From 956ad6bcf533dfa92f94caafc1c063d46a487a2e Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Fri, 28 Apr 2023 00:41:52 -0400 Subject: [PATCH 001/225] add redesigned model cache for diffusers & transformers --- .../backend/model_management/model_cache.py | 203 ++++++++++++++++++ .../backend/model_management/model_manager.py | 7 +- .../backend/stable_diffusion/offloading.py | 10 +- pyproject.toml | 1 + 4 files changed, 211 insertions(+), 10 deletions(-) create mode 100644 invokeai/backend/model_management/model_cache.py diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py new file mode 100644 index 0000000000..0ef97e3532 --- /dev/null +++ b/invokeai/backend/model_management/model_cache.py @@ -0,0 +1,203 @@ +""" +Manage a cache of Stable Diffusion model files for fast switching. +They are moved between GPU and CPU as necessary. If the cache +grows larger than a preset maximum, then the least recently used +model will be cleared and (re)loaded from disk when next needed. +""" + +import contextlib +import hashlib +import gc +import time +import os +import psutil + +import safetensors +import safetensors.torch +import torch +import transformers +import warnings + +from pathlib import Path +from diffusers import ( + AutoencoderKL, + UNet2DConditionModel, + SchedulerMixin, + logging as diffusers_logging, +) +from transformers import( + CLIPTokenizer, + CLIPFeatureExtractor, + CLIPTextModel, + logging as transformers_logging, +) +from huggingface_hub import scan_cache_dir +from omegaconf import OmegaConf +from omegaconf.dictconfig import DictConfig +from picklescan.scanner import scan_file_path +from typing import Sequence, Union + +from invokeai.backend.globals import Globals, global_cache_dir +from diffusers.pipelines.stable_diffusion.safety_checker import ( + StableDiffusionSafetyChecker, + ) +from ..stable_diffusion import ( + StableDiffusionGeneratorPipeline, +) +from ..stable_diffusion.offloading import ModelGroup, FullyLoadedModelGroup +from ..util import CUDA_DEVICE, ask_user, download_with_resume + +MAX_MODELS_CACHED = 4 + +class ModelCache(object): + def __init__( + self, + max_models_cached: int=MAX_MODELS_CACHED, + execution_device: torch.device=torch.device('cuda'), + precision: torch.dtype=torch.float16, + sequential_offload: bool=False, + ): + self.model_group: ModelGroup=FullyLoadedModelGroup(execution_device) + self.models: dict = dict() + self.stack: Sequence = list() + self.sequential_offload: bool=sequential_offload + self.precision: torch.dtype=precision + self.max_models_cached: int=max_models_cached + self.device: torch.device=execution_device + + def get_model( + self, + repo_id_or_path: Union[str,Path], + model_class: type=StableDiffusionGeneratorPipeline, + subfolder: Path=None, + revision: str=None, + )->Union[ + AutoencoderKL, + CLIPTokenizer, + CLIPFeatureExtractor, + CLIPTextModel, + UNet2DConditionModel, + StableDiffusionSafetyChecker, + StableDiffusionGeneratorPipeline, + ]: + ''' + Load and return a HuggingFace model, with RAM caching. + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param revision: model revision + :param model_class: class of model to return + ''' + key = self._model_key(repo_id_or_path,model_class,revision,subfolder) # internal unique identifier for the model + if key in self.models: # cached - move to bottom of stack + previous_key = self._current_model_key + with contextlib.suppress(ValueError): + self.stack.remove(key) + self.stack.append(key) + if previous_key != key: + if hasattr(self.current_model,'to'): + print(f'DEBUG: loading {key} into GPU') + self.model_group.offload_current() + self.model_group.load(self.models[key]) + + else: # not cached -load + self._make_cache_room() + self.model_group.offload_current() + print(f'DEBUG: loading {key} from disk/net') + model = self._load_model_from_storage( + repo_id_or_path=repo_id_or_path, + subfolder=subfolder, + revision=revision, + model_class=model_class + ) + if hasattr(model,'to'): + self.model_group.install(model) # register with the model group + self.stack.append(key) # add to LRU cache + self.models[key]=model # keep copy of model in dict + return self.models[key] + + @staticmethod + def _model_key(path,model_class,revision,subfolder)->str: + return ':'.join([str(path),str(model_class),str(revision),str(subfolder)]) + + def _make_cache_room(self): + models_in_ram = len(self.models) + while models_in_ram >= self.max_models_cached: + if least_recently_used_key := self.stack.pop(0): + print(f'DEBUG: maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') + self.model_group.uninstall(self.models[least_recently_used_key]) + del self.models[least_recently_used_key] + models_in_ram = len(self.models) + gc.collect() + + @property + def current_model(self)->Union[ + AutoencoderKL, + CLIPTokenizer, + CLIPFeatureExtractor, + CLIPTextModel, + UNet2DConditionModel, + StableDiffusionSafetyChecker, + StableDiffusionGeneratorPipeline, + ]: + ''' + Returns current model. + ''' + return self.models[self._current_model_key] + + @property + def _current_model_key(self)->str: + ''' + Returns key of currently loaded model. + ''' + return self.stack[-1] + + def _load_model_from_storage( + self, + repo_id_or_path: Union[str,Path], + subfolder: Path=None, + revision: str=None, + model_class: type=StableDiffusionGeneratorPipeline, + )->Union[ + AutoencoderKL, + CLIPTokenizer, + CLIPFeatureExtractor, + CLIPTextModel, + UNet2DConditionModel, + StableDiffusionSafetyChecker, + StableDiffusionGeneratorPipeline, + ]: + ''' + Load and return a HuggingFace model. + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param revision: model revision + :param model_class: class of model to return + ''' + # silence transformer and diffuser warnings + with SilenceWarnings(): + model = model_class.from_pretrained( + repo_id_or_path, + revision=revision, + subfolder=subfolder or '.', + cache_dir=global_cache_dir('hub'), + ) + if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): + model.enable_offload_submodels(self.device) + elif hasattr(model,'to'): + model.to(self.device) + return model + +class SilenceWarnings(object): + def __init__(self): + self.transformers_verbosity = transformers_logging.get_verbosity() + self.diffusers_verbosity = diffusers_logging.get_verbosity() + + def __enter__(self): + transformers_logging.set_verbosity_error() + diffusers_logging.set_verbosity_error() + warnings.simplefilter('ignore') + + def __exit__(self,type,value,traceback): + transformers_logging.set_verbosity(self.transformers_verbosity) + diffusers_logging.set_verbosity(self.diffusers_verbosity) + warnings.simplefilter('default') diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 534b526081..68ecd3bcca 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -1,4 +1,4 @@ -"""enum +""" Manage a cache of Stable Diffusion model files for fast switching. They are moved between GPU and CPU as necessary. If CPU memory falls below a preset minimum, the least recently used model will be @@ -1108,11 +1108,8 @@ class ModelManager(object): >> invokeai/models/diffusers to invokeai/models/hub due to a change introduced by >> diffusers version 0.14. InvokeAI will now move all models from the "diffusers" directory >> into "hub" and then remove the diffusers directory. This is a quick, safe, one-time ->> operation. However if you have customized either of these directories and need to ->> make adjustments, please press ctrl-C now to abort and relaunch InvokeAI when you are ready. ->> Otherwise press to continue.""" +>> operation.""" ) - input("continue> ") # transformer files get moved into the hub directory if cls._is_huggingface_hub_directory_present(): diff --git a/invokeai/backend/stable_diffusion/offloading.py b/invokeai/backend/stable_diffusion/offloading.py index 871f994732..5fc3f765ae 100644 --- a/invokeai/backend/stable_diffusion/offloading.py +++ b/invokeai/backend/stable_diffusion/offloading.py @@ -157,7 +157,7 @@ class LazilyLoadedModelGroup(ModelGroup): def offload_current(self): module = self._current_model_ref() if module is not NO_MODEL: - module.to(device=OFFLOAD_DEVICE) + module.to(OFFLOAD_DEVICE) self.clear_current_model() def _load(self, module: torch.nn.Module) -> torch.nn.Module: @@ -228,7 +228,7 @@ class FullyLoadedModelGroup(ModelGroup): def install(self, *models: torch.nn.Module): for model in models: self._models.add(model) - model.to(device=self.execution_device) + model.to(self.execution_device) def uninstall(self, *models: torch.nn.Module): for model in models: @@ -238,11 +238,11 @@ class FullyLoadedModelGroup(ModelGroup): self.uninstall(*self._models) def load(self, model): - model.to(device=self.execution_device) + model.to(self.execution_device) def offload_current(self): for model in self._models: - model.to(device=OFFLOAD_DEVICE) + model.to(OFFLOAD_DEVICE) def ready(self): for model in self._models: @@ -252,7 +252,7 @@ class FullyLoadedModelGroup(ModelGroup): self.execution_device = device for model in self._models: if model.device != OFFLOAD_DEVICE: - model.to(device=device) + model.to(device) def device_for(self, model): if model not in self: diff --git a/pyproject.toml b/pyproject.toml index 0ab1e0ede6..38547d0d11 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,7 @@ dependencies = [ "picklescan", "pillow", "prompt-toolkit", + "pympler==1.0.1", "pypatchmatch", "pyreadline3", "python-multipart==0.0.6", From 2e2abf6ea69f5615de37c2bde650c80eba2e5c76 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Mon, 1 May 2023 22:57:30 -0400 Subject: [PATCH 002/225] caching of subparts working --- .../backend/model_management/model_cache.py | 76 ++++++++++--------- tests/test_model_cache.py | 53 +++++++++++++ 2 files changed, 95 insertions(+), 34 deletions(-) create mode 100644 tests/test_model_cache.py diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 0ef97e3532..997a42ad3e 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -18,6 +18,7 @@ import torch import transformers import warnings +from enum import Enum from pathlib import Path from diffusers import ( AutoencoderKL, @@ -32,8 +33,6 @@ from transformers import( logging as transformers_logging, ) from huggingface_hub import scan_cache_dir -from omegaconf import OmegaConf -from omegaconf.dictconfig import DictConfig from picklescan.scanner import scan_file_path from typing import Sequence, Union @@ -48,7 +47,21 @@ from ..stable_diffusion.offloading import ModelGroup, FullyLoadedModelGroup from ..util import CUDA_DEVICE, ask_user, download_with_resume MAX_MODELS_CACHED = 4 + +# This is the mapping from the stable diffusion submodel dict key to the class +class SDModelType(Enum): + diffusion_pipeline=StableDiffusionGeneratorPipeline # whole thing + vae=AutoencoderKL # parts + text_encoder=CLIPTextModel + tokenizer=CLIPTokenizer + unet=UNet2DConditionModel + scheduler=SchedulerMixin + safety_checker=StableDiffusionSafetyChecker + feature_extractor=CLIPFeatureExtractor +# List the model classes we know how to fetch +ModelClass = Union[tuple([x.value for x in SDModelType])] + class ModelCache(object): def __init__( self, @@ -65,21 +78,27 @@ class ModelCache(object): self.max_models_cached: int=max_models_cached self.device: torch.device=execution_device + def get_submodel( + self, + repo_id_or_path: Union[str,Path], + submodel: SDModelType=SDModelType.vae, + subfolder: Path=None, + revision: str=None, + )->ModelClass: + parent_model = self.get_model( + repo_id_or_path=repo_id_or_path, + subfolder=subfolder, + revision=revision, + ) + return getattr(parent_model, submodel.name) + def get_model( self, repo_id_or_path: Union[str,Path], - model_class: type=StableDiffusionGeneratorPipeline, + model_type: SDModelType=SDModelType.diffusion_pipeline, subfolder: Path=None, revision: str=None, - )->Union[ - AutoencoderKL, - CLIPTokenizer, - CLIPFeatureExtractor, - CLIPTextModel, - UNet2DConditionModel, - StableDiffusionSafetyChecker, - StableDiffusionGeneratorPipeline, - ]: + )->ModelClass: ''' Load and return a HuggingFace model, with RAM caching. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model @@ -87,7 +106,12 @@ class ModelCache(object): :param revision: model revision :param model_class: class of model to return ''' - key = self._model_key(repo_id_or_path,model_class,revision,subfolder) # internal unique identifier for the model + key = self._model_key( + repo_id_or_path, + model_type.value, + revision, + subfolder + ) # internal unique identifier for the model if key in self.models: # cached - move to bottom of stack previous_key = self._current_model_key with contextlib.suppress(ValueError): @@ -105,9 +129,9 @@ class ModelCache(object): print(f'DEBUG: loading {key} from disk/net') model = self._load_model_from_storage( repo_id_or_path=repo_id_or_path, + model_class=model_type.value, subfolder=subfolder, revision=revision, - model_class=model_class ) if hasattr(model,'to'): self.model_group.install(model) # register with the model group @@ -117,7 +141,7 @@ class ModelCache(object): @staticmethod def _model_key(path,model_class,revision,subfolder)->str: - return ':'.join([str(path),str(model_class),str(revision),str(subfolder)]) + return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')]) def _make_cache_room(self): models_in_ram = len(self.models) @@ -130,15 +154,7 @@ class ModelCache(object): gc.collect() @property - def current_model(self)->Union[ - AutoencoderKL, - CLIPTokenizer, - CLIPFeatureExtractor, - CLIPTextModel, - UNet2DConditionModel, - StableDiffusionSafetyChecker, - StableDiffusionGeneratorPipeline, - ]: + def current_model(self)->ModelClass: ''' Returns current model. ''' @@ -156,16 +172,8 @@ class ModelCache(object): repo_id_or_path: Union[str,Path], subfolder: Path=None, revision: str=None, - model_class: type=StableDiffusionGeneratorPipeline, - )->Union[ - AutoencoderKL, - CLIPTokenizer, - CLIPFeatureExtractor, - CLIPTextModel, - UNet2DConditionModel, - StableDiffusionSafetyChecker, - StableDiffusionGeneratorPipeline, - ]: + model_class: ModelClass=StableDiffusionGeneratorPipeline, + )->ModelClass: ''' Load and return a HuggingFace model. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model diff --git a/tests/test_model_cache.py b/tests/test_model_cache.py new file mode 100644 index 0000000000..4130d8e7d5 --- /dev/null +++ b/tests/test_model_cache.py @@ -0,0 +1,53 @@ +import pytest +import torch + +from invokeai.backend.model_management.model_cache import ModelCache, SDModelType +from invokeai.backend.stable_diffusion import StableDiffusionGeneratorPipeline + +from diffusers import ( + AutoencoderKL, + UNet2DConditionModel, + SchedulerMixin, +) +from transformers import ( + CLIPTokenizer, + CLIPFeatureExtractor, + CLIPTextModel, +) + + +cache = ModelCache() + +def test_pipeline_fetch(): + model0 = cache.get_model('stabilityai/sd-vae-ft-mse',SDModelType.vae) + model1 = cache.get_model('stabilityai/stable-diffusion-2-1',SDModelType.diffusion_pipeline) + model1_2 = cache.get_model('stabilityai/stable-diffusion-2-1') + assert model1==model1_2 + assert model1.device==torch.device('cuda') + model2 = cache.get_model('runwayml/stable-diffusion-v1-5') + assert model2.device==torch.device('cuda') + assert model1.device==torch.device('cpu') + model1 = cache.get_model('stabilityai/stable-diffusion-2-1') + assert model1.device==torch.device('cuda') + +def test_submodel_fetch(): + model1_vae = cache.get_submodel('stabilityai/stable-diffusion-2-1',SDModelType.vae) + assert isinstance(model1_vae,AutoencoderKL) + model1 = cache.get_model('stabilityai/stable-diffusion-2-1',SDModelType.diffusion_pipeline) + assert model1_vae == model1.vae + model1_vae_2 = cache.get_submodel('stabilityai/stable-diffusion-2-1') + assert model1_vae == model1_vae_2 + +def test_transformer_fetch(): + model4 = cache.get_model('openai/clip-vit-large-patch14',SDModelType.tokenizer) + assert isinstance(model4,CLIPTokenizer) + + model5 = cache.get_model('openai/clip-vit-large-patch14',SDModelType.text_encoder) + assert isinstance(model5,CLIPTextModel) + +def test_subfolder_fetch(): + model6 = cache.get_model('stabilityai/stable-diffusion-2',SDModelType.tokenizer,subfolder="tokenizer") + assert isinstance(model6,CLIPTokenizer) + + model7 = cache.get_model('stabilityai/stable-diffusion-2',SDModelType.text_encoder,subfolder="text_encoder") + assert isinstance(model7,CLIPTextModel) From bb959448c18671088ef84a7152ca363b075ef59e Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Tue, 2 May 2023 16:52:27 -0400 Subject: [PATCH 003/225] implement hashing for local & remote models --- .../backend/model_management/model_cache.py | 181 ++++++++++++++++-- 1 file changed, 166 insertions(+), 15 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 997a42ad3e..616693b60f 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -20,12 +20,14 @@ import warnings from enum import Enum from pathlib import Path +from pydantic import BaseModel from diffusers import ( AutoencoderKL, UNet2DConditionModel, SchedulerMixin, logging as diffusers_logging, ) +from huggingface_hub import list_repo_refs,HfApi from transformers import( CLIPTokenizer, CLIPFeatureExtractor, @@ -36,10 +38,11 @@ from huggingface_hub import scan_cache_dir from picklescan.scanner import scan_file_path from typing import Sequence, Union -from invokeai.backend.globals import Globals, global_cache_dir from diffusers.pipelines.stable_diffusion.safety_checker import ( StableDiffusionSafetyChecker, ) +from . import load_pipeline_from_original_stable_diffusion_ckpt +from ..globals import Globals, global_cache_dir from ..stable_diffusion import ( StableDiffusionGeneratorPipeline, ) @@ -59,9 +62,14 @@ class SDModelType(Enum): safety_checker=StableDiffusionSafetyChecker feature_extractor=CLIPFeatureExtractor -# List the model classes we know how to fetch +# The list of model classes we know how to fetch, for typechecking ModelClass = Union[tuple([x.value for x in SDModelType])] +# Legacy information needed to load a legacy checkpoint file +class LegacyInfo(BaseModel): + config_file: Path + vae_file: Path + class ModelCache(object): def __init__( self, @@ -69,7 +77,15 @@ class ModelCache(object): execution_device: torch.device=torch.device('cuda'), precision: torch.dtype=torch.float16, sequential_offload: bool=False, + sha_chunksize: int = 16777216, ): + ''' + :param max_models_cached: Maximum number of models to cache in CPU RAM [4] + :param execution_device: Torch device to load active model into [torch.device('cuda')] + :param precision: Precision for loaded models [torch.float16] + :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially + :param sha_chunksize: Chunksize to use when calculating sha256 model hash + ''' self.model_group: ModelGroup=FullyLoadedModelGroup(execution_device) self.models: dict = dict() self.stack: Sequence = list() @@ -77,6 +93,7 @@ class ModelCache(object): self.precision: torch.dtype=precision self.max_models_cached: int=max_models_cached self.device: torch.device=execution_device + self.sha_chunksize=sha_chunksize def get_submodel( self, @@ -84,7 +101,16 @@ class ModelCache(object): submodel: SDModelType=SDModelType.vae, subfolder: Path=None, revision: str=None, + legacy_info: LegacyInfo=None, )->ModelClass: + ''' + Load and return a HuggingFace model, with RAM caching. + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae + :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param revision: model revision name + :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt + ''' parent_model = self.get_model( repo_id_or_path=repo_id_or_path, subfolder=subfolder, @@ -98,6 +124,7 @@ class ModelCache(object): model_type: SDModelType=SDModelType.diffusion_pipeline, subfolder: Path=None, revision: str=None, + legacy_info: LegacyInfo=None, )->ModelClass: ''' Load and return a HuggingFace model, with RAM caching. @@ -105,13 +132,14 @@ class ModelCache(object): :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" :param revision: model revision :param model_class: class of model to return + :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt ''' - key = self._model_key( + key = self._model_key( # internal unique identifier for the model repo_id_or_path, model_type.value, revision, subfolder - ) # internal unique identifier for the model + ) if key in self.models: # cached - move to bottom of stack previous_key = self._current_model_key with contextlib.suppress(ValueError): @@ -119,19 +147,19 @@ class ModelCache(object): self.stack.append(key) if previous_key != key: if hasattr(self.current_model,'to'): - print(f'DEBUG: loading {key} into GPU') + print(f' | loading {key} into GPU') self.model_group.offload_current() self.model_group.load(self.models[key]) - else: # not cached -load self._make_cache_room() self.model_group.offload_current() - print(f'DEBUG: loading {key} from disk/net') + print(f' | loading model {key} from disk/net') model = self._load_model_from_storage( repo_id_or_path=repo_id_or_path, model_class=model_type.value, subfolder=subfolder, revision=revision, + legacy_info=legacy_info, ) if hasattr(model,'to'): self.model_group.install(model) # register with the model group @@ -139,6 +167,22 @@ class ModelCache(object): self.models[key]=model # keep copy of model in dict return self.models[key] + @staticmethod + def model_hash(repo_id_or_path: Union[str,Path], + revision: str=None)->str: + ''' + Given the HF repo id or path to a model on disk, returns a unique + hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs + :param repo_id_or_path: repo_id string or Path to model file/directory on disk. + :param revision: optional revision string (if fetching a HF repo_id) + ''' + if self.is_legacy_ckpt(repo_id_or_path): + return self._legacy_model_hash(repo_id_or_path) + elif Path(repo_id_or_path).is_dir(): + return self._local_model_hash(repo_id_or_path) + else: + return self._hf_commit_hash(repo_id_or_path,revision) + @staticmethod def _model_key(path,model_class,revision,subfolder)->str: return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')]) @@ -147,7 +191,7 @@ class ModelCache(object): models_in_ram = len(self.models) while models_in_ram >= self.max_models_cached: if least_recently_used_key := self.stack.pop(0): - print(f'DEBUG: maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') + print(f' | maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') self.model_group.uninstall(self.models[least_recently_used_key]) del self.models[least_recently_used_key] models_in_ram = len(self.models) @@ -173,28 +217,135 @@ class ModelCache(object): subfolder: Path=None, revision: str=None, model_class: ModelClass=StableDiffusionGeneratorPipeline, + legacy_info: LegacyInfo=None, )->ModelClass: ''' Load and return a HuggingFace model. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" :param revision: model revision - :param model_class: class of model to return + :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline + :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt ''' # silence transformer and diffuser warnings with SilenceWarnings(): - model = model_class.from_pretrained( - repo_id_or_path, - revision=revision, - subfolder=subfolder or '.', - cache_dir=global_cache_dir('hub'), - ) + if self.is_legacy_ckpt(repo_id_or_path): + model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) + else: + model = self._load_diffusers_from_storage( + repo_id_or_path, + subfolder, + revision, + model_class, + ) if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): model.enable_offload_submodels(self.device) elif hasattr(model,'to'): model.to(self.device) return model + def _load_diffusers_from_storage( + self, + repo_id_or_path: Union[str,Path], + subfolder: Path=None, + revision: str=None, + model_class: ModelClass=StableDiffusionGeneratorPipeline, + )->ModelClass: + ''' + Load and return a HuggingFace model using from_pretrained(). + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param revision: model revision + :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline + ''' + return model_class.from_pretrained( + repo_id_or_path, + revision=revision, + subfolder=subfolder or '.', + cache_dir=global_cache_dir('hub'), + ) + + @classmethod + def is_legacy_ckpt(cls, repo_id_or_path: Union[str,Path])->bool: + ''' + Return true if the indicated path is a legacy checkpoint + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + ''' + path = Path(repo_id_or_path) + return path.is_file() and path.suffix in [".ckpt",".safetensors"] + + def _load_ckpt_from_storage(self, + ckpt_path: Union[str,Path], + legacy_info:LegacyInfo)->StableDiffusionGeneratorPipeline: + ''' + Load a legacy checkpoint, convert it, and return a StableDiffusionGeneratorPipeline. + :param ckpt_path: string or Path pointing to the weights file (.ckpt or .safetensors) + :param legacy_info: LegacyInfo object containing paths to legacy config file and alternate vae if required + ''' + assert legacy_info is not None + pipeline = load_pipeline_from_original_stable_diffusion_ckpt( + checkpoint_path=ckpt_path, + original_config_file=legacy_info.config_file, + vae_path=legacy_info.vae_file, + return_generator_pipeline=True, + precision=self.precision, + ) + return pipeline + + def _legacy_model_hash(self, checkpoint_path: Union[str,Path])->str: + sha = hashlib.sha256() + path = Path(checkpoint_path) + assert path.is_file() + + hashpath = path.parent / f"{path.name}.sha256" + if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime: + with open(hashpath) as f: + hash = f.read() + return hash + + print(f' | computing hash of model {path.name}') + with open(path, "rb") as f: + while chunk := f.read(self.sha_chunksize): + sha.update(chunk) + hash = sha.hexdigest() + + with open(hashpath, "w") as f: + f.write(hash) + return hash + + def _local_model_hash(self, model_path: Union[str,Path])->str: + sha = hashlib.sha256() + path = Path(model_path) + + hashpath = path / "checksum.sha256" + if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime: + with open(hashpath) as f: + hash = f.read() + return hash + + print(f' | computing hash of model {path.name}') + for file in list(path.rglob("*.ckpt")) \ + + list(path.rglob("*.safetensors")) \ + + list(path.rglob("*.pth")): + with open(file, "rb") as f: + while chunk := f.read(self.sha_chunksize): + sha.update(chunk) + hash = sha.hexdigest() + with open(hashpath, "w") as f: + f.write(hash) + return hash + + def _hf_commit_hash(self, repo_id: str, revision: str='main')->str: + api = HfApi() + info = api.list_repo_refs( + repo_id=repo_id, + repo_type='model', + ) + desired_revisions = [branch for branch in info.branches if branch.name==revision] + if not desired_revisions: + raise KeyError(f"Revision '{revision}' not found in {repo_id}") + return desired_revisions[0].target_commit + class SilenceWarnings(object): def __init__(self): self.transformers_verbosity = transformers_logging.get_verbosity() From e1fed52c66d6318207f0115cb83aa5ba777a81d5 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Wed, 3 May 2023 12:38:18 -0400 Subject: [PATCH 004/225] work on model cache and its regression test finished --- .../backend/model_management/model_cache.py | 203 ++++++++++-------- tests/test_model_cache.py | 115 ++++++---- 2 files changed, 188 insertions(+), 130 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 616693b60f..2eb675c3e3 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -1,55 +1,46 @@ """ -Manage a cache of Stable Diffusion model files for fast switching. -They are moved between GPU and CPU as necessary. If the cache +Manage a RAM cache of diffusion/transformer models for fast switching. +They are moved between GPU VRAM and CPU RAM as necessary. If the cache grows larger than a preset maximum, then the least recently used model will be cleared and (re)loaded from disk when next needed. + +The cache returns context manager generators designed to load the +model into the GPU within the context, and unload outside the +context. Use like this: + + cache = ModelCache(max_models_cached=6) + with cache.get_model('runwayml/stable-diffusion-1-5') as SD1, + cache.get_model('stabilityai/stable-diffusion-2') as SD2: + do_something_in_GPU(SD1,SD2) + + """ import contextlib -import hashlib import gc -import time -import os -import psutil - -import safetensors -import safetensors.torch -import torch -import transformers +import hashlib import warnings - +from collections.abc import Generator from enum import Enum from pathlib import Path -from pydantic import BaseModel -from diffusers import ( - AutoencoderKL, - UNet2DConditionModel, - SchedulerMixin, - logging as diffusers_logging, -) -from huggingface_hub import list_repo_refs,HfApi -from transformers import( - CLIPTokenizer, - CLIPFeatureExtractor, - CLIPTextModel, - logging as transformers_logging, -) -from huggingface_hub import scan_cache_dir -from picklescan.scanner import scan_file_path from typing import Sequence, Union -from diffusers.pipelines.stable_diffusion.safety_checker import ( - StableDiffusionSafetyChecker, - ) -from . import load_pipeline_from_original_stable_diffusion_ckpt -from ..globals import Globals, global_cache_dir -from ..stable_diffusion import ( - StableDiffusionGeneratorPipeline, -) -from ..stable_diffusion.offloading import ModelGroup, FullyLoadedModelGroup -from ..util import CUDA_DEVICE, ask_user, download_with_resume +import torch +from diffusers import AutoencoderKL, SchedulerMixin, UNet2DConditionModel +from diffusers import logging as diffusers_logging +from diffusers.pipelines.stable_diffusion.safety_checker import \ + StableDiffusionSafetyChecker +from huggingface_hub import HfApi +from picklescan.scanner import scan_file_path +from pydantic import BaseModel +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer +from transformers import logging as transformers_logging -MAX_MODELS_CACHED = 4 +from ..globals import global_cache_dir +from ..stable_diffusion import StableDiffusionGeneratorPipeline +from . import load_pipeline_from_original_stable_diffusion_ckpt + +MAX_MODELS = 4 # This is the mapping from the stable diffusion submodel dict key to the class class SDModelType(Enum): @@ -68,70 +59,69 @@ ModelClass = Union[tuple([x.value for x in SDModelType])] # Legacy information needed to load a legacy checkpoint file class LegacyInfo(BaseModel): config_file: Path - vae_file: Path + vae_file: Path = None + +class UnsafeModelException(Exception): + "Raised when a legacy model file fails the picklescan test" + pass + +class UnscannableModelException(Exception): + "Raised when picklescan is unable to scan a legacy model file" + pass class ModelCache(object): def __init__( self, - max_models_cached: int=MAX_MODELS_CACHED, + max_models: int=MAX_MODELS, execution_device: torch.device=torch.device('cuda'), + storage_device: torch.device=torch.device('cpu'), precision: torch.dtype=torch.float16, sequential_offload: bool=False, sha_chunksize: int = 16777216, ): ''' - :param max_models_cached: Maximum number of models to cache in CPU RAM [4] + :param max_models: Maximum number of models to cache in CPU RAM [4] :param execution_device: Torch device to load active model into [torch.device('cuda')] + :param storage_device: Torch device to save inactive model in [torch.device('cpu')] :param precision: Precision for loaded models [torch.float16] :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially :param sha_chunksize: Chunksize to use when calculating sha256 model hash ''' - self.model_group: ModelGroup=FullyLoadedModelGroup(execution_device) self.models: dict = dict() self.stack: Sequence = list() self.sequential_offload: bool=sequential_offload self.precision: torch.dtype=precision - self.max_models_cached: int=max_models_cached - self.device: torch.device=execution_device + self.max_models: int=max_models + self.execution_device: torch.device=execution_device + self.storage_device: torch.device=storage_device self.sha_chunksize=sha_chunksize - def get_submodel( - self, - repo_id_or_path: Union[str,Path], - submodel: SDModelType=SDModelType.vae, - subfolder: Path=None, - revision: str=None, - legacy_info: LegacyInfo=None, - )->ModelClass: - ''' - Load and return a HuggingFace model, with RAM caching. - :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model - :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae - :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" - :param revision: model revision name - :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt - ''' - parent_model = self.get_model( - repo_id_or_path=repo_id_or_path, - subfolder=subfolder, - revision=revision, - ) - return getattr(parent_model, submodel.name) - + @contextlib.contextmanager def get_model( self, repo_id_or_path: Union[str,Path], model_type: SDModelType=SDModelType.diffusion_pipeline, subfolder: Path=None, + submodel: SDModelType=None, revision: str=None, legacy_info: LegacyInfo=None, - )->ModelClass: + gpu_load: bool=True, + )->Generator[ModelClass, None, None]: ''' - Load and return a HuggingFace model, with RAM caching. + Load and return a HuggingFace model wrapped in a context manager generator, with RAM caching. + Use like this: + + cache = ModelCache() + with cache.get_model('stabilityai/stable-diffusion-2') as SD2: + do_something_with_the_model(SD2) + + The model will be locked into GPU VRAM for the duration of the context. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae :param revision: model revision :param model_class: class of model to return + :param gpu_load: load the model into GPU [default True] :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt ''' key = self._model_key( # internal unique identifier for the model @@ -141,19 +131,12 @@ class ModelCache(object): subfolder ) if key in self.models: # cached - move to bottom of stack - previous_key = self._current_model_key with contextlib.suppress(ValueError): self.stack.remove(key) self.stack.append(key) - if previous_key != key: - if hasattr(self.current_model,'to'): - print(f' | loading {key} into GPU') - self.model_group.offload_current() - self.model_group.load(self.models[key]) + model = self.models[key] else: # not cached -load self._make_cache_room() - self.model_group.offload_current() - print(f' | loading model {key} from disk/net') model = self._load_model_from_storage( repo_id_or_path=repo_id_or_path, model_class=model_type.value, @@ -161,14 +144,29 @@ class ModelCache(object): revision=revision, legacy_info=legacy_info, ) - if hasattr(model,'to'): - self.model_group.install(model) # register with the model group self.stack.append(key) # add to LRU cache self.models[key]=model # keep copy of model in dict - return self.models[key] + + if submodel: + model = getattr(model, submodel.name) + debugging_name = f'{submodel.name} submodel of {repo_id_or_path}' + else: + debugging_name = repo_id_or_path + + try: + if gpu_load and hasattr(model,'to'): + print(f' | Loading {debugging_name} into GPU') + model.to(self.execution_device) # move into GPU + self._print_cuda_stats() + yield model + finally: + if gpu_load and hasattr(model,'to'): + print(f' | Unloading {debugging_name} from GPU') + model.to(self.storage_device) + self._print_cuda_stats() - @staticmethod - def model_hash(repo_id_or_path: Union[str,Path], + def model_hash(self, + repo_id_or_path: Union[str,Path], revision: str=None)->str: ''' Given the HF repo id or path to a model on disk, returns a unique @@ -183,16 +181,28 @@ class ModelCache(object): else: return self._hf_commit_hash(repo_id_or_path,revision) + def cache_size(self)->int: + "Return the current number of models cached." + return len(self.models) + @staticmethod def _model_key(path,model_class,revision,subfolder)->str: return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')]) + def _has_cuda(self)->bool: + return self.execution_device.type == 'cuda' + + def _print_cuda_stats(self): + print( + " | Current VRAM usage:", + "%4.2fG" % (torch.cuda.memory_allocated() / 1e9), + ) + def _make_cache_room(self): models_in_ram = len(self.models) - while models_in_ram >= self.max_models_cached: + while models_in_ram >= self.max_models: if least_recently_used_key := self.stack.pop(0): - print(f' | maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') - self.model_group.uninstall(self.models[least_recently_used_key]) + print(f' | Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') del self.models[least_recently_used_key] models_in_ram = len(self.models) gc.collect() @@ -239,9 +249,9 @@ class ModelCache(object): model_class, ) if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): - model.enable_offload_submodels(self.device) + model.enable_offload_submodels(self.execution_device) elif hasattr(model,'to'): - model.to(self.device) + model.to(self.execution_device) return model def _load_diffusers_from_storage( @@ -274,6 +284,23 @@ class ModelCache(object): path = Path(repo_id_or_path) return path.is_file() and path.suffix in [".ckpt",".safetensors"] + @classmethod + def scan_model(cls, model_name, checkpoint): + """ + Apply picklescanner to the indicated checkpoint and issue a warning + and option to exit if an infected file is identified. + """ + # scan model + print(f" | Scanning Model: {model_name}") + scan_result = scan_file_path(checkpoint) + if scan_result.infected_files != 0: + if scan_result.infected_files == 1: + raise UnsafeModelException("The legacy model you are trying to load may contain malware. Aborting.") + else: + raise UnscannableModelException("InvokeAI was unable to scan the legacy model you requested. Aborting") + else: + print(" | Model scanned ok") + def _load_ckpt_from_storage(self, ckpt_path: Union[str,Path], legacy_info:LegacyInfo)->StableDiffusionGeneratorPipeline: diff --git a/tests/test_model_cache.py b/tests/test_model_cache.py index 4130d8e7d5..25c61d192d 100644 --- a/tests/test_model_cache.py +++ b/tests/test_model_cache.py @@ -1,53 +1,84 @@ import pytest import torch -from invokeai.backend.model_management.model_cache import ModelCache, SDModelType -from invokeai.backend.stable_diffusion import StableDiffusionGeneratorPipeline +from enum import Enum +from invokeai.backend.model_management.model_cache import ModelCache -from diffusers import ( - AutoencoderKL, - UNet2DConditionModel, - SchedulerMixin, -) -from transformers import ( - CLIPTokenizer, - CLIPFeatureExtractor, - CLIPTextModel, -) +class DummyModelBase(object): + '''Base class for dummy component of a diffusers model''' + def __init__(self, repo_id): + self.repo_id = repo_id + self.device = torch.device('cpu') + @classmethod + def from_pretrained(cls, + repo_id:str, + revision:str=None, + subfolder:str=None, + cache_dir:str=None, + ): + return cls(repo_id) + + def to(self, device): + self.device = device -cache = ModelCache() +class DummyModelType1(DummyModelBase): + pass + +class DummyModelType2(DummyModelBase): + pass + +class DummyPipeline(DummyModelBase): + '''Dummy pipeline object is a composite of several types''' + def __init__(self,repo_id): + super().__init__(repo_id) + self.type1 = DummyModelType1('dummy/type1') + self.type2 = DummyModelType2('dummy/type2') + +class DMType(Enum): + dummy_pipeline = DummyPipeline + type1 = DummyModelType1 + type2 = DummyModelType2 + +cache = ModelCache(max_models=4) def test_pipeline_fetch(): - model0 = cache.get_model('stabilityai/sd-vae-ft-mse',SDModelType.vae) - model1 = cache.get_model('stabilityai/stable-diffusion-2-1',SDModelType.diffusion_pipeline) - model1_2 = cache.get_model('stabilityai/stable-diffusion-2-1') - assert model1==model1_2 - assert model1.device==torch.device('cuda') - model2 = cache.get_model('runwayml/stable-diffusion-v1-5') - assert model2.device==torch.device('cuda') - assert model1.device==torch.device('cpu') - model1 = cache.get_model('stabilityai/stable-diffusion-2-1') - assert model1.device==torch.device('cuda') + assert cache.cache_size()==0 + with cache.get_model('dummy/pipeline1',DMType.dummy_pipeline) as pipeline1,\ + cache.get_model('dummy/pipeline1',DMType.dummy_pipeline) as pipeline1a,\ + cache.get_model('dummy/pipeline2',DMType.dummy_pipeline) as pipeline2: + assert pipeline1 is not None, 'get_model() should not return None' + assert pipeline1a is not None, 'get_model() should not return None' + assert pipeline2 is not None, 'get_model() should not return None' + assert type(pipeline1)==DMType.dummy_pipeline.value,'get_model() did not return model of expected type' + assert pipeline1==pipeline1a,'pipelines with the same repo_id should be the same' + assert pipeline1!=pipeline2,'pipelines with different repo_ids should not be the same' + assert cache.cache_size()==2,'cache should uniquely cache models with same identity' + with cache.get_model('dummy/pipeline3',DMType.dummy_pipeline) as pipeline3,\ + cache.get_model('dummy/pipeline4',DMType.dummy_pipeline) as pipeline4: + assert cache.cache_size()==4,'cache did not grow as expected' + with cache.get_model('dummy/pipeline5',DMType.dummy_pipeline) as pipeline5: + assert cache.cache_size()==4,'cache did not free space as expected' + +def test_signatures(): + with cache.get_model('dummy/pipeline',DMType.dummy_pipeline,revision='main') as pipeline1,\ + cache.get_model('dummy/pipeline',DMType.dummy_pipeline,revision='fp16') as pipeline2,\ + cache.get_model('dummy/pipeline',DMType.dummy_pipeline,revision='main',subfolder='foo') as pipeline3: + assert pipeline1 != pipeline2,'models are distinguished by their revision' + assert pipeline1 != pipeline3,'models are distinguished by their subfolder' + +def test_pipeline_device(): + with cache.get_model('dummy/pipeline1',DMType.type1) as model1: + assert model1.device==torch.device('cuda'),'when in context, model device should be in GPU' + with cache.get_model('dummy/pipeline1',DMType.type1, gpu_load=False) as model1: + assert model1.device==torch.device('cpu'),'when gpu_load=False, model device should be CPU' def test_submodel_fetch(): - model1_vae = cache.get_submodel('stabilityai/stable-diffusion-2-1',SDModelType.vae) - assert isinstance(model1_vae,AutoencoderKL) - model1 = cache.get_model('stabilityai/stable-diffusion-2-1',SDModelType.diffusion_pipeline) - assert model1_vae == model1.vae - model1_vae_2 = cache.get_submodel('stabilityai/stable-diffusion-2-1') - assert model1_vae == model1_vae_2 + with cache.get_model(repo_id_or_path='dummy/pipeline1',model_type=DMType.dummy_pipeline) as pipeline,\ + cache.get_model(repo_id_or_path='dummy/pipeline1',model_type=DMType.dummy_pipeline,submodel=DMType.type1) as part1,\ + cache.get_model(repo_id_or_path='dummy/pipeline2',model_type=DMType.dummy_pipeline,submodel=DMType.type1) as part2: + assert type(part1)==DummyModelType1,'returned submodel is not of expected type' + assert part1.device==torch.device('cuda'),'returned submodel should be in the GPU when in context' + assert pipeline.type1==part1,'returned submodel should match the corresponding subpart of parent model' + assert pipeline.type1!=part2,'returned submodel should not match the subpart of a different parent' -def test_transformer_fetch(): - model4 = cache.get_model('openai/clip-vit-large-patch14',SDModelType.tokenizer) - assert isinstance(model4,CLIPTokenizer) - - model5 = cache.get_model('openai/clip-vit-large-patch14',SDModelType.text_encoder) - assert isinstance(model5,CLIPTextModel) - -def test_subfolder_fetch(): - model6 = cache.get_model('stabilityai/stable-diffusion-2',SDModelType.tokenizer,subfolder="tokenizer") - assert isinstance(model6,CLIPTokenizer) - - model7 = cache.get_model('stabilityai/stable-diffusion-2',SDModelType.text_encoder,subfolder="text_encoder") - assert isinstance(model7,CLIPTextModel) From 68bc0112fa653a347d1a7cc449731e9b41f8f908 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Thu, 4 May 2023 23:15:32 -0400 Subject: [PATCH 005/225] implement lazy GPU offloading and ref counting --- .../backend/model_management/model_cache.py | 67 +++++++++++++------ 1 file changed, 46 insertions(+), 21 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 2eb675c3e3..1af02637d3 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -21,6 +21,7 @@ import gc import hashlib import warnings from collections.abc import Generator +from collections import Counter from enum import Enum from pathlib import Path from typing import Sequence, Union @@ -36,6 +37,7 @@ from pydantic import BaseModel from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from transformers import logging as transformers_logging +import invokeai.backend.util.logging as logger from ..globals import global_cache_dir from ..stable_diffusion import StableDiffusionGeneratorPipeline from . import load_pipeline_from_original_stable_diffusion_ckpt @@ -77,6 +79,7 @@ class ModelCache(object): storage_device: torch.device=torch.device('cpu'), precision: torch.dtype=torch.float16, sequential_offload: bool=False, + lazy_offloading: bool=True, sha_chunksize: int = 16777216, ): ''' @@ -84,17 +87,21 @@ class ModelCache(object): :param execution_device: Torch device to load active model into [torch.device('cuda')] :param storage_device: Torch device to save inactive model in [torch.device('cpu')] :param precision: Precision for loaded models [torch.float16] + :param lazy_offloading: Keep model in VRAM until another model needs to be loaded :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially :param sha_chunksize: Chunksize to use when calculating sha256 model hash ''' self.models: dict = dict() self.stack: Sequence = list() + self.lazy_offloading = lazy_offloading self.sequential_offload: bool=sequential_offload self.precision: torch.dtype=precision self.max_models: int=max_models self.execution_device: torch.device=execution_device self.storage_device: torch.device=storage_device self.sha_chunksize=sha_chunksize + self.loaded_models: set = set() # set of model keys loaded in GPU + self.locked_models: Counter = Counter() # set of model keys locked in GPU @contextlib.contextmanager def get_model( @@ -149,21 +156,39 @@ class ModelCache(object): if submodel: model = getattr(model, submodel.name) - debugging_name = f'{submodel.name} submodel of {repo_id_or_path}' - else: - debugging_name = repo_id_or_path - - try: - if gpu_load and hasattr(model,'to'): - print(f' | Loading {debugging_name} into GPU') + + if gpu_load and hasattr(model,'to'): + try: + self.loaded_models.add(key) + self.locked_models[key] += 1 + if self.lazy_offloading: + self._offload_unlocked_models() + logger.debug(f'Loading {key} into {self.execution_device}') model.to(self.execution_device) # move into GPU self._print_cuda_stats() - yield model - finally: - if gpu_load and hasattr(model,'to'): - print(f' | Unloading {debugging_name} from GPU') - model.to(self.storage_device) + yield model + finally: + self.locked_models[key] -= 1 + if not self.lazy_offloading: + self._offload_unlocked_models() self._print_cuda_stats() + else: + # in the event that the caller wants the model in RAM, we + # move it into CPU if it is in GPU and not locked + if hasattr(model,'to') and (key in self.loaded_models + and self.locked_models[key] == 0): + model.to(self.storage_device) + yield model + + def _offload_unlocked_models(self): + to_offload = set() + for key in self.loaded_models: + if key not in self.locked_models or self.locked_models[key] == 0: + logger.debug(f'Offloading {key} from {self.execution_device} into {self.storage_device}') + to_offload.add(key) + for key in to_offload: + self.models[key].to(self.storage_device) + self.loaded_models.remove(key) def model_hash(self, repo_id_or_path: Union[str,Path], @@ -193,16 +218,16 @@ class ModelCache(object): return self.execution_device.type == 'cuda' def _print_cuda_stats(self): - print( - " | Current VRAM usage:", - "%4.2fG" % (torch.cuda.memory_allocated() / 1e9), - ) + vram = "%4.2fG" % (torch.cuda.memory_allocated() / 1e9) + loaded_models = len(self.loaded_models) + locked_models = len([x for x in self.locked_models if self.locked_models[x]>0]) + logger.debug(f"Current VRAM usage: {vram}; locked_models/loaded_models = {locked_models}/{loaded_models}") def _make_cache_room(self): models_in_ram = len(self.models) while models_in_ram >= self.max_models: if least_recently_used_key := self.stack.pop(0): - print(f' | Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') + logger.debug(f'Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') del self.models[least_recently_used_key] models_in_ram = len(self.models) gc.collect() @@ -291,7 +316,7 @@ class ModelCache(object): and option to exit if an infected file is identified. """ # scan model - print(f" | Scanning Model: {model_name}") + logger.debug(f"Scanning Model: {model_name}") scan_result = scan_file_path(checkpoint) if scan_result.infected_files != 0: if scan_result.infected_files == 1: @@ -299,7 +324,7 @@ class ModelCache(object): else: raise UnscannableModelException("InvokeAI was unable to scan the legacy model you requested. Aborting") else: - print(" | Model scanned ok") + logger.debug("Model scanned ok") def _load_ckpt_from_storage(self, ckpt_path: Union[str,Path], @@ -330,7 +355,7 @@ class ModelCache(object): hash = f.read() return hash - print(f' | computing hash of model {path.name}') + logger.debug(f'computing hash of model {path.name}') with open(path, "rb") as f: while chunk := f.read(self.sha_chunksize): sha.update(chunk) @@ -350,7 +375,7 @@ class ModelCache(object): hash = f.read() return hash - print(f' | computing hash of model {path.name}') + logger.debug(f'computing hash of model {path.name}') for file in list(path.rglob("*.ckpt")) \ + list(path.rglob("*.safetensors")) \ + list(path.rglob("*.pth")): From a4e36bc02a732ad5916d520634dfeb0236f069fb Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Thu, 4 May 2023 23:28:03 -0400 Subject: [PATCH 006/225] when model is forcibly moved into RAM update loaded_models set --- invokeai/backend/model_management/model_cache.py | 1 + 1 file changed, 1 insertion(+) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 1af02637d3..02448c59a6 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -178,6 +178,7 @@ class ModelCache(object): if hasattr(model,'to') and (key in self.loaded_models and self.locked_models[key] == 0): model.to(self.storage_device) + self.loaded_models.remove(key) yield model def _offload_unlocked_models(self): From af8c7c7d293011fa8038b900b8488eefdb94dd7e Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Fri, 5 May 2023 19:32:28 -0400 Subject: [PATCH 007/225] model manager rewritten to use model_cache; API changed! --- invokeai/backend/__init__.py | 2 +- invokeai/backend/globals.py | 2 + invokeai/backend/model_management/__init__.py | 10 +- .../convert_ckpt_to_diffusers.py | 3 +- .../backend/model_management/model_cache.py | 302 ++++--- .../backend/model_management/model_manager.py | 847 ++++-------------- 6 files changed, 353 insertions(+), 813 deletions(-) diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py index dd126a322d..06066dd6b1 100644 --- a/invokeai/backend/__init__.py +++ b/invokeai/backend/__init__.py @@ -10,7 +10,7 @@ from .generator import ( Img2Img, Inpaint ) -from .model_management import ModelManager, SDModelComponent +from .model_management import ModelManager from .safety_checker import SafetyChecker from .args import Args from .globals import Globals diff --git a/invokeai/backend/globals.py b/invokeai/backend/globals.py index c5417e03db..37a59b1135 100644 --- a/invokeai/backend/globals.py +++ b/invokeai/backend/globals.py @@ -93,6 +93,8 @@ def global_converted_ckpts_dir() -> Path: def global_set_root(root_dir: Union[str, Path]): Globals.root = root_dir +def global_resolve_path(path: Union[str,Path]): + return Path(Globals.root,path).resolve() def global_cache_dir(subdir: Union[str, Path] = "") -> Path: """ diff --git a/invokeai/backend/model_management/__init__.py b/invokeai/backend/model_management/__init__.py index 64ef4e9ee9..07b567ce7a 100644 --- a/invokeai/backend/model_management/__init__.py +++ b/invokeai/backend/model_management/__init__.py @@ -1,11 +1,5 @@ """ Initialization file for invokeai.backend.model_management """ -from .convert_ckpt_to_diffusers import ( - convert_ckpt_to_diffusers, - load_pipeline_from_original_stable_diffusion_ckpt, -) -from .model_manager import ModelManager,SDModelComponent - - - +from .model_manager import ModelManager +from .model_cache import ModelCache, ModelStatus diff --git a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py index 8aec5a01d9..aaa69da7f3 100644 --- a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py +++ b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py @@ -29,6 +29,7 @@ import invokeai.backend.util.logging as logger from invokeai.backend.globals import global_cache_dir, global_config_dir from .model_manager import ModelManager, SDLegacyType +from .model_cache import ModelCache try: from omegaconf import OmegaConf @@ -1100,7 +1101,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt( if Path(checkpoint_path).suffix == '.ckpt': if scan_needed: - ModelManager.scan_model(checkpoint_path,checkpoint_path) + ModelCache.scan_model(checkpoint_path,checkpoint_path) checkpoint = torch.load(checkpoint_path) else: checkpoint = load_file(checkpoint_path) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 02448c59a6..95b4e165f6 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -24,10 +24,10 @@ from collections.abc import Generator from collections import Counter from enum import Enum from pathlib import Path -from typing import Sequence, Union +from typing import Sequence, Union, Tuple, types import torch -from diffusers import AutoencoderKL, SchedulerMixin, UNet2DConditionModel +from diffusers import StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel from diffusers import logging as diffusers_logging from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker @@ -40,7 +40,6 @@ from transformers import logging as transformers_logging import invokeai.backend.util.logging as logger from ..globals import global_cache_dir from ..stable_diffusion import StableDiffusionGeneratorPipeline -from . import load_pipeline_from_original_stable_diffusion_ckpt MAX_MODELS = 4 @@ -54,9 +53,17 @@ class SDModelType(Enum): scheduler=SchedulerMixin safety_checker=StableDiffusionSafetyChecker feature_extractor=CLIPFeatureExtractor + +class ModelStatus(Enum): + unknown='unknown' + not_loaded='not loaded' + in_ram='cached' + in_vram='in gpu' + active='locked in gpu' # The list of model classes we know how to fetch, for typechecking ModelClass = Union[tuple([x.value for x in SDModelType])] +DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel) # Legacy information needed to load a legacy checkpoint file class LegacyInfo(BaseModel): @@ -81,6 +88,7 @@ class ModelCache(object): sequential_offload: bool=False, lazy_offloading: bool=True, sha_chunksize: int = 16777216, + logger: types.ModuleType = logger ): ''' :param max_models: Maximum number of models to cache in CPU RAM [4] @@ -100,9 +108,11 @@ class ModelCache(object): self.execution_device: torch.device=execution_device self.storage_device: torch.device=storage_device self.sha_chunksize=sha_chunksize + self.logger = logger self.loaded_models: set = set() # set of model keys loaded in GPU self.locked_models: Counter = Counter() # set of model keys locked in GPU + @contextlib.contextmanager def get_model( self, @@ -112,6 +122,7 @@ class ModelCache(object): submodel: SDModelType=None, revision: str=None, legacy_info: LegacyInfo=None, + attach_model_part: Tuple[SDModelType, str] = (None,None), gpu_load: bool=True, )->Generator[ModelClass, None, None]: ''' @@ -122,10 +133,28 @@ class ModelCache(object): with cache.get_model('stabilityai/stable-diffusion-2') as SD2: do_something_with_the_model(SD2) + You can fetch an individual part of a diffusers model by passing the submodel + argument: + + vae_context = cache.get_model( + 'stabilityai/sd-stable-diffusion-2', + submodel=SDModelType.vae + ) + + Vice versa, you can load and attach an external submodel to a diffusers model + before returning it by passing the attach_submodel argument. This only works with + diffusers models: + + pipeline_context = cache.get_model( + 'runwayml/stable-diffusion-v1-5', + attach_model_part=(SDModelType.vae,'stabilityai/sd-vae-ft-mse') + ) + The model will be locked into GPU VRAM for the duration of the context. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae + :param attach_model_part: load and attach a diffusers model component. Pass a tuple of format (SDModelType,repo_id) :param revision: model revision :param model_class: class of model to return :param gpu_load: load the model into GPU [default True] @@ -151,6 +180,8 @@ class ModelCache(object): revision=revision, legacy_info=legacy_info, ) + if model_type==SDModelType.diffusion_pipeline and attach_model_part[0]: + self.attach_part(model,*attach_model_part) self.stack.append(key) # add to LRU cache self.models[key]=model # keep copy of model in dict @@ -163,7 +194,7 @@ class ModelCache(object): self.locked_models[key] += 1 if self.lazy_offloading: self._offload_unlocked_models() - logger.debug(f'Loading {key} into {self.execution_device}') + self.logger.debug(f'Loading {key} into {self.execution_device}') model.to(self.execution_device) # move into GPU self._print_cuda_stats() yield model @@ -181,25 +212,59 @@ class ModelCache(object): self.loaded_models.remove(key) yield model - def _offload_unlocked_models(self): - to_offload = set() - for key in self.loaded_models: - if key not in self.locked_models or self.locked_models[key] == 0: - logger.debug(f'Offloading {key} from {self.execution_device} into {self.storage_device}') - to_offload.add(key) - for key in to_offload: - self.models[key].to(self.storage_device) - self.loaded_models.remove(key) + def attach_part(self, + diffusers_model: StableDiffusionPipeline, + part_type: SDModelType, + part_id: str + ): + ''' + Attach a diffusers model part to a diffusers model. This can be + used to replace the VAE, tokenizer, textencoder, unet, etc. + :param diffuser_model: The diffusers model to attach the part to. + :param part_type: An SD ModelType indicating the part + :param part_id: A HF repo_id for the part + ''' + part_key = part_type.name + part_class = part_type.value + part = self._load_diffusers_from_storage( + part_id, + model_class=part_class, + ) + part.to(diffusers_model.device) + setattr(diffusers_model,part_key,part) + self.logger.debug(f'Attached {part_key} {part_id}') + + def status(self, + repo_id_or_path: Union[str,Path], + model_type: SDModelType=SDModelType.diffusion_pipeline, + revision: str=None, + subfolder: Path=None, + )->ModelStatus: + key = self._model_key( + repo_id_or_path, + model_type.value, + revision, + subfolder) + if key not in self.models: + return ModelStatus.not_loaded + if key in self.loaded_models: + if self.locked_models[key] > 0: + return ModelStatus.active + else: + return ModelStatus.in_vram + else: + return ModelStatus.in_ram def model_hash(self, repo_id_or_path: Union[str,Path], - revision: str=None)->str: + revision: str="main")->str: ''' Given the HF repo id or path to a model on disk, returns a unique hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs :param repo_id_or_path: repo_id string or Path to model file/directory on disk. :param revision: optional revision string (if fetching a HF repo_id) ''' + revision = revision or "main" if self.is_legacy_ckpt(repo_id_or_path): return self._legacy_model_hash(repo_id_or_path) elif Path(repo_id_or_path).is_dir(): @@ -211,96 +276,6 @@ class ModelCache(object): "Return the current number of models cached." return len(self.models) - @staticmethod - def _model_key(path,model_class,revision,subfolder)->str: - return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')]) - - def _has_cuda(self)->bool: - return self.execution_device.type == 'cuda' - - def _print_cuda_stats(self): - vram = "%4.2fG" % (torch.cuda.memory_allocated() / 1e9) - loaded_models = len(self.loaded_models) - locked_models = len([x for x in self.locked_models if self.locked_models[x]>0]) - logger.debug(f"Current VRAM usage: {vram}; locked_models/loaded_models = {locked_models}/{loaded_models}") - - def _make_cache_room(self): - models_in_ram = len(self.models) - while models_in_ram >= self.max_models: - if least_recently_used_key := self.stack.pop(0): - logger.debug(f'Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') - del self.models[least_recently_used_key] - models_in_ram = len(self.models) - gc.collect() - - @property - def current_model(self)->ModelClass: - ''' - Returns current model. - ''' - return self.models[self._current_model_key] - - @property - def _current_model_key(self)->str: - ''' - Returns key of currently loaded model. - ''' - return self.stack[-1] - - def _load_model_from_storage( - self, - repo_id_or_path: Union[str,Path], - subfolder: Path=None, - revision: str=None, - model_class: ModelClass=StableDiffusionGeneratorPipeline, - legacy_info: LegacyInfo=None, - )->ModelClass: - ''' - Load and return a HuggingFace model. - :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model - :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" - :param revision: model revision - :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline - :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt - ''' - # silence transformer and diffuser warnings - with SilenceWarnings(): - if self.is_legacy_ckpt(repo_id_or_path): - model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) - else: - model = self._load_diffusers_from_storage( - repo_id_or_path, - subfolder, - revision, - model_class, - ) - if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): - model.enable_offload_submodels(self.execution_device) - elif hasattr(model,'to'): - model.to(self.execution_device) - return model - - def _load_diffusers_from_storage( - self, - repo_id_or_path: Union[str,Path], - subfolder: Path=None, - revision: str=None, - model_class: ModelClass=StableDiffusionGeneratorPipeline, - )->ModelClass: - ''' - Load and return a HuggingFace model using from_pretrained(). - :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model - :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" - :param revision: model revision - :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline - ''' - return model_class.from_pretrained( - repo_id_or_path, - revision=revision, - subfolder=subfolder or '.', - cache_dir=global_cache_dir('hub'), - ) - @classmethod def is_legacy_ckpt(cls, repo_id_or_path: Union[str,Path])->bool: ''' @@ -327,6 +302,106 @@ class ModelCache(object): else: logger.debug("Model scanned ok") + @staticmethod + def _model_key(path,model_class,revision,subfolder)->str: + return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')]) + + def _has_cuda(self)->bool: + return self.execution_device.type == 'cuda' + + def _print_cuda_stats(self): + vram = "%4.2fG" % (torch.cuda.memory_allocated() / 1e9) + loaded_models = len(self.loaded_models) + locked_models = len([x for x in self.locked_models if self.locked_models[x]>0]) + logger.debug(f"Current VRAM usage: {vram}; locked_models/loaded_models = {locked_models}/{loaded_models}") + + def _make_cache_room(self): + models_in_ram = len(self.models) + while models_in_ram >= self.max_models: + if least_recently_used_key := self.stack.pop(0): + logger.debug(f'Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') + del self.models[least_recently_used_key] + models_in_ram = len(self.models) + gc.collect() + + def _offload_unlocked_models(self): + to_offload = set() + for key in self.loaded_models: + if key not in self.locked_models or self.locked_models[key] == 0: + self.logger.debug(f'Offloading {key} from {self.execution_device} into {self.storage_device}') + to_offload.add(key) + for key in to_offload: + self.models[key].to(self.storage_device) + self.loaded_models.remove(key) + + def _load_model_from_storage( + self, + repo_id_or_path: Union[str,Path], + subfolder: Path=None, + revision: str=None, + model_class: ModelClass=StableDiffusionGeneratorPipeline, + legacy_info: LegacyInfo=None, + )->ModelClass: + ''' + Load and return a HuggingFace model. + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param revision: model revision + :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline + :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt + ''' + if self.is_legacy_ckpt(repo_id_or_path): + model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) + else: + model = self._load_diffusers_from_storage( + repo_id_or_path, + subfolder, + revision, + model_class, + ) + if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): + model.enable_offload_submodels(self.execution_device) + return model + + def _load_diffusers_from_storage( + self, + repo_id_or_path: Union[str,Path], + subfolder: Path=None, + revision: str=None, + model_class: ModelClass=StableDiffusionGeneratorPipeline, + )->ModelClass: + ''' + Load and return a HuggingFace model using from_pretrained(). + :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" + :param revision: model revision + :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline + ''' + self.logger.info(f'Loading model {repo_id_or_path}') + revisions = [revision] if revision \ + else ['fp16','main'] if self.precision==torch.float16 \ + else ['main'] + extra_args = {'precision': self.precision} \ + if model_class in DiffusionClasses \ + else {} + + # silence transformer and diffuser warnings + with SilenceWarnings(): + for rev in revisions: + try: + model = model_class.from_pretrained( + repo_id_or_path, + revision=rev, + subfolder=subfolder or '.', + cache_dir=global_cache_dir('hub'), + **extra_args, + ) + self.logger.debug(f'Found revision {rev}') + break + except OSError: + pass + return model + def _load_ckpt_from_storage(self, ckpt_path: Union[str,Path], legacy_info:LegacyInfo)->StableDiffusionGeneratorPipeline: @@ -336,13 +411,17 @@ class ModelCache(object): :param legacy_info: LegacyInfo object containing paths to legacy config file and alternate vae if required ''' assert legacy_info is not None - pipeline = load_pipeline_from_original_stable_diffusion_ckpt( - checkpoint_path=ckpt_path, - original_config_file=legacy_info.config_file, - vae_path=legacy_info.vae_file, - return_generator_pipeline=True, - precision=self.precision, - ) + + # deferred loading to avoid circular import errors + from .convert_ckpt_to_diffusers import load_pipeline_from_original_stable_diffusion_ckpt + with SilenceWarnings(): + pipeline = load_pipeline_from_original_stable_diffusion_ckpt( + checkpoint_path=ckpt_path, + original_config_file=legacy_info.config_file, + vae_path=legacy_info.vae_file, + return_generator_pipeline=True, + precision=self.precision, + ) return pipeline def _legacy_model_hash(self, checkpoint_path: Union[str,Path])->str: @@ -399,6 +478,7 @@ class ModelCache(object): raise KeyError(f"Revision '{revision}' not found in {repo_id}") return desired_revisions[0].target_commit + class SilenceWarnings(object): def __init__(self): self.transformers_verbosity = transformers_logging.get_verbosity() diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 9ba1e8779c..3977ac0ed7 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -1,56 +1,88 @@ -""" -Manage a cache of Stable Diffusion model files for fast switching. -They are moved between GPU and CPU as necessary. If CPU memory falls -below a preset minimum, the least recently used model will be -cleared and loaded from disk when next needed. +"""This module manages the InvokeAI `models.yaml` file, mapping +symbolic diffusers model names to the paths and repo_ids used +by the underlying `from_pretrained()` call. + +For fetching models, use manager.get_model('symbolic name'). This will +return a SDModelInfo object that contains the following attributes: + + * context -- a context manager Generator that loads and locks the + model into GPU VRAM and returns the model for use. + See below for usage. + * name -- symbolic name of the model + * hash -- unique hash for the model + * location -- path or repo_id of the model + * revision -- revision of the model if coming from a repo id, + e.g. 