Merge remote-tracking branch 'origin/main' into maryhipp/trigger-phrases-main

docs/nodes/defaultNodes.md

@@ -19,6 +19,8 @@ their descriptions.
 | Conditioning Primitive                                        | A conditioning tensor primitive value                                                                                                                |
 | Content Shuffle Processor                                     | Applies content shuffle processing to image                                                                                                          |
 | ControlNet                                                    | Collects ControlNet info to pass to other nodes                                                                                                      |
+| Create Denoise Mask                                           | Converts a greyscale or transparency image into a mask for denoising.                                                                                |
+| Create Gradient Mask                                          | Creates a mask for Gradient ("soft", "differential") inpainting that gradually expands during denoising. Improves edge coherence.                    |
 | Denoise Latents                                               | Denoises noisy latents to decodable images                                                                                                           |
 | Divide Integers                                               | Divides two numbers                                                                                                                                  |
 | Dynamic Prompt                                                | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator                                                                      |

invokeai/app/api/routers/model_manager.py

@@ -283,6 +283,47 @@ async def update_model_metadata(
     return result
 
 
+@model_manager_router.patch(
+    "/i/{key}/metadata",
+    operation_id="update_model_metadata",
+    responses={
+        201: {
+            "description": "The model metadata was updated successfully",
+            "content": {"application/json": {"example": example_model_metadata}},
+        },
+        400: {"description": "Bad request"},
+    },
+)
+async def update_model_metadata(
+    key: str = Path(description="Key of the model repo metadata to fetch."),
+    changes: ModelMetadataChanges = Body(description="The changes"),
+) -> Optional[AnyModelRepoMetadata]:
+    """Updates or creates a model metadata object."""
+    record_store = ApiDependencies.invoker.services.model_manager.store
+    metadata_store = ApiDependencies.invoker.services.model_manager.store.metadata_store
+
+    try:
+        original_metadata = record_store.get_metadata(key)
+        if original_metadata:
+            if changes.default_settings:
+                original_metadata.default_settings = changes.default_settings
+
+            metadata_store.update_metadata(key, original_metadata)
+        else:
+            metadata_store.add_metadata(
+                key, BaseMetadata(name="", author="", default_settings=changes.default_settings)
+            )
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"An error occurred while updating the model metadata: {e}",
+        )
+
+    result: Optional[AnyModelRepoMetadata] = record_store.get_metadata(key)
+
+    return result
+
+
 @model_manager_router.get(
     "/tags",
     operation_id="list_tags",
@@ -491,6 +532,7 @@ async def add_model_record(
 )
 async def install_model(
     source: str = Query(description="Model source to install, can be a local path, repo_id, or remote URL"),
+    inplace: Optional[bool] = Query(description="Whether or not to install a local model in place", default=False),
     # TODO(MM2): Can we type this?
     config: Optional[Dict[str, Any]] = Body(
         description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
@@ -533,6 +575,7 @@ async def install_model(
             source=source,
             config=config,
             access_token=access_token,
+            inplace=bool(inplace),
         )
         logger.info(f"Started installation of {source}")
     except UnknownModelException as e:

invokeai/app/invocations/latent.py

@@ -173,6 +173,16 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
 
+@invocation_output("gradient_mask_output")
+class GradientMaskOutput(BaseInvocationOutput):
+    """Outputs a denoise mask and an image representing the total gradient of the mask."""
+
+    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+    expanded_mask_area: ImageField = OutputField(
+        description="Image representing the total gradient area of the mask. For paste-back purposes."
+    )
+
+
 @invocation(
     "create_gradient_mask",
     title="Create Gradient Mask",
@@ -193,38 +203,42 @@ class CreateGradientMaskInvocation(BaseInvocation):
     )
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
+    def invoke(self, context: InvocationContext) -> GradientMaskOutput:
         mask_image = context.images.get_pil(self.mask.image_name, mode="L")
-        if self.coherence_mode == "Box Blur":
+        if self.edge_radius > 0:
-            blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
+            if self.coherence_mode == "Box Blur":
-        else:  # Gaussian Blur OR Staged
+                blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
-            # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
+            else:  # Gaussian Blur OR Staged
-            blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
+                # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
+                blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
 
-        mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
-        blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
 
-        # redistribute blur so that the edges are 0 and blur out to 1
+            # redistribute blur so that the original edges are 0 and blur outwards to 1
-        blur_tensor = (blur_tensor - 0.5) * 2
+            blur_tensor = (blur_tensor - 0.5) * 2
 
-        threshold = 1 - self.minimum_denoise
+            threshold = 1 - self.minimum_denoise
+
+            if self.coherence_mode == "Staged":
+                # wherever the blur_tensor is less than fully masked, convert it to threshold
+                blur_tensor = torch.where((blur_tensor < 1) & (blur_tensor > 0), threshold, blur_tensor)
+            else:
+                # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
+                blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
 
-        if self.coherence_mode == "Staged":
-            # wherever the blur_tensor is masked to any degree, convert it to threshold
-            blur_tensor = torch.where((blur_tensor < 1), threshold, blur_tensor)
         else:
-            # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
-            blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
-
-        # multiply original mask to force actually masked regions to 0
-        blur_tensor = mask_tensor * blur_tensor
 
         mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
 
-        return DenoiseMaskOutput.build(
+        # compute a [0, 1] mask from the blur_tensor
-            mask_name=mask_name,
+        expanded_mask = torch.where((blur_tensor < 1), 0, 1)
-            masked_latents_name=None,
+        expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
-            gradient=True,
+        expanded_image_dto = context.images.save(expanded_mask_image)
+
+        return GradientMaskOutput(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=None, gradient=True),
+            expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
         )
 
 
@@ -775,10 +789,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     denoising_end=self.denoising_end,
                 )
 
-                (
+                result_latents = pipeline.latents_from_embeddings(
-                    result_latents,
-                    result_attention_map_saver,
-                ) = pipeline.latents_from_embeddings(
                     latents=latents,
                     timesteps=timesteps,
                     init_timestep=init_timestep,

invokeai/app/services/model_install/model_install_default.py

@@ -7,7 +7,6 @@ import time
 from hashlib import sha256
 from pathlib import Path
 from queue import Empty, Queue
-from random import randbytes
 from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import Any, Dict, List, Optional, Set, Union
@@ -21,6 +20,7 @@ from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
 from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase
+from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -150,7 +150,7 @@ class ModelInstallService(ModelInstallServiceBase):
         config = config or {}
         if not config.get("source"):
             config["source"] = model_path.resolve().as_posix()
-        config["key"] = config.get("key", self._create_key())
+        config["key"] = config.get("key", uuid_string())
 
         info: AnyModelConfig = self._probe_model(Path(model_path), config)
 
@@ -178,13 +178,14 @@ class ModelInstallService(ModelInstallServiceBase):
         source: str,
         config: Optional[Dict[str, Any]] = None,
         access_token: Optional[str] = None,
+        inplace: bool = False,
     ) -> ModelInstallJob:
         variants = "|".join(ModelRepoVariant.__members__.values())
         hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
         source_obj: Optional[StringLikeSource] = None
 
         if Path(source).exists():  # A local file or directory
-            source_obj = LocalModelSource(path=Path(source))
+            source_obj = LocalModelSource(path=Path(source), inplace=inplace)
         elif match := re.match(hf_repoid_re, source):
             source_obj = HFModelSource(
                 repo_id=match.group(1),
@@ -526,16 +527,17 @@ class ModelInstallService(ModelInstallServiceBase):
                 setattr(info, key, value)
         return info
 
-    def _create_key(self) -> str:
-        return sha256(randbytes(100)).hexdigest()[0:32]
-
     def _register(
         self, model_path: Path, config: Optional[Dict[str, Any]] = None, info: Optional[AnyModelConfig] = None
     ) -> str:
         # Note that we may be passed a pre-populated AnyModelConfig object,
         # in which case the key field should have been populated by the caller (e.g. in `install_path`).
-        config["key"] = config.get("key", self._create_key())
+        config["key"] = config.get("key", uuid_string())
         info = info or ModelProbe.probe(model_path, config)
+        override_key: Optional[str] = config.get("key") if config else None
+
+        assert info.original_hash  # always assigned by probe()
+        info.key = override_key or info.original_hash
 
         model_path = model_path.absolute()
         if model_path.is_relative_to(self.app_config.models_path):

invokeai/app/services/model_metadata/metadata_store_base.py

@@ -7,17 +7,23 @@ from abc import ABC, abstractmethod
 from typing import List, Optional, Set, Tuple
 
 from pydantic import Field
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
+from invokeai.backend.model_manager.metadata.metadata_base import ModelDefaultSettings
 
 class ModelMetadataChanges(BaseModelExcludeNull, extra="allow"):
     """A set of changes to apply to model metadata.
 
     Only limited changes are valid:
+      - `default_settings`: the user-configured default settings for this model
       - `trigger_phrases`: the list of trigger phrases for this model
     """
 
+    default_settings: Optional[ModelDefaultSettings] = Field(
+        default=None, description="The user-configured default settings for this model"
+    )
+    """The user-configured default settings for this model"""
     trigger_phrases: Optional[List[str]] = Field(default=None, description="The model's list of trigger phrases")
     """The model's list of trigger phrases"""
 

invokeai/app/services/model_metadata/metadata_store_sql.py

@@ -184,7 +184,7 @@ class ModelMetadataStoreSQL(ModelMetadataStoreBase):
     def _update_tags(self, model_key: str, tags: Optional[Set[str]]) -> None:
         """Update tags for the model referenced by model_key."""
         if tags:
-        # remove previous tags from this model
+            # remove previous tags from this model
             self._cursor.execute(
                 """--sql
                 DELETE FROM model_tags

invokeai/app/services/session_processor/session_processor_default.py

@@ -200,6 +200,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
                                 self._invoker.services.logger.error(
                                     f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
                                 )
+                                self._invoker.services.logger.error(error)
 
                                 # Send error event
                                 self._invoker.services.events.emit_invocation_error(

invokeai/app/services/shared/sqlite_migrator/migrations/util/migrate_yaml_config_1.py

@@ -3,7 +3,6 @@
 
 import json
 import sqlite3
-from hashlib import sha1
 from logging import Logger
 from pathlib import Path
 from typing import Optional
@@ -22,7 +21,7 @@ from invokeai.backend.model_manager.config import (
     ModelConfigFactory,
     ModelType,
 )
-from invokeai.backend.model_manager.hash import FastModelHash
+from invokeai.backend.model_manager.hash import ModelHash
 
 ModelsValidator = TypeAdapter(AnyModelConfig)
 
@@ -73,19 +72,27 @@ class MigrateModelYamlToDb1:
 
             base_type, model_type, model_name = str(model_key).split("/")
             try:
-                hash = FastModelHash.hash(self.config.models_path / stanza.path)
+                hash = ModelHash().hash(self.config.models_path / stanza.path)
             except OSError:
                 self.logger.warning(f"The model at {stanza.path} is not a valid file or directory. Skipping migration.")
                 continue
 
-            assert isinstance(model_key, str)
-            new_key = sha1(model_key.encode("utf-8")).hexdigest()
-
             stanza["base"] = BaseModelType(base_type)
             stanza["type"] = ModelType(model_type)
             stanza["name"] = model_name
             stanza["original_hash"] = hash
             stanza["current_hash"] = hash
+            new_key = hash  # deterministic key assignment
+
+            # special case for ip adapters, which need the new `image_encoder_model_id` field
+            if stanza["type"] == ModelType.IPAdapter:
+                try:
+                    stanza["image_encoder_model_id"] = self._get_image_encoder_model_id(
+                        self.config.models_path / stanza.path
+                    )
+                except OSError:
+                    self.logger.warning(f"Could not determine image encoder for {stanza.path}. Skipping.")
+                    continue
 
             new_config: AnyModelConfig = ModelsValidator.validate_python(stanza)  # type: ignore # see https://github.com/pydantic/pydantic/discussions/7094
 
@@ -95,7 +102,7 @@ class MigrateModelYamlToDb1:
                     self.logger.info(f"Updating model {model_name} with information from models.yaml using key {key}")
                     self._update_model(key, new_config)
                 else:
-                    self.logger.info(f"Adding model {model_name} with key {model_key}")
+                    self.logger.info(f"Adding model {model_name} with key {new_key}")
                     self._add_model(new_key, new_config)
             except DuplicateModelException:
                 self.logger.warning(f"Model {model_name} is already in the database")
@@ -149,3 +156,8 @@ class MigrateModelYamlToDb1:
             )
         except sqlite3.IntegrityError as exc:
             raise DuplicateModelException(f"{record.name}: model is already in database") from exc
+
+    def _get_image_encoder_model_id(self, model_path: Path) -> str:
+        with open(model_path / "image_encoder.txt") as f:
+            encoder = f.read()
+        return encoder.strip()

invokeai/backend/model_manager/hash.py

@@ -11,56 +11,175 @@ from invokeai.backend.model_managre.model_hash import FastModelHash
 import hashlib
 import os
 from pathlib import Path
-from typing import Dict, Union
+from typing import Callable, Literal, Optional, Union
 
-from imohash import hashfile
+from blake3 import blake3
+
+MODEL_FILE_EXTENSIONS = (".ckpt", ".safetensors", ".bin", ".pt", ".pth")
+
+ALGORITHM = Literal[
+    "md5",
+    "sha1",
+    "sha224",
+    "sha256",
+    "sha384",
+    "sha512",
+    "blake2b",
+    "blake2s",
+    "sha3_224",
+    "sha3_256",
+    "sha3_384",
+    "sha3_512",
+    "shake_128",
+    "shake_256",
+    "blake3",
+]
 
 
-class FastModelHash(object):
+class ModelHash:
-    """FastModelHash obect provides one public class method, hash()."""
+    """
+    Creates a hash of a model using a specified algorithm.
 
