diff --git a/docs/index.md b/docs/index.md
index 4587b08f18..0aa99a1747 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -67,7 +67,7 @@ title: Home
 implementation of Stable Diffusion, the open source text-to-image and
 image-to-image generator. It provides a streamlined process with various new
 features and options to aid the image generation process. It runs on Windows,
-Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
+Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
 **Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>]
 [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a
diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py
index 21574c7323..7135e66a02 100644
--- a/invokeai/app/invocations/generate.py
+++ b/invokeai/app/invocations/generate.py
@@ -12,12 +12,19 @@ from invokeai.app.models.image import (ColorField, ImageCategory, ImageField,
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.generator.inpaint import infill_methods
 
-from ...backend.generator import Img2Img, Inpaint, InvokeAIGenerator, Txt2Img
+from ...backend.generator import Inpaint, InvokeAIGenerator
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ..util.step_callback import stable_diffusion_step_callback
 from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
 from .image import ImageOutput
 
+import re
+from ...backend.model_management.lora import ModelPatcher
+from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from .model import UNetField, VaeField
+from .compel import ConditioningField
+from contextlib import contextmanager, ExitStack, ContextDecorator
+
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
 INFILL_METHODS = Literal[tuple(infill_methods())]
 DEFAULT_INFILL_METHOD = (
@@ -25,114 +32,48 @@ DEFAULT_INFILL_METHOD = (
 )
 
 
-class SDImageInvocation(BaseModel):
-    """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
+from .latent import get_scheduler
 
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["stable-diffusion", "image"],
-                "type_hints": {
-                    "model": "model",
-                },
-            },
-        }
+class OldModelContext(ContextDecorator):
+    model: StableDiffusionGeneratorPipeline
+
+    def __init__(self, model):
+        self.model = model
+
+    def __enter__(self):
+        return self.model
+
+    def __exit__(self, *exc):
+        return False
+
+class OldModelInfo:
+    name: str
+    hash: str
+    context: OldModelContext
+
+    def __init__(self, name: str, hash: str, model: StableDiffusionGeneratorPipeline):
+        self.name = name
+        self.hash = hash
+        self.context = OldModelContext(
+            model=model,
+        )
 
 
-# Text to image
-class TextToImageInvocation(BaseInvocation, SDImageInvocation):
-    """Generates an image using text2img."""
+class InpaintInvocation(BaseInvocation):
+    """Generates an image using inpaint."""
 
-    type: Literal["txt2img"] = "txt2img"
+    type: Literal["inpaint"] = "inpaint"
 
-    # Inputs
-    # TODO: consider making prompt optional to enable providing prompt through a link
-    # fmt: off
-    prompt: Optional[str] = Field(description="The prompt to generate an image from")
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
     seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
     steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
     width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
     height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
     cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
     scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-    control_model: Optional[str] = Field(default=None, description="The control model to use")
-    control_image: Optional[ImageField] = Field(default=None, description="The processed control image")
-    # fmt: on
-
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Handle invalid model parameter
-        model = context.services.model_manager.get_model(self.model,node=self,context=context)
-
-        # loading controlnet image (currently requires pre-processed image)
-        control_image = (
-            None if self.control_image is None
-            else context.services.images.get_pil_image(self.control_image.image_name)
-        )
-        # loading controlnet model
-        if (self.control_model is None or self.control_model==''):
-            control_model = None
-        else:
-            # FIXME: change this to dropdown menu?
-            # FIXME: generalize so don't have to hardcode torch_dtype and device
-            control_model = ControlNetModel.from_pretrained(self.control_model,
-                                                            torch_dtype=torch.float16).to("cuda")
-
-        # 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]
-
-        txt2img = Txt2Img(model, control_model=control_model)
-        outputs = txt2img.generate(
-            prompt=self.prompt,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            control_image=control_image,
-            **self.dict(
-                exclude={"prompt", "control_image" }
-            ),  # 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)
-
-        image_dto = context.services.images.create(
-            image=generate_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class ImageToImageInvocation(TextToImageInvocation):
-    """Generates an image using img2img."""
-
-    type: Literal["img2img"] = "img2img"
+    unet: UNetField = Field(default=None, description="UNet model")
+    vae: VaeField = Field(default=None, description="Vae model")
 
     # Inputs
     image: Union[ImageField, None] = Field(description="The input image")
@@ -144,72 +85,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         description="Whether or not the result should be fit to the aspect ratio of the input image",
     )
 
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = (
-            None
-            if self.image is None
-            else context.services.images.get_pil_image(self.image.image_name)
-        )
-
-        if self.fit:
-            image = image.resize((self.width, self.height))
-
-        # Handle invalid model parameter
-        model = context.services.model_manager.get_model(self.model,node=self,context=context)
-
-        # 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]
-
-        outputs = Img2Img(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # 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.
-        generator_output = next(outputs)
-
-        image_dto = context.services.images.create(
-            image=generator_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class InpaintInvocation(ImageToImageInvocation):
-    """Generates an image using inpaint."""
-
-    type: Literal["inpaint"] = "inpaint"
-
     # Inputs
     mask: Union[ImageField, None] = Field(description="The mask")
     seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
@@ -252,6 +127,14 @@ class InpaintInvocation(ImageToImageInvocation):
         description="The amount by which to replace masked areas with latent noise",
     )
 
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["stable-diffusion", "image"],
+            },
+        }
+
     def dispatch_progress(
         self,
         context: InvocationContext,
@@ -265,6 +148,49 @@ class InpaintInvocation(ImageToImageInvocation):
             source_node_id=source_node_id,
         )
 
+    def get_conditioning(self, context):
+        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+
+        return (uc, c, extra_conditioning_info)
+
+    @contextmanager
+    def load_model_old_way(self, context, scheduler):
+        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
+        vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
+
+        #unet = unet_info.context.model
+        #vae = vae_info.context.model
+
+        with ExitStack() as stack:
+            loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
+
+            with vae_info as vae,\
+                 unet_info as unet,\
+                 ModelPatcher.apply_lora_unet(unet, loras):
+
+                device = context.services.model_manager.mgr.cache.execution_device
+                dtype = context.services.model_manager.mgr.cache.precision
+
+                pipeline = StableDiffusionGeneratorPipeline(
+                    vae=vae,
+                    text_encoder=None,
+                    tokenizer=None,
+                    unet=unet,
+                    scheduler=scheduler,
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                    precision="float16" if dtype == torch.float16 else "float32",
+                    execution_device=device,
+                )
+
+                yield OldModelInfo(
+                    name=self.unet.unet.model_name,
+                    hash="<NO-HASH>",
+                    model=pipeline,
+                )
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
             None
@@ -277,25 +203,31 @@ class InpaintInvocation(ImageToImageInvocation):
             else context.services.images.get_pil_image(self.mask.image_name)
         )
 
-        # Handle invalid model parameter
-        model = context.services.model_manager.get_model(self.model,node=self,context=context)
-
         # 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]
 
-        outputs = Inpaint(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            mask_image=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
+        conditioning = self.get_conditioning(context)
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
         )
 
+        with self.load_model_old_way(context, scheduler) as model:
+            outputs = Inpaint(model).generate(
+                conditioning=conditioning,
+                scheduler=scheduler,
+                init_image=image,
+                mask_image=mask,
+                step_callback=partial(self.dispatch_progress, context, source_node_id),
+                **self.dict(
+                    exclude={"positive_conditioning", "negative_conditioning", "scheduler", "image", "mask"}
+                ),  # 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.
         generator_output = next(outputs)
diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index cf216e6c54..63db3d925c 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -7,7 +7,7 @@ import einops
 
 from pydantic import BaseModel, Field, validator
 import torch
-from diffusers import ControlNetModel
+from diffusers import ControlNetModel, DPMSolverMultistepScheduler
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
@@ -233,7 +233,17 @@ class TextToLatentsInvocation(BaseInvocation):
                 h_symmetry_time_pct=None,#h_symmetry_time_pct,
                 v_symmetry_time_pct=None#v_symmetry_time_pct,
             ),
-        ).add_scheduler_args_if_applicable(scheduler, eta=0.0)#ddim_eta)
+        )
+
+        conditioning_data = conditioning_data.add_scheduler_args_if_applicable(
+            scheduler,
+
+            # for ddim scheduler
+            eta=0.0, #ddim_eta
+
+            # for ancestral and sde schedulers
+            generator=torch.Generator(device=uc.device).manual_seed(0),
+        )
         return conditioning_data
 
     def create_pipeline(self, unet, scheduler) -> StableDiffusionGeneratorPipeline:
diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py
index 55782bc445..ff8b4bc8c5 100644
--- a/invokeai/backend/__init__.py
+++ b/invokeai/backend/__init__.py
@@ -5,7 +5,6 @@ from .generator import (
     InvokeAIGeneratorBasicParams,
     InvokeAIGenerator,
     InvokeAIGeneratorOutput,
-    Txt2Img,
     Img2Img,
     Inpaint
 )
diff --git a/invokeai/backend/generator/__init__.py b/invokeai/backend/generator/__init__.py
index 9d6263453a..8a7f1c9167 100644
--- a/invokeai/backend/generator/__init__.py
+++ b/invokeai/backend/generator/__init__.py
@@ -5,7 +5,6 @@ from .base import (
     InvokeAIGenerator,
     InvokeAIGeneratorBasicParams,
     InvokeAIGeneratorOutput,
-    Txt2Img,
     Img2Img,
     Inpaint,
     Generator,
diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py
index fb293ab5b2..462b1a4f4b 100644
--- a/invokeai/backend/generator/base.py
+++ b/invokeai/backend/generator/base.py
@@ -29,7 +29,6 @@ import invokeai.backend.util.logging as logger
 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 ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
@@ -81,13 +80,15 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         self.params=params
         self.kwargs = kwargs
 
-    def generate(self,
-                 prompt: str='',
-                 callback: Optional[Callable]=None,
-                 step_callback: Optional[Callable]=None,
-                 iterations: int=1,
-                 **keyword_args,
-                 )->Iterator[InvokeAIGeneratorOutput]:
+    def generate(
+        self,
+        conditioning: tuple,
+        scheduler,
+        callback: Optional[Callable]=None,
+        step_callback: Optional[Callable]=None,
+        iterations: int=1,
+        **keyword_args,
+    )->Iterator[InvokeAIGeneratorOutput]:
         '''
         Return an iterator across the indicated number of generations.
         Each time the iterator is called it will return an InvokeAIGeneratorOutput
@@ -116,11 +117,6 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         model_name = model_info.name
         model_hash = model_info.hash
         with model_info.context as model:
-            scheduler: Scheduler = self.get_scheduler(
-                model=model,
-                scheduler_name=generator_args.get('scheduler')
-            )
-            uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
             gen_class = self._generator_class()
             generator = gen_class(model, self.params.precision, **self.kwargs)
             if self.params.variation_amount > 0:
@@ -143,12 +139,12 @@ class InvokeAIGenerator(metaclass=ABCMeta):
 
             iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
             for i in iteration_count:
-                results = generator.generate(prompt,
-                                             conditioning=(uc, c, extra_conditioning_info),
-                                             step_callback=step_callback,
-                                             sampler=scheduler,
-                                             **generator_args,
-                                             )
+                results = generator.generate(
+                    conditioning=conditioning,
+                    step_callback=step_callback,
+                    sampler=scheduler,
+                    **generator_args,
+                )
                 output = InvokeAIGeneratorOutput(
                     image=results[0][0],
                     seed=results[0][1],
@@ -170,20 +166,6 @@ class InvokeAIGenerator(metaclass=ABCMeta):
     def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
         return generator_class(model, self.params.precision)
 
-    def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-        scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
-        
-        scheduler_config = model.scheduler.config
-        if "_backup" in scheduler_config:
-            scheduler_config = scheduler_config["_backup"]
-        scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
-        scheduler = scheduler_class.from_config(scheduler_config)
-        
-        # hack copied over from generate.py
-        if not hasattr(scheduler, 'uses_inpainting_model'):
-            scheduler.uses_inpainting_model = lambda: False
-        return scheduler
-
     @classmethod
     def _generator_class(cls)->Type[Generator]:
         '''
@@ -193,13 +175,6 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         '''
         return Generator
 
-# ------------------------------------
-class Txt2Img(InvokeAIGenerator):
-    @classmethod
-    def _generator_class(cls):
-        from .txt2img import Txt2Img
-        return Txt2Img
-
 # ------------------------------------
 class Img2Img(InvokeAIGenerator):
     def generate(self,
@@ -253,24 +228,6 @@ class Inpaint(Img2Img):
         from .inpaint import Inpaint
         return Inpaint
 
-# ------------------------------------
-class Embiggen(Txt2Img):
-    def generate(
-            self,
-            embiggen: list=None,
-            embiggen_tiles: list = None,
-            strength: float=0.75,
-            **kwargs)->Iterator[InvokeAIGeneratorOutput]:
-        return super().generate(embiggen=embiggen,
-                                embiggen_tiles=embiggen_tiles,
-                                strength=strength,
-                                **kwargs)
-
-    @classmethod
-    def _generator_class(cls):
-        from .embiggen import Embiggen
-        return Embiggen
-
 class Generator:
     downsampling_factor: int
     latent_channels: int
@@ -281,7 +238,7 @@ class Generator:
         self.model = model
         self.precision = precision
         self.seed = None
-        self.latent_channels = model.channels
+        self.latent_channels = model.unet.config.in_channels
         self.downsampling_factor = downsampling  # BUG: should come from model or config
         self.safety_checker = None
         self.perlin = 0.0
@@ -292,7 +249,7 @@ class Generator:
         self.free_gpu_mem = None
 
     # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
-    def get_make_image(self, prompt, **kwargs):
+    def get_make_image(self, **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
@@ -308,7 +265,6 @@ class Generator:
 
     def generate(
         self,
-        prompt,
         width,
         height,
         sampler,
@@ -333,7 +289,6 @@ class Generator:
             saver.get_stacked_maps_image()
         )
         make_image = self.get_make_image(
-            prompt,
             sampler=sampler,
             init_image=init_image,
             width=width,
diff --git a/invokeai/backend/generator/embiggen.py b/invokeai/backend/generator/embiggen.py
deleted file mode 100644
index 6eae5732b0..0000000000
--- a/invokeai/backend/generator/embiggen.py
+++ /dev/null
@@ -1,559 +0,0 @@
-"""
-invokeai.backend.generator.embiggen descends from .generator
-and generates with .generator.img2img
-"""
-
-import numpy as np
-import torch
-from PIL import Image
-from tqdm import trange
-
-import invokeai.backend.util.logging as logger
-
-from .base import Generator
-from .img2img import Img2Img
-
-class Embiggen(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None
-
-    # Replace generate because Embiggen doesn't need/use most of what it does normallly
-    def generate(
-        self,
-        prompt,
-        iterations=1,
-        seed=None,
-        image_callback=None,
-        step_callback=None,
-        **kwargs,
-    ):
-        make_image = self.get_make_image(prompt, step_callback=step_callback, **kwargs)
-        results = []
-        seed = seed if seed else self.new_seed()
-
-        # Noise will be generated by the Img2Img generator when called
-        for _ in trange(iterations, desc="Generating"):
-            # make_image will call Img2Img which will do the equivalent of get_noise itself
-            image = make_image()
-            results.append([image, seed])
-            if image_callback is not None:
-                image_callback(image, seed, prompt_in=prompt)
-            seed = self.new_seed()
-        return results
-
-    @torch.no_grad()
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        init_img,
-        strength,
-        width,
-        height,
-        embiggen,
-        embiggen_tiles,
-        step_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and multi-stage twice-baked potato layering over the img2img on the initial image
-        Return value depends on the seed at the time you call it
-        """
-        assert (
-            not sampler.uses_inpainting_model()
-        ), "--embiggen is not supported by inpainting models"
-
-        # Construct embiggen arg array, and sanity check arguments
-        if embiggen == None:  # embiggen can also be called with just embiggen_tiles
-            embiggen = [1.0]  # If not specified, assume no scaling
-        elif embiggen[0] < 0:
-            embiggen[0] = 1.0
-            logger.warning(
-                "Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
-            )
-        if len(embiggen) < 2:
-            embiggen.append(0.75)
-        elif embiggen[1] > 1.0 or embiggen[1] < 0:
-            embiggen[1] = 0.75
-            logger.warning(
-                "Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
-            )
-        if len(embiggen) < 3:
-            embiggen.append(0.25)
-        elif embiggen[2] < 0:
-            embiggen[2] = 0.25
-            logger.warning(
-                "Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
-            )
-
-        # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
-        # and then sort them, because... people.
-        if embiggen_tiles:
-            embiggen_tiles = list(map(lambda n: n - 1, embiggen_tiles))
-            embiggen_tiles.sort()
-
-        if strength >= 0.5:
-            logger.warning(
-                f"Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
-            )
-
-        # Prep img2img generator, since we wrap over it
-        gen_img2img = Img2Img(self.model, self.precision)
-
-        # Open original init image (not a tensor) to manipulate
-        initsuperimage = Image.open(init_img)
-
-        with Image.open(init_img) as img:
-            initsuperimage = img.convert("RGB")
-
-        # Size of the target super init image in pixels
-        initsuperwidth, initsuperheight = initsuperimage.size
-
-        # Increase by scaling factor if not already resized, using ESRGAN as able
-        if embiggen[0] != 1.0:
-            initsuperwidth = round(initsuperwidth * embiggen[0])
-            initsuperheight = round(initsuperheight * embiggen[0])
-            if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
-                from ..restoration.realesrgan import ESRGAN
-
-                esrgan = ESRGAN()
-                logger.info(
-                    f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
-                )
-                if embiggen[0] > 2:
-                    initsuperimage = esrgan.process(
-                        initsuperimage,
-                        embiggen[1],  # upscale strength
-                        self.seed,
-                        4,  # upscale scale
-                    )
-                else:
-                    initsuperimage = esrgan.process(
-                        initsuperimage,
-                        embiggen[1],  # upscale strength
-                        self.seed,
-                        2,  # upscale scale
-                    )
-            # We could keep recursively re-running ESRGAN for a requested embiggen[0] larger than 4x
-            #   but from personal experiance it doesn't greatly improve anything after 4x
-            # Resize to target scaling factor resolution
-            initsuperimage = initsuperimage.resize(
-                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS
-            )
-
-        # Use width and height as tile widths and height
-        # Determine buffer size in pixels
-        if embiggen[2] < 1:
-            if embiggen[2] < 0:
-                embiggen[2] = 0
-            overlap_size_x = round(embiggen[2] * width)
-            overlap_size_y = round(embiggen[2] * height)
-        else:
-            overlap_size_x = round(embiggen[2])
-            overlap_size_y = round(embiggen[2])
-
-        # With overall image width and height known, determine how many tiles we need
-        def ceildiv(a, b):
-            return -1 * (-a // b)
-
-        # X and Y needs to be determined independantly (we may have savings on one based on the buffer pixel count)
-        # (initsuperwidth - width) is the area remaining to the right that we need to layers tiles to fill
-        # (width - overlap_size_x) is how much new we can fill with a single tile
-        emb_tiles_x = 1
-        emb_tiles_y = 1
-        if (initsuperwidth - width) > 0:
-            emb_tiles_x = ceildiv(initsuperwidth - width, width - overlap_size_x) + 1
-        if (initsuperheight - height) > 0:
-            emb_tiles_y = ceildiv(initsuperheight - height, height - overlap_size_y) + 1
-        # Sanity
-        assert (
-            emb_tiles_x > 1 or emb_tiles_y > 1
-        ), f"ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don't need to Embiggen! Check your arguments."
-
-        # Prep alpha layers --------------
-        # https://stackoverflow.com/questions/69321734/how-to-create-different-transparency-like-gradient-with-python-pil
-        # agradientL is Left-side transparent
-        agradientL = (
-            Image.linear_gradient("L").rotate(90).resize((overlap_size_x, height))
-        )
-        # agradientT is Top-side transparent
-        agradientT = Image.linear_gradient("L").resize((width, overlap_size_y))
-        # radial corner is the left-top corner, made full circle then cut to just the left-top quadrant
-        agradientC = Image.new("L", (256, 256))
-        for y in range(256):
-            for x in range(256):
-                # Find distance to lower right corner (numpy takes arrays)
-                distanceToLR = np.sqrt([(255 - x) ** 2 + (255 - y) ** 2])[0]
-                # Clamp values to max 255
-                if distanceToLR > 255:
-                    distanceToLR = 255
-                # Place the pixel as invert of distance
-                agradientC.putpixel((x, y), round(255 - distanceToLR))
-
-        # Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
-        # Fits for a left-fading gradient on the bottom side and full opacity on the right side.
-        agradientAsymC = Image.new("L", (256, 256))
-        for y in range(256):
-            for x in range(256):
-                value = round(max(0, x - (255 - y)) * (255 / max(1, y)))
-                # Clamp values
-                value = max(0, value)
-                value = min(255, value)
-                agradientAsymC.putpixel((x, y), value)
-
-        # Create alpha layers default fully white
-        alphaLayerL = Image.new("L", (width, height), 255)
-        alphaLayerT = Image.new("L", (width, height), 255)
-        alphaLayerLTC = Image.new("L", (width, height), 255)
-        # Paste gradients into alpha layers
-        alphaLayerL.paste(agradientL, (0, 0))
-        alphaLayerT.paste(agradientT, (0, 0))
-        alphaLayerLTC.paste(agradientL, (0, 0))
-        alphaLayerLTC.paste(agradientT, (0, 0))
-        alphaLayerLTC.paste(agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0))
-        # make masks with an asymmetric upper-right corner so when the curved transparent corner of the next tile
-        # to its right is placed it doesn't reveal a hard trailing semi-transparent edge in the overlapping space
-        alphaLayerTaC = alphaLayerT.copy()
-        alphaLayerTaC.paste(
-            agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-            (width - overlap_size_x, 0),
-        )
-        alphaLayerLTaC = alphaLayerLTC.copy()
-        alphaLayerLTaC.paste(
-            agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-            (width - overlap_size_x, 0),
-        )
-
-        if embiggen_tiles:
-            # Individual unconnected sides
-            alphaLayerR = Image.new("L", (width, height), 255)
-            alphaLayerR.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerB = Image.new("L", (width, height), 255)
-            alphaLayerB.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerTB = Image.new("L", (width, height), 255)
-            alphaLayerTB.paste(agradientT, (0, 0))
-            alphaLayerTB.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerLR = Image.new("L", (width, height), 255)
-            alphaLayerLR.paste(agradientL, (0, 0))
-            alphaLayerLR.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-
-            # Sides and corner Layers
-            alphaLayerRBC = Image.new("L", (width, height), 255)
-            alphaLayerRBC.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerRBC.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerRBC.paste(
-                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, height - overlap_size_y),
-            )
-            alphaLayerLBC = Image.new("L", (width, height), 255)
-            alphaLayerLBC.paste(agradientL, (0, 0))
-            alphaLayerLBC.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerLBC.paste(
-                agradientC.rotate(90).resize((overlap_size_x, overlap_size_y)),
-                (0, height - overlap_size_y),
-            )
-            alphaLayerRTC = Image.new("L", (width, height), 255)
-            alphaLayerRTC.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerRTC.paste(agradientT, (0, 0))
-            alphaLayerRTC.paste(
-                agradientC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, 0),
-            )
-
-            # All but X layers
-            alphaLayerABT = Image.new("L", (width, height), 255)
-            alphaLayerABT.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABT.paste(agradientL.rotate(180), (width - overlap_size_x, 0))
-            alphaLayerABT.paste(
-                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, height - overlap_size_y),
-            )
-            alphaLayerABL = Image.new("L", (width, height), 255)
-            alphaLayerABL.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABL.paste(agradientT.rotate(180), (0, height - overlap_size_y))
-            alphaLayerABL.paste(
-                agradientC.rotate(180).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, height - overlap_size_y),
-            )
-            alphaLayerABR = Image.new("L", (width, height), 255)
-            alphaLayerABR.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABR.paste(agradientT, (0, 0))
-            alphaLayerABR.paste(
-                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
-            )
-            alphaLayerABB = Image.new("L", (width, height), 255)
-            alphaLayerABB.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABB.paste(agradientL, (0, 0))
-            alphaLayerABB.paste(
-                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
-            )
-
-            # All-around layer
-            alphaLayerAA = Image.new("L", (width, height), 255)
-            alphaLayerAA.paste(alphaLayerABT, (0, 0))
-            alphaLayerAA.paste(agradientT, (0, 0))
-            alphaLayerAA.paste(
-                agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0)
-            )
-            alphaLayerAA.paste(
-                agradientC.rotate(270).resize((overlap_size_x, overlap_size_y)),
-                (width - overlap_size_x, 0),
-            )
-
-        # Clean up temporary gradients
-        del agradientL
-        del agradientT
-        del agradientC
-
-        def make_image():
-            # Make main tiles -------------------------------------------------
-            if embiggen_tiles:
-                logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
-            else:
-                logger.info(
-                    f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
-                )
-
-            emb_tile_store = []
-            # Although we could use the same seed for every tile for determinism, at higher strengths this may
-            # produce duplicated structures for each tile and make the tiling effect more obvious
-            # instead track and iterate a local seed we pass to Img2Img
-            seed = self.seed
-            seedintlimit = (
-                np.iinfo(np.uint32).max - 1
-            )  # only retreive this one from numpy
-
-            for tile in range(emb_tiles_x * emb_tiles_y):
-                # Don't iterate on first tile
-                if tile != 0:
-                    if seed < seedintlimit:
-                        seed += 1
-                    else:
-                        seed = 0
-
-                # Determine if this is a re-run and replace
-                if embiggen_tiles and not tile in embiggen_tiles:
-                    continue
-                # Get row and column entries
-                emb_row_i = tile // emb_tiles_x
-                emb_column_i = tile % emb_tiles_x
-                # Determine bounds to cut up the init image
-                # Determine upper-left point
-                if emb_column_i + 1 == emb_tiles_x:
-                    left = initsuperwidth - width
-                else:
-                    left = round(emb_column_i * (width - overlap_size_x))
-                if emb_row_i + 1 == emb_tiles_y:
-                    top = initsuperheight - height
-                else:
-                    top = round(emb_row_i * (height - overlap_size_y))
-                right = left + width
-                bottom = top + height
-
-                # Cropped image of above dimension (does not modify the original)
-                newinitimage = initsuperimage.crop((left, top, right, bottom))
-                # DEBUG:
-                # newinitimagepath = init_img[0:-4] + f'_emb_Ti{tile}.png'
-                # newinitimage.save(newinitimagepath)
-
-                if embiggen_tiles:
-                    logger.debug(
-                        f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
-                    )
-                else:
-                    logger.debug(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
-
-                # create a torch tensor from an Image
-                newinitimage = np.array(newinitimage).astype(np.float32) / 255.0
-                newinitimage = newinitimage[None].transpose(0, 3, 1, 2)
-                newinitimage = torch.from_numpy(newinitimage)
-                newinitimage = 2.0 * newinitimage - 1.0
-                newinitimage = newinitimage.to(self.model.device)
-                clear_cuda_cache = (
-                    kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
-                )
-
-                tile_results = gen_img2img.generate(
-                    prompt,
-                    iterations=1,
-                    seed=seed,
-                    sampler=sampler,
-                    steps=steps,
-                    cfg_scale=cfg_scale,
-                    conditioning=conditioning,
-                    ddim_eta=ddim_eta,
-                    image_callback=None,  # called only after the final image is generated
-                    step_callback=step_callback,  # called after each intermediate image is generated
-                    width=width,
-                    height=height,
-                    init_image=newinitimage,  # notice that init_image is different from init_img
-                    mask_image=None,
-                    strength=strength,
-                    clear_cuda_cache=clear_cuda_cache,
-                )
-
-                emb_tile_store.append(tile_results[0][0])
-                # DEBUG (but, also has other uses), worth saving if you want tiles without a transparency overlap to manually composite
-                # emb_tile_store[-1].save(init_img[0:-4] + f'_emb_To{tile}.png')
-                del newinitimage
-
-            # Sanity check we have them all
-            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (
-                embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)
-            ):
-                outputsuperimage = Image.new("RGBA", (initsuperwidth, initsuperheight))
-                if embiggen_tiles:
-                    outputsuperimage.alpha_composite(
-                        initsuperimage.convert("RGBA"), (0, 0)
-                    )
-                for tile in range(emb_tiles_x * emb_tiles_y):
-                    if embiggen_tiles:
-                        if tile in embiggen_tiles:
-                            intileimage = emb_tile_store.pop(0)
-                        else:
-                            continue
-                    else:
-                        intileimage = emb_tile_store[tile]
-                    intileimage = intileimage.convert("RGBA")
-                    # Get row and column entries
-                    emb_row_i = tile // emb_tiles_x
-                    emb_column_i = tile % emb_tiles_x
-                    if emb_row_i == 0 and emb_column_i == 0 and not embiggen_tiles:
-                        left = 0
-                        top = 0
-                    else:
-                        # Determine upper-left point
-                        if emb_column_i + 1 == emb_tiles_x:
-                            left = initsuperwidth - width
-                        else:
-                            left = round(emb_column_i * (width - overlap_size_x))
-                        if emb_row_i + 1 == emb_tiles_y:
-                            top = initsuperheight - height
-                        else:
-                            top = round(emb_row_i * (height - overlap_size_y))
-                        # Handle gradients for various conditions
-                        # Handle emb_rerun case
-                        if embiggen_tiles:
-                            # top of image
-                            if emb_row_i == 0:
-                                if emb_column_i == 0:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) not in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerB)
-                                        # Otherwise do nothing on this tile
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerR)
-                                    else:
-                                        intileimage.putalpha(alphaLayerRBC)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    if (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down
-                                        intileimage.putalpha(alphaLayerL)
-                                    else:
-                                        intileimage.putalpha(alphaLayerLBC)
-                                else:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerL)
-                                        else:
-                                            intileimage.putalpha(alphaLayerLBC)
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerLR)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABT)
-                            # bottom of image
-                            elif emb_row_i == emb_tiles_y - 1:
-                                if emb_column_i == 0:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        intileimage.putalpha(alphaLayerTaC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerRTC)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    # No tiles to look ahead to
-                                    intileimage.putalpha(alphaLayerLTC)
-                                else:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        intileimage.putalpha(alphaLayerLTaC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABB)
-                            # vertical middle of image
-                            else:
-                                if emb_column_i == 0:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerTaC)
-                                        else:
-                                            intileimage.putalpha(alphaLayerTB)
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerRTC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABL)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    if (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down
-                                        intileimage.putalpha(alphaLayerLTC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABR)
-                                else:
-                                    if (tile + 1) in embiggen_tiles:  # Look-ahead right
-                                        if (
-                                            tile + emb_tiles_x
-                                        ) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerLTaC)
-                                        else:
-                                            intileimage.putalpha(alphaLayerABR)
-                                    elif (
-                                        tile + emb_tiles_x
-                                    ) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerABB)
-                                    else:
-                                        intileimage.putalpha(alphaLayerAA)
-                        # Handle normal tiling case (much simpler - since we tile left to right, top to bottom)
-                        else:
-                            if emb_row_i == 0 and emb_column_i >= 1:
-                                intileimage.putalpha(alphaLayerL)
-                            elif emb_row_i >= 1 and emb_column_i == 0:
-                                if (
-                                    emb_column_i + 1 == emb_tiles_x
-                                ):  # If we don't have anything that can be placed to the right
-                                    intileimage.putalpha(alphaLayerT)
-                                else:
-                                    intileimage.putalpha(alphaLayerTaC)
-                            else:
-                                if (
-                                    emb_column_i + 1 == emb_tiles_x
-                                ):  # If we don't have anything that can be placed to the right
-                                    intileimage.putalpha(alphaLayerLTC)
-                                else:
-                                    intileimage.putalpha(alphaLayerLTaC)
-                    # Layer tile onto final image
-                    outputsuperimage.alpha_composite(intileimage, (left, top))
-            else:
-                logger.error(
-                    "Could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
-                )
-
-            # after internal loops and patching up return Embiggen image
-            return outputsuperimage
-
-        # end of function declaration
-        return make_image
diff --git a/invokeai/backend/generator/img2img.py b/invokeai/backend/generator/img2img.py
index 2c62bec4d6..1cfbeb66c0 100644
--- a/invokeai/backend/generator/img2img.py
+++ b/invokeai/backend/generator/img2img.py
@@ -22,7 +22,6 @@ class Img2Img(Generator):
 
     def get_make_image(
         self,
-        prompt,
         sampler,
         steps,
         cfg_scale,
diff --git a/invokeai/backend/generator/inpaint.py b/invokeai/backend/generator/inpaint.py
index a7fec83eb7..eaf4047109 100644
--- a/invokeai/backend/generator/inpaint.py
+++ b/invokeai/backend/generator/inpaint.py
@@ -161,9 +161,7 @@ class Inpaint(Img2Img):
         im: Image.Image,
         seam_size: int,
         seam_blur: int,
-        prompt,
         seed,
-        sampler,
         steps,
         cfg_scale,
         ddim_eta,
@@ -177,8 +175,6 @@ class Inpaint(Img2Img):
         mask = self.mask_edge(hard_mask, seam_size, seam_blur)
 
         make_image = self.get_make_image(
-            prompt,
-            sampler,
             steps,
             cfg_scale,
             ddim_eta,
@@ -203,8 +199,6 @@ class Inpaint(Img2Img):
     @torch.no_grad()
     def get_make_image(
         self,
-        prompt,
-        sampler,
         steps,
         cfg_scale,
         ddim_eta,
@@ -306,7 +300,6 @@ class Inpaint(Img2Img):
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
 
         # todo: support cross-attention control
         uc, c, _ = conditioning
@@ -345,9 +338,7 @@ class Inpaint(Img2Img):
                     result,
                     seam_size,
                     seam_blur,
-                    prompt,
                     seed,
-                    sampler,
                     seam_steps,
                     cfg_scale,
                     ddim_eta,
@@ -360,8 +351,6 @@ class Inpaint(Img2Img):
 
                 # Restore original settings
                 self.get_make_image(
-                    prompt,
-                    sampler,
                     steps,
                     cfg_scale,
                     ddim_eta,
diff --git a/invokeai/backend/generator/txt2img.py b/invokeai/backend/generator/txt2img.py
deleted file mode 100644
index 9ea19bd896..0000000000
--- a/invokeai/backend/generator/txt2img.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""
-invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
-"""
-import PIL.Image
-import torch
-
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
-from diffusers.pipelines.controlnet import MultiControlNetModel
-
-from ..stable_diffusion import (
-    ConditioningData,
-    PostprocessingSettings,
-    StableDiffusionGeneratorPipeline,
-)
-from .base import Generator
-
-
-class Txt2Img(Generator):
-    def __init__(self, model, precision,
-                 control_model: Optional[Union[ControlNetModel, List[ControlNetModel]]] = None,
-                 **kwargs):
-        self.control_model = control_model
-        if isinstance(self.control_model, list):
-            self.control_model = MultiControlNetModel(self.control_model)
-        super().__init__(model, precision, **kwargs)
-
-    @torch.no_grad()
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        width,
-        height,
-        step_callback=None,
-        threshold=0.0,
-        warmup=0.2,
-        perlin=0.0,
-        h_symmetry_time_pct=None,
-        v_symmetry_time_pct=None,
-        attention_maps_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        kwargs are 'width' and 'height'
-        """
-        self.perlin = perlin
-        control_image = kwargs.get("control_image", None)
-        do_classifier_free_guidance = cfg_scale > 1.0
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.control_model = self.control_model
-        pipeline.scheduler = sampler
-
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=threshold,
-                warmup=warmup,
-                h_symmetry_time_pct=h_symmetry_time_pct,
-                v_symmetry_time_pct=v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        # FIXME: still need to test with different widths, heights, devices, dtypes
-        #        and add in batch_size, num_images_per_prompt?
-        if control_image is not None:
-            if isinstance(self.control_model, ControlNetModel):
-                control_image = pipeline.prepare_control_image(
-                    image=control_image,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    width=width,
-                    height=height,
-                    # batch_size=batch_size * num_images_per_prompt,
-                    # num_images_per_prompt=num_images_per_prompt,
-                    device=self.control_model.device,
-                    dtype=self.control_model.dtype,
-                )
-            elif isinstance(self.control_model, MultiControlNetModel):
-                images = []
-                for image_ in control_image:
-                    image_ = pipeline.prepare_control_image(
-                        image=image_,
-                        do_classifier_free_guidance=do_classifier_free_guidance,
-                        width=width,
-                        height=height,
-                        # batch_size=batch_size * num_images_per_prompt,
-                        # num_images_per_prompt=num_images_per_prompt,
-                        device=self.control_model.device,
-                        dtype=self.control_model.dtype,
-                    )
-                    images.append(image_)
-                control_image = images
-            kwargs["control_image"] = control_image
-
-        def make_image(x_T: torch.Tensor, _: int) -> PIL.Image.Image:
-            pipeline_output = pipeline.image_from_embeddings(
-                latents=torch.zeros_like(x_T, dtype=self.torch_dtype()),
-                noise=x_T,
-                num_inference_steps=steps,
-                conditioning_data=conditioning_data,
-                callback=step_callback,
-                **kwargs,
-            )
-
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
-                attention_maps_callback(pipeline_output.attention_map_saver)
-
-            return pipeline.numpy_to_pil(pipeline_output.images)[0]
-
-        return make_image
diff --git a/invokeai/backend/generator/txt2img2img.py b/invokeai/backend/generator/txt2img2img.py
deleted file mode 100644
index 1257a44fb1..0000000000
--- a/invokeai/backend/generator/txt2img2img.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""
-invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
-"""
-
-import math
-from typing import Callable, Optional
-
-import torch
-from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
-
-from ..stable_diffusion import PostprocessingSettings
-from .base import Generator
-from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ..stable_diffusion.diffusers_pipeline import ConditioningData
-from ..stable_diffusion.diffusers_pipeline import trim_to_multiple_of
-
-import invokeai.backend.util.logging as logger
-
-class Txt2Img2Img(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None  # for get_noise()
-
-    def get_make_image(
-        self,
-        prompt: str,
-        sampler,
-        steps: int,
-        cfg_scale: float,
-        ddim_eta,
-        conditioning,
-        width: int,
-        height: int,
-        strength: float,
-        step_callback: Optional[Callable] = None,
-        threshold=0.0,
-        warmup=0.2,
-        perlin=0.0,
-        h_symmetry_time_pct=None,
-        v_symmetry_time_pct=None,
-        attention_maps_callback=None,
-        **kwargs,
-    ):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        kwargs are 'width' and 'height'
-        """
-        self.perlin = perlin
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
-
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=threshold,
-                warmup=0.2,
-                h_symmetry_time_pct=h_symmetry_time_pct,
-                v_symmetry_time_pct=v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        def make_image(x_T: torch.Tensor, _: int):
-            first_pass_latent_output, _ = pipeline.latents_from_embeddings(
-                latents=torch.zeros_like(x_T),
-                num_inference_steps=steps,
-                conditioning_data=conditioning_data,
-                noise=x_T,
-                callback=step_callback,
-            )
-
-            # Get our initial generation width and height directly from the latent output so
-            # the message below is accurate.
-            init_width = first_pass_latent_output.size()[3] * self.downsampling_factor
-            init_height = first_pass_latent_output.size()[2] * self.downsampling_factor
-            logger.info(
-                f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
-            )
-
-            # resizing
-            resized_latents = torch.nn.functional.interpolate(
-                first_pass_latent_output,
-                size=(
-                    height // self.downsampling_factor,
-                    width // self.downsampling_factor,
-                ),
-                mode="bilinear",
-            )
-
-            # Free up memory from the last generation.
-            clear_cuda_cache = kwargs["clear_cuda_cache"] or None
-            if clear_cuda_cache is not None:
-                clear_cuda_cache()
-
-            second_pass_noise = self.get_noise_like(
-                resized_latents, override_perlin=True
-            )
-
-            # Clear symmetry for the second pass
-            from dataclasses import replace
-
-            new_postprocessing_settings = replace(
-                conditioning_data.postprocessing_settings, h_symmetry_time_pct=None
-            )
-            new_postprocessing_settings = replace(
-                new_postprocessing_settings, v_symmetry_time_pct=None
-            )
-            new_conditioning_data = replace(
-                conditioning_data, postprocessing_settings=new_postprocessing_settings
-            )
-
-            verbosity = get_verbosity()
-            set_verbosity_error()
-            pipeline_output = pipeline.img2img_from_latents_and_embeddings(
-                resized_latents,
-                num_inference_steps=steps,
-                conditioning_data=new_conditioning_data,
-                strength=strength,
-                noise=second_pass_noise,
-                callback=step_callback,
-            )
-            set_verbosity(verbosity)
-
-            if (
-                pipeline_output.attention_map_saver is not None
-                and attention_maps_callback is not None
-            ):
-                attention_maps_callback(pipeline_output.attention_map_saver)
-
-            return pipeline.numpy_to_pil(pipeline_output.images)[0]
-
-        # FIXME: do we really need something entirely different for the inpainting model?
-
-        # in the case of the inpainting model being loaded, the trick of
-        # providing an interpolated latent doesn't work, so we transiently
-        # create a 512x512 PIL image, upscale it, and run the inpainting
-        # over it in img2img mode. Because the inpaing model is so conservative
-        # it doesn't change the image (much)
-
-        return make_image
-
-    def get_noise_like(self, like: torch.Tensor, override_perlin: bool = False):
-        device = like.device
-        if device.type == "mps":
-            x = torch.randn_like(like, device="cpu", dtype=self.torch_dtype()).to(
-                device
-            )
-        else:
-            x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
-        if self.perlin > 0.0 and override_perlin == False:
-            shape = like.shape
-            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
-                shape[3], shape[2]
-            )
-        return x
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self, width, height, scale=True):
-        # print(f"Get noise: {width}x{height}")
-        if scale:
-            # Scale the input width and height for the initial generation
-            # Make their area equivalent to the model's resolution area (e.g. 512*512 = 262144),
-            # while keeping the minimum dimension at least 0.5 * resolution (e.g. 512*0.5 = 256)
-
-            aspect = width / height
-            dimension = self.model.unet.config.sample_size * self.model.vae_scale_factor
-            min_dimension = math.floor(dimension * 0.5)
-            model_area = (
-                dimension * dimension
-            )  # hardcoded for now since all models are trained on square images
-
-            if aspect > 1.0:
-                init_height = max(min_dimension, math.sqrt(model_area / aspect))
-                init_width = init_height * aspect
-            else:
-                init_width = max(min_dimension, math.sqrt(model_area * aspect))
-                init_height = init_width / aspect
-
-            scaled_width, scaled_height = trim_to_multiple_of(
-                math.floor(init_width), math.floor(init_height)
-            )
-
-        else:
-            scaled_width = width
-            scaled_height = height
-
-        device = self.model.device
-        channels = self.latent_channels
-        if channels == 9:
-            channels = 4  # we don't really want noise for all the mask channels
-        shape = (
-            1,
-            channels,
-            scaled_height // self.downsampling_factor,
-            scaled_width // self.downsampling_factor,
-        )
-        if self.use_mps_noise or device.type == "mps":
-            tensor = torch.empty(size=shape, device="cpu")
-            tensor = self.get_noise_like(like=tensor).to(device)
-        else:
-            tensor = torch.empty(size=shape, device=device)
-            tensor = self.get_noise_like(like=tensor)
-        return tensor
diff --git a/invokeai/backend/install/legacy_arg_parsing.py b/invokeai/backend/install/legacy_arg_parsing.py
index 85ca588fe2..4a58ff8336 100644
--- a/invokeai/backend/install/legacy_arg_parsing.py
+++ b/invokeai/backend/install/legacy_arg_parsing.py
@@ -9,6 +9,7 @@ SAMPLER_CHOICES = [
     "ddpm",
     "deis",
     "lms",
+    "lms_k",
     "pndm",
     "heun",
     "heun_k",
@@ -18,8 +19,13 @@ SAMPLER_CHOICES = [
     "kdpm_2",
     "kdpm_2_a",
     "dpmpp_2s",
+    "dpmpp_2s_k",
     "dpmpp_2m",
     "dpmpp_2m_k",
+    "dpmpp_2m_sde",
+    "dpmpp_2m_sde_k",
+    "dpmpp_sde",
+    "dpmpp_sde_k",
     "unipc",
 ]
 
diff --git a/invokeai/backend/model_management/lora.py b/invokeai/backend/model_management/lora.py
index 46638878aa..c351a76590 100644
--- a/invokeai/backend/model_management/lora.py
+++ b/invokeai/backend/model_management/lora.py
@@ -556,8 +556,8 @@ class ModelPatcher:
         new_tokens_added = None
 
         try:
-            ti_manager = TextualInversionManager()
             ti_tokenizer = copy.deepcopy(tokenizer)
+            ti_manager = TextualInversionManager(ti_tokenizer)
             init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
 
             def _get_trigger(ti, index):
@@ -650,22 +650,24 @@ class TextualInversionModel:
 
 class TextualInversionManager(BaseTextualInversionManager):
     pad_tokens: Dict[int, List[int]]
+    tokenizer: CLIPTokenizer
 
-    def __init__(self):
+    def __init__(self, tokenizer: CLIPTokenizer):
         self.pad_tokens = dict()
+        self.tokenizer = tokenizer
 
     def expand_textual_inversion_token_ids_if_necessary(
         self, token_ids: list[int]
     ) -> list[int]:
 
-        #if token_ids[0] == self.tokenizer.bos_token_id:
-        #    raise ValueError("token_ids must not start with bos_token_id")
-        #if token_ids[-1] == self.tokenizer.eos_token_id:
-        #    raise ValueError("token_ids must not end with eos_token_id")
-
         if len(self.pad_tokens) == 0:
             return token_ids
 
+        if token_ids[0] == self.tokenizer.bos_token_id:
+            raise ValueError("token_ids must not start with bos_token_id")
+        if token_ids[-1] == self.tokenizer.eos_token_id:
+            raise ValueError("token_ids must not end with eos_token_id")
+
         new_token_ids = []
         for token_id in token_ids:
             new_token_ids.append(token_id)
diff --git a/invokeai/backend/model_management/models/textual_inversion.py b/invokeai/backend/model_management/models/textual_inversion.py
index e8c96ff31e..66847f53eb 100644
--- a/invokeai/backend/model_management/models/textual_inversion.py
+++ b/invokeai/backend/model_management/models/textual_inversion.py
@@ -1,3 +1,4 @@
+import os
 import torch
 from typing import Optional
 from .base import (
diff --git a/invokeai/backend/prompting/__init__.py b/invokeai/backend/prompting/__init__.py
deleted file mode 100644
index b52206dd94..0000000000
--- a/invokeai/backend/prompting/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""
-Initialization file for invokeai.backend.prompting
-"""
-from .conditioning import (
-    get_prompt_structure,
-    get_tokens_for_prompt_object,
-    get_uc_and_c_and_ec,
-    split_weighted_subprompts,
-)
diff --git a/invokeai/backend/prompting/conditioning.py b/invokeai/backend/prompting/conditioning.py
deleted file mode 100644
index d070342794..0000000000
--- a/invokeai/backend/prompting/conditioning.py
+++ /dev/null
@@ -1,297 +0,0 @@
-"""
-This module handles the generation of the conditioning tensors.
-
-Useful function exports:
-
-get_uc_and_c_and_ec()           get the conditioned and unconditioned latent, and edited conditioning if we're doing cross-attention control
-
-"""
-import re
-import torch
-from typing import Optional, Union
-
-from compel import Compel
-from compel.prompt_parser import (
-    Blend,
-    CrossAttentionControlSubstitute,
-    FlattenedPrompt,
-    Fragment,
-    PromptParser,
-    Conjunction,
-)
-
-import invokeai.backend.util.logging as logger
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from ..stable_diffusion import InvokeAIDiffuserComponent
-from ..util import torch_dtype
-
-config = InvokeAIAppConfig.get_config()
-
-def get_uc_and_c_and_ec(prompt_string,
-                        model: InvokeAIDiffuserComponent,
-                        log_tokens=False, skip_normalize_legacy_blend=False):
-    # lazy-load any deferred textual inversions.
-    # this might take a couple of seconds the first time a textual inversion is used.
-    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
-
-    compel = Compel(tokenizer=model.tokenizer,
-                    text_encoder=model.text_encoder,
-                    textual_inversion_manager=model.textual_inversion_manager,
-                    dtype_for_device_getter=torch_dtype,
-                    truncate_long_prompts=False,
-                   )
-
-    # get rid of any newline characters
-    prompt_string = prompt_string.replace("\n", " ")
-    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
-
-    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_conjunction: Conjunction
-    if legacy_blend is not None:
-        positive_conjunction = legacy_blend
-    else:
-        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    positive_prompt = positive_conjunction.prompts[0]
-
-    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
-
-    tokens_count = get_max_token_count(model.tokenizer, positive_prompt)
-    if log_tokens or config.log_tokenization:
-        log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
-
-    c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
-    uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
-    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
-
-    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
-                                                         cross_attention_control_args=options.get(
-                                                             'cross_attention_control', None))
-    return uc, c, ec
-
-def get_prompt_structure(
-    prompt_string, skip_normalize_legacy_blend: bool = False
-) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
-    (
-        positive_prompt_string,
-        negative_prompt_string,
-    ) = split_prompt_to_positive_and_negative(prompt_string)
-    legacy_blend = try_parse_legacy_blend(
-        positive_prompt_string, skip_normalize_legacy_blend
-    )
-    positive_prompt: Conjunction
-    if legacy_blend is not None:
-        positive_conjunction = legacy_blend
-    else:
-        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    positive_prompt = positive_conjunction.prompts[0]
-    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
-
-    return positive_prompt, negative_prompt
-
-def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
-) -> int:
-    if type(prompt) is Blend:
-        blend: Blend = prompt
-        return max(
-            [
-                get_max_token_count(tokenizer, c, truncate_if_too_long)
-                for c in blend.prompts
-            ]
-        )
-    else:
-        return len(
-            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
-        )
-
-
-def get_tokens_for_prompt_object(
-    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
-) -> [str]:
-    if type(parsed_prompt) is Blend:
-        raise ValueError(
-            "Blend is not supported here - you need to get tokens for each of its .children"
-        )
-
-    text_fragments = [
-        x.text
-        if type(x) is Fragment
-        else (
-            " ".join([f.text for f in x.original])
-            if type(x) is CrossAttentionControlSubstitute
-            else str(x)
-        )
-        for x in parsed_prompt.children
-    ]
-    text = " ".join(text_fragments)
-    tokens = tokenizer.tokenize(text)
-    if truncate_if_too_long:
-        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
-        tokens = tokens[0:max_tokens_length]
-    return tokens
-
-
-def split_prompt_to_positive_and_negative(prompt_string_uncleaned: str):
-    unconditioned_words = ""
-    unconditional_regex = r"\[(.*?)\]"
-    unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
-    if len(unconditionals) > 0:
-        unconditioned_words = " ".join(unconditionals)
-
-        # Remove Unconditioned Words From Prompt
-        unconditional_regex_compile = re.compile(unconditional_regex)
-        clean_prompt = unconditional_regex_compile.sub(" ", prompt_string_uncleaned)
-        prompt_string_cleaned = re.sub(" +", " ", clean_prompt)
-    else:
-        prompt_string_cleaned = prompt_string_uncleaned
-    return prompt_string_cleaned, unconditioned_words
-
-
-def log_tokenization(
-    positive_prompt: Union[Blend, FlattenedPrompt],
-    negative_prompt: Union[Blend, FlattenedPrompt],
-    tokenizer,
-):
-    logger.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}")
-    logger.info(f"[TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
-
-    log_tokenization_for_prompt_object(positive_prompt, tokenizer)
-    log_tokenization_for_prompt_object(
-        negative_prompt, tokenizer, display_label_prefix="(negative prompt)"
-    )
-
-
-def log_tokenization_for_prompt_object(
-    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
-):
-    display_label_prefix = display_label_prefix or ""
-    if type(p) is Blend:
-        blend: Blend = p
-        for i, c in enumerate(blend.prompts):
-            log_tokenization_for_prompt_object(
-                c,
-                tokenizer,
-                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
-            )
-    elif type(p) is FlattenedPrompt:
-        flattened_prompt: FlattenedPrompt = p
-        if flattened_prompt.wants_cross_attention_control:
-            original_fragments = []
-            edited_fragments = []
-            for f in flattened_prompt.children:
-                if type(f) is CrossAttentionControlSubstitute:
-                    original_fragments += f.original
-                    edited_fragments += f.edited
-                else:
-                    original_fragments.append(f)
-                    edited_fragments.append(f)
-
-            original_text = " ".join([x.text for x in original_fragments])
-            log_tokenization_for_text(
-                original_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap originals)",
-            )
-            edited_text = " ".join([x.text for x in edited_fragments])
-            log_tokenization_for_text(
-                edited_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap replacements)",
-            )
-        else:
-            text = " ".join([x.text for x in flattened_prompt.children])
-            log_tokenization_for_text(
-                text, tokenizer, display_label=display_label_prefix
-            )
-
-
-def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
-    """shows how the prompt is tokenized
-    # usually tokens have '</w>' to indicate end-of-word,
-    # but for readability it has been replaced with ' '
-    """
-    tokens = tokenizer.tokenize(text)
-    tokenized = ""
-    discarded = ""
-    usedTokens = 0
-    totalTokens = len(tokens)
-
-    for i in range(0, totalTokens):
-        token = tokens[i].replace("</w>", " ")
-        # alternate color
-        s = (usedTokens % 6) + 1
-        if truncate_if_too_long and i >= tokenizer.model_max_length:
-            discarded = discarded + f"\x1b[0;3{s};40m{token}"
-        else:
-            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
-            usedTokens += 1
-
-    if usedTokens > 0:
-        logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        logger.debug(f"{tokenized}\x1b[0m")
-
-    if discarded != "":
-        logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        logger.debug(f"{discarded}\x1b[0m")
-
-def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
-    weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
-    if len(weighted_subprompts) <= 1:
-        return None
-    strings = [x[0] for x in weighted_subprompts]
-
-    pp = PromptParser()
-    parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = []
-    weights = []
-    for i, x in enumerate(parsed_conjunctions):
-        if len(x.prompts)>0:
-            flattened_prompts.append(x.prompts[0])
-            weights.append(weighted_subprompts[i][1])
-    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)])
-
-def split_weighted_subprompts(text, skip_normalize=False) -> list:
-    """
-    Legacy blend parsing.
-
-    grabs all text up to the first occurrence of ':'
-    uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
-    if ':' has no value defined, defaults to 1.0
-    repeats until no text remaining
-    """
-    prompt_parser = re.compile(
-        """
-            (?P<prompt>     # capture group for 'prompt'
-            (?:\\\:|[^:])+  # match one or more non ':' characters or escaped colons '\:'
-            )               # end 'prompt'
-            (?:             # non-capture group
-            :+              # match one or more ':' characters
-            (?P<weight>     # capture group for 'weight'
-            -?\d+(?:\.\d+)? # match positive or negative integer or decimal number
-            )?              # end weight capture group, make optional
-            \s*             # strip spaces after weight
-            |               # OR
-            $               # else, if no ':' then match end of line
-            )               # end non-capture group
-            """,
-        re.VERBOSE,
-    )
-    parsed_prompts = [
-        (match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1))
-        for match in re.finditer(prompt_parser, text)
-    ]
-    if len(parsed_prompts) == 0:
-        return []
-    if skip_normalize:
-        return parsed_prompts
-    weight_sum = sum(map(lambda x: x[1], parsed_prompts))
-    if weight_sum == 0:
-        logger.warning(
-            "Subprompt weights add up to zero. Discarding and using even weights instead."
-        )
-        equal_weight = 1 / max(len(parsed_prompts), 1)
-        return [(x[0], equal_weight) for x in parsed_prompts]
-    return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
diff --git a/invokeai/backend/stable_diffusion/__init__.py b/invokeai/backend/stable_diffusion/__init__.py
index 55333d3589..37024ccace 100644
--- a/invokeai/backend/stable_diffusion/__init__.py
+++ b/invokeai/backend/stable_diffusion/__init__.py
@@ -1,7 +1,6 @@
 """
 Initialization file for the invokeai.backend.stable_diffusion package
 """
-from .concepts_lib import HuggingFaceConceptsLibrary
 from .diffusers_pipeline import (
     ConditioningData,
     PipelineIntermediateState,
@@ -10,4 +9,3 @@ from .diffusers_pipeline import (
 from .diffusion import InvokeAIDiffuserComponent
 from .diffusion.cross_attention_map_saving import AttentionMapSaver
 from .diffusion.shared_invokeai_diffusion import PostprocessingSettings
-from .textual_inversion_manager import TextualInversionManager
diff --git a/invokeai/backend/stable_diffusion/concepts_lib.py b/invokeai/backend/stable_diffusion/concepts_lib.py
deleted file mode 100644
index 5294150783..0000000000
--- a/invokeai/backend/stable_diffusion/concepts_lib.py
+++ /dev/null
@@ -1,275 +0,0 @@
-"""
-Query and install embeddings from the HuggingFace SD Concepts Library
-at https://huggingface.co/sd-concepts-library.
-
-The interface is through the Concepts() object.
-"""
-import os
-import re
-from typing import Callable
-from urllib import error as ul_error
-from urllib import request
-
-from huggingface_hub import (
-    HfApi,
-    HfFolder,
-    ModelFilter,
-    hf_hub_url,
-)
-
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.app.services.config import InvokeAIAppConfig
-logger = InvokeAILogger.getLogger()
-
-class HuggingFaceConceptsLibrary(object):
-    def __init__(self, root=None):
-        """
-        Initialize the Concepts object. May optionally pass a root directory.
-        """
-        self.config = InvokeAIAppConfig.get_config()
-        self.root = root or self.config.root
-        self.hf_api = HfApi()
-        self.local_concepts = dict()
-        self.concept_list = None
-        self.concepts_loaded = dict()
-        self.triggers = dict()  # concept name to trigger phrase
-        self.concept_names = dict()  # trigger phrase to concept name
-        self.match_trigger = re.compile(
-            "(<[\w\- >]+>)"
-        )  # trigger is slightly less restrictive than HF concept name
-        self.match_concept = re.compile(
-            "<([\w\-]+)>"
-        )  # HF concept name can only contain A-Za-z0-9_-
-
-    def list_concepts(self) -> list:
-        """
-        Return a list of all the concepts by name, without the 'sd-concepts-library' part.
-        Also adds local concepts in invokeai/embeddings folder.
-        """
-        local_concepts_now = self.get_local_concepts(
-            os.path.join(self.root, "embeddings")
-        )
-        local_concepts_to_add = set(local_concepts_now).difference(
-            set(self.local_concepts)
-        )
-        self.local_concepts.update(local_concepts_now)
-
-        if self.concept_list is not None:
-            if local_concepts_to_add:
-                self.concept_list.extend(list(local_concepts_to_add))
-                return self.concept_list
-            return self.concept_list
-        elif self.config.internet_available is True:
-            try:
-                models = self.hf_api.list_models(
-                    filter=ModelFilter(model_name="sd-concepts-library/")
-                )
-                self.concept_list = [a.id.split("/")[1] for a in models]
-                # when init, add all in dir. when not init, add only concepts added between init and now
-                self.concept_list.extend(list(local_concepts_to_add))
-            except Exception as e:
-                logger.warning(
-                    f"Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
-                )
-                logger.warning(
-                    "You may load .bin and .pt file(s) manually using the --embedding_directory argument."
-                )
-            return self.concept_list
-        else: 
-            return self.concept_list
-
-    def get_concept_model_path(self, concept_name: str) -> str:
-        """
-        Returns the path to the 'learned_embeds.bin' file in
-        the named concept. Returns None if invalid or cannot
-        be downloaded.
-        """
-        if not concept_name in self.list_concepts():
-            logger.warning(
-                f"{concept_name} is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
-            )
-            return None
-        return self.get_concept_file(concept_name.lower(), "learned_embeds.bin")
-
-    def concept_to_trigger(self, concept_name: str) -> str:
-        """
-        Given a concept name returns its trigger by looking in the
-        "token_identifier.txt" file.
-        """
-        if concept_name in self.triggers:
-            return self.triggers[concept_name]
-        elif self.concept_is_local(concept_name):
-            trigger = f"<{concept_name}>"
-            self.triggers[concept_name] = trigger
-            self.concept_names[trigger] = concept_name
-            return trigger
-
-        file = self.get_concept_file(
-            concept_name, "token_identifier.txt", local_only=True
-        )
-        if not file:
-            return None
-        with open(file, "r") as f:
-            trigger = f.readline()
-            trigger = trigger.strip()
-        self.triggers[concept_name] = trigger
-        self.concept_names[trigger] = concept_name
-        return trigger
-
-    def trigger_to_concept(self, trigger: str) -> str:
-        """
-        Given a trigger phrase, maps it to the concept library name.
-        Only works if concept_to_trigger() has previously been called
-        on this library. There needs to be a persistent database for
-        this.
-        """
-        concept = self.concept_names.get(trigger, None)
-        return f"<{concept}>" if concept else f"{trigger}"
-
-    def replace_triggers_with_concepts(self, prompt: str) -> str:
-        """
-        Given a prompt string that contains <trigger> tags, replace these
-        tags with the concept name. The reason for this is so that the
-        concept names get stored in the prompt metadata. There is no
-        controlling of colliding triggers in the SD library, so it is
-        better to store the concept name (unique) than the concept trigger
-        (not necessarily unique!)
-        """
-        if not prompt:
-            return prompt
-        triggers = self.match_trigger.findall(prompt)
-        if not triggers:
-            return prompt
-
-        def do_replace(match) -> str:
-            return self.trigger_to_concept(match.group(1)) or f"<{match.group(1)}>"
-
-        return self.match_trigger.sub(do_replace, prompt)
-
-    def replace_concepts_with_triggers(
-        self,
-        prompt: str,
-        load_concepts_callback: Callable[[list], any],
-        excluded_tokens: list[str],
-    ) -> str:
-        """
-        Given a prompt string that contains `<concept_name>` tags, replace
-        these tags with the appropriate trigger.
-
-        If any `<concept_name>` tags are found, `load_concepts_callback()` is called with a list
-        of `concepts_name` strings.
-
-        `excluded_tokens` are any tokens that should not be replaced, typically because they
-        are trigger tokens from a locally-loaded embedding.
-        """
-        concepts = self.match_concept.findall(prompt)
-        if not concepts:
-            return prompt
-        load_concepts_callback(concepts)
-
-        def do_replace(match) -> str:
-            if excluded_tokens and f"<{match.group(1)}>" in excluded_tokens:
-                return f"<{match.group(1)}>"
-            return self.concept_to_trigger(match.group(1)) or f"<{match.group(1)}>"
-
-        return self.match_concept.sub(do_replace, prompt)
-
-    def get_concept_file(
-        self,
-        concept_name: str,
-        file_name: str = "learned_embeds.bin",
-        local_only: bool = False,
-    ) -> str:
-        if not (
-            self.concept_is_downloaded(concept_name)
-            or self.concept_is_local(concept_name)
-            or local_only
-        ):
-            self.download_concept(concept_name)
-
-        # get local path in invokeai/embeddings if local concept
-        if self.concept_is_local(concept_name):
-            concept_path = self._concept_local_path(concept_name)
-            path = concept_path
-        else:
-            concept_path = self._concept_path(concept_name)
-            path = os.path.join(concept_path, file_name)
-        return path if os.path.exists(path) else None
-
-    def concept_is_local(self, concept_name) -> bool:
-        return concept_name in self.local_concepts
-
-    def concept_is_downloaded(self, concept_name) -> bool:
-        concept_directory = self._concept_path(concept_name)
-        return os.path.exists(concept_directory)
-
-    def download_concept(self, concept_name) -> bool:
-        repo_id = self._concept_id(concept_name)
-        dest = self._concept_path(concept_name)
-
-        access_token = HfFolder.get_token()
-        header = [("Authorization", f"Bearer {access_token}")] if access_token else []
-        opener = request.build_opener()
-        opener.addheaders = header
-        request.install_opener(opener)
-
-        os.makedirs(dest, exist_ok=True)
-        succeeded = True
-
-        bytes = 0
-
-        def tally_download_size(chunk, size, total):
-            nonlocal bytes
-            if chunk == 0:
-                bytes += total
-
-        logger.info(f"Downloading {repo_id}...", end="")
-        try:
-            for file in (
-                "README.md",
-                "learned_embeds.bin",
-                "token_identifier.txt",
-                "type_of_concept.txt",
-            ):
-                url = hf_hub_url(repo_id, file)
-                request.urlretrieve(
-                    url, os.path.join(dest, file), reporthook=tally_download_size
-                )
-        except ul_error.HTTPError as e:
-            if e.code == 404:
-                logger.warning(
-                    f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept."
-                )
-            else:
-                logger.warning(
-                    f"Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)"
-                )
-            os.rmdir(dest)
-            return False
-        except ul_error.URLError as e:
-            logger.error(
-                f"an error occurred while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
-            )
-            os.rmdir(dest)
-            return False
-        logger.info("...{:.2f}Kb".format(bytes / 1024))
-        return succeeded
-
-    def _concept_id(self, concept_name: str) -> str:
-        return f"sd-concepts-library/{concept_name}"
-
-    def _concept_path(self, concept_name: str) -> str:
-        return os.path.join(self.root, "models", "sd-concepts-library", concept_name)
-
-    def _concept_local_path(self, concept_name: str) -> str:
-        filename = self.local_concepts[concept_name]
-        return os.path.join(self.root, "embeddings", filename)
-
-    def get_local_concepts(self, loc_dir: str):
-        locs_dic = dict()
-        if os.path.isdir(loc_dir):
-            for file in os.listdir(loc_dir):
-                f = os.path.splitext(file)
-                if f[1] == ".bin" or f[1] == ".pt":
-                    locs_dic[f[0]] = file
-        return locs_dic
diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index f4afd880d3..0010f33a0d 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -16,7 +16,6 @@ from accelerate.utils import set_seed
 import psutil
 import torch
 import torchvision.transforms as T
-from compel import EmbeddingsProvider
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
@@ -48,7 +47,6 @@ from .diffusion import (
     PostprocessingSettings,
 )
 from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
-from .textual_inversion_manager import TextualInversionManager
 
