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fix invokeai_configure script to work with new mm; rename CLIs

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This commit is contained in:
Lincoln Stein 2024-02-09 16:42:33 -05:00 committed by psychedelicious
parent dfcf38be91
commit d959276217
13 changed files with 686 additions and 692 deletions
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10
invokeai/app/services/config/config_default.py
View File

@ -185,7 +185,9 @@ from .config_base import InvokeAISettings
INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db")
LEGACY_INIT_FILE = Path("invokeai.init")
DEFAULT_MAX_VRAM = 0.5
DEFAULT_RAM_CACHE = 10.0
DEFAULT_VRAM_CACHE = 0.25
DEFAULT_CONVERT_CACHE = 20.0
class Categories(object):
@ -261,9 +263,9 @@ class InvokeAIAppConfig(InvokeAISettings):
version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
# CACHE
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
convert_cache : float = Field(default=10.0, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
ram : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
convert_cache : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)

2
invokeai/backend/install/install_helper.py
View File

@ -37,7 +37,7 @@ from invokeai.backend.model_manager.metadata import UnknownMetadataException
from invokeai.backend.util.logging import InvokeAILogger
# name of the starter models file
INITIAL_MODELS = "INITIAL_MODELS2.yaml"
INITIAL_MODELS = "INITIAL_MODELS.yaml"
def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:

197
invokeai/backend/install/invokeai_configure.py
View File

@ -18,31 +18,30 @@ from argparse import Namespace
from enum import Enum
from pathlib import Path
from shutil import get_terminal_size
from typing import Any, get_args, get_type_hints
from typing import Any, Optional, Set, Tuple, Type, get_args, get_type_hints
from urllib import request
import npyscreen
import omegaconf
import psutil
import torch
import transformers
import yaml
from diffusers import AutoencoderKL
from diffusers import AutoencoderKL, ModelMixin
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from huggingface_hub import HfFolder
from huggingface_hub import login as hf_hub_login
from omegaconf import OmegaConf
from pydantic import ValidationError
from omegaconf import DictConfig, OmegaConf
from pydantic.error_wrappers import ValidationError
from tqdm import tqdm
from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import invokeai.configs as configs
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.install.install_helper import InstallHelper, InstallSelections
from invokeai.backend.install.legacy_arg_parsing import legacy_parser
from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, hf_download_from_pretrained
from invokeai.backend.model_management.model_probe import BaseModelType, ModelType
from invokeai.backend.model_manager import BaseModelType, ModelType
from invokeai.backend.util import choose_precision, choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
from invokeai.frontend.install.model_install import addModelsForm
# TO DO - Move all the frontend code into invokeai.frontend.install
from invokeai.frontend.install.widgets import (
@ -61,7 +60,7 @@ warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
def get_literal_fields(field) -> list[Any]:
def get_literal_fields(field: str) -> Tuple[Any]:
return get_args(get_type_hints(InvokeAIAppConfig).get(field))
@ -80,8 +79,7 @@ ATTENTION_SLICE_CHOICES = get_literal_fields("attention_slice_size")
GENERATION_OPT_CHOICES = ["sequential_guidance", "force_tiled_decode", "lazy_offload"]
GB = 1073741824 # GB in bytes
HAS_CUDA = torch.cuda.is_available()
_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0.0, 0.0)
MAX_VRAM /= GB
MAX_RAM = psutil.virtual_memory().total / GB
@ -96,13 +94,15 @@ logger = InvokeAILogger.get_logger()
class DummyWidgetValue(Enum):
"""Dummy widget values."""
zero = 0
true = True
false = False
# --------------------------------------------
def postscript(errors: None):
def postscript(errors: Set[str]) -> None:
if not any(errors):
message = f"""
** INVOKEAI INSTALLATION SUCCESSFUL **
@ -143,7 +143,7 @@ def yes_or_no(prompt: str, default_yes=True):
# ---------------------------------------------
def HfLogin(access_token) -> str:
def HfLogin(access_token) -> None:
"""
Helper for logging in to Huggingface
The stdout capture is needed to hide the irrelevant "git credential helper" warning
@ -162,7 +162,7 @@ def HfLogin(access_token) -> str:
# -------------------------------------
class ProgressBar:
def __init__(self, model_name="file"):
def __init__(self, model_name: str = "file"):
self.pbar = None
self.name = model_name
@ -179,6 +179,22 @@ class ProgressBar:
self.pbar.update(block_size)
# ---------------------------------------------
def hf_download_from_pretrained(model_class: Type[ModelMixin], model_name: str, destination: Path, **kwargs: Any):
filter = lambda x: "fp16 is not a valid" not in x.getMessage() # noqa E731
logger.addFilter(filter)
try:
model = model_class.from_pretrained(
model_name,
resume_download=True,
**kwargs,
)
model.save_pretrained(destination, safe_serialization=True)
finally:
logger.removeFilter(filter)
return destination
# ---------------------------------------------
def download_with_progress_bar(model_url: str, model_dest: str, label: str = "the"):
try:
@ -249,6 +265,7 @@ def download_conversion_models():
# ---------------------------------------------
# TO DO: use the download queue here.
def download_realesrgan():
logger.info("Installing ESRGAN Upscaling models...")
URLs = [
@ -288,18 +305,19 @@ def download_lama():
# ---------------------------------------------
def download_support_models():
def download_support_models() -> None:
download_realesrgan()
download_lama()
download_conversion_models()
# -------------------------------------
def get_root(root: str = None) -> str:
def get_root(root: Optional[str] = None) -> str:
if root:
return root
elif os.environ.get("INVOKEAI_ROOT"):
return os.environ.get("INVOKEAI_ROOT")
elif root := os.environ.get("INVOKEAI_ROOT"):
assert root is not None
return root
else:
return str(config.root_path)
@ -455,6 +473,25 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
max_width=110,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Model disk conversion cache size (GB). This is used to cache safetensors files that need to be converted to diffusers..",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.disk = self.add_widget_intelligent(
npyscreen.Slider,
value=clip(old_opts.convert_cache, range=(0, 100), step=0.5),
out_of=100,
lowest=0.0,
step=0.5,
relx=8,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Model RAM cache size (GB). Make this at least large enough to hold a single full model (2GB for SD-1, 6GB for SDXL).",
@ -495,6 +532,14 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
)
else:
self.vram = DummyWidgetValue.zero
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Location of the database used to store model path and configuration information:",
editable=False,
color="CONTROL",
)
self.nextrely += 1
self.outdir = self.add_widget_intelligent(
FileBox,
@ -506,19 +551,21 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
labelColor="GOOD",
begin_entry_at=40,
max_height=3,
max_width=127,
scroll_exit=True,
)
self.autoimport_dirs = {}
self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
FileBox,
name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
value=str(config.root_path / config.autoimport_dir),
name="Optional folder to scan for new checkpoints, ControlNets, LoRAs and TI models",
value=str(config.root_path / config.autoimport_dir) if config.autoimport_dir else "",
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=32,
max_height=3,
max_width=127,
scroll_exit=True,
)
self.nextrely += 1
@ -555,6 +602,10 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
self.attention_slice_label.hidden = not show
self.attention_slice_size.hidden = not show
def show_hide_model_conf_override(self, value):
self.model_conf_override.hidden = value
self.model_conf_override.display()
def on_ok(self):
options = self.marshall_arguments()
if self.validate_field_values(options):
@ -584,18 +635,21 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
else:
return True
def marshall_arguments(self):
def marshall_arguments(self) -> Namespace:
new_opts = Namespace()
for attr in [
"ram",
"vram",
"convert_cache",
"outdir",
]:
if hasattr(self, attr):
setattr(new_opts, attr, getattr(self, attr).value)
for attr in self.autoimport_dirs:
if not self.autoimport_dirs[attr].value:
continue
directory = Path(self.autoimport_dirs[attr].value)
if directory.is_relative_to(config.root_path):
directory = directory.relative_to(config.root_path)
@ -615,13 +669,14 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
class EditOptApplication(npyscreen.NPSAppManaged):
def __init__(self, program_opts: Namespace, invokeai_opts: Namespace):
def __init__(self, program_opts: Namespace, invokeai_opts: InvokeAIAppConfig, install_helper: InstallHelper):
super().__init__()
self.program_opts = program_opts
self.invokeai_opts = invokeai_opts
self.user_cancelled = False
self.autoload_pending = True
self.install_selections = default_user_selections(program_opts)
self.install_helper = install_helper
self.install_selections = default_user_selections(program_opts, install_helper)
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
@ -640,16 +695,10 @@ class EditOptApplication(npyscreen.NPSAppManaged):
cycle_widgets=False,
)
def new_opts(self):
def new_opts(self) -> Namespace:
return self.options.marshall_arguments()
def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Namespace:
editApp = EditOptApplication(program_opts, invokeai_opts)
editApp.run()
return editApp.new_opts()
def default_ramcache() -> float:
"""Run a heuristic for the default RAM cache based on installed RAM."""
@ -660,27 +709,18 @@ def default_ramcache() -> float:
) # 2.1 is just large enough for sd 1.5 ;-)
def default_startup_options(init_file: Path) -> Namespace:
def default_startup_options(init_file: Path) -> InvokeAIAppConfig:
opts = InvokeAIAppConfig.get_config()
opts.ram = opts.ram or default_ramcache()
opts.ram = default_ramcache()
return opts
def default_user_selections(program_opts: Namespace) -> InstallSelections:
try:
installer = ModelInstall(config)
except omegaconf.errors.ConfigKeyError:
logger.warning("Your models.yaml file is corrupt or out of date. Reinitializing")
initialize_rootdir(config.root_path, True)
installer = ModelInstall(config)
models = installer.all_models()
def default_user_selections(program_opts: Namespace, install_helper: InstallHelper) -> InstallSelections:
default_model = install_helper.default_model()
assert default_model is not None
default_models = [default_model] if program_opts.default_only else install_helper.recommended_models()
return InstallSelections(
install_models=[models[installer.default_model()].path or models[installer.default_model()].repo_id]
if program_opts.default_only
else [models[x].path or models[x].repo_id for x in installer.recommended_models()]
if program_opts.yes_to_all
else [],
install_models=default_models if program_opts.yes_to_all else [],
)
@ -716,21 +756,10 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
path.mkdir(parents=True, exist_ok=True)
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"}}))
# -------------------------------------
def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
def run_console_ui(
program_opts: Namespace, initfile: Path, install_helper: InstallHelper
) -> Tuple[Optional[Namespace], Optional[InstallSelections]]:
invokeai_opts = default_startup_options(initfile)
invokeai_opts.root = program_opts.root
@ -739,22 +768,16 @@ def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace
"Could not increase terminal size. Try running again with a larger window or smaller font size."
)
# the install-models application spawns a subprocess to install
# models, and will crash unless this is set before running.
import torch
torch.multiprocessing.set_start_method("spawn")
editApp = EditOptApplication(program_opts, invokeai_opts)
editApp = EditOptApplication(program_opts, invokeai_opts, install_helper)
editApp.run()
if editApp.user_cancelled:
return (None, None)
else:
return (editApp.new_opts, editApp.install_selections)
return (editApp.new_opts(), editApp.install_selections)
# -------------------------------------
def write_opts(opts: Namespace, init_file: Path):
def write_opts(opts: InvokeAIAppConfig, init_file: Path) -> None:
"""
Update the invokeai.yaml file with values from current settings.
"""
@ -762,7 +785,7 @@ def write_opts(opts: Namespace, init_file: Path):
new_config = InvokeAIAppConfig.get_config()
new_config.root = config.root
for key, value in opts.__dict__.items():
for key, value in opts.model_dump().items():
if hasattr(new_config, key):
setattr(new_config, key, value)
@ -779,7 +802,7 @@ def default_output_dir() -> Path:
# -------------------------------------
def write_default_options(program_opts: Namespace, initfile: Path):
def write_default_options(program_opts: Namespace, initfile: Path) -> None:
opt = default_startup_options(initfile)
write_opts(opt, initfile)
@ -789,16 +812,11 @@ def write_default_options(program_opts: Namespace, initfile: Path):
# the legacy Args object in order to parse
# the old init file and write out the new
# yaml format.
def migrate_init_file(legacy_format: Path):
def migrate_init_file(legacy_format: Path) -> None:
old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
new = InvokeAIAppConfig.get_config()
fields = [
x
for x, y in InvokeAIAppConfig.model_fields.items()
if (y.json_schema_extra.get("category", None) if y.json_schema_extra else None) != "DEPRECATED"
]
for attr in fields:
for attr in InvokeAIAppConfig.model_fields.keys():
if hasattr(old, attr):
try:
setattr(new, attr, getattr(old, attr))
@ -819,7 +837,7 @@ def migrate_init_file(legacy_format: Path):
# -------------------------------------
def migrate_models(root: Path):
def migrate_models(root: Path) -> None:
from invokeai.backend.install.migrate_to_3 import do_migrate
do_migrate(root, root)
@ -838,7 +856,9 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
):
logger.info("** Migrating invokeai.init to invokeai.yaml")
migrate_init_file(old_init_file)
config.parse_args(argv=[], conf=OmegaConf.load(new_init_file))
omegaconf = OmegaConf.load(new_init_file)
assert isinstance(omegaconf, DictConfig)
config.parse_args(argv=[], conf=omegaconf)
if old_hub.exists():
migrate_models(config.root_path)
@ -849,7 +869,7 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
# -------------------------------------
def main() -> None:
def main():
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
parser.add_argument(
"--skip-sd-weights",
@ -908,6 +928,7 @@ def main() -> None:
if opt.full_precision:
invoke_args.extend(["--precision", "float32"])
config.parse_args(invoke_args)
config.precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
logger = InvokeAILogger().get_logger(config=config)
errors = set()
@ -921,14 +942,18 @@ def main() -> None:
# 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)
# this will initialize the models.yaml file if not present
install_helper = InstallHelper(config, logger)
models_to_download = default_user_selections(opt, install_helper)
new_init_file = config.root_path / "invokeai.yaml"
if opt.yes_to_all:
write_default_options(opt, new_init_file)
init_options = Namespace(precision="float32" if opt.full_precision else "float16")
else:
init_options, models_to_download = run_console_ui(opt, new_init_file)
init_options, models_to_download = run_console_ui(opt, new_init_file, install_helper)
if init_options:
write_opts(init_options, new_init_file)
else:
@ -943,10 +968,12 @@ def main() -> None:
if opt.skip_sd_weights:
logger.warning("Skipping diffusion weights download per user request")
elif models_to_download:
process_and_execute(opt, models_to_download)
install_helper.add_or_delete(models_to_download)
postscript(errors=errors)
if not opt.yes_to_all:
input("Press any key to continue...")
except WindowTooSmallException as e:

