chore(ui): typgen

to avoid disrupting already-working LoRA

invokeai/app/api/routers/app_info.py

@@ -1,8 +1,7 @@
 import typing
 from enum import Enum
-from importlib.metadata import PackageNotFoundError, version
+from importlib.metadata import distributions
 from pathlib import Path
-from platform import python_version
 from typing import Optional
 
 import torch
@@ -44,24 +43,6 @@ class AppVersion(BaseModel):
     highlights: Optional[list[str]] = Field(default=None, description="Highlights of release")
 
 
-class AppDependencyVersions(BaseModel):
-    """App depencency Versions Response"""
-
-    accelerate: str = Field(description="accelerate version")
-    compel: str = Field(description="compel version")
-    cuda: Optional[str] = Field(description="CUDA version")
-    diffusers: str = Field(description="diffusers version")
-    numpy: str = Field(description="Numpy version")
-    opencv: str = Field(description="OpenCV version")
-    onnx: str = Field(description="ONNX version")
-    pillow: str = Field(description="Pillow (PIL) version")
-    python: str = Field(description="Python version")
-    torch: str = Field(description="PyTorch version")
-    torchvision: str = Field(description="PyTorch Vision version")
-    transformers: str = Field(description="transformers version")
-    xformers: Optional[str] = Field(description="xformers version")
-
-
 class AppConfig(BaseModel):
     """App Config Response"""
 
@@ -76,27 +57,19 @@ async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
-@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
-async def get_app_deps() -> AppDependencyVersions:
+@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=dict[str, str])
+async def get_app_deps() -> dict[str, str]:
+    deps: dict[str, str] = {dist.metadata["Name"]: dist.version for dist in distributions()}
     try:
-        xformers = version("xformers")
-    except PackageNotFoundError:
-        xformers = None
-    return AppDependencyVersions(
-        accelerate=version("accelerate"),
-        compel=version("compel"),
-        cuda=torch.version.cuda,
-        diffusers=version("diffusers"),
-        numpy=version("numpy"),
-        opencv=version("opencv-python"),
-        onnx=version("onnx"),
-        pillow=version("pillow"),
-        python=python_version(),
-        torch=torch.version.__version__,
-        torchvision=version("torchvision"),
-        transformers=version("transformers"),
-        xformers=xformers,
-    )
+        cuda = torch.version.cuda or "N/A"
+    except Exception:
+        cuda = "N/A"
+
+    deps["CUDA"] = cuda
+
+    sorted_deps = dict(sorted(deps.items(), key=lambda item: item[0].lower()))
+
+    return sorted_deps
 
 
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)

invokeai/app/api/routers/images.py

@@ -99,7 +99,9 @@ async def upload_image(
             raise HTTPException(status_code=400, detail="Invalid resize_to format or size")
 
         try:
-            np_image = pil_to_np(pil_image)
+            # heuristic_resize_fast expects an RGB or RGBA image
+            pil_rgba = pil_image.convert("RGBA")
+            np_image = pil_to_np(pil_rgba)
             np_image = heuristic_resize_fast(np_image, (resize_dims.width, resize_dims.height))
             pil_image = np_to_pil(np_image)
         except Exception:

invokeai/app/api_app.py

@@ -158,7 +158,7 @@ web_root_path = Path(list(web_dir.__path__)[0])
 try:
     app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
 except RuntimeError:
-    logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
+    logger.warning(f"No UI found at {web_root_path}/dist, skipping UI mount")
 app.mount(
     "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
 )  # docs favicon is in here

invokeai/app/invocations/baseinvocation.py

@@ -499,7 +499,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         ui_type = field.json_schema_extra.get("ui_type", None)
         if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
+            logger.warning(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
             field.json_schema_extra.pop("ui_type")
     return None
 
@@ -613,7 +613,7 @@ def invocation(
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             uiconfig["version"] = version
         else:
-            logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
+            logger.warning(f'No version specified for node "{invocation_type}", using "1.0.0"')
             uiconfig["version"] = "1.0.0"
 
         cls.UIConfig = UIConfigBase(**uiconfig)

invokeai/app/invocations/compel.py

@@ -114,6 +114,13 @@ class CompelInvocation(BaseInvocation):
 
             c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
+        del compel
+        del patched_tokenizer
+        del tokenizer
+        del ti_manager
+        del text_encoder
+        del text_encoder_info
+
         c = c.detach().to("cpu")
 
         conditioning_data = ConditioningFieldData(conditionings=[BasicConditioningInfo(embeds=c)])
@@ -222,7 +229,10 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
+        del compel
+        del patched_tokenizer
         del tokenizer
+        del ti_manager
         del text_encoder
         del text_encoder_info
 

invokeai/app/invocations/fields.py

@@ -437,7 +437,7 @@ class WithWorkflow:
     workflow = None
 
     def __init_subclass__(cls) -> None:
-        logger.warn(
+        logger.warning(
             f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
         )
         super().__init_subclass__()
@@ -578,7 +578,7 @@ def InputField(
 
     if default_factory is not _Unset and default_factory is not None:
         default = default_factory()
-        logger.warn('"default_factory" is not supported, calling it now to set "default"')
+        logger.warning('"default_factory" is not supported, calling it now to set "default"')
 
     # These are the args we may wish pass to the pydantic `Field()` function
     field_args = {

invokeai/app/services/config/config_default.py

@@ -24,7 +24,6 @@ from invokeai.frontend.cli.arg_parser import InvokeAIArgs
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
-DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
 PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
@@ -93,7 +92,7 @@ class InvokeAIAppConfig(BaseSettings):
         vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
         lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
         pytorch_cuda_alloc_conf: Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to "backend:cudaMallocAsync" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.
-        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
+        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
         precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
@@ -176,7 +175,7 @@ class InvokeAIAppConfig(BaseSettings):
     pytorch_cuda_alloc_conf: Optional[str] = Field(default=None,            description="Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to \"backend:cudaMallocAsync\" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.")
 
     # DEVICE
-    device:                      DEVICE = Field(default="auto",             description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
+    device:                      str = Field(default="auto",                description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)", pattern=r"^(auto|cpu|mps|cuda(:\d+)?)$")
     precision:                PRECISION = Field(default="auto",             description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")
 
     # GENERATION

invokeai/app/services/image_records/image_records_sqlite.py

@@ -196,9 +196,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         # Search term condition
         if search_term:
             query_conditions += """--sql
-            AND images.metadata LIKE ?
+            AND (
+                images.metadata LIKE ?
+                OR images.created_at LIKE ?
+            )
             """
             query_params.append(f"%{search_term.lower()}%")
+            query_params.append(f"%{search_term.lower()}%")
 
         if starred_first:
             query_pagination = f"""--sql

invokeai/app/services/images/images_default.py

@@ -78,7 +78,7 @@ class ImageService(ImageServiceABC):
                         board_id=board_id, image_name=image_name
                     )
                 except Exception as e:
-                    self.__invoker.services.logger.warn(f"Failed to add image to board {board_id}: {str(e)}")
+                    self.__invoker.services.logger.warning(f"Failed to add image to board {board_id}: {str(e)}")
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
             )

invokeai/app/services/model_install/model_install_default.py

@@ -148,7 +148,7 @@ class ModelInstallService(ModelInstallServiceBase):
     def _clear_pending_jobs(self) -> None:
         for job in self.list_jobs():
             if not job.in_terminal_state:
-                self._logger.warning("Cancelling job {job.id}")
+                self._logger.warning(f"Cancelling job {job.id}")
                 self.cancel_job(job)
         while True:
             try:

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,3 +1,4 @@
+import gc
 import traceback
 from contextlib import suppress
 from threading import BoundedSemaphore, Thread
@@ -439,6 +440,12 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         poll_now_event.wait(self._polling_interval)
                         continue
 
+                    # GC-ing here can reduce peak memory usage of the invoke process by freeing allocated memory blocks.
+                    # Most queue items take seconds to execute, so the relative cost of a GC is very small.
+                    # Python will never cede allocated memory back to the OS, so anything we can do to reduce the peak
+                    # allocation is well worth it.
+                    gc.collect()
+
                     self._invoker.services.logger.info(
                         f"Executing queue item {self._queue_item.item_id}, session {self._queue_item.session_id}"
                     )

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -104,11 +104,7 @@ class SqliteSessionQueue(SessionQueueBase):
         return cast(Union[int, None], cursor.fetchone()[0]) or 0
 
     async def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
-        return await asyncio.to_thread(self._enqueue_batch, queue_id, batch, prepend)
-
-    def _enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
         try:
-            cursor = self._conn.cursor()
             # TODO: how does this work in a multi-user scenario?
             current_queue_size = self._get_current_queue_size(queue_id)
             max_queue_size = self.__invoker.services.configuration.max_queue_size
@@ -118,8 +114,12 @@ class SqliteSessionQueue(SessionQueueBase):
             if prepend:
                 priority = self._get_highest_priority(queue_id) + 1
 
-            requested_count = calc_session_count(batch)
-            values_to_insert = prepare_values_to_insert(
+            requested_count = await asyncio.to_thread(
+                calc_session_count,
+                batch=batch,
+            )
+            values_to_insert = await asyncio.to_thread(
+                prepare_values_to_insert,
                 queue_id=queue_id,
                 batch=batch,
                 priority=priority,
@@ -127,19 +127,16 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             enqueued_count = len(values_to_insert)
 
-            if requested_count > enqueued_count:
-                values_to_insert = values_to_insert[:max_new_queue_items]
-
-            cursor.executemany(
-                """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-                """,
-                values_to_insert,
-            )
-            self._conn.commit()
+            with self._conn:
+                cursor = self._conn.cursor()
+                cursor.executemany(
+                    """--sql
+                    INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    """,
+                    values_to_insert,
+                )
         except Exception:
-            self._conn.rollback()
             raise
         enqueue_result = EnqueueBatchResult(
             queue_id=queue_id,

invokeai/backend/ip_adapter/README.md

@@ -42,4 +42,5 @@ IP-Adapters:
 - [InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)
 - [InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)
 - [InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)
-- [InvokeAI/ip_adapter_sdxl_vit_h](https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h)
+- [InvokeAI/ip_adapter_sdxl_vit_h](https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h)
+- [InvokeAI/ip-adapter-plus_sdxl_vit-h](https://huggingface.co/InvokeAI/ip-adapter-plus_sdxl_vit-h)

invokeai/backend/model_manager/config.py

@@ -296,7 +296,7 @@ class LoRAConfigBase(ABC, BaseModel):
         from invokeai.backend.patches.lora_conversions.formats import flux_format_from_state_dict
 
         sd = mod.load_state_dict(mod.path)
-        value = flux_format_from_state_dict(sd)
+        value = flux_format_from_state_dict(sd, mod.metadata())
         mod.cache[key] = value
         return value
 