 @dataclass
 class PipelineIntermediateState:
@@ -317,6 +315,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         requires_safety_checker: bool = False,
         precision: str = "float32",
         control_model: ControlNetModel = None,
+        execution_device: Optional[torch.device] = None,
     ):
         super().__init__(
             vae,
@@ -341,22 +340,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             # control_model=control_model,
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(
-            self.unet, self._unet_forward, is_running_diffusers=True
-        )
-        use_full_precision = precision == "float32" or precision == "autocast"
-        self.textual_inversion_manager = TextualInversionManager(
-            tokenizer=self.tokenizer,
-            text_encoder=self.text_encoder,
-            full_precision=use_full_precision,
-        )
-        # InvokeAI's interface for text embeddings and whatnot
-        self.embeddings_provider = EmbeddingsProvider(
-            tokenizer=self.tokenizer,
-            text_encoder=self.text_encoder,
-            textual_inversion_manager=self.textual_inversion_manager,
+            self.unet, self._unet_forward
         )
 
-        self._model_group = FullyLoadedModelGroup(self.unet.device)
+        self._model_group = FullyLoadedModelGroup(execution_device or self.unet.device)
         self._model_group.install(*self._submodels)
         self.control_model = control_model
 
@@ -404,50 +391,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 else:
                     self.disable_attention_slicing()
 
-    def enable_offload_submodels(self, device: torch.device):
-        """
-        Offload each submodel when it's not in use.
-
-        Useful for low-vRAM situations where the size of the model in memory is a big chunk of
-        the total available resource, and you want to free up as much for inference as possible.
-
-        This requires more moving parts and may add some delay as the U-Net is swapped out for the
-        VAE and vice-versa.
-        """
-        models = self._submodels
-        if self._model_group is not None:
-            self._model_group.uninstall(*models)
-        group = LazilyLoadedModelGroup(device)
-        group.install(*models)
-        self._model_group = group
-
-    def disable_offload_submodels(self):
-        """
-        Leave all submodels loaded.
-
-        Appropriate for cases where the size of the model in memory is small compared to the memory
-        required for inference. Avoids the delay and complexity of shuffling the submodels to and
-        from the GPU.
-        """
-        models = self._submodels
-        if self._model_group is not None:
-            self._model_group.uninstall(*models)
-        group = FullyLoadedModelGroup(self._model_group.execution_device)
-        group.install(*models)
-        self._model_group = group
-
-    def offload_all(self):
-        """Offload all this pipeline's models to CPU."""
-        self._model_group.offload_current()
-
-    def ready(self):
-        """
-        Ready this pipeline's models.
-
-        i.e. preload them to the GPU if appropriate.
-        """
-        self._model_group.ready()
-
     def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False):
         # overridden method; types match the superclass.
         if torch_device is None:
@@ -991,25 +934,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device = self._model_group.device_for(self.safety_checker)
         return super().run_safety_checker(image, device, dtype)
 
-    @torch.inference_mode()
-    def get_learned_conditioning(
-        self, c: List[List[str]], *, return_tokens=True, fragment_weights=None
-    ):
-        """
-        Compatibility function for invokeai.models.diffusion.ddpm.LatentDiffusion.
-        """
-        return self.embeddings_provider.get_embeddings_for_weighted_prompt_fragments(
-            text_batch=c,
-            fragment_weights_batch=fragment_weights,
-            should_return_tokens=return_tokens,
-            device=self._model_group.device_for(self.unet),
-        )
-
-    @property
-    def channels(self) -> int:
-        """Compatible with DiffusionWrapper"""
-        return self.unet.config.in_channels
-
     def decode_latents(self, latents):
         # Explicit call to get the vae loaded, since `decode` isn't the forward method.
         self._model_group.load(self.vae)
diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index eec8097857..f3b09f6a9f 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -18,7 +18,6 @@ from .cross_attention_control import (
     CrossAttentionType,
     SwapCrossAttnContext,
     get_cross_attention_modules,
-    restore_default_cross_attention,
     setup_cross_attention_control_attention_processors,
 )
 from .cross_attention_map_saving import AttentionMapSaver
@@ -66,7 +65,6 @@ class InvokeAIDiffuserComponent:
         self,
         model,
         model_forward_callback: ModelForwardCallback,
-        is_running_diffusers: bool = False,
     ):
         """
         :param model: the unet model to pass through to cross attention control
@@ -75,7 +73,6 @@ class InvokeAIDiffuserComponent:
         config = InvokeAIAppConfig.get_config()
         self.conditioning = None
         self.model = model
-        self.is_running_diffusers = is_running_diffusers
         self.model_forward_callback = model_forward_callback
         self.cross_attention_control_context = None
         self.sequential_guidance = config.sequential_guidance
@@ -112,37 +109,6 @@ class InvokeAIDiffuserComponent:
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
-    # apparently unused code
-    # TODO: delete
-    # def override_cross_attention(
-    #     self, conditioning: ExtraConditioningInfo, step_count: int
-    # ) -> Dict[str, AttentionProcessor]:
-    #     """
-    #     setup cross attention .swap control. for diffusers this replaces the attention processor, so
-    #     the previous attention processor is returned so that the caller can restore it later.
-    #     """
-    #     self.conditioning = conditioning
-    #     self.cross_attention_control_context = Context(
-    #         arguments=self.conditioning.cross_attention_control_args,
-    #         step_count=step_count,
-    #     )
-    #     return override_cross_attention(
-    #         self.model,
-    #         self.cross_attention_control_context,
-    #         is_running_diffusers=self.is_running_diffusers,
-    #     )
-
-    def restore_default_cross_attention(
-        self, restore_attention_processor: Optional["AttentionProcessor"] = None
-    ):
-        self.conditioning = None
-        self.cross_attention_control_context = None
-        restore_default_cross_attention(
-            self.model,
-            is_running_diffusers=self.is_running_diffusers,
-            restore_attention_processor=restore_attention_processor,
-        )
-
     def setup_attention_map_saving(self, saver: AttentionMapSaver):
         def callback(slice, dim, offset, slice_size, key):
             if dim is not None:
@@ -204,9 +170,7 @@ class InvokeAIDiffuserComponent:
         cross_attention_control_types_to_do = []
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
-            percent_through = self.calculate_percent_through(
-                sigma, step_index, total_step_count
-            )
+            percent_through = step_index / total_step_count
             cross_attention_control_types_to_do = (
                 context.get_active_cross_attention_control_types_for_step(
                     percent_through
@@ -264,9 +228,7 @@ class InvokeAIDiffuserComponent:
         total_step_count,
     ) -> torch.Tensor:
         if postprocessing_settings is not None:
-            percent_through = self.calculate_percent_through(
-                sigma, step_index, total_step_count
-            )
+            percent_through = step_index / total_step_count
             latents = self.apply_threshold(
                 postprocessing_settings, latents, percent_through
             )
@@ -275,22 +237,6 @@ class InvokeAIDiffuserComponent:
             )
         return latents
 
-    def calculate_percent_through(self, sigma, step_index, total_step_count):
-        if step_index is not None and total_step_count is not None:
-            # 🧨diffusers codepath
-            percent_through = (
-                step_index / total_step_count
-            )  # will never reach 1.0 - this is deliberate
-        else:
-            # legacy compvis codepath
-            # TODO remove when compvis codepath support is dropped
-            if step_index is None and sigma is None:
-                raise ValueError(
-                    "Either step_index or sigma is required when doing cross attention control, but both are None."
-                )
-            percent_through = self.estimate_percent_through(step_index, sigma)
-        return percent_through
-
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
     def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
@@ -323,6 +269,7 @@ class InvokeAIDiffuserComponent:
             conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
+    # TODO: looks unused
     def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
         assert isinstance(conditioning, dict)
         assert isinstance(unconditioning, dict)
@@ -350,34 +297,6 @@ class InvokeAIDiffuserComponent:
         conditioning,
         cross_attention_control_types_to_do,
         **kwargs,
-    ):
-        if self.is_running_diffusers:
-            return self._apply_cross_attention_controlled_conditioning__diffusers(
-                x,
-                sigma,
-                unconditioning,
-                conditioning,
-                cross_attention_control_types_to_do,
-                **kwargs,
-            )
-        else:
-            return self._apply_cross_attention_controlled_conditioning__compvis(
-                x,
-                sigma,
-                unconditioning,
-                conditioning,
-                cross_attention_control_types_to_do,
-                **kwargs,
-            )
-
-    def _apply_cross_attention_controlled_conditioning__diffusers(
-        self,
-        x: torch.Tensor,
-        sigma,
-        unconditioning,
-        conditioning,
-        cross_attention_control_types_to_do,
-        **kwargs,
     ):
         context: Context = self.cross_attention_control_context
 
@@ -409,54 +328,6 @@ class InvokeAIDiffuserComponent:
         )
         return unconditioned_next_x, conditioned_next_x
 
-    def _apply_cross_attention_controlled_conditioning__compvis(
-        self,
-        x: torch.Tensor,
-        sigma,
-        unconditioning,
-        conditioning,
-        cross_attention_control_types_to_do,
-        **kwargs,
-    ):
-        # print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
-        # slower non-batched path (20% slower on mac MPS)
-        # We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
-        # unconditioned_next_x causes attention maps to *also* be saved for the unconditioned_next_x.
-        # This messes app their application later, due to mismatched shape of dim 0 (seems to be 16 for batched vs. 8)
-        # (For the batched invocation the `wrangler` function gets attention tensor with shape[0]=16,
-        # representing batched uncond + cond, but then when it comes to applying the saved attention, the
-        # wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
-        # todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
-        context: Context = self.cross_attention_control_context
-
-        try:
-            unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning, **kwargs)
-
-            # process x using the original prompt, saving the attention maps
-            # print("saving attention maps for", cross_attention_control_types_to_do)
-            for ca_type in cross_attention_control_types_to_do:
-                context.request_save_attention_maps(ca_type)
-            _ = self.model_forward_callback(x, sigma, conditioning, **kwargs,)
-            context.clear_requests(cleanup=False)
-
-            # process x again, using the saved attention maps to control where self.edited_conditioning will be applied
-            # print("applying saved attention maps for", cross_attention_control_types_to_do)
-            for ca_type in cross_attention_control_types_to_do:
-                context.request_apply_saved_attention_maps(ca_type)
-            edited_conditioning = (
-                self.conditioning.cross_attention_control_args.edited_conditioning
-            )
-            conditioned_next_x = self.model_forward_callback(
-                x, sigma, edited_conditioning, **kwargs,
-            )
-            context.clear_requests(cleanup=True)
-
-        except:
-            context.clear_requests(cleanup=True)
-            raise
-
-        return unconditioned_next_x, conditioned_next_x
-
     def _combine(self, unconditioned_next_x, conditioned_next_x, guidance_scale):
         # to scale how much effect conditioning has, calculate the changes it does and then scale that
         scaled_delta = (conditioned_next_x - unconditioned_next_x) * guidance_scale
diff --git a/invokeai/backend/stable_diffusion/schedulers/schedulers.py b/invokeai/backend/stable_diffusion/schedulers/schedulers.py
index 08f85cf559..77c45d5eb8 100644
--- a/invokeai/backend/stable_diffusion/schedulers/schedulers.py
+++ b/invokeai/backend/stable_diffusion/schedulers/schedulers.py
@@ -1,13 +1,14 @@
 from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, KDPM2DiscreteScheduler, \
     KDPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, \
     HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UniPCMultistepScheduler, \
-    DPMSolverSinglestepScheduler, DEISMultistepScheduler, DDPMScheduler
+    DPMSolverSinglestepScheduler, DEISMultistepScheduler, DDPMScheduler, DPMSolverSDEScheduler
 
