rename ModelType.Pipeline to ModelType.Main

invokeai/app/invocations/model.py

@@ -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(

invokeai/backend/install/invokeai_configure.py

@@ -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)

invokeai/backend/install/migrate_to_3.py

@@ -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)

invokeai/backend/install/model_install_backend.py

@@ -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:

invokeai/backend/model_management/model_probe.py

@@ -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)

invokeai/backend/model_management/models/__init__.py

@@ -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,

invokeai/backend/model_management/models/base.py

@@ -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

invokeai/backend/model_management/models/stable_diffusion.py

@@ -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

invokeai/configs/INITIAL_MODELS.yaml

@@ -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

invokeai/frontend/install/model_install.py

@@ -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]: