Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into feat/onnx

Browse Source
This commit is contained in:
Brandon Rising
2023-07-27 09:54:30 -04:00
parent 4ebde013ea be00a837cc
commit eb1ba8d74b
38 changed files with 1250 additions and 195 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
invokeai/backend/install/check_root.py
View File

@ -8,9 +8,9 @@ from invokeai.app.services.config import (
def check_invokeai_root(config: InvokeAIAppConfig):
try:
assert config.model_conf_path.exists()
assert config.db_path.exists()
assert config.models_path.exists()
assert config.model_conf_path.exists(), f'{config.model_conf_path} not found'
assert config.db_path.parent.exists(), f'{config.db_path.parent} not found'
assert config.models_path.exists(), f'{config.models_path} not found'
for model in [
'CLIP-ViT-bigG-14-laion2B-39B-b160k',
'bert-base-uncased',
@ -18,9 +18,11 @@ def check_invokeai_root(config: InvokeAIAppConfig):
'sd-vae-ft-mse',
'stable-diffusion-2-clip',
'stable-diffusion-safety-checker']:
assert (config.models_path / f'core/convert/{model}').exists()
except:
path = config.models_path / f'core/convert/{model}'
assert path.exists(), f'{path} is missing'
except Exception as e:
print()
print(f'An exception has occurred: {str(e)}')
print('== STARTUP ABORTED ==')
print('** One or more necessary files is missing from your InvokeAI root directory **')
print('** Please rerun the configuration script to fix this problem. **')

82
invokeai/backend/install/invokeai_configure.py
View File

@ -13,8 +13,8 @@ import os
import shutil
import textwrap
import traceback
import warnings
import yaml
import warnings
from argparse import Namespace
from pathlib import Path
from shutil import get_terminal_size
@ -45,6 +45,7 @@ from invokeai.app.services.config import (
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
from invokeai.frontend.install.widgets import (
SingleSelectColumns,
CenteredButtonPress,
FileBox,
IntTitleSlider,
@ -56,7 +57,6 @@ from invokeai.frontend.install.widgets import (
from invokeai.backend.install.legacy_arg_parsing import legacy_parser
from invokeai.backend.install.model_install_backend import (
hf_download_from_pretrained,
hf_download_with_resume,
InstallSelections,
ModelInstall,
)
@ -330,34 +330,49 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
npyscreen.Checkbox,
name="Free GPU memory after each generation",
value=old_opts.free_gpu_mem,
max_width=45,
relx=5,
scroll_exit=True,
)
self.nextrely -= 1
self.xformers_enabled = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Enable xformers support if available",
name="Enable xformers support",
value=old_opts.xformers_enabled,
relx=5,
max_width=30,
relx=50,
scroll_exit=True,
)
self.nextrely -=1
self.always_use_cpu = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Force CPU to be used on GPU systems",
value=old_opts.always_use_cpu,
relx=5,
relx=80,
scroll_exit=True,
)
precision = old_opts.precision or (
"float32" if program_opts.full_precision else "auto"
)
self.nextrely +=1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Floating Point Precision",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -=1
self.precision = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
columns = 2,
SingleSelectColumns,
columns = 3,
name="Precision",
values=PRECISION_CHOICES,
value=PRECISION_CHOICES.index(precision),
begin_entry_at=3,
max_height=len(PRECISION_CHOICES) + 1,
max_height=2,
max_width=80,
scroll_exit=True,
)
self.max_cache_size = self.add_widget_intelligent(
@ -370,12 +385,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models (<tab> autocompletes, ctrl-N advances):",
editable=False,
color="CONTROL",
)
self.outdir = self.add_widget_intelligent(
FileBox,
name="Output directory for images (<tab> autocompletes, ctrl-N advances):",
@ -391,7 +400,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
self.autoimport_dirs = {}
self.autoimport_dirs['autoimport_dir'] = self.add_widget_intelligent(
FileBox,
name=f'Autoimport Folder',
name=f'Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models',
value=str(config.root_path / config.autoimport_dir),
select_dir=True,
must_exist=False,
@ -402,18 +411,10 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
scroll_exit=True
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="== LICENSE ==",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT
https://huggingface.co/spaces/CompVis/stable-diffusion-license
AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSES LOCATED AT
https://huggingface.co/spaces/CompVis/stable-diffusion-license and
https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md
"""
for i in textwrap.wrap(label,width=window_width-6):
self.add_widget_intelligent(
@ -424,7 +425,7 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
)
self.license_acceptance = self.add_widget_intelligent(
npyscreen.Checkbox,
name="I accept the CreativeML Responsible AI License",
name="I accept the CreativeML Responsible AI Licenses",
value=not first_time,
relx=2,
scroll_exit=True,
@ -439,7 +440,6 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
CenteredButtonPress,
name=label,
relx=(window_width - len(label)) // 2,
rely=-3,
when_pressed_function=self.on_ok,
)
@ -558,7 +558,7 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
# -------------------------------------
def initialize_rootdir(root: Path, yes_to_all: bool = False):
logger.info("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
logger.info("Initializing InvokeAI runtime directory")
for name in (
"models",
"databases",
@ -583,7 +583,18 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
path = dest / 'core'
path.mkdir(parents=True, exist_ok=True)
with open(root / 'configs' / 'models.yaml','w') as yaml_file:
maybe_create_models_yaml(root)
def maybe_create_models_yaml(root: Path):
models_yaml = root / 'configs' / 'models.yaml'
if models_yaml.exists():
if OmegaConf.load(models_yaml).get('__metadata__'): # up to date
return
else:
logger.info('Creating new models.yaml, original saved as models.yaml.orig')
models_yaml.rename(models_yaml.parent / 'models.yaml.orig')
with open(models_yaml,'w') as yaml_file:
yaml_file.write(yaml.dump({'__metadata__':
{'version':'3.0.0'}
}
@ -767,8 +778,8 @@ def main():
if migrate_if_needed(opt, config.root_path):
sys.exit(0)
if not config.model_conf_path.exists():
initialize_rootdir(config.root_path, opt.yes_to_all)
# run this unconditionally in case new directories need to be added
initialize_rootdir(config.root_path, opt.yes_to_all)
models_to_download = default_user_selections(opt)
new_init_file = config.root_path / 'invokeai.yaml'
@ -788,15 +799,14 @@ def main():
sys.exit(0)
if opt.skip_support_models:
logger.info("SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST")
logger.info("Skipping support models at user's request")
else:
logger.info("CHECKING/UPDATING SUPPORT MODELS")
logger.info("Installing support models")
download_support_models()
if opt.skip_sd_weights:
logger.warning("SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST")
logger.warning("Skipping diffusion weights download per user request")
elif models_to_download:
logger.info("DOWNLOADING DIFFUSION WEIGHTS")
process_and_execute(opt, models_to_download)
postscript(errors=errors)

