diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py
index 3850fb6cc3..5159d5b89c 100644
--- a/invokeai/app/invocations/compel.py
+++ b/invokeai/app/invocations/compel.py
@@ -69,20 +69,12 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.services.model_manager.load.load_model_by_key(
-            **self.clip.tokenizer.model_dump(),
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.load.load_model_by_key(
-            **self.clip.text_encoder.model_dump(),
-            context=context,
-        )
+        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
+        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
-                lora_info = context.services.model_manager.load.load_model_by_key(
-                    **lora.model_dump(exclude={"weight"}), context=context
-                )
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                 assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
@@ -94,10 +86,7 @@ class CompelInvocation(BaseInvocation):
         for trigger in extract_ti_triggers_from_prompt(self.prompt):
             name = trigger[1:-1]
             try:
-                loaded_model = context.services.model_manager.load.load_model_by_key(
-                    **self.clip.text_encoder.model_dump(),
-                    context=context,
-                ).model
+                loaded_model = context.models.load(**self.clip.text_encoder.model_dump()).model
                 assert isinstance(loaded_model, TextualInversionModelRaw)
                 ti_list.append((name, loaded_model))
             except UnknownModelException:
@@ -165,14 +154,8 @@ class SDXLPromptInvocationBase:
         lora_prefix: str,
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
-        tokenizer_info = context.services.model_manager.load.load_model_by_key(
-            **clip_field.tokenizer.model_dump(),
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.load.load_model_by_key(
-            **clip_field.text_encoder.model_dump(),
-            context=context,
-        )
+        tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
+        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -197,9 +180,7 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in clip_field.loras:
-                lora_info = context.services.model_manager.load.load_model_by_key(
-                    **lora.model_dump(exclude={"weight"}), context=context
-                )
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                 lora_model = lora_info.model
                 assert isinstance(lora_model, LoRAModelRaw)
                 yield (lora_model, lora.weight)
@@ -212,11 +193,8 @@ class SDXLPromptInvocationBase:
         for trigger in extract_ti_triggers_from_prompt(prompt):
             name = trigger[1:-1]
             try:
-                ti_model = context.services.model_manager.load.load_model_by_attr(
-                    model_name=name,
-                    base_model=text_encoder_info.config.base,
-                    model_type=ModelType.TextualInversion,
-                    context=context,
+                ti_model = context.models.load_by_attrs(
+                    model_name=name, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
                 ).model
                 assert isinstance(ti_model, TextualInversionModelRaw)
                 ti_list.append((name, ti_model))
diff --git a/invokeai/app/invocations/ip_adapter.py b/invokeai/app/invocations/ip_adapter.py
index 01124f62f3..15e254010b 100644
--- a/invokeai/app/invocations/ip_adapter.py
+++ b/invokeai/app/invocations/ip_adapter.py
@@ -14,8 +14,7 @@ from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_management.models.base import BaseModelType, ModelType
-from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
 
 
 # LS: Consider moving these two classes into model.py
@@ -90,10 +89,10 @@ class IPAdapterInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.services.model_manager.store.get_model(self.ip_adapter_model.key)
+        ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
         image_encoder_model_id = ip_adapter_info.image_encoder_model_id
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
-        image_encoder_models = context.services.model_manager.store.search_by_attr(
+        image_encoder_models = context.models.search_by_attrs(
             model_name=image_encoder_model_name, base_model=BaseModelType.Any, model_type=ModelType.CLIPVision
         )
         assert len(image_encoder_models) == 1
diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 05293fdfee..5dd0eb074d 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -141,7 +141,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
         if self.image is not None:
-            image = context.services.images.get_pil_image(self.image.image_name)
+            image = context.images.get_pil(self.image.image_name)
             image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
             if image_tensor.dim() == 3:
                 image_tensor = image_tensor.unsqueeze(0)
@@ -153,10 +153,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
         if image_tensor is not None:
-            vae_info = context.services.model_manager.load.load_model_by_key(
-                **self.vae.vae.model_dump(),
-                context=context,
-            )
+            vae_info = context.models.load(**self.vae.vae.model_dump())
 
             img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
@@ -182,10 +179,7 @@ def get_scheduler(
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.services.model_manager.load.load_model_by_key(
-        **scheduler_info.model_dump(),
-        context=context,
-    )
+    orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -399,12 +393,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         #        and if weight is None, populate with default 1.0?
         controlnet_data = []
         for control_info in control_list:
-            control_model = exit_stack.enter_context(
-                context.services.model_manager.load.load_model_by_key(
-                    key=control_info.control_model.key,
-                    context=context,
-                )
-            )
+            control_model = exit_stack.enter_context(context.models.load(key=control_info.control_model.key))
 
