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tidy(mm): remove current_hash
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@ -32,7 +32,6 @@ model. These are the:
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Responsible for loading a model from disk
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into RAM and VRAM and getting it ready for inference.
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## Location of the Code
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The four main services can be found in
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@ -63,12 +62,11 @@ provides the following fields:
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|----------------|-----------------|------------------|
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| `key` | str | Unique identifier for the model |
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| `name` | str | Name of the model (not unique) |
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| `model_type` | ModelType | The type of the model |
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| `model_format` | ModelFormat | The format of the model (e.g. "diffusers"); also used as a Union discriminator |
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| `base_model` | BaseModelType | The base model that the model is compatible with |
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| `model_type` | ModelType | The type of the model |
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| `model_format` | ModelFormat | The format of the model (e.g. "diffusers"); also used as a Union discriminator |
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| `base_model` | BaseModelType | The base model that the model is compatible with |
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| `path` | str | Location of model on disk |
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| `original_hash` | str | Hash of the model when it was first installed |
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| `current_hash` | str | Most recent hash of the model's contents |
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| `description` | str | Human-readable description of the model (optional) |
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| `source` | str | Model's source URL or repo id (optional) |
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@ -78,8 +76,7 @@ contents at install time obtained by sampling several parts of the
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model's files using the `imohash` library. Over the course of the
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model's lifetime it may be transformed in various ways, such as
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changing its precision or converting it from a .safetensors to a
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diffusers model. When this happens, `original_hash` is unchanged, but
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`current_hash` is updated to indicate the current contents.
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diffusers model.
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`ModelType`, `ModelFormat` and `BaseModelType` are string enums that
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are defined in `invokeai.backend.model_manager.config`. They are also
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@ -94,7 +91,6 @@ The `path` field can be absolute or relative. If relative, it is taken
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to be relative to the `models_dir` setting in the user's
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`invokeai.yaml` file.
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### CheckpointConfig
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This adds support for checkpoint configurations, and adds the
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@ -174,7 +170,7 @@ store = context.services.model_manager.store
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or from elsewhere in the code by accessing
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`ApiDependencies.invoker.services.model_manager.store`.
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### Creating a `ModelRecordService`
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### Creating a `ModelRecordService`
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To create a new `ModelRecordService` database or open an existing one,
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you can directly create either a `ModelRecordServiceSQL` or a
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@ -217,27 +213,27 @@ for use in the InvokeAI web server. Its signature is:
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```
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def open(
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cls,
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config: InvokeAIAppConfig,
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conn: Optional[sqlite3.Connection] = None,
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lock: Optional[threading.Lock] = None
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config: InvokeAIAppConfig,
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conn: Optional[sqlite3.Connection] = None,
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lock: Optional[threading.Lock] = None
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) -> Union[ModelRecordServiceSQL, ModelRecordServiceFile]:
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```
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The way it works is as follows:
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1. Retrieve the value of the `model_config_db` option from the user's
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`invokeai.yaml` config file.
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`invokeai.yaml` config file.
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2. If `model_config_db` is `auto` (the default), then:
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- Use the values of `conn` and `lock` to return a `ModelRecordServiceSQL` object
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opened on the passed connection and lock.
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- Open up a new connection to `databases/invokeai.db` if `conn`
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* Use the values of `conn` and `lock` to return a `ModelRecordServiceSQL` object
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opened on the passed connection and lock.
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* Open up a new connection to `databases/invokeai.db` if `conn`
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and/or `lock` are missing (see note below).
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3. If `model_config_db` is a Path, then use `from_db_file`
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to return the appropriate type of ModelRecordService.
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4. If `model_config_db` is None, then retrieve the legacy
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`conf_path` option from `invokeai.yaml` and use the Path
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indicated there. This will default to `configs/models.yaml`.
