diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 34c90f10c5..4985202782 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -119,6 +119,7 @@ jobs:
         run: >
           configure_invokeai.py
           --yes
+          --default_only
           --full-precision  # can't use fp16 weights without a GPU
 
       - name: Run the tests
diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index 45cead0541..94502855da 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -1,18 +1,32 @@
+stable-diffusion-2.1-768:
+   description: Stable Diffusion version 2.1 diffusers model, trained on 768x768 images (5.21 GB)
+   repo_id: stabilityai/stable-diffusion-2-1
+   format: diffusers
+   recommended: True
+stable-diffusion-2.1-base:
+   description: Stable Diffusion version 2.1 diffusers base model, trained on 512x512 images (5.21 GB)
+   repo_id: stabilityai/stable-diffusion-2-1-base
+   format: diffusers
+   recommended: False
 stable-diffusion-1.5:
    description: Stable Diffusion version 1.5 weight file (4.27 GB)
    repo_id: runwayml/stable-diffusion-v1-5
    format: diffusers
    recommended: True
+   default: True
    vae:
      repo_id: stabilityai/sd-vae-ft-mse
-   default: True
-stable-diffusion-2.1:
-   description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-1
+stable-diffusion-1.4:
+   description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
+   repo_id: CompVis/stable-diffusion-v1-4
+   recommended: False
    format: diffusers
-   recommended: True
+   vae:
+      repo_id: stabilityai/sd-vae-ft-mse
+   width: 512
+   height: 512
 inpainting-1.5:
-   description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
+   description: RunwayML SD 1.5 model optimized for inpainting (ckpt version) (4.27 GB)
    repo_id: runwayml/stable-diffusion-inpainting
    config: v1-inpainting-inference.yaml
    file: sd-v1-5-inpainting.ckpt
@@ -23,19 +37,13 @@ inpainting-1.5:
    recommended: True
    width: 512
    height: 512
-stable-diffusion-1.4:
-   description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
-   repo_id: CompVis/stable-diffusion-v1-4
-   recommended: False
-   format: diffusers
-   vae:
-      repo_id: stabilityai/sd-vae-ft-mse
 waifu-diffusion-1.4:
-  description: Waifu diffusion 1.4
+  description: Latest waifu diffusion 1.4 (diffusers version)
   format: diffusers
   repo_id: hakurei/waifu-diffusion
+  recommended: True
 waifu-diffusion-1.3:
-   description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
+   description: Stable Diffusion 1.4 fine tuned on anime-styled images (ckpt version) (4.27 GB)
    repo_id: hakurei/waifu-diffusion-v1-3
    config: v1-inference.yaml
    file: model-epoch09-float32.ckpt
@@ -53,8 +61,8 @@ trinart-2.0:
    recommended: False
    vae:
      repo_id: stabilityai/sd-vae-ft-mse
-trinart_characters-2.0:
-   description: An SD model finetuned with 19.2M anime/manga style images (4.27 GB)
+trinart_characters-2_0:
+   description: An SD model finetuned with 19.2M anime/manga style images (ckpt version) (4.27 GB)
    repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
    config: v1-inference.yaml
    file: derrida_final.ckpt
@@ -65,6 +73,11 @@ trinart_characters-2.0:
    recommended: False
    width: 512
    height: 512
+anything-4.0:
+   description: High-quality, highly detailed anime style images with just a few prompts
+   format: diffusers
+   repo_id: andite/anything-v4.0
+   recommended: False
 papercut-1.0:
    description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
    repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
@@ -72,8 +85,6 @@ papercut-1.0:
    vae:
      repo_id: stabilityai/sd-vae-ft-mse
    recommended: False
-   width: 512
-   height: 512
 voxel_art-1.0:
    description: Stable Diffusion trained on voxel art (use "VoxelArt" in your prompts) (4.27 GB)
    repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model
diff --git a/docs/installation/020_INSTALL_MANUAL.md b/docs/installation/020_INSTALL_MANUAL.md
index c1181db347..ead1eb6536 100644
--- a/docs/installation/020_INSTALL_MANUAL.md
+++ b/docs/installation/020_INSTALL_MANUAL.md
@@ -217,7 +217,7 @@ manager, please follow these steps:
 
     !!! tip
 
-    Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
+        Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
 