-    @classmethod
+    Args:
-    def hash(cls, model_location: Union[str, Path]) -> str:
+        algorithm: Hashing algorithm to use. Defaults to BLAKE3.
-        """
+        file_filter: A function that takes a file name and returns True if the file should be included in the hash.
-        Return hexdigest string for model located at model_location.
 
-        :param model_location: Path to the model
+    If the model is a single file, it is hashed directly using the provided algorithm.
-        """
+
-        model_location = Path(model_location)
+    If the model is a directory, each model weights file in the directory is hashed using the provided algorithm.
-        if model_location.is_file():
+
-            return cls._hash_file(model_location)
+    Only files with the following extensions are hashed: .ckpt, .safetensors, .bin, .pt, .pth
-        elif model_location.is_dir():
+
-            return cls._hash_dir(model_location)
+    The final hash is computed by hashing the hashes of all model files in the directory using BLAKE3, ensuring
+    that directory hashes are never weaker than the file hashes.
+
+    Usage:
+        ```py
+        # BLAKE3 hash
+        ModelHash().hash("path/to/some/model.safetensors")
+        # MD5
+        ModelHash("md5").hash("path/to/model/dir/")
+        ```
+    """
+
+    def __init__(self, algorithm: ALGORITHM = "blake3", file_filter: Optional[Callable[[str], bool]] = None) -> None:
+        if algorithm == "blake3":
+            self._hash_file = self._blake3
+        elif algorithm in hashlib.algorithms_available:
+            self._hash_file = self._get_hashlib(algorithm)
         else:
-            raise OSError(f"Not a valid file or directory: {model_location}")
+            raise ValueError(f"Algorithm {algorithm} not available")
 
-    @classmethod
+        self._file_filter = file_filter or self._default_file_filter
-    def _hash_file(cls, model_location: Union[str, Path]) -> str:
+
+    def hash(self, model_path: Union[str, Path]) -> str:
         """
-        Fasthash a single file and return its hexdigest.
+        Return hexdigest of hash of model located at model_path using the algorithm provided at class instantiation.
 
-        :param model_location: Path to the model file
+        If model_path is a directory, the hash is computed by hashing the hashes of all model files in the
+        directory. The final composite hash is always computed using BLAKE3.
+
+        Args:
+            model_path: Path to the model
+
+        Returns:
+            str: Hexdigest of the hash of the model
         """
-        # we return md5 hash of the filehash to make it shorter
-        # cryptographic security not needed here
-        return hashlib.md5(hashfile(model_location)).hexdigest()
 
-    @classmethod
+        model_path = Path(model_path)
-    def _hash_dir(cls, model_location: Union[str, Path]) -> str:
+        if model_path.is_file():
-        components: Dict[str, str] = {}
+            return self._hash_file(model_path)
+        elif model_path.is_dir():
+            return self._hash_dir(model_path)
+        else:
+            raise OSError(f"Not a valid file or directory: {model_path}")
 
-        for root, _dirs, files in os.walk(model_location):
+    def _hash_dir(self, dir: Path) -> str:
-            for file in files:
+        """Compute the hash for all files in a directory and return a hexdigest.
-                # only tally tensor files because diffusers config files change slightly
-                # depending on how the model was downloaded/converted.
-                if not file.endswith((".ckpt", ".safetensors", ".bin", ".pt", ".pth")):
-                    continue
-                path = (Path(root) / file).as_posix()
-                fast_hash = cls._hash_file(path)
-                components.update({path: fast_hash})
 
-        # hash all the model hashes together, using alphabetic file order
+        Args:
-        md5 = hashlib.md5()
+            dir: Path to the directory
-        for _path, fast_hash in sorted(components.items()):
+
-            md5.update(fast_hash.encode("utf-8"))
+        Returns:
-        return md5.hexdigest()
+            str: Hexdigest of the hash of the directory
+        """
+        model_component_paths = self._get_file_paths(dir, self._file_filter)
+
+        component_hashes: list[str] = []
+        for component in sorted(model_component_paths):
+            component_hashes.append(self._hash_file(component))
+
+        # BLAKE3 is cryptographically secure. We may as well fall back on a secure algorithm
+        # for the composite hash
+        composite_hasher = blake3()
+        for h in component_hashes:
+            composite_hasher.update(h.encode("utf-8"))
+        return composite_hasher.hexdigest()
+
+    @staticmethod
+    def _get_file_paths(model_path: Path, file_filter: Callable[[str], bool]) -> list[Path]:
+        """Return a list of all model files in the directory.
+
+        Args:
+            model_path: Path to the model
+            file_filter: Function that takes a file name and returns True if the file should be included in the list.
+
+        Returns:
+            List of all model files in the directory
+        """
+
+        files: list[Path] = []
+        for root, _dirs, _files in os.walk(model_path):
+            for file in _files:
+                if file_filter(file):
+                    files.append(Path(root, file))
+        return files
+
+    @staticmethod
+    def _blake3(file_path: Path) -> str:
+        """Hashes a file using BLAKE3
+
+        Args:
+            file_path: Path to the file to hash
+
+        Returns:
+            Hexdigest of the hash of the file
+        """
+        file_hasher = blake3(max_threads=blake3.AUTO)
+        file_hasher.update_mmap(file_path)
+        return file_hasher.hexdigest()
+
+    @staticmethod
+    def _get_hashlib(algorithm: ALGORITHM) -> Callable[[Path], str]:
+        """Factory function that returns a function to hash a file with the given algorithm.
+
+        Args:
+            algorithm: Hashing algorithm to use
+
+        Returns:
+            A function that hashes a file using the given algorithm
+        """
+
+        def hashlib_hasher(file_path: Path) -> str:
+            """Hashes a file using a hashlib algorithm. Uses `memoryview` to avoid reading the entire file into memory."""
+            hasher = hashlib.new(algorithm)
+            buffer = bytearray(128 * 1024)
+            mv = memoryview(buffer)
+            with open(file_path, "rb", buffering=0) as f:
+                while n := f.readinto(mv):
+                    hasher.update(mv[:n])
+            return hasher.hexdigest()
+
+        return hashlib_hasher
+
+    @staticmethod
+    def _default_file_filter(file_path: str) -> bool:
+        """A default file filter that only includes files with the following extensions: .ckpt, .safetensors, .bin, .pt, .pth
+
+        Args:
+            file_path: Path to the file
+
+        Returns:
+            True if the file matches the given extensions, otherwise False
+        """
+        return file_path.endswith(MODEL_FILE_EXTENSIONS)

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -25,6 +25,7 @@ from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 from typing_extensions import Annotated
 
+from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.backend.model_manager import ModelRepoVariant
 
 from ..util import select_hf_files
@@ -68,6 +69,15 @@ class RemoteModelFile(BaseModel):
     sha256: Optional[str] = Field(description="SHA256 hash of this model (not always available)", default=None)
 
 
+class ModelDefaultSettings(BaseModel):
+    vae: str | None
+    vae_precision: str | None
+    scheduler: SCHEDULER_NAME_VALUES | None
+    steps: int | None
+    cfg_scale: float | None
+    cfg_rescale_multiplier: float | None
+
+
 class ModelMetadataBase(BaseModel):
     """Base class for model metadata information."""
 
@@ -75,6 +85,9 @@ class ModelMetadataBase(BaseModel):
     author: str = Field(description="model's author")
     tags: Optional[Set[str]] = Field(description="tags provided by model source", default=None)
     trigger_phrases: Optional[List[str]] = Field(description="trigger phrases for this model", default=None)
+    default_settings: Optional[ModelDefaultSettings] = Field(
+        description="default settings for this model", default=None
+    )
 
 
 class BaseMetadata(ModelMetadataBase):

invokeai/backend/model_manager/probe.py

@@ -21,7 +21,7 @@ from .config import (
     ModelVariantType,
     SchedulerPredictionType,
 )
-from .hash import FastModelHash
+from .hash import ModelHash
 from .util.model_util import lora_token_vector_length, read_checkpoint_meta
 
 CkptType = Dict[str, Any]
@@ -147,7 +147,7 @@ class ModelProbe(object):
         if not probe_class:
             raise InvalidModelConfigException(f"Unhandled combination of {format_type} and {model_type}")
 
-        hash = FastModelHash.hash(model_path)
+        hash = ModelHash().hash(model_path)
         probe = probe_class(model_path)
 
         fields["path"] = model_path.as_posix()

invokeai/backend/stable_diffusion/__init__.py

@@ -4,13 +4,11 @@ Initialization file for the invokeai.backend.stable_diffusion package
 
 from .diffusers_pipeline import PipelineIntermediateState, StableDiffusionGeneratorPipeline  # noqa: F401
 from .diffusion import InvokeAIDiffuserComponent  # noqa: F401
-from .diffusion.cross_attention_map_saving import AttentionMapSaver  # noqa: F401
 from .seamless import set_seamless  # noqa: F401
 
 __all__ = [
     "PipelineIntermediateState",
     "StableDiffusionGeneratorPipeline",
     "InvokeAIDiffuserComponent",
-    "AttentionMapSaver",
     "set_seamless",
 ]

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -12,7 +12,6 @@ import torch
 import torchvision.transforms as T
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.models.controlnet import ControlNetModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
@@ -26,9 +25,9 @@ from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
+from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
 from ..util import auto_detect_slice_size, normalize_device
-from .diffusion import AttentionMapSaver, InvokeAIDiffuserComponent
 
 
 @dataclass
@@ -39,7 +38,6 @@ class PipelineIntermediateState:
     timestep: int
     latents: torch.Tensor
     predicted_original: Optional[torch.Tensor] = None
-    attention_map_saver: Optional[AttentionMapSaver] = None
 
 
 @dataclass
@@ -190,19 +188,6 @@ class T2IAdapterData:
     end_step_percent: float = Field(default=1.0)
 
 
-@dataclass
-class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
-    r"""
-    Output class for InvokeAI's Stable Diffusion pipeline.
-
-    Args:
-        attention_map_saver (`AttentionMapSaver`): Object containing attention maps that can be displayed to the user
-         after generation completes. Optional.
-    """
-
-    attention_map_saver: Optional[AttentionMapSaver]
-
-
 class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion.
@@ -343,9 +328,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         masked_latents: Optional[torch.Tensor] = None,
         gradient_mask: Optional[bool] = False,
         seed: Optional[int] = None,
-    ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+    ) -> torch.Tensor:
         if init_timestep.shape[0] == 0:
-            return latents, None
+            return latents
 
         if additional_guidance is None:
             additional_guidance = []
@@ -385,7 +370,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise, gradient_mask))
 
         try:
-            latents, attention_map_saver = self.generate_latents_from_embeddings(
+            latents = self.generate_latents_from_embeddings(
                 latents,
                 timesteps,
                 conditioning_data,
@@ -402,7 +387,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if mask is not None and not gradient_mask:
             latents = torch.lerp(orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype))
 
-        return latents, attention_map_saver
+        return latents
 
     def generate_latents_from_embeddings(
         self,
@@ -415,16 +400,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         ip_adapter_data: Optional[list[IPAdapterData]] = None,
         t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
-    ):
+    ) -> torch.Tensor:
         self._adjust_memory_efficient_attention(latents)
         if additional_guidance is None:
             additional_guidance = []
 
         batch_size = latents.shape[0]
-        attention_map_saver: Optional[AttentionMapSaver] = None
 
         if timesteps.shape[0] == 0:
-            return latents, attention_map_saver
+            return latents
 
         ip_adapter_unet_patcher = None
         extra_conditioning_info = conditioning_data.text_embeddings.extra_conditioning
@@ -432,7 +416,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             attn_ctx = self.invokeai_diffuser.custom_attention_context(
                 self.invokeai_diffuser.model,
                 extra_conditioning_info=extra_conditioning_info,
-                step_count=len(self.scheduler.timesteps),
             )
             self.use_ip_adapter = False
         elif ip_adapter_data is not None:
@@ -483,13 +466,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
                 predicted_original = getattr(step_output, "pred_original_sample", None)
 
-                # TODO resuscitate attention map saving
-                # if i == len(timesteps)-1 and extra_conditioning_info is not None:
-                #    eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
-                #    attention_map_token_ids = range(1, eos_token_index)
-                #    attention_map_saver = AttentionMapSaver(token_ids=attention_map_token_ids, latents_shape=latents.shape[-2:])
-                #    self.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)
-
                 if callback is not None:
                     callback(
                         PipelineIntermediateState(
@@ -499,11 +475,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                             timestep=int(t),
                             latents=latents,
                             predicted_original=predicted_original,
-                            attention_map_saver=attention_map_saver,
                         )
                     )
 
