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InvokeAI/pyproject.toml

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replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
[build-system]
fix dependencies/requirements
2023-01-19 02:12:59 +00:00
requires = ["setuptools~=65.5", "pip~=22.3", "wheel"]
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
build-backend = "setuptools.build_meta"
[project]
name = "InvokeAI"
description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
update requirements to python 3.10-11
2023-09-28 13:28:41 +00:00
requires-python = ">=3.10, <3.12"
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
readme = { content-type = "text/markdown", file = "README.md" }
keywords = ["stable-diffusion", "AI"]
dynamic = ["version"]
license = { file = "LICENSE" }
authors = [{ name = "The InvokeAI Project", email = "lincoln.stein@gmail.com" }]
classifiers = [
'Development Status :: 4 - Beta',
'Environment :: GPU',
'Environment :: GPU :: NVIDIA CUDA',
'Environment :: MacOS X',
'Intended Audience :: End Users/Desktop',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Operating System :: POSIX :: Linux',
'Operating System :: MacOS',
'Operating System :: Microsoft :: Windows',
'Programming Language :: Python :: 3 :: Only',
'Programming Language :: Python :: 3.10',
'Topic :: Artistic Software',
'Topic :: Internet :: WWW/HTTP :: WSGI :: Application',
'Topic :: Internet :: WWW/HTTP :: WSGI :: Server',
'Topic :: Multimedia :: Graphics',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: Scientific/Engineering :: Image Processing',
]
dependencies = [
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
# Core generation dependencies, pinned for reproducible builds.
bump
2024-02-14 09:10:32 +00:00
"accelerate==0.27.2",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"clip_anytorch==2.5.2", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
"compel==2.0.2",
"controlnet-aux==0.0.7",
chore: bump diffusers 0.26.2 -> 0.26.3 https://github.com/huggingface/diffusers/releases/tag/v0.26.3 This fixes an issue with `DPMSolverSinglestepScheduler` with even numbers of steps.
2024-02-13 12:14:40 +00:00
"diffusers[torch]==0.26.3",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
"mediapipe==0.10.7", # needed for "mediapipeface" controlnet model
Minor dep updates
2024-02-07 16:58:28 +00:00
"numpy==1.26.4", # >1.24.0 is needed to use the 'strict' argument to np.testing.assert_array_equal()
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"onnx==1.15.0",
"onnxruntime==1.16.3",
"opencv-python==4.9.0.80",
"pytorch-lightning==2.1.3",
Update safetensors and transformers to their latest versions (#5562) * Update Safetensors to the lastest version * Update Transformers while at it * Update transformers again
2024-01-31 04:54:56 +00:00
"safetensors==0.4.2",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26
"torch==2.1.2",
"torchmetrics==0.11.4",
"torchsde==0.2.6",
"torchvision==0.16.2",
Update safetensors and transformers to their latest versions (#5562) * Update Safetensors to the lastest version * Update Transformers while at it * Update transformers again
2024-01-31 04:54:56 +00:00
"transformers==4.37.2",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
# Core application dependencies, pinned for reproducible builds.
Upgrade version of fastapi and socketio
2024-02-08 21:49:07 +00:00
"fastapi-events==0.10.1",
"fastapi==0.109.2",
Minor dep updates
2024-02-04 17:21:34 +00:00
"huggingface-hub==0.20.3",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"pydantic-settings==2.1.0",
chore: bump deps - pydantic 2.5.3 -> 2.6.1 - uvicorn 0.25.0 -> 0.27.1
2024-02-10 13:34:47 +00:00
"pydantic==2.6.1",
Upgrade version of fastapi and socketio
2024-02-08 21:49:07 +00:00
"python-socketio==5.11.1",
chore: bump deps - pydantic 2.5.3 -> 2.6.1 - uvicorn 0.25.0 -> 0.27.1
2024-02-10 13:34:47 +00:00
"uvicorn[standard]==0.27.1",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
# Auxiliary dependencies, pinned only if necessary.
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"albumentations",
Allow multiple models to be imported by passing a directory.
2023-02-05 03:05:27 +00:00
"click",
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"datasets",
feat(nodes): restore previous invocation context methods with deprecation warnings
2024-01-14 09:16:51 +00:00
"Deprecated",
tested on 3.11 and 3.10
2023-07-24 21:13:32 +00:00
"dnspython~=2.4.0",
fix: add dynamicprompts to pyproject.toml
2023-06-13 12:05:19 +00:00
"dynamicprompts",
Feat/easy param (#3504) * 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>
2023-06-11 06:27:44 +00:00
"easing-functions",
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"einops",
"facexlib",
sync pydantic and sql field names; merge routes
2023-11-06 23:08:57 +00:00
"imohash",
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"matplotlib", # needed for plotting of Penner easing functions
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"npyscreen",
"omegaconf",
"picklescan",
"pillow",
(scripts) rename/reorganize CLI scripts - add torch MPS fallback directly to CLI.py - rename CLI scripts with `invoke-...` prefix - delete long-deprecated scripts - add a missing package dependency - delete setup.py as obsolete
2023-01-27 06:20:50 +00:00
"prompt-toolkit",
tested on 3.11 and 3.10
2023-07-24 21:13:32 +00:00
"pympler~=1.0.1",
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"pypatchmatch",
many TUI improvements: 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.
2023-06-03 20:17:53 +00:00
'pyperclip',
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"pyreadline3",
tested on 3.11 and 3.10
2023-07-24 21:13:32 +00:00
"python-multipart",
"requests~=2.28.2",
deps: add explicitly dependency to `rich` was previously pulled in as a secondary dependency of something else.
2023-03-05 02:37:39 +00:00
"rich~=13.3",
tested on 3.11 and 3.10
2023-07-24 21:13:32 +00:00
"scikit-image~=0.21.0",
feat(nodes): add version to node schemas 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.
2023-09-04 08:11:56 +00:00
"semver~=3.0.1",
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"send2trash",
tested on 3.11 and 3.10
2023-07-24 21:13:32 +00:00
"test-tube~=0.7.5",
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"windows-curses; sys_platform=='win32'",
]
[project.optional-dependencies]
feat: pin deps Organise deps into ~3 categories: - Core generation dependencies, pinned for reproducible builds. - Core application dependencies, pinned for reproducible builds. - Auxiliary dependencies, pinned only if necessary. I pinned / bumped these to latest: - `controlnet_aux` - `fastapi` - `fastapi-events` - `huggingface-hub` - `numpy` - `python-socketio` - `torchmetrics` - `transformers` - `uvicorn` I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us. I tested on a fresh venv. The app still works and I can generate on macOS. Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch. It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
2024-01-10 04:46:07 +00:00
"xformers" = [
# Core generation dependencies, pinned for reproducible builds.
"xformers==0.0.23.post1; sys_platform!='darwin'",
# Auxiliary dependencies, pinned only if necessary.
"triton; sys_platform=='linux'",
]
"onnx" = ["onnxruntime"]
"onnx-cuda" = ["onnxruntime-gpu"]
"onnx-directml" = ["onnxruntime-directml"]
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
"dist" = ["pip-tools", "pipdeptree", "twine"]
"docs" = [
"mkdocs-material<9.0",
"mkdocs-git-revision-date-localized-plugin",
"mkdocs-redirects==1.2.0",
]
feat: add profiler util (#5601) * feat(config): add profiling config settings - `profile_graphs` enables graph profiling with cProfile - `profiles_dir` sets the output for profiles * feat(nodes): add Profiler util Simple wrapper around cProfile. * feat(nodes): use Profiler in invocation processor * scripts: add generate_profile_graphs.sh script Helper to generate graphs for profiles. * pkg: add snakeviz and gprof2dot to dev deps These are useful for profiling. * tests: add tests for profiler util * fix(profiler): handle previous profile not stopped cleanly * feat(profiler): add profile_prefix config setting The prefix is used when writing profile output files. Useful to organise profiles into sessions. * tidy(profiler): add `_` to private API * feat(profiler): simplify API * feat(profiler): use child logger for profiler logs * chore(profiler): update docstrings * feat(profiler): stop() returns output path * chore(profiler): fix docstring * tests(profiler): update tests * chore: ruff
2024-01-31 10:51:57 +00:00
"dev" = ["jurigged", "pudb", "snakeviz", "gprof2dot"]
Resolving merge conflicts for flake8
2023-08-17 22:45:25 +00:00
"test" = [
chore: bump deps - ruff 0.1.11 -> 0.2.1 - update config format
2024-02-10 13:36:54 +00:00
"ruff==0.2.1",
feat: add ruff-lsp to support most editors
2023-11-10 23:24:53 +00:00
"ruff-lsp",
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
"mypy",
Adding pre-commit to test dependencies
2023-08-20 01:17:44 +00:00
"pre-commit",
Resolving merge conflicts for flake8
2023-08-17 22:45:25 +00:00
"pytest>6.0.0",
