list_models() API call now working

invokeai/app/api/routers/models.py

@@ -29,12 +29,9 @@ ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 
-# class ModelsList(BaseModel):
-#    models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
-
 
 class ModelsList(BaseModel):
-    models: List[ModelConfigBase]
+    models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
 
 
 @models_router.get(
@@ -51,9 +48,9 @@ async def list_models(
     if base_models and len(base_models) > 0:
         models_raw = list()
         for base_model in base_models:
-            models_raw.extend(manager.list_models(base_model=base_model, model_type=model_type))
+            models_raw.extend([x.dict() for x in manager.list_models(base_model=base_model, model_type=model_type)])
     else:
-        models_raw = manager.list_models(model_type=model_type)
+        models_raw = [x.dict() for x in manager.list_models(model_type=model_type)]
     models = parse_obj_as(ModelsList, {"models": models_raw})
     return models
 

invokeai/backend/model_manager/config.py

@@ -10,7 +10,7 @@ Typical usage:
              base_model='sd-1',
              model_type='main',
              config='configs/stable-diffusion/v1-inference.yaml',
-             model_variant='normal',
+             variant='normal',
              model_format='checkpoint'
             )
   config = ModelConfigFactory.make_config(raw)
@@ -173,12 +173,18 @@ class VaeDiffusersConfig(ModelConfigBase):
     model_format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
 
 
-class ControlNetDiffusersConfig(ModelConfigBase):
+class ControlNetDiffusersConfig(DiffusersConfig):
     """Model config for ControlNet models (diffusers version)."""
 
     model_format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
 
 
+class ControlNetCheckpointConfig(CheckpointConfig):
+    """Model config for ControlNet models (diffusers version)."""
+
+    model_format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
+
+
 class TextualInversionConfig(ModelConfigBase):
     """Model config for textual inversion embeddings."""
 
@@ -189,12 +195,14 @@ class MainConfig(ModelConfigBase):
     """Model config for main models."""
 
     vae: Optional[str] = Field(None)
-    model_variant: ModelVariantType = ModelVariantType.Normal
+    variant: ModelVariantType = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfig, MainConfig):
     """Model config for main checkpoint models."""
 
+    config: str
+
 
 class MainDiffusersConfig(DiffusersConfig, MainConfig):
     """Model config for main diffusers models."""

invokeai/backend/model_manager/download/__init__.py

@@ -7,6 +7,8 @@ from .base import (  # noqa F401
     UnknownJobIDException,
     DownloadJobBase,
     ModelSourceMetadata,
+    REPO_ID_RE,
+    HTTP_RE,
 )
 
 from .queue import DownloadQueue  # noqa F401

invokeai/backend/model_manager/download/base.py

@@ -11,6 +11,10 @@ from typing import List, Optional, Callable, Union
 from pydantic import BaseModel, Field
 from pydantic.networks import AnyHttpUrl
 
+# Used to distinguish between repo_id sources and URL sources
+REPO_ID_RE = r"^[\w-]+/[.\w-]+$"
+HTTP_RE = r"^https?://"
+
 
 class DownloadJobStatus(str, Enum):
     """State of a download job."""
@@ -49,7 +53,9 @@ class DownloadJobBase(BaseModel):
     id: int = Field(description="Numeric ID of this job", default=-1)  # default id is a placeholder
     source: str = Field(description="URL or repo_id to download")
     destination: Path = Field(description="Destination of URL on local disk")
-    metadata: Optional[ModelSourceMetadata] = Field(description="Model metadata (source-specific)", default=None)
+    metadata: ModelSourceMetadata = Field(
+        description="Model metadata (source-specific)", default_factory=ModelSourceMetadata
+    )
     access_token: Optional[str] = Field(description="access token needed to access this resource")
     status: DownloadJobStatus = Field(default=DownloadJobStatus.IDLE, description="Status of the download")
     bytes: int = Field(default=0, description="Bytes downloaded so far")

invokeai/backend/model_manager/download/queue.py

@@ -22,6 +22,7 @@ from huggingface_hub import HfApi, hf_hub_url
 
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.app.services.config import InvokeAIAppConfig
+from . import REPO_ID_RE, HTTP_RE
 from .base import (
     DownloadQueueBase,
     DownloadJobStatus,
@@ -59,7 +60,7 @@ class DownloadJobRepoID(DownloadJobBase):
     @validator("source")
     @classmethod
     def _validate_source(cls, v: str) -> str:
-        if not re.match(r"^[\w-]+/[\w-]+$", v):
+        if not re.match(REPO_ID_RE, v):
             raise ValidationError(f"{v} invalid repo_id")
         return v
 