2
invokeai/backend/model_manager/load/load_default.py
View File

@ -19,7 +19,7 @@ from invokeai.backend.model_manager import (
)
from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats, ModelCacheBase, ModelLockerBase
from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data, calc_model_size_by_fs
from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
from invokeai.backend.util.devices import choose_torch_device, torch_dtype

6
invokeai/backend/util/devices.py
View File

@ -1,7 +1,7 @@
from __future__ import annotations
from contextlib import nullcontext
from typing import Optional, Union
from typing import Literal, Optional, Union
import torch
from torch import autocast
@ -31,7 +31,9 @@ def choose_torch_device() -> torch.device:
# We are in transition here from using a single global AppConfig to allowing multiple
# configurations. It is strongly recommended to pass the app_config to this function.
def choose_precision(device: torch.device, app_config: Optional[InvokeAIAppConfig] = None) -> str:
def choose_precision(
device: torch.device, app_config: Optional[InvokeAIAppConfig] = None
) -> Literal["float32", "float16", "bfloat16"]:
"""Return an appropriate precision for the given torch device."""
app_config = app_config or config
if device.type == "cuda":

106
invokeai/configs/INITIAL_MODELS.yaml
View File

@ -1,153 +1,157 @@
# This file predefines a few models that the user may want to install.
sd-1/main/stable-diffusion-v1-5:
description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
repo_id: runwayml/stable-diffusion-v1-5
source: runwayml/stable-diffusion-v1-5
recommended: True
default: True
sd-1/main/stable-diffusion-v1-5-inpainting:
description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
source: runwayml/stable-diffusion-inpainting
recommended: True
sd-2/main/stable-diffusion-2-1:
description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
source: stabilityai/stable-diffusion-2-1
recommended: False
sd-2/main/stable-diffusion-2-inpainting:
description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-inpainting
source: stabilityai/stable-diffusion-2-inpainting
recommended: False
sdxl/main/stable-diffusion-xl-base-1-0:
description: Stable Diffusion XL base model (12 GB)
repo_id: stabilityai/stable-diffusion-xl-base-1.0
source: stabilityai/stable-diffusion-xl-base-1.0
recommended: True
sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
description: Stable Diffusion XL refiner model (12 GB)
repo_id: stabilityai/stable-diffusion-xl-refiner-1.0
source: stabilityai/stable-diffusion-xl-refiner-1.0
recommended: False
sdxl/vae/sdxl-1-0-vae-fix:
description: Fine tuned version of the SDXL-1.0 VAE
repo_id: madebyollin/sdxl-vae-fp16-fix
sdxl/vae/sdxl-vae-fp16-fix:
description: Version of the SDXL-1.0 VAE that works in half precision mode
source: madebyollin/sdxl-vae-fp16-fix
recommended: True
sd-1/main/Analog-Diffusion:
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
repo_id: wavymulder/Analog-Diffusion
source: wavymulder/Analog-Diffusion
recommended: False
sd-1/main/Deliberate_v5:
sd-1/main/Deliberate:
description: Versatile model that produces detailed images up to 768px (4.27 GB)
path: https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors
source: XpucT/Deliberate
recommended: False
sd-1/main/Dungeons-and-Diffusion:
description: Dungeons & Dragons characters (2.13 GB)
repo_id: 0xJustin/Dungeons-and-Diffusion
source: 0xJustin/Dungeons-and-Diffusion
recommended: False
sd-1/main/dreamlike-photoreal-2:
description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
repo_id: dreamlike-art/dreamlike-photoreal-2.0
source: dreamlike-art/dreamlike-photoreal-2.0
recommended: False
sd-1/main/Inkpunk-Diffusion:
description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
repo_id: Envvi/Inkpunk-Diffusion
source: Envvi/Inkpunk-Diffusion
recommended: False
sd-1/main/openjourney:
description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
repo_id: prompthero/openjourney
source: prompthero/openjourney
recommended: False
sd-1/main/seek.art_MEGA:
repo_id: coreco/seek.art_MEGA
source: coreco/seek.art_MEGA
description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
recommended: False
sd-1/main/trinart_stable_diffusion_v2:
description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
repo_id: naclbit/trinart_stable_diffusion_v2
source: naclbit/trinart_stable_diffusion_v2
recommended: False
sd-1/controlnet/qrcode_monster:
repo_id: monster-labs/control_v1p_sd15_qrcode_monster
source: monster-labs/control_v1p_sd15_qrcode_monster
subfolder: v2
sd-1/controlnet/canny:
repo_id: lllyasviel/control_v11p_sd15_canny
source: lllyasviel/control_v11p_sd15_canny
recommended: True
sd-1/controlnet/inpaint:
repo_id: lllyasviel/control_v11p_sd15_inpaint
source: lllyasviel/control_v11p_sd15_inpaint
sd-1/controlnet/mlsd:
repo_id: lllyasviel/control_v11p_sd15_mlsd
source: lllyasviel/control_v11p_sd15_mlsd
sd-1/controlnet/depth:
repo_id: lllyasviel/control_v11f1p_sd15_depth
source: lllyasviel/control_v11f1p_sd15_depth
recommended: True
sd-1/controlnet/normal_bae:
repo_id: lllyasviel/control_v11p_sd15_normalbae
source: lllyasviel/control_v11p_sd15_normalbae
sd-1/controlnet/seg:
repo_id: lllyasviel/control_v11p_sd15_seg
source: lllyasviel/control_v11p_sd15_seg
sd-1/controlnet/lineart:
repo_id: lllyasviel/control_v11p_sd15_lineart
source: lllyasviel/control_v11p_sd15_lineart
recommended: True
sd-1/controlnet/lineart_anime:
repo_id: lllyasviel/control_v11p_sd15s2_lineart_anime
source: lllyasviel/control_v11p_sd15s2_lineart_anime
sd-1/controlnet/openpose:
repo_id: lllyasviel/control_v11p_sd15_openpose
source: lllyasviel/control_v11p_sd15_openpose
recommended: True
sd-1/controlnet/scribble:
repo_id: lllyasviel/control_v11p_sd15_scribble
source: lllyasviel/control_v11p_sd15_scribble
recommended: False
sd-1/controlnet/softedge:
repo_id: lllyasviel/control_v11p_sd15_softedge
source: lllyasviel/control_v11p_sd15_softedge
sd-1/controlnet/shuffle:
repo_id: lllyasviel/control_v11e_sd15_shuffle
source: lllyasviel/control_v11e_sd15_shuffle
sd-1/controlnet/tile:
repo_id: lllyasviel/control_v11f1e_sd15_tile
source: lllyasviel/control_v11f1e_sd15_tile
sd-1/controlnet/ip2p:
repo_id: lllyasviel/control_v11e_sd15_ip2p
source: lllyasviel/control_v11e_sd15_ip2p
sd-1/t2i_adapter/canny-sd15:
repo_id: TencentARC/t2iadapter_canny_sd15v2
source: TencentARC/t2iadapter_canny_sd15v2
sd-1/t2i_adapter/sketch-sd15:
repo_id: TencentARC/t2iadapter_sketch_sd15v2
source: TencentARC/t2iadapter_sketch_sd15v2
sd-1/t2i_adapter/depth-sd15:
repo_id: TencentARC/t2iadapter_depth_sd15v2
source: TencentARC/t2iadapter_depth_sd15v2
sd-1/t2i_adapter/zoedepth-sd15:
repo_id: TencentARC/t2iadapter_zoedepth_sd15v1
source: TencentARC/t2iadapter_zoedepth_sd15v1
sdxl/t2i_adapter/canny-sdxl:
repo_id: TencentARC/t2i-adapter-canny-sdxl-1.0
source: TencentARC/t2i-adapter-canny-sdxl-1.0
sdxl/t2i_adapter/zoedepth-sdxl:
repo_id: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
source: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
sdxl/t2i_adapter/lineart-sdxl:
repo_id: TencentARC/t2i-adapter-lineart-sdxl-1.0
source: TencentARC/t2i-adapter-lineart-sdxl-1.0
sdxl/t2i_adapter/sketch-sdxl:
repo_id: TencentARC/t2i-adapter-sketch-sdxl-1.0
source: TencentARC/t2i-adapter-sketch-sdxl-1.0
sd-1/embedding/EasyNegative:
path: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
source: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
recommended: True
sd-1/embedding/ahx-beta-453407d:
repo_id: sd-concepts-library/ahx-beta-453407d
description: A textual inversion to use in the negative prompt to reduce bad anatomy
sd-1/lora/FlatColor:
source: https://civitai.com/models/6433/loraflatcolor
recommended: True
description: A LoRA that generates scenery using solid blocks of color
sd-1/lora/Ink scenery:
path: https://civitai.com/api/download/models/83390
source: https://civitai.com/api/download/models/83390
description: Generate india ink-like landscapes
sd-1/ip_adapter/ip_adapter_sd15:
repo_id: InvokeAI/ip_adapter_sd15
source: InvokeAI/ip_adapter_sd15
recommended: True
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: IP-Adapter for SD 1.5 models
sd-1/ip_adapter/ip_adapter_plus_sd15:
repo_id: InvokeAI/ip_adapter_plus_sd15
source: InvokeAI/ip_adapter_plus_sd15
recommended: False
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: Refined IP-Adapter for SD 1.5 models
sd-1/ip_adapter/ip_adapter_plus_face_sd15:
repo_id: InvokeAI/ip_adapter_plus_face_sd15
source: InvokeAI/ip_adapter_plus_face_sd15
recommended: False
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: Refined IP-Adapter for SD 1.5 models, adapted for faces
sdxl/ip_adapter/ip_adapter_sdxl:
repo_id: InvokeAI/ip_adapter_sdxl
source: InvokeAI/ip_adapter_sdxl
recommended: False
requires:
- InvokeAI/ip_adapter_sdxl_image_encoder
description: IP-Adapter for SDXL models
any/clip_vision/ip_adapter_sd_image_encoder:
repo_id: InvokeAI/ip_adapter_sd_image_encoder
source: InvokeAI/ip_adapter_sd_image_encoder
recommended: False
description: Required model for using IP-Adapters with SD-1/2 models
any/clip_vision/ip_adapter_sdxl_image_encoder:
repo_id: InvokeAI/ip_adapter_sdxl_image_encoder
source: InvokeAI/ip_adapter_sdxl_image_encoder
recommended: False
description: Required model for using IP-Adapters with SDXL models

106
invokeai/configs/INITIAL_MODELS2.yaml → invokeai/configs/INITIAL_MODELS.yaml.OLD
View File

@ -1,157 +1,153 @@
# This file predefines a few models that the user may want to install.
sd-1/main/stable-diffusion-v1-5:
description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
source: runwayml/stable-diffusion-v1-5
repo_id: runwayml/stable-diffusion-v1-5
recommended: True
default: True
sd-1/main/stable-diffusion-v1-5-inpainting:
description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
source: runwayml/stable-diffusion-inpainting
repo_id: runwayml/stable-diffusion-inpainting
recommended: True
sd-2/main/stable-diffusion-2-1:
description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
source: stabilityai/stable-diffusion-2-1
repo_id: stabilityai/stable-diffusion-2-1
recommended: False
sd-2/main/stable-diffusion-2-inpainting:
description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
source: stabilityai/stable-diffusion-2-inpainting
repo_id: stabilityai/stable-diffusion-2-inpainting
recommended: False
sdxl/main/stable-diffusion-xl-base-1-0:
description: Stable Diffusion XL base model (12 GB)
source: stabilityai/stable-diffusion-xl-base-1.0
repo_id: stabilityai/stable-diffusion-xl-base-1.0
recommended: True
sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
description: Stable Diffusion XL refiner model (12 GB)
source: stabilityai/stable-diffusion-xl-refiner-1.0
repo_id: stabilityai/stable-diffusion-xl-refiner-1.0
recommended: False
sdxl/vae/sdxl-vae-fp16-fix:
description: Version of the SDXL-1.0 VAE that works in half precision mode
source: madebyollin/sdxl-vae-fp16-fix
sdxl/vae/sdxl-1-0-vae-fix:
description: Fine tuned version of the SDXL-1.0 VAE
repo_id: madebyollin/sdxl-vae-fp16-fix
recommended: True
sd-1/main/Analog-Diffusion:
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
source: wavymulder/Analog-Diffusion
repo_id: wavymulder/Analog-Diffusion
recommended: False
sd-1/main/Deliberate:
sd-1/main/Deliberate_v5:
description: Versatile model that produces detailed images up to 768px (4.27 GB)
source: XpucT/Deliberate
path: https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors
recommended: False
sd-1/main/Dungeons-and-Diffusion:
description: Dungeons & Dragons characters (2.13 GB)
source: 0xJustin/Dungeons-and-Diffusion
repo_id: 0xJustin/Dungeons-and-Diffusion
recommended: False
sd-1/main/dreamlike-photoreal-2:
description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
source: dreamlike-art/dreamlike-photoreal-2.0
repo_id: dreamlike-art/dreamlike-photoreal-2.0
recommended: False
sd-1/main/Inkpunk-Diffusion:
description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
source: Envvi/Inkpunk-Diffusion
repo_id: Envvi/Inkpunk-Diffusion
recommended: False
sd-1/main/openjourney:
description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
source: prompthero/openjourney
repo_id: prompthero/openjourney
recommended: False
sd-1/main/seek.art_MEGA:
source: coreco/seek.art_MEGA
repo_id: coreco/seek.art_MEGA
description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
recommended: False
sd-1/main/trinart_stable_diffusion_v2:
description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
source: naclbit/trinart_stable_diffusion_v2
repo_id: naclbit/trinart_stable_diffusion_v2
recommended: False
sd-1/controlnet/qrcode_monster:
source: monster-labs/control_v1p_sd15_qrcode_monster
repo_id: monster-labs/control_v1p_sd15_qrcode_monster
subfolder: v2
sd-1/controlnet/canny:
source: lllyasviel/control_v11p_sd15_canny
repo_id: lllyasviel/control_v11p_sd15_canny
recommended: True
sd-1/controlnet/inpaint:
source: lllyasviel/control_v11p_sd15_inpaint
repo_id: lllyasviel/control_v11p_sd15_inpaint
sd-1/controlnet/mlsd:
source: lllyasviel/control_v11p_sd15_mlsd
repo_id: lllyasviel/control_v11p_sd15_mlsd
sd-1/controlnet/depth:
source: lllyasviel/control_v11f1p_sd15_depth
repo_id: lllyasviel/control_v11f1p_sd15_depth
recommended: True
sd-1/controlnet/normal_bae:
source: lllyasviel/control_v11p_sd15_normalbae
repo_id: lllyasviel/control_v11p_sd15_normalbae
sd-1/controlnet/seg:
source: lllyasviel/control_v11p_sd15_seg
repo_id: lllyasviel/control_v11p_sd15_seg
sd-1/controlnet/lineart:
source: lllyasviel/control_v11p_sd15_lineart
repo_id: lllyasviel/control_v11p_sd15_lineart
recommended: True
sd-1/controlnet/lineart_anime:
source: lllyasviel/control_v11p_sd15s2_lineart_anime
repo_id: lllyasviel/control_v11p_sd15s2_lineart_anime
sd-1/controlnet/openpose:
source: lllyasviel/control_v11p_sd15_openpose
repo_id: lllyasviel/control_v11p_sd15_openpose
recommended: True
sd-1/controlnet/scribble:
source: lllyasviel/control_v11p_sd15_scribble
repo_id: lllyasviel/control_v11p_sd15_scribble
recommended: False
sd-1/controlnet/softedge:
source: lllyasviel/control_v11p_sd15_softedge
repo_id: lllyasviel/control_v11p_sd15_softedge
sd-1/controlnet/shuffle:
source: lllyasviel/control_v11e_sd15_shuffle
repo_id: lllyasviel/control_v11e_sd15_shuffle
sd-1/controlnet/tile:
source: lllyasviel/control_v11f1e_sd15_tile
repo_id: lllyasviel/control_v11f1e_sd15_tile
sd-1/controlnet/ip2p:
source: lllyasviel/control_v11e_sd15_ip2p
repo_id: lllyasviel/control_v11e_sd15_ip2p
sd-1/t2i_adapter/canny-sd15:
source: TencentARC/t2iadapter_canny_sd15v2
repo_id: TencentARC/t2iadapter_canny_sd15v2
sd-1/t2i_adapter/sketch-sd15:
source: TencentARC/t2iadapter_sketch_sd15v2
repo_id: TencentARC/t2iadapter_sketch_sd15v2
sd-1/t2i_adapter/depth-sd15:
source: TencentARC/t2iadapter_depth_sd15v2
repo_id: TencentARC/t2iadapter_depth_sd15v2
sd-1/t2i_adapter/zoedepth-sd15:
source: TencentARC/t2iadapter_zoedepth_sd15v1
repo_id: TencentARC/t2iadapter_zoedepth_sd15v1
sdxl/t2i_adapter/canny-sdxl:
source: TencentARC/t2i-adapter-canny-sdxl-1.0
repo_id: TencentARC/t2i-adapter-canny-sdxl-1.0
sdxl/t2i_adapter/zoedepth-sdxl:
source: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
repo_id: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
sdxl/t2i_adapter/lineart-sdxl:
source: TencentARC/t2i-adapter-lineart-sdxl-1.0
repo_id: TencentARC/t2i-adapter-lineart-sdxl-1.0
sdxl/t2i_adapter/sketch-sdxl:
source: TencentARC/t2i-adapter-sketch-sdxl-1.0
repo_id: TencentARC/t2i-adapter-sketch-sdxl-1.0
sd-1/embedding/EasyNegative:
source: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
path: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
recommended: True
description: A textual inversion to use in the negative prompt to reduce bad anatomy
sd-1/lora/FlatColor:
source: https://civitai.com/models/6433/loraflatcolor
recommended: True
description: A LoRA that generates scenery using solid blocks of color
sd-1/embedding/ahx-beta-453407d:
repo_id: sd-concepts-library/ahx-beta-453407d
sd-1/lora/Ink scenery:
source: https://civitai.com/api/download/models/83390
description: Generate india ink-like landscapes
path: https://civitai.com/api/download/models/83390
sd-1/ip_adapter/ip_adapter_sd15:
source: InvokeAI/ip_adapter_sd15
repo_id: InvokeAI/ip_adapter_sd15
recommended: True
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: IP-Adapter for SD 1.5 models
sd-1/ip_adapter/ip_adapter_plus_sd15:
source: InvokeAI/ip_adapter_plus_sd15
repo_id: InvokeAI/ip_adapter_plus_sd15
recommended: False
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: Refined IP-Adapter for SD 1.5 models
sd-1/ip_adapter/ip_adapter_plus_face_sd15:
source: InvokeAI/ip_adapter_plus_face_sd15
repo_id: InvokeAI/ip_adapter_plus_face_sd15
recommended: False
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: Refined IP-Adapter for SD 1.5 models, adapted for faces
sdxl/ip_adapter/ip_adapter_sdxl:
source: InvokeAI/ip_adapter_sdxl
repo_id: InvokeAI/ip_adapter_sdxl
recommended: False
requires:
- InvokeAI/ip_adapter_sdxl_image_encoder
description: IP-Adapter for SDXL models
any/clip_vision/ip_adapter_sd_image_encoder:
source: InvokeAI/ip_adapter_sd_image_encoder
repo_id: InvokeAI/ip_adapter_sd_image_encoder
recommended: False
description: Required model for using IP-Adapters with SD-1/2 models
any/clip_vision/ip_adapter_sdxl_image_encoder:
source: InvokeAI/ip_adapter_sdxl_image_encoder
repo_id: InvokeAI/ip_adapter_sdxl_image_encoder
recommended: False
description: Required model for using IP-Adapters with SDXL models