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -20,6 +20,10 @@ from invokeai.backend.model_manager.taxonomy import (
     ModelType,
     SubModelType,
 )
+from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_aitoolkit_format,
+    lora_model_from_flux_aitoolkit_state_dict,
+)
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
     is_state_dict_likely_flux_control,
     lora_model_from_flux_control_state_dict,
@@ -92,6 +96,8 @@ class LoRALoader(ModelLoader):
                     model = lora_model_from_flux_onetrainer_state_dict(state_dict=state_dict)
                 elif is_state_dict_likely_flux_control(state_dict=state_dict):
                     model = lora_model_from_flux_control_state_dict(state_dict=state_dict)
+                elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict=state_dict):
+                    model = lora_model_from_flux_aitoolkit_state_dict(state_dict=state_dict)
                 else:
                     raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
             else:

invokeai/backend/model_manager/starter_models.py

@@ -297,6 +297,15 @@ ip_adapter_sdxl = StarterModel(
     dependencies=[ip_adapter_sdxl_image_encoder],
     previous_names=["IP Adapter SDXL"],
 )
+ip_adapter_plus_sdxl = StarterModel(
+    name="Precise Reference (IP Adapter Plus ViT-H)",
+    base=BaseModelType.StableDiffusionXL,
+    source="https://huggingface.co/InvokeAI/ip-adapter-plus_sdxl_vit-h/resolve/main/ip-adapter-plus_sdxl_vit-h.safetensors",
+    description="References images with a higher degree of precision.",
+    type=ModelType.IPAdapter,
+    dependencies=[ip_adapter_sdxl_image_encoder],
+    previous_names=["IP Adapter Plus SDXL"],
+)
 ip_adapter_flux = StarterModel(
     name="Standard Reference (XLabs FLUX IP-Adapter v2)",
     base=BaseModelType.Flux,
@@ -672,6 +681,7 @@ STARTER_MODELS: list[StarterModel] = [
     ip_adapter_plus_sd1,
     ip_adapter_plus_face_sd1,
     ip_adapter_sdxl,
+    ip_adapter_plus_sdxl,
     ip_adapter_flux,
     qr_code_cnet_sd1,
     qr_code_cnet_sdxl,
@@ -744,6 +754,7 @@ sdxl_bundle: list[StarterModel] = [
     juggernaut_sdxl,
     sdxl_fp16_vae_fix,
     ip_adapter_sdxl,
+    ip_adapter_plus_sdxl,
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,

invokeai/backend/model_manager/taxonomy.py

@@ -137,6 +137,7 @@ class FluxLoRAFormat(str, Enum):
     Kohya = "flux.kohya"
     OneTrainer = "flux.onetrainer"
     Control = "flux.control"
+    AIToolkit = "flux.aitoolkit"
 
 
 AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]

invokeai/backend/model_patcher.py

@@ -46,6 +46,10 @@ class ModelPatcher:
         text_encoder: Union[CLIPTextModel, CLIPTextModelWithProjection],
         ti_list: List[Tuple[str, TextualInversionModelRaw]],
     ) -> Iterator[Tuple[CLIPTokenizer, TextualInversionManager]]:
+        if len(ti_list) == 0:
+            yield tokenizer, TextualInversionManager(tokenizer)
+            return
+
         init_tokens_count = None
         new_tokens_added = None
 

invokeai/backend/patches/lora_conversions/flux_aitoolkit_lora_conversion_utils.py

@@ -0,0 +1,63 @@
+import json
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
+from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import _group_by_layer
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.util import InvokeAILogger
+
+
+def is_state_dict_likely_in_flux_aitoolkit_format(state_dict: dict[str, Any], metadata: dict[str, Any] = None) -> bool:
+    if metadata:
+        try:
+            software = json.loads(metadata.get("software", "{}"))
+        except json.JSONDecodeError:
+            return False
+        return software.get("name") == "ai-toolkit"
+    # metadata got lost somewhere
+    return any("diffusion_model" == k.split(".", 1)[0] for k in state_dict.keys())
+
+
+@dataclass
+class GroupedStateDict:
+    transformer: dict[str, Any] = field(default_factory=dict)
+    # might also grow CLIP and T5 submodels
+
+
+def _group_state_by_submodel(state_dict: dict[str, Any]) -> GroupedStateDict:
+    logger = InvokeAILogger.get_logger()
+    grouped = GroupedStateDict()
+    for key, value in state_dict.items():
+        submodel_name, param_name = key.split(".", 1)
+        match submodel_name:
+            case "diffusion_model":
+                grouped.transformer[param_name] = value
+            case _:
+                logger.warning(f"Unexpected submodel name: {submodel_name}")
+    return grouped
+
+
+def _rename_peft_lora_keys(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+    """Renames keys from the PEFT LoRA format to the InvokeAI format."""
+    renamed_state_dict = {}
+    for key, value in state_dict.items():
+        renamed_key = key.replace(".lora_A.", ".lora_down.").replace(".lora_B.", ".lora_up.")
+        renamed_state_dict[renamed_key] = value
+    return renamed_state_dict
+
+
+def lora_model_from_flux_aitoolkit_state_dict(state_dict: dict[str, torch.Tensor]) -> ModelPatchRaw:
+    state_dict = _rename_peft_lora_keys(state_dict)
+    by_layer = _group_by_layer(state_dict)
+    by_model = _group_state_by_submodel(by_layer)
+
+    layers: dict[str, BaseLayerPatch] = {}
+    for layer_key, layer_state_dict in by_model.transformer.items():
+        layers[FLUX_LORA_TRANSFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
+
+    return ModelPatchRaw(layers=layers)

invokeai/backend/patches/lora_conversions/formats.py

@@ -1,4 +1,7 @@
 from invokeai.backend.model_manager.taxonomy import FluxLoRAFormat
+from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_aitoolkit_format,
+)
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import is_state_dict_likely_flux_control
 from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import (
     is_state_dict_likely_in_flux_diffusers_format,
@@ -11,7 +14,7 @@ from invokeai.backend.patches.lora_conversions.flux_onetrainer_lora_conversion_u
 )
 
