Remove image generation node dependencies on generate.py

invokeai/app/api/dependencies.py

@@ -4,7 +4,8 @@
 from argparse import Namespace
 
 from ...backend import Globals
-from ..services.generate_initializer import get_generate
+from ..services.model_manager_initializer import get_model_manager
+from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
@@ -37,18 +38,16 @@ class ApiDependencies:
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(args, config, event_handler_id: int):
-        Globals.try_patchmatch = args.patchmatch
-        Globals.always_use_cpu = args.always_use_cpu
-        Globals.internet_available = args.internet_available and check_internet()
-        Globals.disable_xformers = not args.xformers
-        Globals.ckpt_convert = args.ckpt_convert
+    def initialize(config, event_handler_id: int):
+        Globals.try_patchmatch = config.patchmatch
+        Globals.always_use_cpu = config.always_use_cpu
+        Globals.internet_available = config.internet_available and check_internet()
+        Globals.disable_xformers = not config.xformers
+        Globals.ckpt_convert = config.ckpt_convert
 
         # TODO: Use a logger
         print(f">> Internet connectivity is {Globals.internet_available}")
 
-        generate = get_generate(args, config)
-
         events = FastAPIEventService(event_handler_id)
 
         output_folder = os.path.abspath(
@@ -61,14 +60,15 @@ def initialize(args, config, event_handler_id: int):
         db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            generate=generate,
+            model_manager=get_model_manager(config),
             events=events,
             images=images,
             queue=MemoryInvocationQueue(),
             graph_execution_manager=SqliteItemStorage[GraphExecutionState](
                 filename=db_location, table_name="graph_executions"
             ),
             processor=DefaultInvocationProcessor(),
+            restoration=RestorationServices(config),
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api_app.py

@@ -1,5 +1,4 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
 import asyncio
 from inspect import signature
 
@@ -53,11 +52,11 @@
 # Add startup event to load dependencies
 @app.on_event("startup")
 async def startup_event():
-    args = Args()
-    config = args.parse_args()
+    config = Args()
+    config.parse_args()
 
     ApiDependencies.initialize(
-        args=args, config=config, event_handler_id=event_handler_id
+        config=config, event_handler_id=event_handler_id
     )
 
 

invokeai/app/cli_app.py

@@ -17,7 +17,8 @@
 from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
-from .services.generate_initializer import get_generate
+from .services.model_manager_initializer import get_model_manager
+from .services.restoration_services import RestorationServices
 from .services.graph import EdgeConnection, GraphExecutionState
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
@@ -126,14 +127,9 @@ def invoke_all(context: CliContext):
 
 
 def invoke_cli():
-    args = Args()
-    config = args.parse_args()
-
-    generate = get_generate(args, config)
-
-    # NOTE: load model on first use, uncomment to load at startup
-    # TODO: Make this a config option?
-    # generate.load_model()
+    config = Args()
+    config.parse_args()
+    model_manager = get_model_manager(config)
 
     events = EventServiceBase()
 
@@ -145,14 +141,15 @@ def invoke_cli():
     db_location = os.path.join(output_folder, "invokeai.db")
 
     services = InvocationServices(
-        generate=generate,
+        model_manager=model_manager,
         events=events,
         images=DiskImageStorage(output_folder),
         queue=MemoryInvocationQueue(),
         graph_execution_manager=SqliteItemStorage[GraphExecutionState](
             filename=db_location, table_name="graph_executions"
         ),
         processor=DefaultInvocationProcessor(),
+        restoration=RestorationServices(config),
     )
 
     invoker = Invoker(services)

invokeai/app/invocations/generate.py

@@ -12,12 +12,12 @@
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput
+from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
 
 SAMPLER_NAME_VALUES = Literal[
-    "ddim", "plms", "k_lms", "k_dpm_2", "k_dpm_2_a", "k_euler", "k_euler_a", "k_heun"
+    tuple(InvokeAIGenerator.schedulers())
 ]
 
-
 # Text to image
 class TextToImageInvocation(BaseInvocation):
     """Generates an image using text2img."""
@@ -57,19 +57,18 @@ def step_callback(sample, step=0):
         # 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?
-        if self.model is None or self.model == "":
-            self.model = context.services.generate.model_name
-
-        # Set the model (if already cached, this does nothing)
-        context.services.generate.set_model(self.model)
-
-        results = context.services.generate.prompt2image(
+        #       (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(
                 exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
         )
+        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
+        # each time it is called. We only need the first one.
+        generate_output = next(outputs)
 
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?
@@ -78,7 +77,7 @@ def step_callback(sample, step=0):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, results[0][0])
+        context.services.images.save(image_type, image_name, generate_output.image)
         return ImageOutput(
             image=ImageField(image_type=image_type, image_name=image_name)
         )
@@ -115,23 +114,20 @@ def step_callback(sample, step=0):
         # 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?
-        if self.model is None or self.model == "":
-            self.model = context.services.generate.model_name
-
-        # Set the model (if already cached, this does nothing)
-        context.services.generate.set_model(self.model)
-
-        results = context.services.generate.prompt2image(
-            prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
-            step_callback=step_callback,
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
+        model = context.services.model_manager.get_model()
+        generator_output = next(
+            Img2Img(model).generate(
+                prompt=self.prompt,
+                init_img=image,
+                init_mask=mask,
+                step_callback=step_callback,
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
         )
 
-        result_image = results[0][0]
+        result_image = generator_output.image
 
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?
@@ -145,7 +141,6 @@ def step_callback(sample, step=0):
             image=ImageField(image_type=image_type, image_name=image_name)
         )
 
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -180,23 +175,20 @@ def step_callback(sample, step=0):
         # 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?
-        if self.model is None or self.model == "":
-            self.model = context.services.generate.model_name
-
-        # Set the model (if already cached, this does nothing)
-        context.services.generate.set_model(self.model)
-
-        results = context.services.generate.prompt2image(
-            prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
-            step_callback=step_callback,
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
+        manager = context.services.model_manager.get_model()
+        generator_output = next(
+            Inpaint(model).generate(
+                prompt=self.prompt,
+                init_img=image,
+                init_mask=mask,
+                step_callback=step_callback,
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
         )
 
-        result_image = results[0][0]
+        result_image = generator_output.image
 
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?

invokeai/app/invocations/reconstruct.py

@@ -8,7 +8,6 @@
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput
 
-
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
     #fmt: off
@@ -23,7 +22,7 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
         )
-        results = context.services.generate.upscale_and_reconstruct(
+        results = context.services.restoration.upscale_and_reconstruct(
             image_list=[[image, 0]],
             upscale=None,
             strength=self.strength,  # GFPGAN strength

invokeai/app/invocations/upscale.py

@@ -26,7 +26,7 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
         )
-        results = context.services.generate.upscale_and_reconstruct(
+        results = context.services.restoration.upscale_and_reconstruct(
             image_list=[[image, 0]],
             upscale=(self.level, self.strength),
             strength=0.0,  # GFPGAN strength