Add a compute_dtype field to GGMLTensor.

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

README.md

@@ -105,7 +105,7 @@ Invoke features an organized gallery system for easily storing, accessing, and r
 ### Other features
 
 - Support for both ckpt and diffusers models
-- SD1.5, SD2.0, SDXL, and FLUX support
+- SD1.5, SD2.0, and SDXL support
 - Upscaling Tools
 - Embedding Manager & Support
 - Model Manager & Support

docker/Dockerfile

@@ -38,9 +38,9 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
 
     # xformers + triton fails to install on arm64

docker/docker-compose.yml

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
 
 x-invokeai: &invokeai
-    image: "ghcr.io/invoke-ai/invokeai:latest"
+    image: "local/invokeai:latest"
     build:
       context: ..
       dockerfile: docker/Dockerfile

docs/contributing/INVOCATIONS.md

@@ -144,7 +144,7 @@ As you might have noticed, we added two new arguments to the `InputField`
 definition for `width` and `height`, called `gt` and `le`. They stand for
 _greater than or equal to_ and _less than or equal to_.
 
-These impose constraints on those fields, and will raise an exception if the
+These impose contraints on those fields, and will raise an exception if the
 values do not meet the constraints. Field constraints are provided by
 **pydantic**, so anything you see in the **pydantic docs** will work.
 

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -239,7 +239,7 @@ Consult the
 get it set up.
 
 Suggest using VSCode's included settings sync so that your remote dev host has
-all the same app settings and extensions automatically.
+all the same app settings and extensions automagically.
 
 ##### One remote dev gotcha
 

docs/contributing/contribution_guides/development.md

@@ -2,7 +2,7 @@
 
 ## **What do I need to know to help?**
 
-If you are looking to help with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
+If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
 
 
 ## **Get Started**

docs/contributing/contribution_guides/tutorials.md

@@ -1,6 +1,6 @@
 # Tutorials
 
-Tutorials help new & existing users expand their ability to use InvokeAI to the full extent of our features and services.  
+Tutorials help new & existing users expand their abilty to use InvokeAI to the full extent of our features and services.  
 
 Currently, we have a set of tutorials available on our [YouTube channel](https://www.youtube.com/@invokeai), but as InvokeAI continues to evolve with new updates, we want to ensure that we are giving our users the resources they need to succeed. 
 
@@ -8,4 +8,4 @@ Tutorials can be in the form of videos or article walkthroughs on a subject of y
 
 ## Contributing
 
-Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
+Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.

docs/nodes/communityNodes.md

@@ -21,7 +21,6 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [Clothing Mask](#clothing-mask)
     + [Contrast Limited Adaptive Histogram Equalization](#contrast-limited-adaptive-histogram-equalization)
     + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
-    + [Enhance Detail](#enhance-detail)
     + [Film Grain](#film-grain)
     + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
@@ -41,7 +40,6 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [Metadata-Linked](#metadata-linked-nodes)
     + [Negative Image](#negative-image)
     + [Nightmare Promptgen](#nightmare-promptgen)
-    + [Ollama](#ollama-node)
     + [One Button Prompt](#one-button-prompt)
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
@@ -82,7 +80,7 @@ Note: These are inherited from the core nodes so any update to the core nodes sh
 
 **Example Usage:**
 </br>
-<img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider.png" width="200" /> -> <img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider-depth.png" width="200" /> -> <img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider-dots.png" width="200" /> <img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider-pattern.png" width="200" />
+<img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider-depth.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-dots.png" width="200" /> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-pattern.png" width="200" />
 
 --------------------------------
 ### Average Images
@@ -143,17 +141,6 @@ To be imported, an .obj must use triangulated meshes, so make sure to enable tha
 **Example Usage:**
 </br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
 
---------------------------------
-### Enhance Detail
-
-**Description:** A single node that can enhance the detail in an image. Increase or decrease details in an image using a guided filter (as opposed to the typical Gaussian blur used by most sharpening filters.) Based on the `Enhance Detail` ComfyUI node from  https://github.com/spacepxl/ComfyUI-Image-Filters
-
-**Node Link:** https://github.com/skunkworxdark/enhance-detail-node
-
-**Example Usage:**
-</br>
-<img src="https://raw.githubusercontent.com/skunkworxdark/enhance-detail-node/refs/heads/main/images/Comparison.png" />
-
 --------------------------------
 ### Film Grain
 
@@ -320,7 +307,7 @@ View:
 **Node Link:** https://github.com/helix4u/load_video_frame
 
 **Output Example:** 
-<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/refs/heads/main/_git_assets/dance1736978273.gif" width="500" />
+<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
 
 --------------------------------
 ### Make 3D
@@ -361,7 +348,7 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **Output Examples** 
 
-<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" />
+<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
 
 --------------------------------
 ### Metadata Linked Nodes
@@ -403,23 +390,10 @@ View:
 
 **Node Link:** [https://github.com/gogurtenjoyer/nightmare-promptgen](https://github.com/gogurtenjoyer/nightmare-promptgen)
 
---------------------------------
-### Ollama Node
-
-**Description:** Uses Ollama API to expand text prompts for text-to-image generation using local LLMs. Works great for expanding basic prompts into detailed natural language prompts for Flux. Also provides a toggle to unload the LLM model immediately after expanding, to free up VRAM for Invoke to continue the image generation workflow.
-
-**Node Link:** https://github.com/Jonseed/Ollama-Node
-
-**Example Node Graph:**  https://github.com/Jonseed/Ollama-Node/blob/main/Ollama-Node-Flux-example.json
-
-**View:** 
-
-![ollama node](https://raw.githubusercontent.com/Jonseed/Ollama-Node/a3e7cdc55e394cb89c1ea7ed54e106c212c85e8c/ollama-node-screenshot.png)
-
 --------------------------------
 ### One Button Prompt
 
-<img src="https://raw.githubusercontent.com/AIrjen/OneButtonPrompt_X_InvokeAI/refs/heads/main/images/background.png" width="800" />
+<img src="https://github.com/AIrjen/OneButtonPrompt_X_InvokeAI/blob/main/images/background.png" width="800" />
 
 **Description:** an extensive suite of auto prompt generation and prompt helper nodes based on extensive logic. Get creative with the best prompt generator in the world. 
 
@@ -429,7 +403,7 @@ The main node generates interesting prompts based on a set of parameters. There
 
 **Nodes:**
 
-<img src="https://raw.githubusercontent.com/AIrjen/OneButtonPrompt_X_InvokeAI/refs/heads/main/images/OBP_nodes_invokeai.png" width="800" />
+<img src="https://github.com/AIrjen/OneButtonPrompt_X_InvokeAI/blob/main/images/OBP_nodes_invokeai.png" width="800" />
 
 --------------------------------
 ### Oobabooga
@@ -482,7 +456,7 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **Workflow Examples** 
 
-<img src="https://raw.githubusercontent.com/skunkworxdark/prompt-tools/refs/heads/main/images/CSVToIndexStringNode.png"/>
+<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
 
 --------------------------------
 ### Remote Image
@@ -620,7 +594,7 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
 
 **Output Examples** 
 
-<img src="https://raw.githubusercontent.com/skunkworxdark/XYGrid_nodes/refs/heads/main/images/collage.png" />
+<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
 
 
 --------------------------------

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1727955264,
-        "narHash": "sha256-lrd+7mmb5NauRoMa8+J1jFKYVa+rc8aq2qc9+CxPDKc=",
+        "lastModified": 1690630721,
+        "narHash": "sha256-Y04onHyBQT4Erfr2fc82dbJTfXGYrf4V0ysLUYnPOP8=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "71cd616696bd199ef18de62524f3df3ffe8b9333",
+        "rev": "d2b52322f35597c62abf56de91b0236746b2a03d",
         "type": "github"
       },
       "original": {

flake.nix

@@ -34,7 +34,7 @@
             cudaPackages.cudnn
             cudaPackages.cuda_nvrtc
             cudatoolkit
-            pkg-config
+            pkgconfig
             libconfig
             cmake
             blas
@@ -66,7 +66,7 @@
             black
 
             # Frontend.
-            pnpm_8
+            yarn
             nodejs
           ];
           LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;

installer/install.sh.in

@@ -12,7 +12,7 @@ MINIMUM_PYTHON_VERSION=3.10.0
 MAXIMUM_PYTHON_VERSION=3.11.100
 PYTHON=""
 for candidate in python3.11 python3.10 python3 python ; do
-    if ppath=`which $candidate 2>/dev/null`; then
+    if ppath=`which $candidate`; then
         # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
         # we check that this found executable can actually run
         if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
@@ -30,11 +30,10 @@ done
 if [ -z "$PYTHON" ]; then
     echo "A suitable Python interpreter could not be found"
     echo "Please install Python $MINIMUM_PYTHON_VERSION or higher (maximum $MAXIMUM_PYTHON_VERSION) before running this script. See instructions at $INSTRUCTIONS for help."
+    echo "For the best user experience we suggest enlarging or maximizing this window now."
     read -p "Press any key to exit"
     exit -1
 fi
 
-echo "For the best user experience we suggest enlarging or maximizing this window now."
-
 exec $PYTHON ./lib/main.py ${@}
 read -p "Press any key to exit"

installer/lib/installer.py

@@ -245,9 +245,6 @@ class InvokeAiInstance:
 
         pip = local[self.pip]
 
-        # Uninstall xformers if it is present; the correct version of it will be reinstalled if needed
-        _ = pip["uninstall", "-yqq", "xformers"] & FG
-
         pipeline = pip[
             "install",
             "--require-virtualenv",
@@ -285,6 +282,12 @@ class InvokeAiInstance:
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
 
+    def update(self):
+        pass
+
+    def remove(self):
+        pass
+
 
 ### Utility functions ###
 
@@ -399,7 +402,7 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     :rtype: list
     """
 
-    from messages import GpuType, select_gpu
+    from messages import select_gpu
 
     # device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
     device = select_gpu()
@@ -409,22 +412,16 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     url = None
     optional_modules: str | None = None
     if OS == "Linux":
-        if device == GpuType.ROCM:
-            url = "https://download.pytorch.org/whl/rocm6.1"
-        elif device == GpuType.CPU:
+        if device.value == "rocm":
+            url = "https://download.pytorch.org/whl/rocm5.6"
+        elif device.value == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
-        elif device == GpuType.CUDA:
-            url = "https://download.pytorch.org/whl/cu124"
-            optional_modules = "[onnx-cuda]"
-        elif device == GpuType.CUDA_WITH_XFORMERS:
-            url = "https://download.pytorch.org/whl/cu124"
+        elif device.value == "cuda":
+            # CUDA uses the default PyPi index
             optional_modules = "[xformers,onnx-cuda]"
     elif OS == "Windows":
-        if device == GpuType.CUDA:
-            url = "https://download.pytorch.org/whl/cu124"
-            optional_modules = "[onnx-cuda]"
-        elif device == GpuType.CUDA_WITH_XFORMERS:
-            url = "https://download.pytorch.org/whl/cu124"
+        if device.value == "cuda":
+            url = "https://download.pytorch.org/whl/cu121"
             optional_modules = "[xformers,onnx-cuda]"
         elif device.value == "cpu":
             # CPU  uses the default PyPi index, no optional modules

installer/lib/messages.py

@@ -206,7 +206,6 @@ def dest_path(dest: Optional[str | Path] = None) -> Path | None:
 
 
 class GpuType(Enum):
-    CUDA_WITH_XFORMERS = "xformers"
     CUDA = "cuda"
     ROCM = "rocm"
     CPU = "cpu"
@@ -222,15 +221,11 @@ def select_gpu() -> GpuType:
         return GpuType.CPU
 
     nvidia = (
-        "an [gold1 b]NVIDIA[/] RTX 3060 or newer GPU using CUDA",
+        "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
         GpuType.CUDA,
     )
-    vintage_nvidia = (
-        "an [gold1 b]NVIDIA[/] RTX 20xx or older GPU using CUDA+xFormers",
-        GpuType.CUDA_WITH_XFORMERS,
-    )
     amd = (
-        "an [gold1 b]AMD[/] GPU using ROCm",
+        "an [gold1 b]AMD[/] GPU (using ROCm™)",
         GpuType.ROCM,
     )
     cpu = (
@@ -240,13 +235,14 @@ def select_gpu() -> GpuType:
 
     options = []
     if OS == "Windows":
-        options = [nvidia, vintage_nvidia, cpu]
+        options = [nvidia, cpu]
     if OS == "Linux":
-        options = [nvidia, vintage_nvidia, amd, cpu]
+        options = [nvidia, amd, cpu]
     elif OS == "Darwin":
         options = [cpu]
 
     if len(options) == 1:
+        print(f'Your platform [gold1]{OS}-{ARCH}[/] only supports the "{options[0][1]}" driver. Proceeding with that.')
         return options[0][1]
 
     options = {str(i): opt for i, opt in enumerate(options, 1)}

invokeai/app/api/routers/boards.py

@@ -5,10 +5,9 @@ from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
+from invokeai.app.services.board_records.board_records_common import BoardChanges
 from invokeai.app.services.boards.boards_common import BoardDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 boards_router = APIRouter(prefix="/v1/boards", tags=["boards"])
 
@@ -116,8 +115,6 @@ async def delete_board(
     response_model=Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]],
 )
 async def list_boards(
-    order_by: BoardRecordOrderBy = Query(default=BoardRecordOrderBy.CreatedAt, description="The attribute to order by"),
-    direction: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The direction to order by"),
     all: Optional[bool] = Query(default=None, description="Whether to list all boards"),
     offset: Optional[int] = Query(default=None, description="The page offset"),
     limit: Optional[int] = Query(default=None, description="The number of boards per page"),
@@ -125,9 +122,9 @@ async def list_boards(
 ) -> Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]]:
     """Gets a list of boards"""
     if all:
-        return ApiDependencies.invoker.services.boards.get_all(order_by, direction, include_archived)
+        return ApiDependencies.invoker.services.boards.get_all(include_archived)
     elif offset is not None and limit is not None:
-        return ApiDependencies.invoker.services.boards.get_many(order_by, direction, offset, limit, include_archived)
+        return ApiDependencies.invoker.services.boards.get_many(offset, limit, include_archived)
     else:
         raise HTTPException(
             status_code=400,

invokeai/app/api/routers/model_manager.py

@@ -38,12 +38,7 @@ from invokeai.backend.model_manager.load.model_cache.model_cache_base import Cac
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
-from invokeai.backend.model_manager.starter_models import (
-    STARTER_BUNDLES,
-    STARTER_MODELS,
-    StarterModel,
-    StarterModelWithoutDependencies,
-)
+from invokeai.backend.model_manager.starter_models import STARTER_MODELS, StarterModel, StarterModelWithoutDependencies
 
 model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
 
@@ -797,48 +792,22 @@ async def convert_model(
     return new_config
 
 
-class StarterModelResponse(BaseModel):
-    starter_models: list[StarterModel]
-    starter_bundles: dict[str, list[StarterModel]]
-
-
-def get_is_installed(
-    starter_model: StarterModel | StarterModelWithoutDependencies, installed_models: list[AnyModelConfig]
-) -> bool:
-    for model in installed_models:
-        if model.source == starter_model.source:
-            return True
-        if model.name == starter_model.name and model.base == starter_model.base and model.type == starter_model.type:
-            return True
-    return False
-
-
-@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=StarterModelResponse)
-async def get_starter_models() -> StarterModelResponse:
+@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=list[StarterModel])
+async def get_starter_models() -> list[StarterModel]:
     installed_models = ApiDependencies.invoker.services.model_manager.store.search_by_attr()
+    installed_model_sources = {m.source for m in installed_models}
     starter_models = deepcopy(STARTER_MODELS)
-    starter_bundles = deepcopy(STARTER_BUNDLES)
     for model in starter_models:
-        model.is_installed = get_is_installed(model, installed_models)
+        if model.source in installed_model_sources:
+            model.is_installed = True
         # Remove already-installed dependencies
         missing_deps: list[StarterModelWithoutDependencies] = []
-
         for dep in model.dependencies or []:
-            if not get_is_installed(dep, installed_models):
+            if dep.source not in installed_model_sources:
                 missing_deps.append(dep)
         model.dependencies = missing_deps
 
-    for bundle in starter_bundles.values():
-        for model in bundle:
-            model.is_installed = get_is_installed(model, installed_models)
-            # Remove already-installed dependencies
-            missing_deps: list[StarterModelWithoutDependencies] = []
-            for dep in model.dependencies or []:
-                if not get_is_installed(dep, installed_models):
-                    missing_deps.append(dep)
-            model.dependencies = missing_deps
-
-    return StarterModelResponse(starter_models=starter_models, starter_bundles=starter_bundles)
+    return starter_models
 
 
 @model_manager_router.get(

invokeai/app/api/routers/workflows.py

@@ -83,7 +83,7 @@ async def create_workflow(
 )
 async def list_workflows(
     page: int = Query(default=0, description="The page to get"),
-    per_page: Optional[int] = Query(default=None, description="The number of workflows per page"),
+    per_page: int = Query(default=10, description="The number of workflows per page"),
     order_by: WorkflowRecordOrderBy = Query(
         default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
     ),
@@ -93,5 +93,5 @@ async def list_workflows(
 ) -> PaginatedResults[WorkflowRecordListItemDTO]:
     """Gets a page of workflows"""
     return ApiDependencies.invoker.services.workflow_records.get_many(
-        order_by=order_by, direction=direction, page=page, per_page=per_page, query=query, category=category
+        page=page, per_page=per_page, order_by=order_by, direction=direction, query=query, category=category
     )

invokeai/app/invocations/denoise_latents.py

@@ -547,9 +547,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 if not isinstance(single_ipa_image_fields, list):
                     single_ipa_image_fields = [single_ipa_image_fields]
 
-                single_ipa_images = [
-                    context.images.get_pil(image.image_name, mode="RGB") for image in single_ipa_image_fields
-                ]
+                single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_image_fields]
                 with image_encoder_model_info as image_encoder_model:
                     assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                     # Get image embeddings from CLIP and ImageProjModel.

invokeai/app/invocations/fields.py

@@ -133,7 +133,6 @@ class FieldDescriptions:
     clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
-    mmditx = "MMDiTX"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
@@ -141,7 +140,6 @@ class FieldDescriptions:
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
     flux_model = "Flux model (Transformer) to load"
-    sd3_model = "SD3 model (MMDiTX) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -194,7 +192,6 @@ class FieldDescriptions:
     freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
     freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
     freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
-    instantx_control_mode = "The control mode for InstantX ControlNet union models. Ignored for other ControlNet models. The standard mapping is: canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6). Negative values will be treated as 'None'."
 
 
 class ImageField(BaseModel):

invokeai/app/invocations/flux_controlnet.py

@@ -1,99 +0,0 @@
-from pydantic import BaseModel, Field, field_validator, model_validator
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
-from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES
-
-
-class FluxControlNetField(BaseModel):
-    image: ImageField = Field(description="The control image")
-    control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
-    control_weight: float | list[float] = Field(default=1, description="The weight given to the ControlNet")
-    begin_step_percent: float = Field(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
-    )
-    end_step_percent: float = Field(
-        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
-    )
-    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
-    instantx_control_mode: int | None = Field(default=-1, description=FieldDescriptions.instantx_control_mode)
-
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
-
-@invocation_output("flux_controlnet_output")
-class FluxControlNetOutput(BaseInvocationOutput):
-    """FLUX ControlNet info"""
-
-    control: FluxControlNetField = OutputField(description=FieldDescriptions.control)
-
-
-@invocation(
-    "flux_controlnet",
-    title="FLUX ControlNet",
-    tags=["controlnet", "flux"],
-    category="controlnet",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxControlNetInvocation(BaseInvocation):
-    """Collect FLUX ControlNet info to pass to other nodes."""
-
-    image: ImageField = InputField(description="The control image")
-    control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
-    )
-    control_weight: float | list[float] = InputField(
-        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
-    )
-    begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
-    )
-    end_step_percent: float = InputField(
-        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
-    )
-    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
-    # Note: We default to -1 instead of None, because in the workflow editor UI None is not currently supported.
-    instantx_control_mode: int | None = InputField(default=-1, description=FieldDescriptions.instantx_control_mode)
-
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
-    def invoke(self, context: InvocationContext) -> FluxControlNetOutput:
-        return FluxControlNetOutput(
-            control=FluxControlNetField(
-                image=self.image,
-                control_model=self.control_model,
-                control_weight=self.control_weight,
-                begin_step_percent=self.begin_step_percent,
-                end_step_percent=self.end_step_percent,
-                resize_mode=self.resize_mode,
-                instantx_control_mode=self.instantx_control_mode,
-            ),
-        )

invokeai/app/invocations/flux_denoise.py

@@ -1,38 +1,26 @@
 from contextlib import ExitStack
 from typing import Callable, Iterator, Optional, Tuple
 
-import numpy as np
-import numpy.typing as npt
 import torch
 import torchvision.transforms as tv_transforms
 from torchvision.transforms.functional import resize as tv_resize
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
     FluxConditioningField,
-    ImageField,
     Input,
     InputField,
     LatentsField,
     WithBoard,
     WithMetadata,
 )
-from invokeai.app.invocations.flux_controlnet import FluxControlNetField
-from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.model import TransformerField, VAEField
+from invokeai.app.invocations.model import TransformerField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
-from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
 from invokeai.backend.flux.denoise import denoise
-from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
-from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
-from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
-from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
+from invokeai.backend.flux.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.sampling_utils import (
     clip_timestep_schedule_fractional,
@@ -42,7 +30,7 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import FLUX_KOHYA_TRANFORMER_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
@@ -56,7 +44,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.2.0",
+    version="3.0.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -89,24 +77,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     positive_text_conditioning: FluxConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
-    negative_text_conditioning: FluxConditioningField | None = InputField(
-        default=None,
-        description="Negative conditioning tensor. Can be None if cfg_scale is 1.0.",
-        input=Input.Connection,
-    )
-    cfg_scale: float | list[float] = InputField(default=1.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
-    cfg_scale_start_step: int = InputField(
-        default=0,
-        title="CFG Scale Start Step",
-        description="Index of the first step to apply cfg_scale. Negative indices count backwards from the "
-        + "the last step (e.g. a value of -1 refers to the final step).",
-    )
-    cfg_scale_end_step: int = InputField(
-        default=-1,
-        title="CFG Scale End Step",
-        description="Index of the last step to apply cfg_scale. Negative indices count backwards from the "
-        + "last step (e.g. a value of -1 refers to the final step).",
-    )
     width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
     height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
     num_steps: int = InputField(
@@ -117,18 +87,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
     )
     seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
-    control: FluxControlNetField | list[FluxControlNetField] | None = InputField(
-        default=None, input=Input.Connection, description="ControlNet models."
-    )
-    controlnet_vae: VAEField | None = InputField(
-        default=None,
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-
-    ip_adapter: IPAdapterField | list[IPAdapterField] | None = InputField(
-        description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection
-    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
@@ -138,19 +96,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
-    def _load_text_conditioning(
-        self, context: InvocationContext, conditioning_name: str, dtype: torch.dtype
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-        flux_conditioning = flux_conditioning.to(dtype=dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-        return t5_embeddings, clip_embeddings
-
     def _run_diffusion(
         self,
         context: InvocationContext,
@@ -158,15 +103,13 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         inference_dtype = torch.bfloat16
 
         # Load the conditioning data.
-        pos_t5_embeddings, pos_clip_embeddings = self._load_text_conditioning(
-            context, self.positive_text_conditioning.conditioning_name, inference_dtype
-        )
-        neg_t5_embeddings: torch.Tensor | None = None
-        neg_clip_embeddings: torch.Tensor | None = None
-        if self.negative_text_conditioning is not None:
-            neg_t5_embeddings, neg_clip_embeddings = self._load_text_conditioning(
-                context, self.negative_text_conditioning.conditioning_name, inference_dtype
-            )
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
 
         # Load the input latents, if provided.
         init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
@@ -224,19 +167,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
-        b, _c, latent_h, latent_w = x.shape
-        img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
+        b, _c, h, w = x.shape
+        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
 
-        pos_bs, pos_t5_seq_len, _ = pos_t5_embeddings.shape
-        pos_txt_ids = torch.zeros(
-            pos_bs, pos_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-        )
-        neg_txt_ids: torch.Tensor | None = None
-        if neg_t5_embeddings is not None:
-            neg_bs, neg_t5_seq_len, _ = neg_t5_embeddings.shape
-            neg_txt_ids = torch.zeros(
-                neg_bs, neg_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-            )
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
 
         # Pack all latent tensors.
         init_latents = pack(init_latents) if init_latents is not None else None
@@ -257,36 +192,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 noise=noise,
             )
 
-        # Compute the IP-Adapter image prompt clip embeddings.
-        # We do this before loading other models to minimize peak memory.
-        # TODO(ryand): We should really do this in a separate invocation to benefit from caching.
-        ip_adapter_fields = self._normalize_ip_adapter_fields()
-        pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds = self._prep_ip_adapter_image_prompt_clip_embeds(
-            ip_adapter_fields, context
-        )
-
-        cfg_scale = self.prep_cfg_scale(
-            cfg_scale=self.cfg_scale,
-            timesteps=timesteps,
-            cfg_scale_start_step=self.cfg_scale_start_step,
-            cfg_scale_end_step=self.cfg_scale_end_step,
-        )
-
-        with ExitStack() as exit_stack:
-            # Prepare ControlNet extensions.
-            # Note: We do this before loading the transformer model to minimize peak memory (see implementation).
-            controlnet_extensions = self._prep_controlnet_extensions(
-                context=context,
-                exit_stack=exit_stack,
-                latent_height=latent_h,
-                latent_width=latent_w,
-                dtype=inference_dtype,
-                device=x.device,
-            )
-
-            # Load the transformer model.
-            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
+        with (
+            transformer_info.model_on_device() as (cached_weights, transformer),
+            ExitStack() as exit_stack,
+        ):
             assert isinstance(transformer, Flux)
+
             config = transformer_info.config
             assert config is not None
 
@@ -298,7 +209,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     LoRAPatcher.apply_lora_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
-                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
+                        prefix=FLUX_KOHYA_TRANFORMER_PREFIX,
                         cached_weights=cached_weights,
                     )
                 )
@@ -313,95 +224,29 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     LoRAPatcher.apply_lora_sidecar_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
-                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
+                        prefix=FLUX_KOHYA_TRANFORMER_PREFIX,
                         dtype=inference_dtype,
                     )
                 )
             else:
                 raise ValueError(f"Unsupported model format: {config.format}")
 
-            # Prepare IP-Adapter extensions.
-            pos_ip_adapter_extensions, neg_ip_adapter_extensions = self._prep_ip_adapter_extensions(
-                pos_image_prompt_clip_embeds=pos_image_prompt_clip_embeds,
-                neg_image_prompt_clip_embeds=neg_image_prompt_clip_embeds,
-                ip_adapter_fields=ip_adapter_fields,
-                context=context,
-                exit_stack=exit_stack,
-                dtype=inference_dtype,
-            )
-
             x = denoise(
                 model=transformer,
                 img=x,
                 img_ids=img_ids,
-                txt=pos_t5_embeddings,
-                txt_ids=pos_txt_ids,
-                vec=pos_clip_embeddings,
-                neg_txt=neg_t5_embeddings,
-                neg_txt_ids=neg_txt_ids,
-                neg_vec=neg_clip_embeddings,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
                 timesteps=timesteps,
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
-                cfg_scale=cfg_scale,
                 inpaint_extension=inpaint_extension,
-                controlnet_extensions=controlnet_extensions,
-                pos_ip_adapter_extensions=pos_ip_adapter_extensions,
-                neg_ip_adapter_extensions=neg_ip_adapter_extensions,
             )
 
         x = unpack(x.float(), self.height, self.width)
         return x
 
-    @classmethod
-    def prep_cfg_scale(
-        cls, cfg_scale: float | list[float], timesteps: list[float], cfg_scale_start_step: int, cfg_scale_end_step: int
-    ) -> list[float]:
-        """Prepare the cfg_scale schedule.
-
-        - Clips the cfg_scale schedule based on cfg_scale_start_step and cfg_scale_end_step.
-        - If cfg_scale is a list, then it is assumed to be a schedule and is returned as-is.
-        - If cfg_scale is a scalar, then a linear schedule is created from cfg_scale_start_step to cfg_scale_end_step.
-        """
-        # num_steps is the number of denoising steps, which is one less than the number of timesteps.
-        num_steps = len(timesteps) - 1
-
-        # Normalize cfg_scale to a list if it is a scalar.
-        cfg_scale_list: list[float]
-        if isinstance(cfg_scale, float):
-            cfg_scale_list = [cfg_scale] * num_steps
-        elif isinstance(cfg_scale, list):
-            cfg_scale_list = cfg_scale
-        else:
-            raise ValueError(f"Unsupported cfg_scale type: {type(cfg_scale)}")
-        assert len(cfg_scale_list) == num_steps
-
-        # Handle negative indices for cfg_scale_start_step and cfg_scale_end_step.
-        start_step_index = cfg_scale_start_step
-        if start_step_index < 0:
-            start_step_index = num_steps + start_step_index
-        end_step_index = cfg_scale_end_step
-        if end_step_index < 0:
-            end_step_index = num_steps + end_step_index
-
-        # Validate the start and end step indices.
-        if not (0 <= start_step_index < num_steps):
-            raise ValueError(f"Invalid cfg_scale_start_step. Out of range: {cfg_scale_start_step}.")
-        if not (0 <= end_step_index < num_steps):
-            raise ValueError(f"Invalid cfg_scale_end_step. Out of range: {cfg_scale_end_step}.")
-        if start_step_index > end_step_index:
-            raise ValueError(
-                f"cfg_scale_start_step ({cfg_scale_start_step}) must be before cfg_scale_end_step "
-                + f"({cfg_scale_end_step})."
-            )
-
-        # Set values outside the start and end step indices to 1.0. This is equivalent to disabling cfg_scale for those
-        # steps.
-        clipped_cfg_scale = [1.0] * num_steps
-        clipped_cfg_scale[start_step_index : end_step_index + 1] = cfg_scale_list[start_step_index : end_step_index + 1]
-
-        return clipped_cfg_scale
-
     def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
         """Prepare the inpaint mask.
 
@@ -443,210 +288,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # `latents`.
         return mask.expand_as(latents)
 
-    def _prep_controlnet_extensions(
-        self,
-        context: InvocationContext,
-        exit_stack: ExitStack,
-        latent_height: int,
-        latent_width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-    ) -> list[XLabsControlNetExtension | InstantXControlNetExtension]:
-        # Normalize the controlnet input to list[ControlField].
-        controlnets: list[FluxControlNetField]
-        if self.control is None:
-            controlnets = []
-        elif isinstance(self.control, FluxControlNetField):
-            controlnets = [self.control]
-        elif isinstance(self.control, list):
-            controlnets = self.control
-        else:
-            raise ValueError(f"Unsupported controlnet type: {type(self.control)}")
-
-        # TODO(ryand): Add a field to the model config so that we can distinguish between XLabs and InstantX ControlNets
-        # before loading the models. Then make sure that all VAE encoding is done before loading the ControlNets to
-        # minimize peak memory.
-
-        # First, load the ControlNet models so that we can determine the ControlNet types.
-        controlnet_models = [context.models.load(controlnet.control_model) for controlnet in controlnets]
-
-        # Calculate the controlnet conditioning tensors.
-        # We do this before loading the ControlNet models because it may require running the VAE, and we are trying to
-        # keep peak memory down.
-        controlnet_conds: list[torch.Tensor] = []
-        for controlnet, controlnet_model in zip(controlnets, controlnet_models, strict=True):
-            image = context.images.get_pil(controlnet.image.image_name)
-            if isinstance(controlnet_model.model, InstantXControlNetFlux):
-                if self.controlnet_vae is None:
-                    raise ValueError("A ControlNet VAE is required when using an InstantX FLUX ControlNet.")
-                vae_info = context.models.load(self.controlnet_vae.vae)
-                controlnet_conds.append(
-                    InstantXControlNetExtension.prepare_controlnet_cond(
-                        controlnet_image=image,
-                        vae_info=vae_info,
-                        latent_height=latent_height,
-                        latent_width=latent_width,
-                        dtype=dtype,
-                        device=device,
-                        resize_mode=controlnet.resize_mode,
-                    )
-                )
-            elif isinstance(controlnet_model.model, XLabsControlNetFlux):
-                controlnet_conds.append(
-                    XLabsControlNetExtension.prepare_controlnet_cond(
-                        controlnet_image=image,
-                        latent_height=latent_height,
-                        latent_width=latent_width,
-                        dtype=dtype,
-                        device=device,
-                        resize_mode=controlnet.resize_mode,
-                    )
-                )
-
-        # Finally, load the ControlNet models and initialize the ControlNet extensions.
-        controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension] = []
-        for controlnet, controlnet_cond, controlnet_model in zip(
-            controlnets, controlnet_conds, controlnet_models, strict=True
-        ):
-            model = exit_stack.enter_context(controlnet_model)
-
-            if isinstance(model, XLabsControlNetFlux):
-                controlnet_extensions.append(
-                    XLabsControlNetExtension(
-                        model=model,
-                        controlnet_cond=controlnet_cond,
-                        weight=controlnet.control_weight,
-                        begin_step_percent=controlnet.begin_step_percent,
-                        end_step_percent=controlnet.end_step_percent,
-                    )
-                )
-            elif isinstance(model, InstantXControlNetFlux):
-                instantx_control_mode: torch.Tensor | None = None
-                if controlnet.instantx_control_mode is not None and controlnet.instantx_control_mode >= 0:
-                    instantx_control_mode = torch.tensor(controlnet.instantx_control_mode, dtype=torch.long)
-                    instantx_control_mode = instantx_control_mode.reshape([-1, 1])
-
-                controlnet_extensions.append(
-                    InstantXControlNetExtension(
-                        model=model,
-                        controlnet_cond=controlnet_cond,
-                        instantx_control_mode=instantx_control_mode,
-                        weight=controlnet.control_weight,
-                        begin_step_percent=controlnet.begin_step_percent,
-                        end_step_percent=controlnet.end_step_percent,
-                    )
-                )
-            else:
-                raise ValueError(f"Unsupported ControlNet model type: {type(model)}")
-
-        return controlnet_extensions
-
-    def _normalize_ip_adapter_fields(self) -> list[IPAdapterField]:
-        if self.ip_adapter is None:
-            return []
-        elif isinstance(self.ip_adapter, IPAdapterField):
-            return [self.ip_adapter]
-        elif isinstance(self.ip_adapter, list):
-            return self.ip_adapter
-        else:
-            raise ValueError(f"Unsupported IP-Adapter type: {type(self.ip_adapter)}")
-
-    def _prep_ip_adapter_image_prompt_clip_embeds(
-        self,
-        ip_adapter_fields: list[IPAdapterField],
-        context: InvocationContext,
-    ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
-        """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
-        clip_image_processor = CLIPImageProcessor()
-
-        pos_image_prompt_clip_embeds: list[torch.Tensor] = []
-        neg_image_prompt_clip_embeds: list[torch.Tensor] = []
-        for ip_adapter_field in ip_adapter_fields:
-            # `ip_adapter_field.image` could be a list or a single ImageField. Normalize to a list here.
-            ipa_image_fields: list[ImageField]
-            if isinstance(ip_adapter_field.image, ImageField):
-                ipa_image_fields = [ip_adapter_field.image]
-            elif isinstance(ip_adapter_field.image, list):
-                ipa_image_fields = ip_adapter_field.image
-            else:
-                raise ValueError(f"Unsupported IP-Adapter image type: {type(ip_adapter_field.image)}")
-
-            if len(ipa_image_fields) != 1:
-                raise ValueError(
-                    f"FLUX IP-Adapter only supports a single image prompt (received {len(ipa_image_fields)})."
-                )
-
-            ipa_images = [context.images.get_pil(image.image_name, mode="RGB") for image in ipa_image_fields]
-
-            pos_images: list[npt.NDArray[np.uint8]] = []
-            neg_images: list[npt.NDArray[np.uint8]] = []
-            for ipa_image in ipa_images:
-                assert ipa_image.mode == "RGB"
-                pos_image = np.array(ipa_image)
-                # We use a black image as the negative image prompt for parity with
-                # https://github.com/XLabs-AI/x-flux-comfyui/blob/45c834727dd2141aebc505ae4b01f193a8414e38/nodes.py#L592-L593
-                # An alternative scheme would be to apply zeros_like() after calling the clip_image_processor.
-                neg_image = np.zeros_like(pos_image)
-                pos_images.append(pos_image)
-                neg_images.append(neg_image)
-
-            with context.models.load(ip_adapter_field.image_encoder_model) as image_encoder_model:
-                assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
-
-                clip_image: torch.Tensor = clip_image_processor(images=pos_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
-                pos_clip_image_embeds = image_encoder_model(clip_image).image_embeds
-
-                clip_image = clip_image_processor(images=neg_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
-                neg_clip_image_embeds = image_encoder_model(clip_image).image_embeds
-
-            pos_image_prompt_clip_embeds.append(pos_clip_image_embeds)
-            neg_image_prompt_clip_embeds.append(neg_clip_image_embeds)
-
-        return pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds
-
-    def _prep_ip_adapter_extensions(
-        self,
-        ip_adapter_fields: list[IPAdapterField],
-        pos_image_prompt_clip_embeds: list[torch.Tensor],
-        neg_image_prompt_clip_embeds: list[torch.Tensor],
-        context: InvocationContext,
-        exit_stack: ExitStack,
-        dtype: torch.dtype,
-    ) -> tuple[list[XLabsIPAdapterExtension], list[XLabsIPAdapterExtension]]:
-        pos_ip_adapter_extensions: list[XLabsIPAdapterExtension] = []
-        neg_ip_adapter_extensions: list[XLabsIPAdapterExtension] = []
-        for ip_adapter_field, pos_image_prompt_clip_embed, neg_image_prompt_clip_embed in zip(
-            ip_adapter_fields, pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds, strict=True
-        ):
-            ip_adapter_model = exit_stack.enter_context(context.models.load(ip_adapter_field.ip_adapter_model))
-            assert isinstance(ip_adapter_model, XlabsIpAdapterFlux)
-            ip_adapter_model = ip_adapter_model.to(dtype=dtype)
-            if ip_adapter_field.mask is not None:
-                raise ValueError("IP-Adapter masks are not yet supported in Flux.")
-            ip_adapter_extension = XLabsIPAdapterExtension(
-                model=ip_adapter_model,
-                image_prompt_clip_embed=pos_image_prompt_clip_embed,
-                weight=ip_adapter_field.weight,
-                begin_step_percent=ip_adapter_field.begin_step_percent,
-                end_step_percent=ip_adapter_field.end_step_percent,
-            )
-            ip_adapter_extension.run_image_proj(dtype=dtype)
-            pos_ip_adapter_extensions.append(ip_adapter_extension)
-
-            ip_adapter_extension = XLabsIPAdapterExtension(
-                model=ip_adapter_model,
-                image_prompt_clip_embed=neg_image_prompt_clip_embed,
-                weight=ip_adapter_field.weight,
-                begin_step_percent=ip_adapter_field.begin_step_percent,
-                end_step_percent=ip_adapter_field.end_step_percent,
-            )
-            ip_adapter_extension.run_image_proj(dtype=dtype)
-            neg_ip_adapter_extensions.append(ip_adapter_extension)
-
-        return pos_ip_adapter_extensions, neg_ip_adapter_extensions
-
     def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in self.transformer.loras:
             lora_info = context.models.load(lora.lora)