 
-def flux_format_from_state_dict(state_dict):
+def flux_format_from_state_dict(state_dict: dict, metadata: dict | None = None) -> FluxLoRAFormat | None:
     if is_state_dict_likely_in_flux_kohya_format(state_dict):
         return FluxLoRAFormat.Kohya
     elif is_state_dict_likely_in_flux_onetrainer_format(state_dict):
@@ -20,5 +23,7 @@ def flux_format_from_state_dict(state_dict):
         return FluxLoRAFormat.Diffusers
     elif is_state_dict_likely_flux_control(state_dict):
         return FluxLoRAFormat.Control
+    elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict, metadata):
+        return FluxLoRAFormat.AIToolkit
     else:
         return None

invokeai/frontend/web/package.json

@@ -68,7 +68,7 @@
     "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
     "filesize": "^10.1.6",
-    "fracturedjsonjs": "^4.0.2",
+    "fracturedjsonjs": "^4.1.0",
     "framer-motion": "^11.10.0",
     "i18next": "^25.0.1",
     "i18next-http-backend": "^3.0.2",

invokeai/frontend/web/pnpm-lock.yaml

@@ -54,8 +54,8 @@ dependencies:
     specifier: ^10.1.6
     version: 10.1.6
   fracturedjsonjs:
-    specifier: ^4.0.2
-    version: 4.0.2
+    specifier: ^4.1.0
+    version: 4.1.0
   framer-motion:
     specifier: ^11.10.0
     version: 11.10.0(react-dom@18.3.1)(react@18.3.1)
@@ -5280,8 +5280,8 @@ packages:
       signal-exit: 4.1.0
     dev: true
 
-  /fracturedjsonjs@4.0.2:
-    resolution: {integrity: sha512-+vGJH9wK0EEhbbn50V2sOebLRaar1VL3EXr02kxchIwpkhQk0ItrPjIOtYPYuU9hNFpVzxjrPgzjtMJih+ae4A==}
+  /fracturedjsonjs@4.1.0:
+    resolution: {integrity: sha512-qy6LPA8OOiiyRHt5/sNKDayD7h5r3uHmHxSOLbBsgtU/hkt5vOVWOR51MdfDbeCNfj7k/dKCRbXYm8FBAJcgWQ==}
     dev: false
 
   /framer-motion@10.18.0(react-dom@18.3.1)(react@18.3.1):

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/DataViewer.tsx

@@ -2,7 +2,7 @@ import type { FlexProps } from '@invoke-ai/ui-library';
 import { Box, chakra, Flex, IconButton, Tooltip, useShiftModifier } from '@invoke-ai/ui-library';
 import { getOverlayScrollbarsParams } from 'common/components/OverlayScrollbars/constants';
 import { useClipboard } from 'common/hooks/useClipboard';
-import { Formatter } from 'fracturedjsonjs';
+import { Formatter, TableCommaPlacement } from 'fracturedjsonjs';
 import { isString } from 'lodash-es';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
 import type { CSSProperties } from 'react';
@@ -11,6 +11,8 @@ import { useTranslation } from 'react-i18next';
 import { PiCopyBold, PiDownloadSimpleBold } from 'react-icons/pi';
 
 const formatter = new Formatter();
+formatter.Options.TableCommaPlacement = TableCommaPlacement.BeforePadding;
+formatter.Options.OmitTrailingWhitespace = true;
 
 type Props = {
   label: string;
@@ -19,6 +21,7 @@ type Props = {
   withDownload?: boolean;
   withCopy?: boolean;
   extraCopyActions?: { label: string; getData: (data: unknown) => unknown }[];
+  wrapData?: boolean;
 } & FlexProps;
 
 const overlayscrollbarsOptions = getOverlayScrollbarsParams({
@@ -29,7 +32,16 @@ const overlayscrollbarsOptions = getOverlayScrollbarsParams({
 const ChakraPre = chakra('pre');
 
 const DataViewer = (props: Props) => {
-  const { label, data, fileName, withDownload = true, withCopy = true, extraCopyActions, ...rest } = props;
+  const {
+    label,
+    data,
+    fileName,
+    withDownload = true,
+    withCopy = true,
+    extraCopyActions,
+    wrapData = true,
+    ...rest
+  } = props;
   const dataString = useMemo(() => (isString(data) ? data : formatter.Serialize(data)) ?? '', [data]);
   const shift = useShiftModifier();
   const clipboard = useClipboard();
@@ -53,7 +65,7 @@ const DataViewer = (props: Props) => {
     <Flex bg="base.800" borderRadius="base" flexGrow={1} w="full" h="full" position="relative" {...rest}>
       <Box position="absolute" top={0} left={0} right={0} bottom={0} overflow="auto" p={2} fontSize="sm">
         <OverlayScrollbarsComponent defer style={overlayScrollbarsStyles} options={overlayscrollbarsOptions}>
-          <ChakraPre whiteSpace="pre-wrap">{dataString}</ChakraPre>
+          <ChakraPre whiteSpace={wrapData ? 'pre-wrap' : undefined}>{dataString}</ChakraPre>
         </OverlayScrollbarsComponent>
       </Box>
       <Flex position="absolute" top={0} insetInlineEnd={0} p={2}>