 ---
 
diff --git a/environments-and-requirements/requirements-lin-amd.txt b/environments-and-requirements/requirements-lin-amd.txt
index 0aa6092b30..0e707be483 100644
--- a/environments-and-requirements/requirements-lin-amd.txt
+++ b/environments-and-requirements/requirements-lin-amd.txt
@@ -1,6 +1,6 @@
 -r environments-and-requirements/requirements-base.txt
 # Get hardware-appropriate torch/torchvision
---extra-index-url https://download.pytorch.org/whl/rocm5.1.1 --trusted-host https://download.pytorch.org
+--extra-index-url https://download.pytorch.org/whl/rocm5.2 --trusted-host https://download.pytorch.org
 torch>=1.13.1
 torchvision>=0.14.1
 -e .
diff --git a/environments-and-requirements/requirements-lin-cuda.txt b/environments-and-requirements/requirements-lin-cuda.txt
index d7ea4b4542..1f1ebcf947 100644
--- a/environments-and-requirements/requirements-lin-cuda.txt
+++ b/environments-and-requirements/requirements-lin-cuda.txt
@@ -1,4 +1,4 @@
---extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
+--trusted-host https://download.pytorch.org
 -r environments-and-requirements/requirements-base.txt
 torch>=1.13.1
 torchvision>=0.14.1
diff --git a/environments-and-requirements/requirements-win-colab-cuda.txt b/environments-and-requirements/requirements-win-colab-cuda.txt
index 01b25144b6..7fc55c538a 100644
--- a/environments-and-requirements/requirements-win-colab-cuda.txt
+++ b/environments-and-requirements/requirements-win-colab-cuda.txt
@@ -1,6 +1,6 @@
 -r environments-and-requirements/requirements-base.txt
 # Get hardware-appropriate torch/torchvision
---extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
+--extra-index-url https://download.pytorch.org/whl/cu117 --trusted-host https://download.pytorch.org
 torch==1.13.1
 torchvision==0.14.1
 -e .
diff --git a/ldm/generate.py b/ldm/generate.py
index 7670448a93..63eaf79b50 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -29,7 +29,7 @@ from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.conditioning import get_uc_and_c_and_ec
 from ldm.invoke.devices import choose_torch_device, choose_precision
 from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import global_cache_dir
+from ldm.invoke.globals import global_cache_dir, Globals
 from ldm.invoke.image_util import InitImageResizer
 from ldm.invoke.model_manager import ModelManager
 from ldm.invoke.pngwriter import PngWriter
@@ -201,6 +201,7 @@ class Generate:
             self.precision = 'float32'
         if self.precision == 'auto':
             self.precision = choose_precision(self.device)
+        Globals.full_precision = self.precision=='float32'
 
         # model caching system for fast switching
         self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index cfe9a64ed5..6fb0efeb8d 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -613,8 +613,6 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
             description = model_description):
         print('** model failed to import')
         return None
-    if input('Make this the default model? [n] ').startswith(('y','Y')):
-        manager.set_default_model(model_name)
     return model_name
 
 def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
@@ -647,8 +645,6 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
         print('** model failed to import')
         return None
 
-    if input('Make this the default model? [n] ').startswith(('y','Y')):
-        manager.set_model_default(model_name)
     return model_name
 
 def _verify_load(model_name:str, gen)->bool:
@@ -726,6 +722,9 @@ def del_config(model_name:str, gen, opt, completer):
     if model_name == current_model:
         print("** Can't delete active model. !switch to another model first. **")
         return
+    if model_name not in gen.model_manager.config:
+        print(f"** Unknown model {model_name}")
+        return
     gen.model_manager.del_model(model_name)
     gen.model_manager.commit(opt.conf)
     print(f'** {model_name} deleted')
diff --git a/ldm/invoke/ckpt_generator/base.py b/ldm/invoke/ckpt_generator/base.py
index c73bb50c9b..c84550a6e3 100644
--- a/ldm/invoke/ckpt_generator/base.py
+++ b/ldm/invoke/ckpt_generator/base.py
@@ -335,4 +335,5 @@ class CkptGenerator():
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath,'PNG')
 
