initialize InvokeAIGenerator object with model, not manager

invokeai/app/invocations/generate.py

@@ -57,8 +57,9 @@ class TextToImageInvocation(BaseInvocation):
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        manager = context.services.model_manager
-        outputs = Txt2Img(manager).generate(
+        #       (right now uses whatever current model is set in model manager)
+        model= context.services.model_manager.get_model()
+        outputs = Txt2Img(model).generate(
             prompt=self.prompt,
             step_callback=step_callback,
             **self.dict(
@@ -113,9 +114,9 @@ class ImageToImageInvocation(TextToImageInvocation):
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        manager = context.services.model_manager
+        model = context.services.model_manager.get_model()
         generator_output = next(
-            Img2Img(manager).generate(
+            Img2Img(model).generate(
                 prompt=self.prompt,
                 init_img=image,
                 init_mask=mask,
@@ -174,9 +175,9 @@ class InpaintInvocation(ImageToImageInvocation):
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        manager = context.services.model_manager
+        manager = context.services.model_manager.get_model()
         generator_output = next(
-            Inpaint(manager).generate(
+            Inpaint(model).generate(
                 prompt=self.prompt,
                 init_img=image,
                 init_mask=mask,

invokeai/backend/generator/base.py

@@ -29,14 +29,12 @@ from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
 from ..safety_checker import SafetyChecker
 from ..prompting.conditioning import get_uc_and_c_and_ec
-from ..model_management.model_manager import ModelManager
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 downsampling = 8
 
 @dataclass
 class InvokeAIGeneratorBasicParams:
-    model_name: str='stable-diffusion-1.5'
     seed: int=None
     width: int=512
     height: int=512
@@ -86,10 +84,10 @@ class InvokeAIGenerator(metaclass=ABCMeta):
     )
 
     def __init__(self,
-                 model_manager: ModelManager,
+                 model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
                  ):
-        self.model_manager=model_manager
+        self.model_info=model_info
         self.params=params
 
     def generate(self,
@@ -123,8 +121,8 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         generator_args = dataclasses.asdict(self.params)
         generator_args.update(keyword_args)
 
-        model_name = generator_args.get('model_name') or self.model_manager.current_model
-        model_info: dict = self.model_manager.get_model(model_name)
+        model_info = self.model_info
+        model_name = model_info['model_name']
         model:StableDiffusionGeneratorPipeline = model_info['model']
         model_hash = model_info['hash']
         scheduler: Scheduler = self.get_scheduler(
@@ -164,7 +162,7 @@ class InvokeAIGenerator(metaclass=ABCMeta):
                 seed=results[0][1],
                 attention_maps_images=results[0][2],
                 model_hash = model_hash,
-                params=Namespace(**generator_args),
+                params=Namespace(model_name=model_name,**generator_args),
             )
             if callback:
                 callback(output)

invokeai/backend/model_management/model_manager.py

@@ -88,12 +88,15 @@ class ModelManager(object):
         """
         return model_name in self.config
 
-    def get_model(self, model_name: str):
+    def get_model(self, model_name: str=None):
         """
         Given a model named identified in models.yaml, return
         the model object. If in RAM will load into GPU VRAM.
         If on disk, will load from there.
         """
+        if not model_name:
+            return self.current_model if self.current_model else self.get_model(self.default_model())
+        
         if not self.valid_model(model_name):
             print(
                 f'** "{model_name}" is not a known model name. Please check your models.yaml file'
@@ -116,6 +119,7 @@ class ModelManager(object):
         else:  # we're about to load a new model, so potentially offload the least recently used one
             requested_model, width, height, hash = self._load_model(model_name)
             self.models[model_name] = {
+                "model_name": model_name,
                 "model": requested_model,
                 "width": width,
                 "height": height,
@@ -125,6 +129,7 @@ class ModelManager(object):
         self.current_model = model_name
         self._push_newest_model(model_name)
         return {
+            "model_name": model_name,
             "model": requested_model,
             "width": width,
             "height": height,