 SCHEDULER_MAP = dict(
     ddim=(DDIMScheduler, dict()),
     ddpm=(DDPMScheduler, dict()),
     deis=(DEISMultistepScheduler, dict()),
-    lms=(LMSDiscreteScheduler, dict()),
+    lms=(LMSDiscreteScheduler, dict(use_karras_sigmas=False)),
+    lms_k=(LMSDiscreteScheduler, dict(use_karras_sigmas=True)),
     pndm=(PNDMScheduler, dict()),
     heun=(HeunDiscreteScheduler, dict(use_karras_sigmas=False)),
     heun_k=(HeunDiscreteScheduler, dict(use_karras_sigmas=True)),
@@ -16,8 +17,13 @@ SCHEDULER_MAP = dict(
     euler_a=(EulerAncestralDiscreteScheduler, dict()),
     kdpm_2=(KDPM2DiscreteScheduler, dict()),
     kdpm_2_a=(KDPM2AncestralDiscreteScheduler, dict()),
-    dpmpp_2s=(DPMSolverSinglestepScheduler, dict()),
+    dpmpp_2s=(DPMSolverSinglestepScheduler, dict(use_karras_sigmas=False)),
+    dpmpp_2s_k=(DPMSolverSinglestepScheduler, dict(use_karras_sigmas=True)),
     dpmpp_2m=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False)),
     dpmpp_2m_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)),
+    dpmpp_2m_sde=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False, algorithm_type='sde-dpmsolver++')),
+    dpmpp_2m_sde_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True, algorithm_type='sde-dpmsolver++')),
+    dpmpp_sde=(DPMSolverSDEScheduler, dict(use_karras_sigmas=False, noise_sampler_seed=0)),
+    dpmpp_sde_k=(DPMSolverSDEScheduler, dict(use_karras_sigmas=True, noise_sampler_seed=0)),
     unipc=(UniPCMultistepScheduler, dict(cpu_only=True))
 )
diff --git a/invokeai/backend/stable_diffusion/textual_inversion_manager.py b/invokeai/backend/stable_diffusion/textual_inversion_manager.py
deleted file mode 100644
index 9476c12dc5..0000000000
--- a/invokeai/backend/stable_diffusion/textual_inversion_manager.py
+++ /dev/null
@@ -1,429 +0,0 @@
-import traceback
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Optional, Union, List
-
-import safetensors.torch
-import torch
-
-from compel.embeddings_provider import BaseTextualInversionManager
-from picklescan.scanner import scan_file_path
-from transformers import CLIPTextModel, CLIPTokenizer
-
-import invokeai.backend.util.logging as logger
-from .concepts_lib import HuggingFaceConceptsLibrary
-
-@dataclass
-class EmbeddingInfo:
-    name: str
-    embedding: torch.Tensor
-    num_vectors_per_token: int
-    token_dim: int 
-    trained_steps: int = None
-    trained_model_name: str = None
-    trained_model_checksum: str = None
-
-@dataclass
-class TextualInversion:
-    trigger_string: str
-    embedding: torch.Tensor
-    trigger_token_id: Optional[int] = None
-    pad_token_ids: Optional[list[int]] = None
-
-    @property
-    def embedding_vector_length(self) -> int:
-        return self.embedding.shape[0]
-
-
-class TextualInversionManager(BaseTextualInversionManager):
-    def __init__(
-        self,
-        tokenizer: CLIPTokenizer,
-        text_encoder: CLIPTextModel,
-        full_precision: bool = True,
-    ):
-        self.tokenizer = tokenizer
-        self.text_encoder = text_encoder
-        self.full_precision = full_precision
-        self.hf_concepts_library = HuggingFaceConceptsLibrary()
-        self.trigger_to_sourcefile = dict()
-        default_textual_inversions: list[TextualInversion] = []
-        self.textual_inversions = default_textual_inversions
-
-    def load_huggingface_concepts(self, concepts: list[str]):
-        for concept_name in concepts:
-            if concept_name in self.hf_concepts_library.concepts_loaded:
-                continue
-            trigger = self.hf_concepts_library.concept_to_trigger(concept_name)
-            if (
-                self.has_textual_inversion_for_trigger_string(trigger)
-                or self.has_textual_inversion_for_trigger_string(concept_name)
-                or self.has_textual_inversion_for_trigger_string(f"<{concept_name}>")
-            ):  # in case a token with literal angle brackets encountered
-                logger.info(f"Loaded local embedding for trigger {concept_name}")
-                continue
-            bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
-            if not bin_file:
-                continue
-            logger.info(f"Loaded remote embedding for trigger {concept_name}")
-            self.load_textual_inversion(bin_file)
-            self.hf_concepts_library.concepts_loaded[concept_name] = True
-
-    def get_all_trigger_strings(self) -> list[str]:
-        return [ti.trigger_string for ti in self.textual_inversions]
-
-    def load_textual_inversion(
-        self, ckpt_path: Union[str, Path], defer_injecting_tokens: bool = False
-    ):
-        ckpt_path = Path(ckpt_path)
-
-        if not ckpt_path.is_file():
-            return
-
-        if str(ckpt_path).endswith(".DS_Store"):
-            return
-
-        embedding_list = self._parse_embedding(str(ckpt_path))
-        for embedding_info in embedding_list:
-            if (self.text_encoder.get_input_embeddings().weight.data[0].shape[0] != embedding_info.token_dim):
-                logger.warning(
-                    f"Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}."
-                )
-                continue
-
-            # Resolve the situation in which an earlier embedding has claimed the same
-            # trigger string. We replace the trigger with '<source_file>', as we used to.
-            trigger_str = embedding_info.name
-            sourcefile = (
-                f"{ckpt_path.parent.name}/{ckpt_path.name}"
-                if ckpt_path.name == "learned_embeds.bin"
-                else ckpt_path.name
-            )
-
-            if trigger_str in self.trigger_to_sourcefile:
-                replacement_trigger_str = (
-                    f"<{ckpt_path.parent.name}>"
-                    if ckpt_path.name == "learned_embeds.bin"
-                    else f"<{ckpt_path.stem}>"
-                )
-                logger.info(
-                    f"{sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}"
-                )
-                trigger_str = replacement_trigger_str
-
-            try:
-                self._add_textual_inversion(
-                    trigger_str,
-                    embedding_info.embedding,
-                    defer_injecting_tokens=defer_injecting_tokens,
-                )
-                # remember which source file claims this trigger
-                self.trigger_to_sourcefile[trigger_str] = sourcefile
-
-            except ValueError as e:
-                logger.debug(f'Ignoring incompatible embedding {embedding_info["name"]}')
-                logger.debug(f"The error was {str(e)}")
-
-    def _add_textual_inversion(
-        self, trigger_str, embedding, defer_injecting_tokens=False
-    ) -> Optional[TextualInversion]:
-        """
-        Add a textual inversion to be recognised.
-        :param trigger_str: The trigger text in the prompt that activates this textual inversion. If unknown to the embedder's tokenizer, will be added.
-        :param embedding: The actual embedding data that will be inserted into the conditioning at the point where the token_str appears.
-        :return: The token id for the added embedding, either existing or newly-added.
-        """
-        if trigger_str in [ti.trigger_string for ti in self.textual_inversions]:
-            logger.warning(
-                f"TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
-            )
-            return
-        if not self.full_precision:
-            embedding = embedding.half()
-        if len(embedding.shape) == 1:
-            embedding = embedding.unsqueeze(0)
-        elif len(embedding.shape) > 2:
-            raise ValueError(
-                f"** TextualInversionManager cannot add {trigger_str} because the embedding shape {embedding.shape} is incorrect. The embedding must have shape [token_dim] or [V, token_dim] where V is vector length and token_dim is 768 for SD1 or 1280 for SD2."
-            )
-
-        try:
-            ti = TextualInversion(trigger_string=trigger_str, embedding=embedding)
-            if not defer_injecting_tokens:
-                self._inject_tokens_and_assign_embeddings(ti)
-            self.textual_inversions.append(ti)
-            return ti
-
-        except ValueError as e:
-            if str(e).startswith("Warning"):
-                logger.warning(f"{str(e)}")
-            else:
-                traceback.print_exc()
-                logger.error(
-                    f"TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
-                )
-                raise
-
-    def _inject_tokens_and_assign_embeddings(self, ti: TextualInversion) -> int:
-        if ti.trigger_token_id is not None:
-            raise ValueError(
-                f"Tokens already injected for textual inversion with trigger '{ti.trigger_string}'"
-            )
-
-        trigger_token_id = self._get_or_create_token_id_and_assign_embedding(
-            ti.trigger_string, ti.embedding[0]
-        )
-
-        if ti.embedding_vector_length > 1:
-            # for embeddings with vector length > 1
-            pad_token_strings = [
-                ti.trigger_string + "-!pad-" + str(pad_index)
-                for pad_index in range(1, ti.embedding_vector_length)
-            ]
-            # todo: batched UI for faster loading when vector length >2
-            pad_token_ids = [
-                self._get_or_create_token_id_and_assign_embedding(
-                    pad_token_str, ti.embedding[1 + i]
-                )
-                for (i, pad_token_str) in enumerate(pad_token_strings)
-            ]
-        else:
-            pad_token_ids = []
-
-        ti.trigger_token_id = trigger_token_id
-        ti.pad_token_ids = pad_token_ids
-        return ti.trigger_token_id
-
-    def has_textual_inversion_for_trigger_string(self, trigger_string: str) -> bool:
-        try:
-            ti = self.get_textual_inversion_for_trigger_string(trigger_string)
-            return ti is not None
-        except StopIteration:
-            return False
-
-    def get_textual_inversion_for_trigger_string(
-        self, trigger_string: str
-    ) -> TextualInversion:
-        return next(
-            ti for ti in self.textual_inversions if ti.trigger_string == trigger_string
-        )
-
-    def get_textual_inversion_for_token_id(self, token_id: int) -> TextualInversion:
-        return next(
-            ti for ti in self.textual_inversions if ti.trigger_token_id == token_id
-        )
-
-    def create_deferred_token_ids_for_any_trigger_terms(
-        self, prompt_string: str
-    ) -> list[int]:
-        injected_token_ids = []
-        for ti in self.textual_inversions:
-            if ti.trigger_token_id is None and ti.trigger_string in prompt_string:
-                if ti.embedding_vector_length > 1:
-                    logger.info(
-                        f"Preparing tokens for textual inversion {ti.trigger_string}..."
-                    )
-                try:
-                    self._inject_tokens_and_assign_embeddings(ti)
-                except ValueError as e:
-                    logger.debug(
-                        f"Ignoring incompatible embedding trigger {ti.trigger_string}"
-                    )
-                    logger.debug(f"The error was {str(e)}")
-                    continue
-                injected_token_ids.append(ti.trigger_token_id)
-                injected_token_ids.extend(ti.pad_token_ids)
-        return injected_token_ids
-
-    def expand_textual_inversion_token_ids_if_necessary(
-        self, prompt_token_ids: list[int]
-    ) -> list[int]:
-        """
-        Insert padding tokens as necessary into the passed-in list of token ids to match any textual inversions it includes.
-
-        :param prompt_token_ids: The prompt as a list of token ids (`int`s). Should not include bos and eos markers.
-        :return: The prompt token ids with any necessary padding to account for textual inversions inserted. May be too
-                long - caller is responsible for prepending/appending eos and bos token ids, and truncating if necessary.
-        """
-        if len(prompt_token_ids) == 0:
-            return prompt_token_ids
-
-        if prompt_token_ids[0] == self.tokenizer.bos_token_id:
-            raise ValueError("prompt_token_ids must not start with bos_token_id")
-        if prompt_token_ids[-1] == self.tokenizer.eos_token_id:
-            raise ValueError("prompt_token_ids must not end with eos_token_id")
-        textual_inversion_trigger_token_ids = [
-            ti.trigger_token_id for ti in self.textual_inversions
-        ]
-        prompt_token_ids = prompt_token_ids.copy()
-        for i, token_id in reversed(list(enumerate(prompt_token_ids))):
-            if token_id in textual_inversion_trigger_token_ids:
-                textual_inversion = next(
-                    ti
-                    for ti in self.textual_inversions
-                    if ti.trigger_token_id == token_id
-                )
-                for pad_idx in range(0, textual_inversion.embedding_vector_length - 1):
-                    prompt_token_ids.insert(
-                        i + pad_idx + 1, textual_inversion.pad_token_ids[pad_idx]
-                    )
-
-        return prompt_token_ids
-
-    def _get_or_create_token_id_and_assign_embedding(
-        self, token_str: str, embedding: torch.Tensor
-    ) -> int:
-        if len(embedding.shape) != 1:
-            raise ValueError(
-                "Embedding has incorrect shape - must be [token_dim] where token_dim is 768 for SD1 or 1280 for SD2"
-            )
-        existing_token_id = self.tokenizer.convert_tokens_to_ids(token_str)
-        if existing_token_id == self.tokenizer.unk_token_id:
-            num_tokens_added = self.tokenizer.add_tokens(token_str)
-            current_embeddings = self.text_encoder.resize_token_embeddings(None)
-            current_token_count = current_embeddings.num_embeddings
-            new_token_count = current_token_count + num_tokens_added
-            # the following call is slow - todo make batched for better performance with vector length >1
-            self.text_encoder.resize_token_embeddings(new_token_count)
-
-        token_id = self.tokenizer.convert_tokens_to_ids(token_str)
-        if token_id == self.tokenizer.unk_token_id:
-            raise RuntimeError(f"Unable to find token id for token '{token_str}'")
-        if (
-            self.text_encoder.get_input_embeddings().weight.data[token_id].shape
-            != embedding.shape
-        ):
-            raise ValueError(
-                f"Warning. Cannot load embedding for {token_str}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {self.text_encoder.get_input_embeddings().weight.data[token_id].shape[0]}."
-            )
-        self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding
-
-        return token_id
-
-
-    def _parse_embedding(self, embedding_file: str)->List[EmbeddingInfo]:
-        suffix = Path(embedding_file).suffix
-        try:
-            if suffix in [".pt",".ckpt",".bin"]:
-                scan_result = scan_file_path(embedding_file)
-                if scan_result.infected_files > 0:
-                    logger.critical(
-                        f"Security Issues Found in Model: {scan_result.issues_count}"
-                    )
-                    logger.critical("For your safety, InvokeAI will not load this embed.")
-                    return list()
-                ckpt = torch.load(embedding_file,map_location="cpu")
-            else:
-                ckpt = safetensors.torch.load_file(embedding_file)
-        except Exception as e:
-            logger.warning(f"Notice: unrecognized embedding file format: {embedding_file}: {e}")
-            return list()
-        
-        # try to figure out what kind of embedding file it is and parse accordingly
-        keys = list(ckpt.keys())
-        if all(x in keys for x in ['string_to_token','string_to_param','name','step']):
-            return self._parse_embedding_v1(ckpt, embedding_file)     # example rem_rezero.pt
-        
-        elif all(x in keys for x in ['string_to_token','string_to_param']):
-            return self._parse_embedding_v2(ckpt, embedding_file)     # example midj-strong.pt
-        
-        elif 'emb_params' in keys:
-            return self._parse_embedding_v3(ckpt, embedding_file)     # example easynegative.safetensors
-        
-        else:
-            return self._parse_embedding_v4(ckpt, embedding_file)     # usually a '.bin' file
-
-    def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
-        basename = Path(file_path).stem
-        logger.debug(f'Loading v1 embedding file: {basename}')
-
-        embeddings = list()
-        token_counter = -1
-        for token,embedding in embedding_ckpt["string_to_param"].items():
-            if token_counter < 0:
-                trigger = embedding_ckpt["name"]
-            elif token_counter == 0:
-                trigger = '<basename>'
-            else:
-                trigger = f'<{basename}-{int(token_counter:=token_counter)}>'
-            token_counter += 1
-            embedding_info = EmbeddingInfo(
-                name = trigger,
-                embedding = embedding,
-                num_vectors_per_token = embedding.size()[0],
-                token_dim = embedding.size()[1],
-                trained_steps = embedding_ckpt["step"],
-                trained_model_name = embedding_ckpt["sd_checkpoint_name"],
-                trained_model_checksum = embedding_ckpt["sd_checkpoint"]
-            )
-            embeddings.append(embedding_info)
-        return embeddings
-
-    def _parse_embedding_v2 (
-        self, embedding_ckpt: dict, file_path: str
-    ) -> List[EmbeddingInfo]:
-        """
-        This handles embedding .pt file variant #2.
-        """
-        basename = Path(file_path).stem
-        logger.debug(f'Loading v2 embedding file: {basename}')
-        embeddings = list()
-        
-        if isinstance(
-            list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
-        ):
-            token_counter = 0
-            for token,embedding in embedding_ckpt["string_to_param"].items():
-                trigger = token if token != '*' \
-                    else f'<{basename}>' if token_counter == 0 \
-                         else f'<{basename}-{int(token_counter:=token_counter+1)}>'
-                embedding_info = EmbeddingInfo(
-                    name = trigger,
-                    embedding = embedding,
-                    num_vectors_per_token = embedding.size()[0],
-                    token_dim = embedding.size()[1],
-                )
-                embeddings.append(embedding_info)
-        else:
-            logger.warning(f"{basename}: Unrecognized embedding format")
-
-        return embeddings
-
-    def _parse_embedding_v3(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
-        """
-        Parse 'version 3' of the .pt textual inversion embedding files.
-        """
-        basename = Path(file_path).stem
-        logger.debug(f'Loading v3 embedding file: {basename}')
-        embedding = embedding_ckpt['emb_params']
-        embedding_info = EmbeddingInfo(
-            name = f'<{basename}>',
-            embedding = embedding,
-            num_vectors_per_token = embedding.size()[0],
-            token_dim = embedding.size()[1],
-        )
-        return [embedding_info]
-    
-    def _parse_embedding_v4(self, embedding_ckpt: dict, filepath: str)->List[EmbeddingInfo]:
-        """
-        Parse 'version 4' of the textual inversion embedding files. This one
-        is usually associated with .bin files trained by HuggingFace diffusers.
-        """
-        basename = Path(filepath).stem
-        short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
-        
-        logger.debug(f'Loading v4 embedding file: {short_path}')
-        
-        embeddings = list()
-        if list(embedding_ckpt.keys()) == 0:
-            logger.warning(f"Invalid embeddings file: {short_path}")
-        else:
-            for token,embedding in embedding_ckpt.items():
-                embedding_info = EmbeddingInfo(
-                    name = token or f"<{basename}>",
-                    embedding = embedding,
-                    num_vectors_per_token = 1,  # All Concepts seem to default to 1
-                    token_dim = embedding.size()[0],
-                )
-                embeddings.append(embedding_info)
-        return embeddings
diff --git a/invokeai/backend/web/modules/parameters.py b/invokeai/backend/web/modules/parameters.py
index 9a4bc0aec3..440f21a947 100644
--- a/invokeai/backend/web/modules/parameters.py
+++ b/invokeai/backend/web/modules/parameters.py
@@ -7,6 +7,7 @@ SAMPLER_CHOICES = [
     "ddpm",
     "deis",
     "lms",
+    "lms_k",
     "pndm",
     "heun",
     'heun_k',
@@ -16,8 +17,13 @@ SAMPLER_CHOICES = [
     "kdpm_2",
     "kdpm_2_a",
     "dpmpp_2s",
+    "dpmpp_2s_k",
     "dpmpp_2m",
     "dpmpp_2m_k",
+    "dpmpp_2m_sde",
+    "dpmpp_2m_sde_k",
+    "dpmpp_sde",
+    "dpmpp_sde_k",
     "unipc",
 ]
 
diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json
index 7a73bae411..eae0c07eff 100644
--- a/invokeai/frontend/web/public/locales/en.json
+++ b/invokeai/frontend/web/public/locales/en.json
@@ -547,7 +547,8 @@
         "general": "General",
         "generation": "Generation",
         "ui": "User Interface",
-        "availableSchedulers": "Available Schedulers"
+        "favoriteSchedulers": "Favorite Schedulers",
+        "favoriteSchedulersPlaceholder": "No schedulers favorited"
     },
     "toast": {
         "serverError": "Server Error",
diff --git a/invokeai/frontend/web/src/app/constants.ts b/invokeai/frontend/web/src/app/constants.ts
index c2e525ad7d..5fd413d915 100644
--- a/invokeai/frontend/web/src/app/constants.ts
+++ b/invokeai/frontend/web/src/app/constants.ts
@@ -1,25 +1,62 @@
-// TODO: use Enums?
+import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
 
-export const SCHEDULERS = [
-  'ddim',
-  'lms',
+// zod needs the array to be `as const` to infer the type correctly
+// this is the source of the `SchedulerParam` type, which is generated by zod
+export const SCHEDULER_NAMES_AS_CONST = [
   'euler',
-  'euler_k',
-  'euler_a',
+  'deis',
+  'ddim',
+  'ddpm',
   'dpmpp_2s',
   'dpmpp_2m',
-  'dpmpp_2m_k',
-  'kdpm_2',
-  'kdpm_2_a',
-  'deis',
-  'ddpm',
-  'pndm',
+  'dpmpp_2m_sde',
+  'dpmpp_sde',
   'heun',
-  'heun_k',
+  'kdpm_2',
+  'lms',
+  'pndm',
   'unipc',
+  'euler_k',
+  'dpmpp_2s_k',
+  'dpmpp_2m_k',
+  'dpmpp_2m_sde_k',
+  'dpmpp_sde_k',
+  'heun_k',
+  'lms_k',
+  'euler_a',
+  'kdpm_2_a',
 ] as const;
 
-export type Scheduler = (typeof SCHEDULERS)[number];
+export const DEFAULT_SCHEDULER_NAME = 'euler';
+
+export const SCHEDULER_NAMES: SchedulerParam[] = [...SCHEDULER_NAMES_AS_CONST];
+
+export const SCHEDULER_LABEL_MAP: Record<SchedulerParam, string> = {
+  euler: 'Euler',
+  deis: 'DEIS',
+  ddim: 'DDIM',
+  ddpm: 'DDPM',
+  dpmpp_sde: 'DPM++ SDE',
+  dpmpp_2s: 'DPM++ 2S',
+  dpmpp_2m: 'DPM++ 2M',
+  dpmpp_2m_sde: 'DPM++ 2M SDE',
+  heun: 'Heun',
+  kdpm_2: 'KDPM 2',
+  lms: 'LMS',
+  pndm: 'PNDM',
+  unipc: 'UniPC',
+  euler_k: 'Euler Karras',
+  dpmpp_sde_k: 'DPM++ SDE Karras',
+  dpmpp_2s_k: 'DPM++ 2S Karras',
+  dpmpp_2m_k: 'DPM++ 2M Karras',
+  dpmpp_2m_sde_k: 'DPM++ 2M SDE Karras',
+  heun_k: 'Heun Karras',
+  lms_k: 'LMS Karras',
+  euler_a: 'Euler Ancestral',
+  kdpm_2_a: 'KDPM 2 Ancestral',
+};
+
+export type Scheduler = (typeof SCHEDULER_NAMES)[number];
 
 // Valid upscaling levels
 export const UPSCALING_LEVELS: Array<{ label: string; value: string }> = [
diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts
index 4d8177d7f3..a26d872d50 100644
--- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts
+++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts
@@ -1,11 +1,10 @@
 import { startAppListening } from '..';
 import { sessionCreated } from 'services/thunks/session';
-import { buildCanvasGraphComponents } from 'features/nodes/util/graphBuilders/buildCanvasGraph';
+import { buildCanvasGraph } from 'features/nodes/util/graphBuilders/buildCanvasGraph';
 import { log } from 'app/logging/useLogger';
 import { canvasGraphBuilt } from 'features/nodes/store/actions';
 import { imageUpdated, imageUploaded } from 'services/thunks/image';
-import { v4 as uuidv4 } from 'uuid';
-import { Graph } from 'services/api';
+import { ImageDTO } from 'services/api';
 import {
   canvasSessionIdChanged,
   stagingAreaInitialized,
@@ -67,112 +66,106 @@ export const addUserInvokedCanvasListener = () => {
 
       moduleLog.debug(`Generation mode: ${generationMode}`);
 
-      // Build the canvas graph
-      const graphComponents = await buildCanvasGraphComponents(
-        state,
-        generationMode
-      );
+      // Temp placeholders for the init and mask images
+      let canvasInitImage: ImageDTO | undefined;
+      let canvasMaskImage: ImageDTO | undefined;
 
-      if (!graphComponents) {
-        moduleLog.error('Problem building graph');
-        return;
-      }
-
-      const { rangeNode, iterateNode, baseNode, edges } = graphComponents;
-
-      // Assemble! Note that this graph *does not have the init or mask image set yet!*
-      const nodes: Graph['nodes'] = {
-        [rangeNode.id]: rangeNode,
-        [iterateNode.id]: iterateNode,
-        [baseNode.id]: baseNode,
-      };
-
-      const graph = { nodes, edges };
-
-      dispatch(canvasGraphBuilt(graph));
-
-      moduleLog.debug({ data: graph }, 'Canvas graph built');
-
-      // If we are generating img2img or inpaint, we need to upload the init images
-      if (baseNode.type === 'img2img' || baseNode.type === 'inpaint') {
-        const baseFilename = `${uuidv4()}.png`;
-        dispatch(
+      // For img2img and inpaint/outpaint, we need to upload the init images
+      if (['img2img', 'inpaint', 'outpaint'].includes(generationMode)) {
+        // upload the image, saving the request id
+        const { requestId: initImageUploadedRequestId } = dispatch(
           imageUploaded({
             formData: {
-              file: new File([baseBlob], baseFilename, { type: 'image/png' }),
+              file: new File([baseBlob], 'canvasInitImage.png', {
+                type: 'image/png',
+              }),
             },
             imageCategory: 'general',
             isIntermediate: true,
           })
         );
 
-        // Wait for the image to be uploaded
-        const [{ payload: baseImageDTO }] = await take(
+        // Wait for the image to be uploaded, matching by request id
+        const [{ payload }] = await take(
           (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
             imageUploaded.fulfilled.match(action) &&
-            action.meta.arg.formData.file.name === baseFilename
+            action.meta.requestId === initImageUploadedRequestId
         );
 
-        // Update the base node with the image name and type
-        baseNode.image = {
-          image_name: baseImageDTO.image_name,
-        };
+        canvasInitImage = payload;
       }
 
-      // For inpaint, we also need to upload the mask layer
-      if (baseNode.type === 'inpaint') {
-        const maskFilename = `${uuidv4()}.png`;
-        dispatch(
+      // For inpaint/outpaint, we also need to upload the mask layer
+      if (['inpaint', 'outpaint'].includes(generationMode)) {
+        // upload the image, saving the request id
+        const { requestId: maskImageUploadedRequestId } = dispatch(
           imageUploaded({
             formData: {
-              file: new File([maskBlob], maskFilename, { type: 'image/png' }),
+              file: new File([maskBlob], 'canvasMaskImage.png', {
+                type: 'image/png',
+              }),
             },
             imageCategory: 'mask',
             isIntermediate: true,
           })
         );
 
-        // Wait for the mask to be uploaded
-        const [{ payload: maskImageDTO }] = await take(
+        // Wait for the image to be uploaded, matching by request id
+        const [{ payload }] = await take(
           (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
             imageUploaded.fulfilled.match(action) &&
-            action.meta.arg.formData.file.name === maskFilename
+            action.meta.requestId === maskImageUploadedRequestId
         );
 
-        // Update the base node with the image name and type
-        baseNode.mask = {
-          image_name: maskImageDTO.image_name,
-        };
+        canvasMaskImage = payload;
       }
 
-      // Create the session and wait for response
-      dispatch(sessionCreated({ graph }));
-      const [sessionCreatedAction] = await take(sessionCreated.fulfilled.match);
+      const graph = buildCanvasGraph(
+        state,
+        generationMode,
+        canvasInitImage,
+        canvasMaskImage
+      );
+
+      moduleLog.debug({ graph }, `Canvas graph built`);
+
+      // currently this action is just listened to for logging
+      dispatch(canvasGraphBuilt(graph));
+
+      // Create the session, store the request id
+      const { requestId: sessionCreatedRequestId } = dispatch(
+        sessionCreated({ graph })
+      );
+
+      // Take the session created action, matching by its request id
+      const [sessionCreatedAction] = await take(
+        (action): action is ReturnType<typeof sessionCreated.fulfilled> =>
+          sessionCreated.fulfilled.match(action) &&
+          action.meta.requestId === sessionCreatedRequestId
+      );
       const sessionId = sessionCreatedAction.payload.id;
 
       // Associate the init image with the session, now that we have the session ID
-      if (
-        (baseNode.type === 'img2img' || baseNode.type === 'inpaint') &&
-        baseNode.image
-      ) {
+      if (['img2img', 'inpaint'].includes(generationMode) && canvasInitImage) {
         dispatch(
           imageUpdated({
-            imageName: baseNode.image.image_name,
+            imageName: canvasInitImage.image_name,
             requestBody: { session_id: sessionId },
           })
         );
       }
 
       // Associate the mask image with the session, now that we have the session ID
-      if (baseNode.type === 'inpaint' && baseNode.mask) {
+      if (['inpaint'].includes(generationMode) && canvasMaskImage) {
         dispatch(
           imageUpdated({
-            imageName: baseNode.mask.image_name,
+            imageName: canvasMaskImage.image_name,
             requestBody: { session_id: sessionId },
           })
         );
       }
 
+      // Prep the canvas staging area if it is not yet initialized
       if (!state.canvas.layerState.stagingArea.boundingBox) {
         dispatch(
           stagingAreaInitialized({
diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts
index 7dcbe8a41d..368d97a10f 100644
--- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts
+++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts
@@ -1,10 +1,10 @@
 import { startAppListening } from '..';
-import { buildImageToImageGraph } from 'features/nodes/util/graphBuilders/buildImageToImageGraph';
 import { sessionCreated } from 'services/thunks/session';
 import { log } from 'app/logging/useLogger';
 import { imageToImageGraphBuilt } from 'features/nodes/store/actions';
 import { userInvoked } from 'app/store/actions';
 import { sessionReadyToInvoke } from 'features/system/store/actions';
+import { buildLinearImageToImageGraph } from 'features/nodes/util/graphBuilders/buildLinearImageToImageGraph';
 
 const moduleLog = log.child({ namespace: 'invoke' });
 
@@ -15,7 +15,7 @@ export const addUserInvokedImageToImageListener = () => {
     effect: async (action, { getState, dispatch, take }) => {
       const state = getState();
 
-      const graph = buildImageToImageGraph(state);
+      const graph = buildLinearImageToImageGraph(state);
       dispatch(imageToImageGraphBuilt(graph));
       moduleLog.debug({ data: graph }, 'Image to Image graph built');
 
diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts
index 6042d86cb7..c76e0dfd4f 100644
--- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts
+++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts
@@ -1,10 +1,10 @@
 import { startAppListening } from '..';
-import { buildTextToImageGraph } from 'features/nodes/util/graphBuilders/buildTextToImageGraph';
 import { sessionCreated } from 'services/thunks/session';
 import { log } from 'app/logging/useLogger';
 import { textToImageGraphBuilt } from 'features/nodes/store/actions';
 import { userInvoked } from 'app/store/actions';
 import { sessionReadyToInvoke } from 'features/system/store/actions';
+import { buildLinearTextToImageGraph } from 'features/nodes/util/graphBuilders/buildLinearTextToImageGraph';
 
 const moduleLog = log.child({ namespace: 'invoke' });
 
@@ -15,7 +15,7 @@ export const addUserInvokedTextToImageListener = () => {
     effect: async (action, { getState, dispatch, take }) => {
       const state = getState();
 
-      const graph = buildTextToImageGraph(state);
+      const graph = buildLinearTextToImageGraph(state);
 
       dispatch(textToImageGraphBuilt(graph));
 
diff --git a/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx b/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx
new file mode 100644
index 0000000000..c7ce1de4c1
--- /dev/null
+++ b/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx
@@ -0,0 +1,94 @@
+import { Tooltip } from '@chakra-ui/react';
+import { MultiSelect, MultiSelectProps } from '@mantine/core';
+import { memo } from 'react';
+
+type IAIMultiSelectProps = MultiSelectProps & {
+  tooltip?: string;
+};
+
+const IAIMantineMultiSelect = (props: IAIMultiSelectProps) => {
+  const { searchable = true, tooltip, ...rest } = props;
+  return (
+    <Tooltip label={tooltip} placement="top" hasArrow>
+      <MultiSelect
+        searchable={searchable}
+        styles={() => ({
+          label: {
+            color: 'var(--invokeai-colors-base-300)',
+            fontWeight: 'normal',
+          },
+          searchInput: {
+            '::placeholder': {
+              color: 'var(--invokeai-colors-base-700)',
+            },
+          },
+          input: {
+            backgroundColor: 'var(--invokeai-colors-base-900)',
+            borderWidth: '2px',
+            borderColor: 'var(--invokeai-colors-base-800)',
+            color: 'var(--invokeai-colors-base-100)',
+            padding: 10,
+            paddingRight: 24,
+            fontWeight: 600,
+            '&:hover': { borderColor: 'var(--invokeai-colors-base-700)' },
+            '&:focus': {
+              borderColor: 'var(--invokeai-colors-accent-600)',
+            },
+            '&:focus-within': {
+              borderColor: 'var(--invokeai-colors-accent-600)',
+            },
+          },
+          value: {
+            backgroundColor: 'var(--invokeai-colors-base-800)',
+            color: 'var(--invokeai-colors-base-100)',
+            button: {
+              color: 'var(--invokeai-colors-base-100)',
+            },
+            '&:hover': {
+              backgroundColor: 'var(--invokeai-colors-base-700)',
+              cursor: 'pointer',
+            },
+          },
+          dropdown: {
+            backgroundColor: 'var(--invokeai-colors-base-800)',
+            borderColor: 'var(--invokeai-colors-base-700)',
+          },
+          item: {
+            backgroundColor: 'var(--invokeai-colors-base-800)',
+            color: 'var(--invokeai-colors-base-200)',
+            padding: 6,
+            '&[data-hovered]': {
+              color: 'var(--invokeai-colors-base-100)',
+              backgroundColor: 'var(--invokeai-colors-base-750)',
+            },
+            '&[data-active]': {
+              backgroundColor: 'var(--invokeai-colors-base-750)',
+              '&:hover': {
+                color: 'var(--invokeai-colors-base-100)',
+                backgroundColor: 'var(--invokeai-colors-base-750)',
+              },
+            },
+            '&[data-selected]': {
+              color: 'var(--invokeai-colors-base-50)',
+              backgroundColor: 'var(--invokeai-colors-accent-650)',
+              fontWeight: 600,
+              '&:hover': {
+                backgroundColor: 'var(--invokeai-colors-accent-600)',
+              },
+            },
+          },
+          rightSection: {
+            width: 24,
+            padding: 20,
+            button: {
+              color: 'var(--invokeai-colors-base-100)',
+            },
+          },
+        })}
+        {...rest}
+      />
+    </Tooltip>
+  );
+};
+
+export default memo(IAIMantineMultiSelect);
diff --git a/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts b/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
index 1fd7eb2dba..dd5a97e2f1 100644
--- a/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
@@ -2,8 +2,7 @@ import { RootState } from 'app/store/store';
 import { filter, forEach, size } from 'lodash-es';
 import { CollectInvocation, ControlNetInvocation } from 'services/api';
 import { NonNullableGraph } from '../types/types';
-
-const CONTROL_NET_COLLECT = 'control_net_collect';
+import { CONTROL_NET_COLLECT } from './graphBuilders/constants';
 
 export const addControlNetToLinearGraph = (
   graph: NonNullableGraph,
@@ -37,7 +36,7 @@ export const addControlNetToLinearGraph = (
       });
     }
 
