remove factory pattern

Factory pattern is now removed. Typical usage of the InvokeAIGenerator is now:

```
from invokeai.backend.generator import (
    InvokeAIGeneratorBasicParams,
    Txt2Img,
    Img2Img,
    Inpaint,
)
    params = InvokeAIGeneratorBasicParams(
        model_name = 'stable-diffusion-1.5',
        steps = 30,
        scheduler = 'k_lms',
        cfg_scale = 8.0,
        height = 640,
        width = 640
        )
    print ('=== TXT2IMG TEST ===')
    txt2img = Txt2Img(manager, params)
    outputs = txt2img.generate(prompt='banana sushi', iterations=2)

    for i in outputs:
        print(f'image={output.image}, seed={output.seed}, model={output.params.model_name}, hash={output.model_hash}, steps={output.params.steps}')
```

The `params` argument is optional, so if you wish to accept default
parameters and selectively override them, just do this:

```
    outputs = Txt2Img(manager).generate(prompt='banana sushi',
                                        steps=50,
					scheduler='k_heun',
					model_name='stable-diffusion-2.1'
					)
```

invokeai/app/api/dependencies.py

@@ -4,7 +4,7 @@ import os
 from argparse import Namespace
 
 from ...backend import Globals
-from ..services.generate_initializer import get_generator_factory
+from ..services.generate_initializer import get_model_manager
 from ..services.graph import GraphExecutionState
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
@@ -47,7 +47,7 @@ class ApiDependencies:
         # TODO: Use a logger
         print(f">> Internet connectivity is {Globals.internet_available}")
 
-        generator_factory = get_generator_factory(args, config)
+        model_manager = get_model_manager(args, config)
 
         events = FastAPIEventService(event_handler_id)
 

invokeai/app/cli_app.py

@@ -17,7 +17,7 @@ from .cli.commands import BaseCommand, CliContext, ExitCli, add_parsers, get_gra
 from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
-from .services.generate_initializer import get_generator_factory
+from .services.generate_initializer import get_model_manager
 from .services.graph import EdgeConnection, GraphExecutionState
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
@@ -129,7 +129,7 @@ def invoke_cli():
     args = Args()
     config = args.parse_args()
 
-    generator_factory = get_generator_factory(args, config)
+    model_manager = get_model_manager(args, config)
 
     events = EventServiceBase()
 
@@ -141,7 +141,7 @@ def invoke_cli():
     db_location = os.path.join(output_folder, "invokeai.db")
 
     services = InvocationServices(
-        generator_factory=generator_factory,
+        model_manager=model_manager,
         events=events,
         images=DiskImageStorage(output_folder),
         queue=MemoryInvocationQueue(),

invokeai/app/invocations/generate.py

@@ -18,7 +18,6 @@ SAMPLER_NAME_VALUES = Literal[
     tuple(InvokeAIGenerator.schedulers())
 ]
 
-
 # Text to image
 class TextToImageInvocation(BaseInvocation):
     """Generates an image using text2img."""
@@ -58,15 +57,8 @@ 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?
-        factory = context.services.generator_factory
+        manager = context.services.model_manager
-        if self.model:
+        outputs = Txt2Img(manager).generate(
-            factory.model_name = self.model
-        else:
-            self.model = factory.model_name
-
-        txt2img = factory.make_generator(Txt2Img)
-
-        outputs = txt2img.generate(
             prompt=self.prompt,
             step_callback=step_callback,
             **self.dict(
@@ -121,13 +113,9 @@ 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?
-        factory = context.services.generator_factory
+        manager = context.services.model_manager
-        self.model = self.model or factory.model_name
-        factory.model_name = self.model
-        img2img = factory.make_generator(Img2Img)
-
         generator_output = next(
-            img2img.generate(
+            Img2Img(manager).generate(
                 prompt=self.prompt,
                 init_img=image,
                 init_mask=mask,
@@ -186,13 +174,9 @@ 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?
-        factory = context.services.generator_factory
+        manager = context.services.model_manager
-        self.model = self.model or factory.model_name
-        factory.model_name = self.model
-        inpaint = factory.make_generator(Inpaint)
-
         generator_output = next(
-            inpaint.generate(
+            Inpaint(manager).generate(
                 prompt=self.prompt,
                 init_img=image,
                 init_mask=mask,

invokeai/app/services/generate_initializer.py

@@ -6,12 +6,12 @@ from argparse import Namespace
 from omegaconf import OmegaConf
 
 import invokeai.version
-from ...backend import ModelManager, InvokeAIGeneratorBasicParams, InvokeAIGeneratorFactory
+from ...backend import ModelManager
 from ...backend.util import choose_precision, choose_torch_device
 from ...backend import Globals
 
