a crash when no remaining uninstalled
models remained. So had to fix this.
The sample_to_image method in `ldm.invoke.generator.base` was still
using ckpt-era code. As a result when the WebUI was set to show
"accurate" intermediate images, there'd be a crash. This PR corrects the
problem.
- Closes#2784
- Closes#2775
- Discord member @marcus.llewellyn reported that some civitai
2.1-derived checkpoints were not converting properly (probably
dreambooth-generated):
https://discord.com/channels/1020123559063990373/1078386197589655582/1078387806122025070
- @blessedcoolant tracked this down to a missing key that was used to
derive vector length of the CLIP model used by fetching the second
dimension of the tensor at "cond_stage_model.model.text_projection".
- On inspection, I found that the same second dimension can be recovered
from key 'cond_stage_model.model.ln_final.bias', and use that instead. I
hope this is correct; tested on multiple v1, v2 and inpainting models
and they converted correctly.
- While debugging this, I found and fixed several other issues:
- model download script was not pre-downloading the OpenCLIP
text_encoder or text_tokenizer. This is fixed.
- got rid of legacy code in `ckpt_to_diffuser.py` and replaced with
calls into `model_manager`
- more consistent status reporting in the CLI.
without this change, the project can be installed on 3.9 but not used
this also fixes the container images
Maybe we should re-enable Python 3.9 checks which would have prevented
this.
- Discord member @marcus.llewellyn reported that some civitai 2.1-derived checkpoints were
not converting properly (probably dreambooth-generated):
https://discord.com/channels/1020123559063990373/1078386197589655582/1078387806122025070
- @blessedcoolant tracked this down to a missing key that was used to
derive vector length of the CLIP model used by fetching the second
dimension of the tensor at "cond_stage_model.model.text_projection".
His proposed solution was to hardcode a value of 1024.
- On inspection, I found that the same second dimension can be
recovered from key 'cond_stage_model.model.ln_final.bias', and use
that instead. I hope this is correct; tested on multiple v1, v2 and
inpainting models and they converted correctly.
- While debugging this, I found and fixed several other issues:
- model download script was not pre-downloading the OpenCLIP
text_encoder or text_tokenizer. This is fixed.
- got rid of legacy code in `ckpt_to_diffuser.py` and replaced
with calls into `model_manager`
- more consistent status reporting in the CLI.
Root directory finding algorithm is:
2) use --root argument
2) use INVOKEAI_ROOT environment variable
3) use VIRTUAL_ENV environment variable
4) use ~/invokeai
Since developers are liable to put virtual environments in their
favorite places, not necessarily in the invokeai root directory, this PR
adds a sanity check that looks for the existence of
`VIRTUAL_ENV/invokeai.init`, and moves on to (4) if not found.
# This will constitute v2.3.1+rc2
## Windows installer enhancements
1. resize installer window to give more room for configure and download
forms
2. replace '\' with '/' in directory names to allow user to
drag-and-drop
folders into the dialogue boxes that accept directories.
3. similar change in CLI for the !import_model and !convert_model
commands
4. better error reporting when a model download fails due to network
errors
5. put the launcher scripts into a loop so that menu reappears after
invokeai, merge script, etc exits. User can quit with "Q".
6. do not try to download fp16 of sd-ft-mse-vae, since it doesn't exist.
7. cleaned up status reporting when installing models
8. Detect when install failed for some reason and print helpful error
message rather than stack trace.
9. Detect window size and resize to minimum acceptable values to provide
better display of configure and install forms.
10. Fix a bug in the CLI which prevented diffusers imported by their
repo_ids
from being correctly registered in the current session (though they
install
correctly)
11. Capitalize the "i" in Imported in the autogenerated descriptions.
Root directory finding algorithm is:
2) use --root argument
2) use INVOKEAI_ROOT environment variable
3) use VIRTUAL_ENV environment variable
4) use ~/invokeai
Since developer's are liable to put virtual environments in their
favorite places, not necessarily in the invokeai root directory, this
PR adds a sanity check that looks for the existence of
VIRTUAL_ENV/invokeai.init, and moves to (4) if not found.
- Fix a bug in the CLI which prevented diffusers imported by their repo_ids
from being correctly registered in the current session (though they install
correctly)
- Capitalize the "i" in Imported in the autogenerated descriptions.
1. resize installer window to give more room for configure and download forms
2. replace '\' with '/' in directory names to allow user to drag-and-drop
folders into the dialogue boxes that accept directories.
3. similar change in CLI for the !import_model and !convert_model commands
4. better error reporting when a model download fails due to network errors
5. put the launcher scripts into a loop so that menu reappears after
invokeai, merge script, etc exits. User can quit with "Q".
6. do not try to download fp16 of sd-ft-mse-vae, since it doesn't exist.
7. cleaned up status reporting when installing models
- Detect when install failed for some reason and print helpful error
message rather than stack trace.
- Detect window size and resize to minimum acceptable values to provide
better display of configure and install forms.
Currently translated at 81.4% (382 of 469 strings)
translationBot(ui): update translation (Russian)
Currently translated at 81.6% (382 of 468 strings)
Co-authored-by: Sergey Krashevich <svk@svk.su>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
## Major Changes
The invokeai-configure script has now been refactored. The work of
selecting and downloading initial models at install time is now done by
a script named `invokeai-model-install` (module name is
`ldm.invoke.config.model_install`)
Screen 1 - adjust startup options:

Screen 2 - select SD models:

The calling arguments for `invokeai-configure` have not changed, so
nothing should break. After initializing the root directory, the script
calls `invokeai-model-install` to let the user select the starting
models to install.
`invokeai-model-install puts up a console GUI with checkboxes to
indicate which models to install. It respects the `--default_only` and
`--yes` arguments so that CI will continue to work. Here are the various
effects you can achieve:
`invokeai-configure`
This will use console-based UI to initialize invokeai.init,
download support models, and choose and download SD models
`invokeai-configure --yes`
Without activating the GUI, populate invokeai.init with default values,
download support models and download the "recommended" SD models
`invokeai-configure --default_only`
Activate the GUI for changing init options, but don't show the SD
download
form, and automatically download the default SD model (currently SD-1.5)
`invokeai-model-install`
Select and install models. This can be used to download arbitrary
models from the Internet, install HuggingFace models using their
repo_id,
or watch a directory for models to load at startup time
`invokeai-model-install --yes`
Import the recommended SD models without a GUI
`invokeai-model-install --default_only`
As above, but only import the default model
## Flexible Model Imports
The console GUI allows the user to import arbitrary models into InvokeAI
using:
1. A HuggingFace Repo_id
2. A URL (http/https/ftp) that points to a checkpoint or safetensors
file
3. A local path on disk pointing to a checkpoint/safetensors file or
diffusers directory
4. A directory to be scanned for all checkpoint/safetensors files to be
imported
The UI allows the user to specify multiple models to bulk import. The
user can specify whether to import the ckpt/safetensors as-is, or
convert to `diffusers`. The user can also designate a directory to be
scanned at startup time for checkpoint/safetensors files.
## Backend Changes
To support the model selection GUI PR introduces a new method in
`ldm.invoke.model_manager` called `heuristic_import(). This accepts a
string-like object which can be a repo_id, URL, local path or directory.
It will figure out what the object is and import it. It interrogates the
contents of checkpoint and safetensors files to determine what type of
SD model they are -- v1.x, v2.x or v1.x inpainting.
## Installer
I am attaching a zip file of the installer if you would like to try the
process from end to end.
[InvokeAI-installer-v2.3.0.zip](https://github.com/invoke-ai/InvokeAI/files/10785474/InvokeAI-installer-v2.3.0.zip)
motivation: i want to be doing future prompting development work in the
`compel` lib (https://github.com/damian0815/compel) - which is currently
pip installable with `pip install compel`.
-At some point pathlib was added to the list of imported modules and
this broken the os.path code that assembled the sample data set.
-Now fixed by replacing os.path calls with Path methods
-At some point pathlib was added to the list of imported modules and this
broken the os.path code that assembled the sample data set.
-Now fixed by replacing os.path calls with Path methods
- Disable responsive resizing below starting dimensions (you can make
form larger, but not smaller than what it was at startup)
- Fix bug that caused multiple --ckpt_convert entries (and similar) to
be written to init file.
This bug is related to the format in which we stored prompts for some time: an array of weighted subprompts.
This caused some strife when recalling a prompt if the prompt had colons in it, due to our recently introduced handling of negative prompts.
Currently there is no need to store a prompt as anything other than a string, so we revert to doing that.
Compatibility with structured prompts is maintained via helper hook.
Lots of earlier embeds use a common trigger token such as * or the
hebrew letter shan. Previously, the textual inversion manager would
refuse to load the second and subsequent embeddings that used a
previously-claimed trigger. Now, when this case is encountered, the
trigger token is replaced by <filename> and the user is informed of the
fact.
1. Fixed display crash when the number of installed models is less than
the number of desired columns to display them.
2. Added --ckpt_convert option to init file.
Enhancements:
1. Directory-based imports will not attempt to import components of diffusers models.
2. Diffuser directory imports now supported
3. Files that end with .ckpt that are not Stable Diffusion models (such as VAEs) are
skipped during import.
Bugs identified in Psychedelicious's review:
1. The invokeai-configure form now tracks the current contents of `invokeai.init` correctly.
2. The autoencoders are no longer treated like installable models, but instead are
mandatory support models. They will no longer appear in `models.yaml`
Bugs identified in Damian's review:
1. If invokeai-model-install is started before the root directory is initialized, it will
call invokeai-configure to fix the matter.
2. Fix bug that was causing empty `models.yaml` under certain conditions.
3. Made import textbox smaller
4. Hide the "convert to diffusers" options if nothing to import.
In theory, this reduces peak memory consumption by doing the conditioned
and un-conditioned predictions one after the other instead of in a
single mini-batch.
In practice, it doesn't reduce the reported "Max VRAM used for this
generation" for me, even without xformers. (But it does slow things down
by a good 18%.)
That suggests to me that the peak memory usage is during VAE decoding,
not the diffusion unet, but ymmv. It does [improve things for gogurt's
16 GB
M1](https://github.com/invoke-ai/InvokeAI/pull/2732#issuecomment-1436187407),
so it seems worthwhile.
To try it out, use the `--sequential_guidance` option:
2dded68267/ldm/invoke/args.py (L487-L492)
- Adds an update action to launcher script
- This action calls new python script `invokeai-update`, which prompts
user to update to latest release version, main development version, or
an arbitrary git tag or branch name.
- It then uses `pip` to update to whatever tag was specified.
The user interface (such as it is) looks like this:

- The TI script was looping over all files in the training image
directory, regardless of whether they were image files or not. This PR
adds a check for image file extensions.
-
- Closes#2715
- Fixes longstanding bug in the token vector size code which caused .pt
files to be assigned the wrong token vector length. These were then
tossed out during directory scanning.
- Fixes longstanding bug in the token vector size code which caused
.pt files to be assigned the wrong token vector length. These
were then tossed out during directory scanning.
- Fixed the test for token length; tested on several .pt and .bin files
- Also added a __main__ entrypoint for CLI.py, to make pdb debugging a
bit more convenient.
When selecting the last model of the third model-list in the
model-merging-TUI it crashed because the code forgot about the "None"
element.
Additionally it seems that it accidentally always took the wrong model
as third model if selected?
This simple fix resolves both issues.
Added symmetry to Invoke based on discussions with @damian0815. This can currently only be activated via the CLI with the `--h_symmetry_time_pct` and `--v_symmetry_time_pct` options. Those take values from 0.0-1.0, exclusive, indicating the percentage through generation at which symmetry is applied as a one-time operation. To have symmetry in either axis applied after the first step, use a very low value like 0.001.
- not sure why, but at some pont --ckpt_convert (which converts legacy checkpoints)
into diffusers in memory, stopped working due to float16/float32 issues.
- this commit repairs the problem
- also removed some debugging messages I found in passing
- Fixed the test for token length; tested on several .pt and .bin files
- Also added a __main__ entrypoint for CLI.py, to make pdb debugging a bit
more convenient.
- You can now achieve several effects:
`invokeai-configure`
This will use console-based UI to initialize invokeai.init,
download support models, and choose and download SD models
`invokeai-configure --yes`
Without activating the GUI, populate invokeai.init with default values,
download support models and download the "recommended" SD models
`invokeai-configure --default_only`
As above, but only download the default SD model (currently SD-1.5)
`invokeai-model-install`
Select and install models. This can be used to download arbitrary
models from the Internet, install HuggingFace models using their repo_id,
or watch a directory for models to load at startup time
`invokeai-model-install --yes`
Import the recommended SD models without a GUI
`invokeai-model-install --default_only`
As above, but only import the default model
A few bugs fixed.
- After the recent update to the Cancel Button, it was no longer
respecting sizing in Floating Mode and the Beta Canvas. Fixed that.
- After the recent dependency update, useHotkeys was bugging out for the
fullscreen hotkey `f`. Realized this was happening because the hotkey
was initialized in two places -- in both the gallery and the parameter
floating button. Removed it from both those places and moved it to the
InvokeTabs component. It makes sense to reside it here because it is a
global hotkey.
- Also added index `0` to the default Accordion index in state in order
to ensure that the main accordions stay open. Conveniently this works
great on all tabs. We have all the primary options in accordions so they
stay open. And as for advanced settings, the first one is always Seed
which is an important accordion, so it opens up by default.
Think there may be some more bugs. Looking in to them.
After upgrading the deps, the full screen hotkey started to bug out. I believe this was happening because it was triggered in two different components causing it to run twice. Removed it from both floating buttons and moved it to the Invoke tab. Makes sense to keep it there as it is a global hotkey.
After the recent changes the Cancel button wasn't maintaining min height in floating mode. Also the new button group was not scaling in width correctly on the Canvas Beta UI. Fixed both.