-    forEach(controlNets, (controlNet, index) => {
+    forEach(controlNets, (controlNet) => {
       const {
         controlNetId,
         isEnabled,
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts
index 2d23b882ea..3ea513fe7e 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts
@@ -1,116 +1,39 @@
 import { RootState } from 'app/store/store';
-import {
-  Edge,
-  ImageToImageInvocation,
-  InpaintInvocation,
-  IterateInvocation,
-  RandomRangeInvocation,
-  RangeInvocation,
-  TextToImageInvocation,
-} from 'services/api';
-import { buildImg2ImgNode } from '../nodeBuilders/buildImageToImageNode';
-import { buildTxt2ImgNode } from '../nodeBuilders/buildTextToImageNode';
-import { buildRangeNode } from '../nodeBuilders/buildRangeNode';
-import { buildIterateNode } from '../nodeBuilders/buildIterateNode';
-import { buildEdges } from '../edgeBuilders/buildEdges';
+import { ImageDTO } from 'services/api';
 import { log } from 'app/logging/useLogger';
-import { buildInpaintNode } from '../nodeBuilders/buildInpaintNode';
+import { forEach } from 'lodash-es';
+import { buildCanvasInpaintGraph } from './buildCanvasInpaintGraph';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { buildCanvasImageToImageGraph } from './buildCanvasImageToImageGraph';
+import { buildCanvasTextToImageGraph } from './buildCanvasTextToImageGraph';
 
 const moduleLog = log.child({ namespace: 'nodes' });
 
-const buildBaseNode = (
-  nodeType: 'txt2img' | 'img2img' | 'inpaint' | 'outpaint',
-  state: RootState
-):
-  | TextToImageInvocation
-  | ImageToImageInvocation
-  | InpaintInvocation
-  | undefined => {
-  const overrides = {
-    ...state.canvas.boundingBoxDimensions,
-    is_intermediate: true,
-  };
-
-  if (nodeType === 'txt2img') {
-    return buildTxt2ImgNode(state, overrides);
-  }
-
-  if (nodeType === 'img2img') {
-    return buildImg2ImgNode(state, overrides);
-  }
-
-  if (nodeType === 'inpaint' || nodeType === 'outpaint') {
-    return buildInpaintNode(state, overrides);
-  }
-};
-
-/**
- * Builds the Canvas workflow graph and image blobs.
- */
-export const buildCanvasGraphComponents = async (
+export const buildCanvasGraph = (
   state: RootState,
-  generationMode: 'txt2img' | 'img2img' | 'inpaint' | 'outpaint'
-): Promise<
-  | {
-      rangeNode: RangeInvocation | RandomRangeInvocation;
-      iterateNode: IterateInvocation;
-      baseNode:
-        | TextToImageInvocation
-        | ImageToImageInvocation
-        | InpaintInvocation;
-      edges: Edge[];
-    }
-  | undefined
-> => {
-  // The base node is a txt2img, img2img or inpaint node
-  const baseNode = buildBaseNode(generationMode, state);
+  generationMode: 'txt2img' | 'img2img' | 'inpaint' | 'outpaint',
+  canvasInitImage: ImageDTO | undefined,
+  canvasMaskImage: ImageDTO | undefined
+) => {
+  let graph: NonNullableGraph;
 
-  if (!baseNode) {
-    moduleLog.error('Problem building base node');
-    return;
+  if (generationMode === 'txt2img') {
+    graph = buildCanvasTextToImageGraph(state);
+  } else if (generationMode === 'img2img') {
+    if (!canvasInitImage) {
+      throw new Error('Missing canvas init image');
+    }
+    graph = buildCanvasImageToImageGraph(state, canvasInitImage);
+  } else {
+    if (!canvasInitImage || !canvasMaskImage) {
+      throw new Error('Missing canvas init and mask images');
+    }
+    graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
   }
 
-  if (baseNode.type === 'inpaint') {
-    const {
-      seamSize,
-      seamBlur,
-      seamSteps,
-      seamStrength,
-      tileSize,
-      infillMethod,
-    } = state.generation;
+  forEach(graph.nodes, (node) => {
+    graph.nodes[node.id].is_intermediate = true;
+  });
 
-    const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } =
-      state.canvas;
-
-    if (boundingBoxScaleMethod !== 'none') {
-      baseNode.inpaint_width = scaledBoundingBoxDimensions.width;
-      baseNode.inpaint_height = scaledBoundingBoxDimensions.height;
-    }
-
-    baseNode.seam_size = seamSize;
-    baseNode.seam_blur = seamBlur;
-    baseNode.seam_strength = seamStrength;
-    baseNode.seam_steps = seamSteps;
-    baseNode.infill_method = infillMethod as InpaintInvocation['infill_method'];
-
-    if (infillMethod === 'tile') {
-      baseNode.tile_size = tileSize;
-    }
-  }
-
-  // We always range and iterate nodes, no matter the iteration count
-  // This is required to provide the correct seeds to the backend engine
-  const rangeNode = buildRangeNode(state);
-  const iterateNode = buildIterateNode();
-
-  // Build the edges for the nodes selected.
-  const edges = buildEdges(baseNode, rangeNode, iterateNode);
-
-  return {
-    rangeNode,
-    iterateNode,
-    baseNode,
-    edges,
-  };
+  return graph;
 };
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
new file mode 100644
index 0000000000..efaeaddff2
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
@@ -0,0 +1,331 @@
+import { RootState } from 'app/store/store';
+import {
+  ImageDTO,
+  ImageResizeInvocation,
+  RandomIntInvocation,
+  RangeOfSizeInvocation,
+} from 'services/api';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { log } from 'app/logging/useLogger';
+import {
+  ITERATE,
+  LATENTS_TO_IMAGE,
+  MODEL_LOADER,
+  NEGATIVE_CONDITIONING,
+  NOISE,
+  POSITIVE_CONDITIONING,
+  RANDOM_INT,
+  RANGE_OF_SIZE,
+  IMAGE_TO_IMAGE_GRAPH,
+  IMAGE_TO_LATENTS,
+  LATENTS_TO_LATENTS,
+  RESIZE,
+} from './constants';
+import { set } from 'lodash-es';
+import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
+
+const moduleLog = log.child({ namespace: 'nodes' });
+
+/**
+ * Builds the Canvas tab's Image to Image graph.
+ */
+export const buildCanvasImageToImageGraph = (
+  state: RootState,
+  initialImage: ImageDTO
+): NonNullableGraph => {
+  const {
+    positivePrompt,
+    negativePrompt,
+    model: model_name,
+    cfgScale: cfg_scale,
+    scheduler,
+    steps,
+    img2imgStrength: strength,
+    iterations,
+    seed,
+    shouldRandomizeSeed,
+  } = state.generation;
+
+  // The bounding box determines width and height, not the width and height params
+  const { width, height } = state.canvas.boundingBoxDimensions;
+
+  /**
+   * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
+   * full graph here as a template. Then use the parameters from app state and set friendlier node
+   * ids.
+   *
+   * The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
+   * the `fit` param. These are added to the graph at the end.
+   */
+
+  // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  const graph: NonNullableGraph = {
+    id: IMAGE_TO_IMAGE_GRAPH,
+    nodes: {
+      [POSITIVE_CONDITIONING]: {
+        type: 'compel',
+        id: POSITIVE_CONDITIONING,
+        prompt: positivePrompt,
+      },
+      [NEGATIVE_CONDITIONING]: {
+        type: 'compel',
+        id: NEGATIVE_CONDITIONING,
+        prompt: negativePrompt,
+      },
+      [RANGE_OF_SIZE]: {
+        type: 'range_of_size',
+        id: RANGE_OF_SIZE,
+        // seed - must be connected manually
+        // start: 0,
+        size: iterations,
+        step: 1,
+      },
+      [NOISE]: {
+        type: 'noise',
+        id: NOISE,
+      },
+      [MODEL_LOADER]: {
+        type: 'sd1_model_loader',
+        id: MODEL_LOADER,
+        model_name,
+      },
+      [LATENTS_TO_IMAGE]: {
+        type: 'l2i',
+        id: LATENTS_TO_IMAGE,
+      },
+      [ITERATE]: {
+        type: 'iterate',
+        id: ITERATE,
+      },
+      [LATENTS_TO_LATENTS]: {
+        type: 'l2l',
+        id: LATENTS_TO_LATENTS,
+        cfg_scale,
+        scheduler,
+        steps,
+        strength,
+      },
+      [IMAGE_TO_LATENTS]: {
+        type: 'i2l',
+        id: IMAGE_TO_LATENTS,
+        // must be set manually later, bc `fit` parameter may require a resize node inserted
+        // image: {
+        //   image_name: initialImage.image_name,
+        // },
+      },
+    },
+    edges: [
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: RANGE_OF_SIZE,
+          field: 'collection',
+        },
+        destination: {
+          node_id: ITERATE,
+          field: 'collection',
+        },
+      },
+      {
+        source: {
+          node_id: ITERATE,
+          field: 'item',
+        },
+        destination: {
+          node_id: NOISE,
+          field: 'seed',
+        },
+      },
+      {
+        source: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'latents',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'latents',
+        },
+      },
+      {
+        source: {
+          node_id: IMAGE_TO_LATENTS,
+          field: 'latents',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'latents',
+        },
+      },
+      {
+        source: {
+          node_id: NOISE,
+          field: 'noise',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'noise',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: IMAGE_TO_LATENTS,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'unet',
+        },
+      },
+      {
+        source: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'negative_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'positive_conditioning',
+        },
+      },
+    ],
+  };
+
+  // handle seed
+  if (shouldRandomizeSeed) {
+    // Random int node to generate the starting seed
+    const randomIntNode: RandomIntInvocation = {
+      id: RANDOM_INT,
+      type: 'rand_int',
+    };
+
+    graph.nodes[RANDOM_INT] = randomIntNode;
+
+    // Connect random int to the start of the range of size so the range starts on the random first seed
+    graph.edges.push({
+      source: { node_id: RANDOM_INT, field: 'a' },
+      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
+    });
+  } else {
+    // User specified seed, so set the start of the range of size to the seed
+    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
+  }
+
+  // handle `fit`
+  if (initialImage.width !== width || initialImage.height !== height) {
+    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
+
+    // Create a resize node, explicitly setting its image
+    const resizeNode: ImageResizeInvocation = {
+      id: RESIZE,
+      type: 'img_resize',
+      image: {
+        image_name: initialImage.image_name,
+      },
+      is_intermediate: true,
+      width,
+      height,
+    };
+
+    graph.nodes[RESIZE] = resizeNode;
+
+    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'image' },
+      destination: {
+        node_id: IMAGE_TO_LATENTS,
+        field: 'image',
+      },
+    });
+
+    // The `RESIZE` node also passes its width and height to `NOISE`
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'width' },
+      destination: {
+        node_id: NOISE,
+        field: 'width',
+      },
+    });
+
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'height' },
+      destination: {
+        node_id: NOISE,
+        field: 'height',
+      },
+    });
+  } else {
+    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
+    set(graph.nodes[IMAGE_TO_LATENTS], 'image', {
+      image_name: initialImage.image_name,
+    });
+
+    // Pass the image's dimensions to the `NOISE` node
+    graph.edges.push({
+      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
+      destination: {
+        node_id: NOISE,
+        field: 'width',
+      },
+    });
+    graph.edges.push({
+      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
+      destination: {
+        node_id: NOISE,
+        field: 'height',
+      },
+    });
+  }
+
+  // add controlnet
+  addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
+
+  return graph;
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
new file mode 100644
index 0000000000..785e1d2fdb
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
@@ -0,0 +1,224 @@
+import { RootState } from 'app/store/store';
+import {
+  ImageDTO,
+  InpaintInvocation,
+  RandomIntInvocation,
+  RangeOfSizeInvocation,
+} from 'services/api';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { log } from 'app/logging/useLogger';
+import {
+  ITERATE,
+  MODEL_LOADER,
+  NEGATIVE_CONDITIONING,
+  POSITIVE_CONDITIONING,
+  RANDOM_INT,
+  RANGE_OF_SIZE,
+  INPAINT_GRAPH,
+  INPAINT,
+} from './constants';
+
+const moduleLog = log.child({ namespace: 'nodes' });
+
+/**
+ * Builds the Canvas tab's Inpaint graph.
+ */
+export const buildCanvasInpaintGraph = (
+  state: RootState,
+  canvasInitImage: ImageDTO,
+  canvasMaskImage: ImageDTO
+): NonNullableGraph => {
+  const {
+    positivePrompt,
+    negativePrompt,
+    model: model_name,
+    cfgScale: cfg_scale,
+    scheduler,
+    steps,
+    img2imgStrength: strength,
+    shouldFitToWidthHeight,
+    iterations,
+    seed,
+    shouldRandomizeSeed,
+    seamSize,
+    seamBlur,
+    seamSteps,
+    seamStrength,
+    tileSize,
+    infillMethod,
+  } = state.generation;
+
+  // The bounding box determines width and height, not the width and height params
+  const { width, height } = state.canvas.boundingBoxDimensions;
+
+  // We may need to set the inpaint width and height to scale the image
+  const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
+
+  const graph: NonNullableGraph = {
+    id: INPAINT_GRAPH,
+    nodes: {
+      [INPAINT]: {
+        type: 'inpaint',
+        id: INPAINT,
+        steps,
+        width,
+        height,
+        cfg_scale,
+        scheduler,
+        image: {
+          image_name: canvasInitImage.image_name,
+        },
+        strength,
+        fit: shouldFitToWidthHeight,
+        mask: {
+          image_name: canvasMaskImage.image_name,
+        },
+        seam_size: seamSize,
+        seam_blur: seamBlur,
+        seam_strength: seamStrength,
+        seam_steps: seamSteps,
+        tile_size: infillMethod === 'tile' ? tileSize : undefined,
+        infill_method: infillMethod as InpaintInvocation['infill_method'],
+        inpaint_width:
+          boundingBoxScaleMethod !== 'none'
+            ? scaledBoundingBoxDimensions.width
+            : undefined,
+        inpaint_height:
+          boundingBoxScaleMethod !== 'none'
+            ? scaledBoundingBoxDimensions.height
+            : undefined,
+      },
+      [POSITIVE_CONDITIONING]: {
+        type: 'compel',
+        id: POSITIVE_CONDITIONING,
+        prompt: positivePrompt,
+      },
+      [NEGATIVE_CONDITIONING]: {
+        type: 'compel',
+        id: NEGATIVE_CONDITIONING,
+        prompt: negativePrompt,
+      },
+      [MODEL_LOADER]: {
+        type: 'sd1_model_loader',
+        id: MODEL_LOADER,
+        model_name,
+      },
+      [RANGE_OF_SIZE]: {
+        type: 'range_of_size',
+        id: RANGE_OF_SIZE,
+        // seed - must be connected manually
+        // start: 0,
+        size: iterations,
+        step: 1,
+      },
+      [ITERATE]: {
+        type: 'iterate',
+        id: ITERATE,
+      },
+    },
+    edges: [
+      {
+        source: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: INPAINT,
+          field: 'negative_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: INPAINT,
+          field: 'positive_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: INPAINT,
+          field: 'unet',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: INPAINT,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: RANGE_OF_SIZE,
+          field: 'collection',
+        },
+        destination: {
+          node_id: ITERATE,
+          field: 'collection',
+        },
+      },
+      {
+        source: {
+          node_id: ITERATE,
+          field: 'item',
+        },
+        destination: {
+          node_id: INPAINT,
+          field: 'seed',
+        },
+      },
+    ],
+  };
+
+  // handle seed
+  if (shouldRandomizeSeed) {
+    // Random int node to generate the starting seed
+    const randomIntNode: RandomIntInvocation = {
+      id: RANDOM_INT,
+      type: 'rand_int',
+    };
+
+    graph.nodes[RANDOM_INT] = randomIntNode;
+
+    // Connect random int to the start of the range of size so the range starts on the random first seed
+    graph.edges.push({
+      source: { node_id: RANDOM_INT, field: 'a' },
+      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
+    });
+  } else {
+    // User specified seed, so set the start of the range of size to the seed
+    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
+  }
+
+  return graph;
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
new file mode 100644
index 0000000000..ca0e56e849
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
@@ -0,0 +1,224 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { RandomIntInvocation, RangeOfSizeInvocation } from 'services/api';
+import {
+  ITERATE,
+  LATENTS_TO_IMAGE,
+  MODEL_LOADER,
+  NEGATIVE_CONDITIONING,
+  NOISE,
+  POSITIVE_CONDITIONING,
+  RANDOM_INT,
+  RANGE_OF_SIZE,
+  TEXT_TO_IMAGE_GRAPH,
+  TEXT_TO_LATENTS,
+} from './constants';
+import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
+
+/**
+ * Builds the Canvas tab's Text to Image graph.
+ */
+export const buildCanvasTextToImageGraph = (
+  state: RootState
+): NonNullableGraph => {
+  const {
+    positivePrompt,
+    negativePrompt,
+    model: model_name,
+    cfgScale: cfg_scale,
+    scheduler,
+    steps,
+    iterations,
+    seed,
+    shouldRandomizeSeed,
+  } = state.generation;
+
+  // The bounding box determines width and height, not the width and height params
+  const { width, height } = state.canvas.boundingBoxDimensions;
+
+  /**
+   * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
+   * full graph here as a template. Then use the parameters from app state and set friendlier node
+   * ids.
+   *
+   * The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
+   * the `fit` param. These are added to the graph at the end.
+   */
+
+  // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  const graph: NonNullableGraph = {
+    id: TEXT_TO_IMAGE_GRAPH,
+    nodes: {
+      [POSITIVE_CONDITIONING]: {
+        type: 'compel',
+        id: POSITIVE_CONDITIONING,
+        prompt: positivePrompt,
+      },
+      [NEGATIVE_CONDITIONING]: {
+        type: 'compel',
+        id: NEGATIVE_CONDITIONING,
+        prompt: negativePrompt,
+      },
+      [RANGE_OF_SIZE]: {
+        type: 'range_of_size',
+        id: RANGE_OF_SIZE,
+        // start: 0, // seed - must be connected manually
+        size: iterations,
+        step: 1,
+      },
+      [NOISE]: {
+        type: 'noise',
+        id: NOISE,
+        width,
+        height,
+      },
+      [TEXT_TO_LATENTS]: {
+        type: 't2l',
+        id: TEXT_TO_LATENTS,
+        cfg_scale,
+        scheduler,
+        steps,
+      },
+      [MODEL_LOADER]: {
+        type: 'sd1_model_loader',
+        id: MODEL_LOADER,
+        model_name,
+      },
+      [LATENTS_TO_IMAGE]: {
+        type: 'l2i',
+        id: LATENTS_TO_IMAGE,
+      },
+      [ITERATE]: {
+        type: 'iterate',
+        id: ITERATE,
+      },
+    },
+    edges: [
+      {
+        source: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'negative_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'positive_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'unet',
+        },
+      },
+      {
+        source: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'latents',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'latents',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: RANGE_OF_SIZE,
+          field: 'collection',
+        },
+        destination: {
+          node_id: ITERATE,
+          field: 'collection',
+        },
+      },
+      {
+        source: {
+          node_id: ITERATE,
+          field: 'item',
+        },
+        destination: {
+          node_id: NOISE,
+          field: 'seed',
+        },
+      },
+      {
+        source: {
+          node_id: NOISE,
+          field: 'noise',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'noise',
+        },
+      },
+    ],
+  };
+
+  // handle seed
+  if (shouldRandomizeSeed) {
+    // Random int node to generate the starting seed
+    const randomIntNode: RandomIntInvocation = {
+      id: RANDOM_INT,
+      type: 'rand_int',
+    };
+
+    graph.nodes[RANDOM_INT] = randomIntNode;
+
+    // Connect random int to the start of the range of size so the range starts on the random first seed
+    graph.edges.push({
+      source: { node_id: RANDOM_INT, field: 'a' },
+      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
+    });
+  } else {
+    // User specified seed, so set the start of the range of size to the seed
+    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
+  }
+
+  // add controlnet
+  addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
+
+  return graph;
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildImageToImageGraph.ts
deleted file mode 100644
index 4986d86713..0000000000
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildImageToImageGraph.ts
+++ /dev/null
@@ -1,416 +0,0 @@
-import { RootState } from 'app/store/store';
-import {
-  CompelInvocation,
-  Graph,
-  ImageResizeInvocation,
-  ImageToLatentsInvocation,
-  IterateInvocation,
-  LatentsToImageInvocation,
-  LatentsToLatentsInvocation,
-  NoiseInvocation,
-  RandomIntInvocation,
-  RangeOfSizeInvocation,
-} from 'services/api';
-import { NonNullableGraph } from 'features/nodes/types/types';
-import { log } from 'app/logging/useLogger';
-import { set } from 'lodash-es';
-import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
-
-const moduleLog = log.child({ namespace: 'nodes' });
-
-const POSITIVE_CONDITIONING = 'positive_conditioning';
-const NEGATIVE_CONDITIONING = 'negative_conditioning';
-const IMAGE_TO_LATENTS = 'image_to_latents';
-const LATENTS_TO_LATENTS = 'latents_to_latents';
-const LATENTS_TO_IMAGE = 'latents_to_image';
-const RESIZE = 'resize_image';
-const NOISE = 'noise';
-const RANDOM_INT = 'rand_int';
-const RANGE_OF_SIZE = 'range_of_size';
-const ITERATE = 'iterate';
-
-/**
- * Builds the Image to Image tab graph.
- */
-export const buildImageToImageGraph = (state: RootState): Graph => {
-  const {
-    positivePrompt,
-    negativePrompt,
-    model,
-    cfgScale: cfg_scale,
-    scheduler,
-    steps,
-    initialImage,
-    img2imgStrength: strength,
-    shouldFitToWidthHeight,
-    width,
-    height,
-    iterations,
-    seed,
-    shouldRandomizeSeed,
-  } = state.generation;
-
-  if (!initialImage) {
-    moduleLog.error('No initial image found in state');
-    throw new Error('No initial image found in state');
-  }
-
-  const graph: NonNullableGraph = {
-    nodes: {},
-    edges: [],
-  };
-
-  // Create the positive conditioning (prompt) node
-  const positiveConditioningNode: CompelInvocation = {
-    id: POSITIVE_CONDITIONING,
-    type: 'compel',
-    prompt: positivePrompt,
-    model,
-  };
-
-  // Negative conditioning
-  const negativeConditioningNode: CompelInvocation = {
-    id: NEGATIVE_CONDITIONING,
-    type: 'compel',
-    prompt: negativePrompt,
-    model,
-  };
-
-  // This will encode the raster image to latents - but it may get its `image` from a resize node,
-  // so we do not set its `image` property yet
-  const imageToLatentsNode: ImageToLatentsInvocation = {
-    id: IMAGE_TO_LATENTS,
-    type: 'i2l',
-    model,
-  };
-
-  // This does the actual img2img inference
-  const latentsToLatentsNode: LatentsToLatentsInvocation = {
-    id: LATENTS_TO_LATENTS,
-    type: 'l2l',
-    cfg_scale,
-    model,
-    scheduler,
-    steps,
-    strength,
-  };
-
-  // Finally we decode the latents back to an image
-  const latentsToImageNode: LatentsToImageInvocation = {
-    id: LATENTS_TO_IMAGE,
-    type: 'l2i',
-    model,
-  };
-
-  // Add all those nodes to the graph
-  graph.nodes[POSITIVE_CONDITIONING] = positiveConditioningNode;
-  graph.nodes[NEGATIVE_CONDITIONING] = negativeConditioningNode;
-  graph.nodes[IMAGE_TO_LATENTS] = imageToLatentsNode;
-  graph.nodes[LATENTS_TO_LATENTS] = latentsToLatentsNode;
-  graph.nodes[LATENTS_TO_IMAGE] = latentsToImageNode;
-
-  // Connect the prompt nodes to the imageToLatents node
-  graph.edges.push({
-    source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning' },
-    destination: {
-      node_id: LATENTS_TO_LATENTS,
-      field: 'positive_conditioning',
-    },
-  });
-  graph.edges.push({
-    source: { node_id: NEGATIVE_CONDITIONING, field: 'conditioning' },
-    destination: {
-      node_id: LATENTS_TO_LATENTS,
-      field: 'negative_conditioning',
-    },
-  });
-
-  // Connect the image-encoding node
-  graph.edges.push({
-    source: { node_id: IMAGE_TO_LATENTS, field: 'latents' },
-    destination: {
-      node_id: LATENTS_TO_LATENTS,
-      field: 'latents',
-    },
-  });
-
-  // Connect the image-decoding node
-  graph.edges.push({
-    source: { node_id: LATENTS_TO_LATENTS, field: 'latents' },
-    destination: {
-      node_id: LATENTS_TO_IMAGE,
-      field: 'latents',
-    },
-  });
-
-  /**
-   * Now we need to handle iterations and random seeds. There are four possible scenarios:
-   * - Single iteration, explicit seed
-   * - Single iteration, random seed
-   * - Multiple iterations, explicit seed
-   * - Multiple iterations, random seed
-   *
-   * They all have different graphs and connections.
-   */
-
-  // Single iteration, explicit seed
-  if (!shouldRandomizeSeed && iterations === 1) {
-    // Noise node using the explicit seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-      seed: seed,
-    };
-
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect noise to l2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: LATENTS_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  // Single iteration, random seed
-  if (shouldRandomizeSeed && iterations === 1) {
-    // Random int node to generate the seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
-
-    // Noise node without any seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-    };
-
-    graph.nodes[RANDOM_INT] = randomIntNode;
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect random int to the seed of the noise node
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: {
-        node_id: NOISE,
-        field: 'seed',
-      },
-    });
-
-    // Connect noise to l2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: LATENTS_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  // Multiple iterations, explicit seed
-  if (!shouldRandomizeSeed && iterations > 1) {
-    // Range of size node to generate `iterations` count of seeds - range of size generates a collection
-    // of ints from `start` to `start + size`. The `start` is the seed, and the `size` is the number of
-    // iterations.
-    const rangeOfSizeNode: RangeOfSizeInvocation = {
-      id: RANGE_OF_SIZE,
-      type: 'range_of_size',
-      start: seed,
-      size: iterations,
-    };
-
-    // Iterate node to iterate over the seeds generated by the range of size node
-    const iterateNode: IterateInvocation = {
-      id: ITERATE,
-      type: 'iterate',
-    };
-
-    // Noise node without any seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-    };
-
-    // Adding to the graph
-    graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
-    graph.nodes[ITERATE] = iterateNode;
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect range of size to iterate
-    graph.edges.push({
-      source: { node_id: RANGE_OF_SIZE, field: 'collection' },
-      destination: {
-        node_id: ITERATE,
-        field: 'collection',
-      },
-    });
-
-    // Connect iterate to noise
-    graph.edges.push({
-      source: {
-        node_id: ITERATE,
-        field: 'item',
-      },
-      destination: {
-        node_id: NOISE,
-        field: 'seed',
-      },
-    });
-
-    // Connect noise to l2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: LATENTS_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  // Multiple iterations, random seed
-  if (shouldRandomizeSeed && iterations > 1) {
-    // Random int node to generate the seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
-
-    // Range of size node to generate `iterations` count of seeds - range of size generates a collection
-    const rangeOfSizeNode: RangeOfSizeInvocation = {
-      id: RANGE_OF_SIZE,
-      type: 'range_of_size',
-      size: iterations,
-    };
-
-    // Iterate node to iterate over the seeds generated by the range of size node
-    const iterateNode: IterateInvocation = {
-      id: ITERATE,
-      type: 'iterate',
-    };
-
-    // Noise node without any seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-      width,
-      height,
-    };
-
-    // Adding to the graph
-    graph.nodes[RANDOM_INT] = randomIntNode;
-    graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
-    graph.nodes[ITERATE] = iterateNode;
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect random int to the start of the range of size so the range starts on the random first seed
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
-    });
-
-    // Connect range of size to iterate
-    graph.edges.push({
-      source: { node_id: RANGE_OF_SIZE, field: 'collection' },
-      destination: {
-        node_id: ITERATE,
-        field: 'collection',
-      },
-    });
-
-    // Connect iterate to noise
-    graph.edges.push({
-      source: {
-        node_id: ITERATE,
-        field: 'item',
-      },
-      destination: {
-        node_id: NOISE,
-        field: 'seed',
-      },
-    });
-
-    // Connect noise to l2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: LATENTS_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  if (
-    shouldFitToWidthHeight &&
-    (initialImage.width !== width || initialImage.height !== height)
-  ) {
-    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
-
-    // Create a resize node, explicitly setting its image
-    const resizeNode: ImageResizeInvocation = {
-      id: RESIZE,
-      type: 'img_resize',
-      image: {
-        image_name: initialImage.image_name,
-      },
-      is_intermediate: true,
-      height,
-      width,
-    };
-
-    graph.nodes[RESIZE] = resizeNode;
-
-    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'image' },
-      destination: {
-        node_id: IMAGE_TO_LATENTS,
-        field: 'image',
-      },
-    });
-
-    // The `RESIZE` node also passes its width and height to `NOISE`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'width' },
-      destination: {
-        node_id: NOISE,
-        field: 'width',
-      },
-    });
-
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'height' },
-      destination: {
-        node_id: NOISE,
-        field: 'height',
-      },
-    });
-  } else {
-    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
-    set(graph.nodes[IMAGE_TO_LATENTS], 'image', {
-      image_name: initialImage.image_name,
-    });
-
-    // Pass the image's dimensions to the `NOISE` node
-    graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
-      destination: {
-        node_id: NOISE,
-        field: 'width',
-      },
-    });
-    graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
-      destination: {
-        node_id: NOISE,
-        field: 'height',
-      },
-    });
-  }
-
-  addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
-
-  return graph;
-};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
new file mode 100644
index 0000000000..1f2c8327e0
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
@@ -0,0 +1,338 @@
+import { RootState } from 'app/store/store';
+import {
+  ImageResizeInvocation,
+  RandomIntInvocation,
+  RangeOfSizeInvocation,
+} from 'services/api';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { log } from 'app/logging/useLogger';
+import {
+  ITERATE,
+  LATENTS_TO_IMAGE,
+  MODEL_LOADER,
+  NEGATIVE_CONDITIONING,
+  NOISE,
+  POSITIVE_CONDITIONING,
+  RANDOM_INT,
+  RANGE_OF_SIZE,
+  IMAGE_TO_IMAGE_GRAPH,
+  IMAGE_TO_LATENTS,
+  LATENTS_TO_LATENTS,
+  RESIZE,
+} from './constants';
+import { set } from 'lodash-es';
+import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
+
+const moduleLog = log.child({ namespace: 'nodes' });
+
+/**
+ * Builds the Image to Image tab graph.
+ */
+export const buildLinearImageToImageGraph = (
+  state: RootState
+): NonNullableGraph => {
+  const {
+    positivePrompt,
+    negativePrompt,
+    model: model_name,
+    cfgScale: cfg_scale,
+    scheduler,
+    steps,
+    initialImage,
+    img2imgStrength: strength,
+    shouldFitToWidthHeight,
+    width,
+    height,
+    iterations,
+    seed,
+    shouldRandomizeSeed,
+  } = state.generation;
+
+  /**
+   * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
+   * full graph here as a template. Then use the parameters from app state and set friendlier node
+   * ids.
+   *
+   * The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
+   * the `fit` param. These are added to the graph at the end.
+   */
+
+  if (!initialImage) {
+    moduleLog.error('No initial image found in state');
+    throw new Error('No initial image found in state');
+  }
+
+  // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  const graph: NonNullableGraph = {
+    id: IMAGE_TO_IMAGE_GRAPH,
+    nodes: {
+      [POSITIVE_CONDITIONING]: {
+        type: 'compel',
+        id: POSITIVE_CONDITIONING,
+        prompt: positivePrompt,
+      },
+      [NEGATIVE_CONDITIONING]: {
+        type: 'compel',
+        id: NEGATIVE_CONDITIONING,
+        prompt: negativePrompt,
+      },
+      [RANGE_OF_SIZE]: {
+        type: 'range_of_size',
+        id: RANGE_OF_SIZE,
+        // seed - must be connected manually
+        // start: 0,
+        size: iterations,
+        step: 1,
+      },
+      [NOISE]: {
+        type: 'noise',
+        id: NOISE,
+      },
+      [MODEL_LOADER]: {
+        type: 'sd1_model_loader',
+        id: MODEL_LOADER,
+        model_name,
+      },
+      [LATENTS_TO_IMAGE]: {
+        type: 'l2i',
+        id: LATENTS_TO_IMAGE,
+      },
+      [ITERATE]: {
+        type: 'iterate',
+        id: ITERATE,
+      },
+      [LATENTS_TO_LATENTS]: {
+        type: 'l2l',
+        id: LATENTS_TO_LATENTS,
+        cfg_scale,
+        scheduler,
+        steps,
+        strength,
+      },
+      [IMAGE_TO_LATENTS]: {
+        type: 'i2l',
+        id: IMAGE_TO_LATENTS,
+        // must be set manually later, bc `fit` parameter may require a resize node inserted
+        // image: {
+        //   image_name: initialImage.image_name,
+        // },
+      },
+    },
+    edges: [
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: RANGE_OF_SIZE,
+          field: 'collection',
+        },
+        destination: {
+          node_id: ITERATE,
+          field: 'collection',
+        },
+      },
+      {
+        source: {
+          node_id: ITERATE,
+          field: 'item',
+        },
+        destination: {
+          node_id: NOISE,
+          field: 'seed',
+        },
+      },
+      {
+        source: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'latents',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'latents',
+        },
+      },
+      {
+        source: {
+          node_id: IMAGE_TO_LATENTS,
+          field: 'latents',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'latents',
+        },
+      },
+      {
+        source: {
+          node_id: NOISE,
+          field: 'noise',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'noise',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: IMAGE_TO_LATENTS,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'unet',
+        },
+      },
+      {
+        source: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'negative_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: LATENTS_TO_LATENTS,
+          field: 'positive_conditioning',
+        },
+      },
+    ],
+  };
+
+  // handle seed
+  if (shouldRandomizeSeed) {
+    // Random int node to generate the starting seed
+    const randomIntNode: RandomIntInvocation = {
+      id: RANDOM_INT,
+      type: 'rand_int',
+    };
+
+    graph.nodes[RANDOM_INT] = randomIntNode;
+
+    // Connect random int to the start of the range of size so the range starts on the random first seed
+    graph.edges.push({
+      source: { node_id: RANDOM_INT, field: 'a' },
+      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
+    });
+  } else {
+    // User specified seed, so set the start of the range of size to the seed
+    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
+  }
+
+  // handle `fit`
+  if (
+    shouldFitToWidthHeight &&
+    (initialImage.width !== width || initialImage.height !== height)
+  ) {
+    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
+
+    // Create a resize node, explicitly setting its image
+    const resizeNode: ImageResizeInvocation = {
+      id: RESIZE,
+      type: 'img_resize',
+      image: {
+        image_name: initialImage.image_name,
+      },
+      is_intermediate: true,
+      width,
+      height,
+    };
+
+    graph.nodes[RESIZE] = resizeNode;
+
+    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'image' },
+      destination: {
+        node_id: IMAGE_TO_LATENTS,
+        field: 'image',
+      },
+    });
+
+    // The `RESIZE` node also passes its width and height to `NOISE`
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'width' },
+      destination: {
+        node_id: NOISE,
+        field: 'width',
+      },
+    });
+
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'height' },
+      destination: {
+        node_id: NOISE,
+        field: 'height',
+      },
+    });
+  } else {
+    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
+    set(graph.nodes[IMAGE_TO_LATENTS], 'image', {
+      image_name: initialImage.image_name,
+    });
+
+    // Pass the image's dimensions to the `NOISE` node
+    graph.edges.push({
+      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
+      destination: {
+        node_id: NOISE,
+        field: 'width',
+      },
+    });
+    graph.edges.push({
+      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
+      destination: {
+        node_id: NOISE,
+        field: 'height',
+      },
+    });
+  }
+
+  // add controlnet
+  addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
+
+  return graph;
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
new file mode 100644
index 0000000000..c179a89504
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
@@ -0,0 +1,226 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { RandomIntInvocation, RangeOfSizeInvocation } from 'services/api';
+import {
+  ITERATE,
+  LATENTS_TO_IMAGE,
+  MODEL_LOADER,
+  NEGATIVE_CONDITIONING,
+  NOISE,
+  POSITIVE_CONDITIONING,
+  RANDOM_INT,
+  RANGE_OF_SIZE,
+  TEXT_TO_IMAGE_GRAPH,
+  TEXT_TO_LATENTS,
+} from './constants';
+import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
+
+type TextToImageGraphOverrides = {
+  width: number;
+  height: number;
+};
+
+export const buildLinearTextToImageGraph = (
+  state: RootState,
+  overrides?: TextToImageGraphOverrides
+): NonNullableGraph => {
+  const {
+    positivePrompt,
+    negativePrompt,
+    model: model_name,
+    cfgScale: cfg_scale,
+    scheduler,
+    steps,
+    width,
+    height,
+    iterations,
+    seed,
+    shouldRandomizeSeed,
+  } = state.generation;
+
+  /**
+   * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
+   * full graph here as a template. Then use the parameters from app state and set friendlier node
+   * ids.
+   *
+   * The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
+   * the `fit` param. These are added to the graph at the end.
+   */
+
+  // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  const graph: NonNullableGraph = {
+    id: TEXT_TO_IMAGE_GRAPH,
+    nodes: {
+      [POSITIVE_CONDITIONING]: {
+        type: 'compel',
+        id: POSITIVE_CONDITIONING,
+        prompt: positivePrompt,
+      },
+      [NEGATIVE_CONDITIONING]: {
+        type: 'compel',
+        id: NEGATIVE_CONDITIONING,
+        prompt: negativePrompt,
+      },
+      [RANGE_OF_SIZE]: {
+        type: 'range_of_size',
+        id: RANGE_OF_SIZE,
+        // start: 0, // seed - must be connected manually
+        size: iterations,
+        step: 1,
+      },
+      [NOISE]: {
+        type: 'noise',
+        id: NOISE,
+        width: overrides?.width || width,
+        height: overrides?.height || height,
+      },
+      [TEXT_TO_LATENTS]: {
+        type: 't2l',
+        id: TEXT_TO_LATENTS,
+        cfg_scale,
+        scheduler,
+        steps,
+      },
+      [MODEL_LOADER]: {
+        type: 'sd1_model_loader',
+        id: MODEL_LOADER,
+        model_name,
+      },
+      [LATENTS_TO_IMAGE]: {
+        type: 'l2i',
+        id: LATENTS_TO_IMAGE,
+      },
+      [ITERATE]: {
+        type: 'iterate',
+        id: ITERATE,
+      },
+    },
+    edges: [
+      {
+        source: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'negative_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'conditioning',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'positive_conditioning',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'unet',
+        },
+      },
+      {
+        source: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'latents',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'latents',
+        },
+      },
+      {
+        source: {
+          node_id: MODEL_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: LATENTS_TO_IMAGE,
+          field: 'vae',
+        },
+      },
+      {
+        source: {
+          node_id: RANGE_OF_SIZE,
+          field: 'collection',
+        },
+        destination: {
+          node_id: ITERATE,
+          field: 'collection',
+        },
+      },
+      {
+        source: {
+          node_id: ITERATE,
+          field: 'item',
+        },
+        destination: {
+          node_id: NOISE,
+          field: 'seed',
+        },
+      },
+      {
+        source: {
+          node_id: NOISE,
+          field: 'noise',
+        },
+        destination: {
+          node_id: TEXT_TO_LATENTS,
+          field: 'noise',
+        },
+      },
+    ],
+  };
+
+  // handle seed
+  if (shouldRandomizeSeed) {
+    // Random int node to generate the starting seed
+    const randomIntNode: RandomIntInvocation = {
+      id: RANDOM_INT,
+      type: 'rand_int',
+    };
+
+    graph.nodes[RANDOM_INT] = randomIntNode;
+
+    // Connect random int to the start of the range of size so the range starts on the random first seed
+    graph.edges.push({
+      source: { node_id: RANDOM_INT, field: 'a' },
+      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
+    });
+  } else {
+    // User specified seed, so set the start of the range of size to the seed
+    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
+  }
+
+  // add controlnet
+  addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
+
+  return graph;
+};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildTextToImageGraph.ts
deleted file mode 100644
index ae71f569b6..0000000000
--- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildTextToImageGraph.ts
+++ /dev/null
@@ -1,316 +0,0 @@
-import { RootState } from 'app/store/store';
-import {
-  CompelInvocation,
-  Graph,
-  IterateInvocation,
-  LatentsToImageInvocation,
-  NoiseInvocation,
-  RandomIntInvocation,
-  RangeOfSizeInvocation,
-  TextToLatentsInvocation,
-} from 'services/api';
-import { NonNullableGraph } from 'features/nodes/types/types';
-import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
-
-const POSITIVE_CONDITIONING = 'positive_conditioning';
-const NEGATIVE_CONDITIONING = 'negative_conditioning';
-const TEXT_TO_LATENTS = 'text_to_latents';
-const LATENTS_TO_IMAGE = 'latents_to_image';
-const NOISE = 'noise';
-const RANDOM_INT = 'rand_int';
-const RANGE_OF_SIZE = 'range_of_size';
-const ITERATE = 'iterate';
-
-/**
- * Builds the Text to Image tab graph.
- */
-export const buildTextToImageGraph = (state: RootState): Graph => {
-  const {
-    positivePrompt,
-    negativePrompt,
-    model,
-    cfgScale: cfg_scale,
-    scheduler,
-    steps,
-    width,
-    height,
-    iterations,
-    seed,
-    shouldRandomizeSeed,
-  } = state.generation;
-
-  const graph: NonNullableGraph = {
-    nodes: {},
-    edges: [],
-  };
-
-  // Create the conditioning, t2l and l2i nodes
-  const positiveConditioningNode: CompelInvocation = {
-    id: POSITIVE_CONDITIONING,
-    type: 'compel',
-    prompt: positivePrompt,
-    model,
-  };
-
-  const negativeConditioningNode: CompelInvocation = {
-    id: NEGATIVE_CONDITIONING,
-    type: 'compel',
-    prompt: negativePrompt,
-    model,
-  };
-
-  const textToLatentsNode: TextToLatentsInvocation = {
-    id: TEXT_TO_LATENTS,
-    type: 't2l',
-    cfg_scale,
-    model,
-    scheduler,
-    steps,
-  };
-
-  const latentsToImageNode: LatentsToImageInvocation = {
-    id: LATENTS_TO_IMAGE,
-    type: 'l2i',
-    model,
-  };
-
-  // Add to the graph
-  graph.nodes[POSITIVE_CONDITIONING] = positiveConditioningNode;
-  graph.nodes[NEGATIVE_CONDITIONING] = negativeConditioningNode;
-  graph.nodes[TEXT_TO_LATENTS] = textToLatentsNode;
-  graph.nodes[LATENTS_TO_IMAGE] = latentsToImageNode;
-
-  // Connect them
-  graph.edges.push({
-    source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning' },
-    destination: {
-      node_id: TEXT_TO_LATENTS,
-      field: 'positive_conditioning',
-    },
-  });
-
-  graph.edges.push({
-    source: { node_id: NEGATIVE_CONDITIONING, field: 'conditioning' },
-    destination: {
-      node_id: TEXT_TO_LATENTS,
-      field: 'negative_conditioning',
-    },
-  });
-
-  graph.edges.push({
-    source: { node_id: TEXT_TO_LATENTS, field: 'latents' },
-    destination: {
-      node_id: LATENTS_TO_IMAGE,
-      field: 'latents',
-    },
-  });
-
-  /**
-   * Now we need to handle iterations and random seeds. There are four possible scenarios:
-   * - Single iteration, explicit seed
-   * - Single iteration, random seed
-   * - Multiple iterations, explicit seed
-   * - Multiple iterations, random seed
-   *
-   * They all have different graphs and connections.
-   */
-
-  // Single iteration, explicit seed
-  if (!shouldRandomizeSeed && iterations === 1) {
-    // Noise node using the explicit seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-      seed: seed,
-      width,
-      height,
-    };
-
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect noise to l2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: TEXT_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  // Single iteration, random seed
-  if (shouldRandomizeSeed && iterations === 1) {
-    // Random int node to generate the seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
-
-    // Noise node without any seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-      width,
-      height,
-    };
-
-    graph.nodes[RANDOM_INT] = randomIntNode;
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect random int to the seed of the noise node
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: {
-        node_id: NOISE,
-        field: 'seed',
-      },
-    });
-
-    // Connect noise to t2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: TEXT_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  // Multiple iterations, explicit seed
-  if (!shouldRandomizeSeed && iterations > 1) {
-    // Range of size node to generate `iterations` count of seeds - range of size generates a collection
-    // of ints from `start` to `start + size`. The `start` is the seed, and the `size` is the number of
-    // iterations.
-    const rangeOfSizeNode: RangeOfSizeInvocation = {
-      id: RANGE_OF_SIZE,
-      type: 'range_of_size',
-      start: seed,
-      size: iterations,
-    };
-
-    // Iterate node to iterate over the seeds generated by the range of size node
-    const iterateNode: IterateInvocation = {
-      id: ITERATE,
-      type: 'iterate',
-    };
-
-    // Noise node without any seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-      width,
-      height,
-    };
-
-    // Adding to the graph
-    graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
-    graph.nodes[ITERATE] = iterateNode;
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect range of size to iterate
-    graph.edges.push({
-      source: { node_id: RANGE_OF_SIZE, field: 'collection' },
-      destination: {
-        node_id: ITERATE,
-        field: 'collection',
-      },
-    });
-
-    // Connect iterate to noise
-    graph.edges.push({
-      source: {
-        node_id: ITERATE,
-        field: 'item',
-      },
-      destination: {
-        node_id: NOISE,
-        field: 'seed',
-      },
-    });
-
-    // Connect noise to t2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: TEXT_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  // Multiple iterations, random seed
-  if (shouldRandomizeSeed && iterations > 1) {
-    // Random int node to generate the seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
-
-    // Range of size node to generate `iterations` count of seeds - range of size generates a collection
-    const rangeOfSizeNode: RangeOfSizeInvocation = {
-      id: RANGE_OF_SIZE,
-      type: 'range_of_size',
-      size: iterations,
-    };
-
-    // Iterate node to iterate over the seeds generated by the range of size node
-    const iterateNode: IterateInvocation = {
-      id: ITERATE,
-      type: 'iterate',
-    };
-
-    // Noise node without any seed
-    const noiseNode: NoiseInvocation = {
-      id: NOISE,
-      type: 'noise',
-      width,
-      height,
-    };
-
-    // Adding to the graph
-    graph.nodes[RANDOM_INT] = randomIntNode;
-    graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
-    graph.nodes[ITERATE] = iterateNode;
-    graph.nodes[NOISE] = noiseNode;
-
-    // Connect random int to the start of the range of size so the range starts on the random first seed
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
-    });
-
-    // Connect range of size to iterate
-    graph.edges.push({
-      source: { node_id: RANGE_OF_SIZE, field: 'collection' },
-      destination: {
-        node_id: ITERATE,
-        field: 'collection',
-      },
-    });
-
-    // Connect iterate to noise
-    graph.edges.push({
-      source: {
-        node_id: ITERATE,
-        field: 'item',
-      },
-      destination: {
-        node_id: NOISE,
-        field: 'seed',
-      },
-    });
-
-    // Connect noise to t2l
-    graph.edges.push({
-      source: { node_id: NOISE, field: 'noise' },
-      destination: {
-        node_id: TEXT_TO_LATENTS,
-        field: 'noise',
-      },
-    });
-  }
-
-  addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
-
-  return graph;
-};
diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
new file mode 100644
index 0000000000..39e0080d11
--- /dev/null
+++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
@@ -0,0 +1,20 @@
+// friendly node ids
+export const POSITIVE_CONDITIONING = 'positive_conditioning';
+export const NEGATIVE_CONDITIONING = 'negative_conditioning';
+export const TEXT_TO_LATENTS = 'text_to_latents';
+export const LATENTS_TO_IMAGE = 'latents_to_image';
+export const NOISE = 'noise';
+export const RANDOM_INT = 'rand_int';
+export const RANGE_OF_SIZE = 'range_of_size';
+export const ITERATE = 'iterate';
+export const MODEL_LOADER = 'model_loader';
+export const IMAGE_TO_LATENTS = 'image_to_latents';
+export const LATENTS_TO_LATENTS = 'latents_to_latents';
+export const RESIZE = 'resize_image';
+export const INPAINT = 'inpaint';
+export const CONTROL_NET_COLLECT = 'control_net_collect';
+
+// friendly graph ids
+export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';
+export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph';
+export const INPAINT_GRAPH = 'inpaint_graph';
diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
index cf29636ea3..8818dcba9b 100644
--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
@@ -1,12 +1,11 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { Scheduler } from 'app/constants';
+import { SCHEDULER_LABEL_MAP, SCHEDULER_NAMES } from 'app/constants';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIMantineSelect, {
-  IAISelectDataType,
-} from 'common/components/IAIMantineSelect';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { setScheduler } from 'features/parameters/store/generationSlice';
+import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
 import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -14,30 +13,36 @@ import { useTranslation } from 'react-i18next';
 const selector = createSelector(
   [uiSelector, generationSelector],
   (ui, generation) => {
-    const allSchedulers: string[] = ui.schedulers
-      .slice()
-      .sort((a, b) => a.localeCompare(b));
+    const { scheduler } = generation;
+    const { favoriteSchedulers: enabledSchedulers } = ui;
+
+    const data = SCHEDULER_NAMES.map((schedulerName) => ({
+      value: schedulerName,
+      label: SCHEDULER_LABEL_MAP[schedulerName as SchedulerParam],
+      group: enabledSchedulers.includes(schedulerName)
+        ? 'Favorites'
+        : undefined,
+    })).sort((a, b) => a.label.localeCompare(b.label));
 