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So a typical startup pattern would be:
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```
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@ -255,7 +251,7 @@ store = ModelRecordServiceBase.open(config, db_conn, lock)
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Configurations can be retrieved in several ways.
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#### get_model(key) -> AnyModelConfig:
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#### get_model(key) -> AnyModelConfig
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The basic functionality is to call the record store object's
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`get_model()` method with the desired model's unique key. It returns
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@ -272,28 +268,28 @@ print(model_conf.path)
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If the key is unrecognized, this call raises an
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`UnknownModelException`.
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#### exists(key) -> AnyModelConfig:
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#### exists(key) -> AnyModelConfig
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Returns True if a model with the given key exists in the databsae.
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#### search_by_path(path) -> AnyModelConfig:
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#### search_by_path(path) -> AnyModelConfig
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Returns the configuration of the model whose path is `path`. The path
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is matched using a simple string comparison and won't correctly match
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models referred to by different paths (e.g. using symbolic links).
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#### search_by_name(name, base, type) -> List[AnyModelConfig]:
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#### search_by_name(name, base, type) -> List[AnyModelConfig]
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This method searches for models that match some combination of `name`,
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`BaseType` and `ModelType`. Calling without any arguments will return
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all the models in the database.
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#### all_models() -> List[AnyModelConfig]:
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#### all_models() -> List[AnyModelConfig]
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Return all the model configs in the database. Exactly equivalent to
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calling `search_by_name()` with no arguments.
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#### search_by_tag(tags) -> List[AnyModelConfig]:
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#### search_by_tag(tags) -> List[AnyModelConfig]
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`tags` is a list of strings. This method returns a list of model
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configs that contain all of the given tags. Examples:
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@ -312,11 +308,11 @@ commercializable_models = [x for x in store.all_models() \
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if x.license.contains('allowCommercialUse=Sell')]
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```
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#### version() -> str:
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#### version() -> str
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Returns the version of the database, currently at `3.2`
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#### model_info_by_name(name, base_model, model_type) -> ModelConfigBase:
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#### model_info_by_name(name, base_model, model_type) -> ModelConfigBase
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This method exists to ease the transition from the previous version of
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the model manager, in which `get_model()` took the three arguments
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@ -337,7 +333,7 @@ model and pass its key to `get_model()`.
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Several methods allow you to create and update stored model config
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records.
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#### add_model(key, config) -> AnyModelConfig:
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#### add_model(key, config) -> AnyModelConfig
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Given a key and a configuration, this will add the model's
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configuration record to the database. `config` can either be a subclass of
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@ -352,7 +348,7 @@ model with the same key is already in the database, or an
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`InvalidModelConfigException` if a dict was passed and Pydantic
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experienced a parse or validation error.
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### update_model(key, config) -> AnyModelConfig:
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### update_model(key, config) -> AnyModelConfig
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Given a key and a configuration, this will update the model
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configuration record in the database. `config` can be either a
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@ -370,31 +366,31 @@ The `ModelInstallService` class implements the
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shop for all your model install needs. It provides the following
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functionality:
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- Registering a model config record for a model already located on the
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* Registering a model config record for a model already located on the
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local filesystem, without moving it or changing its path.
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- Installing a model alreadiy located on the local filesystem, by
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* Installing a model alreadiy located on the local filesystem, by
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moving it into the InvokeAI root directory under the
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`models` folder (or wherever config parameter `models_dir`
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specifies).
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- Probing of models to determine their type, base type and other key
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* Probing of models to determine their type, base type and other key
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information.
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- Interface with the InvokeAI event bus to provide status updates on
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* Interface with the InvokeAI event bus to provide status updates on
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the download, installation and registration process.
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- Downloading a model from an arbitrary URL and installing it in
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* Downloading a model from an arbitrary URL and installing it in
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`models_dir`.