- Adds a translation status badge
- Adds a blurb about contributing a translation (we want Weblate to be
the source of truth for translations, and to avoid updating translations
directly here)
- Upgraded all dependencies
- Removed beta TS 5.0 as it conflicted with some packages
- Added types for `Array.prototype.findLast` and
`Array.prototype.findLastIndex` (these definitions are provided in TS
5.0
- Fixed fixed type import syntax in a few components
- Re-patched `redux-deep-persist` and tested to ensure the patch still
works
The husky pre-commit command was `npx run lint`, but it should run
`lint-staged`. Also, `npx` wasn't working for me. Changed the command to
`npm run lint-staged` and it all works. Extended the `lint-staged`
triggers to hit `json`, `scss` and `html`.
When encountering a bad embedding, InvokeAI was asking about reconfiguring models. This is because the embedding load error was never handled - it now is.
- Upgraded all dependencies
- Removed beta TS 5.0 as it conflicted with some packages
- Added types for `Array.prototype.findLast` and `Array.prototype.findLastIndex` (these definitions are provided in TS 5.0
- Fixed fixed type import syntax in a few components
- Re-patched `redux-deep-persist` and tested to ensure the patch still works
Model Manager lags a bit if you have a lot of models.
Basically added a fake delay to rendering the model list so the modal
has time to load first. Hacky but if it works it works.
## What was the problem/requirement? (What/Why)
Frequently, I wish to cancel the processing of images, but also want the
current image to finalize before I do. To work around this, I need to
wait until the current one finishes before pressing the cancel.
## What was the solution? (How)
* Implemented a button that allows to "Cancel after current iteration,"
which stores a state in the UI that will attempt to cancel the
processing after the current image finishes
* If the button is pressed again, while it is spinning and before the
next iteration happens, this will stop the scheduling of the cancel, and
behave as if the button was never pressed.
### Minor
* Added `.yarn` to `.gitignore` as this was an output folder produced
from following Frontend's README
### Revision 2
#### Major
* Changed from a standalone button to a context menu next to the
original cancel button. Pressing the context menu will give the
drop-down option to select which type of cancel method the user prefers,
and they can press that button for canceling in the specified type
* Moved states to system state for cross-screen and toggled cancel types
management
* Added in distribution for the target yarn version (allowing any
version of yarn to compile successfully), and updated the README to
ensure `--immutable` is passed for onboarding developers
#### Minor
* Updated `.gitignore` to ignore specific yarn folders, as specified by
their team -
https://yarnpkg.com/getting-started/qa#which-files-should-be-gitignored
## How were these changes tested?
* `yarn dev` => Server started successfully
* Manual testing on the development server to ensure the button behaved
as expected
* `yarn run build` => Success
### Artifacts
#### Revision 1
* Video showing the UI changes in action
https://user-images.githubusercontent.com/89283782/218347722-3a15ce61-2d8c-4c38-b681-e7a3e79dd595.mov
* Images showing the basic UI changes


#### Revision 2
* Video showing the UI changes in action
https://user-images.githubusercontent.com/89283782/219901217-048d2912-9b61-4415-85fd-9e8fedb00c79.mov
* Images showing the basic UI changes
(Default state)

(Drop-down context menu active)

(Scheduled cancel selected and running)

(Scheduled cancel started)

## Notes
* Using `SystemState`'s `currentStatus` variable, when the value is
`common:statusIterationComplete` is an alternative to this approach (and
would be more optimal as it should prevent the next iteration from even
starting), but since the names are within the translations, rather than
an enum or other type, this method of tracking the current iteration was
used instead.
* `isLoading` on `IAIIconButton` caused the Icon Button to also be
disabled, so the current solution works around that with conditionally
rendering the icon of the button instead of passing that value.
* I don't have context on the development expectation for `dist` folder
interactions (and couldn't find any documentation outside of the
`.gitignore` mentioning that the folder should remain. Let me know if
they need to be modified a certain way.
- The checkpoint conversion script was generating diffusers models with
the safety checker set to null. This resulted in models that could not
be merged with ones that have the safety checker activated.
- This PR fixes the issue by incorporating the safety checker into all
1.x-derived checkpoints, regardless of user's nsfw_checker setting.
- The checkpoint conversion script was generating diffusers models
with the safety checker set to null. This resulted in models
that could not be merged with ones that have the safety checker
activated.
- This PR fixes the issue by incorporating the safety checker into
all 1.x-derived checkpoints, regardless of user's nsfw_checker setting.
Also tighten up the typing of `device` attributes in general.
Fixes
> ValueError: Expected a torch.device with a specified index or an
integer, but got:cuda
Weblate's first PR was it attempting to fix some translation issues we
had overlooked!
It wanted to remove some keys which it did not see in our translation
source due to typos.
This PR instead corrects the key names to resolve the issues.
# Weblate Translation
After doing a full integration test of 3 translation service providers
on my fork of InvokeAI, we have chosen
[Weblate](https://hosted.weblate.org). The other two viable options were
[Crowdin](https://crowdin.com/) and
[Transifex](https://www.transifex.com/).
Weblate was the choice because its hosted service provides a very solid
UX / DX, can scale as much as we may ever need, is FOSS itself, and
generously offers free hosted service to other libre projects like ours.
## How it works
Weblate hosts its own fork of our repo and establishes a kind of
unidirectional relationship between our repo and its fork.
### InvokeAI --> Weblate
The `invoke-ai/InvokeAI` repo has had the Weblate GitHub app added to
it. This app watches for changes to our translation source
(`invokeai/frontend/public/locales/en.json`) and then updates the
Weblate fork. The Weblate UI then knows there are new strings to be
translated, or changes to be made.
### Translation
Our translators can then update the translations on the Weblate UI. The
plan now is to invite individual community members who have expressed
interest in maintaining a language or two and give them access to the
app. We can also open the doors to the general public if desired.
### Weblate --> InvokeAI
When a translation is ready or changed, the system will make a PR to
`main`. We have a substantial degree of control over this and will
likely manually trigger these PRs instead of letting them fire off
automatically.
Once a PR is merged, we will still need to rebuild the web UI. I think
we can set things up so that we only need the rebuild when a totally new
language is added, but for now, we will stick to this relatively simple
setup.
## This PR
This PR sets up the web UI's translation stuff to work with Weblate:
- merged each locale into a single file
- updated the i18next config and UI to work with this simpler file
structure
- update our eslint and prettier rules to ensure the locale files have
the same format as what Weblate outputs (`tabWidth: 4`)
- added a thank you to Weblate in our README
Once this is merged, I'll link Weblate to `main` and do a couple tests
to ensure it is all working as expected.
This fixes a few cosmetic bugs in the merge models console GUI:
1) Fix the minimum and maximum ranges on alpha. Was 0.05 to 0.95. Now
0.01 to 0.99.
2) Don't show the 'add_difference' interpolation method when 2 models
selected, or the other three methods when three models selected
## Convert v2 models in CLI
- This PR introduces a CLI prompt for the proper configuration file to
use when converting a ckpt file, in order to support both inpainting
and v2 models files.
- When user tries to directly !import a v2 model, it prints out a proper
warning that v2 ckpts are not directly supported and converts it into a
diffusers model automatically.
The user interaction looks like this:
```
(stable-diffusion-1.5) invoke> !import_model /home/lstein/graphic-art.ckpt
Short name for this model [graphic-art]: graphic-art-test
Description for this model [Imported model graphic-art]: Imported model graphic-art
What type of model is this?:
[1] A model based on Stable Diffusion 1.X
[2] A model based on Stable Diffusion 2.X
[3] An inpainting model based on Stable Diffusion 1.X
[4] Something else
Your choice: [1] 2
```
In addition, this PR enhances the bulk checkpoint import function. If a
directory path is passed to `!import_model` then it will be scanned for
`.ckpt` and `.safetensors` files. The user will be prompted to import
all the files found, or select which ones to import.
Addresses
https://discord.com/channels/1020123559063990373/1073730061380894740/1073954728544845855
- fix alpha slider to show values from 0.01 to 0.99
- fix interpolation list to show 'difference' method for 3 models,
- and weighted_sum, sigmoid and inverse_sigmoid methods for 2
Porting over as many usable options to slider as possible.
- Ported Face Restoration settings to Sliders.
- Ported Upscale Settings to Sliders.
- Ported Variation Amount to Sliders.
- Ported Noise Threshold to Sliders <-- Optimized slider so the values
actually make sense.
- Ported Perlin Noise to Sliders.
- Added a suboption hook for the High Res Strength Slider.
- Fixed a couple of small issues with the Slider component.
- Ported Main Options to Sliders.
- Corrected error that caused --full-precision argument to be ignored
when models downloaded using the --yes argument.
- Improved autodetection of v1 inpainting files; no longer relies on the
file having 'inpaint' in the name.
* new OffloadingDevice loads one model at a time, on demand
* fixup! new OffloadingDevice loads one model at a time, on demand
* fix(prompt_to_embeddings): call the text encoder directly instead of its forward method
allowing any associated hooks to run with it.
* more attempts to get things on the right device from the offloader
* more attempts to get things on the right device from the offloader
* make offloading methods an explicit part of the pipeline interface
* inlining some calls where device is only used once
* ensure model group is ready after pipeline.to is called
* fixup! Strategize slicing based on free [V]RAM (#2572)
* doc(offloading): docstrings for offloading.ModelGroup
* doc(offloading): docstrings for offloading-related pipeline methods
* refactor(offloading): s/SimpleModelGroup/FullyLoadedModelGroup
* refactor(offloading): s/HotSeatModelGroup/LazilyLoadedModelGroup
to frame it is the same terms as "FullyLoadedModelGroup"
---------
Co-authored-by: Damian Stewart <null@damianstewart.com>
- filter paths for `build-container.yml` and `test-invoke-pip.yml`
- add workflow to pass required checks on PRs with `paths-ignore`
- this triggers if `test-invoke-pip.yml` does not
- fix "CI checks on main link" in `/README.md`
- filter paths for `build-container.yml` and `test-invoke-pip.yml`
- add workflow to pass required checks on PRs with `paths-ignore`
- this triggers if `test-invoke-pip.yml` does not
- fix "CI checks on main link" in `/README.md`
Assuming that mixing `"literal strings"` and `{'JSX expressions'}`
throughout the code is not for a explicit reason but just a result IDE
autocompletion, I changed all props to be consistent with the
conventional style of using simple string literals where it is
sufficient.
This is a somewhat trivial change, but it makes the code a little more
readable and uniform
- quashed multiple bugs in model conversion and importing
- found old issue in handling of resume of interrupted downloads
- will require extensive testing
### WebUI Model Conversion
**Model Search Updates**
- Model Search now has a radio group that allows users to pick the type
of model they are importing. If they know their model has a custom
config file, they can assign it right here. Based on their pick, the
model config data is automatically populated. And this same information
is used when converting the model to `diffusers`.

- Files named `model.safetensors` and
`diffusion_pytorch_model.safetensors` are excluded from the search
because these are naming conventions used by diffusers models and they
will end up showing on the list because our conversion saves safetensors
and not bin files.
**Model Conversion UI**
- The **Convert To Diffusers** button can be found on the Edit page of
any **Checkpoint Model**.

- When converting the model, the entire process is handled
automatically. The corresponding config while at the time of the Ckpt
addition is used in the process.
- Users are presented with the choice on where to save the diffusers
converted model - same location as the ckpt, InvokeAI models root folder
or a completely custom location.

- When the model is converted, the checkpoint entry is replaced with the
diffusers model entry. A user can readd the ckpt if they wish to.
---
More or less done. Might make some minor UX improvements as I refine
things.
Tensors with diffusers no longer have to be multiples of 8. This broke Perlin noise generation. We now generate noise for the next largest multiple of 8 and return a cropped result. Fixes#2674.
`generator` now asks `InvokeAIDiffuserComponent` to do postprocessing work on latents after every step. Thresholding - now implemented as replacing latents outside of the threshold with random noise - is called at this point. This postprocessing step is also where we can hook up symmetry and other image latent manipulations in the future.
Note: code at this layer doesn't need to worry about MPS as relevant torch functions are wrapped and made MPS-safe by `generator.py`.
1. Now works with sites that produce lots of redirects, such as CIVITAI
2. Derive name of destination model file from HTTP Content-Disposition header,
if present.
3. Swap \\ for / in file paths provided by users, to hopefully fix issues with
Windows.
This PR adds a new attributer to ldm.generate, `embedding_trigger_strings`:
```
gen = Generate(...)
strings = gen.embedding_trigger_strings
strings = gen.embedding_trigger_strings()
```
The trigger strings will change when the model is updated to show only
those strings which are compatible with the current
model. Dynamically-downloaded triggers from the HF Concepts Library
will only show up after they are used for the first time. However, the
full list of concepts available for download can be retrieved
programatically like this:
```
from ldm.invoke.concepts_lib import HuggingFAceConceptsLibrary
concepts = HuggingFaceConceptsLibrary()
trigger_strings = concepts.list_concepts()
```
I have added the arabic locale files. There need to be some
modifications to the code in order to detect the language direction and
add it to the current document body properties.
For example we can use this:
import { appWithTranslation, useTranslation } from "next-i18next";
import React, { useEffect } from "react";
const { t, i18n } = useTranslation();
const direction = i18n.dir();
useEffect(() => {
document.body.dir = direction;
}, [direction]);
This should be added to the app file. It uses next-i18next to
automatically get the current language and sets the body text direction
(ltr or rtl) depending on the selected language.
## Provide informative error messages when TI and Merge scripts have
insufficient space for console UI
- The invokeai-ti and invokeai-merge scripts will crash if there is not
enough space in the console to fit the user interface (even after
responsive formatting).
- This PR intercepts the errors and prints a useful error message
advising user to make window larger.
1. The invokeai-configure script has now been refactored. The work of
selecting and downloading initial models at install time is now done
by a script named invokeai-initial-models (module
name is ldm.invoke.config.initial_model_select)
The calling arguments for invokeai-configure have not changed, so
nothing should break. After initializing the root directory, the
script calls invokeai-initial-models to let the user select the
starting models to install.
2. invokeai-initial-models puts up a console GUI with checkboxes to
indicate which models to install. It respects the --default_only
and --yes arguments so that CI will continue to work.
3. User can now edit the VAE assigned to diffusers models in the CLI.
4. Fixed a bug that caused a crash during model loading when the VAE
is set to None, rather than being empty.
- The invokeai-ti and invokeai-merge scripts will crash if there is not enough space
in the console to fit the user interface (even after responsive formatting).
- This PR intercepts the errors and prints a useful error message advising user to
make window larger.
- fix unused variables and f-strings found by pyflakes
- use global_converted_ckpts_dir() to find location of diffusers
- fixed bug in model_manager that was causing the description of converted
models to read "Optimized version of {model_name}'
Strategize slicing based on free [V]RAM when not using xformers. Free [V]RAM is evaluated at every generation. When there's enough memory, the entire generation occurs without slicing. If there is not enough free memory, we use diffusers' sliced attention.
- Adds an update action to launcher script
- This action calls new python script `invokeai-update`, which prompts
user to update to latest release version, main development version,
or an arbitrary git tag or branch name.
- It then uses `pip` to update to whatever tag was specified.
Some of the core features of this PR include:
- optional push image to dockerhub (will be skipped in repos which
didn't set it up)
- stop using the root user at runtime
- trigger builds also for update/docker/* and update/ci/docker/*
- always cache image from current branch and main branch
- separate caches for container flavors
- updated comments with instructions in build.sh and run.sh
This commit cleans up the code that did bulk imports of legacy model
files. The code has been refactored, and the user is now offered the
option of importing all the model files found in the directory, or
selecting which ones to import.
Users can now pick the folder to save their diffusers converted model. It can either be the same folder as the ckpt, or the invoke root models folder or a totally custom location.
Fixed a couple of bugs:
1. The original config file for the ckpt file is derived from the entry in
`models.yaml` rather than relying on the user to select. The implication
of this is that V2 ckpt models need to be assigned `v2-inference-v.yaml`
when they are first imported. Otherwise they won't convert right. Note
that currently V2 ckpts are imported with `v1-inference.yaml`, which
isn't right either.
2. Fixed a backslash in the output diffusers path, which was causing
load failures on Linux.
Remaining issues:
1. The radio buttons for selecting the model type are
nonfunctional. It feels to me like these should be moved into the
dialogue for importing ckpt/safetensors files, because this is
where the algorithm needs help from the user.
2. The output diffusers model is written into the same directory as
the input ckpt file. The CLI does it differently and stores the
diffusers model in `ROOTDIR/models/converted-ckpts`. We should
settle on one way or the other.
Converted the picker options to a Radio Group and also updated the backend to use the appropriate config if it is a v2 model that needs to be converted.
- This PR introduces a CLI prompt for the proper configuration file to
use when converting a ckpt file, in order to support both inpainting
and v2 models files.
- When user tries to directly !import a v2 model, it prints out a proper
warning that v2 ckpts are not directly supported.
## What was the problem/requirement? (What/Why)
* Windows location for the Python environment activate location is
currently incorrect
* Due to this, this command will fail for Windows-based users
* The contributing link within the `Developer Install` sections leads to
a [404](https://invoke-ai.github.io/index.md#Contributing)
* `Developer Install`'s numbered list currently lists 1, 1, 2, . . .
## What was the solution? (How)
* Changed the location of Windows script based on actual location -
[reference](https://docs.python.org/3/library/venv.html)
* Moved the link to point to one directory higher -- the main index.md
* Minor format adjustments to allow for the numbered list to appear as
expected
## How were these changes tested?
* `mkdocs serve` => Verified on local server that the changes reflected
as expected
## Notes
Contributing mentions to set the upstream towards the `development`
branch, but that branch has been untouched for several months, so I've
pointed to the `main` branch. Let me know if we need to switch to a
different one.
…odels
- If CLI asked to convert the currently loaded model, the model would
crash on the first rendering. CLI will now refuse to convert a model
loaded in memory (probably a good idea in any case).
- CLI will offer the `v1-inpainting-inference.yaml` as the configuration
file when importing an inpainting a .ckpt or .safetensors file that has
"inpainting" in the name. Otherwise it offers `v1-inference.yaml` as the
default.
rather than bypassing any path with diffusers in it, im specifically bypassing model.safetensors and diffusion_pytorch_model.safetensors both of which should be diffusers files in most cases.
- If CLI asked to convert the currently loaded model, the model would crash
on the first rendering. CLI will now refuse to convert a model loaded
in memory (probably a good idea in any case).
- CLI will offer the `v1-inpainting-inference.yaml` as the configuration
file when importing an inpainting a .ckpt or .safetensors file that
has "inpainting" in the name. Otherwise it offers `v1-inference.yaml`
as the default.
Found a couple of places where the formatting was messed up. I also
added a "Quick Start Guide" to the README for people who encounter
InvokeAI through PyPi. It features the PyPi install!
pulling in denoising support from upstream (its already there, invoke
just isn't using it). I've enabled this as a command line argument as
construction of the ESRGAN handler happens once. Ideally this would be a
UI option that could be adjusted for each upscaling task. Unfortunately
that is beyond my current level of InvokeAI-foo.
Upstream reference is here, starting on line 99 "use dni to control the
denoise strength"
https://github.com/xinntao/Real-ESRGAN/blob/master/inference_realesrgan.py
- This makes the launcher options menu on Windows look and act the same
as the Linux/Mac launcher, which previously was lacking the command-line
help option and didn't list item (6) as an option.
Work in progress. I am reviewing and updating the documentation for
2.3.0. The following sections need to be done:
- [x] index.md
- [x] installation/010_INSTALL_AUTOMATED.md
- [x] installation/020_INSTALL_MANUAL.md
- [x] installation/030_INSTALL_CUDA_AND_ROCM.md (needs to be written
from scratch)
- [x] installation/040_INSTALL_DOCKER.md
- [x] installation/050_INSTALLING_MODELS.md
- [x] features/CLI.md
- [x] features/WEB.md
Using Windows 10 I found I needed to use double backslashes to import a
new model, when using single backslash the output would say
"e:_ProjectsCodemodelsldmstable-diffusion-model-to-import.ckpt is
neither the path to a .ckpt file nor a diffusers repository id. Can't
import." This added tip in the documentation will help Windows users
overcome this.
- The following were supposed to be equivalent, but the latter crashes:
```
invoke> banana sushi
invoke> --prompt="banana sushi"
```
This PR fixes the problem.
- Fixes#2548
- This makes the launcher options menu on Windows look and act the same
as the Linux/Mac launcher, which previously was lacking the command-line
help option and didn't list item (6) as an option.
The `useHotkeys` hook for this hotkey didn't have `isConnected` or `isProcessing` in its dependencies array. This prevented `handleDelete()` from dispatching the delete request.
This is an early draft of a codeowners file for InvokeAI. It has plenty
of gaps in it. Please use this PR to add yourself and others where
appropriate.
- The following were supposed to be equivalent, but the latter crashes:
```
invoke> banana sushi
invoke> --prompt="banana sushi"
```
This PR fixes the problem.
- Fixes#2548
This adds some platform-specific help messages to the installer welcome
screen:
- For Windows, the message encourages them to install VC++ core
libraries and the registry long name patch
- For MacOSX, the message warns the user to install the XCode tools.
I found I needed to use double backslashes to import a new model, when using single backslash the output would say "e:_ProjectsCodemodelsldmstable-diffusion-model-to-import.ckpt is neither the path to a .ckpt file nor a diffusers repository id. Can't import." This added tip in the documentation will help Windows users overcome this.
- `eslint` and `prettier` configs
- `husky` to format and lint via pre-commit hook
- `babel-plugin-transform-imports` to treeshake `lodash` and other packages if needed
Lints and formats codebase.
`options` slice was huge and managed a mix of generation parameters and general app settings. It has been split up:
- Generation parameters are now in `generationSlice`.
- Postprocessing parameters are now in `postprocessingSlice`
- UI related things are now in `uiSlice`
There is probably more to be done, like `gallerySlice` perhaps should only manage internal gallery state, and not if the gallery is displayed.
Full-slice selectors have been made for each slice.
Other organisational tweaks.
Previously conversions of .ckpt and .safetensors files to diffusers
models were failing with channel mismatch errors. This is corrected
with this PR.
- The model_manager convert_and_import() method now accepts the path
to the checkpoint file's configuration file, using the parameter
`original_config_file`. For inpainting files this should be set to
the full path to `v1-inpainting-inference.yaml`.
- If no configuration file is provided in the call, then the presence
of an inpainting file will be inferred at the
`ldm.ckpt_to_diffuser.convert_ckpt_to_diffUser()` level by looking
for the string "inpaint" in the path. AUTO1111 does something
similar to this, but it is brittle and not recommended.
- This PR also changes the model manager model_names() method to return
the model names in case folded sort order.
- `eslint` and `prettier` configs
- `husky` to format and lint via pre-commit hook
- `babel-plugin-transform-imports` to treeshake `lodash` and other packages if needed
Lints and formats codebase.
`options` slice was huge and managed a mix of generation parameters and general app settings. It has been split up:
- Generation parameters are now in `generationSlice`.
- Postprocessing parameters are now in `postprocessingSlice`
- UI related things are now in `uiSlice`
There is probably more to be done, like `gallerySlice` perhaps should only manage internal gallery state, and not if the gallery is displayed.
Full-slice selectors have been made for each slice.
Other organisational tweaks.
# enhance model_manager support for converting inpainting ckpt files
Previously conversions of .ckpt and .safetensors files to diffusers
models were failing with channel mismatch errors. This is corrected
with this PR.
- The model_manager convert_and_import() method now accepts the path
to the checkpoint file's configuration file, using the parameter
`original_config_file`. For inpainting files this should be set to
the full path to `v1-inpainting-inference.yaml`.
- If no configuration file is provided in the call, then the presence
of an inpainting file will be inferred at the
`ldm.ckpt_to_diffuser.convert_ckpt_to_diffUser()` level by looking
for the string "inpaint" in the path. AUTO1111 does something
similar to this, but it is brittle and not recommended.
- This PR also changes the model manager model_names() method to return
the model names in case folded sort order.
- Diffusers Sampler list is independent from CKPT Sampler list. And the
app will load the correct list based on what model you have loaded.
- Isolated the activeModelSelector coz this is used in multiple places.
- Possible fix to the white screen bug that some users face. This was
happening because of a possible null in the active model list
description tag. Which should hopefully now be fixed with the new
activeModelSelector.
I'll keep tabs on the last thing. Good to go.
For the torch and torchvision libraries **only**, the installer will now
pass the pip `--force-reinstall` option. This is intended to fix issues
with the user getting a CPU-only version of torch and then not being
able to replace it.
Previously conversions of .ckpt and .safetensors files to diffusers
models were failing with channel mismatch errors. This is corrected
with this PR.
- The model_manager convert_and_import() method now accepts the path
to the checkpoint file's configuration file, using the parameter
`original_config_file`. For inpainting files this should be set to
the full path to `v1-inpainting-inference.yaml`.
- If no configuration file is provided in the call, then the presence
of an inpainting file will be inferred at the
`ldm.ckpt_to_diffuser.convert_ckpt_to_diffUser()` level by looking
for the string "inpaint" in the path. AUTO1111 does something
similar to this, but it is brittle and not recommended.
- This PR also changes the model manager model_names() method to return
the model names in case folded sort order.
test-invoke-pip.yml:
- enable caching of pip dependencies in `actions/setup-python@v4`
- add workflow_dispatch trigger
- fix indentation in concurrency
- set env `PIP_USE_PEP517: '1'`
- cache python dependencies
- remove models cache (since we currently use 190.96 GB of 10 GB while I
am writing this)
- add step to set `INVOKEAI_OUTDIR`
- add outdir arg to invokeai
- fix path in archive results
model_manager.py:
- read files in chunks when calculating sha (windows runner is crashing
otherwise)
- help users to avoid glossing over per-platform prerequisites
- better link colouring
- update link to community instructions to install xcode command line tools
- Issue is that if insufficient diffusers models are defined in
models.yaml the frontend would ungraciously crash.
- Now it emits appropriate error messages telling user what the problem
is.
- Issue is that if insufficient diffusers models are defined in
models.yaml the frontend would ungraciously crash.
- Now it emits appropriate error messages telling user what the problem
is.
- dont build frontend since complications with QEMU
- set pip cache dir
- add pip cache to all pip related build steps
- dont lock pip cache
- update dockerignore to exclude uneeded files
env.sh:
- move check for torch to CONVTAINER_FLAVOR detection
Dockerfile
- only mount `/var/cache/apt` for apt related steps
- remove `docker-clean` from `/etc/apt/apt.conf.d` for BuildKit cache
- remove apt-get clean for BuildKit cache
- only copy frontend to frontend-builder
- mount `/usr/local/share/.cache/yarn` in frountend-builder
- separate steps for yarn install and yarn build
- build pytorch in pyproject-builder
build.sh
- prepare for installation with extras
This change allows passing a directory with multiple models in it to be
imported.
Ensures that diffusers directories will still work.
Fixed up some minor type issues.
This allows the --log_tokenization option to be used as a command line
argument (or from invokeai.init), making it possible to view
tokenization information in the terminal when using the web interface.
- This fixes an edge case crash when the textual inversion frontend
tried to display the list of models and no default model defined
in models.yaml
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
This allows the --log_tokenization option to be used as a command line argument (or from invokeai.init), making it possible to view tokenization information in the terminal when using the web interface.
- Rename configure_invokeai.py to invokeai_configure.py to be consistent
with installed script name
- Remove warning message about half-precision models not being available
during the model download process.
- adjust estimated file size reported by configure
- guesstimate disk space needed for "all" models
- fix up the "latest" tag to be named 'v2.3-latest'
- To ensure a clean environment, the installer will now detect whether a
previous .venv exists in the install location, and move it to .venv-backup
before creating a fresh .venv.
- Any previous .venv-backup is deleted.
- User is informed of process.
- Rename configure_invokeai.py to invokeai_configure.py to be
consistent with installed script name
- Remove warning message about half-precision models not being
available during the model download process.
- adjust estimated file size reported by configure
- guesstimate disk space needed for "all" models
- fix up the "latest" tag to be named 'v2.3-latest'
`torch` wasn't seeing the environment variable. I suspect this is
because it was imported before the variable was set, so was running with
a different environment.
Many `torch` ops are supported on MPS so this wasn't noticed
immediately, but some samplers like k_dpm_2 still use unsupported
operations and need this fallback.
This PR forces the installer to install the official torch-cu117 wheel
from download.torch.org, rather than relying on PyPi.org to return the
correct version. It ought to correct the problems that some people have
experienced with cuda support not being installed.
1. The convert module was converting ckpt models into
StableDiffusionGeneratorPipeline objects for use in-memory, but then
when saved to disk created files that could not be merged with
StableDiffusionPipeline models. I have added a flag that selects which
pipeline class to return, so that both in-memory and disk conversions
work properly.
2. This PR also fixes an issue with `invoke.sh` not using the correct
path for the textual inversion and merge scripts.
3. Quench nags during the merge process about the safety checker being
turned off.
`torch` wasn't seeing the environment variable. I suspect this is because it was imported before the variable was set, so was running with a different environment.
Many `torch` ops are supported on MPS so this wasn't noticed immediately, but some samplers like k_dpm_2 still use unsupported operations and need this fallback.
* remove non maintained Dockerfile
* adapt Docker related files to latest changes
- also build the frontend when building the image
- skip user response if INVOKE_MODEL_RECONFIGURE is set
- split INVOKE_MODEL_RECONFIGURE to support more than one argument
* rename `docker-build` dir to `docker`
* update build-container.yml
- rename image to invokeai
- add cpu flavor
- add metadata to build summary
- enable caching
- remove build-cloud-img.yml
* fix yarn cache path, link copyjob
Crashes would occur in the invokeai-configure script if no HF token
was found in cache and the user declines to provide one when prompted.
The reason appears to be that on Linux systems getpass_asterisk()
raises an EOFError when no input is provided
On windows10, getpass_asterisk() does not raise the EOFError, but
returns an empty string instead. This patch detects this and raises
the exception so that the control logic is preserved.
if reinstalling over an existing installation where the .venv was
created with symlinks to system python instead of copies of the python
executable, the installer would raise a `SameFileError`, because it
would attempt to copy Python over itself. This fixes the issue.
Copying the executable is still preferred for new environments, because
this guarantees the stable Python version.
- fixes bug in finding the source of the configs dir;
- updates the docs for manual install to clarify the preference to
keeping the `.venv` inside the runtime dir, and the caveat/extra steps
required if done otherwise
if reinstalling over an existing installation where the .venv
was created with symlinks to system python instead of copies
of the python executable, the installer would raise a
SameFileError, because it would attempt to copy Python over
itself. This fixes the issue.
- Added modest adaptive behavior; if the screen is wide enough the three
checklists of models will be arranged in a horizontal row.
- Added color support
## Summary
This PR rewrites the core of the installer in Python for cross-platform
compatibility. Filesystem path manipulation, platform/arch decisions and
various edge cases are handled in a more convenient fashion. The
original `install.bat.in`/`install.sh.in` scripts are kept as
entrypoints for their respective OSs, but only serve as thin wrappers to
the Python module.
In addition, it:
- builds and **packages the .whl with the installer**, so that
downloading a versioned installer will guarantee installation of the
same version of the application.
- updates shell entrypoints:
- new commands are `invokeai`, `invokeai-configure`, `invokeai-ti`,
`invokeai-merge`.
- these commands will be available in the activated `.venv` or via the
launch scripts
- `invoke.py` and `configure_invokeai.py` scripts are deprecated but
kept around for backwards compatibility and keeping users' surprise to a
minimum.
- introduces a new `ldm/invoke/config` package and moves the
`configure_invokeai` script into it. Similarly, movers Textual Inversion
script and TUI to `ldm/invoke/training`.
- moves the `configs` directory into the `ldm/invoke/config` package for
easy distribution.
- updates documentation to reflect all of the above changes
- fixes a failing test
- reduces wheel size to 3MB (from 27MB) by excluding unnecessary image
files under `assets`
⚠️ self-updating functionality and ability to install arbitrary
versions are still WIP. For now we can recommend downloading and running
the installer for a specific version as desired.
## Testing the source install
From the cloned source, check out this branch, and:
`$ python3 installer/main.py --root <path_to_destination>`
Also try:
`$ python3 installer/main.py ` - will prompt for paths
`$ python3 installer/main.py --yes` - will not prompt for any input
- try to combine the `--yes` and `--root` options
- try to install in destinations with "quirky" paths, such as paths
containing spaces in the directory name, etc.
## Testing the packaged install ("Automated Installer"):
Download the
[InvokeAI-installer-v2.3.0+a0.zip](https://github.com/invoke-ai/InvokeAI/files/10533913/InvokeAI-installer-v2.3.0%2Ba0.zip)
file, unzip it, and run the install script for your platform (preferably
in a terminal window)
OR make your own: from the cloned source, check out this branch, and:
```
cd installer
./create_installer.sh
# (do NOT tag/push when prompted! just say "no")
```
This will create the installation media:
`InvokeAI-installer-v2.3.0+a0.zip`. The installer is now
*platform-agnostic* - meaning, both Windows and *nix install resources
are packaged together.
Copy it somewhere as if it had been downloaded from the internet. Unzip
the file, enter the created `InvokeAI-Installer` directory, and run
`install.sh` or `install.bat` as applicable your platform.
⚠️ NOTE!!! `install.sh` accepts the same arguments as are
applicable to the Python script, i.e. you can `install.sh --yes --root
....`. This is NOT yet supported by the Windows `.bat` script. Only
interactive installation is supported on Windows. (this is still a
TODO).
* refactor ckpt_to_diffuser to allow converted pipeline to remain in memory
- This idea was introduced by Damian
- Note that although I attempted to use the updated HuggingFace module
pipelines/stable_diffusion/convert_from_ckpt.py, it was unable to
convert safetensors files for reasons I didn't dig into.
- Default is to extract EMA weights.
* add --ckpt_convert option to load legacy ckpt files as diffusers models
- not quite working - I'm getting artifacts and glitches in the
converted diffuser models
- leave as draft for time being
* do not include safety checker in converted files
* add ability to control which vae is used
API now allows the caller to pass an external VAE model to the
checkpoint conversion process. In this way, if an external VAE is
specified in the checkpoint's config stanza, this VAE will be used
when constructing the diffusers model.
Tested with both regular and inpainting 1.X models.
Not tested with SD 2.X models!
---------
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
Co-authored-by: Damian Stewart <null@damianstewart.com>
This PR changes the codeowner for the installer directory from
@tildebyte to @ebr due to the former's time commitments.
Further reorganization of the codeowners is pending.
1. only load triton on linux machines
2. require pip >= 23.0 so that editable installs can run without setup.py
3. model files default to SD-1.5, not 2.1
4. use diffusers model of inpainting rather than ckpt
5. selected a new set of initial models based on # of likes at huggingface
- launcher scripts are installed *before* the configure script runs,
so that if something goes wrong in the configure script, the user
can run invoke.{sh,bat} and get the option to re-run configure
- fixed typo in invoke.sh which misspelled name of invokeai-configure
Draft PRs are triggering actions on every commit (except
`test-invoke-pip.yml`).
I've added a conditional to each job to only run when the PR is not a
draft.
(maybe there is a reason we are running all applicable workflows on
draft PRs?)
- also remove conda related things
- rename `invoke` to `invokeai`
- rename `configure_invokeai` to `invokeai-configure`
- rename venv back to common `.venv` but add `--prompt InvokeAI`
- remove outdated information
A new infill method, **solid:** solid color. currently using middle
gray.
Fixes#2417
It seems like the runwayml inpainting model specifically expects those
masked areas to be blanked out like this.
I haven't tried the SD 2.0 inpainting model with it yet.
Otherwise the model seems too reluctant to change these areas, even
though the mask channel should allow it to.
This makes the solid infill method proposed by #2441 less necessary,
though I think there's still a place for an infill method that is faster
than patchmatch and more predictable than tiles.
Even with #2441, this PR is still useful because it influences all areas
to be painted, not just the infill area.
Fixes#2417
- implement the following pattern for finding data files under both
regular and editable install conditions:
import invokeai.foo.bar as bar
path = bar.__path__[0]
- this *seems* to work reliably with Python 3.9. Testing on 3.10 needs
to be performed.
- fixes a spurious "unknown model name" error when trying to edit the
short name of an existing model.
- relaxes naming requirements to include the ':' and '/' characters
in model names
1) Downgrade numpy to avoid dependency conflict with numba
2) Move all non ldm/invoke files into `invokeai`. This includes assets, backend, frontend, and configs.
3) Fix up way that the backend finds the frontend and the generator finds the NSFW caution.png icon.
if running `python3 installer/main.py` from the source distribution,
it would fail because it expected to find a wheel.
this PR tries to perform a source install by going one level up the directory
tree and checking for `pyproject.toml` and `ldm` directory entries to
confirm (to a degree) that this is an InvokeAI distribution
* Update --hires_fix
Change `--hires_fix` to calculate initial width and height based on the model's resolution (if available) and with a minimum size.
- install.sh is now a thin wrapper around the pythonized install script
- install.bat not done yet - to follow
- user messaging is tailored to the current platform (paste shortcuts, file paths, etc)
- emit invoke.sh/invoke.bat scripts to the runtime dir
- improve launch scripts (add help option, etc)
- only emit the platform-specific scripts
if the config directory is missing, initialize it using the standard
process of copying it over, instead of failing to create the config file
this can happen if the user is re-running the config script in a directory which
already has the init file, but no configs dir
the 'setup.py install' method is deprecated in favour of a
build-system independent format: https://peps.python.org/pep-0517/
this is needed to install dependencies that don't have a pyproject.toml
file (only setup.py) in a forward-compatible way
This allows reliable distribution of the initial 'configs' directory
with the Python package, and enables the configuration script to be running
from anywhere, as long as the virtual environment is available on the sys.path
There is a race condition affecting the 'tempfile' module on Windows.
A PermissionsError is raised when cleaning up the temp dir
Python3.10 introduced a flag to suppress this error.
Windows + Python3.9 users will receive an unpleasant stack trace for now
The original textual inversion script in scripts is now superseded. The
replacement can be found in ldm/invoke/textual_inversion.py and is a
merging of the command line and front end scripts. After running `pip
install -e .` there will be a `textual_inversion` command on your path.
You can activate the front end this way:
`textual_inversion -gui`
Adds double-click to reset canvas view to 100%.
- Adds hook to manage single and double clicks
- Single Click `Reset Canvas View` --> scale to fit, no change to
current behaviour
- Double Click `Reset Canvas View` --> set scale to 1
Testing suggests that the diffusers versions of Waifu-1.4 anything-v4.0
require the `sd-vae-ft-mse` to generate decent images, so the
appropriate arguments have been added to the initial model file.
- Model merging and textual inversion scripts have been moved into
`ldm/invoke`, which allows them to be installed properly by
pyproject.toml.
- As part of the pyproject install, the .py suffix is removed from the
command. I.e. use `invoke`, `configure_invokeai`, `merge_models` and
`textual_inversion`.
- GUI versions are activated by adding `--gui` to the command. Without
this, you get a classical argv-based command. Example: `merge_models
--gui`
- Fixed up the launcher scripts to accommodate new naming scheme.
- Keyboard behavior of the GUI front ends has been improved. You can now
use up and down arrow to move from field to field, in addition to <tab>
and ctrl-N/ctrl-P
So far the slider component was unable to take typed input due to a
bunch of issues that were a pain to solve. This PR fixes it.
Things to test:
- Moving the slider also updates the value in the input text box.
- Input text box next to slider can be changed in two ways: If you type
a manual value, the slider will be updated when you lose focus from the
input box. If you use the stepper icons to update the values, the slider
should update immediately.
- Make sure the reset buttons next to the slider are updating correctly
and make sure this updates both the slider and the input box values.
- Brush Size slider -> make sure the hotkeys are updating the input box
too.
- This replaces the original clipseg library with the transformers
version from HuggingFace.
- This should make it possible to register InvokeAI at PyPi and do a
fully automated pip-based install.
- Minor regression: it is no longer possible to specify which device the
clipseg model will be loaded into, and it will reside in CPU. However,
performance is more than acceptable.
- This replaces the original clipseg library with the transformers
version from HuggingFace.
- This should make it possible to register InvokeAI at PyPi and do
a fully automated pip-based install.
- Minor regression: it is no longer possible to specify which device
the clipseg model will be loaded into, and it will reside in CPU.
However, performance is more than acceptable.
Fix two deficiencies in the CLI's support for model management:
1. `!import_model` did not allow user to specify VAE file. This is now
fixed.
2. `!del_model` did not offer the user the opportunity to delete the
underlying
weights file or diffusers directory. This is now fixed.
This PR improves the console reporting of the process of recognizing
trigger tokens and loading their embeds.
1. Do not report "concept is not known to HuggingFace" if the trigger
term is in fact a local embedding trigger.
2. When a trigger term is first recognized during a session, report the
fact.
This should help debug embedding issues in the future.
Note that the local embeddings produced by the new InvokeAI TI training
script default to the format <trigger> with literal angle brackets. This
sets them off from the rest of the text well and will enable
autocomplete at some point in the future. However, this means that they
supersede like-named HuggingFace concepts, and may cause problems for
people uploading them to the HuggingFace repository (although that
problem already exists).
This PR attempts to fix `--free_gpu_mem` option that was not working in
CKPT-based diffuser model after #1583.
I noticed that the memory usage after #1583 did not decrease after
generating an image when `--free_gpu_mem` option was enabled.
It turns out that the option was not propagated into `Generator`
instance, hence the generation will always run without the memory saving
procedure.
This PR also related to #2326. Initially, I was trying to make
`--free_gpu_mem` works on 🤗 diffuser model as well.
In the process, I noticed that InvokeAI will raise an exception when
`--free_gpu_mem` is enabled.
I tried to quickly fix it by simply ignoring the exception and produce a
warning message to user's console.
This PR adds `scripts/merge_fe.py`, which will merge any 2-3 diffusers
models registered in InvokeAI's `models.yaml`, producing a new merged
model that will be registered as well.
Currently this script will only work if all models to be merged are
known by their repo_ids. Local models, including those converted from
ckpt files, will cause a crash due to a bug in the diffusers
`checkpoint_merger.py` code. I have made a PR against
huggingface/diffusers which fixes this:
https://github.com/huggingface/diffusers/pull/2060
I've written up the install procedure for xFormers on Linux systems.
I need help with the Windows install; I don't know what the build
dependencies (compiler, etc) are. This section of the docs is currently
empty.
Please see `docs/installation/070_INSTALL_XFORMERS.md`
other changes which where required:
- move configure_invokeai.py into ldm.invoke
- update files which imported configure_invokeai to use new location:
- ldm/invoke/CLI.py
- scripts/load_models.py
- scripts/preload_models.py
- update test-invoke-pip.yml:
- remove pr type "converted_to_draft"
- remove reference to dev/diffusers
- remove no more needed requirements from matrix
- add pytorch to matrix
- install via `pip3 install --use-pep517 .`
- use the created executables
- this should also fix configure_invoke not executed in windows
To install use `pip install --use-pep517 -e .` where `-e` is optional
- Added new documentation for textual inversion training process
- Move `main.py` into the deprecated scripts folder
- Fix bug in `textual_inversion.py` which was causing it to not load
the globals module correctly.
- Sort models alphabetically in console front end
- Only show diffusers models in console front end
Starting `invoke.py` with --no-xformers will disable
memory-efficient-attention support if xformers is installed.
For symmetry, `--xformers` will enable support, but this is already the
default if xformers is available.
This commit suppresses a few irrelevant warning messages that the
diffusers module produces:
1. The warning that turning off the NSFW detector makes you an
irresponsible person.
2. Warnings about running fp16 models stored in CPU (we are not running
them in CPU, just caching them in CPU RAM)
This commit suppresses a few irrelevant warning messages that the
diffusers module produces:
1. The warning that turning off the NSFW detector makes you an
irresponsible person.
2. Warnings about running fp16 models stored in CPU (we are not running
them in CPU, just caching them in CPU RAM)
Starting `invoke.py` with --no-xformers will disable
memory-efficient-attention support if xformers is installed.
--xformers will enable support, but this is already the
default.
- During trigger token processing, emit better status messages indicating
which triggers were found.
- Suppress message "<token> is not known to HuggingFace library, when
token is in fact a local embed.
- When a ckpt or safetensors file uses an external autoencoder and we
don't know which diffusers model corresponds to this (if any!), then
we fallback to using stabilityai/sd-vae-ft-mse
- This commit improves error reporting so that user knows what is happening.
- After successfully converting a ckt file to diffusers, model_manager
will attempt to create an equivalent 'vae' entry to the resulting
diffusers stanza.
- This is a bit of a hack, as it relies on a hard-coded dictionary
to map ckpt VAEs to diffusers VAEs. The correct way to do this
would be to convert the VAE to a diffusers model and then point
to that. But since (almost) all models are using vae-ft-mse-840000-ema-pruned,
I did it the easy way first and will work on the better solution later.
1. !import_model did not allow user to specify VAE file. This is now fixed.
2. !del_model did not offer the user the opportunity to delete the underlying
weights file or diffusers directory. This is now fixed.
This commit allows InvokeAI to store & load 🤗 models at a location set
by `XDG_CACHE_HOME` environment variable if `HF_HOME` is not set.
By integrating this commit, a user who either use `HF_HOME` or
`XDG_CACHE_HOME` environment variables in their environment can let
InvokeAI to reuse the existing cache directory used by 🤗 library by
default. I happened to benefit from this commit because I have a Jupyter
Notebook that uses 🤗 diffusers model stored at `XDG_CACHE_HOME`
directory.
Reference:
https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
Updated the link for the MS Visual C libraries - I'm not sure if MS
changed the location of the files but this new one leads right to the
file downloads.
- Migration process will not crash if duplicate model files are found,
one in legacy location and the other in new location. The model in the
legacy location will be deleted in this case.
- Added a hint to stable-diffusion-2.1 telling people it will work best
with 768 pixel images.
- Added the anything-4.0 model.
Added a --default_only argument that limits model downloads to the
single default model, for use in continuous integration.
New behavior
- switch -
--yes --default_only Behavior
----- -------------- --------
<not set> <not set> interactive download
--yes <not set> non-interactively download all
recommended models
--yes --default_only non-interactively download the
default model
Added a --default_only argument that limits model downloads to the single
default model, for use in continuous integration.
New behavior
- switch -
--yes --default_only Behavior
----- -------------- --------
<not set> <not set> interactive download
--yes <not set> non-interactively download all
recommended models
--yes --default_only non-interactively download the
default model
- All tensors in diffusers code path are now set explicitly to
float32 or float16, depending on the --precision flag.
- autocast is still used in the ckpt path, since it is being
deprecated.
- Work around problem with OmegaConf.update() that prevented model names
from containing periods.
- Fix logic bug in !delete_model that didn't check for existence of model
in config file.
* docs: Fix links to pip and Conda installation methods
* docs: Improve installation script readability
This commit adds a space between `-m` option and the module name.
* docs: Fix alignments of step 4 & 9 in `pip` installation method
* docs: Rewrite step 10 of the ` pip` installation method
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
- Migration process will not crash if duplicate model files are found,
one in legacy location and the other in new location.
The model in the legacy location will be deleted in this case.
- Added a hint to stable-diffusion-2.1 telling people it will work best
with 768 pixel images.
- Added the anything-4.0 model.
* initial commit of DiffusionPipeline class
* spike: proof of concept using diffusers for txt2img
* doc: type hints for Generator
* refactor(model_cache): factor out load_ckpt
* model_cache: add ability to load a diffusers model pipeline
and update associated things in Generate & Generator to not instantly fail when that happens
* model_cache: fix model default image dimensions
* txt2img: support switching diffusers schedulers
* diffusers: let the scheduler do its scaling of the initial latents
Remove IPNDM scheduler; it is not behaving.
* web server: update image_progress callback for diffusers data
* diffusers: restore prompt weighting feature
* diffusers: fix set-sampler error following model switch
* diffusers: use InvokeAIDiffuserComponent for conditioning
* cross_attention_control: stub (no-op) implementations for diffusers
* model_cache: let offload_model work with DiffusionPipeline, sorta.
* models.yaml.example: add diffusers-format model, set as default
* test-invoke-conda: use diffusers-format model
test-invoke-conda: put huggingface-token where the library can use it
* environment-mac: upgrade to diffusers 0.7 (from 0.6)
this was already done for linux; mac must have been lost in the merge.
* preload_models: explicitly load diffusers models
In non-interactive mode too, as long as you're logged in.
* fix(model_cache): don't check `model.config` in diffusers format
clean-up from recent merge.
* diffusers integration: support img2img
* dev: upgrade to diffusers 0.8 (from 0.7.1)
We get to remove some code by using methods that were factored out in the base class.
* refactor: remove backported img2img.get_timesteps
now that we can use it directly from diffusers 0.8.1
* ci: use diffusers model
* dev: upgrade to diffusers 0.9 (from 0.8.1)
* lint: correct annotations for Python 3.9.
* lint: correct AttributeError.name reference for Python 3.9.
* CI: prefer diffusers-1.4 because it no longer requires a token
The RunwayML models still do.
* build: there's yet another place to update requirements?
* configure: try to download models even without token
Models in the CompVis and stabilityai repos no longer require them. (But runwayml still does.)
* configure: add troubleshooting info for config-not-found
* fix(configure): prepend root to config path
* fix(configure): remove second `default: true` from models example
* CI: simplify test-on-push logic now that we don't need secrets
The "test on push but only in forks" logic was only necessary when tests didn't work for PRs-from-forks.
* create an embedding_manager for diffusers
* internal: avoid importing diffusers DummyObject
see https://github.com/huggingface/diffusers/issues/1479
* fix "config attributes…not expected" diffusers warnings.
* fix deprecated scheduler construction
* work around an apparent MPS torch bug that causes conditioning to have no effect
* 🚧 post-rebase repair
* preliminary support for outpainting (no masking yet)
* monkey-patch diffusers.attention and use Invoke lowvram code
* add always_use_cpu arg to bypass MPS
* add cross-attention control support to diffusers (fails on MPS)
For unknown reasons MPS produces garbage output with .swap(). Use
--always_use_cpu arg to invoke.py for now to test this code on MPS.
* diffusers support for the inpainting model
* fix debug_image to not crash with non-RGB images.
* inpainting for the normal model [WIP]
This seems to be performing well until the LAST STEP, at which point it dissolves to confetti.
* fix off-by-one bug in cross-attention-control (#1774)
prompt token sequences begin with a "beginning-of-sequence" marker <bos> and end with a repeated "end-of-sequence" marker <eos> - to make a default prompt length of <bos> + 75 prompt tokens + <eos>. the .swap() code was failing to take the column for <bos> at index 0 into account. the changes here do that, and also add extra handling for a single <eos> (which may be redundant but which is included for completeness).
based on my understanding and some assumptions about how this all works, the reason .swap() nevertheless seemed to do the right thing, to some extent, is because over multiple steps the conditioning process in Stable Diffusion operates as a feedback loop. a change to token n-1 has flow-on effects to how the [1x4x64x64] latent tensor is modified by all the tokens after it, - and as the next step is processed, all the tokens before it as well. intuitively, a token's conditioning effects "echo" throughout the whole length of the prompt. so even though the token at n-1 was being edited when what the user actually wanted was to edit the token at n, it nevertheless still had some non-negligible effect, in roughly the right direction, often enough that it seemed like it was working properly.
* refactor common CrossAttention stuff into a mixin so that the old ldm code can still work if necessary
* inpainting for the normal model. I think it works this time.
* diffusers: reset num_vectors_per_token
sync with 44a0055571
* diffusers: txt2img2img (hires_fix)
with so much slicing and dicing of pipeline methods to stitch them together
* refactor(diffusers): reduce some code duplication amongst the different tasks
* fixup! refactor(diffusers): reduce some code duplication amongst the different tasks
* diffusers: enable DPMSolver++ scheduler
* diffusers: upgrade to diffusers 0.10, add Heun scheduler
* diffusers(ModelCache): stopgap to make from_cpu compatible with diffusers
* CI: default to diffusers-1.5 now that runwayml token requirement is gone
* diffusers: update to 0.10 (and transformers to 4.25)
* diffusers: use xformers when available
diffusers no longer auto-enables this as of 0.10.2.
* diffusers: make masked img2img behave better with multi-step schedulers
re-randomizing the noise each step was confusing them.
* diffusers: work more better with more models.
fixed relative path problem with local models.
fixed models on hub not always having a `fp16` branch.
* diffusers: stopgap fix for attention_maps_callback crash after recent merge
* fixup import merge conflicts
correction for 061c5369a2
* test: add tests/inpainting inputs for masked img2img
* diffusers(AddsMaskedGuidance): partial fix for k-schedulers
Prevents them from crashing, but results are still hot garbage.