"pytest-cov",
"pytest-datadir",
[feature] Download Queue (#5225) * add base definition of download manager * basic functionality working * add unit tests for download queue * add documentation and FastAPI route * fix docs * add missing test dependency; fix import ordering * fix file path length checking on windows * fix ruff check error * move release() into the __del__ method * disable testing of stderr messages due to issues with pytest capsys fixture * fix unsorted imports * harmonized implementation of start() and stop() calls in download and & install modules * Update invokeai/app/services/download/download_base.py Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> * replace test datadir fixture with tmp_path * replace DownloadJobBase->DownloadJob in download manager documentation * make source and dest arguments to download_queue.download() an AnyHttpURL and Path respectively * fix pydantic typecheck errors in the download unit test * ruff formatting * add "job cancelled" as an event rather than an exception * fix ruff errors * Update invokeai/app/services/download/download_default.py Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com> * use threading.Event to stop service worker threads; handle unfinished job edge cases * remove dangling STOP job definition * fix ruff complaint * fix ruff check again * avoid race condition when start() and stop() are called simultaneously from different threads * avoid race condition in stop() when a job becomes active while shutting down --------- Co-authored-by: Lincoln Stein <lstein@gmail.com> Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com> Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
2023-12-22 17:35:57 +00:00
"requests_testadapter",
Resolving merge conflicts for flake8
2023-08-17 22:45:25 +00:00
]
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
[project.scripts]
(scripts) improved script entrypoints
2023-01-28 21:56:14 +00:00
# legacy entrypoints; provided for backwards compatibility
clip cache settings to permissible values; remove redundant imports in install __init__ file
2023-08-09 18:55:02 +00:00
"configure_invokeai.py" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
all vestiges of ldm.invoke removed
2023-03-03 06:02:00 +00:00
"textual_inversion.py" = "invokeai.frontend.training:invokeai_textual_inversion"
(scripts) improved script entrypoints
2023-01-28 21:56:14 +00:00
Update pyproject.toml
2023-10-18 00:20:06 +00:00
# shortcut commands to start web ui
restore ability to launch web server with invokeai --web
2023-07-07 14:07:15 +00:00
# "invokeai --web" will launch the web interface
remove all references to CLI
2023-10-17 16:59:48 +00:00
# "invokeai" = "invokeai.frontend.legacy_launch_invokeai:main"
restore ability to launch web server with invokeai --web
2023-07-07 14:07:15 +00:00
# new shortcut to launch web interface
partial port of invokeai-configure
2023-05-16 05:50:01 +00:00
"invokeai-web" = "invokeai.app.api_app:invoke_api"
# full commands
clip cache settings to permissible values; remove redundant imports in install __init__ file
2023-08-09 18:55:02 +00:00
"invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
all files migrated; tweaks needed
2023-03-03 05:02:15 +00:00
"invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers"
Port the command-line tools to use model_manager2 (#5546) * Port the command-line tools to use model_manager2 1.Reimplement the following: - invokeai-model-install - invokeai-merge - invokeai-ti To avoid breaking the original modeal manager, the udpated tools have been renamed invokeai-model-install2 and invokeai-merge2. The textual inversion training script should continue to work with existing installations. The "starter" models now live in `invokeai/configs/INITIAL_MODELS2.yaml`. When the full model manager 2 is in place and working, I'll rename these files and commands. 2. Add the `merge` route to the web API. This will merge two or three models, resulting a new one. - Note that because the model installer selectively installs the `fp16` variant of models (rather than both 16- and 32-bit versions as previous), the diffusers merge script will choke on any huggingface diffuserse models that were downloaded with the new installer. Previously-downloaded models should continue to merge correctly. I have a PR upstream https://github.com/huggingface/diffusers/pull/6670 to fix this. 3. (more important!) During implementation of the CLI tools, found and fixed a number of small runtime bugs in the model_manager2 implementation: - During model database migration, if a registered models file was not found on disk, the migration would be aborted. Now the offending model is skipped with a log warning. - Caught and fixed a condition in which the installer would download the entire diffusers repo when the user provided a single `.safetensors` file URL. - Caught and fixed a condition in which the installer would raise an exception and stop the app when a request for an unknown model's metadata was passed to Civitai. Now an error is logged and the installer continues. - Replaced the LoWRA starter LoRA with FlatColor. The former has been removed from Civitai. * fix ruff issue --------- Co-authored-by: Lincoln Stein <lstein@gmail.com>