'fp16' + * precision -- torch precision of the model + * status -- a ModelStatus enum corresponding to one of + 'not_loaded', 'in_ram', 'in_vram' or 'active' + +Typical usage: + + from invokeai.backend import ModelManager + manager = ModelManager(config_path='./configs/models.yaml',max_models=4) + model_info = manager.get_model('stable-diffusion-1.5') + with model_info.context as my_model: + my_model.latents_from_embeddings(...) + +The manager uses the underlying ModelCache class to keep +frequently-used models in RAM and move them into GPU as needed for +generation operations. The ModelCache object can be accessed using +the manager's "cache" attribute. + +Other methods provided by ModelManager support importing, editing, +converting and deleting models. """ from __future__ import annotations -import contextlib -import gc -import hashlib import os import re -import sys import textwrap -import time -import warnings +from dataclasses import dataclass from enum import Enum, auto from pathlib import Path -from shutil import move, rmtree -from typing import Any, Optional, Union, Callable, types +from shutil import rmtree +from typing import Union, Callable, types import safetensors import safetensors.torch import torch -import transformers import invokeai.backend.util.logging as logger -from diffusers import ( - AutoencoderKL, - UNet2DConditionModel, - SchedulerMixin, - logging as dlogging, -) from huggingface_hub import scan_cache_dir from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig -from picklescan.scanner import scan_file_path -from invokeai.backend.globals import Globals, global_cache_dir +from invokeai.backend.globals import Globals, global_cache_dir, global_resolve_path +from .model_cache import Generator, ModelClass, ModelCache, SDModelType, ModelStatus, LegacyInfo -from transformers import ( - CLIPTextModel, - CLIPTokenizer, - CLIPFeatureExtractor, -) -from diffusers.pipelines.stable_diffusion.safety_checker import ( - StableDiffusionSafetyChecker, - ) -from ..stable_diffusion import ( - StableDiffusionGeneratorPipeline, -) -from ..util import CUDA_DEVICE, ask_user, download_with_resume +from ..util import CUDA_DEVICE +# wanted to use pydantic here, but Generator objects not supported +@dataclass +class SDModelInfo(): + context: Generator[ModelClass, None, None] + name: str + hash: str + location: Union[Path,str] + precision: torch.dtype + subfolder: Path = None + revision: str = None + _cache: ModelCache = None + + + def status(self)->ModelStatus: + '''Return load status of this model as a model_cache.ModelStatus enum''' + if not self._cache: + return ModelStatus.unknown + return self._cache.status( + self.location, + revision = self.revision, + subfolder = self.subfolder + ) + +class InvalidModelError(Exception): + "Raised when an invalid model is requested" + pass class SDLegacyType(Enum): V1 = auto() @@ -60,54 +92,39 @@ class SDLegacyType(Enum): V2_v = auto() UNKNOWN = auto() -class SDModelComponent(Enum): - vae="vae" - text_encoder="text_encoder" - tokenizer="tokenizer" - unet="unet" - scheduler="scheduler" - safety_checker="safety_checker" - feature_extractor="feature_extractor" - DEFAULT_MAX_MODELS = 2 class ModelManager(object): """ - Model manager handles loading, caching, importing, deleting, converting, and editing models. + High-level interface to model management. """ logger: types.ModuleType = logger def __init__( - self, - config: OmegaConf | Path, - device_type: torch.device = CUDA_DEVICE, - precision: str = "float16", - max_loaded_models=DEFAULT_MAX_MODELS, - sequential_offload=False, - embedding_path: Path = None, - logger: types.ModuleType = logger, + self, + config_path: Path, + device_type: torch.device = CUDA_DEVICE, + precision: torch.dtype = torch.float16, + max_models=DEFAULT_MAX_MODELS, + sequential_offload=False, + logger: types.ModuleType = logger, ): """ - Initialize with the path to the models.yaml config file or - an initialized OmegaConf dictionary. Optional parameters - are the torch device type, precision, max_loaded_models, + Initialize with the path to the models.yaml config file. + Optional parameters are the torch device type, precision, max_models, and sequential_offload boolean. Note that the default device type and precision are set up for a CUDA system running at half precision. """ - # prevent nasty-looking CLIP log message - transformers.logging.set_verbosity_error() - if not isinstance(config, DictConfig): - config = OmegaConf.load(config) - self.config = config - self.precision = precision - self.device = torch.device(device_type) - self.max_loaded_models = max_loaded_models - self.models = {} - self.stack = [] # this is an LRU FIFO - self.current_model = None - self.sequential_offload = sequential_offload - self.embedding_path = embedding_path + self.config_path = config_path + self.config = OmegaConf.load(self.config_path) + self.cache = ModelCache( + max_models=max_models, + execution_device = device_type, + precision = precision, + sequential_offload = sequential_offload, + logger = logger, + ) self.logger = logger def valid_model(self, model_name: str) -> bool: @@ -117,138 +134,69 @@ class ModelManager(object): """ return model_name in self.config - def get_model(self, model_name: str = None) -> dict: - """Given a model named identified in models.yaml, return a dict - containing the model object and some of its key features. If - in RAM will load into GPU VRAM. If on disk, will load from - there. - The dict has the following keys: - 'model': The StableDiffusionGeneratorPipeline object - 'model_name': The name of the model in models.yaml - 'width': The width of images trained by this model - 'height': The height of images trained by this model - 'hash': A unique hash of this model's files on disk. + def get_model(self, + model_name: str = None, + submodel: SDModelType=None, + ) -> SDModelInfo: + """Given a model named identified in models.yaml, return + an SDModelInfo object describing it. + :param model_name: symbolic name of the model in models.yaml + :param submodel: an SDModelType enum indicating the portion of + the model to retrieve (e.g. SDModelType.vae) """ if not model_name: - return ( - self.get_model(self.current_model) - if self.current_model - else self.get_model(self.default_model()) - ) + model_name = self.default_model() if not self.valid_model(model_name): - self.logger.error( + raise InvalidModelError( f'"{model_name}" is not a known model name. Please check your models.yaml file' ) - return self.current_model - if self.current_model != model_name: - if model_name not in self.models: # make room for a new one - self._make_cache_room() - self.offload_model(self.current_model) - - if model_name in self.models: - requested_model = self.models[model_name]["model"] - self.logger.info(f"Retrieving model {model_name} from system RAM cache") - requested_model.ready() - width = self.models[model_name]["width"] - height = self.models[model_name]["height"] - hash = self.models[model_name]["hash"] - - else: # we're about to load a new model, so potentially offload the least recently used one - requested_model, width, height, hash = self._load_model(model_name) - self.models[model_name] = { - "model_name": model_name, - "model": requested_model, - "width": width, - "height": height, - "hash": hash, - } - - self.current_model = model_name - self._push_newest_model(model_name) - return { - "model_name": model_name, - "model": requested_model, - "width": width, - "height": height, - "hash": hash, - } - - def get_model_vae(self, model_name: str=None)->AutoencoderKL: - """Given a model name identified in models.yaml, load the model into - GPU if necessary and return its assigned VAE as an - AutoencoderKL object. If no model name is provided, return the - vae from the model currently in the GPU. - """ - return self._get_sub_model(model_name, SDModelComponent.vae) - - def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer: - """Given a model name identified in models.yaml, load the model into - GPU if necessary and return its assigned CLIPTokenizer. If no - model name is provided, return the tokenizer from the model - currently in the GPU. - """ - return self._get_sub_model(model_name, SDModelComponent.tokenizer) - - def get_model_unet(self, model_name: str=None)->UNet2DConditionModel: - """Given a model name identified in models.yaml, load the model into - GPU if necessary and return its assigned UNet2DConditionModel. If no model - name is provided, return the UNet from the model - currently in the GPU. - """ - return self._get_sub_model(model_name, SDModelComponent.unet) - - def get_model_text_encoder(self, model_name: str=None)->CLIPTextModel: - """Given a model name identified in models.yaml, load the model into - GPU if necessary and return its assigned CLIPTextModel. If no - model name is provided, return the text encoder from the model - currently in the GPU. - """ - return self._get_sub_model(model_name, SDModelComponent.text_encoder) - - def get_model_feature_extractor(self, model_name: str=None)->CLIPFeatureExtractor: - """Given a model name identified in models.yaml, load the model into - GPU if necessary and return its assigned CLIPFeatureExtractor. If no - model name is provided, return the text encoder from the model - currently in the GPU. - """ - return self._get_sub_model(model_name, SDModelComponent.feature_extractor) - - def get_model_scheduler(self, model_name: str=None)->SchedulerMixin: - """Given a model name identified in models.yaml, load the model into - GPU if necessary and return its assigned scheduler. If no - model name is provided, return the text encoder from the model - currently in the GPU. - """ - return self._get_sub_model(model_name, SDModelComponent.scheduler) - - def _get_sub_model( - self, - model_name: str=None, - model_part: SDModelComponent=SDModelComponent.vae, - ) -> Union[ - AutoencoderKL, - CLIPTokenizer, - CLIPFeatureExtractor, - UNet2DConditionModel, - CLIPTextModel, - StableDiffusionSafetyChecker, - ]: - """Given a model name identified in models.yaml, and the part of the - model you wish to retrieve, return that part. Parts are in an Enum - class named SDModelComponent, and consist of: - SDModelComponent.vae - SDModelComponent.text_encoder - SDModelComponent.tokenizer - SDModelComponent.unet - SDModelComponent.scheduler - SDModelComponent.safety_checker - SDModelComponent.feature_extractor - """ - model_dict = self.get_model(model_name) - model = model_dict["model"] - return getattr(model, model_part.value) + # get the required loading info out of the config file + mconfig = self.config[model_name] + format = mconfig.get('format','diffusers') + legacy = None + if format=='ckpt': + location = global_resolve_path(mconfig.weights) + legacy = LegacyInfo( + config_file = global_resolve_path(mconfig.config), + ) + if mconfig.get('vae'): + legacy.vae_file = global_resolve_path(mconfig.vae) + elif format=='diffusers': + location = mconfig.repo_id + revision = mconfig.get('revision') + else: + raise InvalidModelError( + f'"{model_name}" has an unknown format {format}' + ) + + subfolder = mconfig.get('subfolder') + hash = self.cache.model_hash(location,revision) + vae = (None,None) + try: + vae_id = mconfig.vae.repo_id + vae = (SDModelType.vae,vae_id) + except Exception: + pass + model_context = self.cache.get_model( + location, + revision = revision, + subfolder = subfolder, + legacy_info = legacy, + submodel = submodel, + attach_model_part=vae, + ) + return SDModelInfo( + context = model_context, + name = model_name, + hash = hash, + location = location, + revision = revision, + precision = self.cache.precision, + subfolder = subfolder, + _cache = self.cache + ) def default_model(self) -> str | None: """ @@ -324,19 +272,19 @@ class ModelManager(object): format = stanza.get("format", "ckpt") # Determine Format # Common Attribs + status = self.cache.status( + stanza.get('weights') or stanza.get('repo_id'), + revision=stanza.get('revision'), + subfolder=stanza.get('subfolder') + ) description = stanza.get("description", None) - if self.current_model == name: - status = "active" - elif name in self.models: - status = "cached" - else: - status = "not loaded" models[name].update( description=description, format=format, - status=status, + status=status.value ) + # Checkpoint Config Parse if format == "ckpt": models[name].update( @@ -373,7 +321,7 @@ class ModelManager(object): for name in models: if models[name]["format"] == "vae": continue - line = f'{name:25s} {models[name]["status"]:>10s} {models[name]["format"]:10s} {models[name]["description"]}' + line = f'{name:25s} {models[name]["status"]:>15s} {models[name]["format"]:10s} {models[name]["description"]}' if models[name]["status"] == "active": line = f"\033[1m{line}\033[0m" print(line) @@ -441,233 +389,6 @@ class ModelManager(object): if clobber: self._invalidate_cached_model(model_name) - def _load_model(self, model_name: str): - """Load and initialize the model from configuration variables passed at object creation time""" - if model_name not in self.config: - self.logger.error( - f'"{model_name}" is not a known model name. Please check your models.yaml file' - ) - return - - mconfig = self.config[model_name] - - # for usage statistics - if self._has_cuda(): - torch.cuda.reset_peak_memory_stats() - torch.cuda.empty_cache() - - tic = time.time() - - # this does the work - model_format = mconfig.get("format", "ckpt") - if model_format == "ckpt": - weights = mconfig.weights - self.logger.info(f"Loading {model_name} from {weights}") - model, width, height, model_hash = self._load_ckpt_model( - model_name, mconfig - ) - elif model_format == "diffusers": - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - model, width, height, model_hash = self._load_diffusers_model(mconfig) - else: - raise NotImplementedError( - f"Unknown model format {model_name}: {model_format}" - ) - self._add_embeddings_to_model(model) - - # usage statistics - toc = time.time() - self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic)) - if self._has_cuda(): - self.logger.info( - "Max VRAM used to load the model: "+ - "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9) - ) - self.logger.info( - "Current VRAM usage: "+ - "%4.2fG" % (torch.cuda.memory_allocated() / 1e9) - ) - return model, width, height, model_hash - - def _load_diffusers_model(self, mconfig): - name_or_path = self.model_name_or_path(mconfig) - using_fp16 = self.precision == "float16" - - self.logger.info(f"Loading diffusers model from {name_or_path}") - if using_fp16: - self.logger.debug("Using faster float16 precision") - else: - self.logger.debug("Using more accurate float32 precision") - - # TODO: scan weights maybe? - pipeline_args: dict[str, Any] = dict( - safety_checker=None, local_files_only=not Globals.internet_available - ) - if "vae" in mconfig and mconfig["vae"] is not None: - if vae := self._load_vae(mconfig["vae"]): - pipeline_args.update(vae=vae) - if not isinstance(name_or_path, Path): - pipeline_args.update(cache_dir=global_cache_dir("hub")) - if using_fp16: - pipeline_args.update(torch_dtype=torch.float16) - fp_args_list = [{"revision": "fp16"}, {}] - else: - fp_args_list = [{}] - - verbosity = dlogging.get_verbosity() - dlogging.set_verbosity_error() - - pipeline = None - for fp_args in fp_args_list: - try: - pipeline = StableDiffusionGeneratorPipeline.from_pretrained( - name_or_path, - **pipeline_args, - **fp_args, - ) - except OSError as e: - if str(e).startswith("fp16 is not a valid"): - pass - else: - self.logger.error( - f"An unexpected error occurred while downloading the model: {e})" - ) - if pipeline: - break - - dlogging.set_verbosity(verbosity) - assert pipeline is not None, OSError(f'"{name_or_path}" could not be loaded') - - if self.sequential_offload: - pipeline.enable_offload_submodels(self.device) - else: - pipeline.to(self.device) - - model_hash = self._diffuser_sha256(name_or_path) - - # square images??? - width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor - height = width - self.logger.debug(f"Default image dimensions = {width} x {height}") - - return pipeline, width, height, model_hash - - def _load_ckpt_model(self, model_name, mconfig): - config = mconfig.config - weights = mconfig.weights - vae = mconfig.get("vae") - width = mconfig.width - height = mconfig.height - - if not os.path.isabs(config): - config = os.path.join(Globals.root, config) - if not os.path.isabs(weights): - weights = os.path.normpath(os.path.join(Globals.root, weights)) - - # Convert to diffusers and return a diffusers pipeline - self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...") - - from . import load_pipeline_from_original_stable_diffusion_ckpt - - try: - if self.list_models()[self.current_model]["status"] == "active": - self.offload_model(self.current_model) - except Exception: - pass - - vae_path = None - if vae: - vae_path = ( - vae - if os.path.isabs(vae) - else os.path.normpath(os.path.join(Globals.root, vae)) - ) - if self._has_cuda(): - torch.cuda.empty_cache() - pipeline = load_pipeline_from_original_stable_diffusion_ckpt( - checkpoint_path=weights, - original_config_file=config, - vae_path=vae_path, - return_generator_pipeline=True, - precision=torch.float16 if self.precision == "float16" else torch.float32, - ) - if self.sequential_offload: - pipeline.enable_offload_submodels(self.device) - else: - pipeline.to(self.device) - return ( - pipeline, - width, - height, - "NOHASH", - ) - - def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path: - if isinstance(model_name, DictConfig) or isinstance(model_name, dict): - mconfig = model_name - elif model_name in self.config: - mconfig = self.config[model_name] - else: - raise ValueError( - f'"{model_name}" is not a known model name. Please check your models.yaml file' - ) - - if "path" in mconfig and mconfig["path"] is not None: - path = Path(mconfig["path"]) - if not path.is_absolute(): - path = Path(Globals.root, path).resolve() - return path - elif "repo_id" in mconfig: - return mconfig["repo_id"] - else: - raise ValueError("Model config must specify either repo_id or path.") - - def offload_model(self, model_name: str) -> None: - """ - Offload the indicated model to CPU. Will call - _make_cache_room() to free space if needed. - """ - if model_name not in self.models: - return - - self.logger.info(f"Offloading {model_name} to CPU") - model = self.models[model_name]["model"] - model.offload_all() - self.current_model = None - - gc.collect() - if self._has_cuda(): - torch.cuda.empty_cache() - - @classmethod - def scan_model(self, model_name, checkpoint): - """ - Apply picklescanner to the indicated checkpoint and issue a warning - and option to exit if an infected file is identified. - """ - # scan model - self.logger.debug(f"Scanning Model: {model_name}") - scan_result = scan_file_path(checkpoint) - if scan_result.infected_files != 0: - if scan_result.infected_files == 1: - self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}") - self.logger.critical("The model you are trying to load seems to be infected.") - self.logger.critical("For your safety, InvokeAI will not load this model.") - self.logger.critical("Please use checkpoints from trusted sources.") - self.logger.critical("Exiting InvokeAI") - sys.exit() - else: - self.logger.warning("InvokeAI was unable to scan the model you are using.") - model_safe_check_fail = ask_user( - "Do you want to to continue loading the model?", ["y", "n"] - ) - if model_safe_check_fail.lower() != "y": - self.logger.critical("Exiting InvokeAI") - sys.exit() - else: - self.logger.debug("Model scanned ok") - def import_diffuser_model( self, repo_or_path: Union[str, Path], @@ -949,8 +670,6 @@ class ModelManager(object): new_config = None - from . import convert_ckpt_to_diffusers - if diffusers_path.exists(): self.logger.error( f"The path {str(diffusers_path)} already exists. Please move or remove it and try again." @@ -960,6 +679,10 @@ class ModelManager(object): model_name = model_name or diffusers_path.name model_description = model_description or f"Converted version of {model_name}" self.logger.debug(f"Converting {model_name} to diffusers (30-60s)") + + # to avoid circular import errors + from .convert_ckpt_to_diffusers import convert_ckpt_to_diffusers + try: # By passing the specified VAE to the conversion function, the autoencoder # will be built into the model rather than tacked on afterward via the config file @@ -1020,33 +743,12 @@ class ModelManager(object): return search_folder, found_models - def _make_cache_room(self) -> None: - num_loaded_models = len(self.models) - if num_loaded_models >= self.max_loaded_models: - least_recent_model = self._pop_oldest_model() - self.logger.info( - f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}" - ) - if least_recent_model is not None: - del self.models[least_recent_model] - gc.collect() - - def print_vram_usage(self) -> None: - if self._has_cuda: - self.logger.info( - "Current VRAM usage:"+ - "%4.2fG" % (torch.cuda.memory_allocated() / 1e9), - ) - - def commit(self, config_file_path: str) -> None: + def commit(self) -> None: """ Write current configuration out to the indicated file. """ yaml_str = OmegaConf.to_yaml(self.config) - if not os.path.isabs(config_file_path): - config_file_path = os.path.normpath( - os.path.join(Globals.root, config_file_path) - ) + config_file_path = self.config_path tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp") with open(tmpfile, "w", encoding="utf-8") as outfile: outfile.write(self.preamble()) @@ -1069,240 +771,6 @@ class ModelManager(object): """ ) - @classmethod - def migrate_models(cls): - """ - Migrate the ~/invokeai/models directory from the legacy format used through 2.2.5 - to the 2.3.0 "diffusers" version. This should be a one-time operation, called at - script startup time. - """ - # Three transformer models to check: bert, clip and safety checker, and - # the diffusers as well - models_dir = Path(Globals.root, "models") - legacy_locations = [ - Path( - models_dir, - "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker", - ), - Path(models_dir, "bert-base-uncased/models--bert-base-uncased"), - Path( - models_dir, - "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14", - ), - ] - legacy_locations.extend(list(global_cache_dir("diffusers").glob("*"))) - - legacy_layout = False - for model in legacy_locations: - legacy_layout = legacy_layout or model.exists() - if not legacy_layout: - return - - print( - """ ->> ALERT: ->> The location of your previously-installed diffusers models needs to move from ->> invokeai/models/diffusers to invokeai/models/hub due to a change introduced by ->> diffusers version 0.14. InvokeAI will now move all models from the "diffusers" directory ->> into "hub" and then remove the diffusers directory. This is a quick, safe, one-time ->> operation.""" - ) - - # transformer files get moved into the hub directory - if cls._is_huggingface_hub_directory_present(): - hub = global_cache_dir("hub") - else: - hub = models_dir / "hub" - - os.makedirs(hub, exist_ok=True) - for model in legacy_locations: - source = models_dir / model - dest = hub / model.stem - if dest.exists() and not source.exists(): - continue - cls.logger.info(f"{source} => {dest}") - if source.exists(): - if dest.is_symlink(): - logger.warning(f"Found symlink at {dest.name}. Not migrating.") - elif dest.exists(): - if source.is_dir(): - rmtree(source) - else: - source.unlink() - else: - move(source, dest) - - # now clean up by removing any empty directories - empty = [ - root - for root, dirs, files, in os.walk(models_dir) - if not len(dirs) and not len(files) - ] - for d in empty: - os.rmdir(d) - cls.logger.info("Migration is done. Continuing...") - - def _resolve_path( - self, source: Union[str, Path], dest_directory: str - ) -> Optional[Path]: - resolved_path = None - if str(source).startswith(("http:", "https:", "ftp:")): - dest_directory = Path(dest_directory) - if not dest_directory.is_absolute(): - dest_directory = Globals.root / dest_directory - dest_directory.mkdir(parents=True, exist_ok=True) - resolved_path = download_with_resume(str(source), dest_directory) - else: - if not os.path.isabs(source): - source = os.path.join(Globals.root, source) - resolved_path = Path(source) - return resolved_path - - def _invalidate_cached_model(self, model_name: str) -> None: - self.offload_model(model_name) - if model_name in self.stack: - self.stack.remove(model_name) - self.models.pop(model_name, None) - - def _pop_oldest_model(self): - """ - Remove the first element of the FIFO, which ought - to be the least recently accessed model. Do not - pop the last one, because it is in active use! - """ - return self.stack.pop(0) - - def _push_newest_model(self, model_name: str) -> None: - """ - Maintain a simple FIFO. First element is always the - least recent, and last element is always the most recent. - """ - with contextlib.suppress(ValueError): - self.stack.remove(model_name) - self.stack.append(model_name) - - def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline): - if self.embedding_path is not None: - self.logger.info(f"Loading embeddings from {self.embedding_path}") - for root, _, files in os.walk(self.embedding_path): - for name in files: - ti_path = os.path.join(root, name) - model.textual_inversion_manager.load_textual_inversion( - ti_path, defer_injecting_tokens=True - ) - self.logger.info( - f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}' - ) - - def _has_cuda(self) -> bool: - return self.device.type == "cuda" - - def _diffuser_sha256( - self, name_or_path: Union[str, Path], chunksize=16777216 - ) -> Union[str, bytes]: - path = None - if isinstance(name_or_path, Path): - path = name_or_path - else: - owner, repo = name_or_path.split("/") - path = Path(global_cache_dir("hub") / f"models--{owner}--{repo}") - if not path.exists(): - return None - hashpath = path / "checksum.sha256" - if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime: - with open(hashpath) as f: - hash = f.read() - return hash - self.logger.debug("Calculating sha256 hash of model files") - tic = time.time() - sha = hashlib.sha256() - count = 0 - for root, dirs, files in os.walk(path, followlinks=False): - for name in files: - count += 1 - with open(os.path.join(root, name), "rb") as f: - while chunk := f.read(chunksize): - sha.update(chunk) - hash = sha.hexdigest() - toc = time.time() - self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic)) - with open(hashpath, "w") as f: - f.write(hash) - return hash - - def _cached_sha256(self, path, data) -> Union[str, bytes]: - dirname = os.path.dirname(path) - basename = os.path.basename(path) - base, _ = os.path.splitext(basename) - hashpath = os.path.join(dirname, base + ".sha256") - - if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime( - hashpath - ): - with open(hashpath) as f: - hash = f.read() - return hash - - self.logger.debug("Calculating sha256 hash of weights file") - tic = time.time() - sha = hashlib.sha256() - sha.update(data) - hash = sha.hexdigest() - toc = time.time() - self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic)) - - with open(hashpath, "w") as f: - f.write(hash) - return hash - - def _load_vae(self, vae_config) -> AutoencoderKL: - vae_args = {} - try: - name_or_path = self.model_name_or_path(vae_config) - except Exception: - return None - if name_or_path is None: - return None - using_fp16 = self.precision == "float16" - - vae_args.update( - cache_dir=global_cache_dir("hub"), - local_files_only=not Globals.internet_available, - ) - - self.logger.debug(f"Loading diffusers VAE from {name_or_path}") - if using_fp16: - vae_args.update(torch_dtype=torch.float16) - fp_args_list = [{"revision": "fp16"}, {}] - else: - self.logger.debug("Using more accurate float32 precision") - fp_args_list = [{}] - - vae = None - deferred_error = None - - # A VAE may be in a subfolder of a model's repository. - if "subfolder" in vae_config: - vae_args["subfolder"] = vae_config["subfolder"] - - for fp_args in fp_args_list: - # At some point we might need to be able to use different classes here? But for now I think - # all Stable Diffusion VAE are AutoencoderKL. - try: - vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args, **fp_args) - except OSError as e: - if str(e).startswith("fp16 is not a valid"): - pass - else: - deferred_error = e - if vae: - break - - if not vae and deferred_error: - self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}") - - return vae - @classmethod def _delete_model_from_cache(cls,repo_id): cache_info = scan_cache_dir(global_cache_dir("hub")) @@ -1326,8 +794,3 @@ class ModelManager(object): return path return Path(Globals.root, path).resolve() - @staticmethod - def _is_huggingface_hub_directory_present() -> bool: - return ( - os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None - ) From e0214a32bc7102d2e710fdf7d3b60763ee8eceb2 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 6 May 2023 00:44:12 -0400 Subject: [PATCH 008/225] mostly ported to new manager API; needs testing --- invokeai/app/invocations/latent.py | 93 ++--- invokeai/app/invocations/util/choose_model.py | 4 +- .../app/services/model_manager_initializer.py | 17 +- invokeai/backend/__init__.py | 2 +- invokeai/backend/generate.py | 375 +++++++++--------- invokeai/backend/generator/base.py | 81 ++-- invokeai/backend/model_management/__init__.py | 2 +- .../backend/model_management/model_cache.py | 79 ++-- .../backend/model_management/model_manager.py | 8 +- invokeai/backend/prompting/conditioning.py | 1 + invokeai/backend/web/invoke_ai_web_server.py | 14 +- invokeai/frontend/CLI/CLI.py | 9 +- 12 files changed, 353 insertions(+), 332 deletions(-) diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index c6ea8a686a..84af41b1c9 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -180,31 +180,33 @@ class TextToLatentsInvocation(BaseInvocation): def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline: model_info = choose_model(model_manager, self.model) - model_name = model_info['model_name'] - model_hash = model_info['hash'] - model: StableDiffusionGeneratorPipeline = model_info['model'] - model.scheduler = get_scheduler( - model=model, - scheduler_name=self.scheduler - ) + model_name = model_info.name + model_hash = model_info.hash + model_ctx: StableDiffusionGeneratorPipeline = model_info.context + with model_ctx as model: + model.scheduler = get_scheduler( + model=model, + scheduler_name=self.scheduler + ) - if isinstance(model, DiffusionPipeline): - for component in [model.unet, model.vae]: - configure_model_padding(component, + if isinstance(model, DiffusionPipeline): + for component in [model.unet, model.vae]: + configure_model_padding(component, + self.seamless, + self.seamless_axes + ) + else: + configure_model_padding(model, self.seamless, self.seamless_axes ) - else: - configure_model_padding(model, - self.seamless, - self.seamless_axes - ) - return model + return model_ctx def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData: uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model) + print(f'DEBUG: uc.dtype={uc.dtype}, c.dtype={c.dtype}') conditioning_data = ConditioningData( uc, c, @@ -230,18 +232,17 @@ class TextToLatentsInvocation(BaseInvocation): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - model = self.get_model(context.services.model_manager) - conditioning_data = self.get_conditioning_data(model) + with self.get_model(context.services.model_manager) as model: + conditioning_data = self.get_conditioning_data(model) - # TODO: Verify the noise is the right size - - result_latents, result_attention_map_saver = model.latents_from_embeddings( - latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)), - noise=noise, - num_inference_steps=self.steps, - conditioning_data=conditioning_data, - callback=step_callback - ) + # TODO: Verify the noise is the right size + result_latents, result_attention_map_saver = model.latents_from_embeddings( + latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)), + noise=noise, + num_inference_steps=self.steps, + conditioning_data=conditioning_data, + callback=step_callback + ) # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 torch.cuda.empty_cache() @@ -284,29 +285,29 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - model = self.get_model(context.services.model_manager) - conditioning_data = self.get_conditioning_data(model) + with self.get_model(context.services.model_manager) as model: + conditioning_data = self.get_conditioning_data(model) - # TODO: Verify the noise is the right size + # TODO: Verify the noise is the right size - initial_latents = latent if self.strength < 1.0 else torch.zeros_like( - latent, device=model.device, dtype=latent.dtype - ) + initial_latents = latent if self.strength < 1.0 else torch.zeros_like( + latent, device=model.device, dtype=latent.dtype + ) - timesteps, _ = model.get_img2img_timesteps( - self.steps, - self.strength, - device=model.device, - ) + timesteps, _ = model.get_img2img_timesteps( + self.steps, + self.strength, + device=model.device, + ) - result_latents, result_attention_map_saver = model.latents_from_embeddings( - latents=initial_latents, - timesteps=timesteps, - noise=noise, - num_inference_steps=self.steps, - conditioning_data=conditioning_data, - callback=step_callback - ) + result_latents, result_attention_map_saver = model.latents_from_embeddings( + latents=initial_latents, + timesteps=timesteps, + noise=noise, + num_inference_steps=self.steps, + conditioning_data=conditioning_data, + callback=step_callback + ) # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 torch.cuda.empty_cache() diff --git a/invokeai/app/invocations/util/choose_model.py b/invokeai/app/invocations/util/choose_model.py index cd03ce87a8..0e888d7f43 100644 --- a/invokeai/app/invocations/util/choose_model.py +++ b/invokeai/app/invocations/util/choose_model.py @@ -7,7 +7,7 @@ def choose_model(model_manager: ModelManager, model_name: str): if model_manager.valid_model(model_name): model = model_manager.get_model(model_name) else: - model = model_manager.get_model() - logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.") + model = model_manager.get_model(model_manager.default_model()) + logger.warning(f"'{model_name}' is not a valid model name. Using default model \'{model.name}\' instead.") return model diff --git a/invokeai/app/services/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py index 2b1aac1f36..c7924e797d 100644 --- a/invokeai/app/services/model_manager_initializer.py +++ b/invokeai/app/services/model_manager_initializer.py @@ -47,22 +47,21 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager: else: embedding_path = None - # migrate legacy models - ModelManager.migrate_models() - # creating the model manager try: device = torch.device(choose_torch_device()) - precision = 'float16' if config.precision=='float16' \ - else 'float32' if config.precision=='float32' \ - else choose_precision(device) + if config.precision=="auto": + precision = choose_precision(device) + dtype = torch.float32 if precision=='float32' \ + else torch.float16 model_manager = ModelManager( - OmegaConf.load(config.conf), - precision=precision, + config.conf, + precision=dtype, device_type=device, max_loaded_models=config.max_loaded_models, - embedding_path = Path(embedding_path), +# temporarily disabled until model manager stabilizes +# embedding_path = Path(embedding_path), logger = logger, ) except (FileNotFoundError, TypeError, AssertionError) as e: diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py index 06066dd6b1..dc2eeca67a 100644 --- a/invokeai/backend/__init__.py +++ b/invokeai/backend/__init__.py @@ -10,7 +10,7 @@ from .generator import ( Img2Img, Inpaint ) -from .model_management import ModelManager +from .model_management import ModelManager, ModelCache, ModelStatus, SDModelType from .safety_checker import SafetyChecker from .args import Args from .globals import Globals diff --git a/invokeai/backend/generate.py b/invokeai/backend/generate.py index 4f3df60f1c..c7e2558db1 100644 --- a/invokeai/backend/generate.py +++ b/invokeai/backend/generate.py @@ -37,7 +37,7 @@ from .safety_checker import SafetyChecker from .prompting import get_uc_and_c_and_ec from .prompting.conditioning import log_tokenization from .stable_diffusion import HuggingFaceConceptsLibrary -from .util import choose_precision, choose_torch_device +from .util import choose_precision, choose_torch_device, torch_dtype def fix_func(orig): if hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): @@ -50,7 +50,6 @@ def fix_func(orig): return new_func return orig - torch.rand = fix_func(torch.rand) torch.rand_like = fix_func(torch.rand_like) torch.randn = fix_func(torch.randn) @@ -156,7 +155,6 @@ class Generate: weights=None, config=None, ): - mconfig = OmegaConf.load(conf) self.height = None self.width = None self.model_manager = None @@ -171,7 +169,7 @@ class Generate: self.seamless_axes = {"x", "y"} self.hires_fix = False self.embedding_path = embedding_path - self.model = None # empty for now + self.model_context = None # empty for now self.model_hash = None self.sampler = None self.device = None @@ -219,12 +217,12 @@ class Generate: # model caching system for fast switching self.model_manager = ModelManager( - mconfig, + conf, self.device, - self.precision, + torch_dtype(self.device), max_loaded_models=max_loaded_models, sequential_offload=self.free_gpu_mem, - embedding_path=Path(self.embedding_path), +# embedding_path=Path(self.embedding_path), ) # don't accept invalid models fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME @@ -418,170 +416,171 @@ class Generate: with_variations = [] if with_variations is None else with_variations # will instantiate the model or return it from cache - model = self.set_model(self.model_name) + model_context = self.set_model(self.model_name) # self.width and self.height are set by set_model() # to the width and height of the image training set width = width or self.width height = height or self.height - if isinstance(model, DiffusionPipeline): - configure_model_padding(model.unet, seamless, seamless_axes) - configure_model_padding(model.vae, seamless, seamless_axes) - else: - configure_model_padding(model, seamless, seamless_axes) - - assert cfg_scale > 1.0, "CFG_Scale (-C) must be >1.0" - assert threshold >= 0.0, "--threshold must be >=0.0" - assert ( - 0.0 < strength <= 1.0 - ), "img2img and inpaint strength can only work with 0.0 < strength < 1.0" - assert ( - 0.0 <= variation_amount <= 1.0 - ), "-v --variation_amount must be in [0.0, 1.0]" - assert 0.0 <= perlin <= 1.0, "--perlin must be in [0.0, 1.0]" - assert (embiggen == None and embiggen_tiles == None) or ( - (embiggen != None or embiggen_tiles != None) and init_img != None - ), "Embiggen requires an init/input image to be specified" - - if len(with_variations) > 0 or variation_amount > 1.0: - assert seed is not None, "seed must be specified when using with_variations" - if variation_amount == 0.0: - assert ( - iterations == 1 - ), "when using --with_variations, multiple iterations are only possible when using --variation_amount" - assert all( - 0 <= weight <= 1 for _, weight in with_variations - ), f"variation weights must be in [0.0, 1.0]: got {[weight for _, weight in with_variations]}" - - width, height, _ = self._resolution_check(width, height, log=True) - assert ( - inpaint_replace >= 0.0 and inpaint_replace <= 1.0 - ), "inpaint_replace must be between 0.0 and 1.0" - - if sampler_name and (sampler_name != self.sampler_name): - self.sampler_name = sampler_name - self._set_scheduler() - - # apply the concepts library to the prompt - prompt = self.huggingface_concepts_library.replace_concepts_with_triggers( - prompt, - lambda concepts: self.load_huggingface_concepts(concepts), - self.model.textual_inversion_manager.get_all_trigger_strings(), - ) - - tic = time.time() - if self._has_cuda(): - torch.cuda.reset_peak_memory_stats() - - results = list() - - try: - uc, c, extra_conditioning_info = get_uc_and_c_and_ec( - prompt, - model=self.model, - skip_normalize_legacy_blend=skip_normalize, - log_tokens=self.log_tokenization, - ) - - init_image, mask_image = self._make_images( - init_img, - init_mask, - width, - height, - fit=fit, - text_mask=text_mask, - invert_mask=invert_mask, - force_outpaint=force_outpaint, - ) - - # TODO: Hacky selection of operation to perform. Needs to be refactored. - generator = self.select_generator( - init_image, mask_image, embiggen, hires_fix, force_outpaint - ) - - generator.set_variation(self.seed, variation_amount, with_variations) - generator.use_mps_noise = use_mps_noise - - results = generator.generate( - prompt, - iterations=iterations, - seed=self.seed, - sampler=self.sampler, - steps=steps, - cfg_scale=cfg_scale, - conditioning=(uc, c, extra_conditioning_info), - ddim_eta=ddim_eta, - image_callback=image_callback, # called after the final image is generated - step_callback=step_callback, # called after each intermediate image is generated - width=width, - height=height, - init_img=init_img, # embiggen needs to manipulate from the unmodified init_img - init_image=init_image, # notice that init_image is different from init_img - mask_image=mask_image, - strength=strength, - threshold=threshold, - perlin=perlin, - h_symmetry_time_pct=h_symmetry_time_pct, - v_symmetry_time_pct=v_symmetry_time_pct, - embiggen=embiggen, - embiggen_tiles=embiggen_tiles, - embiggen_strength=embiggen_strength, - inpaint_replace=inpaint_replace, - mask_blur_radius=mask_blur_radius, - safety_checker=self.safety_checker, - seam_size=seam_size, - seam_blur=seam_blur, - seam_strength=seam_strength, - seam_steps=seam_steps, - tile_size=tile_size, - infill_method=infill_method, - force_outpaint=force_outpaint, - inpaint_height=inpaint_height, - inpaint_width=inpaint_width, - enable_image_debugging=enable_image_debugging, - free_gpu_mem=self.free_gpu_mem, - clear_cuda_cache=self.clear_cuda_cache, - ) - - if init_color: - self.correct_colors( - image_list=results, - reference_image_path=init_color, - image_callback=image_callback, - ) - - if upscale is not None or facetool_strength > 0: - self.upscale_and_reconstruct( - results, - upscale=upscale, - upscale_denoise_str=upscale_denoise_str, - facetool=facetool, - strength=facetool_strength, - codeformer_fidelity=codeformer_fidelity, - save_original=save_original, - image_callback=image_callback, - ) - - except KeyboardInterrupt: - # Clear the CUDA cache on an exception - self.clear_cuda_cache() - - if catch_interrupts: - logger.warning("Interrupted** Partial results will be returned.") + with model_context as model: + if isinstance(model, DiffusionPipeline): + configure_model_padding(model.unet, seamless, seamless_axes) + configure_model_padding(model.vae, seamless, seamless_axes) else: - raise KeyboardInterrupt - except RuntimeError: - # Clear the CUDA cache on an exception - self.clear_cuda_cache() + configure_model_padding(model, seamless, seamless_axes) - print(traceback.format_exc(), file=sys.stderr) - logger.info("Could not generate image.") + assert cfg_scale > 1.0, "CFG_Scale (-C) must be >1.0" + assert threshold >= 0.0, "--threshold must be >=0.0" + assert ( + 0.0 < strength <= 1.0 + ), "img2img and inpaint strength can only work with 0.0 < strength < 1.0" + assert ( + 0.0 <= variation_amount <= 1.0 + ), "-v --variation_amount must be in [0.0, 1.0]" + assert 0.0 <= perlin <= 1.0, "--perlin must be in [0.0, 1.0]" + assert (embiggen == None and embiggen_tiles == None) or ( + (embiggen != None or embiggen_tiles != None) and init_img != None + ), "Embiggen requires an init/input image to be specified" - toc = time.time() - logger.info("Usage stats:") - logger.info(f"{len(results)} image(s) generated in "+"%4.2fs" % (toc - tic)) - self.print_cuda_stats() + if len(with_variations) > 0 or variation_amount > 1.0: + assert seed is not None, "seed must be specified when using with_variations" + if variation_amount == 0.0: + assert ( + iterations == 1 + ), "when using --with_variations, multiple iterations are only possible when using --variation_amount" + assert all( + 0 <= weight <= 1 for _, weight in with_variations + ), f"variation weights must be in [0.0, 1.0]: got {[weight for _, weight in with_variations]}" + + width, height, _ = self._resolution_check(width, height, log=True) + assert ( + inpaint_replace >= 0.0 and inpaint_replace <= 1.0 + ), "inpaint_replace must be between 0.0 and 1.0" + + if sampler_name and (sampler_name != self.sampler_name): + self.sampler_name = sampler_name + self._set_scheduler(model) + + # apply the concepts library to the prompt + prompt = self.huggingface_concepts_library.replace_concepts_with_triggers( + prompt, + lambda concepts: self.load_huggingface_concepts(concepts), + model.textual_inversion_manager.get_all_trigger_strings(), + ) + + tic = time.time() + if self._has_cuda(): + torch.cuda.reset_peak_memory_stats() + + results = list() + + try: + uc, c, extra_conditioning_info = get_uc_and_c_and_ec( + prompt, + model=model, + skip_normalize_legacy_blend=skip_normalize, + log_tokens=self.log_tokenization, + ) + + init_image, mask_image = self._make_images( + init_img, + init_mask, + width, + height, + fit=fit, + text_mask=text_mask, + invert_mask=invert_mask, + force_outpaint=force_outpaint, + ) + + # TODO: Hacky selection of operation to perform. Needs to be refactored. + generator = self.select_generator( + init_image, mask_image, embiggen, hires_fix, force_outpaint + ) + + generator.set_variation(self.seed, variation_amount, with_variations) + generator.use_mps_noise = use_mps_noise + + results = generator.generate( + prompt, + iterations=iterations, + seed=self.seed, + sampler=self.sampler, + steps=steps, + cfg_scale=cfg_scale, + conditioning=(uc, c, extra_conditioning_info), + ddim_eta=ddim_eta, + image_callback=image_callback, # called after the final image is generated + step_callback=step_callback, # called after each intermediate image is generated + width=width, + height=height, + init_img=init_img, # embiggen needs to manipulate from the unmodified init_img + init_image=init_image, # notice that init_image is different from init_img + mask_image=mask_image, + strength=strength, + threshold=threshold, + perlin=perlin, + h_symmetry_time_pct=h_symmetry_time_pct, + v_symmetry_time_pct=v_symmetry_time_pct, + embiggen=embiggen, + embiggen_tiles=embiggen_tiles, + embiggen_strength=embiggen_strength, + inpaint_replace=inpaint_replace, + mask_blur_radius=mask_blur_radius, + safety_checker=self.safety_checker, + seam_size=seam_size, + seam_blur=seam_blur, + seam_strength=seam_strength, + seam_steps=seam_steps, + tile_size=tile_size, + infill_method=infill_method, + force_outpaint=force_outpaint, + inpaint_height=inpaint_height, + inpaint_width=inpaint_width, + enable_image_debugging=enable_image_debugging, + free_gpu_mem=self.free_gpu_mem, + clear_cuda_cache=self.clear_cuda_cache, + ) + + if init_color: + self.correct_colors( + image_list=results, + reference_image_path=init_color, + image_callback=image_callback, + ) + + if upscale is not None or facetool_strength > 0: + self.upscale_and_reconstruct( + results, + upscale=upscale, + upscale_denoise_str=upscale_denoise_str, + facetool=facetool, + strength=facetool_strength, + codeformer_fidelity=codeformer_fidelity, + save_original=save_original, + image_callback=image_callback, + ) + + except KeyboardInterrupt: + # Clear the CUDA cache on an exception + self.clear_cuda_cache() + + if catch_interrupts: + logger.warning("Interrupted** Partial results will be returned.") + else: + raise KeyboardInterrupt + except RuntimeError: + # Clear the CUDA cache on an exception + self.clear_cuda_cache() + + print(traceback.format_exc(), file=sys.stderr) + logger.info("Could not generate image.") + + toc = time.time() + logger.info("Usage stats:") + logger.info(f"{len(results)} image(s) generated in "+"%4.2fs" % (toc - tic)) + self.print_cuda_stats() return results def gather_cuda_stats(self): @@ -662,12 +661,13 @@ class Generate: # used by multiple postfixers # todo: cross-attention control - uc, c, extra_conditioning_info = get_uc_and_c_and_ec( - prompt, - model=self.model, - skip_normalize_legacy_blend=opt.skip_normalize, - log_tokens=log_tokenization, - ) + with self.model_context as model: + uc, c, extra_conditioning_info = get_uc_and_c_and_ec( + prompt, + model=model, + skip_normalize_legacy_blend=opt.skip_normalize, + log_tokens=log_tokenization, + ) if tool in ("gfpgan", "codeformer", "upscale"): if tool == "gfpgan": @@ -852,7 +852,8 @@ class Generate: cn = class_name module = importlib.import_module(mn) constructor = getattr(module, cn) - return constructor(self.model, self.precision) + with self.model_context as model: + return constructor(model, self.precision) def load_model(self): """ @@ -869,8 +870,8 @@ class Generate: If the model fails to load for some reason, will attempt to load the previously- loaded model (if any). If that fallback fails, will raise an AssertionError """ - if self.model_name == model_name and self.model is not None: - return self.model + if self.model_name == model_name and self.model_context is not None: + return self.model_context previous_model_name = self.model_name @@ -881,11 +882,9 @@ class Generate: f'** "{model_name}" is not a known model name. Cannot change.' ) - cache.print_vram_usage() - # have to get rid of all references to model in order # to free it from GPU memory - self.model = None + self.model_context = None self.sampler = None self.generators = {} gc.collect() @@ -902,29 +901,33 @@ class Generate: raise e model_name = previous_model_name - self.model = model_data["model"] - self.width = model_data["width"] - self.height = model_data["height"] - self.model_hash = model_data["hash"] + self.model_context = model_data.context + self.width = 512 + self.height = 512 + self.model_hash = model_data.hash # uncache generators so they pick up new models self.generators = {} set_seed(random.randrange(0, np.iinfo(np.uint32).max)) self.model_name = model_name - self._set_scheduler() # requires self.model_name to be set first - return self.model + with self.model_context as model: + self._set_scheduler(model) # requires self.model_name to be set first + return self.model_context def load_huggingface_concepts(self, concepts: list[str]): - self.model.textual_inversion_manager.load_huggingface_concepts(concepts) + with self.model_context as model: + model.textual_inversion_manager.load_huggingface_concepts(concepts) @property def huggingface_concepts_library(self) -> HuggingFaceConceptsLibrary: - return self.model.textual_inversion_manager.hf_concepts_library + with self.model_context as model: + return model.textual_inversion_manager.hf_concepts_library @property def embedding_trigger_strings(self) -> List[str]: - return self.model.textual_inversion_manager.get_all_trigger_strings() + with self.model_context as model: + return model.textual_inversion_manager.get_all_trigger_strings() def correct_colors(self, image_list, reference_image_path, image_callback=None): reference_image = Image.open(reference_image_path) @@ -1044,8 +1047,8 @@ class Generate: def is_legacy_model(self, model_name) -> bool: return self.model_manager.is_legacy(model_name) - def _set_scheduler(self): - default = self.model.scheduler + def _set_scheduler(self,model): + default = model.scheduler # See https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672 scheduler_map = dict( @@ -1069,7 +1072,7 @@ class Generate: msg = ( f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})" ) - self.sampler = sampler_class.from_config(self.model.scheduler.config) + self.sampler = sampler_class.from_config(model.scheduler.config) else: msg = ( f" Unsupported Sampler: {self.sampler_name} "+ diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py index 8ad9dec026..1f0670bef3 100644 --- a/invokeai/backend/generator/base.py +++ b/invokeai/backend/generator/base.py @@ -123,51 +123,51 @@ class InvokeAIGenerator(metaclass=ABCMeta): generator_args.update(keyword_args) model_info = self.model_info - model_name = model_info['model_name'] - model:StableDiffusionGeneratorPipeline = model_info['model'] - model_hash = model_info['hash'] - scheduler: Scheduler = self.get_scheduler( - model=model, - scheduler_name=generator_args.get('scheduler') - ) - uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model) - gen_class = self._generator_class() - generator = gen_class(model, self.params.precision) - if self.params.variation_amount > 0: - generator.set_variation(generator_args.get('seed'), - generator_args.get('variation_amount'), - generator_args.get('with_variations') - ) + model_name = model_info.name + model_hash = model_info.hash + with model_info.context as model: + scheduler: Scheduler = self.get_scheduler( + model=model, + scheduler_name=generator_args.get('scheduler') + ) + uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model) + gen_class = self._generator_class() + generator = gen_class(model, self.params.precision) + if self.params.variation_amount > 0: + generator.set_variation(generator_args.get('seed'), + generator_args.get('variation_amount'), + generator_args.get('with_variations') + ) - if isinstance(model, DiffusionPipeline): - for component in [model.unet, model.vae]: - configure_model_padding(component, + if isinstance(model, DiffusionPipeline): + for component in [model.unet, model.vae]: + configure_model_padding(component, + generator_args.get('seamless',False), + generator_args.get('seamless_axes') + ) + else: + configure_model_padding(model, generator_args.get('seamless',False), generator_args.get('seamless_axes') ) - else: - configure_model_padding(model, - generator_args.get('seamless',False), - generator_args.get('seamless_axes') - ) - iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1) - for i in iteration_count: - results = generator.generate(prompt, - conditioning=(uc, c, extra_conditioning_info), - step_callback=step_callback, - sampler=scheduler, - **generator_args, - ) - output = InvokeAIGeneratorOutput( - image=results[0][0], - seed=results[0][1], - attention_maps_images=results[0][2], - model_hash = model_hash, - params=Namespace(model_name=model_name,**generator_args), - ) - if callback: - callback(output) + iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1) + for i in iteration_count: + results = generator.generate(prompt, + conditioning=(uc, c, extra_conditioning_info), + step_callback=step_callback, + sampler=scheduler, + **generator_args, + ) + output = InvokeAIGeneratorOutput( + image=results[0][0], + seed=results[0][1], + attention_maps_images=results[0][2], + model_hash = model_hash, + params=Namespace(model_name=model_name,**generator_args), + ) + if callback: + callback(output) yield output @classmethod @@ -275,7 +275,6 @@ class Embiggen(Txt2Img): from .embiggen import Embiggen return Embiggen - class Generator: downsampling_factor: int latent_channels: int diff --git a/invokeai/backend/model_management/__init__.py b/invokeai/backend/model_management/__init__.py index 07b567ce7a..44b51e6a2a 100644 --- a/invokeai/backend/model_management/__init__.py +++ b/invokeai/backend/model_management/__init__.py @@ -2,4 +2,4 @@ Initialization file for invokeai.backend.model_management """ from .model_manager import ModelManager -from .model_cache import ModelCache, ModelStatus +from .model_cache import ModelCache, ModelStatus, SDModelType diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 95b4e165f6..265d363475 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -78,6 +78,10 @@ class UnscannableModelException(Exception): "Raised when picklescan is unable to scan a legacy model file" pass +class ModelLocker(object): + "Forward declaration" + pass + class ModelCache(object): def __init__( self, @@ -112,8 +116,6 @@ class ModelCache(object): self.loaded_models: set = set() # set of model keys loaded in GPU self.locked_models: Counter = Counter() # set of model keys locked in GPU - - @contextlib.contextmanager def get_model( self, repo_id_or_path: Union[str,Path], @@ -124,7 +126,7 @@ class ModelCache(object): legacy_info: LegacyInfo=None, attach_model_part: Tuple[SDModelType, str] = (None,None), gpu_load: bool=True, - )->Generator[ModelClass, None, None]: + )->ModelLocker: # ?? what does it return ''' Load and return a HuggingFace model wrapped in a context manager generator, with RAM caching. Use like this: @@ -188,29 +190,45 @@ class ModelCache(object): if submodel: model = getattr(model, submodel.name) - if gpu_load and hasattr(model,'to'): - try: - self.loaded_models.add(key) - self.locked_models[key] += 1 - if self.lazy_offloading: - self._offload_unlocked_models() - self.logger.debug(f'Loading {key} into {self.execution_device}') - model.to(self.execution_device) # move into GPU - self._print_cuda_stats() - yield model - finally: - self.locked_models[key] -= 1 - if not self.lazy_offloading: - self._offload_unlocked_models() - self._print_cuda_stats() - else: - # in the event that the caller wants the model in RAM, we - # move it into CPU if it is in GPU and not locked - if hasattr(model,'to') and (key in self.loaded_models - and self.locked_models[key] == 0): - model.to(self.storage_device) - self.loaded_models.remove(key) - yield model + return self.ModelLocker(self, key, model, gpu_load) + + class ModelLocker(object): + def __init__(self, cache, key, model, gpu_load): + self.gpu_load = gpu_load + self.cache = cache + self.key = key + # This will keep a copy of the model in RAM until the locker + # is garbage collected. Needs testing! + self.model = model + + def __enter__(self)->ModelClass: + cache = self.cache + key = self.key + model = self.model + if self.gpu_load and hasattr(model,'to'): + cache.loaded_models.add(key) + cache.locked_models[key] += 1 + if cache.lazy_offloading: + cache._offload_unlocked_models() + cache.logger.debug(f'Loading {key} into {cache.execution_device}') + model.to(cache.execution_device) # move into GPU + cache._print_cuda_stats() + else: + # in the event that the caller wants the model in RAM, we + # move it into CPU if it is in GPU and not locked + if hasattr(model,'to') and (key in cache.loaded_models + and cache.locked_models[key] == 0): + model.to(cache.storage_device) + cache.loaded_models.remove(key) + return model + + def __exit__(self, type, value, traceback): + key = self.key + cache = self.cache + cache.locked_models[key] -= 1 + if not cache.lazy_offloading: + cache._offload_unlocked_models() + cache._print_cuda_stats() def attach_part(self, diffusers_model: StableDiffusionPipeline, @@ -381,10 +399,11 @@ class ModelCache(object): revisions = [revision] if revision \ else ['fp16','main'] if self.precision==torch.float16 \ else ['main'] - extra_args = {'precision': self.precision} \ - if model_class in DiffusionClasses \ - else {} - + extra_args = {'torch_dtype': self.precision, + 'safety_checker': None}\ + if model_class in DiffusionClasses\ + else {} + # silence transformer and diffuser warnings with SilenceWarnings(): for rev in revisions: diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 3977ac0ed7..8b6704eb8a 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -69,7 +69,7 @@ class SDModelInfo(): revision: str = None _cache: ModelCache = None - + @property def status(self)->ModelStatus: '''Return load status of this model as a model_cache.ModelStatus enum''' if not self._cache: @@ -106,7 +106,7 @@ class ModelManager(object): config_path: Path, device_type: torch.device = CUDA_DEVICE, precision: torch.dtype = torch.float16, - max_models=DEFAULT_MAX_MODELS, + max_loaded_models=DEFAULT_MAX_MODELS, sequential_offload=False, logger: types.ModuleType = logger, ): @@ -119,7 +119,7 @@ class ModelManager(object): self.config_path = config_path self.config = OmegaConf.load(self.config_path) self.cache = ModelCache( - max_models=max_models, + max_models=max_loaded_models, execution_device = device_type, precision = precision, sequential_offload = sequential_offload, @@ -164,7 +164,7 @@ class ModelManager(object): if mconfig.get('vae'): legacy.vae_file = global_resolve_path(mconfig.vae) elif format=='diffusers': - location = mconfig.repo_id + location = mconfig.get('repo_id') or mconfig.get('path') revision = mconfig.get('revision') else: raise InvalidModelError( diff --git a/invokeai/backend/prompting/conditioning.py b/invokeai/backend/prompting/conditioning.py index d9130ace04..7c6cc0eea2 100644 --- a/invokeai/backend/prompting/conditioning.py +++ b/invokeai/backend/prompting/conditioning.py @@ -7,6 +7,7 @@ get_uc_and_c_and_ec() get the conditioned and unconditioned latent, an """ import re +import torch from typing import Optional, Union from compel import Compel diff --git a/invokeai/backend/web/invoke_ai_web_server.py b/invokeai/backend/web/invoke_ai_web_server.py index 84478d5cb6..eec02cd9dc 100644 --- a/invokeai/backend/web/invoke_ai_web_server.py +++ b/invokeai/backend/web/invoke_ai_web_server.py @@ -78,7 +78,6 @@ class InvokeAIWebServer: mimetypes.add_type("application/javascript", ".js") mimetypes.add_type("text/css", ".css") # Socket IO - logger = True if args.web_verbose else False engineio_logger = True if args.web_verbose else False max_http_buffer_size = 10000000 @@ -1278,13 +1277,14 @@ class InvokeAIWebServer: eventlet.sleep(0) parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"]) - tokens = ( - None - if type(parsed_prompt) is Blend - else get_tokens_for_prompt_object( - self.generate.model.tokenizer, parsed_prompt + with self.generate.model_context as model: + tokens = ( + None + if type(parsed_prompt) is Blend + else get_tokens_for_prompt_object( + model.tokenizer, parsed_prompt + ) ) - ) attention_maps_image_base64_url = ( None if attention_maps_image is None diff --git a/invokeai/frontend/CLI/CLI.py b/invokeai/frontend/CLI/CLI.py index aa0c4bea5f..0c984080a6 100644 --- a/invokeai/frontend/CLI/CLI.py +++ b/invokeai/frontend/CLI/CLI.py @@ -109,9 +109,6 @@ def main(): else: embedding_path = None - # migrate legacy models - ModelManager.migrate_models() - # load the infile as a list of lines if opt.infile: try: @@ -197,7 +194,7 @@ def main_loop(gen, opt): # changing the history file midstream when the output directory is changed. completer = get_completer(opt, models=gen.model_manager.list_models()) set_default_output_dir(opt, completer) - if gen.model: + if gen.model_context: add_embedding_terms(gen, completer) output_cntr = completer.get_current_history_length() + 1 @@ -1080,7 +1077,8 @@ def add_embedding_terms(gen, completer): Called after setting the model, updates the autocompleter with any terms loaded by the embedding manager. """ - trigger_strings = gen.model.textual_inversion_manager.get_all_trigger_strings() + with gen.model_context as model: + trigger_strings = model.textual_inversion_manager.get_all_trigger_strings() completer.add_embedding_terms(trigger_strings) @@ -1222,6 +1220,7 @@ def report_model_error(opt: Namespace, e: Exception): logger.warning( "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models." ) + traceback.print_exc() yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE") if yes_to_all: logger.warning( From 05a27bda5e4375630d1ff8255bf2373d547f8a5f Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 6 May 2023 15:58:44 -0400 Subject: [PATCH 009/225] generalize model loading support, include loras/embeds --- .../backend/model_management/model_cache.py | 87 ++++++----- .../backend/model_management/model_manager.py | 138 +++++++++++++++++- 2 files changed, 182 insertions(+), 43 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 265d363475..c316c4292f 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -20,13 +20,13 @@ import contextlib import gc import hashlib import warnings -from collections.abc import Generator from collections import Counter from enum import Enum from pathlib import Path from typing import Sequence, Union, Tuple, types import torch +import safetensors.torch from diffusers import StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel from diffusers import logging as diffusers_logging from diffusers.pipelines.stable_diffusion.safety_checker import \ @@ -46,13 +46,18 @@ MAX_MODELS = 4 # This is the mapping from the stable diffusion submodel dict key to the class class SDModelType(Enum): diffusion_pipeline=StableDiffusionGeneratorPipeline # whole thing - vae=AutoencoderKL # parts + diffusers=StableDiffusionGeneratorPipeline # same thing + vae=AutoencoderKL # diffusers parts text_encoder=CLIPTextModel tokenizer=CLIPTokenizer unet=UNet2DConditionModel scheduler=SchedulerMixin safety_checker=StableDiffusionSafetyChecker feature_extractor=CLIPFeatureExtractor + # These are all loaded as dicts of tensors + lora=dict + textual_inversion=dict + ckpt=dict class ModelStatus(Enum): unknown='unknown' @@ -192,6 +197,15 @@ class ModelCache(object): return self.ModelLocker(self, key, model, gpu_load) + def uncache_model(self, key: str): + '''Remove corresponding model from the cache''' + if key is not None and key in self.models: + with contextlib.suppress(ValueError): + del self.models[key] + del self.locked_models[key] + self.stack.remove(key) + self.loaded_models.remove(key) + class ModelLocker(object): def __init__(self, cache, key, model, gpu_load): self.gpu_load = gpu_load @@ -301,7 +315,7 @@ class ModelCache(object): :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model ''' path = Path(repo_id_or_path) - return path.is_file() and path.suffix in [".ckpt",".safetensors"] + return path.suffix in [".ckpt",".safetensors",".pt"] @classmethod def scan_model(cls, model_name, checkpoint): @@ -368,15 +382,17 @@ class ModelCache(object): :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt ''' - if self.is_legacy_ckpt(repo_id_or_path): - model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) - else: - model = self._load_diffusers_from_storage( - repo_id_or_path, - subfolder, - revision, - model_class, - ) + # silence transformer and diffuser warnings + with SilenceWarnings(): + if self.is_legacy_ckpt(repo_id_or_path): + model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) + else: + model = self._load_diffusers_from_storage( + repo_id_or_path, + subfolder, + revision, + model_class, + ) if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): model.enable_offload_submodels(self.execution_device) return model @@ -404,21 +420,19 @@ class ModelCache(object): if model_class in DiffusionClasses\ else {} - # silence transformer and diffuser warnings - with SilenceWarnings(): - for rev in revisions: - try: - model = model_class.from_pretrained( - repo_id_or_path, - revision=rev, - subfolder=subfolder or '.', - cache_dir=global_cache_dir('hub'), - **extra_args, - ) - self.logger.debug(f'Found revision {rev}') - break - except OSError: - pass + for rev in revisions: + try: + model = model_class.from_pretrained( + repo_id_or_path, + revision=rev, + subfolder=subfolder or '.', + cache_dir=global_cache_dir('hub'), + **extra_args, + ) + self.logger.debug(f'Found revision {rev}') + break + except OSError: + pass return model def _load_ckpt_from_storage(self, @@ -429,24 +443,27 @@ class ModelCache(object): :param ckpt_path: string or Path pointing to the weights file (.ckpt or .safetensors) :param legacy_info: LegacyInfo object containing paths to legacy config file and alternate vae if required ''' - assert legacy_info is not None - - # deferred loading to avoid circular import errors - from .convert_ckpt_to_diffusers import load_pipeline_from_original_stable_diffusion_ckpt - with SilenceWarnings(): + if legacy_info is None or legacy_info.config_file is None: + if Path(ckpt_path).suffix == '.safetensors': + return safetensors.torch.load_file(ckpt_path) + else: + return torch.load(ckpt_path) + else: + # deferred loading to avoid circular import errors + from .convert_ckpt_to_diffusers import load_pipeline_from_original_stable_diffusion_ckpt pipeline = load_pipeline_from_original_stable_diffusion_ckpt( checkpoint_path=ckpt_path, original_config_file=legacy_info.config_file, - vae_path=legacy_info.vae_file, + vae_path=legacy_info.vae_file, return_generator_pipeline=True, precision=self.precision, ) - return pipeline + return pipeline def _legacy_model_hash(self, checkpoint_path: Union[str,Path])->str: sha = hashlib.sha256() path = Path(checkpoint_path) - assert path.is_file() + assert path.is_file(),f"File {checkpoint_path} not found" hashpath = path.parent / f"{path.name}.sha256" if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime: diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 8b6704eb8a..8929d8cdd6 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -31,7 +31,56 @@ generation operations. The ModelCache object can be accessed using the manager's "cache" attribute. Other methods provided by ModelManager support importing, editing, -converting and deleting models. +converting and deleting models. + +The general format of a models.yaml section is: + + name-of-model: + format: diffusers|ckpt|vae|text_encoder|tokenizer... + repo_id: owner/repo + path: /path/to/local/file/or/directory + subfolder: subfolder-name + submodel: vae|text_encoder|tokenizer... + +The format is one of {diffusers, ckpt, vae, text_encoder, tokenizer, +unet, scheduler, safety_checker, feature_extractor}, and correspond to +items in the SDModelType enum defined in model_cache.py + +One, but not both, of repo_id and path are provided. repo_id is the +HuggingFace repository ID of the model, and path points to the file or +directory on disk. + +If subfolder is provided, then the model exists in a subdirectory of +the main model. These are usually named after the model type, such as +"unet". + +Finally, if submodel is provided, then the path/repo_id is treated as +a diffusers model, the whole thing is ready into memory, and then the +requested part (e.g. "unet") is retrieved. + +This summarizes the three ways of getting a non-diffuser model: + + clip-test-1: + format: text_encoder + repo_id: openai/clip-vit-large-patch14 + description: Returns standalone CLIPTextModel + + clip-test-2: + format: diffusers + repo_id: stabilityai/stable-diffusion-2 + submodel: text_encoder + description: Returns the text_encoder part of whole diffusers model (whole thing in RAM) + + clip-test-3: + format: text_encoder + repo_id: stabilityai/stable-diffusion-2 + subfolder: text_encoder + description: Returns the text_encoder in the subfolder of the diffusers model (just the encoder in RAM) + + clip-token: + format: tokenizer + repo_id: openai/clip-vit-large-patch14 + description: Returns standalone tokenizer """ from __future__ import annotations @@ -53,14 +102,14 @@ from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig from invokeai.backend.globals import Globals, global_cache_dir, global_resolve_path -from .model_cache import Generator, ModelClass, ModelCache, SDModelType, ModelStatus, LegacyInfo +from .model_cache import ModelClass, ModelCache, ModelLocker, SDModelType, ModelStatus, LegacyInfo from ..util import CUDA_DEVICE # wanted to use pydantic here, but Generator objects not supported @dataclass class SDModelInfo(): - context: Generator[ModelClass, None, None] + context: ModelLocker name: str hash: str location: Union[Path,str] @@ -125,6 +174,7 @@ class ModelManager(object): sequential_offload = sequential_offload, logger = logger, ) + self.cache_keys = dict() self.logger = logger def valid_model(self, model_name: str) -> bool: @@ -154,8 +204,12 @@ class ModelManager(object): # get the required loading info out of the config file mconfig = self.config[model_name] + format = mconfig.get('format','diffusers') + model_type = SDModelType.diffusion_pipeline + model_parts = dict([(x.name,x) for x in SDModelType]) legacy = None + if format=='ckpt': location = global_resolve_path(mconfig.weights) legacy = LegacyInfo( @@ -165,14 +219,22 @@ class ModelManager(object): legacy.vae_file = global_resolve_path(mconfig.vae) elif format=='diffusers': location = mconfig.get('repo_id') or mconfig.get('path') - revision = mconfig.get('revision') + if sm := mconfig.get('submodel'): + submodel = model_parts[sm] + elif format in model_parts: + location = mconfig.get('repo_id') or mconfig.get('path') or mconfig.get('weights') + model_type = model_parts[format] else: raise InvalidModelError( f'"{model_name}" has an unknown format {format}' ) subfolder = mconfig.get('subfolder') + revision = mconfig.get('revision') hash = self.cache.model_hash(location,revision) + + # to support the traditional way of attaching a VAE + # to a model, we hacked in `attach_model_part` vae = (None,None) try: vae_id = mconfig.vae.repo_id @@ -181,12 +243,19 @@ class ModelManager(object): pass model_context = self.cache.get_model( location, + model_type = model_type, revision = revision, subfolder = subfolder, legacy_info = legacy, submodel = submodel, attach_model_part=vae, ) + + # in case we need to communicate information about this + # model to the cache manager, then we need to remember + # the cache key + self.cache_keys[model_name] = model_context.key + return SDModelInfo( context = model_context, name = model_name, @@ -365,6 +434,7 @@ class ModelManager(object): attributes are incorrect or the model name is missing. """ omega = self.config + assert "format" in model_attributes, 'missing required field "format"' if model_attributes["format"] == "diffusers": assert ( @@ -373,9 +443,11 @@ class ModelManager(object): assert ( "path" in model_attributes or "repo_id" in model_attributes ), 'model must have either the "path" or "repo_id" fields defined' - else: + elif model_attributes["format"] == "ckpt": for field in ("description", "weights", "height", "width", "config"): assert field in model_attributes, f"required field {field} is missing" + else: + assert "weights" in model_attributes and "description" in model_attributes assert ( clobber or model_name not in omega @@ -385,9 +457,10 @@ class ModelManager(object): if "weights" in omega[model_name]: omega[model_name]["weights"].replace("\\", "/") - - if clobber: - self._invalidate_cached_model(model_name) + + if clobber and model_name in self.cache_keys: + self.cache.uncache_model(self.cache_keys[model_name]) + del self.cache_keys[model_name] def import_diffuser_model( self, @@ -425,6 +498,55 @@ class ModelManager(object): self.commit(commit_to_conf) return model_name + def import_lora( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated lora file. Call + mgr.commit() to write out the configuration to models.yaml + """ + path = Path(path) + model_name = model_name or path.stem + model_description = description or f"LoRA model {model_name}" + self.add_model(model_name, + dict( + format="lora", + weights=str(path), + description=model_description, + ), + True + ) + + def import_embedding( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated lora file. Call + mgr.commit() to write out the configuration to models.yaml + """ + path = Path(path) + if path.is_directory() and (path / "learned_embeds.bin").exists(): + weights = path / "learned_embeds.bin" + else: + weights = path + + model_name = model_name or path.stem + model_description = description or f"Textual embedding model {model_name}" + self.add_model(model_name, + dict( + format="textual_inversion", + weights=str(path), + description=model_description, + ), + True + ) + @classmethod def probe_model_type(self, checkpoint: dict) -> SDLegacyType: """ From 647ffb2a0f5e617708ffef09891ee530354032ee Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 6 May 2023 22:41:19 -0400 Subject: [PATCH 010/225] defined abstract baseclass for model manager service --- invokeai/app/services/model_manager.py | 179 ++++++++++++++++++ invokeai/backend/__init__.py | 2 +- invokeai/backend/model_management/__init__.py | 2 +- .../backend/model_management/model_manager.py | 10 +- 4 files changed, 186 insertions(+), 7 deletions(-) create mode 100644 invokeai/app/services/model_manager.py diff --git a/invokeai/app/services/model_manager.py b/invokeai/app/services/model_manager.py new file mode 100644 index 0000000000..044310fccb --- /dev/null +++ b/invokeai/app/services/model_manager.py @@ -0,0 +1,179 @@ +# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team + +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Union, Callable + +from invokeai.backend import ModelManager, SDModelType, SDModelInfo + +class ModelManagerBase(ABC): + """Responsible for managing models on disk and in memory""" + + @abstractmethod + def get(self, model_name: str, submodel: SDModelType=None)->SDModelInfo: + """Retrieve the indicated model. submodel can be used to get a + part (such as the vae) of a diffusers mode.l""" + pass + + @abstractmethod + def valid_model(self, model_name: str) -> bool: + """ + Given a model name, returns True if it is a valid + identifier. + """ + pass + + @abstractmethod + def default_model(self) -> Union[str,None]: + """ + Returns the name of the default model, or None + if none is defined. + """ + pass + + @abstractmethod + def set_default_model(self, model_name:str): + """Sets the default model to the indicated name.""" + pass + + @abstractmethod + def model_info(self, model_name: str)->dict: + """ + Given a model name returns a dict-like (OmegaConf) object describing it. + """ + pass + + @abstractmethod + def model_names(self)->list[str]: + """ + Returns a list of all the model names known. + """ + pass + + @abstractmethod + def list_models(self)->dict: + """ + Return a dict of models in the format: + { model_name1: {'status': ('active'|'cached'|'not loaded'), + 'description': description, + 'format': ('ckpt'|'diffusers'|'vae'|'text_encoder'|'tokenizer'|'lora'...), + }, + model_name2: { etc } + """ + pass + + + @abstractmethod + def add_model( + self, model_name: str, model_attributes: dict, clobber: bool = False)->None: + """ + Update the named model with a dictionary of attributes. Will fail with an + assertion error if the name already exists. Pass clobber=True to overwrite. + On a successful update, the config will be changed in memory. Will fail + with an assertion error if provided attributes are incorrect or + the model name is missing. Call commit() to write changes to disk. + """ + pass + + @abstractmethod + def del_model(self, model_name: str, delete_files: bool = False) -> None: + """ + Delete the named model from configuration. If delete_files is true, + then the underlying weight file or diffusers directory will be deleted + as well. Call commit() to write to disk. + """ + pass + + @abstractmethod + def import_diffuser_model( + repo_or_path: Union[str, Path], + model_name: str = None, + description: str = None, + vae: dict = None, + ) -> bool: + """ + Install the indicated diffuser model and returns True if successful. + + "repo_or_path" can be either a repo-id or a path-like object corresponding to the + top of a downloaded diffusers directory. + + You can optionally provide a model name and/or description. If not provided, + then these will be derived from the repo name. Call commit() to write to disk. + """ + pass + + @abstractmethod + def import_lora( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated lora file. Call + mgr.commit() to write out the configuration to models.yaml + """ + pass + + @abstractmethod + def import_embedding( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated textual inversion embedding file. + Call commit() to write out the configuration to models.yaml + """ + pass + + @abstractmethod + def heuristic_import( + self, + path_url_or_repo: str, + model_name: str = None, + description: str = None, + model_config_file: Path = None, + commit_to_conf: Path = None, + config_file_callback: Callable[[Path], Path] = None, + ) -> str: + """Accept a string which could be: + - a HF diffusers repo_id + - a URL pointing to a legacy .ckpt or .safetensors file + - a local path pointing to a legacy .ckpt or .safetensors file + - a local directory containing .ckpt and .safetensors files + - a local directory containing a diffusers model + + After determining the nature of the model and downloading it + (if necessary), the file is probed to determine the correct + configuration file (if needed) and it is imported. + + The model_name and/or description can be provided. If not, they will + be generated automatically. + + If commit_to_conf is provided, the newly loaded model will be written + to the `models.yaml` file at the indicated path. Otherwise, the changes + will only remain in memory. + + The routine will do its best to figure out the config file + needed to convert legacy checkpoint file, but if it can't it + will call the config_file_callback routine, if provided. The + callback accepts a single argument, the Path to the checkpoint + file, and returns a Path to the config file to use. + + The (potentially derived) name of the model is returned on + success, or None on failure. When multiple models are added + from a directory, only the last imported one is returned. + + """ + pass + + @abstractmethod + def commit(self, conf_file: Path=None) -> None: + """ + Write current configuration out to the indicated file. + If no conf_file is provided, then replaces the + original file/database used to initialize the object. + """ + pass diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py index dc2eeca67a..e06e220ffe 100644 --- a/invokeai/backend/__init__.py +++ b/invokeai/backend/__init__.py @@ -10,7 +10,7 @@ from .generator import ( Img2Img, Inpaint ) -from .model_management import ModelManager, ModelCache, ModelStatus, SDModelType +from .model_management import ModelManager, ModelCache, ModelStatus, SDModelType, SDModelInfo from .safety_checker import SafetyChecker from .args import Args from .globals import Globals diff --git a/invokeai/backend/model_management/__init__.py b/invokeai/backend/model_management/__init__.py index 44b51e6a2a..89114d7de4 100644 --- a/invokeai/backend/model_management/__init__.py +++ b/invokeai/backend/model_management/__init__.py @@ -1,5 +1,5 @@ """ Initialization file for invokeai.backend.model_management """ -from .model_manager import ModelManager +from .model_manager import ModelManager, SDModelInfo from .model_cache import ModelCache, ModelStatus, SDModelType diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 8929d8cdd6..7c3c32c004 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -102,7 +102,7 @@ from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig from invokeai.backend.globals import Globals, global_cache_dir, global_resolve_path -from .model_cache import ModelClass, ModelCache, ModelLocker, SDModelType, ModelStatus, LegacyInfo +from .model_cache import ModelCache, ModelLocker, SDModelType, ModelStatus, LegacyInfo from ..util import CUDA_DEVICE @@ -267,7 +267,7 @@ class ModelManager(object): _cache = self.cache ) - def default_model(self) -> str | None: + def default_model(self) -> Union[str,None]: """ Returns the name of the default model, or None if none is defined. @@ -541,7 +541,7 @@ class ModelManager(object): self.add_model(model_name, dict( format="textual_inversion", - weights=str(path), + weights=str(weights), description=model_description, ), True @@ -865,12 +865,12 @@ class ModelManager(object): return search_folder, found_models - def commit(self) -> None: + def commit(self, conf_file: Path=None) -> None: """ Write current configuration out to the indicated file. """ yaml_str = OmegaConf.to_yaml(self.config) - config_file_path = self.config_path + config_file_path = conf_file or self.config_path tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp") with open(tmpfile, "w", encoding="utf-8") as outfile: outfile.write(self.preamble()) From 667171ed904d74bd28d18ba904ab430e70e5c5fc Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sun, 7 May 2023 18:07:28 -0400 Subject: [PATCH 011/225] cap model cache size using bytes, not # models --- .../app/services/model_manager_initializer.py | 2 +- invokeai/backend/args.py | 10 +- .../backend/model_management/model_cache.py | 119 +++++++++++++----- .../backend/model_management/model_manager.py | 6 +- 4 files changed, 99 insertions(+), 38 deletions(-) diff --git a/invokeai/app/services/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py index c7924e797d..cae650fcfa 100644 --- a/invokeai/app/services/model_manager_initializer.py +++ b/invokeai/app/services/model_manager_initializer.py @@ -59,7 +59,7 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager: config.conf, precision=dtype, device_type=device, - max_loaded_models=config.max_loaded_models, + max_cache_size=config.max_cache_size, # temporarily disabled until model manager stabilizes # embedding_path = Path(embedding_path), logger = logger, diff --git a/invokeai/backend/args.py b/invokeai/backend/args.py index eb8b396ee0..2cd44b8f7b 100644 --- a/invokeai/backend/args.py +++ b/invokeai/backend/args.py @@ -502,11 +502,11 @@ class Args(object): help="Deprecated way to set --precision=float32", ) model_group.add_argument( - "--max_loaded_models", - dest="max_loaded_models", - type=int, - default=2, - help="Maximum number of models to keep in memory for fast switching, including the one in GPU", + "--max_cache_size", + dest="max_cache_size", + type=float, + default=6.0, + help="Maximum size of the model RAM cache (in GB). 