* fix --safety_checker arg parsing
and add note to diffusers loader about where safety checker gets called
* generate: fix import error
* CI: don't try to read the old init location
* diffusers: support loading an alternate VAE
* CI: remove sh-syntax if-statement so it doesn't crash powershell
* CI: fold strings in yaml because backslash is not line-continuation in powershell
* attention maps callback stuff for diffusers
* build: fix syntax error in environment-mac
* diffusers: add INITIAL_MODELS with diffusers-compatible repos
* re-enable the embedding manager; closes#1778
* Squashed commit of the following:
commit e4a956abc37fcb5cf188388b76b617bc5c8fda7d
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 15:43:07 2022 +0100
import new load handling from EmbeddingManager and cleanup
commit c4abe91a5ba0d415b45bf734068385668b7a66e6
Merge: 032e856e 1efc6397
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 15:09:53 2022 +0100
Merge branch 'feature_textual_inversion_mgr' into dev/diffusers_with_textual_inversion_manager
commit 032e856eefb3bbc39534f5daafd25764bcfcef8b
Merge: 8b4f0fe9 bc515e24
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 15:08:01 2022 +0100
Merge remote-tracking branch 'upstream/dev/diffusers' into dev/diffusers_with_textual_inversion_manager
commit 1efc6397fc6e61c1aff4b0258b93089d61de5955
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 15:04:28 2022 +0100
cleanup and add performance notes
commit e400f804ac471a0ca2ba432fd658778b20c7bdab
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 14:45:07 2022 +0100
fix bug and update unit tests
commit deb9ae0ae1016750e93ce8275734061f7285a231
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 14:28:29 2022 +0100
textual inversion manager seems to work
commit 162e02505dec777e91a983c4d0fb52e950d25ff0
Merge: cbad4583 12769b3d
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 11:58:03 2022 +0100
Merge branch 'main' into feature_textual_inversion_mgr
commit cbad45836c6aace6871a90f2621a953f49433131
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 11:54:10 2022 +0100
use position embeddings
commit 070344c69b0e0db340a183857d0a787b348681d3
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 11:53:47 2022 +0100
Don't crash CLI on exceptions
commit b035ac8c6772dfd9ba41b8eeb9103181cda028f8
Author: Damian Stewart <d@damianstewart.com>
Date: Sun Dec 18 11:11:55 2022 +0100
add missing position_embeddings
commit 12769b3d3562ef71e0f54946b532ad077e10043c
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 13:33:25 2022 +0100
debugging why it don't work
commit bafb7215eabe1515ca5e8388fd3bb2f3ac5362cf
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 13:21:33 2022 +0100
debugging why it don't work
commit 664a6e9e14
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 12:48:38 2022 +0100
use TextualInversionManager in place of embeddings (wip, doesn't work)
commit 8b4f0fe9d6e4e2643b36dfa27864294785d7ba4e
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 12:48:38 2022 +0100
use TextualInversionManager in place of embeddings (wip, doesn't work)
commit ffbe1ab11163ba712e353d89404e301d0e0c6cdf
Merge: 6e4dad60023df37e
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 02:37:31 2022 +0100
Merge branch 'feature_textual_inversion_mgr' into dev/diffusers
commit 023df37eff
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 02:36:54 2022 +0100
cleanup
commit 05fac594ea
Author: Damian Stewart <d@damianstewart.com>
Date: Fri Dec 16 02:07:49 2022 +0100
tweak error checking
commit 009f32ed39
Author: damian <null@damianstewart.com>
Date: Thu Dec 15 21:29:47 2022 +0100
unit tests passing for embeddings with vector length >1
commit beb1b08d9a
Author: Damian Stewart <d@damianstewart.com>
Date: Thu Dec 15 13:39:09 2022 +0100
more explicit equality tests when overwriting
commit 44d8a5a7c8
Author: Damian Stewart <d@damianstewart.com>
Date: Thu Dec 15 13:30:13 2022 +0100
wip textual inversion manager (unit tests passing for 1v embedding overwriting)
commit 417c2b57d9
Author: Damian Stewart <d@damianstewart.com>
Date: Thu Dec 15 12:30:55 2022 +0100
wip textual inversion manager (unit tests passing for base stuff + padding)
commit 2e80872e3b
Author: Damian Stewart <d@damianstewart.com>
Date: Thu Dec 15 10:57:57 2022 +0100
wip new TextualInversionManager
* stop using WeightedFrozenCLIPEmbedder
* store diffusion models locally
- configure_invokeai.py reconfigured to store diffusion models rather than
CompVis models
- hugging face caching model is used, but cache is set to ~/invokeai/models/repo_id
- models.yaml does **NOT** use path, just repo_id
- "repo_name" changed to "repo_id" to following hugging face conventions
- Models are loaded with full precision pending further work.
* allow non-local files during development
* path takes priority over repo_id
* MVP for model_cache and configure_invokeai
- Feature complete (almost)
- configure_invokeai.py downloads both .ckpt and diffuser models,
along with their VAEs. Both types of download are controlled by
a unified INITIAL_MODELS.yaml file.
- model_cache can load both type of model and switches back and forth
in CPU. No memory leaks detected
TO DO:
1. I have not yet turned on the LocalOnly flag for diffuser models, so
the code will check the Hugging Face repo for updates before using the
locally cached models. This will break firewalled systems. I am thinking
of putting in a global check for internet connectivity at startup time
and setting the LocalOnly flag based on this. It would be good to check
updates if there is connectivity.
2. I have not gone completely through INITIAL_MODELS.yaml to check which
models are available as diffusers and which are not. So models like
PaperCut and VoxelArt may not load properly. The runway and stability
models are checked, as well as the Trinart models.
3. Add stanzas for SD 2.0 and 2.1 in INITIAL_MODELS.yaml
REMAINING PROBLEMS NOT DIRECTLY RELATED TO MODEL_CACHE:
1. When loading a .ckpt file there are lots of messages like this:
Warning! ldm.modules.attention.CrossAttention is no longer being
maintained. Please use InvokeAICrossAttention instead.
I'm not sure how to address this.
2. The ckpt models ***don't actually run*** due to the lack of special-case
support for them in the generator objects. For example, here's the hard
crash you get when you run txt2img against the legacy waifu-diffusion-1.3
model:
```
>> An error occurred:
Traceback (most recent call last):
File "/data/lstein/InvokeAI/ldm/invoke/CLI.py", line 140, in main
main_loop(gen, opt)
File "/data/lstein/InvokeAI/ldm/invoke/CLI.py", line 371, in main_loop
gen.prompt2image(
File "/data/lstein/InvokeAI/ldm/generate.py", line 496, in prompt2image
results = generator.generate(
File "/data/lstein/InvokeAI/ldm/invoke/generator/base.py", line 108, in generate
image = make_image(x_T)
File "/data/lstein/InvokeAI/ldm/invoke/generator/txt2img.py", line 33, in make_image
pipeline_output = pipeline.image_from_embeddings(
File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1265, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'LatentDiffusion' object has no attribute 'image_from_embeddings'
```
3. The inpainting diffusion model isn't working. Here's the output of "banana
sushi" when inpainting-1.5 is loaded:
```
Traceback (most recent call last):
File "/data/lstein/InvokeAI/ldm/generate.py", line 496, in prompt2image
results = generator.generate(
File "/data/lstein/InvokeAI/ldm/invoke/generator/base.py", line 108, in generate
image = make_image(x_T)
File "/data/lstein/InvokeAI/ldm/invoke/generator/txt2img.py", line 33, in make_image
pipeline_output = pipeline.image_from_embeddings(
File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 301, in image_from_embeddings
result_latents, result_attention_map_saver = self.latents_from_embeddings(
File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 330, in latents_from_embeddings
result: PipelineIntermediateState = infer_latents_from_embeddings(
File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 185, in __call__
for result in self.generator_method(*args, **kwargs):
File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 367, in generate_latents_from_embeddings
step_output = self.step(batched_t, latents, guidance_scale,
File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 409, in step
step_output = self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/diffusers/schedulers/scheduling_lms_discrete.py", line 223, in step
pred_original_sample = sample - sigma * model_output
RuntimeError: The size of tensor a (9) must match the size of tensor b (4) at non-singleton dimension 1
```
* proper support for float32/float16
- configure script now correctly detects user's preference for
fp16/32 and downloads the correct diffuser version. If fp16
version not available, falls back to fp32 version.
- misc code cleanup and simplification in model_cache
* add on-the-fly conversion of .ckpt to diffusers models
1. On-the-fly conversion code can be found in the file ldm/invoke/ckpt_to_diffusers.py.
2. A new !optimize command has been added to the CLI. Should be ported to Web GUI.
User experience on the CLI is this:
```
invoke> !optimize /home/lstein/invokeai/models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
INFO: Converting legacy weights file /home/lstein/invokeai/models/ldm/stable-diffusion-v1/sd-v1-4.ckpt to optimized diffuser model.
This operation will take 30-60s to complete.
Success. Optimized model is now located at /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
Writing new config file entry for sd-v1-4...
>> New configuration:
sd-v1-4:
description: Optimized version of sd-v1-4
format: diffusers
path: /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
OK to import [n]? y
>> Verifying that new model loads...
>> Current VRAM usage: 2.60G
>> Offloading stable-diffusion-2.1 to CPU
>> Loading diffusers model from /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
| Using faster float16 precision
You have disabled the safety checker for <class 'ldm.invoke.generator.diffusers_pipeline.StableDiffusionGeneratorPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion \
license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances,\
disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
| training width x height = (512 x 512)
>> Model loaded in 3.48s
>> Max VRAM used to load the model: 2.17G
>> Current VRAM usage:2.17G
>> Textual inversions available:
>> Setting Sampler to k_lms (LMSDiscreteScheduler)
Keep model loaded? [y]
```
* add parallel set of generator files for ckpt legacy generation
* generation using legacy ckpt models now working
* diffusers: fix missing attention_maps_callback
fix for 23eb80b404
* associate legacy CrossAttention with .ckpt models
* enable autoconvert
New --autoconvert CLI option will scan a designated directory for
new .ckpt files, convert them into diffuser models, and import
them into models.yaml.
Works like this:
invoke.py --autoconvert /path/to/weights/directory
In ModelCache added two new methods:
autoconvert_weights(config_path, weights_directory_path, models_directory_path)
convert_and_import(ckpt_path, diffuser_path)
* diffusers: update to diffusers 0.11 (from 0.10.2)
* fix vae loading & width/height calculation
* refactor: encapsulate these conditioning data into one container
* diffusers: fix some noise-scaling issues by pushing the noise-mixing down to the common function
* add support for safetensors and accelerate
* set local_files_only when internet unreachable
* diffusers: fix error-handling path when model repo has no fp16 branch
* fix generatorinpaint error
Fixes :
"ModuleNotFoundError: No module named 'ldm.invoke.generatorinpaint'
https://github.com/invoke-ai/InvokeAI/pull/1583#issuecomment-1363634318
* quench diffuser safety-checker warning
* diffusers: support stochastic DDIM eta parameter
* fix conda env creation on macos
* fix cross-attention with diffusers 0.11
* diffusers: the VAE needs to be tiling as well as the U-Net
* diffusers: comment on subfolders
* diffusers: embiggen!
* diffusers: make model_cache.list_models serializable
* diffusers(inpaint): restore scaling functionality
* fix requirements clash between numba and numpy 1.24
* diffusers: allow inpainting model to do non-inpainting tasks
* start expanding model_cache functionality
* add import_ckpt_model() and import_diffuser_model() methods to model_manager
- in addition, model_cache.py is now renamed to model_manager.py
* allow "recommended" flag to be optional in INITIAL_MODELS.yaml
* configure_invokeai now downloads VAE diffusers in advance
* rename ModelCache to ModelManager
* remove support for `repo_name` in models.yaml
* check for and refuse to load embeddings trained on incompatible models
* models.yaml.example: s/repo_name/repo_id
and remove extra INITIAL_MODELS now that the main one has diffusers models in it.
* add MVP textual inversion script
* refactor(InvokeAIDiffuserComponent): factor out _combine()
* InvokeAIDiffuserComponent: implement threshold
* InvokeAIDiffuserComponent: diagnostic logs for threshold
...this does not look right
* add a curses-based frontend to textual inversion
- not quite working yet
- requires npyscreen installed
- on windows will also have the windows-curses requirement, but not added
to requirements yet
* add curses-based interface for textual inversion
* fix crash in convert_and_import()
- This corrects a "local variable referenced before assignment" error
in model_manager.convert_and_import()
* potential workaround for no 'state_dict' key error
- As reported in https://github.com/huggingface/diffusers/issues/1876
* create TI output dir if needed
* Update environment-lin-cuda.yml (#2159)
Fixing line 42 to be the proper order to define the transformers requirement: ~= instead of =~
* diffusers: update sampler-to-scheduler mapping
based on https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672
* improve user exp for ckt to diffusers conversion
- !optimize_models command now operates on an existing ckpt file entry in models.yaml
- replaces existing entry, rather than adding a new one
- offers to delete the ckpt file after conversion
* web: adapt progress callback to deal with old generator or new diffusers pipeline
* clean-up model_manager code
- add_model() verified to work for .ckpt local paths,
.ckpt remote URLs, diffusers local paths, and
diffusers repo_ids
- convert_and_import() verified to work for local and
remove .ckpt files
* handle edge cases for import_model() and convert_model()
* add support for safetensor .ckpt files
* fix name error
* code cleanup with pyflake
* improve model setting behavior
- If the user enters an invalid model name at startup time, will not
try to load it, warn, and use default model
- CLI UI enhancement: include currently active model in the command
line prompt.
* update test-invoke-pip.yml
- fix model cache path to point to runwayml/stable-diffusion-v1-5
- remove `skip-sd-weights` from configure_invokeai.py args
* exclude dev/diffusers from "fail for draft PRs"
* disable "fail on PR jobs"
* re-add `--skip-sd-weights` since no space
* update workflow environments
- include `INVOKE_MODEL_RECONFIGURE: '--yes'`
* clean up model load failure handling
- Allow CLI to run even when no model is defined or loadable.
- Inhibit stack trace when model load fails - only show last error
- Give user *option* to run configure_invokeai.py when no models
successfully load.
- Restart invokeai after reconfiguration.
* further edge-case handling
1) only one model in models.yaml file, and that model is broken
2) no models in models.yaml
3) models.yaml doesn't exist at all
* fix incorrect model status listing
- "cached" was not being returned from list_models()
- normalize handling of exceptions during model loading:
- Passing an invalid model name to generate.set_model() will return
a KeyError
- All other exceptions are returned as the appropriate Exception
* CI: do download weights (if not already cached)
* diffusers: fix scheduler loading in offline mode
* CI: fix model name (no longer has `diffusers-` prefix)
* Update txt2img2img.py (#2256)
* fixes to share models with HuggingFace cache system
- If HF_HOME environment variable is defined, then all huggingface models
are stored in that directory following the standard conventions.
- For seamless interoperability, set HF_HOME to ~/.cache/huggingface
- If HF_HOME not defined, then models are stored in ~/invokeai/models.
This is equivalent to setting HF_HOME to ~/invokeai/models
A future commit will add a migration mechanism so that this change doesn't
break previous installs.
* feat - make model storage compatible with hugging face caching system
This commit alters the InvokeAI model directory to be compatible with
hugging face, making it easier to share diffusers (and other models)
across different programs.
- If the HF_HOME environment variable is not set, then models are
cached in ~/invokeai/models in a format that is identical to the
HuggingFace cache.
- If HF_HOME is set, then models are cached wherever HF_HOME points.