 # TODO: most of this code should be split into individual services as the Generate.py code is deprecated
-def get_generator_factory(args, config) -> InvokeAIGeneratorFactory:
+def get_model_manager(args, config) -> ModelManager:
     if not args.conf:
         config_file = os.path.join(Globals.root, "configs", "models.yaml")
         if not os.path.exists(config_file):
@@ -64,7 +64,7 @@ def get_generator_factory(args, config) -> InvokeAIGeneratorFactory:
             print(f"{e}. Aborting.")
             sys.exit(-1)
 
-    # creating an InvokeAIGeneratorFactory object:
+    # creating the model manager
     try:
         device = torch.device(choose_torch_device())
         precision = 'float16' if args.precision=='float16' \
@@ -77,11 +77,6 @@ def get_generator_factory(args, config) -> InvokeAIGeneratorFactory:
             device_type=device,
             max_loaded_models=args.max_loaded_models,
         )
-        # TO DO: initialize and pass safety checker!!!
-        params = InvokeAIGeneratorBasicParams(
-            precision=precision,
-        )
-        factory = InvokeAIGeneratorFactory(model_manager, params)
     except (FileNotFoundError, TypeError, AssertionError) as e:
         report_model_error(args, e)
     except (IOError, KeyError) as e:
@@ -100,7 +95,7 @@ def get_generator_factory(args, config) -> InvokeAIGeneratorFactory:
             weights_directory=path,
         )
 
-    return factory
+    return model_manager
 
 def load_face_restoration(opt):
     try:

invokeai/app/services/invocation_services.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-from invokeai.backend import InvokeAIGeneratorFactory
+from invokeai.backend import ModelManager
 
 from .events import EventServiceBase
 from .image_storage import ImageStorageBase
@@ -10,7 +10,7 @@ from .item_storage import ItemStorageABC
 class InvocationServices:
     """Services that can be used by invocations"""
 
-    generator_factory: InvokeAIGeneratorFactory
+    model_manager: ModelManager
     events: EventServiceBase
     images: ImageStorageBase
     queue: InvocationQueueABC
@@ -21,14 +21,14 @@ class InvocationServices:
 
     def __init__(
             self,
-            generator_factory: InvokeAIGeneratorFactory,
+            model_manager: ModelManager,
             events: EventServiceBase,
             images: ImageStorageBase,
             queue: InvocationQueueABC,
             graph_execution_manager: ItemStorageABC["GraphExecutionState"],
             processor: "InvocationProcessorABC",
     ):
-        self.generator_factory = generator_factory
+        self.model_manager = model_manager
         self.events = events
         self.images = images
         self.queue = queue

invokeai/backend/__init__.py

@@ -4,7 +4,6 @@ Initialization file for invokeai.backend
 from .generate import Generate
 from .generator import (
     InvokeAIGeneratorBasicParams,
-    InvokeAIGeneratorFactory,
     InvokeAIGenerator,
     InvokeAIGeneratorOutput,
     Txt2Img,

invokeai/backend/generator/__init__.py

@@ -2,7 +2,6 @@
 Initialization file for the invokeai.generator package
 """
 from .base import (
-    InvokeAIGeneratorFactory,
     InvokeAIGenerator,
     InvokeAIGeneratorBasicParams,
     InvokeAIGeneratorOutput,

invokeai/backend/generator/base.py

@@ -11,7 +11,8 @@ import diffusers
 import os
 import random
 import traceback
-from abc import ABCMeta, abstractmethod
+from abc import ABCMeta
+from argparse import Namespace
 from contextlib import nullcontext
 
 import cv2
@@ -21,7 +22,7 @@ from PIL import Image, ImageChops, ImageFilter
 from accelerate.utils import set_seed
 from diffusers import DiffusionPipeline
 from tqdm import trange
-from typing import List, Type, Iterator
+from typing import List, Iterator
 from dataclasses import dataclass, field
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
@@ -35,13 +36,13 @@ downsampling = 8
 