             # control_models.append(control_model)
             control_image_field = control_info.image
@@ -466,25 +455,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
         conditioning_data.ip_adapter_conditioning = []
         for single_ip_adapter in ip_adapter:
             ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-                context.services.model_manager.load.load_model_by_key(
-                    key=single_ip_adapter.ip_adapter_model.key,
-                    context=context,
-                )
+                context.models.load(key=single_ip_adapter.ip_adapter_model.key)
             )
 
-            image_encoder_model_info = context.services.model_manager.load.load_model_by_key(
-                key=single_ip_adapter.image_encoder_model.key,
-                context=context,
-            )
+            image_encoder_model_info = context.models.load(key=single_ip_adapter.image_encoder_model.key)
 
             # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
             single_ipa_image_fields = single_ip_adapter.image
             if not isinstance(single_ipa_image_fields, list):
                 single_ipa_image_fields = [single_ipa_image_fields]
 
-            single_ipa_images = [
-                context.services.images.get_pil_image(image.image_name) for image in single_ipa_image_fields
-            ]
+            single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_image_fields]
 
             # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
             # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
@@ -528,10 +509,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            t2i_adapter_model_info = context.services.model_manager.load.load_model_by_key(
-                key=t2i_adapter_field.t2i_adapter_model.key,
-                context=context,
-            )
+            t2i_adapter_model_info = context.models.load(key=t2i_adapter_field.t2i_adapter_model.key)
             image = context.images.get_pil(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
@@ -676,30 +654,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            # Get the source node id (we are invoking the prepared node)
-            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
             # get the unet's config so that we can pass the base to dispatch_progress()
-            unet_config = context.services.model_manager.store.get_model(self.unet.unet.key)
+            unet_config = context.models.get_config(self.unet.unet.key)
 
             def step_callback(state: PipelineIntermediateState) -> None:
-                self.dispatch_progress(context, source_node_id, state, unet_config.base)
+                context.util.sd_step_callback(state, unet_config.base)
 
             def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
                 for lora in self.unet.loras:
-                    lora_info = context.services.model_manager.load.load_model_by_key(
-                        **lora.model_dump(exclude={"weight"}),
-                        context=context,
-                    )
+                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                     yield (lora_info.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.services.model_manager.load.load_model_by_key(
-                **self.unet.unet.model_dump(),
-                context=context,
-            )
+            unet_info = context.models.load(**self.unet.unet.model_dump())
             assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
                 ExitStack() as exit_stack,
@@ -806,10 +774,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        vae_info = context.services.model_manager.load.load_model_by_key(
-            **self.vae.vae.model_dump(),
-            context=context,
-        )
+        vae_info = context.models.load(**self.vae.vae.model_dump())
 
         with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, torch.nn.Module)
@@ -1032,10 +997,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.services.model_manager.load.load_model_by_key(
-            **self.vae.vae.model_dump(),
-            context=context,
-        )
+        vae_info = context.models.load(**self.vae.vae.model_dump())
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
@@ -1239,10 +1201,7 @@ class IdealSizeInvocation(BaseInvocation):
         return tuple((x - x % multiple_of) for x in args)
 
     def invoke(self, context: InvocationContext) -> IdealSizeOutput:
-        unet_config = context.services.model_manager.load.load_model_by_key(
-            **self.unet.unet.model_dump(),
-            context=context,
-        )
+        unet_config = context.models.get_config(**self.unet.unet.model_dump())
         aspect = self.width / self.height
         dimension: float = 512
         if unet_config.base == BaseModelType.StableDiffusion2:
diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index 71a71a63c8..6087bc82db 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -103,7 +103,7 @@ class MainModelLoaderInvocation(BaseInvocation):
         key = self.model.key
 
