Merge branch 'main' into ti-ui

.gitignore

@@ -201,8 +201,6 @@ checkpoints
 # If it's a Mac
 .DS_Store
 
-invokeai/frontend/web/dist/*
-
 # Let the frontend manage its own gitignore
 !invokeai/frontend/web/*
 

invokeai/app/invocations/compel.py

@@ -9,6 +9,7 @@ from compel.prompt_parser import (Blend, Conjunction,
                                   FlattenedPrompt, Fragment)
 from pydantic import BaseModel, Field
 
+from ...backend.model_management.models import ModelNotFoundException
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
@@ -86,10 +87,10 @@ class CompelInvocation(BaseInvocation):
                         model_type=ModelType.TextualInversion,
                     ).context.model
                 )
-            except Exception:
+            except ModelNotFoundException:
                 # print(e)
                 #import traceback
-                # print(traceback.format_exc())
+                #print(traceback.format_exc())
                 print(f"Warn: trigger: \"{trigger}\" not found")
 
         with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\

invokeai/backend/install/migrate_to_3.py

@@ -228,6 +228,7 @@ class MigrateTo3(object):
             self._migrate_pretrained(CLIPTextModel,
                                      repo_id = repo_id,
                                      dest = target_dir / 'clip-vit-large-patch14',
+                                     force = True,
                                      **kwargs)
 
             # sd-2
@@ -291,21 +292,21 @@ class MigrateTo3(object):
     def _model_probe_to_path(self, info: ModelProbeInfo)->Path:
         return Path(self.dest_models, info.base_type.value, info.model_type.value)
 
-    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, **kwargs):
-        if dest.exists():
+    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force:bool=False, **kwargs):
+        if dest.exists() and not force:
             logger.info(f'Skipping existing {dest}')
             return
         model = model_class.from_pretrained(repo_id, **kwargs)
-        self._save_pretrained(model, dest)
+        self._save_pretrained(model, dest, overwrite=force)
 
-    def _save_pretrained(self, model, dest: Path):
-        if dest.exists():
-            logger.info(f'Skipping existing {dest}')
-            return
+    def _save_pretrained(self, model, dest: Path, overwrite: bool=False):
         model_name = dest.name
-        download_path = dest.with_name(f'{model_name}.downloading')
-        model.save_pretrained(download_path, safe_serialization=True)
-        download_path.replace(dest)
+        if overwrite:
+            model.save_pretrained(dest, safe_serialization=True)
+        else:
+            download_path = dest.with_name(f'{model_name}.downloading')
+            model.save_pretrained(download_path, safe_serialization=True)
+            download_path.replace(dest)
 
     def _download_vae(self, repo_id: str, subfolder:str=None)->Path:
         vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / 'models/hub', subfolder=subfolder)
@@ -573,8 +574,10 @@ script, which will perform a full upgrade in place."""
 
     dest_directory = args.dest_directory
     assert dest_directory.is_dir(), f"{dest_directory} is not a valid directory"
-    assert (dest_directory / 'models').is_dir(), f"{dest_directory} does not contain a 'models' subdirectory"
-    assert (dest_directory / 'invokeai.yaml').exists(), f"{dest_directory} does not contain an InvokeAI init file."
+
+    # TODO: revisit
+    # assert (dest_directory / 'models').is_dir(), f"{dest_directory} does not contain a 'models' subdirectory"
+    # assert (dest_directory / 'invokeai.yaml').exists(), f"{dest_directory} does not contain an InvokeAI init file."
 
     do_migrate(root_directory,dest_directory)
 

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -29,7 +29,7 @@ import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 
 from .model_manager import ModelManager
-from .model_cache import ModelCache
+from picklescan.scanner import scan_file_path
 from .models import BaseModelType, ModelVariantType
 
 try:
@@ -1014,7 +1014,10 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             checkpoint = load_file(checkpoint_path)
         else:
             if scan_needed:
-                ModelCache.scan_model(checkpoint_path, checkpoint_path)
+                # scan model
+                scan_result = scan_file_path(checkpoint_path)
+                if scan_result.infected_files != 0:
+                    raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
             checkpoint = torch.load(checkpoint_path)
 