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- Special handling for Civitai model URLs which allow the user to
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* Special handling for Civitai model URLs which allow the user to
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paste in a model page's URL or download link
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- Special handling for HuggingFace repo_ids to recursively download
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* Special handling for HuggingFace repo_ids to recursively download
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the contents of the repository, paying attention to alternative
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variants such as fp16.
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- Saving tags and other metadata about the model into the invokeai database
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* Saving tags and other metadata about the model into the invokeai database
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when fetching from a repo that provides that type of information,
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(currently only Civitai and HuggingFace).
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@ -427,8 +423,8 @@ queue.start()
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installer = ModelInstallService(app_config=config,
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record_store=record_store,
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download_queue=queue
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)
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download_queue=queue
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)
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installer.start()
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```
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@ -443,7 +439,6 @@ required parameters:
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| `metadata_store` | Optional[ModelMetadataStore] | Metadata storage object |
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|`session` | Optional[requests.Session] | Swap in a different Session object (usually for debugging) |
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Once initialized, the installer will provide the following methods:
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#### install_job = installer.heuristic_import(source, [config], [access_token])
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@ -457,15 +452,15 @@ The `source` is a string that can be any of these forms
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1. A path on the local filesystem (`C:\\users\\fred\\model.safetensors`)
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2. A Url pointing to a single downloadable model file (`https://civitai.com/models/58390/detail-tweaker-lora-lora`)
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3. A HuggingFace repo_id with any of the following formats:
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- `model/name` -- entire model
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- `model/name:fp32` -- entire model, using the fp32 variant
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- `model/name:fp16:vae` -- vae submodel, using the fp16 variant
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- `model/name::vae` -- vae submodel, using default precision
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- `model/name:fp16:path/to/model.safetensors` -- an individual model file, fp16 variant
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- `model/name::path/to/model.safetensors` -- an individual model file, default variant
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* `model/name` -- entire model
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* `model/name:fp32` -- entire model, using the fp32 variant
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* `model/name:fp16:vae` -- vae submodel, using the fp16 variant
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* `model/name::vae` -- vae submodel, using default precision
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* `model/name:fp16:path/to/model.safetensors` -- an individual model file, fp16 variant
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* `model/name::path/to/model.safetensors` -- an individual model file, default variant
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Note that by specifying a relative path to the top of the HuggingFace
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repo, you can download and install arbitrary models files.
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repo, you can download and install arbitrary models files.
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The variant, if not provided, will be automatically filled in with
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`fp32` if the user has requested full precision, and `fp16`
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@ -491,9 +486,9 @@ following illustrates basic usage:
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```
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from invokeai.app.services.model_install import (
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LocalModelSource,
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HFModelSource,
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URLModelSource,
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LocalModelSource,
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HFModelSource,
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URLModelSource,
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)
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source1 = LocalModelSource(path='/opt/models/sushi.safetensors') # a local safetensors file
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@ -513,13 +508,13 @@ for source in [source1, source2, source3, source4, source5, source6, source7]:
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source2job = installer.wait_for_installs(timeout=120)
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for source in sources:
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job = source2job[source]
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if job.complete:
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model_config = job.config_out
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model_key = model_config.key
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print(f"{source} installed as {model_key}")
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elif job.errored:
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print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
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if job.complete:
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model_config = job.config_out
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model_key = model_config.key
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print(f"{source} installed as {model_key}")
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elif job.errored:
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print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
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```
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As shown here, the `import_model()` method accepts a variety of
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@ -528,7 +523,7 @@ HuggingFace repo_ids with and without a subfolder designation,
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Civitai model URLs and arbitrary URLs that point to checkpoint files
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(but not to folders).
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Each call to `import_model()` return a `ModelInstallJob` job,
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Each call to `import_model()` return a `ModelInstallJob` job,
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an object which tracks the progress of the install.
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If a remote model is requested, the model's files are downloaded in
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@ -555,7 +550,7 @@ The full list of arguments to `import_model()` is as follows:
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| `config` | Dict[str, Any] | None | Override all or a portion of model's probed attributes |
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The next few sections describe the various types of ModelSource that
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can be passed to `import_model()`.