- To enable sharing with other HuggingFace library clients, set
HF_HOME to ~/.cache/huggingface to set the default cache location
or to ~/invokeai/models to have huggingface cache inside InvokeAI.
* fixes to share models with HuggingFace cache system
- If HF_HOME environment variable is defined, then all huggingface models
are stored in that directory following the standard conventions.
- For seamless interoperability, set HF_HOME to ~/.cache/huggingface
- If HF_HOME not defined, then models are stored in ~/invokeai/models.
This is equivalent to setting HF_HOME to ~/invokeai/models
A future commit will add a migration mechanism so that this change doesn't
break previous installs.
* fix error "no attribute CkptInpaint"
* model_manager.list_models() returns entire model config stanza+status
* Initial Draft - Model Manager Diffusers
* added hash function to diffusers
* implement sha256 hashes on diffusers models
* Add Model Manager Support for Diffusers
* fix various problems with model manager
- in cli import functions, fix not enough values to unpack from
_get_name_and_desc()
- fix crash when using old-style vae: value with new-style diffuser
* rebuild frontend
* fix dictconfig-not-serializable issue
* fix NoneType' object is not subscriptable crash in model_manager
* fix "str has no attribute get" error in model_manager list_models()
* Add path and repo_id support for Diffusers Model Manager
Also fixes bugs
* Fix tooltip IT localization not working
* Add Version Number To WebUI
* Optimize Model Search
* Fix incorrect font on the Model Manager UI
* Fix image degradation on merge fixes - [Experimental]
This change should effectively fix a couple of things.
- Fix image degradation on subsequent merges of the canvas layers.
- Fix the slight transparent border that is left behind when filling the bounding box with a color.
- Fix the left over line of color when filling a bounding box with color.
So far there are no side effects for this. If any, please report.
* Add local model filtering for Diffusers / Checkpoints
* Go to home on modal close for the Add Modal UI
* Styling Fixes
* Model Manager Diffusers Localization Update
* Add Safe Tensor scanning to Model Manager
* Fix model edit form dispatching string values instead of numbers.
* Resolve VAE handling / edge cases for supplied repos
* defer injecting tokens for textual inversions until they're used for the first time
* squash a console warning
* implement model migration check
* add_model() overwrites previous config rather than merges
* fix model config file attribute merging
* fix precision handling in textual inversion script
* allow ckpt conversion script to work with safetensors .ckpts
Applied patch here:
beb932c5d1
* fix name "args" is not defined crash in textual_inversion_training
* fix a second NameError: name 'args' is not defined crash
* fix loading of the safety checker from the global cache dir
* add installation step to textual inversion frontend
- After a successful training run, the script will copy learned_embeds.bin
to a subfolder of the embeddings directory.
- User given the option to delete the logs and intermediate checkpoints
(which together use 7-8G of space)
- If textual inversion training fails, reports the error gracefully.
* don't crash out on incompatible embeddings
- put try: blocks around places where the system tries to load an embedding
which is incompatible with the currently loaded model
* add support for checkpoint resuming
* textual inversion preferences are saved and restored between sessions
- Preferences are stored in a file named text-inversion-training/preferences.conf
- Currently the resume-from-checkpoint option is not working correctly. Possible
bug in textual_inversion_training.py?
* copy learned_embeddings.bin into right location
* add front end for diffusers model merging
- Front end doesn't do anything yet!!!!
- Made change to model name parsing in CLI to support ability to have merged models
with the "+" character in their names.
* improve inpainting experience
- recommend ckpt version of inpainting-1.5 to user
- fix get_noise() bug in ckpt version of omnibus.py
* update environment*yml
* tweak instructions to install HuggingFace token
* bump version number
* enhance update scripts
- update scripts will now fetch new INITIAL_MODELS.yaml so that
configure_invokeai.py will know about the diffusers versions.
* enhance invoke.sh/invoke.bat launchers
- added configure_invokeai.py to menu
- menu defaults to browser-based invoke
* remove conda workflow (#2321)
* fix `token_ids has shape torch.Size([79]) - expected [77]`
* update CHANGELOG.md with 2.3.* info
- Add information on how formats have changed and the upgrade process.
- Add short bug list.
Co-authored-by: Damian Stewart <d@damianstewart.com>
Co-authored-by: Damian Stewart <null@damianstewart.com>
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
Co-authored-by: Wybartel-luxmc <37852506+Wybartel-luxmc@users.noreply.github.com>
Co-authored-by: mauwii <Mauwii@outlook.de>
Co-authored-by: mickr777 <115216705+mickr777@users.noreply.github.com>
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
Co-authored-by: Matthias Wild <40327258+mauwii@users.noreply.github.com>
* update version number
* print version number at startup
* move version number into ldm/invoke/_version.py
* bump version to 2.2.6+a0
* handle whitespace better
* resolve issues raised by mauwii during PR review
1. create_installers.sh now asks before tagging and committing the
current repo
2. trailing whitespace removed from user-provided location of invokeai
directory in install.bat
Updated the link for the MS Visual C libraries - I'm not sure if MS changed the location of the files but this new one leads right to the file downloads.
- Removed links from the install instructions to the installer zip files.
- Replaced "2.2.4" with "2.X.X" globally, to avoid the docs going out of
date.
* Permit cmd override for CORS modification
* Enable multiple origins for CORS
* Remove CMD_OVERRIDE
* Revert executable bit change
* Defensively convert list into string
* Bad if statement
* Retry rebase
* Retry rebase
Co-authored-by: Chris Dawson <chris@vivoh.com>
- fix problem of facexlib weights being downloaded into the .venv
package directory when codeformer restoration requested.
- now users pre-downloaded weights in ~/invokeai/models/gfpgan/weights
(which is shared with gfpgan)
Co-authored-by: Matthias Wild <40327258+mauwii@users.noreply.github.com>
- Fixed codeformer module so that the facexlib files are downloaded
into their pre-stored location in models/gfpgan/weights (shared
with the GFPGAN module)
* installer tweaks in preparation for v2.2.5
- pin numpy to 1.23.* to avoid requirements conflict with numba
- update.sh and update.bat now accept a tag or branch string, not a URL
- update scripts download latest requirements-base before updating.
* update.bat.in debugged and working
* update pulls from "latest" now
* bump version number
* fix permissions on create_installer.sh
* give Linux user option of installing ROCm or CUDA
* rc2.2.5 (install.sh) relative path fixes (#2155)
* (installer) fix bug in resolution of relative paths in linux install script
point installer at 2.2.5-rc1
selecting ~/Data/myapps/ as location would create a ./~/Data/myapps
instead of expanding the ~/ to the value of ${HOME}
also, squash the trailing slash in path, if it was entered by the user
* (installer) add option to automatically start the app after install
also: when exiting, print the command to get back into the app
* remove extraneous whitespace
* model_cache applies rootdir to config path
* bring installers up to date with 2.2.5-rc2
* bump rc version
* create_installer now adds version number
* rebuild frontend
* bump rc#
* add locales to frontend dist package
- bump to patchlevel 6
* bump patchlevel
* use invoke-ai version of GFPGAN
- This version is very slightly modified to allow weights files
to be pre-downloaded by the configure script.
* fix formatting error during startup
* bump patch level
* workaround #2 for GFPGAN facexlib() weights downloading
* bump patch
* ready for merge and release
* remove extraneous comment
* set PYTORCH_ENABLE_MPS_FALLBACK directly in invoke.py
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
* Update WEBUIHOTKEYS.md
Fixed display errors so it no longer show extra plus signs on the site
* Update WEBUIHOTKEYS.md
Correction to keycap look to have symbols on special keys like enter, shift, and ctrl.
2022-12-31 23:48:17 +01:00
1794 changed files with 339688 additions and 78214 deletions
label:What version did you experience this issue on?
description:|
Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
description:Commit a idea or Request a new feature
description:Contribute a idea or request a new feature
title: '[enhancement]:'
labels: ['enhancement']
# assignees:
@@ -9,14 +9,14 @@ body:
- type:markdown
attributes:
value:|
Thanks for taking the time to fill out this Feature request!
Thanks for taking the time to fill out this feature request!
- type:checkboxes
attributes:
label:Is there an existing issue for this?
description:|
Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
to see if a simmilar issue already exists for the feature you want to request
to see if a similar issue already exists for the feature you want to request
options:
- label:I have searched the existing issues
required:true
@@ -34,12 +34,9 @@ body:
id:whatisexpected
attributes:
label:What should this feature add?
description:Please try to explain the functionality this feature should add
description:Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
placeholder:|
Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
Great benefits with automatic labeling, assigning and other functionalitys not available in that form
via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
validations:
required:true
@@ -51,6 +48,6 @@ body:
- type:textarea
attributes:
label:Aditional Content
label:Additional Content
description:Add any other context or screenshots about the feature request here.
placeholder:This is a Mockup of the design how I imagine it <screenshot>
placeholder:This is a mockup of the design how I imagine it <screenshot>
stale-issue-message:"There has been no activity in this issue for ${{ env.DAYS_BEFORE_ISSUE_STALE }} days. If this issue is still being experienced, please reply with an updated confirmation that the issue is still being experienced with the latest release."
close-issue-message:"Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
[![CI checks on main badge]][CI checks on main link] [![CI checks on dev badge]][CI checks on dev link] [![latest commit to dev badge]][latest commit to dev link]
[![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link]
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
[CI checks on dev badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
[CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
[latest commit to dev link]: https://github.com/invoke-ai/InvokeAI/commits/development
[latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
[latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
2. Download the .zip file for your OS (Windows/macOS/Linux).
3. Unzip the file.
4. If you are on Windows, double-click on the `install.bat` script. On macOS, open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press return. On Linux, run `install.sh`.
5. Wait a while, until it is done.
6. The folder where you ran the installer from will now be filled with lots of files. If you are on Windows, double-click on the `invoke.bat` file. On macOS, open a Terminal window, drag `invoke.sh` from the folder into the Terminal, and press return. On Linux, run `invoke.sh`
7. Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090.
8. Type `banana sushi` in the box on the top left and click `Invoke`:
(Replace `v3.0.0` with the current release number if this document is out of date).
The first command will install and upgrade new software to run
InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
You may now launch the WebUI in the usual way, by selecting option [1]
from the launcher script
#### Migrating Images
The migration script will migrate your invokeai settings and models,
including textual inversion models, LoRAs and merges that you may have
installed previously. However it does **not** migrate the generated
images stored in your 2.3-format outputs directory. To do this, you
need to run an additional step:
1. From a working InvokeAI 3.0 root directory, start the launcher and
enter menu option [8] to open the "developer's console".
2. At the developer's console command line, type the command:
```bash
invokeai-import-images
```
3. This will lead you through the process of confirming the desired
source and destination for the imported images. The images will
appear in the gallery board of your choice, and contain the
original prompt, model name, and other parameters used to generate
the image.
(Many kudos to **techjedi** for contributing this script.)
## Hardware Requirements
InvokeAI is supported across Linux, Windows and macOS. Linux
users can use either an Nvidia-based card (with CUDA support) or an
AMD card (using the ROCm driver).
#### System
You wil need one of the following:
### System
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
You will need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB
of VRAM is highly recommended for rendering using the Stable
Diffusion XL models
- An Apple computer with an M1 chip.
- An AMD-based graphics card with 4GB or more VRAM memory (Linux
only), 6-8 GB for XL rendering.
We do not recommend the GTX 1650 or 1660 series video cards. They are
unable to run in half-precision mode and do not have sufficient VRAM
to render 512x512 images.
#### Memory
**Memory** - At least 12 GB Main Memory RAM.
- At least 12 GB Main Memory RAM.
**Disk** - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
#### Disk
## Features
- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
**Note**
### *Web Server & UI*
If you have a Nvidia 10xx series card (e.g. the 1080ti), please
run the dream script in full-precision mode as shown below.
InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
Similarly, specify full-precision mode on Apple M1 hardware.
### *Unified Canvas*
Precision is auto configured based on the device. If however you encounter
errors like 'expected type Float but found Half' or 'not implemented for Half'
you can try starting `invoke.py` with the `--precision=float32` flag to your initialization command
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
Or by updating your InvokeAI configuration file with this argument.
### *Workflows & Nodes*
### Features
InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
- [Simplified API for text to image generation](https://invoke-ai.github.io/InvokeAI/features/OTHER/#simplified-api)
Invoke AI provides an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
For our latest changes, view our [Release Notes](https://github.com/invoke-ai/InvokeAI/releases)
For our latest changes, view our [Release
Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
[CHANGELOG](docs/CHANGELOG.md).
### Troubleshooting
Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
problems and other issues.
problems and other issues. For more help, please join our [Discord][discord link]
# Contributing
## Contributing
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
cleanup, testing, or code reviews, is very much encouraged to do so.
To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
If you are unfamiliar with how
to contribute to GitHub projects, here is a
[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contributionguidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.
to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing:
All commands are to be run from the `docker` directory: `cd docker`
#### Linux
1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
- The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
3. Ensure docker daemon is able to access the GPU.
- You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
#### macOS
1. Ensure Docker has at least 16GB RAM
2. Enable VirtioFS for file sharing
3. Enable `docker compose` V2 support
This is done via Docker Desktop preferences
## Quickstart
1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
a. the desired location of the InvokeAI runtime directory, or
b. an existing, v3.0.0 compatible runtime directory.
1.`docker compose up`
The image will be built automatically if needed.
The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.
### Use a GPU
- Linux is *recommended* for GPU support in Docker.
- WSL2 is *required* for Windows.
- only `x86_64` architecture is supported.
The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
## Customize
Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
Example (most values are optional):
```
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
GPU_DRIVER=cuda
```
## Even Moar Customizing!
See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
### Reconfigure the runtime directory
Can be used to download additional models from the supported model list
In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
Stable Diffusion distribution by InvokeAI: https://github.com/invoke-ai
The Docker image tracks the `main` branch of the InvokeAI project, which means it includes the latest features, but may contain some bugs.