         # TODO: not found exceptions
-        if not context.services.model_manager.store.exists(key):
+        if not context.models.exists(key):
             raise Exception(f"Unknown model {key}")
 
         return ModelLoaderOutput(
@@ -172,7 +172,7 @@ class LoraLoaderInvocation(BaseInvocation):
 
         lora_key = self.lora.key
 
-        if not context.services.model_manager.store.exists(lora_key):
+        if not context.models.exists(lora_key):
             raise Exception(f"Unkown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
@@ -252,7 +252,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
 
         lora_key = self.lora.key
 
-        if not context.services.model_manager.store.exists(lora_key):
+        if not context.models.exists(lora_key):
             raise Exception(f"Unknown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
@@ -318,7 +318,7 @@ class VaeLoaderInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> VAEOutput:
         key = self.vae_model.key
 
-        if not context.services.model_manager.store.exists(key):
+        if not context.models.exists(key):
             raise Exception(f"Unkown vae: {key}!")
 
         return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))
diff --git a/invokeai/app/invocations/sdxl.py b/invokeai/app/invocations/sdxl.py
index 85e6fb787f..0df27c0011 100644
--- a/invokeai/app/invocations/sdxl.py
+++ b/invokeai/app/invocations/sdxl.py
@@ -43,7 +43,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
         model_key = self.model.key
 
         # TODO: not found exceptions
-        if not context.services.model_manager.store.exists(model_key):
+        if not context.models.exists(model_key):
             raise Exception(f"Unknown model: {model_key}")
 
         return SDXLModelLoaderOutput(
@@ -112,7 +112,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
         model_key = self.model.key
 
         # TODO: not found exceptions
-        if not context.services.model_manager.store.exists(model_key):
+        if not context.models.exists(model_key):
             raise Exception(f"Unknown model: {model_key}")
 
         return SDXLRefinerModelLoaderOutput(
diff --git a/invokeai/app/services/model_load/model_load_base.py b/invokeai/app/services/model_load/model_load_base.py
index 45eaf4652f..f4dd905135 100644
--- a/invokeai/app/services/model_load/model_load_base.py
+++ b/invokeai/app/services/model_load/model_load_base.py
@@ -4,7 +4,7 @@
 from abc import ABC, abstractmethod
 from typing import Optional
 
-from invokeai.app.invocations.baseinvocation import InvocationContext
+from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
@@ -19,14 +19,14 @@ class ModelLoadServiceBase(ABC):
         self,
         key: str,
         submodel_type: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         """
         Given a model's key, load it and return the LoadedModel object.
 
         :param key: Key of model config to be fetched.
         :param submodel: For main (pipeline models), the submodel to fetch.
-        :param context: Invocation context used for event reporting
+        :param context_data: Invocation context data used for event reporting
         """
         pass
 
@@ -35,14 +35,14 @@ class ModelLoadServiceBase(ABC):
         self,
         model_config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         """
         Given a model's configuration, load it and return the LoadedModel object.
 
         :param model_config: Model configuration record (as returned by ModelRecordBase.get_model())
         :param submodel: For main (pipeline models), the submodel to fetch.
-        :param context: Invocation context used for event reporting
+        :param context_data: Invocation context data used for event reporting
         """
         pass
 
@@ -53,7 +53,7 @@ class ModelLoadServiceBase(ABC):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         """
         Given a model's attributes, search the database for it, and if found, load and return the LoadedModel object.
@@ -66,7 +66,7 @@ class ModelLoadServiceBase(ABC):
         :param base_model: Base model
         :param model_type: Type of the model
         :param submodel: For main (pipeline models), the submodel to fetch
-        :param context: The invocation context.
+        :param context_data: The invocation context data.
 
