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
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
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
```
