* 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.