invokeai/app/invocations/flux_ip_adapter.py

@@ -1,89 +0,0 @@
-from builtins import float
-from typing import List, Literal, Union
-
-from pydantic import field_validator, model_validator
-from typing_extensions import Self
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import InputField, UIType
-from invokeai.app.invocations.ip_adapter import (
-    CLIP_VISION_MODEL_MAP,
-    IPAdapterField,
-    IPAdapterInvocation,
-    IPAdapterOutput,
-)
-from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import (
-    IPAdapterCheckpointConfig,
-    IPAdapterInvokeAIConfig,
-)
-
-
-@invocation(
-    "flux_ip_adapter",
-    title="FLUX IP-Adapter",
-    tags=["ip_adapter", "control"],
-    category="ip_adapter",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxIPAdapterInvocation(BaseInvocation):
-    """Collects FLUX IP-Adapter info to pass to other nodes."""
-
-    # FLUXIPAdapterInvocation is based closely on IPAdapterInvocation, but with some unsupported features removed.
-
-    image: ImageField = InputField(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: ModelIdentifierField = InputField(
-        description="The IP-Adapter model.", title="IP-Adapter Model", ui_type=UIType.IPAdapterModel
-    )
-    # Currently, the only known ViT model used by FLUX IP-Adapters is ViT-L.
-    clip_vision_model: Literal["ViT-L"] = InputField(description="CLIP Vision model to use.", default="ViT-L")
-    weight: Union[float, List[float]] = InputField(
-        default=1, description="The weight given to the IP-Adapter", title="Weight"
-    )
-    begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
-    )
-    end_step_percent: float = InputField(
-        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
-    )
-
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v: float) -> float:
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> Self:
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
-    def invoke(self, context: InvocationContext) -> IPAdapterOutput:
-        # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
-        assert isinstance(ip_adapter_info, (IPAdapterInvokeAIConfig, IPAdapterCheckpointConfig))
-
-        # Note: There is a IPAdapterInvokeAIConfig.image_encoder_model_id field, but it isn't trustworthy.
-        image_encoder_starter_model = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
-        image_encoder_model_id = image_encoder_starter_model.source
-        image_encoder_model_name = image_encoder_starter_model.name
-        image_encoder_model = IPAdapterInvocation.get_clip_image_encoder(
-            context, image_encoder_model_id, image_encoder_model_name
-        )
-
-        return IPAdapterOutput(
-            ip_adapter=IPAdapterField(
-                image=self.image,
-                ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
-                weight=self.weight,
-                target_blocks=[],  # target_blocks is currently unused for FLUX IP-Adapters.
-                begin_step_percent=self.begin_step_percent,
-                end_step_percent=self.end_step_percent,
-                mask=None,  # mask is currently unused for FLUX IP-Adapters.
-            ),
-        )

invokeai/app/invocations/flux_model_loader.py

@@ -1,86 +0,0 @@
-from typing import Literal
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.util import max_seq_lengths
-from invokeai.backend.model_manager.config import CheckpointConfigBase, SubModelType
-
-
-@invocation_output("flux_model_loader_output")
-class FluxModelLoaderOutput(BaseInvocationOutput):
-    """Flux base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-    max_seq_len: Literal[256, 512] = OutputField(
-        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
-        title="Max Seq Length",
-    )
-
-
-@invocation(
-    "flux_model_loader",
-    title="Flux Main Model",
-    tags=["model", "flux"],
-    category="model",
-    version="1.0.4",
-    classification=Classification.Prototype,
-)
-class FluxModelLoaderInvocation(BaseInvocation):
-    """Loads a flux base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.flux_model,
-        ui_type=UIType.FluxMainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-
-        transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
-            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-            max_seq_len=max_seq_lengths[transformer_config.config_path],
-        )

invokeai/app/invocations/flux_text_encoder.py

@@ -10,7 +10,7 @@ from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import FLUX_KOHYA_CLIP_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
@@ -101,7 +101,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
                     LoRAPatcher.apply_lora_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context),
-                        prefix=FLUX_LORA_CLIP_PREFIX,
+                        prefix=FLUX_KOHYA_CLIP_PREFIX,
                         cached_weights=cached_weights,
                     )
                 )

invokeai/app/invocations/ip_adapter.py

@@ -9,7 +9,6 @@ from invokeai.app.invocations.fields import FieldDescriptions, InputField, Outpu
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
@@ -18,12 +17,6 @@ from invokeai.backend.model_manager.config import (
     IPAdapterInvokeAIConfig,
     ModelType,
 )
-from invokeai.backend.model_manager.starter_models import (
-    StarterModel,
-    clip_vit_l_image_encoder,
-    ip_adapter_sd_image_encoder,
-    ip_adapter_sdxl_image_encoder,
-)
 
 
 class IPAdapterField(BaseModel):
@@ -62,14 +55,10 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-CLIP_VISION_MODEL_MAP: dict[Literal["ViT-L", "ViT-H", "ViT-G"], StarterModel] = {
-    "ViT-L": clip_vit_l_image_encoder,
-    "ViT-H": ip_adapter_sd_image_encoder,
-    "ViT-G": ip_adapter_sdxl_image_encoder,
-}
+CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.5.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -81,7 +70,7 @@ class IPAdapterInvocation(BaseInvocation):
         ui_order=-1,
         ui_type=UIType.IPAdapterModel,
     )
-    clip_vision_model: Literal["ViT-H", "ViT-G", "ViT-L"] = InputField(
+    clip_vision_model: Literal["ViT-H", "ViT-G"] = InputField(
         description="CLIP Vision model to use. Overrides model settings. Mandatory for checkpoint models.",
         default="ViT-H",
         ui_order=2,
@@ -122,11 +111,9 @@ class IPAdapterInvocation(BaseInvocation):
             image_encoder_model_id = ip_adapter_info.image_encoder_model_id
             image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         else:
-            image_encoder_starter_model = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
-            image_encoder_model_id = image_encoder_starter_model.source
-            image_encoder_model_name = image_encoder_starter_model.name
+            image_encoder_model_name = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
 
-        image_encoder_model = self.get_clip_image_encoder(context, image_encoder_model_id, image_encoder_model_name)
+        image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
 
         if self.method == "style":
             if ip_adapter_info.base == "sd-1":
@@ -160,10 +147,7 @@ class IPAdapterInvocation(BaseInvocation):
             ),
         )
 
-    @classmethod
-    def get_clip_image_encoder(
-        cls, context: InvocationContext, image_encoder_model_id: str, image_encoder_model_name: str
-    ) -> AnyModelConfig:
+    def _get_image_encoder(self, context: InvocationContext, image_encoder_model_name: str) -> AnyModelConfig:
         image_encoder_models = context.models.search_by_attrs(
             name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
         )
@@ -175,11 +159,7 @@ class IPAdapterInvocation(BaseInvocation):
             )
 
             installer = context._services.model_manager.install
-            # Note: We hard-code the type to CLIPVision here because if the model contains both a CLIPVision and a
-            # CLIPText model, the probe may treat it as a CLIPText model.
-            job = installer.heuristic_import(
-                image_encoder_model_id, ModelRecordChanges(name=image_encoder_model_name, type=ModelType.CLIPVision)
-            )
+            job = installer.heuristic_import(f"InvokeAI/{image_encoder_model_name}")
             installer.wait_for_job(job, timeout=600)  # Wait for up to 10 minutes
             image_encoder_models = context.models.search_by_attrs(
                 name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision

invokeai/app/invocations/mask.py

@@ -5,7 +5,6 @@ from PIL import Image
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
 from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithBoard, WithMetadata
 from invokeai.app.invocations.primitives import ImageOutput, MaskOutput
-from invokeai.backend.image_util.util import pil_to_np
 
 
 @invocation(
@@ -149,51 +148,3 @@ class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         mask_pil = Image.fromarray(mask_np, mode="L")
         image_dto = context.images.save(image=mask_pil)
         return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "apply_tensor_mask_to_image",
-    title="Apply Tensor Mask to Image",
-    tags=["mask"],
-    category="mask",
-    version="1.0.0",
-)
-class ApplyMaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Applies a tensor mask to an image.
-
-    The image is converted to RGBA and the mask is applied to the alpha channel."""
-
-    mask: TensorField = InputField(description="The mask tensor to apply.")
-    image: ImageField = InputField(description="The image to apply the mask to.")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
-        mask = context.tensors.load(self.mask.tensor_name)
-
-        # Squeeze the channel dimension if it exists.
-        if mask.dim() == 3:
-            mask = mask.squeeze(0)
-
-        # Ensure that the mask is binary.
-        if mask.dtype != torch.bool:
-            mask = mask > 0.5
-        mask_np = (mask.float() * 255).byte().cpu().numpy().astype(np.uint8)
-
-        # Apply the mask only to the alpha channel where the original alpha is non-zero. This preserves the original
-        # image's transparency - else the transparent regions would end up as opaque black.
-
-        # Separate the image into R, G, B, and A channels
-        image_np = pil_to_np(image)
-        r, g, b, a = np.split(image_np, 4, axis=-1)
-
-        # Apply the mask to the alpha channel
-        new_alpha = np.where(a.squeeze() > 0, mask_np, a.squeeze())
-
-        # Stack the RGB channels with the modified alpha
-        masked_image_np = np.dstack([r.squeeze(), g.squeeze(), b.squeeze(), new_alpha])
-
-        # Convert back to an image (RGBA)
-        masked_image = Image.fromarray(masked_image_np.astype(np.uint8), "RGBA")
-        image_dto = context.images.save(image=masked_image)
-
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/metadata.py

@@ -40,7 +40,7 @@ class IPAdapterMetadataField(BaseModel):
 
     image: ImageField = Field(description="The IP-Adapter image prompt.")
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
-    clip_vision_model: Literal["ViT-L", "ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
+    clip_vision_model: Literal["ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
     method: Literal["full", "style", "composition"] = Field(description="Method to apply IP Weights with")
     weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
     begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Optional
+from typing import List, Literal, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,9 +13,11 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
+from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    CheckpointConfigBase,
     ModelType,
     SubModelType,
 )
@@ -137,6 +139,78 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
+@invocation_output("flux_model_loader_output")
+class FluxModelLoaderOutput(BaseInvocationOutput):
+    """Flux base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    max_seq_len: Literal[256, 512] = OutputField(
+        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
+        title="Max Seq Length",
+    )
+
+
+@invocation(
+    "flux_model_loader",
+    title="Flux Main Model",
+    tags=["model", "flux"],
+    category="model",
+    version="1.0.4",
+    classification=Classification.Prototype,
+)
+class FluxModelLoaderInvocation(BaseInvocation):
+    """Loads a flux base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.flux_model,
+        ui_type=UIType.FluxMainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
+    )
+
+    clip_embed_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPEmbedModel,
+        input=Input.Direct,
+        title="CLIP Embed",
+    )
+
+    vae_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
+        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+
+        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+
+        transformer_config = context.models.get_config(transformer)
+        assert isinstance(transformer_config, CheckpointConfigBase)
+
+        return FluxModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+            max_seq_len=max_seq_lengths[transformer_config.config_path],
+        )
+
+
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/sd3_model_loader.py

@@ -1,102 +0,0 @@
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import CheckpointConfigBase, SubModelType
-
-
-@invocation_output("sd3_model_loader_output")
-class Sd3ModelLoaderOutput(BaseInvocationOutput):
-    """SD3 base model loader output."""
-
-    mmditx: TransformerField = OutputField(description=FieldDescriptions.mmditx, title="MMDiTX")
-    clip_l: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP L")
-    clip_g: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP G")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-
-
-@invocation(
-    "sd3_model_loader",
-    title="SD3 Main Model",
-    tags=["model", "sd3"],
-    category="model",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class Sd3ModelLoaderInvocation(BaseInvocation):
-    """Loads a SD3 base model, outputting its submodels."""
-
-    # TODO(ryand): Create a UIType.Sd3MainModelField to use here.
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sd3_model,
-        ui_type=UIType.MainModel,
-        input=Input.Direct,
-    )
-
-    # TODO(ryand): Make the text encoders optional.
-    # Note: The text encoders are optional for SD3. The model was trained with dropout, so any can be left out at
-    # inference time. Typically, only the T5 encoder is omitted, since it is the largest by far.
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_l_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP L Embed",
-    )
-
-    clip_g_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP G Embed",
-    )
-
-    # TODO(ryand): Create a UIType.Sd3VaModelField to use here.
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.VAEModel, title="VAE"
-    )
-
-    def invoke(self, context: InvocationContext) -> Sd3ModelLoaderOutput:
-        for key in [
-            self.model.key,
-            self.t5_encoder_model.key,
-            self.clip_l_embed_model.key,
-            self.clip_g_embed_model.key,
-            self.vae_model.key,
-        ]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        # TODO(ryand): Figure out the sub-model types for SD3.
-        mmditx = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer_l = self.clip_l_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder_l = self.clip_l_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer_g = self.clip_g_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder_g = self.clip_g_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer_t5 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-
-        transformer_config = context.models.get_config(mmditx)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return Sd3ModelLoaderOutput(
-            mmditx=TransformerField(transformer=mmditx, loras=[]),
-            clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
-            clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-        )

invokeai/app/invocations/segment_anything.py

@@ -1,11 +1,9 @@
-from enum import Enum
 from pathlib import Path
 from typing import Literal
 
 import numpy as np
 import torch
 from PIL import Image
-from pydantic import BaseModel, Field, model_validator
 from transformers import AutoModelForMaskGeneration, AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
@@ -25,31 +23,12 @@ SEGMENT_ANYTHING_MODEL_IDS: dict[SegmentAnythingModelKey, str] = {
 }
 
 
-class SAMPointLabel(Enum):
-    negative = -1
-    neutral = 0
-    positive = 1
-
-
-class SAMPoint(BaseModel):
-    x: int = Field(..., description="The x-coordinate of the point")
-    y: int = Field(..., description="The y-coordinate of the point")
-    label: SAMPointLabel = Field(..., description="The label of the point")
-
-
-class SAMPointsField(BaseModel):
-    points: list[SAMPoint] = Field(..., description="The points of the object")
-
-    def to_list(self) -> list[list[int]]:
-        return [[point.x, point.y, point.label.value] for point in self.points]
-
-
 @invocation(
     "segment_anything",
     title="Segment Anything",
     tags=["prompt", "segmentation"],
     category="segmentation",
-    version="1.1.0",
+    version="1.0.0",
 )
 class SegmentAnythingInvocation(BaseInvocation):
     """Runs a Segment Anything Model."""
@@ -61,13 +40,7 @@ class SegmentAnythingInvocation(BaseInvocation):
 
     model: SegmentAnythingModelKey = InputField(description="The Segment Anything model to use.")
     image: ImageField = InputField(description="The image to segment.")
-    bounding_boxes: list[BoundingBoxField] | None = InputField(
-        default=None, description="The bounding boxes to prompt the SAM model with."
-    )
-    point_lists: list[SAMPointsField] | None = InputField(
-        default=None,
-        description="The list of point lists to prompt the SAM model with. Each list of points represents a single object.",
-    )
+    bounding_boxes: list[BoundingBoxField] = InputField(description="The bounding boxes to prompt the SAM model with.")
     apply_polygon_refinement: bool = InputField(
         description="Whether to apply polygon refinement to the masks. This will smooth the edges of the masks slightly and ensure that each mask consists of a single closed polygon (before merging).",
         default=True,
@@ -77,22 +50,12 @@ class SegmentAnythingInvocation(BaseInvocation):
         default="all",
     )
 
-    @model_validator(mode="after")
-    def check_point_lists_or_bounding_box(self):
-        if self.point_lists is None and self.bounding_boxes is None:
-            raise ValueError("Either point_lists or bounding_box must be provided.")
-        elif self.point_lists is not None and self.bounding_boxes is not None:
-            raise ValueError("Only one of point_lists or bounding_box can be provided.")
-        return self
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> MaskOutput:
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
-        if (not self.bounding_boxes or len(self.bounding_boxes) == 0) and (
-            not self.point_lists or len(self.point_lists) == 0
-        ):
+        if len(self.bounding_boxes) == 0:
             combined_mask = torch.zeros(image_pil.size[::-1], dtype=torch.bool)
         else:
             masks = self._segment(context=context, image=image_pil)
@@ -120,13 +83,14 @@ class SegmentAnythingInvocation(BaseInvocation):
         assert isinstance(sam_processor, SamProcessor)
         return SegmentAnythingPipeline(sam_model=sam_model, sam_processor=sam_processor)
 
-    def _segment(self, context: InvocationContext, image: Image.Image) -> list[torch.Tensor]:
+    def _segment(
+        self,
+        context: InvocationContext,
+        image: Image.Image,
+    ) -> list[torch.Tensor]:
         """Use Segment Anything (SAM) to generate masks given an image + a set of bounding boxes."""
         # Convert the bounding boxes to the SAM input format.
-        sam_bounding_boxes = (
-            [[bb.x_min, bb.y_min, bb.x_max, bb.y_max] for bb in self.bounding_boxes] if self.bounding_boxes else None
-        )
-        sam_points = [p.to_list() for p in self.point_lists] if self.point_lists else None
+        sam_bounding_boxes = [[bb.x_min, bb.y_min, bb.x_max, bb.y_max] for bb in self.bounding_boxes]
 
         with (
             context.models.load_remote_model(
@@ -134,7 +98,7 @@ class SegmentAnythingInvocation(BaseInvocation):
             ) as sam_pipeline,
         ):
             assert isinstance(sam_pipeline, SegmentAnythingPipeline)
-            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes, point_lists=sam_points)
+            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes)
 
         masks = self._process_masks(masks)
         if self.apply_polygon_refinement:
@@ -177,10 +141,9 @@ class SegmentAnythingInvocation(BaseInvocation):
 
         return masks
 
-    def _filter_masks(
-        self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField] | None
-    ) -> list[torch.Tensor]:
+    def _filter_masks(self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField]) -> list[torch.Tensor]:
         """Filter the detected masks based on the specified mask filter."""
+        assert len(masks) == len(bounding_boxes)
 
         if self.mask_filter == "all":
             return masks
@@ -188,10 +151,6 @@ class SegmentAnythingInvocation(BaseInvocation):
             # Find the largest mask.
             return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
-            assert (
-                bounding_boxes is not None
-            ), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
-            assert len(masks) == len(bounding_boxes)
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most
             # cases the scores should all be non-None when using this filtering mode. That being said, -1.0 is a

invokeai/app/services/board_records/board_records_base.py

@@ -1,8 +1,7 @@
 from abc import ABC, abstractmethod
 
-from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecord, BoardRecordOrderBy
+from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecord
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class BoardRecordStorageBase(ABC):
@@ -40,19 +39,12 @@ class BoardRecordStorageBase(ABC):
 
     @abstractmethod
     def get_many(
-        self,
-        order_by: BoardRecordOrderBy,
-        direction: SQLiteDirection,
-        offset: int = 0,
-        limit: int = 10,
-        include_archived: bool = False,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardRecord]:
         """Gets many board records."""
         pass
 
     @abstractmethod
-    def get_all(
-        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
-    ) -> list[BoardRecord]:
+    def get_all(self, include_archived: bool = False) -> list[BoardRecord]:
         """Gets all board records."""
         pass

invokeai/app/services/board_records/board_records_common.py

@@ -1,10 +1,8 @@
 from datetime import datetime
-from enum import Enum
 from typing import Optional, Union
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import get_iso_timestamp
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
@@ -62,13 +60,6 @@ class BoardChanges(BaseModel, extra="forbid"):
     archived: Optional[bool] = Field(default=None, description="Whether or not the board is archived")
 
 
-class BoardRecordOrderBy(str, Enum, metaclass=MetaEnum):
-    """The order by options for board records"""
-
-    CreatedAt = "created_at"
-    Name = "board_name"
-
-
 class BoardRecordNotFoundException(Exception):
     """Raised when an board record is not found."""
 

invokeai/app/services/board_records/board_records_sqlite.py

@@ -8,12 +8,10 @@ from invokeai.app.services.board_records.board_records_common import (
     BoardRecord,
     BoardRecordDeleteException,
     BoardRecordNotFoundException,
-    BoardRecordOrderBy,
     BoardRecordSaveException,
     deserialize_board_record,
 )
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.app.util.misc import uuid_string
 
@@ -146,12 +144,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         return self.get(board_id)
 
     def get_many(
-        self,
-        order_by: BoardRecordOrderBy,
-        direction: SQLiteDirection,
-        offset: int = 0,
-        limit: int = 10,
-        include_archived: bool = False,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardRecord]:
         try:
             self._lock.acquire()
@@ -161,16 +154,17 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
                 SELECT *
                 FROM boards
                 {archived_filter}
-                ORDER BY {order_by} {direction}
+                ORDER BY created_at DESC
                 LIMIT ? OFFSET ?;
             """
 
             # Determine archived filter condition
-            archived_filter = "" if include_archived else "WHERE archived = 0"
+            if include_archived:
+                archived_filter = ""
+            else:
+                archived_filter = "WHERE archived = 0"
 
-            final_query = base_query.format(
-                archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
-            )
+            final_query = base_query.format(archived_filter=archived_filter)
 
             # Execute query to fetch boards
             self._cursor.execute(final_query, (limit, offset))
@@ -204,32 +198,23 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         finally:
             self._lock.release()
 
-    def get_all(
-        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
-    ) -> list[BoardRecord]:
+    def get_all(self, include_archived: bool = False) -> list[BoardRecord]:
         try:
             self._lock.acquire()
 
-            if order_by == BoardRecordOrderBy.Name:
-                base_query = """
-                    SELECT *
-                    FROM boards
-                    {archived_filter}
-                    ORDER BY LOWER(board_name) {direction}
-                """
+            base_query = """
+                SELECT *
+                FROM boards
+                {archived_filter}
+                ORDER BY created_at DESC
+            """
+
+            if include_archived:
+                archived_filter = ""
             else:
-                base_query = """
-                    SELECT *
-                    FROM boards
-                    {archived_filter}
-                    ORDER BY {order_by} {direction}
-                """
+                archived_filter = "WHERE archived = 0"
 
-            archived_filter = "" if include_archived else "WHERE archived = 0"
-
-            final_query = base_query.format(
-                archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
-            )
+            final_query = base_query.format(archived_filter=archived_filter)
 
             self._cursor.execute(final_query)
 

invokeai/app/services/boards/boards_base.py

@@ -1,9 +1,8 @@
 from abc import ABC, abstractmethod
 
-from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
+from invokeai.app.services.board_records.board_records_common import BoardChanges
 from invokeai.app.services.boards.boards_common import BoardDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class BoardServiceABC(ABC):
@@ -44,19 +43,12 @@ class BoardServiceABC(ABC):
 
     @abstractmethod
     def get_many(
-        self,
-        order_by: BoardRecordOrderBy,
-        direction: SQLiteDirection,
-        offset: int = 0,
-        limit: int = 10,
-        include_archived: bool = False,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardDTO]:
         """Gets many boards."""
         pass
 
     @abstractmethod
-    def get_all(
-        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
-    ) -> list[BoardDTO]:
+    def get_all(self, include_archived: bool = False) -> list[BoardDTO]:
         """Gets all boards."""
         pass

invokeai/app/services/boards/boards_default.py

@@ -1,9 +1,8 @@
-from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
+from invokeai.app.services.board_records.board_records_common import BoardChanges
 from invokeai.app.services.boards.boards_base import BoardServiceABC
 from invokeai.app.services.boards.boards_common import BoardDTO, board_record_to_dto
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class BoardService(BoardServiceABC):
@@ -48,16 +47,9 @@ class BoardService(BoardServiceABC):
         self.__invoker.services.board_records.delete(board_id)
 
     def get_many(
-        self,
-        order_by: BoardRecordOrderBy,
-        direction: SQLiteDirection,
-        offset: int = 0,
-        limit: int = 10,
-        include_archived: bool = False,
+        self, offset: int = 0, limit: int = 10, include_archived: bool = False
     ) -> OffsetPaginatedResults[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_many(
-            order_by, direction, offset, limit, include_archived
-        )
+        board_records = self.__invoker.services.board_records.get_many(offset, limit, include_archived)
         board_dtos = []
         for r in board_records.items:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
@@ -71,10 +63,8 @@ class BoardService(BoardServiceABC):
 
         return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
-    def get_all(
-        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
-    ) -> list[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_all(order_by, direction, include_archived)
+    def get_all(self, include_archived: bool = False) -> list[BoardDTO]:
+        board_records = self.__invoker.services.board_records.get_all(include_archived)
         board_dtos = []
         for r in board_records:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)

invokeai/app/services/config/config_default.py

@@ -250,9 +250,9 @@ class InvokeAIAppConfig(BaseSettings):
             )
 
             if as_example:
-                file.write("# This is an example file with default and example settings.\n")
-                file.write("# You should not copy this whole file into your config.\n")
-                file.write("# Only add the settings you need to change to your config file.\n\n")
+                file.write(
+                    "# This is an example file with default and example settings. Use the values here as a baseline.\n\n"
+                )
             file.write("# Internal metadata - do not edit:\n")
             file.write(yaml.dump(meta_dict, sort_keys=False))
             file.write("\n")

invokeai/app/services/image_files/image_files_disk.py

@@ -110,26 +110,15 @@ class DiskImageFileStorage(ImageFileStorageBase):
         except Exception as e:
             raise ImageFileDeleteException from e
 
+    # TODO: make this a bit more flexible for e.g. cloud storage
     def get_path(self, image_name: str, thumbnail: bool = False) -> Path:
-        base_folder = self.__thumbnails_folder if thumbnail else self.__output_folder
-        filename = get_thumbnail_name(image_name) if thumbnail else image_name
+        path = self.__output_folder / image_name
 
-        # Strip any path information from the filename
-        basename = Path(filename).name
+        if thumbnail:
+            thumbnail_name = get_thumbnail_name(image_name)
+            path = self.__thumbnails_folder / thumbnail_name
 
-        if basename != filename:
-            raise ValueError("Invalid image name, potential directory traversal detected")
-
-        image_path = base_folder / basename
-
-        # Ensure the image path is within the base folder to prevent directory traversal
-        resolved_base = base_folder.resolve()
-        resolved_image_path = image_path.resolve()
-
-        if not resolved_image_path.is_relative_to(resolved_base):
-            raise ValueError("Image path outside outputs folder, potential directory traversal detected")
-
-        return resolved_image_path
+        return path
 
     def validate_path(self, path: Union[str, Path]) -> bool:
         """Validates the path given for an image or thumbnail."""

invokeai/app/services/workflow_records/workflow_records_base.py

@@ -39,11 +39,11 @@ class WorkflowRecordsStorageBase(ABC):
     @abstractmethod
     def get_many(
         self,
+        page: int,
+        per_page: int,
         order_by: WorkflowRecordOrderBy,
         direction: SQLiteDirection,
         category: WorkflowCategory,
-        page: int,
-        per_page: Optional[int],
         query: Optional[str],
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
         """Gets many workflows."""

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -125,11 +125,11 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
 
     def get_many(
         self,
+        page: int,
+        per_page: int,
         order_by: WorkflowRecordOrderBy,
         direction: SQLiteDirection,
         category: WorkflowCategory,
-        page: int = 0,
-        per_page: Optional[int] = None,
         query: Optional[str] = None,
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
         try:
@@ -153,7 +153,6 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 """
             main_params: list[int | str] = [category.value]
             count_params: list[int | str] = [category.value]
-
             stripped_query = query.strip() if query else None
             if stripped_query:
                 wildcard_query = "%" + stripped_query + "%"
@@ -162,28 +161,20 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 main_params.extend([wildcard_query, wildcard_query])
                 count_params.extend([wildcard_query, wildcard_query])
 
-            main_query += f" ORDER BY {order_by.value} {direction.value}"
-
-            if per_page:
-                main_query += " LIMIT ? OFFSET ?"
-                main_params.extend([per_page, page * per_page])
-
+            main_query += f" ORDER BY {order_by.value} {direction.value} LIMIT ? OFFSET ?;"
+            main_params.extend([per_page, page * per_page])
             self._cursor.execute(main_query, main_params)
             rows = self._cursor.fetchall()
             workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
 
             self._cursor.execute(count_query, count_params)
             total = self._cursor.fetchone()[0]
-
-            if per_page:
-                pages = total // per_page + (total % per_page > 0)
-            else:
-                pages = 1  # If no pagination, there is only one page
+            pages = total // per_page + (total % per_page > 0)
 
             return PaginatedResults(
                 items=workflows,
                 page=page,
-                per_page=per_page if per_page else total,
+                per_page=per_page,
                 pages=pages,
                 total=total,
             )

invokeai/backend/flux/controlnet/controlnet_flux_output.py

@@ -1,58 +0,0 @@
-from dataclasses import dataclass
-
-import torch
-
-
-@dataclass
-class ControlNetFluxOutput:
-    single_block_residuals: list[torch.Tensor] | None
-    double_block_residuals: list[torch.Tensor] | None
-
-    def apply_weight(self, weight: float):
-        if self.single_block_residuals is not None:
-            for i in range(len(self.single_block_residuals)):
-                self.single_block_residuals[i] = self.single_block_residuals[i] * weight
-        if self.double_block_residuals is not None:
-            for i in range(len(self.double_block_residuals)):
-                self.double_block_residuals[i] = self.double_block_residuals[i] * weight
-
-
-def add_tensor_lists_elementwise(
-    list1: list[torch.Tensor] | None, list2: list[torch.Tensor] | None
-) -> list[torch.Tensor] | None:
-    """Add two tensor lists elementwise that could be None."""
-    if list1 is None and list2 is None:
-        return None
-    if list1 is None:
-        return list2
-    if list2 is None:
-        return list1
-
-    new_list: list[torch.Tensor] = []
-    for list1_tensor, list2_tensor in zip(list1, list2, strict=True):
-        new_list.append(list1_tensor + list2_tensor)
-    return new_list
-
-
-def add_controlnet_flux_outputs(
-    controlnet_output_1: ControlNetFluxOutput, controlnet_output_2: ControlNetFluxOutput
-) -> ControlNetFluxOutput:
-    return ControlNetFluxOutput(
-        single_block_residuals=add_tensor_lists_elementwise(
-            controlnet_output_1.single_block_residuals, controlnet_output_2.single_block_residuals
-        ),
-        double_block_residuals=add_tensor_lists_elementwise(
-            controlnet_output_1.double_block_residuals, controlnet_output_2.double_block_residuals
-        ),
-    )
-
-
-def sum_controlnet_flux_outputs(
-    controlnet_outputs: list[ControlNetFluxOutput],
-) -> ControlNetFluxOutput:
-    controlnet_output_sum = ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
-
-    for controlnet_output in controlnet_outputs:
-        controlnet_output_sum = add_controlnet_flux_outputs(controlnet_output_sum, controlnet_output)
-
-    return controlnet_output_sum

invokeai/backend/flux/controlnet/instantx_controlnet_flux.py

@@ -1,180 +0,0 @@
-# This file was initially copied from:
-# https://github.com/huggingface/diffusers/blob/99f608218caa069a2f16dcf9efab46959b15aec0/src/diffusers/models/controlnet_flux.py
-
-
-from dataclasses import dataclass
-
-import torch
-import torch.nn as nn
-
-from invokeai.backend.flux.controlnet.zero_module import zero_module
-from invokeai.backend.flux.model import FluxParams
-from invokeai.backend.flux.modules.layers import (
-    DoubleStreamBlock,
-    EmbedND,
-    MLPEmbedder,
-    SingleStreamBlock,
-    timestep_embedding,
-)
-
-
-@dataclass
-class InstantXControlNetFluxOutput:
-    controlnet_block_samples: list[torch.Tensor] | None
-    controlnet_single_block_samples: list[torch.Tensor] | None
-
-
-# NOTE(ryand): Mapping between diffusers FLUX transformer params and BFL FLUX transformer params:
-# - Diffusers: BFL
-# - in_channels: in_channels
-# - num_layers: depth
-# - num_single_layers: depth_single_blocks
-# - attention_head_dim: hidden_size // num_heads
-# - num_attention_heads: num_heads
-# - joint_attention_dim: context_in_dim
-# - pooled_projection_dim: vec_in_dim
-# - guidance_embeds: guidance_embed
-# - axes_dims_rope: axes_dim
-
-
-class InstantXControlNetFlux(torch.nn.Module):
-    def __init__(self, params: FluxParams, num_control_modes: int | None = None):
-        """
-        Args:
-            params (FluxParams): The parameters for the FLUX model.
-            num_control_modes (int | None, optional): The number of controlnet modes. If non-None, then the model is a
-                'union controlnet' model and expects a mode conditioning input at runtime.
-        """
-        super().__init__()
-
-        # The following modules mirror the base FLUX transformer model.
-        # -------------------------------------------------------------
-        self.params = params
-        self.in_channels = params.in_channels
-        self.out_channels = self.in_channels
-        if params.hidden_size % params.num_heads != 0:
-            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
-        pe_dim = params.hidden_size // params.num_heads
-        if sum(params.axes_dim) != pe_dim:
-            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
-        self.hidden_size = params.hidden_size
-        self.num_heads = params.num_heads
-        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
-        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
-        self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
-        )
-        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
-
-        self.double_blocks = nn.ModuleList(
-            [
-                DoubleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    qkv_bias=params.qkv_bias,
-                )
-                for _ in range(params.depth)
-            ]
-        )
-
-        self.single_blocks = nn.ModuleList(
-            [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
-                for _ in range(params.depth_single_blocks)
-            ]
-        )
-
-        # The following modules are specific to the ControlNet model.
-        # -----------------------------------------------------------
-        self.controlnet_blocks = nn.ModuleList([])
-        for _ in range(len(self.double_blocks)):
-            self.controlnet_blocks.append(zero_module(nn.Linear(self.hidden_size, self.hidden_size)))
-
-        self.controlnet_single_blocks = nn.ModuleList([])
-        for _ in range(len(self.single_blocks)):
-            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.hidden_size, self.hidden_size)))
-
-        self.is_union = False
-        if num_control_modes is not None:
-            self.is_union = True
-            self.controlnet_mode_embedder = nn.Embedding(num_control_modes, self.hidden_size)
-
-        self.controlnet_x_embedder = zero_module(torch.nn.Linear(self.in_channels, self.hidden_size))
-
-    def forward(
-        self,
-        controlnet_cond: torch.Tensor,
-        controlnet_mode: torch.Tensor | None,
-        img: torch.Tensor,
-        img_ids: torch.Tensor,
-        txt: torch.Tensor,
-        txt_ids: torch.Tensor,
-        timesteps: torch.Tensor,
-        y: torch.Tensor,
-        guidance: torch.Tensor | None = None,
-    ) -> InstantXControlNetFluxOutput:
-        if img.ndim != 3 or txt.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
-        img = self.img_in(img)
-
-        # Add controlnet_cond embedding.
-        img = img + self.controlnet_x_embedder(controlnet_cond)
-
-        vec = self.time_in(timestep_embedding(timesteps, 256))
-        if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
-        vec = vec + self.vector_in(y)
-        txt = self.txt_in(txt)
-
-        # If this is a union ControlNet, then concat the control mode embedding to the T5 text embedding.
-        if self.is_union:
-            if controlnet_mode is None:
-                # We allow users to enter 'None' as the controlnet_mode if they don't want to worry about this input.
-                # We've chosen to use a zero-embedding in this case.
-                zero_index = torch.zeros([1, 1], dtype=torch.long, device=txt.device)
-                controlnet_mode_emb = torch.zeros_like(self.controlnet_mode_embedder(zero_index))
-            else:
-                controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
-            txt = torch.cat([controlnet_mode_emb, txt], dim=1)
-            txt_ids = torch.cat([txt_ids[:, :1, :], txt_ids], dim=1)
-        else:
-            assert controlnet_mode is None
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        pe = self.pe_embedder(ids)
-
-        double_block_samples: list[torch.Tensor] = []
-        for block in self.double_blocks:
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
-            double_block_samples.append(img)
-
-        img = torch.cat((txt, img), 1)
-
-        single_block_samples: list[torch.Tensor] = []
-        for block in self.single_blocks:
-            img = block(img, vec=vec, pe=pe)
-            single_block_samples.append(img[:, txt.shape[1] :])
-
-        # ControlNet Block
-        controlnet_double_block_samples: list[torch.Tensor] = []
-        for double_block_sample, controlnet_block in zip(double_block_samples, self.controlnet_blocks, strict=True):
-            double_block_sample = controlnet_block(double_block_sample)
-            controlnet_double_block_samples.append(double_block_sample)
-
-        controlnet_single_block_samples: list[torch.Tensor] = []
-        for single_block_sample, controlnet_block in zip(
-            single_block_samples, self.controlnet_single_blocks, strict=True
-        ):
-            single_block_sample = controlnet_block(single_block_sample)
-            controlnet_single_block_samples.append(single_block_sample)
-
-        return InstantXControlNetFluxOutput(
-            controlnet_block_samples=controlnet_double_block_samples or None,
-            controlnet_single_block_samples=controlnet_single_block_samples or None,
-        )

invokeai/backend/flux/controlnet/state_dict_utils.py

@@ -1,295 +0,0 @@
-from typing import Any, Dict
-
-import torch
-
-from invokeai.backend.flux.model import FluxParams
-
-
-def is_state_dict_xlabs_controlnet(sd: Dict[str, Any]) -> bool:
-    """Is the state dict for an XLabs ControlNet model?
-
-    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
-    """
-    # If all of the expected keys are present, then this is very likely an XLabs ControlNet model.
-    expected_keys = {
-        "controlnet_blocks.0.bias",
-        "controlnet_blocks.0.weight",
-        "input_hint_block.0.bias",
-        "input_hint_block.0.weight",
-        "pos_embed_input.bias",
-        "pos_embed_input.weight",
-    }
-
-    if expected_keys.issubset(sd.keys()):
-        return True
-    return False
-
-
-def is_state_dict_instantx_controlnet(sd: Dict[str, Any]) -> bool:
-    """Is the state dict for an InstantX ControlNet model?
-
-    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
-    """
-    # If all of the expected keys are present, then this is very likely an InstantX ControlNet model.
-    expected_keys = {
-        "controlnet_blocks.0.bias",
-        "controlnet_blocks.0.weight",
-        "controlnet_x_embedder.bias",
-        "controlnet_x_embedder.weight",
-    }
-
-    if expected_keys.issubset(sd.keys()):
-        return True
-    return False
-
-
-def _fuse_weights(*t: torch.Tensor) -> torch.Tensor:
-    """Fuse weights along dimension 0.
-
-    Used to fuse q, k, v attention weights into a single qkv tensor when converting from diffusers to BFL format.
-    """
-    # TODO(ryand): Double check dim=0 is correct.
-    return torch.cat(t, dim=0)
-
-
-def _convert_flux_double_block_sd_from_diffusers_to_bfl_format(
-    sd: Dict[str, torch.Tensor], double_block_index: int
-) -> Dict[str, torch.Tensor]:
-    """Convert the state dict for a double block from diffusers format to BFL format."""
-    to_prefix = f"double_blocks.{double_block_index}"
-    from_prefix = f"transformer_blocks.{double_block_index}"
-
-    new_sd: dict[str, torch.Tensor] = {}
-
-    # Check one key to determine if this block exists.
-    if f"{from_prefix}.attn.add_q_proj.bias" not in sd:
-        return new_sd
-
-    # txt_attn.qkv
-    new_sd[f"{to_prefix}.txt_attn.qkv.bias"] = _fuse_weights(
-        sd.pop(f"{from_prefix}.attn.add_q_proj.bias"),
-        sd.pop(f"{from_prefix}.attn.add_k_proj.bias"),
-        sd.pop(f"{from_prefix}.attn.add_v_proj.bias"),
-    )
-    new_sd[f"{to_prefix}.txt_attn.qkv.weight"] = _fuse_weights(
-        sd.pop(f"{from_prefix}.attn.add_q_proj.weight"),
-        sd.pop(f"{from_prefix}.attn.add_k_proj.weight"),
-        sd.pop(f"{from_prefix}.attn.add_v_proj.weight"),
-    )
-
-    # img_attn.qkv
-    new_sd[f"{to_prefix}.img_attn.qkv.bias"] = _fuse_weights(
-        sd.pop(f"{from_prefix}.attn.to_q.bias"),
-        sd.pop(f"{from_prefix}.attn.to_k.bias"),
-        sd.pop(f"{from_prefix}.attn.to_v.bias"),
-    )
-    new_sd[f"{to_prefix}.img_attn.qkv.weight"] = _fuse_weights(
-        sd.pop(f"{from_prefix}.attn.to_q.weight"),
-        sd.pop(f"{from_prefix}.attn.to_k.weight"),
-        sd.pop(f"{from_prefix}.attn.to_v.weight"),
-    )
-
-    # Handle basic 1-to-1 key conversions.
-    key_map = {
-        # img_attn
-        "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
-        "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
-        "attn.to_out.0.weight": "img_attn.proj.weight",
-        "attn.to_out.0.bias": "img_attn.proj.bias",
-        # img_mlp
-        "ff.net.0.proj.weight": "img_mlp.0.weight",
-        "ff.net.0.proj.bias": "img_mlp.0.bias",
-        "ff.net.2.weight": "img_mlp.2.weight",
-        "ff.net.2.bias": "img_mlp.2.bias",
-        # img_mod
-        "norm1.linear.weight": "img_mod.lin.weight",
-        "norm1.linear.bias": "img_mod.lin.bias",
-        # txt_attn
-        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
-        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
-        "attn.to_add_out.weight": "txt_attn.proj.weight",
-        "attn.to_add_out.bias": "txt_attn.proj.bias",
-        # txt_mlp
-        "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
-        "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
-        "ff_context.net.2.weight": "txt_mlp.2.weight",
-        "ff_context.net.2.bias": "txt_mlp.2.bias",
-        # txt_mod
-        "norm1_context.linear.weight": "txt_mod.lin.weight",
-        "norm1_context.linear.bias": "txt_mod.lin.bias",
-    }
-    for from_key, to_key in key_map.items():
-        new_sd[f"{to_prefix}.{to_key}"] = sd.pop(f"{from_prefix}.{from_key}")
-
-    return new_sd
-
-
-def _convert_flux_single_block_sd_from_diffusers_to_bfl_format(
-    sd: Dict[str, torch.Tensor], single_block_index: int
-) -> Dict[str, torch.Tensor]:
-    """Convert the state dict for a single block from diffusers format to BFL format."""
-    to_prefix = f"single_blocks.{single_block_index}"
-    from_prefix = f"single_transformer_blocks.{single_block_index}"
-
-    new_sd: dict[str, torch.Tensor] = {}
-
-    # Check one key to determine if this block exists.
-    if f"{from_prefix}.attn.to_q.bias" not in sd:
-        return new_sd
-
-    # linear1 (qkv)
-    new_sd[f"{to_prefix}.linear1.bias"] = _fuse_weights(
-        sd.pop(f"{from_prefix}.attn.to_q.bias"),
-        sd.pop(f"{from_prefix}.attn.to_k.bias"),
-        sd.pop(f"{from_prefix}.attn.to_v.bias"),
-        sd.pop(f"{from_prefix}.proj_mlp.bias"),
-    )
-    new_sd[f"{to_prefix}.linear1.weight"] = _fuse_weights(
-        sd.pop(f"{from_prefix}.attn.to_q.weight"),
-        sd.pop(f"{from_prefix}.attn.to_k.weight"),
-        sd.pop(f"{from_prefix}.attn.to_v.weight"),
-        sd.pop(f"{from_prefix}.proj_mlp.weight"),
-    )
-
-    # Handle basic 1-to-1 key conversions.
-    key_map = {
-        # linear2
-        "proj_out.weight": "linear2.weight",
-        "proj_out.bias": "linear2.bias",
-        # modulation
-        "norm.linear.weight": "modulation.lin.weight",
-        "norm.linear.bias": "modulation.lin.bias",
-        # norm
-        "attn.norm_k.weight": "norm.key_norm.scale",
-        "attn.norm_q.weight": "norm.query_norm.scale",
-    }
-    for from_key, to_key in key_map.items():
-        new_sd[f"{to_prefix}.{to_key}"] = sd.pop(f"{from_prefix}.{from_key}")
-
-    return new_sd
-
-
-def convert_diffusers_instantx_state_dict_to_bfl_format(sd: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-    """Convert an InstantX ControlNet state dict to the format that can be loaded by our internal
-    InstantXControlNetFlux model.
-
-    The original InstantX ControlNet model was developed to be used in diffusers. We have ported the original
-    implementation to InstantXControlNetFlux to make it compatible with BFL-style models. This function converts the
-    original state dict to the format expected by InstantXControlNetFlux.
-    """
-    # Shallow copy sd so that we can pop keys from it without modifying the original.
-    sd = sd.copy()
-
-    new_sd: dict[str, torch.Tensor] = {}
-
-    # Handle basic 1-to-1 key conversions.
-    basic_key_map = {
-        # Base model keys.
-        # ----------------
-        # txt_in keys.
-        "context_embedder.bias": "txt_in.bias",
-        "context_embedder.weight": "txt_in.weight",
-        # guidance_in MLPEmbedder keys.
-        "time_text_embed.guidance_embedder.linear_1.bias": "guidance_in.in_layer.bias",
-        "time_text_embed.guidance_embedder.linear_1.weight": "guidance_in.in_layer.weight",
-        "time_text_embed.guidance_embedder.linear_2.bias": "guidance_in.out_layer.bias",
-        "time_text_embed.guidance_embedder.linear_2.weight": "guidance_in.out_layer.weight",
-        # vector_in MLPEmbedder keys.
-        "time_text_embed.text_embedder.linear_1.bias": "vector_in.in_layer.bias",
-        "time_text_embed.text_embedder.linear_1.weight": "vector_in.in_layer.weight",
-        "time_text_embed.text_embedder.linear_2.bias": "vector_in.out_layer.bias",
-        "time_text_embed.text_embedder.linear_2.weight": "vector_in.out_layer.weight",
-        # time_in MLPEmbedder keys.
-        "time_text_embed.timestep_embedder.linear_1.bias": "time_in.in_layer.bias",
-        "time_text_embed.timestep_embedder.linear_1.weight": "time_in.in_layer.weight",
-        "time_text_embed.timestep_embedder.linear_2.bias": "time_in.out_layer.bias",
-        "time_text_embed.timestep_embedder.linear_2.weight": "time_in.out_layer.weight",
-        # img_in keys.
-        "x_embedder.bias": "img_in.bias",
-        "x_embedder.weight": "img_in.weight",
-    }
-    for old_key, new_key in basic_key_map.items():
-        v = sd.pop(old_key, None)
-        if v is not None:
-            new_sd[new_key] = v
-
-    # Handle the double_blocks.
-    block_index = 0
-    while True:
-        converted_double_block_sd = _convert_flux_double_block_sd_from_diffusers_to_bfl_format(sd, block_index)
-        if len(converted_double_block_sd) == 0:
-            break
-        new_sd.update(converted_double_block_sd)
-        block_index += 1
-
-    # Handle the single_blocks.
-    block_index = 0
-    while True:
-        converted_singe_block_sd = _convert_flux_single_block_sd_from_diffusers_to_bfl_format(sd, block_index)
-        if len(converted_singe_block_sd) == 0:
-            break
-        new_sd.update(converted_singe_block_sd)
-        block_index += 1
-
-    # Transfer controlnet keys as-is.
-    for k in list(sd.keys()):
-        if k.startswith("controlnet_"):
-            new_sd[k] = sd.pop(k)
-
-    # Assert that all keys have been handled.
-    assert len(sd) == 0
-    return new_sd
-
-
-def infer_flux_params_from_state_dict(sd: Dict[str, torch.Tensor]) -> FluxParams:
-    """Infer the FluxParams from the shape of a FLUX state dict. When a model is distributed in diffusers format, this
-    information is all contained in the config.json file that accompanies the model. However, being apple to infer the
-    params from the state dict enables us to load models (e.g. an InstantX ControlNet) from a single weight file.
-    """
-    hidden_size = sd["img_in.weight"].shape[0]
-    mlp_hidden_dim = sd["double_blocks.0.img_mlp.0.weight"].shape[0]
-    # mlp_ratio is a float, but we treat it as an int here to avoid having to think about possible float precision
-    # issues. In practice, mlp_ratio is usually 4.
-    mlp_ratio = mlp_hidden_dim // hidden_size
-
-    head_dim = sd["double_blocks.0.img_attn.norm.query_norm.scale"].shape[0]
-    num_heads = hidden_size // head_dim
-
-    # Count the number of double blocks.
-    double_block_index = 0
-    while f"double_blocks.{double_block_index}.img_attn.qkv.weight" in sd:
-        double_block_index += 1
-
-    # Count the number of single blocks.
-    single_block_index = 0
-    while f"single_blocks.{single_block_index}.linear1.weight" in sd:
-        single_block_index += 1
-
-    return FluxParams(
-        in_channels=sd["img_in.weight"].shape[1],
-        vec_in_dim=sd["vector_in.in_layer.weight"].shape[1],
-        context_in_dim=sd["txt_in.weight"].shape[1],
-        hidden_size=hidden_size,
-        mlp_ratio=mlp_ratio,
-        num_heads=num_heads,
-        depth=double_block_index,
-        depth_single_blocks=single_block_index,
-        # axes_dim cannot be inferred from the state dict. The hard-coded value is correct for dev/schnell models.
-        axes_dim=[16, 56, 56],
-        # theta cannot be inferred from the state dict. The hard-coded value is correct for dev/schnell models.
-        theta=10_000,
-        qkv_bias="double_blocks.0.img_attn.qkv.bias" in sd,
-        guidance_embed="guidance_in.in_layer.weight" in sd,
-    )
-
-
-def infer_instantx_num_control_modes_from_state_dict(sd: Dict[str, torch.Tensor]) -> int | None:
-    """Infer the number of ControlNet Union modes from the shape of a InstantX ControlNet state dict.
-
-    Returns None if the model is not a ControlNet Union model. Otherwise returns the number of modes.
-    """
-    mode_embedder_key = "controlnet_mode_embedder.weight"
-    if mode_embedder_key not in sd:
-        return None
-
-    return sd[mode_embedder_key].shape[0]

invokeai/backend/flux/controlnet/xlabs_controlnet_flux.py

@@ -1,130 +0,0 @@
-# This file was initially based on:
-# https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/controlnet.py
-
-
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-
-from invokeai.backend.flux.controlnet.zero_module import zero_module
-from invokeai.backend.flux.model import FluxParams
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock, EmbedND, MLPEmbedder, timestep_embedding
-
-
-@dataclass
-class XLabsControlNetFluxOutput:
-    controlnet_double_block_residuals: list[torch.Tensor] | None
-
-
-class XLabsControlNetFlux(torch.nn.Module):
-    """A ControlNet model for FLUX.
-
-    The architecture is very similar to the base FLUX model, with the following differences:
-    - A `controlnet_depth` parameter is passed to control the number of double_blocks that the ControlNet is applied to.
-      In order to keep the ControlNet small, this is typically much less than the depth of the base FLUX model.
-    - There is a set of `controlnet_blocks` that are applied to the output of each double_block.
-    """
-
-    def __init__(self, params: FluxParams, controlnet_depth: int = 2):
-        super().__init__()
-
-        self.params = params
-        self.in_channels = params.in_channels
-        self.out_channels = self.in_channels
-        if params.hidden_size % params.num_heads != 0:
-            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
-        pe_dim = params.hidden_size // params.num_heads
-        if sum(params.axes_dim) != pe_dim:
-            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
-        self.hidden_size = params.hidden_size
-        self.num_heads = params.num_heads
-        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
-        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
-        self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else torch.nn.Identity()
-        )
-        self.txt_in = torch.nn.Linear(params.context_in_dim, self.hidden_size)
-
-        self.double_blocks = torch.nn.ModuleList(
-            [
-                DoubleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    qkv_bias=params.qkv_bias,
-                )
-                for _ in range(controlnet_depth)
-            ]
-        )
-
-        # Add ControlNet blocks.
-        self.controlnet_blocks = torch.nn.ModuleList([])
-        for _ in range(controlnet_depth):
-            controlnet_block = torch.nn.Linear(self.hidden_size, self.hidden_size)
-            controlnet_block = zero_module(controlnet_block)
-            self.controlnet_blocks.append(controlnet_block)
-        self.pos_embed_input = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
-        self.input_hint_block = torch.nn.Sequential(
-            torch.nn.Conv2d(3, 16, 3, padding=1),
-            torch.nn.SiLU(),
-            torch.nn.Conv2d(16, 16, 3, padding=1),
-            torch.nn.SiLU(),
-            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
-            torch.nn.SiLU(),
-            torch.nn.Conv2d(16, 16, 3, padding=1),
-            torch.nn.SiLU(),
-            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
-            torch.nn.SiLU(),
-            torch.nn.Conv2d(16, 16, 3, padding=1),
-            torch.nn.SiLU(),
-            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
-            torch.nn.SiLU(),
-            zero_module(torch.nn.Conv2d(16, 16, 3, padding=1)),
-        )
-
-    def forward(
-        self,
-        img: torch.Tensor,
-        img_ids: torch.Tensor,
-        controlnet_cond: torch.Tensor,
-        txt: torch.Tensor,
-        txt_ids: torch.Tensor,
-        timesteps: torch.Tensor,
-        y: torch.Tensor,
-        guidance: torch.Tensor | None = None,
-    ) -> XLabsControlNetFluxOutput:
-        if img.ndim != 3 or txt.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
-        # running on sequences img
-        img = self.img_in(img)
-        controlnet_cond = self.input_hint_block(controlnet_cond)
-        controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-        controlnet_cond = self.pos_embed_input(controlnet_cond)
-        img = img + controlnet_cond
-        vec = self.time_in(timestep_embedding(timesteps, 256))
-        if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
-        vec = vec + self.vector_in(y)
-        txt = self.txt_in(txt)
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        pe = self.pe_embedder(ids)
-
-        block_res_samples: list[torch.Tensor] = []
-
-        for block in self.double_blocks:
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
-            block_res_samples.append(img)
-
-        controlnet_block_res_samples: list[torch.Tensor] = []
-        for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks, strict=True):
-            block_res_sample = controlnet_block(block_res_sample)
-            controlnet_block_res_samples.append(block_res_sample)
-
-        return XLabsControlNetFluxOutput(controlnet_double_block_residuals=controlnet_block_res_samples)

invokeai/backend/flux/controlnet/zero_module.py

@@ -1,12 +0,0 @@
-from typing import TypeVar
-
-import torch
-
-T = TypeVar("T", bound=torch.nn.Module)
-
-
-def zero_module(module: T) -> T:
-    """Initialize the parameters of a module to zero."""
-    for p in module.parameters():
-        torch.nn.init.zeros_(p)
-    return module

invokeai/backend/flux/custom_block_processor.py

@@ -1,83 +0,0 @@
-import einops
-import torch
-
-from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
-from invokeai.backend.flux.math import attention
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
-
-
-class CustomDoubleStreamBlockProcessor:
-    """A class containing a custom implementation of DoubleStreamBlock.forward() with additional features
-    (IP-Adapter, etc.).
-    """
-
-    @staticmethod
-    def _double_stream_block_forward(
-        block: DoubleStreamBlock, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """This function is a direct copy of DoubleStreamBlock.forward(), but it returns some of the intermediate
-        values.
-        """
-        img_mod1, img_mod2 = block.img_mod(vec)
-        txt_mod1, txt_mod2 = block.txt_mod(vec)
-
-        # prepare image for attention
-        img_modulated = block.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = block.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = einops.rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
-        img_q, img_k = block.img_attn.norm(img_q, img_k, img_v)
-
-        # prepare txt for attention
-        txt_modulated = block.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = block.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = einops.rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
-        txt_q, txt_k = block.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        # run actual attention
-        q = torch.cat((txt_q, img_q), dim=2)
-        k = torch.cat((txt_k, img_k), dim=2)
-        v = torch.cat((txt_v, img_v), dim=2)
-
-        attn = attention(q, k, v, pe=pe)
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-        # calculate the img bloks
-        img = img + img_mod1.gate * block.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * block.img_mlp((1 + img_mod2.scale) * block.img_norm2(img) + img_mod2.shift)
-
-        # calculate the txt bloks
-        txt = txt + txt_mod1.gate * block.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * block.txt_mlp((1 + txt_mod2.scale) * block.txt_norm2(txt) + txt_mod2.shift)
-        return img, txt, img_q
-
-    @staticmethod
-    def custom_double_block_forward(
-        timestep_index: int,
-        total_num_timesteps: int,
-        block_index: int,
-        block: DoubleStreamBlock,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        vec: torch.Tensor,
-        pe: torch.Tensor,
-        ip_adapter_extensions: list[XLabsIPAdapterExtension],
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """A custom implementation of DoubleStreamBlock.forward() with additional features:
-        - IP-Adapter support
-        """
-        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(block, img, txt, vec, pe)
-
-        # Apply IP-Adapter conditioning.
-        for ip_adapter_extension in ip_adapter_extensions:
-            img = ip_adapter_extension.run_ip_adapter(
-                timestep_index=timestep_index,
-                total_num_timesteps=total_num_timesteps,
-                block_index=block_index,
-                block=block,
-                img_q=img_q,
-                img=img,
-            )
-
-        return img, txt

invokeai/backend/flux/denoise.py

@@ -1,14 +1,9 @@
-import math
 from typing import Callable
 
 import torch
 from tqdm import tqdm
 
-from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
-from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
-from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
-from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
-from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
+from invokeai.backend.flux.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 
@@ -18,23 +13,14 @@ def denoise(
     # model input
     img: torch.Tensor,
     img_ids: torch.Tensor,
-    # positive text conditioning
     txt: torch.Tensor,
     txt_ids: torch.Tensor,
     vec: torch.Tensor,
-    # negative text conditioning
-    neg_txt: torch.Tensor | None,
-    neg_txt_ids: torch.Tensor | None,
-    neg_vec: torch.Tensor | None,
     # sampling parameters
     timesteps: list[float],
     step_callback: Callable[[PipelineIntermediateState], None],
     guidance: float,
-    cfg_scale: list[float],
     inpaint_extension: InpaintExtension | None,
-    controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
-    pos_ip_adapter_extensions: list[XLabsIPAdapterExtension],
-    neg_ip_adapter_extensions: list[XLabsIPAdapterExtension],
 ):
     # step 0 is the initial state
     total_steps = len(timesteps) - 1
@@ -47,34 +33,11 @@ def denoise(
             latents=img,
         ),
     )
+    step = 1
     # guidance_vec is ignored for schnell.
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    for step_index, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
+    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
-
-        # Run ControlNet models.
-        controlnet_residuals: list[ControlNetFluxOutput] = []
-        for controlnet_extension in controlnet_extensions:
-            controlnet_residuals.append(
-                controlnet_extension.run_controlnet(
-                    timestep_index=step_index,
-                    total_num_timesteps=total_steps,
-                    img=img,
-                    img_ids=img_ids,
-                    txt=txt,
-                    txt_ids=txt_ids,
-                    y=vec,
-                    timesteps=t_vec,
-                    guidance=guidance_vec,
-                )
-            )
-
-        # Merge the ControlNet residuals from multiple ControlNets.
-        # TODO(ryand): We may want to calculate the sum just-in-time to keep peak memory low. Keep in mind, that the
-        # controlnet_residuals datastructure is efficient in that it likely contains multiple references to the same
-        # tensors. Calculating the sum materializes each tensor into its own instance.
-        merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
-
         pred = model(
             img=img,
             img_ids=img_ids,
@@ -83,39 +46,8 @@ def denoise(
             y=vec,
             timesteps=t_vec,
             guidance=guidance_vec,
-            timestep_index=step_index,
-            total_num_timesteps=total_steps,
-            controlnet_double_block_residuals=merged_controlnet_residuals.double_block_residuals,
-            controlnet_single_block_residuals=merged_controlnet_residuals.single_block_residuals,
-            ip_adapter_extensions=pos_ip_adapter_extensions,
         )
 
-        step_cfg_scale = cfg_scale[step_index]
-
-        # If step_cfg_scale, is 1.0, then we don't need to run the negative prediction.
-        if not math.isclose(step_cfg_scale, 1.0):
-            # TODO(ryand): Add option to run positive and negative predictions in a single batch for better performance
-            # on systems with sufficient VRAM.
-
-            if neg_txt is None or neg_txt_ids is None or neg_vec is None:
-                raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
-
-            neg_pred = model(
-                img=img,
-                img_ids=img_ids,
-                txt=neg_txt,
-                txt_ids=neg_txt_ids,
-                y=neg_vec,
-                timesteps=t_vec,
-                guidance=guidance_vec,
-                timestep_index=step_index,
-                total_num_timesteps=total_steps,
-                controlnet_double_block_residuals=None,
-                controlnet_single_block_residuals=None,
-                ip_adapter_extensions=neg_ip_adapter_extensions,
-            )
-            pred = neg_pred + step_cfg_scale * (pred - neg_pred)
-
         preview_img = img - t_curr * pred
         img = img + (t_prev - t_curr) * pred
 
@@ -125,12 +57,13 @@ def denoise(
 
         step_callback(
             PipelineIntermediateState(
-                step=step_index + 1,
+                step=step,
                 order=1,
                 total_steps=total_steps,
                 timestep=int(t_curr),
                 latents=preview_img,
             ),
         )
+        step += 1
 
     return img

invokeai/backend/flux/extensions/base_controlnet_extension.py

@@ -1,45 +0,0 @@
-import math
-from abc import ABC, abstractmethod
-from typing import List, Union
-
-import torch
-
-from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
-
-
-class BaseControlNetExtension(ABC):
-    def __init__(
-        self,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        self._weight = weight
-        self._begin_step_percent = begin_step_percent
-        self._end_step_percent = end_step_percent
-
-    def _get_weight(self, timestep_index: int, total_num_timesteps: int) -> float:
-        first_step = math.floor(self._begin_step_percent * total_num_timesteps)
-        last_step = math.ceil(self._end_step_percent * total_num_timesteps)
-
-        if timestep_index < first_step or timestep_index > last_step:
-            return 0.0
-
-        if isinstance(self._weight, list):
-            return self._weight[timestep_index]
-
-        return self._weight
-
-    @abstractmethod
-    def run_controlnet(
-        self,
-        timestep_index: int,
-        total_num_timesteps: int,
-        img: torch.Tensor,
-        img_ids: torch.Tensor,
-        txt: torch.Tensor,
-        txt_ids: torch.Tensor,
-        y: torch.Tensor,
-        timesteps: torch.Tensor,
-        guidance: torch.Tensor | None,
-    ) -> ControlNetFluxOutput: ...

invokeai/backend/flux/extensions/instantx_controlnet_extension.py

@@ -1,194 +0,0 @@
-import math
-from typing import List, Union
-
-import torch
-from PIL.Image import Image
-
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
-from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES, prepare_control_image
-from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
-from invokeai.backend.flux.controlnet.instantx_controlnet_flux import (
-    InstantXControlNetFlux,
-    InstantXControlNetFluxOutput,
-)
-from invokeai.backend.flux.extensions.base_controlnet_extension import BaseControlNetExtension
-from invokeai.backend.flux.sampling_utils import pack
-from invokeai.backend.model_manager.load.load_base import LoadedModel
-
-
-class InstantXControlNetExtension(BaseControlNetExtension):
-    def __init__(
-        self,
-        model: InstantXControlNetFlux,
-        controlnet_cond: torch.Tensor,
-        instantx_control_mode: torch.Tensor | None,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        super().__init__(
-            weight=weight,
-            begin_step_percent=begin_step_percent,
-            end_step_percent=end_step_percent,
-        )
-        self._model = model
-        # The VAE-encoded and 'packed' control image to pass to the ControlNet model.
-        self._controlnet_cond = controlnet_cond
-        # TODO(ryand): Should we define an enum for the instantx_control_mode? Is it likely to change for future models?
-        # The control mode for InstantX ControlNet union models.
-        # See the values defined here: https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union#control-mode
-        # Expected shape: (batch_size, 1), Expected dtype: torch.long
-        # If None, a zero-embedding will be used.
-        self._instantx_control_mode = instantx_control_mode
-
-        # TODO(ryand): Pass in these params if a new base transformer / InstantX ControlNet pair get released.
-        self._flux_transformer_num_double_blocks = 19
-        self._flux_transformer_num_single_blocks = 38
-
-    @classmethod
-    def prepare_controlnet_cond(
-        cls,
-        controlnet_image: Image,
-        vae_info: LoadedModel,
-        latent_height: int,
-        latent_width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        resize_mode: CONTROLNET_RESIZE_VALUES,
-    ):
-        image_height = latent_height * LATENT_SCALE_FACTOR
-        image_width = latent_width * LATENT_SCALE_FACTOR
-
-        resized_controlnet_image = prepare_control_image(
-            image=controlnet_image,
-            do_classifier_free_guidance=False,
-            width=image_width,
-            height=image_height,
-            device=device,
-            dtype=dtype,
-            control_mode="balanced",
-            resize_mode=resize_mode,
-        )
-
-        # Shift the image from [0, 1] to [-1, 1].
-        resized_controlnet_image = resized_controlnet_image * 2 - 1
-
-        # Run VAE encoder.
-        controlnet_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=resized_controlnet_image)
-        controlnet_cond = pack(controlnet_cond)
-
-        return controlnet_cond
-
-    @classmethod
-    def from_controlnet_image(
-        cls,
-        model: InstantXControlNetFlux,
-        controlnet_image: Image,
-        instantx_control_mode: torch.Tensor | None,
-        vae_info: LoadedModel,
-        latent_height: int,
-        latent_width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        resize_mode: CONTROLNET_RESIZE_VALUES,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        image_height = latent_height * LATENT_SCALE_FACTOR
-        image_width = latent_width * LATENT_SCALE_FACTOR
-
-        resized_controlnet_image = prepare_control_image(
-            image=controlnet_image,
-            do_classifier_free_guidance=False,
-            width=image_width,
-            height=image_height,
-            device=device,
-            dtype=dtype,
-            control_mode="balanced",
-            resize_mode=resize_mode,
-        )
-
-        # Shift the image from [0, 1] to [-1, 1].
-        resized_controlnet_image = resized_controlnet_image * 2 - 1
-
-        # Run VAE encoder.
-        controlnet_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=resized_controlnet_image)
-        controlnet_cond = pack(controlnet_cond)
-
-        return cls(
-            model=model,
-            controlnet_cond=controlnet_cond,
-            instantx_control_mode=instantx_control_mode,
-            weight=weight,
-            begin_step_percent=begin_step_percent,
-            end_step_percent=end_step_percent,
-        )
-
-    def _instantx_output_to_controlnet_output(
-        self, instantx_output: InstantXControlNetFluxOutput
-    ) -> ControlNetFluxOutput:
-        # The `interval_control` logic here is based on
-        # https://github.com/huggingface/diffusers/blob/31058cdaef63ca660a1a045281d156239fba8192/src/diffusers/models/transformers/transformer_flux.py#L507-L511
-
-        # Handle double block residuals.
-        double_block_residuals: list[torch.Tensor] = []
-        double_block_samples = instantx_output.controlnet_block_samples
-        if double_block_samples:
-            interval_control = self._flux_transformer_num_double_blocks / len(double_block_samples)
-            interval_control = int(math.ceil(interval_control))
-            for i in range(self._flux_transformer_num_double_blocks):
-                double_block_residuals.append(double_block_samples[i // interval_control])
-
-        # Handle single block residuals.
-        single_block_residuals: list[torch.Tensor] = []
-        single_block_samples = instantx_output.controlnet_single_block_samples
-        if single_block_samples:
-            interval_control = self._flux_transformer_num_single_blocks / len(single_block_samples)
-            interval_control = int(math.ceil(interval_control))
-            for i in range(self._flux_transformer_num_single_blocks):
-                single_block_residuals.append(single_block_samples[i // interval_control])
-
-        return ControlNetFluxOutput(
-            double_block_residuals=double_block_residuals or None,
-            single_block_residuals=single_block_residuals or None,
-        )
-
-    def run_controlnet(
-        self,
-        timestep_index: int,
-        total_num_timesteps: int,
-        img: torch.Tensor,
-        img_ids: torch.Tensor,
-        txt: torch.Tensor,
-        txt_ids: torch.Tensor,
-        y: torch.Tensor,
-        timesteps: torch.Tensor,
-        guidance: torch.Tensor | None,
-    ) -> ControlNetFluxOutput:
-        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
-        if weight < 1e-6:
-            return ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
-
-        # Make sure inputs have correct device and dtype.
-        self._controlnet_cond = self._controlnet_cond.to(device=img.device, dtype=img.dtype)
-        self._instantx_control_mode = (
-            self._instantx_control_mode.to(device=img.device) if self._instantx_control_mode is not None else None
-        )
-
-        instantx_output: InstantXControlNetFluxOutput = self._model(
-            controlnet_cond=self._controlnet_cond,
-            controlnet_mode=self._instantx_control_mode,
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            timesteps=timesteps,
-            y=y,
-            guidance=guidance,
-        )
-
-        controlnet_output = self._instantx_output_to_controlnet_output(instantx_output)
-        controlnet_output.apply_weight(weight)
-        return controlnet_output