444
invokeai/frontend/install/model_install.py
View File

@ -6,47 +6,45 @@
"""
This is the npyscreen frontend to the model installation application.
The work is actually done in backend code in model_install_backend.py.
It is currently named model_install2.py, but will ultimately replace model_install.py.
"""
import argparse
import curses
import logging
import sys
import textwrap
import traceback
import warnings
from argparse import Namespace
from multiprocessing import Process
from multiprocessing.connection import Connection, Pipe
from pathlib import Path
from shutil import get_terminal_size
from typing import Optional
from typing import Any, Dict, List, Optional, Set
import npyscreen
import torch
from npyscreen import widget
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, SchedulerPredictionType
from invokeai.backend.model_management import ModelManager, ModelType
from invokeai.app.services.model_install import ModelInstallServiceBase
from invokeai.backend.install.install_helper import InstallHelper, InstallSelections, UnifiedModelInfo
from invokeai.backend.model_manager import ModelType
from invokeai.backend.util import choose_precision, choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.widgets import (
MIN_COLS,
MIN_LINES,
BufferBox,
CenteredTitleText,
CyclingForm,
MultiSelectColumns,
SingleSelectColumns,
TextBox,
WindowTooSmallException,
select_stable_diffusion_config_file,
set_min_terminal_size,
)
warnings.filterwarnings("ignore", category=UserWarning) # noqa: E402
config = InvokeAIAppConfig.get_config()
logger = InvokeAILogger.get_logger()
logger = InvokeAILogger.get_logger("ModelInstallService")
logger.setLevel("WARNING")
# logger.setLevel('DEBUG')
# build a table mapping all non-printable characters to None
# for stripping control characters
@ -58,44 +56,42 @@ MAX_OTHER_MODELS = 72
def make_printable(s: str) -> str:
"""Replace non-printable characters in a string"""
"""Replace non-printable characters in a string."""
return s.translate(NOPRINT_TRANS_TABLE)
class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
"""Main form for interactive TUI."""
# for responsive resizing set to False, but this seems to cause a crash!
FIX_MINIMUM_SIZE_WHEN_CREATED = True
# for persistence
current_tab = 0
def __init__(self, parentApp, name, multipage=False, *args, **keywords):
def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, multipage: bool = False, **keywords: Any):
self.multipage = multipage
self.subprocess = None
super().__init__(parentApp=parentApp, name=name, *args, **keywords) # noqa: B026 # TODO: maybe this is bad?
super().__init__(parentApp=parentApp, name=name, **keywords)
def create(self):
def create(self) -> None:
self.installer = self.parentApp.install_helper.installer
self.model_labels = self._get_model_labels()
self.keypress_timeout = 10
self.counter = 0
self.subprocess_connection = None
if not config.model_conf_path.exists():
with open(config.model_conf_path, "w") as file:
print("# InvokeAI model configuration file", file=file)
self.installer = ModelInstall(config)
self.all_models = self.installer.all_models()
self.starter_models = self.installer.starter_models()
self.model_labels = self._get_model_labels()
window_width, window_height = get_terminal_size()
self.nextrely -= 1
# npyscreen has no typing hints
self.nextrely -= 1 # type: ignore
self.add_widget_intelligent(
npyscreen.FixedText,
value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
editable=False,
color="CAUTION",
)
self.nextrely += 1
self.nextrely += 1 # type: ignore
self.tabs = self.add_widget_intelligent(
SingleSelectColumns,
values=[
@ -115,9 +111,9 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
)
self.tabs.on_changed = self._toggle_tables
top_of_table = self.nextrely
top_of_table = self.nextrely # type: ignore
self.starter_pipelines = self.add_starter_pipelines()
bottom_of_table = self.nextrely
bottom_of_table = self.nextrely # type: ignore
self.nextrely = top_of_table
self.pipeline_models = self.add_pipeline_widgets(
@ -162,15 +158,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.nextrely = bottom_of_table + 1
self.monitor = self.add_widget_intelligent(
BufferBox,
name="Log Messages",
editable=False,
max_height=6,
)
self.nextrely += 1
done_label = "APPLY CHANGES"
back_label = "BACK"
cancel_label = "CANCEL"
current_position = self.nextrely
@ -186,14 +174,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
)
self.nextrely = current_position
self.ok_button = self.add_widget_intelligent(
npyscreen.ButtonPress,
name=done_label,
relx=(window_width - len(done_label)) // 2,
when_pressed_function=self.on_execute,
)
label = "APPLY CHANGES & EXIT"
label = "APPLY CHANGES"
self.nextrely = current_position
self.done = self.add_widget_intelligent(
npyscreen.ButtonPress,
@ -210,17 +192,16 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
############# diffusers tab ##########
def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
"""Add widgets responsible for selecting diffusers models"""
widgets = {}
models = self.all_models
starters = self.starter_models
starter_model_labels = self.model_labels
widgets: Dict[str, npyscreen.widget] = {}
self.installed_models = sorted([x for x in starters if models[x].installed])
all_models = self.all_models # master dict of all models, indexed by key
model_list = [x for x in self.starter_models if all_models[x].type in ["main", "vae"]]
model_labels = [self.model_labels[x] for x in model_list]
widgets.update(
label1=self.add_widget_intelligent(
CenteredTitleText,
name="Select from a starter set of Stable Diffusion models from HuggingFace.",
name="Select from a starter set of Stable Diffusion models from HuggingFace and Civitae.",
editable=False,
labelColor="CAUTION",
)
@ -230,23 +211,24 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
# if user has already installed some initial models, then don't patronize them
# by showing more recommendations
show_recommended = len(self.installed_models) == 0
keys = [x for x in models.keys() if x in starters]
checked = [
model_list.index(x)
for x in model_list
if (show_recommended and all_models[x].recommended) or all_models[x].installed
]
widgets.update(
models_selected=self.add_widget_intelligent(
MultiSelectColumns,
columns=1,
name="Install Starter Models",
values=[starter_model_labels[x] for x in keys],
value=[
keys.index(x)
for x in keys
if (show_recommended and models[x].recommended) or (x in self.installed_models)
],
max_height=len(starters) + 1,
values=model_labels,
value=checked,
max_height=len(model_list) + 1,
relx=4,
scroll_exit=True,
),
models=keys,
models=model_list,
)
self.nextrely += 1
@ -257,14 +239,18 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self,
model_type: ModelType,
window_width: int = 120,
install_prompt: str = None,
exclude: set = None,
install_prompt: Optional[str] = None,
exclude: Optional[Set[str]] = None,
) -> dict[str, npyscreen.widget]:
"""Generic code to create model selection widgets"""
if exclude is None:
exclude = set()
widgets = {}
model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and x not in exclude]
widgets: Dict[str, npyscreen.widget] = {}
all_models = self.all_models
model_list = sorted(
[x for x in all_models if all_models[x].type == model_type and x not in exclude],
key=lambda x: all_models[x].name or "",
)
model_labels = [self.model_labels[x] for x in model_list]
show_recommended = len(self.installed_models) == 0
@ -300,7 +286,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
value=[
model_list.index(x)
for x in model_list
if (show_recommended and self.all_models[x].recommended) or self.all_models[x].installed
if (show_recommended and all_models[x].recommended) or all_models[x].installed
],
max_height=len(model_list) // columns + 1,
relx=4,
@ -324,7 +310,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
download_ids=self.add_widget_intelligent(
TextBox,
name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
max_height=4,
max_height=6,
scroll_exit=True,
editable=True,
)
@ -349,13 +335,13 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
return widgets
def resize(self):
def resize(self) -> None:
super().resize()
if s := self.starter_pipelines.get("models_selected"):
keys = [x for x in self.all_models.keys() if x in self.starter_models]
s.values = [self.model_labels[x] for x in keys]
if model_list := self.starter_pipelines.get("models"):
s.values = [self.model_labels[x] for x in model_list]
def _toggle_tables(self, value=None):
def _toggle_tables(self, value: List[int]) -> None:
selected_tab = value[0]
widgets = [
self.starter_pipelines,
@ -385,17 +371,18 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.display()
def _get_model_labels(self) -> dict[str, str]:
"""Return a list of trimmed labels for all models."""
window_width, window_height = get_terminal_size()
checkbox_width = 4
spacing_width = 2
result = {}
models = self.all_models
label_width = max([len(models[x].name) for x in models])
label_width = max([len(models[x].name or "") for x in self.starter_models])
description_width = window_width - label_width - checkbox_width - spacing_width
result = {}
for x in models.keys():
description = models[x].description
for key in self.all_models:
description = models[key].description
description = (
description[0 : description_width - 3] + "..."
if description and len(description) > description_width
@ -403,7 +390,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
if description
else ""
)
result[x] = f"%-{label_width}s %s" % (models[x].name, description)
result[key] = f"%-{label_width}s %s" % (models[key].name, description)
return result
def _get_columns(self) -> int:
@ -413,50 +401,40 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
def confirm_deletions(self, selections: InstallSelections) -> bool:
remove_models = selections.remove_models
if len(remove_models) > 0:
mods = "\n".join([ModelManager.parse_key(x)[0] for x in remove_models])
return npyscreen.notify_ok_cancel(
if remove_models:
model_names = [self.all_models[x].name or "" for x in remove_models]
mods = "\n".join(model_names)
is_ok = npyscreen.notify_ok_cancel(
f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
)
assert isinstance(is_ok, bool) # npyscreen doesn't have return type annotations
return is_ok
else:
return True
def on_execute(self):
self.marshall_arguments()
app = self.parentApp
if not self.confirm_deletions(app.install_selections):
return
@property
def all_models(self) -> Dict[str, UnifiedModelInfo]:
# npyscreen doesn't having typing hints
return self.parentApp.install_helper.all_models # type: ignore
self.monitor.entry_widget.buffer(["Processing..."], scroll_end=True)
self.ok_button.hidden = True
self.display()
@property
def starter_models(self) -> List[str]:
return self.parentApp.install_helper._starter_models # type: ignore
# TO DO: Spawn a worker thread, not a subprocess
parent_conn, child_conn = Pipe()
p = Process(
target=process_and_execute,
kwargs={
"opt": app.program_opts,
"selections": app.install_selections,
"conn_out": child_conn,
},
)
p.start()
child_conn.close()
self.subprocess_connection = parent_conn