-            return latents, attention_map_saver
+            return latents
 
     @torch.inference_mode()
     def step(

invokeai/backend/stable_diffusion/diffusion/__init__.py

@@ -2,6 +2,4 @@
 Initialization file for invokeai.models.diffusion
 """
 
-from .cross_attention_control import InvokeAICrossAttentionMixin  # noqa: F401
-from .cross_attention_map_saving import AttentionMapSaver  # noqa: F401
 from .shared_invokeai_diffusion import InvokeAIDiffuserComponent  # noqa: F401

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -3,19 +3,13 @@
 
 
 import enum
-import math
 from dataclasses import dataclass, field
-from typing import Callable, Optional
+from typing import Optional
 
-import diffusers
-import psutil
 import torch
 from compel.cross_attention_control import Arguments
-from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, SlicedAttnProcessor
+from diffusers.models.attention_processor import Attention, SlicedAttnProcessor
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from torch import nn
-
-import invokeai.backend.util.logging as logger
 
 from ...util import torch_dtype
 
@@ -25,72 +19,14 @@ class CrossAttentionType(enum.Enum):
     TOKENS = 2
 
 
-class Context:
+class CrossAttnControlContext:
-    cross_attention_mask: Optional[torch.Tensor]
+    def __init__(self, arguments: Arguments):
-    cross_attention_index_map: Optional[torch.Tensor]
-
-    class Action(enum.Enum):
-        NONE = 0
-        SAVE = (1,)
-        APPLY = 2
-
-    def __init__(self, arguments: Arguments, step_count: int):
         """
         :param arguments: Arguments for the cross-attention control process
-        :param step_count: The absolute total number of steps of diffusion (for img2img this is likely larger than the number of steps that will actually run)
         """
-        self.cross_attention_mask = None
+        self.cross_attention_mask: Optional[torch.Tensor] = None
-        self.cross_attention_index_map = None
+        self.cross_attention_index_map: Optional[torch.Tensor] = None
-        self.self_cross_attention_action = Context.Action.NONE
-        self.tokens_cross_attention_action = Context.Action.NONE
         self.arguments = arguments
-        self.step_count = step_count
-
-        self.self_cross_attention_module_identifiers = []
-        self.tokens_cross_attention_module_identifiers = []
-
-        self.saved_cross_attention_maps = {}
-
-        self.clear_requests(cleanup=True)
-
-    def register_cross_attention_modules(self, model):
-        for name, _module in get_cross_attention_modules(model, CrossAttentionType.SELF):
-            if name in self.self_cross_attention_module_identifiers:
-                raise AssertionError(f"name {name} cannot appear more than once")
-            self.self_cross_attention_module_identifiers.append(name)
-        for name, _module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
-            if name in self.tokens_cross_attention_module_identifiers:
-                raise AssertionError(f"name {name} cannot appear more than once")
-            self.tokens_cross_attention_module_identifiers.append(name)
-
-    def request_save_attention_maps(self, cross_attention_type: CrossAttentionType):
-        if cross_attention_type == CrossAttentionType.SELF:
-            self.self_cross_attention_action = Context.Action.SAVE
-        else:
-            self.tokens_cross_attention_action = Context.Action.SAVE
-
-    def request_apply_saved_attention_maps(self, cross_attention_type: CrossAttentionType):
-        if cross_attention_type == CrossAttentionType.SELF:
-            self.self_cross_attention_action = Context.Action.APPLY
-        else:
-            self.tokens_cross_attention_action = Context.Action.APPLY
-
-    def is_tokens_cross_attention(self, module_identifier) -> bool:
-        return module_identifier in self.tokens_cross_attention_module_identifiers
-
-    def get_should_save_maps(self, module_identifier: str) -> bool:
-        if module_identifier in self.self_cross_attention_module_identifiers:
-            return self.self_cross_attention_action == Context.Action.SAVE
-        elif module_identifier in self.tokens_cross_attention_module_identifiers:
-            return self.tokens_cross_attention_action == Context.Action.SAVE
-        return False
-
-    def get_should_apply_saved_maps(self, module_identifier: str) -> bool:
-        if module_identifier in self.self_cross_attention_module_identifiers:
-            return self.self_cross_attention_action == Context.Action.APPLY
-        elif module_identifier in self.tokens_cross_attention_module_identifiers:
-            return self.tokens_cross_attention_action == Context.Action.APPLY
-        return False
 
     def get_active_cross_attention_control_types_for_step(
         self, percent_through: float = None
@@ -111,219 +47,8 @@ class Context:
             to_control.append(CrossAttentionType.TOKENS)
         return to_control
 
-    def save_slice(
-        self,
-        identifier: str,
-        slice: torch.Tensor,
-        dim: Optional[int],
-        offset: int,
-        slice_size: Optional[int],
-    ):
-        if identifier not in self.saved_cross_attention_maps:
-            self.saved_cross_attention_maps[identifier] = {
-                "dim": dim,
-                "slice_size": slice_size,
-                "slices": {offset or 0: slice},
-            }
-        else:
-            self.saved_cross_attention_maps[identifier]["slices"][offset or 0] = slice
 
-    def get_slice(
+def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: CrossAttnControlContext):
-        self,
-        identifier: str,
-        requested_dim: Optional[int],
-        requested_offset: int,
-        slice_size: int,
-    ):
-        saved_attention_dict = self.saved_cross_attention_maps[identifier]
-        if requested_dim is None:
-            if saved_attention_dict["dim"] is not None:
-                raise RuntimeError(f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}")
-            return saved_attention_dict["slices"][0]
-
-        if saved_attention_dict["dim"] == requested_dim:
-            if slice_size != saved_attention_dict["slice_size"]:
-                raise RuntimeError(
-                    f"slice_size mismatch: expected slice_size={slice_size}, have {saved_attention_dict['slice_size']}"
-                )
-            return saved_attention_dict["slices"][requested_offset]
-
-        if saved_attention_dict["dim"] is None:
-            whole_saved_attention = saved_attention_dict["slices"][0]
-            if requested_dim == 0:
-                return whole_saved_attention[requested_offset : requested_offset + slice_size]
-            elif requested_dim == 1:
-                return whole_saved_attention[:, requested_offset : requested_offset + slice_size]
-
-        raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
-
-    def get_slicing_strategy(self, identifier: str) -> tuple[Optional[int], Optional[int]]:
-        saved_attention = self.saved_cross_attention_maps.get(identifier, None)
-        if saved_attention is None:
-            return None, None
-        return saved_attention["dim"], saved_attention["slice_size"]
-
-    def clear_requests(self, cleanup=True):
-        self.tokens_cross_attention_action = Context.Action.NONE
-        self.self_cross_attention_action = Context.Action.NONE
-        if cleanup:
-            self.saved_cross_attention_maps = {}
-
-    def offload_saved_attention_slices_to_cpu(self):
-        for _key, map_dict in self.saved_cross_attention_maps.items():
-            for offset, slice in map_dict["slices"].items():
-                map_dict[offset] = slice.to("cpu")
-
-
-class InvokeAICrossAttentionMixin:
-    """
-    Enable InvokeAI-flavoured Attention calculation, which does aggressive low-memory slicing and calls
-    through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
-    and dymamic slicing strategy selection.
-    """
-
-    def __init__(self):
-        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-        self.attention_slice_wrangler = None
-        self.slicing_strategy_getter = None
-        self.attention_slice_calculated_callback = None
-
-    def set_attention_slice_wrangler(
-        self,
-        wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]],
-    ):
-        """
-        Set custom attention calculator to be called when attention is calculated
-        :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
-        which returns either the suggested_attention_slice or an adjusted equivalent.
-            `module` is the current Attention module for which the callback is being invoked.
-            `suggested_attention_slice` is the default-calculated attention slice
-            `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
-                If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
-
-        Pass None to use the default attention calculation.
-        :return:
-        """
-        self.attention_slice_wrangler = wrangler
-
-    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int, int]]]):
-        self.slicing_strategy_getter = getter
-
-    def set_attention_slice_calculated_callback(self, callback: Optional[Callable[[torch.Tensor], None]]):
-        self.attention_slice_calculated_callback = callback
-
-    def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
-        # calculate attention scores
-        # attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
-        attention_scores = torch.baddbmm(
-            torch.empty(
-                query.shape[0],
-                query.shape[1],
-                key.shape[1],
-                dtype=query.dtype,
-                device=query.device,
-            ),
-            query,
-            key.transpose(-1, -2),
-            beta=0,
-            alpha=self.scale,
-        )
-
-        # calculate attention slice by taking the best scores for each latent pixel
-        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
-        attention_slice_wrangler = self.attention_slice_wrangler
-        if attention_slice_wrangler is not None:
-            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
-        else:
-            attention_slice = default_attention_slice
-
-        if self.attention_slice_calculated_callback is not None:
-            self.attention_slice_calculated_callback(attention_slice, dim, offset, slice_size)
-
-        hidden_states = torch.bmm(attention_slice, value)
-        return hidden_states
-
-    def einsum_op_slice_dim0(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[0], slice_size):
-            end = i + slice_size
-            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_slice_dim1(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_mps_v1(self, q, k, v):
-        if q.shape[1] <= 4096:  # (512x512) max q.shape[1]: 4096
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
-            return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-    def einsum_op_mps_v2(self, q, k, v):
-        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            return self.einsum_op_slice_dim0(q, k, v, 1)
-
-    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
-        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
-        if size_mb <= max_tensor_mb:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
-        if div <= q.shape[0]:
-            return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
-        return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
-
-    def einsum_op_cuda(self, q, k, v):
-        # check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
-        slicing_strategy_getter = self.slicing_strategy_getter
-        if slicing_strategy_getter is not None:
-            (dim, slice_size) = slicing_strategy_getter(self)
-            if dim is not None:
-                # print("using saved slicing strategy with dim", dim, "slice size", slice_size)
-                if dim == 0:
-                    return self.einsum_op_slice_dim0(q, k, v, slice_size)
-                elif dim == 1:
-                    return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-        # fallback for when there is no saved strategy, or saved strategy does not slice
-        mem_free_total = get_mem_free_total(q.device)
-        # Divide factor of safety as there's copying and fragmentation
-        return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
-
-    def get_invokeai_attention_mem_efficient(self, q, k, v):
-        if q.device.type == "cuda":
-            # print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
-            return self.einsum_op_cuda(q, k, v)
-
-        if q.device.type == "mps" or q.device.type == "cpu":
-            if self.mem_total_gb >= 32:
-                return self.einsum_op_mps_v1(q, k, v)
-            return self.einsum_op_mps_v2(q, k, v)
-
-        # Smaller slices are faster due to L2/L3/SLC caches.
-        # Tested on i7 with 8MB L3 cache.
-        return self.einsum_op_tensor_mem(q, k, v, 32)
-
-
-def restore_default_cross_attention(
-    model,
-    is_running_diffusers: bool,
-    restore_attention_processor: Optional[AttentionProcessor] = None,
-):
-    if is_running_diffusers:
-        unet = model
-        unet.set_attn_processor(restore_attention_processor or AttnProcessor())
-    else:
-        remove_attention_function(model)
-
-
-def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -362,170 +87,6 @@ def setup_cross_attention_control_attention_processors(unet: UNet2DConditionMode
         unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
 
 
-def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
-    cross_attention_class: type = InvokeAIDiffusersCrossAttention
-    which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
-    attention_module_tuples = [
-        (name, module)
-        for name, module in model.named_modules()
-        if isinstance(module, cross_attention_class) and which_attn in name
-    ]
-    cross_attention_modules_in_model_count = len(attention_module_tuples)
-    expected_count = 16
-    if cross_attention_modules_in_model_count != expected_count:
-        # non-fatal error but .swap() won't work.
-        logger.error(
-            f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
-            f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching "
-            "failed or some assumption has changed about the structure of the model itself. Please fix the "
-            f"monkey-patching, and/or update the {expected_count} above to an appropriate number, and/or find and "
-            "inform someone who knows what it means. This error is non-fatal, but it is likely that .swap() and "
-            "attention map display will not work properly until it is fixed."
-        )
-    return attention_module_tuples
-
-
-def inject_attention_function(unet, context: Context):
-    # ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
-
-    def attention_slice_wrangler(module, suggested_attention_slice: torch.Tensor, dim, offset, slice_size):
-        # memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
-
-        attention_slice = suggested_attention_slice
-
-        if context.get_should_save_maps(module.identifier):
-            # print(module.identifier, "saving suggested_attention_slice of shape",
-            #      suggested_attention_slice.shape, "dim", dim, "offset", offset)
-            slice_to_save = attention_slice.to("cpu") if dim is not None else attention_slice
-            context.save_slice(
-                module.identifier,
-                slice_to_save,
-                dim=dim,
-                offset=offset,
-                slice_size=slice_size,
-            )
-        elif context.get_should_apply_saved_maps(module.identifier):
-            # print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
-            saved_attention_slice = context.get_slice(module.identifier, dim, offset, slice_size)
-
-            # slice may have been offloaded to CPU
-            saved_attention_slice = saved_attention_slice.to(suggested_attention_slice.device)
-
-            if context.is_tokens_cross_attention(module.identifier):
-                index_map = context.cross_attention_index_map
-                remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
-                this_attention_slice = suggested_attention_slice
-
-                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 + this_attention_slice * this_mask
-            else:
-                # just use everything
-                attention_slice = saved_attention_slice
-
-        return attention_slice
-
-    cross_attention_modules = get_cross_attention_modules(
-        unet, CrossAttentionType.TOKENS
-    ) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
-    for identifier, module in cross_attention_modules:
-        module.identifier = identifier
-        try:
-            module.set_attention_slice_wrangler(attention_slice_wrangler)
-            module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier))  # noqa: B023
-        except AttributeError as e:
-            if is_attribute_error_about(e, "set_attention_slice_wrangler"):
-                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO
-            else:
-                raise
-
-
-def remove_attention_function(unet):
-    cross_attention_modules = get_cross_attention_modules(
-        unet, CrossAttentionType.TOKENS
-    ) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
-    for _identifier, module in cross_attention_modules:
-        try:
-            # clear wrangler callback
-            module.set_attention_slice_wrangler(None)
-            module.set_slicing_strategy_getter(None)
-        except AttributeError as e:
-            if is_attribute_error_about(e, "set_attention_slice_wrangler"):
-                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
-            else:
-                raise
-
-
-def is_attribute_error_about(error: AttributeError, attribute: str):
-    if hasattr(error, "name"):  # Python 3.10
-        return error.name == attribute
-    else:  # Python 3.9
-        return attribute in str(error)
-
-
-def get_mem_free_total(device):
-    # only on cuda
-    if not torch.cuda.is_available():
-        return None
-    stats = torch.cuda.memory_stats(device)
-    mem_active = stats["active_bytes.all.current"]
-    mem_reserved = stats["reserved_bytes.all.current"]
-    mem_free_cuda, _ = torch.cuda.mem_get_info(device)
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
-    return mem_free_total
-
-
-class InvokeAIDiffusersCrossAttention(diffusers.models.attention.Attention, InvokeAICrossAttentionMixin):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        InvokeAICrossAttentionMixin.__init__(self)
-
-    def _attention(self, query, key, value, attention_mask=None):
-        # default_result = super()._attention(query,  key, value)
-        if attention_mask is not None:
-            print(f"{type(self).__name__} ignoring passed-in attention_mask")
-        attention_result = self.get_invokeai_attention_mem_efficient(query, key, value)
-
-        hidden_states = self.reshape_batch_dim_to_heads(attention_result)
-        return hidden_states
-
-
-## 🧨diffusers implementation follows
-
-
-"""
-# base implementation
-
-class AttnProcessor:
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        batch_size, sequence_length, _ = hidden_states.shape
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
-
-        query = attn.to_q(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states
-
-"""
-
-
 @dataclass
 class SwapCrossAttnContext:
     modified_text_embeddings: torch.Tensor

invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py

@@ -1,100 +0,0 @@
-import math
-from typing import Optional
-
-import torch
-from PIL import Image
-from torchvision.transforms.functional import InterpolationMode
-from torchvision.transforms.functional import resize as tv_resize
-
-
-class AttentionMapSaver:
-    def __init__(self, token_ids: range, latents_shape: torch.Size):
-        self.token_ids = token_ids
-        self.latents_shape = latents_shape
-        # self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]])
-        self.collated_maps: dict[str, torch.Tensor] = {}
-
-    def clear_maps(self):
-        self.collated_maps = {}
-
-    def add_attention_maps(self, maps: torch.Tensor, key: str):
-        """
-        Accumulate the given attention maps and store by summing with existing maps at the passed-in key (if any).
-        :param maps: Attention maps to store. Expected shape [A, (H*W), N] where A is attention heads count, H and W are the map size (fixed per-key) and N is the number of tokens (typically 77).
-        :param key: Storage key. If a map already exists for this key it will be summed with the incoming data. In this case the maps sizes (H and W) should match.
-        :return: None
-        """
-        key_and_size = f"{key}_{maps.shape[1]}"
-
-        # extract desired tokens
-        maps = maps[:, :, self.token_ids]
-
-        # merge attention heads to a single map per token
-        maps = torch.sum(maps, 0)
-
-        # store
-        if key_and_size not in self.collated_maps:
-            self.collated_maps[key_and_size] = torch.zeros_like(maps, device="cpu")
-        self.collated_maps[key_and_size] += maps.cpu()
-
-    def write_maps_to_disk(self, path: str):
-        pil_image = self.get_stacked_maps_image()
-        if pil_image is not None:
-            pil_image.save(path, "PNG")
-
-    def get_stacked_maps_image(self) -> Optional[Image.Image]:
-        """
-        Scale all collected attention maps to the same size, blend them together and return as an image.
-        :return: An image containing a vertical stack of blended attention maps, one for each requested token.
-        """
-        num_tokens = len(self.token_ids)
-        if num_tokens == 0:
-            return None
-
-        latents_height = self.latents_shape[0]
-        latents_width = self.latents_shape[1]
-
-        merged = None
-
-        for _key, maps in self.collated_maps.items():
-            # maps has shape [(H*W), N] for N tokens
-            # but we want [N, H, W]
-            this_scale_factor = math.sqrt(maps.shape[0] / (latents_width * latents_height))
-            this_maps_height = int(float(latents_height) * this_scale_factor)
-            this_maps_width = int(float(latents_width) * this_scale_factor)
-            # and we need to do some dimension juggling
-            maps = torch.reshape(
-                torch.swapdims(maps, 0, 1),
-                [num_tokens, this_maps_height, this_maps_width],
-            )
-
-            # scale to output size if necessary
-            if this_scale_factor != 1:
-                maps = tv_resize(maps, [latents_height, latents_width], InterpolationMode.BICUBIC)
-
-            # normalize
-            maps_min = torch.min(maps)
-            maps_range = torch.max(maps) - maps_min
-            # print(f"map {key} size {[this_maps_width, this_maps_height]} range {[maps_min, maps_min + maps_range]}")
-            maps_normalized = (maps - maps_min) / maps_range
-            # expand to (-0.1, 1.1) and clamp
-            maps_normalized_expanded = maps_normalized * 1.1 - 0.05
-            maps_normalized_expanded_clamped = torch.clamp(maps_normalized_expanded, 0, 1)
-
-            # merge together, producing a vertical stack
-            maps_stacked = torch.reshape(
-                maps_normalized_expanded_clamped,
-                [num_tokens * latents_height, latents_width],
-            )
-
-            if merged is None:
-                merged = maps_stacked
-            else:
-                # screen blend
-                merged = 1 - (1 - maps_stacked) * (1 - merged)
-
-        if merged is None:
-            return None
-
-        merged_bytes = merged.mul(0xFF).byte()
-        return Image.fromarray(merged_bytes.numpy(), mode="L")

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -17,13 +17,11 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 )
 
 from .cross_attention_control import (
-    Context,
     CrossAttentionType,
+    CrossAttnControlContext,
     SwapCrossAttnContext,
-    get_cross_attention_modules,
     setup_cross_attention_control_attention_processors,
 )
-from .cross_attention_map_saving import AttentionMapSaver
 
 ModelForwardCallback: TypeAlias = Union[
     # x, t, conditioning, Optional[cross-attention kwargs]
@@ -69,14 +67,12 @@ class InvokeAIDiffuserComponent:
         self,
         unet: UNet2DConditionModel,
         extra_conditioning_info: Optional[ExtraConditioningInfo],
-        step_count: int,
     ):
         old_attn_processors = unet.attn_processors
 
         try:
-            self.cross_attention_control_context = Context(
+            self.cross_attention_control_context = CrossAttnControlContext(
                 arguments=extra_conditioning_info.cross_attention_control_args,
-                step_count=step_count,
             )
             setup_cross_attention_control_attention_processors(
                 unet,
@@ -87,27 +83,6 @@ class InvokeAIDiffuserComponent:
         finally:
             self.cross_attention_control_context = None
             unet.set_attn_processor(old_attn_processors)
-            # TODO resuscitate attention map saving
-            # self.remove_attention_map_saving()
-
-    def setup_attention_map_saving(self, saver: AttentionMapSaver):
-        def callback(slice, dim, offset, slice_size, key):
-            if dim is not None:
-                # sliced tokens attention map saving is not implemented
-                return
-            saver.add_attention_maps(slice, key)
-
-        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
-        for identifier, module in tokens_cross_attention_modules:
-            key = "down" if identifier.startswith("down") else "up" if identifier.startswith("up") else "mid"
-            module.set_attention_slice_calculated_callback(
-                lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key)
-            )
-
-    def remove_attention_map_saving(self):
-        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
-        for _, module in tokens_cross_attention_modules:
-            module.set_attention_slice_calculated_callback(None)
 
     def do_controlnet_step(
         self,
@@ -592,54 +567,3 @@ class InvokeAIDiffuserComponent:
 
         self.last_percent_through = percent_through
         return latents.to(device=dev)
-
-    # todo: make this work
-    @classmethod
-    def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
-        x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2)  # aka sigmas
-
-        deltas = None
-        uncond_latents = None
-        weighted_cond_list = (
-            c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
-        )
-
-        # below is fugly omg
-        conditionings = [uc] + [c for c, weight in weighted_cond_list]
-        weights = [1] + [weight for c, weight in weighted_cond_list]
-        chunk_count = math.ceil(len(conditionings) / 2)
-        deltas = None
-        for chunk_index in range(chunk_count):
-            offset = chunk_index * 2
-            chunk_size = min(2, len(conditionings) - offset)
-
-            if chunk_size == 1:
-                c_in = conditionings[offset]
-                latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
-                latents_b = None
-            else:
-                c_in = torch.cat(conditionings[offset : offset + 2])
-                latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)
-
-            # first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
-            if chunk_index == 0:
-                uncond_latents = latents_a
-                deltas = latents_b - uncond_latents
-            else:
-                deltas = torch.cat((deltas, latents_a - uncond_latents))
-                if latents_b is not None:
-                    deltas = torch.cat((deltas, latents_b - uncond_latents))
-
-        # merge the weighted deltas together into a single merged delta
-        per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
-        normalize = False
-        if normalize:
-            per_delta_weights /= torch.sum(per_delta_weights)
-        reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
-        deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
-
-        # old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)
-        # assert(0 == len(torch.nonzero(old_return_value - (uncond_latents + deltas_merged * cond_scale))))
-
-        return uncond_latents + deltas_merged * global_guidance_scale

invokeai/frontend/web/public/locales/de.json

@@ -134,8 +134,6 @@
         "loadMore": "Mehr laden",
         "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
-        "preparingDownload": "bereite Download vor",
-        "preparingDownloadFailed": "Problem beim Download vorbereiten",
         "deleteImage": "Lösche Bild",
         "copy": "Kopieren",
         "download": "Runterladen",
@@ -967,7 +965,7 @@
         "resumeFailed": "Problem beim Fortsetzen des Prozesses",
         "pruneFailed": "Problem beim leeren der Warteschlange",
         "pauseTooltip": "Prozess anhalten",
-        "back": "Hinten",
+        "back": "Ende",
         "resumeSucceeded": "Prozess wird fortgesetzt",
         "resumeTooltip": "Prozess wieder aufnehmen",
         "time": "Zeit",

invokeai/frontend/web/public/locales/en.json

@@ -741,6 +741,8 @@
         "customConfig": "Custom Config",
         "customConfigFileLocation": "Custom Config File Location",
         "customSaveLocation": "Custom Save Location",
+        "defaultSettings": "Default Settings",
+        "defaultSettingsSaved": "Default Settings Saved",
         "delete": "Delete",
         "deleteConfig": "Delete Config",
         "deleteModel": "Delete Model",
@@ -852,6 +854,7 @@
         "upcastAttention": "Upcast Attention",
         "updateModel": "Update Model",
         "useCustomConfig": "Use Custom Config",
+        "useDefaultSettings": "Use Default Settings",
         "v1": "v1",
         "v2_768": "v2 (768px)",
         "v2_base": "v2 (512px)",
@@ -870,6 +873,7 @@
     "models": {
         "addLora": "Add LoRA",
         "allLoRAsAdded": "All LoRAs added",
+        "concepts": "Concepts",
         "loraAlreadyAdded": "LoRA already added",
         "esrganModel": "ESRGAN Model",
         "loading": "loading",

invokeai/frontend/web/public/locales/es.json

@@ -505,8 +505,6 @@
         "seamLowThreshold": "Bajo",
         "coherencePassHeader": "Parámetros de la coherencia",
         "compositingSettingsHeader": "Ajustes de la composición",
-        "coherenceSteps": "Pasos",
-        "coherenceStrength": "Fuerza",
         "patchmatchDownScaleSize": "Reducir a escala",
         "coherenceMode": "Modo"
     },