invokeai/backend/flux/extensions/xlabs_controlnet_extension.py

@@ -1,150 +0,0 @@
-from typing import List, Union
-
-import torch
-from PIL.Image import Image
-
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES, prepare_control_image
-from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
-from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux, XLabsControlNetFluxOutput
-from invokeai.backend.flux.extensions.base_controlnet_extension import BaseControlNetExtension
-
-
-class XLabsControlNetExtension(BaseControlNetExtension):
-    def __init__(
-        self,
-        model: XLabsControlNetFlux,
-        controlnet_cond: torch.Tensor,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        super().__init__(
-            weight=weight,
-            begin_step_percent=begin_step_percent,
-            end_step_percent=end_step_percent,
-        )
-
-        self._model = model
-        # _controlnet_cond is the control image passed to the ControlNet model.
-        # Pixel values are in the range [-1, 1]. Shape: (batch_size, 3, height, width).
-        self._controlnet_cond = controlnet_cond
-
-        # TODO(ryand): Pass in these params if a new base transformer / XLabs ControlNet pair get released.
-        self._flux_transformer_num_double_blocks = 19
-        self._flux_transformer_num_single_blocks = 38
-
-    @classmethod
-    def prepare_controlnet_cond(
-        cls,
-        controlnet_image: Image,
-        latent_height: int,
-        latent_width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        resize_mode: CONTROLNET_RESIZE_VALUES,
-    ):
-        image_height = latent_height * LATENT_SCALE_FACTOR
-        image_width = latent_width * LATENT_SCALE_FACTOR
-
-        controlnet_cond = prepare_control_image(
-            image=controlnet_image,
-            do_classifier_free_guidance=False,
-            width=image_width,
-            height=image_height,
-            device=device,
-            dtype=dtype,
-            control_mode="balanced",
-            resize_mode=resize_mode,
-        )
-
-        # Map pixel values from [0, 1] to [-1, 1].
-        controlnet_cond = controlnet_cond * 2 - 1
-
-        return controlnet_cond
-
-    @classmethod
-    def from_controlnet_image(
-        cls,
-        model: XLabsControlNetFlux,
-        controlnet_image: Image,
-        latent_height: int,
-        latent_width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        resize_mode: CONTROLNET_RESIZE_VALUES,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        image_height = latent_height * LATENT_SCALE_FACTOR
-        image_width = latent_width * LATENT_SCALE_FACTOR
-
-        controlnet_cond = prepare_control_image(
-            image=controlnet_image,
-            do_classifier_free_guidance=False,
-            width=image_width,
-            height=image_height,
-            device=device,
-            dtype=dtype,
-            control_mode="balanced",
-            resize_mode=resize_mode,
-        )
-
-        # Map pixel values from [0, 1] to [-1, 1].
-        controlnet_cond = controlnet_cond * 2 - 1
-
-        return cls(
-            model=model,
-            controlnet_cond=controlnet_cond,
-            weight=weight,
-            begin_step_percent=begin_step_percent,
-            end_step_percent=end_step_percent,
-        )
-
-    def _xlabs_output_to_controlnet_output(self, xlabs_output: XLabsControlNetFluxOutput) -> ControlNetFluxOutput:
-        # The modulo index logic used here is based on:
-        # https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/model.py#L198-L200
-
-        # Handle double block residuals.
-        double_block_residuals: list[torch.Tensor] = []
-        xlabs_double_block_residuals = xlabs_output.controlnet_double_block_residuals
-        if xlabs_double_block_residuals is not None:
-            for i in range(self._flux_transformer_num_double_blocks):
-                double_block_residuals.append(xlabs_double_block_residuals[i % len(xlabs_double_block_residuals)])
-
-        return ControlNetFluxOutput(
-            double_block_residuals=double_block_residuals,
-            single_block_residuals=None,
-        )
-
-    def run_controlnet(
-        self,
-        timestep_index: int,
-        total_num_timesteps: int,
-        img: torch.Tensor,
-        img_ids: torch.Tensor,
-        txt: torch.Tensor,
-        txt_ids: torch.Tensor,
-        y: torch.Tensor,
-        timesteps: torch.Tensor,
-        guidance: torch.Tensor | None,
-    ) -> ControlNetFluxOutput:
-        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
-        if weight < 1e-6:
-            return ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
-
-        xlabs_output: XLabsControlNetFluxOutput = self._model(
-            img=img,
-            img_ids=img_ids,
-            controlnet_cond=self._controlnet_cond,
-            txt=txt,
-            txt_ids=txt_ids,
-            timesteps=timesteps,
-            y=y,
-            guidance=guidance,
-        )
-
-        controlnet_output = self._xlabs_output_to_controlnet_output(xlabs_output)
-        controlnet_output.apply_weight(weight)
-        return controlnet_output

invokeai/backend/flux/extensions/xlabs_ip_adapter_extension.py

@@ -1,89 +0,0 @@
-import math
-from typing import List, Union
-
-import einops
-import torch
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
-
-
-class XLabsIPAdapterExtension:
-    def __init__(
-        self,
-        model: XlabsIpAdapterFlux,
-        image_prompt_clip_embed: torch.Tensor,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        self._model = model
-        self._image_prompt_clip_embed = image_prompt_clip_embed
-        self._weight = weight
-        self._begin_step_percent = begin_step_percent
-        self._end_step_percent = end_step_percent
-
-        self._image_proj: torch.Tensor | None = None
-
-    def _get_weight(self, timestep_index: int, total_num_timesteps: int) -> float:
-        first_step = math.floor(self._begin_step_percent * total_num_timesteps)
-        last_step = math.ceil(self._end_step_percent * total_num_timesteps)
-
-        if timestep_index < first_step or timestep_index > last_step:
-            return 0.0
-
-        if isinstance(self._weight, list):
-            return self._weight[timestep_index]
-
-        return self._weight
-
-    @staticmethod
-    def run_clip_image_encoder(
-        pil_image: List[Image.Image], image_encoder: CLIPVisionModelWithProjection
-    ) -> torch.Tensor:
-        clip_image_processor = CLIPImageProcessor()
-        clip_image: torch.Tensor = clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(device=image_encoder.device, dtype=image_encoder.dtype)
-        clip_image_embeds = image_encoder(clip_image).image_embeds
-        return clip_image_embeds
-
-    def run_image_proj(self, dtype: torch.dtype):
-        image_prompt_clip_embed = self._image_prompt_clip_embed.to(dtype=dtype)
-        self._image_proj = self._model.image_proj(image_prompt_clip_embed)
-
-    def run_ip_adapter(
-        self,
-        timestep_index: int,
-        total_num_timesteps: int,
-        block_index: int,
-        block: DoubleStreamBlock,
-        img_q: torch.Tensor,
-        img: torch.Tensor,
-    ) -> torch.Tensor:
-        """The logic in this function is based on:
-        https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/modules/layers.py#L245-L301
-        """
-        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
-        if weight < 1e-6:
-            return img
-
-        ip_adapter_block = self._model.ip_adapter_double_blocks.double_blocks[block_index]
-
-        ip_key = ip_adapter_block.ip_adapter_double_stream_k_proj(self._image_proj)
-        ip_value = ip_adapter_block.ip_adapter_double_stream_v_proj(self._image_proj)
-
-        # Reshape projections for multi-head attention.
-        ip_key = einops.rearrange(ip_key, "B L (H D) -> B H L D", H=block.num_heads)
-        ip_value = einops.rearrange(ip_value, "B L (H D) -> B H L D", H=block.num_heads)
-
-        # Compute attention between IP projections and the latent query.
-        ip_attn = torch.nn.functional.scaled_dot_product_attention(
-            img_q, ip_key, ip_value, dropout_p=0.0, is_causal=False
-        )
-        ip_attn = einops.rearrange(ip_attn, "B H L D -> B L (H D)", H=block.num_heads)
-
-        img = img + weight * ip_attn
-
-        return img

invokeai/backend/flux/ip_adapter/ip_double_stream_block_processor.py

@@ -1,93 +0,0 @@
-# This file is based on:
-# https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/modules/layers.py#L221
-import einops
-import torch
-
-from invokeai.backend.flux.math import attention
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
-
-
-class IPDoubleStreamBlockProcessor(torch.nn.Module):
-    """Attention processor for handling IP-adapter with double stream block."""
-
-    def __init__(self, context_dim: int, hidden_dim: int):
-        super().__init__()
-
-        # Ensure context_dim matches the dimension of image_proj
-        self.context_dim = context_dim
-        self.hidden_dim = hidden_dim
-
-        # Initialize projections for IP-adapter
-        self.ip_adapter_double_stream_k_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-        self.ip_adapter_double_stream_v_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_k_proj.weight)
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_k_proj.bias)
-
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_v_proj.weight)
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_v_proj.bias)
-
-    def __call__(
-        self,
-        attn: DoubleStreamBlock,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        vec: torch.Tensor,
-        pe: torch.Tensor,
-        image_proj: torch.Tensor,
-        ip_scale: float = 1.0,
-    ):
-        # Prepare image for attention
-        img_mod1, img_mod2 = attn.img_mod(vec)
-        txt_mod1, txt_mod2 = attn.txt_mod(vec)
-
-        img_modulated = attn.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = attn.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = einops.rearrange(
-            img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim
-        )
-        img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v)
-
-        txt_modulated = attn.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = attn.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = einops.rearrange(
-            txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim
-        )
-        txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        q = torch.cat((txt_q, img_q), dim=2)
-        k = torch.cat((txt_k, img_k), dim=2)
-        v = torch.cat((txt_v, img_v), dim=2)
-
-        attn1 = attention(q, k, v, pe=pe)
-        txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :]
-
-        # print(f"txt_attn shape: {txt_attn.size()}")
-        # print(f"img_attn shape: {img_attn.size()}")
-
-        img = img + img_mod1.gate * attn.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift)
-
-        txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift)
-
-        # IP-adapter processing
-        ip_query = img_q  # latent sample query
-        ip_key = self.ip_adapter_double_stream_k_proj(image_proj)
-        ip_value = self.ip_adapter_double_stream_v_proj(image_proj)
-
-        # Reshape projections for multi-head attention
-        ip_key = einops.rearrange(ip_key, "B L (H D) -> B H L D", H=attn.num_heads, D=attn.head_dim)
-        ip_value = einops.rearrange(ip_value, "B L (H D) -> B H L D", H=attn.num_heads, D=attn.head_dim)
-
-        # Compute attention between IP projections and the latent query
-        ip_attention = torch.nn.functional.scaled_dot_product_attention(
-            ip_query, ip_key, ip_value, dropout_p=0.0, is_causal=False
-        )
-        ip_attention = einops.rearrange(ip_attention, "B H L D -> B L (H D)", H=attn.num_heads, D=attn.head_dim)
-
-        img = img + ip_scale * ip_attention
-
-        return img, txt

invokeai/backend/flux/ip_adapter/state_dict_utils.py

@@ -1,50 +0,0 @@
-from typing import Any, Dict
-
-import torch
-
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterParams
-
-
-def is_state_dict_xlabs_ip_adapter(sd: Dict[str, Any]) -> bool:
-    """Is the state dict for an XLabs FLUX IP-Adapter model?
-
-    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
-    """
-    # If all of the expected keys are present, then this is very likely an XLabs IP-Adapter model.
-    expected_keys = {
-        "double_blocks.0.processor.ip_adapter_double_stream_k_proj.bias",
-        "double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight",
-        "double_blocks.0.processor.ip_adapter_double_stream_v_proj.bias",
-        "double_blocks.0.processor.ip_adapter_double_stream_v_proj.weight",
-        "ip_adapter_proj_model.norm.bias",
-        "ip_adapter_proj_model.norm.weight",
-        "ip_adapter_proj_model.proj.bias",
-        "ip_adapter_proj_model.proj.weight",
-    }
-
-    if expected_keys.issubset(sd.keys()):
-        return True
-    return False
-
-
-def infer_xlabs_ip_adapter_params_from_state_dict(state_dict: dict[str, torch.Tensor]) -> XlabsIpAdapterParams:
-    num_double_blocks = 0
-    context_dim = 0
-    hidden_dim = 0
-
-    # Count the number of double blocks.
-    double_block_index = 0
-    while f"double_blocks.{double_block_index}.processor.ip_adapter_double_stream_k_proj.weight" in state_dict:
-        double_block_index += 1
-    num_double_blocks = double_block_index
-
-    hidden_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[0]
-    context_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[1]
-    clip_embeddings_dim = state_dict["ip_adapter_proj_model.proj.weight"].shape[1]
-
-    return XlabsIpAdapterParams(
-        num_double_blocks=num_double_blocks,
-        context_dim=context_dim,
-        hidden_dim=hidden_dim,
-        clip_embeddings_dim=clip_embeddings_dim,
-    )

invokeai/backend/flux/ip_adapter/xlabs_ip_adapter_flux.py

@@ -1,67 +0,0 @@
-from dataclasses import dataclass
-
-import torch
-
-from invokeai.backend.ip_adapter.ip_adapter import ImageProjModel
-
-
-class IPDoubleStreamBlock(torch.nn.Module):
-    def __init__(self, context_dim: int, hidden_dim: int):
-        super().__init__()
-
-        self.context_dim = context_dim
-        self.hidden_dim = hidden_dim
-
-        self.ip_adapter_double_stream_k_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-        self.ip_adapter_double_stream_v_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-
-
-class IPAdapterDoubleBlocks(torch.nn.Module):
-    def __init__(self, num_double_blocks: int, context_dim: int, hidden_dim: int):
-        super().__init__()
-        self.double_blocks = torch.nn.ModuleList(
-            [IPDoubleStreamBlock(context_dim, hidden_dim) for _ in range(num_double_blocks)]
-        )
-
-
-@dataclass
-class XlabsIpAdapterParams:
-    num_double_blocks: int
-    context_dim: int
-    hidden_dim: int
-
-    clip_embeddings_dim: int
-
-
-class XlabsIpAdapterFlux(torch.nn.Module):
-    def __init__(self, params: XlabsIpAdapterParams):
-        super().__init__()
-        self.image_proj = ImageProjModel(
-            cross_attention_dim=params.context_dim, clip_embeddings_dim=params.clip_embeddings_dim
-        )
-        self.ip_adapter_double_blocks = IPAdapterDoubleBlocks(
-            num_double_blocks=params.num_double_blocks, context_dim=params.context_dim, hidden_dim=params.hidden_dim
-        )
-
-    def load_xlabs_state_dict(self, state_dict: dict[str, torch.Tensor], assign: bool = False):
-        """We need this custom function to load state dicts rather than using .load_state_dict(...) because the model
-        structure does not match the state_dict structure.
-        """
-        # Split the state_dict into the image projection model and the double blocks.
-        image_proj_sd: dict[str, torch.Tensor] = {}
-        double_blocks_sd: dict[str, torch.Tensor] = {}
-        for k, v in state_dict.items():
-            if k.startswith("ip_adapter_proj_model."):
-                image_proj_sd[k] = v
-            elif k.startswith("double_blocks."):
-                double_blocks_sd[k] = v
-            else:
-                raise ValueError(f"Unexpected key: {k}")
-
-        # Initialize the image projection model.
-        image_proj_sd = {k.replace("ip_adapter_proj_model.", ""): v for k, v in image_proj_sd.items()}
-        self.image_proj.load_state_dict(image_proj_sd, assign=assign)
-
-        # Initialize the double blocks.
-        double_blocks_sd = {k.replace("processor.", ""): v for k, v in double_blocks_sd.items()}
-        self.ip_adapter_double_blocks.load_state_dict(double_blocks_sd, assign=assign)

invokeai/backend/flux/math.py

@@ -16,10 +16,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    scale = (
-        torch.arange(0, dim, 2, dtype=torch.float32 if pos.device.type == "mps" else torch.float64, device=pos.device)
-        / dim
-    )
+    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
     omega = 1.0 / (theta**scale)
     out = torch.einsum("...n,d->...nd", pos, omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)

invokeai/backend/flux/model.py

@@ -5,8 +5,6 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 
-from invokeai.backend.flux.custom_block_processor import CustomDoubleStreamBlockProcessor
-from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.modules.layers import (
     DoubleStreamBlock,
     EmbedND,
@@ -89,12 +87,7 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
-        guidance: Tensor | None,
-        timestep_index: int,
-        total_num_timesteps: int,
-        controlnet_double_block_residuals: list[Tensor] | None,
-        controlnet_single_block_residuals: list[Tensor] | None,
-        ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        guidance: Tensor | None = None,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -112,39 +105,12 @@ class Flux(nn.Module):
         ids = torch.cat((txt_ids, img_ids), dim=1)
         pe = self.pe_embedder(ids)
 
-        # Validate double_block_residuals shape.
-        if controlnet_double_block_residuals is not None:
-            assert len(controlnet_double_block_residuals) == len(self.double_blocks)
-        for block_index, block in enumerate(self.double_blocks):
-            assert isinstance(block, DoubleStreamBlock)
-
-            img, txt = CustomDoubleStreamBlockProcessor.custom_double_block_forward(
-                timestep_index=timestep_index,
-                total_num_timesteps=total_num_timesteps,
-                block_index=block_index,
-                block=block,
-                img=img,
-                txt=txt,
-                vec=vec,
-                pe=pe,
-                ip_adapter_extensions=ip_adapter_extensions,
-            )
-
-            if controlnet_double_block_residuals is not None:
-                img += controlnet_double_block_residuals[block_index]
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
 
         img = torch.cat((txt, img), 1)
-
-        # Validate single_block_residuals shape.
-        if controlnet_single_block_residuals is not None:
-            assert len(controlnet_single_block_residuals) == len(self.single_blocks)
-
-        for block_index, block in enumerate(self.single_blocks):
+        for block in self.single_blocks:
             img = block(img, vec=vec, pe=pe)
-
-            if controlnet_single_block_residuals is not None:
-                img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]
-
         img = img[:, txt.shape[1] :, ...]
 
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)

invokeai/backend/flux/sampling_utils.py

@@ -168,17 +168,8 @@ def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtyp
     Returns:
         torch.Tensor: Image position ids.
     """
-
-    if device.type == "mps":
-        orig_dtype = dtype
-        dtype = torch.float16
-
     img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
     img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
     img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
     img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
-
-    if device.type == "mps":
-        img_ids.to(orig_dtype)
-
     return img_ids

invokeai/backend/image_util/segment_anything/segment_anything_pipeline.py

@@ -1,4 +1,4 @@
-from typing import Optional, TypeAlias
+from typing import Optional
 
 import torch
 from PIL import Image
@@ -7,14 +7,6 @@ from transformers.models.sam.processing_sam import SamProcessor
 
 from invokeai.backend.raw_model import RawModel
 
-# Type aliases for the inputs to the SAM model.
-ListOfBoundingBoxes: TypeAlias = list[list[int]]
-"""A list of bounding boxes. Each bounding box is in the format [xmin, ymin, xmax, ymax]."""
-ListOfPoints: TypeAlias = list[list[int]]
-"""A list of points. Each point is in the format [x, y]."""
-ListOfPointLabels: TypeAlias = list[int]
-"""A list of SAM point labels. Each label is an integer where -1 is background, 0 is neutral, and 1 is foreground."""
-
 
 class SegmentAnythingPipeline(RawModel):
     """A wrapper class for the transformers SAM model and processor that makes it compatible with the model manager."""
@@ -35,53 +27,20 @@ class SegmentAnythingPipeline(RawModel):
 
         return calc_module_size(self._sam_model)
 
-    def segment(
-        self,
-        image: Image.Image,
-        bounding_boxes: list[list[int]] | None = None,
-        point_lists: list[list[list[int]]] | None = None,
-    ) -> torch.Tensor:
+    def segment(self, image: Image.Image, bounding_boxes: list[list[int]]) -> torch.Tensor:
         """Run the SAM model.
 
-        Either bounding_boxes or point_lists must be provided. If both are provided, bounding_boxes will be used and
-        point_lists will be ignored.
-
         Args:
             image (Image.Image): The image to segment.
             bounding_boxes (list[list[int]]): The bounding box prompts. Each bounding box is in the format
                 [xmin, ymin, xmax, ymax].
-            point_lists (list[list[list[int]]]): The points prompts. Each point is in the format [x, y, label].
-                `label` is an integer where -1 is background, 0 is neutral, and 1 is foreground.
 
         Returns:
             torch.Tensor: The segmentation masks. dtype: torch.bool. shape: [num_masks, channels, height, width].
         """
-
-        # Prep the inputs:
-        # - Create a list of bounding boxes or points and labels.
-        # - Add a batch dimension of 1 to the inputs.
-        if bounding_boxes:
-            input_boxes: list[ListOfBoundingBoxes] | None = [bounding_boxes]
-            input_points: list[ListOfPoints] | None = None
-            input_labels: list[ListOfPointLabels] | None = None
-        elif point_lists:
-            input_boxes: list[ListOfBoundingBoxes] | None = None
-            input_points: list[ListOfPoints] | None = []
-            input_labels: list[ListOfPointLabels] | None = []
-            for point_list in point_lists:
-                input_points.append([[p[0], p[1]] for p in point_list])
-                input_labels.append([p[2] for p in point_list])
-
-        else:
-            raise ValueError("Either bounding_boxes or points and labels must be provided.")
-
-        inputs = self._sam_processor(
-            images=image,
-            input_boxes=input_boxes,
-            input_points=input_points,
-            input_labels=input_labels,
-            return_tensors="pt",
-        ).to(self._sam_model.device)
+        # Add batch dimension of 1 to the bounding boxes.
+        boxes = [bounding_boxes]
+        inputs = self._sam_processor(images=image, input_boxes=boxes, return_tensors="pt").to(self._sam_model.device)
         outputs = self._sam_model(**inputs)
         masks = self._sam_processor.post_process_masks(
             masks=outputs.pred_masks,

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -2,7 +2,6 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
 from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
 from invokeai.backend.lora.layers.lora_layer import LoRALayer
@@ -190,9 +189,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
     # Assert that all keys were processed.
     assert len(grouped_state_dict) == 0
 
-    layers_with_prefix = {f"{FLUX_LORA_TRANSFORMER_PREFIX}{k}": v for k, v in layers.items()}
-
-    return LoRAModelRaw(layers=layers_with_prefix)
+    return LoRAModelRaw(layers=layers)
 
 
 def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:

invokeai/backend/lora/conversions/flux_kohya_lora_conversion_utils.py

@@ -3,7 +3,6 @@ from typing import Any, Dict, TypeVar
 
 import torch
 
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
 from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
@@ -24,6 +23,11 @@ FLUX_KOHYA_TRANSFORMER_KEY_REGEX = (
 FLUX_KOHYA_CLIP_KEY_REGEX = r"lora_te1_text_model_encoder_layers_(\d+)_(mlp|self_attn)_(\w+)\.?.*"
 
 
+# Prefixes used to distinguish between transformer and CLIP text encoder keys in the InvokeAI LoRA format.
+FLUX_KOHYA_TRANFORMER_PREFIX = "lora_transformer-"
+FLUX_KOHYA_CLIP_PREFIX = "lora_clip-"
+
+
 def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> bool:
     """Checks if the provided state dict is likely in the Kohya FLUX LoRA format.
 
@@ -63,9 +67,9 @@ def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -
     # Create LoRA layers.
     layers: dict[str, AnyLoRALayer] = {}
     for layer_key, layer_state_dict in transformer_grouped_sd.items():
-        layers[FLUX_LORA_TRANSFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
+        layers[FLUX_KOHYA_TRANFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
     for layer_key, layer_state_dict in clip_grouped_sd.items():
-        layers[FLUX_LORA_CLIP_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
+        layers[FLUX_KOHYA_CLIP_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
 
     # Create and return the LoRAModelRaw.
     return LoRAModelRaw(layers=layers)

invokeai/backend/lora/conversions/flux_lora_constants.py

@@ -1,3 +0,0 @@
-# Prefixes used to distinguish between transformer and CLIP text encoder keys in the FLUX InvokeAI LoRA format.
-FLUX_LORA_TRANSFORMER_PREFIX = "lora_transformer-"
-FLUX_LORA_CLIP_PREFIX = "lora_clip-"

invokeai/backend/model_manager/config.py

@@ -53,8 +53,6 @@ class BaseModelType(str, Enum):
     Any = "any"
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
-    # TODO(ryand): Should this just be StableDiffusion3?
-    StableDiffusion35 = "sd-3.5"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
@@ -396,8 +394,6 @@ class IPAdapterBaseConfig(ModelConfigBase):
 class IPAdapterInvokeAIConfig(IPAdapterBaseConfig):
     """Model config for IP Adapter diffusers format models."""
 
-    # TODO(ryand): Should we deprecate this field? From what I can tell, it hasn't been probed correctly for a long
-    # time. Need to go through the history to make sure I'm understanding this fully.
     image_encoder_model_id: str
     format: Literal[ModelFormat.InvokeAI]
 

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

@@ -1,41 +0,0 @@
-from pathlib import Path
-from typing import Optional
-
-from transformers import CLIPVisionModelWithProjection
-
-from invokeai.backend.model_manager.config import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    DiffusersConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
-class ClipVisionLoader(ModelLoader):
-    """Class to load CLIPVision models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, DiffusersConfigBase):
-            raise ValueError("Only DiffusersConfigBase models are currently supported here.")
-
-        if submodel_type is not None:
-            raise Exception("There are no submodels in CLIP Vision models.")
-
-        model_path = Path(config.path)
-
-        model = CLIPVisionModelWithProjection.from_pretrained(
-            model_path, torch_dtype=self._torch_dtype, local_files_only=True
-        )
-        assert isinstance(model, CLIPVisionModelWithProjection)
-
-        return model

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

@@ -8,36 +8,17 @@ from diffusers import ControlNetModel
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
-)
-from invokeai.backend.model_manager.config import (
     BaseModelType,
-    ControlNetCheckpointConfig,
     ModelFormat,
     ModelType,
-    SubModelType,
 )
+from invokeai.backend.model_manager.config import ControlNetCheckpointConfig, SubModelType
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Diffusers
-)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
-)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Diffusers
-)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
-)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Diffusers
-)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
-)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
 class ControlNetLoader(GenericDiffusersLoader):
     """Class to load ControlNet models."""
 

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

@@ -10,19 +10,6 @@ from safetensors.torch import load_file
 from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
 from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
-from invokeai.backend.flux.controlnet.state_dict_utils import (
-    convert_diffusers_instantx_state_dict_to_bfl_format,
-    infer_flux_params_from_state_dict,
-    infer_instantx_num_control_modes_from_state_dict,
-    is_state_dict_instantx_controlnet,
-    is_state_dict_xlabs_controlnet,
-)
-from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
-from invokeai.backend.flux.ip_adapter.state_dict_utils import infer_xlabs_ip_adapter_params_from_state_dict
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import (
-    XlabsIpAdapterFlux,
-)
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.util import ae_params, params
@@ -37,9 +24,6 @@ from invokeai.backend.model_manager import (
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
     CLIPEmbedDiffusersConfig,
-    ControlNetCheckpointConfig,
-    ControlNetDiffusersConfig,
-    IPAdapterCheckpointConfig,
     MainBnbQuantized4bCheckpointConfig,
     MainCheckpointConfig,
     MainGGUFCheckpointConfig,
@@ -53,7 +37,6 @@ from invokeai.backend.model_manager.util.model_util import (
     convert_bundle_to_flux_transformer_checkpoint,
 )
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
-from invokeai.backend.quantization.gguf.utils import TORCH_COMPATIBLE_QTYPES
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 try:
@@ -175,7 +158,7 @@ class T5EncoderCheckpointModel(ModelLoader):
             case SubModelType.Tokenizer2:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
             case SubModelType.TextEncoder2:
-                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2", torch_dtype="auto")
+                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2")
 
         raise ValueError(
             f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
@@ -251,21 +234,11 @@ class FluxGGUFCheckpointModel(ModelLoader):
         model_path = Path(config.path)
 
         with SilenceWarnings():
-            model = Flux(params[config.config_path])
-
+            # Load the state dict and patcher
             # HACK(ryand): We shouldn't be hard-coding the compute_dtype here.
             sd = gguf_sd_loader(model_path, compute_dtype=torch.bfloat16)
-
-        # HACK(ryand): There are some broken GGUF models in circulation that have the wrong shape for img_in.weight.
-        # We override the shape here to fix the issue.
-        # Example model with this issue (Q4_K_M): https://civitai.com/models/705823/ggufk-flux-unchained-km-quants
-        img_in_weight = sd.get("img_in.weight", None)
-        if img_in_weight is not None and img_in_weight._ggml_quantization_type in TORCH_COMPATIBLE_QTYPES:
-            expected_img_in_weight_shape = model.img_in.weight.shape
-            img_in_weight.quantized_data = img_in_weight.quantized_data.view(expected_img_in_weight_shape)
-            img_in_weight.tensor_shape = expected_img_in_weight_shape
-
-        model.load_state_dict(sd, assign=True)
+            model = Flux(params[config.config_path])
+            model.load_state_dict(sd, assign=True)
         return model
 
 
@@ -309,74 +282,3 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
                 sd = convert_bundle_to_flux_transformer_checkpoint(sd)
             model.load_state_dict(sd, assign=True)
         return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
-class FluxControlnetModel(ModelLoader):
-    """Class to load FLUX ControlNet models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if isinstance(config, ControlNetCheckpointConfig):
-            model_path = Path(config.path)
-        elif isinstance(config, ControlNetDiffusersConfig):
-            # If this is a diffusers directory, we simply ignore the config file and load from the weight file.
-            model_path = Path(config.path) / "diffusion_pytorch_model.safetensors"
-        else:
-            raise ValueError(f"Unexpected ControlNet model config type: {type(config)}")
-
-        sd = load_file(model_path)
-
-        # Detect the FLUX ControlNet model type from the state dict.
-        if is_state_dict_xlabs_controlnet(sd):
-            return self._load_xlabs_controlnet(sd)
-        elif is_state_dict_instantx_controlnet(sd):
-            return self._load_instantx_controlnet(sd)
-        else:
-            raise ValueError("Do not recognize the state dict as an XLabs or InstantX ControlNet model.")
-
-    def _load_xlabs_controlnet(self, sd: dict[str, torch.Tensor]) -> AnyModel:
-        with accelerate.init_empty_weights():
-            # HACK(ryand): Is it safe to assume dev here?
-            model = XLabsControlNetFlux(params["flux-dev"])
-
-        model.load_state_dict(sd, assign=True)
-        return model
-
-    def _load_instantx_controlnet(self, sd: dict[str, torch.Tensor]) -> AnyModel:
-        sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
-        flux_params = infer_flux_params_from_state_dict(sd)
-        num_control_modes = infer_instantx_num_control_modes_from_state_dict(sd)
-
-        with accelerate.init_empty_weights():
-            model = InstantXControlNetFlux(flux_params, num_control_modes)
-
-        model.load_state_dict(sd, assign=True)
-        return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.IPAdapter, format=ModelFormat.Checkpoint)
-class FluxIpAdapterModel(ModelLoader):
-    """Class to load FLUX IP-Adapter models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, IPAdapterCheckpointConfig):
-            raise ValueError(f"Unexpected model config type: {type(config)}.")
-
-        sd = load_file(Path(config.path))
-
-        params = infer_xlabs_ip_adapter_params_from_state_dict(sd)
-
-        with accelerate.init_empty_weights():
-            model = XlabsIpAdapterFlux(params=params)
-
-        model.load_xlabs_state_dict(sd, assign=True)
-        return model

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

@@ -22,6 +22,7 @@ from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)
 class GenericDiffusersLoader(ModelLoader):
     """Class to load simple diffusers models."""

invokeai/backend/model_manager/load/model_loaders/sd3.py

@@ -1,55 +0,0 @@
-from pathlib import Path
-from typing import Optional
-
-from invokeai.backend.model_manager.config import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    CheckpointConfigBase,
-    MainCheckpointConfig,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion35, type=ModelType.Main, format=ModelFormat.Checkpoint)
-class FluxCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CheckpointConfigBase):
-            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Transformer:
-                return self._load_from_singlefile(config)
-
-        raise ValueError(
-            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    def _load_from_singlefile(
-        self,
-        config: AnyModelConfig,
-    ) -> AnyModel:
-        assert isinstance(config, MainCheckpointConfig)
-        model_path = Path(config.path)
-
-        # model = Flux(params[config.config_path])
-        # sd = load_file(model_path)
-        # if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
-        #     sd = convert_bundle_to_flux_transformer_checkpoint(sd)
-        # new_sd_size = sum([ten.nelement() * torch.bfloat16.itemsize for ten in sd.values()])
-        # self._ram_cache.make_room(new_sd_size)
-        # for k in sd.keys():
-        #     # We need to cast to bfloat16 due to it being the only currently supported dtype for inference
-        #     sd[k] = sd[k].to(torch.bfloat16)
-        # model.load_state_dict(sd, assign=True)
-        return model

invokeai/backend/model_manager/probe.py

@@ -10,11 +10,6 @@ from picklescan.scanner import scan_file_path
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
-from invokeai.backend.flux.controlnet.state_dict_utils import (
-    is_state_dict_instantx_controlnet,
-    is_state_dict_xlabs_controlnet,
-)
-from invokeai.backend.flux.ip_adapter.state_dict_utils import is_state_dict_xlabs_ip_adapter
 from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
     is_state_dict_likely_in_flux_diffusers_format,
 )
@@ -37,7 +32,6 @@ from invokeai.backend.model_manager.config import (
 from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
 from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
-from invokeai.backend.sd3.sd3_state_dict_utils import is_sd3_checkpoint
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
@@ -121,9 +115,7 @@ class ModelProbe(object):
         "T2IAdapter": ModelType.T2IAdapter,
         "CLIPModel": ModelType.CLIPEmbed,
         "CLIPTextModel": ModelType.CLIPEmbed,
-        "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
-        "FluxControlNetModel": ModelType.ControlNet,
     }
 
     @classmethod
@@ -243,14 +235,11 @@ class ModelProbe(object):
         for key in [str(k) for k in ckpt.keys()]:
             if key.startswith(
                 (
-                    # The following prefixes appear when multiple models have been bundled together in a single file (I
-                    # believe the format originated in ComfyUI).
-                    # first_stage_model = VAE
-                    # cond_stage_model = Text Encoder
-                    # model.diffusion_model = UNet / Transformer
                     "cond_stage_model.",
                     "first_stage_model.",
                     "model.diffusion_model.",
+                    # FLUX models in the official BFL format contain keys with the "double_blocks." prefix.
+                    "double_blocks.",
                     # Some FLUX checkpoint files contain transformer keys prefixed with "model.diffusion_model".
                     # This prefix is typically used to distinguish between multiple models bundled in a single file.
                     "model.diffusion_model.double_blocks.",
@@ -258,10 +247,6 @@ class ModelProbe(object):
             ):
                 # Keys starting with double_blocks are associated with Flux models
                 return ModelType.Main
-            # FLUX models in the official BFL format contain keys with the "double_blocks." prefix, but we must be
-            # careful to avoid false positives on XLabs FLUX IP-Adapter models.
-            elif key.startswith("double_blocks.") and "ip_adapter" not in key:
-                return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE
             elif key.startswith(("lora_te_", "lora_unet_")):
@@ -270,28 +255,9 @@ class ModelProbe(object):
             # LoRA models, but as of the time of writing, we support Diffusers FLUX PEFT LoRA models.
             elif key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight", "lora_A.weight", "lora_B.weight")):
                 return ModelType.LoRA
-            elif key.startswith(
-                (
-                    "controlnet",
-                    "control_model",
-                    "input_blocks",
-                    # XLabs FLUX ControlNet models have keys starting with "controlnet_blocks."
-                    # For example: https://huggingface.co/XLabs-AI/flux-controlnet-collections/blob/86ab1e915a389d5857135c00e0d350e9e38a9048/flux-canny-controlnet_v2.safetensors
-                    # TODO(ryand): This is very fragile. XLabs FLUX ControlNet models also contain keys starting with
-                    # "double_blocks.", which we check for above. But, I'm afraid to modify this logic because it is so
-                    # delicate.
-                    "controlnet_blocks",
-                )
-            ):
+            elif key.startswith(("controlnet", "control_model", "input_blocks")):
                 return ModelType.ControlNet
-            elif key.startswith(
-                (
-                    "image_proj.",
-                    "ip_adapter.",
-                    # XLabs FLUX IP-Adapter models have keys startinh with "ip_adapter_proj_model.".
-                    "ip_adapter_proj_model.",
-                )
-            ):
+            elif key.startswith(("image_proj.", "ip_adapter.")):
                 return ModelType.IPAdapter
             elif key in {"emb_params", "string_to_param"}:
                 return ModelType.TextualInversion
@@ -315,10 +281,12 @@ class ModelProbe(object):
             return ModelType.SpandrelImageToImage
         except spandrel.UnsupportedModelError:
             pass
-        except Exception as e:
-            logger.warning(
-                f"Encountered error while probing to determine if {model_path} is a Spandrel model. Ignoring. Error: {e}"
-            )
+        except RuntimeError as e:
+            if "No such file or directory" in str(e):
+                # This error is expected if the model_path does not exist (which is the case in some unit tests).
+                pass
+            else:
+                raise e
 
         raise InvalidModelConfigException(f"Unable to determine model type for {model_path}")
 