self.subprocess = p
app.install_selections = InstallSelections()
@property
def installed_models(self) -> List[str]:
return self.parentApp.install_helper._installed_models # type: ignore
def on_back(self):
def on_back(self) -> None:
self.parentApp.switchFormPrevious()
self.editing = False
def on_cancel(self):
def on_cancel(self) -> None:
self.parentApp.setNextForm(None)
self.parentApp.user_cancelled = True
self.editing = False
def on_done(self):
def on_done(self) -> None:
self.marshall_arguments()
if not self.confirm_deletions(self.parentApp.install_selections):
return
@ -464,77 +442,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.parentApp.user_cancelled = False
self.editing = False
########## This routine monitors the child process that is performing model installation and removal #####
def while_waiting(self):
"""Called during idle periods. Main task is to update the Log Messages box with messages
from the child process that does the actual installation/removal"""
c = self.subprocess_connection
if not c:
return
monitor_widget = self.monitor.entry_widget
while c.poll():
try:
data = c.recv_bytes().decode("utf-8")
data.strip("\n")
# processing child is requesting user input to select the
# right configuration file
if data.startswith("*need v2 config"):
_, model_path, *_ = data.split(":", 2)
self._return_v2_config(model_path)
# processing child is done
elif data == "*done*":
self._close_subprocess_and_regenerate_form()
break
# update the log message box
else:
data = make_printable(data)
data = data.replace("[A", "")
monitor_widget.buffer(
textwrap.wrap(
data,
width=monitor_widget.width,
subsequent_indent=" ",
),
scroll_end=True,
)
self.display()
except (EOFError, OSError):
self.subprocess_connection = None
def _return_v2_config(self, model_path: str):
c = self.subprocess_connection
model_name = Path(model_path).name
message = select_stable_diffusion_config_file(model_name=model_name)
c.send_bytes(message.encode("utf-8"))
def _close_subprocess_and_regenerate_form(self):
app = self.parentApp
self.subprocess_connection.close()
self.subprocess_connection = None
self.monitor.entry_widget.buffer(["** Action Complete **"])
self.display()
# rebuild the form, saving and restoring some of the fields that need to be preserved.
saved_messages = self.monitor.entry_widget.values
app.main_form = app.addForm(
"MAIN",
addModelsForm,
name="Install Stable Diffusion Models",
multipage=self.multipage,
)
app.switchForm("MAIN")
app.main_form.monitor.entry_widget.values = saved_messages
app.main_form.monitor.entry_widget.buffer([""], scroll_end=True)
# app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
# app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
def marshall_arguments(self):
def marshall_arguments(self) -> None:
"""
Assemble arguments and store as attributes of the application:
.starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml
@ -564,46 +472,24 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
models_to_install = [x for x in selected if not self.all_models[x].installed]
models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
selections.remove_models.extend(models_to_remove)
selections.install_models.extend(
all_models[x].path or all_models[x].repo_id
for x in models_to_install
if all_models[x].path or all_models[x].repo_id
)
selections.install_models.extend([all_models[x] for x in models_to_install])
# models located in the 'download_ids" section
for section in ui_sections:
if downloads := section.get("download_ids"):
selections.install_models.extend(downloads.value.split())
# NOT NEEDED - DONE IN BACKEND NOW
# # special case for the ipadapter_models. If any of the adapters are
# # chosen, then we add the corresponding encoder(s) to the install list.
# section = self.ipadapter_models
# if section.get("models_selected"):
# selected_adapters = [
# self.all_models[section["models"][x]].name for x in section.get("models_selected").value
# ]
# encoders = []
# if any(["sdxl" in x for x in selected_adapters]):
# encoders.append("ip_adapter_sdxl_image_encoder")
# if any(["sd15" in x for x in selected_adapters]):
# encoders.append("ip_adapter_sd_image_encoder")
# for encoder in encoders:
# key = f"any/clip_vision/{encoder}"
# repo_id = f"InvokeAI/{encoder}"
# if key not in self.all_models:
# selections.install_models.append(repo_id)
models = [UnifiedModelInfo(source=x) for x in downloads.value.split()]
selections.install_models.extend(models)
class AddModelApplication(npyscreen.NPSAppManaged):
def __init__(self, opt):
class AddModelApplication(npyscreen.NPSAppManaged): # type: ignore
def __init__(self, opt: Namespace, install_helper: InstallHelper):
super().__init__()
self.program_opts = opt
self.user_cancelled = False
# self.autoload_pending = True
self.install_selections = InstallSelections()
self.install_helper = install_helper
def onStart(self):
def onStart(self) -> None:
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.main_form = self.addForm(
"MAIN",
@ -613,138 +499,62 @@ class AddModelApplication(npyscreen.NPSAppManaged):
)
class StderrToMessage:
def __init__(self, connection: Connection):
self.connection = connection
def write(self, data: str):
self.connection.send_bytes(data.encode("utf-8"))
def flush(self):
pass
def list_models(installer: ModelInstallServiceBase, model_type: ModelType):
"""Print out all models of type model_type."""
models = installer.record_store.search_by_attr(model_type=model_type)
print(f"Installed models of type `{model_type}`:")
for model in models:
path = (config.models_path / model.path).resolve()
print(f"{model.name:40}{model.base.value:5}{model.type.value:8}{model.format.value:12}{path}")
# --------------------------------------------------------
def ask_user_for_prediction_type(model_path: Path, tui_conn: Connection = None) -> SchedulerPredictionType:
if tui_conn:
logger.debug("Waiting for user response...")
return _ask_user_for_pt_tui(model_path, tui_conn)
else:
return _ask_user_for_pt_cmdline(model_path)
def _ask_user_for_pt_cmdline(model_path: Path) -> Optional[SchedulerPredictionType]:
choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
print(
f"""
Please select the scheduler prediction type of the checkpoint named {model_path.name}:
[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
[3] Accept the best guess; you can fix it in the Web UI later
"""
)
choice = None
ok = False
while not ok:
try:
choice = input("select [3]> ").strip()
if not choice:
return None
choice = choices[int(choice) - 1]
ok = True
except (ValueError, IndexError):
print(f"{choice} is not a valid choice")
except EOFError:
return
return choice
def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPredictionType:
tui_conn.send_bytes(f"*need v2 config for:{model_path}".encode("utf-8"))
# note that we don't do any status checking here
response = tui_conn.recv_bytes().decode("utf-8")
if response is None:
return None
elif response == "epsilon":
return SchedulerPredictionType.epsilon
elif response == "v":
return SchedulerPredictionType.VPrediction
elif response == "guess":
return None
else:
return None
# --------------------------------------------------------
def process_and_execute(
opt: Namespace,
selections: InstallSelections,
conn_out: Connection = None,
):
# need to reinitialize config in subprocess
config = InvokeAIAppConfig.get_config()
args = ["--root", opt.root] if opt.root else []
config.parse_args(args)
# set up so that stderr is sent to conn_out
if conn_out:
translator = StderrToMessage(conn_out)
sys.stderr = translator
sys.stdout = translator
logger = InvokeAILogger.get_logger()
logger.handlers.clear()
logger.addHandler(logging.StreamHandler(translator))
installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x, conn_out))
installer.install(selections)
if conn_out:
conn_out.send_bytes("*done*".encode("utf-8"))
conn_out.close()
# --------------------------------------------------------
def select_and_download_models(opt: Namespace):
def select_and_download_models(opt: Namespace) -> None:
"""Prompt user for install/delete selections and execute."""
precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
config.precision = precision
installer = ModelInstall(config, prediction_type_helper=ask_user_for_prediction_type)
# unsure how to avoid a typing complaint in the next line: config.precision is an enumerated Literal
config.precision = precision # type: ignore
install_helper = InstallHelper(config, logger)
installer = install_helper.installer
if opt.list_models:
installer.list_models(opt.list_models)
list_models(installer, opt.list_models)
elif opt.add or opt.delete:
selections = InstallSelections(install_models=opt.add or [], remove_models=opt.delete or [])
installer.install(selections)
selections = InstallSelections(
install_models=[UnifiedModelInfo(source=x) for x in (opt.add or [])], remove_models=opt.delete or []
)
install_helper.add_or_delete(selections)
elif opt.default_only:
selections = InstallSelections(install_models=installer.default_model())
installer.install(selections)
default_model = install_helper.default_model()
assert default_model is not None
selections = InstallSelections(install_models=[default_model])
install_helper.add_or_delete(selections)
elif opt.yes_to_all:
selections = InstallSelections(install_models=installer.recommended_models())
installer.install(selections)
selections = InstallSelections(install_models=install_helper.recommended_models())
install_helper.add_or_delete(selections)
# this is where the TUI is called
else:
# needed to support the probe() method running under a subprocess
torch.multiprocessing.set_start_method("spawn")
if not set_min_terminal_size(MIN_COLS, MIN_LINES):
raise WindowTooSmallException(
"Could not increase terminal size. Try running again with a larger window or smaller font size."
)
installApp = AddModelApplication(opt)
installApp = AddModelApplication(opt, install_helper)
try:
installApp.run()
except KeyboardInterrupt as e:
if hasattr(installApp, "main_form"):
if installApp.main_form.subprocess and installApp.main_form.subprocess.is_alive():
logger.info("Terminating subprocesses")
installApp.main_form.subprocess.terminate()
installApp.main_form.subprocess = None
raise e
process_and_execute(opt, installApp.install_selections)
except KeyboardInterrupt:
print("Aborted...")
sys.exit(-1)
install_helper.add_or_delete(installApp.install_selections)
# -------------------------------------
def main():
def main() -> None:
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
parser.add_argument(
"--add",
@ -754,7 +564,7 @@ def main():
parser.add_argument(
"--delete",
nargs="*",
help="List of names of models to idelete",
help="List of names of models to delete. Use type:name to disambiguate, as in `controlnet:my_model`",
)
parser.add_argument(
"--full-precision",
@ -781,14 +591,6 @@ def main():
choices=[x.value for x in ModelType],
help="list installed models",
)
parser.add_argument(
"--config_file",
"-c",
dest="config_file",
type=str,
default=None,
help="path to configuration file to create",
)
parser.add_argument(
"--root_dir",
dest="root",