2024-02-02 17:18:47 +00:00
"invokeai-merge2" = "invokeai.frontend.merge.merge_diffusers2:main"
all files migrated; tweaks needed
2023-03-03 05:02:15 +00:00
"invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion"
clip cache settings to permissible values; remove redundant imports in install __init__ file
2023-08-09 18:55:02 +00:00
"invokeai-model-install" = "invokeai.frontend.install.model_install:main"
chore: bump deps - ruff 0.1.11 -> 0.2.1 - update config format
2024-02-10 13:36:54 +00:00
"invokeai-model-install2" = "invokeai.frontend.install.model_install2:main" # will eventually be renamed to invokeai-model-install
migration script working well
2023-06-22 19:47:12 +00:00
"invokeai-migrate3" = "invokeai.backend.install.migrate_to_3:main"
clip cache settings to permissible values; remove redundant imports in install __init__ file
2023-08-09 18:55:02 +00:00
"invokeai-update" = "invokeai.frontend.install.invokeai_update:main"
reimplement the old invokeai-metadata command
2023-09-19 18:59:44 +00:00
"invokeai-metadata" = "invokeai.backend.image_util.invoke_metadata:main"
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
2023-03-04 01:19:37 +00:00
"invokeai-node-web" = "invokeai.app.api_app:invoke_api"
add techjedi's import script, with some filecompletion tweaks
2023-08-05 16:19:24 +00:00
"invokeai-import-images" = "invokeai.frontend.install.import_images:main"
add techjedi's database maintenance script
2023-09-19 04:08:00 +00:00
"invokeai-db-maintenance" = "invokeai.backend.util.db_maintenance:main"
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
[project.urls]
"Homepage" = "https://invoke-ai.github.io/InvokeAI/"
"Documentation" = "https://invoke-ai.github.io/InvokeAI/"
"Source" = "https://github.com/invoke-ai/InvokeAI/"
"Bug Reports" = "https://github.com/invoke-ai/InvokeAI/issues"
"Discord" = "https://discord.gg/ZmtBAhwWhy"
[tool.setuptools.dynamic]
almost all of backend migrated; restoration next
2023-03-02 18:28:17 +00:00
version = { attr = "invokeai.version.__version__" }
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
[tool.setuptools.packages.find]
"where" = ["."]
first phase of source tree restructure 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 )
2023-02-28 04:52:46 +00:00
"include" = [
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
"invokeai.assets.fonts*",
"invokeai.version*",
"invokeai.generator*",
"invokeai.backend*",
"invokeai.frontend*",
"invokeai.frontend.web.dist*",
"invokeai.frontend.web.static*",
"invokeai.configs*",
"invokeai.app*",
feat(nodes): create invocation_api.py This is the public API for invocations. Everything a custom node might need should be re-exported from this file.
2024-01-14 23:48:33 +00:00
"invokeai.invocation_api*",
first phase of source tree restructure 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 )
2023-02-28 04:52:46 +00:00
]
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
[tool.setuptools.package-data]
fix(assets): re-add missing caution image
2023-10-19 16:13:36 +00:00
"invokeai.app.assets" = ["**/*.png"]
feat: workflow library (#5148) * chore: bump pydantic to 2.5.2 This release fixes pydantic/pydantic#8175 and allows us to use `JsonValue` * fix(ui): exclude public/en.json from prettier config * fix(workflow_records): fix SQLite workflow insertion to ignore duplicates * feat(backend): update workflows handling Update workflows handling for Workflow Library. **Updated Workflow Storage** "Embedded Workflows" are workflows associated with images, and are now only stored in the image files. "Library Workflows" are not associated with images, and are stored only in DB. This works out nicely. We have always saved workflows to files, but recently began saving them to the DB in addition to in image files. When that happened, we stopped reading workflows from files, so all the workflows that only existed in images were inaccessible. With this change, access to those workflows is restored, and no workflows are lost. **Updated Workflow Handling in Nodes** Prior to this change, workflows were embedded in images by passing the whole workflow JSON to a special workflow field on a node. In the node's `invoke()` function, the node was able to access this workflow and save it with the image. This (inaccurately) models workflows as a property of an image and is rather awkward technically. A workflow is now a property of a batch/session queue item. It is available in the InvocationContext and therefore available to all nodes during `invoke()`. **Database Migrations** Added a `SQLiteMigrator` class to handle database migrations. Migrations were needed to accomodate the DB-related changes in this PR. See the code for details. The `images`, `workflows` and `session_queue` tables required migrations for this PR, and are using the new migrator. Other tables/services are still creating tables themselves. A followup PR will adapt them to use the migrator. **Other/Support Changes** - Add a `has_workflow` column to `images` table to indicate that the image has an embedded workflow. - Add handling for retrieving the workflow from an image in python. The image file must be fetched, the workflow extracted, and then sent to client, avoiding needing the browser to parse the image file. With the `has_workflow` column, the UI knows if there is a workflow to be fetched, and only fetches when the user requests to load the workflow. - Add route to get the workflow from an image - Add CRUD service/routes for the library workflows - `workflow_images` table and services removed (no longer needed now that embedded workflows are not in the DB) * feat(ui): updated workflow handling (WIP) Clientside updates for the backend workflow changes. Includes roughed-out workflow library UI. * feat: revert SQLiteMigrator class Will pursue this in a separate PR. * feat(nodes): do not overwrite custom node module names Use a different, simpler method to detect if a node is custom. * feat(nodes): restore WithWorkflow as no-op class This class is deprecated and no longer needed. Set its workflow attr value to None (meaning it is now a no-op), and issue a warning when an invocation subclasses it. * fix(nodes): fix get_workflow from queue item dict func * feat(backend): add WorkflowRecordListItemDTO This is the id, name, description, created at and updated at workflow columns/attrs. Used to display lists of workflowsl * chore(ui): typegen * feat(ui): add workflow loading, deleting to workflow library UI * feat(ui): workflow library pagination button styles * wip * feat: workflow library WIP - Save to library - Duplicate - Filter/sort - UI/queries * feat: workflow library - system graphs - wip * feat(backend): sync system workflows to db * fix: merge conflicts * feat: simplify default workflows - Rename "system" -> "default" - Simplify syncing logic - Update UI to match * feat(workflows): update default workflows - Update TextToImage_SD15 - Add TextToImage_SDXL - Add README * feat(ui): refine workflow list UI * fix(workflow_records): typo * fix(tests): fix tests * feat(ui): clean up workflow library hooks * fix(db): fix mis-ordered db cleanup step It was happening before pruning queue items - should happen afterwards, else you have to restart the app again to free disk space made available by the pruning. * feat(ui): tweak reset workflow editor translations * feat(ui): split out workflow redux state The `nodes` slice is a rather complicated slice. Removing `workflow` makes it a bit more reasonable. Also helps to flatten state out a bit. * docs: update default workflows README * fix: tidy up unused files, unrelated changes * fix(backend): revert unrelated service organisational changes * feat(backend): workflow_records.get_many arg "filter_text" -> "query" * feat(ui): use custom hook in current image buttons Already in use elsewhere, forgot to use it here. * fix(ui): remove commented out property * fix(ui): fix workflow loading - Different handling for loading from library vs external - Fix bug where only nodes and edges loaded * fix(ui): fix save/save-as workflow naming * fix(ui): fix circular dependency * fix(db): fix bug with releasing without lock in db.clean() * fix(db): remove extraneous lock * chore: bump ruff * fix(workflow_records): default `category` to `WorkflowCategory.User` This allows old workflows to validate when reading them from the db or image files. * hide workflow library buttons if feature is disabled --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
2023-12-08 22:48:38 +00:00
"invokeai.app.services.workflow_records.default_workflows" = ["*.json"]
add font Inter-Regular.ttf to installed assets
2023-10-03 12:48:50 +00:00
"invokeai.assets.fonts" = ["**/*.ttf"]
Various fixes 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.
2023-01-30 23:42:17 +00:00
"invokeai.backend" = ["**.png"]
"invokeai.configs" = ["*.example", "**/*.yaml", "*.txt"]
all files migrated; tweaks needed
2023-03-03 05:02:15 +00:00
"invokeai.frontend.web.dist" = ["**"]
move static into invokeai.frontend.web directory for dist install
2023-05-22 20:48:17 +00:00
"invokeai.frontend.web.static" = ["**"]
fix(nodes): explicitly include custom nodes files setuptools ignores markdown files - explicitly include all files in `"invokeai.app.invocations"` to ensure all custom node files are included
2023-10-20 04:18:29 +00:00
"invokeai.app.invocations" = ["**"]
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
combine pytest.ini with pyproject.toml
2023-03-05 17:00:08 +00:00
#=== Begin: PyTest and Coverage
replace legacy setup.py with pyproject.toml 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
2023-01-17 02:00:02 +00:00
[tool.pytest.ini_options]
POC of a test that depends on models.
