merged with main

docs/installation/010_INSTALL_AUTOMATED.md

@@ -150,7 +150,7 @@ experimental versions later.
 
     ```cmd
     C:\Documents\Linco> cd InvokeAI-Installer
-    C:\Documents\Linco\invokeAI> install.bat
+    C:\Documents\Linco\invokeAI> .\install.bat
     ```
 
 7.  **Select the location to install InvokeAI**: The script will ask you to choose where to install InvokeAI. Select a
@@ -167,6 +167,11 @@ experimental versions later.
       `/home/YourName/invokeai` on Linux systems, and `/Users/YourName/invokeai`
       on Macintoshes, where "YourName" is your login name.
 
+    -If you have previously installed InvokeAI, you will be asked to
+     confirm whether you want to reinstall into this directory.  You
+     may choose to reinstall, in which case your version will be upgraded,
+     or choose a different directory.
+
     - The script uses tab autocompletion to suggest directory path completions.
       Type part of the path (e.g. "C:\Users") and press ++tab++ repeatedly
       to suggest completions.
@@ -181,11 +186,6 @@ experimental versions later.
     are unsure what GPU you are using, you can ask the installer to
     guess.
 
-    <figure markdown>
-    ![choose-gpu-screenshot](../assets/installer-walkthrough/choose-gpu.png)
-    </figure>
-
-
 9.  **Watch it go!**: Sit back and let the install script work. It will install the third-party
     libraries needed by InvokeAI and the application itself.
 
@@ -197,25 +197,138 @@ experimental versions later.
     minutes and nothing is happening, you can interrupt the script with ^C. You
     may restart it and it will pick up where it left off.
 
-10. **Post-install Configuration**: After installation completes, the installer will launch the
-    configuration script, which will guide you through the first-time
-    process of selecting one or more Stable Diffusion model weights
-    files, downloading and configuring them. We provide a list of
-    popular models that InvokeAI performs well with. However, you can
-    add more weight files later on using the command-line client or
-    the Web UI. See [Installing Models](050_INSTALLING_MODELS.md) for
-    details.
-
     <figure markdown>
-    ![downloading-models-screenshot](../assets/installer-walkthrough/downloading-models.png)
+    ![initial-settings-screenshot](../assets/installer-walkthrough/settings-form.png)
     </figure>
 
-    If you have already downloaded the weights file(s) for another Stable
+10. **Post-install Configuration**: After installation completes, the
-    Diffusion distribution, you may skip this step (by selecting "skip" when
+    installer will launch the configuration form, which will guide you
-    prompted) and configure InvokeAI to use the previously-downloaded files. The
+    through the first-time process of adjusting some of InvokeAI's
-    process for this is described in [Installing Models](050_INSTALLING_MODELS.md).
+    startup settings. To move around this form use ctrl-N for
+    &lt;N&gt;ext and ctrl-P for &lt;P&gt;revious, or use &lt;tab&gt;
+    and shift-&lt;tab&gt; to move forward and back. Once you are in a
+    multi-checkbox field use the up and down cursor keys to select the
+    item you want, and &lt;space&gt; to toggle it on and off.  Within
+    a directory field, pressing &lt;tab&gt; will provide autocomplete
+    options.
 
-11. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
+    Generally the defaults are fine, and you can come back to this screen at
+    any time to tweak your system. Here are the options you can adjust:
+
+    - ***Output directory for images***
+      This is the path to a directory in which InvokeAI will store all its
+      generated images.
+
+    - ***NSFW checker***
+      If checked, InvokeAI will test images for potential sexual content
+      and blur them out if found.
+
+    - ***HuggingFace Access Token***
+      InvokeAI has the ability to download embedded styles and subjects
+      from the HuggingFace Concept Library on-demand. However, some of
+      the concept library files are password protected. To make download
+      smoother, you can set up an account at huggingface.co, obtain an
+      access token, and paste it into this field. Note that you paste
+      to this screen using ctrl-shift-V
+
+    - ***Free GPU memory after each generation***
+        This is useful for low-memory machines and helps minimize the
+	amount of GPU VRAM used by InvokeAI.
+
+    - ***Enable xformers support if available***
+        If the xformers library was successfully installed, this will activate
+	it to reduce memory consumption and increase rendering speed noticeably.
+	Note that xformers has the side effect of generating slightly different
+	images even when presented with the same seed and other settings.
+
+    - ***Force CPU to be used on GPU systems***
+        This will use the (slow) CPU rather than the accelerated GPU. This
+	can be used to generate images on systems that don't have a compatible
+	GPU.
+
+    - ***Precision***
+        This controls whether to use float32 or float16 arithmetic.
+	float16 uses less memory but is also slightly less accurate.
+	Ordinarily the right arithmetic is picked automatically ("auto"),
+	but you may have to use float32 to get images on certain systems
+	and graphics cards. The "autocast" option is deprecated and
+	shouldn't be used unless you are asked to by a member of the team.
+
+    - ***Number of models to cache in CPU memory***
+        This allows you to keep models in memory and switch rapidly among
+	them rather than having them load from disk each time. This slider
+	controls how many models to keep loaded at once. Each
+	model will use 2-4 GB of RAM, so use this cautiously
+
+    - ***Directory containing embedding/textual inversion files***
+        This is the directory in which you can place custom embedding
+	files (.pt or .bin). During startup, this directory will be
+	scanned and InvokeAI will print out the text terms that
+	are available to trigger the embeddings.
+
+    At the bottom of the screen you will see a checkbox for accepting
+    the CreativeML Responsible AI License. You need to accept the license
+    in order to download Stable Diffusion models from the next screen.
+
+    _You can come back to the startup options form_ as many times as you like.
+    From the `invoke.sh` or `invoke.bat` launcher, select option (6) to relaunch
+    this script. On the command line, it is named `invokeai-configure`.
+
+11. **Downloading Models**: After you press `[NEXT]` on the screen, you will be taken
+    to another screen that prompts you to download a series of starter models. The ones
+    we recommend are preselected for you, but you are encouraged to use the checkboxes to
+    pick and choose.
+    You will probably wish to download `autoencoder-840000` for use with models that
+    were trained with an older version of the Stability VAE.
+
+    <figure markdown>
+    ![select-models-screenshot](../assets/installer-walkthrough/installing-models.png)
+    </figure>
+    
+    Below the preselected list of starter models is a large text field which you can use
+    to specify a series of models to import. You can specify models in a variety of formats,
+    each separated by a space or newline. The formats accepted are:
+
+    - The path to a .ckpt or .safetensors file. On most systems, you can drag a file from
+      the file browser to the textfield to automatically paste the path. Be sure to remove
+      extraneous quotation marks and other things that come along for the ride.
+
+    - The path to a directory containing a combination of `.ckpt` and `.safetensors` files.
+      The directory will be scanned from top to bottom (including subfolders) and any
+      file that can be imported will be.
+
+    - A URL pointing to a `.ckpt` or `.safetensors` file. You can cut
+      and paste directly from a web page, or simply drag the link from the web page
+      or navigation bar. (You can also use ctrl-shift-V to paste into this field)
+      The file will be downloaded and installed.
+      
+    - The HuggingFace repository ID (repo_id) for a `diffusers` model. These IDs have
+       the format _author_name/model_name_, as in `andite/anything-v4.0`
+      
+    - The path to a local directory containing a `diffusers`
+      model. These directories always have the file `model_index.json`
+      at their top level.
+
+    _Select a directory for models to import_ You may select a local
+    directory for autoimporting at startup time. If you select this
+    option, the directory you choose will be scanned for new
+    .ckpt/.safetensors files each time InvokeAI starts up, and any new
+    files will be automatically imported and made available for your
+    use.
+
+    _Convert imported models into diffusers_ When legacy checkpoint
+    files are imported, you may select to use them unmodified (the
+    default) or to convert them into `diffusers` models. The latter
+    load much faster and have slightly better rendering performance,
+    but not all checkpoint files can be converted. Note that Stable Diffusion
+    Version 2.X files are **only** supported in `diffusers` format and will
+    be converted regardless.
+     
+     _You can come back to the model install form_ as many times as you like.
+     From the `invoke.sh` or `invoke.bat` launcher, select option (5) to relaunch
+     this script. On the command line, it is named `invokeai-model-install`.
+
+12. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
     for the directory `invokeai` installed in the location you chose at the
     beginning of the install session. Look for a shell script named `invoke.sh`
     (Linux/Mac) or `invoke.bat` (Windows). Launch the script by double-clicking
@@ -348,25 +461,11 @@ version (recommended), follow these steps:
 1. Start the `invoke.sh`/`invoke.bat` launch script from within the
    `invokeai` root directory.
 