 @dataclass
 class InvokeAIGeneratorBasicParams:
+    model_name: str='stable-diffusion-1.5'
     seed: int=None
     width: int=512
     height: int=512
     cfg_scale: int=7.5
     steps: int=20
     ddim_eta: float=0.0
-    model_name: str='stable-diffusion-1.5'
     scheduler: int='ddim'
     precision: str='float16'
     perlin: float=0.0
@@ -62,41 +63,8 @@ class InvokeAIGeneratorOutput:
     '''
     image: Image
     seed: int
-    model_name: str
     model_hash: str
-    params: dict
+    params: Namespace
-
-    def __getattribute__(self,name):
-        try:
-            return object.__getattribute__(self, name)
-        except AttributeError:
-            params = object.__getattribute__(self, 'params')
-            if name in params:
-                return params[name]
-        raise AttributeError(f"'{self.__class__.__name__}' has no attribute '{name}'")
-
-class InvokeAIGeneratorFactory(object):
-    def __init__(self,
-                 model_manager: ModelManager,
-                 params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
-                 ):
-        self.model_manager = model_manager
-        self.params = params
-                 
-    def make_generator(self, generatorclass: Type[InvokeAIGenerator], **keyword_args)->InvokeAIGenerator:
-        return generatorclass(self.model_manager,
-                              self.params,
-                              **keyword_args
-                              )
-
-    # getter and setter shortcuts for commonly used parameters
-    @property
-    def model_name(self)->str:
-        return self.params.model_name
-
-    @model_name.setter
-    def model_name(self, model_name: str):
-        self.params.model_name=model_name
 
 # we are interposing a wrapper around the original Generator classes so that
 # old code that calls Generate will continue to work.
@@ -116,7 +84,7 @@ class InvokeAIGenerator(metaclass=ABCMeta):
 
     def __init__(self,
                  model_manager: ModelManager,
-                 params: InvokeAIGeneratorBasicParams,
+                 params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
                  ):
         self.model_manager=model_manager
         self.params=params
@@ -149,23 +117,24 @@ class InvokeAIGenerator(metaclass=ABCMeta):
                print(o.image, o.seed)
         
         '''
-        model_name = self.params.model_name or self.model_manager.current_model
+        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:StableDiffusionGeneratorPipeline = model_info['model']
         model_hash = model_info['hash']
         scheduler: Scheduler = self.get_scheduler(
             model=model,
-            scheduler_name=self.params.scheduler
+            scheduler_name=generator_args.get('scheduler')
         )
         uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
         generator = self.load_generator(model, self._generator_name())
         if self.params.variation_amount > 0:
-            generator.set_variation(self.params.seed,
+            generator.set_variation(generator_args.get('seed'),
-                                    self.params.variation_amount,
+                                    generator_args.get('variation_amount'),
-                                    self.params.with_variations)
+                                    generator_args.get('with_variations')
-
+                                    )
-        generator_args = dataclasses.asdict(self.params)
-        generator_args.update(keyword_args)
 
         iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
         for i in iteration_count:
@@ -177,9 +146,8 @@ class InvokeAIGenerator(metaclass=ABCMeta):
             output = InvokeAIGeneratorOutput(
                 image=results[0][0],
                 seed=results[0][1],
-                model_name = model_name,
                 model_hash = model_hash,
-                params=generator_args,
+                params=Namespace(**generator_args),
             )
             if callback:
                 callback(output)
@@ -206,17 +174,18 @@ class InvokeAIGenerator(metaclass=ABCMeta):
             scheduler.uses_inpainting_model = lambda: False
         return scheduler
 
-    @abstractmethod
+    @classmethod
-    def _generator_name(self)->str:
+    def _generator_name(cls):
         '''
-        In derived classes will return the name of the generator to use.
+        In derived classes return the name of the generator to apply.
+        If you don't override will return the name of the derived
+        class, which nicely parallels the generator class names.
         '''
-        pass
+        return cls.__name__
 
 # ------------------------------------
 class Txt2Img(InvokeAIGenerator):
-    def _generator_name(self)->str:
+    pass
-        return 'Txt2Img'
 
 # ------------------------------------
 class Img2Img(InvokeAIGenerator):
@@ -230,9 +199,6 @@ class Img2Img(InvokeAIGenerator):
                                 **keyword_args
                                 )
 
-    def _generator_name(self)->str:
-        return 'Img2Img'
-
 # ------------------------------------
 # Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
 class Inpaint(Img2Img):
@@ -266,9 +232,6 @@ class Inpaint(Img2Img):
             **keyword_args
         )
 
-    def _generator_name(self)->str:
-        return 'Inpaint'
-    
 class Generator:
     downsampling_factor: int
     latent_channels: int

invokeai/backend/model_management/model_manager.py

@@ -34,7 +34,7 @@ from picklescan.scanner import scan_file_path
 from invokeai.backend.globals import Globals, global_cache_dir
 
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
-from ..util import CUDA_DEVICE, ask_user, download_with_resume
+from ..util import CUDA_DEVICE, CPU_DEVICE, ask_user, download_with_resume
 
 class SDLegacyType(Enum):
     V1 = 1