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fix(mm): regression from change to legacy conf dir change

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This commit is contained in:
psychedelicious
2024-03-19 18:12:23 +11:00
parent 040ea8f41b
commit 6c13fa13ea
4 changed files with 11 additions and 13 deletions
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2
invokeai/app/services/model_install/model_install_default.py
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@ -604,7 +604,7 @@ class ModelInstallService(ModelInstallServiceBase):
info.path = model_path.as_posix() info.path = model_path.as_posix()
# add 'main' specific fields # Checkpoints have a config file needed for conversion - resolve this to an absolute path
if isinstance(info, CheckpointConfigBase): if isinstance(info, CheckpointConfigBase):
legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve() legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve()
info.config_path = legacy_conf.as_posix() info.config_path = legacy_conf.as_posix()

4
invokeai/backend/model_manager/load/model_loaders/controlnet.py
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@ -35,8 +35,6 @@ class ControlNetLoader(GenericDiffusersLoader):
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path: def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
assert isinstance(config, CheckpointConfigBase) assert isinstance(config, CheckpointConfigBase)
config_file = config.config_path
image_size = ( image_size = (
512 512
if config.base == BaseModelType.StableDiffusion1 if config.base == BaseModelType.StableDiffusion1
@ -46,7 +44,7 @@ class ControlNetLoader(GenericDiffusersLoader):
) )
self._logger.info(f"Converting {model_path} to diffusers format") self._logger.info(f"Converting {model_path} to diffusers format")
with open(self._app_config.root_path / config_file, "r") as config_stream: with open(config.config_path, "r") as config_stream:
convert_controlnet_to_diffusers( convert_controlnet_to_diffusers(
model_path, model_path,
output_path, output_path,

3
invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
View File

@ -76,7 +76,6 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
assert isinstance(config, MainCheckpointConfig) assert isinstance(config, MainCheckpointConfig)
base = config.base base = config.base
config_file = config.config_path
prediction_type = config.prediction_type.value prediction_type = config.prediction_type.value
upcast_attention = config.upcast_attention upcast_attention = config.upcast_attention
image_size = ( image_size = (
@ -92,7 +91,7 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
model_path, model_path,
output_path, output_path,
model_type=self.model_base_to_model_type[base], model_type=self.model_base_to_model_type[base],
original_config_file=self._app_config.root_path / config_file, original_config_file=config.config_path,
extract_ema=True, extract_ema=True,
from_safetensors=model_path.suffix == ".safetensors", from_safetensors=model_path.suffix == ".safetensors",
precision=self._torch_dtype, precision=self._torch_dtype,

15
invokeai/backend/model_manager/probe.py
View File

@ -178,13 +178,14 @@ class ModelProbe(object):
fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE] fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
and fields["format"] is ModelFormat.Checkpoint and fields["format"] is ModelFormat.Checkpoint
): ):
fields["config_path"] = cls._get_checkpoint_config_path( ckpt_config_path = cls._get_checkpoint_config_path(
model_path, model_path,
model_type=fields["type"], model_type=fields["type"],
base_type=fields["base"], base_type=fields["base"],
variant_type=fields["variant"], variant_type=fields["variant"],
prediction_type=fields["prediction_type"], prediction_type=fields["prediction_type"],
).as_posix() )
fields["config_path"] = str(ckpt_config_path)
# additional fields needed for main non-checkpoint models # additional fields needed for main non-checkpoint models
elif fields["type"] == ModelType.Main and fields["format"] in [ elif fields["type"] == ModelType.Main and fields["format"] in [
@ -298,23 +299,23 @@ class ModelProbe(object):
config_file = LEGACY_CONFIGS[base_type][variant_type] config_file = LEGACY_CONFIGS[base_type][variant_type]
if isinstance(config_file, dict): # need another tier for sd-2.x models if isinstance(config_file, dict): # need another tier for sd-2.x models
config_file = config_file[prediction_type] config_file = config_file[prediction_type]
config_file = f"stable-diffusion/{config_file}"
elif model_type is ModelType.ControlNet: elif model_type is ModelType.ControlNet:
config_file = ( config_file = (
"../controlnet/cldm_v15.yaml" "controlnet/cldm_v15.yaml"
if base_type is BaseModelType.StableDiffusion1 if base_type is BaseModelType.StableDiffusion1
else "../controlnet/cldm_v21.yaml" else "controlnet/cldm_v21.yaml"
) )
elif model_type is ModelType.VAE: elif model_type is ModelType.VAE:
config_file = ( config_file = (
"../stable-diffusion/v1-inference.yaml" "stable-diffusion/v1-inference.yaml"
if base_type is BaseModelType.StableDiffusion1 if base_type is BaseModelType.StableDiffusion1
else "../stable-diffusion/v2-inference.yaml" else "stable-diffusion/v2-inference.yaml"
) )
else: else:
raise InvalidModelConfigException( raise InvalidModelConfigException(
f"{model_path}: Unrecognized combination of model_type={model_type}, base_type={base_type}" f"{model_path}: Unrecognized combination of model_type={model_type}, base_type={base_type}"
) )
assert isinstance(config_file, str)
return Path(config_file) return Path(config_file)
@classmethod @classmethod