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Merge branch 'main' into feat/select-vram-in-config

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This commit is contained in:
Lincoln Stein
2023-08-07 15:51:59 -04:00
committed by GitHub
parent da96a41103 ae17d01e1d
commit 0fd13d3604
18 changed files with 511 additions and 333 deletions
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6
invokeai/backend/install/model_install_backend.py
View File

@@ -101,9 +101,9 @@ class ModelInstall(object):
def __init__(
self,
config: InvokeAIAppConfig,
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
model_manager: ModelManager = None,
access_token: str = None,
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
model_manager: Optional[ModelManager] = None,
access_token: Optional[str] = None,
):
self.config = config
self.mgr = model_manager or ModelManager(config.model_conf_path)

30
invokeai/backend/model_management/model_manager.py
View File

@@ -234,7 +234,7 @@ import textwrap
import yaml
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, List, Tuple, Union, Dict, Set, Callable, types
from typing import Literal, Optional, List, Tuple, Union, Dict, Set, Callable, types
from shutil import rmtree, move
import torch
@@ -518,7 +518,7 @@ class ModelManager(object):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> dict:
) -> Union[dict, None]:
"""
Given a model name returns the OmegaConf (dict-like) object describing it.
"""
@@ -540,13 +540,15 @@ class ModelManager(object):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> dict:
) -> Union[dict, None]:
"""
Returns a dict describing one installed model, using
the combined format of the list_models() method.
"""
models = self.list_models(base_model, model_type, model_name)
return models[0] if models else None
if len(models) > 1:
return models[0]
return None
def list_models(
self,
@@ -560,7 +562,7 @@ class ModelManager(object):
model_keys = (
[self.create_key(model_name, base_model, model_type)]
if model_name
if model_name and base_model and model_type
else sorted(self.models, key=str.casefold)
)
models = []
@@ -596,7 +598,7 @@ class ModelManager(object):
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():
for model_dict in self.list_models():
for model_name, model_info in model_dict.items():
line = f'{model_info["name"]:25s} {model_info["type"]:10s} {model_info["description"]}'
print(line)
@@ -699,8 +701,8 @@ class ModelManager(object):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
new_name: Optional[str] = None,
new_base: Optional[BaseModelType] = None,
):
"""
Rename or rebase a model.
@@ -753,7 +755,7 @@ class ModelManager(object):
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main, ModelType.Vae],
model_type: Literal[ModelType.Main, ModelType.Vae],
dest_directory: Optional[Path] = None,
) -> AddModelResult:
"""
@@ -767,6 +769,10 @@ class ModelManager(object):
This will raise a ValueError unless the model is a checkpoint.
"""
info = self.model_info(model_name, base_model, model_type)
if info is None:
raise FileNotFoundError(f"model not found: {model_name}")
if info["model_format"] != "checkpoint":
raise ValueError(f"not a checkpoint format model: {model_name}")
@@ -836,7 +842,7 @@ class ModelManager(object):
return search_folder, found_models
def commit(self, conf_file: Path = None) -> None:
def commit(self, conf_file: Optional[Path] = None) -> None:
"""
Write current configuration out to the indicated file.
"""
@@ -983,7 +989,7 @@ class ModelManager(object):
# LS: hacky
# Patch in the SD VAE from core so that it is available for use by the UI
try:
self.heuristic_import({self.resolve_model_path("core/convert/sd-vae-ft-mse")})
self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))})
except:
pass
@@ -1011,7 +1017,7 @@ class ModelManager(object):
def heuristic_import(
self,
items_to_import: Set[str],
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> Dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.

6
invokeai/backend/model_management/model_merge.py
View File

@@ -33,7 +33,7 @@ class ModelMerger(object):
self,
model_paths: List[Path],
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
interp: Optional[MergeInterpolationMethod] = None,
force: bool = False,
**kwargs,
) -> DiffusionPipeline:
@@ -73,7 +73,7 @@ class ModelMerger(object):
base_model: Union[BaseModelType, str],
merged_model_name: str,
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
interp: Optional[MergeInterpolationMethod] = None,
force: bool = False,
merge_dest_directory: Optional[Path] = None,
**kwargs,
@@ -122,7 +122,7 @@ class ModelMerger(object):
dump_path.mkdir(parents=True, exist_ok=True)
dump_path = dump_path / merged_model_name
merged_pipe.save_pretrained(dump_path, safe_serialization=1)
merged_pipe.save_pretrained(dump_path, safe_serialization=True)
attributes = dict(
path=str(dump_path),
description=f"Merge of models {', '.join(model_names)}",

4
invokeai/backend/util/devices.py
View File

@@ -1,6 +1,8 @@
from __future__ import annotations
from contextlib import nullcontext
from packaging import version
import platform
import torch
from torch import autocast
@@ -30,7 +32,7 @@ def choose_precision(device: torch.device) -> str:
device_name = torch.cuda.get_device_name(device)
if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
return "float16"
elif device.type == "mps":
elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"):
return "float16"
return "float32"