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model manager rewritten to use model_cache; API changed!

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This commit is contained in:
Lincoln Stein 2023-05-05 19:32:28 -04:00
parent a4e36bc02a
commit af8c7c7d29
6 changed files with 353 additions and 813 deletions
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2
invokeai/backend/__init__.py
View File

@ -10,7 +10,7 @@ from .generator import (
Img2Img,
Inpaint
)
from .model_management import ModelManager, SDModelComponent
from .model_management import ModelManager
from .safety_checker import SafetyChecker
from .args import Args
from .globals import Globals

2
invokeai/backend/globals.py
View File

@ -93,6 +93,8 @@ def global_converted_ckpts_dir() -> Path:
def global_set_root(root_dir: Union[str, Path]):
Globals.root = root_dir
def global_resolve_path(path: Union[str,Path]):
return Path(Globals.root,path).resolve()
def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
"""

10
invokeai/backend/model_management/__init__.py
View File

@ -1,11 +1,5 @@
"""
Initialization file for invokeai.backend.model_management
"""
from .convert_ckpt_to_diffusers import (
convert_ckpt_to_diffusers,
load_pipeline_from_original_stable_diffusion_ckpt,
)
from .model_manager import ModelManager,SDModelComponent
from .model_manager import ModelManager
from .model_cache import ModelCache, ModelStatus

3
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -29,6 +29,7 @@ import invokeai.backend.util.logging as logger
from invokeai.backend.globals import global_cache_dir, global_config_dir
from .model_manager import ModelManager, SDLegacyType
from .model_cache import ModelCache
try:
from omegaconf import OmegaConf
@ -1100,7 +1101,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
if Path(checkpoint_path).suffix == '.ckpt':
if scan_needed:
ModelManager.scan_model(checkpoint_path,checkpoint_path)
ModelCache.scan_model(checkpoint_path,checkpoint_path)
checkpoint = torch.load(checkpoint_path)
else:
checkpoint = load_file(checkpoint_path)

288
invokeai/backend/model_management/model_cache.py
View File

@ -24,10 +24,10 @@ from collections.abc import Generator
from collections import Counter
from enum import Enum
from pathlib import Path
from typing import Sequence, Union
from typing import Sequence, Union, Tuple, types
import torch
from diffusers import AutoencoderKL, SchedulerMixin, UNet2DConditionModel
from diffusers import StableDiffusionPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel
from diffusers import logging as diffusers_logging
from diffusers.pipelines.stable_diffusion.safety_checker import \
StableDiffusionSafetyChecker
@ -40,7 +40,6 @@ from transformers import logging as transformers_logging
import invokeai.backend.util.logging as logger
from ..globals import global_cache_dir
from ..stable_diffusion import StableDiffusionGeneratorPipeline
from . import load_pipeline_from_original_stable_diffusion_ckpt
MAX_MODELS = 4
@ -55,8 +54,16 @@ class SDModelType(Enum):
safety_checker=StableDiffusionSafetyChecker
feature_extractor=CLIPFeatureExtractor
class ModelStatus(Enum):
unknown='unknown'
not_loaded='not loaded'
in_ram='cached'
in_vram='in gpu'
active='locked in gpu'
# The list of model classes we know how to fetch, for typechecking
ModelClass = Union[tuple([x.value for x in SDModelType])]
DiffusionClasses = (StableDiffusionGeneratorPipeline, AutoencoderKL, SchedulerMixin, UNet2DConditionModel)
# Legacy information needed to load a legacy checkpoint file
class LegacyInfo(BaseModel):
@ -81,6 +88,7 @@ class ModelCache(object):
sequential_offload: bool=False,
lazy_offloading: bool=True,
sha_chunksize: int = 16777216,
logger: types.ModuleType = logger
):
'''
:param max_models: Maximum number of models to cache in CPU RAM [4]
@ -100,9 +108,11 @@ class ModelCache(object):
self.execution_device: torch.device=execution_device
self.storage_device: torch.device=storage_device
self.sha_chunksize=sha_chunksize
self.logger = logger
self.loaded_models: set = set() # set of model keys loaded in GPU
self.locked_models: Counter = Counter() # set of model keys locked in GPU
@contextlib.contextmanager
def get_model(
self,
@ -112,6 +122,7 @@ class ModelCache(object):
submodel: SDModelType=None,
revision: str=None,
legacy_info: LegacyInfo=None,
attach_model_part: Tuple[SDModelType, str] = (None,None),
gpu_load: bool=True,
)->Generator[ModelClass, None, None]:
'''
@ -122,10 +133,28 @@ class ModelCache(object):
with cache.get_model('stabilityai/stable-diffusion-2') as SD2:
do_something_with_the_model(SD2)
You can fetch an individual part of a diffusers model by passing the submodel
argument:
vae_context = cache.get_model(
'stabilityai/sd-stable-diffusion-2',
submodel=SDModelType.vae
)
Vice versa, you can load and attach an external submodel to a diffusers model
before returning it by passing the attach_submodel argument. This only works with
diffusers models:
pipeline_context = cache.get_model(
'runwayml/stable-diffusion-v1-5',
attach_model_part=(SDModelType.vae,'stabilityai/sd-vae-ft-mse')
)
The model will be locked into GPU VRAM for the duration of the context.