444
invokeai/frontend/install/model_install2.py → invokeai/frontend/install/model_install.py.OLD
View File

@ -6,45 +6,47 @@
"""
This is the npyscreen frontend to the model installation application.
It is currently named model_install2.py, but will ultimately replace model_install.py.
The work is actually done in backend code in model_install_backend.py.
"""
import argparse
import curses
import logging
import sys
import textwrap
import traceback
import warnings
from argparse import Namespace
from multiprocessing import Process
from multiprocessing.connection import Connection, Pipe
from pathlib import Path
from shutil import get_terminal_size
from typing import Any, Dict, List, Optional, Set
from typing import Optional
import npyscreen
import torch
from npyscreen import widget
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.model_install import ModelInstallServiceBase
from invokeai.backend.install.install_helper import InstallHelper, InstallSelections, UnifiedModelInfo
from invokeai.backend.model_manager import ModelType
from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, SchedulerPredictionType
from invokeai.backend.model_management import ModelManager, ModelType
from invokeai.backend.util import choose_precision, choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.widgets import (
MIN_COLS,
MIN_LINES,
BufferBox,
CenteredTitleText,
CyclingForm,
MultiSelectColumns,
SingleSelectColumns,
TextBox,
WindowTooSmallException,
select_stable_diffusion_config_file,
set_min_terminal_size,
)
warnings.filterwarnings("ignore", category=UserWarning) # noqa: E402
config = InvokeAIAppConfig.get_config()
logger = InvokeAILogger.get_logger("ModelInstallService")
logger.setLevel("WARNING")
# logger.setLevel('DEBUG')
logger = InvokeAILogger.get_logger()
# build a table mapping all non-printable characters to None
# for stripping control characters
@ -56,42 +58,44 @@ MAX_OTHER_MODELS = 72
def make_printable(s: str) -> str:
"""Replace non-printable characters in a string."""
"""Replace non-printable characters in a string"""
return s.translate(NOPRINT_TRANS_TABLE)
class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
"""Main form for interactive TUI."""
# for responsive resizing set to False, but this seems to cause a crash!
FIX_MINIMUM_SIZE_WHEN_CREATED = True
# for persistence
current_tab = 0
def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, multipage: bool = False, **keywords: Any):
def __init__(self, parentApp, name, multipage=False, *args, **keywords):
self.multipage = multipage
self.subprocess = None
super().__init__(parentApp=parentApp, name=name, **keywords)
super().__init__(parentApp=parentApp, name=name, *args, **keywords) # noqa: B026 # TODO: maybe this is bad?
def create(self) -> None:
self.installer = self.parentApp.install_helper.installer
self.model_labels = self._get_model_labels()
def create(self):
self.keypress_timeout = 10
self.counter = 0
self.subprocess_connection = None
if not config.model_conf_path.exists():
with open(config.model_conf_path, "w") as file:
print("# InvokeAI model configuration file", file=file)
self.installer = ModelInstall(config)
self.all_models = self.installer.all_models()
self.starter_models = self.installer.starter_models()
self.model_labels = self._get_model_labels()
window_width, window_height = get_terminal_size()
# npyscreen has no typing hints
self.nextrely -= 1 # type: ignore
self.nextrely -= 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
editable=False,
color="CAUTION",
)
self.nextrely += 1 # type: ignore
self.nextrely += 1
self.tabs = self.add_widget_intelligent(
SingleSelectColumns,
values=[
@ -111,9 +115,9 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
)
self.tabs.on_changed = self._toggle_tables
top_of_table = self.nextrely # type: ignore
top_of_table = self.nextrely
self.starter_pipelines = self.add_starter_pipelines()
bottom_of_table = self.nextrely # type: ignore
bottom_of_table = self.nextrely
self.nextrely = top_of_table
self.pipeline_models = self.add_pipeline_widgets(
@ -158,7 +162,15 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.nextrely = bottom_of_table + 1
self.monitor = self.add_widget_intelligent(
BufferBox,
name="Log Messages",
editable=False,
max_height=6,
)
self.nextrely += 1
done_label = "APPLY CHANGES"
back_label = "BACK"
cancel_label = "CANCEL"
current_position = self.nextrely
@ -174,8 +186,14 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
)
self.nextrely = current_position
self.ok_button = self.add_widget_intelligent(
npyscreen.ButtonPress,
name=done_label,
relx=(window_width - len(done_label)) // 2,
when_pressed_function=self.on_execute,
)
label = "APPLY CHANGES"
label = "APPLY CHANGES & EXIT"
self.nextrely = current_position
self.done = self.add_widget_intelligent(
npyscreen.ButtonPress,
@ -192,16 +210,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
############# diffusers tab ##########
def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
"""Add widgets responsible for selecting diffusers models"""
widgets: Dict[str, npyscreen.widget] = {}
widgets = {}
models = self.all_models
starters = self.starter_models
starter_model_labels = self.model_labels
all_models = self.all_models # master dict of all models, indexed by key
model_list = [x for x in self.starter_models if all_models[x].type in ["main", "vae"]]
model_labels = [self.model_labels[x] for x in model_list]
self.installed_models = sorted([x for x in starters if models[x].installed])
widgets.update(
label1=self.add_widget_intelligent(
CenteredTitleText,
name="Select from a starter set of Stable Diffusion models from HuggingFace and Civitae.",
name="Select from a starter set of Stable Diffusion models from HuggingFace.",
editable=False,
labelColor="CAUTION",
)
@ -211,24 +230,23 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
# if user has already installed some initial models, then don't patronize them
# by showing more recommendations
show_recommended = len(self.installed_models) == 0
checked = [
model_list.index(x)
for x in model_list
if (show_recommended and all_models[x].recommended) or all_models[x].installed
]
keys = [x for x in models.keys() if x in starters]
widgets.update(
models_selected=self.add_widget_intelligent(
MultiSelectColumns,
columns=1,
name="Install Starter Models",
values=model_labels,
value=checked,
max_height=len(model_list) + 1,
values=[starter_model_labels[x] for x in keys],
value=[
keys.index(x)
for x in keys
if (show_recommended and models[x].recommended) or (x in self.installed_models)
],
max_height=len(starters) + 1,
relx=4,
scroll_exit=True,
),
models=model_list,
models=keys,
)
self.nextrely += 1
@ -239,18 +257,14 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self,
model_type: ModelType,
window_width: int = 120,
install_prompt: Optional[str] = None,
exclude: Optional[Set[str]] = None,
install_prompt: str = None,
exclude: set = None,
) -> dict[str, npyscreen.widget]:
"""Generic code to create model selection widgets"""
if exclude is None:
exclude = set()
widgets: Dict[str, npyscreen.widget] = {}
all_models = self.all_models
model_list = sorted(
[x for x in all_models if all_models[x].type == model_type and x not in exclude],
key=lambda x: all_models[x].name or "",
)
widgets = {}
model_list = [x for x in self.all_models if self.all_models[x].model_type == model_type and x not in exclude]
model_labels = [self.model_labels[x] for x in model_list]
show_recommended = len(self.installed_models) == 0
@ -286,7 +300,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
value=[
model_list.index(x)
for x in model_list
if (show_recommended and all_models[x].recommended) or all_models[x].installed
if (show_recommended and self.all_models[x].recommended) or self.all_models[x].installed
],
max_height=len(model_list) // columns + 1,
relx=4,
@ -310,7 +324,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
download_ids=self.add_widget_intelligent(
TextBox,
name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
max_height=6,
max_height=4,
scroll_exit=True,
editable=True,
)
@ -335,13 +349,13 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
return widgets
def resize(self) -> None:
def resize(self):
super().resize()
if s := self.starter_pipelines.get("models_selected"):
if model_list := self.starter_pipelines.get("models"):
s.values = [self.model_labels[x] for x in model_list]
keys = [x for x in self.all_models.keys() if x in self.starter_models]
s.values = [self.model_labels[x] for x in keys]
def _toggle_tables(self, value: List[int]) -> None:
def _toggle_tables(self, value=None):
selected_tab = value[0]
widgets = [
self.starter_pipelines,
@ -371,18 +385,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.display()
def _get_model_labels(self) -> dict[str, str]:
"""Return a list of trimmed labels for all models."""
window_width, window_height = get_terminal_size()
checkbox_width = 4
spacing_width = 2
result = {}
models = self.all_models
label_width = max([len(models[x].name or "") for x in self.starter_models])
label_width = max([len(models[x].name) for x in models])
description_width = window_width - label_width - checkbox_width - spacing_width
for key in self.all_models:
description = models[key].description
result = {}
for x in models.keys():
description = models[x].description
description = (
description[0 : description_width - 3] + "..."
if description and len(description) > description_width
@ -390,8 +403,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
if description
else ""
)
result[key] = f"%-{label_width}s %s" % (models[key].name, description)
result[x] = f"%-{label_width}s %s" % (models[x].name, description)
return result
def _get_columns(self) -> int:
@ -401,40 +413,50 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
def confirm_deletions(self, selections: InstallSelections) -> bool:
remove_models = selections.remove_models
if remove_models:
model_names = [self.all_models[x].name or "" for x in remove_models]
mods = "\n".join(model_names)
is_ok = npyscreen.notify_ok_cancel(
if len(remove_models) > 0:
mods = "\n".join([ModelManager.parse_key(x)[0] for x in remove_models])
return npyscreen.notify_ok_cancel(
f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
)
assert isinstance(is_ok, bool) # npyscreen doesn't have return type annotations
return is_ok
else:
return True
@property
def all_models(self) -> Dict[str, UnifiedModelInfo]:
# npyscreen doesn't having typing hints
return self.parentApp.install_helper.all_models # type: ignore
def on_execute(self):
self.marshall_arguments()
app = self.parentApp
if not self.confirm_deletions(app.install_selections):
return
@property
def starter_models(self) -> List[str]:
return self.parentApp.install_helper._starter_models # type: ignore
self.monitor.entry_widget.buffer(["Processing..."], scroll_end=True)
self.ok_button.hidden = True
self.display()
@property
def installed_models(self) -> List[str]:
return self.parentApp.install_helper._installed_models # type: ignore
# TO DO: Spawn a worker thread, not a subprocess
parent_conn, child_conn = Pipe()
p = Process(
target=process_and_execute,
kwargs={
"opt": app.program_opts,