invokeai/frontend/web/public/locales/it.json

@@ -114,7 +114,8 @@
         "checkpoint": "Checkpoint",
         "safetensors": "Safetensors",
         "ai": "ia",
-        "file": "File"
+        "file": "File",
+        "toResolve": "Da risolvere"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -142,8 +143,6 @@
         "copy": "Copia",
         "download": "Scarica",
         "setCurrentImage": "Imposta come immagine corrente",
-        "preparingDownload": "Preparazione del download",
-        "preparingDownloadFailed": "Problema durante la preparazione del download",
         "downloadSelection": "Scarica gli elementi selezionati",
         "noImageSelected": "Nessuna immagine selezionata",
         "deleteSelection": "Elimina la selezione",
@@ -609,8 +608,6 @@
         "seamLowThreshold": "Basso",
         "seamHighThreshold": "Alto",
         "coherencePassHeader": "Passaggio di coerenza",
-        "coherenceSteps": "Passi",
-        "coherenceStrength": "Forza",
         "compositingSettingsHeader": "Impostazioni di composizione",
         "patchmatchDownScaleSize": "Ridimensiona",
         "coherenceMode": "Modalità",
@@ -1400,19 +1397,6 @@
                 "Regola la maschera."
             ]
         },
-        "compositingCoherenceSteps": {
-            "heading": "Passi",
-            "paragraphs": [
-                "Numero di passi utilizzati nel Passaggio di Coerenza.",
-                "Simile ai passi di generazione."
-            ]
-        },
-        "compositingBlur": {
-            "heading": "Sfocatura",
-            "paragraphs": [
-                "Il raggio di sfocatura della maschera."
-            ]
-        },
         "compositingCoherenceMode": {
             "heading": "Modalità",
             "paragraphs": [
@@ -1431,13 +1415,6 @@
                 "Un secondo ciclo di riduzione del rumore aiuta a comporre l'immagine Inpaint/Outpaint."
             ]
         },
-        "compositingStrength": {
-            "heading": "Forza",
-            "paragraphs": [
-                "Quantità di rumore aggiunta per il Passaggio di Coerenza.",
-                "Simile alla forza di riduzione del rumore."
-            ]
-        },
         "paramNegativeConditioning": {
             "paragraphs": [
                 "Il processo di generazione evita i concetti nel prompt negativo. Utilizzatelo per escludere qualità o oggetti dall'output.",

invokeai/frontend/web/public/locales/ko.json

@@ -123,8 +123,6 @@
         "autoSwitchNewImages": "새로운 이미지로 자동 전환",
         "loading": "불러오는 중",
         "unableToLoad": "갤러리를 로드할 수 없음",
-        "preparingDownload": "다운로드 준비",
-        "preparingDownloadFailed": "다운로드 준비 중 발생한 문제",
         "singleColumnLayout": "단일 열 레이아웃",
         "image": "이미지",
         "loadMore": "더 불러오기",

invokeai/frontend/web/public/locales/nl.json

@@ -97,8 +97,6 @@
         "featuresWillReset": "Als je deze afbeelding verwijdert, dan worden deze functies onmiddellijk teruggezet.",
         "loading": "Bezig met laden",
         "unableToLoad": "Kan galerij niet laden",
-        "preparingDownload": "Bezig met voorbereiden van download",
-        "preparingDownloadFailed": "Fout bij voorbereiden van download",
         "downloadSelection": "Download selectie",
         "currentlyInUse": "Deze afbeelding is momenteel in gebruik door de volgende functies:",
         "copy": "Kopieer",
@@ -535,8 +533,6 @@
         "coherencePassHeader": "Coherentiestap",
         "maskBlur": "Vervaag",
         "maskBlurMethod": "Vervagingsmethode",
-        "coherenceSteps": "Stappen",
-        "coherenceStrength": "Sterkte",
         "seamHighThreshold": "Hoog",
         "seamLowThreshold": "Laag",
         "invoke": {
@@ -1139,13 +1135,6 @@
                 "Een afbeeldingsgrootte (in aantal pixels) equivalent aan 512x512 wordt aanbevolen voor SD1.5-modellen. Een grootte-equivalent van 1024x1024 wordt aanbevolen voor SDXL-modellen."
             ]
         },
-        "compositingCoherenceSteps": {
-            "heading": "Stappen",
-            "paragraphs": [
-                "Het aantal te gebruiken ontruisingsstappen in de coherentiefase.",
-                "Gelijk aan de hoofdparameter Stappen."
-            ]
-        },
         "dynamicPrompts": {
             "paragraphs": [
                 "Dynamische prompts vormt een enkele prompt om in vele.",
@@ -1160,12 +1149,6 @@
             ],
             "heading": "VAE"
         },
-        "compositingBlur": {
-            "heading": "Vervaging",
-            "paragraphs": [
-                "De vervagingsstraal van het masker."
-            ]
-        },
         "paramIterations": {
             "paragraphs": [
                 "Het aantal te genereren afbeeldingen.",
@@ -1240,13 +1223,6 @@
             ],
             "heading": "Ontruisingssterkte"
         },
-        "compositingStrength": {
-            "heading": "Sterkte",
-            "paragraphs": [
-                "Ontruisingssterkte voor de coherentiefase.",
-                "Gelijk aan de parameter Ontruisingssterkte Afbeelding naar afbeelding."
-            ]
-        },
         "paramNegativeConditioning": {
             "paragraphs": [
                 "Het genereerproces voorkomt de gegeven begrippen in de negatieve prompt. Gebruik dit om bepaalde zaken of voorwerpen uit te sluiten van de uitvoerafbeelding.",

invokeai/frontend/web/public/locales/ru.json

@@ -143,8 +143,6 @@
         "problemDeletingImagesDesc": "Не удалось удалить одно или несколько изображений",
         "loading": "Загрузка",
         "unableToLoad": "Невозможно загрузить галерею",
-        "preparingDownload": "Подготовка к скачиванию",
-        "preparingDownloadFailed": "Проблема с подготовкой к скачиванию",
         "image": "изображение",
         "drop": "перебросить",
         "problemDeletingImages": "Проблема с удалением изображений",
@@ -612,9 +610,7 @@
         "maskBlurMethod": "Метод размытия",
         "seamLowThreshold": "Низкий",
         "seamHighThreshold": "Высокий",
-        "coherenceSteps": "Шагов",
         "coherencePassHeader": "Порог Coherence",
-        "coherenceStrength": "Сила",
         "compositingSettingsHeader": "Настройки компоновки",
         "invoke": {
             "noNodesInGraph": "Нет узлов в графе",
@@ -1321,13 +1317,6 @@
                 "Размер изображения (в пикселях), эквивалентный 512x512, рекомендуется для моделей SD1.5, а размер, эквивалентный 1024x1024, рекомендуется для моделей SDXL."
             ]
         },
-        "compositingCoherenceSteps": {
-            "heading": "Шаги",
-            "paragraphs": [
-                "Количество шагов снижения шума, используемых при прохождении когерентности.",
-                "То же, что и основной параметр «Шаги»."
-            ]
-        },
         "dynamicPrompts": {
             "paragraphs": [
                 "Динамические запросы превращают одно приглашение на множество.",
@@ -1342,12 +1331,6 @@
             ],
             "heading": "VAE"
         },
-        "compositingBlur": {
-            "heading": "Размытие",
-            "paragraphs": [
-                "Радиус размытия маски."
-            ]
-        },
         "paramIterations": {
             "paragraphs": [
                 "Количество изображений, которые нужно сгенерировать.",
@@ -1422,13 +1405,6 @@
             ],
             "heading": "Шумоподавление"
         },
-        "compositingStrength": {
-            "heading": "Сила",
-            "paragraphs": [
-                null,
-                "То же, что параметр «Сила шумоподавления img2img»."
-            ]
-        },
         "paramNegativeConditioning": {
             "paragraphs": [
                 "Stable Diffusion пытается избежать указанных в отрицательном запросе концепций. Используйте это, чтобы исключить качества или объекты из вывода.",

invokeai/frontend/web/public/locales/tr.json

@@ -355,7 +355,6 @@
         "starImage": "Yıldız Koy",
         "download": "İndir",
         "deleteSelection": "Seçileni Sil",
-        "preparingDownloadFailed": "İndirme Hazırlanırken Sorun",
         "problemDeletingImages": "Görsel Silmede Sorun",
         "featuresWillReset": "Bu görseli silerseniz, o özellikler resetlenecektir.",
         "galleryImageResetSize": "Boyutu Resetle",
@@ -377,7 +376,6 @@
         "setCurrentImage": "Çalışma Görseli Yap",
         "unableToLoad": "Galeri Yüklenemedi",
         "downloadSelection": "Seçileni İndir",
-        "preparingDownload": "İndirmeye Hazırlanıyor",
         "singleColumnLayout": "Tek Sütun Düzen",
         "generations": "Çıktılar",
         "showUploads": "Yüklenenleri Göster",
@@ -723,7 +721,6 @@
         "clipSkip": "CLIP Atlama",
         "randomizeSeed": "Rastgele Tohum",
         "cfgScale": "CFG Ölçeği",
-        "coherenceStrength": "Etki",
         "controlNetControlMode": "Yönetim Kipi",
         "general": "Genel",
         "img2imgStrength": "Görselden Görsel Ölçüsü",
@@ -793,7 +790,6 @@
         "cfgRescaleMultiplier": "CFG Rescale Çarpanı",
         "cfgRescale": "CFG Rescale",
         "coherencePassHeader": "Uyum Geçişi",
-        "coherenceSteps": "Adım",
         "infillMethod": "Doldurma Yöntemi",
         "maskBlurMethod": "Bulandırma Yöntemi",
         "steps": "Adım",

invokeai/frontend/web/public/locales/zh_CN.json

@@ -136,8 +136,6 @@
         "copy": "复制",
         "download": "下载",
         "setCurrentImage": "设为当前图像",
-        "preparingDownload": "准备下载",
-        "preparingDownloadFailed": "准备下载时出现问题",
         "downloadSelection": "下载所选内容",
         "noImageSelected": "无选中的图像",
         "deleteSelection": "删除所选内容",
@@ -616,11 +614,9 @@
             "incompatibleBaseModelForControlAdapter": "有 #{{number}} 个 Control Adapter 模型与主模型不兼容。"
         },
         "patchmatchDownScaleSize": "缩小",
-        "coherenceSteps": "步数",
         "clipSkip": "CLIP 跳过层",
         "compositingSettingsHeader": "合成设置",
         "useCpuNoise": "使用 CPU 噪声",
-        "coherenceStrength": "强度",
         "enableNoiseSettings": "启用噪声设置",
         "coherenceMode": "模式",
         "cpuNoise": "CPU 噪声",
@@ -1402,19 +1398,6 @@
                 "图像尺寸（单位：像素）建议 SD 1.5 模型使用等效 512x512 的尺寸，SDXL 模型使用等效 1024x1024 的尺寸。"
             ]
         },
-        "compositingCoherenceSteps": {
-            "heading": "步数",
-            "paragraphs": [
-                "一致性层中使用的去噪步数。",
-                "与主参数中的步数相同。"
-            ]
-        },
-        "compositingBlur": {
-            "heading": "模糊",
-            "paragraphs": [
-                "遮罩模糊半径。"
-            ]
-        },
         "noiseUseCPU": {
             "heading": "使用 CPU 噪声",
             "paragraphs": [
@@ -1467,13 +1450,6 @@
                 "第二轮去噪有助于合成内补/外扩图像。"
             ]
         },
-        "compositingStrength": {
-            "heading": "强度",
-            "paragraphs": [
-                "一致性层使用的去噪强度。",
-                "去噪强度与图生图的参数相同。"
-            ]
-        },
         "paramNegativeConditioning": {
             "paragraphs": [
                 "生成过程会避免生成负向提示词中的概念。使用此选项来使输出排除部分质量或对象。",

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -55,6 +55,8 @@ import { addUpscaleRequestedListener } from 'app/store/middleware/listenerMiddle
 import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
 
+import { addSetDefaultSettingsListener } from './listeners/setDefaultSettings';
+
 export const listenerMiddleware = createListenerMiddleware();
 
 export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
@@ -153,3 +155,5 @@ addUpscaleRequestedListener(startAppListening);
 
 // Dynamic prompts
 addDynamicPromptsListener(startAppListening);
+
+addSetDefaultSettingsListener(startAppListening);