-2. Choose menu item (6) "Developer's Console". This will launch a new
+2. Choose menu item (10) "Update InvokeAI".
-   command line.
-
-3. Type the following command:
-
-   ```bash
-   pip install InvokeAI --upgrade
-   ```
-4.  Watch the installation run. Once it is complete, you may exit the
-    command line by typing `exit`, and then start InvokeAI from the
-    launch script as per usual.
-
-
-Alternatively, if you wish to get the most recent unreleased
-development version, perform the same steps to enter the developer's
-console, and then type:
-
-   ```bash
-   pip install https://github.com/invoke-ai/InvokeAI/archive/refs/heads/main.zip
-   ```
 
+3. This will launch a menu that gives you the option of:
 
+   1. Updating to the latest official release;
+   2. Updating to the bleeding-edge development version; or
+   3. Manually entering the tag or branch name of a version of
+      InvokeAI you wish to try out.

installer/templates/invoke.bat.in

@@ -15,8 +15,9 @@ echo 5. download and install models
 echo 6. change InvokeAI startup options
 echo 7. re-run the configure script to fix a broken install
 echo 8. open the developer console
-echo 9. command-line help
+echo 9. update InvokeAI
-set /P restore="Please enter 1-9: [2] "
+echo 10. command-line help
+set /P restore="Please enter 1-10: [2] "
 if not defined restore set restore=2
 IF /I "%restore%" == "1" (
     echo Starting the InvokeAI command-line..
@@ -51,7 +52,10 @@ IF /I "%restore%" == "1" (
     echo *************************
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
-) ELSE IF /I "%restore%" == "8" (
+) ELSE IF /I "%restore%" == "9" (
+   echo Running invokeai-update...
+   python .venv\Scripts\invokeai-update.exe %*
+) ELSE IF /I "%restore%" == "10" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai.exe --help %*
     pause

installer/templates/invoke.sh.in

@@ -34,9 +34,10 @@ if [ "$0" != "bash" ]; then
     echo "6. change InvokeAI startup options"
     echo "7. re-run the configure script to fix a broken install"
     echo "8. open the developer console"
-    echo "9. command-line help "
+    echo "9. update InvokeAI"
+    echo "10. command-line help "
     echo ""
-    read -p "Please enter 1-9: [2] " yn
+    read -p "Please enter 1-10: [2] " yn
     choice=${yn:='2'}
     case $choice in
         1)
@@ -65,11 +66,15 @@ if [ "$0" != "bash" ]; then
             exec invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
 	    ;;
 	8)
-            echo "Developer Console:"
+	    echo "Developer Console:"
             file_name=$(basename "${BASH_SOURCE[0]}")
             bash --init-file "$file_name"
             ;;
         9)
+	    echo "Update:"
+            exec invokeai-update
+            ;;
+        10)
             exec invokeai --help
             ;;
         *)

ldm/generate.py

@@ -970,6 +970,7 @@ class Generate:
 
