InvokeAI/pyproject.toml

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[build-system]
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requires = ["setuptools~=65.5", "pip~=22.3", "wheel"]
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"
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requires-python = ">=3.9, <3.12"
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 = [
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"accelerate~=0.21.0",
"albumentations",
"click",
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"clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
"compel~=2.0.0",
"controlnet-aux>=0.0.6",
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"timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26
"datasets",
"diffusers[torch]~=0.20.0",
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"dnspython~=2.4.0",
"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>
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"easing-functions",
"einops",
"facexlib",
"fastapi==0.88.0",
"fastapi-events==0.8.0",
"fastapi-socketio==0.0.10",
"huggingface-hub~=0.16.4",
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"invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
"matplotlib", # needed for plotting of Penner easing functions
"mediapipe", # needed for "mediapipeface" controlnet model
"numpy",
"npyscreen",
"omegaconf",
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"onnx",
"onnxruntime",
"opencv-python",
"pydantic==1.*",
"picklescan",
"pillow",
"prompt-toolkit",
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"pympler~=1.0.1",
"pypatchmatch",
'pyperclip',
"pyreadline3",
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"python-multipart",
"pytorch-lightning",
"realesrgan",
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"requests~=2.28.2",
"rich~=13.3",
"safetensors==0.3.1",
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"scikit-image~=0.21.0",
"send2trash",
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"test-tube~=0.7.5",
"torch~=2.0.1",
"torchvision~=0.15.2",
"torchmetrics~=0.11.0",
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"torchsde~=0.2.5",
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"transformers~=4.31.0",
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"uvicorn[standard]~=0.21.1",
"windows-curses; sys_platform=='win32'",
]
[project.optional-dependencies]
"dist" = ["pip-tools", "pipdeptree", "twine"]
"docs" = [
"mkdocs-material<9.0",
"mkdocs-git-revision-date-localized-plugin",
"mkdocs-redirects==1.2.0",
]
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"dev" = [
"jurigged",
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"pudb",
]
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"test" = [
"black",
"flake8",
"Flake8-pyproject",
"pytest>6.0.0",
"pytest-cov",
"pytest-datadir",
]
"xformers" = [
"xformers~=0.0.19; sys_platform!='darwin'",
"triton; sys_platform=='linux'",
]
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"onnx" = [
"onnxruntime",
]
"onnx-cuda" = [
"onnxruntime-gpu",
]
"onnx-directml" = [
"onnxruntime-directml",
]
[project.scripts]
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# legacy entrypoints; provided for backwards compatibility
"configure_invokeai.py" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
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"textual_inversion.py" = "invokeai.frontend.training:invokeai_textual_inversion"
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# shortcut commands to start cli and web
# "invokeai --web" will launch the web interface
# "invokeai" will launch the CLI
"invokeai" = "invokeai.frontend.legacy_launch_invokeai:main"
# new shortcut to launch web interface
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"invokeai-web" = "invokeai.app.api_app:invoke_api"
# full commands
"invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
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"invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers"
"invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion"
"invokeai-model-install" = "invokeai.frontend.install.model_install:main"
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"invokeai-migrate3" = "invokeai.backend.install.migrate_to_3:main"
"invokeai-update" = "invokeai.frontend.install.invokeai_update:main"
"invokeai-metadata" = "invokeai.frontend.CLI.sd_metadata:print_metadata"
"invokeai-node-cli" = "invokeai.app.cli_app:invoke_cli"
"invokeai-node-web" = "invokeai.app.api_app:invoke_api"
"invokeai-import-images" = "invokeai.frontend.install.import_images:main"
[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]
version = { attr = "invokeai.version.__version__" }
[tool.setuptools.packages.find]
"where" = ["."]
"include" = [
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"invokeai.assets.web*","invokeai.version*",
"invokeai.generator*","invokeai.backend*",
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"invokeai.frontend*", "invokeai.frontend.web.dist*",
"invokeai.frontend.web.static*",
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"invokeai.configs*",
"invokeai.app*","ldm*",
]
[tool.setuptools.package-data]
"invokeai.assets.web" = ["**.png","**.js","**.woff2","**.css"]
"invokeai.backend" = ["**.png"]
"invokeai.configs" = ["*.example", "**/*.yaml", "*.txt"]
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"invokeai.frontend.web.dist" = ["**"]
"invokeai.frontend.web.static" = ["**"]
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#=== Begin: PyTest and Coverage
[tool.pytest.ini_options]
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addopts = "--cov-report term --cov-report html --cov-report xml"
[tool.coverage.run]
branch = true
source = ["invokeai"]
omit = ["*tests*", "*migrations*", ".venv/*", "*.env"]
[tool.coverage.report]
show_missing = true
fail_under = 85 # let's set something sensible on Day 1 ...
[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
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[tool.flake8]
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max-line-length = 120
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ignore = ["E203", "E266", "E501", "W503"]
select = ["B", "C", "E", "F", "W", "T4"]
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[tool.black]
line-length = 120