Fixes

invokeai/app/invocations/model.py

@@ -64,7 +64,7 @@ class ModelLoaderInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
 
-        base_model = BaseModelType.StableDiffusion2 # TODO:
+        base_model = BaseModelType.StableDiffusion1_5 # TODO:
 
         # TODO: not found exceptions
         if not context.services.model_manager.model_exists(
@@ -116,7 +116,7 @@ class ModelLoaderInvocation(BaseInvocation):
                     model_name=self.model_name,
                     base_model=base_model,
                     model_type=ModelType.Pipeline,
-                    submodel=SDModelType.Scheduler,
+                    submodel=SubModelType.Scheduler,
                 ),
                 loras=[],
             ),
@@ -125,13 +125,13 @@ class ModelLoaderInvocation(BaseInvocation):
                     model_name=self.model_name,
                     base_model=base_model,
                     model_type=ModelType.Pipeline,
-                    submodel=SDModelType.Tokenizer,
+                    submodel=SubModelType.Tokenizer,
                 ),
                 text_encoder=ModelInfo(
                     model_name=self.model_name,
                     base_model=base_model,
                     model_type=ModelType.Pipeline,
-                    submodel=SDModelType.TextEncoder,
+                    submodel=SubModelType.TextEncoder,
                 ),
                 loras=[],
             ),
@@ -140,7 +140,7 @@ class ModelLoaderInvocation(BaseInvocation):
                     model_name=self.model_name,
                     base_model=base_model,
                     model_type=ModelType.Pipeline,
-                    submodel=SDModelType.Vae,
+                    submodel=SubModelType.Vae,
                 ),
             )
         )

invokeai/backend/model_management/model_cache.py

@@ -162,7 +162,8 @@ class ModelCache(object):
         if model_info_key not in self.model_infos:
             self.model_infos[model_info_key] = model_class(
                 model_path,
-                model_class,
+                base_model,
+                model_type,
             )
 
         return self.model_infos[model_info_key]
@@ -208,7 +209,7 @@ class ModelCache(object):
 
             # clean memory to make MemoryUsage() more accurate
             gc.collect()
-            model = model_info.get_model(submodel, torch_dtype=self.precision)
+            model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
             if mem_used := model_info.get_size(submodel):
                 self.logger.debug(f'CPU RAM used for load: {(mem_used/GIG):.2f} GB')
 

invokeai/backend/model_management/model_manager.py

@@ -430,10 +430,10 @@ class ModelManager(object):
 
         # vae/movq override
         # TODO: 
-        if submodel is not None and submodel in model_config:
+        if submodel_type is not None and submodel_type in model_config:
-            model_path = model_config[submodel]
+            model_path = model_config[submodel_type]
-            model_type = submodel
+            model_type = submodel_type
-            submodel = None
+            submodel_type = None
 
         dst_convert_path = None # TODO:
         model_path = model_class.convert_if_required(
@@ -443,9 +443,11 @@ class ModelManager(object):
         )
 
         model_context = self.cache.get_model(
-            model_path,
+            model_path=model_path,
-            model_class,
+            model_class=model_class,
-            submodel_type,
+            base_model=base_model,
+            model_type=model_type,
+            submodel=submodel_type,
         )
 
         hash = "<NO_HASH>" # TODO:

invokeai/backend/model_management/models/base.py

@@ -1,3 +1,4 @@
+import os
 import sys
 import typing
 import inspect

invokeai/backend/model_management/models/stable_diffusion.py

@@ -102,7 +102,8 @@ class StableDiffusion15Model(DiffusersModel):
             elif kwargs["format"] == "diffusers":
                 unet_config_path = os.path.join(kwargs["path"], "unet", "config.json")
                 if os.path.exists(unet_config_path):
-                    unet_config = json.loads(unet_config_path)
+                    with open(unet_config_path, "r") as f:
+                        unet_config = json.loads(f.read())
                     in_channels = unet_config['in_channels']
 
                 else:
@@ -135,10 +136,14 @@ class StableDiffusion15Model(DiffusersModel):
             return "checkpoint"
 
     @classmethod
-    def convert_if_required(cls, model_path: str, dst_cache_path: str, config: Optional[dict]) -> str:
+    def convert_if_required(cls, model_path: str, dst_cache_path: str, config: ModelConfigBase) -> str:
-        cfg = cls.build_config(**config)
+        if isinstance(config, cls.CheckpointConfig):
-        if isinstance(cfg, cls.CheckpointConfig):
+            return _convert_ckpt_and_cache(
-            return _convert_ckpt_and_cache(cfg) # TODO: args
+                version=BaseModelType.StableDiffusion1_5,
+                config=config.dict(),
+                in_path=model_path,
+                out_path=dst_cache_path,
+            ) # TODO: args
         else:
             return model_path
 
@@ -154,10 +159,22 @@ class StableDiffusion2BaseModel(StableDiffusion15Model):
             model_type=ModelType.Pipeline,
         )
 
-class StableDiffusion2Model(DiffusersModel):
+    @classmethod
+    def convert_if_required(cls, model_path: str, dst_cache_path: str, config: ModelConfigBase) -> str:
+        if isinstance(config, cls.CheckpointConfig):
+            return _convert_ckpt_and_cache(
+                version=BaseModelType.StableDiffusion2Base,
+                config=config.dict(),
+                in_path=model_path,
+                out_path=dst_cache_path,
+            ) # TODO: args
+        else:
+            return model_path
+
+class StableDiffusion2Model(StableDiffusion15Model):
 
     # TODO: str -> Path?
-    # overwrite configs
+    # TODO: check that configs overwriten
     class DiffusersConfig(ModelConfigBase):
         format: Literal["diffusers"]
         vae: Optional[str] = Field(None)
@@ -174,22 +191,49 @@ class StableDiffusion2Model(DiffusersModel):
         # skip StableDiffusion15Model __init__
         assert base_model == BaseModelType.StableDiffusion2
         assert model_type == ModelType.Pipeline
-        super().__init__(
+        # skip StableDiffusion15Model __init__
+        super(StableDiffusion15Model, self).__init__(
             model_path=model_path,
             base_model=BaseModelType.StableDiffusion2,
             model_type=ModelType.Pipeline,
         )
 
+    @classmethod
+    def convert_if_required(cls, model_path: str, dst_cache_path: str, config: ModelConfigBase) -> str:
+        if isinstance(config, cls.CheckpointConfig):
+            return _convert_ckpt_and_cache(
+                version=BaseModelType.StableDiffusion2,
+                config=config.dict(),
+                in_path=model_path,
+                out_path=dst_cache_path,
+            ) # TODO: args
+        else:
+            return model_path
+
 
 # TODO: rework
 DictConfig = dict
-def _convert_ckpt_and_cache(self, mconfig: DictConfig) -> str:
+def _convert_ckpt_and_cache(
+    self,
+    version: BaseModelType,
+    mconfig: dict, # TODO:
+    in_path: str,
+    out_path: str,
+) -> str:
     """
     Convert the checkpoint model indicated in mconfig into a
     diffusers, cache it to disk, and return Path to converted
     file. If already on disk then just returns Path.
     """
+    raise NotImplementedError()
     app_config = InvokeAIAppConfig.get_config()
+
+    #if "config" not in mconfig:
+    #    if version == BaseModelType.StableDiffusion1_5:
+            #if 
+            #mconfig["config"] = app_config.config_dir / "stable-diffusion" / "v1-inference.yaml"
+
+
     weights = app_config.root_dir / mconfig.path
     config_file = app_config.root_dir / mconfig.config
     diffusers_path = app_config.converted_ckpts_dir / weights.stem