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can be passed to `import_model()`.
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`config` can be used to override all or a portion of the configuration
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attributes returned by the model prober. See the section below for
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@ -566,7 +561,6 @@ details.
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This is used for a model that is located on a locally-accessible Posix
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filesystem, such as a local disk or networked fileshare.
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| **Argument** | **Type** | **Default** | **Description** |
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|------------------|------------------------------|-------------|-------------------------------------------|
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| `path` | str | Path | None | Path to the model file or directory |
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@ -625,7 +619,6 @@ HuggingFace has the most complicated `ModelSource` structure:
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| `subfolder` | Path | None | Look for the model in a subfolder of the repo. |
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| `access_token` | str | None | An access token needed to gain access to a subscriber's-only model. |
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The `repo_id` is the repository ID, such as `stabilityai/sdxl-turbo`.
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The `variant` is one of the various diffusers formats that HuggingFace
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@ -661,7 +654,6 @@ in. To download these files, you must provide an
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`HfFolder.get_token()` will be called to fill it in with the cached
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one.
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#### Monitoring the install job process
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When you create an install job with `import_model()`, it launches the
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@ -675,14 +667,13 @@ The `ModelInstallJob` class has the following structure:
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| `id` | `int` | Integer ID for this job |
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| `status` | `InstallStatus` | An enum of [`waiting`, `downloading`, `running`, `completed`, `error` and `cancelled`]|
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| `config_in` | `dict` | Overriding configuration values provided by the caller |
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| `config_out` | `AnyModelConfig`| After successful completion, contains the configuration record written to the database |
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| `inplace` | `boolean` | True if the caller asked to install the model in place using its local path |
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| `source` | `ModelSource` | The local path, remote URL or repo_id of the model to be installed |
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| `config_out` | `AnyModelConfig`| After successful completion, contains the configuration record written to the database |
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| `inplace` | `boolean` | True if the caller asked to install the model in place using its local path |
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| `source` | `ModelSource` | The local path, remote URL or repo_id of the model to be installed |
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| `local_path` | `Path` | If a remote model, holds the path of the model after it is downloaded; if a local model, same as `source` |
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| `error_type` | `str` | Name of the exception that led to an error status |
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| `error` | `str` | Traceback of the error |
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If the `event_bus` argument was provided, events will also be
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broadcast to the InvokeAI event bus. The events will appear on the bus
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as an event of type `EventServiceBase.model_event`, a timestamp and
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@ -702,14 +693,13 @@ following keys:
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| `total_bytes` | int | Total size of all the files that make up the model |
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| `parts` | List[Dict]| Information on the progress of the individual files that make up the model |
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The parts is a list of dictionaries that give information on each of
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the components pieces of the download. The dictionary's keys are
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`source`, `local_path`, `bytes` and `total_bytes`, and correspond to
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the like-named keys in the main event.
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Note that downloading events will not be issued for local models, and
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that downloading events occur *before* the running event.
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that downloading events occur _before_ the running event.
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##### `model_install_running`
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@ -752,14 +742,13 @@ properties: `waiting`, `downloading`, `running`, `complete`, `errored`
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and `cancelled`, as well as `in_terminal_state`. The last will return
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True if the job is in the complete, errored or cancelled states.
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#### Model configuration and probing
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The install service uses the `invokeai.backend.model_manager.probe`
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module during import to determine the model's type, base type, and
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other configuration parameters. Among other things, it assigns a
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default name and description for the model based on probed
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fields.
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fields.
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When downloading remote models is implemented, additional
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configuration information, such as list of trigger terms, will be
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@ -774,11 +763,11 @@ attributes. Here is an example of setting the
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```
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install_job = installer.import_model(
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source=HFModelSource(repo_id='stabilityai/stable-diffusion-2-1',variant='fp32'),
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config=dict(
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prediction_type=SchedulerPredictionType('v_prediction')
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name='stable diffusion 2 base model',
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)
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)
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config=dict(
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prediction_type=SchedulerPredictionType('v_prediction')
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name='stable diffusion 2 base model',
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)
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)
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```
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### Other installer methods
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@ -862,7 +851,6 @@ This method is similar to `unregister()`, but also unconditionally
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deletes the corresponding model weights file(s), regardless of whether
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they are inside or outside the InvokeAI models hierarchy.
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#### path = installer.download_and_cache(remote_source, [access_token], [timeout])
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This utility routine will download the model file located at source,
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@ -953,7 +941,7 @@ following fields:
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When you create a job, you can assign it a `priority`. If multiple
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jobs are queued, the job with the lowest priority runs first. (Don't
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blame me! The Unix developers came up with this convention.)
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blame me! The Unix developers came up with this convention.)
|
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Every job has a `source` and a `destination`. `source` is a string in
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the base class, but subclassses redefine it more specifically.
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@ -974,7 +962,7 @@ is in its lifecycle. Values are defined in the string enum
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`DownloadJobStatus`, a symbol available from
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`invokeai.app.services.download_manager`. Possible values are:
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|
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| **Value** | **String Value** | ** Description ** |
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| **Value** | **String Value** | **Description** |
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|--------------|---------------------|-------------------|
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| `IDLE` | idle | Job created, but not submitted to the queue |
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| `ENQUEUED` | enqueued | Job is patiently waiting on the queue |
|
||||
@ -991,7 +979,7 @@ debugging and performance testing.
|
||||
|
||||
In case of an error, the Exception that caused the error will be
|
||||
placed in the `error` field, and the job's status will be set to
|
||||
`DownloadJobStatus.ERROR`.
|
||||
`DownloadJobStatus.ERROR`.
|
||||
|
||||
After an error occurs, any partially downloaded files will be deleted
|
||||
from disk, unless `preserve_partial_downloads` was set to True at job
|
||||
@ -1040,11 +1028,11 @@ While a job is being downloaded, the queue will emit events at
|
||||
periodic intervals. A typical series of events during a successful
|
||||
download session will look like this:
|
||||
|
||||
- enqueued
|
||||
- running
|
||||
- running
|
||||
- running
|
||||
- completed
|
||||
* enqueued
|
||||
* running
|
||||
* running
|
||||
* running
|
||||
* completed
|
||||
|
||||
There will be a single enqueued event, followed by one or more running
|
||||
events, and finally one `completed`, `error` or `cancelled`
|
||||
@ -1053,12 +1041,12 @@ events.
