bump rc version number

- This variant, exemplified by "easynegative.safetensors" has a single
'embparam' key containing a Tensor.
- Also refactored code to make it easier to read.
- Handle both pickle and safetensor formats.

docs/features/CONCEPTS.md

@@ -109,21 +109,43 @@ For example, TI files generated by the Hugging Face toolkit share the named
 `learned_embedding.bin`. You can use subdirectories to keep them distinct.
 
 At startup time, InvokeAI will scan the `embeddings` directory and load any TI
-files it finds there. At startup you will see a message similar to this one:
+files it finds there. At startup you will see messages similar to these:
 
 ```bash
->> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
+>> Loading embeddings from /data/lstein/invokeai-2.3/embeddings
+   | Loading v1 embedding file: style-hamunaptra
+   | Loading v4 embedding file: embeddings/learned_embeds-steps-500.bin
+   | Loading v2 embedding file: lfa
+   | Loading v3 embedding file: easynegative
+   | Loading v1 embedding file: rem_rezero
+   | Loading v2 embedding file: midj-strong
+   | Loading v4 embedding file: anime-background-style-v2/learned_embeds.bin
+   | Loading v4 embedding file: kamon-style/learned_embeds.bin
+   ** Notice: kamon-style/learned_embeds.bin was trained on a model with an incompatible token dimension: 768 vs 1024.
+>> Textual inversion triggers: <anime-background-style-v2>, <easynegative>, <lfa>, <midj-strong>, <milo>, Rem3-2600, Style-Hamunaptra
 ```
 
-Note the `*` trigger term. This is a placeholder term that many early TI
-tutorials taught people to use rather than a more descriptive term.
-Unfortunately, if you have multiple TI files that all use this term, only the
-first one loaded will be triggered by use of the term.
+Textual Inversion embeddings trained on version 1.X stable diffusion
+models are incompatible with version 2.X models and vice-versa.
 
-To avoid this problem, you can use the `merge_embeddings.py` script to merge two
-or more TI files together. If it encounters a collision of terms, the script
-will prompt you to select new terms that do not collide. See
-[Textual Inversion](TEXTUAL_INVERSION.md) for details.
+After the embeddings load, InvokeAI will print out a list of all the
+recognized trigger terms. To trigger the term, include it in the
+prompt exactly as written, including angle brackets if any and
+respecting the capitalization.
+
+There are at least four different embedding file formats, and each uses
+a different convention for the trigger terms. In some cases, the
+trigger term is specified in the file contents and may or may not be
+surrounded by angle brackets. In the example above, `Rem3-2600`,
+`Style-Hamunaptra`, and `<midj-strong>` were specified this way and
+there is no easy way to change the term.
+
+In other cases the trigger term is not contained within the embedding
+file. In this case, InvokeAI constructs a trigger term consisting of
+the base name of the file (without the file extension) surrounded by
+angle brackets. In the example above `<easynegative`> is such a file
+(the filename was `easynegative.safetensors`). In such cases, you can
+change the trigger term simply by renaming the file.
 
 ## Further Reading
 

docs/installation/050_INSTALLING_MODELS.md

@@ -11,7 +11,7 @@ The model checkpoint files ('\*.ckpt') are the Stable Diffusion
 captioned images gathered from multiple sources.
 
 Originally there was only a single Stable Diffusion weights file,
-which many people named `model.ckpt`. Now there are dozens or more
+which many people named `model.ckpt`. Now there are hundreds
 that have been fine tuned to provide particulary styles, genres, or
 other features. In addition, there are several new formats that
 improve on the original checkpoint format: a `.safetensors` format
@@ -29,9 +29,10 @@ and performance are being made at a rapid pace. Among other features
 is the ability to download and install a `diffusers` model just by
 providing its HuggingFace repository ID.
 
-While InvokeAI will continue to support `.ckpt` and `.safetensors`
+While InvokeAI will continue to support legacy `.ckpt` and `.safetensors`
 models for the near future, these are deprecated and support will
-likely be withdrawn at some point in the not-too-distant future.
+be withdrawn in version 3.0, after which all legacy models will be
+converted into diffusers at the time they are loaded.
 