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/setDefaultSettings.ts

@@ -0,0 +1,96 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { setDefaultSettings } from 'features/parameters/store/actions';
+import {
+  setCfgRescaleMultiplier,
+  setCfgScale,
+  setScheduler,
+  setSteps,
+  vaePrecisionChanged,
+  vaeSelected,
+} from 'features/parameters/store/generationSlice';
+import {
+  isParameterCFGRescaleMultiplier,
+  isParameterCFGScale,
+  isParameterPrecision,
+  isParameterScheduler,
+  isParameterSteps,
+  zParameterVAEModel,
+} from 'features/parameters/types/parameterSchemas';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
+import { t } from 'i18next';
+import { map } from 'lodash-es';
+import { modelsApi } from 'services/api/endpoints/models';
+
+export const addSetDefaultSettingsListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: setDefaultSettings,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const currentModel = state.generation.model;
+
+      if (!currentModel) {
+        return;
+      }
+
+      const metadata = await dispatch(modelsApi.endpoints.getModelMetadata.initiate(currentModel.key)).unwrap();
+
+      if (!metadata || !metadata.default_settings) {
+        return;
+      }
+
+      const { vae, vae_precision, cfg_scale, cfg_rescale_multiplier, steps, scheduler } = metadata.default_settings;
+
+      if (vae) {
+        // we store this as "default" within default settings
+        // to distinguish it from no default set
+        if (vae === 'default') {
+          dispatch(vaeSelected(null));
+        } else {
+          const { data } = modelsApi.endpoints.getVaeModels.select()(state);
+          const vaeArray = map(data?.entities);
+          const validVae = vaeArray.find((model) => model.key === vae);
+
+          const result = zParameterVAEModel.safeParse(validVae);
+          if (!result.success) {
+            return;
+          }
+          dispatch(vaeSelected(result.data));
+        }
+      }
+
+      if (vae_precision) {
+        if (isParameterPrecision(vae_precision)) {
+          dispatch(vaePrecisionChanged(vae_precision));
+        }
+      }
+
+      if (cfg_scale) {
+        if (isParameterCFGScale(cfg_scale)) {
+          dispatch(setCfgScale(cfg_scale));
+        }
+      }
+
+      if (cfg_rescale_multiplier) {
+        if (isParameterCFGRescaleMultiplier(cfg_rescale_multiplier)) {
+          dispatch(setCfgRescaleMultiplier(cfg_rescale_multiplier));
+        }
+      }
+
+      if (steps) {
+        if (isParameterSteps(steps)) {
+          dispatch(setSteps(steps));
+        }
+      }
+
+      if (scheduler) {
+        if (isParameterScheduler(scheduler)) {
+          dispatch(setScheduler(scheduler));
+        }
+      }
+
+      dispatch(addToast(makeToast({ title: t('toast.parameterSet', { parameter: 'Default settings' }) })));
+    },
+  });
+};

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -1,4 +1,5 @@
 import type { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
+import type { ParameterPrecision, ParameterScheduler } from 'features/parameters/types/parameterSchemas';
 import type { InvokeTabName } from 'features/ui/store/tabMap';
 import type { O } from 'ts-toolbelt';
 
@@ -82,6 +83,8 @@ export type AppConfig = {
     guidance: NumericalParameterConfig;
     cfgRescaleMultiplier: NumericalParameterConfig;
     img2imgStrength: NumericalParameterConfig;
+    scheduler?: ParameterScheduler;
+    vaePrecision?: ParameterPrecision;
     // Canvas
     boundingBoxHeight: NumericalParameterConfig; // initial value comes from model
     boundingBoxWidth: NumericalParameterConfig; // initial value comes from model

invokeai/frontend/web/src/features/lora/components/LoRASelect.tsx

@@ -59,7 +59,7 @@ const LoRASelect = () => {
   return (
     <FormControl isDisabled={!options.length}>
       <InformationalPopover feature="lora">
-        <FormLabel>{t('models.lora')} </FormLabel>
+        <FormLabel>{t('models.concepts')} </FormLabel>
       </InformationalPopover>
       <Combobox
         placeholder={placeholder}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ImportQueue/ImportQueueBadge.tsx

@@ -15,7 +15,7 @@ const STATUSES = {
 const ImportQueueBadge = ({ status, errorReason }: { status?: ModelInstallStatus; errorReason?: string | null }) => {
   const { t } = useTranslation();
 
-  if (!status) {
+  if (!status || !Object.keys(STATUSES).includes(status)) {
     return <></>;
   }
 

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings.tsx

@@ -0,0 +1,66 @@
+import { skipToken } from '@reduxjs/toolkit/query';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import Loading from 'common/components/Loading/Loading';
+import { selectConfigSlice } from 'features/system/store/configSlice';
+import { isNil } from 'lodash-es';
+import { useMemo } from 'react';
+import { useGetModelMetadataQuery } from 'services/api/endpoints/models';
+
+import { DefaultSettingsForm } from './DefaultSettings/DefaultSettingsForm';
+
+const initialStatesSelector = createMemoizedSelector(selectConfigSlice, (config) => {
+  const { steps, guidance, scheduler, cfgRescaleMultiplier, vaePrecision } = config.sd;
+
+  return {
+    initialSteps: steps.initial,
+    initialCfg: guidance.initial,
+    initialScheduler: scheduler,
+    initialCfgRescaleMultiplier: cfgRescaleMultiplier.initial,
+    initialVaePrecision: vaePrecision,
+  };
+});
+
+export const DefaultSettings = () => {
+  const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+
+  const { data, isLoading } = useGetModelMetadataQuery(selectedModelKey ?? skipToken);
+  const { initialSteps, initialCfg, initialScheduler, initialCfgRescaleMultiplier, initialVaePrecision } =
+    useAppSelector(initialStatesSelector);
+
+  const defaultSettingsDefaults = useMemo(() => {
+    return {
+      vae: { isEnabled: !isNil(data?.default_settings?.vae), value: data?.default_settings?.vae || 'default' },
+      vaePrecision: {
+        isEnabled: !isNil(data?.default_settings?.vae_precision),
+        value: data?.default_settings?.vae_precision || initialVaePrecision || 'fp32',
+      },
+      scheduler: {
+        isEnabled: !isNil(data?.default_settings?.scheduler),
+        value: data?.default_settings?.scheduler || initialScheduler || 'euler',
+      },
+      steps: { isEnabled: !isNil(data?.default_settings?.steps), value: data?.default_settings?.steps || initialSteps },
+      cfgScale: {
+        isEnabled: !isNil(data?.default_settings?.cfg_scale),
+        value: data?.default_settings?.cfg_scale || initialCfg,
+      },
+      cfgRescaleMultiplier: {
+        isEnabled: !isNil(data?.default_settings?.cfg_rescale_multiplier),
+        value: data?.default_settings?.cfg_rescale_multiplier || initialCfgRescaleMultiplier,
+      },
+    };
+  }, [
+    data?.default_settings,
+    initialSteps,
+    initialCfg,
+    initialScheduler,
+    initialCfgRescaleMultiplier,
+    initialVaePrecision,
+  ]);
+
+  if (isLoading) {
+    return <Loading />;
+  }
+
+  return <DefaultSettingsForm defaultSettingsDefaults={defaultSettingsDefaults} />;
+};

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultCfgRescaleMultiplier.tsx

@@ -0,0 +1,72 @@
+import { CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+
+import type { DefaultSettingsFormData } from './DefaultSettingsForm';
+
+type DefaultCfgRescaleMultiplierType = DefaultSettingsFormData['cfgRescaleMultiplier'];
+
+export function DefaultCfgRescaleMultiplier(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { field } = useController(props);
+
+  const sliderMin = useAppSelector((s) => s.config.sd.cfgRescaleMultiplier.sliderMin);
+  const sliderMax = useAppSelector((s) => s.config.sd.cfgRescaleMultiplier.sliderMax);
+  const numberInputMin = useAppSelector((s) => s.config.sd.cfgRescaleMultiplier.numberInputMin);
+  const numberInputMax = useAppSelector((s) => s.config.sd.cfgRescaleMultiplier.numberInputMax);
+  const coarseStep = useAppSelector((s) => s.config.sd.cfgRescaleMultiplier.coarseStep);
+  const fineStep = useAppSelector((s) => s.config.sd.cfgRescaleMultiplier.fineStep);
+  const { t } = useTranslation();
+  const marks = useMemo(() => [sliderMin, Math.floor(sliderMax / 2), sliderMax], [sliderMax, sliderMin]);
+
+  const onChange = useCallback(
+    (v: number) => {
+      const updatedValue = {
+        ...(field.value as DefaultCfgRescaleMultiplierType),
+        value: v,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  const value = useMemo(() => {
+    return (field.value as DefaultCfgRescaleMultiplierType).value;
+  }, [field.value]);
+
+  const isDisabled = useMemo(() => {
+    return !(field.value as DefaultCfgRescaleMultiplierType).isEnabled;
+  }, [field.value]);
+
+  return (
+    <FormControl flexDir="column" gap={1} alignItems="flex-start">
+      <InformationalPopover feature="paramCFGRescaleMultiplier">
+        <FormLabel>{t('parameters.cfgRescaleMultiplier')}</FormLabel>
+      </InformationalPopover>
+      <Flex w="full" gap={1}>
+        <CompositeSlider
+          value={value}
+          min={sliderMin}
+          max={sliderMax}
+          step={coarseStep}
+          fineStep={fineStep}
+          onChange={onChange}
+          marks={marks}
+          isDisabled={isDisabled}
+        />
+        <CompositeNumberInput
+          value={value}
+          min={numberInputMin}
+          max={numberInputMax}
+          step={coarseStep}
+          fineStep={fineStep}
+          onChange={onChange}
+          isDisabled={isDisabled}
+        />
+      </Flex>
+    </FormControl>
+  );
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultCfgScale.tsx

@@ -0,0 +1,72 @@
+import { CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+
+import type { DefaultSettingsFormData } from './DefaultSettingsForm';
+
+type DefaultCfgType = DefaultSettingsFormData['cfgScale'];
+
+export function DefaultCfgScale(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { field } = useController(props);
+
+  const sliderMin = useAppSelector((s) => s.config.sd.guidance.sliderMin);
+  const sliderMax = useAppSelector((s) => s.config.sd.guidance.sliderMax);
+  const numberInputMin = useAppSelector((s) => s.config.sd.guidance.numberInputMin);
+  const numberInputMax = useAppSelector((s) => s.config.sd.guidance.numberInputMax);
+  const coarseStep = useAppSelector((s) => s.config.sd.guidance.coarseStep);
+  const fineStep = useAppSelector((s) => s.config.sd.guidance.fineStep);
+  const { t } = useTranslation();
+  const marks = useMemo(() => [sliderMin, Math.floor(sliderMax / 2), sliderMax], [sliderMax, sliderMin]);
+
+  const onChange = useCallback(
+    (v: number) => {
+      const updatedValue = {
+        ...(field.value as DefaultCfgType),
+        value: v,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  const value = useMemo(() => {
+    return (field.value as DefaultCfgType).value;
+  }, [field.value]);
+
+  const isDisabled = useMemo(() => {
+    return !(field.value as DefaultCfgType).isEnabled;
+  }, [field.value]);
+
+  return (
+    <FormControl flexDir="column" gap={1} alignItems="flex-start">
+      <InformationalPopover feature="paramCFGScale">
+        <FormLabel>{t('parameters.cfgScale')}</FormLabel>
+      </InformationalPopover>
+      <Flex w="full" gap={1}>
+        <CompositeSlider
+          value={value}
+          min={sliderMin}
+          max={sliderMax}
+          step={coarseStep}
+          fineStep={fineStep}
+          onChange={onChange}
+          marks={marks}
+          isDisabled={isDisabled}
+        />
+        <CompositeNumberInput
+          value={value}
+          min={numberInputMin}
+          max={numberInputMax}
+          step={coarseStep}
+          fineStep={fineStep}
+          onChange={onChange}
+          isDisabled={isDisabled}
+        />
+      </Flex>
+    </FormControl>
+  );
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultScheduler.tsx

@@ -0,0 +1,50 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { SCHEDULER_OPTIONS } from 'features/parameters/types/constants';
+import { isParameterScheduler } from 'features/parameters/types/parameterSchemas';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+
+import type { DefaultSettingsFormData } from './DefaultSettingsForm';
+
+type DefaultSchedulerType = DefaultSettingsFormData['scheduler'];
+
+export function DefaultScheduler(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { t } = useTranslation();
+  const { field } = useController(props);
+
+  const onChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      if (!isParameterScheduler(v?.value)) {
+        return;
+      }
+      const updatedValue = {
+        ...(field.value as DefaultSchedulerType),
+        value: v.value,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  const value = useMemo(
+    () => SCHEDULER_OPTIONS.find((o) => o.value === (field.value as DefaultSchedulerType).value),
+    [field]
+  );
+
+  const isDisabled = useMemo(() => {
+    return !(field.value as DefaultSchedulerType).isEnabled;
+  }, [field.value]);
+
+  return (
+    <FormControl flexDir="column" gap={1} alignItems="flex-start">
+      <InformationalPopover feature="paramScheduler">
+        <FormLabel>{t('parameters.scheduler')}</FormLabel>
+      </InformationalPopover>
+      <Combobox isDisabled={isDisabled} value={value} options={SCHEDULER_OPTIONS} onChange={onChange} />
+    </FormControl>
+  );
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultSettingsForm.tsx