Your working directory is mounted under the `/workspace` path inside the pod. The models are in `/workspace/invokeai/models`, and outputs are in `/workspace/invokeai/outputs`.
> **Only the /workspace directory will persist between pod restarts!**
> **If you _terminate_ (not just _stop_) the pod, the /workspace will be lost.**
## Quickstart
1. Launch a pod from this template. **It will take about 5-10 minutes to run through the initial setup**. Be patient.
1. Wait for the application to load.
- TIP: you know it's ready when the CPU usage goes idle
- You can also check the logs for a line that says "_Point your browser at..._"
1. Open the Invoke AI web UI: click the `Connect` => `connect over HTTP` button.
1. Generate some art!
## Other things you can do
At any point you may edit the pod configuration and set an arbitrary Docker command. For example, you could run a command to downloads some models using `curl`, or fetch some images and place them into your outputs to continue a working session.
If you need to run *multiple commands*, define them in the Docker Command field like this:
This image includes a couple of handy tools to help you get the data into the pod (such as your custom models or embeddings), and out of the pod (such as downloading your outputs). Here are your options for getting your data in and out of the pod:
- **SSH server**:
1. Make sure to create and set your Public Key in the RunPod settings (follow the official instructions)
1. Add an exposed port 22 (TCP) in the pod settings!
1. When your pod restarts, you will see a new entry in the `Connect` dialog. Use this SSH server to `scp` or `sftp` your files as necessary, or SSH into the pod using the fully fledged SSH server.
1. On your computer, `pip install magic-wormhole` (see above instructions for details)
1. Connect to the command line **using the "light" SSH client** or the browser-based console. _Currently there's a bug where `wormhole` isn't available when connected to "full" SSH server, as described above_.
1.`wormhole send /workspace/invokeai/outputs` will send the entire `outputs` directory. You can also send individual files.
1. Once packaged, you will see a `wormhole receive <123-some-words>` command. Copy it
1. Paste this command into the terminal on your local machine to securely download the payload.
1. It works the same in reverse: you can `wormhole send` some models from your computer to the pod. Again, save your files somewhere in `/workspace` or they will be lost when the pod is stopped.
- **RunPod's Cloud Sync feature** may be used to sync the persistent volume to cloud storage. You could, for example, copy the entire `/workspace` to S3, add some custom models to it, and copy it back from S3 when launching new pod configurations. Follow the Cloud Sync instructions.
### Disable the NSFW checker
The NSFW checker is enabled by default. To disable it, edit the pod configuration and set the following command:
This release (along with the post1 and post2 follow-on releases) expands support for additional LoRA and LyCORIS models, upgrades diffusers versions, and fixes a few bugs.
### LoRA and LyCORIS Support Improvement
A number of LoRA/LyCORIS fine-tune files (those which alter the text encoder as well as the unet model) were not having the desired effect in InvokeAI. This bug has now been fixed. Full documentation of LoRA support is available at InvokeAI LoRA Support.
Previously, InvokeAI did not distinguish between LoRA/LyCORIS models based on Stable Diffusion v1.5 vs those based on v2.0 and 2.1, leading to a crash when an incompatible model was loaded. This has now been fixed. In addition, the web pulldown menus for LoRA and Textual Inversion selection have been enhanced to show only those files that are compatible with the currently-selected Stable Diffusion model.
Support for the newer LoKR LyCORIS files has been added.
### Library Updates and Speed/Reproducibility Advancements
The major enhancement in this version is that NVIDIA users no longer need to decide between speed and reproducibility. Previously, if you activated the Xformers library, you would see improvements in speed and memory usage, but multiple images generated with the same seed and other parameters would be slightly different from each other. This is no longer the case. Relative to 2.3.5 you will see improved performance when running without Xformers, and even better performance when Xformers is activated. In both cases, images generated with the same settings will be identical.
Here are the new library versions:
Library Version
Torch 2.0.0
Diffusers 0.16.1
Xformers 0.0.19
Compel 1.1.5
Other Improvements
### Performance Improvements
When a model is loaded for the first time, InvokeAI calculates its checksum for incorporation into the PNG metadata. This process could take up to a minute on network-mounted disks and WSL mounts. This release noticeably speeds up the process.
### Bug Fixes
The "import models from directory" and "import from URL" functionality in the console-based model installer has now been fixed.
When running the WebUI, we have reduced the number of times that InvokeAI reaches out to HuggingFace to fetch the list of embeddable Textual Inversion models. We have also caught and fixed a problem with the updater not correctly detecting when another instance of the updater is running
## v2.3.4 <small>(7 April 2023)</small>
What's New in 2.3.4
This features release adds support for LoRA (Low-Rank Adaptation) and LyCORIS (Lora beYond Conventional) models, as well as some minor bug fixes.
### LoRA and LyCORIS Support
LoRA files contain fine-tuning weights that enable particular styles, subjects or concepts to be applied to generated images. LyCORIS files are an extended variant of LoRA. InvokeAI supports the most common LoRA/LyCORIS format, which ends in the suffix .safetensors. You will find numerous LoRA and LyCORIS models for download at Civitai, and a small but growing number at Hugging Face. Full documentation of LoRA support is available at InvokeAI LoRA Support.( Pre-release note: this page will only be available after release)
To use LoRA/LyCORIS models in InvokeAI:
Download the .safetensors files of your choice and place in /path/to/invokeai/loras. This directory was not present in earlier version of InvokeAI but will be created for you the first time you run the command-line or web client. You can also create the directory manually.
Add withLora(lora-file,weight) to your prompts. The weight is optional and will default to 1.0. A few examples, assuming that a LoRA file named loras/sushi.safetensors is present:
family sitting at dinner table eating sushi withLora(sushi,0.9)
family sitting at dinner table eating sushi withLora(sushi, 0.75)
family sitting at dinner table eating sushi withLora(sushi)
Multiple withLora() prompt fragments are allowed. The weight can be arbitrarily large, but the useful range is roughly 0.5 to 1.0. Higher weights make the LoRA's influence stronger. Negative weights are also allowed, which can lead to some interesting effects.
Generate as you usually would! If you find that the image is too "crisp" try reducing the overall CFG value or reducing individual LoRA weights. As is the case with all fine-tunes, you'll get the best results when running the LoRA on top of the model similar to, or identical with, the one that was used during the LoRA's training. Don't try to load a SD 1.x-trained LoRA into a SD 2.x model, and vice versa. This will trigger a non-fatal error message and generation will not proceed.
You can change the location of the loras directory by passing the --lora_directory option to `invokeai.
### New WebUI LoRA and Textual Inversion Buttons
This version adds two new web interface buttons for inserting LoRA and Textual Inversion triggers into the prompt as shown in the screenshot below.
Clicking on one or the other of the buttons will bring up a menu of available LoRA/LyCORIS or Textual Inversion trigger terms. Select a menu item to insert the properly-formatted withLora() or <textual-inversion> prompt fragment into the positive prompt. The number in parentheses indicates the number of trigger terms currently in the prompt. You may click the button again and deselect the LoRA or trigger to remove it from the prompt, or simply edit the prompt directly.
Currently terms are inserted into the positive prompt textbox only. However, some textual inversion embeddings are designed to be used with negative prompts. To move a textual inversion trigger into the negative prompt, simply cut and paste it.
By default the Textual Inversion menu only shows locally installed models found at startup time in /path/to/invokeai/embeddings. However, InvokeAI has the ability to dynamically download and install additional Textual Inversion embeddings from the HuggingFace Concepts Library. You may choose to display the most popular of these (with five or more likes) in the Textual Inversion menu by going to Settings and turning on "Show Textual Inversions from HF Concepts Library." When this option is activated, the locally-installed TI embeddings will be shown first, followed by uninstalled terms from Hugging Face. See The Hugging Face Concepts Library and Importing Textual Inversion files for more information.
### Minor features and fixes
This release changes model switching behavior so that the command-line and Web UIs save the last model used and restore it the next time they are launched. It also improves the behavior of the installer so that the pip utility is kept up to date.
### Known Bugs in 2.3.4
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
## v2.3.3 <small>(28 March 2023)</small>
This is a bugfix and minor feature release.
### Bugfixes
Since version 2.3.2 the following bugs have been fixed:
Bugs
When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
Textual inversion will select an appropriate batchsize based on whether xformers is active, and will default to xformers enabled if the library is detected.
The batch script log file names have been fixed to be compatible with Windows.
Occasional corruption of the .next_prefix file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
Support loading of legacy config files that have no personalization (textual inversion) section.
An infinite loop when opening the developer's console from within the invoke.sh script has been corrected.
Documentation fixes, including a recipe for detecting and fixing problems with the AMD GPU ROCm driver.
Enhancements
It is now possible to load and run several community-contributed SD-2.0 based models, including the often-requested "Illuminati" model.
The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI embeddings directory.
If no --model is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
On Linux systems, the invoke.sh launcher now uses a prettier console-based interface. To take advantage of it, install the dialog package using your package manager (e.g. sudo apt install dialog).
When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
my-favorite-model.ckpt
my-favorite-model.yaml
my-favorite-model.vae.pt # or my-favorite-model.vae.safetensors
### Known Bugs in 2.3.3
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
## v2.3.2 <small>(11 March 2023)</small>
This is a bugfix and minor feature release.
### Bugfixes
Since version 2.3.1 the following bugs have been fixed:
Black images appearing for potential NSFW images when generating with legacy checkpoint models and both --no-nsfw_checker and --ckpt_convert turned on.
Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
Crashes that occurred during model merging.
Restore previous naming of Stable Diffusion base and 768 models.
Upgraded to latest versions of diffusers, transformers, safetensors and accelerate libraries upstream. We hope that this will fix the assertion NDArray > 2**32 issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
As part of the upgrade to diffusers, the location of the diffusers-based models has changed from models/diffusers to models/hub. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your models/diffusers directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
New "Invokeai-batch" script
### Invoke AI Batch
2.3.2 introduces a new command-line only script called invokeai-batch that can be used to generate hundreds of images from prompts and settings that vary systematically. This can be used to try the same prompt across multiple combinations of models, steps, CFG settings and so forth. It also allows you to template prompts and generate a combinatorial list like:
a shack in the mountains, photograph
a shack in the mountains, watercolor
a shack in the mountains, oil painting
a chalet in the mountains, photograph
a chalet in the mountains, watercolor
a chalet in the mountains, oil painting
a shack in the desert, photograph
...
If you have a system with multiple GPUs, or a single GPU with lots of VRAM, you can parallelize generation across the combinatorial set, reducing wait times and using your system's resources efficiently (make sure you have good GPU cooling).
To try invokeai-batch out. Launch the "developer's console" using the invoke launcher script, or activate the invokeai virtual environment manually. From the console, give the command invokeai-batch --help in order to learn how the script works and create your first template file for dynamic prompt generation.
### Known Bugs in 2.3.2
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise a Trojan alert for the codeformer.pth face restoration model. As far as we have been able to determine, this is a false positive and can be safely whitelisted.
## v2.3.1 <small>(22 February 2023)</small>
This is primarily a bugfix release, but it does provide several new features that will improve the user experience.
### Enhanced support for model management
InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
There are three ways of accessing the model management features:
From the WebUI, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
Using the Model Installer App
Choose option (5) download and install models from the invoke launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
Command-line users can start this app using the command invokeai-model-install.
Using the Command Line Client (CLI)
The !install_model and !convert_model commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do not need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
Please see INSTALLING MODELS for more information on model management.
### An Improved Installer Experience
The installer now launches a console-based UI for setting and changing commonly-used startup options:
After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time, you can edit the startup options by launching invoke.sh/invoke.bat and entering option (6) change InvokeAI startup options
Command-line users can launch the new configure app using invokeai-configure.
This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch invoke.sh or invoke.bat and choose option (9) update InvokeAI . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
Command-line users can run this interface by typing invokeai-configure
### Image Symmetry Options
There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting Symmetry from the image generation settings, or within the CLI by using the options --h_symmetry_time_pct and --v_symmetry_time_pct (these can be abbreviated to --h_sym and --v_sym like all other options).
### A New Unified Canvas Look
This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select Use Canvas Beta Layout:
Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
Model conversion and merging within the WebUI
The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the invoke.sh/invoke.bat scripts.
An easier way to contribute translations to the WebUI
We have migrated our translation efforts to Weblate, a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief translation guide for more information on how to contribute.
Numerous internal bugfixes and performance issues
### Bug Fixes
This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to diffusers 0.13.0, and using the compel library for prompt parsing. See Detailed Change Log for a detailed list of bugs caught and squished.
Summary of InvokeAI command line scripts (all accessible via the launcher menu)
Command Description
invokeai Command line interface
invokeai --web Web interface
invokeai-model-install Model installer with console forms-based front end
invokeai-ti --gui Textual inversion, with a console forms-based front end
invokeai-merge --gui Model merging, with a console forms-based front end
invokeai-configure Startup configuration; can also be used to reinstall support models
invokeai-update InvokeAI software updater
### Known Bugs in 2.3.1
These are known bugs in the release.
MacOS users generating 768x768 pixel images or greater using diffusers models may experience a hard crash with assertion NDArray > 2**32 This appears to be an issu...
## v2.3.0 <small>(15 January 2023)</small>
**Transition to diffusers
Version 2.3 provides support for both the traditional `.ckpt` weight
checkpoint files as well as the HuggingFace `diffusers` format. This
introduces several changes you should know about.
1. The models.yaml format has been updated. There are now two
different type of configuration stanza. The traditional ckpt
one will look like this, with a `format` of `ckpt` and a
`weights` field that points to the absolute or ROOTDIR-relative
location of the ckpt file.
```
inpainting-1.5:
description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
Some files were not shown because too many files have changed in this diff
Show More
Reference in New Issue
Block a user
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.