mirror of
https://github.com/invoke-ai/InvokeAI.git
synced 2026-02-02 21:25:21 -05:00
3d6d89feb4
* pass model config to _load_model * make conversion work again * do not write diffusers to disk when convert_cache set to 0 * adding same model to cache twice is a no-op, not an assertion error * fix issues identified by psychedelicious during pr review * following conversion, avoid redundant read of cached submodels * fix error introduced while merging --------- Co-authored-by: Lincoln Stein <lstein@gmail.com>
117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
|
|
"""Class for StableDiffusion model loading in InvokeAI."""
|
|
|
|
from pathlib import Path
|
|
from typing import Optional
|
|
|
|
from invokeai.backend.model_manager import (
|
|
AnyModel,
|
|
AnyModelConfig,
|
|
BaseModelType,
|
|
ModelFormat,
|
|
ModelType,
|
|
SchedulerPredictionType,
|
|
SubModelType,
|
|
)
|
|
from invokeai.backend.model_manager.config import (
|
|
CheckpointConfigBase,
|
|
DiffusersConfigBase,
|
|
MainCheckpointConfig,
|
|
ModelVariantType,
|
|
)
|
|
from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
|
|
|
|
from .. import ModelLoaderRegistry
|
|
from .generic_diffusers import GenericDiffusersLoader
|
|
|
|
VARIANT_TO_IN_CHANNEL_MAP = {
|
|
ModelVariantType.Normal: 4,
|
|
ModelVariantType.Depth: 5,
|
|
ModelVariantType.Inpaint: 9,
|
|
}
|
|
|
|
|
|
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Diffusers)
|
|
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Checkpoint)
|
|
class StableDiffusionDiffusersModel(GenericDiffusersLoader):
|
|
"""Class to load main models."""
|
|
|
|
model_base_to_model_type = {
|
|
BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
|
|
BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
|
|
BaseModelType.StableDiffusionXL: "SDXL",
|
|
BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
|
|
}
|
|
|
|
def _load_model(
|
|
self,
|
|
config: AnyModelConfig,
|
|
submodel_type: Optional[SubModelType] = None,
|
|
) -> AnyModel:
|
|
if not submodel_type is not None:
|
|
raise Exception("A submodel type must be provided when loading main pipelines.")
|
|
model_path = Path(config.path)
|
|
load_class = self.get_hf_load_class(model_path, submodel_type)
|
|
repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
|
|
variant = repo_variant.value if repo_variant else None
|
|
model_path = model_path / submodel_type.value
|
|
try:
|
|
result: AnyModel = load_class.from_pretrained(
|
|
model_path,
|
|
torch_dtype=self._torch_dtype,
|
|
variant=variant,
|
|
)
|
|
except OSError as e:
|
|
if variant and "no file named" in str(
|
|
e
|
|
): # try without the variant, just in case user's preferences changed
|
|
result = load_class.from_pretrained(model_path, torch_dtype=self._torch_dtype)
|
|
else:
|
|
raise e
|
|
|
|
return result
|
|
|
|
def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
|
|
if not isinstance(config, CheckpointConfigBase):
|
|
return False
|
|
elif (
|
|
dest_path.exists()
|
|
and (dest_path / "model_index.json").stat().st_mtime >= (config.converted_at or 0.0)
|
|
and (dest_path / "model_index.json").stat().st_mtime >= model_path.stat().st_mtime
|
|
):
|
|
return False
|
|
else:
|
|
return True
|
|
|
|
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
|
|
assert isinstance(config, MainCheckpointConfig)
|
|
base = config.base
|
|
|
|
prediction_type = config.prediction_type.value
|
|
upcast_attention = config.upcast_attention
|
|
image_size = (
|
|
1024
|
|
if base == BaseModelType.StableDiffusionXL
|
|
else 768
|
|
if config.prediction_type == SchedulerPredictionType.VPrediction and base == BaseModelType.StableDiffusion2
|
|
else 512
|
|
)
|
|
|
|
self._logger.info(f"Converting {model_path} to diffusers format")
|
|
|
|
loaded_model = convert_ckpt_to_diffusers(
|
|
model_path,
|
|
output_path,
|
|
model_type=self.model_base_to_model_type[base],
|
|
original_config_file=self._app_config.legacy_conf_path / config.config_path,
|
|
extract_ema=True,
|
|
from_safetensors=model_path.suffix == ".safetensors",
|
|
precision=self._torch_dtype,
|
|
prediction_type=prediction_type,
|
|
image_size=image_size,
|
|
upcast_attention=upcast_attention,
|
|
load_safety_checker=False,
|
|
num_in_channels=VARIANT_TO_IN_CHANNEL_MAP[config.variant],
|
|
)
|
|
return loaded_model
|