:param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
:param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
:param submodel: an SDModelType enum indicating the model part to return, e.g. SDModelType.vae
:param attach_model_part: load and attach a diffusers model component. Pass a tuple of format (SDModelType,repo_id)
:param revision: model revision
:param model_class: class of model to return
:param gpu_load: load the model into GPU [default True]
@ -151,6 +180,8 @@ class ModelCache(object):
revision=revision,
legacy_info=legacy_info,
)
if model_type==SDModelType.diffusion_pipeline and attach_model_part[0]:
self.attach_part(model,*attach_model_part)
self.stack.append(key) # add to LRU cache
self.models[key]=model # keep copy of model in dict
@ -163,7 +194,7 @@ class ModelCache(object):
self.locked_models[key] += 1
if self.lazy_offloading:
self._offload_unlocked_models()
logger.debug(f'Loading {key} into {self.execution_device}')
self.logger.debug(f'Loading {key} into {self.execution_device}')
model.to(self.execution_device) # move into GPU
self._print_cuda_stats()
yield model
@ -181,25 +212,59 @@ class ModelCache(object):
self.loaded_models.remove(key)
yield model
def _offload_unlocked_models(self):
to_offload = set()
for key in self.loaded_models:
if key not in self.locked_models or self.locked_models[key] == 0:
logger.debug(f'Offloading {key} from {self.execution_device} into {self.storage_device}')
to_offload.add(key)
for key in to_offload:
self.models[key].to(self.storage_device)
self.loaded_models.remove(key)
def attach_part(self,
diffusers_model: StableDiffusionPipeline,
part_type: SDModelType,
part_id: str
):
'''
Attach a diffusers model part to a diffusers model. This can be
used to replace the VAE, tokenizer, textencoder, unet, etc.
:param diffuser_model: The diffusers model to attach the part to.
:param part_type: An SD ModelType indicating the part
:param part_id: A HF repo_id for the part
'''
part_key = part_type.name
part_class = part_type.value
part = self._load_diffusers_from_storage(
part_id,
model_class=part_class,
)
part.to(diffusers_model.device)
setattr(diffusers_model,part_key,part)
self.logger.debug(f'Attached {part_key} {part_id}')
def status(self,
repo_id_or_path: Union[str,Path],
model_type: SDModelType=SDModelType.diffusion_pipeline,
revision: str=None,
subfolder: Path=None,
)->ModelStatus:
key = self._model_key(
repo_id_or_path,
model_type.value,
revision,
subfolder)
if key not in self.models:
return ModelStatus.not_loaded
if key in self.loaded_models:
if self.locked_models[key] > 0:
return ModelStatus.active
else:
return ModelStatus.in_vram
else:
return ModelStatus.in_ram
def model_hash(self,
repo_id_or_path: Union[str,Path],
revision: str=None)->str:
revision: str="main")->str:
'''
Given the HF repo id or path to a model on disk, returns a unique
hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs
:param repo_id_or_path: repo_id string or Path to model file/directory on disk.
:param revision: optional revision string (if fetching a HF repo_id)
'''
revision = revision or "main"
if self.is_legacy_ckpt(repo_id_or_path):
return self._legacy_model_hash(repo_id_or_path)
elif Path(repo_id_or_path).is_dir():
@ -211,96 +276,6 @@ class ModelCache(object):
"Return the current number of models cached."
return len(self.models)
@staticmethod
def _model_key(path,model_class,revision,subfolder)->str:
return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')])
def _has_cuda(self)->bool:
return self.execution_device.type == 'cuda'
def _print_cuda_stats(self):
vram = "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
loaded_models = len(self.loaded_models)
locked_models = len([x for x in self.locked_models if self.locked_models[x]>0])
logger.debug(f"Current VRAM usage: {vram}; locked_models/loaded_models = {locked_models}/{loaded_models}")
def _make_cache_room(self):
models_in_ram = len(self.models)
while models_in_ram >= self.max_models:
if least_recently_used_key := self.stack.pop(0):
logger.debug(f'Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}')
del self.models[least_recently_used_key]
models_in_ram = len(self.models)
gc.collect()
@property
def current_model(self)->ModelClass:
'''
Returns current model.
'''
return self.models[self._current_model_key]
@property
def _current_model_key(self)->str:
'''
Returns key of currently loaded model.
'''
return self.stack[-1]
def _load_model_from_storage(
self,
repo_id_or_path: Union[str,Path],
subfolder: Path=None,
revision: str=None,
model_class: ModelClass=StableDiffusionGeneratorPipeline,
legacy_info: LegacyInfo=None,
)->ModelClass:
'''
Load and return a HuggingFace model.
:param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
:param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
:param revision: model revision
:param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline
:param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt
'''
# silence transformer and diffuser warnings
with SilenceWarnings():
if self.is_legacy_ckpt(repo_id_or_path):
model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info)
else:
model = self._load_diffusers_from_storage(
repo_id_or_path,
subfolder,
revision,
model_class,
)
if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline):
model.enable_offload_submodels(self.execution_device)
elif hasattr(model,'to'):
model.to(self.execution_device)
return model
def _load_diffusers_from_storage(
self,
repo_id_or_path: Union[str,Path],
subfolder: Path=None,
revision: str=None,
model_class: ModelClass=StableDiffusionGeneratorPipeline,
)->ModelClass:
'''
Load and return a HuggingFace model using from_pretrained().
