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This commit is contained in:
Lincoln Stein
2023-06-05 22:03:44 -04:00
parent 54e5301b35 b31fc43bfa
commit 90333c0074
45 changed files with 377 additions and 9254 deletions
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4
invokeai/backend/image_util/patchmatch.py
View File

@ -6,7 +6,8 @@ be suppressed or deferred
"""
import numpy as np
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
config = InvokeAIAppConfig.get_config()
class PatchMatch:
"""
@ -21,7 +22,6 @@ class PatchMatch:
@classmethod
def _load_patch_match(self):
config = get_invokeai_config()
if self.tried_load:
return
if config.try_patchmatch:

4
invokeai/backend/image_util/txt2mask.py
View File

@ -33,10 +33,11 @@ from PIL import Image, ImageOps
from transformers import AutoProcessor, CLIPSegForImageSegmentation
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
CLIPSEG_SIZE = 352
config = InvokeAIAppConfig.get_config()
class SegmentedGrayscale(object):
def __init__(self, image: Image, heatmap: torch.Tensor):
@ -83,7 +84,6 @@ class Txt2Mask(object):
def __init__(self, device="cpu", refined=False):
logger.info("Initializing clipseg model for text to mask inference")
config = get_invokeai_config()
# BUG: we are not doing anything with the device option at this time
self.device = device

30
invokeai/backend/install/invokeai_configure.py
View File

@ -55,6 +55,8 @@ from invokeai.backend.install.model_install_backend import (
UserSelections,
)
from invokeai.app.services.config import InvokeAIAppConfig
warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
@ -62,7 +64,7 @@ transformers.logging.set_verbosity_error()
# --------------------------globals-----------------------
config = get_invokeai_config(argv=[])
config = InvokeAIAppConfig.get_config()
Model_dir = "models"
Weights_dir = "ldm/stable-diffusion-v1/"
@ -301,7 +303,7 @@ def download_vaes():
if not hf_download_with_resume(
repo_id=repo_id,
model_name=model_name,
model_dir=str(config.root / Model_dir / Weights_dir),
model_dir=str(config.root_path / Model_dir / Weights_dir),
):
raise Exception(f"download of {model_name} failed")
except Exception as e:
@ -326,7 +328,7 @@ class editOptsForm(npyscreen.FormMultiPage):
def create(self):
program_opts = self.parentApp.program_opts
old_opts = self.parentApp.invokeai_opts
first_time = not (config.root / 'invokeai.yaml').exists()
first_time = not (config.root_path / 'invokeai.yaml').exists()
access_token = HfFolder.get_token()
window_width, window_height = get_terminal_size()
for i in [
@ -641,7 +643,7 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
def default_startup_options(init_file: Path) -> Namespace:
opts = InvokeAIAppConfig(argv=[])
opts = InvokeAIAppConfig.get_config()
if not init_file.exists():
opts.nsfw_checker = True
return opts
@ -709,10 +711,10 @@ def write_opts(opts: Namespace, init_file: Path):
"""
Update the invokeai.yaml file with values from current settings.
"""
# this will load default settings
new_config = InvokeAIAppConfig(argv=[])
# this will load current settings
new_config = InvokeAIAppConfig.get_config()
new_config.root = config.root
for key,value in opts.__dict__.items():
if hasattr(new_config,key):
setattr(new_config,key,value)
@ -722,19 +724,19 @@ def write_opts(opts: Namespace, init_file: Path):
# -------------------------------------
def default_output_dir() -> Path:
return config.root / "outputs"
return config.root_path / "outputs"
# -------------------------------------
def default_embedding_dir() -> Path:
return config.root / "embeddings"
return config.root_path / "embeddings"
# -------------------------------------
def default_lora_dir() -> Path:
return config.root / "loras"
return config.root_path / "loras"
# -------------------------------------
def default_controlnet_dir() -> Path:
return config.root / "controlnets"
return config.root_path / "controlnets"
# -------------------------------------
def write_default_options(program_opts: Namespace, initfile: Path):
@ -748,7 +750,7 @@ def write_default_options(program_opts: Namespace, initfile: Path):
# yaml format.
def migrate_init_file(legacy_format:Path):
old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
new = InvokeAIAppConfig(conf={})
new = InvokeAIAppConfig.get_config()
fields = list(get_type_hints(InvokeAIAppConfig).keys())
for attr in fields:
@ -840,7 +842,8 @@ def main():
if old_init_file.exists() and not new_init_file.exists():
print('** Migrating invokeai.init to invokeai.yaml')
migrate_init_file(old_init_file)
config.parse_args([]) # reread defaults
# Load new init file into config
config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
if not config.model_conf_path.exists():
initialize_rootdir(config.root, opt.yes_to_all)
@ -877,7 +880,6 @@ def main():
if opt.skip_sd_weights:
print("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
elif models_to_download:
print(models_to_download)
print("\n** DOWNLOADING DIFFUSION WEIGHTS **")
process_and_execute(opt, models_to_download)