6 GB is sufficient to keep 2-3 diffusers models in RAM simultaneously.", ) model_group.add_argument( "--free_gpu_mem", diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index c316c4292f..97c21159b4 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -19,11 +19,13 @@ context. Use like this: import contextlib import gc import hashlib +import logging import warnings from collections import Counter from enum import Enum from pathlib import Path -from typing import Sequence, Union, Tuple, types +from psutil import Process +from typing import Dict, Sequence, Union, Tuple, types import torch import safetensors.torch @@ -41,7 +43,12 @@ import invokeai.backend.util.logging as logger from ..globals import global_cache_dir from ..stable_diffusion import StableDiffusionGeneratorPipeline -MAX_MODELS = 4 +# Maximum size of the cache, in gigs +# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously +DEFAULT_MAX_CACHE_SIZE = 6.0 + +# actual size of a gig +GIG = 1073741824 # This is the mapping from the stable diffusion submodel dict key to the class class SDModelType(Enum): @@ -65,6 +72,24 @@ class ModelStatus(Enum): in_ram='cached' in_vram='in gpu' active='locked in gpu' + +# This is used to guesstimate the size of a model before we load it. +# After loading, we will know it exactly. +# Sizes are in Gigs, estimated for float16; double for float32 +SIZE_GUESSTIMATE = { + SDModelType.diffusion_pipeline: 2.5, + SDModelType.diffusers: 2.5, + SDModelType.vae: 0.35, + SDModelType.text_encoder: 0.5, + SDModelType.tokenizer: 0.0001, + SDModelType.unet: 3.4, + SDModelType.scheduler: 0.0001, + SDModelType.safety_checker: 1.2, + SDModelType.feature_extractor: 0.0001, + SDModelType.lora: 0.1, + SDModelType.textual_inversion: 0.0001, + SDModelType.ckpt: 4.2, +} # The list of model classes we know how to fetch, for typechecking ModelClass = Union[tuple([x.value for x in SDModelType])] @@ -90,7 +115,7 @@ class ModelLocker(object): class ModelCache(object): def __init__( self, - max_models: int=MAX_MODELS, + max_cache_size: float=DEFAULT_MAX_CACHE_SIZE, execution_device: torch.device=torch.device('cuda'), storage_device: torch.device=torch.device('cpu'), precision: torch.dtype=torch.float16, @@ -113,13 +138,15 @@ class ModelCache(object): self.lazy_offloading = lazy_offloading self.sequential_offload: bool=sequential_offload self.precision: torch.dtype=precision - self.max_models: int=max_models + self.current_cache_size: int=0 + self.max_cache_size: int=max_cache_size self.execution_device: torch.device=execution_device self.storage_device: torch.device=storage_device self.sha_chunksize=sha_chunksize self.logger = logger self.loaded_models: set = set() # set of model keys loaded in GPU self.locked_models: Counter = Counter() # set of model keys locked in GPU + self.model_sizes: Dict[str,int] = dict() def get_model( self, @@ -172,21 +199,33 @@ class ModelCache(object): model_type.value, revision, subfolder - ) + ) + if key in self.models: # cached - move to bottom of stack with contextlib.suppress(ValueError): self.stack.remove(key) self.stack.append(key) model = self.models[key] + else: # not cached -load - self._make_cache_room() - model = self._load_model_from_storage( - repo_id_or_path=repo_id_or_path, - model_class=model_type.value, - subfolder=subfolder, - revision=revision, - legacy_info=legacy_info, - ) + self.logger.info(f'Loading model {repo_id_or_path}, type {model_type}') + + # this will remove older cached models until + # there is sufficient room to load the requested model + self._make_cache_room(key, model_type) + + with MemoryUsage() as usage: + model = self._load_model_from_storage( + repo_id_or_path=repo_id_or_path, + model_class=model_type.value, + subfolder=subfolder, + revision=revision, + legacy_info=legacy_info, + ) + logger.debug(f'Actual memory used to load model: {(usage.mem_used/GIG):.2f} GB') + self.model_sizes[key] = usage.mem_used + self.current_cache_size += usage.mem_used + if model_type==SDModelType.diffusion_pipeline and attach_model_part[0]: self.attach_part(model,*attach_model_part) self.stack.append(key) # add to LRU cache @@ -200,11 +239,11 @@ class ModelCache(object): def uncache_model(self, key: str): '''Remove corresponding model from the cache''' if key is not None and key in self.models: - with contextlib.suppress(ValueError): + with contextlib.suppress(ValueError), contextlib.suppress(KeyError): del self.models[key] del self.locked_models[key] - self.stack.remove(key) self.loaded_models.remove(key) + self.stack.remove(key) class ModelLocker(object): def __init__(self, cache, key, model, gpu_load): @@ -304,9 +343,9 @@ class ModelCache(object): else: return self._hf_commit_hash(repo_id_or_path,revision) - def cache_size(self)->int: - "Return the current number of models cached." - return len(self.models) + def cache_size(self)->float: + "Return the current size of the cache, in GB" + return self.current_cache_size / GIG @classmethod def is_legacy_ckpt(cls, repo_id_or_path: Union[str,Path])->bool: @@ -342,18 +381,29 @@ class ModelCache(object): return self.execution_device.type == 'cuda' def _print_cuda_stats(self): - vram = "%4.2fG" % (torch.cuda.memory_allocated() / 1e9) + vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG) + ram = "%4.2fG" % (self.current_cache_size / GIG) loaded_models = len(self.loaded_models) locked_models = len([x for x in self.locked_models if self.locked_models[x]>0]) - logger.debug(f"Current VRAM usage: {vram}; locked_models/loaded_models = {locked_models}/{loaded_models}") + logger.debug(f"Current VRAM/RAM usage: {vram}/{ram}; locked_models/loaded_models = {locked_models}/{loaded_models}") - def _make_cache_room(self): - models_in_ram = len(self.models) - while models_in_ram >= self.max_models: + def _make_cache_room(self, key, model_type): + # calculate how much memory this model will require + multiplier = 2 if self.precision==torch.float32 else 1 + bytes_needed = int(self.model_sizes.get(key,0) or SIZE_GUESSTIMATE[model_type]*GIG*multiplier) + maximum_size = self.max_cache_size * GIG # stored in GB, convert to bytes + current_size = self.current_cache_size + + adjective = 'guesstimated' if key not in self.model_sizes else 'known from previous load' + logger.debug(f'{(bytes_needed/GIG):.2f} GB needed to load this model ({adjective})') + while current_size+bytes_needed > maximum_size: if least_recently_used_key := self.stack.pop(0): - logger.debug(f'Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}') - del self.models[least_recently_used_key] - models_in_ram = len(self.models) + model_size = self.model_sizes.get(least_recently_used_key,0) + logger.debug(f'Max cache size exceeded: cache_size={(current_size/GIG):.2f} GB, need an additional {(bytes_needed/GIG):.2f} GB') + logger.debug(f'Unloading model {least_recently_used_key} to free {(model_size/GIG):.2f} GB') + self.uncache_model(least_recently_used_key) + current_size -= model_size + self.current_cache_size = current_size gc.collect() def _offload_unlocked_models(self): @@ -393,8 +443,8 @@ class ModelCache(object): revision, model_class, ) - if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): - model.enable_offload_submodels(self.execution_device) + if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): + model.enable_offload_submodels(self.execution_device) return model def _load_diffusers_from_storage( @@ -411,7 +461,6 @@ class ModelCache(object): :param revision: model revision :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline ''' - self.logger.info(f'Loading model {repo_id_or_path}') revisions = [revision] if revision \ else ['fp16','main'] if self.precision==torch.float16 \ else ['main'] @@ -529,3 +578,15 @@ class SilenceWarnings(object): transformers_logging.set_verbosity(self.transformers_verbosity) diffusers_logging.set_verbosity(self.diffusers_verbosity) warnings.simplefilter('default') + +class MemoryUsage(object): + def __init__(self): + self.vms = None + self.mem_used = 0 + + def __enter__(self): + self.vms = Process().memory_info().vms + return self + + def __exit__(self, *args): + self.mem_used = Process().memory_info().vms - self.vms diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 7c3c32c004..368d581cd8 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -141,7 +141,7 @@ class SDLegacyType(Enum): V2_v = auto() UNKNOWN = auto() -DEFAULT_MAX_MODELS = 2 +MAX_CACHE_SIZE = 6.0 # GB class ModelManager(object): """ @@ -155,7 +155,7 @@ class ModelManager(object): config_path: Path, device_type: torch.device = CUDA_DEVICE, precision: torch.dtype = torch.float16, - max_loaded_models=DEFAULT_MAX_MODELS, + max_cache_size=MAX_CACHE_SIZE, sequential_offload=False, logger: types.ModuleType = logger, ): @@ -168,7 +168,7 @@ class ModelManager(object): self.config_path = config_path self.config = OmegaConf.load(self.config_path) self.cache = ModelCache( - max_models=max_loaded_models, + max_cache_size=max_cache_size, execution_device = device_type, precision = precision, sequential_offload = sequential_offload, From 4649920074eca72b637a3e1b790e3a554c2a1530 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sun, 7 May 2023 19:06:49 -0400 Subject: [PATCH 012/225] adjust t2i to work with new model structure --- invokeai/app/invocations/latent.py | 11 +++++------ .../app/services/model_manager_initializer.py | 4 ++++ invokeai/backend/args.py | 19 +++++++++++++------ .../backend/model_management/model_cache.py | 4 +++- 4 files changed, 25 insertions(+), 13 deletions(-) diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 84af41b1c9..ea16c90030 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -206,7 +206,6 @@ class TextToLatentsInvocation(BaseInvocation): def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData: uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model) - print(f'DEBUG: uc.dtype={uc.dtype}, c.dtype={c.dtype}') conditioning_data = ConditioningData( uc, c, @@ -346,11 +345,11 @@ class LatentsToImageInvocation(BaseInvocation): # TODO: this only really needs the vae model_info = choose_model(context.services.model_manager, self.model) - model: StableDiffusionGeneratorPipeline = model_info['model'] - - with torch.inference_mode(): - np_image = model.decode_latents(latents) - image = model.numpy_to_pil(np_image)[0] + + with model_info.context as model: + with torch.inference_mode(): + np_image = model.decode_latents(latents) + image = model.numpy_to_pil(np_image)[0] image_type = ImageType.RESULT image_name = context.services.images.create_name( diff --git a/invokeai/app/services/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py index cae650fcfa..3c0d9dbef6 100644 --- a/invokeai/app/services/model_manager_initializer.py +++ b/invokeai/app/services/model_manager_initializer.py @@ -54,6 +54,10 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager: precision = choose_precision(device) dtype = torch.float32 if precision=='float32' \ else torch.float16 + + max_cache_size = config.max_cache_size \ + if hasattr(config,'max_cache_size') \ + else config.max_loaded_models * 2.5 model_manager = ModelManager( config.conf, diff --git a/invokeai/backend/args.py b/invokeai/backend/args.py index 2cd44b8f7b..64f000a45d 100644 --- a/invokeai/backend/args.py +++ b/invokeai/backend/args.py @@ -557,12 +557,6 @@ class Args(object): default=False, help="Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.", ) - model_group.add_argument( - "--autoimport", - default=None, - type=str, - help="(DEPRECATED - NONFUNCTIONAL). Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly", - ) model_group.add_argument( "--autoconvert", default=None, @@ -765,6 +759,19 @@ class Args(object): action="store_true", help="Start InvokeAI GUI", ) + deprecated_group.add_argument( + "--autoimport", + default=None, + type=str, + help="(DEPRECATED - NONFUNCTIONAL). Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly", + ) + deprecated_group.add_argument( + "--max_loaded_models", + dest="max_loaded_models", + type=int, + default=3, + help="Maximum number of models to keep in RAM cache (deprecated - use max_cache_size)", + ) return parser # This creates the parser that processes commands on the invoke> command line diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 97c21159b4..540ec0b0fd 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -213,7 +213,9 @@ class ModelCache(object): # this will remove older cached models until # there is sufficient room to load the requested model self._make_cache_room(key, model_type) - + + # clean memory to make MemoryUsage() more accurate + gc.collect() with MemoryUsage() as usage: model = self._load_model_from_storage( repo_id_or_path=repo_id_or_path, From fd63e36822a454bf9c24d7f62b5111727b4510e7 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sun, 7 May 2023 21:39:11 -0400 Subject: [PATCH 013/225] optimize subfolder so that it returns submodel if parent is in RAM --- .../backend/model_management/model_cache.py | 58 +++++++++++++++---- 1 file changed, 47 insertions(+), 11 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 540ec0b0fd..20dac8a985 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -29,6 +29,7 @@ from typing import Dict, Sequence, Union, Tuple, types import torch import safetensors.torch + from diffusers import StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel from diffusers import logging as diffusers_logging from diffusers.pipelines.stable_diffusion.safety_checker import \ @@ -53,7 +54,7 @@ GIG = 1073741824 # This is the mapping from the stable diffusion submodel dict key to the class class SDModelType(Enum): diffusion_pipeline=StableDiffusionGeneratorPipeline # whole thing - diffusers=StableDiffusionGeneratorPipeline # same thing + diffusers=StableDiffusionGeneratorPipeline # same thing, different name vae=AutoencoderKL # diffusers parts text_encoder=CLIPTextModel tokenizer=CLIPTokenizer @@ -164,8 +165,19 @@ class ModelCache(object): Use like this: cache = ModelCache() - with cache.get_model('stabilityai/stable-diffusion-2') as SD2: - do_something_with_the_model(SD2) + with cache.get_model('stabilityai/stable-diffusion-2') as model: + do_something_with_the_model(model) + + While in context, model will be locked into GPU. If you want to do something + with the model while it is in RAM, just use the context's `model` attribute: + + context = cache.get_model('stabilityai/stable-diffusion-2') + context.model.device + # device(type='cpu') + + with context as model: + model.device + # device(type='cuda') You can fetch an individual part of a diffusers model by passing the submodel argument: @@ -175,6 +187,14 @@ class ModelCache(object): submodel=SDModelType.vae ) + This is equivalent to: + + vae_context = cache.get_model( + 'stabilityai/sd-stable-diffusion-2', + model_type = SDModelType.vae, + subfolder='vae' + ) + Vice versa, you can load and attach an external submodel to a diffusers model before returning it by passing the attach_submodel argument. This only works with diffusers models: @@ -196,12 +216,26 @@ class ModelCache(object): ''' key = self._model_key( # internal unique identifier for the model repo_id_or_path, - model_type.value, revision, - subfolder + subfolder, + model_type.value, ) - - if key in self.models: # cached - move to bottom of stack + + # optimization: if caller is asking to load a submodel of a diffusers pipeline, then + # check whether it is already cached in RAM and return it instead of loading from disk again + if subfolder and not submodel: + possible_parent_key = self._model_key( + repo_id_or_path, + None, + revision, + SDModelType.diffusers.value + ) + if possible_parent_key in self.models: + key = possible_parent_key + submodel=model_type + + # Look for the model in the cache RAM + if key in self.models: # cached - move to bottom of stack (most recently used) with contextlib.suppress(ValueError): self.stack.remove(key) self.stack.append(key) @@ -225,11 +259,13 @@ class ModelCache(object): legacy_info=legacy_info, ) logger.debug(f'Actual memory used to load model: {(usage.mem_used/GIG):.2f} GB') - self.model_sizes[key] = usage.mem_used - self.current_cache_size += usage.mem_used + self.model_sizes[key] = usage.mem_used # remember size of this model for cache cleansing + self.current_cache_size += usage.mem_used # increment size of the cache + # this is a bit of legacy work needed to support the old-style "load this diffuser with custom VAE" if model_type==SDModelType.diffusion_pipeline and attach_model_part[0]: self.attach_part(model,*attach_model_part) + self.stack.append(key) # add to LRU cache self.models[key]=model # keep copy of model in dict @@ -376,8 +412,8 @@ class ModelCache(object): logger.debug("Model scanned ok") @staticmethod - def _model_key(path,model_class,revision,subfolder)->str: - return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')]) + def _model_key(path,revision,subfolder,model_class)->str: + return ':'.join([str(path),str(revision or ''),str(subfolder or ''),model_class.__name__]) def _has_cuda(self)->bool: return self.execution_device.type == 'cuda' From c15b49c805b50fa3476fdaec3610b3c98a2d09cb Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sun, 7 May 2023 23:18:17 -0400 Subject: [PATCH 014/225] implement StALKeR7779 requested API for fetching submodels --- .../backend/model_management/model_cache.py | 47 +++-- .../backend/model_management/model_manager.py | 192 +++++++++++++----- tests/test_model_cache.py | 8 +- 3 files changed, 175 insertions(+), 72 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 20dac8a985..04af1f43f3 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -19,10 +19,9 @@ context. Use like this: import contextlib import gc import hashlib -import logging import warnings from collections import Counter -from enum import Enum +from enum import Enum,auto from pathlib import Path from psutil import Process from typing import Dict, Sequence, Union, Tuple, types @@ -52,9 +51,15 @@ DEFAULT_MAX_CACHE_SIZE = 6.0 GIG = 1073741824 # This is the mapping from the stable diffusion submodel dict key to the class +class LoraType(dict): + pass +class TIType(dict): + pass +class CkptType(dict): + pass + class SDModelType(Enum): - diffusion_pipeline=StableDiffusionGeneratorPipeline # whole thing - diffusers=StableDiffusionGeneratorPipeline # same thing, different name + diffusers=StableDiffusionGeneratorPipeline # whole pipeline vae=AutoencoderKL # diffusers parts text_encoder=CLIPTextModel tokenizer=CLIPTokenizer @@ -62,10 +67,11 @@ class SDModelType(Enum): scheduler=SchedulerMixin safety_checker=StableDiffusionSafetyChecker feature_extractor=CLIPFeatureExtractor - # These are all loaded as dicts of tensors - lora=dict - textual_inversion=dict - ckpt=dict + # These are all loaded as dicts of tensors, and we + # distinguish them by class + lora=LoraType + textual_inversion=TIType + ckpt=CkptType class ModelStatus(Enum): unknown='unknown' @@ -78,17 +84,16 @@ class ModelStatus(Enum): # After loading, we will know it exactly. # Sizes are in Gigs, estimated for float16; double for float32 SIZE_GUESSTIMATE = { - SDModelType.diffusion_pipeline: 2.5, SDModelType.diffusers: 2.5, SDModelType.vae: 0.35, SDModelType.text_encoder: 0.5, - SDModelType.tokenizer: 0.0001, + SDModelType.tokenizer: 0.001, SDModelType.unet: 3.4, - SDModelType.scheduler: 0.0001, + SDModelType.scheduler: 0.001, SDModelType.safety_checker: 1.2, - SDModelType.feature_extractor: 0.0001, + SDModelType.feature_extractor: 0.001, SDModelType.lora: 0.1, - SDModelType.textual_inversion: 0.0001, + SDModelType.textual_inversion: 0.001, SDModelType.ckpt: 4.2, } @@ -152,7 +157,7 @@ class ModelCache(object): def get_model( self, repo_id_or_path: Union[str,Path], - model_type: SDModelType=SDModelType.diffusion_pipeline, + model_type: SDModelType=SDModelType.diffusers, subfolder: Path=None, submodel: SDModelType=None, revision: str=None, @@ -263,7 +268,7 @@ class ModelCache(object): self.current_cache_size += usage.mem_used # increment size of the cache # this is a bit of legacy work needed to support the old-style "load this diffuser with custom VAE" - if model_type==SDModelType.diffusion_pipeline and attach_model_part[0]: + if model_type==SDModelType.diffusers and attach_model_part[0]: self.attach_part(model,*attach_model_part) self.stack.append(key) # add to LRU cache @@ -301,8 +306,10 @@ class ModelCache(object): cache.locked_models[key] += 1 if cache.lazy_offloading: cache._offload_unlocked_models() - cache.logger.debug(f'Loading {key} into {cache.execution_device}') - model.to(cache.execution_device) # move into GPU + if model.device != cache.execution_device: + cache.logger.debug(f'Moving {key} into {cache.execution_device}') + model.to(cache.execution_device) # move into GPU + cache.logger.debug(f'Locking {key} in {cache.execution_device}') cache._print_cuda_stats() else: # in the event that the caller wants the model in RAM, we @@ -345,7 +352,7 @@ class ModelCache(object): def status(self, repo_id_or_path: Union[str,Path], - model_type: SDModelType=SDModelType.diffusion_pipeline, + model_type: SDModelType=SDModelType.diffusers, revision: str=None, subfolder: Path=None, )->ModelStatus: @@ -428,7 +435,7 @@ class ModelCache(object): def _make_cache_room(self, key, model_type): # calculate how much memory this model will require multiplier = 2 if self.precision==torch.float32 else 1 - bytes_needed = int(self.model_sizes.get(key,0) or SIZE_GUESSTIMATE[model_type]*GIG*multiplier) + bytes_needed = int(self.model_sizes.get(key,0) or SIZE_GUESSTIMATE.get(model_type,0.5)*GIG*multiplier) maximum_size = self.max_cache_size * GIG # stored in GB, convert to bytes current_size = self.current_cache_size @@ -473,7 +480,7 @@ class ModelCache(object): # silence transformer and diffuser warnings with SilenceWarnings(): if self.is_legacy_ckpt(repo_id_or_path): - model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) + model = model_class(self._load_ckpt_from_storage(repo_id_or_path, legacy_info)) else: model = self._load_diffusers_from_storage( repo_id_or_path, diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 368d581cd8..94e514a013 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -20,18 +20,37 @@ return a SDModelInfo object that contains the following attributes: Typical usage: from invokeai.backend import ModelManager - manager = ModelManager(config_path='./configs/models.yaml',max_models=4) + + manager = ModelManager( + config='./configs/models.yaml', + max_cache_size=8 + ) # gigabytes + model_info = manager.get_model('stable-diffusion-1.5') with model_info.context as my_model: my_model.latents_from_embeddings(...) The manager uses the underlying ModelCache class to keep frequently-used models in RAM and move them into GPU as needed for -generation operations. The ModelCache object can be accessed using -the manager's "cache" attribute. +generation operations. The optional `max_cache_size` argument +indicates the maximum size the cache can grow to, in gigabytes. The +underlying ModelCache object can be accessed using the manager's "cache" +attribute. -Other methods provided by ModelManager support importing, editing, -converting and deleting models. +Because the model manager can return multiple different types of +models, you may wish to add additional type checking on the class +of model returned. To do this, provide the option `model_type` +parameter: + + model_info = manager.get_model( + 'clip-tokenizer', + model_type=SDModelType.tokenizer + ) + +This will raise an InvalidModelError if the format defined in the +config file doesn't match the requested model type. + +MODELS.YAML The general format of a models.yaml section is: @@ -40,7 +59,6 @@ The general format of a models.yaml section is: repo_id: owner/repo path: /path/to/local/file/or/directory subfolder: subfolder-name - submodel: vae|text_encoder|tokenizer... The format is one of {diffusers, ckpt, vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor}, and correspond to @@ -54,11 +72,7 @@ If subfolder is provided, then the model exists in a subdirectory of the main model. These are usually named after the model type, such as "unet". -Finally, if submodel is provided, then the path/repo_id is treated as -a diffusers model, the whole thing is ready into memory, and then the -requested part (e.g. "unet") is retrieved. - -This summarizes the three ways of getting a non-diffuser model: +This example summarizes the two ways of getting a non-diffuser model: clip-test-1: format: text_encoder @@ -66,21 +80,48 @@ This summarizes the three ways of getting a non-diffuser model: description: Returns standalone CLIPTextModel clip-test-2: - format: diffusers - repo_id: stabilityai/stable-diffusion-2 - submodel: text_encoder - description: Returns the text_encoder part of whole diffusers model (whole thing in RAM) - - clip-test-3: format: text_encoder repo_id: stabilityai/stable-diffusion-2 subfolder: text_encoder description: Returns the text_encoder in the subfolder of the diffusers model (just the encoder in RAM) - clip-token: +SUBMODELS: + +It is also possible to fetch an isolated submodel from a diffusers +model. Use the `submodel` parameter to select which part: + + vae = manager.get_model('stable-diffusion-1.5',submodel=SDModelType.vae) + with vae.context as my_vae: + print(type(my_vae)) + # "AutoencoderKL" + +DISAMBIGUATION: + +You may wish to use the same name for a related family of models. To +do this, disambiguate the stanza key with the model and and format +separated by "/". Example: + + clip-large/tokenizer: format: tokenizer repo_id: openai/clip-vit-large-patch14 description: Returns standalone tokenizer + + clip-large/text_encoder: + format: text_encoder + repo_id: openai/clip-vit-large-patch14 + description: Returns standalone text encoder + +You can now use the `model_type` argument to indicate which model you +want: + + tokenizer = mgr.get('clip-large',model_type=SDModelType.tokenizer) + encoder = mgr.get('clip-large',model_type=SDModelType.text_encoder) + +OTHER FUNCTIONS: + +Other methods provided by ModelManager support importing, editing, +converting and deleting models. + """ from __future__ import annotations @@ -152,7 +193,7 @@ class ModelManager(object): def __init__( self, - config_path: Path, + config: Union[Path, DictConfig, str], device_type: torch.device = CUDA_DEVICE, precision: torch.dtype = torch.float16, max_cache_size=MAX_CACHE_SIZE, @@ -165,8 +206,15 @@ class ModelManager(object): and sequential_offload boolean. Note that the default device type and precision are set up for a CUDA system running at half precision. """ - self.config_path = config_path - self.config = OmegaConf.load(self.config_path) + if isinstance(config, DictConfig): + self.config = config + self.config_path = None + elif type(config) in [str,DictConfig]: + self.config_path = config + self.config = OmegaConf.load(self.config_path) + else: + raise ValueError('config argument must be an OmegaConf object, a Path or a string') + self.cache = ModelCache( max_cache_size=max_cache_size, execution_device = device_type, @@ -185,28 +233,64 @@ class ModelManager(object): return model_name in self.config def get_model(self, - model_name: str = None, + model_name: str, + model_type: SDModelType=None, submodel: SDModelType=None, ) -> SDModelInfo: """Given a model named identified in models.yaml, return an SDModelInfo object describing it. :param model_name: symbolic name of the model in models.yaml + :param model_type: SDModelType enum indicating the type of model to return :param submodel: an SDModelType enum indicating the portion of the model to retrieve (e.g. SDModelType.vae) + + If not provided, the model_type will be read from the `format` field + of the corresponding stanza. If provided, the model_type will be used + to disambiguate stanzas in the configuration file. The default is to + assume a diffusers pipeline. The behavior is illustrated here: + + [models.yaml] + test1/diffusers: + repo_id: foo/bar + format: diffusers + description: Typical diffusers pipeline + + test1/lora: + repo_id: /tmp/loras/test1.safetensors + format: lora + description: Typical lora file + + test1_pipeline = mgr.get_model('test1') + # returns a StableDiffusionGeneratorPipeline + + test1_vae1 = mgr.get_model('test1',submodel=SDModelType.vae) + # returns the VAE part of a diffusers model as an AutoencoderKL + + test1_vae2 = mgr.get_model('test1',model_type=SDModelType.diffusers,submodel=SDModelType.vae) + # does the same thing as the previous statement. Note that model_type + # is for the parent model, and submodel is for the part + + test1_lora = mgr.get_model('test1',model_type=SDModelType.lora) + # returns a LoRA embed (as a 'dict' of tensors) + + test1_encoder = mgr.get_modelI('test1',model_type=SDModelType.textencoder) + # raises an InvalidModelError + """ if not model_name: model_name = self.default_model() - if not self.valid_model(model_name): - raise InvalidModelError( - f'"{model_name}" is not a known model name. Please check your models.yaml file' - ) - + model_key = self._disambiguate_name(model_name, model_type) + # get the required loading info out of the config file - mconfig = self.config[model_name] + mconfig = self.config[model_key] format = mconfig.get('format','diffusers') - model_type = SDModelType.diffusion_pipeline + if model_type and model_type.name != format: + raise InvalidModelError( + f'Inconsistent model definition; {model_key} has format {format}, but type {model_type.name} was requested' + ) + model_parts = dict([(x.name,x) for x in SDModelType]) legacy = None @@ -219,16 +303,14 @@ class ModelManager(object): legacy.vae_file = global_resolve_path(mconfig.vae) elif format=='diffusers': location = mconfig.get('repo_id') or mconfig.get('path') - if sm := mconfig.get('submodel'): - submodel = model_parts[sm] elif format in model_parts: location = mconfig.get('repo_id') or mconfig.get('path') or mconfig.get('weights') - model_type = model_parts[format] else: raise InvalidModelError( - f'"{model_name}" has an unknown format {format}' + f'"{model_key}" has an unknown format {format}' ) - + + model_type = model_parts[format] subfolder = mconfig.get('subfolder') revision = mconfig.get('revision') hash = self.cache.model_hash(location,revision) @@ -254,7 +336,7 @@ class ModelManager(object): # in case we need to communicate information about this # model to the cache manager, then we need to remember # the cache key - self.cache_keys[model_name] = model_context.key + self.cache_keys[model_key] = model_context.key return SDModelInfo( context = model_context, @@ -449,18 +531,20 @@ class ModelManager(object): else: assert "weights" in model_attributes and "description" in model_attributes + model_key = f'{model_name}/{format}' + assert ( - clobber or model_name not in omega - ), f'attempt to overwrite existing model definition "{model_name}"' + clobber or model_key not in omega + ), f'attempt to overwrite existing model definition "{model_key}"' - omega[model_name] = model_attributes + omega[model_key] = model_attributes - if "weights" in omega[model_name]: - omega[model_name]["weights"].replace("\\", "/") + if "weights" in omega[model_key]: + omega[model_key]["weights"].replace("\\", "/") - if clobber and model_name in self.cache_keys: - self.cache.uncache_model(self.cache_keys[model_name]) - del self.cache_keys[model_name] + if clobber and model_key in self.cache_keys: + self.cache.uncache_model(self.cache_keys[model_key]) + del self.cache_keys[model_key] def import_diffuser_model( self, @@ -482,6 +566,7 @@ class ModelManager(object): models.yaml file. """ model_name = model_name or Path(repo_or_path).stem + model_key = f'{model_name}/diffusers' model_description = description or f"Imported diffusers model {model_name}" new_config = dict( description=model_description, @@ -493,10 +578,10 @@ class ModelManager(object): else: new_config.update(repo_id=repo_or_path) - self.add_model(model_name, new_config, True) + self.add_model(model_key, new_config, True) if commit_to_conf: self.commit(commit_to_conf) - return model_name + return model_key def import_lora( self, @@ -511,7 +596,7 @@ class ModelManager(object): path = Path(path) model_name = model_name or path.stem model_description = description or f"LoRA model {model_name}" - self.add_model(model_name, + self.add_model(f'{model_name}/{SDModelType.lora.name}', dict( format="lora", weights=str(path), @@ -538,7 +623,7 @@ class ModelManager(object): model_name = model_name or path.stem model_description = description or f"Textual embedding model {model_name}" - self.add_model(model_name, + self.add_model(f'{model_name}/{SDModelType.textual_inversion.name}', dict( format="textual_inversion", weights=str(weights), @@ -871,6 +956,7 @@ class ModelManager(object): """ yaml_str = OmegaConf.to_yaml(self.config) config_file_path = conf_file or self.config_path + assert config_file_path is not None,'no config file path to write to' tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp") with open(tmpfile, "w", encoding="utf-8") as outfile: outfile.write(self.preamble()) @@ -893,6 +979,18 @@ class ModelManager(object): """ ) + def _disambiguate_name(self, model_name:str, model_type:SDModelType)->str: + model_type = model_type or SDModelType.diffusers + full_name = f"{model_name}/{model_type.name}" + if self.valid_model(full_name): + return full_name + if self.valid_model(model_name): + return model_name + raise InvalidModelError( + f'Neither "{model_name}" nor "{full_name}" are known model names. Please check your models.yaml file' + ) + + @classmethod def _delete_model_from_cache(cls,repo_id): cache_info = scan_cache_dir(global_cache_dir("hub")) diff --git a/tests/test_model_cache.py b/tests/test_model_cache.py index 25c61d192d..6510f7ac1f 100644 --- a/tests/test_model_cache.py +++ b/tests/test_model_cache.py @@ -40,7 +40,7 @@ class DMType(Enum): type1 = DummyModelType1 type2 = DummyModelType2 -cache = ModelCache(max_models=4) +cache = ModelCache(max_cache_size=4) def test_pipeline_fetch(): assert cache.cache_size()==0 @@ -53,12 +53,10 @@ def test_pipeline_fetch(): assert type(pipeline1)==DMType.dummy_pipeline.value,'get_model() did not return model of expected type' assert pipeline1==pipeline1a,'pipelines with the same repo_id should be the same' assert pipeline1!=pipeline2,'pipelines with different repo_ids should not be the same' - assert cache.cache_size()==2,'cache should uniquely cache models with same identity' + assert len(cache.models)==2,'cache should uniquely cache models with same identity' with cache.get_model('dummy/pipeline3',DMType.dummy_pipeline) as pipeline3,\ cache.get_model('dummy/pipeline4',DMType.dummy_pipeline) as pipeline4: - assert cache.cache_size()==4,'cache did not grow as expected' - with cache.get_model('dummy/pipeline5',DMType.dummy_pipeline) as pipeline5: - assert cache.cache_size()==4,'cache did not free space as expected' + assert len(cache.models)==4,'cache did not grow as expected' def test_signatures(): with cache.get_model('dummy/pipeline',DMType.dummy_pipeline,revision='main') as pipeline1,\ From a10815554480ba9f266c1c97722b623291d06bf7 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Mon, 8 May 2023 21:47:03 -0400 Subject: [PATCH 015/225] added StALKeR779's great model size calculating routine --- .../backend/model_management/model_cache.py | 72 +++++++++++++------ 1 file changed, 50 insertions(+), 22 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 04af1f43f3..173fd87623 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -21,15 +21,14 @@ import gc import hashlib import warnings from collections import Counter -from enum import Enum,auto +from enum import Enum from pathlib import Path -from psutil import Process from typing import Dict, Sequence, Union, Tuple, types import torch import safetensors.torch -from diffusers import StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel +from diffusers import DiffusionPipeline, StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel from diffusers import logging as diffusers_logging from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker @@ -84,7 +83,7 @@ class ModelStatus(Enum): # After loading, we will know it exactly. # Sizes are in Gigs, estimated for float16; double for float32 SIZE_GUESSTIMATE = { - SDModelType.diffusers: 2.5, + SDModelType.diffusers: 2.2, SDModelType.vae: 0.35, SDModelType.text_encoder: 0.5, SDModelType.tokenizer: 0.001, @@ -255,17 +254,18 @@ class ModelCache(object): # clean memory to make MemoryUsage() more accurate gc.collect() - with MemoryUsage() as usage: - model = self._load_model_from_storage( - repo_id_or_path=repo_id_or_path, - model_class=model_type.value, - subfolder=subfolder, - revision=revision, - legacy_info=legacy_info, - ) - logger.debug(f'Actual memory used to load model: {(usage.mem_used/GIG):.2f} GB') - self.model_sizes[key] = usage.mem_used # remember size of this model for cache cleansing - self.current_cache_size += usage.mem_used # increment size of the cache + model = self._load_model_from_storage( + repo_id_or_path=repo_id_or_path, + model_class=model_type.value, + subfolder=subfolder, + revision=revision, + legacy_info=legacy_info, + ) + + if mem_used := self.calc_model_size(model): + logger.debug(f'CPU RAM used for load: {(mem_used/GIG):.2f} GB') + self.model_sizes[key] = mem_used # remember size of this model for cache cleansing + self.current_cache_size += mem_used # increment size of the cache # this is a bit of legacy work needed to support the old-style "load this diffuser with custom VAE" if model_type==SDModelType.diffusers and attach_model_part[0]: @@ -308,7 +308,10 @@ class ModelCache(object): cache._offload_unlocked_models() if model.device != cache.execution_device: cache.logger.debug(f'Moving {key} into {cache.execution_device}') - model.to(cache.execution_device) # move into GPU + with VRAMUsage() as mem: + model.to(cache.execution_device) # move into GPU + cache.logger.debug(f'GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB') + cache.model_sizes[key] = mem.vram_used # more accurate size cache.logger.debug(f'Locking {key} in {cache.execution_device}') cache._print_cuda_stats() else: @@ -479,8 +482,9 @@ class ModelCache(object): ''' # silence transformer and diffuser warnings with SilenceWarnings(): + # !!! NOTE: conversion should not happen here, but in ModelManager if self.is_legacy_ckpt(repo_id_or_path): - model = model_class(self._load_ckpt_from_storage(repo_id_or_path, legacy_info)) + model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) else: model = self._load_diffusers_from_storage( repo_id_or_path, @@ -608,6 +612,30 @@ class ModelCache(object): raise KeyError(f"Revision '{revision}' not found in {repo_id}") return desired_revisions[0].target_commit + @staticmethod + def calc_model_size(model)->int: + if isinstance(model,DiffusionPipeline): + return ModelCache._calc_pipeline(model) + elif isinstance(model,torch.nn.Module): + return ModelCache._calc_model(model) + else: + return None + + @staticmethod + def _calc_pipeline(pipeline)->int: + res = 0 + for submodel_key in pipeline.components.keys(): + submodel = getattr(pipeline, submodel_key) + if submodel is not None and isinstance(submodel, torch.nn.Module): + res += ModelCache._calc_model(submodel) + return res + + @staticmethod + def _calc_model(model)->int: + mem_params = sum([param.nelement()*param.element_size() for param in model.parameters()]) + mem_bufs = sum([buf.nelement()*buf.element_size() for buf in model.buffers()]) + mem = mem_params + mem_bufs # in bytes + return mem class SilenceWarnings(object): def __init__(self): @@ -624,14 +652,14 @@ class SilenceWarnings(object): diffusers_logging.set_verbosity(self.diffusers_verbosity) warnings.simplefilter('default') -class MemoryUsage(object): +class VRAMUsage(object): def __init__(self): - self.vms = None - self.mem_used = 0 + self.vram = None + self.vram_used = 0 def __enter__(self): - self.vms = Process().memory_info().vms + self.vram = torch.cuda.memory_allocated() return self def __exit__(self, *args): - self.mem_used = Process().memory_info().vms - self.vms + self.vram_used = torch.cuda.memory_allocated() - self.vram From 9cb962cad7fedcbfcb138fa4d7c2420b0ce1b037 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Mon, 8 May 2023 23:39:44 -0400 Subject: [PATCH 016/225] ckpt model conversion now done in ModelCache --- invokeai/backend/generate.py | 6 +- invokeai/backend/globals.py | 2 + .../backend/model_management/model_cache.py | 34 ++-- .../backend/model_management/model_manager.py | 147 ++++++++++++++---- invokeai/frontend/CLI/CLI.py | 6 +- 5 files changed, 134 insertions(+), 61 deletions(-) diff --git a/invokeai/backend/generate.py b/invokeai/backend/generate.py index c7e2558db1..8cddc1496b 100644 --- a/invokeai/backend/generate.py +++ b/invokeai/backend/generate.py @@ -150,7 +150,7 @@ class Generate: esrgan=None, free_gpu_mem: bool = False, safety_checker: bool = False, - max_loaded_models: int = 2, + max_cache_size: int = 6, # these are deprecated; if present they override values in the conf file weights=None, config=None, @@ -183,7 +183,7 @@ class Generate: self.codeformer = codeformer self.esrgan = esrgan self.free_gpu_mem = free_gpu_mem - self.max_loaded_models = (max_loaded_models,) + self.max_cache_size = max_cache_size self.size_matters = True # used to warn once about large image sizes and VRAM self.txt2mask = None self.safety_checker = None @@ -220,7 +220,7 @@ class Generate: conf, self.device, torch_dtype(self.device), - max_loaded_models=max_loaded_models, + max_cache_size=max_cache_size, sequential_offload=self.free_gpu_mem, # embedding_path=Path(self.embedding_path), ) diff --git a/invokeai/backend/globals.py b/invokeai/backend/globals.py index 37a59b1135..5106ddb67d 100644 --- a/invokeai/backend/globals.py +++ b/invokeai/backend/globals.py @@ -94,6 +94,8 @@ def global_set_root(root_dir: Union[str, Path]): Globals.root = root_dir def global_resolve_path(path: Union[str,Path]): + if path is None: + return None return Path(Globals.root,path).resolve() def global_cache_dir(subdir: Union[str, Path] = "") -> Path: diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 173fd87623..b8f44f82ec 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -361,9 +361,10 @@ class ModelCache(object): )->ModelStatus: key = self._model_key( repo_id_or_path, - model_type.value, revision, - subfolder) + subfolder, + model_type.value, + ) if key not in self.models: return ModelStatus.not_loaded if key in self.loaded_models: @@ -384,9 +385,7 @@ class ModelCache(object): :param revision: optional revision string (if fetching a HF repo_id) ''' revision = revision or "main" - if self.is_legacy_ckpt(repo_id_or_path): - return self._legacy_model_hash(repo_id_or_path) - elif Path(repo_id_or_path).is_dir(): + if Path(repo_id_or_path).is_dir(): return self._local_model_hash(repo_id_or_path) else: return self._hf_commit_hash(repo_id_or_path,revision) @@ -395,15 +394,6 @@ class ModelCache(object): "Return the current size of the cache, in GB" return self.current_cache_size / GIG - @classmethod - def is_legacy_ckpt(cls, repo_id_or_path: Union[str,Path])->bool: - ''' - Return true if the indicated path is a legacy checkpoint - :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model - ''' - path = Path(repo_id_or_path) - return path.suffix in [".ckpt",".safetensors",".pt"] - @classmethod def scan_model(cls, model_name, checkpoint): """ @@ -482,16 +472,12 @@ class ModelCache(object): ''' # silence transformer and diffuser warnings with SilenceWarnings(): - # !!! NOTE: conversion should not happen here, but in ModelManager - if self.is_legacy_ckpt(repo_id_or_path): - model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info) - else: - model = self._load_diffusers_from_storage( - repo_id_or_path, - subfolder, - revision, - model_class, - ) + model = self._load_diffusers_from_storage( + repo_id_or_path, + subfolder, + revision, + model_class, + ) if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): model.enable_offload_submodels(self.execution_device) return model diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 94e514a013..4c0b1b3ad9 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -143,7 +143,7 @@ from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig from invokeai.backend.globals import Globals, global_cache_dir, global_resolve_path -from .model_cache import ModelCache, ModelLocker, SDModelType, ModelStatus, LegacyInfo +from .model_cache import ModelCache, ModelLocker, SDModelType, ModelStatus, SilenceWarnings from ..util import CUDA_DEVICE @@ -225,12 +225,16 @@ class ModelManager(object): self.cache_keys = dict() self.logger = logger - def valid_model(self, model_name: str) -> bool: + def valid_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers) -> bool: """ Given a model name, returns True if it is a valid identifier. """ - return model_name in self.config + try: + self._disambiguate_name(model_name, model_type) + return True + except InvalidModelError: + return False def get_model(self, model_name: str, @@ -294,17 +298,17 @@ class ModelManager(object): model_parts = dict([(x.name,x) for x in SDModelType]) legacy = None - if format=='ckpt': - location = global_resolve_path(mconfig.weights) - legacy = LegacyInfo( - config_file = global_resolve_path(mconfig.config), - ) - if mconfig.get('vae'): - legacy.vae_file = global_resolve_path(mconfig.vae) - elif format=='diffusers': - location = mconfig.get('repo_id') or mconfig.get('path') + if format == 'diffusers': + # intercept stanzas that point to checkpoint weights and replace them + # with the equivalent diffusers model + if 'weights' in mconfig: + location = self.convert_ckpt_and_cache(mconfig) + else: + location = global_resolve_path(mconfig.get('path')) or mconfig.get('repo_id') elif format in model_parts: - location = mconfig.get('repo_id') or mconfig.get('path') or mconfig.get('weights') + location = global_resolve_path(mconfig.get('path')) \ + or mconfig.get('repo_id') \ + or global_resolve_path(mconfig.get('weights')) else: raise InvalidModelError( f'"{model_key}" has an unknown format {format}' @@ -531,7 +535,7 @@ class ModelManager(object): else: assert "weights" in model_attributes and "description" in model_attributes - model_key = f'{model_name}/{format}' + model_key = f'{model_name}/{model_attributes["format"]}' assert ( clobber or model_key not in omega @@ -776,7 +780,7 @@ class ModelManager(object): # another round of heuristics to guess the correct config file. checkpoint = None if model_path.suffix in [".ckpt", ".pt"]: - self.scan_model(model_path, model_path) + self.cache.scan_model(model_path, model_path) checkpoint = torch.load(model_path) else: checkpoint = safetensors.torch.load_file(model_path) @@ -840,19 +844,86 @@ class ModelManager(object): diffuser_path = Path( Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem ) - model_name = self.convert_and_import( - model_path, - diffusers_path=diffuser_path, - vae=vae, - vae_path=str(vae_path), - model_name=model_name, - model_description=description, - original_config_file=model_config_file, - commit_to_conf=commit_to_conf, - scan_needed=False, - ) + with SilenceWarnings(): + model_name = self.convert_and_import( + model_path, + diffusers_path=diffuser_path, + vae=vae, + vae_path=str(vae_path), + model_name=model_name, + model_description=description, + original_config_file=model_config_file, + commit_to_conf=commit_to_conf, + scan_needed=False, + ) return model_name + def convert_ckpt_and_cache(self, mconfig:DictConfig)->Path: + """ + Convert the checkpoint model indicated in mconfig into a + diffusers, cache it to disk, and return Path to converted + file. If already on disk then just returns Path. + """ + weights = global_resolve_path(mconfig.weights) + config_file = global_resolve_path(mconfig.config) + diffusers_path = global_resolve_path(Path('models',Globals.converted_ckpts_dir)) / weights.stem + + # return cached version if it exists + if diffusers_path.exists(): + return diffusers_path + + vae_ckpt_path, vae_model = self._get_vae_for_conversion(weights, mconfig) + # to avoid circular import errors + from .convert_ckpt_to_diffusers import convert_ckpt_to_diffusers + with SilenceWarnings(): + convert_ckpt_to_diffusers( + weights, + diffusers_path, + extract_ema=True, + original_config_file=config_file, + vae=vae_model, + vae_path=str(global_resolve_path(vae_ckpt_path)), + scan_needed=True, + ) + return diffusers_path + + def _get_vae_for_conversion(self, + weights: Path, + mconfig: DictConfig + )->tuple(Path,SDModelType.vae): + # VAE handling is convoluted + # 1. If there is a .vae.ckpt file sharing same stem as weights, then use + # it as the vae_path passed to convert + vae_ckpt_path = None + vae_diffusers_location = None + vae_model = None + for suffix in ["pt", "ckpt", "safetensors"]: + if (weights.with_suffix(f".vae.{suffix}")).exists(): + vae_ckpt_path = weights.with_suffix(f".vae.{suffix}") + self.logger.debug(f"Using VAE file {vae_ckpt_path.name}") + if vae_ckpt_path: + return (vae_ckpt_path, None) + + # 2. If mconfig has a vae weights path, then we use that as vae_path + vae_config = mconfig.get('vae') + if vae_config and isinstance(vae_config,str): + vae_ckpt_path = vae_config + return (vae_ckpt_path, None) + + # 3. If mconfig has a vae dict, then we use it as the diffusers-style vae + if vae_config and isinstance(vae_config,DictConfig): + vae_diffusers_location = global_resolve_path(vae_config.get('path')) or vae_config.get('repo_id') + + # 4. Otherwise, we use stabilityai/sd-vae-ft-mse "because it works" + else: + vae_diffusers_location = "stabilityai/sd-vae-ft-mse" + + if vae_diffusers_location: + vae_model = self.cache.get_model(vae_diffusers_location, SDModelType.vae).model + return (None, vae_model) + + return (None, None) + def convert_and_import( self, ckpt_path: Path, @@ -895,7 +966,8 @@ class ModelManager(object): # will be built into the model rather than tacked on afterward via the config file vae_model = None if vae: - vae_model = self._load_vae(vae) + vae_location = global_resolve_path(vae.get('path')) or vae.get('repo_id') + vae_model = self.cache.get_model(vae_location,SDModelType.vae).model vae_path = None convert_ckpt_to_diffusers( ckpt_path, @@ -982,9 +1054,9 @@ class ModelManager(object): def _disambiguate_name(self, model_name:str, model_type:SDModelType)->str: model_type = model_type or SDModelType.diffusers full_name = f"{model_name}/{model_type.name}" - if self.valid_model(full_name): + if full_name in self.config: return full_name - if self.valid_model(model_name): + if model_name in self.config: return model_name raise InvalidModelError( f'Neither "{model_name}" nor "{full_name}" are known model names. Please check your models.yaml file' @@ -1014,3 +1086,20 @@ class ModelManager(object): return path return Path(Globals.root, path).resolve() + # This is not the same as global_resolve_path(), which prepends + # Globals.root. + def _resolve_path( + self, source: Union[str, Path], dest_directory: str + ) -> Optional[Path]: + resolved_path = None + if str(source).startswith(("http:", "https:", "ftp:")): + dest_directory = Path(dest_directory) + if not dest_directory.is_absolute(): + dest_directory = Globals.root / dest_directory + dest_directory.mkdir(parents=True, exist_ok=True) + resolved_path = download_with_resume(str(source), dest_directory) + else: + if not os.path.isabs(source): + source = os.path.join(Globals.root, source) + resolved_path = Path(source) + return resolved_path diff --git a/invokeai/frontend/CLI/CLI.py b/invokeai/frontend/CLI/CLI.py index 0c984080a6..8525853e93 100644 --- a/invokeai/frontend/CLI/CLI.py +++ b/invokeai/frontend/CLI/CLI.py @@ -54,10 +54,6 @@ def main(): "--weights argument has been deprecated. Please edit ./configs/models.yaml, and select the weights using --model instead." ) sys.exit(-1) - if args.max_loaded_models is not None: - if args.max_loaded_models <= 0: - print("--max_loaded_models must be >= 1; using 1") - args.max_loaded_models = 1 # alert - setting a few globals here Globals.try_patchmatch = args.patchmatch @@ -136,7 +132,7 @@ def main(): esrgan=esrgan, free_gpu_mem=opt.free_gpu_mem, safety_checker=opt.safety_checker, - max_loaded_models=opt.max_loaded_models, + max_cache_size=opt.max_cache_size, ) except (FileNotFoundError, TypeError, AssertionError) as e: report_model_error(opt, e) From 3d85e769ce8c41cf84f51a07745a2cc76ebfc3d3 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Tue, 9 May 2023 22:44:58 -0400 Subject: [PATCH 017/225] clean up ckpt handling - remove legacy ckpt loading code from model_cache - added placeholders for lora and textual inversion model loading --- .../backend/model_management/model_cache.py | 72 ++++++------------- .../backend/model_management/model_manager.py | 2 - 2 files changed, 22 insertions(+), 52 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index b8f44f82ec..64f95699c9 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -54,8 +54,6 @@ class LoraType(dict): pass class TIType(dict): pass -class CkptType(dict): - pass class SDModelType(Enum): diffusers=StableDiffusionGeneratorPipeline # whole pipeline @@ -70,7 +68,6 @@ class SDModelType(Enum): # distinguish them by class lora=LoraType textual_inversion=TIType - ckpt=CkptType class ModelStatus(Enum): unknown='unknown' @@ -93,18 +90,12 @@ SIZE_GUESSTIMATE = { SDModelType.feature_extractor: 0.001, SDModelType.lora: 0.1, SDModelType.textual_inversion: 0.001, - SDModelType.ckpt: 4.2, } # The list of model classes we know how to fetch, for typechecking ModelClass = Union[tuple([x.value for x in SDModelType])] DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel) -# Legacy information needed to load a legacy checkpoint file -class LegacyInfo(BaseModel): - config_file: Path - vae_file: Path = None - class UnsafeModelException(Exception): "Raised when a legacy model file fails the picklescan test" pass @@ -160,7 +151,6 @@ class ModelCache(object): subfolder: Path=None, submodel: SDModelType=None, revision: str=None, - legacy_info: LegacyInfo=None, attach_model_part: Tuple[SDModelType, str] = (None,None), gpu_load: bool=True, )->ModelLocker: # ?? what does it return @@ -210,13 +200,12 @@ class ModelCache(object): The model will be locked into GPU VRAM for the duration of the context. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model + :param model_type: An SDModelType enum indicating the type of the (parent) model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae :param attach_model_part: load and attach a diffusers model component. Pass a tuple of format (SDModelType,repo_id) :param revision: model revision - :param model_class: class of model to return :param gpu_load: load the model into GPU [default True] - :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt ''' key = self._model_key( # internal unique identifier for the model repo_id_or_path, @@ -256,10 +245,9 @@ class ModelCache(object): gc.collect() model = self._load_model_from_storage( repo_id_or_path=repo_id_or_path, - model_class=model_type.value, + model_type=model_type, subfolder=subfolder, revision=revision, - legacy_info=legacy_info, ) if mem_used := self.calc_model_size(model): @@ -459,27 +447,30 @@ class ModelCache(object): repo_id_or_path: Union[str,Path], subfolder: Path=None, revision: str=None, - model_class: ModelClass=StableDiffusionGeneratorPipeline, - legacy_info: LegacyInfo=None, + model_type: SDModelType=SDModelType.diffusers, )->ModelClass: ''' Load and return a HuggingFace model. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" :param revision: model revision - :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline - :param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt + :param model_type: type of model to return, defaults to SDModelType.diffusers ''' # silence transformer and diffuser warnings with SilenceWarnings(): - model = self._load_diffusers_from_storage( - repo_id_or_path, - subfolder, - revision, - model_class, - ) - if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): - model.enable_offload_submodels(self.execution_device) + if model_type==SDModelType.lora: + model = self._load_lora_from_storage(repo_id_or_path) + elif model_type==SDModelType.textual_inversion: + model = self._load_ti_from_storage(repo_id_or_path) + else: + model = self._load_diffusers_from_storage( + repo_id_or_path, + subfolder, + revision, + model_type.value, + ) + if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): + model.enable_offload_submodels(self.execution_device) return model def _load_diffusers_from_storage( @@ -519,30 +510,11 @@ class ModelCache(object): pass return model - def _load_ckpt_from_storage(self, - ckpt_path: Union[str,Path], - legacy_info:LegacyInfo)->StableDiffusionGeneratorPipeline: - ''' - Load a legacy checkpoint, convert it, and return a StableDiffusionGeneratorPipeline. - :param ckpt_path: string or Path pointing to the weights file (.ckpt or .safetensors) - :param legacy_info: LegacyInfo object containing paths to legacy config file and alternate vae if required - ''' - if legacy_info is None or legacy_info.config_file is None: - if Path(ckpt_path).suffix == '.safetensors': - return safetensors.torch.load_file(ckpt_path) - else: - return torch.load(ckpt_path) - else: - # deferred loading to avoid circular import errors - from .convert_ckpt_to_diffusers import load_pipeline_from_original_stable_diffusion_ckpt - pipeline = load_pipeline_from_original_stable_diffusion_ckpt( - checkpoint_path=ckpt_path, - original_config_file=legacy_info.config_file, - vae_path=legacy_info.vae_file, - return_generator_pipeline=True, - precision=self.precision, - ) - return pipeline + def _load_lora_from_storage(self, lora_path: Path)->SDModelType.lora.value: + assert False,"_load_lora_from_storage() is not yet implemented" + + def _load_ti_from_storage(self, lora_path: Path)->SDModelType.textual_inversion.value: + assert False,"_load_ti_from_storage() is not yet implemented" def _legacy_model_hash(self, checkpoint_path: Union[str,Path])->str: sha = hashlib.sha256() diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 4c0b1b3ad9..0905d5bf40 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -296,7 +296,6 @@ class ModelManager(object): ) model_parts = dict([(x.name,x) for x in SDModelType]) - legacy = None if format == 'diffusers': # intercept stanzas that point to checkpoint weights and replace them @@ -332,7 +331,6 @@ class ModelManager(object): model_type = model_type, revision = revision, subfolder = subfolder, - legacy_info = legacy, submodel = submodel, attach_model_part=vae, ) From 99c692f39704b642914731062a14e7807d0025e2 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Tue, 9 May 2023 23:46:59 -0400 Subject: [PATCH 018/225] check that model name matches format --- invokeai/backend/model_management/model_cache.py | 7 +++++++ invokeai/backend/model_management/model_manager.py | 7 +++++-- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 64f95699c9..08aa2f3044 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -289,19 +289,26 @@ class ModelCache(object): cache = self.cache key = self.key model = self.model + + # NOTE that the model has to have the to() method in order for this + # code to move it into GPU! if self.gpu_load and hasattr(model,'to'): cache.loaded_models.add(key) cache.locked_models[key] += 1 + if cache.lazy_offloading: cache._offload_unlocked_models() + if model.device != cache.execution_device: cache.logger.debug(f'Moving {key} into {cache.execution_device}') with VRAMUsage() as mem: model.to(cache.execution_device) # move into GPU cache.logger.debug(f'GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB') cache.model_sizes[key] = mem.vram_used # more accurate size + cache.logger.debug(f'Locking {key} in {cache.execution_device}') cache._print_cuda_stats() + else: # in the event that the caller wants the model in RAM, we # move it into CPU if it is in GPU and not locked diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 0905d5bf40..f4093ab116 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -1054,10 +1054,13 @@ class ModelManager(object): full_name = f"{model_name}/{model_type.name}" if full_name in self.config: return full_name - if model_name in self.config: + # special case - if diffusers requested, then allow name without type appended + if model_type==SDModelType.diffusers \ + and model_name in self.config \ + and self.config[model_name].format=='diffusers': return model_name raise InvalidModelError( - f'Neither "{model_name}" nor "{full_name}" are known model names. Please check your models.yaml file' + f'"{full_name}" is not a known model name. Please check your models.yaml file' ) From 4627910c5dfdbc9b10feae523a1b0f17f48d8dbf Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Thu, 11 May 2023 00:09:19 -0400 Subject: [PATCH 019/225] added a wrapper model_manager_service and model events --- invokeai/app/invocations/compel.py | 2 +- invokeai/app/services/events.py | 44 +- invokeai/app/services/model_manager.py | 34 +- .../app/services/model_manager_initializer.py | 8 +- .../app/services/model_manager_service.py | 412 ++++++++++++++++++ .../backend/model_management/model_manager.py | 3 +- 6 files changed, 495 insertions(+), 8 deletions(-) create mode 100644 invokeai/app/services/model_manager_service.py diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index 1fb7832031..e251ff8226 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -59,7 +59,7 @@ class CompelInvocation(BaseInvocation): # TODO: load without model model = choose_model(context.services.model_manager, self.model) - pipeline = model["model"] + pipeline = model.context.model tokenizer = pipeline.tokenizer text_encoder = pipeline.text_encoder diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py index 5f26c42c17..a25549dfc5 100644 --- a/invokeai/app/services/events.py +++ b/invokeai/app/services/events.py @@ -3,7 +3,7 @@ from typing import Any from invokeai.app.api.models.images import ProgressImage from invokeai.app.util.misc import get_timestamp - +from invokeai.app.services.model_manager_service import SDModelType, SDModelInfo class EventServiceBase: session_event: str = "session_event" @@ -101,3 +101,45 @@ class EventServiceBase: graph_execution_state_id=graph_execution_state_id, ), ) + + def emit_model_load_started ( + self, + graph_execution_state_id: str, + node: dict, + source_node_id: str, + model_name: str, + submodel: SDModelType, + ) -> None: + """Emitted when a model is requested""" + self.__emit_session_event( + event_name="model_load_started", + payload=dict( + graph_execution_state_id=graph_execution_state_id, + node=node, + source_node_id=source_node_id, + model_name=str, + submodel=submodel, + ), + ) + + def emit_model_load_completed ( + self, + graph_execution_state_id: str, + node: dict, + source_node_id: str, + model_name: str, + submodel: SDModelType, + model_info: SDModelInfo, + ) -> None: + """Emitted when a model is correctly loaded (returns model info)""" + self.__emit_session_event( + event_name="model_load_started", + payload=dict( + graph_execution_state_id=graph_execution_state_id, + node=node, + source_node_id=source_node_id, + model_name=str, + submodel=submodel, + model_info=model_info, + ), + ) diff --git a/invokeai/app/services/model_manager.py b/invokeai/app/services/model_manager.py index 044310fccb..e28a37db99 100644 --- a/invokeai/app/services/model_manager.py +++ b/invokeai/app/services/model_manager.py @@ -6,7 +6,7 @@ from typing import Union, Callable from invokeai.backend import ModelManager, SDModelType, SDModelInfo -class ModelManagerBase(ABC): +class ModelManagerServiceBase(ABC): """Responsible for managing models on disk and in memory""" @abstractmethod @@ -177,3 +177,35 @@ class ModelManagerBase(ABC): original file/database used to initialize the object. """ pass + + # simple implementation + class ModelManagerService(ModelManagerServiceBase): + """Responsible for managing models on disk and in memory""" + def __init__( + self, + config: Union[Path, DictConfig, str], + device_type: torch.device = CUDA_DEVICE, + precision: torch.dtype = torch.float16, + max_cache_size=MAX_CACHE_SIZE, + sequential_offload=False, + logger: types.ModuleType = logger, + ): + """ + Initialize with the path to the models.yaml config file. + Optional parameters are the torch device type, precision, max_models, + and sequential_offload boolean. Note that the default device + type and precision are set up for a CUDA system running at half precision. + """ + self.mgr = ModelManager(config=config, + device_type=device_type, + precision=precision, + max_cache_size=max_cache_size, + sequential_offload=sequential_offload, + logger=logger + ) + + def get(self, model_name: str, submodel: SDModelType=None)->SDModelInfo: + """Retrieve the indicated model. submodel can be used to get a + part (such as the vae) of a diffusers mode.""" + self.mgr.get_model( + diff --git a/invokeai/app/services/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py index 3c0d9dbef6..6645c50f15 100644 --- a/invokeai/app/services/model_manager_initializer.py +++ b/invokeai/app/services/model_manager_initializer.py @@ -8,12 +8,12 @@ from pathlib import Path from typing import types import invokeai.version -from ...backend import ModelManager +from .model_manager_service import ModelManagerService from ...backend.util import choose_precision, choose_torch_device from ...backend import Globals -# TODO: Replace with an abstract class base ModelManagerBase -def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager: +# temporary function - should call ModelManagerService() directly +def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManagerService: if not config.conf: config_file = os.path.join(Globals.root, "configs", "models.yaml") if not os.path.exists(config_file): @@ -59,7 +59,7 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager: if hasattr(config,'max_cache_size') \ else config.max_loaded_models * 2.5 - model_manager = ModelManager( + model_manager = ModelManagerService( config.conf, precision=dtype, device_type=device, diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py new file mode 100644 index 0000000000..b3f13bc7dd --- /dev/null +++ b/invokeai/app/services/model_manager_service.py @@ -0,0 +1,412 @@ +# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team + + +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Union, Callable + +from invokeai.backend.util import CUDA_DEVICE +from invokeai.backend.model_management.model_manager import ( + ModelManager, + SDModelType, + SDModelInfo, + DictConfig, + MAX_CACHE_SIZE, + types, + torch, + logger, +) + +class ModelManagerServiceBase(ABC): + """Responsible for managing models on disk and in memory""" + + @abstractmethod + def get_model(self, + model_name: str, + model_type: SDModelType=SDModelType.diffusers, + submodel: SDModelType=None + )->SDModelInfo: + """Retrieve the indicated model with name and type. + submodel can be used to get a part (such as the vae) + of a diffusers pipeline.""" + pass + + @property + @abstractmethod + def logger(self): + pass + + @abstractmethod + def valid_model(self, model_name: str) -> bool: + """ + Given a model name, returns True if it is a valid + identifier. + """ + pass + + @abstractmethod + def default_model(self) -> Union[str,None]: + """ + Returns the name of the default model, or None + if none is defined. + """ + pass + + @abstractmethod + def set_default_model(self, model_name:str): + """Sets the default model to the indicated name.""" + pass + + @abstractmethod + def model_info(self, model_name: str)->dict: + """ + Given a model name returns a dict-like (OmegaConf) object describing it. + """ + pass + + @abstractmethod + def model_names(self)->list[str]: + """ + Returns a list of all the model names known. + """ + pass + + @abstractmethod + def list_models(self)->dict: + """ + Return a dict of models in the format: + { model_name1: {'status': ('active'|'cached'|'not loaded'), + 'description': description, + 'format': ('ckpt'|'diffusers'|'vae'|'text_encoder'|'tokenizer'|'lora'...), + }, + model_name2: { etc } + """ + pass + + + @abstractmethod + def add_model( + self, model_name: str, model_attributes: dict, clobber: bool = False)->None: + """ + Update the named model with a dictionary of attributes. Will fail with an + assertion error if the name already exists. Pass clobber=True to overwrite. + On a successful update, the config will be changed in memory. Will fail + with an assertion error if provided attributes are incorrect or + the model name is missing. Call commit() to write changes to disk. + """ + pass + + @abstractmethod + def del_model(self, + model_name: str, + model_type: SDModelType=SDModelType.diffusers, + delete_files: bool = False): + """ + Delete the named model from configuration. If delete_files is true, + then the underlying weight file or diffusers directory will be deleted + as well. Call commit() to write to disk. + """ + pass + + @abstractmethod + def import_diffuser_model( + repo_or_path: Union[str, Path], + model_name: str = None, + description: str = None, + vae: dict = None, + ) -> bool: + """ + Install the indicated diffuser model and returns True if successful. + + "repo_or_path" can be either a repo-id or a path-like object corresponding to the + top of a downloaded diffusers directory. + + You can optionally provide a model name and/or description. If not provided, + then these will be derived from the repo name. Call commit() to write to disk. + """ + pass + + @abstractmethod + def import_lora( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated lora file. Call + mgr.commit() to write out the configuration to models.yaml + """ + pass + + @abstractmethod + def import_embedding( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated textual inversion embedding file. + Call commit() to write out the configuration to models.yaml + """ + pass + + @abstractmethod + def heuristic_import( + self, + path_url_or_repo: str, + model_name: str = None, + description: str = None, + model_config_file: Path = None, + commit_to_conf: Path = None, + config_file_callback: Callable[[Path], Path] = None, + ) -> str: + """Accept a string which could be: + - a HF diffusers repo_id + - a URL pointing to a legacy .ckpt or .safetensors file + - a local path pointing to a legacy .ckpt or .safetensors file + - a local directory containing .ckpt and .safetensors files + - a local directory containing a diffusers model + + After determining the nature of the model and downloading it + (if necessary), the file is probed to determine the correct + configuration file (if needed) and it is imported. + + The model_name and/or description can be provided. If not, they will + be generated automatically. + + If commit_to_conf is provided, the newly loaded model will be written + to the `models.yaml` file at the indicated path. Otherwise, the changes + will only remain in memory. + + The routine will do its best to figure out the config file + needed to convert legacy checkpoint file, but if it can't it + will call the config_file_callback routine, if provided. The + callback accepts a single argument, the Path to the checkpoint + file, and returns a Path to the config file to use. + + The (potentially derived) name of the model is returned on + success, or None on failure. When multiple models are added + from a directory, only the last imported one is returned. + + """ + pass + + @abstractmethod + def commit(self, conf_file: Path=None) -> None: + """ + Write current configuration out to the indicated file. + If no conf_file is provided, then replaces the + original file/database used to initialize the object. + """ + pass + +# simple implementation +class ModelManagerService(ModelManagerServiceBase): + """Responsible for managing models on disk and in memory""" + def __init__( + self, + config: Union[Path, DictConfig, str], + device_type: torch.device = CUDA_DEVICE, + precision: torch.dtype = torch.float16, + max_cache_size=MAX_CACHE_SIZE, + sequential_offload=False, + logger: types.ModuleType = logger, + ): + """ + Initialize with the path to the models.yaml config file. + Optional parameters are the torch device type, precision, max_models, + and sequential_offload boolean. Note that the default device + type and precision are set up for a CUDA system running at half precision. + """ + self.mgr = ModelManager(config=config, + device_type=device_type, + precision=precision, + max_cache_size=max_cache_size, + sequential_offload=sequential_offload, + logger=logger + ) + + def get_model(self, + model_name: str, + model_type: SDModelType=SDModelType.diffusers, + submodel: SDModelType=None, + )->SDModelInfo: + """ + Retrieve the indicated model. submodel can be used to get a + part (such as the vae) of a diffusers mode. + """ + return self.mgr.get_model( + model_name, + model_type, + submodel, + ) + + def valid_model(self, *args, **kwargs) -> bool: + """ + Given a model name, returns True if it is a valid + identifier. + """ + return self.mgr.valid_model(*args, **kwargs) + + def default_model(self) -> Union[str,None]: + """ + Returns the name of the default model, or None + if none is defined. + """ + return self.mgr.default_model() + + def set_default_model(self, model_name:str): + """Sets the default model to the indicated name.""" + self.mgr.set_default_model(model_name) + + def model_info(self, model_name: str)->dict: + """ + Given a model name returns a dict-like (OmegaConf) object describing it. + """ + return self.mgr.model_info(model_name) + + def model_names(self)->list[str]: + """ + Returns a list of all the model names known. + """ + return self.mgr.model_names() + + def list_models(self)->dict: + """ + Return a dict of models in the format: + { model_name1: {'status': ('active'|'cached'|'not loaded'), + 'description': description, + 'format': ('ckpt'|'diffusers'|'vae'|'text_encoder'|'tokenizer'|'lora'...), + }, + model_name2: { etc } + """ + return self.mgr.list_models() + + def add_model( + self, model_name: str, model_attributes: dict, clobber: bool = False)->None: + """ + Update the named model with a dictionary of attributes. Will fail with an + assertion error if the name already exists. Pass clobber=True to overwrite. + On a successful update, the config will be changed in memory. Will fail + with an assertion error if provided attributes are incorrect or + the model name is missing. Call commit() to write changes to disk. + """ + return self.mgr.add_model(model_name, model_attributes, dict, clobber) + + + def del_model(self, + model_name: str, + model_type: SDModelType=SDModelType.diffusers, + delete_files: bool = False + ): + """ + Delete the named model from configuration. If delete_files is true, + then the underlying weight file or diffusers directory will be deleted + as well. Call commit() to write to disk. + """ + self.mgr.del_model(model_name, model_type, delete_files) + + def import_diffuser_model( + self, + repo_or_path: Union[str, Path], + model_name: str = None, + description: str = None, + vae: dict = None, + ) -> bool: + """ + Install the indicated diffuser model and returns True if successful. + + "repo_or_path" can be either a repo-id or a path-like object corresponding to the + top of a downloaded diffusers directory. + + You can optionally provide a model name and/or description. If not provided, + then these will be derived from the repo name. Call commit() to write to disk. + """ + return self.mgr.import_diffuser_model(repo_or_path, model_name, description, vae) + + def import_lora( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated lora file. Call + mgr.commit() to write out the configuration to models.yaml + """ + self.mgr.import_lora(path, model_name, description) + + def import_embedding( + self, + path: Path, + model_name: str=None, + description: str=None, + ): + """ + Creates an entry for the indicated textual inversion embedding file. + Call commit() to write out the configuration to models.yaml + """ + self.mgr(path, model_name, description) + + def heuristic_import( + self, + path_url_or_repo: str, + model_name: str = None, + description: str = None, + model_config_file: Path = None, + commit_to_conf: Path = None, + config_file_callback: Callable[[Path], Path] = None, + ) -> str: + """Accept a string which could be: + - a HF diffusers repo_id + - a URL pointing to a legacy .ckpt or .safetensors file + - a local path pointing to a legacy .ckpt or .safetensors file + - a local directory containing .ckpt and .safetensors files + - a local directory containing a diffusers model + + After determining the nature of the model and downloading it + (if necessary), the file is probed to determine the correct + configuration file (if needed) and it is imported. + + The model_name and/or description can be provided. If not, they will + be generated automatically. + + If commit_to_conf is provided, the newly loaded model will be written + to the `models.yaml` file at the indicated path. Otherwise, the changes + will only remain in memory. + + The routine will do its best to figure out the config file + needed to convert legacy checkpoint file, but if it can't it + will call the config_file_callback routine, if provided. The + callback accepts a single argument, the Path to the checkpoint + file, and