@@ -123,10 +124,10 @@ class DownloadQueue(DownloadQueueBase):
 
         if Path(source).exists():
             cls = DownloadJobPath
-        elif re.match(r"^[\w-]+/[\w-]+$", str(source)):
+        elif re.match(REPO_ID_RE, str(source)):
             cls = DownloadJobRepoID
             kwargs = dict(variant=variant)
-        elif re.match(r"^https?://", str(source)):
+        elif re.match(HTTP_RE, str(source)):
             cls = DownloadJobURL
         else:
             raise NotImplementedError(f"Don't know what to do with this type of source: {source}")

invokeai/backend/model_manager/install.py

@@ -66,6 +66,8 @@ from .download import (
     DownloadJobBase,
     ModelSourceMetadata,
     DownloadEventHandler,
+    REPO_ID_RE,
+    HTTP_RE,
 )
 from .download.queue import DownloadJobURL, DownloadJobRepoID, DownloadJobPath
 from .hash import FastModelHash
@@ -481,10 +483,10 @@ class ModelInstall(ModelInstallBase):
         models_dir = self._config.models_path
         self._tmpdir = self._tmpdir or tempfile.TemporaryDirectory(dir=models_dir)
 
-        if re.match(r"^[\w-]+/[\w-]+$", str(source)):
+        if re.match(REPO_ID_RE, str(source)):
             cls = ModelInstallRepoIDJob
             kwargs = dict(variant=variant)
-        elif re.match(r"^https?://", str(source)):
+        elif re.match(HTTP_RE, str(source)):
             cls = ModelInstallURLJob
             kwargs = {}
         else:

invokeai/backend/model_manager/models/controlnet.py

@@ -7,7 +7,7 @@ import torch
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
-
+from ..config import ControlNetDiffusersConfig, ControlNetCheckpointConfig
 from .base import (
     BaseModelType,
     EmptyConfigLoader,
@@ -32,12 +32,11 @@ class ControlNetModel(ModelBase):
     # model_class: Type
     # model_size: int
 
-    class DiffusersConfig(ModelConfigBase):
-        model_format: Literal[ControlNetModelFormat.Diffusers]
+    class DiffusersConfig(ControlNetDiffusersConfig):
+        model_format: Literal[ControlNetModelFormat.Diffusers] = ControlNetModelFormat.Diffusers
 
-    class CheckpointConfig(ModelConfigBase):
-        model_format: Literal[ControlNetModelFormat.Checkpoint]
-        config: str
+    class CheckpointConfig(ControlNetCheckpointConfig):
+        model_format: Literal[ControlNetModelFormat.Checkpoint] = ControlNetModelFormat.Checkpoint
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.ControlNet

invokeai/backend/model_manager/models/lora.py

@@ -2,11 +2,11 @@ import bisect
 import os
 from enum import Enum
 from pathlib import Path
-from typing import Dict, Optional, Union
+from typing import Dict, Optional, Union, Literal
 
 import torch
 from safetensors.torch import load_file
-
+from ..config import LoRAConfig
 from .base import (
     BaseModelType,
     InvalidModelException,
@@ -27,8 +27,8 @@ class LoRAModelFormat(str, Enum):
 class LoRAModel(ModelBase):
     # model_size: int
 
-    class Config(ModelConfigBase):
-        model_format: LoRAModelFormat  # TODO:
+    class Config(LoRAConfig):
+        model_format: Literal[LoRAModelFormat.LyCORIS]  # TODO:
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.Lora

invokeai/backend/model_manager/models/sdxl.py

@@ -5,7 +5,7 @@ from typing import Literal, Optional
 
 from omegaconf import OmegaConf
 from pydantic import Field
-
+from ..config import MainDiffusersConfig, MainCheckpointConfig
 from .base import (
     BaseModelType,
     DiffusersModel,
@@ -25,16 +25,11 @@ class StableDiffusionXLModelFormat(str, Enum):
 
 class StableDiffusionXLModel(DiffusersModel):
     # TODO: check that configs overwriten properly
-    class DiffusersConfig(ModelConfigBase):
+    class DiffusersConfig(MainDiffusersConfig):
         model_format: Literal[StableDiffusionXLModelFormat.Diffusers]
-        vae: Optional[str] = Field(None)
-        variant: ModelVariantType
 
     class CheckpointConfig(ModelConfigBase):
         model_format: Literal[StableDiffusionXLModelFormat.Checkpoint]
-        vae: Optional[str] = Field(None)
-        config: str
-        variant: ModelVariantType
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert base_model in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner}

