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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
fix model manager documentation
This commit is contained in:
Lincoln Stein
parent
c3c4a71173
commit
60b37b7ff4
@ -374,7 +374,8 @@ setting environment variables INVOKEAI_<setting>.
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tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
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root : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
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autoconvert_dir : Path = Field(default=None, description='Path to a directory of ckpt files to be converted into diffusers and imported on startup.', category='Paths')
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autoimport_dir : Path = Field(default='models/autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
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autoconvert_dir : Path = Field(default=None, description='Deprecated configuration option.', category='Paths')
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conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
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models_dir : Path = Field(default='./models', description='Path to the models directory', category='Paths')
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legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
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@ -179,9 +179,9 @@ class ModelInstall(object):
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self.mgr.commit()
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if selections.autoscan_on_startup and Path(selections.scan_directory).is_dir():
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update_autoconvert_dir(selections.scan_directory)
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update_autoimport_dir(selections.scan_directory)
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else:
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update_autoconvert_dir(None)
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update_autoimport_dir(None)
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def heuristic_install(self, model_path_id_or_url: Union[str,Path]):
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# A little hack to allow nested routines to retrieve info on the requested ID
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@ -375,13 +375,13 @@ class ModelInstall(object):
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'''
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return {v.get('path') or v.get('repo_id') : k for k, v in datasets.items()}
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def update_autoconvert_dir(autodir: Path):
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def update_autoimport_dir(autodir: Path):
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'''
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Update the "autoconvert_dir" option in invokeai.yaml
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Update the "autoimport_dir" option in invokeai.yaml
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'''
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invokeai_config_path = config.init_file_path
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conf = OmegaConf.load(invokeai_config_path)
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conf.InvokeAI.Paths.autoconvert_dir = str(autodir) if autodir else None
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conf.InvokeAI.Paths.autoimport_dir = str(autodir) if autodir else None
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yaml = OmegaConf.to_yaml(conf)
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tmpfile = invokeai_config_path.parent / "new_config.tmp"
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with open(tmpfile, "w", encoding="utf-8") as outfile:
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@ -1,53 +1,193 @@
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"""This module manages the InvokeAI `models.yaml` file, mapping
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symbolic diffusers model names to the paths and repo_ids used
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by the underlying `from_pretrained()` call.
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symbolic diffusers model names to the paths and repo_ids used by the
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underlying `from_pretrained()` call.
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For fetching models, use manager.get_model('symbolic name'). This will
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return a ModelInfo object that contains the following attributes:
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* context -- a context manager Generator that loads and locks the
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model into GPU VRAM and returns the model for use.
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See below for usage.
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* name -- symbolic name of the model
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* type -- SubModelType of the model
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* hash -- unique hash for the model
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* location -- path or repo_id of the model
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* revision -- revision of the model if coming from a repo id,
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e.g. 'fp16'
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* precision -- torch precision of the model
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SYNOPSIS:
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Typical usage:
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mgr = ModelManager('/home/phi/invokeai/configs/models.yaml')
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sd1_5 = mgr.get_model('stable-diffusion-v1-5',
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model_type=ModelType.Main,
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base_model=BaseModelType.StableDiffusion1,
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submodel_type=SubModelType.Unet)
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with sd1_5 as unet:
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run_some_inference(unet)
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from invokeai.backend import ModelManager
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FETCHING MODELS:
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manager = ModelManager(
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config='./configs/models.yaml',
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max_cache_size=8
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) # gigabytes
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Models are described using four attributes:
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model_info = manager.get_model('stable-diffusion-1.5', SubModelType.Diffusers)
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with model_info.context as my_model:
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my_model.latents_from_embeddings(...)
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1) model_name -- the symbolic name for the model
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The manager uses the underlying ModelCache class to keep
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frequently-used models in RAM and move them into GPU as needed for
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generation operations. The optional `max_cache_size` argument
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indicates the maximum size the cache can grow to, in gigabytes. The
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underlying ModelCache object can be accessed using the manager's "cache"
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attribute.
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2) ModelType -- an enum describing the type of the model. Currently
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defined types are:
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ModelType.Main -- a full model capable of generating images
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ModelType.Vae -- a VAE model
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ModelType.Lora -- a LoRA or LyCORIS fine-tune
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ModelType.TextualInversion -- a textual inversion embedding
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ModelType.ControlNet -- a ControlNet model
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Because the model manager can return multiple different types of
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models, you may wish to add additional type checking on the class
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of model returned. To do this, provide the option `model_type`
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parameter:
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3) BaseModelType -- an enum indicating the stable diffusion base model, one of:
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BaseModelType.StableDiffusion1
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BaseModelType.StableDiffusion2
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model_info = manager.get_model(
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'clip-tokenizer',
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model_type=SubModelType.Tokenizer
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)
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4) SubModelType (optional) -- an enum that refers to one of the submodels contained
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within the main model. Values are:
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This will raise an InvalidModelError if the format defined in the
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config file doesn't match the requested model type.