2023-09-22 22:44:10 +00:00
addopts = "--cov-report term --cov-report html --cov-report xml --strict-markers -m \"not slow\""
markers = [
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
"slow: Marks tests as slow. Disabled by default. To run all tests, use -m \"\". To run only slow tests, use -m \"slow\".",
POC of a test that depends on models.
2023-09-22 22:44:10 +00:00
]
combine pytest.ini with pyproject.toml
2023-03-05 17:00:08 +00:00
[tool.coverage.run]
branch = true
source = ["invokeai"]
omit = ["*tests*", "*migrations*", ".venv/*", "*.env"]
[tool.coverage.report]
show_missing = true
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
fail_under = 85 # let's set something sensible on Day 1 ...
combine pytest.ini with pyproject.toml
2023-03-05 17:00:08 +00:00
[tool.coverage.json]
output = "coverage/coverage.json"
pretty_print = true
[tool.coverage.html]
directory = "coverage/html"
[tool.coverage.xml]
output = "coverage/index.xml"
#=== End: PyTest and Coverage
all vestiges of ldm.invoke removed
2023-03-03 06:02:00 +00:00
feat: replace isort, flake8 & black with ruff
2023-11-10 23:06:21 +00:00
#=== Begin: Ruff
[tool.ruff]
line-length = 120
Adding excludes to flake8 config
2023-09-16 15:47:05 +00:00
exclude = [
".git",
"__pycache__",
"build",
"dist",
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
"invokeai/frontend/web/node_modules/",
Enforce Unix line endings in container (#4990) * (fix) enforce Unix (LF) line endings in docker/ directory * (fix) update docker docs wrt line endings on Windows * (fix) static check fixes
2023-10-30 16:34:30 +00:00
".venv*",
Adding excludes to flake8 config
2023-09-16 15:47:05 +00:00
]
chore: bump deps - ruff 0.1.11 -> 0.2.1 - update config format
2024-02-10 13:36:54 +00:00
[tool.ruff.lint]
ignore = [
"E501", # https://docs.astral.sh/ruff/rules/line-too-long/
"C901", # https://docs.astral.sh/ruff/rules/complex-structure/
"B008", # https://docs.astral.sh/ruff/rules/function-call-in-default-argument/
"B904", # https://docs.astral.sh/ruff/rules/raise-without-from-inside-except/
]
select = ["B", "C", "E", "F", "W", "I"]
feat: replace isort, flake8 & black with ruff
2023-11-10 23:06:21 +00:00
#=== End: Ruff
Run python black
2023-07-28 13:46:44 +00:00
feat: replace isort, flake8 & black with ruff
2023-11-10 23:06:21 +00:00
#=== Begin: MyPy
feat: update mypy config - Ignore one additional module - Add comments
2023-11-20 20:13:17 +00:00
# global mypy config
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
[tool.mypy]
ignore_missing_imports = true # ignores missing types in third-party libraries
make install_path and register_path work; refactor model probing
2023-11-24 04:15:32 +00:00
strict = true
Model Manager Refactor: Install remote models and store their tags and other metadata (#5361) * add basic functionality for model metadata fetching from hf and civitai * add storage * start unit tests * add unit tests and documentation * add missing dependency for pytests * remove redundant fetch; add modified/published dates; updated docs * add code to select diffusers files based on the variant type * implement Civitai installs * make huggingface parallel downloading work * add unit tests for model installation manager - Fixed race condition on selection of download destination path - Add fixtures common to several model_manager_2 unit tests - Added dummy model files for testing diffusers and safetensors downloading/probing - Refactored code for selecting proper variant from list of huggingface repo files - Regrouped ordering of methods in model_install_default.py * improve Civitai model downloading - Provide a better error message when Civitai requires an access token (doesn't give a 403 forbidden, but redirects to the HTML of an authorization page -- arrgh) - Handle case of Civitai providing a primary download link plus additional links for VAEs, config files, etc * add routes for retrieving metadata and tags * code tidying and documentation * fix ruff errors * add file needed to maintain test root diretory in repo for unit tests * fix self->cls in classmethod * add pydantic plugin for mypy * use TestSession instead of requests.Session to prevent any internet activity improve logging fix error message formatting fix logging again fix forward vs reverse slash issue in Windows install tests * Several fixes of problems detected during PR review: - Implement cancel_model_install_job and get_model_install_job routes to allow for better control of model download and install. - Fix thread deadlock that occurred after cancelling an install. - Remove unneeded pytest_plugins section from tests/conftest.py - Remove unused _in_terminal_state() from model_install_default. - Remove outdated documentation from several spots. - Add workaround for Civitai API results which don't return correct URL for the default model. * fix docs and tests to match get_job_by_source() rather than get_job() * Update