"selections": app.install_selections,
"conn_out": child_conn,
},
)
p.start()
child_conn.close()
self.subprocess_connection = parent_conn
self.subprocess = p
app.install_selections = InstallSelections()
def on_back(self) -> None:
def on_back(self):
self.parentApp.switchFormPrevious()
self.editing = False
def on_cancel(self) -> None:
def on_cancel(self):
self.parentApp.setNextForm(None)
self.parentApp.user_cancelled = True
self.editing = False
def on_done(self) -> None:
def on_done(self):
self.marshall_arguments()
if not self.confirm_deletions(self.parentApp.install_selections):
return
@ -442,7 +464,77 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.parentApp.user_cancelled = False
self.editing = False
def marshall_arguments(self) -> None:
########## This routine monitors the child process that is performing model installation and removal #####
def while_waiting(self):
"""Called during idle periods. Main task is to update the Log Messages box with messages
from the child process that does the actual installation/removal"""
c = self.subprocess_connection
if not c:
return
monitor_widget = self.monitor.entry_widget
while c.poll():
try:
data = c.recv_bytes().decode("utf-8")
data.strip("\n")
# processing child is requesting user input to select the
# right configuration file
if data.startswith("*need v2 config"):
_, model_path, *_ = data.split(":", 2)
self._return_v2_config(model_path)
# processing child is done
elif data == "*done*":
self._close_subprocess_and_regenerate_form()
break
# update the log message box
else:
data = make_printable(data)
data = data.replace("[A", "")
monitor_widget.buffer(
textwrap.wrap(
data,
width=monitor_widget.width,
subsequent_indent=" ",
),
scroll_end=True,
)
self.display()
except (EOFError, OSError):
self.subprocess_connection = None
def _return_v2_config(self, model_path: str):
c = self.subprocess_connection
model_name = Path(model_path).name
message = select_stable_diffusion_config_file(model_name=model_name)
c.send_bytes(message.encode("utf-8"))
def _close_subprocess_and_regenerate_form(self):
app = self.parentApp
self.subprocess_connection.close()
self.subprocess_connection = None
self.monitor.entry_widget.buffer(["** Action Complete **"])
self.display()
# rebuild the form, saving and restoring some of the fields that need to be preserved.
saved_messages = self.monitor.entry_widget.values
app.main_form = app.addForm(
"MAIN",
addModelsForm,
name="Install Stable Diffusion Models",
multipage=self.multipage,
)
app.switchForm("MAIN")
app.main_form.monitor.entry_widget.values = saved_messages
app.main_form.monitor.entry_widget.buffer([""], scroll_end=True)
# app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
# app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
def marshall_arguments(self):
"""
Assemble arguments and store as attributes of the application:
.starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml
@ -472,24 +564,46 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
models_to_install = [x for x in selected if not self.all_models[x].installed]
models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
selections.remove_models.extend(models_to_remove)
selections.install_models.extend([all_models[x] for x in models_to_install])
selections.install_models.extend(
all_models[x].path or all_models[x].repo_id
for x in models_to_install
if all_models[x].path or all_models[x].repo_id
)
# models located in the 'download_ids" section
for section in ui_sections:
if downloads := section.get("download_ids"):
models = [UnifiedModelInfo(source=x) for x in downloads.value.split()]
selections.install_models.extend(models)
selections.install_models.extend(downloads.value.split())
# NOT NEEDED - DONE IN BACKEND NOW
# # special case for the ipadapter_models. If any of the adapters are
# # chosen, then we add the corresponding encoder(s) to the install list.
# section = self.ipadapter_models
# if section.get("models_selected"):
# selected_adapters = [
# self.all_models[section["models"][x]].name for x in section.get("models_selected").value
# ]
# encoders = []
# if any(["sdxl" in x for x in selected_adapters]):
# encoders.append("ip_adapter_sdxl_image_encoder")
# if any(["sd15" in x for x in selected_adapters]):
# encoders.append("ip_adapter_sd_image_encoder")
# for encoder in encoders:
# key = f"any/clip_vision/{encoder}"
# repo_id = f"InvokeAI/{encoder}"
# if key not in self.all_models:
# selections.install_models.append(repo_id)
class AddModelApplication(npyscreen.NPSAppManaged): # type: ignore
def __init__(self, opt: Namespace, install_helper: InstallHelper):
class AddModelApplication(npyscreen.NPSAppManaged):
def __init__(self, opt):
super().__init__()
self.program_opts = opt
self.user_cancelled = False
# self.autoload_pending = True
self.install_selections = InstallSelections()
self.install_helper = install_helper
def onStart(self) -> None:
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.main_form = self.addForm(
"MAIN",
@ -499,62 +613,138 @@ class AddModelApplication(npyscreen.NPSAppManaged): # type: ignore
)
def list_models(installer: ModelInstallServiceBase, model_type: ModelType):
"""Print out all models of type model_type."""
models = installer.record_store.search_by_attr(model_type=model_type)
print(f"Installed models of type `{model_type}`:")
for model in models:
path = (config.models_path / model.path).resolve()
print(f"{model.name:40}{model.base.value:5}{model.type.value:8}{model.format.value:12}{path}")
class StderrToMessage:
def __init__(self, connection: Connection):
self.connection = connection
def write(self, data: str):
self.connection.send_bytes(data.encode("utf-8"))
def flush(self):
pass
# --------------------------------------------------------
def select_and_download_models(opt: Namespace) -> None:
"""Prompt user for install/delete selections and execute."""
def ask_user_for_prediction_type(model_path: Path, tui_conn: Connection = None) -> SchedulerPredictionType:
if tui_conn:
logger.debug("Waiting for user response...")
return _ask_user_for_pt_tui(model_path, tui_conn)
else:
return _ask_user_for_pt_cmdline(model_path)
def _ask_user_for_pt_cmdline(model_path: Path) -> Optional[SchedulerPredictionType]:
choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
print(
f"""
Please select the scheduler prediction type of the checkpoint named {model_path.name}:
[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
[3] Accept the best guess; you can fix it in the Web UI later
"""
)
choice = None
ok = False
while not ok:
try:
choice = input("select [3]> ").strip()
if not choice:
return None
choice = choices[int(choice) - 1]
ok = True
except (ValueError, IndexError):
print(f"{choice} is not a valid choice")
except EOFError:
return
return choice
def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPredictionType:
tui_conn.send_bytes(f"*need v2 config for:{model_path}".encode("utf-8"))
# note that we don't do any status checking here
response = tui_conn.recv_bytes().decode("utf-8")
if response is None:
return None
elif response == "epsilon":
return SchedulerPredictionType.epsilon
elif response == "v":
return SchedulerPredictionType.VPrediction
elif response == "guess":
return None
else:
return None
# --------------------------------------------------------
def process_and_execute(
opt: Namespace,
selections: InstallSelections,
conn_out: Connection = None,
):
# need to reinitialize config in subprocess
config = InvokeAIAppConfig.get_config()
args = ["--root", opt.root] if opt.root else []
config.parse_args(args)
# set up so that stderr is sent to conn_out
if conn_out:
translator = StderrToMessage(conn_out)
sys.stderr = translator
sys.stdout = translator
logger = InvokeAILogger.get_logger()
logger.handlers.clear()
logger.addHandler(logging.StreamHandler(translator))
installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x, conn_out))
installer.install(selections)
if conn_out:
conn_out.send_bytes("*done*".encode("utf-8"))
conn_out.close()
# --------------------------------------------------------
def select_and_download_models(opt: Namespace):
precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
# unsure how to avoid a typing complaint in the next line: config.precision is an enumerated Literal
config.precision = precision # type: ignore
install_helper = InstallHelper(config, logger)
installer = install_helper.installer
config.precision = precision
installer = ModelInstall(config, prediction_type_helper=ask_user_for_prediction_type)
if opt.list_models:
list_models(installer, opt.list_models)
installer.list_models(opt.list_models)
elif opt.add or opt.delete:
selections = InstallSelections(
install_models=[UnifiedModelInfo(source=x) for x in (opt.add or [])], remove_models=opt.delete or []
)
install_helper.add_or_delete(selections)
selections = InstallSelections(install_models=opt.add or [], remove_models=opt.delete or [])
installer.install(selections)
elif opt.default_only:
default_model = install_helper.default_model()
assert default_model is not None
selections = InstallSelections(install_models=[default_model])
install_helper.add_or_delete(selections)
selections = InstallSelections(install_models=installer.default_model())
installer.install(selections)
elif opt.yes_to_all:
selections = InstallSelections(install_models=install_helper.recommended_models())
install_helper.add_or_delete(selections)
selections = InstallSelections(install_models=installer.recommended_models())
installer.install(selections)
# this is where the TUI is called
else:
# needed to support the probe() method running under a subprocess
torch.multiprocessing.set_start_method("spawn")
if not set_min_terminal_size(MIN_COLS, MIN_LINES):
raise WindowTooSmallException(
"Could not increase terminal size. Try running again with a larger window or smaller font size."
)
installApp = AddModelApplication(opt, install_helper)
installApp = AddModelApplication(opt)
try:
installApp.run()
except KeyboardInterrupt:
print("Aborted...")
sys.exit(-1)
install_helper.add_or_delete(installApp.install_selections)
except KeyboardInterrupt as e:
if hasattr(installApp, "main_form"):
if installApp.main_form.subprocess and installApp.main_form.subprocess.is_alive():
logger.info("Terminating subprocesses")
installApp.main_form.subprocess.terminate()
installApp.main_form.subprocess = None
raise e
process_and_execute(opt, installApp.install_selections)
# -------------------------------------
def main() -> None:
def main():
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
parser.add_argument(
"--add",
@ -564,7 +754,7 @@ def main() -> None:
parser.add_argument(
"--delete",
nargs="*",
help="List of names of models to delete. Use type:name to disambiguate, as in `controlnet:my_model`",
help="List of names of models to idelete",
)
parser.add_argument(
"--full-precision",
@ -591,6 +781,14 @@ def main() -> None:
choices=[x.value for x in ModelType],
help="list installed models",
)
parser.add_argument(
"--config_file",
"-c",
dest="config_file",
type=str,
default=None,
help="path to configuration file to create",
)
parser.add_argument(
"--root_dir",
dest="root",

