mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into release/invokeai-3-0-beta
This commit is contained in:
Lincoln Stein
commit
e943913f58
@ -328,6 +328,25 @@ class ModelCache(object):
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refs = sys.getrefcount(cache_entry.model)
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# manualy clear local variable references of just finished function calls
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# for some reason python don't want to collect it even by gc.collect() immidiately
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if refs > 2:
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while True:
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cleared = False
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for referrer in gc.get_referrers(cache_entry.model):
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if type(referrer).__name__ == "frame":
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# RuntimeError: cannot clear an executing frame
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with suppress(RuntimeError):
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referrer.clear()
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cleared = True
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#break
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# repeat if referrers changes(due to frame clear), else exit loop
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if cleared:
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gc.collect()
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else:
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break
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device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
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self.logger.debug(f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}")
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@ -363,6 +382,9 @@ class ModelCache(object):
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self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
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vram_in_use += mem.vram_used # note vram_used is negative
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self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
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gc.collect()
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torch.cuda.empty_cache()
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def _local_model_hash(self, model_path: Union[str, Path]) -> str:
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sha = hashlib.sha256()
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@ -106,16 +106,16 @@ providing information about a model defined in models.yaml. For example:
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>>> models = mgr.list_models()
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>>> json.dumps(models[0])
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{"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny",
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"model_format": "diffusers",
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"name": "canny",
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"base_model": "sd-1",
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{"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny",
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"model_format": "diffusers",
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"name": "canny",
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"base_model": "sd-1",
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"type": "controlnet"
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}
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You can filter by model type and base model as shown here:
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controlnets = mgr.list_models(model_type=ModelType.ControlNet,
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base_model=BaseModelType.StableDiffusion1)
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for c in controlnets:
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@ -140,14 +140,14 @@ Layout of the `models` directory:
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models
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├── sd-1
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│ ├── controlnet
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│ ├── lora
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│ ├── main
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│ └── embedding
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│ ├── controlnet
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│ ├── lora
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│ ├── main
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│ └── embedding
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├── sd-2
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│ ├── controlnet
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│ ├── lora
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│ ├── main
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│ ├── controlnet
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│ ├── lora
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│ ├── main
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│ └── embedding
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└── core
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├── face_reconstruction
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@ -195,7 +195,7 @@ name, base model, type and a dict of model attributes. See
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`invokeai/backend/model_management/models` for the attributes required
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by each model type.
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A model can be deleted using `del_model()`, providing the same
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A model can be deleted using `del_model()`, providing the same
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identifying information as `get_model()`
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The `heuristic_import()` method will take a set of strings
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@ -304,7 +304,7 @@ class ModelManager(object):
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logger: types.ModuleType = logger,
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):
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"""
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Initialize with the path to the models.yaml config file.
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Initialize with the path to the models.yaml config file.
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Optional parameters are the torch device type, precision, max_models,
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and sequential_offload boolean. Note that the default device
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type and precision are set up for a CUDA system running at half precision.
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@ -323,7 +323,7 @@ class ModelManager(object):
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self.config_meta = ConfigMeta(**config.pop("__metadata__"))
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# TODO: metadata not found
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# TODO: version check
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self.app_config = InvokeAIAppConfig.get_config()
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self.logger = logger
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self.cache = ModelCache(
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@ -431,7 +431,7 @@ class ModelManager(object):
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:param model_name: symbolic name of the model in models.yaml
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:param model_type: ModelType enum indicating the type of model to return
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:param base_model: BaseModelType enum indicating the base model used by this model
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:param submode_typel: an ModelType enum indicating the portion of
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:param submode_typel: an ModelType enum indicating the portion of
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the model to retrieve (e.g. ModelType.Vae)
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"""
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model_class = MODEL_CLASSES[base_model][model_type]
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@ -456,7 +456,7 @@ class ModelManager(object):
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raise ModelNotFoundException(f"Model not found - {model_key}")
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# vae/movq override
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# TODO:
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# TODO:
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if submodel_type is not None and hasattr(model_config, submodel_type):
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override_path = getattr(model_config, submodel_type)
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if override_path:
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@ -489,7 +489,7 @@ class ModelManager(object):
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self.cache_keys[model_key].add(model_context.key)
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model_hash = "<NO_HASH>" # TODO:
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return ModelInfo(
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context = model_context,
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name = model_name,
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@ -518,7 +518,7 @@ class ModelManager(object):
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def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
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"""
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Return a list of (str, BaseModelType, ModelType) corresponding to all models
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Return a list of (str, BaseModelType, ModelType) corresponding to all models
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known to the configuration.