:param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
:param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
:param revision: model revision
:param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline
'''
return model_class.from_pretrained(
repo_id_or_path,
revision=revision,
subfolder=subfolder or '.',
cache_dir=global_cache_dir('hub'),
)
@classmethod
def is_legacy_ckpt(cls, repo_id_or_path: Union[str,Path])->bool:
'''
@ -327,6 +302,106 @@ class ModelCache(object):
else:
logger.debug("Model scanned ok")
@staticmethod
def _model_key(path,model_class,revision,subfolder)->str:
return ':'.join([str(path),model_class.__name__,str(revision or ''),str(subfolder or '')])
def _has_cuda(self)->bool:
return self.execution_device.type == 'cuda'
def _print_cuda_stats(self):
vram = "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
loaded_models = len(self.loaded_models)
locked_models = len([x for x in self.locked_models if self.locked_models[x]>0])
logger.debug(f"Current VRAM usage: {vram}; locked_models/loaded_models = {locked_models}/{loaded_models}")
def _make_cache_room(self):
models_in_ram = len(self.models)
while models_in_ram >= self.max_models:
if least_recently_used_key := self.stack.pop(0):
logger.debug(f'Maximum cache size reached: cache_size={models_in_ram}; unloading model {least_recently_used_key}')
del self.models[least_recently_used_key]
models_in_ram = len(self.models)
gc.collect()
def _offload_unlocked_models(self):
to_offload = set()
for key in self.loaded_models:
if key not in self.locked_models or self.locked_models[key] == 0:
self.logger.debug(f'Offloading {key} from {self.execution_device} into {self.storage_device}')
to_offload.add(key)
for key in to_offload:
self.models[key].to(self.storage_device)
self.loaded_models.remove(key)
def _load_model_from_storage(
self,
repo_id_or_path: Union[str,Path],
subfolder: Path=None,
revision: str=None,
model_class: ModelClass=StableDiffusionGeneratorPipeline,
legacy_info: LegacyInfo=None,
)->ModelClass:
'''
Load and return a HuggingFace model.
:param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
:param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
:param revision: model revision
:param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline
:param legacy_info: a LegacyInfo object containing additional info needed to load a legacy ckpt
'''
if self.is_legacy_ckpt(repo_id_or_path):
model = self._load_ckpt_from_storage(repo_id_or_path, legacy_info)
else:
model = self._load_diffusers_from_storage(
repo_id_or_path,
subfolder,
revision,
model_class,
)
if self.sequential_offload and isinstance(model,StableDiffusionGeneratorPipeline):
model.enable_offload_submodels(self.execution_device)
return model
def _load_diffusers_from_storage(
self,
repo_id_or_path: Union[str,Path],
subfolder: Path=None,
revision: str=None,
model_class: ModelClass=StableDiffusionGeneratorPipeline,
)->ModelClass:
'''
Load and return a HuggingFace model using from_pretrained().
:param repo_id_or_path: either the HuggingFace repo_id or a Path to a local model
:param subfolder: name of a subfolder in which the model can be found, e.g. "vae"
:param revision: model revision
:param model_class: class of model to return, defaults to StableDiffusionGeneratorPIpeline
'''
self.logger.info(f'Loading model {repo_id_or_path}')
revisions = [revision] if revision \
else ['fp16','main'] if self.precision==torch.float16 \
else ['main']
extra_args = {'precision': self.precision} \
if model_class in DiffusionClasses \
else {}
# silence transformer and diffuser warnings
with SilenceWarnings():
for rev in revisions:
try:
model = model_class.from_pretrained(
repo_id_or_path,
revision=rev,
subfolder=subfolder or '.',
cache_dir=global_cache_dir('hub'),
**extra_args,
)
self.logger.debug(f'Found revision {rev}')
break
except OSError:
pass
return model
def _load_ckpt_from_storage(self,
ckpt_path: Union[str,Path],
legacy_info:LegacyInfo)->StableDiffusionGeneratorPipeline:
@ -336,6 +411,10 @@ class ModelCache(object):
:param legacy_info: LegacyInfo object containing paths to legacy config file and alternate vae if required
'''
assert legacy_info is not None
# deferred loading to avoid circular import errors
from .convert_ckpt_to_diffusers import load_pipeline_from_original_stable_diffusion_ckpt
with SilenceWarnings():
pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
checkpoint_path=ckpt_path,
original_config_file=legacy_info.config_file,
@ -399,6 +478,7 @@ class ModelCache(object):
raise KeyError(f"Revision '{revision}' not found in {repo_id}")
return desired_revisions[0].target_commit
class SilenceWarnings(object):
def __init__(self):
self.transformers_verbosity = transformers_logging.get_verbosity()

835
invokeai/backend/model_management/model_manager.py
View File

@ -1,56 +1,88 @@
"""
Manage a cache of Stable Diffusion model files for fast switching.
They are moved between GPU and CPU as necessary. If CPU memory falls
below a preset minimum, the least recently used model will be
cleared and loaded from disk when next needed.
"""This module manages the InvokeAI `models.yaml` file, mapping
symbolic diffusers model names to the paths and repo_ids used
by the underlying `from_pretrained()` call.