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/PublishWorkflowPanelContent.tsx

@@ -22,6 +22,7 @@ import { NodeFieldElementOverlay } from 'features/nodes/components/sidePanel/bui
 import { useDoesWorkflowHaveUnsavedChanges } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
 import {
   $isInPublishFlow,
+  $isPublishing,
   $isReadyToDoValidationRun,
   $isSelectingOutputNode,
   $outputNodeId,
@@ -183,13 +184,14 @@ SelectOutputNodeButton.displayName = 'SelectOutputNodeButton';
 
 const CancelPublishButton = memo(() => {
   const { t } = useTranslation();
+  const isPublishing = useStore($isPublishing);
   const onClick = useCallback(() => {
     $isInPublishFlow.set(false);
     $isSelectingOutputNode.set(false);
     $outputNodeId.set(null);
   }, []);
   return (
-    <Button leftIcon={<PiXBold />} onClick={onClick}>
+    <Button leftIcon={<PiXBold />} onClick={onClick} isDisabled={isPublishing}>
       {t('common.cancel')}
     </Button>
   );
@@ -198,6 +200,7 @@ CancelPublishButton.displayName = 'CancelDeployButton';
 
 const PublishWorkflowButton = memo(() => {
   const { t } = useTranslation();
+  const isPublishing = useStore($isPublishing);
   const isReadyToDoValidationRun = useStore($isReadyToDoValidationRun);
   const isReadyToEnqueue = useStore($isReadyToEnqueue);
   const doesWorkflowHaveUnsavedChanges = useDoesWorkflowHaveUnsavedChanges();
@@ -211,6 +214,7 @@ const PublishWorkflowButton = memo(() => {
 
   const enqueue = useEnqueueWorkflows();
   const onClick = useCallback(async () => {
+    $isPublishing.set(true);
     const result = await withResultAsync(() => enqueue(true, true));
     if (result.isErr()) {
       toast({
@@ -244,8 +248,30 @@ const PublishWorkflowButton = memo(() => {
       });
       log.debug(parseify(result.value), 'Enqueued batch');
     }
+    $isPublishing.set(false);
   }, [enqueue, projectUrl, t]);
 
+  const isDisabled = useMemo(() => {
+    return (
+      !allowPublishWorkflows ||
+      !isReadyToEnqueue ||
+      doesWorkflowHaveUnsavedChanges ||
+      hasUnpublishableNodes ||
+      !isReadyToDoValidationRun ||
+      !(outputNodeId !== null && !isSelectingOutputNode) ||
+      isPublishing
+    );
+  }, [
+    allowPublishWorkflows,
+    doesWorkflowHaveUnsavedChanges,
+    hasUnpublishableNodes,
+    isReadyToDoValidationRun,
+    isReadyToEnqueue,
+    isSelectingOutputNode,
+    outputNodeId,
+    isPublishing,
+  ]);
+
   return (
     <PublishTooltip
       isWorkflowSaved={!doesWorkflowHaveUnsavedChanges}
@@ -255,19 +281,8 @@ const PublishWorkflowButton = memo(() => {
       hasPublishableInputs={inputs.publishable.length > 0}
       hasUnpublishableInputs={inputs.unpublishable.length > 0}
     >
-      <Button
-        leftIcon={<PiLightningFill />}
-        isDisabled={
-          !allowPublishWorkflows ||
-          !isReadyToEnqueue ||
-          doesWorkflowHaveUnsavedChanges ||
-          hasUnpublishableNodes ||
-          !isReadyToDoValidationRun ||
-          !(outputNodeId !== null && !isSelectingOutputNode)
-        }
-        onClick={onClick}
-      >
-        {t('workflows.builder.publish')}
+      <Button leftIcon={<PiLightningFill />} isDisabled={isDisabled} onClick={onClick}>
+        {isPublishing ? t('workflows.builder.publishing') : t('workflows.builder.publish')}
       </Button>
     </PublishTooltip>
   );
@@ -337,6 +352,10 @@ export const StartPublishFlowButton = memo(() => {
     $isInPublishFlow.set(true);
   }, []);
 
+  const isDisabled = useMemo(() => {
+    return !allowPublishWorkflows || !isReadyToEnqueue || doesWorkflowHaveUnsavedChanges || hasUnpublishableNodes;
+  }, [allowPublishWorkflows, doesWorkflowHaveUnsavedChanges, hasUnpublishableNodes, isReadyToEnqueue]);
+
   return (
     <PublishTooltip
       isWorkflowSaved={!doesWorkflowHaveUnsavedChanges}
@@ -346,15 +365,7 @@ export const StartPublishFlowButton = memo(() => {
       hasPublishableInputs={inputs.publishable.length > 0}
       hasUnpublishableInputs={inputs.unpublishable.length > 0}
     >
-      <Button
-        onClick={onClick}
-        leftIcon={<PiLightningFill />}
-        variant="ghost"
-        size="sm"
-        isDisabled={
-          !allowPublishWorkflows || !isReadyToEnqueue || doesWorkflowHaveUnsavedChanges || hasUnpublishableNodes
-        }
-      >
+      <Button onClick={onClick} leftIcon={<PiLightningFill />} variant="ghost" size="sm" isDisabled={isDisabled}>
         {t('workflows.builder.publish')}
       </Button>
     </PublishTooltip>