|
||||
It is possible for a caller to pause download temporarily, in which
|
||||
case the events may look something like this:
|
||||
|
||||
- enqueued
|
||||
- running
|
||||
- running
|
||||
- paused
|
||||
- running
|
||||
- completed
|
||||
* enqueued
|
||||
* running
|
||||
* running
|
||||
* paused
|
||||
* running
|
||||
* completed
|
||||
|
||||
The download queue logs when downloads start and end (unless `quiet`
|
||||
is set to True at initialization time) but doesn't log any progress
|
||||
@ -1120,11 +1108,11 @@ A typical initialization sequence will look like:
|
||||
from invokeai.app.services.download_manager import DownloadQueueService
|
||||
|
||||
def log_download_event(job: DownloadJobBase):
|
||||
logger.info(f'job={job.id}: status={job.status}')
|
||||
logger.info(f'job={job.id}: status={job.status}')
|
||||
|
||||
queue = DownloadQueueService(
|
||||
event_handlers=[log_download_event]
|
||||
)
|
||||
event_handlers=[log_download_event]
|
||||
)
|
||||
```
|
||||
|
||||
Event handlers can be provided to the queue at initialization time as
|
||||
@ -1155,9 +1143,9 @@ To use the former method, follow this example:
|
||||
```
|
||||
job = DownloadJobRemoteSource(
|
||||
source='http://www.civitai.com/models/13456',
|
||||
destination='/tmp/models/',
|
||||
event_handlers=[my_handler1, my_handler2], # if desired
|
||||
)
|
||||
destination='/tmp/models/',
|
||||
event_handlers=[my_handler1, my_handler2], # if desired
|
||||
)
|
||||
queue.submit_download_job(job, start=True)
|
||||
```
|
||||
|
||||
@ -1172,13 +1160,13 @@ To have the queue create the job for you, follow this example instead:
|
||||
```
|
||||
job = queue.create_download_job(
|
||||
source='http://www.civitai.com/models/13456',
|
||||
destdir='/tmp/models/',
|
||||
filename='my_model.safetensors',
|
||||
event_handlers=[my_handler1, my_handler2], # if desired
|
||||
start=True,
|
||||
)
|
||||
destdir='/tmp/models/',
|
||||
filename='my_model.safetensors',
|
||||
event_handlers=[my_handler1, my_handler2], # if desired
|
||||
start=True,
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
The `filename` argument forces the downloader to use the specified
|
||||
name for the file rather than the name provided by the remote source,
|
||||
and is equivalent to manually specifying a destination of
|
||||
@ -1187,7 +1175,6 @@ and is equivalent to manually specifying a destination of
|
||||
Here is the full list of arguments that can be provided to
|
||||
`create_download_job()`:
|
||||
|
||||
|
||||
| **Argument** | **Type** | **Default** | **Description** |
|
||||
|------------------|------------------------------|-------------|-------------------------------------------|
|
||||
| `source` | Union[str, Path, AnyHttpUrl] | | Download remote or local source |
|
||||
@ -1200,7 +1187,7 @@ Here is the full list of arguments that can be provided to
|
||||
|
||||
Internally, `create_download_job()` has a little bit of internal logic
|
||||
that looks at the type of the source and selects the right subclass of
|
||||
`DownloadJobBase` to create and enqueue.
|
||||
`DownloadJobBase` to create and enqueue.
|
||||
|
||||
**TODO**: move this logic into its own method for overriding in
|
||||
subclasses.
|
||||
@ -1275,7 +1262,7 @@ for getting the model to run. For example "author" is metadata, while
|
||||
"type", "base" and "format" are not. The latter fields are part of the
|
||||
model's config, as defined in `invokeai.backend.model_manager.config`.
|
||||
|
||||
### Example Usage:
|
||||
### Example Usage
|
||||
|
||||
```
|
||||
from invokeai.backend.model_manager.metadata import (
|
||||
@ -1328,7 +1315,6 @@ This is the common base class for metadata:
|
||||
| `author` | str | Model's author |
|
||||
| `tags` | Set[str] | Model tags |
|
||||
|
||||
|
||||
Note that the model config record also has a `name` field. It is
|
||||
intended that the config record version be locally customizable, while
|
||||
the metadata version is read-only. However, enforcing this is expected
|
||||
@ -1348,7 +1334,6 @@ This descends from `ModelMetadataBase` and adds the following fields:
|
||||
| `last_modified`| datetime | Date of last commit of this model to the repo |
|
||||
| `files` | List[Path] | List of the files in the model repo |
|
||||
|
||||
|
||||
#### `CivitaiMetadata`
|
||||
|
||||
This descends from `ModelMetadataBase` and adds the following fields:
|
||||
@ -1415,7 +1400,6 @@ testing suite to avoid hitting the internet.
|
||||
The HuggingFace and Civitai fetcher subclasses add additional
|
||||
repo-specific fetching methods:
|
||||
|
||||
|
||||
#### HuggingFaceMetadataFetch
|
||||
|
||||
This overrides its base class `from_json()` method to return a
|
||||
@ -1434,13 +1418,12 @@ retrieves its metadata. Functionally equivalent to `from_id()`, the
|
||||
only difference is that it returna a `CivitaiMetadata` object rather
|
||||
than an `AnyModelRepoMetadata`.
|
||||
|
||||
|
||||
### Metadata Storage
|
||||
|
||||
The `ModelMetadataStore` provides a simple facility to store model
|
||||
metadata in the `invokeai.db` database. The data is stored as a JSON
|
||||
blob, with a few common fields (`name`, `author`, `tags`) broken out
|
||||
to be searchable.
|
||||
to be searchable.
|
||||
|
||||
When a metadata object is saved to the database, it is identified
|
||||
using the model key, _and this key must correspond to an existing
|
||||
@ -1535,16 +1518,16 @@ from invokeai.app.services.model_load import ModelLoadService, ModelLoaderRegist
|
||||
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
ram_cache = ModelCache(
|
||||
max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
|
||||
max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
|
||||
)
|
||||
convert_cache = ModelConvertCache(
|
||||
cache_path=config.models_convert_cache_path, max_size=config.convert_cache_size
|
||||
cache_path=config.models_convert_cache_path, max_size=config.convert_cache_size
|
||||
)
|
||||
loader = ModelLoadService(
|
||||
app_config=config,
|
||||
ram_cache=ram_cache,
|
||||
convert_cache=convert_cache,
|
||||
registry=ModelLoaderRegistry
|
||||
app_config=config,
|
||||
ram_cache=ram_cache,
|
||||
convert_cache=convert_cache,
|
||||
registry=ModelLoaderRegistry
|
||||
)
|
||||
```
|
||||
|
||||
@ -1567,7 +1550,6 @@ The returned `LoadedModel` object contains a copy of the configuration
|
||||
record returned by the model record `get_model()` method, as well as
|
||||
the in-memory loaded model:
|
||||
|
||||
|
||||
| **Attribute Name** | **Type** | **Description** |
|
||||
|----------------|-----------------|------------------|
|
||||
| `config` | AnyModelConfig | A copy of the model's configuration record for retrieving base type, etc. |
|
||||
@ -1581,7 +1563,6 @@ return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
|
||||
models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
|
||||
models. The others are obvious.
|
||||
|
||||
|
||||
`LoadedModel` acts as a context manager. The context loads the model
|
||||
into the execution device (e.g. VRAM on CUDA systems), locks the model
|
||||
in the execution device for the duration of the context, and returns
|
||||
@ -1590,14 +1571,14 @@ the model. Use it like this:
|
||||
```
|
||||
model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
|
||||
with model_info as vae:
|
||||
image = vae.decode(latents)[0]
|
||||
image = vae.decode(latents)[0]
|
||||
```
|
||||
|
||||
`get_model_by_key()` may raise any of the following exceptions:
|
||||
|
||||
- `UnknownModelException` -- key not in database
|
||||
- `ModelNotFoundException` -- key in database but model not found at path
|
||||
- `NotImplementedException` -- the loader doesn't know how to load this type of model
|
||||
* `UnknownModelException` -- key not in database
|
||||
* `ModelNotFoundException` -- key in database but model not found at path
|
||||
* `NotImplementedException` -- the loader doesn't know how to load this type of model
|
||||
|
||||
### Emitting model loading events
|
||||
|
||||
@ -1609,15 +1590,15 @@ following payload:
|
||||
|
||||
```
|
||||
payload=dict(
|
||||
queue_id=queue_id,
|
||||
queue_item_id=queue_item_id,
|
||||
queue_batch_id=queue_batch_id,
|
||||
graph_execution_state_id=graph_execution_state_id,
|
||||
model_key=model_key,
|
||||
submodel_type=submodel,
|
||||
hash=model_info.hash,
|
||||
location=str(model_info.location),
|
||||
precision=str(model_info.precision),
|
||||
queue_id=queue_id,
|
||||
queue_item_id=queue_item_id,
|
||||
queue_batch_id=queue_batch_id,
|
||||
graph_execution_state_id=graph_execution_state_id,
|
||||
model_key=model_key,
|
||||
submodel_type=submodel,
|
||||
hash=model_info.hash,
|
||||
location=str(model_info.location),
|
||||
precision=str(model_info.precision),
|
||||
)
|
||||
```
|
||||
|
||||
@ -1724,6 +1705,7 @@ object, or in `context.services.model_manager` from within an
|
||||
invocation.