@@ -0,0 +1,147 @@
+import { Button, Flex, Heading } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import type { ParameterScheduler } from 'features/parameters/types/parameterSchemas';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
+import { useCallback } from 'react';
+import type { SubmitHandler } from 'react-hook-form';
+import { useForm } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+import { IoPencil } from 'react-icons/io5';
+import { useUpdateModelMetadataMutation } from 'services/api/endpoints/models';
+
+import { DefaultCfgRescaleMultiplier } from './DefaultCfgRescaleMultiplier';
+import { DefaultCfgScale } from './DefaultCfgScale';
+import { DefaultScheduler } from './DefaultScheduler';
+import { DefaultSteps } from './DefaultSteps';
+import { DefaultVae } from './DefaultVae';
+import { DefaultVaePrecision } from './DefaultVaePrecision';
+import { SettingToggle } from './SettingToggle';
+
+export interface FormField<T> {
+  value: T;
+  isEnabled: boolean;
+}
+
+export type DefaultSettingsFormData = {
+  vae: FormField<string>;
+  vaePrecision: FormField<string>;
+  scheduler: FormField<ParameterScheduler>;
+  steps: FormField<number>;
+  cfgScale: FormField<number>;
+  cfgRescaleMultiplier: FormField<number>;
+};
+
+export const DefaultSettingsForm = ({
+  defaultSettingsDefaults,
+}: {
+  defaultSettingsDefaults: DefaultSettingsFormData;
+}) => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+
+  const [editModelMetadata, { isLoading }] = useUpdateModelMetadataMutation();
+
+  const { handleSubmit, control, formState } = useForm<DefaultSettingsFormData>({
+    defaultValues: defaultSettingsDefaults,
+  });
+
+  const onSubmit = useCallback<SubmitHandler<DefaultSettingsFormData>>(
+    (data) => {
+      if (!selectedModelKey) {
+        return;
+      }
+
+      const body = {
+        vae: data.vae.isEnabled ? data.vae.value : null,
+        vae_precision: data.vaePrecision.isEnabled ? data.vaePrecision.value : null,
+        cfg_scale: data.cfgScale.isEnabled ? data.cfgScale.value : null,
+        cfg_rescale_multiplier: data.cfgRescaleMultiplier.isEnabled ? data.cfgRescaleMultiplier.value : null,
+        steps: data.steps.isEnabled ? data.steps.value : null,
+        scheduler: data.scheduler.isEnabled ? data.scheduler.value : null,
+      };
+
+      editModelMetadata({
+        key: selectedModelKey,
+        body: { default_settings: body },
+      })
+        .unwrap()
+        .then((_) => {
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.defaultSettingsSaved'),
+                status: 'success',
+              })
+            )
+          );
+        })
+        .catch((error) => {
+          if (error) {
+            dispatch(
+              addToast(
+                makeToast({
+                  title: `${error.data.detail} `,
+                  status: 'error',
+                })
+              )
+            );
+          }
+        });
+    },
+    [selectedModelKey, dispatch, editModelMetadata, t]
+  );
+
+  return (
+    <>
+      <Flex gap="2" justifyContent="space-between" w="full" mb={5}>
+        <Heading fontSize="md">{t('modelManager.defaultSettings')}</Heading>
+        <Button
+          size="sm"
+          leftIcon={<IoPencil />}
+          colorScheme="invokeYellow"
+          isDisabled={!formState.isDirty}
+          onClick={handleSubmit(onSubmit)}
+          type="submit"
+          isLoading={isLoading}
+        >
+          {t('common.save')}
+        </Button>
+      </Flex>
+
+      <Flex flexDir="column" gap={8}>
+        <Flex gap={8}>
+          <Flex gap={4} w="full">
+            <SettingToggle control={control} name="vae" />
+            <DefaultVae control={control} name="vae" />
+          </Flex>
+          <Flex gap={4} w="full">
+            <SettingToggle control={control} name="vaePrecision" />
+            <DefaultVaePrecision control={control} name="vaePrecision" />
+          </Flex>
+        </Flex>
+        <Flex gap={8}>
+          <Flex gap={4} w="full">
+            <SettingToggle control={control} name="scheduler" />
+            <DefaultScheduler control={control} name="scheduler" />
+          </Flex>
+          <Flex gap={4} w="full">
+            <SettingToggle control={control} name="steps" />
+            <DefaultSteps control={control} name="steps" />
+          </Flex>
+        </Flex>
+        <Flex gap={8}>
+          <Flex gap={4} w="full">
+            <SettingToggle control={control} name="cfgScale" />
+            <DefaultCfgScale control={control} name="cfgScale" />
+          </Flex>
+          <Flex gap={4} w="full">
+            <SettingToggle control={control} name="cfgRescaleMultiplier" />
+            <DefaultCfgRescaleMultiplier control={control} name="cfgRescaleMultiplier" />
+          </Flex>
+        </Flex>
+      </Flex>
+    </>
+  );
+};

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultSteps.tsx

@@ -0,0 +1,72 @@
+import { CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+
+import type { DefaultSettingsFormData } from './DefaultSettingsForm';
+
+type DefaultSteps = DefaultSettingsFormData['steps'];
+
+export function DefaultSteps(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { field } = useController(props);
+
+  const sliderMin = useAppSelector((s) => s.config.sd.steps.sliderMin);
+  const sliderMax = useAppSelector((s) => s.config.sd.steps.sliderMax);
+  const numberInputMin = useAppSelector((s) => s.config.sd.steps.numberInputMin);
+  const numberInputMax = useAppSelector((s) => s.config.sd.steps.numberInputMax);
+  const coarseStep = useAppSelector((s) => s.config.sd.steps.coarseStep);
+  const fineStep = useAppSelector((s) => s.config.sd.steps.fineStep);
+  const { t } = useTranslation();
+  const marks = useMemo(() => [sliderMin, Math.floor(sliderMax / 2), sliderMax], [sliderMax, sliderMin]);
+
+  const onChange = useCallback(
+    (v: number) => {
+      const updatedValue = {
+        ...(field.value as DefaultSteps),
+        value: v,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  const value = useMemo(() => {
+    return (field.value as DefaultSteps).value;
+  }, [field.value]);
+
+  const isDisabled = useMemo(() => {
+    return !(field.value as DefaultSteps).isEnabled;
+  }, [field.value]);
+
+  return (
+    <FormControl flexDir="column" gap={1} alignItems="flex-start">
+      <InformationalPopover feature="paramSteps">
+        <FormLabel>{t('parameters.steps')}</FormLabel>
+      </InformationalPopover>
+      <Flex w="full" gap={1}>
+        <CompositeSlider
+          value={value}
+          min={sliderMin}
+          max={sliderMax}
+          step={coarseStep}
+          fineStep={fineStep}
+          onChange={onChange}
+          marks={marks}
+          isDisabled={isDisabled}
+        />
+        <CompositeNumberInput
+          value={value}
+          min={numberInputMin}
+          max={numberInputMax}
+          step={coarseStep}
+          fineStep={fineStep}
+          onChange={onChange}
+          isDisabled={isDisabled}
+        />
+      </Flex>
+    </FormControl>
+  );
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultVae.tsx

@@ -0,0 +1,65 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { skipToken } from '@reduxjs/toolkit/query';
+import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { map } from 'lodash-es';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+import { useGetModelConfigQuery, useGetVaeModelsQuery } from 'services/api/endpoints/models';
+
+import type { DefaultSettingsFormData } from './DefaultSettingsForm';
+
+type DefaultVaeType = DefaultSettingsFormData['vae'];
+
+export function DefaultVae(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { t } = useTranslation();
+  const { field } = useController(props);
+  const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+  const { data: modelData } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
+
+  const { compatibleOptions } = useGetVaeModelsQuery(undefined, {
+    selectFromResult: ({ data }) => {
+      const modelArray = map(data?.entities);
+      const compatibleOptions = modelArray
+        .filter((vae) => vae.base === modelData?.base)
+        .map((vae) => ({ label: vae.name, value: vae.key }));
+
+      const defaultOption = { label: 'Default VAE', value: 'default' };
+
+      return { compatibleOptions: [defaultOption, ...compatibleOptions] };
+    },
+  });
+
+  const onChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      const newValue = !v?.value ? 'default' : v.value;
+
+      const updatedValue = {
+        ...(field.value as DefaultVaeType),
+        value: newValue,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  const value = useMemo(() => {
+    return compatibleOptions.find((vae) => vae.value === (field.value as DefaultVaeType).value);
+  }, [compatibleOptions, field.value]);
+
+  const isDisabled = useMemo(() => {
+    return !(field.value as DefaultVaeType).isEnabled;
+  }, [field.value]);
+
+  return (
+    <FormControl flexDir="column" gap={1} alignItems="flex-start">
+      <InformationalPopover feature="paramVAE">
+        <FormLabel>{t('modelManager.vae')}</FormLabel>
+      </InformationalPopover>
+      <Combobox isDisabled={isDisabled} value={value} options={compatibleOptions} onChange={onChange} />
+    </FormControl>
+  );
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultVaePrecision.tsx

@@ -0,0 +1,51 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { isParameterPrecision } from 'features/parameters/types/parameterSchemas';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+import { useTranslation } from 'react-i18next';
+
+import type { DefaultSettingsFormData } from './DefaultSettingsForm';
+
+const options = [
+  { label: 'FP16', value: 'fp16' },
+  { label: 'FP32', value: 'fp32' },
+];
+
+type DefaultVaePrecisionType = DefaultSettingsFormData['vaePrecision'];
+
+export function DefaultVaePrecision(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { t } = useTranslation();
+  const { field } = useController(props);
+
+  const onChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      if (!isParameterPrecision(v?.value)) {
+        return;
+      }
+      const updatedValue = {
+        ...(field.value as DefaultVaePrecisionType),
+        value: v.value,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  const value = useMemo(() => options.find((o) => o.value === (field.value as DefaultVaePrecisionType).value), [field]);
+
+  const isDisabled = useMemo(() => {
+    return !(field.value as DefaultVaePrecisionType).isEnabled;
+  }, [field.value]);
+
+  return (
+    <FormControl flexDir="column" gap={1} alignItems="flex-start">
+      <InformationalPopover feature="paramVAEPrecision">
+        <FormLabel>{t('modelManager.vaePrecision')}</FormLabel>
+      </InformationalPopover>
+      <Combobox isDisabled={isDisabled} value={value} options={options} onChange={onChange} />
+    </FormControl>
+  );
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/SettingToggle.tsx

@@ -0,0 +1,28 @@
+import { Switch } from '@invoke-ai/ui-library';
+import type { ChangeEvent } from 'react';
+import { useCallback, useMemo } from 'react';
+import type { UseControllerProps } from 'react-hook-form';
+import { useController } from 'react-hook-form';
+
+import type { DefaultSettingsFormData, FormField } from './DefaultSettingsForm';
+
+export function SettingToggle<T>(props: UseControllerProps<DefaultSettingsFormData>) {
+  const { field } = useController(props);
+
+  const value = useMemo(() => {
+    return !!(field.value as FormField<T>).isEnabled;
+  }, [field.value]);
+
+  const onChange = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      const updatedValue: FormField<T> = {
+        ...(field.value as FormField<T>),
+        isEnabled: e.target.checked,
+      };
+      field.onChange(updatedValue);
+    },
+    [field]
+  );
+
+  return <Switch isChecked={value} onChange={onChange} />;
+}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelView.tsx

@@ -17,6 +17,7 @@ import type {
   VAEModelConfig,
 } from 'services/api/types';
 
+import { DefaultSettings } from './DefaultSettings';
 import { ModelAttrView } from './ModelAttrView';
 import { ModelConvert } from './ModelConvert';
 
@@ -71,7 +72,7 @@ export const ModelView = () => {
     return <Text>{t('common.somethingWentWrong')}</Text>;
   }
   return (
-    <Flex flexDir="column" h="full">
+    <Flex flexDir="column" h="full" gap="2">
       <Box layerStyle="second" borderRadius="base" p={3}>
         <Flex gap="2" justifyContent="flex-end" w="full">
           <Button size="sm" leftIcon={<IoPencil />} colorScheme="invokeYellow" onClick={handleEditModel}>
@@ -118,6 +119,9 @@ export const ModelView = () => {
           )}
         </Flex>
       </Box>
+      <Box layerStyle="second" borderRadius="base" p={3}>
+        <DefaultSettings />
+      </Box>
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasInpaintGraph.ts

@@ -344,8 +344,8 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
+          node_id: INPAINT_CREATE_MASK,
-          field: 'image',
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasOutpaintGraph.ts

@@ -439,8 +439,8 @@ export const buildCanvasOutpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
+          node_id: INPAINT_CREATE_MASK,
-          field: 'image',
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLInpaintGraph.ts

@@ -355,8 +355,8 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
+          node_id: INPAINT_CREATE_MASK,
-          field: 'image',
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLOutpaintGraph.ts

@@ -448,8 +448,8 @@ export const buildCanvasSDXLOutpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
+          node_id: INPAINT_CREATE_MASK,
-          field: 'image',
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/parameters/components/MainModel/NavigateToModelManagerButton.tsx

@@ -0,0 +1,36 @@
+import type { IconButtonProps } from '@invoke-ai/ui-library';
+import { IconButton } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { setActiveTab } from 'features/ui/store/uiSlice';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiGearSixBold } from 'react-icons/pi';
+
+export const NavigateToModelManagerButton = memo((props: Omit<IconButtonProps, 'aria-label'>) => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
+  const shouldShowButton = useMemo(() => !disabledTabs.includes('modelManager'), [disabledTabs]);
+
+  const handleClick = useCallback(() => {
+    dispatch(setActiveTab('modelManager'));
+  }, [dispatch]);
+
+  if (!shouldShowButton) {
+    return null;
+  }
+
+  return (
+    <IconButton
+      icon={<PiGearSixBold />}
+      tooltip={t('modelManager.modelManager')}
+      aria-label={t('modelManager.modelManager')}
+      onClick={handleClick}
+      size="sm"
+      variant="ghost"
+      {...props}
+    />
+  );
+});
+
+NavigateToModelManagerButton.displayName = 'NavigateToModelManagerButton';

invokeai/frontend/web/src/features/parameters/components/MainModel/UseDefaultSettingsButton.tsx