For fetching models, use manager.get_model('symbolic name'). This will
return a SDModelInfo object that contains the following attributes:
* context -- a context manager Generator that loads and locks the
model into GPU VRAM and returns the model for use.
See below for usage.
* name -- symbolic name of the model
* hash -- unique hash for the model
* location -- path or repo_id of the model
* revision -- revision of the model if coming from a repo id,
e.g. 'fp16'
* precision -- torch precision of the model
* status -- a ModelStatus enum corresponding to one of
'not_loaded', 'in_ram', 'in_vram' or 'active'
Typical usage:
from invokeai.backend import ModelManager
manager = ModelManager(config_path='./configs/models.yaml',max_models=4)
model_info = manager.get_model('stable-diffusion-1.5')
with model_info.context as my_model:
my_model.latents_from_embeddings(...)
The manager uses the underlying ModelCache class to keep
frequently-used models in RAM and move them into GPU as needed for
generation operations. The ModelCache object can be accessed using
the manager's "cache" attribute.
Other methods provided by ModelManager support importing, editing,
converting and deleting models.
"""
from __future__ import annotations
import contextlib
import gc
import hashlib
import os
import re
import sys
import textwrap
import time
import warnings
from dataclasses import dataclass
from enum import Enum, auto
from pathlib import Path
from shutil import move, rmtree
from typing import Any, Optional, Union, Callable, types
from shutil import rmtree
from typing import Union, Callable, types
import safetensors
import safetensors.torch
import torch
import transformers
import invokeai.backend.util.logging as logger
from diffusers import (
AutoencoderKL,
UNet2DConditionModel,
SchedulerMixin,
logging as dlogging,
)
from huggingface_hub import scan_cache_dir
from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from picklescan.scanner import scan_file_path
from invokeai.backend.globals import Globals, global_cache_dir
from invokeai.backend.globals import Globals, global_cache_dir, global_resolve_path
from .model_cache import Generator, ModelClass, ModelCache, SDModelType, ModelStatus, LegacyInfo
from transformers import (
CLIPTextModel,
CLIPTokenizer,
CLIPFeatureExtractor,
)
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
from ..util import CUDA_DEVICE
# wanted to use pydantic here, but Generator objects not supported
@dataclass
class SDModelInfo():
context: Generator[ModelClass, None, None]
name: str
hash: str
location: Union[Path,str]
precision: torch.dtype
subfolder: Path = None
revision: str = None
_cache: ModelCache = None
def status(self)->ModelStatus:
'''Return load status of this model as a model_cache.ModelStatus enum'''
if not self._cache:
return ModelStatus.unknown
return self._cache.status(
self.location,
revision = self.revision,
subfolder = self.subfolder
)
from ..stable_diffusion import (
StableDiffusionGeneratorPipeline,
)
from ..util import CUDA_DEVICE, ask_user, download_with_resume
class InvalidModelError(Exception):
"Raised when an invalid model is requested"
pass
class SDLegacyType(Enum):
V1 = auto()
@ -60,54 +92,39 @@ class SDLegacyType(Enum):
V2_v = auto()
UNKNOWN = auto()
class SDModelComponent(Enum):
vae="vae"
text_encoder="text_encoder"
tokenizer="tokenizer"
unet="unet"
scheduler="scheduler"
safety_checker="safety_checker"
feature_extractor="feature_extractor"
DEFAULT_MAX_MODELS = 2
class ModelManager(object):
"""
Model manager handles loading, caching, importing, deleting, converting, and editing models.
High-level interface to model management.
"""
logger: types.ModuleType = logger
def __init__(
self,
config: OmegaConf | Path,
config_path: Path,
device_type: torch.device = CUDA_DEVICE,
precision: str = "float16",
max_loaded_models=DEFAULT_MAX_MODELS,
precision: torch.dtype = torch.float16,
max_models=DEFAULT_MAX_MODELS,
sequential_offload=False,
embedding_path: Path = None,
logger: types.ModuleType = logger,
):
"""
Initialize with the path to the models.yaml config file or
an initialized OmegaConf dictionary. Optional parameters
are the torch device type, precision, max_loaded_models,
Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision.
"""
# prevent nasty-looking CLIP log message
transformers.logging.set_verbosity_error()
if not isinstance(config, DictConfig):
config = OmegaConf.load(config)
self.config = config
self.precision = precision
self.device = torch.device(device_type)
self.max_loaded_models = max_loaded_models
self.models = {}
self.stack = [] # this is an LRU FIFO
self.current_model = None
self.sequential_offload = sequential_offload
self.embedding_path = embedding_path
self.config_path = config_path
self.config = OmegaConf.load(self.config_path)
self.cache = ModelCache(
max_models=max_models,
execution_device = device_type,
precision = precision,
sequential_offload = sequential_offload,
logger = logger,
)
self.logger = logger
def valid_model(self, model_name: str) -> bool:
@ -117,138 +134,69 @@ class ModelManager(object):
"""
return model_name in self.config
def get_model(self, model_name: str = None) -> dict:
"""Given a model named identified in models.yaml, return a dict
containing the model object and some of its key features. If
in RAM will load into GPU VRAM. If on disk, will load from
there.