invokeai/backend/model_manager/metadata/fetch/huggingface.py Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> * Call CivitaiMetadata.model_validate_json() directly Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> * Second round of revisions suggested by @ryanjdick: - Fix type mismatch in `list_all_metadata()` route. - Do not have a default value for the model install job id - Remove static class variable declarations from non Pydantic classes - Change `id` field to `model_id` for the sqlite3 `model_tags` table. - Changed AFTER DELETE triggers to ON DELETE CASCADE for the metadata and tags tables. - Made the `id` field of the `model_metadata` table into a primary key to achieve uniqueness. * Code cleanup suggested in PR review: - Narrowed the declaration of the `parts` attribute of the download progress event - Removed auto-conversion of str to Url in Url-containing sources - Fixed handling of `InvalidModelConfigException` - Made unknown sources raise `NotImplementedError` rather than `Exception` - Improved status reporting on cached HuggingFace access tokens * Multiple fixes: - `job.total_size` returns a valid size for locally installed models - new route `list_models` returns a paged summary of model, name, description, tags and other essential info - fix a few type errors * consolidated all invokeai root pytest fixtures into a single location * Update invokeai/backend/model_manager/metadata/metadata_store.py Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com> * Small tweaks in response to review comments: - Remove flake8 configuration from pyproject.toml - Use `id` rather than `modelId` for huggingface `ModelInfo` object - Use `last_modified` rather than `LastModified` for huggingface `ModelInfo` object - Add `sha256` field to file metadata downloaded from huggingface - Add `Invoker` argument to the model installer `start()` and `stop()` routines (but made it optional in order to facilitate use of the service outside the API) - Removed redundant `PRAGMA foreign_keys` from metadata store initialization code. * Additional tweaks and minor bug fixes - Fix calculation of aggregate diffusers model size to only count the size of files, not files + directories (which gives different unit test results on different filesystems). - Refactor _get_metadata() and _get_download_urls() to have distinct code paths for Civitai, HuggingFace and URL sources. - Forward the `inplace` flag from the source to the job and added unit test for this. - Attach cached model metadata to the job rather than to the model install service. * fix unit test that was breaking on windows due to CR/LF changing size of test json files * fix ruff formatting * a few last minor fixes before merging: - Turn job `error` and `error_type` into properties derived from the exception. - Add TODO comment about the reason for handling temporary directory destruction manually rather than using tempfile.tmpdir(). * add unit tests for reporting HTTP download errors --------- Co-authored-by: Lincoln Stein <lstein@gmail.com> Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2024-01-14 19:54:53 +00:00
plugins = "pydantic.mypy"
make install_path and register_path work; refactor model probing
2023-11-24 04:15:32 +00:00
exclude = ["tests/*"]
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
feat: update mypy config - Ignore one additional module - Add comments
2023-11-20 20:13:17 +00:00
# overrides for specific modules
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
[[tool.mypy.overrides]]
feat: update mypy config - Ignore one additional module - Add comments
2023-11-20 20:13:17 +00:00
follow_imports = "skip" # skips type checking of the modules listed below
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
module = [
"invokeai.app.api.routers.models",
"invokeai.app.invocations.compel",
"invokeai.app.invocations.latent",
"invokeai.app.services.invocation_stats.invocation_stats_default",
"invokeai.app.services.model_manager.model_manager_base",
"invokeai.app.services.model_manager.model_manager_default",
feat: update mypy config - Ignore one additional module - Add comments
2023-11-20 20:13:17 +00:00
"invokeai.app.services.model_records.model_records_sql",
