mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into fix/detect-more-loras
This commit is contained in:
Lincoln Stein
GitHub
@ -10,12 +10,15 @@ import sys
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import argparse
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import io
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import os
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import psutil
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import shutil
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import textwrap
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import torch
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import traceback
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import yaml
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import warnings
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from argparse import Namespace
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from enum import Enum
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from pathlib import Path
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from shutil import get_terminal_size
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from typing import get_type_hints
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@ -44,6 +47,8 @@ from invokeai.app.services.config import (
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)
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from invokeai.backend.util.logging import InvokeAILogger
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from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
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# TO DO - Move all the frontend code into invokeai.frontend.install
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from invokeai.frontend.install.widgets import (
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SingleSelectColumns,
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CenteredButtonPress,
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@ -53,6 +58,7 @@ from invokeai.frontend.install.widgets import (
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CyclingForm,
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MIN_COLS,
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MIN_LINES,
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WindowTooSmallException,
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)
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from invokeai.backend.install.legacy_arg_parsing import legacy_parser
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from invokeai.backend.install.model_install_backend import (
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@ -61,6 +67,7 @@ from invokeai.backend.install.model_install_backend import (
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ModelInstall,
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)
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from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
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from pydantic.error_wrappers import ValidationError
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warnings.filterwarnings("ignore")
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transformers.logging.set_verbosity_error()
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@ -76,6 +83,13 @@ Default_config_file = config.model_conf_path
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SD_Configs = config.legacy_conf_path
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PRECISION_CHOICES = ["auto", "float16", "float32"]
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GB = 1073741824 # GB in bytes
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HAS_CUDA = torch.cuda.is_available()
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_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
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MAX_VRAM /= GB
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MAX_RAM = psutil.virtual_memory().total / GB
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INIT_FILE_PREAMBLE = """# InvokeAI initialization file
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# This is the InvokeAI initialization file, which contains command-line default values.
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@ -86,6 +100,12 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
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logger = InvokeAILogger.getLogger()
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class DummyWidgetValue(Enum):
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zero = 0
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true = True
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false = False
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# --------------------------------------------
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def postscript(errors: None):
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if not any(errors):
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@ -378,13 +398,35 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
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)
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self.max_cache_size = self.add_widget_intelligent(
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IntTitleSlider,
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name="Size of the RAM cache used for fast model switching (GB)",
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name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
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value=old_opts.max_cache_size,
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out_of=20,
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out_of=MAX_RAM,
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lowest=3,
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begin_entry_at=6,
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scroll_exit=True,
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)
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if HAS_CUDA:
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self.nextrely += 1
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self.add_widget_intelligent(
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npyscreen.TitleFixedText,
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name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
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begin_entry_at=0,
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editable=False,
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color="CONTROL",
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scroll_exit=True,
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)
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self.nextrely -= 1
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self.max_vram_cache_size = self.add_widget_intelligent(
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npyscreen.Slider,
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value=old_opts.max_vram_cache_size,
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out_of=round(MAX_VRAM * 2) / 2,
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lowest=0.0,
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relx=8,
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step=0.25,
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scroll_exit=True,
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)
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else:
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self.max_vram_cache_size = DummyWidgetValue.zero
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self.nextrely += 1
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self.outdir = self.add_widget_intelligent(
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FileBox,
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@ -401,7 +443,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
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self.autoimport_dirs = {}
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self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
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FileBox,
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name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
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name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
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value=str(config.root_path / config.autoimport_dir),
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select_dir=True,
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must_exist=False,
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@ -476,6 +518,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
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"outdir",
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"free_gpu_mem",
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"max_cache_size",
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"max_vram_cache_size",
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"xformers_enabled",
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"always_use_cpu",
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]:
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@ -592,13 +635,13 @@ def maybe_create_models_yaml(root: Path):
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# -------------------------------------
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def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
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# parse_args() will read from init file if present
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invokeai_opts = default_startup_options(initfile)
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invokeai_opts.root = program_opts.root
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# The third argument is needed in the Windows 11 environment to
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# launch a console window running this program.
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set_min_terminal_size(MIN_COLS, MIN_LINES)
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if not set_min_terminal_size(MIN_COLS, MIN_LINES):
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raise WindowTooSmallException(
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"Could not increase terminal size. Try running again with a larger window or smaller font size."
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)
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# the install-models application spawns a subprocess to install
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# models, and will crash unless this is set before running.
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@ -654,10 +697,13 @@ def migrate_init_file(legacy_format: Path):
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old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
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new = InvokeAIAppConfig.get_config()
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fields = list(get_type_hints(InvokeAIAppConfig).keys())
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fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
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for attr in fields:
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if hasattr(old, attr):
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setattr(new, attr, getattr(old, attr))
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try:
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setattr(new, attr, getattr(old, attr))
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except ValidationError as e:
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print(f"* Ignoring incompatible value for field {attr}:\n {str(e)}")
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# a few places where the field names have changed and we have to
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# manually add in the new names/values
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@ -777,6 +823,7 @@ def main():
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models_to_download = default_user_selections(opt)
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new_init_file = config.root_path / "invokeai.yaml"
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if opt.yes_to_all:
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write_default_options(opt, new_init_file)
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init_options = Namespace(precision="float32" if opt.full_precision else "float16")
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@ -802,6 +849,8 @@ def main():
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postscript(errors=errors)
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if not opt.yes_to_all:
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input("Press any key to continue...")
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except WindowTooSmallException as e:
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logger.error(str(e))
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except KeyboardInterrupt:
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print("\nGoodbye! Come back soon.")
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@ -595,9 +595,10 @@ class ModelManager(object):
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the combined format of the list_models() method.
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"""
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models = self.list_models(base_model, model_type, model_name)
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if len(models) > 1:
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if len(models) >= 1:
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return models[0]
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return None
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else:
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return None
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def list_models(
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self,
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@ -990,7 +991,9 @@ class ModelManager(object):
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raise DuplicateModelException(f"Model with key {model_key} added twice")
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model_path = self.relative_model_path(model_path)
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model_config: ModelConfigBase = model_class.probe_config(str(model_path))
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model_config: ModelConfigBase = model_class.probe_config(
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str(model_path), model_base=cur_base_model
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)
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self.models[model_key] = model_config
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new_models_found = True
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except DuplicateModelException as e:
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@ -80,8 +80,10 @@ class StableDiffusionXLModel(DiffusersModel):
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raise Exception("Unkown stable diffusion 2.* model format")
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if ckpt_config_path is None:
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# TO DO: implement picking
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pass
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# avoid circular import
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from .stable_diffusion import _select_ckpt_config
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ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant)
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return cls.create_config(
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path=path,
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