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/publish.ts

@@ -19,6 +19,7 @@ import { useGetBatchStatusQuery } from 'services/api/endpoints/queue';
 import { useGetWorkflowQuery } from 'services/api/endpoints/workflows';
 import { assert } from 'tsafe';
 
+export const $isPublishing = atom(false);
 export const $isInPublishFlow = atom(false);
 export const $outputNodeId = atom<string | null>(null);
 export const $isSelectingOutputNode = atom(false);

invokeai/frontend/web/src/features/system/components/AboutModal/AboutModal.tsx

@@ -58,7 +58,7 @@ const AboutModal = ({ children }: AboutModalProps) => {
       {cloneElement(children, {
         onClick: onOpen,
       })}
-      <Modal isOpen={isOpen} onClose={onClose} isCentered size="2xl" useInert={false}>
+      <Modal isOpen={isOpen} onClose={onClose} isCentered size="5xl" useInert={false}>
         <ModalOverlay />
         <ModalContent maxH="80vh" h="34rem">
           <ModalHeader>{t('accessibility.about')}</ModalHeader>
@@ -66,7 +66,7 @@ const AboutModal = ({ children }: AboutModalProps) => {
           <ModalBody display="flex" flexDir="column" gap={4}>
             <Grid templateColumns="repeat(2, 1fr)" h="full">
               <GridItem backgroundColor="base.750" borderRadius="base" p="4" h="full">
-                <DataViewer label={t('common.systemInformation')} data={localData} />
+                <DataViewer label={t('common.systemInformation')} data={localData} wrapData={false} />
               </GridItem>
               <GridItem>
                 <Flex flexDir="column" gap={3} justifyContent="center" alignItems="center" h="full">

invokeai/frontend/web/src/services/api/endpoints/appInfo.ts

@@ -1,7 +1,7 @@
 import { $openAPISchemaUrl } from 'app/store/nanostores/openAPISchemaUrl';
 import type { OpenAPIV3_1 } from 'openapi-types';
 import type { paths } from 'services/api/schema';
-import type { AppConfig, AppDependencyVersions, AppVersion } from 'services/api/types';
+import type { AppConfig, AppVersion } from 'services/api/types';
 
 import { api, buildV1Url } from '..';
 
@@ -22,7 +22,10 @@ export const appInfoApi = api.injectEndpoints({
       }),
       providesTags: ['FetchOnReconnect'],
     }),
-    getAppDeps: build.query<AppDependencyVersions, void>({
+    getAppDeps: build.query<
+      paths['/api/v1/app/app_deps']['get']['responses']['200']['content']['application/json'],
+      void
+    >({
       query: () => ({
         url: buildAppInfoUrl('app_deps'),
         method: 'GET',

invokeai/frontend/web/src/services/api/schema.ts

@@ -1925,77 +1925,6 @@ export type components = {
              */
             watermarking_methods: string[];
         };
-        /**
-         * AppDependencyVersions
-         * @description App depencency Versions Response
-         */
-        AppDependencyVersions: {
-            /**
-             * Accelerate
-             * @description accelerate version
-             */
-            accelerate: string;
-            /**
-             * Compel
-             * @description compel version
-             */
-            compel: string;
-            /**
-             * Cuda
-             * @description CUDA version
-             */
-            cuda: string | null;
-            /**
-             * Diffusers
-             * @description diffusers version
-             */
-            diffusers: string;
-            /**
-             * Numpy
-             * @description Numpy version
-             */
-            numpy: string;
-            /**
-             * Opencv
-             * @description OpenCV version
-             */
-            opencv: string;
-            /**
-             * Onnx
-             * @description ONNX version
-             */
-            onnx: string;
-            /**
-             * Pillow
-             * @description Pillow (PIL) version
-             */
-            pillow: string;
-            /**
-             * Python
-             * @description Python version
-             */
-            python: string;
-            /**
-             * Torch
-             * @description PyTorch version
-             */
-            torch: string;
-            /**
-             * Torchvision
-             * @description PyTorch Vision version
-             */
-            torchvision: string;
-            /**
-             * Transformers
-             * @description transformers version
-             */
-            transformers: string;
-            /**
-             * Xformers
-             * @description xformers version
-             */
-            xformers: string | null;
-        };
         /**
          * AppVersion
          * @description App Version Response
@@ -12062,7 +11991,7 @@ export type components = {
          *         vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
          *         lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
          *         pytorch_cuda_alloc_conf: Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to "backend:cudaMallocAsync" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.
-         *         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
+         *         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
          *         precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
          *         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
          *         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
@@ -12337,11 +12266,10 @@ export type components = {
             pytorch_cuda_alloc_conf?: string | null;
             /**
              * Device
-             * @description Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.
+             * @description Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
              * @default auto
-             * @enum {string}
              */
-            device?: "auto" | "cpu" | "cuda" | "cuda:1" | "mps";
+            device?: string;
             /**
              * Precision
              * @description Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.
@@ -24226,7 +24154,9 @@ export interface operations {
                     [name: string]: unknown;
                 };
                 content: {
-                    "application/json": components["schemas"]["AppDependencyVersions"];
+                    "application/json": {
+                        [key: string]: string;
+                    };
                 };
             };
         };

invokeai/frontend/web/src/services/api/types.ts

@@ -31,7 +31,6 @@ export type InvocationJSONSchemaExtra = S['UIConfigBase'];
 // App Info
 export type AppVersion = S['AppVersion'];
 export type AppConfig = S['AppConfig'];
-export type AppDependencyVersions = S['AppDependencyVersions'];
 