11
invokeai/frontend/install/widgets.py
View File

@ -267,6 +267,17 @@ class SingleSelectWithChanged(npyscreen.SelectOne):
self.on_changed(self.value)
class CheckboxWithChanged(npyscreen.Checkbox):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.on_changed = None
def whenToggled(self):
super().whenToggled()
if self.on_changed:
self.on_changed(self.value)
class SingleSelectColumnsSimple(SelectColumnBase, SingleSelectWithChanged):
"""Row of radio buttons. Spacebar to select."""

0
invokeai/frontend/merge/merge_diffusers2.py → invokeai/frontend/merge/merge_diffusers.py.OLD
View File

3
pyproject.toml
View File

@ -136,8 +136,7 @@ dependencies = [
# full commands
"invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
"invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers"
"invokeai-merge2" = "invokeai.frontend.merge.merge_diffusers2:main"
"invokeai-merge" = "invokeai.frontend.merge.merge_diffusers:main"
"invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion"
"invokeai-model-install" = "invokeai.frontend.install.model_install:main"
"invokeai-model-install2" = "invokeai.frontend.install.model_install2:main" # will eventually be renamed to invokeai-model-install

47
tests/test_model_manager.py
View File

@ -1,47 +0,0 @@
from pathlib import Path
import pytest
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.backend import BaseModelType, ModelManager, ModelType, SubModelType
BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main)
VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
VAE_NULL_OVERRIDE_MODEL_NAME = ("SDXL with empty VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
@pytest.fixture
def model_manager(datadir) -> ModelManager:
InvokeAIAppConfig.get_config(root=datadir)
return ModelManager(datadir / "configs" / "relative_sub.models.yaml")
def test_get_model_names(model_manager: ModelManager):
names = model_manager.model_names()
assert names[:2] == [BASIC_MODEL_NAME, VAE_OVERRIDE_MODEL_NAME]
def test_get_model_path_for_diffusers(model_manager: ModelManager, datadir: Path):
model_config = model_manager._get_model_config(BASIC_MODEL_NAME[1], BASIC_MODEL_NAME[0], BASIC_MODEL_NAME[2])
top_model_path, is_override = model_manager._get_model_path(model_config)
expected_model_path = datadir / "models" / "sdxl" / "main" / "SDXL base 1_0"
assert top_model_path == expected_model_path
assert not is_override
def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir: Path):
model_config = model_manager._get_model_config(
VAE_OVERRIDE_MODEL_NAME[1], VAE_OVERRIDE_MODEL_NAME[0], VAE_OVERRIDE_MODEL_NAME[2]
)
vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix"
assert vae_model_path == expected_vae_path
assert is_override
def test_get_model_path_for_null_overridden_vae(model_manager: ModelManager, datadir: Path):
model_config = model_manager._get_model_config(
VAE_NULL_OVERRIDE_MODEL_NAME[1], VAE_NULL_OVERRIDE_MODEL_NAME[0], VAE_NULL_OVERRIDE_MODEL_NAME[2]
)
vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
assert not is_override