invokeai/backend/model_manager/models/stable_diffusion.py

@@ -12,6 +12,7 @@ from ..config import SilenceWarnings
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 
+from ..config import MainCheckpointConfig, MainDiffusersConfig
 from .base import (
     BaseModelType,
     DiffusersModel,
@@ -32,16 +33,11 @@ class StableDiffusion1ModelFormat(str, Enum):
 
 
 class StableDiffusion1Model(DiffusersModel):
-    class DiffusersConfig(ModelConfigBase):
+    class DiffusersConfig(MainDiffusersConfig):
         model_format: Literal[StableDiffusion1ModelFormat.Diffusers]
-        vae: Optional[str] = Field(None)
-        variant: ModelVariantType
 
-    class CheckpointConfig(ModelConfigBase):
+    class CheckpointConfig(MainCheckpointConfig):
         model_format: Literal[StableDiffusion1ModelFormat.Checkpoint]
-        vae: Optional[str] = Field(None)
-        config: str
-        variant: ModelVariantType
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert base_model == BaseModelType.StableDiffusion1

invokeai/backend/model_manager/models/stable_diffusion_onnx.py

@@ -2,7 +2,7 @@ from enum import Enum
 from typing import Literal
 
 from diffusers import OnnxRuntimeModel
-
+from ..config import ONNXSD1Config, ONNXSD2Config
 from .base import (
     BaseModelType,
     DiffusersModel,
@@ -21,9 +21,8 @@ class StableDiffusionOnnxModelFormat(str, Enum):
 
 
 class ONNXStableDiffusion1Model(DiffusersModel):
-    class Config(ModelConfigBase):
+    class Config(ONNXSD1Config):
         model_format: Literal[StableDiffusionOnnxModelFormat.Onnx]
-        variant: ModelVariantType
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert base_model == BaseModelType.StableDiffusion1
@@ -80,11 +79,8 @@ class ONNXStableDiffusion1Model(DiffusersModel):
 
 class ONNXStableDiffusion2Model(DiffusersModel):
     # TODO: check that configs overwriten properly
-    class Config(ModelConfigBase):
+    class Config(ONNXSD2Config):
         model_format: Literal[StableDiffusionOnnxModelFormat.Onnx]
-        variant: ModelVariantType
-        prediction_type: SchedulerPredictionType
-        upcast_attention: bool
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert base_model == BaseModelType.StableDiffusion2

invokeai/backend/model_manager/models/textual_inversion.py

@@ -1,10 +1,11 @@
 import os
-from typing import Optional
+from typing import Optional, Literal
 
 import torch
 
 # TODO: naming
 from ..lora import TextualInversionModel as TextualInversionModelRaw
+from ..config import ModelFormat, TextualInversionConfig
 from .base import (
     BaseModelType,
     InvalidModelException,
@@ -20,8 +21,15 @@ from .base import (
 class TextualInversionModel(ModelBase):
     # model_size: int
 
-    class Config(ModelConfigBase):
-        model_format: None
+    class FolderConfig(TextualInversionConfig):
+        """Config for embeddings that are represented as a folder containing learned_embeds.bin."""
+
+        model_format: Literal[ModelFormat.EmbeddingFolder]
+
+    class FileConfig(TextualInversionConfig):
+        """Config for embeddings that are contained in safetensors/checkpoint files."""
+
+        model_format: Literal[ModelFormat.EmbeddingFile]
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.TextualInversion

invokeai/backend/model_manager/models/vae.py

@@ -1,14 +1,14 @@
 import os
 from enum import Enum
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Literal
 
 import safetensors
 import torch
 from omegaconf import OmegaConf
 
 from invokeai.app.services.config import InvokeAIAppConfig
-
+from ..config import VaeDiffusersConfig, VaeCheckpointConfig
 from .base import (
     BaseModelType,
     EmptyConfigLoader,
@@ -34,8 +34,11 @@ class VaeModel(ModelBase):
     # vae_class: Type
     # model_size: int
 
-    class Config(ModelConfigBase):
-        model_format: VaeModelFormat
+    class DiffusersConfig(VaeDiffusersConfig):
+        model_format: Literal[VaeModelFormat.Diffusers] = VaeModelFormat.Diffusers
+
+    class CheckpointConfig(VaeCheckpointConfig):
+        model_format: Literal[VaeModelFormat.Checkpoint] = VaeModelFormat.Checkpoint
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert model_type == ModelType.Vae