returns a Path to the config file to use. + + The (potentially derived) name of the model is returned on + success, or None on failure. When multiple models are added + from a directory, only the last imported one is returned. + + """ + return self.mgr.heuristic_import( + path_url_or_repo, + model_name, + description, + model_config_file, + commit_to_conf, + config_file_callback + ) + + + def commit(self, conf_file: Path=None): + """ + Write current configuration out to the indicated file. + If no conf_file is provided, then replaces the + original file/database used to initialize the object. + """ + return self.mgr.commit(conf_file) + + @property + def logger(self): + return self.mgr.logger + diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index f4093ab116..2c158cc953 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -479,10 +479,11 @@ class ModelManager(object): line = f"\033[1m{line}\033[0m" print(line) - def del_model(self, model_name: str, delete_files: bool = False) -> None: + def del_model(self, model_name: str, model_type: SDModelType.diffusers, delete_files: bool = False): """ Delete the named model. """ + model_name = self._disambiguate_name(model_name, model_type) omega = self.config if model_name not in omega: self.logger.error(f"Unknown model {model_name}") From df5b96895444d5a24093dfa136e7203581a23e5b Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Thu, 11 May 2023 21:24:29 -0400 Subject: [PATCH 020/225] model manager now running as a service --- invokeai/app/cli_app.py | 14 +- invokeai/app/invocations/compel.py | 107 +++++---- invokeai/app/invocations/generate.py | 3 +- invokeai/app/invocations/latent.py | 3 +- invokeai/app/invocations/util/choose_model.py | 14 -- invokeai/app/services/model_manager.py | 211 ------------------ .../app/services/model_manager_initializer.py | 124 ---------- .../app/services/model_manager_service.py | 66 ++++-- .../backend/model_management/model_manager.py | 2 +- pyproject.toml | 2 +- 10 files changed, 118 insertions(+), 428 deletions(-) delete mode 100644 invokeai/app/invocations/util/choose_model.py delete mode 100644 invokeai/app/services/model_manager.py delete mode 100644 invokeai/app/services/model_manager_initializer.py diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py index abe672820b..89bef69e11 100644 --- a/invokeai/app/cli_app.py +++ b/invokeai/app/cli_app.py @@ -13,18 +13,20 @@ from typing import ( from pydantic import BaseModel from pydantic.fields import Field - import invokeai.backend.util.logging as logger +import invokeai.version + from invokeai.app.services.metadata import PngMetadataService from .services.default_graphs import create_system_graphs from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage from ..backend import Args +from ..backend import Globals # this should go when pr 3340 merged + from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers from .cli.completer import set_autocompleter from .invocations.baseinvocation import BaseInvocation from .services.events import EventServiceBase -from .services.model_manager_initializer import get_model_manager from .services.restoration_services import RestorationServices from .services.graph import Edge, EdgeConnection, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible from .services.default_graphs import default_text_to_image_graph_id @@ -34,7 +36,7 @@ from .services.invocation_services import InvocationServices from .services.invoker import Invoker from .services.processor import DefaultInvocationProcessor from .services.sqlite import SqliteItemStorage - +from .services.model_manager_service import ModelManagerService class CliCommand(BaseModel): command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore @@ -191,7 +193,11 @@ def invoke_all(context: CliContext): def invoke_cli(): config = Args() config.parse_args() - model_manager = get_model_manager(config,logger=logger) + + logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}") + logger.info(f'InvokeAI runtime directory is "{Globals.root}"') + + model_manager = ModelManagerService(config,logger) # This initializes the autocompleter and returns it. # Currently nothing is done with the returned Completer diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index e251ff8226..6c2d703bc0 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -1,7 +1,6 @@ from typing import Literal, Optional, Union from pydantic import BaseModel, Field -from invokeai.app.invocations.util.choose_model import choose_model from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig from ...backend.util.devices import choose_torch_device, torch_dtype @@ -58,74 +57,74 @@ class CompelInvocation(BaseInvocation): def invoke(self, context: InvocationContext) -> CompelOutput: # TODO: load without model - model = choose_model(context.services.model_manager, self.model) - pipeline = model.context.model - tokenizer = pipeline.tokenizer - text_encoder = pipeline.text_encoder + model = context.services.model_manager.get_model(self.model) + with model.context as pipeline: + tokenizer = pipeline.tokenizer + text_encoder = pipeline.text_encoder - # TODO: global? input? - #use_full_precision = precision == "float32" or precision == "autocast" - #use_full_precision = False + # TODO: global? input? + #use_full_precision = precision == "float32" or precision == "autocast" + #use_full_precision = False - # TODO: redo TI when separate model loding implemented - #textual_inversion_manager = TextualInversionManager( - # tokenizer=tokenizer, - # text_encoder=text_encoder, - # full_precision=use_full_precision, - #) + # TODO: redo TI when separate model loding implemented + #textual_inversion_manager = TextualInversionManager( + # tokenizer=tokenizer, + # text_encoder=text_encoder, + # full_precision=use_full_precision, + #) - def load_huggingface_concepts(concepts: list[str]): - pipeline.textual_inversion_manager.load_huggingface_concepts(concepts) + def load_huggingface_concepts(concepts: list[str]): + pipeline.textual_inversion_manager.load_huggingface_concepts(concepts) - # apply the concepts library to the prompt - prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers( - self.prompt, - lambda concepts: load_huggingface_concepts(concepts), - pipeline.textual_inversion_manager.get_all_trigger_strings(), - ) + # apply the concepts library to the prompt + prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers( + self.prompt, + lambda concepts: load_huggingface_concepts(concepts), + pipeline.textual_inversion_manager.get_all_trigger_strings(), + ) - # lazy-load any deferred textual inversions. - # this might take a couple of seconds the first time a textual inversion is used. - pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms( - prompt_str - ) + # lazy-load any deferred textual inversions. + # this might take a couple of seconds the first time a textual inversion is used. + pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms( + prompt_str + ) - compel = Compel( - tokenizer=tokenizer, - text_encoder=text_encoder, - textual_inversion_manager=pipeline.textual_inversion_manager, - dtype_for_device_getter=torch_dtype, - truncate_long_prompts=True, # TODO: - ) + compel = Compel( + tokenizer=tokenizer, + text_encoder=text_encoder, + textual_inversion_manager=pipeline.textual_inversion_manager, + dtype_for_device_getter=torch_dtype, + truncate_long_prompts=True, # TODO: + ) - # TODO: support legacy blend? + # TODO: support legacy blend? - prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str) + prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str) - if getattr(Globals, "log_tokenization", False): - log_tokenization_for_prompt_object(prompt, tokenizer) + if getattr(Globals, "log_tokenization", False): + log_tokenization_for_prompt_object(prompt, tokenizer) - c, options = compel.build_conditioning_tensor_for_prompt_object(prompt) + c, options = compel.build_conditioning_tensor_for_prompt_object(prompt) - # TODO: long prompt support - #if not self.truncate_long_prompts: - # [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc]) + # TODO: long prompt support + #if not self.truncate_long_prompts: + # [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc]) - ec = InvokeAIDiffuserComponent.ExtraConditioningInfo( - tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt), - cross_attention_control_args=options.get("cross_attention_control", None), - ) + ec = InvokeAIDiffuserComponent.ExtraConditioningInfo( + tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt), + cross_attention_control_args=options.get("cross_attention_control", None), + ) - conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning" + conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning" - # TODO: hacky but works ;D maybe rename latents somehow? - context.services.latents.set(conditioning_name, (c, ec)) + # TODO: hacky but works ;D maybe rename latents somehow? + context.services.latents.set(conditioning_name, (c, ec)) - return CompelOutput( - conditioning=ConditioningField( - conditioning_name=conditioning_name, - ), - ) + return CompelOutput( + conditioning=ConditioningField( + conditioning_name=conditioning_name, + ), + ) def get_max_token_count( diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py index 9a29502048..3d5dd7f5ce 100644 --- a/invokeai/app/invocations/generate.py +++ b/invokeai/app/invocations/generate.py @@ -9,7 +9,6 @@ from torch import Tensor from pydantic import BaseModel, Field from invokeai.app.models.image import ColorField, ImageField, ImageType -from invokeai.app.invocations.util.choose_model import choose_model from invokeai.app.util.misc import SEED_MAX, get_random_seed from invokeai.backend.generator.inpaint import infill_methods from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig @@ -72,7 +71,7 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation): def invoke(self, context: InvocationContext) -> ImageOutput: # Handle invalid model parameter - model = choose_model(context.services.model_manager, self.model) + model = context.services.model_manager.get_model(self.model) # Get the source node id (we are invoking the prepared node) graph_execution_state = context.services.graph_execution_manager.get( diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 55da8a4909..5229ace95f 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -6,7 +6,6 @@ import einops from pydantic import BaseModel, Field import torch -from invokeai.app.invocations.util.choose_model import choose_model from invokeai.app.util.misc import SEED_MAX, get_random_seed from invokeai.app.util.step_callback import stable_diffusion_step_callback @@ -177,7 +176,7 @@ class TextToLatentsInvocation(BaseInvocation): ) def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline: - model_info = choose_model(model_manager, self.model) + model_info = model_manager.get_model(self.model) model_name = model_info.name model_hash = model_info.hash model_ctx: StableDiffusionGeneratorPipeline = model_info.context diff --git a/invokeai/app/invocations/util/choose_model.py b/invokeai/app/invocations/util/choose_model.py deleted file mode 100644 index c62640620b..0000000000 --- a/invokeai/app/invocations/util/choose_model.py +++ /dev/null @@ -1,14 +0,0 @@ -from invokeai.backend.model_management.model_manager_service import ModelManagerService, SDModelType - - -def choose_model(model_manager: ModelManagerService, model_name: str, model_type: SDModelType=SDModelType.diffusers): - """Returns the default model if the `model_name` not a valid model, else returns the selected model.""" - logger = model_manager.logger - if model_name and not model_manager.valid_model(model_name, model_type): - default_model_name = model_manager.default_model() - logger.warning(f"\'{model_name}\' is not a valid model name. Using default model \'{default_model_name}\' instead.") - model = model_manager.get_model() - else: - model = model_manager.get_model(model_name, model_type) - - return model diff --git a/invokeai/app/services/model_manager.py b/invokeai/app/services/model_manager.py deleted file mode 100644 index e28a37db99..0000000000 --- a/invokeai/app/services/model_manager.py +++ /dev/null @@ -1,211 +0,0 @@ -# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team - -from abc import ABC, abstractmethod -from pathlib import Path -from typing import Union, Callable - -from invokeai.backend import ModelManager, SDModelType, SDModelInfo - -class ModelManagerServiceBase(ABC): - """Responsible for managing models on disk and in memory""" - - @abstractmethod - def get(self, model_name: str, submodel: SDModelType=None)->SDModelInfo: - """Retrieve the indicated model. submodel can be used to get a - part (such as the vae) of a diffusers mode.l""" - pass - - @abstractmethod - def valid_model(self, model_name: str) -> bool: - """ - Given a model name, returns True if it is a valid - identifier. - """ - pass - - @abstractmethod - def default_model(self) -> Union[str,None]: - """ - Returns the name of the default model, or None - if none is defined. - """ - pass - - @abstractmethod - def set_default_model(self, model_name:str): - """Sets the default model to the indicated name.""" - pass - - @abstractmethod - def model_info(self, model_name: str)->dict: - """ - Given a model name returns a dict-like (OmegaConf) object describing it. - """ - pass - - @abstractmethod - def model_names(self)->list[str]: - """ - Returns a list of all the model names known. - """ - pass - - @abstractmethod - def list_models(self)->dict: - """ - Return a dict of models in the format: - { model_name1: {'status': ('active'|'cached'|'not loaded'), - 'description': description, - 'format': ('ckpt'|'diffusers'|'vae'|'text_encoder'|'tokenizer'|'lora'...), - }, - model_name2: { etc } - """ - pass - - - @abstractmethod - def add_model( - self, model_name: str, model_attributes: dict, clobber: bool = False)->None: - """ - Update the named model with a dictionary of attributes. Will fail with an - assertion error if the name already exists. Pass clobber=True to overwrite. - On a successful update, the config will be changed in memory. Will fail - with an assertion error if provided attributes are incorrect or - the model name is missing. Call commit() to write changes to disk. - """ - pass - - @abstractmethod - def del_model(self, model_name: str, delete_files: bool = False) -> None: - """ - Delete the named model from configuration. If delete_files is true, - then the underlying weight file or diffusers directory will be deleted - as well. Call commit() to write to disk. - """ - pass - - @abstractmethod - def import_diffuser_model( - repo_or_path: Union[str, Path], - model_name: str = None, - description: str = None, - vae: dict = None, - ) -> bool: - """ - Install the indicated diffuser model and returns True if successful. - - "repo_or_path" can be either a repo-id or a path-like object corresponding to the - top of a downloaded diffusers directory. - - You can optionally provide a model name and/or description. If not provided, - then these will be derived from the repo name. Call commit() to write to disk. - """ - pass - - @abstractmethod - def import_lora( - self, - path: Path, - model_name: str=None, - description: str=None, - ): - """ - Creates an entry for the indicated lora file. Call - mgr.commit() to write out the configuration to models.yaml - """ - pass - - @abstractmethod - def import_embedding( - self, - path: Path, - model_name: str=None, - description: str=None, - ): - """ - Creates an entry for the indicated textual inversion embedding file. - Call commit() to write out the configuration to models.yaml - """ - pass - - @abstractmethod - def heuristic_import( - self, - path_url_or_repo: str, - model_name: str = None, - description: str = None, - model_config_file: Path = None, - commit_to_conf: Path = None, - config_file_callback: Callable[[Path], Path] = None, - ) -> str: - """Accept a string which could be: - - a HF diffusers repo_id - - a URL pointing to a legacy .ckpt or .safetensors file - - a local path pointing to a legacy .ckpt or .safetensors file - - a local directory containing .ckpt and .safetensors files - - a local directory containing a diffusers model - - After determining the nature of the model and downloading it - (if necessary), the file is probed to determine the correct - configuration file (if needed) and it is imported. - - The model_name and/or description can be provided. If not, they will - be generated automatically. - - If commit_to_conf is provided, the newly loaded model will be written - to the `models.yaml` file at the indicated path. Otherwise, the changes - will only remain in memory. - - The routine will do its best to figure out the config file - needed to convert legacy checkpoint file, but if it can't it - will call the config_file_callback routine, if provided. The - callback accepts a single argument, the Path to the checkpoint - file, and returns a Path to the config file to use. - - The (potentially derived) name of the model is returned on - success, or None on failure. When multiple models are added - from a directory, only the last imported one is returned. - - """ - pass - - @abstractmethod - def commit(self, conf_file: Path=None) -> None: - """ - Write current configuration out to the indicated file. - If no conf_file is provided, then replaces the - original file/database used to initialize the object. - """ - pass - - # simple implementation - class ModelManagerService(ModelManagerServiceBase): - """Responsible for managing models on disk and in memory""" - def __init__( - self, - config: Union[Path, DictConfig, str], - device_type: torch.device = CUDA_DEVICE, - precision: torch.dtype = torch.float16, - max_cache_size=MAX_CACHE_SIZE, - sequential_offload=False, - logger: types.ModuleType = logger, - ): - """ - Initialize with the path to the models.yaml config file. - Optional parameters are the torch device type, precision, max_models, - and sequential_offload boolean. Note that the default device - type and precision are set up for a CUDA system running at half precision. - """ - self.mgr = ModelManager(config=config, - device_type=device_type, - precision=precision, - max_cache_size=max_cache_size, - sequential_offload=sequential_offload, - logger=logger - ) - - def get(self, model_name: str, submodel: SDModelType=None)->SDModelInfo: - """Retrieve the indicated model. submodel can be used to get a - part (such as the vae) of a diffusers mode.""" - self.mgr.get_model( - diff --git a/invokeai/app/services/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py deleted file mode 100644 index 6645c50f15..0000000000 --- a/invokeai/app/services/model_manager_initializer.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import sys -import torch -from argparse import Namespace -from invokeai.backend import Args -from omegaconf import OmegaConf -from pathlib import Path -from typing import types - -import invokeai.version -from .model_manager_service import ModelManagerService -from ...backend.util import choose_precision, choose_torch_device -from ...backend import Globals - -# temporary function - should call ModelManagerService() directly -def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManagerService: - if not config.conf: - config_file = os.path.join(Globals.root, "configs", "models.yaml") - if not os.path.exists(config_file): - report_model_error( - config, FileNotFoundError(f"The file {config_file} could not be found."), logger - ) - - logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}") - logger.info(f'InvokeAI runtime directory is "{Globals.root}"') - - # these two lines prevent a horrible warning message from appearing - # when the frozen CLIP tokenizer is imported - import transformers # type: ignore - - transformers.logging.set_verbosity_error() - import diffusers - - diffusers.logging.set_verbosity_error() - - # normalize the config directory relative to root - if not os.path.isabs(config.conf): - config.conf = os.path.normpath(os.path.join(Globals.root, config.conf)) - - if config.embeddings: - if not os.path.isabs(config.embedding_path): - embedding_path = os.path.normpath( - os.path.join(Globals.root, config.embedding_path) - ) - else: - embedding_path = config.embedding_path - else: - embedding_path = None - - # creating the model manager - try: - device = torch.device(choose_torch_device()) - if config.precision=="auto": - precision = choose_precision(device) - dtype = torch.float32 if precision=='float32' \ - else torch.float16 - - max_cache_size = config.max_cache_size \ - if hasattr(config,'max_cache_size') \ - else config.max_loaded_models * 2.5 - - model_manager = ModelManagerService( - config.conf, - precision=dtype, - device_type=device, - max_cache_size=config.max_cache_size, -# temporarily disabled until model manager stabilizes -# embedding_path = Path(embedding_path), - logger = logger, - ) - except (FileNotFoundError, TypeError, AssertionError) as e: - report_model_error(config, e, logger) - except (IOError, KeyError) as e: - logger.error(f"{e}. Aborting.") - sys.exit(-1) - - # try to autoconvert new models - # autoimport new .ckpt files - if path := config.autoconvert: - model_manager.autoconvert_weights( - conf_path=config.conf, - weights_directory=path, - ) - logger.info('Model manager initialized') - return model_manager - -def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType): - logger.error(f'An error occurred while attempting to initialize the model: "{str(e)}"') - logger.error( - "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models." - ) - yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE") - if yes_to_all: - logger.warning( - "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE" - ) - else: - response = input( - "Do you want to run invokeai-configure script to select and/or reinstall models? [y] " - ) - if response.startswith(("n", "N")): - return - - logger.info("invokeai-configure is launching....\n") - - # Match arguments that were set on the CLI - # only the arguments accepted by the configuration script are parsed - root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else [] - config = ["--config", opt.conf] if opt.conf is not None else [] - sys.argv = ["invokeai-configure"] - sys.argv.extend(root_dir) - sys.argv.extend(config.to_dict()) - if yes_to_all is not None: - for arg in yes_to_all.split(): - sys.argv.append(arg) - - from invokeai.frontend.install import invokeai_configure - - invokeai_configure() - # TODO: Figure out how to restart - # print('** InvokeAI will now restart') - # sys.argv = previous_args - # main() # would rather do a os.exec(), but doesn't exist? - # sys.exit(0) diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index 19dc8ddc88..e9c959d5e0 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -3,23 +3,35 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Union, Callable +from typing import Union, Callable, types -from invokeai.backend.util import CUDA_DEVICE from invokeai.backend.model_management.model_manager import ( ModelManager, SDModelType, SDModelInfo, - DictConfig, - MAX_CACHE_SIZE, types, torch, - logger, ) +from ...backend import Args,Globals # this must go when pr 3340 merged +from ...backend.util import choose_precision, choose_torch_device class ModelManagerServiceBase(ABC): """Responsible for managing models on disk and in memory""" + @abstractmethod + def __init__( + self, + config: Args, + logger: types.ModuleType + ): + """ + Initialize with the path to the models.yaml config file. + Optional parameters are the torch device type, precision, max_models, + and sequential_offload boolean. Note that the default device + type and precision are set up for a CUDA system running at half precision. + """ + pass + @abstractmethod def get_model(self, model_name: str, @@ -207,26 +219,50 @@ class ModelManagerService(ModelManagerServiceBase): """Responsible for managing models on disk and in memory""" def __init__( self, - config: Union[Path, DictConfig, str], - device_type: torch.device = CUDA_DEVICE, - precision: torch.dtype = torch.float16, - max_cache_size=MAX_CACHE_SIZE, - sequential_offload=False, - logger: types.ModuleType = logger, - ): + config: Args, + logger: types.ModuleType + ): """ Initialize with the path to the models.yaml config file. Optional parameters are the torch device type, precision, max_models, and sequential_offload boolean. Note that the default device type and precision are set up for a CUDA system running at half precision. """ - self.mgr = ModelManager(config=config, - device_type=device_type, - precision=precision, + if config.conf and Path(config.conf).exists(): + config_file = config.conf + else: + config_file = Path(Globals.root, "configs", "models.yaml") + if not config_file.exists(): + raise IOError(f"The file {config_file} could not be found.") + + logger.debug(f'config file={config_file}') + + device = torch.device(choose_torch_device()) + if config.precision=="auto": + precision = choose_precision(device) + dtype = torch.float32 if precision=='float32' \ + else torch.float16 + + # this is transitional backward compatibility + # support for the deprecated `max_loaded_models` + # configuration value. If present, then the + # cache size is set to 2.5 GB times + # the number of max_loaded_models. Otherwise + # use new `max_cache_size` config setting + max_cache_size = config.max_cache_size \ + if hasattr(config,'max_cache_size') \ + else config.max_loaded_models * 2.5 + + sequential_offload = config.sequential_guidance + + self.mgr = ModelManager(config=config_file, + device_type=device, + precision=dtype, max_cache_size=max_cache_size, sequential_offload=sequential_offload, logger=logger ) + logger.info('Model manager service initialized') def get_model(self, model_name: str, diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 2c158cc953..f80e845f17 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -209,7 +209,7 @@ class ModelManager(object): if isinstance(config, DictConfig): self.config = config self.config_path = None - elif type(config) in [str,DictConfig]: + elif isinstance(config,(str,Path)): self.config_path = config self.config = OmegaConf.load(self.config_path) else: diff --git a/pyproject.toml b/pyproject.toml index 82130e7640..54fb9f7165 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,7 @@ dependencies = [ "albumentations", "click", "clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip", - "compel~=1.1.5", + "compel~=1.0.5", "datasets", "diffusers[torch]~=0.16.1", "dnspython==2.2.1", From 11ecf438f51e1ccdba2d05eb2f48ea6213a99e62 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Thu, 11 May 2023 23:33:24 -0400 Subject: [PATCH 021/225] latents.py converted to use model manager service; events emitted --- invokeai/app/invocations/latent.py | 135 +++++++++++------- invokeai/app/services/events.py | 1 + .../app/services/model_manager_service.py | 22 +++ 3 files changed, 104 insertions(+), 54 deletions(-) diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 5229ace95f..91320173ed 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -1,31 +1,32 @@ # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) -import random from typing import Literal, Optional, Union + +import diffusers import einops -from pydantic import BaseModel, Field import torch +from diffusers import DiffusionPipeline +from diffusers.schedulers import SchedulerMixin as Scheduler +from pydantic import BaseModel, Field +from invokeai.app.models.exceptions import CanceledException from invokeai.app.util.misc import SEED_MAX, get_random_seed - from invokeai.app.util.step_callback import stable_diffusion_step_callback -from ...backend.model_management.model_manager import ModelManager -from ...backend.util.devices import choose_torch_device, torch_dtype -from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings from ...backend.image_util.seamless import configure_model_padding -from ...backend.prompting.conditioning import get_uc_and_c_and_ec -from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline, image_resized_to_grid_as_tensor -from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig -import numpy as np -from ..services.image_storage import ImageType -from .baseinvocation import BaseInvocation, InvocationContext -from .image import ImageField, ImageOutput, build_image_output -from .compel import ConditioningField +from ...backend.model_management.model_manager import SDModelType from ...backend.stable_diffusion import PipelineIntermediateState -from diffusers.schedulers import SchedulerMixin as Scheduler -import diffusers -from diffusers import DiffusionPipeline +from ...backend.stable_diffusion.diffusers_pipeline import ( + ConditioningData, StableDiffusionGeneratorPipeline, + image_resized_to_grid_as_tensor) +from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \ + PostprocessingSettings +from ...backend.util.devices import choose_torch_device, torch_dtype +from ..services.image_storage import ImageType +from .baseinvocation import (BaseInvocation, BaseInvocationOutput, + InvocationConfig, InvocationContext) +from .compel import ConditioningField +from .image import ImageField, ImageOutput, build_image_output class LatentsField(BaseModel): @@ -103,6 +104,37 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c # x = (1 - self.perlin) * x + self.perlin * perlin_noise return x +class ModelChooser: + def choose_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: + + if context.services.queue.is_canceled(context.graph_execution_state_id): + raise CanceledException + + # Get the source node id (we are invoking the prepared node) + graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) + source_node_id = graph_execution_state.prepared_source_mapping[self.id] + + context.services.events.emit_model_load_started( + graph_execution_state_id=context.graph_execution_state_id, + node=self.dict(), + source_node_id=source_node_id, + model_name=self.model, + submodel=SDModelType.diffusers + ) + + model_manager = context.services.model_manager + model_info = model_manager.get_model(self.model) + model_ctx: StableDiffusionGeneratorPipeline = model_info.context + context.services.events.emit_model_load_completed ( + graph_execution_state_id=context.graph_execution_state_id, + node=self.dict(), + source_node_id=source_node_id, + model_name=self.model, + submodel=SDModelType.diffusers, + model_info=model_info + ) + + return model_ctx class NoiseInvocation(BaseInvocation): """Generates latent noise.""" @@ -135,7 +167,7 @@ class NoiseInvocation(BaseInvocation): # Text to image -class TextToLatentsInvocation(BaseInvocation): +class TextToLatentsInvocation(BaseInvocation, ModelChooser): """Generates latents from conditionings.""" type: Literal["t2l"] = "t2l" @@ -175,32 +207,6 @@ class TextToLatentsInvocation(BaseInvocation): source_node_id=source_node_id, ) - def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline: - model_info = model_manager.get_model(self.model) - model_name = model_info.name - model_hash = model_info.hash - model_ctx: StableDiffusionGeneratorPipeline = model_info.context - with model_ctx as model: - model.scheduler = get_scheduler( - model=model, - scheduler_name=self.scheduler - ) - - if isinstance(model, DiffusionPipeline): - for component in [model.unet, model.vae]: - configure_model_padding(component, - self.seamless, - self.seamless_axes - ) - else: - configure_model_padding(model, - self.seamless, - self.seamless_axes - ) - - return model_ctx - - def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData: c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name) uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name) @@ -230,8 +236,8 @@ class TextToLatentsInvocation(BaseInvocation): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - model = self.get_model(context.services.model_manager) - conditioning_data = self.get_conditioning_data(context, model) + with self.choose_model(context) as model: + conditioning_data = self.get_conditioning_data(context, model) # TODO: Verify the noise is the right size result_latents, result_attention_map_saver = model.latents_from_embeddings( @@ -251,8 +257,30 @@ class TextToLatentsInvocation(BaseInvocation): latents=LatentsField(latents_name=name) ) + def choose_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: + model_ctx = super().choose_model(context) -class LatentsToLatentsInvocation(TextToLatentsInvocation): + with model_ctx as model: + model.scheduler = get_scheduler( + model=model, + scheduler_name=self.scheduler + ) + + if isinstance(model, DiffusionPipeline): + for component in [model.unet, model.vae]: + configure_model_padding(component, + self.seamless, + self.seamless_axes + ) + else: + configure_model_padding(model, + self.seamless, + self.seamless_axes + ) + return model_ctx + + +class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelChooser): """Generates latents using latents as base image.""" type: Literal["l2l"] = "l2l" @@ -283,7 +311,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - with self.get_model(context.services.model_manager) as model: + with self.choose_model(context) as model: conditioning_data = self.get_conditioning_data(model) # TODO: Verify the noise is the right size @@ -318,7 +346,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation): # Latent to image -class LatentsToImageInvocation(BaseInvocation): +class LatentsToImageInvocation(BaseInvocation, ModelChooser): """Generates an image from latents.""" type: Literal["l2i"] = "l2i" @@ -343,9 +371,7 @@ class LatentsToImageInvocation(BaseInvocation): latents = context.services.latents.get(self.latents.latents_name) # TODO: this only really needs the vae - model_info = choose_model(context.services.model_manager, self.model) - - with model_info.context as model: + with self.choose_model(context) as model: with torch.inference_mode(): np_image = model.decode_latents(latents) image = model.numpy_to_pil(np_image)[0] @@ -432,7 +458,7 @@ class ScaleLatentsInvocation(BaseInvocation): return LatentsOutput(latents=LatentsField(latents_name=name)) -class ImageToLatentsInvocation(BaseInvocation): +class ImageToLatentsInvocation(BaseInvocation, ModelChooser): """Encodes an image into latents.""" type: Literal["i2l"] = "i2l" @@ -457,7 +483,7 @@ class ImageToLatentsInvocation(BaseInvocation): ) # TODO: this only really needs the vae - model_info = choose_model(context.services.model_manager, self.model) + model_info = self.choose_model(context) model: StableDiffusionGeneratorPipeline = model_info["model"] image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB")) @@ -474,3 +500,4 @@ class ImageToLatentsInvocation(BaseInvocation): name = f"{context.graph_execution_state_id}__{self.id}" context.services.latents.set(name, latents) return LatentsOutput(latents=LatentsField(latents_name=name)) + diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py index a25549dfc5..dda5557315 100644 --- a/invokeai/app/services/events.py +++ b/invokeai/app/services/events.py @@ -4,6 +4,7 @@ from typing import Any from invokeai.app.api.models.images import ProgressImage from invokeai.app.util.misc import get_timestamp from invokeai.app.services.model_manager_service import SDModelType, SDModelInfo +from invokeai.app.models.exceptions import CanceledException class EventServiceBase: session_event: str = "session_event" diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index e9c959d5e0..3e245bc47e 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -4,6 +4,7 @@ from abc import ABC, abstractmethod from pathlib import Path from typing import Union, Callable, types +from dataclasses import dataclass from invokeai.backend.model_management.model_manager import ( ModelManager, @@ -15,6 +16,11 @@ from invokeai.backend.model_management.model_manager import ( from ...backend import Args,Globals # this must go when pr 3340 merged from ...backend.util import choose_precision, choose_torch_device +@dataclass +class LastUsedModel: + model_name: str + model_type: SDModelType + class ModelManagerServiceBase(ABC): """Responsible for managing models on disk and in memory""" @@ -273,6 +279,22 @@ class ModelManagerService(ModelManagerServiceBase): Retrieve the indicated model. submodel can be used to get a part (such as the vae) of a diffusers mode. """ + + # Temporary hack here: we remember the last model fetched + # so that when executing a graph, the first node called gets + # to set default model for subsequent nodes in the event that + # they do not set the model explicitly. This should be + # displaced by model loader mechanism. + # This is to work around lack of model loader at current time, + # which was causing inconsistent model usage throughout graph. + if not model_name: + self.logger.debug('No model name provided, defaulting to last loaded model') + model_name = LastUsedModel.name + model_type = model_type or LastUsedModel.type + else: + LastUsedModel.name = model_name + LastUsedModel.model_type = model_type + return self.mgr.get_model( model_name, model_type, From 2ef79b8bf324802ef5b8df32bcdf276fc8e1704d Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Fri, 12 May 2023 00:14:56 -0400 Subject: [PATCH 022/225] fix bug in persistent model scheme --- invokeai/app/invocations/generate.py | 2 +- invokeai/app/invocations/latent.py | 56 ++++--------- invokeai/app/services/events.py | 4 + .../app/services/model_manager_service.py | 84 ++++++++++++++++--- 4 files changed, 93 insertions(+), 53 deletions(-) diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py index 3d5dd7f5ce..1f0d99ba46 100644 --- a/invokeai/app/invocations/generate.py +++ b/invokeai/app/invocations/generate.py @@ -71,7 +71,7 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation): def invoke(self, context: InvocationContext) -> ImageOutput: # Handle invalid model parameter - model = context.services.model_manager.get_model(self.model) + model = context.services.model_manager.get_model(self.model,node=self,context=context) # Get the source node id (we are invoking the prepared node) graph_execution_state = context.services.graph_execution_manager.get( diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 91320173ed..3f99d08bd1 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -9,12 +9,10 @@ from diffusers import DiffusionPipeline from diffusers.schedulers import SchedulerMixin as Scheduler from pydantic import BaseModel, Field -from invokeai.app.models.exceptions import CanceledException from invokeai.app.util.misc import SEED_MAX, get_random_seed from invokeai.app.util.step_callback import stable_diffusion_step_callback from ...backend.image_util.seamless import configure_model_padding -from ...backend.model_management.model_manager import SDModelType from ...backend.stable_diffusion import PipelineIntermediateState from ...backend.stable_diffusion.diffusers_pipeline import ( ConditioningData, StableDiffusionGeneratorPipeline, @@ -104,37 +102,11 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c # x = (1 - self.perlin) * x + self.perlin * perlin_noise return x -class ModelChooser: - def choose_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: - - if context.services.queue.is_canceled(context.graph_execution_state_id): - raise CanceledException - - # Get the source node id (we are invoking the prepared node) - graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) - source_node_id = graph_execution_state.prepared_source_mapping[self.id] - - context.services.events.emit_model_load_started( - graph_execution_state_id=context.graph_execution_state_id, - node=self.dict(), - source_node_id=source_node_id, - model_name=self.model, - submodel=SDModelType.diffusers - ) - +class ModelGetter: + def get_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: model_manager = context.services.model_manager - model_info = model_manager.get_model(self.model) - model_ctx: StableDiffusionGeneratorPipeline = model_info.context - context.services.events.emit_model_load_completed ( - graph_execution_state_id=context.graph_execution_state_id, - node=self.dict(), - source_node_id=source_node_id, - model_name=self.model, - submodel=SDModelType.diffusers, - model_info=model_info - ) - - return model_ctx + model_info = model_manager.get_model(self.model,node=self,context=context) + return model_info.context class NoiseInvocation(BaseInvocation): """Generates latent noise.""" @@ -167,7 +139,7 @@ class NoiseInvocation(BaseInvocation): # Text to image -class TextToLatentsInvocation(BaseInvocation, ModelChooser): +class TextToLatentsInvocation(BaseInvocation, ModelGetter): """Generates latents from conditionings.""" type: Literal["t2l"] = "t2l" @@ -236,7 +208,7 @@ class TextToLatentsInvocation(BaseInvocation, ModelChooser): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - with self.choose_model(context) as model: + with self.get_model(context) as model: conditioning_data = self.get_conditioning_data(context, model) # TODO: Verify the noise is the right size @@ -257,8 +229,8 @@ class TextToLatentsInvocation(BaseInvocation, ModelChooser): latents=LatentsField(latents_name=name) ) - def choose_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: - model_ctx = super().choose_model(context) + def get_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: + model_ctx = super().get_model(context) with model_ctx as model: model.scheduler = get_scheduler( @@ -280,7 +252,7 @@ class TextToLatentsInvocation(BaseInvocation, ModelChooser): return model_ctx -class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelChooser): +class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter): """Generates latents using latents as base image.""" type: Literal["l2l"] = "l2l" @@ -311,7 +283,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelChooser): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - with self.choose_model(context) as model: + with self.get_model(context) as model: conditioning_data = self.get_conditioning_data(model) # TODO: Verify the noise is the right size @@ -346,7 +318,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelChooser): # Latent to image -class LatentsToImageInvocation(BaseInvocation, ModelChooser): +class LatentsToImageInvocation(BaseInvocation, ModelGetter): """Generates an image from latents.""" type: Literal["l2i"] = "l2i" @@ -371,7 +343,7 @@ class LatentsToImageInvocation(BaseInvocation, ModelChooser): latents = context.services.latents.get(self.latents.latents_name) # TODO: this only really needs the vae - with self.choose_model(context) as model: + with self.get_model(context) as model: with torch.inference_mode(): np_image = model.decode_latents(latents) image = model.numpy_to_pil(np_image)[0] @@ -458,7 +430,7 @@ class ScaleLatentsInvocation(BaseInvocation): return LatentsOutput(latents=LatentsField(latents_name=name)) -class ImageToLatentsInvocation(BaseInvocation, ModelChooser): +class ImageToLatentsInvocation(BaseInvocation, ModelGetter): """Encodes an image into latents.""" type: Literal["i2l"] = "i2l" @@ -483,7 +455,7 @@ class ImageToLatentsInvocation(BaseInvocation, ModelChooser): ) # TODO: this only really needs the vae - model_info = self.choose_model(context) + model_info = self.get_model(context) model: StableDiffusionGeneratorPipeline = model_info["model"] image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB")) diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py index dda5557315..03a36962fc 100644 --- a/invokeai/app/services/events.py +++ b/invokeai/app/services/events.py @@ -109,6 +109,7 @@ class EventServiceBase: node: dict, source_node_id: str, model_name: str, + model_type: SDModelType, submodel: SDModelType, ) -> None: """Emitted when a model is requested""" @@ -119,6 +120,7 @@ class EventServiceBase: node=node, source_node_id=source_node_id, model_name=str, + model_type=model_type, submodel=submodel, ), ) @@ -129,6 +131,7 @@ class EventServiceBase: node: dict, source_node_id: str, model_name: str, + model_type: SDModelType, submodel: SDModelType, model_info: SDModelInfo, ) -> None: @@ -140,6 +143,7 @@ class EventServiceBase: node=node, source_node_id=source_node_id, model_name=str, + model_type=model_type, submodel=submodel, model_info=model_info, ), diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index 3e245bc47e..3c50d2ba2d 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -10,16 +10,18 @@ from invokeai.backend.model_management.model_manager import ( ModelManager, SDModelType, SDModelInfo, - types, torch, ) +from invokeai.app.models.exceptions import CanceledException from ...backend import Args,Globals # this must go when pr 3340 merged from ...backend.util import choose_precision, choose_torch_device @dataclass class LastUsedModel: - model_name: str - model_type: SDModelType + model_name: str=None + model_type: SDModelType=None + +last_used_model = LastUsedModel() class ModelManagerServiceBase(ABC): """Responsible for managing models on disk and in memory""" @@ -42,7 +44,9 @@ class ModelManagerServiceBase(ABC): def get_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers, - submodel: SDModelType=None + submodel: SDModelType=None, + node=None, # circular dependency issues, so untyped at moment + context=None, )->SDModelInfo: """Retrieve the indicated model with name and type. submodel can be used to get a part (such as the vae) @@ -274,6 +278,8 @@ class ModelManagerService(ModelManagerServiceBase): model_name: str, model_type: SDModelType=SDModelType.diffusers, submodel: SDModelType=None, + node=None, + context=None, )->SDModelInfo: """ Retrieve the indicated model. submodel can be used to get a @@ -287,20 +293,45 @@ class ModelManagerService(ModelManagerServiceBase): # displaced by model loader mechanism. # This is to work around lack of model loader at current time, # which was causing inconsistent model usage throughout graph. + global last_used_model + if not model_name: self.logger.debug('No model name provided, defaulting to last loaded model') - model_name = LastUsedModel.name - model_type = model_type or LastUsedModel.type + model_name = last_used_model.model_name + model_type = model_type or last_used_model.model_type else: - LastUsedModel.name = model_name - LastUsedModel.model_type = model_type - - return self.mgr.get_model( + last_used_model.model_name = model_name + last_used_model.model_type = model_type + + # if we are called from within a node, then we get to emit + # load start and complete events + if node and context: + self._emit_load_event( + node=node, + context=context, + model_name=model_name, + model_type=model_type, + submodel=submodel + ) + + model_info = self.mgr.get_model( model_name, model_type, submodel, ) + if node and context: + self._emit_load_event( + node=node, + context=context, + model_name=model_name, + model_type=model_type, + submodel=submodel, + model_info=model_info + ) + + return model_info + def valid_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers) -> bool: """ Given a model name, returns True if it is a valid @@ -466,6 +497,39 @@ class ModelManagerService(ModelManagerServiceBase): """ return self.mgr.commit(conf_file) + def _emit_load_event( + self, + node, + context, + model_name: str, + model_type: SDModelType, + submodel: SDModelType, + model_info: SDModelInfo=None, + ): + if context.services.queue.is_canceled(context.graph_execution_state_id): + raise CanceledException + graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) + source_node_id = graph_execution_state.prepared_source_mapping[node.id] + if context: + context.services.events.emit_model_load_started( + graph_execution_state_id=context.graph_execution_state_id, + node=node.dict(), + source_node_id=source_node_id, + model_name=model_name, + model_type=model_type, + submodel=submodel, + ) + else: + context.services.events.emit_model_load_completed ( + graph_execution_state_id=context.graph_execution_state_id, + node=node.dict(), + source_node_id=source_node_id, + model_name=model_name, + model_type=model_type, + submodel=submodel, + model_info=model_info + ) + @property def logger(self): return self.mgr.logger From 79fecba274effaefb849fa24236553fe000c2af3 Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Fri, 12 May 2023 23:05:08 +0300 Subject: [PATCH 023/225] Fix model manager initialization in web ui --- invokeai/app/api/dependencies.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py index 99127c4332..0e0d7481a7 100644 --- a/invokeai/app/api/dependencies.py +++ b/invokeai/app/api/dependencies.py @@ -8,7 +8,6 @@ from typing import types from ..services.default_graphs import create_system_graphs from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage from ...backend import Globals -from ..services.model_manager_initializer import get_model_manager from ..services.restoration_services import RestorationServices from ..services.graph import GraphExecutionState, LibraryGraph from ..services.image_storage import DiskImageStorage @@ -18,6 +17,7 @@ from ..services.invoker import Invoker from ..services.processor import DefaultInvocationProcessor from ..services.sqlite import SqliteItemStorage from ..services.metadata import PngMetadataService +from ..services.model_manager_service import ModelManagerService from .events import FastAPIEventService @@ -70,7 +70,7 @@ class ApiDependencies: db_location = os.path.join(output_folder, "invokeai.db") services = InvocationServices( - model_manager=get_model_manager(config,logger), + model_manager=ModelManagerService(config,logger), events=events, logger=logger, latents=latents, From 5431dd5f501a17b8e0ceb7f173d79a8ea4391056 Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Fri, 12 May 2023 23:08:03 +0300 Subject: [PATCH 024/225] Fix event args --- invokeai/app/services/events.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py index 03a36962fc..d3f61a70b8 100644 --- a/invokeai/app/services/events.py +++ b/invokeai/app/services/events.py @@ -119,13 +119,13 @@ class EventServiceBase: graph_execution_state_id=graph_execution_state_id, node=node, source_node_id=source_node_id, - model_name=str, - model_type=model_type, + model_name=model_name, + model_type=model_type.name, submodel=submodel, ), ) - def emit_model_load_completed ( + def emit_model_load_completed( self, graph_execution_state_id: str, node: dict, @@ -137,13 +137,13 @@ class EventServiceBase: ) -> None: """Emitted when a model is correctly loaded (returns model info)""" self.__emit_session_event( - event_name="model_load_started", + event_name="model_load_completed", payload=dict( graph_execution_state_id=graph_execution_state_id, node=node, source_node_id=source_node_id, - model_name=str, - model_type=model_type, + model_name=model_name, + model_type=model_type.name, submodel=submodel, model_info=model_info, ), From 4492044d29305088121302248bca6e6e7fa544e3 Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Fri, 12 May 2023 23:09:33 +0300 Subject: [PATCH 025/225] Redo compel node to separate model loading --- invokeai/app/invocations/compel.py | 32 ++++++++++-------------------- 1 file changed, 11 insertions(+), 21 deletions(-) diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index 6c2d703bc0..25bb421544 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -6,6 +6,7 @@ from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationCont from ...backend.util.devices import choose_torch_device, torch_dtype from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager +from ...backend.model_management import SDModelType from compel import Compel from compel.prompt_parser import ( @@ -58,9 +59,14 @@ class CompelInvocation(BaseInvocation): # TODO: load without model model = context.services.model_manager.get_model(self.model) - with model.context as pipeline: - tokenizer = pipeline.tokenizer - text_encoder = pipeline.text_encoder + text_encoder_info = context.services.model_manager.get_model( + self.model, SDModelType.diffusers, SDModelType.text_encoder + ) + tokenizer_info = context.services.model_manager.get_model( + self.model, SDModelType.diffusers, SDModelType.tokenizer + ) + with text_encoder_info.context as text_encoder,\ + tokenizer_info.context as tokenizer: # TODO: global? input? #use_full_precision = precision == "float32" or precision == "autocast" @@ -73,33 +79,17 @@ class CompelInvocation(BaseInvocation): # full_precision=use_full_precision, #) - def load_huggingface_concepts(concepts: list[str]): - pipeline.textual_inversion_manager.load_huggingface_concepts(concepts) - - # apply the concepts library to the prompt - prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers( - self.prompt, - lambda concepts: load_huggingface_concepts(concepts), - pipeline.textual_inversion_manager.get_all_trigger_strings(), - ) - - # lazy-load any deferred textual inversions. - # this might take a couple of seconds the first time a textual inversion is used. - pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms( - prompt_str - ) - compel = Compel( tokenizer=tokenizer, text_encoder=text_encoder, - textual_inversion_manager=pipeline.textual_inversion_manager, + textual_inversion_manager=None, # TODO: dtype_for_device_getter=torch_dtype, truncate_long_prompts=True, # TODO: ) # TODO: support legacy blend? - prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str) + prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(self.prompt) if getattr(Globals, "log_tokenization", False): log_tokenization_for_prompt_object(prompt, tokenizer) From 131145eab1e746e2f477320e0cce222d358257fd Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Fri, 12 May 2023 23:13:34 +0300 Subject: [PATCH 026/225] A big refactor of model manager(according to IMHO) --- .../backend/model_management/model_manager.py | 355 ++++++++++-------- 1 file changed, 190 insertions(+), 165 deletions(-) diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index f80e845f17..bd70e23f5b 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -133,6 +133,7 @@ from enum import Enum, auto from pathlib import Path from shutil import rmtree from typing import Union, Callable, types +from contextlib import suppress import safetensors import safetensors.torch @@ -192,13 +193,13 @@ class ModelManager(object): logger: types.ModuleType = logger def __init__( - self, - config: Union[Path, DictConfig, str], - device_type: torch.device = CUDA_DEVICE, - precision: torch.dtype = torch.float16, - max_cache_size=MAX_CACHE_SIZE, - sequential_offload=False, - logger: types.ModuleType = logger, + self, + config: Union[Path, DictConfig, str], + device_type: torch.device = CUDA_DEVICE, + precision: torch.dtype = torch.float16, + max_cache_size=MAX_CACHE_SIZE, + sequential_offload=False, + logger: types.ModuleType = logger, ): """ Initialize with the path to the models.yaml config file. @@ -225,22 +226,36 @@ class ModelManager(object): self.cache_keys = dict() self.logger = logger - def valid_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers) -> bool: + # TODO: rename to smth like - is_model_exists + def valid_model( + self, + model_name: str, + model_type: SDModelType = SDModelType.diffusers, + ) -> bool: """ Given a model name, returns True if it is a valid identifier. """ - try: - self._disambiguate_name(model_name, model_type) - return True - except InvalidModelError: - return False + model_key = self.create_key(model_name, model_class) + return model_key in self.config - def get_model(self, - model_name: str, - model_type: SDModelType=None, - submodel: SDModelType=None, - ) -> SDModelInfo: + def create_key(self, model_name: str, model_type: SDModelType) -> str: + return f"{model_type.name}/{model_name}" + + def parse_key(self, model_key: str) -> Tuple[str, SDModelType]: + model_type_str, model_name = model_key.split('/', 1) + if model_type_str not in SDModelType.__members__: + # TODO: + raise Exception(f"Unkown model type: {model_type_str}") + + return (model_name, SDModelType[model_type_str]) + + def get_model( + self, + model_name: str, + model_type: SDModelType=None, + submodel: SDModelType=None, + ) -> SDModelInfo: """Given a model named identified in models.yaml, return an SDModelInfo object describing it. :param model_name: symbolic name of the model in models.yaml @@ -254,85 +269,77 @@ class ModelManager(object): assume a diffusers pipeline. The behavior is illustrated here: [models.yaml] - test1/diffusers: + diffusers/test1: repo_id: foo/bar - format: diffusers description: Typical diffusers pipeline - test1/lora: + lora/test1: repo_id: /tmp/loras/test1.safetensors - format: lora description: Typical lora file test1_pipeline = mgr.get_model('test1') # returns a StableDiffusionGeneratorPipeline - test1_vae1 = mgr.get_model('test1',submodel=SDModelType.vae) + test1_vae1 = mgr.get_model('test1', submodel=SDModelType.vae) # returns the VAE part of a diffusers model as an AutoencoderKL - test1_vae2 = mgr.get_model('test1',model_type=SDModelType.diffusers,submodel=SDModelType.vae) + test1_vae2 = mgr.get_model('test1', model_type=SDModelType.diffusers, submodel=SDModelType.vae) # does the same thing as the previous statement. Note that model_type # is for the parent model, and submodel is for the part - test1_lora = mgr.get_model('test1',model_type=SDModelType.lora) + test1_lora = mgr.get_model('test1', model_type=SDModelType.lora) # returns a LoRA embed (as a 'dict' of tensors) - test1_encoder = mgr.get_modelI('test1',model_type=SDModelType.textencoder) + test1_encoder = mgr.get_modelI('test1', model_type=SDModelType.textencoder) # raises an InvalidModelError """ - if not model_name: - model_name = self.default_model() + # TODO: delete default model or add check that this stable diffusion model + # if not model_name: + # model_name = self.default_model() - model_key = self._disambiguate_name(model_name, model_type) + model_key = self.create_key(model_name, model_type) + if model_key not in self.config: + raise InvalidModelError( + f'"{model_key}" is not a known model name. Please check your models.yaml file' + ) # get the required loading info out of the config file mconfig = self.config[model_key] - format = mconfig.get('format','diffusers') - if model_type and model_type.name != format: - raise InvalidModelError( - f'Inconsistent model definition; {model_key} has format {format}, but type {model_type.name} was requested' - ) - - model_parts = dict([(x.name,x) for x in SDModelType]) - - if format == 'diffusers': + # type already checked as it's part of key + if model_type == SDModelType.diffusers: # intercept stanzas that point to checkpoint weights and replace them # with the equivalent diffusers model if 'weights' in mconfig: location = self.convert_ckpt_and_cache(mconfig) else: location = global_resolve_path(mconfig.get('path')) or mconfig.get('repo_id') - elif format in model_parts: - location = global_resolve_path(mconfig.get('path')) \ - or mconfig.get('repo_id') \ - or global_resolve_path(mconfig.get('weights')) else: - raise InvalidModelError( - f'"{model_key}" has an unknown format {format}' + location = global_resolve_path( + mconfig.get('path')) \ + or mconfig.get('repo_id') \ + or global_resolve_path(mconfig.get('weights') ) - model_type = model_parts[format] subfolder = mconfig.get('subfolder') revision = mconfig.get('revision') - hash = self.cache.model_hash(location,revision) + hash = self.cache.model_hash(location, revision) # to support the traditional way of attaching a VAE # to a model, we hacked in `attach_model_part` - vae = (None,None) - try: + vae = (None, None) + with suppress(Exception): vae_id = mconfig.vae.repo_id - vae = (SDModelType.vae,vae_id) - except Exception: - pass + vae = (SDModelType.vae, vae_id) + model_context = self.cache.get_model( location, model_type = model_type, revision = revision, subfolder = subfolder, submodel = submodel, - attach_model_part=vae, + attach_model_part = vae, ) # in case we need to communicate information about this @@ -402,27 +409,28 @@ class ModelManager(object): def list_models(self) -> dict: """ - Return a dict of models in the format: - { model_name1: {'status': ('active'|'cached'|'not loaded'), - 'description': description, - 'format': ('ckpt'|'diffusers'|'vae'), - }, - model_name2: { etc } + Return a dict of models Please use model_manager.models() to get all the model names, model_manager.model_info('model-name') to get the stanza for the model named 'model-name', and model_manager.config to get the full OmegaConf object derived from models.yaml """ models = {} - for name in sorted(self.config, key=str.casefold): - stanza = self.config[name] + for model_key in sorted(self.config, key=str.casefold): + stanza = self.config[model_key] # don't include VAEs in listing (legacy style) if "config" in stanza and "/VAE/" in stanza["config"]: continue - models[name] = dict() - format = stanza.get("format", "ckpt") # Determine Format + model_name, model_type = self.parse_key(model_key) + models[model_name] = dict() + + # TODO: return all models in future + if model_type != SDModelType.diffusers: + continue + + model_format = "ckpt" if "weights" in stanza else "diffusers" # Common Attribs status = self.cache.status( @@ -431,37 +439,38 @@ class ModelManager(object): subfolder=stanza.get('subfolder') ) description = stanza.get("description", None) - models[name].update( + models[model_name].update( description=description, - format=format, + type=model_type, + format=model_format, status=status.value ) # Checkpoint Config Parse - if format == "ckpt": - models[name].update( - config=str(stanza.get("config", None)), - weights=str(stanza.get("weights", None)), - vae=str(stanza.get("vae", None)), - width=str(stanza.get("width", 512)), - height=str(stanza.get("height", 512)), + if model_format == "ckpt": + models[model_name].update( + config = str(stanza.get("config", None)), + weights = str(stanza.get("weights", None)), + vae = str(stanza.get("vae", None)), + width = str(stanza.get("width", 512)), + height = str(stanza.get("height", 512)), ) # Diffusers Config Parse - if vae := stanza.get("vae", None): - if isinstance(vae, DictConfig): - vae = dict( - repo_id=str(vae.get("repo_id", None)), - path=str(vae.get("path", None)), - subfolder=str(vae.get("subfolder", None)), - ) + elif model_format == "diffusers": + if vae := stanza.get("vae", None): + if isinstance(vae, DictConfig): + vae = dict( + repo_id = str(vae.get("repo_id", None)), + path = str(vae.get("path", None)), + subfolder = str(vae.get("subfolder", None)), + ) - if format == "diffusers": - models[name].update( - vae=vae, - repo_id=str(stanza.get("repo_id", None)), - path=str(stanza.get("path", None)), + models[model_name].update( + vae = vae, + repo_id = str(stanza.get("repo_id", None)), + path = str(stanza.get("path", None)), ) return models @@ -472,44 +481,60 @@ class ModelManager(object): """ models = self.list_models() for name in models: - if models[name]["format"] == "vae": + if models[name]["type"] == "vae": continue - line = f'{name:25s} {models[name]["status"]:>15s} {models[name]["format"]:10s} {models[name]["description"]}' + line = f'{name:25s} {models[name]["status"]:>15s} {models[name]["type"]:10s} {models[name]["description"]}' if models[name]["status"] == "active": line = f"\033[1m{line}\033[0m" print(line) - def del_model(self, model_name: str, model_type: SDModelType.diffusers, delete_files: bool = False): + def del_model( + self, + model_name: str, + model_type: SDModelType.diffusers, + delete_files: bool = False + ): """ Delete the named model. """ - model_name = self._disambiguate_name(model_name, model_type) - omega = self.config - if model_name not in omega: - self.logger.error(f"Unknown model {model_name}") - return - # save these for use in deletion later - conf = omega[model_name] - repo_id = conf.get("repo_id", None) - path = self._abs_path(conf.get("path", None)) - weights = self._abs_path(conf.get("weights", None)) + model_key = self.create_key(model_name, model_type) + model_cfg = self.pop(model_key, None) + + if model_cfg is None: + self.logger.error( + f"Unknown model {model_key}" + ) + return + + # TODO: some legacy? + #if model_name in self.stack: + # self.stack.remove(model_name) - del omega[model_name] - if model_name in self.stack: - self.stack.remove(model_name) if delete_files: - if weights: + repo_id = conf.get("repo_id", None) + path = self._abs_path(conf.get("path", None)) + weights = self._abs_path(conf.get("weights", None)) + if "weights" in model_cfg: + weights = self._abs_path(model_cfg["weights"]) self.logger.info(f"Deleting file {weights}") Path(weights).unlink(missing_ok=True) - elif path: + + elif "path" in model_cfg: + path = self._abs_path(model_cfg["path"]) self.logger.info(f"Deleting directory {path}") rmtree(path, ignore_errors=True) - elif repo_id: + + elif "repo_id" in model_cfg: + repo_id = model_cfg["repo_id"] self.logger.info(f"Deleting the cached model directory for {repo_id}") self._delete_model_from_cache(repo_id) def add_model( - self, model_name: str, model_attributes: dict, clobber: bool = False + self, + model_name: str, + model_type: SDModelType, + model_attributes: dict, + clobber: bool = False ) -> None: """ Update the named model with a dictionary of attributes. Will fail with an @@ -518,37 +543,47 @@ class ModelManager(object): method will return True. Will fail with an assertion error if provided attributes are incorrect or the model name is missing. """ - omega = self.config - assert "format" in model_attributes, 'missing required field "format"' - if model_attributes["format"] == "diffusers": - assert ( - "description" in model_attributes - ), 'required field "description" is missing' - assert ( - "path" in model_attributes or "repo_id" in model_attributes - ), 'model must have either the "path" or "repo_id" fields defined' - elif model_attributes["format"] == "ckpt": - for field in ("description", "weights", "height", "width", "config"): - assert field in model_attributes, f"required field {field} is missing" + if model_type == SDModelType.diffusers: + # TODO: automaticaly or manualy? + #assert "format" in model_attributes, 'missing required field "format"' + model_format = "ckpt" if "weights" in model_attributes else "diffusers" + + if model_format == "diffusers": + assert ( + "description" in model_attributes + ), 'required field "description" is missing' + assert ( + "path" in model_attributes or "repo_id" in model_attributes + ), 'model must have either the "path" or "repo_id" fields defined' + + elif model_format == "ckpt": + for field in ("description", "weights", "height", "width", "config"): + assert field in model_attributes, f"required field {field} is missing" + else: assert "weights" in model_attributes and "description" in model_attributes - model_key = f'{model_name}/{model_attributes["format"]}' + model_key = self.create_key(model_name, model_type) assert ( - clobber or model_key not in omega + clobber or model_key not in self.config ), f'attempt to overwrite existing model definition "{model_key}"' - omega[model_key] = model_attributes + self.config[model_key] = model_attributes - if "weights" in omega[model_key]: - omega[model_key]["weights"].replace("\\", "/") + if "weights" in self.config[model_key]: + self.config[model_key]["weights"].replace("\\", "/") if clobber and model_key in self.cache_keys: self.cache.uncache_model(self.cache_keys[model_key]) del self.cache_keys[model_key] + + + + + def import_diffuser_model( self, repo_or_path: Union[str, Path], @@ -587,10 +622,10 @@ class ModelManager(object): return model_key def import_lora( - self, - path: Path, - model_name: str=None, - description: str=None, + self, + path: Path, + model_name: str=None, + description: str=None, ): """ Creates an entry for the indicated lora file. Call @@ -599,20 +634,21 @@ class ModelManager(object): path = Path(path) model_name = model_name or path.stem model_description = description or f"LoRA model {model_name}" - self.add_model(f'{model_name}/{SDModelType.lora.name}', - dict( - format="lora", - weights=str(path), - description=model_description, - ), - True - ) + self.add_model( + f'{model_name}/{SDModelType.lora.name}', + dict( + format="lora", + weights=str(path), + description=model_description, + ), + True + ) def import_embedding( - self, - path: Path, - model_name: str=None, - description: str=None, + self, + path: Path, + model_name: str=None, + description: str=None, ): """ Creates an entry for the indicated lora file. Call @@ -626,14 +662,15 @@ class ModelManager(object): model_name = model_name or path.stem model_description = description or f"Textual embedding model {model_name}" - self.add_model(f'{model_name}/{SDModelType.textual_inversion.name}', - dict( - format="textual_inversion", - weights=str(weights), - description=model_description, - ), - True - ) + self.add_model( + f'{model_name}/{SDModelType.textual_inversion.name}', + dict( + format="textual_inversion", + weights=str(weights), + description=model_description, + ), + True + ) @classmethod def probe_model_type(self, checkpoint: dict) -> SDLegacyType: @@ -857,7 +894,7 @@ class ModelManager(object): ) return model_name - def convert_ckpt_and_cache(self, mconfig:DictConfig)->Path: + def convert_ckpt_and_cache(self, mconfig: DictConfig)->Path: """ Convert the checkpoint model indicated in mconfig into a diffusers, cache it to disk, and return Path to converted @@ -872,6 +909,7 @@ class ModelManager(object): return diffusers_path vae_ckpt_path, vae_model = self._get_vae_for_conversion(weights, mconfig) + # to avoid circular import errors from .convert_ckpt_to_diffusers import convert_ckpt_to_diffusers with SilenceWarnings(): @@ -881,15 +919,16 @@ class ModelManager(object): extract_ema=True, original_config_file=config_file, vae=vae_model, - vae_path=str(global_resolve_path(vae_ckpt_path)), + vae_path=str(global_resolve_path(vae_ckpt_path)) if vae_ckpt_path else None, scan_needed=True, ) return diffusers_path - def _get_vae_for_conversion(self, - weights: Path, - mconfig: DictConfig - )->tuple(Path,SDModelType.vae): + def _get_vae_for_conversion( + self, + weights: Path, + mconfig: DictConfig + ) -> Tuple[Path, SDModelType.vae]: # VAE handling is convoluted # 1. If there is a .vae.ckpt file sharing same stem as weights, then use # it as the vae_path passed to convert @@ -1047,21 +1086,7 @@ class ModelManager(object): # model requires a model config file, a weights file, # and the width and height of the images it # was trained on. - """ - ) - - def _disambiguate_name(self, model_name:str, model_type:SDModelType)->str: - model_type = model_type or SDModelType.diffusers - full_name = f"{model_name}/{model_type.name}" - if full_name in self.config: - return full_name - # special case - if diffusers requested, then allow name without type appended - if model_type==SDModelType.diffusers \ - and model_name in self.config \ - and self.config[model_name].format=='diffusers': - return model_name - raise InvalidModelError( - f'"{full_name}" is not a known model name. Please check your models.yaml file' + """ ) From 3b2a054f7a62351ffa6162aa881bdad065d95b3c Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Sat, 13 May 2023 04:37:20 +0300 Subject: [PATCH 027/225] Add model loader node; unet, clip, vae fields; change compel node to clip field --- invokeai/app/invocations/compel.py | 13 +- invokeai/app/invocations/model.py | 131 +++++++++++++++++ .../backend/model_management/model_manager.py | 2 +- .../web/src/common/util/parseMetadata.ts | 133 +++++++++++++++++- .../nodes/components/InputFieldComponent.tsx | 33 +++++ .../fields/ClipInputFieldComponent.tsx | 16 +++ .../fields/UNetInputFieldComponent.tsx | 16 +++ .../fields/VaeInputFieldComponent.tsx | 16 +++ .../web/src/features/nodes/types/constants.ts | 21 +++ .../web/src/features/nodes/types/types.ts | 24 ++++ .../nodes/util/fieldTemplateBuilders.ts | 57 ++++++++ .../features/nodes/util/fieldValueBuilders.ts | 12 ++ 12 files changed, 466 insertions(+), 8 deletions(-) create mode 100644 invokeai/app/invocations/model.py create mode 100644 invokeai/frontend/web/src/features/nodes/components/fields/ClipInputFieldComponent.tsx create mode 100644 invokeai/frontend/web/src/features/nodes/components/fields/UNetInputFieldComponent.tsx create mode 100644 invokeai/frontend/web/src/features/nodes/components/fields/VaeInputFieldComponent.tsx diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index 25bb421544..584e14ea0f 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -3,6 +3,8 @@ from pydantic import BaseModel, Field from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig +from .model import ClipField + from ...backend.util.devices import choose_torch_device, torch_dtype from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager @@ -41,7 +43,7 @@ class CompelInvocation(BaseInvocation): type: Literal["compel"] = "compel" prompt: str = Field(default="", description="Prompt") - model: str = Field(default="", description="Model to use") + clip: ClipField = Field(None, description="Clip to use") # Schema customisation class Config(InvocationConfig): @@ -58,12 +60,15 @@ class CompelInvocation(BaseInvocation): def invoke(self, context: InvocationContext) -> CompelOutput: # TODO: load without model - model = context.services.model_manager.get_model(self.model) text_encoder_info = context.services.model_manager.get_model( - self.model, SDModelType.diffusers, SDModelType.text_encoder + model_name=self.clip.text_encoder.model_name, + model_type=SDModelType[self.clip.text_encoder.model_type], + submodel=SDModelType[self.clip.text_encoder.submodel], ) tokenizer_info = context.services.model_manager.get_model( - self.model, SDModelType.diffusers, SDModelType.tokenizer + model_name=self.clip.tokenizer.model_name, + model_type=SDModelType[self.clip.tokenizer.model_type], + submodel=SDModelType[self.clip.tokenizer.submodel], ) with text_encoder_info.context as text_encoder,\ tokenizer_info.context as tokenizer: diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py new file mode 100644 index 0000000000..11f1fd5c5f --- /dev/null +++ b/invokeai/app/invocations/model.py @@ -0,0 +1,131 @@ +from typing import Literal, Optional, Union +from pydantic import BaseModel, Field + +from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig + +from ...backend.util.devices import choose_torch_device, torch_dtype +from ...backend.model_management import SDModelType + +class ModelInfo(BaseModel): + model_name: str = Field(description="Info to load unet submodel") + model_type: str = Field(description="Info to load unet submodel") + submodel: Optional[str] = Field(description="Info to load unet submodel") + +class UNetField(BaseModel): + unet: ModelInfo = Field(description="Info to load unet submodel") + scheduler: ModelInfo = Field(description="Info to load scheduler submodel") + # loras: List[ModelInfo] + +class ClipField(BaseModel): + tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel") + text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel") + # loras: List[ModelInfo] + +class VaeField(BaseModel): + # TODO: better naming? + vae: ModelInfo = Field(description="Info to load vae submodel") + + +class ModelLoaderOutput(BaseInvocationOutput): + """Model loader output""" + + #fmt: off + type: Literal["model_loader_output"] = "model_loader_output" + + unet: UNetField = Field(default=None, description="UNet submodel") + clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels") + vae: VaeField = Field(default=None, description="Vae submodel") + #fmt: on + + +class ModelLoaderInvocation(BaseInvocation): + """Loading submodels of selected model.""" + + type: Literal["model_loader"] = "model_loader" + + model_name: str = Field(default="", description="Model to load") + # TODO: precision? + + # Schema customisation + class Config(InvocationConfig): + schema_extra = { + "ui": { + "tags": ["model", "loader"], + "type_hints": { + "model_name": "model" # TODO: rename to model_name? + } + }, + } + + def invoke(self, context: InvocationContext) -> ModelLoaderOutput: + + # TODO: not found exceptions + if not context.services.model_manager.valid_model( + model_name=self.model_name, + model_type=SDModelType.diffusers, + ): + raise Exception(f"Unkown model name: {self.model_name}!") + + """ + if not context.services.model_manager.valid_model( + model_name=self.model_name, + model_type=SDModelType.diffusers, + submodel=SDModelType.tokenizer, + ): + raise Exception( + f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted" + ) + + if not context.services.model_manager.valid_model( + model_name=self.model_name, + model_type=SDModelType.diffusers, + submodel=SDModelType.text_encoder, + ): + raise Exception( + f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted" + ) + + if not context.services.model_manager.valid_model( + model_name=self.model_name, + model_type=SDModelType.diffusers, + submodel=SDModelType.unet, + ): + raise Exception( + f"Failed to find unet submodel from {self.model_name}! Check if model corrupted" + ) + """ + + + return ModelLoaderOutput( + unet=UNetField( + unet=ModelInfo( + model_name=self.model_name, + model_type=SDModelType.diffusers.name, + submodel=SDModelType.unet.name, + ), + scheduler=ModelInfo( + model_name=self.model_name, + model_type=SDModelType.diffusers.name, + submodel=SDModelType.scheduler.name, + ), + ), + clip=ClipField( + tokenizer=ModelInfo( + model_name=self.model_name, + model_type=SDModelType.diffusers.name, + submodel=SDModelType.tokenizer.name, + ), + text_encoder=ModelInfo( + model_name=self.model_name, + model_type=SDModelType.diffusers.name, + submodel=SDModelType.text_encoder.name, + ), + ), + vae=VaeField( + vae=ModelInfo( + model_name=self.model_name, + model_type=SDModelType.diffusers.name, + submodel=SDModelType.vae.name, + ), + ) + ) diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index bd70e23f5b..e32ba3b22a 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -236,7 +236,7 @@ class ModelManager(object): Given a model name, returns True if it is a valid identifier. """ - model_key = self.create_key(model_name, model_class) + model_key = self.create_key(model_name, model_type) return model_key in self.config def create_key(self, model_name: str, model_type: SDModelType) -> str: diff --git a/invokeai/frontend/web/src/common/util/parseMetadata.ts b/invokeai/frontend/web/src/common/util/parseMetadata.ts index c27833218b..95d08db3e0 100644 --- a/invokeai/frontend/web/src/common/util/parseMetadata.ts +++ b/invokeai/frontend/web/src/common/util/parseMetadata.ts @@ -1,5 +1,12 @@ import { forEach, size } from 'lodash-es'; -import { ImageField, LatentsField, ConditioningField } from 'services/api'; +import { + ImageField, + LatentsField, + ConditioningField, + UNetField, + ClipField, + VaeField, +} from 'services/api'; const OBJECT_TYPESTRING = '[object Object]'; const STRING_TYPESTRING = '[object String]'; @@ -98,6 +105,101 @@ const parseConditioningField = ( }; }; +const _parseModelInfo = (modelInfo: unknown): ModelInfo | undefined => { + // Must be an object + if (!isObject(modelInfo)) { + return; + } + + if (!('model_name' in modelInfo && typeof modelInfo.model_name == 'string')) { + return; + } + + if (!('model_type' in modelInfo && typeof modelInfo.model_type == 'string')) { + return; + } + + if (!('submodel' in modelInfo && typeof modelInfo.submodel == 'string')) { + return; + } + + return { + model_name: modelInfo.model_name, + model_type: modelInfo.model_type, + submodel: modelInfo.submodel, + }; +}; + +const parseUNetField = (unetField: unknown): UNetField | undefined => { + // Must be an object + if (!isObject(unetField)) { + return; + } + + if (!('unet' in unetField && 'scheduler' in unetField)) { + return; + } + + const unet = _parseModelInfo(unetField.unet); + const scheduler = _parseModelInfo(unetField.scheduler); + + if (!(unet && scheduler)) { + return; + } + + // Build a valid UNetField + return { + unet: unet, + scheduler: scheduler, + }; +}; + +const parseClipField = (clipField: unknown): ClipField | undefined => { + // Must be an object + if (!isObject(clipField)) { + return; + } + + if (!('tokenizer' in clipField && 'text_encoder' in clipField)) { + return; + } + + const tokenizer = _parseModelInfo(clipField.tokenizer); + const text_encoder = _parseModelInfo(clipField.text_encoder); + + if (!(tokenizer && text_encoder)) { + return; + } + + // Build a valid ClipField + return { + tokenizer: tokenizer, + text_encoder: text_encoder, + }; +}; + +const parseVaeField = (vaeField: unknown): VaeField | undefined => { + // Must be an object + if (!isObject(vaeField)) { + return; + } + + if (!('vae' in vaeField)) { + return; + } + + const vae = _parseModelInfo(vaeField.vae); + + if (!vae) { + return; + } + + // Build a valid VaeField + return { + vae: vae, + }; +}; + type NodeMetadata = { [key: string]: | string @@ -105,7 +207,10 @@ type NodeMetadata = { | boolean | ImageField | LatentsField - | ConditioningField; + | ConditioningField + | UNetField + | ClipField + | VaeField; }; type InvokeAIMetadata = { @@ -131,7 +236,8 @@ export const parseNodeMetadata = ( return; } - // the only valid object types are ImageField, LatentsField and ConditioningField + // valid object types are: + // ImageField, LatentsField ConditioningField, UNetField, ClipField, VaeField if (isObject(nodeItem)) { if ('image_name' in nodeItem || 'image_type' in nodeItem) { const imageField = parseImageField(nodeItem); @@ -156,6 +262,27 @@ export const parseNodeMetadata = ( } return; } + + if ('unet' in nodeItem && 'tokenizer' in nodeItem) { + const unetField = parseUNetField(nodeItem); + if (unetField) { + parsed[nodeKey] = unetField; + } + } + + if ('tokenizer' in nodeItem && 'text_encoder' in nodeItem) { + const clipField = parseClipField(nodeItem); + if (clipField) { + parsed[nodeKey] = clipField; + } + } + + if ('vae' in nodeItem) { + const vaeField = parseVaeField(nodeItem); + if (vaeField) { + parsed[nodeKey] = vaeField; + } + } } // otherwise we accept any string, number or boolean diff --git a/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx b/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx index 9527708c40..341ca19fa9 100644 --- a/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx +++ b/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx @@ -7,6 +7,9 @@ import EnumInputFieldComponent from './fields/EnumInputFieldComponent'; import ImageInputFieldComponent from './fields/ImageInputFieldComponent'; import LatentsInputFieldComponent from './fields/LatentsInputFieldComponent'; import ConditioningInputFieldComponent from './fields/ConditioningInputFieldComponent'; +import UNetInputFieldComponent from './fields/UNetInputFieldComponent'; +import ClipInputFieldComponent from './fields/ClipInputFieldComponent'; +import VaeInputFieldComponent from './fields/VaeInputFieldComponent'; import ModelInputFieldComponent from './fields/ModelInputFieldComponent'; import NumberInputFieldComponent from './fields/NumberInputFieldComponent'; import StringInputFieldComponent from './fields/StringInputFieldComponent'; @@ -97,6 +100,36 @@ const InputFieldComponent = (props: InputFieldComponentProps) => { ); } + if (type === 'unet' && template.type === 'unet') { + return ( + + ); + } + + if (type === 'clip' && template.type === 'clip') { + return ( + + ); + } + + if (type === 'vae' && template.type === 'vae') { + return ( + + ); + } + if (type === 'model' && template.type === 'model') { return ( +) => { + const { nodeId, field } = props; + + return null; +}; + +export default memo(ClipInputFieldComponent); diff --git a/invokeai/frontend/web/src/features/nodes/components/fields/UNetInputFieldComponent.tsx b/invokeai/frontend/web/src/features/nodes/components/fields/UNetInputFieldComponent.tsx new file mode 100644 index 0000000000..5926bf113a --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/components/fields/UNetInputFieldComponent.tsx @@ -0,0 +1,16 @@ +import { + UNetInputFieldTemplate, + UNetInputFieldValue, +} from 'features/nodes/types/types'; +import { memo } from 'react'; +import { FieldComponentProps } from './types'; + +const UNetInputFieldComponent = ( + props: FieldComponentProps +) => { + const { nodeId, field } = props; + + return null; +}; + +export default memo(UNetInputFieldComponent); diff --git a/invokeai/frontend/web/src/features/nodes/components/fields/VaeInputFieldComponent.tsx b/invokeai/frontend/web/src/features/nodes/components/fields/VaeInputFieldComponent.tsx new file mode 100644 index 0000000000..0fa11ae34e --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/components/fields/VaeInputFieldComponent.tsx @@ -0,0 +1,16 @@ +import { + VaeInputFieldTemplate, + VaeInputFieldValue, +} from 'features/nodes/types/types'; +import { memo } from 'react'; +import { FieldComponentProps } from './types'; + +const VaeInputFieldComponent = ( + props: FieldComponentProps +) => { + const { nodeId, field } = props; + + return null; +}; + +export default memo(VaeInputFieldComponent); diff --git a/invokeai/frontend/web/src/features/nodes/types/constants.ts b/invokeai/frontend/web/src/features/nodes/types/constants.ts index 7e4dadc21d..36c3514eeb 100644 --- a/invokeai/frontend/web/src/features/nodes/types/constants.ts +++ b/invokeai/frontend/web/src/features/nodes/types/constants.ts @@ -11,6 +11,9 @@ export const FIELD_TYPE_MAP: Record = { ImageField: 'image', LatentsField: 'latents', ConditioningField: 'conditioning', + UNetField: 'unet', + ClipField: 'clip', + VaeField: 'vae', model: 'model', array: 'array', item: 'item', @@ -71,6 +74,24 @@ export const FIELDS: Record = { title: 'Conditioning', description: 'Conditioning may be passed between nodes.', }, + unet: { + color: 'red', + colorCssVar: getColorTokenCssVariable('red'), + title: 'UNet', + description: 'UNet submodel.', + }, + clip: { + color: 'green', + colorCssVar: getColorTokenCssVariable('green'), + title: 'Clip', + description: 'Tokenizer and text_encoder submodels.', + }, + vae: { + color: 'blue', + colorCssVar: getColorTokenCssVariable('blue'), + title: 'Vae', + description: 'Vae submodel.', + }, model: { color: 'teal', colorCssVar: getColorTokenCssVariable('teal'), diff --git a/invokeai/frontend/web/src/features/nodes/types/types.ts b/invokeai/frontend/web/src/features/nodes/types/types.ts index 876ba95cac..0ed0a46964 100644 --- a/invokeai/frontend/web/src/features/nodes/types/types.ts +++ b/invokeai/frontend/web/src/features/nodes/types/types.ts @@ -58,6 +58,9 @@ export type FieldType = | 'image' | 'latents' | 'conditioning' + | 'unet' + | 'clip' + | 'vae' | 'model' | 'array' | 'item' @@ -79,6 +82,9 @@ export type InputFieldValue = | ImageInputFieldValue | LatentsInputFieldValue | ConditioningInputFieldValue + | UNetInputFieldValue + | ClipInputFieldValue + | VaeInputFieldValue | EnumInputFieldValue | ModelInputFieldValue | ArrayInputFieldValue @@ -99,6 +105,9 @@ export type InputFieldTemplate = | ImageInputFieldTemplate | LatentsInputFieldTemplate | ConditioningInputFieldTemplate + | UNetInputFieldTemplate + | ClipInputFieldTemplate + | VaeInputFieldTemplate | EnumInputFieldTemplate | ModelInputFieldTemplate | ArrayInputFieldTemplate @@ -177,6 +186,21 @@ export type ConditioningInputFieldValue = FieldValueBase & { value?: undefined; }; +export type UNetInputFieldValue = FieldValueBase & { + type: 'unet'; + value?: undefined; +}; + +export type ClipInputFieldValue = FieldValueBase & { + type: 'clip'; + value?: undefined; +}; + +export type VaeInputFieldValue = FieldValueBase & { + type: 'vae'; + value?: undefined; +}; + export type ImageInputFieldValue = FieldValueBase & { type: 'image'; value?: Pick; diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts index 11f0087488..b275c84248 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts @@ -10,6 +10,9 @@ import { IntegerInputFieldTemplate, LatentsInputFieldTemplate, ConditioningInputFieldTemplate, + UNetInputFieldTemplate, + ClipInputFieldTemplate, + VaeInputFieldTemplate, StringInputFieldTemplate, ModelInputFieldTemplate, ArrayInputFieldTemplate, @@ -215,6 +218,51 @@ const buildConditioningInputFieldTemplate = ({ return template; }; +const buildUNetInputFieldTemplate = ({ + schemaObject, + baseField, +}: BuildInputFieldArg): UNetInputFieldTemplate => { + const template: UNetInputFieldTemplate = { + ...baseField, + type: 'unet', + inputRequirement: 'always', + inputKind: 'connection', + default: schemaObject.default ?? undefined, + }; + + return template; +}; + +const buildClipInputFieldTemplate = ({ + schemaObject, + baseField, +}: BuildInputFieldArg): ClipInputFieldTemplate => { + const template: ClipInputFieldTemplate = { + ...baseField, + type: 'clip', + inputRequirement: 'always', + inputKind: 'connection', + default: schemaObject.default ?? undefined, + }; + + return template; +}; + +const buildVaeInputFieldTemplate = ({ + schemaObject, + baseField, +}: BuildInputFieldArg): VaeInputFieldTemplate => { + const template: VaeInputFieldTemplate = { + ...baseField, + type: 'vae', + inputRequirement: 'always', + inputKind: 'connection', + default: schemaObject.default ?? undefined, + }; + + return template; +}; + const buildEnumInputFieldTemplate = ({ schemaObject, baseField, @@ -331,6 +379,15 @@ export const buildInputFieldTemplate = ( if (['conditioning'].includes(fieldType)) { return buildConditioningInputFieldTemplate({ schemaObject, baseField }); } + if (['unet'].includes(fieldType)) { + return buildUNetInputFieldTemplate({ schemaObject, baseField }); + } + if (['clip'].includes(fieldType)) { + return buildClipInputFieldTemplate({ schemaObject, baseField }); + } + if (['vae'].includes(fieldType)) { + return buildVaeInputFieldTemplate({ schemaObject, baseField }); + } if (['model'].includes(fieldType)) { return buildModelInputFieldTemplate({ schemaObject, baseField }); } diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts index 9221e5f7ac..c0c19708c7 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts @@ -52,6 +52,18 @@ export const buildInputFieldValue = ( fieldValue.value = undefined; } + if (template.type === 'unet') { + fieldValue.value = undefined; + } + + if (template.type === 'clip') { + fieldValue.value = undefined; + } + + if (template.type === 'vae') { + fieldValue.value = undefined; + } + if (template.type === 'model') { fieldValue.value = undefined; } From bc96727cbe4399cbfde4d1f2daaf73830ac502f5 Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Sat, 13 May 2023 16:08:03 +0300 Subject: [PATCH 028/225] Rewrite latent nodes to new model manager --- invokeai/app/invocations/latent.py | 251 ++++++++++++++++++----------- 1 file changed, 160 insertions(+), 91 deletions(-) diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 3f99d08bd1..9ca1c81a3d 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -7,6 +7,7 @@ import einops import torch from diffusers import DiffusionPipeline from diffusers.schedulers import SchedulerMixin as Scheduler +from diffusers.image_processor import VaeImageProcessor from pydantic import BaseModel, Field from invokeai.app.util.misc import SEED_MAX, get_random_seed @@ -26,6 +27,9 @@ from .baseinvocation import (BaseInvocation, BaseInvocationOutput, from .compel import ConditioningField from .image import ImageField, ImageOutput, build_image_output +from .model import ModelInfo, UNetField, VaeField +from ...backend.model_management import SDModelType + class LatentsField(BaseModel): """A latents field used for passing latents between invocations""" @@ -70,9 +74,21 @@ SAMPLER_NAME_VALUES = Literal[ ] -def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler: +def get_scheduler( + context: InvocationContext, + scheduler_info: ModelInfo, + scheduler_name: str, +) -> Scheduler: + orig_scheduler_info = context.services.model_manager.get_model( + model_name=scheduler_info.model_name, + model_type=SDModelType[scheduler_info.model_type], + submodel=SDModelType[scheduler_info.submodel], + ) + with orig_scheduler_info.context as orig_scheduler: + scheduler_config = orig_scheduler.config + scheduler_class = scheduler_map.get(scheduler_name,'ddim') - scheduler = scheduler_class.from_config(model.scheduler.config) + scheduler = scheduler_class.from_config(scheduler_config) # hack copied over from generate.py if not hasattr(scheduler, 'uses_inpainting_model'): scheduler.uses_inpainting_model = lambda: False @@ -102,12 +118,6 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c # x = (1 - self.perlin) * x + self.perlin * perlin_noise return x -class ModelGetter: - def get_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: - model_manager = context.services.model_manager - model_info = model_manager.get_model(self.model,node=self,context=context) - return model_info.context - class NoiseInvocation(BaseInvocation): """Generates latent noise.""" @@ -139,7 +149,7 @@ class NoiseInvocation(BaseInvocation): # Text to image -class TextToLatentsInvocation(BaseInvocation, ModelGetter): +class TextToLatentsInvocation(BaseInvocation): """Generates latents from conditionings.""" type: Literal["t2l"] = "t2l" @@ -152,9 +162,10 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter): steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image") cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", ) scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" ) - model: str = Field(default="", description="The model to use (currently ignored)") seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", ) seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") + + unet: UNetField = Field(default=None, description="UNet submodel") # fmt: on # Schema customisation @@ -162,9 +173,6 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter): schema_extra = { "ui": { "tags": ["latents", "image"], - "type_hints": { - "model": "model" - } }, } @@ -179,7 +187,7 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter): source_node_id=source_node_id, ) - def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData: + def get_conditioning_data(self, context: InvocationContext, scheduler) -> ConditioningData: c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name) uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name) @@ -194,9 +202,36 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter): h_symmetry_time_pct=None,#h_symmetry_time_pct, v_symmetry_time_pct=None#v_symmetry_time_pct, ), - ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta) + ).add_scheduler_args_if_applicable(scheduler, eta=None)#ddim_eta) return conditioning_data + def create_pipeline(self, unet, scheduler) -> StableDiffusionGeneratorPipeline: + configure_model_padding( + unet, + self.seamless, + self.seamless_axes, + ) + + class FakeVae: + class FakeVaeConfig: + def __init__(self): + self.block_out_channels = [0] + + def __init__(self): + self.config = FakeVae.FakeVaeConfig() + + return StableDiffusionGeneratorPipeline( + vae=FakeVae(), # TODO: oh... + text_encoder=None, + tokenizer=None, + unet=unet, + scheduler=scheduler, + safety_checker=None, + feature_extractor=None, + requires_safety_checker=False, + precision="float16" if unet.dtype == torch.float16 else "float32", + #precision="float16", # TODO: + ) def invoke(self, context: InvocationContext) -> LatentsOutput: noise = context.services.latents.get(self.noise.latents_name) @@ -207,19 +242,33 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - - with self.get_model(context) as model: - conditioning_data = self.get_conditioning_data(context, model) - # TODO: Verify the noise is the right size - result_latents, result_attention_map_saver = model.latents_from_embeddings( - latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)), - noise=noise, - num_inference_steps=self.steps, - conditioning_data=conditioning_data, - callback=step_callback + #unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) + unet_info = context.services.model_manager.get_model( + model_name=self.unet.unet.model_name, + model_type=SDModelType[self.unet.unet.model_type], + submodel=SDModelType[self.unet.unet.submodel] if self.unet.unet.submodel else None, ) + with unet_info.context as unet: + scheduler = get_scheduler( + context=context, + scheduler_info=self.unet.scheduler, + scheduler_name=self.scheduler, + ) + + pipeline = self.create_pipeline(unet, scheduler) + conditioning_data = self.get_conditioning_data(context, scheduler) + + # TODO: Verify the noise is the right size + result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( + latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)), + noise=noise, + num_inference_steps=self.steps, + conditioning_data=conditioning_data, + callback=step_callback + ) + # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 torch.cuda.empty_cache() @@ -229,30 +278,8 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter): latents=LatentsField(latents_name=name) ) - def get_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline: - model_ctx = super().get_model(context) - with model_ctx as model: - model.scheduler = get_scheduler( - model=model, - scheduler_name=self.scheduler - ) - - if isinstance(model, DiffusionPipeline): - for component in [model.unet, model.vae]: - configure_model_padding(component, - self.seamless, - self.seamless_axes - ) - else: - configure_model_padding(model, - self.seamless, - self.seamless_axes - ) - return model_ctx - - -class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter): +class LatentsToLatentsInvocation(TextToLatentsInvocation): """Generates latents using latents as base image.""" type: Literal["l2l"] = "l2l" @@ -266,9 +293,6 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter): schema_extra = { "ui": { "tags": ["latents"], - "type_hints": { - "model": "model" - } }, } @@ -283,22 +307,35 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - with self.get_model(context) as model: - conditioning_data = self.get_conditioning_data(model) + #unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) + unet_info = context.services.model_manager.get_model( + model_name=self.unet.unet.model_name, + model_type=SDModelType[self.unet.unet.model_type], + submodel=SDModelType[self.unet.unet.submodel] if self.unet.unet.submodel else None, + ) + + with unet_info.context as unet: + scheduler = get_scheduler( + context=context, + scheduler_info=self.unet.scheduler, + scheduler_name=self.scheduler, + ) + + pipeline = self.create_pipeline(unet, scheduler) + conditioning_data = self.get_conditioning_data(context, scheduler) # TODO: Verify the noise is the right size - initial_latents = latent if self.strength < 1.0 else torch.zeros_like( - latent, device=model.device, dtype=latent.dtype + latent, device=unet.device, dtype=latent.dtype ) - timesteps, _ = model.get_img2img_timesteps( + timesteps, _ = pipeline.get_img2img_timesteps( self.steps, self.strength, - device=model.device, + device=unet.device, ) - result_latents, result_attention_map_saver = model.latents_from_embeddings( + result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( latents=initial_latents, timesteps=timesteps, noise=noise, @@ -318,23 +355,21 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter): # Latent to image -class LatentsToImageInvocation(BaseInvocation, ModelGetter): +class LatentsToImageInvocation(BaseInvocation): """Generates an image from latents.""" type: Literal["l2i"] = "l2i" # Inputs latents: Optional[LatentsField] = Field(description="The latents to generate an image from") - model: str = Field(default="", description="The model to use") + vae: VaeField = Field(default=None, description="Vae submodel") + tiled: bool = Field(default=False, description="Decode latents by overlaping tiles(less memory consumption)") # Schema customisation class Config(InvocationConfig): schema_extra = { "ui": { "tags": ["latents", "image"], - "type_hints": { - "model": "model" - } }, } @@ -342,27 +377,45 @@ class LatentsToImageInvocation(BaseInvocation, ModelGetter): def invoke(self, context: InvocationContext) -> ImageOutput: latents = context.services.latents.get(self.latents.latents_name) - # TODO: this only really needs the vae - with self.get_model(context) as model: + #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict()) + vae_info = context.services.model_manager.get_model( + model_name=self.vae.vae.model_name, + model_type=SDModelType[self.vae.vae.model_type], + submodel=SDModelType[self.vae.vae.submodel] if self.vae.vae.submodel else None, + ) + + with vae_info.context as vae: + # TODO: check if it works + if self.tiled: + vae.enable_tiling() + else: + vae.disable_tiling() + with torch.inference_mode(): - np_image = model.decode_latents(latents) - image = model.numpy_to_pil(np_image)[0] + # copied from diffusers pipeline + latents = latents / vae.config.scaling_factor + image = vae.decode(latents, return_dict=False)[0] + image = (image / 2 + 0.5).clamp(0, 1) # denormalize + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + np_image = image.cpu().permute(0, 2, 3, 1).float().numpy() - image_type = ImageType.RESULT - image_name = context.services.images.create_name( - context.graph_execution_state_id, self.id - ) + image = VaeImageProcessor.numpy_to_pil(np_image)[0] - metadata = context.services.metadata.build_metadata( - session_id=context.graph_execution_state_id, node=self - ) + image_type = ImageType.RESULT + image_name = context.services.images.create_name( + context.graph_execution_state_id, self.id + ) - torch.cuda.empty_cache() + metadata = context.services.metadata.build_metadata( + session_id=context.graph_execution_state_id, node=self + ) - context.services.images.save(image_type, image_name, image, metadata) - return build_image_output( - image_type=image_type, image_name=image_name, image=image - ) + torch.cuda.empty_cache() + + context.services.images.save(image_type, image_name, image, metadata) + return build_image_output( + image_type=image_type, image_name=image_name, image=image + ) LATENTS_INTERPOLATION_MODE = Literal[ @@ -430,21 +483,21 @@ class ScaleLatentsInvocation(BaseInvocation): return LatentsOutput(latents=LatentsField(latents_name=name)) -class ImageToLatentsInvocation(BaseInvocation, ModelGetter): +class ImageToLatentsInvocation(BaseInvocation): """Encodes an image into latents.""" type: Literal["i2l"] = "i2l" # Inputs image: Union[ImageField, None] = Field(description="The image to encode") - model: str = Field(default="", description="The model to use") + vae: VaeField = Field(default=None, description="Vae submodel") + tiled: bool = Field(default=False, description="Encode latents by overlaping tiles(less memory consumption)") # Schema customisation class Config(InvocationConfig): schema_extra = { "ui": { "tags": ["latents", "image"], - "type_hints": {"model": "model"}, }, } @@ -454,22 +507,38 @@ class ImageToLatentsInvocation(BaseInvocation, ModelGetter): self.image.image_type, self.image.image_name ) - # TODO: this only really needs the vae - model_info = self.get_model(context) - model: StableDiffusionGeneratorPipeline = model_info["model"] + #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict()) + vae_info = context.services.model_manager.get_model( + model_name=self.vae.vae.model_name, + model_type=SDModelType[self.vae.vae.model_type], + submodel=SDModelType[self.vae.vae.submodel] if self.vae.vae.submodel else None, + ) image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB")) - if image_tensor.dim() == 3: image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w") - latents = model.non_noised_latents_from_image( - image_tensor, - device=model._model_group.device_for(model.unet), - dtype=model.unet.dtype, - ) + with vae_info.context as vae: + # TODO: check if it works + if self.tiled: + vae.enable_tiling() + else: + vae.disable_tiling() + + latents = self.non_noised_latents_from_image(vae, image_tensor) name = f"{context.graph_execution_state_id}__{self.id}" context.services.latents.set(name, latents) return LatentsOutput(latents=LatentsField(latents_name=name)) + + def non_noised_latents_from_image(self, vae, init_image): + init_image = init_image.to(device=vae.device, dtype=vae.dtype) + with torch.inference_mode(): + init_latent_dist = vae.encode(init_image).latent_dist + init_latents = init_latent_dist.sample().to( + dtype=vae.dtype + ) # FIXME: uses torch.randn. make reproducible! + + init_latents = 0.18215 * init_latents + return init_latents \ No newline at end of file From 72967bf1180c151638e2c1c4a26517557c8f60e8 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 14:44:44 -0400 Subject: [PATCH 029/225] convert add_model(), del_model(), list_models() etc to use bifurcated names --- invokeai/app/api/routers/models.py | 26 +- invokeai/app/invocations/model.py | 8 +- .../app/services/model_manager_service.py | 72 +++--- invokeai/backend/generate.py | 4 +- .../backend/model_management/model_manager.py | 230 ++++++++++++------ invokeai/frontend/CLI/readline.py | 5 +- 6 files changed, 217 insertions(+), 128 deletions(-) diff --git a/invokeai/app/api/routers/models.py b/invokeai/app/api/routers/models.py index ca83b44bf3..97c0265c50 100644 --- a/invokeai/app/api/routers/models.py +++ b/invokeai/app/api/routers/models.py @@ -1,12 +1,9 @@ # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and 2023 Kent Keirsey (https://github.com/hipsterusername) -import shutil -import asyncio -from typing import Annotated, Any, List, Literal, Optional, Union +from typing import Annotated, Literal, Optional, Union from fastapi.routing import APIRouter, HTTPException from pydantic import BaseModel, Field, parse_obj_as -from pathlib import Path from ..dependencies import ApiDependencies models_router = APIRouter(prefix="/v1/models", tags=["models"]) @@ -19,6 +16,15 @@ class VaeRepo(BaseModel): class ModelInfo(BaseModel): description: Optional[str] = Field(description="A description of the model") + model_name: str = Field(description="The name of the model") + model_type: str = Field(description="The type of the model") + +class DiffusersModelInfo(ModelInfo): + format: Literal['folder'] = 'folder' + + vae: Optional[VaeRepo] = Field(description="The VAE repo to use for this model") + repo_id: Optional[str] = Field(description="The repo ID to use for this model") + path: Optional[str] = Field(description="The path to the model") class CkptModelInfo(ModelInfo): format: Literal['ckpt'] = 'ckpt' @@ -29,12 +35,8 @@ class CkptModelInfo(ModelInfo): width: Optional[int] = Field(description="The width of the model") height: Optional[int] = Field(description="The height of the model") -class DiffusersModelInfo(ModelInfo): - format: Literal['diffusers'] = 'diffusers' - - vae: Optional[VaeRepo] = Field(description="The VAE repo to use for this model") - repo_id: Optional[str] = Field(description="The repo ID to use for this model") - path: Optional[str] = Field(description="The path to the model") +class SafetensorsModelInfo(CkptModelInfo): + format: Literal['safetensors'] = 'safetensors' class CreateModelRequest(BaseModel): name: str = Field(description="The name of the model") @@ -56,7 +58,7 @@ class ConvertedModelResponse(BaseModel): info: DiffusersModelInfo = Field(description="The converted model info") class ModelsList(BaseModel): - models: dict[str, Annotated[Union[(CkptModelInfo,DiffusersModelInfo)], Field(discriminator="format")]] + models: dict[str, Annotated[Union[(DiffusersModelInfo,CkptModelInfo,SafetensorsModelInfo)], Field(discriminator="format")]] @models_router.get( @@ -121,7 +123,7 @@ async def delete_model(model_name: str) -> None: raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully") else: - logger.error(f"Model not found") + logger.error("Model not found") raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found") diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py index 11f1fd5c5f..74467d7ab3 100644 --- a/invokeai/app/invocations/model.py +++ b/invokeai/app/invocations/model.py @@ -60,14 +60,14 @@ class ModelLoaderInvocation(BaseInvocation): def invoke(self, context: InvocationContext) -> ModelLoaderOutput: # TODO: not found exceptions - if not context.services.model_manager.valid_model( + if not context.services.model_manager.model_exists( model_name=self.model_name, model_type=SDModelType.diffusers, ): raise Exception(f"Unkown model name: {self.model_name}!") """ - if not context.services.model_manager.valid_model( + if not context.services.model_manager.model_exists( model_name=self.model_name, model_type=SDModelType.diffusers, submodel=SDModelType.tokenizer, @@ -76,7 +76,7 @@ class ModelLoaderInvocation(BaseInvocation): f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted" ) - if not context.services.model_manager.valid_model( + if not context.services.model_manager.model_exists( model_name=self.model_name, model_type=SDModelType.diffusers, submodel=SDModelType.text_encoder, @@ -85,7 +85,7 @@ class ModelLoaderInvocation(BaseInvocation): f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted" ) - if not context.services.model_manager.valid_model( + if not context.services.model_manager.model_exists( model_name=self.model_name, model_type=SDModelType.diffusers, submodel=SDModelType.unet, diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index 3c50d2ba2d..fd24aca81b 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -59,35 +59,35 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def valid_model(self, model_name: str) -> bool: - """ - Given a model name, returns True if it is a valid - identifier. - """ + def model_exists( + self, + model_name: str, + model_type: SDModelType + ) -> bool: pass @abstractmethod - def default_model(self) -> Union[str,None]: + def default_model(self) -> Union[Tuple(str, SDModelType),None], """ - Returns the name of the default model, or None + Returns the name and typeof the default model, or None if none is defined. """ pass @abstractmethod - def set_default_model(self, model_name:str): + def set_default_model(self, model_name: str, model_type: SDModelType): """Sets the default model to the indicated name.""" pass @abstractmethod - def model_info(self, model_name: str)->dict: + def model_info(self, model_name: str, model_type: SDModelType)->dict: """ Given a model name returns a dict-like (OmegaConf) object describing it. """ pass @abstractmethod - def model_names(self)->list[str]: + def model_names(self)->List[Tuple(str, SDModelType)]: """ Returns a list of all the model names known. """ @@ -97,18 +97,25 @@ class ModelManagerServiceBase(ABC): def list_models(self)->dict: """ Return a dict of models in the format: - { model_name1: {'status': ('active'|'cached'|'not loaded'), - 'description': description, - 'format': ('ckpt'|'diffusers'|'vae'|'text_encoder'|'tokenizer'|'lora'...), + { model_key1: {'status': 'active'|'cached'|'not loaded', + 'model_name' : name, + 'model_type' : SDModelType, + 'description': description, + 'format': 'folder'|'safetensors'|'ckpt' }, - model_name2: { etc } + model_key2: { etc } """ pass @abstractmethod def add_model( - self, model_name: str, model_attributes: dict, clobber: bool = False)->None: + self, + model_name: str, + model_type: SDModelType, + model_attributes: dict, + clobber: bool = False + )->None: """ Update the named model with a dictionary of attributes. Will fail with an assertion error if the name already exists. Pass clobber=True to overwrite. @@ -121,7 +128,7 @@ class ModelManagerServiceBase(ABC): @abstractmethod def del_model(self, model_name: str, - model_type: SDModelType=SDModelType.diffusers, + model_type: SDModelType, delete_files: bool = False): """ Delete the named model from configuration. If delete_files is true, @@ -332,33 +339,37 @@ class ModelManagerService(ModelManagerServiceBase): return model_info - def valid_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers) -> bool: + def model_exists( + self, + model_name: str, + model_type: SDModelType + ) -> bool: """ Given a model name, returns True if it is a valid identifier. """ - return self.mgr.valid_model( + return self.mgr.model_exists( model_name, model_type) - def default_model(self) -> Union[str,None]: + def default_model(self) -> Union[Tuple(str, SDModelType),None], """ Returns the name of the default model, or None if none is defined. """ return self.mgr.default_model() - def set_default_model(self, model_name:str): + def set_default_model(self, model_name:str, model_type: SDModelType): """Sets the default model to the indicated name.""" self.mgr.set_default_model(model_name) - def model_info(self, model_name: str)->dict: + def model_info(self, model_name: str, model_type: SDModelType)->dict: """ Given a model name returns a dict-like (OmegaConf) object describing it. """ return self.mgr.model_info(model_name) - def model_names(self)->list[str]: + def model_names(self)->List[Tuple(str, SDModelType)]: """ Returns a list of all the model names known. """ @@ -367,16 +378,21 @@ class ModelManagerService(ModelManagerServiceBase): def list_models(self)->dict: """ Return a dict of models in the format: - { model_name1: {'status': ('active'|'cached'|'not loaded'), + { model_key: {'status': 'active'|'cached'|'not loaded', + 'model_name' : name, + 'model_type' : SDModelType, 'description': description, - 'format': ('ckpt'|'diffusers'|'vae'|'text_encoder'|'tokenizer'|'lora'...), + 'format': 'folder'|'safetensors'|'ckpt' }, - model_name2: { etc } """ return self.mgr.list_models() def add_model( - self, model_name: str, model_attributes: dict, clobber: bool = False)->None: + self, + model_name: str, + model_type: SDModelType, + model_attributes: dict, + clobber: bool = False)->None: """ Update the named model with a dictionary of attributes. Will fail with an assertion error if the name already exists. Pass clobber=True to overwrite. @@ -384,7 +400,7 @@ class ModelManagerService(ModelManagerServiceBase): with an assertion error if provided attributes are incorrect or the model name is missing. Call commit() to write changes to disk. """ - return self.mgr.add_model(model_name, model_attributes, dict, clobber) + return self.mgr.add_model(model_name, model_type, model_attributes, dict, clobber) def del_model(self, @@ -439,7 +455,7 @@ class ModelManagerService(ModelManagerServiceBase): Creates an entry for the indicated textual inversion embedding file. Call commit() to write out the configuration to models.yaml """ - self.mgr(path, model_name, description) + self.mgr.import_embedding(path, model_name, description) def heuristic_import( self, diff --git a/invokeai/backend/generate.py b/invokeai/backend/generate.py index 8cddc1496b..bdf9f25b6f 100644 --- a/invokeai/backend/generate.py +++ b/invokeai/backend/generate.py @@ -227,7 +227,7 @@ class Generate: # don't accept invalid models fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME model = model or fallback - if not self.model_manager.valid_model(model): + if not self.model_manager.model_exists(model): logger.warning( f'"{model}" is not a known model name; falling back to {fallback}.' ) @@ -877,7 +877,7 @@ class Generate: # the model cache does the loading and offloading cache = self.model_manager - if not cache.valid_model(model_name): + if not cache.model_exists(model_name): raise KeyError( f'** "{model_name}" is not a known model name. Cannot change.' ) diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index e32ba3b22a..301f29f2c9 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -9,6 +9,7 @@ return a SDModelInfo object that contains the following attributes: model into GPU VRAM and returns the model for use. See below for usage. * name -- symbolic name of the model + * type -- SDModelType of the model * hash -- unique hash for the model * location -- path or repo_id of the model * revision -- revision of the model if coming from a repo id, @@ -26,7 +27,7 @@ Typical usage: max_cache_size=8 ) # gigabytes - model_info = manager.get_model('stable-diffusion-1.5') + model_info = manager.get_model('stable-diffusion-1.5', SDModelType.diffusers) with model_info.context as my_model: my_model.latents_from_embeddings(...) @@ -54,15 +55,20 @@ MODELS.YAML The general format of a models.yaml section is: - name-of-model: - format: diffusers|ckpt|vae|text_encoder|tokenizer... + type-of-model/name-of-model: + format: folder|ckpt|safetensors repo_id: owner/repo path: /path/to/local/file/or/directory subfolder: subfolder-name -The format is one of {diffusers, ckpt, vae, text_encoder, tokenizer, -unet, scheduler, safety_checker, feature_extractor}, and correspond to -items in the SDModelType enum defined in model_cache.py +The type of model is given in the stanza key, and is one of +{diffusers, ckpt, vae, text_encoder, tokenizer, unet, scheduler, +safety_checker, feature_extractor, lora, textual_inversion}, and +correspond to items in the SDModelType enum defined in model_cache.py + +The format indicates whether the model is organized as a folder with +model subdirectories, or is contained in a single checkpoint or +safetensors file. One, but not both, of repo_id and path are provided. repo_id is the HuggingFace repository ID of the model, and path points to the file or @@ -74,13 +80,13 @@ the main model. These are usually named after the model type, such as This example summarizes the two ways of getting a non-diffuser model: - clip-test-1: - format: text_encoder + text_encoder/clip-test-1: + format: folder repo_id: openai/clip-vit-large-patch14 description: Returns standalone CLIPTextModel - clip-test-2: - format: text_encoder + text_encoder/clip-test-2: + format: folder repo_id: stabilityai/stable-diffusion-2 subfolder: text_encoder description: Returns the text_encoder in the subfolder of the diffusers model (just the encoder in RAM) @@ -101,12 +107,12 @@ You may wish to use the same name for a related family of models. To do this, disambiguate the stanza key with the model and and format separated by "/". Example: - clip-large/tokenizer: + tokenizer/clip-large: format: tokenizer repo_id: openai/clip-vit-large-patch14 description: Returns standalone tokenizer - clip-large/text_encoder: + text_encoder/clip-large: format: text_encoder repo_id: openai/clip-vit-large-patch14 description: Returns standalone text encoder @@ -128,31 +134,41 @@ from __future__ import annotations import os import re import textwrap +from contextlib import suppress from dataclasses import dataclass from enum import Enum, auto +from packaging import version from pathlib import Path from shutil import rmtree -from typing import Union, Callable, types -from contextlib import suppress +from typing import Callable, Optional, List, Tuple, Union, types import safetensors import safetensors.torch import torch -import invokeai.backend.util.logging as logger from huggingface_hub import scan_cache_dir from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig -from invokeai.backend.globals import Globals, global_cache_dir, global_resolve_path -from .model_cache import ModelCache, ModelLocker, SDModelType, ModelStatus, SilenceWarnings +import invokeai.backend.util.logging as logger +from invokeai.backend.globals import (Globals, global_cache_dir, + global_resolve_path) +from invokeai.backend.util import download_with_resume from ..util import CUDA_DEVICE +from .model_cache import (ModelCache, ModelLocker, ModelStatus, SDModelType, + SilenceWarnings) + +# We are only starting to number the config file with release 3. +# The config file version doesn't have to start at release version, but it will help +# reduce confusion. +CONFIG_FILE_VERSION='3.0.0' # wanted to use pydantic here, but Generator objects not supported @dataclass class SDModelInfo(): context: ModelLocker name: str + type: SDModelType hash: str location: Union[Path,str] precision: torch.dtype @@ -208,14 +224,17 @@ class ModelManager(object): type and precision are set up for a CUDA system running at half precision. """ if isinstance(config, DictConfig): - self.config = config self.config_path = None + self.config = config elif isinstance(config,(str,Path)): self.config_path = config self.config = OmegaConf.load(self.config_path) else: raise ValueError('config argument must be an OmegaConf object, a Path or a string') + # check config version number and update on disk/RAM if necessary + self._update_config_file_version() + self.cache = ModelCache( max_cache_size=max_cache_size, execution_device = device_type, @@ -226,8 +245,7 @@ class ModelManager(object): self.cache_keys = dict() self.logger = logger - # TODO: rename to smth like - is_model_exists - def valid_model( + def model_exists( self, model_name: str, model_type: SDModelType = SDModelType.diffusers, @@ -246,14 +264,14 @@ class ModelManager(object): model_type_str, model_name = model_key.split('/', 1) if model_type_str not in SDModelType.__members__: # TODO: - raise Exception(f"Unkown model type: {model_type_str}") + raise Exception(f"Unknown model type: {model_type_str}") return (model_name, SDModelType[model_type_str]) def get_model( self, model_name: str, - model_type: SDModelType=None, + model_type: SDModelType=SDModelType.diffusers, submodel: SDModelType=None, ) -> SDModelInfo: """Given a model named identified in models.yaml, return @@ -311,7 +329,7 @@ class ModelManager(object): if model_type == SDModelType.diffusers: # intercept stanzas that point to checkpoint weights and replace them # with the equivalent diffusers model - if 'weights' in mconfig: + if mconfig.format in ["ckpt", "diffusers"]: location = self.convert_ckpt_and_cache(mconfig) else: location = global_resolve_path(mconfig.get('path')) or mconfig.get('repo_id') @@ -350,6 +368,7 @@ class ModelManager(object): return SDModelInfo( context = model_context, name = model_name, + type = submodel or model_type, hash = hash, location = location, revision = revision, @@ -358,43 +377,50 @@ class ModelManager(object): _cache = self.cache ) - def default_model(self) -> Union[str,None]: + def default_model(self) -> Union[Tuple(str, SDModelType),None]: """ Returns the name of the default model, or None if none is defined. """ - for model_name in self.config: - if self.config[model_name].get("default"): - return model_name - return list(self.config.keys())[0] # first one + for model_name, model_type in self.model_names(): + model_key = self.create_key(model_name, model_type) + if self.config[model_key].get("default"): + return (model_name, model_type) + return self.model_names()[0][0] - def set_default_model(self, model_name: str) -> None: + def set_default_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers) -> None: """ Set the default model. The change will not take effect until you call model_manager.commit() """ - assert model_name in self.model_names(), f"unknown model '{model_name}'" + assert self.model_exists(model_name, model_type), f"unknown model '{model_name}'" config = self.config - for model in config: - config[model].pop("default", None) - config[model_name]["default"] = True + for model_name, model_type in self.model_names(): + key = self.create_key(model_name, model_type) + config[key].pop("default", None) + config[self.create_key(model_name, model_type)]["default"] = True - def model_info(self, model_name: str) -> dict: + def model_info( + self, + model_name: str, + model_type: SDModelType=SDModelType.diffusers + ) -> dict: """ Given a model name returns the OmegaConf (dict-like) object describing it. """ - if model_name not in self.config: + if not self.exists(model_name, model_type): return None - return self.config[model_name] + return self.config[self.create_key(model_name,model_type)] - def model_names(self) -> list[str]: + def model_names(self) -> List[Tuple(str, SDModelType)]: """ - Return a list consisting of all the names of models defined in models.yaml + Return a list of (str, SDModelType) corresponding to all models + known to the configuration. """ - return list(self.config.keys()) + return [(self.parse_key(x)) for x in self.config.keys() if isinstance(self.config[x],DictConfig)] - def is_legacy(self, model_name: str) -> bool: + def is_legacy(self, model_name: str, model_type: SDModelType.diffusers) -> bool: """ Return true if this is a legacy (.ckpt) model """ @@ -402,7 +428,7 @@ class ModelManager(object): # there are no legacy ckpts! if Globals.ckpt_convert: return False - info = self.model_info(model_name) + info = self.model_info(model_name, model_type) if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")): return True return False @@ -422,15 +448,17 @@ class ModelManager(object): # don't include VAEs in listing (legacy style) if "config" in stanza and "/VAE/" in stanza["config"]: continue + if model_key=='config_file_version': + continue model_name, model_type = self.parse_key(model_key) - models[model_name] = dict() + models[model_key] = dict() # TODO: return all models in future if model_type != SDModelType.diffusers: continue - model_format = "ckpt" if "weights" in stanza else "diffusers" + model_format = stanza.get('format') # Common Attribs status = self.cache.status( @@ -439,26 +467,25 @@ class ModelManager(object): subfolder=stanza.get('subfolder') ) description = stanza.get("description", None) - models[model_name].update( - description=description, - type=model_type, + models[model_key].update( + model_name=model_name, + model_type=model_type.name, format=model_format, + description=description, status=status.value ) # Checkpoint Config Parse - if model_format == "ckpt": - models[model_name].update( + if model_format in ["ckpt","safetensors"]: + models[model_key].update( config = str(stanza.get("config", None)), weights = str(stanza.get("weights", None)), vae = str(stanza.get("vae", None)), - width = str(stanza.get("width", 512)), - height = str(stanza.get("height", 512)), ) # Diffusers Config Parse - elif model_format == "diffusers": + elif model_format == "folder": if vae := stanza.get("vae", None): if isinstance(vae, DictConfig): vae = dict( @@ -467,7 +494,7 @@ class ModelManager(object): subfolder = str(vae.get("subfolder", None)), ) - models[model_name].update( + models[model_key].update( vae = vae, repo_id = str(stanza.get("repo_id", None)), path = str(stanza.get("path", None)), @@ -479,12 +506,9 @@ class ModelManager(object): """ Print a table of models, their descriptions, and load status """ - models = self.list_models() - for name in models: - if models[name]["type"] == "vae": - continue - line = f'{name:25s} {models[name]["status"]:>15s} {models[name]["type"]:10s} {models[name]["description"]}' - if models[name]["status"] == "active": + for model_key, model_info in self.list_models().items(): + line = f'{model_info["model_name"]:25s} {model_info["status"]:>15s} {model_info["model_type"]:10s} {model_info["description"]}' + if model_info["status"] in ["in gpu","locked in gpu"]: line = f"\033[1m{line}\033[0m" print(line) @@ -511,9 +535,9 @@ class ModelManager(object): # self.stack.remove(model_name) if delete_files: - repo_id = conf.get("repo_id", None) - path = self._abs_path(conf.get("path", None)) - weights = self._abs_path(conf.get("weights", None)) + repo_id = model_cfg.get("repo_id", None) + path = self._abs_path(model_cfg.get("path", None)) + weights = self._abs_path(model_cfg.get("weights", None)) if "weights" in model_cfg: weights = self._abs_path(model_cfg["weights"]) self.logger.info(f"Deleting file {weights}") @@ -558,7 +582,7 @@ class ModelManager(object): ), 'model must have either the "path" or "repo_id" fields defined' elif model_format == "ckpt": - for field in ("description", "weights", "height", "width", "config"): + for field in ("description", "weights", "config"): assert field in model_attributes, f"required field {field} is missing" else: @@ -579,11 +603,6 @@ class ModelManager(object): self.cache.uncache_model(self.cache_keys[model_key]) del self.cache_keys[model_key] - - - - - def import_diffuser_model( self, repo_or_path: Union[str, Path], @@ -604,7 +623,6 @@ class ModelManager(object): models.yaml file. """ model_name = model_name or Path(repo_or_path).stem - model_key = f'{model_name}/diffusers' model_description = description or f"Imported diffusers model {model_name}" new_config = dict( description=model_description, @@ -616,10 +634,10 @@ class ModelManager(object): else: new_config.update(repo_id=repo_or_path) - self.add_model(model_key, new_config, True) + self.add_model(model_name, SDModelType.diffusers, new_config, True) if commit_to_conf: self.commit(commit_to_conf) - return model_key + return self.create_key(model_name, SDModelType.diffusers) def import_lora( self, @@ -635,7 +653,8 @@ class ModelManager(object): model_name = model_name or path.stem model_description = description or f"LoRA model {model_name}" self.add_model( - f'{model_name}/{SDModelType.lora.name}', + model_name, + SDModelType.lora, dict( format="lora", weights=str(path), @@ -663,7 +682,8 @@ class ModelManager(object): model_name = model_name or path.stem model_description = description or f"Textual embedding model {model_name}" self.add_model( - f'{model_name}/{SDModelType.textual_inversion.name}', + model_name, + SDModelType.textual_inversion, dict( format="textual_inversion", weights=str(weights), @@ -1025,9 +1045,14 @@ class ModelManager(object): description=model_description, format="diffusers", ) - if model_name in self.config: - self.del_model(model_name) - self.add_model(model_name, new_config, True) + if self.model_exists(model_name, SDModelType.diffusers): + self.del_model(model_name, SDModelType.diffusers) + self.add_model( + model_name, + SDModelType.diffusers, + new_config, + True + ) if commit_to_conf: self.commit(commit_to_conf) self.logger.debug("Conversion succeeded") @@ -1081,11 +1106,6 @@ class ModelManager(object): """\ # This file describes the alternative machine learning models # available to InvokeAI script. - # - # To add a new model, follow the examples below. Each - # model requires a model config file, a weights file, - # and the width and height of the images it - # was trained on. """ ) @@ -1130,3 +1150,53 @@ class ModelManager(object): source = os.path.join(Globals.root, source) resolved_path = Path(source) return resolved_path + + def _update_config_file_version(self): + """ + This gets called at object init time and will update + from older versions of the config file to new ones + as necessary. + """ + current_version = self.config.get("config_file_version","1.0.0") + if version.parse(current_version) < version.parse(CONFIG_FILE_VERSION): + self.logger.info(f'models.yaml version {current_version} detected. Updating to {CONFIG_FILE_VERSION}') + + new_config = OmegaConf.create() + new_config["config_file_version"] = CONFIG_FILE_VERSION + + for model_key in self.config: + + old_stanza = self.config[model_key] + + # ignore old and ugly way of associating a legacy + # vae with a legacy checkpont model + if old_stanza.get("config") and '/VAE/' in old_stanza.get("config"): + continue + + # bare keys are updated to be prefixed with 'diffusers/' + if '/' not in model_key: + new_key = f'diffusers/{model_key}' + else: + new_key = model_key + + if old_stanza.get('format')=='diffusers': + model_format = 'folder' + elif old_stanza.get('weights') and Path(old_stanza.get('weights')).suffix == '.ckpt': + model_format = 'ckpt' + elif old_stanza.get('weights') and Path(old_stanza.get('weights')).suffix == '.safetensors': + model_format = 'safetensors' + + # copy fields over manually rather than doing a copy() or deepcopy() + # in order to avoid bringing in unwanted fields. + new_config[new_key] = dict( + description = old_stanza.get('description'), + format = model_format, + ) + for field in ["repo_id", "path", "weights", "config", "vae"]: + if field_value := old_stanza.get(field): + new_config[new_key].update({field: field_value}) + + self.config = new_config + if self.config_path: + self.commit() + diff --git a/invokeai/frontend/CLI/readline.py b/invokeai/frontend/CLI/readline.py index 5a877ae810..228ab88b57 100644 --- a/invokeai/frontend/CLI/readline.py +++ b/invokeai/frontend/CLI/readline.py @@ -345,13 +345,14 @@ class Completer(object): partial = text matches = list() for s in self.models: + name = self.models[s]["model_name"] format = self.models[s]["format"] if format == "vae": continue if ckpt_only and format != "ckpt": continue - if s.startswith(partial): - matches.append(switch + s) + if name.startswith(partial): + matches.append(switch + name) matches.sort() return matches From 5e8e3cf4640c4e358533f930a3564a16a423fe34 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 14:55:59 -0400 Subject: [PATCH 030/225] correct typos in model_manager_service --- invokeai/app/services/model_manager_service.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index fd24aca81b..8384a97435 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -3,7 +3,7 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Union, Callable, types +from typing import Union, Callable, List, Tuple, types from dataclasses import dataclass from invokeai.backend.model_management.model_manager import ( @@ -67,7 +67,7 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def default_model(self) -> Union[Tuple(str, SDModelType),None], + def default_model(self) -> Union[Tuple(str, SDModelType),None]: """ Returns the name and typeof the default model, or None if none is defined. @@ -352,7 +352,7 @@ class ModelManagerService(ModelManagerServiceBase): model_name, model_type) - def default_model(self) -> Union[Tuple(str, SDModelType),None], + def default_model(self) -> Union[Tuple(str, SDModelType),None]: """ Returns the name of the default model, or None if none is defined. From b23c9f1da5f19167669ac044179884092187253a Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 14:59:21 -0400 Subject: [PATCH 031/225] get Tuple type hint syntax right --- invokeai/app/services/model_manager_service.py | 8 ++++---- invokeai/backend/model_management/model_manager.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index 8384a97435..5c57c5462c 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -67,7 +67,7 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def default_model(self) -> Union[Tuple(str, SDModelType),None]: + def default_model(self) -> Union[Tuple[str, SDModelType],None]: """ Returns the name and typeof the default model, or None if none is defined. @@ -87,7 +87,7 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def model_names(self)->List[Tuple(str, SDModelType)]: + def model_names(self)->List[Tuple[str, SDModelType]]: """ Returns a list of all the model names known. """ @@ -352,7 +352,7 @@ class ModelManagerService(ModelManagerServiceBase): model_name, model_type) - def default_model(self) -> Union[Tuple(str, SDModelType),None]: + def default_model(self) -> Union[Tuple[str, SDModelType],None]: """ Returns the name of the default model, or None if none is defined. @@ -369,7 +369,7 @@ class ModelManagerService(ModelManagerServiceBase): """ return self.mgr.model_info(model_name) - def model_names(self)->List[Tuple(str, SDModelType)]: + def model_names(self)->List[Tuple[str, SDModelType]]: """ Returns a list of all the model names known. """ diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 301f29f2c9..f1a6dc18dc 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -377,7 +377,7 @@ class ModelManager(object): _cache = self.cache ) - def default_model(self) -> Union[Tuple(str, SDModelType),None]: + def default_model(self) -> Union[Tuple[str, SDModelType],None]: """ Returns the name of the default model, or None if none is defined. @@ -413,7 +413,7 @@ class ModelManager(object): return None return self.config[self.create_key(model_name,model_type)] - def model_names(self) -> List[Tuple(str, SDModelType)]: + def model_names(self) -> List[Tuple[str, SDModelType]]: """ Return a list of (str, SDModelType) corresponding to all models known to the configuration. From d8b1f29066d1b93c0aaca093a67ac9bbec0ccf05 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 16:29:18 -0400 Subject: [PATCH 032/225] proxy SDModelInfo so that it can be used directly as context --- invokeai/backend/model_management/model_cache.py | 2 +- invokeai/backend/model_management/model_manager.py | 6 ++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 08aa2f3044..558de0f28c 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -68,7 +68,7 @@ class SDModelType(Enum): # distinguish them by class lora=LoraType textual_inversion=TIType - + class ModelStatus(Enum): unknown='unknown' not_loaded='not loaded' diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index f1a6dc18dc..4fbc80703a 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -176,6 +176,12 @@ class SDModelInfo(): revision: str = None _cache: ModelCache = None + def __enter__(self): + return self.context.__enter__() + + def __exit__(self,*args, **kwargs): + self.context.__exit__(*args, **kwargs) + @property def status(self)->ModelStatus: '''Return load status of this model as a model_cache.ModelStatus enum''' From 2204e4759669d100a3f13e0069241bb7f35570ce Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 16:54:47 -0400 Subject: [PATCH 033/225] allow submodels to be fetched independent of parent pipeline --- invokeai/backend/model_management/model_manager.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 4fbc80703a..87fb938338 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -357,6 +357,13 @@ class ModelManager(object): vae_id = mconfig.vae.repo_id vae = (SDModelType.vae, vae_id) + # optimization - don't load whole model if the user + # is asking for just a piece of it + if model_type == SDModelType.diffusers and submodel and not subfolder: + model_type = submodel + subfolder = submodel.name + submodel = None + model_context = self.cache.get_model( location, model_type = model_type, From 039fa73269f2decb55e75347e3b19dad534de9ac Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Sun, 14 May 2023 03:06:26 +0300 Subject: [PATCH 034/225] Change SDModelType enum to string, fixes(model unload negative locks count, scheduler load error, saftensors convert, wrong logic in del_model, wrong parse metadata in web) --- invokeai/app/invocations/compel.py | 13 +- invokeai/app/invocations/latent.py | 63 ++-- invokeai/app/invocations/model.py | 38 +-- invokeai/app/services/events.py | 4 +- .../app/services/model_manager_service.py | 228 +++++++------- .../backend/model_management/model_cache.py | 277 ++++++++++-------- .../backend/model_management/model_manager.py | 126 ++++---- .../web/src/common/util/parseMetadata.ts | 2 +- 8 files changed, 388 insertions(+), 363 deletions(-) diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index 584e14ea0f..633b53accd 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -59,19 +59,14 @@ class CompelInvocation(BaseInvocation): def invoke(self, context: InvocationContext) -> CompelOutput: - # TODO: load without model text_encoder_info = context.services.model_manager.get_model( - model_name=self.clip.text_encoder.model_name, - model_type=SDModelType[self.clip.text_encoder.model_type], - submodel=SDModelType[self.clip.text_encoder.submodel], + **self.clip.text_encoder.dict(), ) tokenizer_info = context.services.model_manager.get_model( - model_name=self.clip.tokenizer.model_name, - model_type=SDModelType[self.clip.tokenizer.model_type], - submodel=SDModelType[self.clip.tokenizer.submodel], + **self.clip.tokenizer.dict(), ) - with text_encoder_info.context as text_encoder,\ - tokenizer_info.context as tokenizer: + with text_encoder_info as text_encoder,\ + tokenizer_info as tokenizer: # TODO: global? input? #use_full_precision = precision == "float32" or precision == "autocast" diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 9ca1c81a3d..cedadde4d0 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -79,12 +79,8 @@ def get_scheduler( scheduler_info: ModelInfo, scheduler_name: str, ) -> Scheduler: - orig_scheduler_info = context.services.model_manager.get_model( - model_name=scheduler_info.model_name, - model_type=SDModelType[scheduler_info.model_type], - submodel=SDModelType[scheduler_info.submodel], - ) - with orig_scheduler_info.context as orig_scheduler: + orig_scheduler_info = context.services.model_manager.get_model(**scheduler_info.dict()) + with orig_scheduler_info as orig_scheduler: scheduler_config = orig_scheduler.config scheduler_class = scheduler_map.get(scheduler_name,'ddim') @@ -243,14 +239,8 @@ class TextToLatentsInvocation(BaseInvocation): def step_callback(state: PipelineIntermediateState): self.dispatch_progress(context, source_node_id, state) - #unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) - unet_info = context.services.model_manager.get_model( - model_name=self.unet.unet.model_name, - model_type=SDModelType[self.unet.unet.model_type], - submodel=SDModelType[self.unet.unet.submodel] if self.unet.unet.submodel else None, - ) - - with unet_info.context as unet: + unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) + with unet_info as unet: scheduler = get_scheduler( context=context, scheduler_info=self.unet.scheduler, @@ -309,12 +299,10 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation): #unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) unet_info = context.services.model_manager.get_model( - model_name=self.unet.unet.model_name, - model_type=SDModelType[self.unet.unet.model_type], - submodel=SDModelType[self.unet.unet.submodel] if self.unet.unet.submodel else None, + **self.unet.unet.dict(), ) - with unet_info.context as unet: + with unet_info as unet: scheduler = get_scheduler( context=context, scheduler_info=self.unet.scheduler, @@ -379,18 +367,18 @@ class LatentsToImageInvocation(BaseInvocation): #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict()) vae_info = context.services.model_manager.get_model( - model_name=self.vae.vae.model_name, - model_type=SDModelType[self.vae.vae.model_type], - submodel=SDModelType[self.vae.vae.submodel] if self.vae.vae.submodel else None, + **self.vae.vae.dict(), ) - with vae_info.context as vae: - # TODO: check if it works + with vae_info as vae: if self.tiled: vae.enable_tiling() else: vae.disable_tiling() + # clear memory as vae decode can request a lot + torch.cuda.empty_cache() + with torch.inference_mode(): # copied from diffusers pipeline latents = latents / vae.config.scaling_factor @@ -509,36 +497,29 @@ class ImageToLatentsInvocation(BaseInvocation): #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict()) vae_info = context.services.model_manager.get_model( - model_name=self.vae.vae.model_name, - model_type=SDModelType[self.vae.vae.model_type], - submodel=SDModelType[self.vae.vae.submodel] if self.vae.vae.submodel else None, + **self.vae.vae.dict(), ) image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB")) if image_tensor.dim() == 3: image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w") - with vae_info.context as vae: - # TODO: check if it works + with vae_info as vae: if self.tiled: vae.enable_tiling() else: vae.disable_tiling() - latents = self.non_noised_latents_from_image(vae, image_tensor) + # non_noised_latents_from_image + image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype) + with torch.inference_mode(): + image_tensor_dist = vae.encode(image_tensor).latent_dist + latents = image_tensor_dist.sample().to( + dtype=vae.dtype + ) # FIXME: uses torch.randn. make reproducible! + + latents = 0.18215 * latents name = f"{context.graph_execution_state_id}__{self.id}" context.services.latents.set(name, latents) return LatentsOutput(latents=LatentsField(latents_name=name)) - - - def non_noised_latents_from_image(self, vae, init_image): - init_image = init_image.to(device=vae.device, dtype=vae.dtype) - with torch.inference_mode(): - init_latent_dist = vae.encode(init_image).latent_dist - init_latents = init_latent_dist.sample().to( - dtype=vae.dtype - ) # FIXME: uses torch.randn. make reproducible! - - init_latents = 0.18215 * init_latents - return init_latents \ No newline at end of file diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py index 74467d7ab3..c473451c6c 100644 --- a/invokeai/app/invocations/model.py +++ b/invokeai/app/invocations/model.py @@ -8,8 +8,8 @@ from ...backend.model_management import SDModelType class ModelInfo(BaseModel): model_name: str = Field(description="Info to load unet submodel") - model_type: str = Field(description="Info to load unet submodel") - submodel: Optional[str] = Field(description="Info to load unet submodel") + model_type: SDModelType = Field(description="Info to load unet submodel") + submodel: Optional[SDModelType] = Field(description="Info to load unet submodel") class UNetField(BaseModel): unet: ModelInfo = Field(description="Info to load unet submodel") @@ -62,15 +62,15 @@ class ModelLoaderInvocation(BaseInvocation): # TODO: not found exceptions if not context.services.model_manager.model_exists( model_name=self.model_name, - model_type=SDModelType.diffusers, + model_type=SDModelType.Diffusers, ): raise Exception(f"Unkown model name: {self.model_name}!") """ if not context.services.model_manager.model_exists( model_name=self.model_name, - model_type=SDModelType.diffusers, - submodel=SDModelType.tokenizer, + model_type=SDModelType.Diffusers, + submodel=SDModelType.Tokenizer, ): raise Exception( f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted" @@ -78,8 +78,8 @@ class ModelLoaderInvocation(BaseInvocation): if not context.services.model_manager.model_exists( model_name=self.model_name, - model_type=SDModelType.diffusers, - submodel=SDModelType.text_encoder, + model_type=SDModelType.Diffusers, + submodel=SDModelType.TextEncoder, ): raise Exception( f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted" @@ -87,8 +87,8 @@ class ModelLoaderInvocation(BaseInvocation): if not context.services.model_manager.model_exists( model_name=self.model_name, - model_type=SDModelType.diffusers, - submodel=SDModelType.unet, + model_type=SDModelType.Diffusers, + submodel=SDModelType.UNet, ): raise Exception( f"Failed to find unet submodel from {self.model_name}! Check if model corrupted" @@ -100,32 +100,32 @@ class ModelLoaderInvocation(BaseInvocation): unet=UNetField( unet=ModelInfo( model_name=self.model_name, - model_type=SDModelType.diffusers.name, - submodel=SDModelType.unet.name, + model_type=SDModelType.Diffusers, + submodel=SDModelType.UNet, ), scheduler=ModelInfo( model_name=self.model_name, - model_type=SDModelType.diffusers.name, - submodel=SDModelType.scheduler.name, + model_type=SDModelType.Diffusers, + submodel=SDModelType.Scheduler, ), ), clip=ClipField( tokenizer=ModelInfo( model_name=self.model_name, - model_type=SDModelType.diffusers.name, - submodel=SDModelType.tokenizer.name, + model_type=SDModelType.Diffusers, + submodel=SDModelType.Tokenizer, ), text_encoder=ModelInfo( model_name=self.model_name, - model_type=SDModelType.diffusers.name, - submodel=SDModelType.text_encoder.name, + model_type=SDModelType.Diffusers, + submodel=SDModelType.TextEncoder, ), ), vae=VaeField( vae=ModelInfo( model_name=self.model_name, - model_type=SDModelType.diffusers.name, - submodel=SDModelType.vae.name, + model_type=SDModelType.Diffusers, + submodel=SDModelType.Vae, ), ) ) diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py index d3f61a70b8..9e2db4bfe8 100644 --- a/invokeai/app/services/events.py +++ b/invokeai/app/services/events.py @@ -120,7 +120,7 @@ class EventServiceBase: node=node, source_node_id=source_node_id, model_name=model_name, - model_type=model_type.name, + model_type=model_type, submodel=submodel, ), ) @@ -143,7 +143,7 @@ class EventServiceBase: node=node, source_node_id=source_node_id, model_name=model_name, - model_type=model_type.name, + model_type=model_type, submodel=submodel, model_info=model_info, ), diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index 5c57c5462c..0d140511e0 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -1,21 +1,25 @@ # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team +from __future__ import annotations +import torch from abc import ABC, abstractmethod from pathlib import Path -from typing import Union, Callable, List, Tuple, types +from typing import Union, Callable, List, Tuple, types, TYPE_CHECKING from dataclasses import dataclass from invokeai.backend.model_management.model_manager import ( ModelManager, SDModelType, SDModelInfo, - torch, ) from invokeai.app.models.exceptions import CanceledException -from ...backend import Args,Globals # this must go when pr 3340 merged +from ...backend import Args, Globals # this must go when pr 3340 merged from ...backend.util import choose_precision, choose_torch_device +if TYPE_CHECKING: + from ..invocations.baseinvocation import BaseInvocation, InvocationContext + @dataclass class LastUsedModel: model_name: str=None @@ -28,9 +32,9 @@ class ModelManagerServiceBase(ABC): @abstractmethod def __init__( - self, - config: Args, - logger: types.ModuleType + self, + config: Args, + logger: types.ModuleType, ): """ Initialize with the path to the models.yaml config file. @@ -41,13 +45,14 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def get_model(self, - model_name: str, - model_type: SDModelType=SDModelType.diffusers, - submodel: SDModelType=None, - node=None, # circular dependency issues, so untyped at moment - context=None, - )->SDModelInfo: + def get_model( + self, + model_name: str, + model_type: SDModelType = SDModelType.Diffusers, + submodel: Optional[SDModelType] = None, + node: Optional[BaseInvocation] = None, + context: Optional[InvocationContext] = None, + ) -> SDModelInfo: """Retrieve the indicated model with name and type. submodel can be used to get a part (such as the vae) of a diffusers pipeline.""" @@ -60,14 +65,14 @@ class ModelManagerServiceBase(ABC): @abstractmethod def model_exists( - self, - model_name: str, - model_type: SDModelType + self, + model_name: str, + model_type: SDModelType, ) -> bool: pass @abstractmethod - def default_model(self) -> Union[Tuple[str, SDModelType],None]: + def default_model(self) -> Optional[Tuple[str, SDModelType]]: """ Returns the name and typeof the default model, or None if none is defined. @@ -80,21 +85,21 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def model_info(self, model_name: str, model_type: SDModelType)->dict: + def model_info(self, model_name: str, model_type: SDModelType) -> dict: """ Given a model name returns a dict-like (OmegaConf) object describing it. """ pass @abstractmethod - def model_names(self)->List[Tuple[str, SDModelType]]: + def model_names(self) -> List[Tuple[str, SDModelType]]: """ Returns a list of all the model names known. """ pass @abstractmethod - def list_models(self)->dict: + def list_models(self) -> dict: """ Return a dict of models in the format: { model_key1: {'status': 'active'|'cached'|'not loaded', @@ -110,12 +115,12 @@ class ModelManagerServiceBase(ABC): @abstractmethod def add_model( - self, - model_name: str, - model_type: SDModelType, - model_attributes: dict, - clobber: bool = False - )->None: + self, + model_name: str, + model_type: SDModelType, + model_attributes: dict, + clobber: bool = False + ) -> None: """ Update the named model with a dictionary of attributes. Will fail with an assertion error if the name already exists. Pass clobber=True to overwrite. @@ -126,10 +131,12 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def del_model(self, - model_name: str, - model_type: SDModelType, - delete_files: bool = False): + def del_model( + self, + model_name: str, + model_type: SDModelType, + delete_files: bool = False, + ): """ Delete the named model from configuration. If delete_files is true, then the underlying weight file or diffusers directory will be deleted @@ -140,9 +147,9 @@ class ModelManagerServiceBase(ABC): @abstractmethod def import_diffuser_model( repo_or_path: Union[str, Path], - model_name: str = None, - description: str = None, - vae: dict = None, + model_name: Optional[str] = None, + description: Optional[str] = None, + vae: Optional[dict] = None, ) -> bool: """ Install the indicated diffuser model and returns True if successful. @@ -157,10 +164,10 @@ class ModelManagerServiceBase(ABC): @abstractmethod def import_lora( - self, - path: Path, - model_name: str=None, - description: str=None, + self, + path: Path, + model_name: Optional[str] = None, + description: Optional[str] = None, ): """ Creates an entry for the indicated lora file. Call @@ -170,10 +177,10 @@ class ModelManagerServiceBase(ABC): @abstractmethod def import_embedding( - self, - path: Path, - model_name: str=None, - description: str=None, + self, + path: Path, + model_name: str=None, + description: str=None, ): """ Creates an entry for the indicated textual inversion embedding file. @@ -223,7 +230,7 @@ class ModelManagerServiceBase(ABC): pass @abstractmethod - def commit(self, conf_file: Path=None) -> None: + def commit(self, conf_file: Path = None) -> None: """ Write current configuration out to the indicated file. If no conf_file is provided, then replaces the @@ -235,10 +242,10 @@ class ModelManagerServiceBase(ABC): class ModelManagerService(ModelManagerServiceBase): """Responsible for managing models on disk and in memory""" def __init__( - self, - config: Args, - logger: types.ModuleType - ): + self, + config: Args, + logger: types.ModuleType, + ): """ Initialize with the path to the models.yaml config file. Optional parameters are the torch device type, precision, max_models, @@ -255,7 +262,7 @@ class ModelManagerService(ModelManagerServiceBase): logger.debug(f'config file={config_file}') device = torch.device(choose_torch_device()) - if config.precision=="auto": + if config.precision == "auto": precision = choose_precision(device) dtype = torch.float32 if precision=='float32' \ else torch.float16 @@ -272,22 +279,24 @@ class ModelManagerService(ModelManagerServiceBase): sequential_offload = config.sequential_guidance - self.mgr = ModelManager(config=config_file, - device_type=device, - precision=dtype, - max_cache_size=max_cache_size, - sequential_offload=sequential_offload, - logger=logger - ) + self.mgr = ModelManager( + config=config_file, + device_type=device, + precision=dtype, + max_cache_size=max_cache_size, + sequential_offload=sequential_offload, + logger=logger, + ) logger.info('Model manager service initialized') - def get_model(self, - model_name: str, - model_type: SDModelType=SDModelType.diffusers, - submodel: SDModelType=None, - node=None, - context=None, - )->SDModelInfo: + def get_model( + self, + model_name: str, + model_type: SDModelType = SDModelType.Diffusers, + submodel: Optional[SDModelType] = None, + node: Optional[BaseInvocation] = None, + context: Optional[InvocationContext] = None, + ) -> SDModelInfo: """ Retrieve the indicated model. submodel can be used to get a part (such as the vae) of a diffusers mode. @@ -340,9 +349,9 @@ class ModelManagerService(ModelManagerServiceBase): return model_info def model_exists( - self, - model_name: str, - model_type: SDModelType + self, + model_name: str, + model_type: SDModelType, ) -> bool: """ Given a model name, returns True if it is a valid @@ -350,32 +359,33 @@ class ModelManagerService(ModelManagerServiceBase): """ return self.mgr.model_exists( model_name, - model_type) + model_type, + ) - def default_model(self) -> Union[Tuple[str, SDModelType],None]: + def default_model(self) -> Optional[Tuple[str, SDModelType]]: """ Returns the name of the default model, or None if none is defined. """ return self.mgr.default_model() - def set_default_model(self, model_name:str, model_type: SDModelType): + def set_default_model(self, model_name: str, model_type: SDModelType): """Sets the default model to the indicated name.""" self.mgr.set_default_model(model_name) - def model_info(self, model_name: str, model_type: SDModelType)->dict: + def model_info(self, model_name: str, model_type: SDModelType) -> dict: """ Given a model name returns a dict-like (OmegaConf) object describing it. """ return self.mgr.model_info(model_name) - def model_names(self)->List[Tuple[str, SDModelType]]: + def model_names(self) -> List[Tuple[str, SDModelType]]: """ Returns a list of all the model names known. """ return self.mgr.model_names() - def list_models(self)->dict: + def list_models(self) -> dict: """ Return a dict of models in the format: { model_key: {'status': 'active'|'cached'|'not loaded', @@ -388,11 +398,12 @@ class ModelManagerService(ModelManagerServiceBase): return self.mgr.list_models() def add_model( - self, - model_name: str, - model_type: SDModelType, - model_attributes: dict, - clobber: bool = False)->None: + self, + model_name: str, + model_type: SDModelType, + model_attributes: dict, + clobber: bool = False, + )->None: """ Update the named model with a dictionary of attributes. Will fail with an assertion error if the name already exists. Pass clobber=True to overwrite. @@ -400,14 +411,15 @@ class ModelManagerService(ModelManagerServiceBase): with an assertion error if provided attributes are incorrect or the model name is missing. Call commit() to write changes to disk. """ - return self.mgr.add_model(model_name, model_type, model_attributes, dict, clobber) + return self.mgr.add_model(model_name, model_type, model_attributes, clobber) - def del_model(self, - model_name: str, - model_type: SDModelType=SDModelType.diffusers, - delete_files: bool = False - ): + def del_model( + self, + model_name: str, + model_type: SDModelType = SDModelType.Diffusers, + delete_files: bool = False, + ): """ Delete the named model from configuration. If delete_files is true, then the underlying weight file or diffusers directory will be deleted @@ -416,11 +428,11 @@ class ModelManagerService(ModelManagerServiceBase): self.mgr.del_model(model_name, model_type, delete_files) def import_diffuser_model( - self, - repo_or_path: Union[str, Path], - model_name: str = None, - description: str = None, - vae: dict = None, + self, + repo_or_path: Union[str, Path], + model_name: Optional[str] = None, + description: Optional[str] = None, + vae: Optional[dict] = None, ) -> bool: """ Install the indicated diffuser model and returns True if successful. @@ -431,13 +443,13 @@ class ModelManagerService(ModelManagerServiceBase): You can optionally provide a model name and/or description. If not provided, then these will be derived from the repo name. Call commit() to write to disk. """ - return self.mgr.import_diffuser_model(repo_or_path, model_name, description, vae) + return self.mgr.import_diffuser_model(repo_or_path, model_name, description, vae) def import_lora( - self, - path: Path, - model_name: str=None, - description: str=None, + self, + path: Path, + model_name: Optional[str] = None, + description: Optional[str] = None, ): """ Creates an entry for the indicated lora file. Call @@ -446,10 +458,10 @@ class ModelManagerService(ModelManagerServiceBase): self.mgr.import_lora(path, model_name, description) def import_embedding( - self, - path: Path, - model_name: str=None, - description: str=None, + self, + path: Path, + model_name: Optional[str] = None, + description: Optional[str] = None, ): """ Creates an entry for the indicated textual inversion embedding file. @@ -462,9 +474,9 @@ class ModelManagerService(ModelManagerServiceBase): path_url_or_repo: str, model_name: str = None, description: str = None, - model_config_file: Path = None, - commit_to_conf: Path = None, - config_file_callback: Callable[[Path], Path] = None, + model_config_file: Optional[Path] = None, + commit_to_conf: Optional[Path] = None, + config_file_callback: Optional[Callable[[Path], Path]] = None, ) -> str: """Accept a string which could be: - a HF diffusers repo_id @@ -505,7 +517,7 @@ class ModelManagerService(ModelManagerServiceBase): ) - def commit(self, conf_file: Path=None): + def commit(self, conf_file: Optional[Path]=None): """ Write current configuration out to the indicated file. If no conf_file is provided, then replaces the @@ -514,16 +526,16 @@ class ModelManagerService(ModelManagerServiceBase): return self.mgr.commit(conf_file) def _emit_load_event( - self, - node, - context, - model_name: str, - model_type: SDModelType, - submodel: SDModelType, - model_info: SDModelInfo=None, + self, + node, + context, + model_name: str, + model_type: SDModelType, + submodel: SDModelType, + model_info: Optional[SDModelInfo] = None, ): if context.services.queue.is_canceled(context.graph_execution_state_id): - raise CanceledException + raise CanceledException() graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) source_node_id = graph_execution_state.prepared_source_mapping[node.id] if context: @@ -536,7 +548,7 @@ class ModelManagerService(ModelManagerServiceBase): submodel=submodel, ) else: - context.services.events.emit_model_load_completed ( + context.services.events.emit_model_load_completed( graph_execution_state_id=context.graph_execution_state_id, node=node.dict(), source_node_id=source_node_id, diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 558de0f28c..5c062483db 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -23,12 +23,12 @@ import warnings from collections import Counter from enum import Enum from pathlib import Path -from typing import Dict, Sequence, Union, Tuple, types +from typing import Dict, Sequence, Union, Tuple, types, Optional import torch import safetensors.torch -from diffusers import DiffusionPipeline, StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel +from diffusers import DiffusionPipeline, StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel, ConfigMixin from diffusers import logging as diffusers_logging from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker @@ -55,20 +55,38 @@ class LoraType(dict): class TIType(dict): pass -class SDModelType(Enum): - diffusers=StableDiffusionGeneratorPipeline # whole pipeline - vae=AutoencoderKL # diffusers parts - text_encoder=CLIPTextModel - tokenizer=CLIPTokenizer - unet=UNet2DConditionModel - scheduler=SchedulerMixin - safety_checker=StableDiffusionSafetyChecker - feature_extractor=CLIPFeatureExtractor +class SDModelType(str, Enum): + Diffusers="diffusers" # whole pipeline + Vae="vae" # diffusers parts + TextEncoder="text_encoder" + Tokenizer="tokenizer" + UNet="unet" + Scheduler="scheduler" + SafetyChecker="safety_checker" + FeatureExtractor="feature_extractor" # These are all loaded as dicts of tensors, and we # distinguish them by class - lora=LoraType - textual_inversion=TIType - + Lora="lora" + TextualInversion="textual_inversion" + +# TODO: +class EmptyScheduler(SchedulerMixin, ConfigMixin): + pass + +MODEL_CLASSES = { + SDModelType.Diffusers: StableDiffusionGeneratorPipeline, + SDModelType.Vae: AutoencoderKL, + SDModelType.TextEncoder: CLIPTextModel, # TODO: t5 + SDModelType.Tokenizer: CLIPTokenizer, # TODO: t5 + SDModelType.UNet: UNet2DConditionModel, + SDModelType.Scheduler: EmptyScheduler, + SDModelType.SafetyChecker: StableDiffusionSafetyChecker, + SDModelType.FeatureExtractor: CLIPFeatureExtractor, + + SDModelType.Lora: LoraType, + SDModelType.TextualInversion: TIType, +} + class ModelStatus(Enum): unknown='unknown' not_loaded='not loaded' @@ -80,21 +98,21 @@ class ModelStatus(Enum): # After loading, we will know it exactly. # Sizes are in Gigs, estimated for float16; double for float32 SIZE_GUESSTIMATE = { - SDModelType.diffusers: 2.2, - SDModelType.vae: 0.35, - SDModelType.text_encoder: 0.5, - SDModelType.tokenizer: 0.001, - SDModelType.unet: 3.4, - SDModelType.scheduler: 0.001, - SDModelType.safety_checker: 1.2, - SDModelType.feature_extractor: 0.001, - SDModelType.lora: 0.1, - SDModelType.textual_inversion: 0.001, + SDModelType.Diffusers: 2.2, + SDModelType.Vae: 0.35, + SDModelType.TextEncoder: 0.5, + SDModelType.Tokenizer: 0.001, + SDModelType.UNet: 3.4, + SDModelType.Scheduler: 0.001, + SDModelType.SafetyChecker: 1.2, + SDModelType.FeatureExtractor: 0.001, + SDModelType.Lora: 0.1, + SDModelType.TextualInversion: 0.001, } # The list of model classes we know how to fetch, for typechecking -ModelClass = Union[tuple([x.value for x in SDModelType])] -DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel) +ModelClass = Union[tuple([x for x in MODEL_CLASSES.values()])] +DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, EmptyScheduler, UNet2DConditionModel) class UnsafeModelException(Exception): "Raised when a legacy model file fails the picklescan test" @@ -110,15 +128,15 @@ class ModelLocker(object): class ModelCache(object): def __init__( - self, - max_cache_size: float=DEFAULT_MAX_CACHE_SIZE, - execution_device: torch.device=torch.device('cuda'), - storage_device: torch.device=torch.device('cpu'), - precision: torch.dtype=torch.float16, - sequential_offload: bool=False, - lazy_offloading: bool=True, - sha_chunksize: int = 16777216, - logger: types.ModuleType = logger + self, + max_cache_size: float=DEFAULT_MAX_CACHE_SIZE, + execution_device: torch.device=torch.device('cuda'), + storage_device: torch.device=torch.device('cpu'), + precision: torch.dtype=torch.float16, + sequential_offload: bool=False, + lazy_offloading: bool=True, + sha_chunksize: int = 16777216, + logger: types.ModuleType = logger ): ''' :param max_models: Maximum number of models to cache in CPU RAM [4] @@ -145,15 +163,15 @@ class ModelCache(object): self.model_sizes: Dict[str,int] = dict() def get_model( - self, - repo_id_or_path: Union[str,Path], - model_type: SDModelType=SDModelType.diffusers, - subfolder: Path=None, - submodel: SDModelType=None, - revision: str=None, - attach_model_part: Tuple[SDModelType, str] = (None,None), - gpu_load: bool=True, - )->ModelLocker: # ?? what does it return + self, + repo_id_or_path: Union[str, Path], + model_type: SDModelType = SDModelType.Diffusers, + subfolder: Path = None, + submodel: SDModelType = None, + revision: str = None, + attach_model_part: Tuple[SDModelType, str] = (None, None), + gpu_load: bool = True, + ) -> ModelLocker: # ?? what does it return ''' Load and return a HuggingFace model wrapped in a context manager generator, with RAM caching. Use like this: @@ -178,14 +196,14 @@ class ModelCache(object): vae_context = cache.get_model( 'stabilityai/sd-stable-diffusion-2', - submodel=SDModelType.vae + submodel=SDModelType.Vae ) This is equivalent to: vae_context = cache.get_model( 'stabilityai/sd-stable-diffusion-2', - model_type = SDModelType.vae, + model_type = SDModelType.Vae, subfolder='vae' ) @@ -195,14 +213,14 @@ class ModelCache(object): pipeline_context = cache.get_model( 'runwayml/stable-diffusion-v1-5', - attach_model_part=(SDModelType.vae,'stabilityai/sd-vae-ft-mse') + attach_model_part=(SDModelType.Vae,'stabilityai/sd-vae-ft-mse') ) The model will be locked into GPU VRAM for the duration of the context. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model :param model_type: An SDModelType enum indicating the type of the (parent) model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" - :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae + :param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.Vae :param attach_model_part: load and attach a diffusers model component. Pass a tuple of format (SDModelType,repo_id) :param revision: model revision :param gpu_load: load the model into GPU [default True] @@ -211,7 +229,7 @@ class ModelCache(object): repo_id_or_path, revision, subfolder, - model_type.value, + model_type, ) # optimization: if caller is asking to load a submodel of a diffusers pipeline, then @@ -221,11 +239,11 @@ class ModelCache(object): repo_id_or_path, None, revision, - SDModelType.diffusers.value + SDModelType.Diffusers ) if possible_parent_key in self.models: key = possible_parent_key - submodel=model_type + submodel = model_type # Look for the model in the cache RAM if key in self.models: # cached - move to bottom of stack (most recently used) @@ -256,24 +274,24 @@ class ModelCache(object): self.current_cache_size += mem_used # increment size of the cache # this is a bit of legacy work needed to support the old-style "load this diffuser with custom VAE" - if model_type==SDModelType.diffusers and attach_model_part[0]: - self.attach_part(model,*attach_model_part) + if model_type == SDModelType.Diffusers and attach_model_part[0]: + self.attach_part(model, *attach_model_part) self.stack.append(key) # add to LRU cache - self.models[key]=model # keep copy of model in dict + self.models[key] = model # keep copy of model in dict if submodel: - model = getattr(model, submodel.name) + model = getattr(model, submodel) return self.ModelLocker(self, key, model, gpu_load) def uncache_model(self, key: str): '''Remove corresponding model from the cache''' if key is not None and key in self.models: - with contextlib.suppress(ValueError), contextlib.suppress(KeyError): - del self.models[key] - del self.locked_models[key] - self.loaded_models.remove(key) + self.models.pop(key, None) + self.locked_models.pop(key, None) + self.loaded_models.discard(key) + with contextlib.suppress(ValueError): self.stack.remove(key) class ModelLocker(object): @@ -302,7 +320,7 @@ class ModelCache(object): if model.device != cache.execution_device: cache.logger.debug(f'Moving {key} into {cache.execution_device}') with VRAMUsage() as mem: - model.to(cache.execution_device) # move into GPU + model.to(cache.execution_device, dtype=cache.precision) # move into GPU cache.logger.debug(f'GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB') cache.model_sizes[key] = mem.vram_used # more accurate size @@ -312,13 +330,16 @@ class ModelCache(object): else: # in the event that the caller wants the model in RAM, we # move it into CPU if it is in GPU and not locked - if hasattr(model,'to') and (key in cache.loaded_models + if hasattr(model, 'to') and (key in cache.loaded_models and cache.locked_models[key] == 0): model.to(cache.storage_device) cache.loaded_models.remove(key) return model def __exit__(self, type, value, traceback): + if not hasattr(self.model, 'to'): + return + key = self.key cache = self.cache cache.locked_models[key] -= 1 @@ -326,11 +347,12 @@ class ModelCache(object): cache._offload_unlocked_models() cache._print_cuda_stats() - def attach_part(self, - diffusers_model: StableDiffusionPipeline, - part_type: SDModelType, - part_id: str - ): + def attach_part( + self, + diffusers_model: StableDiffusionPipeline, + part_type: SDModelType, + part_id: str, + ): ''' Attach a diffusers model part to a diffusers model. This can be used to replace the VAE, tokenizer, textencoder, unet, etc. @@ -338,27 +360,26 @@ class ModelCache(object): :param part_type: An SD ModelType indicating the part :param part_id: A HF repo_id for the part ''' - part_key = part_type.name - part_class = part_type.value part = self._load_diffusers_from_storage( part_id, - model_class=part_class, + model_class=MODEL_CLASSES[part_type], ) part.to(diffusers_model.device) - setattr(diffusers_model,part_key,part) - self.logger.debug(f'Attached {part_key} {part_id}') + setattr(diffusers_model, part_type, part) + self.logger.debug(f'Attached {part_type} {part_id}') - def status(self, - repo_id_or_path: Union[str,Path], - model_type: SDModelType=SDModelType.diffusers, - revision: str=None, - subfolder: Path=None, - )->ModelStatus: + def status( + self, + repo_id_or_path: Union[str, Path], + model_type: SDModelType = SDModelType.Diffusers, + revision: str = None, + subfolder: Path = None, + ) -> ModelStatus: key = self._model_key( repo_id_or_path, revision, subfolder, - model_type.value, + model_type, ) if key not in self.models: return ModelStatus.not_loaded @@ -370,9 +391,11 @@ class ModelCache(object): else: return ModelStatus.in_ram - def model_hash(self, - repo_id_or_path: Union[str,Path], - revision: str="main")->str: + def model_hash( + self, + repo_id_or_path: Union[str, Path], + revision: str = "main", + ) -> str: ''' Given the HF repo id or path to a model on disk, returns a unique hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs @@ -385,7 +408,7 @@ class ModelCache(object): else: return self._hf_commit_hash(repo_id_or_path,revision) - def cache_size(self)->float: + def cache_size(self) -> float: "Return the current size of the cache, in GB" return self.current_cache_size / GIG @@ -407,10 +430,15 @@ class ModelCache(object): logger.debug("Model scanned ok") @staticmethod - def _model_key(path,revision,subfolder,model_class)->str: - return ':'.join([str(path),str(revision or ''),str(subfolder or ''),model_class.__name__]) + def _model_key(path, revision, subfolder, model_class) -> str: + return ':'.join([ + str(path), + str(revision or ''), + str(subfolder or ''), + model_class, + ]) - def _has_cuda(self)->bool: + def _has_cuda(self) -> bool: return self.execution_device.type == 'cuda' def _print_cuda_stats(self): @@ -450,43 +478,43 @@ class ModelCache(object): self.loaded_models.remove(key) def _load_model_from_storage( - self, - repo_id_or_path: Union[str,Path], - subfolder: Path=None, - revision: str=None, - model_type: SDModelType=SDModelType.diffusers, - )->ModelClass: + self, + repo_id_or_path: Union[str, Path], + subfolder: Optional[Path] = None, + revision: Optional[str] = None, + model_type: SDModelType = SDModelType.Diffusers, + ) -> ModelClass: ''' Load and return a HuggingFace model. :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model :param subfolder: name of a subfolder in which the model can be found, e.g. "vae" :param revision: model revision - :param model_type: type of model to return, defaults to SDModelType.diffusers + :param model_type: type of model to return, defaults to SDModelType.Diffusers ''' # silence transformer and diffuser warnings with SilenceWarnings(): - if model_type==SDModelType.lora: + if model_type==SDModelType.Lora: model = self._load_lora_from_storage(repo_id_or_path) - elif model_type==SDModelType.textual_inversion: + elif model_type==SDModelType.TextualInversion: model = self._load_ti_from_storage(repo_id_or_path) else: model = self._load_diffusers_from_storage( repo_id_or_path, subfolder, revision, - model_type.value, + model_type, ) - if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline): + if self.sequential_offload and isinstance(model, StableDiffusionGeneratorPipeline): model.enable_offload_submodels(self.execution_device) return model def _load_diffusers_from_storage( - self, - repo_id_or_path: Union[str,Path], - subfolder: Path=None, - revision: str=None, - model_class: ModelClass=StableDiffusionGeneratorPipeline, - )->ModelClass: + self, + repo_id_or_path: Union[str, Path], + subfolder: Optional[Path] = None, + revision: Optional[str] = None, + model_type: ModelClass = StableDiffusionGeneratorPipeline, + ) -> ModelClass: ''' Load and return a HuggingFace model using from_pretrained(). :param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model @@ -494,17 +522,26 @@ class ModelCache(object): :param revision: model revision :param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline ''' - revisions = [revision] if revision \ - else ['fp16','main'] if self.precision==torch.float16 \ - else ['main'] - extra_args = {'torch_dtype': self.precision, - 'safety_checker': None}\ - if model_class in DiffusionClasses\ - else {} + + model_class = MODEL_CLASSES[model_type] + + if revision is not None: + revisions = [revision] + elif self.precision == torch.float16: + revisions = ['fp16', 'main'] + else: + revisions = ['main'] + + extra_args = dict() + if model_class in DiffusionClasses: + extra_args = dict( + torch_dtype=self.precision, + safety_checker=None, + ) for rev in revisions: try: - model = model_class.from_pretrained( + model = model_class.from_pretrained( repo_id_or_path, revision=rev, subfolder=subfolder or '.', @@ -517,13 +554,13 @@ class ModelCache(object): pass return model - def _load_lora_from_storage(self, lora_path: Path)->SDModelType.lora.value: - assert False,"_load_lora_from_storage() is not yet implemented" + def _load_lora_from_storage(self, lora_path: Path) -> LoraType: + assert False, "_load_lora_from_storage() is not yet implemented" - def _load_ti_from_storage(self, lora_path: Path)->SDModelType.textual_inversion.value: - assert False,"_load_ti_from_storage() is not yet implemented" + def _load_ti_from_storage(self, lora_path: Path) -> TIType: + assert False, "_load_ti_from_storage() is not yet implemented" - def _legacy_model_hash(self, checkpoint_path: Union[str,Path])->str: + def _legacy_model_hash(self, checkpoint_path: Union[str, Path]) -> str: sha = hashlib.sha256() path = Path(checkpoint_path) assert path.is_file(),f"File {checkpoint_path} not found" @@ -544,7 +581,7 @@ class ModelCache(object): f.write(hash) return hash - def _local_model_hash(self, model_path: Union[str,Path])->str: + def _local_model_hash(self, model_path: Union[str, Path]) -> str: sha = hashlib.sha256() path = Path(model_path) @@ -566,7 +603,7 @@ class ModelCache(object): f.write(hash) return hash - def _hf_commit_hash(self, repo_id: str, revision: str='main')->str: + def _hf_commit_hash(self, repo_id: str, revision: str='main') -> str: api = HfApi() info = api.list_repo_refs( repo_id=repo_id, @@ -578,7 +615,7 @@ class ModelCache(object): return desired_revisions[0].target_commit @staticmethod - def calc_model_size(model)->int: + def calc_model_size(model) -> int: if isinstance(model,DiffusionPipeline): return ModelCache._calc_pipeline(model) elif isinstance(model,torch.nn.Module): @@ -587,7 +624,7 @@ class ModelCache(object): return None @staticmethod - def _calc_pipeline(pipeline)->int: + def _calc_pipeline(pipeline) -> int: res = 0 for submodel_key in pipeline.components.keys(): submodel = getattr(pipeline, submodel_key) @@ -596,7 +633,7 @@ class ModelCache(object): return res @staticmethod - def _calc_model(model)->int: + def _calc_model(model) -> int: mem_params = sum([param.nelement()*param.element_size() for param in model.parameters()]) mem_bufs = sum([buf.nelement()*buf.element_size() for buf in model.buffers()]) mem = mem_params + mem_bufs # in bytes diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 87fb938338..05e0afff98 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -27,7 +27,7 @@ Typical usage: max_cache_size=8 ) # gigabytes - model_info = manager.get_model('stable-diffusion-1.5', SDModelType.diffusers) + model_info = manager.get_model('stable-diffusion-1.5', SDModelType.Diffusers) with model_info.context as my_model: my_model.latents_from_embeddings(...) @@ -45,7 +45,7 @@ parameter: model_info = manager.get_model( 'clip-tokenizer', - model_type=SDModelType.tokenizer + model_type=SDModelType.Tokenizer ) This will raise an InvalidModelError if the format defined in the @@ -96,7 +96,7 @@ SUBMODELS: It is also possible to fetch an isolated submodel from a diffusers model. Use the `submodel` parameter to select which part: - vae = manager.get_model('stable-diffusion-1.5',submodel=SDModelType.vae) + vae = manager.get_model('stable-diffusion-1.5',submodel=SDModelType.Vae) with vae.context as my_vae: print(type(my_vae)) # "AutoencoderKL" @@ -120,8 +120,8 @@ separated by "/". Example: You can now use the `model_type` argument to indicate which model you want: - tokenizer = mgr.get('clip-large',model_type=SDModelType.tokenizer) - encoder = mgr.get('clip-large',model_type=SDModelType.text_encoder) + tokenizer = mgr.get('clip-large',model_type=SDModelType.Tokenizer) + encoder = mgr.get('clip-large',model_type=SDModelType.TextEncoder) OTHER FUNCTIONS: @@ -254,7 +254,7 @@ class ModelManager(object): def model_exists( self, model_name: str, - model_type: SDModelType = SDModelType.diffusers, + model_type: SDModelType = SDModelType.Diffusers, ) -> bool: """ Given a model name, returns True if it is a valid @@ -264,28 +264,28 @@ class ModelManager(object): return model_key in self.config def create_key(self, model_name: str, model_type: SDModelType) -> str: - return f"{model_type.name}/{model_name}" + return f"{model_type}/{model_name}" def parse_key(self, model_key: str) -> Tuple[str, SDModelType]: model_type_str, model_name = model_key.split('/', 1) - if model_type_str not in SDModelType.__members__: - # TODO: + try: + model_type = SDModelType(model_type_str) + return (model_name, model_type) + except: raise Exception(f"Unknown model type: {model_type_str}") - return (model_name, SDModelType[model_type_str]) - def get_model( self, model_name: str, - model_type: SDModelType=SDModelType.diffusers, - submodel: SDModelType=None, + model_type: SDModelType = SDModelType.Diffusers, + submodel: Optional[SDModelType] = None, ) -> SDModelInfo: """Given a model named identified in models.yaml, return an SDModelInfo object describing it. :param model_name: symbolic name of the model in models.yaml :param model_type: SDModelType enum indicating the type of model to return :param submodel: an SDModelType enum indicating the portion of - the model to retrieve (e.g. SDModelType.vae) + the model to retrieve (e.g. SDModelType.Vae) If not provided, the model_type will be read from the `format` field of the corresponding stanza. If provided, the model_type will be used @@ -304,17 +304,17 @@ class ModelManager(object): test1_pipeline = mgr.get_model('test1') # returns a StableDiffusionGeneratorPipeline - test1_vae1 = mgr.get_model('test1', submodel=SDModelType.vae) + test1_vae1 = mgr.get_model('test1', submodel=SDModelType.Vae) # returns the VAE part of a diffusers model as an AutoencoderKL - test1_vae2 = mgr.get_model('test1', model_type=SDModelType.diffusers, submodel=SDModelType.vae) + test1_vae2 = mgr.get_model('test1', model_type=SDModelType.Diffusers, submodel=SDModelType.Vae) # does the same thing as the previous statement. Note that model_type # is for the parent model, and submodel is for the part - test1_lora = mgr.get_model('test1', model_type=SDModelType.lora) + test1_lora = mgr.get_model('test1', model_type=SDModelType.Lora) # returns a LoRA embed (as a 'dict' of tensors) - test1_encoder = mgr.get_modelI('test1', model_type=SDModelType.textencoder) + test1_encoder = mgr.get_modelI('test1', model_type=SDModelType.TextEncoder) # raises an InvalidModelError """ @@ -332,10 +332,10 @@ class ModelManager(object): mconfig = self.config[model_key] # type already checked as it's part of key - if model_type == SDModelType.diffusers: + if model_type == SDModelType.Diffusers: # intercept stanzas that point to checkpoint weights and replace them # with the equivalent diffusers model - if mconfig.format in ["ckpt", "diffusers"]: + if mconfig.format in ["ckpt", "safetensors"]: location = self.convert_ckpt_and_cache(mconfig) else: location = global_resolve_path(mconfig.get('path')) or mconfig.get('repo_id') @@ -355,13 +355,13 @@ class ModelManager(object): vae = (None, None) with suppress(Exception): vae_id = mconfig.vae.repo_id - vae = (SDModelType.vae, vae_id) + vae = (SDModelType.Vae, vae_id) # optimization - don't load whole model if the user # is asking for just a piece of it - if model_type == SDModelType.diffusers and submodel and not subfolder: + if model_type == SDModelType.Diffusers and submodel and not subfolder: model_type = submodel - subfolder = submodel.name + subfolder = submodel.value submodel = None model_context = self.cache.get_model( @@ -390,7 +390,7 @@ class ModelManager(object): _cache = self.cache ) - def default_model(self) -> Union[Tuple[str, SDModelType],None]: + def default_model(self) -> Optional[Tuple[str, SDModelType]]: """ Returns the name of the default model, or None if none is defined. @@ -401,7 +401,7 @@ class ModelManager(object): return (model_name, model_type) return self.model_names()[0][0] - def set_default_model(self, model_name: str, model_type: SDModelType=SDModelType.diffusers) -> None: + def set_default_model(self, model_name: str, model_type: SDModelType=SDModelType.Diffusers) -> None: """ Set the default model. The change will not take effect until you call model_manager.commit() @@ -415,25 +415,25 @@ class ModelManager(object): config[self.create_key(model_name, model_type)]["default"] = True def model_info( - self, - model_name: str, - model_type: SDModelType=SDModelType.diffusers + self, + model_name: str, + model_type: SDModelType=SDModelType.Diffusers, ) -> dict: """ Given a model name returns the OmegaConf (dict-like) object describing it. """ if not self.exists(model_name, model_type): return None - return self.config[self.create_key(model_name,model_type)] + return self.config[self.create_key(model_name, model_type)] def model_names(self) -> List[Tuple[str, SDModelType]]: """ Return a list of (str, SDModelType) corresponding to all models known to the configuration. """ - return [(self.parse_key(x)) for x in self.config.keys() if isinstance(self.config[x],DictConfig)] + return [(self.parse_key(x)) for x in self.config.keys() if isinstance(self.config[x], DictConfig)] - def is_legacy(self, model_name: str, model_type: SDModelType.diffusers) -> bool: + def is_legacy(self, model_name: str, model_type: SDModelType.Diffusers) -> bool: """ Return true if this is a legacy (.ckpt) model """ @@ -461,14 +461,14 @@ class ModelManager(object): # don't include VAEs in listing (legacy style) if "config" in stanza and "/VAE/" in stanza["config"]: continue - if model_key=='config_file_version': + if model_key == 'config_file_version': continue model_name, model_type = self.parse_key(model_key) models[model_key] = dict() # TODO: return all models in future - if model_type != SDModelType.diffusers: + if model_type != SDModelType.Diffusers: continue model_format = stanza.get('format') @@ -477,15 +477,15 @@ class ModelManager(object): status = self.cache.status( stanza.get('weights') or stanza.get('repo_id'), revision=stanza.get('revision'), - subfolder=stanza.get('subfolder') + subfolder=stanza.get('subfolder'), ) description = stanza.get("description", None) models[model_key].update( model_name=model_name, - model_type=model_type.name, + model_type=model_type, format=model_format, description=description, - status=status.value + status=status.value, ) @@ -528,8 +528,8 @@ class ModelManager(object): def del_model( self, model_name: str, - model_type: SDModelType.diffusers, - delete_files: bool = False + model_type: SDModelType.Diffusers, + delete_files: bool = False, ): """ Delete the named model. @@ -539,9 +539,9 @@ class ModelManager(object): if model_cfg is None: self.logger.error( - f"Unknown model {model_key}" - ) - return + f"Unknown model {model_key}" + ) + return # TODO: some legacy? #if model_name in self.stack: @@ -571,7 +571,7 @@ class ModelManager(object): model_name: str, model_type: SDModelType, model_attributes: dict, - clobber: bool = False + clobber: bool = False, ) -> None: """ Update the named model with a dictionary of attributes. Will fail with an @@ -581,7 +581,7 @@ class ModelManager(object): attributes are incorrect or the model name is missing. """ - if model_type == SDModelType.diffusers: + if model_type == SDModelType.Fiffusers: # TODO: automaticaly or manualy? #assert "format" in model_attributes, 'missing required field "format"' model_format = "ckpt" if "weights" in model_attributes else "diffusers" @@ -647,16 +647,16 @@ class ModelManager(object): else: new_config.update(repo_id=repo_or_path) - self.add_model(model_name, SDModelType.diffusers, new_config, True) + self.add_model(model_name, SDModelType.Diffusers, new_config, True) if commit_to_conf: self.commit(commit_to_conf) - return self.create_key(model_name, SDModelType.diffusers) + return self.create_key(model_name, SDModelType.Diffusers) def import_lora( self, path: Path, - model_name: str=None, - description: str=None, + model_name: Optional[str] = None, + description: Optional[str] = None, ): """ Creates an entry for the indicated lora file. Call @@ -667,7 +667,7 @@ class ModelManager(object): model_description = description or f"LoRA model {model_name}" self.add_model( model_name, - SDModelType.lora, + SDModelType.Lora, dict( format="lora", weights=str(path), @@ -679,8 +679,8 @@ class ModelManager(object): def import_embedding( self, path: Path, - model_name: str=None, - description: str=None, + model_name: Optional[str] = None, + description: Optional[str] = None, ): """ Creates an entry for the indicated lora file. Call @@ -696,7 +696,7 @@ class ModelManager(object): model_description = description or f"Textual embedding model {model_name}" self.add_model( model_name, - SDModelType.textual_inversion, + SDModelType.TextualInversion, dict( format="textual_inversion", weights=str(weights), @@ -746,11 +746,11 @@ class ModelManager(object): def heuristic_import( self, path_url_or_repo: str, - model_name: str = None, - description: str = None, - model_config_file: Path = None, - commit_to_conf: Path = None, - config_file_callback: Callable[[Path], Path] = None, + model_name: Optional[str] = None, + description: Optional[str] = None, + model_config_file: Optional[Path] = None, + commit_to_conf: Optional[Path] = None, + config_file_callback: Optional[Callable[[Path], Path]] = None, ) -> str: """Accept a string which could be: - a HF diffusers repo_id @@ -927,7 +927,7 @@ class ModelManager(object): ) return model_name - def convert_ckpt_and_cache(self, mconfig: DictConfig)->Path: + def convert_ckpt_and_cache(self, mconfig: DictConfig) -> Path: """ Convert the checkpoint model indicated in mconfig into a diffusers, cache it to disk, and return Path to converted @@ -961,7 +961,7 @@ class ModelManager(object): self, weights: Path, mconfig: DictConfig - ) -> Tuple[Path, SDModelType.vae]: + ) -> Tuple[Path, AutoencoderKL]: # VAE handling is convoluted # 1. If there is a .vae.ckpt file sharing same stem as weights, then use # it as the vae_path passed to convert @@ -990,7 +990,7 @@ class ModelManager(object): vae_diffusers_location = "stabilityai/sd-vae-ft-mse" if vae_diffusers_location: - vae_model = self.cache.get_model(vae_diffusers_location, SDModelType.vae).model + vae_model = self.cache.get_model(vae_diffusers_location, SDModelType.Vae).model return (None, vae_model) return (None, None) @@ -1038,7 +1038,7 @@ class ModelManager(object): vae_model = None if vae: vae_location = global_resolve_path(vae.get('path')) or vae.get('repo_id') - vae_model = self.cache.get_model(vae_location,SDModelType.vae).model + vae_model = self.cache.get_model(vae_location, SDModelType.Vae).model vae_path = None convert_ckpt_to_diffusers( ckpt_path, @@ -1058,11 +1058,11 @@ class ModelManager(object): description=model_description, format="diffusers", ) - if self.model_exists(model_name, SDModelType.diffusers): - self.del_model(model_name, SDModelType.diffusers) + if self.model_exists(model_name, SDModelType.Diffusers): + self.del_model(model_name, SDModelType.Diffusers) self.add_model( model_name, - SDModelType.diffusers, + SDModelType.Diffusers, new_config, True ) diff --git a/invokeai/frontend/web/src/common/util/parseMetadata.ts b/invokeai/frontend/web/src/common/util/parseMetadata.ts index 95d08db3e0..f1828d95e7 100644 --- a/invokeai/frontend/web/src/common/util/parseMetadata.ts +++ b/invokeai/frontend/web/src/common/util/parseMetadata.ts @@ -263,7 +263,7 @@ export const parseNodeMetadata = ( return; } - if ('unet' in nodeItem && 'tokenizer' in nodeItem) { + if ('unet' in nodeItem && 'scheduler' in nodeItem) { const unetField = parseUNetField(nodeItem); if (unetField) { parsed[nodeKey] = unetField; From 1f602e614305993f5db8bc40c2bc05af48004a6f Mon Sep 17 00:00:00 2001 From: Sergey Borisov Date: Sun, 14 May 2023 03:46:13 +0300 Subject: [PATCH 035/225] Fix - apply precision to text_encoder --- invokeai/backend/model_management/model_cache.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 5c062483db..8d7aaa8af4 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -112,7 +112,7 @@ SIZE_GUESSTIMATE = { # The list of model classes we know how to fetch, for typechecking ModelClass = Union[tuple([x for x in MODEL_CLASSES.values()])] -DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, EmptyScheduler, UNet2DConditionModel) +DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, EmptyScheduler, UNet2DConditionModel, CLIPTextModel) class UnsafeModelException(Exception): "Raised when a legacy model file fails the picklescan test" @@ -320,7 +320,7 @@ class ModelCache(object): if model.device != cache.execution_device: cache.logger.debug(f'Moving {key} into {cache.execution_device}') with VRAMUsage() as mem: - model.to(cache.execution_device, dtype=cache.precision) # move into GPU + model.to(cache.execution_device) # move into GPU cache.logger.debug(f'GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB') cache.model_sizes[key] = mem.vram_used # more accurate size @@ -534,11 +534,14 @@ class ModelCache(object): extra_args = dict() if model_class in DiffusionClasses: - extra_args = dict( + extra_args.update( torch_dtype=self.precision, + ) + if model_class == StableDiffusionGeneratorPipeline: + extra_args.update( safety_checker=None, ) - + for rev in revisions: try: model = model_class.from_pretrained( From b31a6ff605a734fa7b884e7bd7ca4fb5a44f8b0d Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 21:11:06 -0400 Subject: [PATCH 036/225] fix reversed args in _model_key() call --- invokeai/backend/model_management/model_cache.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 8d7aaa8af4..bf77e7fb02 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -237,8 +237,8 @@ class ModelCache(object): if subfolder and not submodel: possible_parent_key = self._model_key( repo_id_or_path, - None, revision, + None, SDModelType.Diffusers ) if possible_parent_key in self.models: From 426f4eaf7e72b03962903a74238a58c93e8f7489 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sat, 13 May 2023 22:29:33 -0400 Subject: [PATCH 037/225] adjusted regression tests to work with new SDModelTypes --- invokeai/app/invocations/latent.py | 14 +++++--------- tests/test_model_cache.py | 28 ++++++++++++++++++---------- 2 files changed, 23 insertions(+), 19 deletions(-) diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index efd9b458c7..54eb0c6630 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -2,10 +2,8 @@ from typing import Literal, Optional, Union -import diffusers import einops import torch -from diffusers import DiffusionPipeline from diffusers.schedulers import SchedulerMixin as Scheduler from diffusers.image_processor import VaeImageProcessor from pydantic import BaseModel, Field @@ -22,18 +20,16 @@ from ...backend.stable_diffusion.diffusers_pipeline import ( from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \ PostprocessingSettings from ...backend.util.devices import choose_torch_device, torch_dtype -from ...backend.prompting.conditioning import get_uc_and_c_and_ec from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP -from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig -import numpy as np +from .baseinvocation import ( + BaseInvocation, BaseInvocationOutput, + InvocationContext, InvocationConfig + ) from ..services.image_storage import ImageType -from .baseinvocation import (BaseInvocation, BaseInvocationOutput, - InvocationConfig, InvocationContext) from .compel import ConditioningField from .image import ImageField, ImageOutput, build_image_output from .model import ModelInfo, UNetField, VaeField -from ...backend.model_management import SDModelType class LatentsField(BaseModel): @@ -213,7 +209,7 @@ class TextToLatentsInvocation(BaseInvocation): h_symmetry_time_pct=None,#h_symmetry_time_pct, v_symmetry_time_pct=None#v_symmetry_time_pct, ), - ).add_scheduler_args_if_applicable(scheduler, eta=None)#ddim_eta) + ).add_scheduler_args_if_applicable(scheduler, eta=0.0)#ddim_eta) return conditioning_data def create_pipeline(self, unet, scheduler) -> StableDiffusionGeneratorPipeline: diff --git a/tests/test_model_cache.py b/tests/test_model_cache.py index 6510f7ac1f..2ced3ea5bd 100644 --- a/tests/test_model_cache.py +++ b/tests/test_model_cache.py @@ -2,7 +2,7 @@ import pytest import torch from enum import Enum -from invokeai.backend.model_management.model_cache import ModelCache +from invokeai.backend.model_management.model_cache import ModelCache, MODEL_CLASSES class DummyModelBase(object): '''Base class for dummy component of a diffusers model''' @@ -32,13 +32,21 @@ class DummyPipeline(DummyModelBase): '''Dummy pipeline object is a composite of several types''' def __init__(self,repo_id): super().__init__(repo_id) - self.type1 = DummyModelType1('dummy/type1') - self.type2 = DummyModelType2('dummy/type2') + self.dummy_model_type1 = DummyModelType1('dummy/type1') + self.dummy_model_type2 = DummyModelType2('dummy/type2') -class DMType(Enum): - dummy_pipeline = DummyPipeline - type1 = DummyModelType1 - type2 = DummyModelType2 +class DMType(str, Enum): + dummy_pipeline = 'dummy_pipeline' + type1 = 'dummy_model_type1' + type2 = 'dummy_model_type2' + +MODEL_CLASSES.update( + { + DMType.dummy_pipeline: DummyPipeline, + DMType.type1: DummyModelType1, + DMType.type2: DummyModelType2, + } +) cache = ModelCache(max_cache_size=4) @@ -50,7 +58,7 @@ def test_pipeline_fetch(): assert pipeline1 is not None, 'get_model() should not return None' assert pipeline1a is not None, 'get_model() should not return None' assert pipeline2 is not None, 'get_model() should not return None' - assert type(pipeline1)==DMType.dummy_pipeline.value,'get_model() did not return model of expected type' + assert type(pipeline1)==DummyPipeline,'get_model() did not return model of expected type' assert pipeline1==pipeline1a,'pipelines with the same repo_id should be the same' assert pipeline1!=pipeline2,'pipelines with different repo_ids should not be the same' assert len(cache.models)==2,'cache should uniquely cache models with same identity' @@ -77,6 +85,6 @@ def test_submodel_fetch(): cache.get_model(repo_id_or_path='dummy/pipeline2',model_type=DMType.dummy_pipeline,submodel=DMType.type1) as part2: assert type(part1)==DummyModelType1,'returned submodel is not of expected type' assert part1.device==torch.device('cuda'),'returned submodel should be in the GPU when in context' - assert pipeline.type1==part1,'returned submodel should match the corresponding subpart of parent model' - assert pipeline.type1!=part2,'returned submodel should not match the subpart of a different parent' + assert pipeline.dummy_model_type1==part1,'returned submodel should match the corresponding subpart of parent model' + assert pipeline.dummy_model_type1!=part2,'returned submodel should not match the subpart of a different parent' From 63e465eb5c35c34370d3c36143cc82213aa0ac7e Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Sun, 14 May 2023 16:45:40 -0400 Subject: [PATCH 038/225] tweaks to `get_model()` behavior 1. If an external VAE is specified in config file, then get_model(submodel=vae) will return the external VAE, not the one burnt into the parent diffusers pipeline. 2. The mechanism in (1) is generalized such that you can now have "unet:", "text_encoder:" and similar stanzas in the config file. Valid formats of these subsections: unet: repo_id: foo/bar unet: path: /path/to/local/folder unet: repo_id: foo/bar subfolder: unet In the near future, these will also be used to attach external parts to the pipeline, generalizing VAE behavior. 3. Accommodate callers (i.e. the WebUI) that are passing the model key ("diffusers/stable-diffusion-1.5") to get_model() instead of the tuple of model_name and model_type. 4. Fixed bug in VAE model attaching code. 5. 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a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -267,16 +267,16 @@ class ModelCache(object): subfolder=subfolder, revision=revision, ) - + if mem_used := self.calc_model_size(model): logger.debug(f'CPU RAM used for load: {(mem_used/GIG):.2f} GB') self.model_sizes[key] = mem_used # remember size of this model for cache cleansing self.current_cache_size += mem_used # increment size of the cache - + # this is a bit of legacy work needed to support the old-style "load this diffuser with custom VAE" if model_type == SDModelType.Diffusers and attach_model_part[0]: self.attach_part(model, *attach_model_part) - + self.stack.append(key) # add to LRU cache self.models[key] = model # keep copy of model in dict @@ -352,6 +352,7 @@ class ModelCache(object): diffusers_model: StableDiffusionPipeline, part_type: SDModelType, part_id: str, + subfolder: Optional[str] = None ): ''' Attach a diffusers model part to a diffusers model. This can be @@ -362,7 +363,8 @@ class ModelCache(object): ''' part = self._load_diffusers_from_storage( part_id, - model_class=MODEL_CLASSES[part_type], + model_type=part_type, + subfolder=subfolder, ) part.to(diffusers_model.device) setattr(diffusers_model, part_type, part) diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 05e0afff98..3c15bb1bfb 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -133,6 +133,7 @@ from __future__ import annotations import os import re +import sys import textwrap from contextlib import suppress from dataclasses import dataclass @@ -318,11 +319,18 @@ class ModelManager(object): # raises an InvalidModelError """ + + # This is a temporary workaround for callers that use "type/name" as the model name + # because they haven't adjusted to the new return format of `list_models()` + if "/" in model_name: + model_key = model_name + else: + model_key = self.create_key(model_name, model_type) + # TODO: delete default model or add check that this stable diffusion model # if not model_name: # model_name = self.default_model() - model_key = self.create_key(model_name, model_type) if model_key not in self.config: raise InvalidModelError( f'"{model_key}" is not a known model name. Please check your models.yaml file' @@ -350,20 +358,30 @@ class ModelManager(object): revision = mconfig.get('revision') hash = self.cache.model_hash(location, revision) - # to support the traditional way of attaching a VAE - # to a model, we hacked in `attach_model_part` - vae = (None, None) - with suppress(Exception): - vae_id = mconfig.vae.repo_id - vae = (SDModelType.Vae, vae_id) + # If the caller is asking for part of the model and the config indicates + # an external replacement for that field, then we fetch the replacement + if submodel and mconfig.get(submodel): + location = mconfig.get(submodel).get('path') \ + or mconfig.get(submodel).get('repo_id') + model_type = submodel + submodel = None - # optimization - don't load whole model if the user - # is asking for just a piece of it - if model_type == SDModelType.Diffusers and submodel and not subfolder: + # We don't need to load whole model if the user is asking for just a piece of it + elif model_type == SDModelType.Diffusers and submodel and not subfolder: model_type = submodel subfolder = submodel.value submodel = None + # to support the traditional way of attaching a VAE + # to a model, we hacked in `attach_model_part` + # TODO: generalize this + vae = (None, None) + if model_type == SDModelType.Diffusers: + with suppress(Exception): + vae_id = mconfig.vae.get('path') or mconfig.vae.get('repo_id') + vae_subfolder = mconfig.vae.get('subfolder') + vae = (SDModelType.Vae, vae_id, vae_subfolder) + model_context = self.cache.get_model( location, model_type = model_type, diff --git a/invokeai/frontend/web/dist/assets/App-08e5c546.css b/invokeai/frontend/web/dist/assets/App-08e5c546.css deleted file mode 100644 index 27d0052b89..0000000000 --- a/invokeai/frontend/web/dist/assets/App-08e5c546.css +++ /dev/null @@ -1 +0,0 @@ -.ltr-image-gallery-css-transition-enter{transform:translate(150%)}.ltr-image-gallery-css-transition-enter-active{transform:translate(0);transition:all .12s 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o=r.parentNode;if(!o)return null;var s=r.window.document.createElement("div");return s.style.width="100%",s.style.height="100%",s.style.position="absolute",s.style.transform="scale(0, 0)",s.style.left="0",s.style.flex="0 0 100%",s.classList?s.classList.add(Jg):s.className+=Jg,o.appendChild(s),s},r.removeBase=function(o){var s=r.parentNode;s&&s.removeChild(o)},r.ref=function(o){o&&(r.resizable=o)},r.state={isResizing:!1,width:typeof(r.propsSize&&r.propsSize.width)>"u"?"auto":r.propsSize&&r.propsSize.width,height:typeof(r.propsSize&&r.propsSize.height)>"u"?"auto":r.propsSize&&r.propsSize.height,direction:"right",original:{x:0,y:0,width:0,height:0},backgroundStyle:{height:"100%",width:"100%",backgroundColor:"rgba(0,0,0,0)",cursor:"auto",opacity:0,position:"fixed",zIndex:9999,top:"0",left:"0",bottom:"0",right:"0"},flexBasis:void 0},r.onResizeStart=r.onResizeStart.bind(r),r.onMouseMove=r.onMouseMove.bind(r),r.onMouseUp=r.onMouseUp.bind(r),r}return Object.defineProperty(t.prototype,"parentNode",{get:function(){return this.resizable?this.resizable.parentNode:null},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"window",{get:function(){return!this.resizable||!this.resizable.ownerDocument?null:this.resizable.ownerDocument.defaultView},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"propsSize",{get:function(){return this.props.size||this.props.defaultSize||m9},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"size",{get:function(){var n=0,r=0;if(this.resizable&&this.window){var o=this.resizable.offsetWidth,s=this.resizable.offsetHeight,c=this.resizable.style.position;c!=="relative"&&(this.resizable.style.position="relative"),n=this.resizable.style.width!=="auto"?this.resizable.offsetWidth:o,r=this.resizable.style.height!=="auto"?this.resizable.offsetHeight:s,this.resizable.style.position=c}return{width:n,height:r}},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"sizeStyle",{get:function(){var n=this,r=this.props.size,o=function(u){if(typeof n.state[u]>"u"||n.state[u]==="auto")return"auto";if(n.propsSize&&n.propsSize[u]&&n.propsSize[u].toString().endsWith("%")){if(n.state[u].toString().endsWith("%"))return n.state[u].toString();var f=n.getParentSize(),p=Number(n.state[u].toString().replace("px","")),h=p/f[u]*100;return h+"%"}return Nd(n.state[u])},s=r&&typeof r.width<"u"&&!this.state.isResizing?Nd(r.width):o("width"),c=r&&typeof 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r&&(this.parentNode.style.flexWrap=o),this.removeBase(n),s},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(!(!this.resizable||!this.window)){var n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var o=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof o>"u"||o==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var o=this.props.boundsByDirection,s=this.state.direction,c=o&&Ti("left",s),u=o&&Ti("top",s),f,p;if(this.props.bounds==="parent"){var h=this.parentNode;h&&(f=c?this.resizableRight-this.parentLeft:h.offsetWidth+(this.parentLeft-this.resizableLeft),p=u?this.resizableBottom-this.parentTop:h.offsetHeight+(this.parentTop-this.resizableTop))}else this.props.bounds==="window"?this.window&&(f=c?this.resizableRight:this.window.innerWidth-this.resizableLeft,p=u?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(f=c?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),p=u?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return f&&Number.isFinite(f)&&(n=n&&n"u"?10:s.width,y=typeof o.width>"u"||o.width<0?n:o.width,x=typeof s.height>"u"?10:s.height,S=typeof o.height>"u"||o.height<0?r:o.height,j=f||0,C=p||0;if(u){var w=(x-j)*this.ratio+C,k=(S-j)*this.ratio+C,I=(h-C)/this.ratio+j,P=(y-C)/this.ratio+j,M=Math.max(h,w),T=Math.min(y,k),D=Math.max(x,I),_=Math.min(S,P);n=Tl(n,M,T),r=Tl(r,D,_)}else n=Tl(n,h,y),r=Tl(r,x,S);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var o=this.props.bounds.getBoundingClientRect();this.targetLeft=o.left,this.targetTop=o.top}if(this.resizable){var s=this.resizable.getBoundingClientRect(),c=s.left,u=s.top,f=s.right,p=s.bottom;this.resizableLeft=c,this.resizableRight=f,this.resizableTop=u,this.resizableBottom=p}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var o=0,s=0;if(n.nativeEvent&&v9(n.nativeEvent)?(o=n.nativeEvent.clientX,s=n.nativeEvent.clientY):n.nativeEvent&&_l(n.nativeEvent)&&(o=n.nativeEvent.touches[0].clientX,s=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var c=this.props.onResizeStart(n,r,this.resizable);if(c===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var u,f=this.window.getComputedStyle(this.resizable);if(f.flexBasis!=="auto"){var p=this.parentNode;if(p){var h=this.window.getComputedStyle(p).flexDirection;this.flexDir=h.startsWith("row")?"row":"column",u=f.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var y={original:{x:o,y:s,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:yr(yr({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:u};this.setState(y)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&_l(n))try{n.preventDefault(),n.stopPropagation()}catch{}var o=this.props,s=o.maxWidth,c=o.maxHeight,u=o.minWidth,f=o.minHeight,p=_l(n)?n.touches[0].clientX:n.clientX,h=_l(n)?n.touches[0].clientY:n.clientY,y=this.state,x=y.direction,S=y.original,j=y.width,C=y.height,w=this.getParentSize(),k=g9(w,this.window.innerWidth,this.window.innerHeight,s,c,u,f);s=k.maxWidth,c=k.maxHeight,u=k.minWidth,f=k.minHeight;var I=this.calculateNewSizeFromDirection(p,h),P=I.newHeight,M=I.newWidth,T=this.calculateNewMaxFromBoundary(s,c);this.props.snap&&this.props.snap.x&&(M=Zg(M,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(P=Zg(P,this.props.snap.y,this.props.snapGap));var D=this.calculateNewSizeFromAspectRatio(M,P,{width:T.maxWidth,height:T.maxHeight},{width:u,height:f});if(M=D.newWidth,P=D.newHeight,this.props.grid){var _=Qg(M,this.props.grid[0]),R=Qg(P,this.props.grid[1]),V=this.props.snapGap||0;M=V===0||Math.abs(_-M)<=V?_:M,P=V===0||Math.abs(R-P)<=V?R:P}var F={width:M-S.width,height:P-S.height};if(j&&typeof j=="string"){if(j.endsWith("%")){var B=M/w.width*100;M=B+"%"}else if(j.endsWith("vw")){var Q=M/this.window.innerWidth*100;M=Q+"vw"}else if(j.endsWith("vh")){var U=M/this.window.innerHeight*100;M=U+"vh"}}if(C&&typeof C=="string"){if(C.endsWith("%")){var B=P/w.height*100;P=B+"%"}else if(C.endsWith("vw")){var Q=P/this.window.innerWidth*100;P=Q+"vw"}else if(C.endsWith("vh")){var U=P/this.window.innerHeight*100;P=U+"vh"}}var X={width:this.createSizeForCssProperty(M,"width"),height:this.createSizeForCssProperty(P,"height")};this.flexDir==="row"?X.flexBasis=X.width:this.flexDir==="column"&&(X.flexBasis=X.height),GS.flushSync(function(){r.setState(X)}),this.props.onResize&&this.props.onResize(n,x,this.resizable,F)}},t.prototype.onMouseUp=function(n){var r=this.state,o=r.isResizing,s=r.direction,c=r.original;if(!(!o||!this.resizable)){var u={width:this.size.width-c.width,height:this.size.height-c.height};this.props.onResizeStop&&this.props.onResizeStop(n,s,this.resizable,u),this.props.size&&this.setState(this.props.size),this.unbindEvents(),this.setState({isResizing:!1,backgroundStyle:yr(yr({},this.state.backgroundStyle),{cursor:"auto"})})}},t.prototype.updateSize=function(n){this.setState({width:n.width,height:n.height})},t.prototype.renderResizer=function(){var n=this,r=this.props,o=r.enable,s=r.handleStyles,c=r.handleClasses,u=r.handleWrapperStyle,f=r.handleWrapperClass,p=r.handleComponent;if(!o)return null;var h=Object.keys(o).map(function(y){return o[y]!==!1?g.createElement(h9,{key:y,direction:y,onResizeStart:n.onResizeStart,replaceStyles:s&&s[y],className:c&&c[y]},p&&p[y]?p[y]:null):null});return g.createElement("div",{className:f,style:u},h)},t.prototype.render=function(){var n=this,r=Object.keys(this.props).reduce(function(c,u){return y9.indexOf(u)!==-1||(c[u]=n.props[u]),c},{}),o=yr(yr(yr({position:"relative",userSelect:this.state.isResizing?"none":"auto"},this.props.style),this.sizeStyle),{maxWidth:this.props.maxWidth,maxHeight:this.props.maxHeight,minWidth:this.props.minWidth,minHeight:this.props.minHeight,boxSizing:"border-box",flexShrink:0});this.state.flexBasis&&(o.flexBasis=this.state.flexBasis);var s=this.props.as||"div";return g.createElement(s,yr({ref:this.ref,style:o,className:this.props.className},r),this.state.isResizing&&g.createElement("div",{style:this.state.backgroundStyle}),this.props.children,this.renderResizer())},t.defaultProps={as:"div",onResizeStart:function(){},onResize:function(){},onResizeStop:function(){},enable:{top:!0,right:!0,bottom:!0,left:!0,topRight:!0,bottomRight:!0,bottomLeft:!0,topLeft:!0},style:{},grid:[1,1],lockAspectRatio:!1,lockAspectRatioExtraWidth:0,lockAspectRatioExtraHeight:0,scale:1,resizeRatio:1,snapGap:0},t}(g.PureComponent),Nx={color:void 0,size:void 0,className:void 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Set,this.wrapperComponent=null,this.contentComponent=null,this.isInitialized=!1,this.bounds=null,this.previousWheelEvent=null,this.wheelStopEventTimer=null,this.wheelAnimationTimer=null,this.isPanning=!1,this.startCoords=null,this.lastTouch=null,this.distance=null,this.lastDistance=null,this.pinchStartDistance=null,this.pinchStartScale=null,this.pinchMidpoint=null,this.doubleClickStopEventTimer=null,this.velocity=null,this.velocityTime=null,this.lastMousePosition=null,this.animate=!1,this.animation=null,this.maxBounds=null,this.pressedKeys={},this.mount=function(){n.initializeWindowEvents()},this.unmount=function(){n.cleanupWindowEvents()},this.update=function(r){ea(n,n.transformState.scale),n.setup=y1(r)},this.initializeWindowEvents=function(){var r,o=Kd(),s=(r=n.wrapperComponent)===null||r===void 0?void 0:r.ownerDocument,c=s==null?void 0:s.defaultView;c==null||c.addEventListener("mousedown",n.onPanningStart,o),c==null||c.addEventListener("mousemove",n.onPanning,o),c==null||c.addEventListener("mouseup",n.onPanningStop,o),s==null||s.addEventListener("mouseleave",n.clearPanning,o),c==null||c.addEventListener("keyup",n.setKeyUnPressed,o),c==null||c.addEventListener("keydown",n.setKeyPressed,o)},this.cleanupWindowEvents=function(){var r,o,s=Kd(),c=(r=n.wrapperComponent)===null||r===void 0?void 0:r.ownerDocument,u=c==null?void 0:c.defaultView;u==null||u.removeEventListener("mousedown",n.onPanningStart,s),u==null||u.removeEventListener("mousemove",n.onPanning,s),u==null||u.removeEventListener("mouseup",n.onPanningStop,s),c==null||c.removeEventListener("mouseleave",n.clearPanning,s),u==null||u.removeEventListener("keyup",n.setKeyUnPressed,s),u==null||u.removeEventListener("keydown",n.setKeyPressed,s),document.removeEventListener("mouseleave",n.clearPanning,s),br(n),(o=n.observer)===null||o===void 0||o.disconnect()},this.handleInitializeWrapperEvents=function(r){var o=Kd();r.addEventListener("wheel",n.onWheelZoom,o),r.addEventListener("dblclick",n.onDoubleClick,o),r.addEventListener("touchstart",n.onTouchPanningStart,o),r.addEventListener("touchmove",n.onTouchPanning,o),r.addEventListener("touchend",n.onTouchPanningStop,o)},this.handleInitialize=function(r){var o=n.setup.centerOnInit;n.applyTransformation(),o&&(n.setCenter(),n.observer=new ResizeObserver(function(){var s;n.setCenter(),(s=n.observer)===null||s===void 0||s.disconnect()}),n.observer.observe(r))},this.onWheelZoom=function(r){var o=n.setup.disabled;if(!o){var s=kF(n,r);if(s){var c=n.isPressingKeys(n.setup.wheel.activationKeys);c&&(RF(n,r),NF(n,r),FF(n,r))}}},this.onPanningStart=function(r){var o=n.setup.disabled,s=n.props.onPanningStart;if(!o){var c=f1(n,r);if(c){var u=n.isPressingKeys(n.setup.panning.activationKeys);u&&(r.preventDefault(),r.stopPropagation(),br(n),m1(n,r),Mt(dt(n),r,s))}}},this.onPanning=function(r){var o=n.setup.disabled,s=n.props.onPanning;if(!o){var c=h1(n);if(c){var u=n.isPressingKeys(n.setup.panning.activationKeys);u&&(r.preventDefault(),r.stopPropagation(),v1(n,r.clientX,r.clientY),Mt(dt(n),r,s))}}},this.onPanningStop=function(r){var o=n.props.onPanningStop;n.isPanning&&(pF(n),Mt(dt(n),r,o))},this.onPinchStart=function(r){var o=n.setup.disabled,s=n.props,c=s.onPinchingStart,u=s.onZoomStart;if(!o){var f=TF(n,r);f&&(LF(n,r),br(n),Mt(dt(n),r,c),Mt(dt(n),r,u))}},this.onPinch=function(r){var o=n.setup.disabled,s=n.props,c=s.onPinching,u=s.onZoom;if(!o){var f=_F(n);f&&(r.preventDefault(),r.stopPropagation(),BF(n,r),Mt(dt(n),r,c),Mt(dt(n),r,u))}},this.onPinchStop=function(r){var o=n.props,s=o.onPinchingStop,c=o.onZoomStop;n.pinchStartScale&&(HF(n),Mt(dt(n),r,s),Mt(dt(n),r,c))},this.onTouchPanningStart=function(r){var o=n.setup.disabled,s=n.props.onPanningStart;if(!o){var c=f1(n,r);if(c){var u=n.lastTouch&&+new Date-n.lastTouch<200;if(u&&r.touches.length===1)n.onDoubleClick(r);else{n.lastTouch=+new Date,br(n);var f=r.touches,p=f.length===1,h=f.length===2;p&&(br(n),m1(n,r),Mt(dt(n),r,s)),h&&n.onPinchStart(r)}}}},this.onTouchPanning=function(r){var o=n.setup.disabled,s=n.props.onPanning;if(n.isPanning&&r.touches.length===1){if(o)return;var c=h1(n);if(!c)return;r.preventDefault(),r.stopPropagation();var u=r.touches[0];v1(n,u.clientX,u.clientY),Mt(dt(n),r,s)}else r.touches.length>1&&n.onPinch(r)},this.onTouchPanningStop=function(r){n.onPanningStop(r),n.onPinchStop(r)},this.onDoubleClick=function(r){var o=n.setup.disabled;if(!o){var s=UF(n,r);s&&VF(n,r)}},this.clearPanning=function(r){n.isPanning&&n.onPanningStop(r)},this.setKeyPressed=function(r){n.pressedKeys[r.key]=!0},this.setKeyUnPressed=function(r){n.pressedKeys[r.key]=!1},this.isPressingKeys=function(r){return r.length?Boolean(r.find(function(o){return n.pressedKeys[o]})):!0},this.setTransformState=function(r,o,s){var c=n.props.onTransformed;if(!Number.isNaN(r)&&!Number.isNaN(o)&&!Number.isNaN(s)){r!==n.transformState.scale&&(n.transformState.previousScale=n.transformState.scale,n.transformState.scale=r),n.transformState.positionX=o,n.transformState.positionY=s;var u=dt(n);n.onChangeCallbacks.forEach(function(f){return f(u)}),Mt(u,{scale:r,positionX:o,positionY:s},c),n.applyTransformation()}else console.error("Detected NaN set state values")},this.setCenter=function(){if(n.wrapperComponent&&n.contentComponent){var r=C4(n.transformState.scale,n.wrapperComponent,n.contentComponent);n.setTransformState(r.scale,r.positionX,r.positionY)}},this.handleTransformStyles=function(r,o,s){return n.props.customTransform?n.props.customTransform(r,o,s):wF(r,o,s)},this.applyTransformation=function(){if(!(!n.mounted||!n.contentComponent)){var r=n.transformState,o=r.scale,s=r.positionX,c=r.positionY,u=n.handleTransformStyles(s,c,o);n.contentComponent.style.transform=u}},this.getContext=function(){return dt(n)},this.onChange=function(r){return n.onChangeCallbacks.has(r)||n.onChangeCallbacks.add(r),function(){n.onChangeCallbacks.delete(r)}},this.init=function(r,o){n.cleanupWindowEvents(),n.wrapperComponent=r,n.contentComponent=o,ea(n,n.transformState.scale),n.handleInitializeWrapperEvents(r),n.handleInitialize(o),n.initializeWindowEvents(),n.isInitialized=!0,Mt(dt(n),void 0,n.props.onInit)},this.props=t,this.setup=y1(this.props),this.transformState=y4(this.props)}return e}(),Kc=_e.createContext(null),GF=function(e,t){return typeof e=="function"?e(t):e},qF=_e.forwardRef(function(e,t){var n=g.useState(0),r=n[1],o=e.children,s=g.useRef(new $F(e)).current,c=GF(e.children,dt(s)),u=g.useCallback(function(){typeof o=="function"&&r(function(f){return f+1})},[o]);return g.useImperativeHandle(t,function(){return dt(s)},[s]),g.useEffect(function(){s.update(e)},[s,e]),g.useEffect(function(){return s.onChange(u)},[s,e,u]),_e.createElement(Kc.Provider,{value:s},c)});function YF(e,t){t===void 0&&(t={});var n=t.insertAt;if(!(!e||typeof document>"u")){var r=document.head||document.getElementsByTagName("head")[0],o=document.createElement("style");o.type="text/css",n==="top"&&r.firstChild?r.insertBefore(o,r.firstChild):r.appendChild(o),o.styleSheet?o.styleSheet.cssText=e:o.appendChild(document.createTextNode(e))}}var KF=`.transform-component-module_wrapper__7HFJe { - position: relative; - width: -moz-fit-content; - width: fit-content; - height: -moz-fit-content; - height: fit-content; - overflow: hidden; - -webkit-touch-callout: none; /* iOS Safari */ - -webkit-user-select: none; /* Safari */ - -khtml-user-select: none; /* Konqueror HTML */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* Internet Explorer/Edge */ - user-select: none; - margin: 0; - padding: 0; -} -.transform-component-module_content__uCDPE { - display: flex; - flex-wrap: wrap; - width: -moz-fit-content; - width: fit-content; - height: -moz-fit-content; - height: fit-content; - margin: 0; - padding: 0; - transform-origin: 0% 0%; -} -.transform-component-module_content__uCDPE img { - pointer-events: none; -} -`,x1={wrapper:"transform-component-module_wrapper__7HFJe",content:"transform-component-module_content__uCDPE"};YF(KF);var XF=function(e){var t=e.children,n=e.wrapperClass,r=n===void 0?"":n,o=e.contentClass,s=o===void 0?"":o,c=e.wrapperStyle,u=e.contentStyle,f=e.wrapperProps,p=f===void 0?{}:f,h=e.contentProps,y=h===void 0?{}:h,x=g.useContext(Kc).init,S=g.useRef(null),j=g.useRef(null);return g.useEffect(function(){var C=S.current,w=j.current;C!==null&&w!==null&&x&&x(C,w)},[]),_e.createElement("div",fo({},p,{ref:S,className:"react-transform-wrapper ".concat(x1.wrapper," ").concat(r),style:c}),_e.createElement("div",fo({},y,{ref:j,className:"react-transform-component ".concat(x1.content," ").concat(s),style:u}),t))},E4=function(){var e=g.useContext(Kc),t=g.useState(dt(e||{})),n=t[0],r=t[1];if(!e)throw new Error("Transform context mus be placed inside TransformWrapper");return g.useEffect(function(){e.onChange(function(o){r(o)})},[e]),n};_e.forwardRef(function(e,t){var n=g.useRef(null),r=g.useContext(Kc);return g.useEffect(function(){return r.onChange(function(o){if(n.current){var s=0,c=0;n.current.style.transform=r.handleTransformStyles(s,c,1/o.state.scale)}})},[r]),_e.createElement("div",fo({},e,{ref:jF([n,t])}))});const QF=()=>{const[e,t]=g.useState(0),[n,r]=g.useState(1),[o,s]=g.useState(1);return{rotation:e,scaleX:n,scaleY:o,flipHorizontally:()=>{r(n*-1)},flipVertically:()=>{s(o*-1)},rotateCounterClockwise:()=>{t(e===-270?0:e-90)},rotateClockwise:()=>{t(e===270?0:e+90)},reset:()=>{t(0),r(1),s(1)}}},ZF=({flipHorizontally:e,flipVertically:t,rotateCounterClockwise:n,rotateClockwise:r,reset:o})=>{const{zoomIn:s,zoomOut:c,resetTransform:u}=E4(),{t:f}=K();return l.jsxs(Qt,{isAttached:!0,orientation:"vertical",children:[l.jsx(ae,{icon:l.jsx(bD,{}),"aria-label":f("accessibility.zoomIn"),tooltip:f("accessibility.zoomIn"),onClick:()=>s(),fontSize:20}),l.jsx(ae,{icon:l.jsx(SD,{}),"aria-label":f("accessibility.zoomOut"),tooltip:f("accessibility.zoomOut"),onClick:()=>c(),fontSize:20}),l.jsx(ae,{icon:l.jsx(yD,{}),"aria-label":f("accessibility.rotateCounterClockwise"),tooltip:f("accessibility.rotateCounterClockwise"),onClick:n,fontSize:20}),l.jsx(ae,{icon:l.jsx(xD,{}),"aria-label":f("accessibility.rotateClockwise"),tooltip:f("accessibility.rotateClockwise"),onClick:r,fontSize:20}),l.jsx(ae,{icon:l.jsx(W0,{}),"aria-label":f("accessibility.flipHorizontally"),tooltip:f("accessibility.flipHorizontally"),onClick:e,fontSize:20}),l.jsx(ae,{icon:l.jsx(W0,{style:{transform:"rotate(90deg)"}}),"aria-label":f("accessibility.flipVertically"),tooltip:f("accessibility.flipVertically"),onClick:t,fontSize:20}),l.jsx(ae,{icon:l.jsx(Mb,{}),"aria-label":f("accessibility.reset"),tooltip:f("accessibility.reset"),onClick:()=>{u(),o()},fontSize:20})]})};function JF({image:e,alt:t,ref:n,styleClass:r,rotation:o,scaleX:s,scaleY:c}){const{centerView:u}=E4();return l.jsx(XF,{wrapperStyle:{width:"100%",height:"100%"},children:l.jsx("img",{style:{transform:`rotate(${o}deg) scaleX(${s}) scaleY(${c})`,width:"100%"},src:e.url,alt:t,ref:n,className:r||"",onLoad:()=>u(1,0,"easeOut")})})}const eL=pe([qr,Pr],(e,t)=>{const{currentImage:n}=e,{shouldShowImageDetails:r}=t;return{viewerImageToDisplay:n,shouldShowImageDetails:r}},{memoizeOptions:{resultEqualityCheck:ee.isEqual}});function tL(){const e=Y(),t=A(x=>x.lightbox.isLightboxOpen),{rotation:n,scaleX:r,scaleY:o,flipHorizontally:s,flipVertically:c,rotateCounterClockwise:u,rotateClockwise:f,reset:p}=QF(),{viewerImageToDisplay:h,shouldShowImageDetails:y}=A(eL);return ce("Esc",()=>{t&&e(Uo(!1))},[t]),l.jsx(xo,{children:t&&l.jsx(ar.div,{initial:{opacity:0},animate:{opacity:1},exit:{opacity:0},transition:{duration:.15,ease:"easeInOut"},style:{display:"flex",width:"100vw",height:`calc(100vh - ${dc*4}px)`,position:"fixed",top:`${dc*4}px`,background:"var(--invokeai-colors-base-900)",zIndex:99},children:l.jsxs(qF,{centerOnInit:!0,minScale:.1,initialPositionX:50,initialPositionY:50,children:[l.jsxs(L,{sx:{flexDir:"column",position:"absolute",insetInlineStart:4,gap:4,zIndex:3,top:4},children:[l.jsx(ae,{icon:l.jsx(gD,{}),"aria-label":"Exit Viewer",className:"lightbox-close-btn",onClick:()=>{e(Uo(!1))},fontSize:20}),l.jsx(ZF,{flipHorizontally:s,flipVertically:c,rotateCounterClockwise:u,rotateClockwise:f,reset:p})]}),l.jsx(L,{sx:{position:"absolute",top:4,zIndex:3,insetInlineStart:"50%",transform:"translate(-50%, 0)"},children:l.jsx(Rb,{})}),h&&l.jsxs(l.Fragment,{children:[l.jsx(JF,{rotation:n,scaleX:r,scaleY:o,image:h,styleClass:"lightbox-image"}),y&&l.jsx(xp,{image:h}),!y&&l.jsx(Ye,{sx:{position:"absolute",top:0,insetInlineStart:0,w:"100vw",h:"100vh",px:16,pointerEvents:"none"},children:l.jsx(Nb,{})})]})]})},"lightbox")})}IN();const aL=e=>{MN();const t=A(r=>r.ui.currentTheme),{setColorMode:n}=jC();return g.useEffect(()=>{n(["light"].includes(t)?"light":"dark")},[n,t]),l.jsxs(ir,{w:"100vw",h:"100vh",children:[l.jsx(tL,{}),l.jsxs(u9,{children:[l.jsx(iP,{}),l.jsxs(ir,{gap:4,p:4,gridAutoRows:"min-content auto",w:eP,h:tP,children:[e.children||l.jsx(_b,{}),l.jsxs(L,{gap:4,w:"full",h:"full",children:[l.jsx(PN,{}),l.jsx(FN,{})]})]}),l.jsx(Ye,{children:l.jsx(Z9,{})})]}),l.jsx($a,{children:l.jsx(AN,{})}),l.jsx($a,{children:l.jsx(_N,{})})]})};export{aL as default}; diff --git a/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-7791bd91.js b/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-7791bd91.js deleted file mode 100644 index 714b252ef2..0000000000 --- a/invokeai/frontend/web/dist/assets/ThemeLocaleProvider-7791bd91.js +++ /dev/null @@ -1,315 +0,0 @@ -import{j as y,cO as Ie,r as _,cP as bt,q as Lr,cQ as o,cR as b,cS as v,cT as S,cU as Vr,cV as ut,cW as vt,cN as ft,cX as mt,n as gt,cY as ht,E as pt}from"./index-e53e8108.js";import{d as yt,i as St,T as xt,j as $t,h as kt}from"./storeHooks-5cde7d31.js";var Or=` - :root { - --chakra-vh: 100vh; - } - - @supports (height: -webkit-fill-available) { - :root { - --chakra-vh: -webkit-fill-available; - } - } - - @supports (height: -moz-fill-available) { - :root { - --chakra-vh: -moz-fill-available; - } - } - - @supports (height: 100dvh) { - :root { - --chakra-vh: 100dvh; - } - } -`,_t=()=>y.jsx(Ie,{styles:Or}),wt=()=>y.jsx(Ie,{styles:` - html { - line-height: 1.5; - -webkit-text-size-adjust: 100%; - font-family: system-ui, sans-serif; - -webkit-font-smoothing: antialiased; - text-rendering: optimizeLegibility; - -moz-osx-font-smoothing: grayscale; - touch-action: manipulation; - } - - body { - position: relative; - min-height: 100%; - font-feature-settings: 'kern'; - } - - *, - *::before, - *::after { - border-width: 0; - border-style: solid; - box-sizing: border-box; - } - - main { - display: block; - } - - hr { - border-top-width: 1px; - box-sizing: content-box; - height: 0; - overflow: visible; - } - - pre, - code, - kbd, - samp { - font-family: SFMono-Regular, Menlo, Monaco, Consolas, monospace; - font-size: 1em; - } - - a { - background-color: transparent; - color: inherit; - text-decoration: inherit; - } - - abbr[title] { - border-bottom: none; - text-decoration: underline; - -webkit-text-decoration: underline dotted; - text-decoration: underline dotted; - } - - b, - strong { - font-weight: bold; - } - - small { - font-size: 80%; - } - - sub, - sup { - font-size: 75%; - line-height: 0; - position: relative; - vertical-align: baseline; - } - - sub { - bottom: -0.25em; - } - - sup { - top: -0.5em; - } - - img { - border-style: none; - } - - button, - input, - optgroup, - select, - textarea { - font-family: inherit; - font-size: 100%; - line-height: 1.15; - margin: 0; - } - - button, - input { - overflow: visible; - } - - button, - select { - text-transform: none; - } - - button::-moz-focus-inner, - 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_r(n,[{key:"changeLanguage",value:function(i){i&&(this.language=i)}},{key:"exists",value:function(i){var a=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(i==null)return!1;var s=this.resolve(i,a);return s&&s.res!==void 0}},{key:"extractFromKey",value:function(i,a){var s=a.nsSeparator!==void 0?a.nsSeparator:this.options.nsSeparator;s===void 0&&(s=":");var u=a.keySeparator!==void 0?a.keySeparator:this.options.keySeparator,c=a.ns||this.options.defaultNS||[],f=s&&i.indexOf(s)>-1,h=!this.options.userDefinedKeySeparator&&!a.keySeparator&&!this.options.userDefinedNsSeparator&&!a.nsSeparator&&!Y5(i,s,u);if(f&&!h){var p=i.match(this.interpolator.nestingRegexp);if(p&&p.length>0)return{key:i,namespaces:c};var g=i.split(s);(s!==u||s===u&&this.options.ns.indexOf(g[0])>-1)&&(c=g.shift()),i=g.join(u)}return typeof c=="string"&&(c=[c]),{key:i,namespaces:c}}},{key:"translate",value:function(i,a,s){var u=this;if(gr(a)!=="object"&&this.options.overloadTranslationOptionHandler&&(a=this.options.overloadTranslationOptionHandler(arguments)),a||(a={}),i==null)return"";Array.isArray(i)||(i=[String(i)]);var c=a.returnDetails!==void 0?a.returnDetails:this.options.returnDetails,f=a.keySeparator!==void 0?a.keySeparator:this.options.keySeparator,h=this.extractFromKey(i[i.length-1],a),p=h.key,g=h.namespaces,y=g[g.length-1],v=a.lng||this.language,w=a.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(v&&v.toLowerCase()==="cimode"){if(w){var x=a.nsSeparator||this.options.nsSeparator;return c?{res:"".concat(y).concat(x).concat(p),usedKey:p,exactUsedKey:p,usedLng:v,usedNS:y}:"".concat(y).concat(x).concat(p)}return c?{res:p,usedKey:p,exactUsedKey:p,usedLng:v,usedNS:y}:p}var _=this.resolve(i,a),S=_&&_.res,C=_&&_.usedKey||p,O=_&&_.exactUsedKey||p,I=Object.prototype.toString.apply(S),L=["[object Number]","[object Function]","[object RegExp]"],D=a.joinArrays!==void 0?a.joinArrays:this.options.joinArrays,U=!this.i18nFormat||this.i18nFormat.handleAsObject,X=typeof S!="string"&&typeof S!="boolean"&&typeof S!="number";if(U&&S&&X&&L.indexOf(I)<0&&!(typeof D=="string"&&I==="[object Array]")){if(!a.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var K=this.options.returnedObjectHandler?this.options.returnedObjectHandler(C,S,mn(mn({},a),{},{ns:g})):"key '".concat(p," (").concat(this.language,")' returned an object instead of string.");return c?(_.res=K,_):K}if(f){var te=I==="[object Array]",ce=te?[]:{},j=te?O:C;for(var T in S)if(Object.prototype.hasOwnProperty.call(S,T)){var $="".concat(j).concat(f).concat(T);ce[T]=this.translate($,mn(mn({},a),{joinArrays:!1,ns:g})),ce[T]===$&&(ce[T]=S[T])}S=ce}}else if(U&&typeof D=="string"&&I==="[object Array]")S=S.join(D),S&&(S=this.extendTranslation(S,i,a,s));else{var V=!1,ie=!1,W=a.count!==void 0&&typeof a.count!="string",Y=n.hasDefaultValue(a),Q=W?this.pluralResolver.getSuffix(v,a.count,a):"",oe=a["defaultValue".concat(Q)]||a.defaultValue;!this.isValidLookup(S)&&Y&&(V=!0,S=oe),this.isValidLookup(S)||(ie=!0,S=p);var ve=a.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,we=ve&&ie?void 0:S,Ue=Y&&oe!==S&&this.options.updateMissing;if(ie||V||Ue){if(this.logger.log(Ue?"updateKey":"missingKey",v,y,p,Ue?oe:S),f){var At=this.resolve(p,mn(mn({},a),{},{keySeparator:!1}));At&&At.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}var me=[],qt=this.languageUtils.getFallbackCodes(this.options.fallbackLng,a.lng||this.language);if(this.options.saveMissingTo==="fallback"&&qt&&qt[0])for(var Bt=0;Bt1&&arguments[1]!==void 0?arguments[1]:{},u,c,f,h,p;return typeof i=="string"&&(i=[i]),i.forEach(function(g){if(!a.isValidLookup(u)){var y=a.extractFromKey(g,s),v=y.key;c=v;var w=y.namespaces;a.options.fallbackNS&&(w=w.concat(a.options.fallbackNS));var x=s.count!==void 0&&typeof s.count!="string",_=x&&!s.ordinal&&s.count===0&&a.pluralResolver.shouldUseIntlApi(),S=s.context!==void 0&&(typeof s.context=="string"||typeof s.context=="number")&&s.context!=="",C=s.lngs?s.lngs:a.languageUtils.toResolveHierarchy(s.lng||a.language,s.fallbackLng);w.forEach(function(O){a.isValidLookup(u)||(p=O,!Fw["".concat(C[0],"-").concat(O)]&&a.utils&&a.utils.hasLoadedNamespace&&!a.utils.hasLoadedNamespace(p)&&(Fw["".concat(C[0],"-").concat(O)]=!0,a.logger.warn('key "'.concat(c,'" for languages "').concat(C.join(", "),`" won't get resolved as namespace "`).concat(p,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),C.forEach(function(I){if(!a.isValidLookup(u)){h=I;var L=[v];if(a.i18nFormat&&a.i18nFormat.addLookupKeys)a.i18nFormat.addLookupKeys(L,v,I,O,s);else{var D;x&&(D=a.pluralResolver.getSuffix(I,s.count,s));var U="".concat(a.options.pluralSeparator,"zero");if(x&&(L.push(v+D),_&&L.push(v+U)),S){var X="".concat(v).concat(a.options.contextSeparator).concat(s.context);L.push(X),x&&(L.push(X+D),_&&L.push(X+U))}}for(var K;K=L.pop();)a.isValidLookup(u)||(f=K,u=a.getResource(I,O,K,s))}}))})}}),{res:u,usedKey:c,exactUsedKey:f,usedLng:h,usedNS:p}}},{key:"isValidLookup",value:function(i){return i!==void 0&&!(!this.options.returnNull&&i===null)&&!(!this.options.returnEmptyString&&i==="")}},{key:"getResource",value:function(i,a,s){var u=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(i,a,s,u):this.resourceStore.getResource(i,a,s,u)}}],[{key:"hasDefaultValue",value:function(i){var a="defaultValue";for(var s in i)if(Object.prototype.hasOwnProperty.call(i,s)&&a===s.substring(0,a.length)&&i[s]!==void 0)return!0;return!1}}]),n}(Aa);function xm(t){return t.charAt(0).toUpperCase()+t.slice(1)}var zw=function(){function t(e){Sr(this,t),this.options=e,this.supportedLngs=this.options.supportedLngs||!1,this.logger=ii.create("languageUtils")}return _r(t,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],i=n.split("-");return this.options.lowerCaseLng?i=i.map(function(a){return a.toLowerCase()}):i.length===2?(i[0]=i[0].toLowerCase(),i[1]=i[1].toUpperCase(),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=xm(i[1].toLowerCase()))):i.length===3&&(i[0]=i[0].toLowerCase(),i[1].length===2&&(i[1]=i[1].toUpperCase()),i[0]!=="sgn"&&i[2].length===2&&(i[2]=i[2].toUpperCase()),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=xm(i[1].toLowerCase())),r.indexOf(i[2].toLowerCase())>-1&&(i[2]=xm(i[2].toLowerCase()))),i.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var i;return n.forEach(function(a){if(!i){var s=r.formatLanguageCode(a);(!r.options.supportedLngs||r.isSupportedCode(s))&&(i=s)}}),!i&&this.options.supportedLngs&&n.forEach(function(a){if(!i){var s=r.getLanguagePartFromCode(a);if(r.isSupportedCode(s))return i=s;i=r.options.supportedLngs.find(function(u){if(u.indexOf(s)===0)return u})}}),i||(i=this.getFallbackCodes(this.options.fallbackLng)[0]),i}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var i=n[r];return i||(i=n[this.getScriptPartFromCode(r)]),i||(i=n[this.formatLanguageCode(r)]),i||(i=n[this.getLanguagePartFromCode(r)]),i||(i=n.default),i||[]}},{key:"toResolveHierarchy",value:function(n,r){var i=this,a=this.getFallbackCodes(r||this.options.fallbackLng||[],n),s=[],u=function(f){f&&(i.isSupportedCode(f)?s.push(f):i.logger.warn("rejecting language code not found in supportedLngs: ".concat(f)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&u(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&u(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&u(this.getLanguagePartFromCode(n))):typeof n=="string"&&u(this.formatLanguageCode(n)),a.forEach(function(c){s.indexOf(c)<0&&u(i.formatLanguageCode(c))}),s}}]),t}(),t4=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],n4={1:function(e){return Number(e>1)},2:function(e){return Number(e!=1)},3:function(e){return 0},4:function(e){return Number(e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2)},5:function(e){return Number(e==0?0:e==1?1:e==2?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5)},6:function(e){return Number(e==1?0:e>=2&&e<=4?1:2)},7:function(e){return Number(e==1?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2)},8:function(e){return Number(e==1?0:e==2?1:e!=8&&e!=11?2:3)},9:function(e){return Number(e>=2)},10:function(e){return Number(e==1?0:e==2?1:e<7?2:e<11?3:4)},11:function(e){return Number(e==1||e==11?0:e==2||e==12?1:e>2&&e<20?2:3)},12:function(e){return Number(e%10!=1||e%100==11)},13:function(e){return Number(e!==0)},14:function(e){return Number(e==1?0:e==2?1:e==3?2:3)},15:function(e){return Number(e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2)},16:function(e){return Number(e%10==1&&e%100!=11?0:e!==0?1:2)},17:function(e){return Number(e==1||e%10==1&&e%100!=11?0:1)},18:function(e){return Number(e==0?0:e==1?1:2)},19:function(e){return Number(e==1?0:e==0||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3)},20:function(e){return Number(e==1?0:e==0||e%100>0&&e%100<20?1:2)},21:function(e){return Number(e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0)},22:function(e){return Number(e==1?0:e==2?1:(e<0||e>10)&&e%10==0?2:3)}},r4=["v1","v2","v3"],Gw={zero:0,one:1,two:2,few:3,many:4,other:5};function i4(){var t={};return t4.forEach(function(e){e.lngs.forEach(function(n){t[n]={numbers:e.nr,plurals:n4[e.fc]}})}),t}var a4=function(){function t(e){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};Sr(this,t),this.languageUtils=e,this.options=n,this.logger=ii.create("pluralResolver"),(!this.options.compatibilityJSON||this.options.compatibilityJSON==="v4")&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=i4()}return _r(t,[{key:"addRule",value:function(n,r){this.rules[n]=r}},{key:"getRule",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(n,{type:r.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[n]||this.rules[this.languageUtils.getLanguagePartFromCode(n)]}},{key:"needsPlural",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},i=this.getRule(n,r);return this.shouldUseIntlApi()?i&&i.resolvedOptions().pluralCategories.length>1:i&&i.numbers.length>1}},{key:"getPluralFormsOfKey",value:function(n,r){var i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(n,i).map(function(a){return"".concat(r).concat(a)})}},{key:"getSuffixes",value:function(n){var r=this,i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},a=this.getRule(n,i);return a?this.shouldUseIntlApi()?a.resolvedOptions().pluralCategories.sort(function(s,u){return Gw[s]-Gw[u]}).map(function(s){return"".concat(r.options.prepend).concat(s)}):a.numbers.map(function(s){return r.getSuffix(n,s,i)}):[]}},{key:"getSuffix",value:function(n,r){var i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},a=this.getRule(n,i);return a?this.shouldUseIntlApi()?"".concat(this.options.prepend).concat(a.select(r)):this.getSuffixRetroCompatible(a,r):(this.logger.warn("no plural rule found for: ".concat(n)),"")}},{key:"getSuffixRetroCompatible",value:function(n,r){var i=this,a=n.noAbs?n.plurals(r):n.plurals(Math.abs(r)),s=n.numbers[a];this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1&&(s===2?s="plural":s===1&&(s=""));var u=function(){return i.options.prepend&&s.toString()?i.options.prepend+s.toString():s.toString()};return this.options.compatibilityJSON==="v1"?s===1?"":typeof s=="number"?"_plural_".concat(s.toString()):u():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1?u():this.options.prepend&&a.toString()?this.options.prepend+a.toString():a.toString()}},{key:"shouldUseIntlApi",value:function(){return!r4.includes(this.options.compatibilityJSON)}}]),t}();function Uw(t,e){var n=Object.keys(t);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(t);e&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(t,i).enumerable})),n.push.apply(n,r)}return n}function Ar(t){for(var e=1;e0&&arguments[0]!==void 0?arguments[0]:{};Sr(this,t),this.logger=ii.create("interpolator"),this.options=e,this.format=e.interpolation&&e.interpolation.format||function(n){return n},this.init(e)}return _r(t,[{key:"init",value:function(){var n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};n.interpolation||(n.interpolation={escapeValue:!0});var r=n.interpolation;this.escape=r.escape!==void 0?r.escape:q5,this.escapeValue=r.escapeValue!==void 0?r.escapeValue:!0,this.useRawValueToEscape=r.useRawValueToEscape!==void 0?r.useRawValueToEscape:!1,this.prefix=r.prefix?es(r.prefix):r.prefixEscaped||"{{",this.suffix=r.suffix?es(r.suffix):r.suffixEscaped||"}}",this.formatSeparator=r.formatSeparator?r.formatSeparator:r.formatSeparator||",",this.unescapePrefix=r.unescapeSuffix?"":r.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":r.unescapeSuffix||"",this.nestingPrefix=r.nestingPrefix?es(r.nestingPrefix):r.nestingPrefixEscaped||es("$t("),this.nestingSuffix=r.nestingSuffix?es(r.nestingSuffix):r.nestingSuffixEscaped||es(")"),this.nestingOptionsSeparator=r.nestingOptionsSeparator?r.nestingOptionsSeparator:r.nestingOptionsSeparator||",",this.maxReplaces=r.maxReplaces?r.maxReplaces:1e3,this.alwaysFormat=r.alwaysFormat!==void 0?r.alwaysFormat:!1,this.resetRegExp()}},{key:"reset",value:function(){this.options&&this.init(this.options)}},{key:"resetRegExp",value:function(){var n="".concat(this.prefix,"(.+?)").concat(this.suffix);this.regexp=new RegExp(n,"g");var r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var i="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(i,"g")}},{key:"interpolate",value:function(n,r,i,a){var s=this,u,c,f,h=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function p(x){return x.replace(/\$/g,"$$$$")}var g=function(_){if(_.indexOf(s.formatSeparator)<0){var S=Dw(r,h,_);return s.alwaysFormat?s.format(S,void 0,i,Ar(Ar(Ar({},a),r),{},{interpolationkey:_})):S}var C=_.split(s.formatSeparator),O=C.shift().trim(),I=C.join(s.formatSeparator).trim();return s.format(Dw(r,h,O),I,i,Ar(Ar(Ar({},a),r),{},{interpolationkey:O}))};this.resetRegExp();var y=a&&a.missingInterpolationHandler||this.options.missingInterpolationHandler,v=a&&a.interpolation&&a.interpolation.skipOnVariables!==void 0?a.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables,w=[{regex:this.regexpUnescape,safeValue:function(_){return p(_)}},{regex:this.regexp,safeValue:function(_){return s.escapeValue?p(s.escape(_)):p(_)}}];return w.forEach(function(x){for(f=0;u=x.regex.exec(n);){var _=u[1].trim();if(c=g(_),c===void 0)if(typeof y=="function"){var S=y(n,u,a);c=typeof S=="string"?S:""}else if(a&&Object.prototype.hasOwnProperty.call(a,_))c="";else if(v){c=u[0];continue}else s.logger.warn("missed to pass in variable ".concat(_," for interpolating ").concat(n)),c="";else typeof c!="string"&&!s.useRawValueToEscape&&(c=Lw(c));var C=x.safeValue(c);if(n=n.replace(u[0],C),v?(x.regex.lastIndex+=c.length,x.regex.lastIndex-=u[0].length):x.regex.lastIndex=0,f++,f>=s.maxReplaces)break}}),n}},{key:"nest",value:function(n,r){var i=this,a=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},s,u,c;function f(y,v){var w=this.nestingOptionsSeparator;if(y.indexOf(w)<0)return y;var x=y.split(new RegExp("".concat(w,"[ ]*{"))),_="{".concat(x[1]);y=x[0],_=this.interpolate(_,c);var S=_.match(/'/g),C=_.match(/"/g);(S&&S.length%2===0&&!C||C.length%2!==0)&&(_=_.replace(/'/g,'"'));try{c=JSON.parse(_),v&&(c=Ar(Ar({},v),c))}catch(O){return this.logger.warn("failed parsing options string in nesting for key ".concat(y),O),"".concat(y).concat(w).concat(_)}return delete c.defaultValue,y}for(;s=this.nestingRegexp.exec(n);){var h=[];c=Ar({},a),c=c.replace&&typeof c.replace!="string"?c.replace:c,c.applyPostProcessor=!1,delete c.defaultValue;var p=!1;if(s[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(s[1])){var g=s[1].split(this.formatSeparator).map(function(y){return y.trim()});s[1]=g.shift(),h=g,p=!0}if(u=r(f.call(this,s[1].trim(),c),c),u&&s[0]===n&&typeof u!="string")return u;typeof 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u=s.split(":"),c=G5(u),f=c[0],h=c.slice(1),p=h.join(":").trim().replace(/^'+|'+$/g,"");n[f.trim()]||(n[f.trim()]=p),p==="false"&&(n[f.trim()]=!1),p==="true"&&(n[f.trim()]=!0),isNaN(p)||(n[f.trim()]=parseInt(p,10))}})}}return{formatName:e,formatOptions:n}}function ts(t){var e={};return function(r,i,a){var s=i+JSON.stringify(a),u=e[s];return u||(u=t(i,a),e[s]=u),u(r)}}var l4=function(){function t(){var e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};Sr(this,t),this.logger=ii.create("formatter"),this.options=e,this.formats={number:ts(function(n,r){var i=new Intl.NumberFormat(n,Ri({},r));return function(a){return i.format(a)}}),currency:ts(function(n,r){var i=new Intl.NumberFormat(n,Ri(Ri({},r),{},{style:"currency"}));return function(a){return i.format(a)}}),datetime:ts(function(n,r){var i=new Intl.DateTimeFormat(n,Ri({},r));return function(a){return i.format(a)}}),relativetime:ts(function(n,r){var i=new Intl.RelativeTimeFormat(n,Ri({},r));return function(a){return i.format(a,r.range||"day")}}),list:ts(function(n,r){var i=new Intl.ListFormat(n,Ri({},r));return function(a){return i.format(a)}})},this.init(e)}return _r(t,[{key:"init",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}},i=r.interpolation;this.formatSeparator=i.formatSeparator?i.formatSeparator:i.formatSeparator||","}},{key:"add",value:function(n,r){this.formats[n.toLowerCase().trim()]=r}},{key:"addCached",value:function(n,r){this.formats[n.toLowerCase().trim()]=ts(r)}},{key:"format",value:function(n,r,i){var a=this,s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{},u=r.split(this.formatSeparator),c=u.reduce(function(f,h){var p=s4(h),g=p.formatName,y=p.formatOptions;if(a.formats[g]){var v=f;try{var w=s&&s.formatParams&&s.formatParams[s.interpolationkey]||{},x=w.locale||w.lng||s.locale||s.lng||i;v=a.formats[g](f,x,Ri(Ri(Ri({},y),s),w))}catch(_){a.logger.warn(_)}return v}else a.logger.warn("there was no format function for 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Sr(this,n),s=e.call(this),Qh&&Aa.call(Ea(s)),s.backend=r,s.store=i,s.services=a,s.languageUtils=a.languageUtils,s.options=u,s.logger=ii.create("backendConnector"),s.waitingReads=[],s.maxParallelReads=u.maxParallelReads||10,s.readingCalls=0,s.maxRetries=u.maxRetries>=0?u.maxRetries:5,s.retryTimeout=u.retryTimeout>=1?u.retryTimeout:350,s.state={},s.queue=[],s.backend&&s.backend.init&&s.backend.init(a,u.backend,u),s}return _r(n,[{key:"queueLoad",value:function(i,a,s,u){var c=this,f={},h={},p={},g={};return i.forEach(function(y){var v=!0;a.forEach(function(w){var x="".concat(y,"|").concat(w);!s.reload&&c.store.hasResourceBundle(y,w)?c.state[x]=2:c.state[x]<0||(c.state[x]===1?h[x]===void 0&&(h[x]=!0):(c.state[x]=1,v=!1,h[x]===void 0&&(h[x]=!0),f[x]===void 0&&(f[x]=!0),g[w]===void 0&&(g[w]=!0)))}),v||(p[y]=!0)}),(Object.keys(f).length||Object.keys(h).length)&&this.queue.push({pending:h,pendingCount:Object.keys(h).length,loaded:{},errors:[],callback:u}),{toLoad:Object.keys(f),pending:Object.keys(h),toLoadLanguages:Object.keys(p),toLoadNamespaces:Object.keys(g)}}},{key:"loaded",value:function(i,a,s){var u=i.split("|"),c=u[0],f=u[1];a&&this.emit("failedLoading",c,f,a),s&&this.store.addResourceBundle(c,f,s),this.state[i]=a?-1:2;var h={};this.queue.forEach(function(p){H5(p.loaded,[c],f),f4(p,i),a&&p.errors.push(a),p.pendingCount===0&&!p.done&&(Object.keys(p.loaded).forEach(function(g){h[g]||(h[g]={});var y=p.loaded[g];y.length&&y.forEach(function(v){h[g][v]===void 0&&(h[g][v]=!0)})}),p.done=!0,p.errors.length?p.callback(p.errors):p.callback())}),this.emit("loaded",h),this.queue=this.queue.filter(function(p){return!p.done})}},{key:"read",value:function(i,a,s){var u=this,c=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,f=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,h=arguments.length>5?arguments[5]:void 0;if(!i.length)return h(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:i,ns:a,fcName:s,tried:c,wait:f,callback:h});return}this.readingCalls++;var p=function(w,x){if(u.readingCalls--,u.waitingReads.length>0){var _=u.waitingReads.shift();u.read(_.lng,_.ns,_.fcName,_.tried,_.wait,_.callback)}if(w&&x&&c2&&arguments[2]!==void 0?arguments[2]:{},c=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),c&&c();typeof i=="string"&&(i=this.languageUtils.toResolveHierarchy(i)),typeof a=="string"&&(a=[a]);var f=this.queueLoad(i,a,u,c);if(!f.toLoad.length)return f.pending.length||c(),null;f.toLoad.forEach(function(h){s.loadOne(h)})}},{key:"load",value:function(i,a,s){this.prepareLoading(i,a,{},s)}},{key:"reload",value:function(i,a,s){this.prepareLoading(i,a,{reload:!0},s)}},{key:"loadOne",value:function(i){var a=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:"",u=i.split("|"),c=u[0],f=u[1];this.read(c,f,"read",void 0,void 0,function(h,p){h&&a.logger.warn("".concat(s,"loading namespace ").concat(f," for language ").concat(c," failed"),h),!h&&p&&a.logger.log("".concat(s,"loaded namespace ").concat(f," for language ").concat(c),p),a.loaded(i,h,p)})}},{key:"saveMissing",value:function(i,a,s,u,c){var f=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{},h=arguments.length>6&&arguments[6]!==void 0?arguments[6]:function(){};if(this.services.utils&&this.services.utils.hasLoadedNamespace&&!this.services.utils.hasLoadedNamespace(a)){this.logger.warn('did not save key "'.concat(s,'" as the namespace "').concat(a,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(s==null||s==="")){if(this.backend&&this.backend.create){var p=Hw(Hw({},f),{},{isUpdate:c}),g=this.backend.create.bind(this.backend);if(g.length<6)try{var y;g.length===5?y=g(i,a,s,u,p):y=g(i,a,s,u),y&&typeof y.then=="function"?y.then(function(v){return h(null,v)}).catch(h):h(null,y)}catch(v){h(v)}else g(i,a,s,u,h,p)}!i||!i[0]||this.store.addResource(i[0],a,s,u)}}}]),n}(Aa);function Vw(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(e){var n={};if(gr(e[1])==="object"&&(n=e[1]),typeof e[1]=="string"&&(n.defaultValue=e[1]),typeof e[2]=="string"&&(n.tDescription=e[2]),gr(e[2])==="object"||gr(e[3])==="object"){var r=e[3]||e[2];Object.keys(r).forEach(function(i){n[i]=r[i]})}return n},interpolation:{escapeValue:!0,format:function(e,n,r,i){return e},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function qw(t){return typeof t.ns=="string"&&(t.ns=[t.ns]),typeof t.fallbackLng=="string"&&(t.fallbackLng=[t.fallbackLng]),typeof t.fallbackNS=="string"&&(t.fallbackNS=[t.fallbackNS]),t.supportedLngs&&t.supportedLngs.indexOf("cimode")<0&&(t.supportedLngs=t.supportedLngs.concat(["cimode"])),t}function Kw(t,e){var n=Object.keys(t);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(t);e&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(t,i).enumerable})),n.push.apply(n,r)}return n}function Qr(t){for(var e=1;e"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function Rf(){}function g4(t){var e=Object.getOwnPropertyNames(Object.getPrototypeOf(t));e.forEach(function(n){typeof t[n]=="function"&&(t[n]=t[n].bind(t))})}var ih=function(t){Xh(n,t);var e=h4(n);function n(){var r,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},a=arguments.length>1?arguments[1]:void 0;if(Sr(this,n),r=e.call(this),Qh&&Aa.call(Ea(r)),r.options=qw(i),r.services={},r.logger=ii,r.modules={external:[]},g4(Ea(r)),a&&!r.isInitialized&&!i.isClone){if(!r.options.initImmediate)return r.init(i,a),Ju(r,Ea(r));setTimeout(function(){r.init(i,a)},0)}return r}return _r(n,[{key:"init",value:function(){var i=this,a=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},s=arguments.length>1?arguments[1]:void 0;typeof a=="function"&&(s=a,a={}),!a.defaultNS&&a.defaultNS!==!1&&a.ns&&(typeof a.ns=="string"?a.defaultNS=a.ns:a.ns.indexOf("translation")<0&&(a.defaultNS=a.ns[0]));var u=Vw();this.options=Qr(Qr(Qr({},u),this.options),qw(a)),this.options.compatibilityAPI!=="v1"&&(this.options.interpolation=Qr(Qr({},u.interpolation),this.options.interpolation)),a.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=a.keySeparator),a.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=a.nsSeparator);function c(_){return _?typeof _=="function"?new _:_:null}if(!this.options.isClone){this.modules.logger?ii.init(c(this.modules.logger),this.options):ii.init(null,this.options);var f;this.modules.formatter?f=this.modules.formatter:typeof Intl<"u"&&(f=l4);var h=new zw(this.options);this.store=new J5(this.options.resources,this.options);var p=this.services;p.logger=ii,p.resourceStore=this.store,p.languageUtils=h,p.pluralResolver=new a4(h,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),f&&(!this.options.interpolation.format||this.options.interpolation.format===u.interpolation.format)&&(p.formatter=c(f),p.formatter.init(p,this.options),this.options.interpolation.format=p.formatter.format.bind(p.formatter)),p.interpolator=new o4(this.options),p.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},p.backendConnector=new d4(c(this.modules.backend),p.resourceStore,p,this.options),p.backendConnector.on("*",function(_){for(var S=arguments.length,C=new Array(S>1?S-1:0),O=1;O1?S-1:0),O=1;O0&&g[0]!=="dev"&&(this.options.lng=g[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var y=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];y.forEach(function(_){i[_]=function(){var S;return(S=i.store)[_].apply(S,arguments)}});var v=["addResource","addResources","addResourceBundle","removeResourceBundle"];v.forEach(function(_){i[_]=function(){var S;return(S=i.store)[_].apply(S,arguments),i}});var w=Bl(),x=function(){var S=function(O,I){i.isInitialized&&!i.initializedStoreOnce&&i.logger.warn("init: i18next is already initialized. You should call init just once!"),i.isInitialized=!0,i.options.isClone||i.logger.log("initialized",i.options),i.emit("initialized",i.options),w.resolve(I),s(O,I)};if(i.languages&&i.options.compatibilityAPI!=="v1"&&!i.isInitialized)return S(null,i.t.bind(i));i.changeLanguage(i.options.lng,S)};return this.options.resources||!this.options.initImmediate?x():setTimeout(x,0),w}},{key:"loadResources",value:function(i){var a=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:Rf,u=s,c=typeof i=="string"?i:this.language;if(typeof i=="function"&&(u=i),!this.options.resources||this.options.partialBundledLanguages){if(c&&c.toLowerCase()==="cimode")return u();var f=[],h=function(y){if(y){var v=a.services.languageUtils.toResolveHierarchy(y);v.forEach(function(w){f.indexOf(w)<0&&f.push(w)})}};if(c)h(c);else{var p=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);p.forEach(function(g){return h(g)})}this.options.preload&&this.options.preload.forEach(function(g){return h(g)}),this.services.backendConnector.load(f,this.options.ns,function(g){!g&&!a.resolvedLanguage&&a.language&&a.setResolvedLanguage(a.language),u(g)})}else u(null)}},{key:"reloadResources",value:function(i,a,s){var u=Bl();return i||(i=this.languages),a||(a=this.options.ns),s||(s=Rf),this.services.backendConnector.reload(i,a,function(c){u.resolve(),s(c)}),u}},{key:"use",value:function(i){if(!i)throw new Error("You are passing an undefined module! Please check the object you are passing to i18next.use()");if(!i.type)throw new Error("You are passing a wrong module! 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n=xk(e,this.socket.binaryType);this.onPacket(n)}onPacket(e){super.emitReserved("packet",e)}onClose(e){this.readyState="closed",super.emitReserved("close",e)}pause(e){}}const Rk="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz-_".split(""),Yv=64,$B={};let hb=0,If=0,pb;function gb(t){let e="";do e=Rk[t%Yv]+e,t=Math.floor(t/Yv);while(t>0);return e}function Ak(){const t=gb(+new Date);return t!==pb?(hb=0,pb=t):t+"."+gb(hb++)}for(;If{this.readyState="paused",e()};if(this.polling||!this.writable){let r=0;this.polling&&(r++,this.once("pollComplete",function(){--r||n()})),this.writable||(r++,this.once("drain",function(){--r||n()}))}else n()}poll(){this.polling=!0,this.doPoll(),this.emitReserved("poll")}onData(e){const n=r=>{if(this.readyState==="opening"&&r.type==="open"&&this.onOpen(),r.type==="close")return this.onClose({description:"transport closed by the server"}),!1;this.onPacket(r)};AB(e,this.socket.binaryType).forEach(n),this.readyState!=="closed"&&(this.polling=!1,this.emitReserved("pollComplete"),this.readyState==="open"&&this.poll())}doClose(){const e=()=>{this.write([{type:"close"}])};this.readyState==="open"?e():this.once("open",e)}write(e){this.writable=!1,RB(e,n=>{this.doWrite(n,()=>{this.writable=!0,this.emitReserved("drain")})})}uri(){let e=this.query||{};const n=this.opts.secure?"https":"http";let r="";this.opts.timestampRequests!==!1&&(e[this.opts.timestampParam]=Ak()),!this.supportsBinary&&!e.sid&&(e.b64=1),this.opts.port&&(n==="https"&&Number(this.opts.port)!==443||n==="http"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port);const i=Ik(e),a=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(a?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}request(e={}){return Object.assign(e,{xd:this.xd,xs:this.xs},this.opts),new ui(this.uri(),e)}doWrite(e,n){const r=this.request({method:"POST",data:e});r.on("success",n),r.on("error",(i,a)=>{this.onError("xhr post error",i,a)})}doPoll(){const e=this.request();e.on("data",this.onData.bind(this)),e.on("error",(n,r)=>{this.onError("xhr poll error",n,r)}),this.pollXhr=e}}class ui extends Rt{constructor(e,n){super(),Jh(this,n),this.opts=n,this.method=n.method||"GET",this.uri=e,this.async=n.async!==!1,this.data=n.data!==void 0?n.data:null,this.create()}create(){const e=Ok(this.opts,"agent","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","autoUnref");e.xdomain=!!this.opts.xd,e.xscheme=!!this.opts.xs;const n=this.xhr=new Mk(e);try{n.open(this.method,this.uri,this.async);try{if(this.opts.extraHeaders){n.setDisableHeaderCheck&&n.setDisableHeaderCheck(!0);for(let r in this.opts.extraHeaders)this.opts.extraHeaders.hasOwnProperty(r)&&n.setRequestHeader(r,this.opts.extraHeaders[r])}}catch{}if(this.method==="POST")try{n.setRequestHeader("Content-type","text/plain;charset=UTF-8")}catch{}try{n.setRequestHeader("Accept","*/*")}catch{}"withCredentials"in n&&(n.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(n.timeout=this.opts.requestTimeout),n.onreadystatechange=()=>{n.readyState===4&&(n.status===200||n.status===1223?this.onLoad():this.setTimeoutFn(()=>{this.onError(typeof n.status=="number"?n.status:0)},0))},n.send(this.data)}catch(r){this.setTimeoutFn(()=>{this.onError(r)},0);return}typeof document<"u"&&(this.index=ui.requestsCount++,ui.requests[this.index]=this)}onError(e){this.emitReserved("error",e,this.xhr),this.cleanup(!0)}cleanup(e){if(!(typeof this.xhr>"u"||this.xhr===null)){if(this.xhr.onreadystatechange=UB,e)try{this.xhr.abort()}catch{}typeof document<"u"&&delete ui.requests[this.index],this.xhr=null}}onLoad(){const e=this.xhr.responseText;e!==null&&(this.emitReserved("data",e),this.emitReserved("success"),this.cleanup())}abort(){this.cleanup()}}ui.requestsCount=0;ui.requests={};if(typeof document<"u"){if(typeof attachEvent=="function")attachEvent("onunload",mb);else if(typeof addEventListener=="function"){const t="onpagehide"in fr?"pagehide":"unload";addEventListener(t,mb,!1)}}function mb(){for(let t in ui.requests)ui.requests.hasOwnProperty(t)&&ui.requests[t].abort()}const Dk=(()=>typeof Promise=="function"&&typeof Promise.resolve=="function"?e=>Promise.resolve().then(e):(e,n)=>n(e,0))(),Lf=fr.WebSocket||fr.MozWebSocket,vb=!0,HB="arraybuffer",yb=typeof navigator<"u"&&typeof navigator.product=="string"&&navigator.product.toLowerCase()==="reactnative";class VB extends Tk{constructor(e){super(e),this.supportsBinary=!e.forceBase64}get name(){return"websocket"}doOpen(){if(!this.check())return;const e=this.uri(),n=this.opts.protocols,r=yb?{}:Ok(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(r.headers=this.opts.extraHeaders);try{this.ws=vb&&!yb?n?new Lf(e,n):new Lf(e):new Lf(e,n,r)}catch(i){return this.emitReserved("error",i)}this.ws.binaryType=this.socket.binaryType||HB,this.addEventListeners()}addEventListeners(){this.ws.onopen=()=>{this.opts.autoUnref&&this.ws._socket.unref(),this.onOpen()},this.ws.onclose=e=>this.onClose({description:"websocket connection closed",context:e}),this.ws.onmessage=e=>this.onData(e.data),this.ws.onerror=e=>this.onError("websocket error",e)}write(e){this.writable=!1;for(let n=0;n{const s={};try{vb&&this.ws.send(a)}catch{}i&&Dk(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})}}doClose(){typeof this.ws<"u"&&(this.ws.close(),this.ws=null)}uri(){let e=this.query||{};const n=this.opts.secure?"wss":"ws";let r="";this.opts.port&&(n==="wss"&&Number(this.opts.port)!==443||n==="ws"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port),this.opts.timestampRequests&&(e[this.opts.timestampParam]=Ak()),this.supportsBinary||(e.b64=1);const i=Ik(e),a=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(a?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}check(){return!!Lf}}const qB={websocket:VB,polling:WB},KB=/^(?:(?![^:@\/?#]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@\/?#]*)(?::([^:@\/?#]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,YB=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function Xv(t){const e=t,n=t.indexOf("["),r=t.indexOf("]");n!=-1&&r!=-1&&(t=t.substring(0,n)+t.substring(n,r).replace(/:/g,";")+t.substring(r,t.length));let i=KB.exec(t||""),a={},s=14;for(;s--;)a[YB[s]]=i[s]||"";return n!=-1&&r!=-1&&(a.source=e,a.host=a.host.substring(1,a.host.length-1).replace(/;/g,":"),a.authority=a.authority.replace("[","").replace("]","").replace(/;/g,":"),a.ipv6uri=!0),a.pathNames=XB(a,a.path),a.queryKey=QB(a,a.query),a}function XB(t,e){const n=/\/{2,9}/g,r=e.replace(n,"/").split("/");return(e.slice(0,1)=="/"||e.length===0)&&r.splice(0,1),e.slice(-1)=="/"&&r.splice(r.length-1,1),r}function QB(t,e){const n={};return e.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,function(r,i,a){i&&(n[i]=a)}),n}let Nk=class os extends Rt{constructor(e,n={}){super(),this.writeBuffer=[],e&&typeof e=="object"&&(n=e,e=null),e?(e=Xv(e),n.hostname=e.host,n.secure=e.protocol==="https"||e.protocol==="wss",n.port=e.port,e.query&&(n.query=e.query)):n.host&&(n.hostname=Xv(n.host).host),Jh(this,n),this.secure=n.secure!=null?n.secure:typeof location<"u"&&location.protocol==="https:",n.hostname&&!n.port&&(n.port=this.secure?"443":"80"),this.hostname=n.hostname||(typeof location<"u"?location.hostname:"localhost"),this.port=n.port||(typeof location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket"],this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},n),this.opts.path=this.opts.path.replace(/\/$/,"")+(this.opts.addTrailingSlash?"/":""),typeof this.opts.query=="string"&&(this.opts.query=zB(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(e){const n=Object.assign({},this.opts.query);n.EIO=Ek,n.transport=e,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts.transportOptions[e],this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new qB[e](r)}open(){let e;if(this.opts.rememberUpgrade&&os.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)e="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else e=this.transports[0];this.readyState="opening";try{e=this.createTransport(e)}catch{this.transports.shift(),this.open();return}e.open(),this.setTransport(e)}setTransport(e){this.transport&&this.transport.removeAllListeners(),this.transport=e,e.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(e){let n=this.createTransport(e),r=!1;os.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",p=>{if(!r)if(p.type==="pong"&&p.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;os.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(h(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const g=new Error("probe error");g.transport=n.name,this.emitReserved("upgradeError",g)}}))};function a(){r||(r=!0,h(),n.close(),n=null)}const s=p=>{const g=new Error("probe error: "+p);g.transport=n.name,a(),this.emitReserved("upgradeError",g)};function u(){s("transport closed")}function c(){s("socket closed")}function f(p){n&&p.name!==n.name&&a()}const h=()=>{n.removeListener("open",i),n.removeListener("error",s),n.removeListener("close",u),this.off("close",c),this.off("upgrading",f)};n.once("open",i),n.once("error",s),n.once("close",u),this.once("close",c),this.once("upgrading",f),n.open()}onOpen(){if(this.readyState="open",os.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade){let e=0;const n=this.upgrades.length;for(;e{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(e,n,r){return this.sendPacket("message",e,n,r),this}send(e,n,r){return this.sendPacket("message",e,n,r),this}sendPacket(e,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const a={type:e,data:n,options:r};this.emitReserved("packetCreate",a),this.writeBuffer.push(a),i&&this.once("flush",i),this.flush()}close(){const e=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),e()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():e()}):this.upgrading?r():e()),this}onError(e){os.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}onClose(e,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(e){const n=[];let r=0;const i=e.length;for(;rtypeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(t):t.buffer instanceof ArrayBuffer,Bk=Object.prototype.toString,tF=typeof Blob=="function"||typeof Blob<"u"&&Bk.call(Blob)==="[object BlobConstructor]",nF=typeof File=="function"||typeof File<"u"&&Bk.call(File)==="[object FileConstructor]";function T0(t){return ZB&&(t instanceof ArrayBuffer||eF(t))||tF&&t instanceof Blob||nF&&t instanceof File}function pd(t,e){if(!t||typeof t!="object")return!1;if(Array.isArray(t)){for(let n=0,r=t.length;n=0&&t.num0;case Be.ACK:case Be.BINARY_ACK:return Array.isArray(n)}}destroy(){this.reconstructor&&(this.reconstructor.finishedReconstruction(),this.reconstructor=null)}}class sF{constructor(e){this.packet=e,this.buffers=[],this.reconPack=e}takeBinaryData(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){const n=iF(this.reconPack,this.buffers);return this.finishedReconstruction(),n}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const lF=Object.freeze(Object.defineProperty({__proto__:null,Decoder:R0,Encoder:oF,get PacketType(){return Be},protocol:aF},Symbol.toStringTag,{value:"Module"}));function Dr(t,e,n){return t.on(e,n),function(){t.off(e,n)}}const uF=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class Fk extends Rt{constructor(e,n,r){super(),this.connected=!1,this.recovered=!1,this.receiveBuffer=[],this.sendBuffer=[],this._queue=[],this._queueSeq=0,this.ids=0,this.acks={},this.flags={},this.io=e,this.nsp=n,r&&r.auth&&(this.auth=r.auth),this._opts=Object.assign({},r),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const e=this.io;this.subs=[Dr(e,"open",this.onopen.bind(this)),Dr(e,"packet",this.onpacket.bind(this)),Dr(e,"error",this.onerror.bind(this)),Dr(e,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected?this:(this.subEvents(),this.io._reconnecting||this.io.open(),this.io._readyState==="open"&&this.onopen(),this)}open(){return this.connect()}send(...e){return e.unshift("message"),this.emit.apply(this,e),this}emit(e,...n){if(uF.hasOwnProperty(e))throw new Error('"'+e.toString()+'" is a reserved event name');if(n.unshift(e),this._opts.retries&&!this.flags.fromQueue&&!this.flags.volatile)return this._addToQueue(n),this;const r={type:Be.EVENT,data:n};if(r.options={},r.options.compress=this.flags.compress!==!1,typeof n[n.length-1]=="function"){const s=this.ids++,u=n.pop();this._registerAckCallback(s,u),r.id=s}const i=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!i||!this.connected)||(this.connected?(this.notifyOutgoingListeners(r),this.packet(r)):this.sendBuffer.push(r)),this.flags={},this}_registerAckCallback(e,n){var r;const i=(r=this.flags.timeout)!==null&&r!==void 0?r:this._opts.ackTimeout;if(i===void 0){this.acks[e]=n;return}const a=this.io.setTimeoutFn(()=>{delete this.acks[e];for(let s=0;s{this.io.clearTimeoutFn(a),n.apply(this,[null,...s])}}emitWithAck(e,...n){const r=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((i,a)=>{n.push((s,u)=>r?s?a(s):i(u):i(s)),this.emit(e,...n)})}_addToQueue(e){let n;typeof e[e.length-1]=="function"&&(n=e.pop());const r={id:this._queueSeq++,tryCount:0,pending:!1,args:e,flags:Object.assign({fromQueue:!0},this.flags)};e.push((i,...a)=>r!==this._queue[0]?void 0:(i!==null?r.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(i)):(this._queue.shift(),n&&n(null,...a)),r.pending=!1,this._drainQueue())),this._queue.push(r),this._drainQueue()}_drainQueue(e=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!e||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(e){e.nsp=this.nsp,this.io._packet(e)}onopen(){typeof this.auth=="function"?this.auth(e=>{this._sendConnectPacket(e)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(e){this.packet({type:Be.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},e):e})}onerror(e){this.connected||this.emitReserved("connect_error",e)}onclose(e,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,n)}onpacket(e){if(e.nsp===this.nsp)switch(e.type){case Be.CONNECT:e.data&&e.data.sid?this.onconnect(e.data.sid,e.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case Be.EVENT:case Be.BINARY_EVENT:this.onevent(e);break;case Be.ACK:case Be.BINARY_ACK:this.onack(e);break;case Be.DISCONNECT:this.ondisconnect();break;case Be.CONNECT_ERROR:this.destroy();const r=new Error(e.data.message);r.data=e.data.data,this.emitReserved("connect_error",r);break}}onevent(e){const n=e.data||[];e.id!=null&&n.push(this.ack(e.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(e){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,e)}super.emit.apply(this,e),this._pid&&e.length&&typeof e[e.length-1]=="string"&&(this._lastOffset=e[e.length-1])}ack(e){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:Be.ACK,id:e,data:i}))}}onack(e){const n=this.acks[e.id];typeof n=="function"&&(n.apply(this,e.data),delete 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this.isListening()&&this.isVisible()&&!a}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var e=this.getDepth(),n=this,r=0,i,a,s,u;function c(f){for(i=[],a=f.length,s=0;s0&&i[0].getDepth()<=e&&c(i)}return n.nodeType!==WF&&c(n.getStage().getChildren()),r}getDepth(){for(var e=0,n=this.parent;n;)e++,n=n.parent;return e}_batchTransformChanges(e){this._batchingTransformChange=!0,e(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(ua),this._clearSelfAndDescendantCache(Ii)),this._needClearTransformCache=!1}setPosition(e){return this._batchTransformChanges(()=>{this.x(e.x),this.y(e.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var e=this.getStage().getPointerPosition();if(!e)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(e)}getAbsolutePosition(e){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!e&&(e=!0);var i=this.getAbsoluteTransform(e).getMatrix(),a=new Wn,s=this.offset();return a.m=i.slice(),a.translate(s.x,s.y),a.getTranslation()}setAbsolutePosition(e){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(ua);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(e.x,e.y),e={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:e.x,y:e.y}),this._clearCache(ua),this._clearSelfAndDescendantCache(Ii),this}_setTransform(e){var n;for(n in e)this.attrs[n]=e[n]}_clearTransform(){var e={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,e}move(e){var n=e.x,r=e.y,i=this.x(),a=this.y();return n!==void 0&&(i+=n),r!==void 0&&(a+=r),this.setPosition({x:i,y:a}),this}_eachAncestorReverse(e,n){var r=[],i=this.getParent(),a,s;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(a=r.length,s=0;s0?(this.parent.children.splice(e,1),this.parent.children.splice(e-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return G.warn("Node has no parent. moveToBottom function is ignored."),!1;var e=this.index;return e>0?(this.parent.children.splice(e,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(e){if(!this.parent)return G.warn("Node has no parent. zIndex parameter is ignored."),this;(e<0||e>=this.parent.children.length)&&G.warn("Unexpected value "+e+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(e,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(md,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var e=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(e*=n.getAbsoluteOpacity()),e}moveTo(e){return this.getParent()!==e&&(this._remove(),e.add(this)),this}toObject(){var e={},n=this.getAttrs(),r,i,a,s,u;e.attrs={};for(r in n)i=n[r],u=G.isObject(i)&&!G._isPlainObject(i)&&!G._isArray(i),!u&&(a=typeof this[r]=="function"&&this[r],delete n[r],s=a?a.call(this):null,n[r]=i,s!==i&&(e.attrs[r]=i));return e.className=this.getClassName(),G._prepareToStringify(e)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(e,n,r){var i=[];n&&this._isMatch(e)&&i.push(this);for(var a=this.parent;a;){if(a===r)return i;a._isMatch(e)&&i.push(a),a=a.parent}return i}isAncestorOf(e){return!1}findAncestor(e,n,r){return this.findAncestors(e,n,r)[0]}_isMatch(e){if(!e)return!1;if(typeof e=="function")return e(this);var n=e.replace(/ /g,"").split(","),r=n.length,i,a;for(i=0;i{try{const i=e==null?void 0:e.callback;i&&delete e.callback,G._urlToImage(this.toDataURL(e),function(a){n(a),i==null||i(a)})}catch(i){r(i)}})}toBlob(e){return new Promise((n,r)=>{try{const i=e==null?void 0:e.callback;i&&delete e.callback,this.toCanvas(e).toBlob(a=>{n(a),i==null||i(a)})}catch(i){r(i)}})}setSize(e){return this.width(e.width),this.height(e.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():pe.dragDistance}_off(e,n,r){var i=this.eventListeners[e],a,s,u;for(a=0;a=0;if(r&&!this.isDragging()){var i=!1;Ge._dragElements.forEach(a=>{this.isAncestorOf(a.node)&&(i=!0)}),i||this._createDragElement(e)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var e=this.getStage();if(!e)return;const n=Ge._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&Ge._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(e={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-e.x,y:-e.y,width:n.width()+2*e.x,height:n.height()+2*e.y};return G.haveIntersection(r,this.getClientRect())}static create(e,n){return G._isString(e)&&(e=JSON.parse(e)),this._createNode(e,n)}static _createNode(e,n){var r=se.prototype.getClassName.call(e),i=e.children,a,s,u;n&&(e.attrs.container=n),pe[r]||(G.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const c=pe[r];if(a=new c(e.attrs),i)for(s=i.length,u=0;u0}removeChildren(){return this.getChildren().forEach(e=>{e.parent=null,e.index=0,e.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(e=>{e.parent=null,e.index=0,e.destroy()}),this.children=[],this._requestDraw(),this}add(...e){if(e.length===0)return this;if(e.length>1){for(var n=0;n0?n[0]:void 0}_generalFind(e,n){var r=[];return this._descendants(i=>{const a=i._isMatch(e);return a&&r.push(i),!!(a&&n)}),r}_descendants(e){let n=!1;const r=this.getChildren();for(const i of r){if(n=e(i),n)return!0;if(i.hasChildren()&&(n=i._descendants(e),n))return!0}return!1}toObject(){var e=se.prototype.toObject.call(this);return e.children=[],this.getChildren().forEach(n=>{e.children.push(n.toObject())}),e}isAncestorOf(e){for(var n=e.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(e){var n=se.prototype.clone.call(this,e);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(e){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(e)&&n.push(r)}),n}_clearSelfAndDescendantCache(e){var n;super._clearSelfAndDescendantCache(e),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(e)}))}_setChildrenIndices(){var e;(e=this.children)===null||e===void 0||e.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(e,n){var r=this.getLayer(),i=e||r&&r.getCanvas(),a=i&&i.getContext(),s=this._getCanvasCache(),u=s&&s.scene,c=i&&i.isCache;if(!this.isVisible()&&!c)return this;if(u){a.save();var f=this.getAbsoluteTransform(n).getMatrix();a.transform(f[0],f[1],f[2],f[3],f[4],f[5]),this._drawCachedSceneCanvas(a),a.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(e,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=e||r&&r.hitCanvas,a=i&&i.getContext(),s=this._getCanvasCache(),u=s&&s.hit;if(u){a.save();var c=this.getAbsoluteTransform(n).getMatrix();a.transform(c[0],c[1],c[2],c[3],c[4],c[5]),this._drawCachedHitCanvas(a),a.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(e,n,r){var i,a=n&&n.getContext(),s=this.clipWidth(),u=this.clipHeight(),c=this.clipFunc(),f=s&&u||c;const h=r===this;if(f){a.save();var p=this.getAbsoluteTransform(r),g=p.getMatrix();if(a.transform(g[0],g[1],g[2],g[3],g[4],g[5]),a.beginPath(),c)c.call(this,a,this);else{var y=this.clipX(),v=this.clipY();a.rect(y,v,s,u)}a.clip(),g=p.copy().invert().getMatrix(),a.transform(g[0],g[1],g[2],g[3],g[4],g[5])}var w=!h&&this.globalCompositeOperation()!=="source-over"&&e==="drawScene";w&&(a.save(),a._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(x){x[e](n,r)}),w&&a.restore(),f&&a.restore()}getClientRect(e){var n;e=e||{};var r=e.skipTransform,i=e.relativeTo,a,s,u,c,f={x:1/0,y:1/0,width:0,height:0},h=this;(n=this.children)===null||n===void 0||n.forEach(function(w){if(w.visible()){var x=w.getClientRect({relativeTo:h,skipShadow:e.skipShadow,skipStroke:e.skipStroke});x.width===0&&x.height===0||(a===void 0?(a=x.x,s=x.y,u=x.x+x.width,c=x.y+x.height):(a=Math.min(a,x.x),s=Math.min(s,x.y),u=Math.max(u,x.x+x.width),c=Math.max(c,x.y+x.height)))}});for(var p=this.find("Shape"),g=!1,y=0;yt.indexOf("pointer")>=0?"pointer":t.indexOf("touch")>=0?"touch":"mouse",is=t=>{const e=iu(t);if(e==="pointer")return pe.pointerEventsEnabled&&Tm.pointer;if(e==="touch")return Tm.touch;if(e==="mouse")return Tm.mouse};function Tb(t={}){return(t.clipFunc||t.clipWidth||t.clipHeight)&&G.warn("Stage does not support clipping. Please use clip for Layers or Groups."),t}const ZF="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);",yd=[];class tp extends qn{constructor(e){super(Tb(e)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),yd.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{Tb(this.attrs)}),this._checkVisibility()}_validateAdd(e){const n=e.getType()==="Layer",r=e.getType()==="FastLayer";n||r||G.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const e=this.visible()?"":"none";this.content.style.display=e}setContainer(e){if(typeof e===KF){if(e.charAt(0)==="."){var n=e.slice(1);e=document.getElementsByClassName(n)[0]}else{var r;e.charAt(0)!=="#"?r=e:r=e.slice(1),e=document.getElementById(r)}if(!e)throw"Can not find container in document with id "+r}return this._setAttr("container",e),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),e.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var e=this.children,n=e.length,r;for(r=0;r-1&&yd.splice(n,1),G.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const e=this._pointerPositions[0]||this._changedPointerPositions[0];return e?{x:e.x,y:e.y}:(G.warn(ZF),null)}_getPointerById(e){return this._pointerPositions.find(n=>n.id===e)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(e){e=e||{},e.x=e.x||0,e.y=e.y||0,e.width=e.width||this.width(),e.height=e.height||this.height();var n=new Ts({width:e.width,height:e.height,pixelRatio:e.pixelRatio||1}),r=n.getContext()._context,i=this.children;return(e.x||e.y)&&r.translate(-1*e.x,-1*e.y),i.forEach(function(a){if(a.isVisible()){var s=a._toKonvaCanvas(e);r.drawImage(s._canvas,e.x,e.y,s.getWidth()/s.getPixelRatio(),s.getHeight()/s.getPixelRatio())}}),n}getIntersection(e){if(!e)return null;var n=this.children,r=n.length,i=r-1,a;for(a=i;a>=0;a--){const s=n[a].getIntersection(e);if(s)return s}return null}_resizeDOM(){var e=this.width(),n=this.height();this.content&&(this.content.style.width=e+Ob,this.content.style.height=n+Ob),this.bufferCanvas.setSize(e,n),this.bufferHitCanvas.setSize(e,n),this.children.forEach(r=>{r.setSize({width:e,height:n}),r.draw()})}add(e,...n){if(arguments.length>1){for(var r=0;rQF&&G.warn("The stage has "+i+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),e.setSize({width:this.width(),height:this.height()}),e.draw(),pe.isBrowser&&this.content.appendChild(e.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(e){return Uk(e,this)}setPointerCapture(e){jk(e,this)}releaseCapture(e){mu(e)}getLayers(){return this.children}_bindContentEvents(){pe.isBrowser&&JF.forEach(([e,n])=>{this.content.addEventListener(e,r=>{this[n](r)},{passive:!1})})}_pointerenter(e){this.setPointersPositions(e);const n=is(e.type);this._fire(n.pointerenter,{evt:e,target:this,currentTarget:this})}_pointerover(e){this.setPointersPositions(e);const n=is(e.type);this._fire(n.pointerover,{evt:e,target:this,currentTarget:this})}_getTargetShape(e){let n=this[e+"targetShape"];return n&&!n.getStage()&&(n=null),n}_pointerleave(e){const n=is(e.type),r=iu(e.type);if(n){this.setPointersPositions(e);var i=this._getTargetShape(r),a=!Ge.isDragging||pe.hitOnDragEnabled;i&&a?(i._fireAndBubble(n.pointerout,{evt:e}),i._fireAndBubble(n.pointerleave,{evt:e}),this._fire(n.pointerleave,{evt:e,target:this,currentTarget:this}),this[r+"targetShape"]=null):a&&(this._fire(n.pointerleave,{evt:e,target:this,currentTarget:this}),this._fire(n.pointerout,{evt:e,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(e){const n=is(e.type),r=iu(e.type);if(n){this.setPointersPositions(e);var i=!1;this._changedPointerPositions.forEach(a=>{var s=this.getIntersection(a);if(Ge.justDragged=!1,pe["_"+r+"ListenClick"]=!0,!(s&&s.isListening()))return;pe.capturePointerEventsEnabled&&s.setPointerCapture(a.id),this[r+"ClickStartShape"]=s,s._fireAndBubble(n.pointerdown,{evt:e,pointerId:a.id}),i=!0;const c=e.type.indexOf("touch")>=0;s.preventDefault()&&e.cancelable&&c&&e.preventDefault()}),i||this._fire(n.pointerdown,{evt:e,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(e){const n=is(e.type),r=iu(e.type);if(!n)return;Ge.isDragging&&Ge.node.preventDefault()&&e.cancelable&&e.preventDefault(),this.setPointersPositions(e);var i=!Ge.isDragging||pe.hitOnDragEnabled;if(!i)return;var a={};let s=!1;var u=this._getTargetShape(r);this._changedPointerPositions.forEach(c=>{const f=Om(c.id)||this.getIntersection(c),h=c.id,p={evt:e,pointerId:h};var g=u!==f;if(g&&u&&(u._fireAndBubble(n.pointerout,Object.assign({},p),f),u._fireAndBubble(n.pointerleave,Object.assign({},p),f)),f){if(a[f._id])return;a[f._id]=!0}f&&f.isListening()?(s=!0,g&&(f._fireAndBubble(n.pointerover,Object.assign({},p),u),f._fireAndBubble(n.pointerenter,Object.assign({},p),u),this[r+"targetShape"]=f),f._fireAndBubble(n.pointermove,Object.assign({},p))):u&&(this._fire(n.pointerover,{evt:e,target:this,currentTarget:this,pointerId:h}),this[r+"targetShape"]=null)}),s||this._fire(n.pointermove,{evt:e,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(e){const n=is(e.type),r=iu(e.type);if(!n)return;this.setPointersPositions(e);const i=this[r+"ClickStartShape"],a=this[r+"ClickEndShape"];var s={};let u=!1;this._changedPointerPositions.forEach(c=>{const f=Om(c.id)||this.getIntersection(c);if(f){if(f.releaseCapture(c.id),s[f._id])return;s[f._id]=!0}const h=c.id,p={evt:e,pointerId:h};let g=!1;pe["_"+r+"InDblClickWindow"]?(g=!0,clearTimeout(this[r+"DblTimeout"])):Ge.justDragged||(pe["_"+r+"InDblClickWindow"]=!0,clearTimeout(this[r+"DblTimeout"])),this[r+"DblTimeout"]=setTimeout(function(){pe["_"+r+"InDblClickWindow"]=!1},pe.dblClickWindow),f&&f.isListening()?(u=!0,this[r+"ClickEndShape"]=f,f._fireAndBubble(n.pointerup,Object.assign({},p)),pe["_"+r+"ListenClick"]&&i&&i===f&&(f._fireAndBubble(n.pointerclick,Object.assign({},p)),g&&a&&a===f&&f._fireAndBubble(n.pointerdblclick,Object.assign({},p)))):(this[r+"ClickEndShape"]=null,pe["_"+r+"ListenClick"]&&this._fire(n.pointerclick,{evt:e,target:this,currentTarget:this,pointerId:h}),g&&this._fire(n.pointerdblclick,{evt:e,target:this,currentTarget:this,pointerId:h}))}),u||this._fire(n.pointerup,{evt:e,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),pe["_"+r+"ListenClick"]=!1,e.cancelable&&r!=="touch"&&e.preventDefault()}_contextmenu(e){this.setPointersPositions(e);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(ny,{evt:e}):this._fire(ny,{evt:e,target:this,currentTarget:this})}_wheel(e){this.setPointersPositions(e);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(ry,{evt:e}):this._fire(ry,{evt:e,target:this,currentTarget:this})}_pointercancel(e){this.setPointersPositions(e);const n=Om(e.pointerId)||this.getIntersection(this.getPointerPosition());n&&n._fireAndBubble(Ss,M0(e)),mu(e.pointerId)}_lostpointercapture(e){mu(e.pointerId)}setPointersPositions(e){var n=this._getContentPosition(),r=null,i=null;e=e||window.event,e.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(e.touches,a=>{this._pointerPositions.push({id:a.identifier,x:(a.clientX-n.left)/n.scaleX,y:(a.clientY-n.top)/n.scaleY})}),Array.prototype.forEach.call(e.changedTouches||e.touches,a=>{this._changedPointerPositions.push({id:a.identifier,x:(a.clientX-n.left)/n.scaleX,y:(a.clientY-n.top)/n.scaleY})})):(r=(e.clientX-n.left)/n.scaleX,i=(e.clientY-n.top)/n.scaleY,this.pointerPos={x:r,y:i},this._pointerPositions=[{x:r,y:i,id:G._getFirstPointerId(e)}],this._changedPointerPositions=[{x:r,y:i,id:G._getFirstPointerId(e)}])}_setPointerPosition(e){G.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(e)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var e=this.content.getBoundingClientRect();return{top:e.top,left:e.left,scaleX:e.width/this.content.clientWidth||1,scaleY:e.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new Ts({width:this.width(),height:this.height()}),this.bufferHitCanvas=new L0({pixelRatio:1,width:this.width(),height:this.height()}),!!pe.isBrowser){var e=this.container();if(!e)throw"Stage has no container. A container is required.";e.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),e.appendChild(this.content),this._resizeDOM()}}cache(){return G.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(e){e.batchDraw()}),this}}tp.prototype.nodeType=qF;yt(tp);A.addGetterSetter(tp,"container");var eP="hasShadow",tP="shadowRGBA",nP="patternImage",rP="linearGradient",iP="radialGradient";let Gf;function Rm(){return Gf||(Gf=G.createCanvasElement().getContext("2d"),Gf)}const vu={};function e$(t){t.fill()}function t$(t){t.stroke()}function n$(t){t.fill()}function r$(t){t.stroke()}function i$(){this._clearCache(eP)}function a$(){this._clearCache(tP)}function o$(){this._clearCache(nP)}function s$(){this._clearCache(rP)}function l$(){this._clearCache(iP)}class re extends se{constructor(e){super(e);let n;for(;n=G.getRandomColor(),!(n&&!(n in vu)););this.colorKey=n,vu[n]=this}getContext(){return G.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return G.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(eP,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(nP,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var e=Rm();const n=e.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(n&&n.setTransform){const r=new Wn;r.translate(this.fillPatternX(),this.fillPatternY()),r.rotate(pe.getAngle(this.fillPatternRotation())),r.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),r.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const i=r.getMatrix(),a=typeof DOMMatrix>"u"?{a:i[0],b:i[1],c:i[2],d:i[3],e:i[4],f:i[5]}:new DOMMatrix(i);n.setTransform(a)}return n}}_getLinearGradient(){return this._getCache(rP,this.__getLinearGradient)}__getLinearGradient(){var e=this.fillLinearGradientColorStops();if(e){for(var n=Rm(),r=this.fillLinearGradientStartPoint(),i=this.fillLinearGradientEndPoint(),a=n.createLinearGradient(r.x,r.y,i.x,i.y),s=0;sthis.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const e=this.hitStrokeWidth();return e==="auto"?this.hasStroke():this.strokeEnabled()&&!!e}intersects(e){var n=this.getStage(),r=n.bufferHitCanvas,i;return r.getContext().clear(),this.drawHit(r,null,!0),i=r.context.getImageData(Math.round(e.x),Math.round(e.y),1,1).data,i[3]>0}destroy(){return se.prototype.destroy.call(this),delete vu[this.colorKey],delete this.colorKey,this}_useBufferCanvas(e){var n;if(!this.getStage()||!((n=this.attrs.perfectDrawEnabled)!==null&&n!==void 0?n:!0))return!1;const i=e||this.hasFill(),a=this.hasStroke(),s=this.getAbsoluteOpacity()!==1;if(i&&a&&s)return!0;const u=this.hasShadow(),c=this.shadowForStrokeEnabled();return!!(i&&a&&u&&c)}setStrokeHitEnabled(e){G.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),e?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var e=this.size();return{x:this._centroid?-e.width/2:0,y:this._centroid?-e.height/2:0,width:e.width,height:e.height}}getClientRect(e={}){const n=e.skipTransform,r=e.relativeTo,i=this.getSelfRect(),s=!e.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,u=i.width+s,c=i.height+s,f=!e.skipShadow&&this.hasShadow(),h=f?this.shadowOffsetX():0,p=f?this.shadowOffsetY():0,g=u+Math.abs(h),y=c+Math.abs(p),v=f&&this.shadowBlur()||0,w=g+v*2,x=y+v*2,_={width:w,height:x,x:-(s/2+v)+Math.min(h,0)+i.x,y:-(s/2+v)+Math.min(p,0)+i.y};return n?_:this._transformedRect(_,r)}drawScene(e,n){var r=this.getLayer(),i=e||r.getCanvas(),a=i.getContext(),s=this._getCanvasCache(),u=this.getSceneFunc(),c=this.hasShadow(),f,h,p,g=i.isCache,y=n===this;if(!this.isVisible()&&!y)return this;if(s){a.save();var v=this.getAbsoluteTransform(n).getMatrix();return a.transform(v[0],v[1],v[2],v[3],v[4],v[5]),this._drawCachedSceneCanvas(a),a.restore(),this}if(!u)return this;if(a.save(),this._useBufferCanvas()&&!g){f=this.getStage(),h=f.bufferCanvas,p=h.getContext(),p.clear(),p.save(),p._applyLineJoin(this);var w=this.getAbsoluteTransform(n).getMatrix();p.transform(w[0],w[1],w[2],w[3],w[4],w[5]),u.call(this,p,this),p.restore();var x=h.pixelRatio;c&&a._applyShadow(this),a._applyOpacity(this),a._applyGlobalCompositeOperation(this),a.drawImage(h._canvas,0,0,h.width/x,h.height/x)}else{if(a._applyLineJoin(this),!y){var w=this.getAbsoluteTransform(n).getMatrix();a.transform(w[0],w[1],w[2],w[3],w[4],w[5]),a._applyOpacity(this),a._applyGlobalCompositeOperation(this)}c&&a._applyShadow(this),u.call(this,a,this)}return a.restore(),this}drawHit(e,n,r=!1){if(!this.shouldDrawHit(n,r))return this;var i=this.getLayer(),a=e||i.hitCanvas,s=a&&a.getContext(),u=this.hitFunc()||this.sceneFunc(),c=this._getCanvasCache(),f=c&&c.hit;if(this.colorKey||G.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),f){s.save();var h=this.getAbsoluteTransform(n).getMatrix();return s.transform(h[0],h[1],h[2],h[3],h[4],h[5]),this._drawCachedHitCanvas(s),s.restore(),this}if(!u)return this;if(s.save(),s._applyLineJoin(this),!(this===n)){var g=this.getAbsoluteTransform(n).getMatrix();s.transform(g[0],g[1],g[2],g[3],g[4],g[5])}return u.call(this,s,this),s.restore(),this}drawHitFromCache(e=0){var n=this._getCanvasCache(),r=this._getCachedSceneCanvas(),i=n.hit,a=i.getContext(),s=i.getWidth(),u=i.getHeight(),c,f,h,p,g,y;a.clear(),a.drawImage(r._canvas,0,0,s,u);try{for(c=a.getImageData(0,0,s,u),f=c.data,h=f.length,p=G._hexToRgb(this.colorKey),g=0;ge?(f[g]=p.r,f[g+1]=p.g,f[g+2]=p.b,f[g+3]=255):f[g+3]=0;a.putImageData(c,0,0)}catch(v){G.error("Unable to draw hit graph from cached scene canvas. "+v.message)}return this}hasPointerCapture(e){return Uk(e,this)}setPointerCapture(e){jk(e,this)}releaseCapture(e){mu(e)}}re.prototype._fillFunc=e$;re.prototype._strokeFunc=t$;re.prototype._fillFuncHit=n$;re.prototype._strokeFuncHit=r$;re.prototype._centroid=!1;re.prototype.nodeType="Shape";yt(re);re.prototype.eventListeners={};re.prototype.on.call(re.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",i$);re.prototype.on.call(re.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",a$);re.prototype.on.call(re.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",o$);re.prototype.on.call(re.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",s$);re.prototype.on.call(re.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",l$);A.addGetterSetter(re,"stroke",void 0,zk());A.addGetterSetter(re,"strokeWidth",2,le());A.addGetterSetter(re,"fillAfterStrokeEnabled",!1);A.addGetterSetter(re,"hitStrokeWidth","auto",I0());A.addGetterSetter(re,"strokeHitEnabled",!0,jr());A.addGetterSetter(re,"perfectDrawEnabled",!0,jr());A.addGetterSetter(re,"shadowForStrokeEnabled",!0,jr());A.addGetterSetter(re,"lineJoin");A.addGetterSetter(re,"lineCap");A.addGetterSetter(re,"sceneFunc");A.addGetterSetter(re,"hitFunc");A.addGetterSetter(re,"dash");A.addGetterSetter(re,"dashOffset",0,le());A.addGetterSetter(re,"shadowColor",void 0,qs());A.addGetterSetter(re,"shadowBlur",0,le());A.addGetterSetter(re,"shadowOpacity",1,le());A.addComponentsGetterSetter(re,"shadowOffset",["x","y"]);A.addGetterSetter(re,"shadowOffsetX",0,le());A.addGetterSetter(re,"shadowOffsetY",0,le());A.addGetterSetter(re,"fillPatternImage");A.addGetterSetter(re,"fill",void 0,zk());A.addGetterSetter(re,"fillPatternX",0,le());A.addGetterSetter(re,"fillPatternY",0,le());A.addGetterSetter(re,"fillLinearGradientColorStops");A.addGetterSetter(re,"strokeLinearGradientColorStops");A.addGetterSetter(re,"fillRadialGradientStartRadius",0);A.addGetterSetter(re,"fillRadialGradientEndRadius",0);A.addGetterSetter(re,"fillRadialGradientColorStops");A.addGetterSetter(re,"fillPatternRepeat","repeat");A.addGetterSetter(re,"fillEnabled",!0);A.addGetterSetter(re,"strokeEnabled",!0);A.addGetterSetter(re,"shadowEnabled",!0);A.addGetterSetter(re,"dashEnabled",!0);A.addGetterSetter(re,"strokeScaleEnabled",!0);A.addGetterSetter(re,"fillPriority","color");A.addComponentsGetterSetter(re,"fillPatternOffset",["x","y"]);A.addGetterSetter(re,"fillPatternOffsetX",0,le());A.addGetterSetter(re,"fillPatternOffsetY",0,le());A.addComponentsGetterSetter(re,"fillPatternScale",["x","y"]);A.addGetterSetter(re,"fillPatternScaleX",1,le());A.addGetterSetter(re,"fillPatternScaleY",1,le());A.addComponentsGetterSetter(re,"fillLinearGradientStartPoint",["x","y"]);A.addComponentsGetterSetter(re,"strokeLinearGradientStartPoint",["x","y"]);A.addGetterSetter(re,"fillLinearGradientStartPointX",0);A.addGetterSetter(re,"strokeLinearGradientStartPointX",0);A.addGetterSetter(re,"fillLinearGradientStartPointY",0);A.addGetterSetter(re,"strokeLinearGradientStartPointY",0);A.addComponentsGetterSetter(re,"fillLinearGradientEndPoint",["x","y"]);A.addComponentsGetterSetter(re,"strokeLinearGradientEndPoint",["x","y"]);A.addGetterSetter(re,"fillLinearGradientEndPointX",0);A.addGetterSetter(re,"strokeLinearGradientEndPointX",0);A.addGetterSetter(re,"fillLinearGradientEndPointY",0);A.addGetterSetter(re,"strokeLinearGradientEndPointY",0);A.addComponentsGetterSetter(re,"fillRadialGradientStartPoint",["x","y"]);A.addGetterSetter(re,"fillRadialGradientStartPointX",0);A.addGetterSetter(re,"fillRadialGradientStartPointY",0);A.addComponentsGetterSetter(re,"fillRadialGradientEndPoint",["x","y"]);A.addGetterSetter(re,"fillRadialGradientEndPointX",0);A.addGetterSetter(re,"fillRadialGradientEndPointY",0);A.addGetterSetter(re,"fillPatternRotation",0);A.backCompat(re,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"});var u$="#",c$="beforeDraw",f$="draw",aP=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],d$=aP.length;class yo extends qn{constructor(e){super(e),this.canvas=new Ts,this.hitCanvas=new L0({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(e){return this.getContext().clear(e),this.getHitCanvas().getContext().clear(e),this}setZIndex(e){super.setZIndex(e);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),e{this.draw(),this._waitingForDraw=!1})),this}getIntersection(e){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i0)return{antialiased:!0};return{}}drawScene(e,n){var r=this.getLayer(),i=e||r&&r.getCanvas();return this._fire(c$,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),qn.prototype.drawScene.call(this,i,n),this._fire(f$,{node:this}),this}drawHit(e,n){var r=this.getLayer(),i=e||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),qn.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(e){G.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(e)}getHitGraphEnabled(e){return G.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var e=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?e.content.removeChild(this.hitCanvas._canvas):e.content.appendChild(this.hitCanvas._canvas)}}destroy(){return G.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}yo.prototype.nodeType="Layer";yt(yo);A.addGetterSetter(yo,"imageSmoothingEnabled",!0);A.addGetterSetter(yo,"clearBeforeDraw",!0);A.addGetterSetter(yo,"hitGraphEnabled",!0,jr());class D0 extends yo{constructor(e){super(e),this.listening(!1),G.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}D0.prototype.nodeType="FastLayer";yt(D0);class zs extends qn{_validateAdd(e){var n=e.getType();n!=="Group"&&n!=="Shape"&&G.throw("You may only add groups and shapes to groups.")}}zs.prototype.nodeType="Group";yt(zs);var Am=function(){return Os.performance&&Os.performance.now?function(){return Os.performance.now()}:function(){return new Date().getTime()}}();class dr{constructor(e,n){this.id=dr.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:Am(),frameRate:0},this.func=e,this.setLayers(n)}setLayers(e){var n=[];return e?e.length>0?n=e:n=[e]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(e){var n=this.layers,r=n.length,i;for(i=0;ithis.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():e<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=e,this.update())}getTime(){return this._time}setPosition(e){this.prevPos=this._pos,this.propFunc(e),this._pos=e}getPosition(e){return e===void 0&&(e=this._time),this.func(e,this.begin,this._change,this.duration)}play(){this.state=Rb,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=Ab,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(e){this.pause(),this._time=e,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var e=this.getTimer()-this._startTime;this.state===Rb?this.setTime(e):this.state===Ab&&this.setTime(this.duration-e)}pause(){this.state=p$,this.fire("onPause")}getTimer(){return new Date().getTime()}}class kt{constructor(e){var n=this,r=e.node,i=r._id,a,s=e.easing||yu.Linear,u=!!e.yoyo,c;typeof e.duration>"u"?a=.3:e.duration===0?a=.001:a=e.duration,this.node=r,this._id=g$++;var f=r.getLayer()||(r instanceof pe.Stage?r.getLayers():null);f||G.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new dr(function(){n.tween.onEnterFrame()},f),this.tween=new m$(c,function(h){n._tweenFunc(h)},s,0,1,a*1e3,u),this._addListeners(),kt.attrs[i]||(kt.attrs[i]={}),kt.attrs[i][this._id]||(kt.attrs[i][this._id]={}),kt.tweens[i]||(kt.tweens[i]={});for(c in e)h$[c]===void 0&&this._addAttr(c,e[c]);this.reset(),this.onFinish=e.onFinish,this.onReset=e.onReset,this.onUpdate=e.onUpdate}_addAttr(e,n){var r=this.node,i=r._id,a,s,u,c,f,h,p,g;if(u=kt.tweens[i][e],u&&delete kt.attrs[i][u][e],a=r.getAttr(e),G._isArray(n))if(s=[],f=Math.max(n.length,a.length),e==="points"&&n.length!==a.length&&(n.length>a.length?(p=a,a=G._prepareArrayForTween(a,n,r.closed())):(h=n,n=G._prepareArrayForTween(n,a,r.closed()))),e.indexOf("fill")===0)for(c=0;c{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var e=this.node,n=kt.attrs[e._id][this._id];n.points&&n.points.trueEnd&&e.setAttr("points",n.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var e=this.node,n=kt.attrs[e._id][this._id];n.points&&n.points.trueStart&&e.points(n.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(e){return this.tween.seek(e*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var e=this.node._id,n=this._id,r=kt.tweens[e],i;this.pause();for(i in r)delete kt.tweens[e][i];delete kt.attrs[e][n]}}kt.attrs={};kt.tweens={};se.prototype.to=function(t){var e=t.onFinish;t.node=this,t.onFinish=function(){this.destroy(),e&&e()};var n=new kt(t);n.play()};const yu={BackEaseIn(t,e,n,r){var i=1.70158;return n*(t/=r)*t*((i+1)*t-i)+e},BackEaseOut(t,e,n,r){var i=1.70158;return n*((t=t/r-1)*t*((i+1)*t+i)+1)+e},BackEaseInOut(t,e,n,r){var i=1.70158;return(t/=r/2)<1?n/2*(t*t*(((i*=1.525)+1)*t-i))+e:n/2*((t-=2)*t*(((i*=1.525)+1)*t+i)+2)+e},ElasticEaseIn(t,e,n,r,i,a){var s=0;return t===0?e:(t/=r)===1?e+n:(a||(a=r*.3),!i||i0?e:n),h=s*n,p=u*(u>0?e:n),g=c*(c>0?n:e);return{x:f,y:r?-1*g:p,width:h-f,height:g-p}}}Hi.prototype._centroid=!0;Hi.prototype.className="Arc";Hi.prototype._attrsAffectingSize=["innerRadius","outerRadius"];yt(Hi);A.addGetterSetter(Hi,"innerRadius",0,le());A.addGetterSetter(Hi,"outerRadius",0,le());A.addGetterSetter(Hi,"angle",0,le());A.addGetterSetter(Hi,"clockwise",!1,jr());function iy(t,e,n,r,i,a,s){var u=Math.sqrt(Math.pow(n-t,2)+Math.pow(r-e,2)),c=Math.sqrt(Math.pow(i-n,2)+Math.pow(a-r,2)),f=s*u/(u+c),h=s*c/(u+c),p=n-f*(i-t),g=r-f*(a-e),y=n+h*(i-t),v=r+h*(a-e);return[p,g,y,v]}function Mb(t,e){var n=t.length,r=[],i,a;for(i=2;i4){for(u=this.getTensionPoints(),c=u.length,f=a?0:4,a||e.quadraticCurveTo(u[0],u[1],u[2],u[3]);fh?f:h,x=f>h?1:f/h,_=f>h?h/f:1;e.translate(u,c),e.rotate(y),e.scale(x,_),e.arc(0,0,w,p,p+g,1-v),e.scale(1/x,1/_),e.rotate(-y),e.translate(-u,-c);break;case"z":r=!0,e.closePath();break}}!r&&!this.hasFill()?e.strokeShape(this):e.fillStrokeShape(this)}getSelfRect(){var e=[];this.dataArray.forEach(function(f){if(f.command==="A"){var h=f.points[4],p=f.points[5],g=f.points[4]+p,y=Math.PI/180;if(Math.abs(h-g)g;v-=y){const w=tt.getPointOnEllipticalArc(f.points[0],f.points[1],f.points[2],f.points[3],v,0);e.push(w.x,w.y)}else for(let v=h+y;vthis.dataArray[r].pathLength;)e-=this.dataArray[r].pathLength,++r;if(r===i)return n=this.dataArray[r-1].points.slice(-2),{x:n[0],y:n[1]};if(e<.01)return n=this.dataArray[r].points.slice(0,2),{x:n[0],y:n[1]};var a=this.dataArray[r],s=a.points;switch(a.command){case"L":return tt.getPointOnLine(e,a.start.x,a.start.y,s[0],s[1]);case"C":return tt.getPointOnCubicBezier(e/a.pathLength,a.start.x,a.start.y,s[0],s[1],s[2],s[3],s[4],s[5]);case"Q":return tt.getPointOnQuadraticBezier(e/a.pathLength,a.start.x,a.start.y,s[0],s[1],s[2],s[3]);case"A":var u=s[0],c=s[1],f=s[2],h=s[3],p=s[4],g=s[5],y=s[6];return p+=g*e/a.pathLength,tt.getPointOnEllipticalArc(u,c,f,h,p,y)}return null}static getLineLength(e,n,r,i){return Math.sqrt((r-e)*(r-e)+(i-n)*(i-n))}static getPointOnLine(e,n,r,i,a,s,u){s===void 0&&(s=n),u===void 0&&(u=r);var c=(a-r)/(i-n+1e-8),f=Math.sqrt(e*e/(1+c*c));i0&&!isNaN(v[0]);){var S=null,C=[],O=c,I=f,L,D,U,X,K,te,ce,j,T,$;switch(y){case"l":c+=v.shift(),f+=v.shift(),S="L",C.push(c,f);break;case"L":c=v.shift(),f=v.shift(),C.push(c,f);break;case"m":var V=v.shift(),ie=v.shift();if(c+=V,f+=ie,S="M",s.length>2&&s[s.length-1].command==="z"){for(var W=s.length-2;W>=0;W--)if(s[W].command==="M"){c=s[W].points[0]+V,f=s[W].points[1]+ie;break}}C.push(c,f),y="l";break;case"M":c=v.shift(),f=v.shift(),S="M",C.push(c,f),y="L";break;case"h":c+=v.shift(),S="L",C.push(c,f);break;case"H":c=v.shift(),S="L",C.push(c,f);break;case"v":f+=v.shift(),S="L",C.push(c,f);break;case"V":f=v.shift(),S="L",C.push(c,f);break;case"C":C.push(v.shift(),v.shift(),v.shift(),v.shift()),c=v.shift(),f=v.shift(),C.push(c,f);break;case"c":C.push(c+v.shift(),f+v.shift(),c+v.shift(),f+v.shift()),c+=v.shift(),f+=v.shift(),S="C",C.push(c,f);break;case"S":D=c,U=f,L=s[s.length-1],L.command==="C"&&(D=c+(c-L.points[2]),U=f+(f-L.points[3])),C.push(D,U,v.shift(),v.shift()),c=v.shift(),f=v.shift(),S="C",C.push(c,f);break;case"s":D=c,U=f,L=s[s.length-1],L.command==="C"&&(D=c+(c-L.points[2]),U=f+(f-L.points[3])),C.push(D,U,c+v.shift(),f+v.shift()),c+=v.shift(),f+=v.shift(),S="C",C.push(c,f);break;case"Q":C.push(v.shift(),v.shift()),c=v.shift(),f=v.shift(),C.push(c,f);break;case"q":C.push(c+v.shift(),f+v.shift()),c+=v.shift(),f+=v.shift(),S="Q",C.push(c,f);break;case"T":D=c,U=f,L=s[s.length-1],L.command==="Q"&&(D=c+(c-L.points[0]),U=f+(f-L.points[1])),c=v.shift(),f=v.shift(),S="Q",C.push(D,U,c,f);break;case"t":D=c,U=f,L=s[s.length-1],L.command==="Q"&&(D=c+(c-L.points[0]),U=f+(f-L.points[1])),c+=v.shift(),f+=v.shift(),S="Q",C.push(D,U,c,f);break;case"A":X=v.shift(),K=v.shift(),te=v.shift(),ce=v.shift(),j=v.shift(),T=c,$=f,c=v.shift(),f=v.shift(),S="A",C=this.convertEndpointToCenterParameterization(T,$,c,f,ce,j,X,K,te);break;case"a":X=v.shift(),K=v.shift(),te=v.shift(),ce=v.shift(),j=v.shift(),T=c,$=f,c+=v.shift(),f+=v.shift(),S="A",C=this.convertEndpointToCenterParameterization(T,$,c,f,ce,j,X,K,te);break}s.push({command:S||y,points:C,start:{x:O,y:I},pathLength:this.calcLength(O,I,S||y,C)})}(y==="z"||y==="Z")&&s.push({command:"z",points:[],start:void 0,pathLength:0})}return s}static calcLength(e,n,r,i){var a,s,u,c,f=tt;switch(r){case"L":return f.getLineLength(e,n,i[0],i[1]);case"C":for(a=0,s=f.getPointOnCubicBezier(0,e,n,i[0],i[1],i[2],i[3],i[4],i[5]),c=.01;c<=1;c+=.01)u=f.getPointOnCubicBezier(c,e,n,i[0],i[1],i[2],i[3],i[4],i[5]),a+=f.getLineLength(s.x,s.y,u.x,u.y),s=u;return a;case"Q":for(a=0,s=f.getPointOnQuadraticBezier(0,e,n,i[0],i[1],i[2],i[3]),c=.01;c<=1;c+=.01)u=f.getPointOnQuadraticBezier(c,e,n,i[0],i[1],i[2],i[3]),a+=f.getLineLength(s.x,s.y,u.x,u.y),s=u;return a;case"A":a=0;var h=i[4],p=i[5],g=i[4]+p,y=Math.PI/180;if(Math.abs(h-g)g;c-=y)u=f.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],c,0),a+=f.getLineLength(s.x,s.y,u.x,u.y),s=u;else for(c=h+y;c1&&(u*=Math.sqrt(y),c*=Math.sqrt(y));var v=Math.sqrt((u*u*(c*c)-u*u*(g*g)-c*c*(p*p))/(u*u*(g*g)+c*c*(p*p)));a===s&&(v*=-1),isNaN(v)&&(v=0);var w=v*u*g/c,x=v*-c*p/u,_=(e+r)/2+Math.cos(h)*w-Math.sin(h)*x,S=(n+i)/2+Math.sin(h)*w+Math.cos(h)*x,C=function(K){return Math.sqrt(K[0]*K[0]+K[1]*K[1])},O=function(K,te){return(K[0]*te[0]+K[1]*te[1])/(C(K)*C(te))},I=function(K,te){return(K[0]*te[1]=1&&(X=0),s===0&&X>0&&(X=X-2*Math.PI),s===1&&X<0&&(X=X+2*Math.PI),[_,S,u,c,L,X,h,s]}}tt.prototype.className="Path";tt.prototype._attrsAffectingSize=["data"];yt(tt);A.addGetterSetter(tt,"data");class So extends Vi{_sceneFunc(e){super._sceneFunc(e);var n=Math.PI*2,r=this.points(),i=r,a=this.tension()!==0&&r.length>4;a&&(i=this.getTensionPoints());var s=this.pointerLength(),u=r.length,c,f;if(a){const g=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[u-2],r[u-1]],y=tt.calcLength(i[i.length-4],i[i.length-3],"C",g),v=tt.getPointOnQuadraticBezier(Math.min(1,1-s/y),g[0],g[1],g[2],g[3],g[4],g[5]);c=r[u-2]-v.x,f=r[u-1]-v.y}else c=r[u-2]-r[u-4],f=r[u-1]-r[u-3];var h=(Math.atan2(f,c)+n)%n,p=this.pointerWidth();this.pointerAtEnding()&&(e.save(),e.beginPath(),e.translate(r[u-2],r[u-1]),e.rotate(h),e.moveTo(0,0),e.lineTo(-s,p/2),e.lineTo(-s,-p/2),e.closePath(),e.restore(),this.__fillStroke(e)),this.pointerAtBeginning()&&(e.save(),e.beginPath(),e.translate(r[0],r[1]),a?(c=(i[0]+i[2])/2-r[0],f=(i[1]+i[3])/2-r[1]):(c=r[2]-r[0],f=r[3]-r[1]),e.rotate((Math.atan2(-f,-c)+n)%n),e.moveTo(0,0),e.lineTo(-s,p/2),e.lineTo(-s,-p/2),e.closePath(),e.restore(),this.__fillStroke(e))}__fillStroke(e){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,e.setLineDash([])),e.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const e=super.getSelfRect(),n=this.pointerWidth()/2;return{x:e.x-n,y:e.y-n,width:e.width+n*2,height:e.height+n*2}}}So.prototype.className="Arrow";yt(So);A.addGetterSetter(So,"pointerLength",10,le());A.addGetterSetter(So,"pointerWidth",10,le());A.addGetterSetter(So,"pointerAtBeginning",!1);A.addGetterSetter(So,"pointerAtEnding",!0);class Ks extends re{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),e.closePath(),e.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(e){this.radius()!==e/2&&this.radius(e/2)}setHeight(e){this.radius()!==e/2&&this.radius(e/2)}}Ks.prototype._centroid=!0;Ks.prototype.className="Circle";Ks.prototype._attrsAffectingSize=["radius"];yt(Ks);A.addGetterSetter(Ks,"radius",0,le());class Na extends re{_sceneFunc(e){var n=this.radiusX(),r=this.radiusY();e.beginPath(),e.save(),n!==r&&e.scale(1,r/n),e.arc(0,0,n,0,Math.PI*2,!1),e.restore(),e.closePath(),e.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(e){this.radiusX(e/2)}setHeight(e){this.radiusY(e/2)}}Na.prototype.className="Ellipse";Na.prototype._centroid=!0;Na.prototype._attrsAffectingSize=["radiusX","radiusY"];yt(Na);A.addComponentsGetterSetter(Na,"radius",["x","y"]);A.addGetterSetter(Na,"radiusX",0,le());A.addGetterSetter(Na,"radiusY",0,le());let hi=class oP extends re{constructor(e){super(e),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const e=this.image();e&&e.complete||e&&e.readyState===4||e&&e.addEventListener&&e.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(e){const n=this.getWidth(),r=this.getHeight(),i=this.cornerRadius(),a=this.attrs.image;let s;if(a){const u=this.attrs.cropWidth,c=this.attrs.cropHeight;u&&c?s=[a,this.cropX(),this.cropY(),u,c,0,0,n,r]:s=[a,0,0,n,r]}(this.hasFill()||this.hasStroke()||i)&&(e.beginPath(),i?G.drawRoundedRectPath(e,n,r,i):e.rect(0,0,n,r),e.closePath(),e.fillStrokeShape(this)),a&&(i&&e.clip(),e.drawImage.apply(e,s))}_hitFunc(e){var n=this.width(),r=this.height(),i=this.cornerRadius();e.beginPath(),i?G.drawRoundedRectPath(e,n,r,i):e.rect(0,0,n,r),e.closePath(),e.fillStrokeShape(this)}getWidth(){var e,n;return(e=this.attrs.width)!==null&&e!==void 0?e:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var e,n;return(e=this.attrs.height)!==null&&e!==void 0?e:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(e,n,r=null){var i=G.createImageElement();i.onload=function(){var a=new oP({image:i});n(a)},i.onerror=r,i.crossOrigin="Anonymous",i.src=e}};hi.prototype.className="Image";yt(hi);A.addGetterSetter(hi,"cornerRadius",0,A0(4));A.addGetterSetter(hi,"image");A.addComponentsGetterSetter(hi,"crop",["x","y","width","height"]);A.addGetterSetter(hi,"cropX",0,le());A.addGetterSetter(hi,"cropY",0,le());A.addGetterSetter(hi,"cropWidth",0,le());A.addGetterSetter(hi,"cropHeight",0,le());var sP=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],v$="Change.konva",y$="none",ay="up",oy="right",sy="down",ly="left",S$=sP.length;class N0 extends zs{constructor(e){super(e),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(e){var n=this,r,i=function(){n._sync()};for(r=0;r{n=Math.min(n,s.x),r=Math.max(r,s.x),i=Math.min(i,s.y),a=Math.max(a,s.y)}),{x:n,y:i,width:r-n,height:a-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(e){this.radius(e/2)}setHeight(e){this.radius(e/2)}}wo.prototype.className="RegularPolygon";wo.prototype._centroid=!0;wo.prototype._attrsAffectingSize=["radius"];yt(wo);A.addGetterSetter(wo,"radius",0,le());A.addGetterSetter(wo,"sides",0,le());var Db=Math.PI*2;class bo extends re{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.innerRadius(),0,Db,!1),e.moveTo(this.outerRadius(),0),e.arc(0,0,this.outerRadius(),Db,0,!0),e.closePath(),e.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}}bo.prototype.className="Ring";bo.prototype._centroid=!0;bo.prototype._attrsAffectingSize=["innerRadius","outerRadius"];yt(bo);A.addGetterSetter(bo,"innerRadius",0,le());A.addGetterSetter(bo,"outerRadius",0,le());class pi extends re{constructor(e){super(e),this._updated=!0,this.anim=new dr(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())})}_sceneFunc(e){var n=this.animation(),r=this.frameIndex(),i=r*4,a=this.animations()[n],s=this.frameOffsets(),u=a[i+0],c=a[i+1],f=a[i+2],h=a[i+3],p=this.image();if((this.hasFill()||this.hasStroke())&&(e.beginPath(),e.rect(0,0,f,h),e.closePath(),e.fillStrokeShape(this)),p)if(s){var g=s[n],y=r*2;e.drawImage(p,u,c,f,h,g[y+0],g[y+1],f,h)}else e.drawImage(p,u,c,f,h,0,0,f,h)}_hitFunc(e){var n=this.animation(),r=this.frameIndex(),i=r*4,a=this.animations()[n],s=this.frameOffsets(),u=a[i+2],c=a[i+3];if(e.beginPath(),s){var f=s[n],h=r*2;e.rect(f[h+0],f[h+1],u,c)}else e.rect(0,0,u,c);e.closePath(),e.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var e=this;this.interval=setInterval(function(){e._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var e=this.getLayer();this.anim.setLayers(e),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var e=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],a=i.length/4;e{e=e.trim();const n=e.indexOf(" ")>=0,r=e.indexOf('"')>=0||e.indexOf("'")>=0;return n&&!r&&(e=`"${e}"`),e}).join(", ")}var jf;function Lm(){return jf||(jf=G.createCanvasElement().getContext(b$),jf)}function L$(t){t.fillText(this._partialText,this._partialTextX,this._partialTextY)}function M$(t){t.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function D$(t){return t=t||{},!t.fillLinearGradientColorStops&&!t.fillRadialGradientColorStops&&!t.fillPatternImage&&(t.fill=t.fill||"black"),t}let vt=class extends re{constructor(e){super(D$(e)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n1&&(_+=s)}}}_hitFunc(e){var n=this.getWidth(),r=this.getHeight();e.beginPath(),e.rect(0,0,n,r),e.closePath(),e.fillStrokeShape(this)}setText(e){var n=G._isString(e)?e:e==null?"":e+"";return this._setAttr(C$,n),this}getWidth(){var e=this.attrs.width===as||this.attrs.width===void 0;return e?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var e=this.attrs.height===as||this.attrs.height===void 0;return e?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return G.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(e){var n=Lm(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(e),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+Uf+this.fontVariant()+Uf+(this.fontSize()+E$)+I$(this.fontFamily())}_addTextLine(e){this.align()===Wl&&(e=e.trim());var r=this._getTextWidth(e);return this.textArr.push({text:e,width:r,lastInParagraph:!1})}_getTextWidth(e){var n=this.letterSpacing(),r=e.length;return Lm().measureText(e).width+(r?n*(r-1):0)}_setTextData(){var e=this.text().split(` -`),n=+this.fontSize(),r=0,i=this.lineHeight()*n,a=this.attrs.width,s=this.attrs.height,u=a!==as&&a!==void 0,c=s!==as&&s!==void 0,f=this.padding(),h=a-f*2,p=s-f*2,g=0,y=this.wrap(),v=y!==Fb,w=y!==R$&&v,x=this.ellipsis();this.textArr=[],Lm().font=this._getContextFont();for(var _=x?this._getTextWidth(Im):0,S=0,C=e.length;Sh)for(;O.length>0;){for(var L=0,D=O.length,U="",X=0;L>>1,te=O.slice(0,K+1),ce=this._getTextWidth(te)+_;ce<=h?(L=K+1,U=te,X=ce):D=K}if(U){if(w){var j,T=O[U.length],$=T===Uf||T===Nb;$&&X<=h?j=U.length:j=Math.max(U.lastIndexOf(Uf),U.lastIndexOf(Nb))+1,j>0&&(L=j,U=U.slice(0,L),X=this._getTextWidth(U))}U=U.trimRight(),this._addTextLine(U),r=Math.max(r,X),g+=i;var V=this._shouldHandleEllipsis(g);if(V){this._tryToAddEllipsisToLastLine();break}if(O=O.slice(L),O=O.trimLeft(),O.length>0&&(I=this._getTextWidth(O),I<=h)){this._addTextLine(O),g+=i,r=Math.max(r,I);break}}else break}else this._addTextLine(O),g+=i,r=Math.max(r,I),this._shouldHandleEllipsis(g)&&Sp)break}this.textHeight=n,this.textWidth=r}_shouldHandleEllipsis(e){var n=+this.fontSize(),r=this.lineHeight()*n,i=this.attrs.height,a=i!==as&&i!==void 0,s=this.padding(),u=i-s*2,c=this.wrap(),f=c!==Fb;return!f||a&&e+r>u}_tryToAddEllipsisToLastLine(){var e=this.attrs.width,n=e!==as&&e!==void 0,r=this.padding(),i=e-r*2,a=this.ellipsis(),s=this.textArr[this.textArr.length-1];if(!(!s||!a)){if(n){var u=this._getTextWidth(s.text+Im)=1){var r=n[0].p0;e.moveTo(r.x,r.y)}for(var i=0;i0&&(u+=e.dataArray[c].pathLength);var f=0;i==="center"&&(f=Math.max(0,u/2-s/2)),i==="right"&&(f=Math.max(0,u-s));for(var h=lP(this.text()),p=this.text().split(" ").length-1,g,y,v,w=-1,x=0,_=function(){x=0;for(var ce=e.dataArray,j=w+1;j0)return w=j,ce[j];ce[j].command==="M"&&(g={x:ce[j].points[0],y:ce[j].points[1]})}return{}},S=function(ce){var j=e._getTextSize(ce).width+r;ce===" "&&i==="justify"&&(j+=(u-s)/p);var T=0,$=0;for(y=void 0;Math.abs(j-T)/j>.01&&$<20;){$++;for(var V=T;v===void 0;)v=_(),v&&V+v.pathLengthj?y=tt.getPointOnLine(j,g.x,g.y,v.points[0],v.points[1],g.x,g.y):v=void 0;break;case"A":var W=v.points[4],Y=v.points[5],Q=v.points[4]+Y;x===0?x=W+1e-8:j>T?x+=Math.PI/180*Y/Math.abs(Y):x-=Math.PI/360*Y/Math.abs(Y),(Y<0&&x=0&&x>Q)&&(x=Q,ie=!0),y=tt.getPointOnEllipticalArc(v.points[0],v.points[1],v.points[2],v.points[3],x,v.points[6]);break;case"C":x===0?j>v.pathLength?x=1e-8:x=j/v.pathLength:j>T?x+=(j-T)/v.pathLength/2:x=Math.max(x-(T-j)/v.pathLength/2,0),x>1&&(x=1,ie=!0),y=tt.getPointOnCubicBezier(x,v.start.x,v.start.y,v.points[0],v.points[1],v.points[2],v.points[3],v.points[4],v.points[5]);break;case"Q":x===0?x=j/v.pathLength:j>T?x+=(j-T)/v.pathLength:x-=(T-j)/v.pathLength,x>1&&(x=1,ie=!0),y=tt.getPointOnQuadraticBezier(x,v.start.x,v.start.y,v.points[0],v.points[1],v.points[2],v.points[3]);break}y!==void 0&&(T=tt.getLineLength(g.x,g.y,y.x,y.y)),ie&&(ie=!1,v=void 0)}},C="C",O=e._getTextSize(C).width+r,I=f/O-1,L=0;Lt+`.${gP}`).join(" "),$b="nodesRect",F$=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],$$={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const z$="ontouchstart"in pe._global;function G$(t,e){if(t==="rotater")return"crosshair";e+=G.degToRad($$[t]||0);var n=(G.radToDeg(e)%360+360)%360;return G._inRange(n,315+22.5,360)||G._inRange(n,0,22.5)?"ns-resize":G._inRange(n,45-22.5,45+22.5)?"nesw-resize":G._inRange(n,90-22.5,90+22.5)?"ew-resize":G._inRange(n,135-22.5,135+22.5)?"nwse-resize":G._inRange(n,180-22.5,180+22.5)?"ns-resize":G._inRange(n,225-22.5,225+22.5)?"nesw-resize":G._inRange(n,270-22.5,270+22.5)?"ew-resize":G._inRange(n,315-22.5,315+22.5)?"nwse-resize":(G.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var sh=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],zb=1e8;function U$(t){return{x:t.x+t.width/2*Math.cos(t.rotation)+t.height/2*Math.sin(-t.rotation),y:t.y+t.height/2*Math.cos(t.rotation)+t.width/2*Math.sin(t.rotation)}}function mP(t,e,n){const r=n.x+(t.x-n.x)*Math.cos(e)-(t.y-n.y)*Math.sin(e),i=n.y+(t.x-n.x)*Math.sin(e)+(t.y-n.y)*Math.cos(e);return Object.assign(Object.assign({},t),{rotation:t.rotation+e,x:r,y:i})}function j$(t,e){const n=U$(t);return mP(t,e,n)}function W$(t,e,n){let r=e;for(let i=0;i{const i=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},a=r._attrsAffectingSize.map(s=>s+"Change."+this._getEventNamespace()).join(" ");r.on(a,i),r.on(F$.map(s=>s+`.${this._getEventNamespace()}`).join(" "),i),r.on(`absoluteTransformChange.${this._getEventNamespace()}`,i),this._proxyDrag(r)}),this._resetTransformCache();var n=!!this.findOne(".top-left");return n&&this.update(),this}_proxyDrag(e){let n;e.on(`dragstart.${this._getEventNamespace()}`,r=>{n=e.getAbsolutePosition(),!this.isDragging()&&e!==this.findOne(".back")&&this.startDrag(r,!1)}),e.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=e.getAbsolutePosition(),a=i.x-n.x,s=i.y-n.y;this.nodes().forEach(u=>{if(u===e||u.isDragging())return;const c=u.getAbsolutePosition();u.setAbsolutePosition({x:c.x+a,y:c.y+s}),u.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(e=>{e.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache($b),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache($b,this.__getNodeRect)}__getNodeShape(e,n=this.rotation(),r){var i=e.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),a=e.getAbsoluteScale(r),s=e.getAbsolutePosition(r),u=i.x*a.x-e.offsetX()*a.x,c=i.y*a.y-e.offsetY()*a.y;const f=(pe.getAngle(e.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),h={x:s.x+u*Math.cos(f)+c*Math.sin(-f),y:s.y+c*Math.cos(f)+u*Math.sin(f),width:i.width*a.x,height:i.height*a.y,rotation:f};return mP(h,-pe.getAngle(n),{x:0,y:0})}__getNodeRect(){var e=this.getNode();if(!e)return{x:-zb,y:-zb,width:0,height:0,rotation:0};const n=[];this.nodes().map(f=>{const h=f.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var p=[{x:h.x,y:h.y},{x:h.x+h.width,y:h.y},{x:h.x+h.width,y:h.y+h.height},{x:h.x,y:h.y+h.height}],g=f.getAbsoluteTransform();p.forEach(function(y){var v=g.point(y);n.push(v)})});const r=new Wn;r.rotate(-pe.getAngle(this.rotation()));var i,a,s,u;n.forEach(function(f){var h=r.point(f);i===void 0&&(i=s=h.x,a=u=h.y),i=Math.min(i,h.x),a=Math.min(a,h.y),s=Math.max(s,h.x),u=Math.max(u,h.y)}),r.invert();const c=r.point({x:i,y:a});return{x:c.x,y:c.y,width:s-i,height:u-a,rotation:pe.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),sh.forEach(function(e){this._createAnchor(e)}.bind(this)),this._createAnchor("rotater")}_createAnchor(e){var n=new Zu({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:e+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:z$?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=pe.getAngle(this.rotation()),a=G$(e,i);n.getStage().content&&(n.getStage().content.style.cursor=a),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var e=new re({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*G._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(e),this._proxyDrag(e),e.on("dragstart",n=>{n.cancelBubble=!0}),e.on("dragmove",n=>{n.cancelBubble=!0}),e.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(e){this._movingAnchorName=e.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,a=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/a),this.cos=Math.abs(r/a),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var s=e.target.getAbsolutePosition(),u=e.target.getStage().getPointerPosition();this._anchorDragOffset={x:u.x-s.x,y:u.y-s.y},this._fire("transformstart",{evt:e.evt,target:this.getNode()}),this._nodes.forEach(c=>{c._fire("transformstart",{evt:e.evt,target:c})})}_handleMouseMove(e){var n,r,i,a=this.findOne("."+this._movingAnchorName),s=a.getStage();s.setPointersPositions(e);const u=s.getPointerPosition();let c={x:u.x-this._anchorDragOffset.x,y:u.y-this._anchorDragOffset.y};const f=a.getAbsolutePosition();this.anchorDragBoundFunc()&&(c=this.anchorDragBoundFunc()(f,c,e)),a.setAbsolutePosition(c);const h=a.getAbsolutePosition();if(!(f.x===h.x&&f.y===h.y)){if(this._movingAnchorName==="rotater"){var p=this._getNodeRect();n=a.x()-p.width/2,r=-a.y()+p.height/2;let ce=Math.atan2(-r,n)+Math.PI/2;p.height<0&&(ce-=Math.PI);var g=pe.getAngle(this.rotation());const j=g+ce,T=pe.getAngle(this.rotationSnapTolerance()),V=W$(this.rotationSnaps(),j,T)-p.rotation,ie=j$(p,V);this._fitNodesInto(ie,e);return}var y=this.keepRatio()||e.shiftKey,S=this.centeredScaling()||e.altKey;if(this._movingAnchorName==="top-left"){if(y){var v=S?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(v.x-a.x(),2)+Math.pow(v.y-a.y(),2));var w=this.findOne(".top-left").x()>v.x?-1:1,x=this.findOne(".top-left").y()>v.y?-1:1;n=i*this.cos*w,r=i*this.sin*x,this.findOne(".top-left").x(v.x-n),this.findOne(".top-left").y(v.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(a.y());else if(this._movingAnchorName==="top-right"){if(y){var v=S?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(a.x()-v.x,2)+Math.pow(v.y-a.y(),2));var w=this.findOne(".top-right").x()v.y?-1:1;n=i*this.cos*w,r=i*this.sin*x,this.findOne(".top-right").x(v.x+n),this.findOne(".top-right").y(v.y-r)}var _=a.position();this.findOne(".top-left").y(_.y),this.findOne(".bottom-right").x(_.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(a.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(a.x());else if(this._movingAnchorName==="bottom-left"){if(y){var v=S?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(v.x-a.x(),2)+Math.pow(a.y()-v.y,2));var w=v.x{r._fire("transformend",{evt:e,target:r})}),this._movingAnchorName=null}}_fitNodesInto(e,n){var r=this._getNodeRect();const i=1;if(G._inRange(e.width,-this.padding()*2-i,i)){this.update();return}if(G._inRange(e.height,-this.padding()*2-i,i)){this.update();return}const a=this.flipEnabled();var s=new Wn;if(s.rotate(pe.getAngle(this.rotation())),this._movingAnchorName&&e.width<0&&this._movingAnchorName.indexOf("left")>=0){const p=s.point({x:-this.padding()*2,y:0});if(e.x+=p.x,e.y+=p.y,e.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,!a){this.update();return}}else if(this._movingAnchorName&&e.width<0&&this._movingAnchorName.indexOf("right")>=0){const p=s.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,e.width+=this.padding()*2,!a){this.update();return}}if(this._movingAnchorName&&e.height<0&&this._movingAnchorName.indexOf("top")>=0){const p=s.point({x:0,y:-this.padding()*2});if(e.x+=p.x,e.y+=p.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,e.height+=this.padding()*2,!a){this.update();return}}else if(this._movingAnchorName&&e.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const p=s.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,e.height+=this.padding()*2,!a){this.update();return}}if(this.boundBoxFunc()){const p=this.boundBoxFunc()(r,e);p?e=p:G.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const u=1e7,c=new Wn;c.translate(r.x,r.y),c.rotate(r.rotation),c.scale(r.width/u,r.height/u);const f=new Wn;f.translate(e.x,e.y),f.rotate(e.rotation),f.scale(e.width/u,e.height/u);const h=f.multiply(c.invert());this._nodes.forEach(p=>{var g;const y=p.getParent().getAbsoluteTransform(),v=p.getTransform().copy();v.translate(p.offsetX(),p.offsetY());const w=new Wn;w.multiply(y.copy().invert()).multiply(h).multiply(y).multiply(v);const x=w.decompose();p.setAttrs(x),this._fire("transform",{evt:n,target:p}),p._fire("transform",{evt:n,target:p}),(g=p.getLayer())===null||g===void 0||g.batchDraw()}),this.rotation(G._getRotation(e.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(e,n){this.findOne(e).setAttrs(n)}update(){var e,n=this._getNodeRect();this.rotation(G._getRotation(n.rotation));var r=n.width,i=n.height,a=this.enabledAnchors(),s=this.resizeEnabled(),u=this.padding(),c=this.anchorSize();this.find("._anchor").forEach(f=>{f.setAttrs({width:c,height:c,offsetX:c/2,offsetY:c/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:c/2+u,offsetY:c/2+u,visible:s&&a.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:c/2+u,visible:s&&a.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:c/2-u,offsetY:c/2+u,visible:s&&a.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:c/2+u,visible:s&&a.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:c/2-u,visible:s&&a.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:c/2+u,offsetY:c/2-u,visible:s&&a.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:c/2-u,visible:s&&a.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:c/2-u,offsetY:c/2-u,visible:s&&a.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*G._sign(i)-u,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0}),(e=this.getLayer())===null||e===void 0||e.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var e=this.findOne("."+this._movingAnchorName);e&&e.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),zs.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return se.prototype.toObject.call(this)}clone(e){var n=se.prototype.clone.call(this,e);return n}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}function H$(t){return t instanceof Array||G.warn("enabledAnchors value should be an array"),t instanceof Array&&t.forEach(function(e){sh.indexOf(e)===-1&&G.warn("Unknown anchor name: "+e+". Available names are: "+sh.join(", "))}),t||[]}Ke.prototype.className="Transformer";yt(Ke);A.addGetterSetter(Ke,"enabledAnchors",sh,H$);A.addGetterSetter(Ke,"flipEnabled",!0,jr());A.addGetterSetter(Ke,"resizeEnabled",!0);A.addGetterSetter(Ke,"anchorSize",10,le());A.addGetterSetter(Ke,"rotateEnabled",!0);A.addGetterSetter(Ke,"rotationSnaps",[]);A.addGetterSetter(Ke,"rotateAnchorOffset",50,le());A.addGetterSetter(Ke,"rotationSnapTolerance",5,le());A.addGetterSetter(Ke,"borderEnabled",!0);A.addGetterSetter(Ke,"anchorStroke","rgb(0, 161, 255)");A.addGetterSetter(Ke,"anchorStrokeWidth",1,le());A.addGetterSetter(Ke,"anchorFill","white");A.addGetterSetter(Ke,"anchorCornerRadius",0,le());A.addGetterSetter(Ke,"borderStroke","rgb(0, 161, 255)");A.addGetterSetter(Ke,"borderStrokeWidth",1,le());A.addGetterSetter(Ke,"borderDash");A.addGetterSetter(Ke,"keepRatio",!0);A.addGetterSetter(Ke,"centeredScaling",!1);A.addGetterSetter(Ke,"ignoreStroke",!1);A.addGetterSetter(Ke,"padding",0,le());A.addGetterSetter(Ke,"node");A.addGetterSetter(Ke,"nodes");A.addGetterSetter(Ke,"boundBoxFunc");A.addGetterSetter(Ke,"anchorDragBoundFunc");A.addGetterSetter(Ke,"shouldOverdrawWholeArea",!1);A.addGetterSetter(Ke,"useSingleNodeRotation",!0);A.backCompat(Ke,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});class qi extends re{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.radius(),0,pe.getAngle(this.angle()),this.clockwise()),e.lineTo(0,0),e.closePath(),e.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return 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Sc{constructor(e){this.openGlobalLock=null,this.isDragging=!1,this.currentDirection=null,this.originPoint={x:0,y:0},this.constraints=!1,this.hasMutatedConstraints=!1,this.elastic=V(),this.visualElement=e}start(e,{snapToCursor:n=!1}={}){const{presenceContext:s}=this.visualElement;if(s&&s.isPresent===!1)return;const i=c=>{this.stopAnimation(),n&&this.snapToCursor(ie(c,"page").point)},r=(c,l)=>{const{drag:u,dragPropagation:f,onDragStart:h}=this.getProps();if(u&&!f&&(this.openGlobalLock&&this.openGlobalLock(),this.openGlobalLock=Zs(u),!this.openGlobalLock))return;this.isDragging=!0,this.currentDirection=null,this.resolveConstraints(),this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!0,this.visualElement.projection.target=void 0),U(p=>{let g=this.getAxisMotionValue(p).get()||0;if(z.test(g)){const{projection:T}=this.visualElement;if(T&&T.layout){const 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s=this.isDragging;if(this.cancel(),!s)return;const{velocity:i}=n;this.startAnimation(i);const{onDragEnd:r}=this.getProps();r&&r(e,n)}cancel(){this.isDragging=!1;const{projection:e,animationState:n}=this.visualElement;e&&(e.isAnimationBlocked=!1),this.panSession&&this.panSession.end(),this.panSession=void 0;const{dragPropagation:s}=this.getProps();!s&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),n&&n.setActive("whileDrag",!1)}updateAxis(e,n,s){const{drag:i}=this.getProps();if(!s||!Nt(e,i,this.currentDirection))return;const r=this.getAxisMotionValue(e);let 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0)||0)}snapToCursor(e){U(n=>{const{drag:s}=this.getProps();if(!Nt(n,s,this.currentDirection))return;const{projection:i}=this.visualElement,r=this.getAxisMotionValue(n);if(i&&i.layout){const{min:o,max:a}=i.layout.layoutBox[n];r.set(e[n]-A(o,a,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:e,dragConstraints:n}=this.getProps(),{projection:s}=this.visualElement;if(!lt(n)||!s||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};U(o=>{const a=this.getAxisMotionValue(o);if(a){const c=a.get();i[o]=pc({min:c,max:c},this.constraints[o])}});const{transformTemplate:r}=this.visualElement.getProps();this.visualElement.current.style.transform=r?r({},""):"none",s.root&&s.root.updateScroll(),s.updateLayout(),this.resolveConstraints(),U(o=>{if(!Nt(o,e,null))return;const a=this.getAxisMotionValue(o),{min:c,max:l}=this.constraints[o];a.set(A(c,l,i[o]))})}addListeners(){if(!this.visualElement.current)return;Cc.set(this.visualElement,this);const e=this.visualElement.current,n=H(e,"pointerdown",c=>{const{drag:l,dragListener:u=!0}=this.getProps();l&&u&&this.start(c)}),s=()=>{const{dragConstraints:c}=this.getProps();lt(c)&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,r=i.addEventListener("measure",s);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),s();const o=$(window,"resize",()=>this.scalePositionWithinConstraints()),a=i.addEventListener("didUpdate",({delta:c,hasLayoutChanged:l})=>{this.isDragging&&l&&(U(u=>{const f=this.getAxisMotionValue(u);f&&(this.originPoint[u]+=c[u].translate,f.set(f.get()+c[u].translate))}),this.visualElement.render())});return()=>{o(),n(),r(),a&&a()}}getProps(){const e=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:s=!1,dragPropagation:i=!1,dragConstraints:r=!1,dragElastic:o=Fe,dragMomentum:a=!0}=e;return{...e,drag:n,dragDirectionLock:s,dragPropagation:i,dragConstraints:r,dragElastic:o,dragMomentum:a}}}function Nt(t,e,n){return(e===!0||e===t)&&(n===null||n===t)}function Ac(t,e=10){let n=null;return Math.abs(t.y)>e?n="y":Math.abs(t.x)>e&&(n="x"),n}class Vc extends tt{constructor(e){super(e),this.removeGroupControls=I,this.removeListeners=I,this.controls=new Sc(e)}mount(){const{dragControls:e}=this.node.getProps();e&&(this.removeGroupControls=e.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||I}unmount(){this.removeGroupControls(),this.removeListeners()}}class wc extends tt{constructor(){super(...arguments),this.removePointerDownListener=I}onPointerDown(e){this.session=new Si(e,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:e,onPanStart:n,onPan:s,onPanEnd:i}=this.node.getProps();return{onSessionStart:e,onStart:n,onMove:s,onEnd:(r,o)=>{delete 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r=window.getComputedStyle(e).getPropertyValue(s);return r?r.trim():Se(i)?Ne(i,e,n+1):i}function Lc(t,{...e},n){const s=t.current;if(!(s instanceof Element))return{target:e,transitionEnd:n};n&&(n={...n}),t.values.forEach(i=>{const r=i.get();if(!Se(r))return;const o=Ne(r,s);o&&i.set(o)});for(const i in e){const r=e[i];if(!Se(r))continue;const o=Ne(r,s);o&&(e[i]=o,n&&n[i]===void 0&&(n[i]=r))}return{target:e,transitionEnd:n}}const Yn="_$css",jc={correct:(t,{treeScale:e,projectionDelta:n})=>{const s=t,i=t.includes("var("),r=[];i&&(t=t.replace(Ri,d=>(r.push(d),Yn)));const o=Q.parse(t);if(o.length>5)return s;const a=Q.createTransformer(t),c=typeof o[0]!="number"?1:0,l=n.x.scale*e.x,u=n.y.scale*e.y;o[0+c]/=l,o[1+c]/=u;const f=A(l,u,.5);typeof o[2+c]=="number"&&(o[2+c]/=f),typeof o[3+c]=="number"&&(o[3+c]/=f);let h=a(o);if(i){let d=0;h=h.replace(Yn,()=>{const p=r[d];return d++,p})}return h}};class Bc extends 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s.start(mn("",s,e,n)),{stop:()=>s.stop(),isAnimating:()=>s.isAnimating()}}const Li=["TopLeft","TopRight","BottomLeft","BottomRight"],Ic=Li.length,Jn=t=>typeof t=="string"?parseFloat(t):t,Qn=t=>typeof t=="number"||b.test(t);function Oc(t,e,n,s,i,r){i?(t.opacity=A(0,n.opacity!==void 0?n.opacity:1,Nc(s)),t.opacityExit=A(e.opacity!==void 0?e.opacity:1,0,Uc(s))):r&&(t.opacity=A(e.opacity!==void 0?e.opacity:1,n.opacity!==void 0?n.opacity:1,s));for(let o=0;ose?1:n(wt(t,e,s))}function es(t,e){t.min=e.min,t.max=e.max}function F(t,e){es(t.x,e.x),es(t.y,e.y)}function ns(t,e,n,s,i){return t-=e,t=Yt(t,1/n,s),i!==void 0&&(t=Yt(t,1/i,s)),t}function _c(t,e=0,n=1,s=.5,i,r=t,o=t){if(z.test(e)&&(e=parseFloat(e),e=A(o.min,o.max,e/100)-o.min),typeof e!="number")return;let a=A(r.min,r.max,s);t===r&&(a-=e),t.min=ns(t.min,e,n,a,i),t.max=ns(t.max,e,n,a,i)}function ss(t,e,[n,s,i],r,o){_c(t,e[n],e[s],e[i],e.scale,r,o)}const zc=["x","scaleX","originX"],$c=["y","scaleY","originY"];function is(t,e,n,s){ss(t.x,e,zc,n?n.x:void 0,s?s.x:void 0),ss(t.y,e,$c,n?n.y:void 0,s?s.y:void 0)}function os(t){return t.translate===0&&t.scale===1}function Bi(t){return os(t.x)&&os(t.y)}function Ue(t,e){return t.x.min===e.x.min&&t.x.max===e.x.max&&t.y.min===e.y.min&&t.y.max===e.y.max}function rs(t){return B(t.x)/B(t.y)}class Hc{constructor(){this.members=[]}add(e){en(this.members,e),e.scheduleRender()}remove(e){if(nn(this.members,e),e===this.prevLead&&(this.prevLead=void 0),e===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(e){const n=this.members.findIndex(i=>e===i);if(n===0)return!1;let s;for(let i=n;i>=0;i--){const r=this.members[i];if(r.isPresent!==!1){s=r;break}}return s?(this.promote(s),!0):!1}promote(e,n){const s=this.lead;if(e!==s&&(this.prevLead=s,this.lead=e,e.show(),s)){s.instance&&s.scheduleRender(),e.scheduleRender(),e.resumeFrom=s,n&&(e.resumeFrom.preserveOpacity=!0),s.snapshot&&(e.snapshot=s.snapshot,e.snapshot.latestValues=s.animationValues||s.latestValues),e.root&&e.root.isUpdating&&(e.isLayoutDirty=!0);const{crossfade:i}=e.options;i===!1&&s.hide()}}exitAnimationComplete(){this.members.forEach(e=>{const{options:n,resumingFrom:s}=e;n.onExitComplete&&n.onExitComplete(),s&&s.options.onExitComplete&&s.options.onExitComplete()})}scheduleRender(){this.members.forEach(e=>{e.instance&&e.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function as(t,e,n){let s="";const i=t.x.translate/e.x,r=t.y.translate/e.y;if((i||r)&&(s=`translate3d(${i}px, ${r}px, 0) `),(e.x!==1||e.y!==1)&&(s+=`scale(${1/e.x}, ${1/e.y}) `),n){const{rotate:c,rotateX:l,rotateY:u}=n;c&&(s+=`rotate(${c}deg) `),l&&(s+=`rotateX(${l}deg) `),u&&(s+=`rotateY(${u}deg) `)}const o=t.x.scale*e.x,a=t.y.scale*e.y;return(o!==1||a!==1)&&(s+=`scale(${o}, ${a})`),s||"none"}const Wc=(t,e)=>t.depth-e.depth;class Gc{constructor(){this.children=[],this.isDirty=!1}add(e){en(this.children,e),this.isDirty=!0}remove(e){nn(this.children,e),this.isDirty=!0}forEach(e){this.isDirty&&this.children.sort(Wc),this.isDirty=!1,this.children.forEach(e)}}function Kc(t){window.MotionDebug&&window.MotionDebug.record(t)}const cs=["","X","Y","Z"],ls=1e3;let Zc=0;const st={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function ki({attachResizeListener:t,defaultParent:e,measureScroll:n,checkIsScrollRoot:s,resetTransform:i}){return class{constructor(o,a={},c=e==null?void 0:e()){this.id=Zc++,this.animationId=0,this.children=new 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l=0;lthis.root.updateBlockedByResize=!1;t(o,()=>{this.root.updateBlockedByResize=!0,f&&f(),f=bi(h,250),yt.hasAnimatedSinceResize&&(yt.hasAnimatedSinceResize=!1,this.nodes.forEach(fs))})}c&&this.root.registerSharedNode(c,this),this.options.animate!==!1&&u&&(c||l)&&this.addEventListener("didUpdate",({delta:f,hasLayoutChanged:h,hasRelativeTargetChanged:d,layout:p})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const g=this.options.transition||u.getDefaultTransition()||al,{onLayoutAnimationStart:T,onLayoutAnimationComplete:P}=u.getProps(),x=!this.targetLayout||!Ue(this.targetLayout,p)||d,y=!h&&d;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||y||h&&(x||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(f,y);const v={...Ti(g,"layout"),onPlay:T,onComplete:P};(u.shouldReduceMotion||this.options.layoutRoot)&&(v.delay=0,v.type=!1),this.startAnimation(v)}else!h&&this.animationProgress===0&&fs(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=p})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const o=this.getStack();o&&o.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,J.preRender(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(sl),this.animationId++)}getTransformTemplate(){const{visualElement:o}=this.options;return o&&o.getProps().transformTemplate}willUpdate(o=!0){if(this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let u=0;u{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let c=0;c{const S=v/1e3;hs(f.x,o.x,S),hs(f.y,o.y,S),this.setTargetDelta(f),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(bt(h,this.layout.layoutBox,this.relativeParent.layout.layoutBox),ol(this.relativeTarget,this.relativeTargetOrigin,h,S),y&&Ue(this.relativeTarget,y)&&(this.isProjectionDirty=!1),y||(y=V()),F(y,this.relativeTarget)),g&&(this.animationValues=u,Oc(u,l,this.latestValues,S,x,P)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=S},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(o){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(J.update(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=R.update(()=>{yt.hasAnimatedSinceResize=!0,this.currentAnimation=Fc(0,ls,{...o,onUpdate:a=>{this.mixTargetDelta(a),o.onUpdate&&o.onUpdate(a)},onComplete:()=>{o.onComplete&&o.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const o=this.getStack();o&&o.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(ls),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const o=this.getLead();let{targetWithTransforms:a,target:c,layout:l,latestValues:u}=o;if(!(!a||!c||!l)){if(this!==o&&this.layout&&l&&Fi(this.options.animationType,this.layout.layoutBox,l.layoutBox)){c=this.target||V();const f=B(this.layout.layoutBox.x);c.x.min=o.target.x.min,c.x.max=c.x.min+f;const h=B(this.layout.layoutBox.y);c.y.min=o.target.y.min,c.y.max=c.y.min+h}F(a,c),ft(a,u),Pt(this.projectionDeltaWithTransform,this.layoutCorrected,a,u)}}registerSharedNode(o,a){this.sharedNodes.has(o)||this.sharedNodes.set(o,new Hc),this.sharedNodes.get(o).add(a);const l=a.options.initialPromotionConfig;a.promote({transition:l?l.transition:void 0,preserveFollowOpacity:l&&l.shouldPreserveFollowOpacity?l.shouldPreserveFollowOpacity(a):void 0})}isLead(){const o=this.getStack();return o?o.lead===this:!0}getLead(){var o;const{layoutId:a}=this.options;return a?((o=this.getStack())===null||o===void 0?void 0:o.lead)||this:this}getPrevLead(){var o;const{layoutId:a}=this.options;return a?(o=this.getStack())===null||o===void 0?void 0:o.prevLead:void 0}getStack(){const{layoutId:o}=this.options;if(o)return this.root.sharedNodes.get(o)}promote({needsReset:o,transition:a,preserveFollowOpacity:c}={}){const l=this.getStack();l&&l.promote(this,c),o&&(this.projectionDelta=void 0,this.needsReset=!0),a&&this.setOptions({transition:a})}relegate(){const o=this.getStack();return o?o.relegate(this):!1}resetRotation(){const{visualElement:o}=this.options;if(!o)return;let a=!1;const{latestValues:c}=o;if((c.rotate||c.rotateX||c.rotateY||c.rotateZ)&&(a=!0),!a)return;const l={};for(let u=0;u{var a;return(a=o.currentAnimation)===null||a===void 0?void 0:a.stop()}),this.root.nodes.forEach(us),this.root.sharedNodes.clear()}}}function qc(t){t.updateLayout()}function Xc(t){var e;const n=((e=t.resumeFrom)===null||e===void 0?void 0:e.snapshot)||t.snapshot;if(t.isLead()&&t.layout&&n&&t.hasListeners("didUpdate")){const{layoutBox:s,measuredBox:i}=t.layout,{animationType:r}=t.options,o=n.source!==t.layout.source;r==="size"?U(f=>{const h=o?n.measuredBox[f]:n.layoutBox[f],d=B(h);h.min=s[f].min,h.max=h.min+d}):Fi(r,n.layoutBox,s)&&U(f=>{const h=o?n.measuredBox[f]:n.layoutBox[f],d=B(s[f]);h.max=h.min+d});const a=Tt();Pt(a,s,n.layoutBox);const c=Tt();o?Pt(c,t.applyTransform(i,!0),n.measuredBox):Pt(c,s,n.layoutBox);const l=!Bi(a);let u=!1;if(!t.resumeFrom){const f=t.getClosestProjectingParent();if(f&&!f.resumeFrom){const{snapshot:h,layout:d}=f;if(h&&d){const p=V();bt(p,n.layoutBox,h.layoutBox);const g=V();bt(g,s,d.layoutBox),Ue(p,g)||(u=!0),f.options.layoutRoot&&(t.relativeTarget=g,t.relativeTargetOrigin=p,t.relativeParent=f)}}}t.notifyListeners("didUpdate",{layout:s,snapshot:n,delta:c,layoutDelta:a,hasLayoutChanged:l,hasRelativeTargetChanged:u})}else if(t.isLead()){const{onExitComplete:s}=t.options;s&&s()}t.options.transition=void 0}function Yc(t){st.totalNodes++,t.parent&&(t.isProjecting()||(t.isProjectionDirty=t.parent.isProjectionDirty),t.isSharedProjectionDirty||(t.isSharedProjectionDirty=Boolean(t.isProjectionDirty||t.parent.isProjectionDirty||t.parent.isSharedProjectionDirty)),t.isTransformDirty||(t.isTransformDirty=t.parent.isTransformDirty))}function Jc(t){t.isProjectionDirty=t.isSharedProjectionDirty=t.isTransformDirty=!1}function Qc(t){t.clearSnapshot()}function us(t){t.clearMeasurements()}function tl(t){const{visualElement:e}=t.options;e&&e.getProps().onBeforeLayoutMeasure&&e.notify("BeforeLayoutMeasure"),t.resetTransform()}function fs(t){t.finishAnimation(),t.targetDelta=t.relativeTarget=t.target=void 0}function el(t){t.resolveTargetDelta()}function nl(t){t.calcProjection()}function sl(t){t.resetRotation()}function il(t){t.removeLeadSnapshot()}function 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C.jsx(Jt,{...i,children:o})};const au=io,cu=eo;export{Ul as A,qi as C,kt as I,Jt as P,iu as T,Rt as a,vo as b,su as c,Ce as d,vn as e,Mc as f,au as g,cu as h,co as i,ru as j,Ll as m,ou as u}; diff --git a/invokeai/frontend/web/dist/index.html b/invokeai/frontend/web/dist/index.html index a02ffb1bc9..a412552051 100644 --- a/invokeai/frontend/web/dist/index.html +++ b/invokeai/frontend/web/dist/index.html @@ -12,8 +12,7 @@ margin: 0; } - - + diff --git a/invokeai/frontend/web/dist/locales/de.json b/invokeai/frontend/web/dist/locales/de.json index deeef34194..cff09d46bb 100644 --- a/invokeai/frontend/web/dist/locales/de.json +++ b/invokeai/frontend/web/dist/locales/de.json @@ -18,7 +18,7 @@ "training": "Training", "trainingDesc1": "Ein spezieller Arbeitsablauf zum Trainieren Ihrer eigenen Embeddings und Checkpoints mit Textual Inversion und Dreambooth über die Weboberfläche.", "trainingDesc2": "InvokeAI unterstützt bereits das Training von benutzerdefinierten Embeddings mit Textual Inversion unter Verwendung des Hauptskripts.", - "upload": "Upload", + "upload": "Hochladen", "close": "Schließen", "load": "Laden", "statusConnected": "Verbunden", @@ -41,12 +41,34 @@ "statusUpscaling": "Hochskalierung", "statusUpscalingESRGAN": "Hochskalierung (ESRGAN)", "statusLoadingModel": "Laden des Modells", - "statusModelChanged": "Modell Geändert" + "statusModelChanged": "Modell Geändert", + "cancel": "Abbruch", + "accept": "Annehmen", + "back": "Zurück", + "langEnglish": "Englisch", + "langDutch": "Niederländisch", + "langFrench": "Französisch", + "oceanTheme": "Ozean", + "langItalian": "Italienisch", + "langPortuguese": "Portogisisch", + "langRussian": "Russisch", + "langUkranian": "Ukrainisch", + "hotkeysLabel": "Tastenkombinationen", + "githubLabel": "Github", + "discordLabel": "Discord", + "txt2img": "Text zu Bild", + "postprocessing": "Nachbearbeitung", + "langPolish": "Polnisch", + "langJapanese": "Japanisch", + "langArabic": "Arabisch", + "langKorean": "Koreanisch", + "langHebrew": "Hebräisch", + "langSpanish": "Spanisch" }, "gallery": { "generations": "Erzeugungen", "showGenerations": "Zeige Erzeugnisse", - "uploads": "Uploads", + "uploads": "Hochgelades", "showUploads": "Zeige Uploads", "galleryImageSize": "Bildgröße", "galleryImageResetSize": "Größe zurücksetzen", @@ -312,7 +334,11 @@ "deleteModel": "Model löschen", "deleteConfig": "Konfiguration löschen", "deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?", - "deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen." + "deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.", + "customConfig": "Benutzerdefinierte Konfiguration", + "invokeRoot": "InvokeAI Ordner", + "formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.", + "checkpointModels": "Kontrollpunkte" }, "parameters": { "images": "Bilder", @@ -370,7 +396,10 @@ "useInitImg": "Ausgangsbild verwenden", "deleteImage": "Bild löschen", "initialImage": "Ursprüngliches Bild", - "showOptionsPanel": "Optionsleiste zeigen" + "showOptionsPanel": "Optionsleiste zeigen", + "cancel": { + "setType": "Abbruchart festlegen" + } }, "settings": { "displayInProgress": "Bilder in Bearbeitung anzeigen", @@ -489,5 +518,25 @@ "betaDarkenOutside": "Außen abdunkeln", "betaLimitToBox": "Begrenzung auf das Feld", "betaPreserveMasked": "Maskiertes bewahren" + }, + "accessibility": { + "modelSelect": "Model Auswahl", + "uploadImage": "Bild hochladen", + "previousImage": "Voriges Bild", + "useThisParameter": "Benutze diesen Parameter", + "copyMetadataJson": "Kopiere metadata JSON", + "zoomIn": "Vergrößern", + "rotateClockwise": "Im Uhrzeigersinn drehen", + "flipHorizontally": "Horizontal drehen", + "flipVertically": "Vertikal drehen", + "modifyConfig": "Optionen einstellen", + "toggleAutoscroll": "Auroscroll ein/ausschalten", + "toggleLogViewer": "Log Betrachter ein/ausschalten", + "showGallery": "Zeige Galerie", + "showOptionsPanel": "Zeige Optionen", + "reset": "Zurücksetzen", + "nextImage": "Nächstes Bild", + "zoomOut": "Verkleinern", + "rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen" } } diff --git a/invokeai/frontend/web/dist/locales/en.json b/invokeai/frontend/web/dist/locales/en.json index a99f54d741..3592e141d0 100644 --- a/invokeai/frontend/web/dist/locales/en.json +++ b/invokeai/frontend/web/dist/locales/en.json @@ -8,7 +8,7 @@ "nextImage": "Next Image", "useThisParameter": "Use this parameter", "copyMetadataJson": "Copy metadata JSON", - "exitViewer": "ExitViewer", + "exitViewer": "Exit Viewer", "zoomIn": "Zoom In", "zoomOut": "Zoom Out", "rotateCounterClockwise": "Rotate Counter-Clockwise", @@ -19,12 +19,13 @@ "toggleAutoscroll": "Toggle autoscroll", "toggleLogViewer": "Toggle Log Viewer", "showGallery": "Show Gallery", - "showOptionsPanel": "Show Options Panel" + "showOptionsPanel": "Show Options Panel", + "menu": "Menu" }, "common": { "hotkeysLabel": "Hotkeys", "themeLabel": "Theme", - "languagePickerLabel": "Language Picker", + "languagePickerLabel": "Language", "reportBugLabel": "Report Bug", "githubLabel": "Github", "discordLabel": "Discord", @@ -52,7 +53,8 @@ "txt2img": "Text To Image", "img2img": "Image To Image", "unifiedCanvas": "Unified Canvas", - "nodes": "Nodes", + "linear": "Linear", + "nodes": "Node Editor", "postprocessing": "Post Processing", "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.", "postProcessing": "Post Processing", @@ -61,7 +63,7 @@ "postProcessDesc3": "The Invoke AI Command Line Interface offers various other features including Embiggen.", "training": "Training", "trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.", - "trainingDesc2": "InvokeAI already supports training custom embeddings using Textual Inversion using the main script.", + "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.", "upload": "Upload", "close": "Close", "cancel": "Cancel", @@ -95,7 +97,13 @@ "statusMergedModels": "Models Merged", "pinOptionsPanel": "Pin Options Panel", "loading": "Loading", - "loadingInvokeAI": "Loading Invoke AI" + "loadingInvokeAI": "Loading Invoke AI", + "random": "Random", + "generate": "Generate", + "openInNewTab": "Open in New Tab", + "dontAskMeAgain": "Don't ask me again", + "areYouSure": "Are you sure?", + "imagePrompt": "Image Prompt" }, "gallery": { "generations": "Generations", @@ -111,7 +119,10 @@ "pinGallery": "Pin Gallery", "allImagesLoaded": "All Images Loaded", "loadMore": "Load More", - "noImagesInGallery": "No Images In Gallery" + "noImagesInGallery": "No Images In Gallery", + "deleteImage": "Delete Image", + "deleteImageBin": "Deleted images will be sent to your operating system's Bin.", + "deleteImagePermanent": "Deleted images cannot be restored." }, "hotkeys": { "keyboardShortcuts": "Keyboard Shortcuts", @@ -443,9 +454,10 @@ "seed": "Seed", "imageToImage": "Image to Image", "randomizeSeed": "Randomize Seed", - "shuffle": "Shuffle", + "shuffle": "Shuffle Seed", "noiseThreshold": "Noise Threshold", "perlinNoise": "Perlin Noise", + "noiseSettings": "Noise", "variations": "Variations", "variationAmount": "Variation Amount", "seedWeights": "Seed Weights", @@ -460,6 +472,8 @@ "scale": "Scale", "otherOptions": "Other Options", "seamlessTiling": "Seamless Tiling", + "seamlessXAxis": "X Axis", + "seamlessYAxis": "Y Axis", "hiresOptim": "High Res Optimization", "hiresStrength": "High Res Strength", "imageFit": "Fit Initial Image To Output Size", @@ -503,7 +517,6 @@ "useAll": "Use All", "useInitImg": "Use Initial Image", "info": "Info", - "deleteImage": "Delete Image", "initialImage": "Initial Image", "showOptionsPanel": "Show Options Panel", "hidePreview": "Hide Preview", @@ -518,12 +531,22 @@ "useCanvasBeta": "Use Canvas Beta Layout", "enableImageDebugging": "Enable Image Debugging", "useSlidersForAll": "Use Sliders For All Options", + "showProgressInViewer": "Show Progress Images in Viewer", + "antialiasProgressImages": "Antialias Progress Images", "resetWebUI": "Reset Web UI", "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.", "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.", - "resetComplete": "Web UI has been reset. Refresh the page to reload." + "resetComplete": "Web UI has been reset. Refresh the page to reload.", + "consoleLogLevel": "Log Level", + "shouldLogToConsole": "Console Logging", + "developer": "Developer", + "general": "General" }, "toast": { + "serverError": "Server Error", + "disconnected": "Disconnected from Server", + "connected": "Connected to Server", + "canceled": "Processing Canceled", "tempFoldersEmptied": "Temp Folder Emptied", "uploadFailed": "Upload failed", "uploadFailedMultipleImagesDesc": "Multiple images pasted, may only upload one image at a time", @@ -531,8 +554,9 @@ "downloadImageStarted": "Image Download Started", "imageCopied": "Image Copied", "imageLinkCopied": "Image Link Copied", + "problemCopyingImageLink": "Unable to Copy Image Link", "imageNotLoaded": "No Image Loaded", - "imageNotLoadedDesc": "No image found to send to image to image module", + "imageNotLoadedDesc": "Could not find image", "imageSavedToGallery": "Image Saved to Gallery", "canvasMerged": "Canvas Merged", "sentToImageToImage": "Sent To Image To Image", @@ -627,6 +651,11 @@ "betaClear": "Clear", "betaDarkenOutside": "Darken Outside", "betaLimitToBox": "Limit To Box", - "betaPreserveMasked": "Preserve Masked" + "betaPreserveMasked": "Preserve Masked", + "antialiasing": "Antialiasing" + }, + "ui": { + "showProgressImages": "Show Progress Images", + "hideProgressImages": "Hide Progress Images" } } diff --git a/invokeai/frontend/web/dist/locales/es.json b/invokeai/frontend/web/dist/locales/es.json index 9c2b6bf983..d17cefaad2 100644 --- a/invokeai/frontend/web/dist/locales/es.json +++ b/invokeai/frontend/web/dist/locales/es.json @@ -73,7 +73,8 @@ "postprocessing": "Tratamiento posterior", "txt2img": "De texto a imagen", "accept": "Aceptar", - "cancel": "Cancelar" + "cancel": "Cancelar", + "linear": "Lineal" }, "gallery": { "generations": "Generaciones", @@ -483,7 +484,9 @@ "negativePrompts": "Preguntas negativas", "imageToImage": "Imagen a imagen", "denoisingStrength": "Intensidad de la eliminación del ruido", - "hiresStrength": "Alta resistencia" + "hiresStrength": "Alta resistencia", + "showPreview": "Mostrar la vista previa", + "hidePreview": "Ocultar la vista previa" }, "settings": { "models": "Modelos", @@ -529,7 +532,11 @@ "metadataLoadFailed": "Error al cargar metadatos", "initialImageSet": "Imágen inicial establecida", "initialImageNotSet": "Imagen inicial no establecida", - "initialImageNotSetDesc": "Error al establecer la imágen inicial" + "initialImageNotSetDesc": "Error al establecer la imágen inicial", + "serverError": "Error en el servidor", + "disconnected": "Desconectado del servidor", + "canceled": "Procesando la cancelación", + "connected": "Conectado al servidor" }, "tooltip": { "feature": { @@ -625,6 +632,7 @@ "toggleAutoscroll": "Activar el autodesplazamiento", "toggleLogViewer": "Alternar el visor de registros", "showGallery": "Mostrar galería", - "showOptionsPanel": "Mostrar el panel de opciones" + "showOptionsPanel": "Mostrar el panel de opciones", + "menu": "Menú" } } diff --git a/invokeai/frontend/web/dist/locales/it.json b/invokeai/frontend/web/dist/locales/it.json index 7df34173df..4ac5b6831d 100644 --- a/invokeai/frontend/web/dist/locales/it.json +++ b/invokeai/frontend/web/dist/locales/it.json @@ -73,7 +73,8 @@ "postprocessing": "Post Elaborazione", "txt2img": "Testo a Immagine", "accept": "Accetta", - "cancel": "Annulla" + "cancel": "Annulla", + "linear": "Lineare" }, "gallery": { "generations": "Generazioni", @@ -483,7 +484,9 @@ }, "hSymmetryStep": "Passi Simmetria Orizzontale", "vSymmetryStep": "Passi Simmetria Verticale", - "symmetry": "Simmetria" + "symmetry": "Simmetria", + "hidePreview": "Nascondi l'anteprima", + "showPreview": "Mostra l'anteprima" }, "settings": { "models": "Modelli", @@ -529,7 +532,11 @@ "metadataLoadFailed": "Impossibile caricare i metadati", "initialImageSet": "Immagine iniziale impostata", "initialImageNotSet": "Immagine iniziale non impostata", - "initialImageNotSetDesc": "Impossibile caricare l'immagine iniziale" + "initialImageNotSetDesc": "Impossibile caricare l'immagine iniziale", + "serverError": "Errore del Server", + "disconnected": "Disconnesso dal Server", + "connected": "Connesso al Server", + "canceled": "Elaborazione annullata" }, "tooltip": { "feature": { @@ -625,6 +632,7 @@ "showOptionsPanel": "Mostra il pannello opzioni", "flipVertically": "Capovolgi verticalmente", "toggleAutoscroll": "Attiva/disattiva lo scorrimento automatico", - "modifyConfig": "Modifica configurazione" + "modifyConfig": "Modifica configurazione", + "menu": "Menu" } } diff --git a/invokeai/frontend/web/dist/locales/ja.json b/invokeai/frontend/web/dist/locales/ja.json index 542cffbb74..007aa9b491 100644 --- a/invokeai/frontend/web/dist/locales/ja.json +++ b/invokeai/frontend/web/dist/locales/ja.json @@ -37,7 +37,43 @@ "statusUpscaling": "アップスケーリング", "statusUpscalingESRGAN": "アップスケーリング (ESRGAN)", "statusLoadingModel": "モデルを読み込む", - "statusModelChanged": "モデルを変更" + "statusModelChanged": "モデルを変更", + "cancel": "キャンセル", + "accept": "同意", + "langBrPortuguese": "Português do Brasil", + "langRussian": "Русский", + "langSimplifiedChinese": "简体中文", + "langUkranian": "Украї́нська", + "langSpanish": "Español", + "img2img": "img2img", + "unifiedCanvas": "Unified Canvas", + "statusMergingModels": "モデルのマージ", + "statusModelConverted": "変換済モデル", + "statusGeneratingInpainting": "Inpaintingを生成", + "statusIterationComplete": "Iteration Complete", + "statusGeneratingOutpainting": "Outpaintingを生成", + "loading": "ロード中", + "loadingInvokeAI": "Invoke AIをロード中", + "statusConvertingModel": "モデルの変換", + "statusMergedModels": "マージ済モデル", + "pinOptionsPanel": "オプションパネルを固定", + "githubLabel": "Github", + "hotkeysLabel": "ホットキー", + "langHebrew": "עברית", + "discordLabel": "Discord", + "langItalian": "Italiano", + "langEnglish": "English", + "oceanTheme": "オーシャン", + "langArabic": "アラビア語", + "langDutch": "Nederlands", + "langFrench": "Français", + "langGerman": "Deutsch", + "langPortuguese": "Português", + "nodes": "ノード", + "langKorean": "한국어", + "langPolish": "Polski", + "txt2img": "txt2img", + "postprocessing": "Post Processing" }, "gallery": { "uploads": "アップロード", @@ -46,11 +82,14 @@ "galleryImageResetSize": "サイズをリセット", "gallerySettings": "ギャラリーの設定", "maintainAspectRatio": "アスペクト比を維持", - "singleColumnLayout": "シングルカラムレイアウト", + "singleColumnLayout": "1カラムレイアウト", "pinGallery": "ギャラリーにピン留め", "allImagesLoaded": "すべての画像を読み込む", "loadMore": "さらに読み込む", - "noImagesInGallery": "ギャラリーに画像がありません" + "noImagesInGallery": "ギャラリーに画像がありません", + "generations": "生成", + "showGenerations": "生成過程を見る", + "autoSwitchNewImages": "新しい画像に自動切替" }, "hotkeys": { "keyboardShortcuts": "キーボードショートカット", @@ -59,14 +98,16 @@ "galleryHotkeys": "ギャラリーのホットキー", "unifiedCanvasHotkeys": "Unified Canvasのホットキー", "invoke": { - "desc": "画像を生成" + "desc": "画像を生成", + "title": "Invoke" }, "cancel": { "title": "キャンセル", "desc": "画像の生成をキャンセル" }, "focusPrompt": { - "desc": "プロンプトテキストボックスにフォーカス" + "desc": "プロンプトテキストボックスにフォーカス", + "title": "プロジェクトにフォーカス" }, "toggleOptions": { "title": "オプションパネルのトグル", @@ -410,5 +451,27 @@ "accept": "同意", "showHide": "表示/非表示", "discardAll": "すべて破棄" + }, + "accessibility": { + "modelSelect": "モデルを選択", + "invokeProgressBar": "進捗バー", + "reset": "リセット", + "uploadImage": "画像をアップロード", + "previousImage": "前の画像", + "nextImage": "次の画像", + "useThisParameter": "このパラメータを使用する", + "copyMetadataJson": "メタデータをコピー(JSON)", + "zoomIn": "ズームイン", + "exitViewer": "ExitViewer", + "zoomOut": "ズームアウト", + "rotateCounterClockwise": "反時計回りに回転", + "rotateClockwise": "時計回りに回転", + "flipHorizontally": "水平方向に反転", + "flipVertically": "垂直方向に反転", + "toggleAutoscroll": "自動スクロールの切替", + "modifyConfig": "Modify Config", + "toggleLogViewer": "Log Viewerの切替", + "showGallery": "ギャラリーを表示", + "showOptionsPanel": "オプションパネルを表示" } } diff --git a/invokeai/frontend/web/dist/locales/nl.json b/invokeai/frontend/web/dist/locales/nl.json index 70b836dbc1..230e3b5b64 100644 --- a/invokeai/frontend/web/dist/locales/nl.json +++ b/invokeai/frontend/web/dist/locales/nl.json @@ -62,7 +62,18 @@ "statusConvertingModel": "Omzetten van model", "statusModelConverted": "Model omgezet", "statusMergingModels": "Samenvoegen van modellen", - "statusMergedModels": "Modellen samengevoegd" + "statusMergedModels": "Modellen samengevoegd", + "cancel": "Annuleer", + "accept": "Akkoord", + "langPortuguese": "Português", + "pinOptionsPanel": "Zet deelscherm Opties vast", + "loading": "Bezig met laden", + "loadingInvokeAI": "Bezig met laden van Invoke AI", + "oceanTheme": "Oceaan", + "langHebrew": "עברית", + "langKorean": "한국어", + "txt2img": "Tekst naar afbeelding", + "postprocessing": "Nabewerking" }, "gallery": { "generations": "Gegenereerde afbeeldingen", @@ -301,7 +312,7 @@ "name": "Naam", "nameValidationMsg": "Geef een naam voor je model", "description": "Beschrijving", - "descriptionValidationMsg": "Voeg een beschrijving toe voor je model.", + "descriptionValidationMsg": "Voeg een beschrijving toe voor je model", "config": "Configuratie", "configValidationMsg": "Pad naar het configuratiebestand van je model.", "modelLocation": "Locatie model", @@ -391,7 +402,13 @@ "modelMergeInterpAddDifferenceHelp": "In deze stand wordt model 3 eerst van model 2 afgehaald. Wat daar uitkomt wordt gemengd met model 1, gebruikmakend van de hierboven ingestelde alfawaarde.", "inverseSigmoid": "Keer Sigmoid om", "sigmoid": "Sigmoid", - "weightedSum": "Gewogen som" + "weightedSum": "Gewogen som", + "v2_base": "v2 (512px)", + "v2_768": "v2 (768px)", + "none": "geen", + "addDifference": "Voeg verschil toe", + "scanForModels": "Scan naar modellen", + "pickModelType": "Kies modelsoort" }, "parameters": { "images": "Afbeeldingen", @@ -561,7 +578,7 @@ "autoSaveToGallery": "Bewaar automatisch naar galerij", "saveBoxRegionOnly": "Bewaar alleen tekengebied", "limitStrokesToBox": "Beperk streken tot tekenvak", - "showCanvasDebugInfo": "Toon foutopsporingsgegevens canvas", + "showCanvasDebugInfo": "Toon aanvullende canvasgegevens", "clearCanvasHistory": "Wis canvasgeschiedenis", "clearHistory": "Wis geschiedenis", "clearCanvasHistoryMessage": "Het wissen van de canvasgeschiedenis laat het huidige canvas ongemoeid, maar wist onherstelbaar de geschiedenis voor het ongedaan maken en herhalen.", @@ -587,5 +604,27 @@ "betaDarkenOutside": "Verduister buiten tekenvak", "betaLimitToBox": "Beperk tot tekenvak", "betaPreserveMasked": "Behoud masker" + }, + "accessibility": { + "exitViewer": "Stop viewer", + "zoomIn": "Zoom in", + "rotateCounterClockwise": "Draai tegen de klok in", + "modelSelect": "Modelkeuze", + "invokeProgressBar": "Voortgangsbalk Invoke", + "reset": "Herstel", + "uploadImage": "Upload afbeelding", + "previousImage": "Vorige afbeelding", + "nextImage": "Volgende afbeelding", + "useThisParameter": "Gebruik deze parameter", + "copyMetadataJson": "Kopieer metagegevens-JSON", + "zoomOut": "Zoom uit", + "rotateClockwise": "Draai met de klok mee", + "flipHorizontally": "Spiegel horizontaal", + "flipVertically": "Spiegel verticaal", + "modifyConfig": "Wijzig configuratie", + "toggleAutoscroll": "Autom. scrollen aan/uit", + "toggleLogViewer": "Logboekviewer aan/uit", + "showGallery": "Toon galerij", + "showOptionsPanel": "Toon deelscherm Opties" } } diff --git a/invokeai/frontend/web/dist/locales/ru.json b/invokeai/frontend/web/dist/locales/ru.json index 0280341dee..822389d78a 100644 --- a/invokeai/frontend/web/dist/locales/ru.json +++ b/invokeai/frontend/web/dist/locales/ru.json @@ -9,7 +9,7 @@ "lightTheme": "Светлая", "greenTheme": "Зеленая", "img2img": "Изображение в изображение (img2img)", - "unifiedCanvas": "Универсальный холст", + "unifiedCanvas": "Единый холст", "nodes": "Ноды", "langRussian": "Русский", "nodesDesc": "Cистема генерации изображений на основе нодов (узлов) уже разрабатывается. Следите за новостями об этой замечательной функции.", @@ -53,7 +53,28 @@ "loading": "Загрузка", "loadingInvokeAI": "Загрузка Invoke AI", "back": "Назад", - "statusConvertingModel": "Конвертация модели" + "statusConvertingModel": "Конвертация модели", + "cancel": "Отменить", + "accept": "Принять", + "oceanTheme": "Океан", + "langUkranian": "Украинский", + "langEnglish": "Английский", + "postprocessing": "Постобработка", + "langArabic": "Арабский", + "langSpanish": "Испанский", + "langSimplifiedChinese": "Китайский (упрощенный)", + "langDutch": "Нидерландский", + "langFrench": "Французский", + "langGerman": "Немецкий", + "langHebrew": "Иврит", + "langItalian": "Итальянский", + "langJapanese": "Японский", + "langKorean": "Корейский", + "langPolish": "Польский", + "langPortuguese": "Португальский", + "txt2img": "Текст в изображение (txt2img)", + "langBrPortuguese": "Португальский (Бразилия)", + "linear": "Линейная обработка" }, "gallery": { "generations": "Генерации", @@ -72,11 +93,11 @@ "noImagesInGallery": "Изображений нет" }, "hotkeys": { - "keyboardShortcuts": "Клавиатурные сокращения", + "keyboardShortcuts": "Горячие клавиши", "appHotkeys": "Горячие клавиши приложения", "generalHotkeys": "Общие горячие клавиши", "galleryHotkeys": "Горячие клавиши галереи", - "unifiedCanvasHotkeys": "Горячие клавиши универсального холста", + "unifiedCanvasHotkeys": "Горячие клавиши Единого холста", "invoke": { "title": "Invoke", "desc": "Сгенерировать изображение" @@ -266,12 +287,12 @@ "desc": "Сбросить вид холста" }, "previousStagingImage": { - "title": "Previous Staging Image", - "desc": "Предыдущее изображение" + "title": "Предыдущее изображение", + "desc": "Предыдущая область изображения" }, "nextStagingImage": { - "title": "Next Staging Image", - "desc": "Следующее изображение" + "title": "Следующее изображение", + "desc": "Следующая область изображения" }, "acceptStagingImage": { "title": "Принять изображение", @@ -353,7 +374,42 @@ "modelConverted": "Модель преобразована", "invokeRoot": "Каталог InvokeAI", "modelsMerged": "Модели объединены", - "mergeModels": "Объединить модели" + "mergeModels": "Объединить модели", + "scanForModels": "Просканировать модели", + "sigmoid": "Сигмоид", + "formMessageDiffusersModelLocation": "Расположение Diffusers-модели", + "modelThree": "Модель 3", + "modelMergeHeaderHelp2": "Только Diffusers-модели доступны для объединения. Если вы хотите объединить checkpoint-модели, сначала преобразуйте их в Diffusers.", + "pickModelType": "Выбрать тип модели", + "formMessageDiffusersVAELocation": "Расположение VAE", + "v1": "v1", + "convertToDiffusersSaveLocation": "Путь сохранения", + "customSaveLocation": "Пользовательский путь сохранения", + "alpha": "Альфа", + "diffusersModels": "Diffusers", + "customConfig": "Пользовательский конфиг", + "pathToCustomConfig": "Путь к пользовательскому конфигу", + "inpainting": "v1 Inpainting", + "sameFolder": "В ту же папку", + "modelOne": "Модель 1", + "mergedModelCustomSaveLocation": "Пользовательский путь", + "none": "пусто", + "addDifference": "Добавить разницу", + "vaeRepoIDValidationMsg": "Онлайн репозиторий VAE", + "convertToDiffusersHelpText2": "Этот процесс заменит вашу запись в Model Manager на версию той же модели в Diffusers.", + "custom": "Пользовательский", + "modelTwo": "Модель 2", + "mergedModelSaveLocation": "Путь сохранения", + "merge": "Объединить", + "interpolationType": "Тип интерполяции", + "modelMergeInterpAddDifferenceHelp": "В этом режиме Модель 3 сначала вычитается из Модели 2. Результирующая версия смешивается с Моделью 1 с установленным выше коэффициентом Альфа.", + "modelMergeHeaderHelp1": "Вы можете объединить до трех разных моделей, чтобы создать смешанную, соответствующую вашим потребностям.", + "modelMergeAlphaHelp": "Альфа влияет на силу смешивания моделей. Более низкие значения альфа приводят к меньшему влиянию второй модели.", + "inverseSigmoid": "Обратный Сигмоид", + "weightedSum": "Взвешенная сумма", + "safetensorModels": "SafeTensors", + "v2_768": "v2 (768px)", + "v2_base": "v2 (512px)" }, "parameters": { "images": "Изображения", @@ -380,7 +436,7 @@ "scale": "Масштаб", "otherOptions": "Другие параметры", "seamlessTiling": "Бесшовный узор", - "hiresOptim": "Высокое разрешение", + "hiresOptim": "Оптимизация High Res", "imageFit": "Уместить изображение", "codeformerFidelity": "Точность", "seamSize": "Размер шва", @@ -397,11 +453,11 @@ "infillScalingHeader": "Заполнение и масштабирование", "img2imgStrength": "Сила обработки img2img", "toggleLoopback": "Зациклить обработку", - "invoke": "Вызвать", + "invoke": "Invoke", "promptPlaceholder": "Введите запрос здесь (на английском). [исключенные токены], (более значимые)++, (менее значимые)--, swap и blend тоже доступны (смотрите Github)", "sendTo": "Отправить", "sendToImg2Img": "Отправить в img2img", - "sendToUnifiedCanvas": "Отправить на холст", + "sendToUnifiedCanvas": "Отправить на Единый холст", "copyImageToLink": "Скопировать ссылку", "downloadImage": "Скачать", "openInViewer": "Открыть в просмотрщике", @@ -413,7 +469,24 @@ "info": "Метаданные", "deleteImage": "Удалить изображение", "initialImage": "Исходное изображение", - "showOptionsPanel": "Показать панель настроек" + "showOptionsPanel": "Показать панель настроек", + "vSymmetryStep": "Шаг верт. симметрии", + "cancel": { + "immediate": "Отменить немедленно", + "schedule": "Отменить после текущей итерации", + "isScheduled": "Отмена", + "setType": "Установить тип отмены" + }, + "general": "Основное", + "hiresStrength": "Сила High Res", + "symmetry": "Симметрия", + "hSymmetryStep": "Шаг гор. симметрии", + "hidePreview": "Скрыть предпросмотр", + "imageToImage": "Изображение в изображение", + "denoisingStrength": "Сила шумоподавления", + "copyImage": "Скопировать изображение", + "negativePrompts": "Исключающий запрос", + "showPreview": "Показать предпросмотр" }, "settings": { "models": "Модели", @@ -423,10 +496,11 @@ "displayHelpIcons": "Показывать значки подсказок", "useCanvasBeta": "Показывать инструменты слева (Beta UI)", "enableImageDebugging": "Включить отладку", - "resetWebUI": "Вернуть умолчания", + "resetWebUI": "Сброс настроек Web UI", "resetWebUIDesc1": "Сброс настроек веб-интерфейса удаляет только локальный кэш браузера с вашими изображениями и настройками. Он не удаляет изображения с диска.", "resetWebUIDesc2": "Если изображения не отображаются в галерее или не работает что-то еще, пожалуйста, попробуйте сбросить настройки, прежде чем сообщать о проблеме на GitHub.", - "resetComplete": "Интерфейс сброшен. Обновите эту страницу." + "resetComplete": "Интерфейс сброшен. Обновите эту страницу.", + "useSlidersForAll": "Использовать ползунки для всех параметров" }, "toast": { "tempFoldersEmptied": "Временная папка очищена", @@ -441,7 +515,7 @@ "imageSavedToGallery": "Изображение сохранено в галерею", "canvasMerged": "Холст объединен", "sentToImageToImage": "Отправить в img2img", - "sentToUnifiedCanvas": "Отправить на холст", + "sentToUnifiedCanvas": "Отправлено на Единый холст", "parametersSet": "Параметры заданы", "parametersNotSet": "Параметры не заданы", "parametersNotSetDesc": "Не найдены метаданные изображения.", @@ -458,7 +532,11 @@ "metadataLoadFailed": "Не удалось загрузить метаданные", "initialImageSet": "Исходное изображение задано", "initialImageNotSet": "Исходное изображение не задано", - "initialImageNotSetDesc": "Не получилось загрузить исходное изображение" + "initialImageNotSetDesc": "Не получилось загрузить исходное изображение", + "serverError": "Ошибка сервера", + "disconnected": "Отключено от сервера", + "connected": "Подключено к серверу", + "canceled": "Обработка отменена" }, "tooltip": { "feature": { @@ -507,7 +585,7 @@ "autoSaveToGallery": "Автосохранение в галерее", "saveBoxRegionOnly": "Сохранять только выделение", "limitStrokesToBox": "Ограничить штрихи выделением", - "showCanvasDebugInfo": "Показать отладку холста", + "showCanvasDebugInfo": "Показать доп. информацию о холсте", "clearCanvasHistory": "Очистить историю холста", "clearHistory": "Очистить историю", "clearCanvasHistoryMessage": "Очистка истории холста оставляет текущий холст нетронутым, но удаляет историю отмен и повторов.", @@ -535,6 +613,26 @@ "betaPreserveMasked": "Сохранять маскируемую область" }, "accessibility": { - "modelSelect": "Выбор модели" + "modelSelect": "Выбор модели", + "uploadImage": "Загрузить изображение", + "nextImage": "Следующее изображение", + "previousImage": "Предыдущее изображение", + "zoomIn": "Приблизить", + "zoomOut": "Отдалить", + "rotateClockwise": "Повернуть по часовой стрелке", + "rotateCounterClockwise": "Повернуть против часовой стрелки", + "flipVertically": "Перевернуть вертикально", + "flipHorizontally": "Отразить горизонтально", + "toggleAutoscroll": "Включить автопрокрутку", + "toggleLogViewer": "Показать или скрыть просмотрщик логов", + "showOptionsPanel": "Показать опции", + "showGallery": "Показать галерею", + "invokeProgressBar": "Индикатор выполнения", + "reset": "Сброс", + "modifyConfig": "Изменить конфиг", + "useThisParameter": "Использовать этот параметр", + "copyMetadataJson": "Скопировать метаданные JSON", + "exitViewer": "Закрыть просмотрщик", + "menu": "Меню" } } diff --git a/invokeai/frontend/web/dist/locales/uk.json b/invokeai/frontend/web/dist/locales/uk.json index 044cea64a4..8261aa82e0 100644 --- a/invokeai/frontend/web/dist/locales/uk.json +++ b/invokeai/frontend/web/dist/locales/uk.json @@ -16,9 +16,9 @@ "postProcessing": "Постобробка", "postProcessDesc1": "Invoke AI пропонує широкий спектр функцій постобробки. Збільшення зображення (upscale) та відновлення облич вже доступні в інтерфейсі. Отримайте доступ до них з меню 'Додаткові параметри' на вкладках 'Зображення із тексту' та 'Зображення із зображення'. Обробляйте зображення безпосередньо, використовуючи кнопки дій із зображеннями над поточним зображенням або в режимі перегляду.", "postProcessDesc2": "Найближчим часом буде випущено спеціальний інтерфейс для більш сучасних процесів постобробки.", - "postProcessDesc3": "Інтерфейс командного рядка Invoke AI пропонує різні інші функції, включаючи збільшення Embiggen", + "postProcessDesc3": "Інтерфейс командного рядка Invoke AI пропонує різні інші функції, включаючи збільшення Embiggen.", "training": "Навчання", - "trainingDesc1": "Спеціальний інтерфейс для навчання власних моделей з використанням Textual Inversion та Dreambooth", + "trainingDesc1": "Спеціальний інтерфейс для навчання власних моделей з використанням Textual Inversion та Dreambooth.", "trainingDesc2": "InvokeAI вже підтримує навчання моделей за допомогою TI, через інтерфейс командного рядка.", "upload": "Завантажити", "close": "Закрити", @@ -43,7 +43,38 @@ "statusUpscaling": "Збільшення", "statusUpscalingESRGAN": "Збільшення (ESRGAN)", "statusLoadingModel": "Завантаження моделі", - "statusModelChanged": "Модель змінено" + "statusModelChanged": "Модель змінено", + "cancel": "Скасувати", + "accept": "Підтвердити", + "back": "Назад", + "postprocessing": "Постобробка", + "statusModelConverted": "Модель сконвертована", + "statusMergingModels": "Злиття моделей", + "loading": "Завантаження", + "loadingInvokeAI": "Завантаження Invoke AI", + "langHebrew": "Іврит", + "langKorean": "Корейська", + "langPortuguese": "Португальська", + "pinOptionsPanel": "Закріпити панель налаштувань", + "oceanTheme": "Океан", + "langArabic": "Арабська", + "langSimplifiedChinese": "Китайська (спрощена)", + "langSpanish": "Іспанська", + "langEnglish": "Англійська", + "langGerman": "Німецька", + "langItalian": "Італійська", + "langJapanese": "Японська", + "langPolish": "Польська", + "langBrPortuguese": "Португальська (Бразилія)", + "langRussian": "Російська", + "githubLabel": "Github", + "txt2img": "Текст в зображення (txt2img)", + "discordLabel": "Discord", + "langDutch": "Голландська", + "langFrench": "Французька", + "statusMergedModels": "Моделі об'єднані", + "statusConvertingModel": "Конвертація моделі", + "linear": "Лінійна обробка" }, "gallery": { "generations": "Генерації", @@ -284,15 +315,15 @@ "description": "Опис", "descriptionValidationMsg": "Введіть опис моделі", "config": "Файл конфігурації", - "configValidationMsg": "Шлях до файлу конфігурації", + "configValidationMsg": "Шлях до файлу конфігурації.", "modelLocation": "Розташування моделі", - "modelLocationValidationMsg": "Шлях до файлу з моделлю", + "modelLocationValidationMsg": "Шлях до файлу з моделлю.", "vaeLocation": "Розтышування VAE", - "vaeLocationValidationMsg": "Шлях до VAE", + "vaeLocationValidationMsg": "Шлях до VAE.", "width": "Ширина", - "widthValidationMsg": "Початкова ширина зображень", + "widthValidationMsg": "Початкова ширина зображень.", "height": "Висота", - "heightValidationMsg": "Початкова висота зображень", + "heightValidationMsg": "Початкова висота зображень.", "addModel": "Додати модель", "updateModel": "Оновити модель", "availableModels": "Доступні моделі", @@ -319,7 +350,66 @@ "deleteModel": "Видалити модель", "deleteConfig": "Видалити конфігурацію", "deleteMsg1": "Ви точно хочете видалити модель із InvokeAI?", - "deleteMsg2": "Це не призведе до видалення файлу моделі з диску. Позніше ви можете додати його знову." + "deleteMsg2": "Це не призведе до видалення файлу моделі з диску. Позніше ви можете додати його знову.", + "allModels": "Усі моделі", + "diffusersModels": "Diffusers", + "scanForModels": "Сканувати моделі", + "convert": "Конвертувати", + "convertToDiffusers": "Конвертувати в Diffusers", + "formMessageDiffusersVAELocationDesc": "Якщо не надано, InvokeAI буде шукати файл VAE в розташуванні моделі, вказаній вище.", + "convertToDiffusersHelpText3": "Файл моделі на диску НЕ буде видалено або змінено. Ви можете знову додати його в Model Manager, якщо потрібно.", + "customConfig": "Користувальницький конфіг", + "invokeRoot": "Каталог InvokeAI", + "custom": "Користувальницький", + "modelTwo": "Модель 2", + "modelThree": "Модель 3", + "mergedModelName": "Назва об'єднаної моделі", + "alpha": "Альфа", + "interpolationType": "Тип інтерполяції", + "mergedModelSaveLocation": "Шлях збереження", + "mergedModelCustomSaveLocation": "Користувальницький шлях", + "invokeAIFolder": "Каталог InvokeAI", + "ignoreMismatch": "Ігнорувати невідповідності між вибраними моделями", + "modelMergeHeaderHelp2": "Тільки Diffusers-моделі доступні для об'єднання. Якщо ви хочете об'єднати checkpoint-моделі, спочатку перетворіть їх на Diffusers.", + "checkpointModels": "Checkpoints", + "repo_id": "ID репозиторію", + "v2_base": "v2 (512px)", + "repoIDValidationMsg": "Онлайн-репозиторій моделі", + "formMessageDiffusersModelLocationDesc": "Вкажіть хоча б одне.", + "formMessageDiffusersModelLocation": "Шлях до Diffusers-моделі", + "v2_768": "v2 (768px)", + "formMessageDiffusersVAELocation": "Шлях до VAE", + "convertToDiffusersHelpText5": "Переконайтеся, що у вас достатньо місця на диску. Моделі зазвичай займають від 4 до 7 Гб.", + "convertToDiffusersSaveLocation": "Шлях збереження", + "v1": "v1", + "convertToDiffusersHelpText6": "Ви хочете перетворити цю модель?", + "inpainting": "v1 Inpainting", + "modelConverted": "Модель перетворено", + "sameFolder": "У ту ж папку", + "statusConverting": "Перетворення", + "merge": "Об'єднати", + "mergeModels": "Об'єднати моделі", + "modelOne": "Модель 1", + "sigmoid": "Сігмоїд", + "weightedSum": "Зважена сума", + "none": "пусто", + "addDifference": "Додати різницю", + "pickModelType": "Вибрати тип моделі", + "convertToDiffusersHelpText4": "Це одноразова дія. Вона може зайняти від 30 до 60 секунд в залежності від характеристик вашого комп'ютера.", + "pathToCustomConfig": "Шлях до конфігу користувача", + "safetensorModels": "SafeTensors", + "addCheckpointModel": "Додати модель Checkpoint/Safetensor", + "addDiffuserModel": "Додати Diffusers", + "vaeRepoID": "ID репозиторію VAE", + "vaeRepoIDValidationMsg": "Онлайн-репозиторій VAE", + "modelMergeInterpAddDifferenceHelp": "У цьому режимі Модель 3 спочатку віднімається з Моделі 2. Результуюча версія змішується з Моделью 1 із встановленим вище коефіцієнтом Альфа.", + "customSaveLocation": "Користувальницький шлях збереження", + "modelMergeAlphaHelp": "Альфа впливає силу змішування моделей. Нижчі значення альфа призводять до меншого впливу другої моделі.", + "convertToDiffusersHelpText1": "Ця модель буде конвертована в формат 🧨 Diffusers.", + "convertToDiffusersHelpText2": "Цей процес замінить ваш запис в Model Manager на версію тієї ж моделі в Diffusers.", + "modelsMerged": "Моделі об'єднані", + "modelMergeHeaderHelp1": "Ви можете об'єднати до трьох різних моделей, щоб створити змішану, що відповідає вашим потребам.", + "inverseSigmoid": "Зворотній Сігмоїд" }, "parameters": { "images": "Зображення", @@ -346,7 +436,7 @@ "scale": "Масштаб", "otherOptions": "інші параметри", "seamlessTiling": "Безшовний узор", - "hiresOptim": "Висока роздільна здатність", + "hiresOptim": "Оптимізація High Res", "imageFit": "Вмістити зображення", "codeformerFidelity": "Точність", "seamSize": "Размір шву", @@ -379,7 +469,24 @@ "info": "Метадані", "deleteImage": "Видалити зображення", "initialImage": "Початкове зображення", - "showOptionsPanel": "Показати панель налаштувань" + "showOptionsPanel": "Показати панель налаштувань", + "general": "Основне", + "cancel": { + "immediate": "Скасувати негайно", + "schedule": "Скасувати після поточної ітерації", + "isScheduled": "Відміна", + "setType": "Встановити тип скасування" + }, + "vSymmetryStep": "Крок верт. симетрії", + "hiresStrength": "Сила High Res", + "hidePreview": "Сховати попередній перегляд", + "showPreview": "Показати попередній перегляд", + "imageToImage": "Зображення до зображення", + "denoisingStrength": "Сила шумоподавлення", + "copyImage": "Копіювати зображення", + "symmetry": "Симетрія", + "hSymmetryStep": "Крок гор. симетрії", + "negativePrompts": "Виключний запит" }, "settings": { "models": "Моделі", @@ -392,7 +499,8 @@ "resetWebUI": "Повернути початкові", "resetWebUIDesc1": "Скидання настройок веб-інтерфейсу видаляє лише локальний кеш браузера з вашими зображеннями та налаштуваннями. Це не призводить до видалення зображень з диску.", "resetWebUIDesc2": "Якщо зображення не відображаються в галереї або не працює ще щось, спробуйте скинути налаштування, перш ніж повідомляти про проблему на GitHub.", - "resetComplete": "Інтерфейс скинуто. Оновіть цю сторінку." + "resetComplete": "Інтерфейс скинуто. Оновіть цю сторінку.", + "useSlidersForAll": "Використовувати повзунки для всіх параметрів" }, "toast": { "tempFoldersEmptied": "Тимчасова папка очищена", @@ -410,21 +518,25 @@ "sentToUnifiedCanvas": "Надіслати на полотно", "parametersSet": "Параметри задані", "parametersNotSet": "Параметри не задані", - "parametersNotSetDesc": "Не знайдені метадані цього зображення", + "parametersNotSetDesc": "Не знайдені метадані цього зображення.", "parametersFailed": "Проблема із завантаженням параметрів", - "parametersFailedDesc": "Неможливо завантажити початкове зображення", + "parametersFailedDesc": "Неможливо завантажити початкове зображення.", "seedSet": "Сід заданий", "seedNotSet": "Сід не заданий", - "seedNotSetDesc": "Не вдалося знайти сід для зображення", + "seedNotSetDesc": "Не вдалося знайти сід для зображення.", "promptSet": "Запит заданий", "promptNotSet": "Запит не заданий", - "promptNotSetDesc": "Не вдалося знайти запит для зображення", + "promptNotSetDesc": "Не вдалося знайти запит для зображення.", "upscalingFailed": "Збільшення не вдалося", "faceRestoreFailed": "Відновлення облич не вдалося", "metadataLoadFailed": "Не вдалося завантажити метадані", "initialImageSet": "Початкове зображення задане", "initialImageNotSet": "Початкове зображення не задане", - "initialImageNotSetDesc": "Не вдалося завантажити початкове зображення" + "initialImageNotSetDesc": "Не вдалося завантажити початкове зображення", + "serverError": "Помилка сервера", + "disconnected": "Відключено від сервера", + "connected": "Підключено до сервера", + "canceled": "Обробку скасовано" }, "tooltip": { "feature": { @@ -473,10 +585,10 @@ "autoSaveToGallery": "Автозбереження до галереї", "saveBoxRegionOnly": "Зберiгати тiльки видiлення", "limitStrokesToBox": "Обмежити штрихи виділенням", - "showCanvasDebugInfo": "Показати налаштування полотна", + "showCanvasDebugInfo": "Показати дод. інформацію про полотно", "clearCanvasHistory": "Очистити iсторiю полотна", "clearHistory": "Очистити iсторiю", - "clearCanvasHistoryMessage": "Очищення історії полотна залишає поточне полотно незайманим, але видаляє історію скасування та повтору", + "clearCanvasHistoryMessage": "Очищення історії полотна залишає поточне полотно незайманим, але видаляє історію скасування та повтору.", "clearCanvasHistoryConfirm": "Ви впевнені, що хочете очистити історію полотна?", "emptyTempImageFolder": "Очистити тимчасову папку", "emptyFolder": "Очистити папку", @@ -499,5 +611,28 @@ "betaDarkenOutside": "Затемнити зовні", "betaLimitToBox": "Обмежити виділенням", "betaPreserveMasked": "Зберiгати замасковану область" + }, + "accessibility": { + "nextImage": "Наступне зображення", + "modelSelect": "Вибір моделі", + "invokeProgressBar": "Індикатор виконання", + "reset": "Скинути", + "uploadImage": "Завантажити зображення", + "useThisParameter": "Використовувати цей параметр", + "exitViewer": "Вийти з переглядача", + "zoomIn": "Збільшити", + "zoomOut": "Зменшити", + "rotateCounterClockwise": "Обертати проти годинникової стрілки", + "rotateClockwise": "Обертати за годинниковою стрілкою", + "toggleAutoscroll": "Увімкнути автопрокручування", + "toggleLogViewer": "Показати або приховати переглядач журналів", + "showGallery": "Показати галерею", + "previousImage": "Попереднє зображення", + "copyMetadataJson": "Скопіювати метадані JSON", + "flipVertically": "Перевернути по вертикалі", + "flipHorizontally": "Відобразити по горизонталі", + "showOptionsPanel": "Показати опції", + "modifyConfig": "Змінити конфігурацію", + "menu": "Меню" } } diff --git a/invokeai/frontend/web/dist/locales/zh_CN.json b/invokeai/frontend/web/dist/locales/zh_CN.json index b23ac8cc99..d4d7746926 100644 --- a/invokeai/frontend/web/dist/locales/zh_CN.json +++ b/invokeai/frontend/web/dist/locales/zh_CN.json @@ -481,5 +481,22 @@ "betaDarkenOutside": "暗化外部区域", "betaLimitToBox": "限制在框内", "betaPreserveMasked": "保留遮罩层" + }, + "accessibility": { + "modelSelect": "模型选择", + "invokeProgressBar": "Invoke 进度条", + "reset": "重置", + "nextImage": "下一张图片", + "useThisParameter": "使用此参数", + "uploadImage": "上传图片", + "previousImage": "上一张图片", + "copyMetadataJson": "复制JSON元数据", + "exitViewer": "退出视口（ExitViewer）", + "zoomIn": "放大", + "zoomOut": "缩小", + "rotateCounterClockwise": "逆时针旋转", + "rotateClockwise": "顺时针旋转", + "flipHorizontally": "水平翻转", + "flipVertically": "垂直翻转" } } diff --git a/invokeai/frontend/web/stats.html b/invokeai/frontend/web/stats.html index 89bfdffeda..0a4f3b313a 100644 --- a/invokeai/frontend/web/stats.html +++ b/invokeai/frontend/web/stats.html @@ -6157,7 +6157,7 @@ var drawChart = (function (exports) {