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SubModelType.UNet
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SubModelType.TextEncoder
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SubModelType.Tokenizer
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SubModelType.Scheduler
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SubModelType.SafetyChecker
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To fetch a model, use `manager.get_model()`. This takes the symbolic
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name of the model, the ModelType, the BaseModelType and the
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SubModelType. The latter is required for ModelType.Main.
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get_model() will return a ModelInfo object that can then be used in
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context to retrieve the model and move it into GPU VRAM (on GPU
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systems).
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A typical example is:
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sd1_5 = mgr.get_model('stable-diffusion-v1-5',
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model_type=ModelType.Main,
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base_model=BaseModelType.StableDiffusion1,
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submodel_type=SubModelType.Unet)
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with sd1_5 as unet:
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run_some_inference(unet)
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The ModelInfo object provides a number of useful fields describing the
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model, including:
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name -- symbolic name of the model
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base_model -- base model (BaseModelType)
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type -- model type (ModelType)
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location -- path to the model file
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precision -- torch precision of the model
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hash -- unique sha256 checksum for this model
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SUBMODELS:
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When fetching a main model, you must specify the submodel. Retrieval
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of full pipelines is not supported.
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vae_info = mgr.get_model('stable-diffusion-1.5',
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model_type = ModelType.Main,
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base_model = BaseModelType.StableDiffusion1,
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submodel_type = SubModelType.Vae
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)
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with vae_info as vae:
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do_something(vae)
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This rule does not apply to controlnets, embeddings, loras and standalone
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VAEs, which do not have submodels.
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LISTING MODELS
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The model_names() method will return a list of Tuples describing each
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model it knows about:
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>> mgr.model_names()
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[
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('stable-diffusion-1.5', <BaseModelType.StableDiffusion1: 'sd-1'>, <ModelType.Main: 'main'>),
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('stable-diffusion-2.1', <BaseModelType.StableDiffusion2: 'sd-2'>, <ModelType.Main: 'main'>),
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('inpaint', <BaseModelType.StableDiffusion1: 'sd-1'>, <ModelType.ControlNet: 'controlnet'>)
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('Ink scenery', <BaseModelType.StableDiffusion1: 'sd-1'>, <ModelType.Lora: 'lora'>)
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...
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]
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The tuple is in the correct order to pass to get_model():
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for m in mgr.model_names():
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info = get_model(*m)
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In contrast, the list_models() method returns a list of dicts, each
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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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"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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name = c['name']
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format = c['model_format']
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path = c['path']
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type = c['type']
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# etc
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ADDING AND REMOVING MODELS
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At startup time, the `models` directory will be scanned for
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checkpoints, diffusers pipelines, controlnets, LoRAs and TI
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embeddings. New entries will be added to the model manager and defunct
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ones removed. Anything that is a main model (ModelType.Main) will be
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added to models.yaml. For scanning to succeed, files need to be in
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their proper places. For example, a controlnet folder built on the
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stable diffusion 2 base, will need to be placed in
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`models/sd-2/controlnet`.
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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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├── sd-2
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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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│ ├── codeformer
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│ └── gfpgan
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├── sd-conversion
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│ ├── clip-vit-large-patch14 - tokenizer, text_encoder subdirs
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│ ├── stable-diffusion-2 - tokenizer, text_encoder subdirs
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│ └── stable-diffusion-safety-checker
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└── upscaling
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└─── esrgan
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class ConfigMeta(BaseModel):Loras, textual_inversion and controlnet models are not listed
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explicitly in models.yaml, but are added to the in-memory data
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structure at initialization time by scanning the models directory. The
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in-memory data structure can be resynchronized by calling
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`manager.scan_models_directory()`.
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Files and folders placed inside the `autoimport_dir` (path defined in
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`invokeai.yaml`, defaulting to `ROOTDIR/autoimport` will also be
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scanned for new models at initialization time and added to
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`models.yaml`. Files will not be moved from this location but
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preserved in-place.
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A model can be manually added using `add_model()` using the model's
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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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identifying information as `get_model()`
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The `heuristic_import()` method will take a set of strings
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corresponding to local paths, remote URLs, and repo_ids, probe the
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object to determine what type of model it is (if any), and import new
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models into the manager. If passed a directory, it will recursively
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scan it for models to import. The return value is a set of the models
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successfully added.