         # sometimes there is a state_dict key and sometimes not

invokeai/backend/model_management/lora.py

@@ -653,6 +653,9 @@ class TextualInversionModel:
         else:
             result.embedding = next(iter(state_dict.values()))
 
+            if len(result.embedding.shape) == 1:
+                result.embedding = result.embedding.unsqueeze(0)
+
             if not isinstance(result.embedding, torch.Tensor):
                 raise ValueError(f"Invalid embeddings file: {file_path.name}")
 

invokeai/backend/model_management/model_cache.py

@@ -100,8 +100,6 @@ class ModelCache(object):
         :param sha_chunksize: Chunksize to use when calculating sha256 model hash
         '''
         #max_cache_size = 9999
-        execution_device = torch.device('cuda')
-
         self.model_infos: Dict[str, ModelBase] = dict()
         self.lazy_offloading = lazy_offloading
         #self.sequential_offload: bool=sequential_offload

invokeai/backend/model_management/model_manager.py

@@ -249,7 +249,7 @@ from .model_cache import ModelCache, ModelLocker
 from .models import (
     BaseModelType, ModelType, SubModelType,
     ModelError, SchedulerPredictionType, MODEL_CLASSES,
-    ModelConfigBase,
+    ModelConfigBase, ModelNotFoundException,
     )
 
 # We are only starting to number the config file with release 3.
@@ -409,7 +409,7 @@ class ModelManager(object):
         if model_key not in self.models:
             self.scan_models_directory(base_model=base_model, model_type=model_type)
             if model_key not in self.models:
-                raise Exception(f"Model not found - {model_key}")
+                raise ModelNotFoundException(f"Model not found - {model_key}")
 
         model_config = self.models[model_key]
         model_path = self.app_config.root_path / model_config.path
@@ -421,7 +421,7 @@ class ModelManager(object):
 
             else:
                 self.models.pop(model_key, None)
-                raise Exception(f"Model not found - {model_key}")
+                raise ModelNotFoundException(f"Model not found - {model_key}")
 
         # vae/movq override
         # TODO: 

invokeai/backend/model_management/models/__init__.py

@@ -2,7 +2,7 @@ import inspect
 from enum import Enum
 from pydantic import BaseModel
 from typing import Literal, get_origin
-from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings
+from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
 from .vae import VaeModel
 from .lora import LoRAModel

invokeai/backend/model_management/models/base.py

@@ -15,6 +15,9 @@ from contextlib import suppress
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 
+class ModelNotFoundException(Exception):
+    pass
+
 class BaseModelType(str, Enum):
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"

invokeai/backend/model_management/models/textual_inversion.py

@@ -8,6 +8,7 @@ from .base import (
     ModelType,
     SubModelType,
     classproperty,
+    ModelNotFoundException,
 )
 # TODO: naming
 from ..lora import TextualInversionModel as TextualInversionModelRaw
@@ -37,8 +38,15 @@ class TextualInversionModel(ModelBase):
         if child_type is not None:
             raise Exception("There is no child models in textual inversion")
 
+        checkpoint_path = self.model_path
+        if os.path.isdir(checkpoint_path):
+            checkpoint_path = os.path.join(checkpoint_path, "learned_embeds.bin")
+
+        if not os.path.exists(checkpoint_path):
+            raise ModelNotFoundException()
+
         model = TextualInversionModelRaw.from_checkpoint(
-            file_path=self.model_path,
+            file_path=checkpoint_path,
             dtype=torch_dtype,
         )
 

invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageModal.tsx

@@ -23,6 +23,7 @@ import { stateSelector } from 'app/store/store';
 import {
   imageDeletionConfirmed,
   imageToDeleteCleared,
+  isModalOpenChanged,
   selectImageUsage,
 } from '../store/imageDeletionSlice';
 
@@ -63,6 +64,7 @@ const DeleteImageModal = () => {
 
   const handleClose = useCallback(() => {
     dispatch(imageToDeleteCleared());
+    dispatch(isModalOpenChanged(false));
   }, [dispatch]);
 
   const handleDelete = useCallback(() => {

invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSlice.ts

@@ -31,6 +31,7 @@ const imageDeletion = createSlice({
     },
     imageToDeleteCleared: (state) => {
       state.imageToDelete = null;
+      state.isModalOpen = false;
     },
   },
 });