         Exceptions: UnknownModelException -- model with these attributes not known
                     NotImplementedException -- a model loader was not provided at initialization time
diff --git a/invokeai/app/services/model_load/model_load_default.py b/invokeai/app/services/model_load/model_load_default.py
index a6ccd5afbc..29b297c814 100644
--- a/invokeai/app/services/model_load/model_load_default.py
+++ b/invokeai/app/services/model_load/model_load_default.py
@@ -3,10 +3,11 @@
 
 from typing import Optional
 
-from invokeai.app.invocations.baseinvocation import InvocationContext
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
+from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_records import ModelRecordServiceBase, UnknownModelException
+from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
 from invokeai.backend.model_manager.load import AnyModelLoader, LoadedModel, ModelCache, ModelConvertCache
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
@@ -46,6 +47,9 @@ class ModelLoadService(ModelLoadServiceBase):
             ),
         )
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+
     @property
     def ram_cache(self) -> ModelCacheBase[AnyModel]:
         """Return the RAM cache used by this loader."""
@@ -60,7 +64,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self,
         key: str,
         submodel_type: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         """
         Given a model's key, load it and return the LoadedModel object.
@@ -70,7 +74,7 @@ class ModelLoadService(ModelLoadServiceBase):
         :param context: Invocation context used for event reporting
         """
         config = self._store.get_model(key)
-        return self.load_model_by_config(config, submodel_type, context)
+        return self.load_model_by_config(config, submodel_type, context_data)
 
     def load_model_by_attr(
         self,
@@ -78,7 +82,7 @@ class ModelLoadService(ModelLoadServiceBase):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         """
         Given a model's attributes, search the database for it, and if found, load and return the LoadedModel object.
@@ -109,7 +113,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self,
         model_config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
+        context_data: Optional[InvocationContextData] = None,
     ) -> LoadedModel:
         """
         Given a model's configuration, load it and return the LoadedModel object.
@@ -118,15 +122,15 @@ class ModelLoadService(ModelLoadServiceBase):
         :param submodel: For main (pipeline models), the submodel to fetch.
         :param context: Invocation context used for event reporting
         """
-        if context:
+        if context_data:
             self._emit_load_event(
-                context=context,
+                context_data=context_data,
                 model_config=model_config,
             )
         loaded_model = self._any_loader.load_model(model_config, submodel_type)
-        if context:
+        if context_data:
             self._emit_load_event(
-                context=context,
+                context_data=context_data,
                 model_config=model_config,
                 loaded=True,
             )
@@ -134,26 +138,28 @@ class ModelLoadService(ModelLoadServiceBase):
 
     def _emit_load_event(
         self,
-        context: InvocationContext,
+        context_data: InvocationContextData,
         model_config: AnyModelConfig,
         loaded: Optional[bool] = False,
     ) -> None:
-        if context.services.queue.is_canceled(context.graph_execution_state_id):
+        if not self._invoker:
+            return
+        if self._invoker.services.queue.is_canceled(context_data.session_id):
             raise CanceledException()
 
         if not loaded:
-            context.services.events.emit_model_load_started(
-                queue_id=context.queue_id,
-                queue_item_id=context.queue_item_id,
-                queue_batch_id=context.queue_batch_id,
-                graph_execution_state_id=context.graph_execution_state_id,
+            self._invoker.services.events.emit_model_load_started(
+                queue_id=context_data.queue_id,
+                queue_item_id=context_data.queue_item_id,
+                queue_batch_id=context_data.batch_id,
+                graph_execution_state_id=context_data.session_id,
                 model_config=model_config,
             )
         else:
-            context.services.events.emit_model_load_completed(
-                queue_id=context.queue_id,
-                queue_item_id=context.queue_item_id,
-                queue_batch_id=context.queue_batch_id,
-                graph_execution_state_id=context.graph_execution_state_id,
+            self._invoker.services.events.emit_model_load_completed(
+                queue_id=context_data.queue_id,
+                queue_item_id=context_data.queue_item_id,
+                queue_batch_id=context_data.batch_id,
+                graph_execution_state_id=context_data.session_id,
                 model_config=model_config,
             )
diff --git a/invokeai/app/services/shared/invocation_context.py b/invokeai/app/services/shared/invocation_context.py
index c68dc1140b..089d09f825 100644
--- a/invokeai/app/services/shared/invocation_context.py
+++ b/invokeai/app/services/shared/invocation_context.py
@@ -1,4 +1,5 @@
 from dataclasses import dataclass
+from pathlib import Path
 from typing import TYPE_CHECKING, Optional
 
 from PIL.Image import Image
@@ -12,8 +13,9 @@ from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend.model_management.model_manager import LoadedModelInfo
-from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.backend.model_manager.metadata.metadata_base import AnyModelRepoMetadata
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 
@@ -259,45 +261,95 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
-    def exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> bool:
+    def exists(self, key: str) -> bool:
         """
         Checks if a model exists.
 