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MODELS.YAML
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@ -56,94 +196,18 @@ The general format of a models.yaml section is:
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type-of-model/name-of-model:
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path: /path/to/local/file/or/directory
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description: a description
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format: folder|ckpt|safetensors|pt
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base: SD-1|SD-2
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subfolder: subfolder-name
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format: diffusers|checkpoint
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variant: normal|inpaint|depth
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The type of model is given in the stanza key, and is one of
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{diffusers, ckpt, vae, text_encoder, tokenizer, unet, scheduler,
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safety_checker, feature_extractor, lora, textual_inversion,
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controlnet}, and correspond to items in the SubModelType enum defined
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in model_cache.py
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{main, vae, lora, controlnet, textual}
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The format indicates whether the model is organized as a folder with
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model subdirectories, or is contained in a single checkpoint or
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safetensors file.
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One, but not both, of repo_id and path are provided. repo_id is the
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HuggingFace repository ID of the model, and path points to the file or
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directory on disk.
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If subfolder is provided, then the model exists in a subdirectory of
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the main model. These are usually named after the model type, such as
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"unet".
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This example summarizes the two ways of getting a non-diffuser model:
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text_encoder/clip-test-1:
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format: folder
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path: /path/to/folder
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description: Returns standalone CLIPTextModel
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text_encoder/clip-test-2:
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format: folder
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repo_id: /path/to/folder
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subfolder: text_encoder
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description: Returns the text_encoder in the subfolder of the diffusers model (just the encoder in RAM)
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SUBMODELS:
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It is also possible to fetch an isolated submodel from a diffusers
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model. Use the `submodel` parameter to select which part:
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vae = manager.get_model('stable-diffusion-1.5',submodel=SubModelType.Vae)
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with vae.context as my_vae:
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print(type(my_vae))
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# "AutoencoderKL"
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DIRECTORY_SCANNING:
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Loras, textual_inversion and controlnet models are usually not listed
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explicitly in models.yaml, but are added to the in-memory data
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structure at initialization time by scanning the models directory. The
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in-memory data structure can be resynchronized by calling
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`manager.scan_models_directory`.
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DISAMBIGUATION:
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You may wish to use the same name for a related family of models. To
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do this, disambiguate the stanza key with the model and and format
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separated by "/". Example:
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tokenizer/clip-large:
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format: tokenizer
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path: /path/to/folder
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description: Returns standalone tokenizer
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text_encoder/clip-large:
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format: text_encoder
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path: /path/to/folder
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description: Returns standalone text encoder
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You can now use the `model_type` argument to indicate which model you
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want:
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tokenizer = mgr.get('clip-large',model_type=SubModelType.Tokenizer)
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encoder = mgr.get('clip-large',model_type=SubModelType.TextEncoder)
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OTHER FUNCTIONS:
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Other methods provided by ModelManager support importing, editing,
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converting and deleting models.
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IMPORTANT CHANGES AND LIMITATIONS SINCE 2.3:
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1. Only local paths are supported. Repo_ids are no longer accepted. This
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simplifies the logic.
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2. VAEs can't be swapped in and out at load time. They must be baked
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into the model when downloaded or converted.
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The format indicates whether the model is organized as a diffusers
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folder with model subdirectories, or is contained in a single
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checkpoint or safetensors file.
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The path points to a file or directory on disk. If a relative path,
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the root is the InvokeAI ROOTDIR.
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"""
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from __future__ import annotations
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@ -185,7 +249,6 @@ class ModelInfo():
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hash: str
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location: Union[Path, str]
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precision: torch.dtype
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revision: str = None
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_cache: ModelCache = None
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def __enter__(self):
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@ -201,31 +264,6 @@ class InvalidModelError(Exception):
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MAX_CACHE_SIZE = 6.0 # GB
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# 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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# │ ├── pipeline
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# │ └── textual_inversion
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# ├── sd-2
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# │ ├── controlnet
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# │ ├── lora
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# │ ├── pipeline
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# │ └── textual_inversion
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# └── core
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# ├── face_reconstruction
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# │ ├── codeformer
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# │ └── gfpgan
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# ├── sd-conversion
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# │ ├── clip-vit-large-patch14 - tokenizer, text_encoder subdirs
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# │ ├── stable-diffusion-2 - tokenizer, text_encoder subdirs
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# │ └── stable-diffusion-safety-checker
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# └── upscaling
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# └─── esrgan
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class ConfigMeta(BaseModel):
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version: str
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@ -330,44 +368,14 @@ class ModelManager(object):
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base_model: BaseModelType,
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model_type: ModelType,
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submodel_type: Optional[SubModelType] = None
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):
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)->ModelInfo:
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"""Given a model named identified in models.yaml, return
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an ModelInfo object describing it.