The dict has the following keys:
'model': The StableDiffusionGeneratorPipeline object
'model_name': The name of the model in models.yaml
'width': The width of images trained by this model
'height': The height of images trained by this model
'hash': A unique hash of this model's files on disk.
def get_model(self,
model_name: str = None,
submodel: SDModelType=None,
) -> SDModelInfo:
"""Given a model named identified in models.yaml, return
an SDModelInfo object describing it.
:param model_name: symbolic name of the model in models.yaml
:param submodel: an SDModelType enum indicating the portion of
the model to retrieve (e.g. SDModelType.vae)
"""
if not model_name:
return (
self.get_model(self.current_model)
if self.current_model
else self.get_model(self.default_model())
)
model_name = self.default_model()
if not self.valid_model(model_name):
self.logger.error(
raise InvalidModelError(
f'"{model_name}" is not a known model name. Please check your models.yaml file'
)
return self.current_model
if self.current_model != model_name:
if model_name not in self.models: # make room for a new one
self._make_cache_room()
self.offload_model(self.current_model)
# get the required loading info out of the config file
mconfig = self.config[model_name]
format = mconfig.get('format','diffusers')
legacy = None
if format=='ckpt':
location = global_resolve_path(mconfig.weights)
legacy = LegacyInfo(
config_file = global_resolve_path(mconfig.config),
)
if mconfig.get('vae'):
legacy.vae_file = global_resolve_path(mconfig.vae)
elif format=='diffusers':
location = mconfig.repo_id
revision = mconfig.get('revision')
else:
raise InvalidModelError(
f'"{model_name}" has an unknown format {format}'
)
if model_name in self.models:
requested_model = self.models[model_name]["model"]
self.logger.info(f"Retrieving model {model_name} from system RAM cache")
requested_model.ready()
width = self.models[model_name]["width"]
height = self.models[model_name]["height"]
hash = self.models[model_name]["hash"]
else: # we're about to load a new model, so potentially offload the least recently used one
requested_model, width, height, hash = self._load_model(model_name)
self.models[model_name] = {
"model_name": model_name,
"model": requested_model,
"width": width,
"height": height,
"hash": hash,
}
self.current_model = model_name
self._push_newest_model(model_name)
return {
"model_name": model_name,
"model": requested_model,
"width": width,
"height": height,
"hash": hash,
}
def get_model_vae(self, model_name: str=None)->AutoencoderKL:
"""Given a model name identified in models.yaml, load the model into
GPU if necessary and return its assigned VAE as an
AutoencoderKL object. If no model name is provided, return the
vae from the model currently in the GPU.
"""
return self._get_sub_model(model_name, SDModelComponent.vae)
def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer:
"""Given a model name identified in models.yaml, load the model into
GPU if necessary and return its assigned CLIPTokenizer. If no
model name is provided, return the tokenizer from the model
currently in the GPU.
"""
return self._get_sub_model(model_name, SDModelComponent.tokenizer)
def get_model_unet(self, model_name: str=None)->UNet2DConditionModel:
"""Given a model name identified in models.yaml, load the model into
GPU if necessary and return its assigned UNet2DConditionModel. If no model
name is provided, return the UNet from the model
currently in the GPU.
"""
return self._get_sub_model(model_name, SDModelComponent.unet)
def get_model_text_encoder(self, model_name: str=None)->CLIPTextModel:
"""Given a model name identified in models.yaml, load the model into
GPU if necessary and return its assigned CLIPTextModel. If no
model name is provided, return the text encoder from the model
currently in the GPU.
"""
return self._get_sub_model(model_name, SDModelComponent.text_encoder)
def get_model_feature_extractor(self, model_name: str=None)->CLIPFeatureExtractor:
"""Given a model name identified in models.yaml, load the model into
GPU if necessary and return its assigned CLIPFeatureExtractor. If no
model name is provided, return the text encoder from the model
currently in the GPU.
"""
return self._get_sub_model(model_name, SDModelComponent.feature_extractor)
def get_model_scheduler(self, model_name: str=None)->SchedulerMixin:
"""Given a model name identified in models.yaml, load the model into
GPU if necessary and return its assigned scheduler. If no
model name is provided, return the text encoder from the model
currently in the GPU.