 This manual will guide you through installing and configuring model
 weight files and converting legacy `.ckpt` and `.safetensors` files
@@ -89,15 +90,18 @@ aware that CIVITAI hosts many models that generate NSFW content.
 !!! note
 
     InvokeAI 2.3.x does not support directly importing and
-    running Stable Diffusion version 2 checkpoint models. You may instead
-    convert them into `diffusers` models using the conversion methods
-    described below.
+    running Stable Diffusion version 2 checkpoint models. If you
+    try to import them, they will be automatically
+    converted into `diffusers` models on the fly. This adds about 20s
+    to loading time. To avoid this overhead, you are encouraged to
+    use one of the conversion methods described below to convert them
+    permanently.
 
 ## Installation
 
 There are multiple ways to install and manage models:
 
-1. The `invokeai-configure` script which will download and install them for you.
+1. The `invokeai-model-install` script which will download and install them for you.
 
 2. The command-line tool (CLI) has commands that allows you to import, configure and modify
    models files.
@@ -105,14 +109,41 @@ There are multiple ways to install and manage models:
 3. The web interface (WebUI) has a GUI for importing and managing
    models.
 
-### Installation via `invokeai-configure`
+### Installation via `invokeai-model-install`
 
-From the `invoke` launcher, choose option (6) "re-run the configure
-script to download new models." This will launch the same script that
-prompted you to select models at install time. You can use this to add
-models that you skipped the first time around. It is all right to
-specify a model that was previously downloaded; the script will just
-confirm that the files are complete.
+From the `invoke` launcher, choose option (5) "Download and install
+models."  This will launch the same script that prompted you to select
+models at install time. You can use this to add models that you
+skipped the first time around. It is all right to specify a model that
+was previously downloaded; the script will just confirm that the files
+are complete.
+
+This script allows you to load 3d party models. Look for a large text
+entry box labeled "IMPORT LOCAL AND REMOTE MODELS." In this box, you
+can cut and paste one or more of any of the following:
+
+1. A URL that points to a downloadable .ckpt or .safetensors file.
+2. A file path pointing to a .ckpt or .safetensors  file.
+3. A diffusers model repo_id (from HuggingFace) in the format
+   "owner/repo_name".
+4. A directory path pointing to a diffusers model directory.
+5. A directory path pointing to a directory containing a bunch of
+   .ckpt and .safetensors files. All will be imported.
+
+You can enter multiple items into the textbox, each one on a separate
+line. You can paste into the textbox using ctrl-shift-V or by dragging
+and dropping a file/directory from the desktop into the box.
+
+The script also lets you designate a directory that will be scanned
+for new model files each time InvokeAI starts up. These models will be
+added automatically.
+
+Lastly, the script gives you a checkbox option to convert legacy models
+into diffusers, or to run the legacy model directly. If you choose to
+convert, the original .ckpt/.safetensors file will **not** be deleted,
+but a new diffusers directory will be created, using twice your disk
+space. However, the diffusers version will load faster, and will be
+compatible with InvokeAI 3.0.
 
 ### Installation via the CLI
 
@@ -144,19 +175,15 @@ invoke> !import_model https://example.org/sd_models/martians.safetensors
 For this to work, the URL must not be password-protected. Otherwise
 you will receive a 404 error.
 
-When you import a legacy model, the CLI will first ask you what type
-of model this is. You can indicate whether it is a model based on
-Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
-or a 1.x inpainting model. Be careful to indicate the correct model
-type, or it will not load correctly. You can correct the model type
-after the fact using the `!edit_model` command.
-
-The system will then ask you a few other questions about the model,
-including what size image it was trained on (usually 512x512), what
-name and description you wish to use for it, and whether you would
-like to install a custom VAE (variable autoencoder) file for the
-model. For recent models, the answer to the VAE question is usually
-"no," but it won't hurt to answer "yes".
+When you import a legacy model, the CLI will try to figure out what
+type of model it is and select the correct load configuration file.
+However, one thing it can't do is to distinguish between Stable
+Diffusion 2.x models trained on 512x512 vs 768x768 images. In this
+case, the CLI will pop up a menu of choices, asking you to select
+which type of model it is. Please consult the model documentation to
+identify the correct answer, as loading with the wrong configuration
+will lead to black images. You can correct the model type after the
+fact using the `!edit_model` command.
 
 After importing, the model will load. If this is successful, you will
 be asked if you want to keep the model loaded in memory to start
@@ -211,129 +238,6 @@ description for the model, whether to make this the default model that
 is loaded at InvokeAI startup time, and whether to replace its
 VAE. Generally the answer to the latter question is "no".
 
-### Specifying a configuration file for legacy checkpoints
-
-Some checkpoint files come with instructions to use a specific .yaml
-configuration file. For InvokeAI load this file correctly, please put
-the config file in the same directory as the corresponding `.ckpt` or
-`.safetensors` file and make sure the file has the same basename as
-the weights file. Here is an example:
-
-```bash
-wonderful-model-v2.ckpt
-wonderful-model-v2.yaml
-```
-
-Similarly, to use a custom VAE, name the VAE like this:
-
-```bash
-wonderful-model-v2.vae.pt
-```
-
-
-### Converting legacy models into `diffusers`
-
-The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
-models file into `diffusers` and install it.This will enable the model
-to load and run faster without loss of image quality.
-
-The usage is identical to `!import_model`. You may point the command
-to either a downloaded model file on disk, or to a (non-password
-protected) URL:
-
-```bash
-invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
-```
-
-After a successful conversion, the CLI will offer you the option of
-deleting the original `.ckpt` or `.safetensors` file.
-
-### Optimizing a previously-installed model
-
-Lastly, if you have previously installed a `.ckpt` or `.safetensors`
-file and wish to convert it into a `diffusers` model, you can do this
-without re-downloading and converting the original file using the
-`!optimize_model` command. Simply pass the short name of an existing
-installed model:
-
-```bash
-invoke> !optimize_model martians-v1.0
-```
-
-The model will be converted into `diffusers` format and replace the
-previously installed version. You will again be offered the
-opportunity to delete the original `.ckpt` or `.safetensors` file.
-
-### Related CLI Commands
-
-There are a whole series of additional model management commands in
-the CLI that you can read about in [Command-Line
-Interface](../features/CLI.md). These include:
-
-* `!models` - List all installed models
-* `!switch <model name>` - Switch to the indicated model
-* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
-* `!del_model <model name>` - Delete the indicated model
-
-### Manually editing `configs/models.yaml`
-
-
-If you are comfortable with a text editor then you may simply edit `models.yaml`
-directly.
-
-You will need to download the desired `.ckpt/.safetensors` file and
-place it somewhere on your machine's filesystem. Alternatively, for a
-`diffusers` model, record the repo_id or download the whole model
-directory. Then using a **text** editor (e.g. the Windows Notepad
-application), open the file `configs/models.yaml`, and add a new
-stanza that follows this model:
-
-#### A legacy model
-
-A legacy `.ckpt` or `.safetensors` entry will look like this:
-
-```yaml
-arabian-nights-1.0:
-  description: A great fine-tune in Arabian Nights style
-  weights: ./path/to/arabian-nights-1.0.ckpt
-  config: ./configs/stable-diffusion/v1-inference.yaml
-  format: ckpt
-  width: 512
-  height: 512
-  default: false
-```
-
-Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
-
-#### A diffusers model
-
-A stanza for a `diffusers` model will look like this for a HuggingFace
-model with a repository ID:
-
-```yaml
-arabian-nights-1.1:
-  description: An even better fine-tune of the Arabian Nights
-  repo_id: captahab/arabian-nights-1.1
-  format: diffusers
-  default: true
-```
-
-And for a downloaded directory:
-
-```yaml
-arabian-nights-1.1:
-  description: An even better fine-tune of the Arabian Nights
-  path: /path/to/captahab-arabian-nights-1.1
-  format: diffusers
-  default: true
-```
-
-There is additional syntax for indicating an external VAE to use with
-this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
-
-After you save the modified `models.yaml` file relaunch
-`invokeai`. The new model will now be available for your use.
-
 ### Installation via the WebUI
 
 To access the WebUI Model Manager, click on the button that looks like
@@ -413,3 +317,143 @@ And here is what the same argument looks like in `invokeai.init`:
 --no-nsfw_checker
 --autoconvert /home/fred/stable-diffusion-checkpoints
 ```
+
+### Specifying a configuration file for legacy checkpoints
+
+Some checkpoint files come with instructions to use a specific .yaml
+configuration file. For InvokeAI load this file correctly, please put
+the config file in the same directory as the corresponding `.ckpt` or
+`.safetensors` file and make sure the file has the same basename as
+the model file. Here is an example:
+
+```bash
+wonderful-model-v2.ckpt
+wonderful-model-v2.yaml
+```
+
+This is not needed for `diffusers` models, which come with their own
+pre-packaged configuration.
+
+### Specifying a custom VAE file for legacy checkpoints
+
+To associate a custom VAE with a legacy file, place the VAE file in
+the same directory as the corresponding `.ckpt` or
+`.safetensors` file and make sure the file has the same basename as
+the model file. Use the suffix `.vae.pt` for VAE checkpoint files, and
+`.vae.safetensors` for VAE safetensors files. There is no requirement
+that both the model and the VAE follow the same format.
+
+Example:
+
+```bash
+wonderful-model-v2.pt
+wonderful-model-v2.vae.safetensors
+```
+
+### Converting legacy models into `diffusers`
+
+The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
+models file into `diffusers` and install it.This will enable the model
+to load and run faster without loss of image quality.
+
+The usage is identical to `!import_model`. You may point the command
+to either a downloaded model file on disk, or to a (non-password
+protected) URL:
+
+```bash
+invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
+```
+
+After a successful conversion, the CLI will offer you the option of
+deleting the original `.ckpt` or `.safetensors` file.
+
+### Optimizing a previously-installed model
+
+Lastly, if you have previously installed a `.ckpt` or `.safetensors`
+file and wish to convert it into a `diffusers` model, you can do this
+without re-downloading and converting the original file using the
+`!optimize_model` command. Simply pass the short name of an existing
+installed model:
+
+```bash
+invoke> !optimize_model martians-v1.0
+```
+
+The model will be converted into `diffusers` format and replace the
+previously installed version. You will again be offered the
+opportunity to delete the original `.ckpt` or `.safetensors` file.
+
+Alternatively you can use the WebUI's model manager to handle diffusers
+optimization. Select the legacy model you wish to convert, and then
+look for a button labeled "Convert to Diffusers" in the upper right of
+the window.
+
+### Related CLI Commands
+
+There are a whole series of additional model management commands in
+the CLI that you can read about in [Command-Line
+Interface](../features/CLI.md). These include:
+
+* `!models` - List all installed models
+* `!switch <model name>` - Switch to the indicated model
+* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
+* `!del_model <model name>` - Delete the indicated model
+
+### Manually editing `configs/models.yaml`
+
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
+
+You will need to download the desired `.ckpt/.safetensors` file and
+place it somewhere on your machine's filesystem. Alternatively, for a
+`diffusers` model, record the repo_id or download the whole model
+directory. Then using a **text** editor (e.g. the Windows Notepad
+application), open the file `configs/models.yaml`, and add a new
+stanza that follows this model:
+
+#### A legacy model
+
+A legacy `.ckpt` or `.safetensors` entry will look like this:
+
+```yaml
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./path/to/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  format: ckpt
+  width: 512
+  height: 512
+  default: false
+```
+
+Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
+
+#### A diffusers model
+
+A stanza for a `diffusers` model will look like this for a HuggingFace
+model with a repository ID:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  repo_id: captahab/arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+And for a downloaded directory:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  path: /path/to/captahab-arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+There is additional syntax for indicating an external VAE to use with
+this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
+
+After you save the modified `models.yaml` file relaunch
+`invokeai`. The new model will now be available for your use.
+

ldm/invoke/CLI.py

@@ -126,11 +126,13 @@ def main():
             print(f"{e}. Aborting.")
             sys.exit(-1)
 
+    model = opt.model or retrieve_last_used_model()
+
     # creating a Generate object:
     try:
         gen = Generate(
             conf=opt.conf,
-            model=opt.model,
+            model=model,
             sampler_name=opt.sampler_name,
             embedding_path=embedding_path,
             full_precision=opt.full_precision,
@@ -179,6 +181,7 @@ def main():
     # web server loops forever
     if opt.web or opt.gui:
         invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan)
+        save_last_used_model(gen.model_name)
         sys.exit(0)
 
     if not infile:
@@ -499,6 +502,7 @@ def main_loop(gen, opt, completer):
     print(
         f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
     )
+    save_last_used_model(gen.model_name)
 
 
 # TO DO: remove repetitive code and the awkward command.replace() trope
@@ -772,11 +776,11 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             original_config_file = Path(model_info["config"])
             model_name = model_name_or_path
             model_description = model_info["description"]
-            vae = model_info["vae"]
+            vae = model_info.get("vae")
         else:
             print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
-        if vae_repo := ldm.invoke.model_manager.VAE_TO_REPO_ID.get(Path(vae).stem):
+        if vae and (vae_repo := ldm.invoke.model_manager.VAE_TO_REPO_ID.get(Path(vae).stem)):
             vae_repo = dict(repo_id=vae_repo)
         else:
             vae_repo = None
@@ -1287,6 +1291,25 @@ def check_internet() -> bool:
     except:
         return False
 
+
+def retrieve_last_used_model()->str:
+    """
+    Return name of the last model used.
+    """
+    model_file_path = Path(Globals.root,'.last_model')
+    if not model_file_path.exists():
+        return None
+    with open(model_file_path,'r') as f:
+        return f.readline()
+
+def save_last_used_model(model_name:str):
+    """
+    Save name of the last model used.
+    """
+    model_file_path = Path(Globals.root,'.last_model')
+    with open(model_file_path,'w') as f:
+        f.write(model_name)
+
 # This routine performs any patch-ups needed after installation
 def run_patches():
     install_missing_config_files()

ldm/invoke/_version.py

@@ -1,2 +1,2 @@
 
-__version__='2.3.3-rc2'
+__version__='2.3.3-rc5'

ldm/invoke/ckpt_to_diffuser.py

@@ -1264,10 +1264,10 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
                 cache_dir=cache_dir,
             )
             pipe = pipeline_class(
-                vae=vae,
-                text_encoder=text_model,
+                vae=vae.to(precision),
+                text_encoder=text_model.to(precision),
                 tokenizer=tokenizer,
-                unet=unet,
+                unet=unet.to(precision),
                 scheduler=scheduler,
                 safety_checker=None,
                 feature_extractor=None,

ldm/invoke/invokeai_metadata.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+
+import sys
+import json
+from ldm.invoke.pngwriter import retrieve_metadata
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
+        print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out their metadata.")
+        exit(-1)
+
+    filenames = sys.argv[1:]
+    for f in filenames:
+        try:
+            metadata = retrieve_metadata(f)
+            print(f'{f}:\n',json.dumps(metadata['sd-metadata'], indent=4))
+        except FileNotFoundError:
+            sys.stderr.write(f'{f} not found\n')
+            continue
+        except PermissionError:
+            sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
+            continue
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        pass

ldm/invoke/model_manager.py

@@ -172,9 +172,9 @@ class ModelManager(object):
         """
         # if we are converting legacy files automatically, then
         # there are no legacy ckpts!
-        if Globals.ckpt_convert:
-            return False
         info = self.model_info(model_name)
+        if Globals.ckpt_convert or info.format=='diffusers' or self.is_v2_config(info.config):
+            return False
         if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
             return True
         return False
@@ -544,6 +544,8 @@ class ModelManager(object):
         return pipeline, width, height, model_hash
 
     def is_v2_config(self, config: Path) -> bool:
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root, config)
         try:
             mconfig = OmegaConf.load(config)
             return (
@@ -1362,7 +1364,7 @@ class ModelManager(object):
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=global_cache_dir("hug"),
+            cache_dir=global_cache_dir("hub"),
             local_files_only=not Globals.internet_available,
         )
 

ldm/modules/textual_inversion_manager.py

@@ -1,9 +1,9 @@
-import os
 import traceback
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional, Union
 
+import safetensors.torch
 import torch
 from picklescan.scanner import scan_file_path
 from transformers import CLIPTextModel, CLIPTokenizer
@@ -71,21 +71,6 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         if str(ckpt_path).endswith(".DS_Store"):
             return
-
-        try:
-            scan_result = scan_file_path(str(ckpt_path))
-            if scan_result.infected_files == 1:
-                print(
-                    f"\n### Security Issues Found in Model: {scan_result.issues_count}"
-                )
-                print("### For your safety, InvokeAI will not load this embed.")
-                return
-        except Exception:
-            print(
-                f"### {ckpt_path.parents[0].name}/{ckpt_path.name} is damaged or corrupt."
-            )
-            return
-
         embedding_info = self._parse_embedding(str(ckpt_path))
 
         if embedding_info is None:
@@ -96,7 +81,7 @@ class TextualInversionManager(BaseTextualInversionManager):
             != embedding_info["token_dim"]
         ):
             print(
-                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']}."
+                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
 
@@ -309,92 +294,72 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         return token_id
 
-    def _parse_embedding(self, embedding_file: str):
-        file_type = embedding_file.split(".")[-1]
-        if file_type == "pt":
-            return self._parse_embedding_pt(embedding_file)
-        elif file_type == "bin":
-            return self._parse_embedding_bin(embedding_file)
-        else:
-            print(f"** Notice: unrecognized embedding file format: {embedding_file}")
+    def _parse_embedding(self, embedding_file: str)->dict:
+        suffix = Path(embedding_file).suffix
+        try:
+            if suffix in [".pt",".ckpt",".bin"]:
+                scan_result = scan_file_path(embedding_file)
+                if scan_result.infected_files == 1:
+                    print(
+                        f"   ** Security Issues Found in Model: {scan_result.issues_count}"
+                    )
+                    print("   ** For your safety, InvokeAI will not load this embed.")
+                    return
+                ckpt = torch.load(embedding_file,map_location="cpu")
+            else:
+                ckpt = safetensors.torch.load_file(embedding_file)
+        except Exception as e:
+            print(f"   ** Notice: unrecognized embedding file format: {embedding_file}: {e}")
             return None
-
-    def _parse_embedding_pt(self, embedding_file):
-        embedding_ckpt = torch.load(embedding_file, map_location="cpu")
-        embedding_info = {}
-
-        # Check if valid embedding file
-        if "string_to_token" and "string_to_param" in embedding_ckpt:
-            # Catch variants that do not have the expected keys or values.
-            try:
-                embedding_info["name"] = embedding_ckpt["name"] or os.path.basename(
-                    os.path.splitext(embedding_file)[0]
-                )
-
-                # Check num of embeddings and warn user only the first will be used
-                embedding_info["num_of_embeddings"] = len(
-                    embedding_ckpt["string_to_token"]
-                )
-                if embedding_info["num_of_embeddings"] > 1:
-                    print(">> More than 1 embedding found. Will use the first one")
-
-                embedding = list(embedding_ckpt["string_to_param"].values())[0]
-            except (AttributeError, KeyError):
-                return self._handle_broken_pt_variants(embedding_ckpt, embedding_file)
-
-            embedding_info["embedding"] = embedding
-            embedding_info["num_vectors_per_token"] = embedding.size()[0]
-            embedding_info["token_dim"] = embedding.size()[1]
-
-            try:
-                embedding_info["trained_steps"] = embedding_ckpt["step"]
-                embedding_info["trained_model_name"] = embedding_ckpt[
-                    "sd_checkpoint_name"
-                ]
-                embedding_info["trained_model_checksum"] = embedding_ckpt[
-                    "sd_checkpoint"
-                ]
-            except AttributeError:
-                print(">> No Training Details Found. Passing ...")
-
-        # .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
-        # They are actually .bin files
-        elif len(embedding_ckpt.keys()) == 1:
-            embedding_info = self._parse_embedding_bin(embedding_file)
-
+        
+        # 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:
-            print(">> Invalid embedding format")
-            embedding_info = None
+            return self._parse_embedding_v4(ckpt, embedding_file)     # usually a '.bin' file
 
+    def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str):
+        basename = Path(file_path).stem
+        print(f'   | Loading v1 embedding file: {basename}')
+
+        embedding_info = {}
+        embedding_info["name"] = embedding_ckpt["name"]
+                
+        # Check num of embeddings and warn user only the first will be used
+        embedding_info["num_of_embeddings"] = len(
+            embedding_ckpt["string_to_token"]
+        )
+        if embedding_info["num_of_embeddings"] > 1:
+            print("   | More than 1 embedding found. Will use the first one")
+        embedding = list(embedding_ckpt["string_to_param"].values())[0]
+        embedding_info["embedding"] = embedding
+        embedding_info["num_vectors_per_token"] = embedding.size()[0]
+        embedding_info["token_dim"] = embedding.size()[1]
+        embedding_info["trained_steps"] = embedding_ckpt["step"]
+        embedding_info["trained_model_name"] = embedding_ckpt[
+            "sd_checkpoint_name"
+        ]
+        embedding_info["trained_model_checksum"] = embedding_ckpt[
+            "sd_checkpoint"
+        ]
         return embedding_info
 
-    def _parse_embedding_bin(self, embedding_file):
-        embedding_ckpt = torch.load(embedding_file, map_location="cpu")
-        embedding_info = {}
-
-        if list(embedding_ckpt.keys()) == 0:
-            print(">> Invalid concepts file")
-            embedding_info = None
-        else:
-            for token in list(embedding_ckpt.keys()):
-                embedding_info["name"] = (
-                    token
-                    or f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>"
-                )
-                embedding_info["embedding"] = embedding_ckpt[token]
-                embedding_info[
-                    "num_vectors_per_token"
-                ] = 1  # All Concepts seem to default to 1
-                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
-
-        return embedding_info
-
-    def _handle_broken_pt_variants(
-        self, embedding_ckpt: dict, embedding_file: str
+    def _parse_embedding_v2 (
+        self, embedding_ckpt: dict, file_path: str
     ) -> dict:
         """
-        This handles the broken .pt file variants. We only know of one at present.
+        This handles embedding .pt file variant #2.
         """
+        basename = Path(file_path).stem
+        print(f'   | Loading v2 embedding file: {basename}')
         embedding_info = {}
         if isinstance(
             list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
@@ -403,7 +368,7 @@ class TextualInversionManager(BaseTextualInversionManager):
                 embedding_info["name"] = (
                     token
                     if token != "*"
-                    else f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>"
+                    else f"<{basename}>"
                 )
                 embedding_info["embedding"] = embedding_ckpt[
                     "string_to_param"
@@ -413,7 +378,46 @@ class TextualInversionManager(BaseTextualInversionManager):
                 ].shape[0]
                 embedding_info["token_dim"] = embedding_info["embedding"].size()[1]
         else:
-            print(">> Invalid embedding format")
+            print(f"   ** {basename}: Unrecognized embedding format")
             embedding_info = None
 
         return embedding_info
+
+    def _parse_embedding_v3(self, embedding_ckpt: dict, file_path: str):
+        """
+        Parse 'version 3' of the .pt textual inversion embedding files.
+        """
+        basename = Path(file_path).stem
+        print(f'   | Loading v3 embedding file: {basename}')
+        embedding_info = {}
+        embedding_info["name"] = f'<{basename}>'
+        embedding_info["num_of_embeddings"] = 1
+        embedding = embedding_ckpt['emb_params']
+        embedding_info["embedding"] = embedding
+        embedding_info["num_vectors_per_token"] = embedding.size()[0]
+        embedding_info["token_dim"] = embedding.size()[1]
+        return embedding_info
+    
+    def _parse_embedding_v4(self, embedding_ckpt: dict, filepath: str):
+        """
+        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
+        
+        print(f'   | Loading v4 embedding file: {short_path}')
+        embedding_info = {}
+        if list(embedding_ckpt.keys()) == 0:
+            print(f"   ** Invalid embeddings file: {short_path}")
+            embedding_info = None
+        else:
+            for token in list(embedding_ckpt.keys()):
+                embedding_info["name"] = (
+                    token
+                    or f"<{basename}>"
+                )
+                embedding_info["embedding"] = embedding_ckpt[token]
+                embedding_info["num_vectors_per_token"] = 1  # All Concepts seem to default to 1
+                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
+        return embedding_info

pyproject.toml

@@ -122,6 +122,7 @@ requires-python = ">=3.9, <3.11"
 "invokeai-ti" = "ldm.invoke.training.textual_inversion:main"
 "invokeai-update" = "ldm.invoke.config.invokeai_update:main"
 "invokeai-batch" = "ldm.invoke.dynamic_prompts:main"
+"invokeai-metadata" = "ldm.invoke.invokeai_metadata:main"
 
 [project.urls]
 "Bug Reports" = "https://github.com/invoke-ai/InvokeAI/issues"