diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py
index 83220d89efa..fc3aca4a195 100644
--- a/invokeai/app/invocations/generate.py
+++ b/invokeai/app/invocations/generate.py
@@ -21,7 +21,8 @@
 import re
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from .model import UNetField, ClipField, VaeField
+from .model import UNetField, VaeField
+from .compel import ConditioningField
 from contextlib import contextmanager, ExitStack, ContextDecorator
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
@@ -63,19 +64,15 @@ class InpaintInvocation(BaseInvocation):
 
     type: Literal["inpaint"] = "inpaint"
 
-    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")
     unet: UNetField = Field(default=None, description="UNet model")
-    clip: ClipField = Field(default=None, description="Clip model")
     vae: VaeField = Field(default=None, description="Vae model")
 
     # Inputs
@@ -151,64 +148,34 @@ def dispatch_progress(
             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):
-        with ExitStack() as stack:
-            unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
-            tokenizer_info = context.services.model_manager.get_model(**self.clip.tokenizer.dict())
-            text_encoder_info = context.services.model_manager.get_model(**self.clip.text_encoder.dict())
-            vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
-
-            #unet = stack.enter_context(unet_info)
-            #tokenizer = stack.enter_context(tokenizer_info)
-            #text_encoder = stack.enter_context(text_encoder_info)
-            #vae = stack.enter_context(vae_info)
-            with vae_info as vae:
-                device = vae.device
-                dtype = vae.dtype
-
-            # not load models to gpu as it should be handled by pipeline
-            unet = unet_info.context.model
-            tokenizer = tokenizer_info.context.model
-            text_encoder = text_encoder_info.context.model
-            vae = vae_info.context.model
-
-            scheduler = get_scheduler(
-                context=context,
-                scheduler_info=self.unet.scheduler,
-                scheduler_name=self.scheduler,
-            )
+    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]
-            ti_list = []
-            for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
-                name = trigger[1:-1]
-                try:
-                    ti_list.append(
-                        stack.enter_context(
-                            context.services.model_manager.get_model(
-                                model_name=name,
-                                base_model=self.clip.text_encoder.base_model,
-                                model_type=ModelType.TextualInversion,
-                            )
-                        )
-                    )
-                except Exception:
-                    #print(e)
-                    #import traceback
-                    #print(traceback.format_exc())
-                    print(f"Warn: trigger: \"{trigger}\" not found")
-
-
-            with ModelPatcher.apply_lora_unet(unet, loras),\
-                 ModelPatcher.apply_lora_text_encoder(text_encoder, loras),\
-                 ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (ti_tokenizer, ti_manager):
+
+            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(
-                    # TODO: ti_manager
                     vae=vae,
-                    text_encoder=text_encoder,
-                    tokenizer=ti_tokenizer,
+                    text_encoder=None,
+                    tokenizer=None,
                     unet=unet,
                     scheduler=scheduler,
                     safety_checker=None,
@@ -242,14 +209,22 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
         )
         source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        with self.load_model_old_way(context) as model:
+        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(
-                prompt=self.prompt,
+                conditioning=conditioning,
+                scheduler=scheduler,
                 init_image=image,
                 mask_image=mask,
                 step_callback=partial(self.dispatch_progress, context, source_node_id),
                 **self.dict(
-                    exclude={"prompt", "image", "mask"}
+                    exclude={"positive_conditioning", "negative_conditioning", "scheduler", "image", "mask"}
                 ),  # Shorthand for passing all of the parameters above manually
             )
 
diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py
index fb293ab5b2d..a379cf63509 100644
--- a/invokeai/backend/generator/base.py
+++ b/invokeai/backend/generator/base.py
@@ -29,7 +29,6 @@
 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 @@ def __init__(self,
         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 @@ def generate(self,
         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 @@ def generate(self,
 
             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 @@ def schedulers(self)->List[str]:
     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]:
         '''
@@ -281,7 +263,7 @@ def __init__(self, model: DiffusionPipeline, precision: str, **kwargs):
         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 +274,7 @@ def __init__(self, model: DiffusionPipeline, precision: str, **kwargs):
         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 +290,6 @@ def set_variation(self, seed, variation_amount, with_variations):
 
     def generate(
         self,
-        prompt,
         width,
         height,
         sampler,
@@ -333,7 +314,6 @@ def generate(
             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 6eae5732b03..00000000000
--- 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 2c62bec4d6b..1cfbeb66c0f 100644
--- a/invokeai/backend/generator/img2img.py
+++ b/invokeai/backend/generator/img2img.py
@@ -22,7 +22,6 @@ def __init__(self, model, precision):
 
     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 a7fec83eb7d..eaf40471095 100644
--- a/invokeai/backend/generator/inpaint.py
+++ b/invokeai/backend/generator/inpaint.py
@@ -161,9 +161,7 @@ def seam_paint(
         im: Image.Image,
         seam_size: int,
         seam_blur: int,
-        prompt,
         seed,
-        sampler,
         steps,
         cfg_scale,
         ddim_eta,
@@ -177,8 +175,6 @@ def seam_paint(
         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 @@ def seam_paint(
     @torch.no_grad()
     def get_make_image(
         self,
-        prompt,
-        sampler,
         steps,
         cfg_scale,
         ddim_eta,
@@ -306,7 +300,6 @@ def get_make_image(
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
 
         # todo: support cross-attention control
         uc, c, _ = conditioning
@@ -345,9 +338,7 @@ def make_image(x_T: torch.Tensor, seed: int):
                     result,
                     seam_size,
                     seam_blur,
-                    prompt,
                     seed,
-                    sampler,
                     seam_steps,
                     cfg_scale,
                     ddim_eta,
@@ -360,8 +351,6 @@ def make_image(x_T: torch.Tensor, seed: int):
 
                 # 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 9ea19bd896c..00000000000
--- 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 1257a44fb15..00000000000
--- 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/model_management/lora.py b/invokeai/backend/model_management/lora.py
index 46638878aaf..c351a76590f 100644
--- a/invokeai/backend/model_management/lora.py
+++ b/invokeai/backend/model_management/lora.py
@@ -556,8 +556,8 @@ def apply_ti(
         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 @@ def from_checkpoint(
 
 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 e8c96ff31e0..66847f53ebd 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 b52206dd944..00000000000
--- 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 d0703427941..00000000000
--- 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 55333d35898..ff47bd5f653 100644
--- a/invokeai/backend/stable_diffusion/__init__.py
+++ b/invokeai/backend/stable_diffusion/__init__.py
@@ -10,4 +10,3 @@
 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/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index 2922238af91..798f398ed63 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -16,7 +16,6 @@
 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 @@
     PostprocessingSettings,
 )
 from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
-from .textual_inversion_manager import TextualInversionManager
 
 @dataclass
 class PipelineIntermediateState:
@@ -344,18 +342,6 @@ def __init__(
         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._model_group = FullyLoadedModelGroup(execution_device or self.unet.device)
         self._model_group.install(*self._submodels)
@@ -405,50 +391,6 @@ def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
                 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:
@@ -992,25 +934,6 @@ def run_safety_checker(self, image, device=None, dtype=None):
             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/textual_inversion_manager.py b/invokeai/backend/stable_diffusion/textual_inversion_manager.py
deleted file mode 100644
index 9476c12dc51..00000000000
--- 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