"""
return self._get_sub_model(model_name, SDModelComponent.scheduler)
def _get_sub_model(
self,
model_name: str=None,
model_part: SDModelComponent=SDModelComponent.vae,
) -> Union[
AutoencoderKL,
CLIPTokenizer,
CLIPFeatureExtractor,
UNet2DConditionModel,
CLIPTextModel,
StableDiffusionSafetyChecker,
]:
"""Given a model name identified in models.yaml, and the part of the
model you wish to retrieve, return that part. Parts are in an Enum
class named SDModelComponent, and consist of:
SDModelComponent.vae
SDModelComponent.text_encoder
SDModelComponent.tokenizer
SDModelComponent.unet
SDModelComponent.scheduler
SDModelComponent.safety_checker
SDModelComponent.feature_extractor
"""
model_dict = self.get_model(model_name)
model = model_dict["model"]
return getattr(model, model_part.value)
subfolder = mconfig.get('subfolder')
hash = self.cache.model_hash(location,revision)
vae = (None,None)
try:
vae_id = mconfig.vae.repo_id
vae = (SDModelType.vae,vae_id)
except Exception:
pass
model_context = self.cache.get_model(
location,
revision = revision,
subfolder = subfolder,
legacy_info = legacy,
submodel = submodel,
attach_model_part=vae,
)
return SDModelInfo(
context = model_context,
name = model_name,
hash = hash,
location = location,
revision = revision,
precision = self.cache.precision,
subfolder = subfolder,
_cache = self.cache
)
def default_model(self) -> str | None:
"""
@ -324,19 +272,19 @@ class ModelManager(object):
format = stanza.get("format", "ckpt") # Determine Format
# Common Attribs
status = self.cache.status(
stanza.get('weights') or stanza.get('repo_id'),
revision=stanza.get('revision'),
subfolder=stanza.get('subfolder')
)
description = stanza.get("description", None)
if self.current_model == name:
status = "active"
elif name in self.models:
status = "cached"
else:
status = "not loaded"
models[name].update(
description=description,
format=format,
status=status,
status=status.value
)
# Checkpoint Config Parse
if format == "ckpt":
models[name].update(
@ -373,7 +321,7 @@ class ModelManager(object):
for name in models:
if models[name]["format"] == "vae":
continue
line = f'{name:25s} {models[name]["status"]:>10s} {models[name]["format"]:10s} {models[name]["description"]}'
line = f'{name:25s} {models[name]["status"]:>15s} {models[name]["format"]:10s} {models[name]["description"]}'
if models[name]["status"] == "active":
line = f"\033[1m{line}\033[0m"
print(line)
@ -441,233 +389,6 @@ class ModelManager(object):
if clobber:
self._invalidate_cached_model(model_name)
def _load_model(self, model_name: str):
"""Load and initialize the model from configuration variables passed at object creation time"""
if model_name not in self.config:
self.logger.error(
f'"{model_name}" is not a known model name. Please check your models.yaml file'
)
return
mconfig = self.config[model_name]
# for usage statistics
if self._has_cuda():
torch.cuda.reset_peak_memory_stats()
torch.cuda.empty_cache()
tic = time.time()
# this does the work
model_format = mconfig.get("format", "ckpt")
if model_format == "ckpt":
weights = mconfig.weights
self.logger.info(f"Loading {model_name} from {weights}")
model, width, height, model_hash = self._load_ckpt_model(
model_name, mconfig
)
elif model_format == "diffusers":
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model, width, height, model_hash = self._load_diffusers_model(mconfig)
else:
raise NotImplementedError(
f"Unknown model format {model_name}: {model_format}"
)
self._add_embeddings_to_model(model)
# usage statistics
toc = time.time()
self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic))
if self._has_cuda():
self.logger.info(
"Max VRAM used to load the model: "+
"%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
)
self.logger.info(
"Current VRAM usage: "+
"%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
)
return model, width, height, model_hash
def _load_diffusers_model(self, mconfig):
name_or_path = self.model_name_or_path(mconfig)
using_fp16 = self.precision == "float16"
self.logger.info(f"Loading diffusers model from {name_or_path}")
if using_fp16:
self.logger.debug("Using faster float16 precision")
else:
self.logger.debug("Using more accurate float32 precision")
# TODO: scan weights maybe?
pipeline_args: dict[str, Any] = dict(
safety_checker=None, local_files_only=not Globals.internet_available
)
if "vae" in mconfig and mconfig["vae"] is not None:
if vae := self._load_vae(mconfig["vae"]):
pipeline_args.update(vae=vae)
if not isinstance(name_or_path, Path):
pipeline_args.update(cache_dir=global_cache_dir("hub"))
if using_fp16:
pipeline_args.update(torch_dtype=torch.float16)
fp_args_list = [{"revision": "fp16"}, {}]
else:
fp_args_list = [{}]
verbosity = dlogging.get_verbosity()
dlogging.set_verbosity_error()
pipeline = None
for fp_args in fp_args_list:
try:
pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
name_or_path,
**pipeline_args,
**fp_args,
)
except OSError as e:
if str(e).startswith("fp16 is not a valid"):
pass
else:
self.logger.error(
f"An unexpected error occurred while downloading the model: {e})"
)
if pipeline:
break
dlogging.set_verbosity(verbosity)
assert pipeline is not None, OSError(f'"{name_or_path}" could not be loaded')
if self.sequential_offload:
pipeline.enable_offload_submodels(self.device)
else:
pipeline.to(self.device)
model_hash = self._diffuser_sha256(name_or_path)
# square images???
width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
height = width
self.logger.debug(f"Default image dimensions = {width} x {height}")
return pipeline, width, height, model_hash
def _load_ckpt_model(self, model_name, mconfig):
config = mconfig.config
weights = mconfig.weights
vae = mconfig.get("vae")
width = mconfig.width
height = mconfig.height
if not os.path.isabs(config):
config = os.path.join(Globals.root, config)
if not os.path.isabs(weights):
weights = os.path.normpath(os.path.join(Globals.root, weights))
# Convert to diffusers and return a diffusers pipeline
self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
from . import load_pipeline_from_original_stable_diffusion_ckpt
try:
if self.list_models()[self.current_model]["status"] == "active":
self.offload_model(self.current_model)
except Exception:
pass
vae_path = None
if vae:
vae_path = (
vae
if os.path.isabs(vae)
else os.path.normpath(os.path.join(Globals.root, vae))
)
if self._has_cuda():
torch.cuda.empty_cache()
pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
checkpoint_path=weights,
original_config_file=config,
vae_path=vae_path,
return_generator_pipeline=True,
precision=torch.float16 if self.precision == "float16" else torch.float32,
)
if self.sequential_offload:
pipeline.enable_offload_submodels(self.device)
else:
pipeline.to(self.device)
return (
pipeline,
width,
height,
"NOHASH",
)
def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
if isinstance(model_name, DictConfig) or isinstance(model_name, dict):
mconfig = model_name
elif model_name in self.config:
mconfig = self.config[model_name]
else:
raise ValueError(
f'"{model_name}" is not a known model name. Please check your models.yaml file'
)
if "path" in mconfig and mconfig["path"] is not None:
path = Path(mconfig["path"])
if not path.is_absolute():
path = Path(Globals.root, path).resolve()
return path
elif "repo_id" in mconfig:
return mconfig["repo_id"]
else:
raise ValueError("Model config must specify either repo_id or path.")