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
+            print(f'>> Loading embeddings from {self.embedding_path}')
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
                     ti_path = os.path.join(root, name)
@@ -977,7 +978,7 @@ class Generate:
                         ti_path, defer_injecting_tokens=True
                     )
             print(
-                f'>> Textual inversions available: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}'
+                f'>> Textual inversion triggers: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}'
             )
 
         self.model_name = model_name

ldm/invoke/CLI.py

@@ -62,6 +62,7 @@ def main():
     Globals.always_use_cpu = args.always_use_cpu
     Globals.internet_available = args.internet_available and check_internet()
     Globals.disable_xformers = not args.xformers
+    Globals.sequential_guidance = args.sequential_guidance
     Globals.ckpt_convert = args.ckpt_convert
 
     print(f">> Internet connectivity is {Globals.internet_available}")
@@ -672,7 +673,6 @@ def import_model(model_path: str, gen, opt, completer, convert=False) -> str:
     completer.update_models(gen.model_manager.list_models())
     print(f">> {model_name} successfully installed")
 
-
 def _verify_load(model_name: str, gen) -> bool:
     print(">> Verifying that new model loads...")
     current_model = gen.model_name
@@ -705,7 +705,6 @@ def _get_model_name_and_desc(
     )
     return model_name, model_description
 
-
 def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer) -> str:
     model_name_or_path = model_name_or_path.replace("\\", "/")  # windows
     manager = gen.model_manager

ldm/invoke/_version.py

@@ -1 +1 @@
-__version__='2.3.0'
+__version__='2.3.1+a0'

ldm/invoke/args.py

@@ -91,15 +91,15 @@ import pydoc
 import re
 import shlex
 import sys
-import ldm.invoke
-import ldm.invoke.pngwriter
-
-from ldm.invoke.globals import Globals
-from ldm.invoke.prompt_parser import split_weighted_subprompts
 from argparse import Namespace
 from pathlib import Path
 from typing import List
 
+import ldm.invoke
+import ldm.invoke.pngwriter
+from ldm.invoke.globals import Globals
+from ldm.invoke.prompt_parser import split_weighted_subprompts
+
 APP_ID = ldm.invoke.__app_id__
 APP_NAME = ldm.invoke.__app_name__
 APP_VERSION = ldm.invoke.__version__
@@ -489,6 +489,13 @@ class Args(object):
             action='store_true',
             help='Force free gpu memory before final decoding',
         )
+        model_group.add_argument(
+            '--sequential_guidance',
+            dest='sequential_guidance',
+            action='store_true',
+            help="Calculate guidance in serial instead of in parallel, lowering memory requirement "
+                 "at the expense of speed",
+        )
         model_group.add_argument(
             '--xformers',
             action=argparse.BooleanOptionalAction,

ldm/invoke/config/invokeai_configure.py

@@ -743,11 +743,10 @@ def default_embedding_dir() -> Path:
 
 # -------------------------------------
 def write_default_options(program_opts: Namespace, initfile: Path):
-    opt = default_startup_options()
+    opt = default_startup_options(initfile)
     opt.hf_token = HfFolder.get_token()
     write_opts(opt, initfile)
 
-
 # -------------------------------------
 def main():
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")