-
+    def torch_dtype(self)->torch.dtype:
+        return torch.float16 if self.precision == 'float16' else torch.float32
diff --git a/ldm/invoke/ckpt_generator/txt2img.py b/ldm/invoke/ckpt_generator/txt2img.py
index 48b83be2ed..825b8583b9 100644
--- a/ldm/invoke/ckpt_generator/txt2img.py
+++ b/ldm/invoke/ckpt_generator/txt2img.py
@@ -72,16 +72,18 @@ class CkptTxt2Img(CkptGenerator):
         device         = self.model.device
         if self.use_mps_noise or device.type == 'mps':
             x = torch.randn([1,
-                                self.latent_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device='cpu').to(device)
+                             self.latent_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device='cpu').to(device)
         else:
             x = torch.randn([1,
-                                self.latent_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device=device)
+                             self.latent_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device=device)
         if self.perlin > 0.0:
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
         return x
diff --git a/ldm/invoke/devices.py b/ldm/invoke/devices.py
index 0fc749c4a4..94ddb74b24 100644
--- a/ldm/invoke/devices.py
+++ b/ldm/invoke/devices.py
@@ -21,10 +21,19 @@ def choose_precision(device) -> str:
             return 'float16'
     return 'float32'
 
+def torch_dtype(device) -> torch.dtype:
+    if Globals.full_precision:
+        return torch.float32
+    if choose_precision(device) == 'float16':
+        return torch.float16
+    else:
+        return torch.float32
+
 def choose_autocast(precision):
     '''Returns an autocast context or nullcontext for the given precision string'''
     # float16 currently requires autocast to avoid errors like:
     # 'expected scalar type Half but found Float'
+    print(f'DEBUG: choose_autocast() called')
     if precision == 'autocast' or precision == 'float16':
         return autocast
     return nullcontext
diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index 68c5ccdeff..3fd34765c6 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -8,6 +8,7 @@ import os
 import os.path as osp
 import random
 import traceback
+from contextlib import nullcontext
 
 import cv2
 import numpy as np
@@ -18,8 +19,6 @@ from einops import rearrange
 from pytorch_lightning import seed_everything
 from tqdm import trange
 
-from ldm.invoke.devices import choose_autocast
-from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
 from ldm.models.diffusion.ddpm import DiffusionWrapper
 from ldm.util import rand_perlin_2d
 
@@ -64,7 +63,7 @@ class Generator:
                  image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
                  safety_checker:dict=None,
                  **kwargs):
-        scope = choose_autocast(self.precision)
+        scope = nullcontext
         self.safety_checker = safety_checker
         attention_maps_images = []
         attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
@@ -236,7 +235,8 @@ class Generator:
 
     def get_perlin_noise(self,width,height):
         fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
-        return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
+        noise = torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
+        return noise
 
     def new_seed(self):
         self.seed = random.randrange(0, np.iinfo(np.uint32).max)
@@ -341,3 +341,6 @@ class Generator:
         image.save(filepath,'PNG')
 
 
+    def torch_dtype(self)->torch.dtype:
+        return torch.float16 if self.precision == 'float16' else torch.float32
+
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index b4a0703f18..5e62abf9df 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -391,7 +391,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, conditioning_data,
-                                    i, additional_guidance=additional_guidance)
+                                    step_index=i,
+                                    total_step_count=len(timesteps),
+                                    additional_guidance=additional_guidance)
             latents = step_output.prev_sample
             predicted_original = getattr(step_output, 'pred_original_sample', None)
 
@@ -410,7 +412,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor,
              conditioning_data: ConditioningData,
-             step_index:int | None = None, additional_guidance: List[Callable] = None):
+             step_index:int, total_step_count:int,
+             additional_guidance: List[Callable] = None):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
 
@@ -427,6 +430,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
             conditioning_data.guidance_scale,
             step_index=step_index,
+            total_step_count=total_step_count,
             threshold=conditioning_data.threshold
         )
 