def offload_model(self, model_name: str) -> None:
"""
Offload the indicated model to CPU. Will call
_make_cache_room() to free space if needed.
"""
if model_name not in self.models:
return
self.logger.info(f"Offloading {model_name} to CPU")
model = self.models[model_name]["model"]
model.offload_all()
self.current_model = None
gc.collect()
if self._has_cuda():
torch.cuda.empty_cache()
@classmethod
def scan_model(self, model_name, checkpoint):
"""
Apply picklescanner to the indicated checkpoint and issue a warning
and option to exit if an infected file is identified.
"""
# scan model
self.logger.debug(f"Scanning Model: {model_name}")
scan_result = scan_file_path(checkpoint)
if scan_result.infected_files != 0:
if scan_result.infected_files == 1:
self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}")
self.logger.critical("The model you are trying to load seems to be infected.")
self.logger.critical("For your safety, InvokeAI will not load this model.")
self.logger.critical("Please use checkpoints from trusted sources.")
self.logger.critical("Exiting InvokeAI")
sys.exit()
else:
self.logger.warning("InvokeAI was unable to scan the model you are using.")
model_safe_check_fail = ask_user(
"Do you want to to continue loading the model?", ["y", "n"]
)
if model_safe_check_fail.lower() != "y":
self.logger.critical("Exiting InvokeAI")
sys.exit()
else:
self.logger.debug("Model scanned ok")
def import_diffuser_model(
self,
repo_or_path: Union[str, Path],
@ -949,8 +670,6 @@ class ModelManager(object):
new_config = None
from . import convert_ckpt_to_diffusers
if diffusers_path.exists():
self.logger.error(
f"The path {str(diffusers_path)} already exists. Please move or remove it and try again."
@ -960,6 +679,10 @@ class ModelManager(object):
model_name = model_name or diffusers_path.name
model_description = model_description or f"Converted version of {model_name}"
self.logger.debug(f"Converting {model_name} to diffusers (30-60s)")
# to avoid circular import errors
from .convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
try:
# By passing the specified VAE to the conversion function, the autoencoder
# will be built into the model rather than tacked on afterward via the config file
@ -1020,33 +743,12 @@ class ModelManager(object):
return search_folder, found_models
def _make_cache_room(self) -> None:
num_loaded_models = len(self.models)
if num_loaded_models >= self.max_loaded_models:
least_recent_model = self._pop_oldest_model()
self.logger.info(
f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
)
if least_recent_model is not None:
del self.models[least_recent_model]
gc.collect()
def print_vram_usage(self) -> None:
if self._has_cuda:
self.logger.info(
"Current VRAM usage:"+
"%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
)
def commit(self, config_file_path: str) -> None:
def commit(self) -> None:
"""
Write current configuration out to the indicated file.
"""
yaml_str = OmegaConf.to_yaml(self.config)
if not os.path.isabs(config_file_path):
config_file_path = os.path.normpath(
os.path.join(Globals.root, config_file_path)
)
config_file_path = self.config_path
tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
with open(tmpfile, "w", encoding="utf-8") as outfile:
outfile.write(self.preamble())
@ -1069,240 +771,6 @@ class ModelManager(object):
"""
)
@classmethod
def migrate_models(cls):
"""
Migrate the ~/invokeai/models directory from the legacy format used through 2.2.5
to the 2.3.0 "diffusers" version. This should be a one-time operation, called at
script startup time.
"""
# Three transformer models to check: bert, clip and safety checker, and
# the diffusers as well
models_dir = Path(Globals.root, "models")
legacy_locations = [
Path(
models_dir,
"CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker",
),
Path(models_dir, "bert-base-uncased/models--bert-base-uncased"),
Path(
models_dir,
"openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14",
),
]
legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
legacy_layout = False
for model in legacy_locations:
legacy_layout = legacy_layout or model.exists()
if not legacy_layout:
return
print(
"""
>> ALERT:
>> The location of your previously-installed diffusers models needs to move from
>> invokeai/models/diffusers to invokeai/models/hub due to a change introduced by
>> diffusers version 0.14. InvokeAI will now move all models from the "diffusers" directory
>> into "hub" and then remove the diffusers directory. This is a quick, safe, one-time
>> operation."""