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"""
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return [(self.parse_key(x)) for x in self.models.keys()]
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@ -692,12 +692,12 @@ class ModelManager(object):
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if new_name is None and new_base is None:
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self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
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return
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model_key = self.create_key(model_name, base_model, model_type)
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model_cfg = self.models.get(model_key, None)
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if not model_cfg:
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raise ModelNotFoundException(f"Unknown model: {model_key}")
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old_path = self.app_config.root_path / model_cfg.path
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new_name = new_name or model_name
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new_base = new_base or base_model
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@ -726,7 +726,7 @@ class ModelManager(object):
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self.models.pop(model_key, None) # delete
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self.models[new_key] = model_cfg
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self.commit()
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def convert_model (
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self,
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model_name: str,
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@ -776,12 +776,12 @@ class ModelManager(object):
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# something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
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rmtree(new_diffusers_path)
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raise
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if checkpoint_path.exists() and checkpoint_path.is_relative_to(self.app_config.models_path):
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checkpoint_path.unlink()
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return result
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def search_models(self, search_folder):
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self.logger.info(f"Finding Models In: {search_folder}")
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models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
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@ -824,10 +824,14 @@ class ModelManager(object):
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assert config_file_path is not None,'no config file path to write to'
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config_file_path = self.app_config.root_path / config_file_path
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tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
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with open(tmpfile, "w", encoding="utf-8") as outfile:
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outfile.write(self.preamble())
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outfile.write(yaml_str)
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os.replace(tmpfile, config_file_path)
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try:
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with open(tmpfile, "w", encoding="utf-8") as outfile:
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outfile.write(self.preamble())
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outfile.write(yaml_str)
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os.replace(tmpfile, config_file_path)
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except OSError as err:
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self.logger.warning(f"Could not modify the config file at {config_file_path}")
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self.logger.warning(err)
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def preamble(self) -> str:
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"""
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@ -977,13 +981,12 @@ class ModelManager(object):
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# avoid circular import here
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from invokeai.backend.install.model_install_backend import ModelInstall
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successfully_installed = dict()
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installer = ModelInstall(config = self.app_config,
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prediction_type_helper = prediction_type_helper,
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model_manager = self)
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for thing in items_to_import:
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installed = installer.heuristic_import(thing)
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successfully_installed.update(installed)
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self.commit()
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self.commit()
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return successfully_installed
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@ -5,7 +5,11 @@ import { useRef } from 'react';
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import { useHoverDirty } from 'react-use';
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import { useGetAppVersionQuery } from 'services/api/endpoints/appInfo';
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const InvokeAILogoComponent = () => {
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interface Props {
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showVersion?: boolean;
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}
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const InvokeAILogoComponent = ({ showVersion = true }: Props) => {
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const { data: appVersion } = useGetAppVersionQuery();
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const ref = useRef(null);
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const isHovered = useHoverDirty(ref);
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@ -28,7 +32,7 @@ const InvokeAILogoComponent = () => {
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invoke <strong>ai</strong>
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</Text>
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<AnimatePresence>
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{isHovered && appVersion && (
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{showVersion && isHovered && appVersion && (
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<motion.div
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key="statusText"
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initial={{
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