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 174c1e469d..77b16a734e 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -36,10 +36,9 @@ class Txt2Img(Generator):
                 threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
-
         def make_image(x_T) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
-                latents=torch.zeros_like(x_T),
+                latents=torch.zeros_like(x_T,dtype=self.torch_dtype()),
                 noise=x_T,
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
@@ -59,16 +58,18 @@ class Txt2Img(Generator):
         input_channels = min(self.latent_channels, 4)
         if self.use_mps_noise or device.type == 'mps':
             x = torch.randn([1,
-                                input_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device='cpu').to(device)
+                             input_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device='cpu').to(device)
         else:
             x = torch.randn([1,
-                                input_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device=device)
+                             input_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device=device)
         if self.perlin > 0.0:
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
         return x
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index e356f719c4..1dba0cfafb 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -90,9 +90,9 @@ class Txt2Img2Img(Generator):
     def get_noise_like(self, like: torch.Tensor):
         device = like.device
         if device.type == 'mps':
-            x = torch.randn_like(like, device='cpu').to(device)
+            x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
         else:
-            x = torch.randn_like(like, device=device)
+            x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
         if self.perlin > 0.0:
             shape = like.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
@@ -117,10 +117,12 @@ class Txt2Img2Img(Generator):
                                 self.latent_channels,
                                 scaled_height // self.downsampling_factor,
                                 scaled_width  // self.downsampling_factor],
-                                device='cpu').to(device)
+                               dtype=self.torch_dtype(),
+                               device='cpu').to(device)
         else:
             return torch.randn([1,
                                 self.latent_channels,
                                 scaled_height // self.downsampling_factor,
                                 scaled_width  // self.downsampling_factor],
-                                device=device)
+                               dtype=self.torch_dtype(),
+                               device=device)
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index c67dbac145..897bf5e204 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -43,6 +43,9 @@ Globals.always_use_cpu = False
 # The CLI will test connectivity at startup time.
 Globals.internet_available = True
 
+# whether we are forcing full precision
+Globals.full_precision = False
+
 def global_config_dir()->Path:
     return Path(Globals.root, Globals.config_dir)
 
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 880d75476f..6dce95b415 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -230,6 +230,9 @@ class ModelManager(object):
         Delete the named model.
         '''
         omega = self.config
+        if model_name not in omega:
+            print(f'** Unknown model {model_name}')
+            return
         del omega[model_name]
         if model_name in self.stack:
             self.stack.remove(model_name)
@@ -253,9 +256,8 @@ class ModelManager(object):
 
         assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
 
-        if model_name not in omega:
-            omega[model_name] = dict()
-        OmegaConf.update(omega,model_name,model_attributes,merge=False)
+        omega[model_name] = model_attributes
+        
         if 'weights' in omega[model_name]:
             omega[model_name]['weights'].replace('\\','/')
 
@@ -349,7 +351,7 @@ class ModelManager(object):
 
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
-            model.to(torch.float16)
+            model = model.to(torch.float16)
         else:
             print('   | Using more accurate float32 precision')
 
@@ -753,16 +755,20 @@ class ModelManager(object):
 
         print('** Legacy version <= 2.2.5 model directory layout detected. Reorganizing.')
         print('** This is a quick one-time operation.')
-        from shutil import move
+        from shutil import move, rmtree
         
         # transformer files get moved into the hub directory
         hub = models_dir / 'hub'
         os.makedirs(hub, exist_ok=True)
         for model in legacy_locations:
-            source = models_dir /model
+            source = models_dir / model
+            dest = hub / model.stem
+            print(f'** {source} => {dest}')
             if source.exists():
-                print(f'DEBUG: Moving {models_dir / model} into hub')
-                move(models_dir / model, hub)
+                if dest.exists():
+                    rmtree(source)
+                else:
+                    move(source, dest)
 
         # anything else gets moved into the diffusers directory
         diffusers = models_dir / 'diffusers'
@@ -773,7 +779,12 @@ class ModelManager(object):
                 if full_path.is_relative_to(hub) or full_path.is_relative_to(diffusers):
                     continue
                 if Path(dir).match('models--*--*'):
-                    move(full_path,diffusers)
+                    dest = diffusers / dir
+                    print(f'** {full_path} => {dest}')
+                    if dest.exists():
+                        rmtree(full_path)
+                    else:
+                        move(full_path,dest)
 
         # now clean up by removing any empty directories
         empty = [root for root, dirs, files, in os.walk(models_dir) if not len(dirs) and not len(files)]
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index 7415f1435b..03d5a5bcec 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -7,6 +7,7 @@ import torch
 import diffusers
 from torch import nn
 from diffusers.models.unet_2d_condition import UNet2DConditionModel
+from ldm.invoke.devices import torch_dtype
 
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
@@ -383,7 +384,7 @@ def inject_attention_function(unet, context: Context):
                 remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
                 this_attention_slice = suggested_attention_slice
 