@@ -0,0 +1,28 @@
+import { IconButton } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { setDefaultSettings } from 'features/parameters/store/actions';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { RiSparklingFill } from 'react-icons/ri';
+
+export const UseDefaultSettingsButton = () => {
+  const model = useAppSelector((s) => s.generation.model);
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  const handleClickDefaultSettings = useCallback(() => {
+    dispatch(setDefaultSettings());
+  }, [dispatch]);
+
+  return (
+    <IconButton
+      icon={<RiSparklingFill />}
+      tooltip={t('modelManager.useDefaultSettings')}
+      aria-label={t('modelManager.useDefaultSettings')}
+      isDisabled={!model}
+      onClick={handleClickDefaultSettings}
+      size="sm"
+      variant="ghost"
+    />
+  );
+};

invokeai/frontend/web/src/features/parameters/store/actions.ts

@@ -5,3 +5,5 @@ import type { ImageDTO } from 'services/api/types';
 export const initialImageSelected = createAction<ImageDTO | undefined>('generation/initialImageSelected');
 
 export const modelSelected = createAction<ParameterModel>('generation/modelSelected');
+
+export const setDefaultSettings = createAction('generation/setDefaultSettings');

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -230,6 +230,12 @@ export const generationSlice = createSlice({
           state.height = optimalDimension;
         }
       }
+      if (action.payload.sd?.scheduler) {
+        state.scheduler = action.payload.sd.scheduler;
+      }
+      if (action.payload.sd?.vaePrecision) {
+        state.vaePrecision = action.payload.sd.vaePrecision;
+      }
     });
 
     // TODO: This is a temp fix to reduce issues with T2I adapter having a different downscaling

invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx

@@ -1,15 +1,5 @@
 import type { FormLabelProps } from '@invoke-ai/ui-library';
-import {
+import { Box, Expander, Flex, FormControlGroup, StandaloneAccordion } from '@invoke-ai/ui-library';
-  Expander,
-  Flex,
-  FormControlGroup,
-  StandaloneAccordion,
-  Tab,
-  TabList,
-  TabPanel,
-  TabPanels,
-  Tabs,
-} from '@invoke-ai/ui-library';
 import { EMPTY_ARRAY } from 'app/store/constants';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
@@ -20,7 +10,9 @@ import { SyncModelsIconButton } from 'features/modelManagerV2/components/SyncMod
 import ParamCFGScale from 'features/parameters/components/Core/ParamCFGScale';
 import ParamScheduler from 'features/parameters/components/Core/ParamScheduler';
 import ParamSteps from 'features/parameters/components/Core/ParamSteps';
+import { NavigateToModelManagerButton } from 'features/parameters/components/MainModel/NavigateToModelManagerButton';
 import ParamMainModelSelect from 'features/parameters/components/MainModel/ParamMainModelSelect';
+import { UseDefaultSettingsButton } from 'features/parameters/components/MainModel/UseDefaultSettingsButton';
 import { useExpanderToggle } from 'features/settingsAccordions/hooks/useExpanderToggle';
 import { useStandaloneAccordionToggle } from 'features/settingsAccordions/hooks/useStandaloneAccordionToggle';
 import { filter } from 'lodash-es';
@@ -39,11 +31,11 @@ export const GenerationSettingsAccordion = memo(() => {
     () =>
       createMemoizedSelector(selectLoraSlice, (lora) => {
         const enabledLoRAsCount = filter(lora.loras, (l) => !!l.isEnabled).length;
-        const loraTabBadges = enabledLoRAsCount ? [enabledLoRAsCount] : EMPTY_ARRAY;
+        const loraTabBadges = enabledLoRAsCount ? [`${enabledLoRAsCount} ${t('models.concepts')}`] : EMPTY_ARRAY;
         const accordionBadges = modelConfig ? [modelConfig.name, modelConfig.base] : EMPTY_ARRAY;
         return { loraTabBadges, accordionBadges };
       }),
-    [modelConfig]
+    [modelConfig, t]
   );
   const { loraTabBadges, accordionBadges } = useAppSelector(selectBadges);
   const { isOpen: isOpenExpander, onToggle: onToggleExpander } = useExpanderToggle({
@@ -58,39 +50,35 @@ export const GenerationSettingsAccordion = memo(() => {
   return (
     <StandaloneAccordion
       label={t('accordions.generation.title')}
-      badges={accordionBadges}
+      badges={[...accordionBadges, ...loraTabBadges]}
       isOpen={isOpenAccordion}
       onToggle={onToggleAccordion}
     >
-      <Tabs variant="collapse">
+      <Box px={4} pt={4}>
-        <TabList>
+        <Flex gap={4} flexDir="column">
-          <Tab>{t('accordions.generation.modelTab')}</Tab>
+          <Flex gap={4} alignItems="center">
-          <Tab badges={loraTabBadges}>{t('accordions.generation.conceptsTab')}</Tab>
+            <ParamMainModelSelect />
-        </TabList>
+            <Flex>
-        <TabPanels>
+              <UseDefaultSettingsButton />
-          <TabPanel overflow="visible" px={4} pt={4}>
-            <Flex gap={4} alignItems="center">
-              <ParamMainModelSelect />
               <SyncModelsIconButton />
+              <NavigateToModelManagerButton />
             </Flex>
-            <Expander isOpen={isOpenExpander} onToggle={onToggleExpander}>
+          </Flex>
-              <Flex gap={4} flexDir="column" pb={4}>
+          <Flex gap={4} flexDir="column">
-                <FormControlGroup formLabelProps={formLabelProps}>
+            <LoRASelect />
-                  <ParamScheduler />
+            <LoRAList />
-                  <ParamSteps />
+          </Flex>
-                  <ParamCFGScale />
+        </Flex>
-                </FormControlGroup>
+        <Expander isOpen={isOpenExpander} onToggle={onToggleExpander}>
-              </Flex>
+          <Flex gap={4} flexDir="column" pb={4}>
-            </Expander>
+            <FormControlGroup formLabelProps={formLabelProps}>
-          </TabPanel>
+              <ParamScheduler />
-          <TabPanel>
+              <ParamSteps />
-            <Flex gap={4} p={4} flexDir="column">
+              <ParamCFGScale />
-              <LoRASelect />
+            </FormControlGroup>
-              <LoRAList />
+          </Flex>
-            </Flex>
+        </Expander>
-          </TabPanel>
+      </Box>
-        </TabPanels>
-      </Tabs>
     </StandaloneAccordion>
   );
 });

invokeai/frontend/web/src/features/system/store/configSlice.ts

@@ -41,6 +41,8 @@ const initialConfigState: AppConfig = {
     boundingBoxHeight: { ...baseDimensionConfig },
     scaledBoundingBoxWidth: { ...baseDimensionConfig },
     scaledBoundingBoxHeight: { ...baseDimensionConfig },
+    scheduler: 'euler',
+    vaePrecision: 'fp32',
     steps: {
       initial: 30,
       sliderMin: 1,

pyproject.toml

@@ -51,12 +51,12 @@ dependencies = [
   "torchmetrics==0.11.4",
   "torchsde==0.2.6",
   "torchvision==0.16.2",
-  "transformers==4.37.2",
+  "transformers==4.38.2",
 
   # Core application dependencies, pinned for reproducible builds.
   "fastapi-events==0.10.1",
   "fastapi==0.109.2",
-  "huggingface-hub==0.20.3",
+  "huggingface-hub==0.21.3",
   "pydantic-settings==2.1.0",
   "pydantic==2.6.1",
   "python-socketio==5.11.1",
@@ -64,6 +64,7 @@ dependencies = [
 
   # Auxiliary dependencies, pinned only if necessary.
   "albumentations",
+  "blake3",
   "click",
   "datasets",
   "Deprecated",
@@ -72,7 +73,6 @@ dependencies = [
   "easing-functions",
   "einops",
   "facexlib",
-  "imohash",
   "matplotlib",                            # needed for plotting of Penner easing functions
   "npyscreen",
   "omegaconf",

tests/app/services/model_install/test_model_install.py

@@ -3,6 +3,7 @@ Test the model installer
 """
 
 import platform
+import uuid
 from pathlib import Path
 
 import pytest
@@ -30,9 +31,8 @@ def test_registration(mm2_installer: ModelInstallServiceBase, embedding_file: Pa
     matches = store.search_by_attr(model_name="test_embedding")
     assert len(matches) == 0
     key = mm2_installer.register_path(embedding_file)
-    assert key is not None
+    # Not raising here is sufficient - key should be UUIDv4
-    assert key != "<NOKEY>"
+    uuid.UUID(key, version=4)
-    assert len(key) == 32
 
 
 def test_registration_meta(mm2_installer: ModelInstallServiceBase, embedding_file: Path) -> None:

tests/test_model_hash.py

@@ -0,0 +1,96 @@
+# pyright:reportPrivateUsage=false
+
+from pathlib import Path
+from typing import Iterable
+
+import pytest
+from blake3 import blake3
+
+from invokeai.backend.model_manager.hash import ALGORITHM, MODEL_FILE_EXTENSIONS, ModelHash
+
+test_cases: list[tuple[ALGORITHM, str]] = [
+    ("md5", "a0cd925fc063f98dbf029eee315060c3"),
+    ("sha1", "9e362940e5603fdc60566ea100a288ba2fe48b8c"),
+    ("sha256", "6dbdb6a147ad4d808455652bf5a10120161678395f6bfbd21eb6fe4e731aceeb"),
+    (
+        "sha512",
+        "c4a10476b21e00042f638ad5755c561d91f2bb599d3504d25409495e1c7eda94543332a1a90fbb4efdaf9ee462c33e0336b5eae4acfb1fa0b186af452dd67dc6",
+    ),
+    ("blake3", "ce3f0c5f3c05d119f4a5dcaf209b50d3149046a0d3a9adee9fed4c83cad6b4d0"),
+]
+
+
+@pytest.mark.parametrize("algorithm,expected_hash", test_cases)
+def test_model_hash_hashes_file(tmp_path: Path, algorithm: ALGORITHM, expected_hash: str):
+    file = Path(tmp_path / "test")
+    file.write_text("model data")
+    md5 = ModelHash(algorithm).hash(file)
+    assert md5 == expected_hash
+
+
+@pytest.mark.parametrize("algorithm", ["md5", "sha1", "sha256", "sha512", "blake3"])
+def test_model_hash_hashes_dir(tmp_path: Path, algorithm: ALGORITHM):
+    model_hash = ModelHash(algorithm)
+    files = [Path(tmp_path, f"{i}.bin") for i in range(5)]
+
+    for f in files:
+        f.write_text("data")
+
+    md5 = model_hash.hash(tmp_path)
+
+    # Manual implementation of composite hash - always uses BLAKE3
+    composite_hasher = blake3()
+    for f in files:
+        h = model_hash.hash(f)
+        composite_hasher.update(h.encode("utf-8"))
+
+    assert md5 == composite_hasher.hexdigest()
+
+
+def test_model_hash_raises_error_on_invalid_algorithm():
+    with pytest.raises(ValueError, match="Algorithm invalid_algorithm not available"):
+        ModelHash("invalid_algorithm")  # pyright: ignore [reportArgumentType]
+
+
+def paths_to_str_set(paths: Iterable[Path]) -> set[str]:
+    return {str(p) for p in paths}
+
+
+def test_model_hash_filters_out_non_model_files(tmp_path: Path):
+    model_files = {Path(tmp_path, f"{i}{ext}") for i, ext in enumerate(MODEL_FILE_EXTENSIONS)}
+
+    for i, f in enumerate(model_files):
+        f.write_text(f"data{i}")
+
+    assert paths_to_str_set(ModelHash._get_file_paths(tmp_path, ModelHash._default_file_filter)) == paths_to_str_set(
+        model_files
+    )
+
+    # Add file that should be ignored - hash should not change
+    file = tmp_path / "test.icecream"
+    file.write_text("data")
+
+    assert paths_to_str_set(ModelHash._get_file_paths(tmp_path, ModelHash._default_file_filter)) == paths_to_str_set(
+        model_files
+    )
+
+    # Add file that should not be ignored - hash should change
+    file = tmp_path / "test.bin"
+    file.write_text("more data")
+    model_files.add(file)
+
+    assert paths_to_str_set(ModelHash._get_file_paths(tmp_path, ModelHash._default_file_filter)) == paths_to_str_set(
+        model_files
+    )
+
+
+def test_model_hash_uses_custom_filter(tmp_path: Path):
+    model_files = {Path(tmp_path, f"file{ext}") for ext in [".pickme", ".ignoreme"]}
+
+    for i, f in enumerate(model_files):
+        f.write_text(f"data{i}")
+
+    def file_filter(file_path: str) -> bool:
+        return file_path.endswith(".pickme")
+
+    assert {p.name for p in ModelHash._get_file_paths(tmp_path, file_filter)} == {"file.pickme"}