|
||||
|
||||
In the examples below, we have retrieved the manager using:
|
||||
|
||||
```
|
||||
mm = ApiDependencies.invoker.services.model_manager
|
||||
```
|
||||
|
||||
@ -71,7 +71,6 @@ example_model_config = {
|
||||
"config_path": "string",
|
||||
"key": "string",
|
||||
"original_hash": "string",
|
||||
"current_hash": "string",
|
||||
"description": "string",
|
||||
"source": "string",
|
||||
"converted_at": 0,
|
||||
|
||||
@ -130,15 +130,12 @@ class ModelConfigBase(BaseModel):
|
||||
original_hash: Optional[str] = Field(
|
||||
description="original fasthash of model contents", default=None
|
||||
) # this is assigned at install time and will not change
|
||||
current_hash: Optional[str] = Field(
|
||||
description="current fasthash of model contents", default=None
|
||||
) # if model is converted or otherwise modified, this will hold updated hash
|
||||
description: Optional[str] = Field(description="human readable description of the model", default=None)
|
||||
source: Optional[str] = Field(description="model original source (path, URL or repo_id)", default=None)
|
||||
|
||||
@staticmethod
|
||||
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
|
||||
schema["required"].extend(["key", "base", "type", "format", "original_hash", "current_hash", "source"])
|
||||
schema["required"].extend(["key", "base", "type", "format", "original_hash", "source"])
|
||||
|
||||
model_config = ConfigDict(
|
||||
use_enum_values=False,
|
||||
|
||||
@ -162,7 +162,6 @@ class ModelProbe(object):
|
||||
)
|
||||
fields["format"] = fields.get("format") or probe.get_format()
|
||||
fields["original_hash"] = fields.get("original_hash") or hash
|
||||
fields["current_hash"] = fields.get("current_hash") or hash
|
||||
|
||||
if format_type == ModelFormat.Diffusers and hasattr(probe, "get_repo_variant"):
|
||||
fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
|
||||
|
||||
@ -59,12 +59,11 @@ def test_registration_meta_override_fail(mm2_installer: ModelInstallServiceBase,
|
||||
def test_registration_meta_override_succeed(mm2_installer: ModelInstallServiceBase, embedding_file: Path) -> None:
|
||||
store = mm2_installer.record_store
|
||||
key = mm2_installer.register_path(
|
||||
embedding_file, {"name": "banana_sushi", "source": "fake/repo_id", "current_hash": "New Hash", "key": "xyzzy"}
|
||||
embedding_file, {"name": "banana_sushi", "source": "fake/repo_id", "key": "xyzzy"}
|
||||
)
|
||||
model_record = store.get_model(key)
|
||||
assert model_record.name == "banana_sushi"
|
||||
assert model_record.source == "fake/repo_id"
|
||||
assert model_record.current_hash == "New Hash"
|
||||
assert model_record.key == "xyzzy"
|
||||
|
||||
|
||||
|
||||
@ -77,7 +77,6 @@ def test_add(store: ModelRecordServiceBase):
|
||||
assert config1.base == BaseModelType.StableDiffusion1
|
||||
assert config1.name == "model1"
|
||||
assert config1.original_hash == "111222333444"
|
||||
assert config1.current_hash is None
|
||||
|
||||
|
||||
def test_dup(store: ModelRecordServiceBase):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user