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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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the model to retrieve (e.g. ModelType.Vae)
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If not provided, the model_type will be read from the `format` field
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of the corresponding stanza. If provided, the model_type will be used
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to disambiguate stanzas in the configuration file. The default is to
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assume a diffusers pipeline. The behavior is illustrated here:
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[models.yaml]
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diffusers/test1:
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repo_id: foo/bar
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description: Typical diffusers pipeline
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lora/test1:
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repo_id: /tmp/loras/test1.safetensors
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description: Typical lora file
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test1_pipeline = mgr.get_model('test1')
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# returns a StableDiffusionGeneratorPipeline
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test1_vae1 = mgr.get_model('test1', submodel=ModelType.Vae)
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# returns the VAE part of a diffusers model as an AutoencoderKL
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||||
|
||||
test1_vae2 = mgr.get_model('test1', model_type=ModelType.Diffusers, submodel=ModelType.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=ModelType.Lora)
|
||||
# returns a LoRA embed (as a 'dict' of tensors)
|
||||
|
||||
test1_encoder = mgr.get_modelI('test1', model_type=ModelType.TextEncoder)
|
||||
# raises an InvalidModelError
|
||||
|
||||
"""
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_key = self.create_key(model_name, base_model, model_type)
|
||||
@ -511,7 +519,7 @@ class ModelManager(object):
|
||||
|
||||
def print_models(self) -> None:
|
||||
"""
|
||||
Print a table of models, their descriptions
|
||||
Print a table of models and their descriptions. This needs to be redone
|
||||
"""
|
||||
# TODO: redo
|
||||
for model_type, model_dict in self.list_models().items():
|
||||
@ -552,7 +560,7 @@ class ModelManager(object):
|
||||
else:
|
||||
model_path.unlink()
|
||||
|
||||
# TODO: test when ui implemented
|
||||
# LS: tested
|
||||
def add_model(
|
||||
self,
|
||||
model_name: str,
|
||||
@ -694,11 +702,18 @@ class ModelManager(object):
|
||||
items_to_import: Set[str],
|
||||
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
|
||||
)->Set[str]:
|
||||
'''
|
||||
Import a list of paths, repo_ids or URLs. Returns the
|
||||
set of successfully imported items. The prediction_type_helper
|
||||
is a callback that receives the Path of a checkpoint or diffusers
|
||||
model and returns a SchedulerPredictionType (or None).
|
||||
'''Import a list of paths, repo_ids or URLs. Returns the set of
|
||||
successfully imported items.
|
||||
:param items_to_import: Set of strings corresponding to models to be imported.
|
||||
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
|
||||
|
||||
The prediction type helper is necessary to distinguish between
|
||||
models based on Stable Diffusion 2 Base (requiring
|
||||
SchedulerPredictionType.Epsilson) and Stable Diffusion 768
|
||||
(requiring SchedulerPredictionType.VPrediction). It is
|
||||
generally impossible to do this programmatically, so the
|
||||
prediction_type_helper usually asks the user to choose.
|
||||
|
||||
'''
|
||||
# avoid circular import here
|
||||
from invokeai.backend.install.model_install_backend import ModelInstall
|
||||
@ -716,6 +731,3 @@ class ModelManager(object):
|
||||
|
||||
self.commit()
|
||||
return successfully_installed
|
||||
|
||||
|
||||
|
||||
|
||||
@ -126,7 +126,10 @@ class ModelBase(metaclass=ABCMeta):
|
||||
if not isinstance(value, type) or not issubclass(value, ModelConfigBase):
|
||||
continue
|
||||
|
||||
fields = inspect.get_annotations(value)
|
||||
if hasattr(inspect,'get_annotations'):
|
||||
fields = inspect.get_annotations(value)
|
||||
else:
|
||||
fields = value.__annotations__
|
||||
try:
|
||||
field = fields["model_format"]
|
||||
except:
|
||||
|
||||
@ -323,7 +323,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
FileBox,
|
||||
max_height=3,
|
||||
name=label,
|
||||
value=str(config.autoconvert_dir) if config.autoconvert_dir else None,
|
||||
value=str(config.autoimport_dir) if config.autoimport_dir else None,
|
||||
select_dir=True,
|
||||
must_exist=True,
|
||||
use_two_lines=False,
|
||||
@ -336,7 +336,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
autoscan_on_startup = self.add_widget_intelligent(
|
||||
npyscreen.Checkbox,
|
||||
name="Scan and import from this directory each time InvokeAI starts",
|
||||
value=config.autoconvert_dir is not None,
|
||||
value=config.autoimport_dir is not None,
|
||||
relx=4,
|
||||
scroll_exit=True,
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user