 // Images
 export type ImageDTO = S['ImageDTO'];

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.14.0"
+__version__ = "5.15.0"

pyproject.toml

@@ -109,6 +109,12 @@ dependencies = [
   "humanize==4.12.1",
 ]
 
+[tool.uv]
+# Prevent opencv-python from ever being chosen during dependency resolution.
+# This prevents conflicts with opencv-contrib-python, which Invoke requires.
+override-dependencies = ["opencv-python; sys_platform=='never'"]
+
+
 [project.scripts]
 "invokeai-web" = "invokeai.app.run_app:run_app"
 

tests/backend/patches/lora_conversions/lora_state_dicts/flux_lora_aitoolkit_format.py

@@ -0,0 +1,458 @@
+state_dict_keys = {
+    "diffusion_model.double_blocks.0.img_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.0.img_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.0.img_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.0.img_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.0.img_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.0.img_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.0.img_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.0.img_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.0.txt_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.0.txt_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.0.txt_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.0.txt_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.0.txt_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.0.txt_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.0.txt_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.0.txt_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.1.img_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.1.img_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.1.img_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.1.img_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.1.img_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.1.img_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.1.img_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.1.img_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.1.txt_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.1.txt_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.1.txt_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.1.txt_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.1.txt_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.1.txt_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.1.txt_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.1.txt_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.10.img_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.10.img_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.10.img_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.10.img_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.10.img_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.10.img_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.10.img_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.10.img_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.10.txt_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.10.txt_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.10.txt_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.10.txt_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.10.txt_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.10.txt_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.10.txt_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.10.txt_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.11.img_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.11.img_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.11.img_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.11.img_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.11.img_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.11.img_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.11.img_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.11.img_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.11.txt_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.11.txt_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.11.txt_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.11.txt_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.11.txt_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.11.txt_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.11.txt_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.11.txt_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.12.img_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.12.img_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.12.img_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.12.img_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.12.img_mlp.0.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.12.img_mlp.0.lora_B.weight": [12288, 16],
+    "diffusion_model.double_blocks.12.img_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.12.img_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.12.txt_attn.proj.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.12.txt_attn.proj.lora_B.weight": [3072, 16],
+    "diffusion_model.double_blocks.12.txt_attn.qkv.lora_A.weight": [16, 3072],
+    "diffusion_model.double_blocks.12.txt_attn.qkv.lora_B.weight": [9216, 16],
+    "diffusion_model.double_blocks.12.txt_mlp.0.lora_A.weight": [16, 3072],
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+    "diffusion_model.double_blocks.9.txt_mlp.2.lora_A.weight": [16, 12288],
+    "diffusion_model.double_blocks.9.txt_mlp.2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.0.linear1.lora_A.weight": [16, 3072],
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+    "diffusion_model.single_blocks.0.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.0.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.1.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.1.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.1.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.1.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.10.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.10.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.10.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.10.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.11.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.11.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.11.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.11.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.12.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.12.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.12.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.12.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.13.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.13.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.13.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.13.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.14.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.14.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.14.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.14.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.15.linear1.lora_A.weight": [16, 3072],
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+    "diffusion_model.single_blocks.15.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.15.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.16.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.16.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.16.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.16.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.17.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.17.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.17.linear2.lora_A.weight": [16, 15360],
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+    "diffusion_model.single_blocks.20.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.20.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.20.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.21.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.21.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.21.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.21.linear2.lora_B.weight": [3072, 16],
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+    "diffusion_model.single_blocks.5.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.5.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.5.linear2.lora_A.weight": [16, 15360],
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+    "diffusion_model.single_blocks.6.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.6.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.6.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.7.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.7.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.7.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.7.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.8.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.8.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.8.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.8.linear2.lora_B.weight": [3072, 16],
+    "diffusion_model.single_blocks.9.linear1.lora_A.weight": [16, 3072],
+    "diffusion_model.single_blocks.9.linear1.lora_B.weight": [21504, 16],
+    "diffusion_model.single_blocks.9.linear2.lora_A.weight": [16, 15360],
+    "diffusion_model.single_blocks.9.linear2.lora_B.weight": [3072, 16],
+}

tests/backend/patches/lora_conversions/test_flux_aitoolkit_lora_conversion_utils.py