     return {
-      scheduler: generation.scheduler,
-      allSchedulers,
+      scheduler,
+      data,
     };
   },
   defaultSelectorOptions
 );
 
 const ParamScheduler = () => {
-  const { allSchedulers, scheduler } = useAppSelector(selector);
-
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
+  const { scheduler, data } = useAppSelector(selector);
 
   const handleChange = useCallback(
     (v: string | null) => {
       if (!v) {
         return;
       }
-      dispatch(setScheduler(v as Scheduler));
+      dispatch(setScheduler(v as SchedulerParam));
     },
     [dispatch]
   );
@@ -46,7 +51,7 @@ const ParamScheduler = () => {
     <IAIMantineSelect
       label={t('parameters.scheduler')}
       value={scheduler}
-      data={allSchedulers}
+      data={data}
       onChange={handleChange}
     />
   );
diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSchedulerAndModel.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSchedulerAndModel.tsx
index 3b53f5005c..65da89b94d 100644
--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSchedulerAndModel.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSchedulerAndModel.tsx
@@ -1,12 +1,12 @@
 import { Box, Flex } from '@chakra-ui/react';
-import { memo } from 'react';
 import ModelSelect from 'features/system/components/ModelSelect';
+import { memo } from 'react';
 import ParamScheduler from './ParamScheduler';
 
 const ParamSchedulerAndModel = () => {
   return (
     <Flex gap={3} w="full">
-      <Box w="16rem">
+      <Box w="20rem">
         <ParamScheduler />
       </Box>
       <Box w="full">
diff --git a/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts b/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
index f516229efe..961ea1b8af 100644
--- a/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
+++ b/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
@@ -1,10 +1,10 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
-import { clamp, sortBy } from 'lodash-es';
-import { receivedModels } from 'services/thunks/model';
-import { Scheduler } from 'app/constants';
-import { ImageDTO } from 'services/api';
 import { configChanged } from 'features/system/store/configSlice';
+import { clamp, sortBy } from 'lodash-es';
+import { ImageDTO } from 'services/api';
+import { imageUrlsReceived } from 'services/thunks/image';
+import { receivedModels } from 'services/thunks/model';
 import {
   CfgScaleParam,
   HeightParam,
@@ -17,7 +17,7 @@ import {
   StrengthParam,
   WidthParam,
 } from './parameterZodSchemas';
-import { imageUrlsReceived } from 'services/thunks/image';
+import { DEFAULT_SCHEDULER_NAME } from 'app/constants';
 
 export interface GenerationState {
   cfgScale: CfgScaleParam;
@@ -63,7 +63,7 @@ export const initialGenerationState: GenerationState = {
   perlin: 0,
   positivePrompt: '',
   negativePrompt: '',
-  scheduler: 'euler',
+  scheduler: DEFAULT_SCHEDULER_NAME,
   seamBlur: 16,
   seamSize: 96,
   seamSteps: 30,
@@ -133,7 +133,7 @@ export const generationSlice = createSlice({
     setWidth: (state, action: PayloadAction<number>) => {
       state.width = action.payload;
     },
-    setScheduler: (state, action: PayloadAction<Scheduler>) => {
+    setScheduler: (state, action: PayloadAction<SchedulerParam>) => {
       state.scheduler = action.payload;
     },
     setSeed: (state, action: PayloadAction<number>) => {
diff --git a/invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts b/invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts
index b99e57bfbb..61567d3fb8 100644
--- a/invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts
+++ b/invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts
@@ -1,4 +1,4 @@
-import { NUMPY_RAND_MAX, SCHEDULERS } from 'app/constants';
+import { NUMPY_RAND_MAX, SCHEDULER_NAMES_AS_CONST } from 'app/constants';
 import { z } from 'zod';
 
 /**
@@ -73,7 +73,7 @@ export const isValidCfgScale = (val: unknown): val is CfgScaleParam =>
 /**
  * Zod schema for scheduler parameter
  */
-export const zScheduler = z.enum(SCHEDULERS);
+export const zScheduler = z.enum(SCHEDULER_NAMES_AS_CONST);
 /**
  * Type alias for scheduler parameter, inferred from its zod schema
  */
diff --git a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx
index e5f4a4cbf7..2e0b3234c7 100644
--- a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx
+++ b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx
@@ -1,47 +1,44 @@
-import {
-  Menu,
-  MenuButton,
-  MenuItemOption,
-  MenuList,
-  MenuOptionGroup,
-} from '@chakra-ui/react';
-import { SCHEDULERS } from 'app/constants';
-
+import { SCHEDULER_LABEL_MAP, SCHEDULER_NAMES } from 'app/constants';
 import { RootState } from 'app/store/store';
+
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIButton from 'common/components/IAIButton';
-import { setSchedulers } from 'features/ui/store/uiSlice';
-import { isArray } from 'lodash-es';
+import IAIMantineMultiSelect from 'common/components/IAIMantineMultiSelect';
+import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
+import { favoriteSchedulersChanged } from 'features/ui/store/uiSlice';
+import { map } from 'lodash-es';
+import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-export default function SettingsSchedulers() {
-  const schedulers = useAppSelector((state: RootState) => state.ui.schedulers);
+const data = map(SCHEDULER_NAMES, (s) => ({
+  value: s,
+  label: SCHEDULER_LABEL_MAP[s],
+})).sort((a, b) => a.label.localeCompare(b.label));
 