ldm/invoke/config/invokeai_update.py

@@ -0,0 +1,102 @@
+'''
+Minimalist updater script. Prompts user for the tag or branch to update to and runs
+pip install <path_to_git_source>.
+'''
+
+import platform
+import requests
+import subprocess
+from rich import box, print
+from rich.console import Console, group
+from rich.panel import Panel
+from rich.prompt import Prompt
+from rich.style import Style
+from rich.text import Text
+from rich.live import Live
+from rich.table import Table
+
+from ldm.invoke import __version__
+
+INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
+INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+
+OS = platform.uname().system
+ARCH = platform.uname().machine
+
+ORANGE_ON_DARK_GREY = Style(bgcolor="grey23", color="orange1")
+
+if OS == "Windows":
+    # Windows terminals look better without a background colour
+    console = Console(style=Style(color="grey74"))
+else:
+    console = Console(style=Style(color="grey74", bgcolor="grey23"))
+
+def get_versions()->dict:
+    return requests.get(url=INVOKE_AI_REL).json()
+
+def welcome(versions: dict):
+
+    @group()
+    def text():
+        yield f'InvokeAI Version: [bold yellow]{__version__}'
+        yield ''
+        yield 'This script will update InvokeAI to the latest release, or to a development version of your choice.'
+        yield ''
+        yield '[bold yellow]Options:'
+        yield f'''[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
+[2] Update to the bleeding-edge development version ([italic]main[/italic])
+[3] Manually enter the tag or branch name you wish to update'''
+
+    console.rule()
+    console.print(
+        Panel(
+            title="[bold wheat1]InvokeAI Updater",
+            renderable=text(),
+            box=box.DOUBLE,
+            expand=True,
+            padding=(1, 2),
+            style=ORANGE_ON_DARK_GREY,
+            subtitle=f"[bold grey39]{OS}-{ARCH}",
+        )
+    )
+    # console.rule is used instead of console.line to maintain dark background
+    # on terminals where light background is the default
+    console.rule(characters=" ")
+
+def main():
+    versions = get_versions()
+    welcome(versions)
+
+    tag = None
+    choice = Prompt.ask(Text.from_markup(('[grey74 on grey23]Choice:')),choices=['1','2','3'],default='1')
+
+    if choice=='1':
+        tag = versions[0]['tag_name']
+    elif choice=='2':
+        tag = 'main'
+    elif choice=='3':
+        tag = Prompt.ask('[grey74 on grey23]Enter an InvokeAI tag or branch name')
+
+    console.print(Panel(f':crossed_fingers: Upgrading to [yellow]{tag}[/yellow]', box=box.MINIMAL, style=ORANGE_ON_DARK_GREY))
+
+    cmd = f'pip install {INVOKE_AI_SRC}/{tag}.zip --use-pep517'
+
+    progress = Table.grid(expand=True)
+    progress_panel = Panel(progress, box=box.MINIMAL, style=ORANGE_ON_DARK_GREY)
+
+    with subprocess.Popen(['bash', '-c', cmd], stdout=subprocess.PIPE, stderr=subprocess.PIPE) as proc:
+        progress.add_column()
+        with Live(progress_panel, console=console, vertical_overflow='visible'):
+            while proc.poll() is None:
+                for l in iter(proc.stdout.readline, b''):
+                    progress.add_row(l.decode().strip(), style=ORANGE_ON_DARK_GREY)
+        if proc.returncode == 0:
+            console.rule(f':heavy_check_mark: Upgrade successful')
+        else:
+            console.rule(f':exclamation: [bold red]Upgrade failed[/red bold]')
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        pass

ldm/invoke/globals.py

@@ -13,8 +13,8 @@ the attributes:
 
 import os
 import os.path as osp
-from pathlib import Path
 from argparse import Namespace
+from pathlib import Path
 from typing import Union
 
 Globals = Namespace()
@@ -48,6 +48,9 @@ Globals.internet_available = True
 # Whether to disable xformers
 Globals.disable_xformers = False
 
+# Low-memory tradeoff for guidance calculations.
+Globals.sequential_guidance = False
+
 # whether we are forcing full precision
 Globals.full_precision = False
 

ldm/invoke/training/textual_inversion_training.py

@@ -441,6 +441,7 @@ class TextualInversionDataset(Dataset):
         self.image_paths = [
             os.path.join(self.data_root, file_path)
             for file_path in os.listdir(self.data_root)
+            if os.path.isfile(file_path) and file_path.endswith(('.png','.PNG','.jpg','.JPG','.jpeg','.JPEG','.gif','.GIF'))
         ]
 