@@ -404,9 +372,6 @@ class ModelProbe(object):
                     #   is used rather than attempting to support flux with separate model types and format
                     #   If changed in the future, please fix me
                     config_file = "flux-schnell"
-            elif base_type == BaseModelType.StableDiffusion35:
-                # TODO(ryand): Think about what to do here.
-                config_file = "sd3.5-large"
             else:
                 config_file = LEGACY_CONFIGS[base_type][variant_type]
                 if isinstance(config_file, dict):  # need another tier for sd-2.x models
@@ -475,7 +440,6 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "lineart": "lineart_image_processor",
     "lineart_anime": "lineart_anime_image_processor",
     "softedge": "hed_image_processor",
-    "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",
     "pose": "dw_openpose_image_processor",
     "mediapipe": "mediapipe_face_processor",
@@ -487,8 +451,7 @@ MODEL_NAME_TO_PREPROCESSOR = {
 
 def get_default_settings_controlnet_t2i_adapter(model_name: str) -> Optional[ControlAdapterDefaultSettings]:
     for k, v in MODEL_NAME_TO_PREPROCESSOR.items():
-        model_name_lower = model_name.lower()
-        if k in model_name_lower:
+        if k in model_name:
             return ControlAdapterDefaultSettings(preprocessor=v)
     return None
 
@@ -526,7 +489,7 @@ class CheckpointProbeBase(ProbeBase):
     def get_variant_type(self) -> ModelVariantType:
         model_type = ModelProbe.get_model_type_from_checkpoint(self.model_path, self.checkpoint)
         base_type = self.get_base_type()
-        if model_type != ModelType.Main or base_type in (BaseModelType.Flux, BaseModelType.StableDiffusion35):
+        if model_type != ModelType.Main or base_type == BaseModelType.Flux:
             return ModelVariantType.Normal
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
         in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
@@ -551,10 +514,6 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
             or "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
         ):
             return BaseModelType.Flux
-
-        if is_sd3_checkpoint(state_dict):
-            return BaseModelType.StableDiffusion35
-
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
             return BaseModelType.StableDiffusion1
@@ -666,11 +625,6 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-        if is_state_dict_xlabs_controlnet(checkpoint) or is_state_dict_instantx_controlnet(checkpoint):
-            # TODO(ryand): Should I distinguish between XLabs, InstantX and other ControlNet models by implementing
-            # get_format()?
-            return BaseModelType.Flux
-
         for key_name in (
             "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
             "controlnet_mid_block.bias",
@@ -696,10 +650,6 @@ class IPAdapterCheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-
-        if is_state_dict_xlabs_ip_adapter(checkpoint):
-            return BaseModelType.Flux
-
         for key in checkpoint.keys():
             if not key.startswith(("image_proj.", "ip_adapter.")):
                 continue
@@ -896,19 +846,22 @@ class ControlNetFolderProbe(FolderProbeBase):
             raise InvalidModelConfigException(f"Cannot determine base type for {self.model_path}")
         with open(config_file, "r") as file:
             config = json.load(file)
-
-        if config.get("_class_name", None) == "FluxControlNetModel":
-            return BaseModelType.Flux
-
         # no obvious way to distinguish between sd2-base and sd2-768
         dimension = config["cross_attention_dim"]
-        if dimension == 768:
-            return BaseModelType.StableDiffusion1
-        if dimension == 1024:
-            return BaseModelType.StableDiffusion2
-        if dimension == 2048:
-            return BaseModelType.StableDiffusionXL
-        raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
+        base_model = (
+            BaseModelType.StableDiffusion1
+            if dimension == 768
+            else (
+                BaseModelType.StableDiffusion2
+                if dimension == 1024
+                else BaseModelType.StableDiffusionXL
+                if dimension == 2048
+                else None
+            )
+        )
+        if not base_model:
+            raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
+        return base_model
 
 
 class LoRAFolderProbe(FolderProbeBase):

invokeai/backend/model_manager/util/model_util.py

@@ -57,7 +57,6 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str
             if scan_result.infected_files != 0:
                 raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
         if str(path).endswith(".gguf"):
-            # The GGUF reader used here uses numpy memmap, so these tensors are not loaded into memory during this function
             checkpoint = gguf_sd_loader(Path(path), compute_dtype=torch.float32)
         else:
             checkpoint = torch.load(path, map_location=torch.device("meta"))

invokeai/backend/quantization/gguf/ggml_tensor.py

@@ -40,7 +40,7 @@ def apply_to_quantized_tensor(func, args, kwargs):
         raise ValueError("Operation changed the dtype of GGMLTensor unexpectedly.")
 
     return GGMLTensor(
-        new_data, ggml_tensor._ggml_quantization_type, ggml_tensor.tensor_shape, ggml_tensor.compute_dtype
+        new_data, ggml_tensor._ggml_quantization_type, ggml_tensor._tensor_shape, ggml_tensor.compute_dtype
     )
 
 
@@ -54,11 +54,6 @@ GGML_TENSOR_OP_TABLE = {
     torch.ops.aten.mul.Tensor: dequantize_and_run,  # pyright: ignore
 }
 
-if torch.backends.mps.is_available():
-    GGML_TENSOR_OP_TABLE.update(
-        {torch.ops.aten.linear.default: dequantize_and_run}  # pyright: ignore
-    )
-
 
 class GGMLTensor(torch.Tensor):
     """A torch.Tensor sub-class holding a quantized GGML tensor.
@@ -96,11 +91,11 @@ class GGMLTensor(torch.Tensor):
         self.quantized_data = data
         self._ggml_quantization_type = ggml_quantization_type
         # The dequantized shape of the tensor.
-        self.tensor_shape = tensor_shape
+        self._tensor_shape = tensor_shape
         self.compute_dtype = compute_dtype
 
     def __repr__(self, *, tensor_contents=None):
-        return f"GGMLTensor(type={self._ggml_quantization_type.name}, dequantized_shape=({self.tensor_shape})"
+        return f"GGMLTensor(type={self._ggml_quantization_type.name}, dequantized_shape=({self._tensor_shape})"
 
     @overload
     def size(self, dim: None = None) -> torch.Size: ...
@@ -111,8 +106,8 @@ class GGMLTensor(torch.Tensor):
     def size(self, dim: int | None = None):
         """Return the size of the tensor after dequantization. I.e. the shape that will be used in any math ops."""
         if dim is not None:
-            return self.tensor_shape[dim]
-        return self.tensor_shape
+            return self._tensor_shape[dim]
+        return self._tensor_shape
 
     @property
     def shape(self) -> torch.Size:  # pyright: ignore[reportIncompatibleVariableOverride] pyright doesn't understand this for some reason.
@@ -141,7 +136,7 @@ class GGMLTensor(torch.Tensor):
         elif self._ggml_quantization_type in DEQUANTIZE_FUNCTIONS:
             # TODO(ryand): Look into how the dtype param is intended to be used.
             return dequantize(
-                data=self.quantized_data, qtype=self._ggml_quantization_type, oshape=self.tensor_shape, dtype=None
+                data=self.quantized_data, qtype=self._ggml_quantization_type, oshape=self._tensor_shape, dtype=None
             ).to(self.compute_dtype)
         else:
             # There is no GPU implementation for this quantization type, so fallback to the numpy implementation.

invokeai/backend/quantization/gguf/utils.py

@@ -277,6 +277,25 @@ def is_quantized(tensor: torch.Tensor):
     return not is_torch_compatible(tensor)
 
 
+def dequantize_tensor(
+    tensor: torch.Tensor, dtype: torch.dtype, dequant_dtype: Union[torch.dtype, str, None] = None
+) -> torch.Tensor:
+    qtype: Optional[gguf.GGMLQuantizationType] = getattr(tensor, "tensor_type", None)
+    oshape: torch.Size = getattr(tensor, "tensor_shape", tensor.shape)
+    if qtype is None:
+        raise ValueError("This is not a valid quantized tensor")
+    if qtype in TORCH_COMPATIBLE_QTYPES:
+        return tensor.to(dtype)
+    elif qtype in DEQUANTIZE_FUNCTIONS:
+        dequant_dtype = dtype if dequant_dtype == "target" else dequant_dtype
+        if not (dequant_dtype is None or isinstance(dequant_dtype, torch.dtype)):
+            raise ValueError("dequant_dtype must be a torch.dtype")
+        return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype)
+    else:
+        new = gguf.quants.dequantize(tensor.cpu().numpy(), qtype)
+        return torch.from_numpy(new).to(tensor.device, dtype=dtype)
+
+
 def dequantize(
     data: torch.Tensor, qtype: gguf.GGMLQuantizationType, oshape: torch.Size, dtype: Optional[torch.dtype] = None
 ):

invokeai/backend/sd3/mmditx.py

@@ -1,891 +0,0 @@
-# This file was originally copied from:
-# https://github.com/Stability-AI/sd3.5/blob/19bf11c4e1e37324c5aa5a61f010d4127848a09c/mmditx.py
-
-
-### This file contains impls for MM-DiT, the core model component of SD3
-
-import math
-from typing import Dict, List, Optional
-
-import numpy as np
-import torch
-from einops import rearrange, repeat
-
-from invokeai.backend.sd3.other_impls import Mlp, attention
-
-
-class PatchEmbed(torch.nn.Module):
-    """2D Image to Patch Embedding"""
-
-    def __init__(
-        self,
-        img_size: Optional[int] = 224,
-        patch_size: int = 16,
-        in_chans: int = 3,
-        embed_dim: int = 768,
-        flatten: bool = True,
-        bias: bool = True,
-        strict_img_size: bool = True,
-        dynamic_img_pad: bool = False,
-        dtype: torch.dtype | None = None,
-        device: torch.device | None = None,
-    ):
-        super().__init__()
-        self.patch_size = (patch_size, patch_size)
-        if img_size is not None:
-            self.img_size = (img_size, img_size)
-            self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size, strict=False)])
-            self.num_patches = self.grid_size[0] * self.grid_size[1]
-        else:
-            self.img_size = None
-            self.grid_size = None
-            self.num_patches = None
-
-        # flatten spatial dim and transpose to channels last, kept for bwd compat
-        self.flatten = flatten
-        self.strict_img_size = strict_img_size
-        self.dynamic_img_pad = dynamic_img_pad
-
-        self.proj = torch.nn.Conv2d(
-            in_chans,
-            embed_dim,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=bias,
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.proj(x)
-        if self.flatten:
-            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
-        return x
-
-
-def modulate(x: torch.Tensor, shift: torch.Tensor | None, scale: torch.Tensor) -> torch.Tensor:
-    if shift is None:
-        shift = torch.zeros_like(scale)
-    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-
-#################################################################################
-#                   Sine/Cosine Positional Embedding Functions                  #
-#################################################################################
-
-
-def get_2d_sincos_pos_embed(
-    embed_dim: int,
-    grid_size: int,
-    cls_token: bool = False,
-    extra_tokens: int = 0,
-    scaling_factor: Optional[float] = None,
-    offset: Optional[float] = None,
-):
-    """
-    grid_size: int of the grid height and width
-    return:
-    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
-    """
-    grid_h = np.arange(grid_size, dtype=np.float32)
-    grid_w = np.arange(grid_size, dtype=np.float32)
-    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    grid = np.stack(grid, axis=0)
-    if scaling_factor is not None:
-        grid = grid / scaling_factor
-    if offset is not None:
-        grid = grid - offset
-    grid = grid.reshape([2, 1, grid_size, grid_size])
-    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
-    if cls_token and extra_tokens > 0:
-        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
-    return pos_embed
-
-
-def get_2d_sincos_pos_embed_from_grid(embed_dim: int, grid):
-    assert embed_dim % 2 == 0
-    # use half of dimensions to encode grid_h
-    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
-    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
-    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
-    return emb
-
-
-def get_1d_sincos_pos_embed_from_grid(embed_dim: int, pos):
-    """
-    embed_dim: output dimension for each position
-    pos: a list of positions to be encoded: size (M,)
-    out: (M, D)
-    """
-    assert embed_dim % 2 == 0
-    omega = np.arange(embed_dim // 2, dtype=np.float64)
-    omega /= embed_dim / 2.0
-    omega = 1.0 / 10000**omega  # (D/2,)
-    pos = pos.reshape(-1)  # (M,)
-    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
-    emb_sin = np.sin(out)  # (M, D/2)
-    emb_cos = np.cos(out)  # (M, D/2)
-    return np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
-
-
-#################################################################################
-#               Embedding Layers for Timesteps and Class Labels                 #
-#################################################################################
-
-
-class TimestepEmbedder(torch.nn.Module):
-    """Embeds scalar timesteps into vector representations."""
-
-    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None):
-        super().__init__()
-        self.mlp = torch.nn.Sequential(
-            torch.nn.Linear(
-                frequency_embedding_size,
-                hidden_size,
-                bias=True,
-                dtype=dtype,
-                device=device,
-            ),
-            torch.nn.SiLU(),
-            torch.nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        """
-        Create sinusoidal timestep embeddings.
-        :param t: a 1-D Tensor of N indices, one per batch element.
-                          These may be fractional.
-        :param dim: the dimension of the output.
-        :param max_period: controls the minimum frequency of the embeddings.
-        :return: an (N, D) Tensor of positional embeddings.
-        """
-        half = dim // 2
-        freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
-            device=t.device
-        )
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-        if torch.is_floating_point(t):
-            embedding = embedding.to(dtype=t.dtype)
-        return embedding
-
-    def forward(self, t, dtype, **kwargs):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class VectorEmbedder(torch.nn.Module):
-    """Embeds a flat vector of dimension input_dim"""
-
-    def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None):
-        super().__init__()
-        self.mlp = torch.nn.Sequential(
-            torch.nn.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
-            torch.nn.SiLU(),
-            torch.nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.mlp(x)
-
-
-#################################################################################
-#                                 Core DiT Model                                #
-#################################################################################
-
-
-def split_qkv(qkv, head_dim):
-    qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
-    return qkv[0], qkv[1], qkv[2]
-
-
-def optimized_attention(qkv, num_heads):
-    return attention(qkv[0], qkv[1], qkv[2], num_heads)
-
-
-class SelfAttention(torch.nn.Module):
-    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int = 8,
-        qkv_bias: bool = False,
-        qk_scale: Optional[float] = None,
-        attn_mode: str = "xformers",
-        pre_only: bool = False,
-        qk_norm: Optional[str] = None,
-        rmsnorm: bool = False,
-        dtype=None,
-        device=None,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        self.qkv = torch.nn.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
-        if not pre_only:
-            self.proj = torch.nn.Linear(dim, dim, dtype=dtype, device=device)
-        assert attn_mode in self.ATTENTION_MODES
-        self.attn_mode = attn_mode
-        self.pre_only = pre_only
-
-        if qk_norm == "rms":
-            self.ln_q = RMSNorm(
-                self.head_dim,
-                elementwise_affine=True,
-                eps=1.0e-6,
-                dtype=dtype,
-                device=device,
-            )
-            self.ln_k = RMSNorm(
-                self.head_dim,
-                elementwise_affine=True,
-                eps=1.0e-6,
-                dtype=dtype,
-                device=device,
-            )
-        elif qk_norm == "ln":
-            self.ln_q = torch.nn.LayerNorm(
-                self.head_dim,
-                elementwise_affine=True,
-                eps=1.0e-6,
-                dtype=dtype,
-                device=device,
-            )
-            self.ln_k = torch.nn.LayerNorm(
-                self.head_dim,
-                elementwise_affine=True,
-                eps=1.0e-6,
-                dtype=dtype,
-                device=device,
-            )
-        elif qk_norm is None:
-            self.ln_q = torch.nn.Identity()
-            self.ln_k = torch.nn.Identity()
-        else:
-            raise ValueError(qk_norm)
-
-    def pre_attention(self, x: torch.Tensor):
-        B, L, C = x.shape
-        qkv = self.qkv(x)
-        q, k, v = split_qkv(qkv, self.head_dim)
-        q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
-        k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
-        return (q, k, v)
-
-    def post_attention(self, x: torch.Tensor) -> torch.Tensor:
-        assert not self.pre_only
-        x = self.proj(x)
-        return x
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        (q, k, v) = self.pre_attention(x)
-        x = attention(q, k, v, self.num_heads)
-        x = self.post_attention(x)
-        return x
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        elementwise_affine: bool = False,
-        eps: float = 1e-6,
-        device=None,
-        dtype=None,
-    ):
-        """
-        Initialize the RMSNorm normalization layer.
-        Args:
-            dim (int): The dimension of the input tensor.
-            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
-        Attributes:
-            eps (float): A small value added to the denominator for numerical stability.
-            weight (torch.nn.Parameter): Learnable scaling parameter.
-        """
-        super().__init__()
-        self.eps = eps
-        self.learnable_scale = elementwise_affine
-        if self.learnable_scale:
-            self.weight = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
-        else:
-            self.register_parameter("weight", None)
-
-    def _norm(self, x):
-        """
-        Apply the RMSNorm normalization to the input tensor.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The normalized tensor.
-        """
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        """
-        Forward pass through the RMSNorm layer.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The output tensor after applying RMSNorm.
-        """
-        x = self._norm(x)
-        if self.learnable_scale:
-            return x * self.weight.to(device=x.device, dtype=x.dtype)
-        else:
-            return x
-
-
-class SwiGLUFeedForward(torch.nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        hidden_dim: int,
-        multiple_of: int,
-        ffn_dim_multiplier: Optional[float] = None,
-    ):
-        """
-        Initialize the FeedForward module.
-
-        Args:
-            dim (int): Input dimension.
-            hidden_dim (int): Hidden dimension of the feedforward layer.
-            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
-            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
-
-        Attributes:
-            w1 (ColumnParallelLinear): Linear transformation for the first layer.
-            w2 (RowParallelLinear): Linear transformation for the second layer.
-            w3 (ColumnParallelLinear): Linear transformation for the third layer.
-
-        """
-        super().__init__()
-        hidden_dim = int(2 * hidden_dim / 3)
-        # custom dim factor multiplier
-        if ffn_dim_multiplier is not None:
-            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
-        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-
-        self.w1 = torch.nn.Linear(dim, hidden_dim, bias=False)
-        self.w2 = torch.nn.Linear(hidden_dim, dim, bias=False)
-        self.w3 = torch.nn.Linear(dim, hidden_dim, bias=False)
-
-    def forward(self, x):
-        return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x))
-
-
-class DismantledBlock(torch.nn.Module):
-    """A DiT block with gated adaptive layer norm (adaLN) conditioning."""
-
-    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        attn_mode: str = "xformers",
-        qkv_bias: bool = False,
-        pre_only: bool = False,
-        rmsnorm: bool = False,
-        scale_mod_only: bool = False,
-        swiglu: bool = False,
-        qk_norm: Optional[str] = None,
-        x_block_self_attn: bool = False,
-        dtype=None,
-        device=None,
-        **block_kwargs,
-    ):
-        super().__init__()
-        assert attn_mode in self.ATTENTION_MODES
-        if not rmsnorm:
-            self.norm1 = torch.nn.LayerNorm(
-                hidden_size,
-                elementwise_affine=False,
-                eps=1e-6,
-                dtype=dtype,
-                device=device,
-            )
-        else:
-            self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.attn = SelfAttention(
-            dim=hidden_size,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            attn_mode=attn_mode,
-            pre_only=pre_only,
-            qk_norm=qk_norm,
-            rmsnorm=rmsnorm,
-            dtype=dtype,
-            device=device,
-        )
-        if x_block_self_attn:
-            assert not pre_only
-            assert not scale_mod_only
-            self.x_block_self_attn = True
-            self.attn2 = SelfAttention(
-                dim=hidden_size,
-                num_heads=num_heads,
-                qkv_bias=qkv_bias,
-                attn_mode=attn_mode,
-                pre_only=False,
-                qk_norm=qk_norm,
-                rmsnorm=rmsnorm,
-                dtype=dtype,
-                device=device,
-            )
-        else:
-            self.x_block_self_attn = False
-        if not pre_only:
-            if not rmsnorm:
-                self.norm2 = torch.nn.LayerNorm(
-                    hidden_size,
-                    elementwise_affine=False,
-                    eps=1e-6,
-                    dtype=dtype,
-                    device=device,
-                )
-            else:
-                self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        if not pre_only:
-            if not swiglu:
-                self.mlp = Mlp(
-                    in_features=hidden_size,
-                    hidden_features=mlp_hidden_dim,
-                    act_layer=torch.nn.GELU(approximate="tanh"),
-                    dtype=dtype,
-                    device=device,
-                )
-            else:
-                self.mlp = SwiGLUFeedForward(dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256)
-        self.scale_mod_only = scale_mod_only
-        if x_block_self_attn:
-            assert not pre_only
-            assert not scale_mod_only
-            n_mods = 9
-        elif not scale_mod_only:
-            n_mods = 6 if not pre_only else 2
-        else:
-            n_mods = 4 if not pre_only else 1
-        self.adaLN_modulation = torch.nn.Sequential(
-            torch.nn.SiLU(),
-            torch.nn.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device),
-        )
-        self.pre_only = pre_only
-
-    def pre_attention(self, x: torch.Tensor, c: torch.Tensor):
-        assert x is not None, "pre_attention called with None input"
-        if not self.pre_only:
-            if not self.scale_mod_only:
-                shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(
-                    6, dim=1
-                )
-            else:
-                shift_msa = None
-                shift_mlp = None
-                scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(4, dim=1)
-            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
-            return qkv, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp)
-        else:
-            if not self.scale_mod_only:
-                shift_msa, scale_msa = self.adaLN_modulation(c).chunk(2, dim=1)
-            else:
-                shift_msa = None
-                scale_msa = self.adaLN_modulation(c)
-            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
-            return qkv, None
-
-    def post_attention(
-        self,
-        attn: torch.Tensor,
-        x: torch.Tensor,
-        gate_msa: torch.Tensor,
-        shift_mlp: torch.Tensor,
-        scale_mlp: torch.Tensor,
-        gate_mlp: torch.Tensor,
-    ) -> torch.Tensor:
-        assert not self.pre_only
-        x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
-        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
-        return x
-
-    def pre_attention_x(
-        self, x: torch.Tensor, c: torch.Tensor
-    ) -> tuple[
-        tuple[torch.Tensor, torch.Tensor, torch.Tensor],
-        tuple[torch.Tensor, torch.Tensor, torch.Tensor],
-        tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
-    ]:
-        assert self.x_block_self_attn
-        (
-            shift_msa,
-            scale_msa,
-            gate_msa,
-            shift_mlp,
-            scale_mlp,
-            gate_mlp,
-            shift_msa2,
-            scale_msa2,
-            gate_msa2,
-        ) = self.adaLN_modulation(c).chunk(9, dim=1)
-        x_norm = self.norm1(x)
-        qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa))
-        qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2))
-        return (
-            qkv,
-            qkv2,
-            (
-                x,
-                gate_msa,
-                shift_mlp,
-                scale_mlp,
-                gate_mlp,
-                gate_msa2,
-            ),
-        )
-
-    def post_attention_x(
-        self,
-        attn: torch.Tensor,
-        attn2: torch.Tensor,
-        x: torch.Tensor,
-        gate_msa: torch.Tensor,
-        shift_mlp: torch.Tensor,
-        scale_mlp: torch.Tensor,
-        gate_mlp: torch.Tensor,
-        gate_msa2: torch.Tensor,
-        attn1_dropout: float = 0.0,
-    ):
-        assert not self.pre_only
-        if attn1_dropout > 0.0:
-            # Use torch.bernoulli to implement dropout, only dropout the batch dimension
-            attn1_dropout = torch.bernoulli(torch.full((attn.size(0), 1, 1), 1 - attn1_dropout, device=attn.device))
-            attn_ = gate_msa.unsqueeze(1) * self.attn.post_attention(attn) * attn1_dropout
-        else:
-            attn_ = gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
-        x = x + attn_
-        attn2_ = gate_msa2.unsqueeze(1) * self.attn2.post_attention(attn2)
-        x = x + attn2_
-        mlp_ = gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
-        x = x + mlp_
-        return x, (gate_msa, gate_msa2, gate_mlp, attn_, attn2_)
-
-    def forward(self, x: torch.Tensor, c: torch.Tensor):
-        assert not self.pre_only
-        if self.x_block_self_attn:
-            (q, k, v), (q2, k2, v2), intermediates = self.pre_attention_x(x, c)
-            attn = attention(q, k, v, self.attn.num_heads)
-            attn2 = attention(q2, k2, v2, self.attn2.num_heads)
-            return self.post_attention_x(attn, attn2, *intermediates)
-        else:
-            (q, k, v), intermediates = self.pre_attention(x, c)
-            attn = attention(q, k, v, self.attn.num_heads)
-            return self.post_attention(attn, *intermediates)
-
-
-def block_mixing(
-    context: torch.Tensor, x: torch.Tensor, context_block: DismantledBlock, x_block: DismantledBlock, c: torch.Tensor
-):
-    assert context is not None, "block_mixing called with None context"
-    context_qkv, context_intermediates = context_block.pre_attention(context, c)
-
-    if x_block.x_block_self_attn:
-        x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c)
-    else:
-        x_qkv, x_intermediates = x_block.pre_attention(x, c)
-
-    o: list[torch.Tensor] = []
-    for t in range(3):
-        o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
-    q, k, v = tuple(o)
-
-    attn = attention(q, k, v, x_block.attn.num_heads)
-    context_attn, x_attn = (
-        attn[:, : context_qkv[0].shape[1]],
-        attn[:, context_qkv[0].shape[1] :],
-    )
-
-    if not context_block.pre_only:
-        context = context_block.post_attention(context_attn, *context_intermediates)
-    else:
-        context = None
-
-    if x_block.x_block_self_attn:
-        x_q2, x_k2, x_v2 = x_qkv2
-        attn2 = attention(x_q2, x_k2, x_v2, x_block.attn2.num_heads)
-    else:
-        x = x_block.post_attention(x_attn, *x_intermediates)
-
-    return context, x
-
-
-class JointBlock(torch.nn.Module):
-    """just a small wrapper to serve as a fsdp unit"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        pre_only = kwargs.pop("pre_only")
-        qk_norm = kwargs.pop("qk_norm", None)
-        x_block_self_attn = kwargs.pop("x_block_self_attn", False)
-        self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
-        self.x_block = DismantledBlock(
-            *args,
-            pre_only=False,
-            qk_norm=qk_norm,
-            x_block_self_attn=x_block_self_attn,
-            **kwargs,
-        )
-
-    def forward(self, *args, **kwargs):
-        return block_mixing(*args, context_block=self.context_block, x_block=self.x_block, **kwargs)
-
-
-class FinalLayer(torch.nn.Module):
-    """
-    The final layer of DiT.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        patch_size: int,
-        out_channels: int,
-        total_out_channels: Optional[int] = None,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-    ):
-        super().__init__()
-        self.norm_final = torch.nn.LayerNorm(
-            hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
-        )
-        self.linear = (
-            torch.nn.Linear(
-                hidden_size,
-                patch_size * patch_size * out_channels,
-                bias=True,
-                dtype=dtype,
-                device=device,
-            )
-            if (total_out_channels is None)
-            else torch.nn.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
-        )
-        self.adaLN_modulation = torch.nn.Sequential(
-            torch.nn.SiLU(),
-            torch.nn.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device),
-        )
-
-    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
-        x = modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
-        return x
-
-
-class MMDiTX(torch.nn.Module):
-    """Diffusion model with a Transformer backbone."""
-
-    def __init__(
-        self,
-        input_size: int | None = 32,
-        patch_size: int = 2,
-        in_channels: int = 4,
-        depth: int = 28,
-        mlp_ratio: float = 4.0,
-        learn_sigma: bool = False,
-        adm_in_channels: Optional[int] = None,
-        context_embedder_config: Optional[Dict] = None,
-        register_length: int = 0,
-        attn_mode: str = "torch",
-        rmsnorm: bool = False,
-        scale_mod_only: bool = False,
-        swiglu: bool = False,
-        out_channels: Optional[int] = None,
-        pos_embed_scaling_factor: Optional[float] = None,
-        pos_embed_offset: Optional[float] = None,
-        pos_embed_max_size: Optional[int] = None,
-        num_patches: Optional[int] = None,
-        qk_norm: Optional[str] = None,
-        x_block_self_attn_layers: Optional[List[int]] = None,
-        qkv_bias: bool = True,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-        verbose: bool = False,
-    ):
-        super().__init__()
-        if verbose:
-            print(
-                f"mmdit initializing with: {input_size=}, {patch_size=}, {in_channels=}, {depth=}, {mlp_ratio=}, {learn_sigma=}, {adm_in_channels=}, {context_embedder_config=}, {register_length=}, {attn_mode=}, {rmsnorm=}, {scale_mod_only=}, {swiglu=}, {out_channels=}, {pos_embed_scaling_factor=}, {pos_embed_offset=}, {pos_embed_max_size=}, {num_patches=}, {qk_norm=}, {qkv_bias=}, {dtype=}, {device=}"
-            )
-        self.dtype = dtype
-        self.learn_sigma = learn_sigma
-        self.in_channels = in_channels
-        default_out_channels = in_channels * 2 if learn_sigma else in_channels
-        self.out_channels = out_channels if out_channels is not None else default_out_channels
-        self.patch_size = patch_size
-        self.pos_embed_scaling_factor = pos_embed_scaling_factor
-        self.pos_embed_offset = pos_embed_offset
-        self.pos_embed_max_size = pos_embed_max_size
-        self.x_block_self_attn_layers = x_block_self_attn_layers or []
-
-        # apply magic --> this defines a head_size of 64
-        hidden_size = 64 * depth
-        num_heads = depth
-
-        self.num_heads = num_heads
-
-        self.x_embedder = PatchEmbed(
-            input_size,
-            patch_size,
-            in_channels,
-            hidden_size,
-            bias=True,
-            strict_img_size=self.pos_embed_max_size is None,
-            dtype=dtype,
-            device=device,
-        )
-        self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device)
-
-        if adm_in_channels is not None:
-            assert isinstance(adm_in_channels, int)
-            self.y_embedder = VectorEmbedder(adm_in_channels, hidden_size, dtype=dtype, device=device)
-
-        self.context_embedder = torch.nn.Identity()
-        if context_embedder_config is not None:
-            if context_embedder_config["target"] == "torch.nn.Linear":
-                self.context_embedder = torch.nn.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
-
-        self.register_length = register_length
-        if self.register_length > 0:
-            self.register = torch.nn.Parameter(torch.randn(1, register_length, hidden_size, dtype=dtype, device=device))
-
-        # num_patches = self.x_embedder.num_patches
-        # Will use fixed sin-cos embedding:
-        # just use a buffer already
-        if num_patches is not None:
-            self.register_buffer(
-                "pos_embed",
-                torch.zeros(1, num_patches, hidden_size, dtype=dtype, device=device),
-            )
-        else:
-            self.pos_embed = None
-
-        self.joint_blocks = torch.nn.ModuleList(
-            [
-                JointBlock(
-                    hidden_size,
-                    num_heads,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    attn_mode=attn_mode,
-                    pre_only=i == depth - 1,
-                    rmsnorm=rmsnorm,
-                    scale_mod_only=scale_mod_only,
-                    swiglu=swiglu,
-                    qk_norm=qk_norm,
-                    x_block_self_attn=(i in self.x_block_self_attn_layers),
-                    dtype=dtype,
-                    device=device,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels, dtype=dtype, device=device)
-
-    def cropped_pos_embed(self, hw: torch.Size) -> torch.Tensor:
-        assert self.pos_embed_max_size is not None
-        p = self.x_embedder.patch_size[0]
-        h, w = hw
-        # patched size
-        h = h // p
-        w = w // p
-        assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
-        assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
-        top = (self.pos_embed_max_size - h) // 2
-        left = (self.pos_embed_max_size - w) // 2
-        spatial_pos_embed: torch.Tensor = rearrange(
-            self.pos_embed,
-            "1 (h w) c -> 1 h w c",
-            h=self.pos_embed_max_size,
-            w=self.pos_embed_max_size,
-        )  # type: ignore Type checking does not correctly infer the type of the self.pos_embed buffer.
-        spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
-        spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
-        return spatial_pos_embed
-
-    def unpatchify(self, x: torch.Tensor, hw: Optional[torch.Size] = None) -> torch.Tensor:
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        if hw is None:
-            h = w = int(x.shape[1] ** 0.5)
-        else:
-            h, w = hw
-            h = h // p
-            w = w // p
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum("nhwpqc->nchpwq", x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
-        return imgs
-
-    def forward_core_with_concat(
-        self,
-        x: torch.Tensor,
-        c_mod: torch.Tensor,
-        context: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if self.register_length > 0:
-            context = torch.cat(
-                (
-                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
-                    context if context is not None else torch.Tensor([]).type_as(x),
-                ),
-                1,
-            )
-
-        # context is B, L', D
-        # x is B, L, D
-        for block in self.joint_blocks:
-            context, x = block(context, x, c=c_mod)
-
-        x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
-        return x
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        t: torch.Tensor,
-        y: Optional[torch.Tensor] = None,
-        context: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        """
-        Forward pass of DiT.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N,) tensor of class labels
-        """
-        hw = x.shape[-2:]
-        x = self.x_embedder(x) + self.cropped_pos_embed(hw)
-        c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
-        if y is not None:
-            y = self.y_embedder(y)  # (N, D)
-            c = c + y  # (N, D)
-
-        context = self.context_embedder(context)
-
-        x = self.forward_core_with_concat(x, c, context)
-
-        x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
-        return x