6
invokeai/backend/install/model_install_backend.py
View File

@ -20,14 +20,16 @@ from tqdm import tqdm
import invokeai.configs as configs
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from ..stable_diffusion import StableDiffusionGeneratorPipeline
from ..util.logging import InvokeAILogger
warnings.filterwarnings("ignore")
# --------------------------globals-----------------------
config = get_invokeai_config(argv=[])
config = InvokeAIAppConfig.get_config()
Model_dir = "models"
Weights_dir = "ldm/stable-diffusion-v1/"

11
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -26,7 +26,7 @@ import torch
from safetensors.torch import load_file
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from .model_manager import ModelManager, SDLegacyType
@ -842,7 +842,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
def convert_ldm_clip_checkpoint(checkpoint):
text_model = CLIPTextModel.from_pretrained(
"openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
"openai/clip-vit-large-patch14", cache_dir=InvokeAIAppConfig.get_config().cache_dir
)
keys = list(checkpoint.keys())
@ -897,7 +897,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
def convert_paint_by_example_checkpoint(checkpoint):
cache_dir = get_invokeai_config().cache_dir
cache_dir = InvokeAIAppConfig.get_config().cache_dir
config = CLIPVisionConfig.from_pretrained(
"openai/clip-vit-large-patch14", cache_dir=cache_dir
)
@ -969,7 +969,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
def convert_open_clip_checkpoint(checkpoint):
cache_dir = get_invokeai_config().cache_dir
cache_dir = InvokeAIAppConfig.get_config().cache_dir
text_model = CLIPTextModel.from_pretrained(
"stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
)
@ -1092,8 +1092,9 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
:param vae: A diffusers VAE to load into the pipeline.
:param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
"""
invoke_config = get_invokeai_config()
config = InvokeAIAppConfig.get_config()
cache_dir = invoke_config.cache_dir
with warnings.catch_warnings():
warnings.simplefilter("ignore")
verbosity = dlogging.get_verbosity()

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

@ -49,7 +49,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
from ..stable_diffusion import (
StableDiffusionGeneratorPipeline,
)
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from ..install.model_install_backend import (
Dataset_path,
hf_download_with_resume,
@ -104,7 +104,7 @@ class ModelManager(object):
if not isinstance(config, DictConfig):
config = OmegaConf.load(config)
self.config = config
self.globals = get_invokeai_config()
self.globals = InvokeAIAppConfig.get_config()
self.precision = precision
self.device = torch.device(device_type)
self.max_loaded_models = max_loaded_models
@ -1063,7 +1063,7 @@ class ModelManager(object):
"""
# Three transformer models to check: bert, clip and safety checker, and
# the diffusers as well
config = get_invokeai_config()
config = InvokeAIAppConfig.get_config()
models_dir = config.root_dir / "models"
legacy_locations = [
Path(
@ -1293,7 +1293,7 @@ class ModelManager(object):
@classmethod
def _delete_model_from_cache(cls,repo_id):
cache_info = scan_cache_dir(get_invokeai_config().cache_dir)
cache_info = scan_cache_dir(InvokeAIAppConfig.get_config().cache_dir)
# I'm sure there is a way to do this with comprehensions
# but the code quickly became incomprehensible!
@ -1310,7 +1310,7 @@ class ModelManager(object):
@staticmethod
def _abs_path(path: str | Path) -> Path:
globals = get_invokeai_config()
globals = InvokeAIAppConfig.get_config()
if path is None or Path(path).is_absolute():
return path
return Path(globals.root_dir, path).resolve()

8
invokeai/backend/prompting/conditioning.py
View File

@ -21,10 +21,12 @@ from compel.prompt_parser import (
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from ..stable_diffusion import InvokeAIDiffuserComponent
from ..util import torch_dtype
config = InvokeAIAppConfig.get_config()
def get_uc_and_c_and_ec(prompt_string,
model: InvokeAIDiffuserComponent,
log_tokens=False, skip_normalize_legacy_blend=False):
@ -37,9 +39,7 @@ def get_uc_and_c_and_ec(prompt_string,
textual_inversion_manager=model.textual_inversion_manager,
dtype_for_device_getter=torch_dtype,
truncate_long_prompts=False,
)
config = get_invokeai_config()
)
# get rid of any newline characters
prompt_string = prompt_string.replace("\n", " ")

4
invokeai/backend/restoration/codeformer.py
View File

@ -6,7 +6,7 @@ import numpy as np
import torch
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
pretrained_model_url = (
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
@ -18,7 +18,7 @@ class CodeFormerRestoration:
self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
) -> None:
self.globals = get_invokeai_config()
self.globals = InvokeAIAppConfig.get_config()
codeformer_dir = self.globals.root_dir / codeformer_dir
self.model_path = codeformer_dir / codeformer_model_path
self.codeformer_model_exists = self.model_path.exists()

4
invokeai/backend/restoration/gfpgan.py
View File

@ -7,11 +7,11 @@ import torch
from PIL import Image
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
class GFPGAN:
def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
self.globals = get_invokeai_config()
self.globals = InvokeAIAppConfig.get_config()
if not os.path.isabs(gfpgan_model_path):
gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
self.model_path = gfpgan_model_path