         self.num_images = len(self.image_paths)

ldm/models/diffusion/shared_invokeai_diffusion.py

@@ -1,4 +1,3 @@
-import math
 from contextlib import contextmanager
 from dataclasses import dataclass
 from math import ceil
@@ -6,13 +5,20 @@ from typing import Callable, Optional, Union, Any, Dict
 
 import numpy as np
 import torch
-
 from diffusers.models.cross_attention import AttnProcessor
+from typing_extensions import TypeAlias
+
+from ldm.invoke.globals import Globals
 from ldm.models.diffusion.cross_attention_control import Arguments, \
     restore_default_cross_attention, override_cross_attention, Context, get_cross_attention_modules, \
     CrossAttentionType, SwapCrossAttnContext
 from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
 
+ModelForwardCallback: TypeAlias = Union[
+    # x, t, conditioning, Optional[cross-attention kwargs]
+    Callable[[torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str, Any]]], torch.Tensor],
+    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
+]
 
 @dataclass(frozen=True)
 class PostprocessingSettings:
@@ -32,7 +38,7 @@ class InvokeAIDiffuserComponent:
     * Hybrid conditioning (used for inpainting)
     '''
     debug_thresholding = False
-    last_percent_through = 0.0
+    sequential_guidance = False
 
     @dataclass
     class ExtraConditioningInfo:
@@ -45,8 +51,7 @@ class InvokeAIDiffuserComponent:
             return self.cross_attention_control_args is not None
 
 
-    def __init__(self, model, model_forward_callback:
+    def __init__(self, model, model_forward_callback: ModelForwardCallback,
-                    Callable[[torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str,Any]]], torch.Tensor],
                  is_running_diffusers: bool=False,
                  ):
         """
@@ -58,7 +63,7 @@ class InvokeAIDiffuserComponent:
         self.is_running_diffusers = is_running_diffusers
         self.model_forward_callback = model_forward_callback
         self.cross_attention_control_context = None
-        self.last_percent_through = 0.0
+        self.sequential_guidance = Globals.sequential_guidance
 
     @contextmanager
     def custom_attention_context(self,
@@ -146,11 +151,20 @@ class InvokeAIDiffuserComponent:
         wants_hybrid_conditioning = isinstance(conditioning, dict)
 
         if wants_hybrid_conditioning:
-            unconditioned_next_x, conditioned_next_x = self.apply_hybrid_conditioning(x, sigma, unconditioning, conditioning)
+            unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(x, sigma, unconditioning,
+                                                                                       conditioning)
         elif wants_cross_attention_control:
-            unconditioned_next_x, conditioned_next_x = self.apply_cross_attention_controlled_conditioning(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
+            unconditioned_next_x, conditioned_next_x = self._apply_cross_attention_controlled_conditioning(x, sigma,
+                                                                                                           unconditioning,
+                                                                                                           conditioning,
+                                                                                                           cross_attention_control_types_to_do)
+        elif self.sequential_guidance:
+            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning_sequentially(
+                x, sigma, unconditioning, conditioning)
+
         else:
-            unconditioned_next_x, conditioned_next_x = self.apply_standard_conditioning(x, sigma, unconditioning, conditioning)
+            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning(
+                x, sigma, unconditioning, conditioning)
 
         combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
 
@@ -185,7 +199,7 @@ class InvokeAIDiffuserComponent:
 
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
-    def apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
+    def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
         # fast batched path
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
@@ -198,7 +212,17 @@ class InvokeAIDiffuserComponent:
         return unconditioned_next_x, conditioned_next_x
 
 
-    def apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
+    def _apply_standard_conditioning_sequentially(self, x: torch.Tensor, sigma, unconditioning: torch.Tensor, conditioning: torch.Tensor):
+        # low-memory sequential path
+        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
+        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning)
+        if conditioned_next_x.device.type == 'mps':
+            # prevent a result filled with zeros. seems to be a torch bug.
+            conditioned_next_x = conditioned_next_x.clone()
+        return unconditioned_next_x, conditioned_next_x
+
+
+    def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
         assert isinstance(conditioning, dict)
         assert isinstance(unconditioning, dict)
         x_twice = torch.cat([x] * 2)
@@ -216,18 +240,21 @@ class InvokeAIDiffuserComponent:
         return unconditioned_next_x, conditioned_next_x
 
 
-    def apply_cross_attention_controlled_conditioning(self,
+    def _apply_cross_attention_controlled_conditioning(self,
                                                      x: torch.Tensor,
                                                      sigma,
                                                      unconditioning,
                                                      conditioning,
                                                      cross_attention_control_types_to_do):
         if self.is_running_diffusers:
-            return self.apply_cross_attention_controlled_conditioning__diffusers(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
+            return self._apply_cross_attention_controlled_conditioning__diffusers(x, sigma, unconditioning,
+                                                                                  conditioning,
+                                                                                  cross_attention_control_types_to_do)
         else:
-            return self.apply_cross_attention_controlled_conditioning__compvis(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
+            return self._apply_cross_attention_controlled_conditioning__compvis(x, sigma, unconditioning, conditioning,
+                                                                                cross_attention_control_types_to_do)
 
-    def apply_cross_attention_controlled_conditioning__diffusers(self,
+    def _apply_cross_attention_controlled_conditioning__diffusers(self,
                                                                  x: torch.Tensor,
                                                                  sigma,
                                                                  unconditioning,
@@ -250,7 +277,7 @@ class InvokeAIDiffuserComponent:
         return unconditioned_next_x, conditioned_next_x
 
 
-    def apply_cross_attention_controlled_conditioning__compvis(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning__compvis(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
         # print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
         # slower non-batched path (20% slower on mac MPS)
         # We are only interested in using attention maps for conditioned_next_x, but batching them with generation of

ldm/modules/textual_inversion_manager.py

@@ -61,8 +61,13 @@ class TextualInversionManager:
 
     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
+        
         try:
             scan_result = scan_file_path(str(ckpt_path))
             if scan_result.infected_files == 1:
@@ -87,7 +92,7 @@ class TextualInversionManager:
             != embedding_info['token_dim']
         ):
             print(
-                f"** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with a different token dimension. It can't be used with this model."
+                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']}."
             )
             return
 
@@ -333,7 +338,6 @@ class TextualInversionManager:
         # .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
         # They are actually .bin files
         elif len(embedding_ckpt.keys()) == 1:
-            print(">> Detected .bin file masquerading as .pt file")
             embedding_info = self._parse_embedding_bin(embedding_file)
 
         else:
@@ -372,9 +376,6 @@ class TextualInversionManager:
         if isinstance(
             list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
         ):
-            print(
-                ">> Detected .pt file variant 1"
-            )  # example at https://github.com/invoke-ai/InvokeAI/issues/1829
             for token in list(embedding_ckpt["string_to_token"].keys()):
                 embedding_info["name"] = (
                     token
@@ -387,7 +388,7 @@ class TextualInversionManager:
                 embedding_info["num_vectors_per_token"] = embedding_info[
                     "embedding"
                 ].shape[0]
-                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
+                embedding_info["token_dim"] = embedding_info["embedding"].size()[1]
         else:
             print(">> Invalid embedding format")
             embedding_info = None

pyproject.toml

@@ -109,6 +109,7 @@ dependencies = [
 "invokeai-merge" = "ldm.invoke.merge_diffusers:main"   # note name munging
 "invokeai-ti" = "ldm.invoke.training.textual_inversion:main"
 "invokeai-model-install" = "ldm.invoke.config.model_install:main"
+"invokeai-update" = "ldm.invoke.config.invokeai_update:main"
 
 [project.urls]
 "Homepage" = "https://invoke-ai.github.io/InvokeAI/"