feat(api): chore: pydantic & fastapi upgrade Upgrade pydantic and fastapi to latest. - pydantic~=2.4.2 - fastapi~=103.2 - fastapi-events~=0.9.1 **Big Changes** There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes. **Invocations** The biggest change relates to invocation creation, instantiation and validation. Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie. Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`. With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation. This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method. In the end, this implementation is cleaner. **Invocation Fields** In pydantic v2, you can no longer directly add or remove fields from a model. Previously, we did this to add the `type` field to invocations. **Invocation Decorators** With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper. A similar technique is used for `invocation_output()`. **Minor Changes** There are a number of minor changes around the pydantic v2 models API. **Protected `model_` Namespace** All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_". Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple. ```py class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") model_config = ConfigDict(protected_namespaces=()) ``` **Model Serialization** Pydantic models no longer have `Model.dict()` or `Model.json()`. Instead, we use `Model.model_dump()` or `Model.model_dump_json()`. **Model Deserialization** Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions. Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model. ```py adapter_graph = TypeAdapter(Graph) deserialized_graph_from_json = adapter_graph.validate_json(graph_json) deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict) ``` **Field Customisation** Pydantic `Field`s no longer accept arbitrary args. Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field. **Schema Customisation** FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec. This necessitates two changes: - Our schema customization logic has been revised - Schema parsing to build node templates has been revised The specific aren't important, but this does present additional surface area for bugs. **Performance Improvements** Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node. I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-09-24 08:11:07 +00:00
"invokeai.app.util.controlnet_utils",
"invokeai.backend.image_util.txt2mask",
"invokeai.backend.image_util.safety_checker",
"invokeai.backend.image_util.patchmatch",
"invokeai.backend.image_util.invisible_watermark",
"invokeai.backend.install.model_install_backend",
"invokeai.backend.ip_adapter.ip_adapter",
"invokeai.backend.ip_adapter.resampler",
"invokeai.backend.ip_adapter.unet_patcher",
"invokeai.backend.model_management.convert_ckpt_to_diffusers",
"invokeai.backend.model_management.lora",
"invokeai.backend.model_management.model_cache",
"invokeai.backend.model_management.model_manager",
"invokeai.backend.model_management.model_merge",
"invokeai.backend.model_management.model_probe",
"invokeai.backend.model_management.model_search",
"invokeai.backend.model_management.models.*", # this is needed to ignore the module's `__init__.py`
"invokeai.backend.model_management.models.base",
"invokeai.backend.model_management.models.controlnet",
"invokeai.backend.model_management.models.ip_adapter",
"invokeai.backend.model_management.models.lora",
"invokeai.backend.model_management.models.sdxl",
"invokeai.backend.model_management.models.stable_diffusion",
"invokeai.backend.model_management.models.vae",
"invokeai.backend.model_management.seamless",
"invokeai.backend.model_management.util",
"invokeai.backend.stable_diffusion.diffusers_pipeline",
"invokeai.backend.stable_diffusion.diffusion.cross_attention_control",
"invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion",
"invokeai.backend.util.hotfixes",
"invokeai.backend.util.mps_fixes",
"invokeai.backend.util.util",
"invokeai.frontend.install.model_install",
]
feat: replace isort, flake8 & black with ruff
2023-11-10 23:06:21 +00:00
#=== End: MyPy
feat: add pyright config I was having issues with mypy bother over- and under-reporting certain problems. I've added a pyright config.
2024-01-13 12:23:01 +00:00
[tool.pyright]
feat: tweak pyright config
2024-01-16 08:19:49 +00:00
# Start from strict mode
typeCheckingMode = "strict"
# This errors whenever an import is missing a type stub file - way too noisy
reportMissingTypeStubs = "none"
# These are the rest of the rules enabled by strict mode - enable them @ warning
reportConstantRedefinition = "warning"
reportDeprecated = "warning"
reportDuplicateImport = "warning"
reportIncompleteStub = "warning"
reportInconsistentConstructor = "warning"
reportInvalidStubStatement = "warning"
reportMatchNotExhaustive = "warning"
reportMissingParameterType = "warning"
reportMissingTypeArgument = "warning"
reportPrivateUsage = "warning"
reportTypeCommentUsage = "warning"
reportUnknownArgumentType = "warning"
reportUnknownLambdaType = "warning"
reportUnknownMemberType = "warning"
reportUnknownParameterType = "warning"
reportUnknownVariableType = "warning"
reportUnnecessaryCast = "warning"
reportUnnecessaryComparison = "warning"
reportUnnecessaryContains = "warning"
reportUnnecessaryIsInstance = "warning"
reportUnusedClass = "warning"
reportUnusedImport = "warning"
reportUnusedFunction = "warning"
reportUnusedVariable = "warning"
reportUntypedBaseClass = "warning"
reportUntypedClassDecorator = "warning"
reportUntypedFunctionDecorator = "warning"
reportUntypedNamedTuple = "warning"
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