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.
Only "real" conflicts were in:
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
* 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>
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.
- 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.
- 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.
* 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.
