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rename ModelType.Pipeline to ModelType.Main

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This commit is contained in:
Lincoln Stein 2023-06-24 11:45:49 -04:00
parent 539d1f3bde
commit ba1371a88f
10 changed files with 35 additions and 35 deletions
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2
invokeai/app/invocations/model.py
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@ -73,7 +73,7 @@ class PipelineModelLoaderInvocation(BaseInvocation):
base_model = self.model.base_model
model_name = self.model.model_name
model_type = ModelType.Pipeline
model_type = ModelType.Main
# TODO: not found exceptions
if not context.services.model_manager.model_exists(

2
invokeai/backend/install/invokeai_configure.py
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@ -618,7 +618,7 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
dest = root / 'models'
for model_base in [BaseModelType.StableDiffusion1,BaseModelType.StableDiffusion2]:
for model_type in [ModelType.Pipeline, ModelType.Vae, ModelType.Lora,
for model_type in [ModelType.Main, ModelType.Vae, ModelType.Lora,
ModelType.ControlNet,ModelType.TextualInversion]:
path = dest / model_base.value / model_type.value
path.mkdir(parents=True, exist_ok=True)

2
invokeai/backend/install/migrate_to_3.py
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@ -94,7 +94,7 @@ class MigrateTo3(object):
Create the basic directory structure for the models folder.
'''
for model_base in [BaseModelType.StableDiffusion1,BaseModelType.StableDiffusion2]:
for model_type in [ModelType.Pipeline, ModelType.Vae, ModelType.Lora,
for model_type in [ModelType.Main, ModelType.Vae, ModelType.Lora,
ModelType.ControlNet,ModelType.TextualInversion]:
path = self.dest_models / model_base.value / model_type.value
path.mkdir(parents=True, exist_ok=True)

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

@ -139,7 +139,7 @@ class ModelInstall(object):
models = set()
for key, value in self.datasets.items():
name,base,model_type = ModelManager.parse_key(key)
if model_type==ModelType.Pipeline:
if model_type==ModelType.Main:
models.add(key)
return models
@ -224,7 +224,7 @@ class ModelInstall(object):
try:
logger.info(f'Probing {path}')
info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
if info.model_type == ModelType.Pipeline:
if info.model_type == ModelType.Main:
model_name = path.stem if info.format=='checkpoint' else path.name
if self.mgr.model_exists(model_name, info.base_type, info.model_type):
raise Exception(f'A model named "{model_name}" is already installed.')
@ -314,7 +314,7 @@ class ModelInstall(object):
description = str(description),
model_format = info.format,
)
if info.model_type == ModelType.Pipeline:
if info.model_type == ModelType.Main:
attributes.update(dict(variant = info.variant_type,))
if info.format=="checkpoint":
try:

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

@ -33,7 +33,7 @@ class ModelProbe(object):
}
CLASS2TYPE = {
'StableDiffusionPipeline' : ModelType.Pipeline,
'StableDiffusionPipeline' : ModelType.Main,
'AutoencoderKL' : ModelType.Vae,
'ControlNetModel' : ModelType.ControlNet,
}
@ -116,7 +116,7 @@ class ModelProbe(object):
if len(checkpoint) < 10 and all(isinstance(v, torch.Tensor) for v in checkpoint.values()):
return ModelType.TextualInversion
if any([x.startswith("model.diffusion_model") for x in state_dict.keys()]):
return ModelType.Pipeline
return ModelType.Main
if any([x.startswith("encoder.conv_in") for x in state_dict.keys()]):
return ModelType.Vae
if "string_to_token" in state_dict or "emb_params" in state_dict:
@ -207,7 +207,7 @@ class CheckpointProbeBase(ProbeBase):
def get_variant_type(self)-> ModelVariantType:
model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path,self.checkpoint)
if model_type != ModelType.Pipeline:
if model_type != ModelType.Main:
return ModelVariantType.Normal
state_dict = self.checkpoint.get('state_dict') or self.checkpoint
in_channels = state_dict[
@ -409,12 +409,12 @@ class LoRAFolderProbe(FolderProbeBase):
return LoRACheckpointProbe(model_file,None).get_base_type()
############## register probe classes ######
ModelProbe.register_probe('diffusers', ModelType.Pipeline, PipelineFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.Main, PipelineFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.Vae, VaeFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.Lora, LoRAFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.TextualInversion, TextualInversionFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.ControlNet, ControlNetFolderProbe)
ModelProbe.register_probe('checkpoint', ModelType.Pipeline, PipelineCheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.Main, PipelineCheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.Vae, VaeCheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.Lora, LoRACheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.TextualInversion, TextualInversionCheckpointProbe)