+export default function SettingsSchedulers() {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const schedulerSettingsHandler = (v: string | string[]) => {
-    if (isArray(v)) dispatch(setSchedulers(v.sort()));
-  };
+  const enabledSchedulers = useAppSelector(
+    (state: RootState) => state.ui.favoriteSchedulers
+  );
+
+  const handleChange = useCallback(
+    (v: string[]) => {
+      dispatch(favoriteSchedulersChanged(v as SchedulerParam[]));
+    },
+    [dispatch]
+  );
 
   return (
-    <Menu closeOnSelect={false}>
-      <MenuButton as={IAIButton}>
-        {t('settings.availableSchedulers')}
-      </MenuButton>
-      <MenuList maxHeight={64} overflowY="scroll">
-        <MenuOptionGroup
-          value={schedulers}
-          type="checkbox"
-          onChange={schedulerSettingsHandler}
-        >
-          {SCHEDULERS.map((scheduler) => (
-            <MenuItemOption key={scheduler} value={scheduler}>
-              {scheduler}
-            </MenuItemOption>
-          ))}
-        </MenuOptionGroup>
-      </MenuList>
-    </Menu>
+    <IAIMantineMultiSelect
+      label={t('settings.favoriteSchedulers')}
+      value={enabledSchedulers}
+      data={data}
+      onChange={handleChange}
+      clearable
+      searchable
+      maxSelectedValues={99}
+      placeholder={t('settings.favoriteSchedulersPlaceholder')}
+    />
   );
 }
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
index 19ef7fd6fa..8e17ff066c 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
@@ -1,5 +1,4 @@
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
-import ParamSeedCollapse from 'features/parameters/components/Parameters/Seed/ParamSeedCollapse';
 import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
@@ -8,6 +7,7 @@ import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
 import { memo } from 'react';
 import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 
 const UnifiedCanvasParameters = () => {
   return (
@@ -16,6 +16,7 @@ const UnifiedCanvasParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <UnifiedCanvasCoreParameters />
+      <ParamControlNetCollapse />
       <ParamVariationCollapse />
       <ParamSymmetryCollapse />
       <ParamSeamCorrectionCollapse />
diff --git a/invokeai/frontend/web/src/features/ui/store/uiSlice.ts b/invokeai/frontend/web/src/features/ui/store/uiSlice.ts
index 65a48bc92c..36c514e995 100644
--- a/invokeai/frontend/web/src/features/ui/store/uiSlice.ts
+++ b/invokeai/frontend/web/src/features/ui/store/uiSlice.ts
@@ -1,10 +1,10 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
+import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { setActiveTabReducer } from './extraReducers';
 import { InvokeTabName } from './tabMap';
 import { AddNewModelType, UIState } from './uiTypes';
-import { initialImageChanged } from 'features/parameters/store/generationSlice';
-import { SCHEDULERS } from 'app/constants';
+import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
 
 export const initialUIState: UIState = {
   activeTab: 0,
@@ -20,7 +20,7 @@ export const initialUIState: UIState = {
   shouldShowGallery: true,
   shouldHidePreview: false,
   shouldShowProgressInViewer: true,
-  schedulers: SCHEDULERS,
+  favoriteSchedulers: [],
 };
 
 export const uiSlice = createSlice({
@@ -94,9 +94,11 @@ export const uiSlice = createSlice({
     setShouldShowProgressInViewer: (state, action: PayloadAction<boolean>) => {
       state.shouldShowProgressInViewer = action.payload;
     },
-    setSchedulers: (state, action: PayloadAction<string[]>) => {
-      state.schedulers = [];
-      state.schedulers = action.payload;
+    favoriteSchedulersChanged: (
+      state,
+      action: PayloadAction<SchedulerParam[]>
+    ) => {
+      state.favoriteSchedulers = action.payload;
     },
   },
   extraReducers(builder) {
@@ -124,7 +126,7 @@ export const {
   toggleParametersPanel,
   toggleGalleryPanel,
   setShouldShowProgressInViewer,
-  setSchedulers,
+  favoriteSchedulersChanged,
 } = uiSlice.actions;
 
 export default uiSlice.reducer;
diff --git a/invokeai/frontend/web/src/features/ui/store/uiTypes.ts b/invokeai/frontend/web/src/features/ui/store/uiTypes.ts
index 18a758cdd6..2a9a82fbe8 100644
--- a/invokeai/frontend/web/src/features/ui/store/uiTypes.ts
+++ b/invokeai/frontend/web/src/features/ui/store/uiTypes.ts
@@ -1,3 +1,5 @@
+import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
+
 export type AddNewModelType = 'ckpt' | 'diffusers' | null;
 
 export type Coordinates = {
@@ -26,5 +28,5 @@ export interface UIState {
   shouldPinGallery: boolean;
   shouldShowGallery: boolean;
   shouldShowProgressInViewer: boolean;
-  schedulers: string[];
+  favoriteSchedulers: SchedulerParam[];
 }
diff --git a/invokeai/frontend/web/src/services/api/index.ts b/invokeai/frontend/web/src/services/api/index.ts
index cd83555f15..7481a5daad 100644
--- a/invokeai/frontend/web/src/services/api/index.ts
+++ b/invokeai/frontend/web/src/services/api/index.ts
@@ -7,9 +7,11 @@ export { OpenAPI } from './core/OpenAPI';
 export type { OpenAPIConfig } from './core/OpenAPI';
 
 export type { AddInvocation } from './models/AddInvocation';
+export type { BaseModelType } from './models/BaseModelType';
 export type { Body_upload_image } from './models/Body_upload_image';
 export type { CannyImageProcessorInvocation } from './models/CannyImageProcessorInvocation';
 export type { CkptModelInfo } from './models/CkptModelInfo';
+export type { ClipField } from './models/ClipField';
 export type { CollectInvocation } from './models/CollectInvocation';
 export type { CollectInvocationOutput } from './models/CollectInvocationOutput';
 export type { ColorField } from './models/ColorField';
@@ -53,7 +55,6 @@ export type { ImageProcessorInvocation } from './models/ImageProcessorInvocation
 export type { ImageRecordChanges } from './models/ImageRecordChanges';
 export type { ImageResizeInvocation } from './models/ImageResizeInvocation';
 export type { ImageScaleInvocation } from './models/ImageScaleInvocation';
-export type { ImageToImageInvocation } from './models/ImageToImageInvocation';
 export type { ImageToLatentsInvocation } from './models/ImageToLatentsInvocation';
 export type { ImageUrlsDTO } from './models/ImageUrlsDTO';
 export type { InfillColorInvocation } from './models/InfillColorInvocation';
@@ -62,6 +63,14 @@ export type { InfillTileInvocation } from './models/InfillTileInvocation';
 export type { InpaintInvocation } from './models/InpaintInvocation';
 export type { IntCollectionOutput } from './models/IntCollectionOutput';
 export type { IntOutput } from './models/IntOutput';
+export type { invokeai__backend__model_management__models__controlnet__ControlNetModel__Config } from './models/invokeai__backend__model_management__models__controlnet__ControlNetModel__Config';
+export type { invokeai__backend__model_management__models__lora__LoRAModel__Config } from './models/invokeai__backend__model_management__models__lora__LoRAModel__Config';
+export type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig } from './models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig';
+export type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig } from './models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig';
+export type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig } from './models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig';
+export type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig } from './models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig';
+export type { invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config } from './models/invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config';
+export type { invokeai__backend__model_management__models__vae__VaeModel__Config } from './models/invokeai__backend__model_management__models__vae__VaeModel__Config';
 export type { IterateInvocation } from './models/IterateInvocation';
 export type { IterateInvocationOutput } from './models/IterateInvocationOutput';
 export type { LatentsField } from './models/LatentsField';
@@ -71,12 +80,20 @@ export type { LatentsToLatentsInvocation } from './models/LatentsToLatentsInvoca
 export type { LineartAnimeImageProcessorInvocation } from './models/LineartAnimeImageProcessorInvocation';
 export type { LineartImageProcessorInvocation } from './models/LineartImageProcessorInvocation';
 export type { LoadImageInvocation } from './models/LoadImageInvocation';
+export type { LoraInfo } from './models/LoraInfo';
+export type { LoraLoaderInvocation } from './models/LoraLoaderInvocation';
+export type { LoraLoaderOutput } from './models/LoraLoaderOutput';
 export type { MaskFromAlphaInvocation } from './models/MaskFromAlphaInvocation';
 export type { MaskOutput } from './models/MaskOutput';
 export type { MediapipeFaceProcessorInvocation } from './models/MediapipeFaceProcessorInvocation';
 export type { MidasDepthImageProcessorInvocation } from './models/MidasDepthImageProcessorInvocation';
 export type { MlsdImageProcessorInvocation } from './models/MlsdImageProcessorInvocation';
+export type { ModelError } from './models/ModelError';
+export type { ModelInfo } from './models/ModelInfo';
+export type { ModelLoaderOutput } from './models/ModelLoaderOutput';
 export type { ModelsList } from './models/ModelsList';
+export type { ModelType } from './models/ModelType';
+export type { ModelVariantType } from './models/ModelVariantType';
 export type { MultiplyInvocation } from './models/MultiplyInvocation';
 export type { NoiseInvocation } from './models/NoiseInvocation';
 export type { NoiseOutput } from './models/NoiseOutput';
@@ -97,12 +114,17 @@ export type { ResizeLatentsInvocation } from './models/ResizeLatentsInvocation';
 export type { ResourceOrigin } from './models/ResourceOrigin';
 export type { RestoreFaceInvocation } from './models/RestoreFaceInvocation';
 export type { ScaleLatentsInvocation } from './models/ScaleLatentsInvocation';
+export type { SchedulerPredictionType } from './models/SchedulerPredictionType';
+export type { SD1ModelLoaderInvocation } from './models/SD1ModelLoaderInvocation';
+export type { SD2ModelLoaderInvocation } from './models/SD2ModelLoaderInvocation';
 export type { ShowImageInvocation } from './models/ShowImageInvocation';
 export type { StepParamEasingInvocation } from './models/StepParamEasingInvocation';
+export type { SubModelType } from './models/SubModelType';
 export type { SubtractInvocation } from './models/SubtractInvocation';
-export type { TextToImageInvocation } from './models/TextToImageInvocation';
 export type { TextToLatentsInvocation } from './models/TextToLatentsInvocation';
+export type { UNetField } from './models/UNetField';
 export type { UpscaleInvocation } from './models/UpscaleInvocation';
+export type { VaeField } from './models/VaeField';
 export type { VaeRepo } from './models/VaeRepo';
 export type { ValidationError } from './models/ValidationError';
 export type { ZoeDepthImageProcessorInvocation } from './models/ZoeDepthImageProcessorInvocation';
diff --git a/invokeai/frontend/web/src/services/api/models/BaseModelType.ts b/invokeai/frontend/web/src/services/api/models/BaseModelType.ts
new file mode 100644
index 0000000000..3f72e68fa4
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/BaseModelType.ts
@@ -0,0 +1,8 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * An enumeration.
+ */
+export type BaseModelType = 'sd-1' | 'sd-2';
diff --git a/invokeai/frontend/web/src/services/api/models/CkptModelInfo.ts b/invokeai/frontend/web/src/services/api/models/CkptModelInfo.ts
index 2ae7c09674..cfa4357725 100644
--- a/invokeai/frontend/web/src/services/api/models/CkptModelInfo.ts
+++ b/invokeai/frontend/web/src/services/api/models/CkptModelInfo.ts
@@ -7,6 +7,14 @@ export type CkptModelInfo = {
    * A description of the model
    */
   description?: string;
+  /**
+   * The name of the model
+   */
+  model_name: string;
+  /**
+   * The type of the model
+   */
+  model_type: string;
   format?: 'ckpt';
   /**
    * The path to the model config
diff --git a/invokeai/frontend/web/src/services/api/models/ClipField.ts b/invokeai/frontend/web/src/services/api/models/ClipField.ts
new file mode 100644
index 0000000000..f9ef2cc683
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/ClipField.ts
@@ -0,0 +1,22 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { LoraInfo } from './LoraInfo';
+import type { ModelInfo } from './ModelInfo';
+
+export type ClipField = {
+  /**
+   * Info to load tokenizer submodel
+   */
+  tokenizer: ModelInfo;
+  /**
+   * Info to load text_encoder submodel
+   */
+  text_encoder: ModelInfo;
+  /**
+   * Loras to apply on model loading
+   */
+  loras: Array<LoraInfo>;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/CompelInvocation.ts b/invokeai/frontend/web/src/services/api/models/CompelInvocation.ts
index 1dc390c1be..dd381ef22c 100644
--- a/invokeai/frontend/web/src/services/api/models/CompelInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/CompelInvocation.ts
@@ -2,6 +2,8 @@
 /* tslint:disable */
 /* eslint-disable */
 
+import type { ClipField } from './ClipField';
+
 /**
  * Parse prompt using compel package to conditioning.
  */
@@ -20,8 +22,8 @@ export type CompelInvocation = {
    */
   prompt?: string;
   /**
-   * Model to use
+   * Clip to use
    */
-  model?: string;
+  clip?: ClipField;
 };
 
diff --git a/invokeai/frontend/web/src/services/api/models/DiffusersModelInfo.ts b/invokeai/frontend/web/src/services/api/models/DiffusersModelInfo.ts
index 5be4801cdd..4e722ddb80 100644
--- a/invokeai/frontend/web/src/services/api/models/DiffusersModelInfo.ts
+++ b/invokeai/frontend/web/src/services/api/models/DiffusersModelInfo.ts
@@ -9,7 +9,15 @@ export type DiffusersModelInfo = {
    * A description of the model
    */
   description?: string;
-  format?: 'diffusers';
+  /**
+   * The name of the model
+   */
+  model_name: string;
+  /**
+   * The type of the model
+   */
+  model_type: string;
+  format?: 'folder';
   /**
    * The VAE repo to use for this model
    */
diff --git a/invokeai/frontend/web/src/services/api/models/Graph.ts b/invokeai/frontend/web/src/services/api/models/Graph.ts
index efac5dabcc..e148954f16 100644
--- a/invokeai/frontend/web/src/services/api/models/Graph.ts
+++ b/invokeai/frontend/web/src/services/api/models/Graph.ts
@@ -26,7 +26,6 @@ import type { ImagePasteInvocation } from './ImagePasteInvocation';
 import type { ImageProcessorInvocation } from './ImageProcessorInvocation';
 import type { ImageResizeInvocation } from './ImageResizeInvocation';
 import type { ImageScaleInvocation } from './ImageScaleInvocation';
-import type { ImageToImageInvocation } from './ImageToImageInvocation';
 import type { ImageToLatentsInvocation } from './ImageToLatentsInvocation';
 import type { InfillColorInvocation } from './InfillColorInvocation';
 import type { InfillPatchMatchInvocation } from './InfillPatchMatchInvocation';
@@ -38,6 +37,7 @@ import type { LatentsToLatentsInvocation } from './LatentsToLatentsInvocation';
 import type { LineartAnimeImageProcessorInvocation } from './LineartAnimeImageProcessorInvocation';
 import type { LineartImageProcessorInvocation } from './LineartImageProcessorInvocation';
 import type { LoadImageInvocation } from './LoadImageInvocation';
+import type { LoraLoaderInvocation } from './LoraLoaderInvocation';
 import type { MaskFromAlphaInvocation } from './MaskFromAlphaInvocation';
 import type { MediapipeFaceProcessorInvocation } from './MediapipeFaceProcessorInvocation';
 import type { MidasDepthImageProcessorInvocation } from './MidasDepthImageProcessorInvocation';
@@ -56,10 +56,11 @@ import type { RangeOfSizeInvocation } from './RangeOfSizeInvocation';
 import type { ResizeLatentsInvocation } from './ResizeLatentsInvocation';
 import type { RestoreFaceInvocation } from './RestoreFaceInvocation';
 import type { ScaleLatentsInvocation } from './ScaleLatentsInvocation';
+import type { SD1ModelLoaderInvocation } from './SD1ModelLoaderInvocation';
+import type { SD2ModelLoaderInvocation } from './SD2ModelLoaderInvocation';
 import type { ShowImageInvocation } from './ShowImageInvocation';
 import type { StepParamEasingInvocation } from './StepParamEasingInvocation';
 import type { SubtractInvocation } from './SubtractInvocation';
-import type { TextToImageInvocation } from './TextToImageInvocation';
 import type { TextToLatentsInvocation } from './TextToLatentsInvocation';
 import type { UpscaleInvocation } from './UpscaleInvocation';
 import type { ZoeDepthImageProcessorInvocation } from './ZoeDepthImageProcessorInvocation';
@@ -72,7 +73,7 @@ export type Graph = {
   /**
    * The nodes in this graph
    */
-  nodes?: Record<string, (LoadImageInvocation | ShowImageInvocation | ImageCropInvocation | ImagePasteInvocation | MaskFromAlphaInvocation | ImageMultiplyInvocation | ImageChannelInvocation | ImageConvertInvocation | ImageBlurInvocation | ImageResizeInvocation | ImageScaleInvocation | ImageLerpInvocation | ImageInverseLerpInvocation | ControlNetInvocation | ImageProcessorInvocation | DynamicPromptInvocation | CompelInvocation | AddInvocation | SubtractInvocation | MultiplyInvocation | DivideInvocation | RandomIntInvocation | ParamIntInvocation | ParamFloatInvocation | NoiseInvocation | TextToLatentsInvocation | LatentsToImageInvocation | ResizeLatentsInvocation | ScaleLatentsInvocation | ImageToLatentsInvocation | CvInpaintInvocation | RangeInvocation | RangeOfSizeInvocation | RandomRangeInvocation | FloatLinearRangeInvocation | StepParamEasingInvocation | UpscaleInvocation | RestoreFaceInvocation | TextToImageInvocation | InfillColorInvocation | InfillTileInvocation | InfillPatchMatchInvocation | GraphInvocation | IterateInvocation | CollectInvocation | CannyImageProcessorInvocation | HedImageProcessorInvocation | LineartImageProcessorInvocation | LineartAnimeImageProcessorInvocation | OpenposeImageProcessorInvocation | MidasDepthImageProcessorInvocation | NormalbaeImageProcessorInvocation | MlsdImageProcessorInvocation | PidiImageProcessorInvocation | ContentShuffleImageProcessorInvocation | ZoeDepthImageProcessorInvocation | MediapipeFaceProcessorInvocation | LatentsToLatentsInvocation | ImageToImageInvocation | InpaintInvocation)>;
+  nodes?: Record<string, (LoadImageInvocation | ShowImageInvocation | ImageCropInvocation | ImagePasteInvocation | MaskFromAlphaInvocation | ImageMultiplyInvocation | ImageChannelInvocation | ImageConvertInvocation | ImageBlurInvocation | ImageResizeInvocation | ImageScaleInvocation | ImageLerpInvocation | ImageInverseLerpInvocation | ControlNetInvocation | ImageProcessorInvocation | SD1ModelLoaderInvocation | SD2ModelLoaderInvocation | LoraLoaderInvocation | DynamicPromptInvocation | CompelInvocation | AddInvocation | SubtractInvocation | MultiplyInvocation | DivideInvocation | RandomIntInvocation | ParamIntInvocation | ParamFloatInvocation | NoiseInvocation | TextToLatentsInvocation | LatentsToImageInvocation | ResizeLatentsInvocation | ScaleLatentsInvocation | ImageToLatentsInvocation | CvInpaintInvocation | RangeInvocation | RangeOfSizeInvocation | RandomRangeInvocation | FloatLinearRangeInvocation | StepParamEasingInvocation | UpscaleInvocation | RestoreFaceInvocation | InpaintInvocation | InfillColorInvocation | InfillTileInvocation | InfillPatchMatchInvocation | GraphInvocation | IterateInvocation | CollectInvocation | CannyImageProcessorInvocation | HedImageProcessorInvocation | LineartImageProcessorInvocation | LineartAnimeImageProcessorInvocation | OpenposeImageProcessorInvocation | MidasDepthImageProcessorInvocation | NormalbaeImageProcessorInvocation | MlsdImageProcessorInvocation | PidiImageProcessorInvocation | ContentShuffleImageProcessorInvocation | ZoeDepthImageProcessorInvocation | MediapipeFaceProcessorInvocation | LatentsToLatentsInvocation)>;
   /**
    * The connections between nodes and their fields in this graph
    */
diff --git a/invokeai/frontend/web/src/services/api/models/GraphExecutionState.ts b/invokeai/frontend/web/src/services/api/models/GraphExecutionState.ts
index ccd5d6f499..602e7a2ebc 100644
--- a/invokeai/frontend/web/src/services/api/models/GraphExecutionState.ts
+++ b/invokeai/frontend/web/src/services/api/models/GraphExecutionState.ts
@@ -14,7 +14,9 @@ import type { IntCollectionOutput } from './IntCollectionOutput';
 import type { IntOutput } from './IntOutput';
 import type { IterateInvocationOutput } from './IterateInvocationOutput';
 import type { LatentsOutput } from './LatentsOutput';
+import type { LoraLoaderOutput } from './LoraLoaderOutput';
 import type { MaskOutput } from './MaskOutput';
+import type { ModelLoaderOutput } from './ModelLoaderOutput';
 import type { NoiseOutput } from './NoiseOutput';
 import type { PromptCollectionOutput } from './PromptCollectionOutput';
 import type { PromptOutput } from './PromptOutput';
@@ -46,7 +48,7 @@ export type GraphExecutionState = {
   /**
    * The results of node executions
    */
-  results: Record<string, (ImageOutput | MaskOutput | ControlOutput | PromptOutput | PromptCollectionOutput | CompelOutput | IntOutput | FloatOutput | LatentsOutput | NoiseOutput | IntCollectionOutput | FloatCollectionOutput | GraphInvocationOutput | IterateInvocationOutput | CollectInvocationOutput)>;
+  results: Record<string, (ImageOutput | MaskOutput | ControlOutput | ModelLoaderOutput | LoraLoaderOutput | PromptOutput | PromptCollectionOutput | CompelOutput | IntOutput | FloatOutput | LatentsOutput | NoiseOutput | IntCollectionOutput | FloatCollectionOutput | GraphInvocationOutput | IterateInvocationOutput | CollectInvocationOutput)>;
   /**
    * Errors raised when executing nodes
    */
diff --git a/invokeai/frontend/web/src/services/api/models/ImageToImageInvocation.ts b/invokeai/frontend/web/src/services/api/models/ImageToImageInvocation.ts
deleted file mode 100644
index e63ec93ada..0000000000
--- a/invokeai/frontend/web/src/services/api/models/ImageToImageInvocation.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-/* istanbul ignore file */
-/* tslint:disable */
-/* eslint-disable */
-
-import type { ImageField } from './ImageField';
-
-/**
- * Generates an image using img2img.
- */
-export type ImageToImageInvocation = {
-  /**
-   * The id of this node. Must be unique among all nodes.
-   */
-  id: string;
-  /**
-   * Whether or not this node is an intermediate node.
-   */
-  is_intermediate?: boolean;
-  type?: 'img2img';
-  /**
-   * The prompt to generate an image from
-   */
-  prompt?: string;
-  /**
-   * The seed to use (omit for random)
-   */
-  seed?: number;
-  /**
-   * The number of steps to use to generate the image
-   */
-  steps?: number;
-  /**
-   * The width of the resulting image
-   */
-  width?: number;
-  /**
-   * The height of the resulting image
-   */
-  height?: number;
-  /**
-   * The Classifier-Free Guidance, higher values may result in a result closer to the prompt
-   */
-  cfg_scale?: number;
-  /**
-   * The scheduler to use
-   */
-  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'unipc';
-  /**
-   * The model to use (currently ignored)
-   */
-  model?: string;
-  /**
-   * Whether or not to produce progress images during generation
-   */
-  progress_images?: boolean;
-  /**
-   * The control model to use
-   */
-  control_model?: string;
-  /**
-   * The processed control image
-   */
-  control_image?: ImageField;
-  /**
-   * The input image
-   */
-  image?: ImageField;
-  /**
-   * The strength of the original image
-   */
-  strength?: number;
-  /**
-   * Whether or not the result should be fit to the aspect ratio of the input image
-   */
-  fit?: boolean;
-};
-
diff --git a/invokeai/frontend/web/src/services/api/models/ImageToLatentsInvocation.ts b/invokeai/frontend/web/src/services/api/models/ImageToLatentsInvocation.ts
index 5569c2fa86..ace0ed8e3c 100644
--- a/invokeai/frontend/web/src/services/api/models/ImageToLatentsInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/ImageToLatentsInvocation.ts
@@ -3,6 +3,7 @@
 /* eslint-disable */
 
 import type { ImageField } from './ImageField';
+import type { VaeField } from './VaeField';
 
 /**
  * Encodes an image into latents.
@@ -22,8 +23,12 @@ export type ImageToLatentsInvocation = {
    */
   image?: ImageField;
   /**
-   * The model to use
+   * Vae submodel
    */
-  model?: string;
+  vae?: VaeField;
+  /**
+   * Encode latents by overlaping tiles(less memory consumption)
+   */
+  tiled?: boolean;
 };
 
diff --git a/invokeai/frontend/web/src/services/api/models/InpaintInvocation.ts b/invokeai/frontend/web/src/services/api/models/InpaintInvocation.ts
index b8ed268ef9..8fb9ad3d54 100644
--- a/invokeai/frontend/web/src/services/api/models/InpaintInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/InpaintInvocation.ts
@@ -3,7 +3,10 @@
 /* eslint-disable */
 
 import type { ColorField } from './ColorField';
+import type { ConditioningField } from './ConditioningField';
 import type { ImageField } from './ImageField';
+import type { UNetField } from './UNetField';
+import type { VaeField } from './VaeField';
 
 /**
  * Generates an image using inpaint.
@@ -19,9 +22,13 @@ export type InpaintInvocation = {
   is_intermediate?: boolean;
   type?: 'inpaint';
   /**
-   * The prompt to generate an image from
+   * Positive conditioning for generation
    */
-  prompt?: string;
+  positive_conditioning?: ConditioningField;
+  /**
+   * Negative conditioning for generation
+   */
+  negative_conditioning?: ConditioningField;
   /**
    * The seed to use (omit for random)
    */
@@ -45,23 +52,15 @@ export type InpaintInvocation = {
   /**
    * The scheduler to use
    */
-  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'unipc';
+  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'lms_k' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2s_k' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'dpmpp_2m_sde' | 'dpmpp_2m_sde_k' | 'dpmpp_sde' | 'dpmpp_sde_k' | 'unipc';
   /**
-   * The model to use (currently ignored)
+   * UNet model
    */
-  model?: string;
+  unet?: UNetField;
   /**
-   * Whether or not to produce progress images during generation
+   * Vae model
    */
-  progress_images?: boolean;
-  /**
-   * The control model to use
-   */
-  control_model?: string;
-  /**
-   * The processed control image
-   */
-  control_image?: ImageField;
+  vae?: VaeField;
   /**
    * The input image
    */
diff --git a/invokeai/frontend/web/src/services/api/models/LatentsToImageInvocation.ts b/invokeai/frontend/web/src/services/api/models/LatentsToImageInvocation.ts
index fcaa37d7e8..865eeff554 100644
--- a/invokeai/frontend/web/src/services/api/models/LatentsToImageInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/LatentsToImageInvocation.ts
@@ -3,6 +3,7 @@
 /* eslint-disable */
 
 import type { LatentsField } from './LatentsField';
+import type { VaeField } from './VaeField';
 
 /**
  * Generates an image from latents.
@@ -22,8 +23,12 @@ export type LatentsToImageInvocation = {
    */
   latents?: LatentsField;
   /**
-   * The model to use
+   * Vae submodel
    */
-  model?: string;
+  vae?: VaeField;
+  /**
+   * Decode latents by overlaping tiles(less memory consumption)
+   */
+  tiled?: boolean;
 };
 
diff --git a/invokeai/frontend/web/src/services/api/models/LatentsToLatentsInvocation.ts b/invokeai/frontend/web/src/services/api/models/LatentsToLatentsInvocation.ts
index 60504459e7..4273115963 100644
--- a/invokeai/frontend/web/src/services/api/models/LatentsToLatentsInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/LatentsToLatentsInvocation.ts
@@ -5,6 +5,7 @@
 import type { ConditioningField } from './ConditioningField';
 import type { ControlField } from './ControlField';
 import type { LatentsField } from './LatentsField';
+import type { UNetField } from './UNetField';
 