4
invokeai/backend/restoration/realesrgan.py
View File

@ -6,8 +6,8 @@ from PIL import Image
from PIL.Image import Image as ImageType
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
config = get_invokeai_config()
from invokeai.app.services.config import InvokeAIAppConfig
config = InvokeAIAppConfig.get_config()
class ESRGAN:
def __init__(self, bg_tile_size=400) -> None:

5
invokeai/backend/safety_checker.py
View File

@ -15,9 +15,11 @@ from transformers import AutoFeatureExtractor
import invokeai.assets.web as web_assets
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from .util import CPU_DEVICE
config = InvokeAIAppConfig.get_config()
class SafetyChecker(object):
CAUTION_IMG = "caution.png"
@ -26,7 +28,6 @@ class SafetyChecker(object):
caution = Image.open(path)
self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
self.device = device
config = get_invokeai_config()
try:
safety_model_id = "CompVis/stable-diffusion-safety-checker"

7
invokeai/backend/stable_diffusion/concepts_lib.py
View File

@ -17,15 +17,16 @@ from huggingface_hub import (
hf_hub_url,
)
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.app.services.config import InvokeAIAppConfig
logger = InvokeAILogger.getLogger()
class HuggingFaceConceptsLibrary(object):
def __init__(self, root=None):
"""
Initialize the Concepts object. May optionally pass a root directory.
"""
self.config = get_invokeai_config()
self.config = InvokeAIAppConfig.get_config()
self.root = root or self.config.root
self.hf_api = HfApi()
self.local_concepts = dict()

4
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -40,7 +40,7 @@ from torchvision.transforms.functional import resize as tv_resize
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from typing_extensions import ParamSpec
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from ..util import CPU_DEVICE, normalize_device
from .diffusion import (
AttentionMapSaver,
@ -364,7 +364,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
"""
if xformers is available, use it, otherwise use sliced attention.
"""
config = get_invokeai_config()
config = InvokeAIAppConfig.get_config()
if (
torch.cuda.is_available()
and is_xformers_available()

40
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -10,7 +10,7 @@ from diffusers.models.attention_processor import AttentionProcessor
from typing_extensions import TypeAlias
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
from .cross_attention_control import (
Arguments,
@ -72,7 +72,7 @@ class InvokeAIDiffuserComponent:
:param model: the unet model to pass through to cross attention control
:param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
"""
config = get_invokeai_config()
config = InvokeAIAppConfig.get_config()
self.conditioning = None
self.model = model
self.is_running_diffusers = is_running_diffusers
@ -112,23 +112,25 @@ class InvokeAIDiffuserComponent:
# TODO resuscitate attention map saving
# self.remove_attention_map_saving()
def override_cross_attention(
self, conditioning: ExtraConditioningInfo, step_count: int
) -> Dict[str, AttentionProcessor]:
"""
setup cross attention .swap control. for diffusers this replaces the attention processor, so
the previous attention processor is returned so that the caller can restore it later.
"""
self.conditioning = conditioning
self.cross_attention_control_context = Context(
arguments=self.conditioning.cross_attention_control_args,
step_count=step_count,
)
return override_cross_attention(
self.model,
self.cross_attention_control_context,
is_running_diffusers=self.is_running_diffusers,
)
# apparently unused code
# TODO: delete
# def override_cross_attention(
# self, conditioning: ExtraConditioningInfo, step_count: int
# ) -> Dict[str, AttentionProcessor]:
# """
# setup cross attention .swap control. for diffusers this replaces the attention processor, so
# the previous attention processor is returned so that the caller can restore it later.
# """
# self.conditioning = conditioning
# self.cross_attention_control_context = Context(
# arguments=self.conditioning.cross_attention_control_args,
# step_count=step_count,
# )
# return override_cross_attention(
# self.model,
# self.cross_attention_control_context,
# is_running_diffusers=self.is_running_diffusers,
# )
def restore_default_cross_attention(
self, restore_attention_processor: Optional["AttentionProcessor"] = None

2
invokeai/backend/training/textual_inversion_training.py
View File

@ -88,7 +88,7 @@ def save_progress(
def parse_args():
config = InvokeAIAppConfig(argv=[])
config = InvokeAIAppConfig.get_config()
parser = PagingArgumentParser(
description="Textual inversion training"
)

5
invokeai/backend/util/devices.py
View File

@ -4,15 +4,15 @@ from contextlib import nullcontext
import torch
from torch import autocast
from invokeai.app.services.config import get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
CPU_DEVICE = torch.device("cpu")
CUDA_DEVICE = torch.device("cuda")
MPS_DEVICE = torch.device("mps")
config = InvokeAIAppConfig.get_config()
def choose_torch_device() -> torch.device:
"""Convenience routine for guessing which GPU device to run model on"""
config = get_invokeai_config()
if config.always_use_cpu:
return CPU_DEVICE
if torch.cuda.is_available():
@ -32,7 +32,6 @@ def choose_precision(device: torch.device) -> str:
def torch_dtype(device: torch.device) -> torch.dtype:
config = get_invokeai_config()
if config.full_precision:
return torch.float32
if choose_precision(device) == "float16":