-                mask = context.cross_attention_mask
+                mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
                 saved_mask = mask
                 this_mask = 1 - mask
                 attention_slice = remapped_saved_attention_slice * saved_mask + \
diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index 0630546394..3be6b10170 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -89,6 +89,7 @@ class InvokeAIDiffuserComponent:
                                 conditioning: Union[torch.Tensor,dict],
                                 unconditional_guidance_scale: float,
                                 step_index: Optional[int]=None,
+                                total_step_count: Optional[int]=None,
                                 threshold: Optional[ThresholdSettings]=None,
                           ):
         """
@@ -106,7 +107,15 @@ class InvokeAIDiffuserComponent:
         cross_attention_control_types_to_do = []
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
-            percent_through = self.estimate_percent_through(step_index, sigma)
+            if step_index is not None and total_step_count is not None:
+                # 🧨diffusers codepath
+                percent_through = step_index / total_step_count  # will never reach 1.0 - this is deliberate
+            else:
+                # legacy compvis codepath
+                # TODO remove when compvis codepath support is dropped
+                if step_index is None and sigma is None:
+                    raise ValueError(f"Either step_index or sigma is required when doing cross attention control, but both are None.")
+                percent_through = self.estimate_percent_through(step_index, sigma)
             cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
 
         wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
diff --git a/ldm/modules/prompt_to_embeddings_converter.py b/ldm/modules/prompt_to_embeddings_converter.py
index b52577c83c..ab989e4892 100644
--- a/ldm/modules/prompt_to_embeddings_converter.py
+++ b/ldm/modules/prompt_to_embeddings_converter.py
@@ -4,7 +4,7 @@ import torch
 from transformers import CLIPTokenizer, CLIPTextModel
 
 from ldm.modules.textual_inversion_manager import TextualInversionManager
-
+from ldm.invoke.devices import torch_dtype
 
 class WeightedPromptFragmentsToEmbeddingsConverter():
 
@@ -207,7 +207,7 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
         per_token_weights += [1.0] * pad_length
 
         all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)
-        per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32, device=device)
+        per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch_dtype(self.text_encoder.device), device=device)
         #print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
         return all_token_ids_tensor, per_token_weights_tensor
 
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index 471a8ee07b..f7ced79a52 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -111,7 +111,6 @@ class TextualInversionManager():
         if ti.trigger_token_id is not None:
             raise ValueError(f"Tokens already injected for textual inversion with trigger '{ti.trigger_string}'")
 
-        print(f'DEBUG: Injecting token {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:
diff --git a/ldm/util.py b/ldm/util.py
index 282a56c3e5..7d44dcd266 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -8,6 +8,7 @@ from threading import Thread
 from urllib import request
 from tqdm import tqdm
 from pathlib import Path
+from ldm.invoke.devices import torch_dtype
 
 import numpy as np
 import torch
@@ -235,7 +236,8 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
     n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]).to(device)
     n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
     t = fade(grid[:shape[0], :shape[1]])
-    return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
+    noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
+    return noise.to(dtype=torch_dtype(device))
 
 def ask_user(question: str, answers: list):
     from itertools import chain, repeat
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 22ade19cdb..9d17a73317 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -197,6 +197,14 @@ def recommended_datasets()->dict:
             datasets[ds]=True
     return datasets
 
+#---------------------------------------------
+def default_dataset()->dict:
+    datasets = dict()
+    for ds in Datasets.keys():
+        if Datasets[ds].get('default',False):
+            datasets[ds]=True
+    return datasets
+
 #---------------------------------------------
 def all_datasets()->dict:
     datasets = dict()
@@ -646,7 +654,7 @@ def download_weights(opt:dict) -> Union[str, None]:
     precision = 'float32' if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
 
     if opt.yes_to_all:
-        models = recommended_datasets()
+        models = default_dataset() if opt.default_only else recommended_datasets()
         access_token = authenticate(opt.yes_to_all)
         if len(models)>0:
             successfully_downloaded = download_weight_datasets(models, access_token, precision=precision)
@@ -808,6 +816,9 @@ def main():
                         dest='yes_to_all',
                         action='store_true',
                         help='answer "yes" to all prompts')
+    parser.add_argument('--default_only',
+                        action='store_true',
+                        help='when --yes specified, only install the default model')
     parser.add_argument('--config_file',
                         '-c',
                         dest='config_file',