)
# transformer files get moved into the hub directory
if cls._is_huggingface_hub_directory_present():
hub = global_cache_dir("hub")
else:
hub = models_dir / "hub"
os.makedirs(hub, exist_ok=True)
for model in legacy_locations:
source = models_dir / model
dest = hub / model.stem
if dest.exists() and not source.exists():
continue
cls.logger.info(f"{source} => {dest}")
if source.exists():
if dest.is_symlink():
logger.warning(f"Found symlink at {dest.name}. Not migrating.")
elif dest.exists():
if source.is_dir():
rmtree(source)
else:
source.unlink()
else:
move(source, dest)
# now clean up by removing any empty directories
empty = [
root
for root, dirs, files, in os.walk(models_dir)
if not len(dirs) and not len(files)
]
for d in empty:
os.rmdir(d)
cls.logger.info("Migration is done. Continuing...")
def _resolve_path(
self, source: Union[str, Path], dest_directory: str
) -> Optional[Path]:
resolved_path = None
if str(source).startswith(("http:", "https:", "ftp:")):
dest_directory = Path(dest_directory)
if not dest_directory.is_absolute():
dest_directory = Globals.root / dest_directory
dest_directory.mkdir(parents=True, exist_ok=True)
resolved_path = download_with_resume(str(source), dest_directory)
else:
if not os.path.isabs(source):
source = os.path.join(Globals.root, source)
resolved_path = Path(source)
return resolved_path
def _invalidate_cached_model(self, model_name: str) -> None:
self.offload_model(model_name)
if model_name in self.stack:
self.stack.remove(model_name)
self.models.pop(model_name, None)
def _pop_oldest_model(self):
"""
Remove the first element of the FIFO, which ought
to be the least recently accessed model. Do not
pop the last one, because it is in active use!
"""
return self.stack.pop(0)
def _push_newest_model(self, model_name: str) -> None:
"""
Maintain a simple FIFO. First element is always the
least recent, and last element is always the most recent.
"""
with contextlib.suppress(ValueError):
self.stack.remove(model_name)
self.stack.append(model_name)
def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
if self.embedding_path is not None:
self.logger.info(f"Loading embeddings from {self.embedding_path}")
for root, _, files in os.walk(self.embedding_path):
for name in files:
ti_path = os.path.join(root, name)
model.textual_inversion_manager.load_textual_inversion(
ti_path, defer_injecting_tokens=True
)
self.logger.info(
f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
)
def _has_cuda(self) -> bool:
return self.device.type == "cuda"
def _diffuser_sha256(
self, name_or_path: Union[str, Path], chunksize=16777216
) -> Union[str, bytes]:
path = None
if isinstance(name_or_path, Path):
path = name_or_path
else:
owner, repo = name_or_path.split("/")
path = Path(global_cache_dir("hub") / f"models--{owner}--{repo}")
if not path.exists():
return None
hashpath = path / "checksum.sha256"
if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
with open(hashpath) as f:
hash = f.read()
return hash
self.logger.debug("Calculating sha256 hash of model files")
tic = time.time()
sha = hashlib.sha256()
count = 0
for root, dirs, files in os.walk(path, followlinks=False):
for name in files:
count += 1
with open(os.path.join(root, name), "rb") as f:
while chunk := f.read(chunksize):
sha.update(chunk)
hash = sha.hexdigest()
toc = time.time()
self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
with open(hashpath, "w") as f:
f.write(hash)
return hash
def _cached_sha256(self, path, data) -> Union[str, bytes]:
dirname = os.path.dirname(path)
basename = os.path.basename(path)
base, _ = os.path.splitext(basename)
hashpath = os.path.join(dirname, base + ".sha256")
if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime(
hashpath
):
with open(hashpath) as f:
hash = f.read()
return hash
self.logger.debug("Calculating sha256 hash of weights file")
tic = time.time()
sha = hashlib.sha256()
sha.update(data)
hash = sha.hexdigest()
toc = time.time()
self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
with open(hashpath, "w") as f:
f.write(hash)
return hash
def _load_vae(self, vae_config) -> AutoencoderKL:
vae_args = {}
try:
name_or_path = self.model_name_or_path(vae_config)
except Exception:
return None
if name_or_path is None:
return None
using_fp16 = self.precision == "float16"
vae_args.update(
cache_dir=global_cache_dir("hub"),
local_files_only=not Globals.internet_available,
)
self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
if using_fp16:
vae_args.update(torch_dtype=torch.float16)
fp_args_list = [{"revision": "fp16"}, {}]
else:
self.logger.debug("Using more accurate float32 precision")
fp_args_list = [{}]
vae = None
deferred_error = None
# A VAE may be in a subfolder of a model's repository.
if "subfolder" in vae_config:
vae_args["subfolder"] = vae_config["subfolder"]
for fp_args in fp_args_list:
# At some point we might need to be able to use different classes here? But for now I think
# all Stable Diffusion VAE are AutoencoderKL.
try:
vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args, **fp_args)
except OSError as e:
if str(e).startswith("fp16 is not a valid"):
pass
else:
deferred_error = e
if vae:
break
if not vae and deferred_error:
self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
return vae
@classmethod
def _delete_model_from_cache(cls,repo_id):
cache_info = scan_cache_dir(global_cache_dir("hub"))
@ -1326,8 +794,3 @@ class ModelManager(object):
return path
return Path(Globals.root, path).resolve()
@staticmethod
def _is_huggingface_hub_directory_present() -> bool:
return (
os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None
)