 /**
  * Generates latents using latents as base image.
@@ -42,11 +43,11 @@ export type LatentsToLatentsInvocation = {
   /**
    * The scheduler to use
    */
-  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'unipc';
+  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'lms_k' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2s_k' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'dpmpp_2m_sde' | 'dpmpp_2m_sde_k' | 'dpmpp_sde' | 'dpmpp_sde_k' | 'unipc';
   /**
-   * The model to use (currently ignored)
+   * UNet submodel
    */
-  model?: string;
+  unet?: UNetField;
   /**
    * The control to use
    */
diff --git a/invokeai/frontend/web/src/services/api/models/LoraInfo.ts b/invokeai/frontend/web/src/services/api/models/LoraInfo.ts
new file mode 100644
index 0000000000..1a575d4147
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/LoraInfo.ts
@@ -0,0 +1,31 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { BaseModelType } from './BaseModelType';
+import type { ModelType } from './ModelType';
+import type { SubModelType } from './SubModelType';
+
+export type LoraInfo = {
+  /**
+   * Info to load submodel
+   */
+  model_name: string;
+  /**
+   * Base model
+   */
+  base_model: BaseModelType;
+  /**
+   * Info to load submodel
+   */
+  model_type: ModelType;
+  /**
+   * Info to load submodel
+   */
+  submodel?: SubModelType;
+  /**
+   * Lora's weight which to use when apply to model
+   */
+  weight: number;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/LoraLoaderInvocation.ts b/invokeai/frontend/web/src/services/api/models/LoraLoaderInvocation.ts
new file mode 100644
index 0000000000..b93281c5a7
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/LoraLoaderInvocation.ts
@@ -0,0 +1,38 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ClipField } from './ClipField';
+import type { UNetField } from './UNetField';
+
+/**
+ * Apply selected lora to unet and text_encoder.
+ */
+export type LoraLoaderInvocation = {
+  /**
+   * The id of this node. Must be unique among all nodes.
+   */
+  id: string;
+  /**
+   * Whether or not this node is an intermediate node.
+   */
+  is_intermediate?: boolean;
+  type?: 'lora_loader';
+  /**
+   * Lora model name
+   */
+  lora_name: string;
+  /**
+   * With what weight to apply lora
+   */
+  weight?: number;
+  /**
+   * UNet model for applying lora
+   */
+  unet?: UNetField;
+  /**
+   * Clip model for applying lora
+   */
+  clip?: ClipField;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/LoraLoaderOutput.ts b/invokeai/frontend/web/src/services/api/models/LoraLoaderOutput.ts
new file mode 100644
index 0000000000..1fed1ebc58
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/LoraLoaderOutput.ts
@@ -0,0 +1,22 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ClipField } from './ClipField';
+import type { UNetField } from './UNetField';
+
+/**
+ * Model loader output
+ */
+export type LoraLoaderOutput = {
+  type?: 'lora_loader_output';
+  /**
+   * UNet submodel
+   */
+  unet?: UNetField;
+  /**
+   * Tokenizer and text_encoder submodels
+   */
+  clip?: ClipField;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/ModelError.ts b/invokeai/frontend/web/src/services/api/models/ModelError.ts
new file mode 100644
index 0000000000..3151a764d6
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/ModelError.ts
@@ -0,0 +1,8 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * An enumeration.
+ */
+export type ModelError = 'not_found';
diff --git a/invokeai/frontend/web/src/services/api/models/ModelInfo.ts b/invokeai/frontend/web/src/services/api/models/ModelInfo.ts
new file mode 100644
index 0000000000..e87799d142
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/ModelInfo.ts
@@ -0,0 +1,27 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { BaseModelType } from './BaseModelType';
+import type { ModelType } from './ModelType';
+import type { SubModelType } from './SubModelType';
+
+export type ModelInfo = {
+  /**
+   * Info to load submodel
+   */
+  model_name: string;
+  /**
+   * Base model
+   */
+  base_model: BaseModelType;
+  /**
+   * Info to load submodel
+   */
+  model_type: ModelType;
+  /**
+   * Info to load submodel
+   */
+  submodel?: SubModelType;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/ModelLoaderOutput.ts b/invokeai/frontend/web/src/services/api/models/ModelLoaderOutput.ts
new file mode 100644
index 0000000000..5b5b51e71f
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/ModelLoaderOutput.ts
@@ -0,0 +1,27 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ClipField } from './ClipField';
+import type { UNetField } from './UNetField';
+import type { VaeField } from './VaeField';
+
+/**
+ * Model loader output
+ */
+export type ModelLoaderOutput = {
+  type?: 'model_loader_output';
+  /**
+   * UNet submodel
+   */
+  unet?: UNetField;
+  /**
+   * Tokenizer and text_encoder submodels
+   */
+  clip?: ClipField;
+  /**
+   * Vae submodel
+   */
+  vae?: VaeField;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/ModelType.ts b/invokeai/frontend/web/src/services/api/models/ModelType.ts
new file mode 100644
index 0000000000..7d7abcafae
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/ModelType.ts
@@ -0,0 +1,8 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * An enumeration.
+ */
+export type ModelType = 'pipeline' | 'vae' | 'lora' | 'controlnet' | 'embedding';
diff --git a/invokeai/frontend/web/src/services/api/models/ModelVariantType.ts b/invokeai/frontend/web/src/services/api/models/ModelVariantType.ts
new file mode 100644
index 0000000000..0527c40bcf
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/ModelVariantType.ts
@@ -0,0 +1,8 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * An enumeration.
+ */
+export type ModelVariantType = 'normal' | 'inpaint' | 'depth';
diff --git a/invokeai/frontend/web/src/services/api/models/ModelsList.ts b/invokeai/frontend/web/src/services/api/models/ModelsList.ts
index 7a7449542d..a2d88d1967 100644
--- a/invokeai/frontend/web/src/services/api/models/ModelsList.ts
+++ b/invokeai/frontend/web/src/services/api/models/ModelsList.ts
@@ -2,10 +2,16 @@
 /* tslint:disable */
 /* eslint-disable */
 
-import type { CkptModelInfo } from './CkptModelInfo';
-import type { DiffusersModelInfo } from './DiffusersModelInfo';
+import type { invokeai__backend__model_management__models__controlnet__ControlNetModel__Config } from './invokeai__backend__model_management__models__controlnet__ControlNetModel__Config';
+import type { invokeai__backend__model_management__models__lora__LoRAModel__Config } from './invokeai__backend__model_management__models__lora__LoRAModel__Config';
+import type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig } from './invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig';
+import type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig } from './invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig';
+import type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig } from './invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig';
+import type { invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig } from './invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig';
+import type { invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config } from './invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config';
+import type { invokeai__backend__model_management__models__vae__VaeModel__Config } from './invokeai__backend__model_management__models__vae__VaeModel__Config';
 
 export type ModelsList = {
-  models: Record<string, (CkptModelInfo | DiffusersModelInfo)>;
+  models: Record<string, Record<string, Record<string, (invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig | invokeai__backend__model_management__models__controlnet__ControlNetModel__Config | invokeai__backend__model_management__models__lora__LoRAModel__Config | invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig | invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config | invokeai__backend__model_management__models__vae__VaeModel__Config | invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig | invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig)>>>;
 };
 
diff --git a/invokeai/frontend/web/src/services/api/models/SD1ModelLoaderInvocation.ts b/invokeai/frontend/web/src/services/api/models/SD1ModelLoaderInvocation.ts
new file mode 100644
index 0000000000..9a8a23077a
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/SD1ModelLoaderInvocation.ts
@@ -0,0 +1,23 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * Loading submodels of selected model.
+ */
+export type SD1ModelLoaderInvocation = {
+  /**
+   * The id of this node. Must be unique among all nodes.
+   */
+  id: string;
+  /**
+   * Whether or not this node is an intermediate node.
+   */
+  is_intermediate?: boolean;
+  type?: 'sd1_model_loader';
+  /**
+   * Model to load
+   */
+  model_name?: string;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/SD2ModelLoaderInvocation.ts b/invokeai/frontend/web/src/services/api/models/SD2ModelLoaderInvocation.ts
new file mode 100644
index 0000000000..f477c11a8d
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/SD2ModelLoaderInvocation.ts
@@ -0,0 +1,23 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * Loading submodels of selected model.
+ */
+export type SD2ModelLoaderInvocation = {
+  /**
+   * The id of this node. Must be unique among all nodes.
+   */
+  id: string;
+  /**
+   * Whether or not this node is an intermediate node.
+   */
+  is_intermediate?: boolean;
+  type?: 'sd2_model_loader';
+  /**
+   * Model to load
+   */
+  model_name?: string;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/SchedulerPredictionType.ts b/invokeai/frontend/web/src/services/api/models/SchedulerPredictionType.ts
new file mode 100644
index 0000000000..fa24aab5a1
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/SchedulerPredictionType.ts
@@ -0,0 +1,8 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * An enumeration.
+ */
+export type SchedulerPredictionType = 'epsilon' | 'v_prediction' | 'sample';
diff --git a/invokeai/frontend/web/src/services/api/models/SubModelType.ts b/invokeai/frontend/web/src/services/api/models/SubModelType.ts
new file mode 100644
index 0000000000..12b055994c
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/SubModelType.ts
@@ -0,0 +1,8 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * An enumeration.
+ */
+export type SubModelType = 'unet' | 'text_encoder' | 'tokenizer' | 'vae' | 'scheduler' | 'safety_checker';
diff --git a/invokeai/frontend/web/src/services/api/models/TextToImageInvocation.ts b/invokeai/frontend/web/src/services/api/models/TextToImageInvocation.ts
deleted file mode 100644
index 7128ea8440..0000000000
--- a/invokeai/frontend/web/src/services/api/models/TextToImageInvocation.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/* istanbul ignore file */
-/* tslint:disable */
-/* eslint-disable */
-
-import type { ImageField } from './ImageField';
-
-/**
- * Generates an image using text2img.
- */
-export type TextToImageInvocation = {
-  /**
-   * The id of this node. Must be unique among all nodes.
-   */
-  id: string;
-  /**
-   * Whether or not this node is an intermediate node.
-   */
-  is_intermediate?: boolean;
-  type?: 'txt2img';
-  /**
-   * The prompt to generate an image from
-   */
-  prompt?: string;
-  /**
-   * The seed to use (omit for random)
-   */
-  seed?: number;
-  /**
-   * The number of steps to use to generate the image
-   */
-  steps?: number;
-  /**
-   * The width of the resulting image
-   */
-  width?: number;
-  /**
-   * The height of the resulting image
-   */
-  height?: number;
-  /**
-   * The Classifier-Free Guidance, higher values may result in a result closer to the prompt
-   */
-  cfg_scale?: number;
-  /**
-   * The scheduler to use
-   */
-  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'unipc';
-  /**
-   * The model to use (currently ignored)
-   */
-  model?: string;
-  /**
-   * Whether or not to produce progress images during generation
-   */
-  progress_images?: boolean;
-  /**
-   * The control model to use
-   */
-  control_model?: string;
-  /**
-   * The processed control image
-   */
-  control_image?: ImageField;
-};
-
diff --git a/invokeai/frontend/web/src/services/api/models/TextToLatentsInvocation.ts b/invokeai/frontend/web/src/services/api/models/TextToLatentsInvocation.ts
index 2db0657e25..cf8229b1f7 100644
--- a/invokeai/frontend/web/src/services/api/models/TextToLatentsInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/TextToLatentsInvocation.ts
@@ -5,6 +5,7 @@
 import type { ConditioningField } from './ConditioningField';
 import type { ControlField } from './ControlField';
 import type { LatentsField } from './LatentsField';
+import type { UNetField } from './UNetField';
 
 /**
  * Generates latents from conditionings.
@@ -42,11 +43,11 @@ export type TextToLatentsInvocation = {
   /**
    * The scheduler to use
    */
-  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'unipc';
+  scheduler?: 'ddim' | 'ddpm' | 'deis' | 'lms' | 'lms_k' | 'pndm' | 'heun' | 'heun_k' | 'euler' | 'euler_k' | 'euler_a' | 'kdpm_2' | 'kdpm_2_a' | 'dpmpp_2s' | 'dpmpp_2s_k' | 'dpmpp_2m' | 'dpmpp_2m_k' | 'dpmpp_2m_sde' | 'dpmpp_2m_sde_k' | 'dpmpp_sde' | 'dpmpp_sde_k' | 'unipc';
   /**
-   * The model to use (currently ignored)
+   * UNet submodel
    */
-  model?: string;
+  unet?: UNetField;
   /**
    * The control to use
    */
diff --git a/invokeai/frontend/web/src/services/api/models/UNetField.ts b/invokeai/frontend/web/src/services/api/models/UNetField.ts
new file mode 100644
index 0000000000..ad3b1ddb5b
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/UNetField.ts
@@ -0,0 +1,22 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { LoraInfo } from './LoraInfo';
+import type { ModelInfo } from './ModelInfo';
+
+export type UNetField = {
+  /**
+   * Info to load unet submodel
+   */
+  unet: ModelInfo;
+  /**
+   * Info to load scheduler submodel
+   */
+  scheduler: ModelInfo;
+  /**
+   * Loras to apply on model loading
+   */
+  loras: Array<LoraInfo>;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/VaeField.ts b/invokeai/frontend/web/src/services/api/models/VaeField.ts
new file mode 100644
index 0000000000..bfe2793887
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/VaeField.ts
@@ -0,0 +1,13 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelInfo } from './ModelInfo';
+
+export type VaeField = {
+  /**
+   * Info to load vae submodel
+   */
+  vae: ModelInfo;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__controlnet__ControlNetModel__Config.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__controlnet__ControlNetModel__Config.ts
new file mode 100644
index 0000000000..f8decdb341
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__controlnet__ControlNetModel__Config.ts
@@ -0,0 +1,14 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+
+export type invokeai__backend__model_management__models__controlnet__ControlNetModel__Config = {
+  path: string;
+  description?: string;
+  format: ('checkpoint' | 'diffusers');
+  default?: boolean;
+  error?: ModelError;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__lora__LoRAModel__Config.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__lora__LoRAModel__Config.ts
new file mode 100644
index 0000000000..614749a2c5
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__lora__LoRAModel__Config.ts
@@ -0,0 +1,14 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+
+export type invokeai__backend__model_management__models__lora__LoRAModel__Config = {
+  path: string;
+  description?: string;
+  format: ('lycoris' | 'diffusers');
+  default?: boolean;
+  error?: ModelError;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig.ts
new file mode 100644
index 0000000000..6bdcb87dd4
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig.ts
@@ -0,0 +1,18 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+import type { ModelVariantType } from './ModelVariantType';
+
+export type invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__CheckpointConfig = {
+  path: string;
+  description?: string;
+  format: 'checkpoint';
+  default?: boolean;
+  error?: ModelError;
+  vae?: string;
+  config?: string;
+  variant: ModelVariantType;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig.ts
new file mode 100644
index 0000000000..c88e042178
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig.ts
@@ -0,0 +1,17 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+import type { ModelVariantType } from './ModelVariantType';
+
+export type invokeai__backend__model_management__models__stable_diffusion__StableDiffusion1Model__DiffusersConfig = {
+  path: string;
+  description?: string;
+  format: 'diffusers';
+  default?: boolean;
+  error?: ModelError;
+  vae?: string;
+  variant: ModelVariantType;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig.ts
new file mode 100644
index 0000000000..ec2ae4a845
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig.ts
@@ -0,0 +1,21 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+import type { ModelVariantType } from './ModelVariantType';
+import type { SchedulerPredictionType } from './SchedulerPredictionType';
+
+export type invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__CheckpointConfig = {
+  path: string;
+  description?: string;
+  format: 'checkpoint';
+  default?: boolean;
+  error?: ModelError;
+  vae?: string;
+  config?: string;
+  variant: ModelVariantType;
+  prediction_type: SchedulerPredictionType;
+  upcast_attention: boolean;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig.ts
new file mode 100644
index 0000000000..67b897d9d9
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig.ts
@@ -0,0 +1,20 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+import type { ModelVariantType } from './ModelVariantType';
+import type { SchedulerPredictionType } from './SchedulerPredictionType';
+
+export type invokeai__backend__model_management__models__stable_diffusion__StableDiffusion2Model__DiffusersConfig = {
+  path: string;
+  description?: string;
+  format: 'diffusers';
+  default?: boolean;
+  error?: ModelError;
+  vae?: string;
+  variant: ModelVariantType;
+  prediction_type: SchedulerPredictionType;
+  upcast_attention: boolean;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config.ts
new file mode 100644
index 0000000000..f23d5002e3
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config.ts
@@ -0,0 +1,14 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+
+export type invokeai__backend__model_management__models__textual_inversion__TextualInversionModel__Config = {
+  path: string;
+  description?: string;
+  format: null;
+  default?: boolean;
+  error?: ModelError;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__vae__VaeModel__Config.ts b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__vae__VaeModel__Config.ts
new file mode 100644
index 0000000000..d9314a6063
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/invokeai__backend__model_management__models__vae__VaeModel__Config.ts
@@ -0,0 +1,14 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ModelError } from './ModelError';
+
+export type invokeai__backend__model_management__models__vae__VaeModel__Config = {
+  path: string;
+  description?: string;
+  format: ('checkpoint' | 'diffusers');
+  default?: boolean;
+  error?: ModelError;
+};
+
diff --git a/invokeai/frontend/web/src/services/api/services/ModelsService.ts b/invokeai/frontend/web/src/services/api/services/ModelsService.ts
index 3f8ae6bf7b..54580ce204 100644
--- a/invokeai/frontend/web/src/services/api/services/ModelsService.ts
+++ b/invokeai/frontend/web/src/services/api/services/ModelsService.ts
@@ -1,8 +1,10 @@
 /* istanbul ignore file */
 /* tslint:disable */
 /* eslint-disable */
+import type { BaseModelType } from '../models/BaseModelType';
 import type { CreateModelRequest } from '../models/CreateModelRequest';
 import type { ModelsList } from '../models/ModelsList';
+import type { ModelType } from '../models/ModelType';
 
 import type { CancelablePromise } from '../core/CancelablePromise';
 import { OpenAPI } from '../core/OpenAPI';
@@ -16,10 +18,29 @@ export class ModelsService {
    * @returns ModelsList Successful Response
    * @throws ApiError
    */
-  public static listModels(): CancelablePromise<ModelsList> {
+  public static listModels({
+    baseModel,
+    modelType,
+  }: {
+    /**
+     * Base model
+     */
+    baseModel?: BaseModelType,
+    /**
+     * The type of model to get
+     */
+    modelType?: ModelType,
+  }): CancelablePromise<ModelsList> {
     return __request(OpenAPI, {
       method: 'GET',
       url: '/api/v1/models/',
+      query: {
+        'base_model': baseModel,
+        'model_type': modelType,
+      },
+      errors: {
+        422: `Validation Error`,
+      },
     });
   }
 
diff --git a/invokeai/frontend/web/src/services/api/services/SessionsService.ts b/invokeai/frontend/web/src/services/api/services/SessionsService.ts
index d850a1ed38..2e4a83b25f 100644
--- a/invokeai/frontend/web/src/services/api/services/SessionsService.ts
+++ b/invokeai/frontend/web/src/services/api/services/SessionsService.ts
@@ -27,7 +27,6 @@ import type { ImagePasteInvocation } from '../models/ImagePasteInvocation';
 import type { ImageProcessorInvocation } from '../models/ImageProcessorInvocation';
 import type { ImageResizeInvocation } from '../models/ImageResizeInvocation';
 import type { ImageScaleInvocation } from '../models/ImageScaleInvocation';
-import type { ImageToImageInvocation } from '../models/ImageToImageInvocation';
 import type { ImageToLatentsInvocation } from '../models/ImageToLatentsInvocation';
 import type { InfillColorInvocation } from '../models/InfillColorInvocation';
 import type { InfillPatchMatchInvocation } from '../models/InfillPatchMatchInvocation';
@@ -39,6 +38,7 @@ import type { LatentsToLatentsInvocation } from '../models/LatentsToLatentsInvoc
 import type { LineartAnimeImageProcessorInvocation } from '../models/LineartAnimeImageProcessorInvocation';
 import type { LineartImageProcessorInvocation } from '../models/LineartImageProcessorInvocation';
 import type { LoadImageInvocation } from '../models/LoadImageInvocation';
+import type { LoraLoaderInvocation } from '../models/LoraLoaderInvocation';
 import type { MaskFromAlphaInvocation } from '../models/MaskFromAlphaInvocation';
 import type { MediapipeFaceProcessorInvocation } from '../models/MediapipeFaceProcessorInvocation';
 import type { MidasDepthImageProcessorInvocation } from '../models/MidasDepthImageProcessorInvocation';
@@ -58,10 +58,11 @@ import type { RangeOfSizeInvocation } from '../models/RangeOfSizeInvocation';
 import type { ResizeLatentsInvocation } from '../models/ResizeLatentsInvocation';
 import type { RestoreFaceInvocation } from '../models/RestoreFaceInvocation';
 import type { ScaleLatentsInvocation } from '../models/ScaleLatentsInvocation';
+import type { SD1ModelLoaderInvocation } from '../models/SD1ModelLoaderInvocation';
+import type { SD2ModelLoaderInvocation } from '../models/SD2ModelLoaderInvocation';
 import type { ShowImageInvocation } from '../models/ShowImageInvocation';
 import type { StepParamEasingInvocation } from '../models/StepParamEasingInvocation';
 import type { SubtractInvocation } from '../models/SubtractInvocation';
-import type { TextToImageInvocation } from '../models/TextToImageInvocation';
 import type { TextToLatentsInvocation } from '../models/TextToLatentsInvocation';
 import type { UpscaleInvocation } from '../models/UpscaleInvocation';
 import type { ZoeDepthImageProcessorInvocation } from '../models/ZoeDepthImageProcessorInvocation';
@@ -174,7 +175,7 @@ export class SessionsService {
      * The id of the session
      */
     sessionId: string,
-    requestBody: (LoadImageInvocation | ShowImageInvocation | ImageCropInvocation | ImagePasteInvocation | MaskFromAlphaInvocation | ImageMultiplyInvocation | ImageChannelInvocation | ImageConvertInvocation | ImageBlurInvocation | ImageResizeInvocation | ImageScaleInvocation | ImageLerpInvocation | ImageInverseLerpInvocation | ControlNetInvocation | ImageProcessorInvocation | DynamicPromptInvocation | CompelInvocation | AddInvocation | SubtractInvocation | MultiplyInvocation | DivideInvocation | RandomIntInvocation | ParamIntInvocation | ParamFloatInvocation | NoiseInvocation | TextToLatentsInvocation | LatentsToImageInvocation | ResizeLatentsInvocation | ScaleLatentsInvocation | ImageToLatentsInvocation | CvInpaintInvocation | RangeInvocation | RangeOfSizeInvocation | RandomRangeInvocation | FloatLinearRangeInvocation | StepParamEasingInvocation | UpscaleInvocation | RestoreFaceInvocation | TextToImageInvocation | InfillColorInvocation | InfillTileInvocation | InfillPatchMatchInvocation | GraphInvocation | IterateInvocation | CollectInvocation | CannyImageProcessorInvocation | HedImageProcessorInvocation | LineartImageProcessorInvocation | LineartAnimeImageProcessorInvocation | OpenposeImageProcessorInvocation | MidasDepthImageProcessorInvocation | NormalbaeImageProcessorInvocation | MlsdImageProcessorInvocation | PidiImageProcessorInvocation | ContentShuffleImageProcessorInvocation | ZoeDepthImageProcessorInvocation | MediapipeFaceProcessorInvocation | LatentsToLatentsInvocation | ImageToImageInvocation | InpaintInvocation),
+    requestBody: (LoadImageInvocation | ShowImageInvocation | ImageCropInvocation | ImagePasteInvocation | MaskFromAlphaInvocation | ImageMultiplyInvocation | ImageChannelInvocation | ImageConvertInvocation | ImageBlurInvocation | ImageResizeInvocation | ImageScaleInvocation | ImageLerpInvocation | ImageInverseLerpInvocation | ControlNetInvocation | ImageProcessorInvocation | SD1ModelLoaderInvocation | SD2ModelLoaderInvocation | LoraLoaderInvocation | DynamicPromptInvocation | CompelInvocation | AddInvocation | SubtractInvocation | MultiplyInvocation | DivideInvocation | RandomIntInvocation | ParamIntInvocation | ParamFloatInvocation | NoiseInvocation | TextToLatentsInvocation | LatentsToImageInvocation | ResizeLatentsInvocation | ScaleLatentsInvocation | ImageToLatentsInvocation | CvInpaintInvocation | RangeInvocation | RangeOfSizeInvocation | RandomRangeInvocation | FloatLinearRangeInvocation | StepParamEasingInvocation | UpscaleInvocation | RestoreFaceInvocation | InpaintInvocation | InfillColorInvocation | InfillTileInvocation | InfillPatchMatchInvocation | GraphInvocation | IterateInvocation | CollectInvocation | CannyImageProcessorInvocation | HedImageProcessorInvocation | LineartImageProcessorInvocation | LineartAnimeImageProcessorInvocation | OpenposeImageProcessorInvocation | MidasDepthImageProcessorInvocation | NormalbaeImageProcessorInvocation | MlsdImageProcessorInvocation | PidiImageProcessorInvocation | ContentShuffleImageProcessorInvocation | ZoeDepthImageProcessorInvocation | MediapipeFaceProcessorInvocation | LatentsToLatentsInvocation),
   }): CancelablePromise<string> {
     return __request(OpenAPI, {
       method: 'POST',
@@ -211,7 +212,7 @@ export class SessionsService {
      * The path to the node in the graph
      */
     nodePath: string,
-    requestBody: (LoadImageInvocation | ShowImageInvocation | ImageCropInvocation | ImagePasteInvocation | MaskFromAlphaInvocation | ImageMultiplyInvocation | ImageChannelInvocation | ImageConvertInvocation | ImageBlurInvocation | ImageResizeInvocation | ImageScaleInvocation | ImageLerpInvocation | ImageInverseLerpInvocation | ControlNetInvocation | ImageProcessorInvocation | DynamicPromptInvocation | CompelInvocation | AddInvocation | SubtractInvocation | MultiplyInvocation | DivideInvocation | RandomIntInvocation | ParamIntInvocation | ParamFloatInvocation | NoiseInvocation | TextToLatentsInvocation | LatentsToImageInvocation | ResizeLatentsInvocation | ScaleLatentsInvocation | ImageToLatentsInvocation | CvInpaintInvocation | RangeInvocation | RangeOfSizeInvocation | RandomRangeInvocation | FloatLinearRangeInvocation | StepParamEasingInvocation | UpscaleInvocation | RestoreFaceInvocation | TextToImageInvocation | InfillColorInvocation | InfillTileInvocation | InfillPatchMatchInvocation | GraphInvocation | IterateInvocation | CollectInvocation | CannyImageProcessorInvocation | HedImageProcessorInvocation | LineartImageProcessorInvocation | LineartAnimeImageProcessorInvocation | OpenposeImageProcessorInvocation | MidasDepthImageProcessorInvocation | NormalbaeImageProcessorInvocation | MlsdImageProcessorInvocation | PidiImageProcessorInvocation | ContentShuffleImageProcessorInvocation | ZoeDepthImageProcessorInvocation | MediapipeFaceProcessorInvocation | LatentsToLatentsInvocation | ImageToImageInvocation | InpaintInvocation),
+    requestBody: (LoadImageInvocation | ShowImageInvocation | ImageCropInvocation | ImagePasteInvocation | MaskFromAlphaInvocation | ImageMultiplyInvocation | ImageChannelInvocation | ImageConvertInvocation | ImageBlurInvocation | ImageResizeInvocation | ImageScaleInvocation | ImageLerpInvocation | ImageInverseLerpInvocation | ControlNetInvocation | ImageProcessorInvocation | SD1ModelLoaderInvocation | SD2ModelLoaderInvocation | LoraLoaderInvocation | DynamicPromptInvocation | CompelInvocation | AddInvocation | SubtractInvocation | MultiplyInvocation | DivideInvocation | RandomIntInvocation | ParamIntInvocation | ParamFloatInvocation | NoiseInvocation | TextToLatentsInvocation | LatentsToImageInvocation | ResizeLatentsInvocation | ScaleLatentsInvocation | ImageToLatentsInvocation | CvInpaintInvocation | RangeInvocation | RangeOfSizeInvocation | RandomRangeInvocation | FloatLinearRangeInvocation | StepParamEasingInvocation | UpscaleInvocation | RestoreFaceInvocation | InpaintInvocation | InfillColorInvocation | InfillTileInvocation | InfillPatchMatchInvocation | GraphInvocation | IterateInvocation | CollectInvocation | CannyImageProcessorInvocation | HedImageProcessorInvocation | LineartImageProcessorInvocation | LineartAnimeImageProcessorInvocation | OpenposeImageProcessorInvocation | MidasDepthImageProcessorInvocation | NormalbaeImageProcessorInvocation | MlsdImageProcessorInvocation | PidiImageProcessorInvocation | ContentShuffleImageProcessorInvocation | ZoeDepthImageProcessorInvocation | MediapipeFaceProcessorInvocation | LatentsToLatentsInvocation),
   }): CancelablePromise<GraphExecutionState> {
     return __request(OpenAPI, {
       method: 'PUT',