6
invokeai/backend/model_management/models/__init__.py
View File

@ -11,21 +11,21 @@ from .textual_inversion import TextualInversionModel
MODEL_CLASSES = {
BaseModelType.StableDiffusion1: {
ModelType.Pipeline: StableDiffusion1Model,
ModelType.Main: StableDiffusion1Model,
ModelType.Vae: VaeModel,
ModelType.Lora: LoRAModel,
ModelType.ControlNet: ControlNetModel,
ModelType.TextualInversion: TextualInversionModel,
},
BaseModelType.StableDiffusion2: {
ModelType.Pipeline: StableDiffusion2Model,
ModelType.Main: StableDiffusion2Model,
ModelType.Vae: VaeModel,
ModelType.Lora: LoRAModel,
ModelType.ControlNet: ControlNetModel,
ModelType.TextualInversion: TextualInversionModel,
},
#BaseModelType.Kandinsky2_1: {
# ModelType.Pipeline: Kandinsky2_1Model,
# ModelType.Main: Kandinsky2_1Model,
# ModelType.MoVQ: MoVQModel,
# ModelType.Lora: LoRAModel,
# ModelType.ControlNet: ControlNetModel,

2
invokeai/backend/model_management/models/base.py
View File

@ -18,7 +18,7 @@ class BaseModelType(str, Enum):
#Kandinsky2_1 = "kandinsky-2.1"
class ModelType(str, Enum):
Pipeline = "pipeline"
Main = "main"
Vae = "vae"
Lora = "lora"
ControlNet = "controlnet" # used by model_probe

8
invokeai/backend/model_management/models/stable_diffusion.py
View File

@ -40,11 +40,11 @@ class StableDiffusion1Model(DiffusersModel):
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
assert base_model == BaseModelType.StableDiffusion1
assert model_type == ModelType.Pipeline
assert model_type == ModelType.Main
super().__init__(
model_path=model_path,
base_model=BaseModelType.StableDiffusion1,
model_type=ModelType.Pipeline,
model_type=ModelType.Main,
)
@classmethod
@ -140,11 +140,11 @@ class StableDiffusion2Model(DiffusersModel):
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
assert base_model == BaseModelType.StableDiffusion2
assert model_type == ModelType.Pipeline
assert model_type == ModelType.Main
super().__init__(
model_path=model_path,
base_model=BaseModelType.StableDiffusion2,
model_type=ModelType.Pipeline,
model_type=ModelType.Main,
)
@classmethod

28
invokeai/configs/INITIAL_MODELS.yaml
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@ -1,58 +1,58 @@
# This file predefines a few models that the user may want to install.
sd-1/pipeline/stable-diffusion-v1-5:
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
recommended: True
default: True
sd-1/pipeline/stable-diffusion-inpainting:
sd-1/main/stable-diffusion-inpainting:
description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
recommended: True
sd-2/pipeline/stable-diffusion-2-1:
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
recommended: True
sd-2/pipeline/stable-diffusion-2-inpainting:
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
recommended: False
sd-1/pipeline/Analog-Diffusion:
sd-1/main/Analog-Diffusion:
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
repo_id: wavymulder/Analog-Diffusion
recommended: false
sd-1/pipeline/Deliberate:
sd-1/main/Deliberate:
description: Versatile model that produces detailed images up to 768px (4.27 GB)
repo_id: XpucT/Deliberate
recommended: False
sd-1/pipeline/Dungeons-and-Diffusion:
sd-1/main/Dungeons-and-Diffusion:
description: Dungeons & Dragons characters (2.13 GB)
repo_id: 0xJustin/Dungeons-and-Diffusion
recommended: False
sd-1/pipeline/dreamlike-photoreal-2:
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
recommended: False
sd-1/pipeline/Inkpunk-Diffusion:
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
recommended: False
sd-1/pipeline/openjourney:
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
recommended: False
sd-1/pipeline/portraitplus:
sd-1/main/portraitplus:
description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
repo_id: wavymulder/portraitplus
recommended: False
sd-1/pipeline/seek.art_MEGA:
sd-1/main/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/pipeline/trinart_stable_diffusion_v2:
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
recommended: False
sd-1/pipeline/waifu-diffusion:
sd-1/main/waifu-diffusion:
description: An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)
repo_id: hakurei/waifu-diffusion
recommended: False

4
invokeai/frontend/install/model_install.py
View File

@ -127,7 +127,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.nextrely = top_of_table
self.pipeline_models = self.add_pipeline_widgets(
model_type=ModelType.Pipeline,
model_type=ModelType.Main,
window_width=window_width,
exclude = self.starter_models
)
@ -303,7 +303,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
### Tab for arbitrary diffusers widgets ###
def add_pipeline_widgets(self,
model_type: ModelType=ModelType.Pipeline,
model_type: ModelType=ModelType.Main,
window_width: int=120,
**kwargs,
)->dict[str,npyscreen.widget]: