change workflow to deploy from v2.3 branch

.github/workflows/mkdocs-material.yml

@@ -41,7 +41,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/main' }}
+        if: ${{ github.ref == 'refs/heads/v2.3' }}
         run: |
           python -m \
             mkdocs gh-deploy \

docs/features/LORAS.md

@@ -41,16 +41,6 @@ Windows systems). If the `loras` folder does not already exist, just
 create it. The vast majority of LoRA models use the Kohya file format,
 which is a type of `.safetensors` file.
 
-!!! warning "LoRA Naming Restrictions"
-
-    InvokeAI will only recognize LoRA files that contain the
-    characters a-z, A-Z, 0-9 and the underscore character
-    _. Other characters, including the hyphen, will cause the
-    LoRA file not to load. These naming restrictions may be
-    relaxed in the future, but for now you will need to rename
-    files that contain hyphens, commas, brackets, and other
-    non-word characters.
-
 You may change where InvokeAI looks for the `loras` folder by passing the
 `--lora_directory` option to the `invoke.sh`/`invoke.bat` launcher, or
 by placing the option in `invokeai.init`. For example:

docs/features/index.md

@@ -33,11 +33,6 @@ title: Overview
         Restore mangled faces and make images larger with upscaling. Also see
         the [Embiggen Upscaling Guide](EMBIGGEN.md).
 
-    -   The [Using LoRA Models](LORAS.md)
-
-        Add custom subjects and styles using HuggingFace's repository of
-        embeddings.
-	
     -   The [Concepts Library](CONCEPTS.md)
 
         Add custom subjects and styles using HuggingFace's repository of

docs/installation/deprecated_documentation/INSTALL_LINUX.md

@@ -79,7 +79,7 @@ title: Manual Installation, Linux
         and obtaining an access token for downloading. It will then download and
         install the weights files for you.
 
-        Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing
+        Please look [here](../INSTALL_MANUAL.md) for a manual process for doing
         the same thing.
 
 7.  Start generating images!

docs/installation/deprecated_documentation/INSTALL_WINDOWS.md

@@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan
           obtaining an access token for downloading. It will then download and install the
           weights files for you.
 
-          Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing the
+          Please look [here](../INSTALL_MANUAL.md) for a manual process for doing the
           same thing.
 
 8. Start generating images!

docs/requirements-mkdocs.txt

@@ -1,5 +0,0 @@
-mkdocs
-mkdocs-material>=8, <9
-mkdocs-git-revision-date-localized-plugin
-mkdocs-redirects==1.2.0
-

installer/lib/installer.py

@@ -243,15 +243,16 @@ class InvokeAiInstance:
 
         # Note that we're installing pinned versions of torch and
         # torchvision here, which *should* correspond to what is
-        # in pyproject.toml.
+        # in pyproject.toml. This is to prevent torch 2.0 from
+        # being installed and immediately uninstalled and replaced with 1.13
         pip = local[self.pip]
 
         (
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch~=2.0.0",
-                "torchvision>=0.14.1",
+                "torch~=1.13.1",
+                "torchvision~=0.14.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

invokeai/backend/invoke_ai_web_server.py

@@ -25,7 +25,7 @@ from invokeai.backend.modules.parameters import parameters_to_command
 import invokeai.frontend.dist as frontend
 from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.concepts_lib import get_hf_concepts_lib
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.conditioning import (
     get_tokens_for_prompt_object,
     get_prompt_structure,
@@ -538,7 +538,7 @@ class InvokeAIWebServer:
             try:
                 local_triggers = self.generate.model.textual_inversion_manager.get_all_trigger_strings()
                 locals = [{'name': x} for x in sorted(local_triggers, key=str.casefold)]
-                concepts = get_hf_concepts_lib().list_concepts(minimum_likes=5)
+                concepts = HuggingFaceConceptsLibrary().list_concepts(minimum_likes=5)
                 concepts = [{'name': f'<{x}>'} for x in sorted(concepts, key=str.casefold) if f'<{x}>' not in local_triggers]
                 socketio.emit("foundTextualInversionTriggers", {'local_triggers': locals, 'huggingface_concepts': concepts})
             except Exception as e:

ldm/generate.py

@@ -13,16 +13,11 @@ import time
 import traceback
 from typing import List
 
-import warnings
-with warnings.catch_warnings():
-    warnings.filterwarnings("ignore", category=UserWarning)
-    import torch
-    
 import cv2
 import diffusers
 import numpy as np
 import skimage
-
+import torch
 import transformers
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.utils.import_utils import is_xformers_available
@@ -984,15 +979,13 @@ class Generate:
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path and not model_data.get("ti_embeddings_loaded"):
             print(f'>> Loading embeddings from {self.embedding_path}')
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=UserWarning)
-                for root, _, files in os.walk(self.embedding_path):
-                    for name in files:
-                        ti_path = os.path.join(root, name)
-                        self.model.textual_inversion_manager.load_textual_inversion(
-                            ti_path, defer_injecting_tokens=True
-                        )
-                model_data["ti_embeddings_loaded"] = True
+            for root, _, files in os.walk(self.embedding_path):
+                for name in files:
+                    ti_path = os.path.join(root, name)
+                    self.model.textual_inversion_manager.load_textual_inversion(
+                        ti_path, defer_injecting_tokens=True
+                    )
+            model_data["ti_embeddings_loaded"] = True
         print(
             f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}'
         )

ldm/invoke/CLI.py

@@ -9,6 +9,7 @@ from pathlib import Path
 from typing import Union
 
 import click
+
 from compel import PromptParser
 
 if sys.platform == "darwin":

ldm/invoke/_version.py

@@ -1,2 +1 @@
-__version__='2.3.5.post1'
-
+__version__='2.3.5'

ldm/invoke/concepts_lib.py

@@ -12,14 +12,6 @@ from urllib import request, error as ul_error
 from huggingface_hub import HfFolder, hf_hub_url, ModelSearchArguments, ModelFilter, HfApi
 from ldm.invoke.globals import Globals
 
-singleton = None
-
-def get_hf_concepts_lib():
-    global singleton
-    if singleton is None:
-        singleton = HuggingFaceConceptsLibrary()
-    return singleton
-
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         '''

ldm/invoke/model_manager.py

@@ -433,7 +433,7 @@ class ModelManager(object):
                     **fp_args,
                 )
             except OSError as e:
-                if 'Revision Not Found' in str(e):
+                if str(e).startswith("fp16 is not a valid"):
                     pass
                 else:
                     print(

ldm/invoke/readline.py

@@ -13,7 +13,7 @@ import re
 import atexit
 from typing import List
 from ldm.invoke.args import Args
-from ldm.invoke.concepts_lib import get_hf_concepts_lib
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.globals import Globals
 from ldm.modules.lora_manager import LoraManager
 
@@ -287,7 +287,7 @@ class Completer(object):
     def _concept_completions(self, text, state):
         if self.concepts is None:
             # cache Concepts() instance so we can check for updates in concepts_list during runtime.
-            self.concepts = get_hf_concepts_lib()
+            self.concepts = HuggingFaceConceptsLibrary()
             self.embedding_terms.update(set(self.concepts.list_concepts()))
         else:
             self.embedding_terms.update(set(self.concepts.list_concepts()))

ldm/modules/embedding_manager.py

@@ -6,7 +6,7 @@ from torch import nn
 
 import sys
 
-from ldm.invoke.concepts_lib import get_hf_concepts_lib
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.data.personalized import per_img_token_list
 from transformers import CLIPTokenizer
 from functools import partial
@@ -39,7 +39,7 @@ class EmbeddingManager(nn.Module):
         super().__init__()
 
         self.embedder = embedder
-        self.concepts_library=get_hf_concepts_lib()
+        self.concepts_library=HuggingFaceConceptsLibrary()
 
         self.string_to_token_dict = {}
         self.string_to_param_dict = nn.ParameterDict()

ldm/modules/textual_inversion_manager.py

@@ -3,16 +3,14 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional, Union
 
-import warnings
-with warnings.catch_warnings():
-    warnings.filterwarnings("ignore", category=UserWarning)
-    import safetensors.torch
-    import torch
+import safetensors.torch
+import torch
 from picklescan.scanner import scan_file_path
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from compel.embeddings_provider import BaseTextualInversionManager
-from ldm.invoke.concepts_lib import get_hf_concepts_lib
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+
 
 @dataclass
 class TextualInversion:
@@ -36,7 +34,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         self.tokenizer = tokenizer
         self.text_encoder = text_encoder
         self.full_precision = full_precision
-        self.hf_concepts_library = get_hf_concepts_lib()
+        self.hf_concepts_library = HuggingFaceConceptsLibrary()
         self.trigger_to_sourcefile = dict()
         default_textual_inversions: list[TextualInversion] = []
         self.textual_inversions = default_textual_inversions

pyproject.toml

@@ -32,9 +32,9 @@ dependencies = [
   "albumentations",
   "click",
   "clip_anytorch",
-  "compel~=1.1.5",
+  "compel~=1.1.0",
   "datasets",
-  "diffusers[torch]~=0.16.1",
+  "diffusers[torch]~=0.15.0",
   "dnspython==2.2.1",
   "einops",
   "eventlet",
@@ -76,7 +76,7 @@ dependencies = [
   "taming-transformers-rom1504",
   "test-tube>=0.7.5",
   "torch-fidelity",
-  "torch~=2.0.0",
+  "torch~=1.13.1",
   "torchmetrics",
   "torchvision>=0.14.1",
   "transformers~=4.26",
@@ -108,7 +108,7 @@ requires-python = ">=3.9, <3.11"
 "test" = ["pytest-cov", "pytest>6.0.0"]
 "xformers" = [
   "triton; sys_platform=='linux'",
-  "xformers~=0.0.19; sys_platform!='darwin'",
+  "xformers~=0.0.16; sys_platform!='darwin'",
 ]
 
 [project.scripts]