@@ -0,0 +1,59 @@
+import accelerate
+import pytest
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.util import params
+from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
+    _group_state_by_submodel,
+    is_state_dict_likely_in_flux_aitoolkit_format,
+    lora_model_from_flux_aitoolkit_state_dict,
+)
+from tests.backend.patches.lora_conversions.lora_state_dicts.flux_dora_onetrainer_format import (
+    state_dict_keys as flux_onetrainer_state_dict_keys,
+)
+from tests.backend.patches.lora_conversions.lora_state_dicts.flux_lora_aitoolkit_format import (
+    state_dict_keys as flux_aitoolkit_state_dict_keys,
+)
+from tests.backend.patches.lora_conversions.lora_state_dicts.flux_lora_diffusers_format import (
+    state_dict_keys as flux_diffusers_state_dict_keys,
+)
+from tests.backend.patches.lora_conversions.lora_state_dicts.utils import keys_to_mock_state_dict
+
+
+def test_is_state_dict_likely_in_flux_aitoolkit_format():
+    state_dict = keys_to_mock_state_dict(flux_aitoolkit_state_dict_keys)
+    assert is_state_dict_likely_in_flux_aitoolkit_format(state_dict)
+
+
+@pytest.mark.parametrize("sd_keys", [flux_diffusers_state_dict_keys, flux_onetrainer_state_dict_keys])
+def test_is_state_dict_likely_in_flux_kohya_format_false(sd_keys: dict[str, list[int]]):
+    state_dict = keys_to_mock_state_dict(sd_keys)
+    assert not is_state_dict_likely_in_flux_aitoolkit_format(state_dict)
+
+
+def test_flux_aitoolkit_transformer_state_dict_is_in_invoke_format():
+    state_dict = keys_to_mock_state_dict(flux_aitoolkit_state_dict_keys)
+    converted_state_dict = _group_state_by_submodel(state_dict).transformer
+
+    # Extract the prefixes from the converted state dict (without the lora suffixes)
+    converted_key_prefixes: list[str] = []
+    for k in converted_state_dict.keys():
+        k = k.replace(".lora_A.weight", "")
+        k = k.replace(".lora_B.weight", "")
+        converted_key_prefixes.append(k)
+
+    # Initialize a FLUX model on the meta device.
+    with accelerate.init_empty_weights():
+        model = Flux(params["flux-schnell"])
+    model_keys = set(model.state_dict().keys())
+
+    for converted_key_prefix in converted_key_prefixes:
+        assert any(model_key.startswith(converted_key_prefix) for model_key in model_keys), (
+            f"'{converted_key_prefix}' did not match any model keys."
+        )
+
+
+def test_lora_model_from_flux_aitoolkit_state_dict():
+    state_dict = keys_to_mock_state_dict(flux_aitoolkit_state_dict_keys)
+
+    assert lora_model_from_flux_aitoolkit_state_dict(state_dict)

tests/backend/util/test_devices.py

@@ -10,7 +10,7 @@ import torch
 from invokeai.app.services.config import get_config
 from invokeai.backend.util.devices import TorchDevice, choose_precision, choose_torch_device, torch_dtype
 
-devices = ["cpu", "cuda:0", "cuda:1", "mps"]
+devices = ["cpu", "cuda:0", "cuda:1", "cuda:2", "mps"]
 device_types_cpu = [("cpu", torch.float32), ("cuda:0", torch.float32), ("mps", torch.float32)]
 device_types_cuda = [("cpu", torch.float32), ("cuda:0", torch.float16), ("mps", torch.float32)]
 device_types_mps = [("cpu", torch.float32), ("cuda:0", torch.float32), ("mps", torch.float16)]

uv.lock

@@ -13,6 +13,9 @@ resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
+[manifest]
+overrides = [{ name = "opencv-python", marker = "sys_platform == 'never'" }]
+
 [[package]]
 name = "absl-py"
 version = "2.2.1"
@@ -948,7 +951,7 @@ version = "0.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
-    { name = "opencv-python" },
+    { name = "opencv-python", marker = "sys_platform == 'never'" },
     { name = "pillow" },
     { name = "pywavelets" },
     { name = "torch" },
@@ -2043,17 +2046,9 @@ name = "opencv-python"
 version = "4.9.0.80"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/25/72/da7c69a3542071bf1e8f65336721b8b2659194425438d988f79bc14ed9cc/opencv-python-4.9.0.80.tar.gz", hash = "sha256:1a9f0e6267de3a1a1db0c54213d022c7c8b5b9ca4b580e80bdc58516c922c9e1", size = 92896686 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/35/69/b657974ddcbba54d59d7d62b01e60a8b815e35f415b996e4d355be0ac7b4/opencv_python-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:7e5f7aa4486651a6ebfa8ed4b594b65bd2d2f41beeb4241a3e4b1b85acbbbadb", size = 55689340 },
-    { url = "https://files.pythonhosted.org/packages/77/df/b56175c3fb5bc058774bdcf35f5a71cf9c3c5b909f98a1c688eb71cd3b1f/opencv_python-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:71dfb9555ccccdd77305fc3dcca5897fbf0cf28b297c51ee55e079c065d812a3", size = 35354525 },
-    { url = "https://files.pythonhosted.org/packages/52/00/2adf376707c7965bb4569f28f73fafe303c404d01047b10e3b52761be086/opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b34a52e9da36dda8c151c6394aed602e4b17fa041df0b9f5b93ae10b0fcca2a", size = 41289855 },
-    { url = "https://files.pythonhosted.org/packages/d9/64/7fdfb9386511cd6805451e012c537073a79a958a58795c4e602e538c388c/opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4088cab82b66a3b37ffc452976b14a3c599269c247895ae9ceb4066d8188a57", size = 62208946 },
-    { url = "https://files.pythonhosted.org/packages/76/9e/db1c2d56c04b97981c06663384f45f28950a73d9acf840c4006d60d0a1ff/opencv_python-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:dcf000c36dd1651118a2462257e3a9e76db789a78432e1f303c7bac54f63ef6c", size = 28546907 },
-    { url = "https://files.pythonhosted.org/packages/c7/ec/9dabb6a9abfdebb3c45b0cc52dec901caafef2b2c7e7d6a839ed86d81e91/opencv_python-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:3f16f08e02b2a2da44259c7cc712e779eff1dd8b55fdb0323e8cab09548086c0", size = 38624911 },
-]
 
 [[package]]
 name = "opt-einsum"