7
invokeai/backend/install/model_install_backend.py
View File

@ -149,16 +149,17 @@ class ModelInstall(object):
for i in installed:
print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}")
def starter_models(self)->Set[str]:
# logic here a little reversed to maintain backward compatibility
def starter_models(self, all_models: bool=False)->Set[str]:
models = set()
for key, value in self.datasets.items():
name,base,model_type = ModelManager.parse_key(key)
if model_type==ModelType.Main:
if all_models or model_type in [ModelType.Main, ModelType.Vae]:
models.add(key)
return models
def recommended_models(self)->Set[str]:
starters = self.starter_models()
starters = self.starter_models(all_models=True)
return set([x for x in starters if self.datasets[x].get('recommended',False)])
def default_model(self)->str:

2
invokeai/backend/model_management/model_manager.py
View File

@ -754,7 +754,7 @@ class ModelManager(object):
# We are taking advantage of a side effect of get_model() that converts check points
# into cached diffusers directories stored at `location`. It doesn't matter
# what submodeltype we request here, so we get the smallest.
submodel = {"submodel_type": SubModelType.Tokenizer} if model_type==ModelType.Main else {}
submodel = {"submodel_type": SubModelType.Scheduler} if model_type==ModelType.Main else {}
model = self.get_model(model_name,
base_model,
model_type,

9
invokeai/backend/model_management/model_probe.py
View File

@ -416,7 +416,14 @@ class PipelineFolderProbe(FolderProbeBase):
class VaeFolderProbe(FolderProbeBase):
def get_base_type(self)->BaseModelType:
return BaseModelType.StableDiffusion1
config_file = self.folder_path / 'config.json'
if not config_file.exists():
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
with open(config_file,'r') as file:
config = json.load(file)
return BaseModelType.StableDiffusionXL \
if config.get('scaling_factor',0)==0.13025 and config.get('sample_size') in [512, 1024] \
else BaseModelType.StableDiffusion1
class TextualInversionFolderProbe(FolderProbeBase):
def get_format(self)->str:

4
invokeai/backend/model_management/models/sdxl.py
View File

@ -112,16 +112,12 @@ class StableDiffusionXLModel(DiffusersModel):
# The convert script adapted from the diffusers package uses
# strings for the base model type. To avoid making too many
# source code changes, we simply translate here
model_base_to_model_type = {BaseModelType.StableDiffusionXL: 'SDXL',
BaseModelType.StableDiffusionXLRefiner: 'SDXL-Refiner',
}
if isinstance(config, cls.CheckpointConfig):
from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
return _convert_ckpt_and_cache(
version=base_model,
model_config=config,
output_path=output_path,
model_type=model_base_to_model_type[base_model],
use_safetensors=False, # corrupts sdxl models for some reason
)
else:

9
invokeai/backend/model_management/models/stable_diffusion.py
View File

@ -14,6 +14,7 @@ from .base import (
read_checkpoint_meta,
classproperty,
InvalidModelException,
ModelNotFoundException,
)
from .sdxl import StableDiffusionXLModel
import invokeai.backend.util.logging as logger
@ -268,12 +269,18 @@ def _convert_ckpt_and_cache(
# to avoid circular import errors
from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
from ...util.devices import choose_torch_device, torch_dtype
model_base_to_model_type = {BaseModelType.StableDiffusion1: 'FrozenCLIPEmbedder',
BaseModelType.StableDiffusion2: 'FrozenOpenCLIPEmbedder',
BaseModelType.StableDiffusionXL: 'SDXL',
BaseModelType.StableDiffusionXLRefiner: 'SDXL-Refiner',
}
logger.info(f'Converting {weights} to diffusers format')
with SilenceWarnings():
convert_ckpt_to_diffusers(
weights,
output_path,
model_type=model_base_to_model_type[version],
model_version=version,
model_variant=model_config.variant,
original_config_file=config_file,