-        :param model_name: The name of the model to check.
-        :param base_model: The base model of the model to check.
-        :param model_type: The type of the model to check.
+        :param key: The key of the model.
         """
-        return self._services.model_manager.model_exists(model_name, base_model, model_type)
+        return self._services.model_manager.store.exists(key)
 
-    def load(
-        self, model_name: str, base_model: BaseModelType, model_type: ModelType, submodel: Optional[SubModelType] = None
-    ) -> LoadedModelInfo:
+    def load(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """
         Loads a model.
 
-        :param model_name: The name of the model to get.
-        :param base_model: The base model of the model to get.
-        :param model_type: The type of the model to get.
-        :param submodel: The submodel of the model to get.
+        :param key: The key of the model.
+        :param submodel_type: The submodel of the model to get.
         :returns: An object representing the loaded model.
         """
 
         # The model manager emits events as it loads the model. It needs the context data to build
         # the event payloads.
 
-        return self._services.model_manager.get_model(
-            model_name, base_model, model_type, submodel, context_data=self._context_data
+        return self._services.model_manager.load.load_model_by_key(
+            key=key, submodel_type=submodel_type, context_data=self._context_data
         )
 
-    def get_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
+    def load_by_attrs(
+        self, model_name: str, base_model: BaseModelType, model_type: ModelType, submodel: Optional[SubModelType] = None
+    ) -> LoadedModel:
+        """
+        Loads a model by its attributes.
+
+        :param model_name: Name of to be fetched.
+        :param base_model: Base model
+        :param model_type: Type of the model
+        :param submodel: For main (pipeline models), the submodel to fetch
+        """
+        return self._services.model_manager.load.load_model_by_attr(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+            submodel=submodel,
+            context_data=self._context_data,
+        )
+
+    def get_config(self, key: str) -> AnyModelConfig:
         """
         Gets a model's info, an dict-like object.
 
-        :param model_name: The name of the model to get.
-        :param base_model: The base model of the model to get.
-        :param model_type: The type of the model to get.
+        :param key: The key of the model.
         """
-        return self._services.model_manager.model_info(model_name, base_model, model_type)
+        return self._services.model_manager.store.get_model(key=key)
+
+    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
+        """
+        Gets a model's metadata, if it has any.
+
+        :param key: The key of the model.
+        """
+        return self._services.model_manager.store.get_metadata(key=key)
+
+    def search_by_path(self, path: Path) -> list[AnyModelConfig]:
+        """
+        Searches for models by path.
+
+        :param path: The path to search for.
+        """
+        return self._services.model_manager.store.search_by_path(path)
+
+    def search_by_attrs(
+        self,
+        model_name: Optional[str] = None,
+        base_model: Optional[BaseModelType] = None,
+        model_type: Optional[ModelType] = None,
+        model_format: Optional[ModelFormat] = None,
+    ) -> list[AnyModelConfig]:
+        """
+        Searches for models by attributes.
+
+        :param model_name: Name of to be fetched.
+        :param base_model: Base model
+        :param model_type: Type of the model
+        :param submodel: For main (pipeline models), the submodel to fetch
+        """
+
+        return self._services.model_manager.store.search_by_attr(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+            model_format=model_format,
+        )
 
 
 class ConfigInterface(InvocationContextInterface):
diff --git a/invokeai/app/util/step_callback.py b/invokeai/app/util/step_callback.py
index d83b380d95..33d00ca366 100644
--- a/invokeai/app/util/step_callback.py
+++ b/invokeai/app/util/step_callback.py
@@ -4,8 +4,8 @@ import torch
 from PIL import Image
 
 from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException, ProgressImage
+from invokeai.backend.model_manager.config import BaseModelType
 
-from ...backend.model_management.models import BaseModelType
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util.util import image_to_dataURL