invokeai/backend/sd3/other_impls.py

@@ -1,795 +0,0 @@
-# This file was originally copied from:
-# https://github.com/Stability-AI/sd3.5/blob/19bf11c4e1e37324c5aa5a61f010d4127848a09c/other_impls.py
-
-### This file contains impls for underlying related models (CLIP, T5, etc)
-
-import math
-from typing import Callable, Optional
-
-import torch
-from transformers import CLIPTokenizer, T5TokenizerFast
-
-#################################################################################################
-### Core/Utility
-#################################################################################################
-
-
-def attention(
-    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask: Optional[torch.Tensor] = None
-) -> torch.Tensor:
-    """Convenience wrapper around a basic attention operation"""
-    b, _, dim_head = q.shape
-    dim_head //= heads
-    q, k, v = map(lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v))
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-    return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
-
-
-class Mlp(torch.nn.Module):
-    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
-
-    def __init__(
-        self,
-        in_features: int,
-        hidden_features: Optional[int] = None,
-        out_features: Optional[int] = None,
-        act_layer: Callable[[torch.Tensor], torch.Tensor] | None = None,
-        bias: bool = True,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        if act_layer is None:
-            act_layer = torch.nn.functional.gelu
-
-        self.fc1 = torch.nn.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
-        self.act = act_layer
-        self.fc2 = torch.nn.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.fc2(x)
-        return x
-
-
-#################################################################################################
-### CLIP
-#################################################################################################
-
-
-class CLIPAttention(torch.nn.Module):
-    def __init__(self, embed_dim, heads, dtype, device):
-        super().__init__()
-        self.heads = heads
-        self.q_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.k_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.v_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-
-    def forward(self, x, mask=None):
-        q = self.q_proj(x)
-        k = self.k_proj(x)
-        v = self.v_proj(x)
-        out = attention(q, k, v, self.heads, mask)
-        return self.out_proj(out)
-
-
-ACTIVATIONS = {
-    "quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
-    "gelu": torch.nn.functional.gelu,
-}
-
-
-class CLIPLayer(torch.nn.Module):
-    def __init__(
-        self,
-        embed_dim,
-        heads,
-        intermediate_size,
-        intermediate_activation,
-        dtype,
-        device,
-    ):
-        super().__init__()
-        self.layer_norm1 = torch.nn.LayerNorm(embed_dim, dtype=dtype, device=device)
-        self.self_attn = CLIPAttention(embed_dim, heads, dtype, device)
-        self.layer_norm2 = torch.nn.LayerNorm(embed_dim, dtype=dtype, device=device)
-        # self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device)
-        self.mlp = Mlp(
-            embed_dim,
-            intermediate_size,
-            embed_dim,
-            act_layer=ACTIVATIONS[intermediate_activation],
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x, mask=None):
-        x += self.self_attn(self.layer_norm1(x), mask)
-        x += self.mlp(self.layer_norm2(x))
-        return x
-
-
-class CLIPEncoder(torch.nn.Module):
-    def __init__(
-        self,
-        num_layers,
-        embed_dim,
-        heads,
-        intermediate_size,
-        intermediate_activation,
-        dtype,
-        device,
-    ):
-        super().__init__()
-        self.layers = torch.nn.ModuleList(
-            [
-                CLIPLayer(
-                    embed_dim,
-                    heads,
-                    intermediate_size,
-                    intermediate_activation,
-                    dtype,
-                    device,
-                )
-                for i in range(num_layers)
-            ]
-        )
-
-    def forward(self, x, mask=None, intermediate_output=None):
-        if intermediate_output is not None:
-            if intermediate_output < 0:
-                intermediate_output = len(self.layers) + intermediate_output
-        intermediate = None
-        for i, l in enumerate(self.layers):
-            x = l(x, mask)
-            if i == intermediate_output:
-                intermediate = x.clone()
-        return x, intermediate
-
-
-class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
-        super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
-
-    def forward(self, input_tokens):
-        return self.token_embedding(input_tokens) + self.position_embedding.weight
-
-
-class CLIPTextModel_(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device):
-        num_layers = config_dict["num_hidden_layers"]
-        embed_dim = config_dict["hidden_size"]
-        heads = config_dict["num_attention_heads"]
-        intermediate_size = config_dict["intermediate_size"]
-        intermediate_activation = config_dict["hidden_act"]
-        super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
-        self.encoder = CLIPEncoder(
-            num_layers,
-            embed_dim,
-            heads,
-            intermediate_size,
-            intermediate_activation,
-            dtype,
-            device,
-        )
-        self.final_layer_norm = torch.nn.LayerNorm(embed_dim, dtype=dtype, device=device)
-
-    def forward(self, input_tokens, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
-        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
-        x, i = self.encoder(x, mask=causal_mask, intermediate_output=intermediate_output)
-        x = self.final_layer_norm(x)
-        if i is not None and final_layer_norm_intermediate:
-            i = self.final_layer_norm(i)
-        pooled_output = x[
-            torch.arange(x.shape[0], device=x.device),
-            input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),
-        ]
-        return x, i, pooled_output
-
-
-class CLIPTextModel(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device):
-        super().__init__()
-        self.num_layers = config_dict["num_hidden_layers"]
-        self.text_model = CLIPTextModel_(config_dict, dtype, device)
-        embed_dim = config_dict["hidden_size"]
-        self.text_projection = torch.nn.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
-        self.text_projection.weight.copy_(torch.eye(embed_dim))
-        self.dtype = dtype
-
-    def get_input_embeddings(self):
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, embeddings):
-        self.text_model.embeddings.token_embedding = embeddings
-
-    def forward(self, *args, **kwargs):
-        x = self.text_model(*args, **kwargs)
-        out = self.text_projection(x[2])
-        return (x[0], x[1], out, x[2])
-
-
-def parse_parentheses(string):
-    result = []
-    current_item = ""
-    nesting_level = 0
-    for char in string:
-        if char == "(":
-            if nesting_level == 0:
-                if current_item:
-                    result.append(current_item)
-                    current_item = "("
-                else:
-                    current_item = "("
-            else:
-                current_item += char
-            nesting_level += 1
-        elif char == ")":
-            nesting_level -= 1
-            if nesting_level == 0:
-                result.append(current_item + ")")
-                current_item = ""
-            else:
-                current_item += char
-        else:
-            current_item += char
-    if current_item:
-        result.append(current_item)
-    return result
-
-
-def token_weights(string, current_weight):
-    a = parse_parentheses(string)
-    out = []
-    for x in a:
-        weight = current_weight
-        if len(x) >= 2 and x[-1] == ")" and x[0] == "(":
-            x = x[1:-1]
-            xx = x.rfind(":")
-            weight *= 1.1
-            if xx > 0:
-                try:
-                    weight = float(x[xx + 1 :])
-                    x = x[:xx]
-                except:
-                    pass
-            out += token_weights(x, weight)
-        else:
-            out += [(x, current_weight)]
-    return out
-
-
-def escape_important(text):
-    text = text.replace("\\)", "\0\1")
-    text = text.replace("\\(", "\0\2")
-    return text
-
-
-def unescape_important(text):
-    text = text.replace("\0\1", ")")
-    text = text.replace("\0\2", "(")
-    return text
-
-
-class SDTokenizer:
-    def __init__(
-        self,
-        max_length=77,
-        pad_with_end=True,
-        tokenizer=None,
-        has_start_token=True,
-        pad_to_max_length=True,
-        min_length=None,
-        extra_padding_token=None,
-    ):
-        self.tokenizer = tokenizer
-        self.max_length = max_length
-        self.min_length = min_length
-
-        empty = self.tokenizer("")["input_ids"]
-        if has_start_token:
-            self.tokens_start = 1
-            self.start_token = empty[0]
-            self.end_token = empty[1]
-        else:
-            self.tokens_start = 0
-            self.start_token = None
-            self.end_token = empty[0]
-        self.pad_with_end = pad_with_end
-        self.pad_to_max_length = pad_to_max_length
-        self.extra_padding_token = extra_padding_token
-
-        vocab = self.tokenizer.get_vocab()
-        self.inv_vocab = {v: k for k, v in vocab.items()}
-        self.max_word_length = 8
-
-    def tokenize_with_weights(self, text: str, return_word_ids=False):
-        """
-        Tokenize the text, with weight values - presume 1.0 for all and ignore other features here.
-        The details aren't relevant for a reference impl, and weights themselves has weak effect on SD3.
-        """
-        if self.pad_with_end:
-            pad_token = self.end_token
-        else:
-            pad_token = 0
-
-        text = escape_important(text)
-        parsed_weights = token_weights(text, 1.0)
-
-        # tokenize words
-        tokens = []
-        for weighted_segment, weight in parsed_weights:
-            to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(" ")
-            to_tokenize = [x for x in to_tokenize if x != ""]
-            for word in to_tokenize:
-                # parse word
-                tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start : -1]])
-
-        # reshape token array to CLIP input size
-        batched_tokens = []
-        batch = []
-        if self.start_token is not None:
-            batch.append((self.start_token, 1.0, 0))
-        batched_tokens.append(batch)
-        for i, t_group in enumerate(tokens):
-            # determine if we're going to try and keep the tokens in a single batch
-            is_large = len(t_group) >= self.max_word_length
-
-            while len(t_group) > 0:
-                if len(t_group) + len(batch) > self.max_length - 1:
-                    remaining_length = self.max_length - len(batch) - 1
-                    # break word in two and add end token
-                    if is_large:
-                        batch.extend([(t, w, i + 1) for t, w in t_group[:remaining_length]])
-                        batch.append((self.end_token, 1.0, 0))
-                        t_group = t_group[remaining_length:]
-                    # add end token and pad
-                    else:
-                        batch.append((self.end_token, 1.0, 0))
-                        if self.pad_to_max_length:
-                            batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
-                    # start new batch
-                    batch = []
-                    if self.start_token is not None:
-                        batch.append((self.start_token, 1.0, 0))
-                    batched_tokens.append(batch)
-                else:
-                    batch.extend([(t, w, i + 1) for t, w in t_group])
-                    t_group = []
-
-        # pad extra padding token first befor getting to the end token
-        if self.extra_padding_token is not None:
-            batch.extend([(self.extra_padding_token, 1.0, 0)] * (self.min_length - len(batch) - 1))
-        # fill last batch
-        batch.append((self.end_token, 1.0, 0))
-        if self.pad_to_max_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
-        if self.min_length is not None and len(batch) < self.min_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch)))
-
-        if not return_word_ids:
-            batched_tokens = [[(t, w) for t, w, _ in x] for x in batched_tokens]
-
-        return batched_tokens
-
-    def untokenize(self, token_weight_pair):
-        return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
-
-
-class SDXLClipGTokenizer(SDTokenizer):
-    def __init__(self, tokenizer):
-        super().__init__(pad_with_end=False, tokenizer=tokenizer)
-
-
-class SD3Tokenizer:
-    def __init__(self):
-        clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-        self.clip_l = SDTokenizer(tokenizer=clip_tokenizer)
-        self.clip_g = SDXLClipGTokenizer(clip_tokenizer)
-        self.t5xxl = T5XXLTokenizer()
-
-    def tokenize_with_weights(self, text: str):
-        out = {}
-        out["l"] = self.clip_l.tokenize_with_weights(text)
-        out["g"] = self.clip_g.tokenize_with_weights(text)
-        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text[:226])
-        return out
-
-
-class ClipTokenWeightEncoder:
-    def encode_token_weights(self, token_weight_pairs):
-        tokens = list(map(lambda a: a[0], token_weight_pairs[0]))
-        out, pooled = self([tokens])
-        if pooled is not None:
-            first_pooled = pooled[0:1].cpu()
-        else:
-            first_pooled = pooled
-        output = [out[0:1]]
-        return torch.cat(output, dim=-2).cpu(), first_pooled
-
-
-class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
-    """Uses the CLIP transformer encoder for text (from huggingface)"""
-
-    LAYERS = ["last", "pooled", "hidden"]
-
-    def __init__(
-        self,
-        device="cpu",
-        max_length=77,
-        layer="last",
-        layer_idx=None,
-        textmodel_json_config=None,
-        dtype=None,
-        model_class=CLIPTextModel,
-        special_tokens={"start": 49406, "end": 49407, "pad": 49407},
-        layer_norm_hidden_state=True,
-        return_projected_pooled=True,
-    ):
-        super().__init__()
-        assert layer in self.LAYERS
-        self.transformer = model_class(textmodel_json_config, dtype, device)
-        self.num_layers = self.transformer.num_layers
-        self.max_length = max_length
-        self.transformer = self.transformer.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-        self.layer = layer
-        self.layer_idx = None
-        self.special_tokens = special_tokens
-        self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
-        self.layer_norm_hidden_state = layer_norm_hidden_state
-        self.return_projected_pooled = return_projected_pooled
-        if layer == "hidden":
-            assert layer_idx is not None
-            assert abs(layer_idx) < self.num_layers
-            self.set_clip_options({"layer": layer_idx})
-        self.options_default = (
-            self.layer,
-            self.layer_idx,
-            self.return_projected_pooled,
-        )
-
-    def set_clip_options(self, options):
-        layer_idx = options.get("layer", self.layer_idx)
-        self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
-        if layer_idx is None or abs(layer_idx) > self.num_layers:
-            self.layer = "last"
-        else:
-            self.layer = "hidden"
-            self.layer_idx = layer_idx
-
-    def forward(self, tokens):
-        backup_embeds = self.transformer.get_input_embeddings()
-        device = backup_embeds.weight.device
-        tokens = torch.LongTensor(tokens).to(device)
-        outputs = self.transformer(
-            tokens,
-            intermediate_output=self.layer_idx,
-            final_layer_norm_intermediate=self.layer_norm_hidden_state,
-        )
-        self.transformer.set_input_embeddings(backup_embeds)
-        if self.layer == "last":
-            z = outputs[0]
-        else:
-            z = outputs[1]
-        pooled_output = None
-        if len(outputs) >= 3:
-            if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
-                pooled_output = outputs[3].float()
-            elif outputs[2] is not None:
-                pooled_output = outputs[2].float()
-        return z.float(), pooled_output
-
-
-class SDXLClipG(SDClipModel):
-    """Wraps the CLIP-G model into the SD-CLIP-Model interface"""
-
-    def __init__(self, config, device="cpu", layer="penultimate", layer_idx=None, dtype=None):
-        if layer == "penultimate":
-            layer = "hidden"
-            layer_idx = -2
-        super().__init__(
-            device=device,
-            layer=layer,
-            layer_idx=layer_idx,
-            textmodel_json_config=config,
-            dtype=dtype,
-            special_tokens={"start": 49406, "end": 49407, "pad": 0},
-            layer_norm_hidden_state=False,
-        )
-
-
-class T5XXLModel(SDClipModel):
-    """Wraps the T5-XXL model into the SD-CLIP-Model interface for convenience"""
-
-    def __init__(self, config, device="cpu", layer="last", layer_idx=None, dtype=None):
-        super().__init__(
-            device=device,
-            layer=layer,
-            layer_idx=layer_idx,
-            textmodel_json_config=config,
-            dtype=dtype,
-            special_tokens={"end": 1, "pad": 0},
-            model_class=T5,
-        )
-
-
-#################################################################################################
-### T5 implementation, for the T5-XXL text encoder portion, largely pulled from upstream impl
-#################################################################################################
-
-
-class T5XXLTokenizer(SDTokenizer):
-    """Wraps the T5 Tokenizer from HF into the SDTokenizer interface"""
-
-    def __init__(self):
-        super().__init__(
-            pad_with_end=False,
-            tokenizer=T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl"),
-            has_start_token=False,
-            pad_to_max_length=False,
-            max_length=99999999,
-            min_length=77,
-        )
-
-
-class T5LayerNorm(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(hidden_size, dtype=dtype, device=device))
-        self.variance_epsilon = eps
-
-    def forward(self, x):
-        variance = x.pow(2).mean(-1, keepdim=True)
-        x = x * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(device=x.device, dtype=x.dtype) * x
-
-
-class T5DenseGatedActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device):
-        super().__init__()
-        self.wi_0 = torch.nn.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
-        self.wi_1 = torch.nn.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
-        self.wo = torch.nn.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
-
-    def forward(self, x):
-        hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
-        hidden_linear = self.wi_1(x)
-        x = hidden_gelu * hidden_linear
-        x = self.wo(x)
-        return x
-
-
-class T5LayerFF(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device):
-        super().__init__()
-        self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device)
-        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
-
-    def forward(self, x):
-        forwarded_states = self.layer_norm(x)
-        forwarded_states = self.DenseReluDense(forwarded_states)
-        x += forwarded_states
-        return x
-
-
-class T5Attention(torch.nn.Module):
-    def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device):
-        super().__init__()
-        # Mesh TensorFlow initialization to avoid scaling before softmax
-        self.q = torch.nn.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.k = torch.nn.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.v = torch.nn.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.o = torch.nn.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
-        self.num_heads = num_heads
-        self.relative_attention_bias = None
-        if relative_attention_bias:
-            self.relative_attention_num_buckets = 32
-            self.relative_attention_max_distance = 128
-            self.relative_attention_bias = torch.nn.Embedding(
-                self.relative_attention_num_buckets, self.num_heads, device=device
-            )
-
-    @staticmethod
-    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
-        """
-        Adapted from Mesh Tensorflow:
-        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-
-        Translate relative position to a bucket number for relative attention. The relative position is defined as
-        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
-        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
-        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
-        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
-        This should allow for more graceful generalization to longer sequences than the model has been trained on
-
-        Args:
-            relative_position: an int32 Tensor
-            bidirectional: a boolean - whether the attention is bidirectional
-            num_buckets: an integer
-            max_distance: an integer
-
-        Returns:
-            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
-        """
-        relative_buckets = 0
-        if bidirectional:
-            num_buckets //= 2
-            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
-            relative_position = torch.abs(relative_position)
-        else:
-            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
-        # now relative_position is in the range [0, inf)
-        # half of the buckets are for exact increments in positions
-        max_exact = num_buckets // 2
-        is_small = relative_position < max_exact
-        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
-        relative_position_if_large = max_exact + (
-            torch.log(relative_position.float() / max_exact)
-            / math.log(max_distance / max_exact)
-            * (num_buckets - max_exact)
-        ).to(torch.long)
-        relative_position_if_large = torch.min(
-            relative_position_if_large,
-            torch.full_like(relative_position_if_large, num_buckets - 1),
-        )
-        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
-        return relative_buckets
-
-    def compute_bias(self, query_length, key_length, device):
-        """Compute binned relative position bias"""
-        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
-        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
-        relative_position = memory_position - context_position  # shape (query_length, key_length)
-        relative_position_bucket = self._relative_position_bucket(
-            relative_position,  # shape (query_length, key_length)
-            bidirectional=True,
-            num_buckets=self.relative_attention_num_buckets,
-            max_distance=self.relative_attention_max_distance,
-        )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
-        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
-        return values
-
-    def forward(self, x, past_bias=None):
-        q = self.q(x)
-        k = self.k(x)
-        v = self.v(x)
-        if self.relative_attention_bias is not None:
-            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
-        if past_bias is not None:
-            mask = past_bias
-        out = attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
-        return self.o(out), past_bias
-
-
-class T5LayerSelfAttention(torch.nn.Module):
-    def __init__(
-        self,
-        model_dim,
-        inner_dim,
-        ff_dim,
-        num_heads,
-        relative_attention_bias,
-        dtype,
-        device,
-    ):
-        super().__init__()
-        self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device)
-        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
-
-    def forward(self, x, past_bias=None):
-        output, past_bias = self.SelfAttention(self.layer_norm(x), past_bias=past_bias)
-        x += output
-        return x, past_bias
-
-
-class T5Block(torch.nn.Module):
-    def __init__(
-        self,
-        model_dim,
-        inner_dim,
-        ff_dim,
-        num_heads,
-        relative_attention_bias,
-        dtype,
-        device,
-    ):
-        super().__init__()
-        self.layer = torch.nn.ModuleList()
-        self.layer.append(
-            T5LayerSelfAttention(
-                model_dim,
-                inner_dim,
-                ff_dim,
-                num_heads,
-                relative_attention_bias,
-                dtype,
-                device,
-            )
-        )
-        self.layer.append(T5LayerFF(model_dim, ff_dim, dtype, device))
-
-    def forward(self, x, past_bias=None):
-        x, past_bias = self.layer[0](x, past_bias)
-        x = self.layer[-1](x)
-        return x, past_bias
-
-
-class T5Stack(torch.nn.Module):
-    def __init__(
-        self,
-        num_layers,
-        model_dim,
-        inner_dim,
-        ff_dim,
-        num_heads,
-        vocab_size,
-        dtype,
-        device,
-    ):
-        super().__init__()
-        self.embed_tokens = torch.nn.Embedding(vocab_size, model_dim, device=device)
-        self.block = torch.nn.ModuleList(
-            [
-                T5Block(
-                    model_dim,
-                    inner_dim,
-                    ff_dim,
-                    num_heads,
-                    relative_attention_bias=(i == 0),
-                    dtype=dtype,
-                    device=device,
-                )
-                for i in range(num_layers)
-            ]
-        )
-        self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
-
-    def forward(self, input_ids, intermediate_output=None, final_layer_norm_intermediate=True):
-        intermediate = None
-        x = self.embed_tokens(input_ids)
-        past_bias = None
-        for i, l in enumerate(self.block):
-            x, past_bias = l(x, past_bias)
-            if i == intermediate_output:
-                intermediate = x.clone()
-        x = self.final_layer_norm(x)
-        if intermediate is not None and final_layer_norm_intermediate:
-            intermediate = self.final_layer_norm(intermediate)
-        return x, intermediate
-
-
-class T5(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device):
-        super().__init__()
-        self.num_layers = config_dict["num_layers"]
-        self.encoder = T5Stack(
-            self.num_layers,
-            config_dict["d_model"],
-            config_dict["d_model"],
-            config_dict["d_ff"],
-            config_dict["num_heads"],
-            config_dict["vocab_size"],
-            dtype,
-            device,
-        )
-        self.dtype = dtype
-
-    def get_input_embeddings(self):
-        return self.encoder.embed_tokens
-
-    def set_input_embeddings(self, embeddings):
-        self.encoder.embed_tokens = embeddings
-
-    def forward(self, *args, **kwargs):
-        return self.encoder(*args, **kwargs)

invokeai/backend/sd3/sd3_impls.py

@@ -1,609 +0,0 @@
-# This file was originally copied from:
-# https://github.com/Stability-AI/sd3.5/blob/19bf11c4e1e37324c5aa5a61f010d4127848a09c/sd3_impls.py
-
-
-### Impls of the SD3 core diffusion model and VAE
-
-import math
-import re
-
-import einops
-import torch
-from PIL import Image
-from tqdm import tqdm
-
-from invokeai.backend.sd3.mmditx import MMDiTX
-
-#################################################################################################
-### MMDiT Model Wrapping
-#################################################################################################
-
-
-class ModelSamplingDiscreteFlow(torch.nn.Module):
-    """Helper for sampler scheduling (ie timestep/sigma calculations) for Discrete Flow models"""
-
-    def __init__(self, shift: float = 1.0):
-        super().__init__()
-        self.shift = shift
-        timesteps = 1000
-        ts = self.sigma(torch.arange(1, timesteps + 1, 1))
-        self.register_buffer("sigmas", ts)
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def timestep(self, sigma: torch.Tensor) -> torch.Tensor:
-        return sigma * 1000
-
-    def sigma(self, timestep: torch.Tensor):
-        timestep = timestep / 1000.0
-        if self.shift == 1.0:
-            return timestep
-        return self.shift * timestep / (1 + (self.shift - 1) * timestep)
-
-    def calculate_denoised(
-        self, sigma: torch.Tensor, model_output: torch.Tensor, model_input: torch.Tensor
-    ) -> torch.Tensor:
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input - model_output * sigma
-
-    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        return sigma * noise + (1.0 - sigma) * latent_image
-
-
-class BaseModel(torch.nn.Module):
-    """Wrapper around the core MM-DiT model"""
-
-    def __init__(
-        self,
-        shift=1.0,
-        device=None,
-        dtype=torch.float32,
-        file=None,
-        prefix="",
-        verbose=False,
-    ):
-        super().__init__()
-        # Important configuration values can be quickly determined by checking shapes in the source file
-        # Some of these will vary between models (eg 2B vs 8B primarily differ in their depth, but also other details change)
-        patch_size = file.get_tensor(f"{prefix}x_embedder.proj.weight").shape[2]
-        depth = file.get_tensor(f"{prefix}x_embedder.proj.weight").shape[0] // 64
-        num_patches = file.get_tensor(f"{prefix}pos_embed").shape[1]
-        pos_embed_max_size = round(math.sqrt(num_patches))
-        adm_in_channels = file.get_tensor(f"{prefix}y_embedder.mlp.0.weight").shape[1]
-        context_shape = file.get_tensor(f"{prefix}context_embedder.weight").shape
-        qk_norm = "rms" if f"{prefix}joint_blocks.0.context_block.attn.ln_k.weight" in file.keys() else None
-        x_block_self_attn_layers = sorted(
-            [
-                int(key.split(".x_block.attn2.ln_k.weight")[0].split(".")[-1])
-                for key in list(filter(re.compile(".*.x_block.attn2.ln_k.weight").match, file.keys()))
-            ]
-        )
-
-        context_embedder_config = {
-            "target": "torch.nn.Linear",
-            "params": {
-                "in_features": context_shape[1],
-                "out_features": context_shape[0],
-            },
-        }
-        self.diffusion_model = MMDiTX(
-            input_size=None,
-            pos_embed_scaling_factor=None,
-            pos_embed_offset=None,
-            pos_embed_max_size=pos_embed_max_size,
-            patch_size=patch_size,
-            in_channels=16,
-            depth=depth,
-            num_patches=num_patches,
-            adm_in_channels=adm_in_channels,
-            context_embedder_config=context_embedder_config,
-            qk_norm=qk_norm,
-            x_block_self_attn_layers=x_block_self_attn_layers,
-            device=device,
-            dtype=dtype,
-            verbose=verbose,
-        )
-        self.model_sampling = ModelSamplingDiscreteFlow(shift=shift)
-
-    def apply_model(
-        self, x: torch.Tensor, sigma: float, c_crossattn: torch.Tensor | None = None, y: torch.Tensor | None = None
-    ):
-        dtype = self.get_dtype()
-        timestep = self.model_sampling.timestep(sigma).float()
-        model_output = self.diffusion_model(x.to(dtype), timestep, context=c_crossattn.to(dtype), y=y.to(dtype)).float()
-        return self.model_sampling.calculate_denoised(sigma, model_output, x)
-
-    def forward(self, *args, **kwargs):
-        return self.apply_model(*args, **kwargs)
-
-    def get_dtype(self):
-        return self.diffusion_model.dtype
-
-
-class CFGDenoiser(torch.nn.Module):
-    """Helper for applying CFG Scaling to diffusion outputs"""
-
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, x, timestep, cond, uncond, cond_scale):
-        # Run cond and uncond in a batch together
-        batched = self.model.apply_model(
-            torch.cat([x, x]),
-            torch.cat([timestep, timestep]),
-            c_crossattn=torch.cat([cond["c_crossattn"], uncond["c_crossattn"]]),
-            y=torch.cat([cond["y"], uncond["y"]]),
-        )
-        # Then split and apply CFG Scaling
-        pos_out, neg_out = batched.chunk(2)
-        scaled = neg_out + (pos_out - neg_out) * cond_scale
-        return scaled
-
-
-class SD3LatentFormat:
-    """Latents are slightly shifted from center - this class must be called after VAE Decode to correct for the shift"""
-
-    def __init__(self):
-        self.scale_factor = 1.5305
-        self.shift_factor = 0.0609
-
-    def process_in(self, latent):
-        return (latent - self.shift_factor) * self.scale_factor
-
-    def process_out(self, latent):
-        return (latent / self.scale_factor) + self.shift_factor
-
-    def decode_latent_to_preview(self, x0):
-        """Quick RGB approximate preview of sd3 latents"""
-        factors = torch.tensor(
-            [
-                [-0.0645, 0.0177, 0.1052],
-                [0.0028, 0.0312, 0.0650],
-                [0.1848, 0.0762, 0.0360],
-                [0.0944, 0.0360, 0.0889],
-                [0.0897, 0.0506, -0.0364],
-                [-0.0020, 0.1203, 0.0284],
-                [0.0855, 0.0118, 0.0283],
-                [-0.0539, 0.0658, 0.1047],
-                [-0.0057, 0.0116, 0.0700],
-                [-0.0412, 0.0281, -0.0039],
-                [0.1106, 0.1171, 0.1220],
-                [-0.0248, 0.0682, -0.0481],
-                [0.0815, 0.0846, 0.1207],
-                [-0.0120, -0.0055, -0.0867],
-                [-0.0749, -0.0634, -0.0456],
-                [-0.1418, -0.1457, -0.1259],
-            ],
-            device="cpu",
-        )
-        latent_image = x0[0].permute(1, 2, 0).cpu() @ factors
-
-        latents_ubyte = (
-            ((latent_image + 1) / 2)
-            .clamp(0, 1)  # change scale from -1..1 to 0..1
-            .mul(0xFF)  # to 0..255
-            .byte()
-        ).cpu()
-
-        return Image.fromarray(latents_ubyte.numpy())
-
-
-#################################################################################################
-### Samplers
-#################################################################################################
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    return x[(...,) + (None,) * dims_to_append]
-
-
-def to_d(x, sigma, denoised):
-    """Converts a denoiser output to a Karras ODE derivative."""
-    return (x - denoised) / append_dims(sigma, x.ndim)
-
-
-@torch.no_grad()
-@torch.autocast("cuda", dtype=torch.float16)
-def sample_euler(model, x, sigmas, extra_args=None):
-    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in tqdm(range(len(sigmas) - 1)):
-        sigma_hat = sigmas[i]
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        dt = sigmas[i + 1] - sigma_hat
-        # Euler method
-        x = x + d * dt
-    return x
-
-
-@torch.no_grad()
-@torch.autocast("cuda", dtype=torch.float16)
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None):
-    """DPM-Solver++(2M)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-    old_denoised = None
-    for i in tqdm(range(len(sigmas) - 1)):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-        h = t_next - t
-        if old_denoised is None or sigmas[i + 1] == 0:
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
-        else:
-            h_last = t - t_fn(sigmas[i - 1])
-            r = h_last / h
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
-        old_denoised = denoised
-    return x
-
-
-#################################################################################################
-### VAE
-#################################################################################################
-
-
-def Normalize(in_channels, num_groups=32, dtype=torch.float32, device=None):
-    return torch.nn.GroupNorm(
-        num_groups=num_groups,
-        num_channels=in_channels,
-        eps=1e-6,
-        affine=True,
-        dtype=dtype,
-        device=device,
-    )
-
-
-class ResnetBlock(torch.nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, dtype=torch.float32, device=None):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-
-        self.norm1 = Normalize(in_channels, dtype=dtype, device=device)
-        self.conv1 = torch.nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-        self.norm2 = Normalize(out_channels, dtype=dtype, device=device)
-        self.conv2 = torch.nn.Conv2d(
-            out_channels,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-        if self.in_channels != self.out_channels:
-            self.nin_shortcut = torch.nn.Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                dtype=dtype,
-                device=device,
-            )
-        else:
-            self.nin_shortcut = None
-        self.swish = torch.nn.SiLU(inplace=True)
-
-    def forward(self, x):
-        hidden = x
-        hidden = self.norm1(hidden)
-        hidden = self.swish(hidden)
-        hidden = self.conv1(hidden)
-        hidden = self.norm2(hidden)
-        hidden = self.swish(hidden)
-        hidden = self.conv2(hidden)
-        if self.in_channels != self.out_channels:
-            x = self.nin_shortcut(x)
-        return x + hidden
-
-
-class AttnBlock(torch.nn.Module):
-    def __init__(self, in_channels, dtype=torch.float32, device=None):
-        super().__init__()
-        self.norm = Normalize(in_channels, dtype=dtype, device=device)
-        self.q = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            dtype=dtype,
-            device=device,
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            dtype=dtype,
-            device=device,
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            dtype=dtype,
-            device=device,
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x):
-        hidden = self.norm(x)
-        q = self.q(hidden)
-        k = self.k(hidden)
-        v = self.v(hidden)
-        b, c, h, w = q.shape
-        q, k, v = map(
-            lambda x: einops.rearrange(x, "b c h w -> b 1 (h w) c").contiguous(),
-            (q, k, v),
-        )
-        hidden = torch.nn.functional.scaled_dot_product_attention(q, k, v)  # scale is dim ** -0.5 per default
-        hidden = einops.rearrange(hidden, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
-        hidden = self.proj_out(hidden)
-        return x + hidden
-
-
-class Downsample(torch.nn.Module):
-    def __init__(self, in_channels, dtype=torch.float32, device=None):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=3,
-            stride=2,
-            padding=0,
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(torch.nn.Module):
-    def __init__(self, in_channels, dtype=torch.float32, device=None):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-        return x
-
-
-class VAEEncoder(torch.nn.Module):
-    def __init__(
-        self,
-        ch=128,
-        ch_mult=(1, 2, 4, 4),
-        num_res_blocks=2,
-        in_channels=3,
-        z_channels=16,
-        dtype=torch.float32,
-        device=None,
-    ):
-        super().__init__()
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(
-            in_channels,
-            ch,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-        in_ch_mult = (1,) + tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = torch.nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = torch.nn.ModuleList()
-            attn = torch.nn.ModuleList()
-            block_in = ch * in_ch_mult[i_level]
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(num_res_blocks):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        dtype=dtype,
-                        device=device,
-                    )
-                )
-                block_in = block_out
-            down = torch.nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in, dtype=dtype, device=device)
-            self.down.append(down)
-        # middle
-        self.mid = torch.nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
-        self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
-        # end
-        self.norm_out = Normalize(block_in, dtype=dtype, device=device)
-        self.conv_out = torch.nn.Conv2d(
-            block_in,
-            2 * z_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-        self.swish = torch.nn.SiLU(inplace=True)
-
-    def forward(self, x):
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1])
-                hs.append(h)
-            if i_level != self.num_resolutions - 1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        # end
-        h = self.norm_out(h)
-        h = self.swish(h)
-        h = self.conv_out(h)
-        return h
-
-
-class VAEDecoder(torch.nn.Module):
-    def __init__(
-        self,
-        ch=128,
-        out_ch=3,
-        ch_mult=(1, 2, 4, 4),
-        num_res_blocks=2,
-        resolution=256,
-        z_channels=16,
-        dtype=torch.float32,
-        device=None,
-    ):
-        super().__init__()
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(
-            z_channels,
-            block_in,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-        # middle
-        self.mid = torch.nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
-        self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
-        # upsampling
-        self.up = torch.nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = torch.nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        dtype=dtype,
-                        device=device,
-                    )
-                )
-                block_in = block_out
-            up = torch.nn.Module()
-            up.block = block
-            if i_level != 0:
-                up.upsample = Upsample(block_in, dtype=dtype, device=device)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-        # end
-        self.norm_out = Normalize(block_in, dtype=dtype, device=device)
-        self.conv_out = torch.nn.Conv2d(
-            block_in,
-            out_ch,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-        self.swish = torch.nn.SiLU(inplace=True)
-
-    def forward(self, z):
-        # z to block_in
-        hidden = self.conv_in(z)
-        # middle
-        hidden = self.mid.block_1(hidden)
-        hidden = self.mid.attn_1(hidden)
-        hidden = self.mid.block_2(hidden)
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                hidden = self.up[i_level].block[i_block](hidden)
-            if i_level != 0:
-                hidden = self.up[i_level].upsample(hidden)
-        # end
-        hidden = self.norm_out(hidden)
-        hidden = self.swish(hidden)
-        hidden = self.conv_out(hidden)
-        return hidden
-
-
-class SDVAE(torch.nn.Module):
-    def __init__(self, dtype=torch.float32, device=None):
-        super().__init__()
-        self.encoder = VAEEncoder(dtype=dtype, device=device)
-        self.decoder = VAEDecoder(dtype=dtype, device=device)
-
-    @torch.autocast("cuda", dtype=torch.float16)
-    def decode(self, latent):
-        return self.decoder(latent)
-
-    @torch.autocast("cuda", dtype=torch.float16)
-    def encode(self, image):
-        hidden = self.encoder(image)
-        mean, logvar = torch.chunk(hidden, 2, dim=1)
-        logvar = torch.clamp(logvar, -30.0, 20.0)
-        std = torch.exp(0.5 * logvar)
-        return mean + std * torch.randn_like(mean)

invokeai/backend/sd3/sd3_infer.py

@@ -1,426 +0,0 @@
-# This file was originally copied from:
-# https://github.com/Stability-AI/sd3.5/blob/19bf11c4e1e37324c5aa5a61f010d4127848a09c/sd3_infer.py
-
-# NOTE: Must have folder `models` with the following files:
-# - `clip_g.safetensors` (openclip bigG, same as SDXL)
-# - `clip_l.safetensors` (OpenAI CLIP-L, same as SDXL)
-# - `t5xxl.safetensors` (google T5-v1.1-XXL)
-# - `sd3_medium.safetensors` (or whichever main MMDiT model file)
-# Also can have
-# - `sd3_vae.safetensors` (holds the VAE separately if needed)
-
-import datetime
-import math
-import os
-
-import fire
-import numpy as np
-import sd3_impls
-import torch
-from other_impls import SD3Tokenizer, SDClipModel, SDXLClipG, T5XXLModel
-from PIL import Image
-from safetensors import safe_open
-from sd3_impls import SDVAE, BaseModel, CFGDenoiser, SD3LatentFormat
-from tqdm import tqdm
-
-#################################################################################################
-### Wrappers for model parts
-#################################################################################################
-
-
-def load_into(f, model, prefix, device, dtype=None):
-    """Just a debugging-friendly hack to apply the weights in a safetensors file to the pytorch module."""
-    for key in f.keys():
-        if key.startswith(prefix) and not key.startswith("loss."):
-            path = key[len(prefix) :].split(".")
-            obj = model
-            for p in path:
-                if obj is list:
-                    obj = obj[int(p)]
-                else:
-                    obj = getattr(obj, p, None)
-                    if obj is None:
-                        print(f"Skipping key '{key}' in safetensors file as '{p}' does not exist in python model")
-                        break
-            if obj is None:
-                continue
-            try:
-                tensor = f.get_tensor(key).to(device=device)
-                if dtype is not None:
-                    tensor = tensor.to(dtype=dtype)
-                obj.requires_grad_(False)
-                obj.set_(tensor)
-            except Exception as e:
-                print(f"Failed to load key '{key}' in safetensors file: {e}")
-                raise e
-
-
-CLIPG_CONFIG = {
-    "hidden_act": "gelu",
-    "hidden_size": 1280,
-    "intermediate_size": 5120,
-    "num_attention_heads": 20,
-    "num_hidden_layers": 32,
-}
-
-
-class ClipG:
-    def __init__(self):
-        with safe_open("models/clip_g.safetensors", framework="pt", device="cpu") as f:
-            self.model = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=torch.float32)
-            load_into(f, self.model.transformer, "", "cpu", torch.float32)
-
-
-CLIPL_CONFIG = {
-    "hidden_act": "quick_gelu",
-    "hidden_size": 768,
-    "intermediate_size": 3072,
-    "num_attention_heads": 12,
-    "num_hidden_layers": 12,
-}
-
-
-class ClipL:
-    def __init__(self):
-        with safe_open("models/clip_l.safetensors", framework="pt", device="cpu") as f:
-            self.model = SDClipModel(
-                layer="hidden",
-                layer_idx=-2,
-                device="cpu",
-                dtype=torch.float32,
-                layer_norm_hidden_state=False,
-                return_projected_pooled=False,
-                textmodel_json_config=CLIPL_CONFIG,
-            )
-            load_into(f, self.model.transformer, "", "cpu", torch.float32)
-
-
-T5_CONFIG = {
-    "d_ff": 10240,
-    "d_model": 4096,
-    "num_heads": 64,
-    "num_layers": 24,
-    "vocab_size": 32128,
-}
-
-
-class T5XXL:
-    def __init__(self):
-        with safe_open("models/t5xxl.safetensors", framework="pt", device="cpu") as f:
-            self.model = T5XXLModel(T5_CONFIG, device="cpu", dtype=torch.float32)
-            load_into(f, self.model.transformer, "", "cpu", torch.float32)
-
-
-class SD3:
-    def __init__(self, model, shift, verbose=False):
-        with safe_open(model, framework="pt", device="cpu") as f:
-            self.model = BaseModel(
-                shift=shift,
-                file=f,
-                prefix="model.diffusion_model.",
-                device="cpu",
-                dtype=torch.float16,
-                verbose=verbose,
-            ).eval()
-            load_into(f, self.model, "model.", "cpu", torch.float16)
-
-
-class VAE:
-    def __init__(self, model):
-        with safe_open(model, framework="pt", device="cpu") as f:
-            self.model = SDVAE(device="cpu", dtype=torch.float16).eval().cpu()
-            prefix = ""
-            if any(k.startswith("first_stage_model.") for k in f.keys()):
-                prefix = "first_stage_model."
-            load_into(f, self.model, prefix, "cpu", torch.float16)
-
-
-#################################################################################################
-### Main inference logic
-#################################################################################################
-
-
-# Note: Sigma shift value, publicly released models use 3.0
-SHIFT = 3.0
-# Naturally, adjust to the width/height of the model you have
-WIDTH = 1024
-HEIGHT = 1024
-# Pick your prompt
-PROMPT = "a photo of a cat"
-# Most models prefer the range of 4-5, but still work well around 7
-CFG_SCALE = 4.5
-# Different models want different step counts but most will be good at 50, albeit that's slow to run
-# sd3_medium is quite decent at 28 steps
-STEPS = 40
-# Seed
-SEED = 23
-# SEEDTYPE = "fixed"
-SEEDTYPE = "rand"
-# SEEDTYPE = "roll"
-# Actual model file path
-# MODEL = "models/sd3_medium.safetensors"
-# MODEL = "models/sd3.5_large_turbo.safetensors"
-MODEL = "models/sd3.5_large.safetensors"
-# VAE model file path, or set None to use the same model file
-VAEFile = None  # "models/sd3_vae.safetensors"
-# Optional init image file path
-INIT_IMAGE = None
-# If init_image is given, this is the percentage of denoising steps to run (1.0 = full denoise, 0.0 = no denoise at all)
-DENOISE = 0.6
-# Output file path
-OUTDIR = "outputs"
-# SAMPLER
-# SAMPLER = "euler"
-SAMPLER = "dpmpp_2m"
-
-
-class SD3Inferencer:
-    def print(self, txt):
-        if self.verbose:
-            print(txt)
-
-    def load(self, model=MODEL, vae=VAEFile, shift=SHIFT, verbose=False):
-        self.verbose = verbose
-        print("Loading tokenizers...")
-        # NOTE: if you need a reference impl for a high performance CLIP tokenizer instead of just using the HF transformers one,
-        # check https://github.com/Stability-AI/StableSwarmUI/blob/master/src/Utils/CliplikeTokenizer.cs
-        # (T5 tokenizer is different though)
-        self.tokenizer = SD3Tokenizer()
-        print("Loading OpenAI CLIP L...")
-        self.clip_l = ClipL()
-        print("Loading OpenCLIP bigG...")
-        self.clip_g = ClipG()
-        print("Loading Google T5-v1-XXL...")
-        self.t5xxl = T5XXL()
-        print(f"Loading SD3 model {os.path.basename(model)}...")
-        self.sd3 = SD3(model, shift, verbose)
-        print("Loading VAE model...")
-        self.vae = VAE(vae or model)
-        print("Models loaded.")
-
-    def get_empty_latent(self, width, height):
-        self.print("Prep an empty latent...")
-        return torch.ones(1, 16, height // 8, width // 8, device="cpu") * 0.0609
-
-    def get_sigmas(self, sampling, steps):
-        start = sampling.timestep(sampling.sigma_max)
-        end = sampling.timestep(sampling.sigma_min)
-        timesteps = torch.linspace(start, end, steps)
-        sigs = []
-        for x in range(len(timesteps)):
-            ts = timesteps[x]
-            sigs.append(sampling.sigma(ts))
-        sigs += [0.0]
-        return torch.FloatTensor(sigs)
-
-    def get_noise(self, seed, latent):
-        generator = torch.manual_seed(seed)
-        self.print(f"dtype = {latent.dtype}, layout = {latent.layout}, device = {latent.device}")
-        return torch.randn(
-            latent.size(),
-            dtype=torch.float32,
-            layout=latent.layout,
-            generator=generator,
-            device="cpu",
-        ).to(latent.dtype)
-
-    def get_cond(self, prompt):
-        self.print("Encode prompt...")
-        tokens = self.tokenizer.tokenize_with_weights(prompt)
-        l_out, l_pooled = self.clip_l.model.encode_token_weights(tokens["l"])
-        g_out, g_pooled = self.clip_g.model.encode_token_weights(tokens["g"])
-        t5_out, t5_pooled = self.t5xxl.model.encode_token_weights(tokens["t5xxl"])
-        lg_out = torch.cat([l_out, g_out], dim=-1)
-        lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
-        return torch.cat([lg_out, t5_out], dim=-2), torch.cat((l_pooled, g_pooled), dim=-1)
-
-    def max_denoise(self, sigmas):
-        max_sigma = float(self.sd3.model.model_sampling.sigma_max)
-        sigma = float(sigmas[0])
-        return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
-
-    def fix_cond(self, cond):
-        cond, pooled = (cond[0].half().cuda(), cond[1].half().cuda())
-        return {"c_crossattn": cond, "y": pooled}
-
-    def do_sampling(
-        self,
-        latent,
-        seed,
-        conditioning,
-        neg_cond,
-        steps,
-        cfg_scale,
-        sampler="dpmpp_2m",
-        denoise=1.0,
-    ) -> torch.Tensor:
-        self.print("Sampling...")
-        latent = latent.half().cuda()
-        self.sd3.model = self.sd3.model.cuda()
-        noise = self.get_noise(seed, latent).cuda()
-        sigmas = self.get_sigmas(self.sd3.model.model_sampling, steps).cuda()
-        sigmas = sigmas[int(steps * (1 - denoise)) :]
-        conditioning = self.fix_cond(conditioning)
-        neg_cond = self.fix_cond(neg_cond)
-        extra_args = {"cond": conditioning, "uncond": neg_cond, "cond_scale": cfg_scale}
-        noise_scaled = self.sd3.model.model_sampling.noise_scaling(sigmas[0], noise, latent, self.max_denoise(sigmas))
-        sample_fn = getattr(sd3_impls, f"sample_{sampler}")
-        latent = sample_fn(CFGDenoiser(self.sd3.model), noise_scaled, sigmas, extra_args=extra_args)
-        latent = SD3LatentFormat().process_out(latent)
-        self.sd3.model = self.sd3.model.cpu()
-        self.print("Sampling done")
-        return latent
-
-    def vae_encode(self, image) -> torch.Tensor:
-        self.print("Encoding image to latent...")
-        image = image.convert("RGB")
-        image_np = np.array(image).astype(np.float32) / 255.0
-        image_np = np.moveaxis(image_np, 2, 0)
-        batch_images = np.expand_dims(image_np, axis=0).repeat(1, axis=0)
-        image_torch = torch.from_numpy(batch_images)
-        image_torch = 2.0 * image_torch - 1.0
-        image_torch = image_torch.cuda()
-        self.vae.model = self.vae.model.cuda()
-        latent = self.vae.model.encode(image_torch).cpu()
-        self.vae.model = self.vae.model.cpu()
-        self.print("Encoded")
-        return latent
-
-    def vae_decode(self, latent) -> Image.Image:
-        self.print("Decoding latent to image...")
-        latent = latent.cuda()
-        self.vae.model = self.vae.model.cuda()
-        image = self.vae.model.decode(latent)
-        image = image.float()
-        self.vae.model = self.vae.model.cpu()
-        image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)[0]
-        decoded_np = 255.0 * np.moveaxis(image.cpu().numpy(), 0, 2)
-        decoded_np = decoded_np.astype(np.uint8)
-        out_image = Image.fromarray(decoded_np)
-        self.print("Decoded")
-        return out_image
-
-    def gen_image(
-        self,
-        prompts=[PROMPT],
-        width=WIDTH,
-        height=HEIGHT,
-        steps=STEPS,
-        cfg_scale=CFG_SCALE,
-        sampler=SAMPLER,
-        seed=SEED,
-        seed_type=SEEDTYPE,
-        out_dir=OUTDIR,
-        init_image=INIT_IMAGE,
-        denoise=DENOISE,
-    ):
-        latent = self.get_empty_latent(width, height)
-        if init_image:
-            image_data = Image.open(init_image)
-            image_data = image_data.resize((width, height), Image.LANCZOS)
-            latent = self.vae_encode(image_data)
-            latent = SD3LatentFormat().process_in(latent)
-        neg_cond = self.get_cond("")
-        seed_num = None
-        pbar = tqdm(enumerate(prompts), total=len(prompts), position=0, leave=True)
-        for i, prompt in pbar:
-            if seed_type == "roll":
-                seed_num = seed if seed_num is None else seed_num + 1
-            elif seed_type == "rand":
-                seed_num = torch.randint(0, 100000, (1,)).item()
-            else:  # fixed
-                seed_num = seed
-            conditioning = self.get_cond(prompt)
-            sampled_latent = self.do_sampling(
-                latent,
-                seed_num,
-                conditioning,
-                neg_cond,
-                steps,
-                cfg_scale,
-                sampler,
-                denoise if init_image else 1.0,
-            )
-            image = self.vae_decode(sampled_latent)
-            save_path = os.path.join(out_dir, f"{i:06d}.png")
-            self.print(f"Will save to {save_path}")
-            image.save(save_path)
-            self.print("Done")
-
-
-CONFIGS = {
-    "sd3_medium": {
-        "shift": 1.0,
-        "cfg": 5.0,
-        "steps": 50,
-        "sampler": "dpmpp_2m",
-    },
-    "sd3.5_large": {
-        "shift": 3.0,
-        "cfg": 4.5,
-        "steps": 40,
-        "sampler": "dpmpp_2m",
-    },
-    "sd3.5_large_turbo": {"shift": 3.0, "cfg": 1.0, "steps": 4, "sampler": "euler"},
-}
-
-
-@torch.no_grad()
-def main(
-    prompt=PROMPT,
-    model=MODEL,
-    out_dir=OUTDIR,
-    postfix=None,
-    seed=SEED,
-    seed_type=SEEDTYPE,
-    sampler=None,
-    steps=None,
-    cfg=None,
-    shift=None,
-    width=WIDTH,
-    height=HEIGHT,
-    vae=VAEFile,
-    init_image=INIT_IMAGE,
-    denoise=DENOISE,
-    verbose=False,
-):
-    steps = steps or CONFIGS[os.path.splitext(os.path.basename(model))[0]]["steps"]
-    cfg = cfg or CONFIGS[os.path.splitext(os.path.basename(model))[0]]["cfg"]
-    shift = shift or CONFIGS[os.path.splitext(os.path.basename(model))[0]]["shift"]
-    sampler = sampler or CONFIGS[os.path.splitext(os.path.basename(model))[0]]["sampler"]
-
-    inferencer = SD3Inferencer()
-    inferencer.load(model, vae, shift, verbose)
-
-    if isinstance(prompt, str):
-        if os.path.splitext(prompt)[-1] == ".txt":
-            with open(prompt, "r") as f:
-                prompts = [l.strip() for l in f.readlines()]
-        else:
-            prompts = [prompt]
-
-    out_dir = os.path.join(
-        out_dir,
-        os.path.splitext(os.path.basename(model))[0],
-        os.path.splitext(os.path.basename(prompt))[0][:50]
-        + (postfix or datetime.datetime.now().strftime("_%Y-%m-%dT%H-%M-%S")),
-    )
-    print(f"Saving images to {out_dir}")
-    os.makedirs(out_dir, exist_ok=False)
-
-    inferencer.gen_image(
-        prompts,
-        width,
-        height,
-        steps,
-        cfg,
-        sampler,
-        seed,
-        seed_type,
-        out_dir,
-        init_image,
-        denoise,
-    )
-
-
-fire.Fire(main)

invokeai/backend/sd3/sd3_mmditx.py

@@ -1,72 +0,0 @@
-from dataclasses import dataclass
-from typing import Literal, TypedDict
-
-import torch
-
-from invokeai.backend.sd3.mmditx import MMDiTX
-from invokeai.backend.sd3.sd3_impls import ModelSamplingDiscreteFlow
-
-
-class ContextEmbedderConfig(TypedDict):
-    target: Literal["torch.nn.Linear"]
-    params: dict[str, int]
-
-
-@dataclass
-class Sd3MMDiTXParams:
-    patch_size: int
-    depth: int
-    num_patches: int
-    pos_embed_max_size: int
-    adm_in_channels: int
-    context_shape: tuple[int, int]
-    qk_norm: Literal["rms", None]
-    x_block_self_attn_layers: list[int]
-    context_embedder_config: ContextEmbedderConfig
-
-
-class Sd3MMDiTX(torch.nn.Module):
-    """This class is based closely on
-    https://github.com/Stability-AI/sd3.5/blob/19bf11c4e1e37324c5aa5a61f010d4127848a09c/sd3_impls.py#L53
-    but has more standard model loading semantics.
-    """
-
-    def __init__(
-        self,
-        params: Sd3MMDiTXParams,
-        shift: float = 1.0,
-        device: torch.device | None = None,
-        dtype: torch.dtype | None = None,
-        verbose: bool = False,
-    ):
-        super().__init__()
-        self.diffusion_model = MMDiTX(
-            input_size=None,
-            pos_embed_scaling_factor=None,
-            pos_embed_offset=None,
-            pos_embed_max_size=params.pos_embed_max_size,
-            patch_size=params.patch_size,
-            in_channels=16,
-            depth=params.depth,
-            num_patches=params.num_patches,
-            adm_in_channels=params.adm_in_channels,
-            context_embedder_config=params.context_embedder_config,
-            qk_norm=params.qk_norm,
-            x_block_self_attn_layers=params.x_block_self_attn_layers,
-            device=device,
-            dtype=dtype,
-            verbose=verbose,
-        )
-        self.model_sampling = ModelSamplingDiscreteFlow(shift=shift)
-
-    def apply_model(self, x: torch.Tensor, sigma: torch.Tensor, c_crossattn: torch.Tensor, y: torch.Tensor):
-        dtype = self.get_dtype()
-        timestep = self.model_sampling.timestep(sigma).float()
-        model_output = self.diffusion_model(x.to(dtype), timestep, context=c_crossattn.to(dtype), y=y.to(dtype)).float()
-        return self.model_sampling.calculate_denoised(sigma, model_output, x)
-
-    def forward(self, x: torch.Tensor, sigma: float, c_crossattn: torch.Tensor, y: torch.Tensor):
-        return self.apply_model(x=x, sigma=sigma, c_crossattn=c_crossattn, y=y)
-
-    def get_dtype(self):
-        return self.diffusion_model.dtype

invokeai/backend/sd3/sd3_state_dict_utils.py

@@ -1,70 +0,0 @@
-import math
-import re
-from typing import Any, Dict
-
-from invokeai.backend.sd3.sd3_mmditx import ContextEmbedderConfig, Sd3MMDiTXParams
-
-
-def is_sd3_checkpoint(sd: Dict[str, Any]) -> bool:
-    """Is the state dict for an SD3 checkpoint like this one?:
-    https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/sd3.5_large.safetensors
-
-    Note that this checkpoint format contains both the VAE and the MMDiTX model.
-
-    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
-    """
-    # If all of the expected keys are present, then this is very likely a SD3 checkpoint.
-    expected_keys = {
-        # VAE decoder and encoder keys.
-        "first_stage_model.decoder.conv_in.bias",
-        "first_stage_model.decoder.conv_in.weight",
-        "first_stage_model.encoder.conv_in.bias",
-        "first_stage_model.encoder.conv_in.weight",
-        # MMDiTX keys.
-        "model.diffusion_model.final_layer.linear.bias",
-        "model.diffusion_model.final_layer.linear.weight",
-        "model.diffusion_model.joint_blocks.0.context_block.attn.ln_k.weight",
-        "model.diffusion_model.joint_blocks.0.context_block.attn.ln_q.weight",
-    }
-
-    return expected_keys.issubset(sd.keys())
-
-
-def infer_sd3_mmditx_params(sd: Dict[str, Any], prefix: str = "model.diffusion_model.") -> Sd3MMDiTXParams:
-    """Infer the MMDiTX model parameters from the state dict.
-
-    This logic is based on:
-    https://github.com/Stability-AI/sd3.5/blob/19bf11c4e1e37324c5aa5a61f010d4127848a09c/sd3_impls.py#L68-L88
-    """
-    patch_size = sd[f"{prefix}x_embedder.proj.weight"].shape[2]
-    depth = sd[f"{prefix}x_embedder.proj.weight"].shape[0] // 64
-    num_patches = sd[f"{prefix}pos_embed"].shape[1]
-    pos_embed_max_size = round(math.sqrt(num_patches))
-    adm_in_channels = sd[f"{prefix}y_embedder.mlp.0.weight"].shape[1]
-    context_shape = sd[f"{prefix}context_embedder.weight"].shape
-    qk_norm = "rms" if f"{prefix}joint_blocks.0.context_block.attn.ln_k.weight" in sd else None
-    x_block_self_attn_layers = sorted(
-        [
-            int(key.split(".x_block.attn2.ln_k.weight")[0].split(".")[-1])
-            for key in list(filter(re.compile(".*.x_block.attn2.ln_k.weight").match, sd.keys()))
-        ]
-    )
-
-    context_embedder_config: ContextEmbedderConfig = {
-        "target": "torch.nn.Linear",
-        "params": {
-            "in_features": context_shape[1],
-            "out_features": context_shape[0],
-        },
-    }
-    return Sd3MMDiTXParams(
-        patch_size=patch_size,
-        depth=depth,
-        num_patches=num_patches,
-        pos_embed_max_size=pos_embed_max_size,
-        adm_in_channels=adm_in_channels,
-        context_shape=context_shape,
-        qk_norm=qk_norm,
-        x_block_self_attn_layers=x_block_self_attn_layers,
-        context_embedder_config=context_embedder_config,
-    )

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -171,19 +171,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-
-        # On 30xx and 40xx series GPUs, `torch-sdp` is faster than `xformers`. This corresponds to a CUDA major
-        # version of 8 or higher. So, for major version 7 or below, we prefer `xformers`.
-        # See:
-        # - https://developer.nvidia.com/cuda-gpus
-        # - https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
-        try:
-            prefer_xformers = torch.cuda.is_available() and torch.cuda.get_device_properties("cuda").major <= 7  # type: ignore # Type of "get_device_properties" is partially unknown
-        except Exception:
-            prefer_xformers = False
-
         config = get_config()
-        if config.attention_type == "xformers" and is_xformers_available() and prefer_xformers:
+        if config.attention_type == "xformers":
             self.enable_xformers_memory_efficient_attention()
             return
         elif config.attention_type == "sliced":
@@ -198,24 +187,20 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             self.disable_attention_slicing()
             return
         elif config.attention_type == "torch-sdp":
-            # torch-sdp is the default in diffusers.
-            return
+            if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
+                # diffusers enables sdp automatically
+                return
+            else:
+                raise Exception("torch-sdp attention slicing not available")
 
-        # See https://github.com/invoke-ai/InvokeAI/issues/7049 for context.
-        # Bumping torch from 2.2.2 to 2.4.1 caused the sliced attention implementation to produce incorrect results.
-        # For now, if a user is on an MPS device and has not explicitly set the attention_type, then we select the
-        # non-sliced torch-sdp implementation. This keeps things working on MPS at the cost of increased peak memory
-        # utilization.
-        if torch.backends.mps.is_available():
-            return
-
-        # The remainder if this code is called when attention_type=='auto'.
+        # the remainder if this code is called when attention_type=='auto'
         if self.unet.device.type == "cuda":
-            if is_xformers_available() and prefer_xformers:
+            if is_xformers_available():
                 self.enable_xformers_memory_efficient_attention()
                 return
-            # torch-sdp is the default in diffusers.
-            return
+            elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
+                # diffusers enables sdp automatically
+                return
 
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free

invokeai/frontend/web/package.json

@@ -52,51 +52,49 @@
     }
   },
   "dependencies": {
-    "@dagrejs/dagre": "^1.1.4",
-    "@dagrejs/graphlib": "^2.2.4",
+    "@dagrejs/dagre": "^1.1.3",
+    "@dagrejs/graphlib": "^2.2.3",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
-    "@fontsource-variable/inter": "^5.1.0",
-    "@invoke-ai/ui-library": "^0.0.42",
+    "@fontsource-variable/inter": "^5.0.20",
+    "@invoke-ai/ui-library": "^0.0.37",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
     "async-mutex": "^0.5.0",
-    "chakra-react-select": "^4.9.2",
+    "chakra-react-select": "^4.9.1",
     "cmdk": "^1.0.0",
     "compare-versions": "^6.1.1",
     "dateformat": "^5.0.3",
     "fracturedjsonjs": "^4.0.2",
-    "framer-motion": "^11.10.0",
-    "i18next": "^23.15.1",
-    "i18next-http-backend": "^2.6.1",
+    "framer-motion": "^11.3.24",
+    "i18next": "^23.12.2",
+    "i18next-http-backend": "^2.5.2",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
-    "konva": "^9.3.15",
+    "konva": "^9.3.14",
     "lodash-es": "^4.17.21",
-    "lru-cache": "^11.0.1",
+    "lru-cache": "^11.0.0",
     "nanoid": "^5.0.7",
-    "nanostores": "^0.11.3",
+    "nanostores": "^0.11.2",
     "new-github-issue-url": "^1.0.0",
     "overlayscrollbars": "^2.10.0",
     "overlayscrollbars-react": "^0.5.6",
-    "perfect-freehand": "^1.2.2",
     "query-string": "^9.1.0",
-    "raf-throttle": "^2.0.6",
     "react": "^18.3.1",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.3.1",
-    "react-dropzone": "^14.2.9",
+    "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.53.0",
+    "react-hook-form": "^7.52.2",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^15.0.2",
-    "react-icons": "^5.3.0",
+    "react-i18next": "^14.1.3",
+    "react-icons": "^5.2.1",
     "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.1.4",
+    "react-resizable-panels": "^2.1.2",
     "react-use": "^17.5.1",
-    "react-virtuoso": "^4.10.4",
+    "react-virtuoso": "^4.9.0",
     "reactflow": "^11.11.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
@@ -104,55 +102,56 @@
     "rfdc": "^1.4.1",
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
-    "socket.io-client": "^4.8.0",
+    "socket.io-client": "^4.7.5",
     "stable-hash": "^0.0.4",
-    "use-debounce": "^10.0.3",
+    "use-debounce": "^10.0.2",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^10.0.0",
     "zod": "^3.23.8",
-    "zod-validation-error": "^3.4.0"
+    "zod-validation-error": "^3.3.1"
   },
   "peerDependencies": {
     "react": "^18.2.0",
-    "react-dom": "^18.2.0"
+    "react-dom": "^18.2.0",
+    "ts-toolbelt": "^9.6.0"
   },
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.3.4",
-    "@storybook/addon-interactions": "^8.3.4",
-    "@storybook/addon-links": "^8.3.4",
-    "@storybook/addon-storysource": "^8.3.4",
-    "@storybook/manager-api": "^8.3.4",
-    "@storybook/react": "^8.3.4",
-    "@storybook/react-vite": "^8.3.4",
-    "@storybook/theming": "^8.3.4",
+    "@storybook/addon-essentials": "^8.2.8",
+    "@storybook/addon-interactions": "^8.2.8",
+    "@storybook/addon-links": "^8.2.8",
+    "@storybook/addon-storysource": "^8.2.8",
+    "@storybook/manager-api": "^8.2.8",
+    "@storybook/react": "^8.2.8",
+    "@storybook/react-vite": "^8.2.8",
+    "@storybook/theming": "^8.2.8",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.16.10",
-    "@types/react": "^18.3.11",
+    "@types/node": "^20.14.15",
+    "@types/react": "^18.3.3",
     "@types/react-dom": "^18.3.0",
     "@types/uuid": "^10.0.0",
-    "@vitejs/plugin-react-swc": "^3.7.1",
-    "@vitest/coverage-v8": "^1.6.0",
-    "@vitest/ui": "^1.6.0",
+    "@vitejs/plugin-react-swc": "^3.7.0",
+    "@vitest/coverage-v8": "^1.5.0",
+    "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
     "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
-    "eslint": "^8.57.1",
-    "eslint-plugin-i18next": "^6.1.0",
+    "eslint": "^8.57.0",
+    "eslint-plugin-i18next": "^6.0.9",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.31.0",
+    "knip": "^5.27.2",
     "openapi-types": "^12.1.3",
-    "openapi-typescript": "^7.4.1",
+    "openapi-typescript": "^7.3.0",
     "prettier": "^3.3.3",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.3.4",
-    "tsafe": "^1.7.5",
-    "type-fest": "^4.26.1",
-    "typescript": "^5.6.2",
-    "vite": "^5.4.8",
-    "vite-plugin-css-injected-by-js": "^3.5.2",
+    "storybook": "^8.2.8",
+    "ts-toolbelt": "^9.6.0",
+    "tsafe": "^1.7.2",
+    "typescript": "^5.5.4",
+    "vite": "^5.4.0",
+    "vite-plugin-css-injected-by-js": "^3.5.1",
     "vite-plugin-dts": "^3.9.1",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.3.2",

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

@@ -4,7 +4,7 @@
         "reportBugLabel": "Fehler melden",
         "settingsLabel": "Einstellungen",
         "img2img": "Bild zu Bild",
-        "nodes": "Arbeitsabläufe",
+        "nodes": "Workflows",
         "upload": "Hochladen",
         "load": "Laden",
         "statusDisconnected": "Getrennt",
@@ -93,9 +93,7 @@
         "placeholderSelectAModel": "Modell auswählen",
         "reset": "Zurücksetzen",
         "none": "Keine",
-        "new": "Neu",
-        "ok": "OK",
-        "close": "Schließen"
+        "new": "Neu"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -158,11 +156,7 @@
         "displayBoardSearch": "Board durchsuchen",
         "displaySearch": "Bild suchen",
         "go": "Los",
-        "jump": "Springen",
-        "assetsTab": "Dateien, die Sie zur Verwendung in Ihren Projekten hochgeladen haben.",
-        "imagesTab": "Bilder, die Sie in Invoke erstellt und gespeichert haben.",
-        "boardsSettings": "Ordnereinstellungen",
-        "imagesSettings": "Galeriebildereinstellungen"
+        "jump": "Springen"
     },
     "hotkeys": {
         "noHotkeysFound": "Kein Hotkey gefunden",
@@ -269,22 +263,6 @@
             "quickSwitch": {
                 "title": "Ebenen schnell umschalten",
                 "desc": "Wechseln Sie zwischen den beiden zuletzt gewählten Ebenen. Wenn eine Ebene mit einem Lesezeichen versehen ist, wird zwischen ihr und der letzten nicht markierten Ebene gewechselt."
-            },
-            "applyFilter": {
-                "title": "Filter anwenden",
-                "desc": "Wende den ausstehenden Filter auf die ausgewählte Ebene an."
-            },
-            "cancelFilter": {
-                "title": "Filter abbrechen",
-                "desc": "Den ausstehenden Filter abbrechen."
-            },
-            "applyTransform": {
-                "desc": "Die ausstehende Transformation auf die ausgewählte Ebene anwenden.",
-                "title": "Transformation anwenden"
-            },
-            "cancelTransform": {
-                "title": "Transformation abbrechen",
-                "desc": "Die ausstehende Transformation abbrechen."
             }
         },
         "viewer": {
@@ -581,18 +559,7 @@
         "scanResults": "Ergebnisse des Scans",
         "urlOrLocalPathHelper": "URLs sollten auf eine einzelne Datei deuten. Lokale Pfade können zusätzlich auch auf einen Ordner für ein einzelnes Diffusers-Modell hinweisen.",
         "inplaceInstallDesc": "Installieren Sie Modelle, ohne die Dateien zu kopieren. Wenn Sie das Modell verwenden, wird es direkt von seinem Speicherort geladen. Wenn deaktiviert, werden die Dateien während der Installation in das von Invoke verwaltete Modellverzeichnis kopiert.",
-        "scanFolderHelper": "Der Ordner wird rekursiv nach Modellen durchsucht. Dies kann bei sehr großen Ordnern etwas dauern.",
-        "includesNModels": "Enthält {{n}} Modelle und deren Abhängigkeiten",
-        "starterBundles": "Starterpakete",
-        "installingXModels_one": "{{count}} Modell wird installiert",
-        "installingXModels_other": "{{count}} Modelle werden installiert",
-        "skippingXDuplicates_one": ", überspringe {{count}} Duplikat",
-        "skippingXDuplicates_other": ", überspringe {{count}} Duplikate",
-        "installingModel": "Modell wird installiert",
-        "loraTriggerPhrases": "LoRA-Auslösephrasen",
-        "installingBundle": "Bündel wird installiert",
-        "triggerPhrases": "Auslösephrasen",
-        "mainModelTriggerPhrases": "Hauptmodell-Auslösephrasen"
+        "scanFolderHelper": "Der Ordner wird rekursiv nach Modellen durchsucht. Dies kann bei sehr großen Ordnern etwas dauern."
     },
     "parameters": {
         "images": "Bilder",
@@ -680,24 +647,22 @@
         "imageCopied": "Bild kopiert",
         "parametersNotSet": "Parameter nicht festgelegt",
         "addedToBoard": "Dem Board hinzugefügt",
-        "loadedWithWarnings": "Workflow mit Warnungen geladen",
-        "imageSaved": "Bild gespeichert"
+        "loadedWithWarnings": "Workflow mit Warnungen geladen"
     },
     "accessibility": {
         "uploadImage": "Bild hochladen",
         "previousImage": "Vorheriges Bild",
+        "showOptionsPanel": "Seitenpanel anzeigen",
         "reset": "Zurücksetzten",
         "nextImage": "Nächstes Bild",
+        "showGalleryPanel": "Galerie-Panel anzeigen",
         "menu": "Menü",
         "invokeProgressBar": "Invoke Fortschrittsanzeige",
         "mode": "Modus",
         "resetUI": "$t(accessibility.reset) von UI",
         "createIssue": "Ticket erstellen",
         "about": "Über",
-        "submitSupportTicket": "Support-Ticket senden",
-        "toggleRightPanel": "Rechtes Bedienfeld umschalten (G)",
-        "toggleLeftPanel": "Linkes Bedienfeld umschalten (T)",
-        "uploadImages": "Bild(er) hochladen"
+        "submitSupportTicket": "Support-Ticket senden"
     },
     "boards": {
         "autoAddBoard": "Board automatisch erstellen",
@@ -732,13 +697,12 @@
         "shared": "Geteilte Ordner",
         "archiveBoard": "Ordner archivieren",
         "archived": "Archiviert",
-        "noBoards": "Kein {{boardType}} Ordner",
+        "noBoards": "Kein {boardType}} Ordner",
         "hideBoards": "Ordner verstecken",
         "viewBoards": "Ordner ansehen",
         "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt.",
         "assetsWithCount_one": "{{count}} in der Sammlung",
-        "assetsWithCount_other": "{{count}} in der Sammlung",
-        "deletedBoardsCannotbeRestored": "Gelöschte Ordner können nicht wiederhergestellt werden. Die Auswahl von \"Nur Ordner löschen\" verschiebt Bilder in einen unkategorisierten Zustand."
+        "assetsWithCount_other": "{{count}} in der Sammlung"
     },
     "queue": {
         "status": "Status",
@@ -841,8 +805,7 @@
         "parameterSet": "Parameter {{parameter}} setzen",
         "recallParameter": "{{label}} Abrufen",
         "parsingFailed": "Parsing Fehlgeschlagen",
-        "canvasV2Metadata": "Leinwand",
-        "guidance": "Führung"
+        "canvasV2Metadata": "Leinwand"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -967,8 +930,7 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "Verwendeter Planer währende des Generierungsprozesses.",
-                "Jeder Planer definiert, wie einem Bild iterativ Rauschen hinzugefügt wird, oder wie ein Sample basierend auf der Ausgabe eines Modells aktualisiert wird."
+                "\"Planer\" definiert, wie iterativ Rauschen zu einem Bild hinzugefügt wird, oder wie ein Sample bei der Ausgabe eines Modells aktualisiert wird."
             ],
             "heading": "Planer"
         },
@@ -994,61 +956,6 @@
         },
         "ipAdapterMethod": {
             "heading": "Methode"
-        },
-        "refinerScheduler": {
-            "heading": "Planer",
-            "paragraphs": [
-                "Planer, der während der Veredelungsphase des Generierungsprozesses verwendet wird.",
-                "Ähnlich wie der Generierungsplaner."
-            ]
-        },
-        "compositingCoherenceMode": {
-            "paragraphs": [
-                "Verwendete Methode zur Erstellung eines kohärenten Bildes mit dem neu generierten maskierten Bereich."
-            ],
-            "heading": "Modus"
-        },
-        "compositingCoherencePass": {
-            "heading": "Kohärenzdurchlauf"
-        },
-        "controlNet": {
-            "heading": "ControlNet"
-        },
-        "compositingMaskAdjustments": {
-            "paragraphs": [
-                "Die Maske anpassen."
-            ],
-            "heading": "Maskenanpassungen"
-        },
-        "compositingMaskBlur": {
-            "paragraphs": [
-                "Der Unschärferadius der Maske."
-            ],
-            "heading": "Maskenunschärfe"
-        },
-        "compositingBlurMethod": {
-            "paragraphs": [
-                "Die auf den maskierten Bereich angewendete Unschärfemethode."
-            ],
-            "heading": "Unschärfemethode"
-        },
-        "controlNetResizeMode": {
-            "heading": "Größenänderungsmodus"
-        },
-        "paramWidth": {
-            "heading": "Breite",
-            "paragraphs": [
-                "Breite des generierten Bildes. Muss ein Vielfaches von 8 sein."
-            ]
-        },
-        "controlNetControlMode": {
-            "heading": "Kontrollmodus"
-        },
-        "controlNetProcessor": {
-            "heading": "Prozessor"
-        },
-        "patchmatchDownScaleSize": {
-            "heading": "Herunterskalieren"
         }
     },
     "invocationCache": {
@@ -1152,25 +1059,7 @@
         "missingFieldTemplate": "Fehlende Feldvorlage",
         "missingNode": "Fehlender Aufrufknoten",
         "missingInvocationTemplate": "Fehlende Aufrufvorlage",
-        "edit": "Bearbeiten",
-        "workflowAuthor": "Autor",
-        "graph": "Graph",
-        "workflowDescription": "Kurze Beschreibung",
-        "versionUnknown": " Version unbekannt",
-        "workflow": "Arbeitsablauf",
-        "noGraph": "Kein Graph",
-        "version": "Version",
-        "zoomInNodes": "Hineinzoomen",
-        "zoomOutNodes": "Herauszoomen",
-        "workflowName": "Name",
-        "unknownNode": "Unbekannter Knoten",
-        "workflowContact": "Kontaktdaten",
-        "workflowNotes": "Notizen",
-        "workflowTags": "Tags",
-        "workflowVersion": "Version",
-        "saveToGallery": "In Galerie speichern",
-        "noWorkflows": "Keine Arbeitsabläufe",
-        "noMatchingWorkflows": "Keine passenden Arbeitsabläufe"
+        "edit": "Bearbeiten"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",
@@ -1238,17 +1127,7 @@
         "openWorkflow": "Arbeitsablauf öffnen",
         "saveWorkflowToProject": "Arbeitsablauf in Projekt speichern",
         "workflowCleared": "Arbeitsablauf gelöscht",
-        "loading": "Lade Arbeitsabläufe",
-        "name": "Name",
-        "ascending": "Aufsteigend",
-        "defaultWorkflows": "Standard Arbeitsabläufe",
-        "userWorkflows": "Benutzer Arbeitsabläufe",
-        "projectWorkflows": "Projekt Arbeitsabläufe",
-        "opened": "Geöffnet",
-        "loadWorkflow": "Arbeitsablauf $t(common.load)",
-        "updated": "Aktualisiert",
-        "created": "Erstellt",
-        "descending": "Absteigend"
+        "loading": "Lade Arbeitsabläufe"
     },
     "sdxl": {
         "concatPromptStyle": "Verknüpfen von Prompt & Stil",
@@ -1340,16 +1219,7 @@
         "searchByName": "Nach Name suchen",
         "promptTemplateCleared": "Promptvorlage gelöscht",
         "preview": "Vorschau",
-        "positivePrompt": "Positiv-Prompt",
-        "active": "Aktiv",
-        "deleteTemplate2": "Sind Sie sicher, dass Sie diese Vorlage löschen möchten? Dies kann nicht rückgängig gemacht werden.",
-        "deleteTemplate": "Vorlage löschen",
-        "copyTemplate": "Vorlage kopieren",
-        "editTemplate": "Vorlage bearbeiten",
-        "deleteImage": "Bild löschen",
-        "defaultTemplates": "Standardvorlagen",
-        "nameColumn": "'name'",
-        "exportDownloaded": "Export heruntergeladen"
+        "positivePrompt": "Positiv-Prompt"
     },
     "newUserExperience": {
         "gettingStartedSeries": "Wünschen Sie weitere Anleitungen? In unserer <LinkComponent>Einführungsserie</LinkComponent> finden Sie Tipps, wie Sie das Potenzial von Invoke Studio voll ausschöpfen können.",
@@ -1362,22 +1232,13 @@
             "bbox": "Bbox"
         },
         "transform": {
-            "fitToBbox": "An Bbox anpassen",
-            "reset": "Zurücksetzen",
-            "apply": "Anwenden",
-            "cancel": "Abbrechen"
+            "fitToBbox": "An Bbox anpassen"
         },
         "pullBboxIntoLayerError": "Problem, Bbox in die Ebene zu ziehen",
         "pullBboxIntoLayer": "Bbox in Ebene ziehen",
         "HUD": {
             "bbox": "Bbox",
-            "scaledBbox": "Skalierte Bbox",
-            "entityStatus": {
-                "isHidden": "{{title}} ist ausgeblendet",
-                "isDisabled": "{{title}} ist deaktiviert",
-                "isLocked": "{{title}} ist gesperrt",
-                "isEmpty": "{{title}} ist leer"
-            }
+            "scaledBbox": "Skalierte Bbox"
         },
         "fitBboxToLayers": "Bbox an Ebenen anpassen",
         "pullBboxIntoReferenceImage": "Bbox ins Referenzbild ziehen",
@@ -1387,108 +1248,13 @@
         "clipToBbox": "Pinselstriche auf Bbox beschränken",
         "canvasContextMenu": {
             "saveBboxToGallery": "Bbox in Galerie speichern",
-            "bboxGroup": "Aus Bbox erstellen",
-            "canvasGroup": "Leinwand",
-            "newGlobalReferenceImage": "Neues globales Referenzbild",
-            "newRegionalReferenceImage": "Neues regionales Referenzbild",
-            "newControlLayer": "Neue Kontroll-Ebene",
-            "newRasterLayer": "Neue Raster-Ebene"
-        },
-        "rectangle": "Rechteck",
-        "saveCanvasToGallery": "Leinwand in Galerie speichern",
-        "newRasterLayerError": "Problem beim Erstellen einer Raster-Ebene",
-        "saveLayerToAssets": "Ebene in Galerie speichern",
-        "deleteReferenceImage": "Referenzbild löschen",
-        "referenceImage": "Referenzbild",
-        "opacity": "Opazität",
-        "resetCanvas": "Leinwand zurücksetzen",
-        "removeBookmark": "Lesezeichen entfernen",
-        "rasterLayer": "Raster-Ebene",
-        "rasterLayers_withCount_visible": "Raster-Ebenen ({{count}})",
-        "controlLayers_withCount_visible": "Kontroll-Ebenen ({{count}})",
-        "deleteSelected": "Ausgewählte löschen",
-        "newRegionalReferenceImageError": "Problem beim Erstellen eines regionalen Referenzbilds",
-        "newControlLayerOk": "Kontroll-Ebene erstellt",
-        "newControlLayerError": "Problem beim Erstellen einer Kontroll-Ebene",
-        "newRasterLayerOk": "Raster-Layer erstellt",
-        "moveToFront": "Nach vorne bringen",
-        "copyToClipboard": "In die Zwischenablage kopieren",
-        "controlLayers_withCount_hidden": "Kontroll-Ebenen ({{count}} ausgeblendet)",
-        "clearCaches": "Cache leeren",
-        "controlLayer": "Kontroll-Ebene",
-        "rasterLayers_withCount_hidden": "Raster-Ebenen ({{count}} ausgeblendet)",
-        "transparency": "Transparenz",
-        "canvas": "Leinwand",
-        "global": "Global",
-        "regional": "Regional",
-        "newGlobalReferenceImageOk": "Globales Referenzbild erstellt",
-        "savedToGalleryError": "Fehler beim Speichern in der Galerie",
-        "savedToGalleryOk": "In Galerie gespeichert",
-        "newGlobalReferenceImageError": "Problem beim Erstellen eines globalen Referenzbilds",
-        "newRegionalReferenceImageOk": "Regionales Referenzbild erstellt",
-        "duplicate": "Duplizieren",
-        "regionalReferenceImage": "Regionales Referenzbild",
-        "globalReferenceImage": "Globales Referenzbild",
-        "regionIsEmpty": "Ausgewählte Region is leer",
-        "mergeVisible": "Sichtbare vereinen",
-        "mergeVisibleOk": "Sichtbare Ebenen vereinen",
-        "mergeVisibleError": "Fehler beim Vereinen sichtbarer Ebenen",
-        "clearHistory": "Verlauf leeren",
-        "addLayer": "Ebene hinzufügen",
-        "width": "Breite",
-        "weight": "Gewichtung",
-        "addReferenceImage": "$t(controlLayers.referenceImage) hinzufügen",
-        "addInpaintMask": "$t(controlLayers.inpaintMask) hinzufügen",
-        "addGlobalReferenceImage": "$t(controlLayers.globalReferenceImage) hinzufügen",
-        "regionalGuidance": "Regionale Führung",
-        "globalReferenceImages_withCount_visible": "Globale Referenzbilder ({{count}})",
-        "addPositivePrompt": "$t(controlLayers.prompt) hinzufügen",
-        "locked": "Gesperrt",
-        "showHUD": "HUD anzeigen",
-        "addNegativePrompt": "$t(controlLayers.negativePrompt) hinzufügen",
-        "addRasterLayer": "$t(controlLayers.rasterLayer) hinzufügen",
-        "addRegionalGuidance": "$t(controlLayers.regionalGuidance) hinzufügen",
-        "addControlLayer": "$t(controlLayers.controlLayer) hinzufügen",
-        "newCanvasSession": "Neue Leinwand-Sitzung",
-        "replaceLayer": "Ebene ersetzen",
-        "newGallerySession": "Neue Galerie-Sitzung",
-        "unlocked": "Entsperrt",
-        "showProgressOnCanvas": "Fortschritt auf Leinwand anzeigen",
-        "controlMode": {
-            "balanced": "Ausgewogen"
-        },
-        "globalReferenceImages_withCount_hidden": "Globale Referenzbilder ({{count}} ausgeblendet)",
-        "sendToGallery": "An Galerie senden",
-        "stagingArea": {
-            "accept": "Annehmen",
-            "next": "Nächste",
-            "discardAll": "Alle verwerfen",
-            "discard": "Verwerfen",
-            "previous": "Vorherige"
-        },
-        "regionalGuidance_withCount_visible": "Regionale Führung ({{count}})",
-        "regionalGuidance_withCount_hidden": "Regionale Führung ({{count}} ausgeblendet)",
-        "settings": {
-            "snapToGrid": {
-                "on": "Ein",
-                "off": "Aus",
-                "label": "Am Raster ausrichten"
-            }
-        },
-        "layer_one": "Ebene",
-        "layer_other": "Ebenen",
-        "layer_withCount_one": "Ebene ({{count}})",
-        "layer_withCount_other": "Ebenen ({{count}})"
+            "bboxGroup": "Aus Bbox erstellen"
+        }
     },
     "upsell": {
         "shareAccess": "Zugang teilen",
         "professional": "Professionell",
         "inviteTeammates": "Teamkollegen einladen",
         "professionalUpsell": "Verfügbar in der Professional Edition von Invoke. Klicken Sie hier oder besuchen Sie invoke.com/pricing für weitere Details."
-    },
-    "upscaling": {
-        "creativity": "Kreativität",
-        "structure": "Struktur",
-        "scale": "Maßstab"
     }
 }

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

@@ -10,10 +10,9 @@
         "previousImage": "Previous Image",
         "reset": "Reset",
         "resetUI": "$t(accessibility.reset) UI",
-        "toggleRightPanel": "Toggle Right Panel (G)",
-        "toggleLeftPanel": "Toggle Left Panel (T)",
-        "uploadImage": "Upload Image",
-        "uploadImages": "Upload Image(s)"
+        "showGalleryPanel": "Show Gallery Panel",
+        "showOptionsPanel": "Show Side Panel",
+        "uploadImage": "Upload Image"
     },
     "boards": {
         "addBoard": "Add Board",
@@ -54,8 +53,7 @@
         "imagesWithCount_one": "{{count}} image",
         "imagesWithCount_other": "{{count}} images",
         "assetsWithCount_one": "{{count}} asset",
-        "assetsWithCount_other": "{{count}} assets",
-        "updateBoardError": "Error updating board"
+        "assetsWithCount_other": "{{count}} assets"
     },
     "accordions": {
         "generation": {
@@ -91,13 +89,11 @@
         "batch": "Batch Manager",
         "beta": "Beta",
         "cancel": "Cancel",
-        "close": "Close",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
         "on": "On",
         "off": "Off",
         "or": "or",
-        "ok": "Ok",
         "checkpoint": "Checkpoint",
         "communityLabel": "Community",
         "controlNet": "ControlNet",
@@ -283,10 +279,8 @@
         "gallery": "Gallery",
         "alwaysShowImageSizeBadge": "Always Show Image Size Badge",
         "assets": "Assets",
-        "assetsTab": "Files you’ve uploaded for use in your projects.",
         "autoAssignBoardOnClick": "Auto-Assign Board on Click",
         "autoSwitchNewImages": "Auto-Switch to New Images",
-        "boardsSettings": "Boards Settings",
         "copy": "Copy",
         "currentlyInUse": "This image is currently in use in the following features:",
         "drop": "Drop",
@@ -305,8 +299,6 @@
         "gallerySettings": "Gallery Settings",
         "go": "Go",
         "image": "image",
-        "imagesTab": "Images you’ve created and saved within Invoke.",
-        "imagesSettings": "Gallery Images Settings",
         "jump": "Jump",
         "loading": "Loading",
         "newestFirst": "Newest First",
@@ -665,7 +657,6 @@
         "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
         "createdBy": "Created By",
         "generationMode": "Generation Mode",
-        "guidance": "Guidance",
         "height": "Height",
         "imageDetails": "Image Details",
         "imageDimensions": "Image Dimensions",
@@ -706,7 +697,7 @@
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
         "convertToDiffusers": "Convert To Diffusers",
-        "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
+        "convertToDiffusersHelpText1": "This model will be converted to the \ud83e\udde8 Diffusers format.",
         "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
         "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
@@ -729,7 +720,6 @@
         "huggingFaceHelper": "If multiple models are found in this repo, you will be prompted to select one to install.",
         "hfToken": "HuggingFace Token",
         "imageEncoderModelId": "Image Encoder Model ID",
-        "includesNModels": "Includes {{n}} models and their dependencies",
         "installQueue": "Install Queue",
         "inplaceInstall": "In-place install",
         "inplaceInstallDesc": "Install models without copying the files. When using the model, it will be loaded from its this location. If disabled, the model file(s) will be copied into the Invoke-managed models directory during installation.",
@@ -783,8 +773,6 @@
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
         "spandrelImageToImage": "Image to Image (Spandrel)",
-        "starterBundles": "Starter Bundles",
-        "starterBundleHelpText": "Easily install all models needed to get started with a base model, including a main model, controlnets, IP adapters, and more. Selecting a bundle will skip any models that you already have installed.",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
@@ -802,13 +790,7 @@
         "vae": "VAE",
         "vaePrecision": "VAE Precision",
         "variant": "Variant",
-        "width": "Width",
-        "installingBundle": "Installing Bundle",
-        "installingModel": "Installing Model",
-        "installingXModels_one": "Installing {{count}} model",
-        "installingXModels_other": "Installing {{count}} models",
-        "skippingXDuplicates_one": ", skipping {{count}} duplicate",
-        "skippingXDuplicates_other": ", skipping {{count}} duplicates"
+        "width": "Width"
     },
     "models": {
         "addLora": "Add LoRA",
@@ -870,8 +852,6 @@
         "ipAdapter": "IP-Adapter",
         "loadingNodes": "Loading Nodes...",
         "loadWorkflow": "Load Workflow",
-        "noWorkflows": "No Workflows",
-        "noMatchingWorkflows": "No Matching Workflows",
         "noWorkflow": "No Workflow",
         "mismatchedVersion": "Invalid node: node {{node}} of type {{type}} has mismatched version (try updating?)",
         "missingTemplate": "Invalid node: node {{node}} of type {{type}} missing template (not installed?)",
@@ -888,7 +868,6 @@
         "nodeType": "Node Type",
         "noFieldsLinearview": "No fields added to Linear View",
         "noFieldsViewMode": "This workflow has no selected fields to display. View the full workflow to configure values.",
-        "workflowHelpText": "Need Help? Check out our guide to <LinkComponent>Getting Started with Workflows</LinkComponent>.",
         "noNodeSelected": "No node selected",
         "nodeOpacity": "Node Opacity",
         "nodeVersion": "Node Version",
@@ -1102,7 +1081,6 @@
         "antialiasProgressImages": "Antialias Progress Images",
         "beta": "Beta",
         "confirmOnDelete": "Confirm On Delete",
-        "confirmOnNewSession": "Confirm On New Session",
         "developer": "Developer",
         "displayInProgress": "Display Progress Images",
         "enableInformationalPopovers": "Enable Informational Popovers",
@@ -1132,15 +1110,13 @@
         "reloadingIn": "Reloading in"
     },
     "toast": {
-        "addedToBoard": "Added to board {{name}}'s assets",
-        "addedToUncategorized": "Added to board $t(boards.uncategorized)'s assets",
+        "addedToBoard": "Added to board",
         "baseModelChanged": "Base Model Changed",
         "baseModelChangedCleared_one": "Cleared or disabled {{count}} incompatible submodel",
         "baseModelChangedCleared_other": "Cleared or disabled {{count}} incompatible submodels",
         "canceled": "Processing Canceled",
         "connected": "Connected to Server",
         "imageCopied": "Image Copied",
-        "linkCopied": "Link Copied",
         "unableToLoadImage": "Unable to Load Image",
         "unableToLoadImageMetadata": "Unable to Load Image Metadata",
         "unableToLoadStylePreset": "Unable to Load Style Preset",
@@ -1182,10 +1158,7 @@
         "setNodeField": "Set as node field",
         "somethingWentWrong": "Something Went Wrong",
         "uploadFailed": "Upload failed",
-        "imagesWillBeAddedTo": "Uploaded images will be added to board {{boardName}}'s assets.",
-        "uploadFailedInvalidUploadDesc_withCount_one": "Must be maximum of 1 PNG or JPEG image.",
-        "uploadFailedInvalidUploadDesc_withCount_other": "Must be maximum of {{count}} PNG or JPEG images.",
-        "uploadFailedInvalidUploadDesc": "Must be PNG or JPEG images.",
+        "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "workflowLoaded": "Workflow Loaded",
         "problemRetrievingWorkflow": "Problem Retrieving Workflow",
         "workflowDeleted": "Workflow Deleted",
@@ -1540,7 +1513,6 @@
         }
     },
     "workflows": {
-        "chooseWorkflowFromLibrary": "Choose Workflow from Library",
         "defaultWorkflows": "Default Workflows",
         "userWorkflows": "User Workflows",
         "projectWorkflows": "Project Workflows",
@@ -1553,9 +1525,7 @@
         "openWorkflow": "Open Workflow",
         "updated": "Updated",
         "uploadWorkflow": "Load from File",
-        "uploadAndSaveWorkflow": "Upload to Library",
         "deleteWorkflow": "Delete Workflow",
-        "deleteWorkflow2": "Are you sure you want to delete this workflow? This cannot be undone.",
         "unnamedWorkflow": "Unnamed Workflow",
         "downloadWorkflow": "Save to File",
         "saveWorkflow": "Save Workflow",
@@ -1578,13 +1548,9 @@
         "loadFromGraph": "Load Workflow from Graph",
         "convertGraph": "Convert Graph",
         "loadWorkflow": "$t(common.load) Workflow",
-        "autoLayout": "Auto Layout",
-        "edit": "Edit",
-        "download": "Download",
-        "copyShareLink": "Copy Share Link",
-        "copyShareLinkForWorkflow": "Copy Share Link for Workflow",
-        "delete": "Delete"
+        "autoLayout": "Auto Layout"
     },
+    "app": {},
     "controlLayers": {
         "regional": "Regional",
         "global": "Global",
@@ -1595,7 +1561,6 @@
         "saveCanvasToGallery": "Save Canvas to Gallery",
         "saveBboxToGallery": "Save Bbox to Gallery",
         "saveLayerToAssets": "Save Layer to Assets",
-        "cropLayerToBbox": "Crop Layer to Bbox",
         "savedToGalleryOk": "Saved to Gallery",
         "savedToGalleryError": "Error saving to gallery",
         "newGlobalReferenceImageOk": "Created Global Reference Image",
@@ -1658,20 +1623,19 @@
         "sendToCanvas": "Send To Canvas",
         "newLayerFromImage": "New Layer from Image",
         "newCanvasFromImage": "New Canvas from Image",
-        "newImg2ImgCanvasFromImage": "New Img2Img from Image",
         "copyToClipboard": "Copy to Clipboard",
         "sendToCanvasDesc": "Pressing Invoke stages your work in progress on the canvas.",
         "viewProgressInViewer": "View progress and outputs in the <Btn>Image Viewer</Btn>.",
         "viewProgressOnCanvas": "View progress and stage outputs on the <Btn>Canvas</Btn>.",
         "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
-        "rasterLayer_withCount_other": "Raster Layers",
         "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
-        "controlLayer_withCount_other": "Control Layers",
         "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
-        "inpaintMask_withCount_other": "Inpaint Masks",
         "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
-        "regionalGuidance_withCount_other": "Regional Guidance",
         "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
+        "rasterLayer_withCount_other": "Raster Layers",
+        "controlLayer_withCount_other": "Control Layers",
+        "inpaintMask_withCount_other": "Inpaint Masks",
+        "regionalGuidance_withCount_other": "Regional Guidance",
         "globalReferenceImage_withCount_other": "Global Reference Images",
         "opacity": "Opacity",
         "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
@@ -1684,6 +1648,7 @@
         "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
         "globalReferenceImages_withCount_visible": "Global Reference Images ({{count}})",
         "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
+        "layer": "Layer",
         "layer_one": "Layer",
         "layer_other": "Layers",
         "layer_withCount_one": "Layer ({{count}})",
@@ -1709,10 +1674,6 @@
         "negativePrompt": "Negative Prompt",
         "beginEndStepPercentShort": "Begin/End %",
         "weight": "Weight",
-        "newGallerySession": "New Gallery Session",
-        "newGallerySessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be sent to the gallery.",
-        "newCanvasSession": "New Canvas Session",
-        "newCanvasSessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be staged on the canvas.",
         "controlMode": {
             "controlMode": "Control Mode",
             "balanced": "Balanced",
@@ -1766,7 +1727,7 @@
                 "label": "Canny Edge Detection",
                 "description": "Generates an edge map from the selected layer using the Canny edge detection algorithm.",
                 "low_threshold": "Low Threshold",
-                "high_threshold": "High Threshold"
+                "high_threshold": "Hight Threshold"
             },
             "color_map": {
                 "label": "Color Map",
@@ -1834,25 +1795,10 @@
         "transform": {
             "transform": "Transform",
             "fitToBbox": "Fit to Bbox",
-            "fitMode": "Fit Mode",
-            "fitModeContain": "Contain",
-            "fitModeCover": "Cover",
-            "fitModeFill": "Fill",
             "reset": "Reset",
             "apply": "Apply",
             "cancel": "Cancel"
         },
-        "segment": {
-            "autoMask": "Auto Mask",
-            "pointType": "Point Type",
-            "foreground": "Foreground",
-            "background": "Background",
-            "neutral": "Neutral",
-            "reset": "Reset",
-            "apply": "Apply",
-            "cancel": "Cancel",
-            "process": "Process"
-        },
         "settings": {
             "snapToGrid": {
                 "label": "Snap to Grid",
@@ -1863,12 +1809,11 @@
                 "label": "Preserve Masked Region",
                 "alert": "Preserving Masked Region"
             },
-            "isolatedStagingPreview": "Isolated Staging Preview",
             "isolatedPreview": "Isolated Preview",
-            "isolatedLayerPreview": "Isolated Layer Preview",
-            "isolatedLayerPreviewDesc": "Whether to show only this layer when performing operations like filtering or transforming.",
-            "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size",
-            "pressureSensitivity": "Pressure Sensitivity"
+            "isolatedStagingPreview": "Isolated Staging Preview",
+            "isolatedFilteringPreview": "Isolated Filtering Preview",
+            "isolatedTransformingPreview": "Isolated Transforming Preview",
+            "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size"
         },
         "HUD": {
             "bbox": "Bbox",
@@ -1883,7 +1828,6 @@
             }
         },
         "canvasContextMenu": {
-            "canvasGroup": "Canvas",
             "saveToGalleryGroup": "Save To Gallery",
             "saveCanvasToGallery": "Save Canvas To Gallery",
             "saveBboxToGallery": "Save Bbox To Gallery",
@@ -1891,8 +1835,7 @@
             "newGlobalReferenceImage": "New Global Reference Image",
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
-            "newRasterLayer": "New Raster Layer",
-            "cropCanvasToBbox": "Crop Canvas to Bbox"
+            "newRasterLayer": "New Raster Layer"
         },
         "stagingArea": {
             "accept": "Accept",
@@ -2015,12 +1958,8 @@
         }
     },
     "newUserExperience": {
-        "toGetStartedLocal": "To get started, make sure to download or import models needed to run Invoke. Then, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. Select a prompt template to improve results. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
-        "toGetStarted": "To get started, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. Select a prompt template to improve results. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
-        "gettingStartedSeries": "Want more guidance? Check out our <LinkComponent>Getting Started Series</LinkComponent> for tips on unlocking the full potential of the Invoke Studio.",
-        "downloadStarterModels": "Download Starter Models",
-        "importModels": "Import Models",
-        "noModelsInstalled": "It looks like you don't have any models installed"
+        "toGetStarted": "To get started, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
+        "gettingStartedSeries": "Want more guidance? Check out our <LinkComponent>Getting Started Series</LinkComponent> for tips on unlocking the full potential of the Invoke Studio."
     },
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",

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

@@ -219,14 +219,14 @@
         "uploadImage": "Cargar imagen",
         "previousImage": "Imagen anterior",
         "nextImage": "Siguiente imagen",
+        "showOptionsPanel": "Mostrar el panel lateral",
         "menu": "Menú",
+        "showGalleryPanel": "Mostrar panel de galería",
         "about": "Acerca de",
         "createIssue": "Crear un problema",
         "resetUI": "Interfaz de usuario $t(accessibility.reset)",
         "mode": "Modo",
-        "submitSupportTicket": "Enviar Ticket de Soporte",
-        "toggleRightPanel": "Activar o desactivar el panel derecho (G)",
-        "toggleLeftPanel": "Activar o desactivar el panel izquierdo (T)"
+        "submitSupportTicket": "Enviar Ticket de Soporte"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -275,12 +275,7 @@
         "addSharedBoard": "Agregar Panel Compartido",
         "boards": "Paneles",
         "archiveBoard": "Archivar Panel",
-        "archived": "Archivado",
-        "selectedForAutoAdd": "Seleccionado para agregar automáticamente",
-        "unarchiveBoard": "Desarchivar el tablero",
-        "noBoards": "No hay tableros {{boardType}}",
-        "shared": "Carpetas compartidas",
-        "deletedPrivateBoardsCannotbeRestored": "Los tableros eliminados no se pueden restaurar. Al elegir \"Eliminar solo tablero\", las imágenes se colocan en un estado privado y sin categoría para el creador de la imagen."
+        "archived": "Archivado"
     },
     "accordions": {
         "compositing": {
@@ -323,13 +318,5 @@
         "inviteTeammates": "Invitar compañeros de equipo",
         "shareAccess": "Compartir acceso",
         "professionalUpsell": "Disponible en la edición profesional de Invoke. Haz clic aquí o visita invoke.com/pricing para obtener más detalles."
-    },
-    "controlLayers": {
-        "layer_one": "Capa",
-        "layer_many": "Capas",
-        "layer_other": "Capas",
-        "layer_withCount_one": "({{count}}) capa",
-        "layer_withCount_many": "({{count}}) capas",
-        "layer_withCount_other": "({{count}}) capas"
     }
 }

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

@@ -4,7 +4,8 @@
         "uploadImage": "Lataa kuva",
         "invokeProgressBar": "Invoken edistymispalkki",
         "nextImage": "Seuraava kuva",
-        "previousImage": "Edellinen kuva"
+        "previousImage": "Edellinen kuva",
+        "showOptionsPanel": "Näytä asetukset"
     },
     "common": {
         "languagePickerLabel": "Kielen valinta",

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

@@ -65,7 +65,7 @@
         "blue": "Blu",
         "alpha": "Alfa",
         "copy": "Copia",
-        "on": "Acceso",
+        "on": "Attivato",
         "checkpoint": "Checkpoint",
         "safetensors": "Safetensors",
         "ai": "ia",
@@ -85,14 +85,13 @@
         "openInViewer": "Apri nel visualizzatore",
         "apply": "Applica",
         "loadingImage": "Caricamento immagine",
-        "off": "Spento",
+        "off": "Disattivato",
         "edit": "Modifica",
         "placeholderSelectAModel": "Seleziona un modello",
         "reset": "Reimposta",
         "none": "Niente",
         "new": "Nuovo",
-        "view": "Vista",
-        "close": "Chiudi"
+        "view": "Vista"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -156,11 +155,7 @@
         "move": "Sposta",
         "gallery": "Galleria",
         "openViewer": "Apri visualizzatore",
-        "closeViewer": "Chiudi visualizzatore",
-        "imagesTab": "Immagini create e salvate in Invoke.",
-        "assetsTab": "File che hai caricato per usarli nei tuoi progetti.",
-        "boardsSettings": "Impostazioni Bacheche",
-        "imagesSettings": "Impostazioni Immagini Galleria"
+        "closeViewer": "Chiudi visualizzatore"
     },
     "hotkeys": {
         "searchHotkeys": "Cerca tasti di scelta rapida",
@@ -326,22 +321,6 @@
             "selectViewTool": {
                 "title": "Strumento Visualizza",
                 "desc": "Seleziona lo strumento Visualizza."
-            },
-            "applyFilter": {
-                "title": "Applica filtro",
-                "desc": "Applica il filtro in sospeso al livello selezionato."
-            },
-            "cancelFilter": {
-                "title": "Annulla filtro",
-                "desc": "Annulla il filtro in sospeso."
-            },
-            "cancelTransform": {
-                "desc": "Annulla la trasformazione in sospeso.",
-                "title": "Annulla Trasforma"
-            },
-            "applyTransform": {
-                "title": "Applica trasformazione",
-                "desc": "Applica la trasformazione in sospeso al livello selezionato."
             }
         },
         "workflows": {
@@ -577,18 +556,7 @@
         "noMatchingModels": "Nessun modello corrispondente",
         "starterModelsInModelManager": "I modelli iniziali possono essere trovati in Gestione Modelli",
         "spandrelImageToImage": "Immagine a immagine (Spandrel)",
-        "learnMoreAboutSupportedModels": "Scopri di più sui modelli che supportiamo",
-        "starterBundles": "Pacchetti per iniziare",
-        "installingBundle": "Installazione del pacchetto",
-        "skippingXDuplicates_one": ", saltando {{count}} duplicato",
-        "skippingXDuplicates_many": ", saltando {{count}} duplicati",
-        "skippingXDuplicates_other": ", saltando {{count}} duplicati",
-        "installingModel": "Installazione del modello",
-        "installingXModels_one": "Installazione di {{count}} modello",
-        "installingXModels_many": "Installazione di {{count}} modelli",
-        "installingXModels_other": "Installazione di {{count}} modelli",
-        "includesNModels": "Include {{n}} modelli e le loro dipendenze",
-        "starterBundleHelpText": "Installa facilmente tutti i modelli necessari per iniziare con un modello base, tra cui un modello principale, controlnet, adattatori IP e altro. Selezionando un pacchetto salterai tutti i modelli che hai già installato."
+        "learnMoreAboutSupportedModels": "Scopri di più sui modelli che supportiamo"
     },
     "parameters": {
         "images": "Immagini",
@@ -606,8 +574,8 @@
         "scale": "Scala",
         "imageFit": "Adatta l'immagine iniziale alle dimensioni di output",
         "scaleBeforeProcessing": "Scala prima dell'elaborazione",
-        "scaledWidth": "Larghezza scalata",
-        "scaledHeight": "Altezza scalata",
+        "scaledWidth": "Larghezza ridimensionata",
+        "scaledHeight": "Altezza ridimensionata",
         "infillMethod": "Metodo di riempimento",
         "tileSize": "Dimensione piastrella",
         "downloadImage": "Scarica l'immagine",
@@ -649,11 +617,7 @@
                 "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
                 "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
                 "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
-                "rgNoRegion": "Nessuna regione selezionata",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza riquadro è {{width}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza riquadro è {{height}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza del riquadro scalato {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza del riquadro scalato {{height}}"
+                "rgNoRegion": "Nessuna regione selezionata"
             },
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), altezza riquadro è {{height}}",
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), larghezza riquadro è {{width}}",
@@ -661,11 +625,7 @@
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), altezza del riquadro scalato è {{height}}",
             "noT5EncoderModelSelected": "Nessun modello di encoder T5 selezionato per la generazione con FLUX",
             "noCLIPEmbedModelSelected": "Nessun modello CLIP Embed selezionato per la generazione con FLUX",
-            "noFLUXVAEModelSelected": "Nessun modello VAE selezionato per la generazione con FLUX",
-            "canvasIsTransforming": "La tela sta trasformando",
-            "canvasIsRasterizing": "La tela sta rasterizzando",
-            "canvasIsCompositing": "La tela è in fase di composizione",
-            "canvasIsFiltering": "La tela sta filtrando"
+            "noFLUXVAEModelSelected": "Nessun modello VAE selezionato per la generazione con FLUX"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -684,12 +644,7 @@
         "processImage": "Elabora Immagine",
         "sendToUpscale": "Invia a Amplia",
         "postProcessing": "Post-elaborazione (Shift + U)",
-        "guidance": "Guida",
-        "gaussianBlur": "Sfocatura Gaussiana",
-        "boxBlur": "Sfocatura Box",
-        "staged": "Maschera espansa",
-        "optimizedImageToImage": "Immagine-a-immagine ottimizzata",
-        "sendToCanvas": "Invia alla Tela"
+        "guidance": "Guida"
     },
     "settings": {
         "models": "Modelli",
@@ -723,8 +678,7 @@
         "enableInformationalPopovers": "Abilita testo informativo a comparsa",
         "reloadingIn": "Ricaricando in",
         "informationalPopoversDisabled": "Testo informativo a comparsa disabilitato",
-        "informationalPopoversDisabledDesc": "I testi informativi a comparsa sono disabilitati. Attivali nelle impostazioni.",
-        "confirmOnNewSession": "Conferma su nuova sessione"
+        "informationalPopoversDisabledDesc": "I testi informativi a comparsa sono disabilitati. Attivali nelle impostazioni."
     },
     "toast": {
         "uploadFailed": "Caricamento fallito",
@@ -733,7 +687,7 @@
         "serverError": "Errore del Server",
         "connected": "Connesso al server",
         "canceled": "Elaborazione annullata",
-        "uploadFailedInvalidUploadDesc": "Devono essere immagini PNG o JPEG.",
+        "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
         "parameterSet": "Parametro richiamato",
         "parameterNotSet": "Parametro non richiamato",
         "problemCopyingImage": "Impossibile copiare l'immagine",
@@ -742,7 +696,7 @@
         "baseModelChangedCleared_other": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
         "loadedWithWarnings": "Flusso di lavoro caricato con avvisi",
         "imageUploaded": "Immagine caricata",
-        "addedToBoard": "Aggiunto alle risorse della bacheca {{name}}",
+        "addedToBoard": "Aggiunto alla bacheca",
         "modelAddedSimple": "Modello aggiunto alla Coda",
         "imageUploadFailed": "Caricamento immagine non riuscito",
         "setControlImage": "Imposta come immagine di controllo",
@@ -767,26 +721,7 @@
         "somethingWentWrong": "Qualcosa è andato storto",
         "outOfMemoryErrorDesc": "Le impostazioni della generazione attuale superano la capacità del sistema. Modifica le impostazioni e riprova.",
         "importFailed": "Importazione non riuscita",
-        "importSuccessful": "Importazione riuscita",
-        "layerSavedToAssets": "Livello salvato nelle risorse",
-        "problemSavingLayer": "Impossibile salvare il livello",
-        "unableToLoadImage": "Impossibile caricare l'immagine",
-        "problemCopyingLayer": "Impossibile copiare il livello",
-        "sentToCanvas": "Inviato alla Tela",
-        "sentToUpscale": "Inviato a Amplia",
-        "unableToLoadStylePreset": "Impossibile caricare lo stile predefinito",
-        "stylePresetLoaded": "Stile predefinito caricato",
-        "unableToLoadImageMetadata": "Impossibile caricare i metadati dell'immagine",
-        "imageSaved": "Immagine salvata",
-        "imageSavingFailed": "Salvataggio dell'immagine non riuscito",
-        "layerCopiedToClipboard": "Livello copiato negli appunti",
-        "imageNotLoadedDesc": "Impossibile trovare l'immagine",
-        "linkCopied": "Collegamento copiato",
-        "addedToUncategorized": "Aggiunto alle risorse della bacheca $t(boards.uncategorized)",
-        "imagesWillBeAddedTo": "Le immagini caricate verranno aggiunte alle risorse della bacheca {{boardName}}.",
-        "uploadFailedInvalidUploadDesc_withCount_one": "Devi caricare al massimo 1 immagine PNG o JPEG.",
-        "uploadFailedInvalidUploadDesc_withCount_many": "Devi caricare al massimo {{count}} immagini PNG o JPEG.",
-        "uploadFailedInvalidUploadDesc_withCount_other": "Devi caricare al massimo {{count}} immagini PNG o JPEG."
+        "importSuccessful": "Importazione riuscita"
     },
     "accessibility": {
         "invokeProgressBar": "Barra di avanzamento generazione",
@@ -794,15 +729,14 @@
         "previousImage": "Immagine precedente",
         "nextImage": "Immagine successiva",
         "reset": "Reimposta",
+        "showOptionsPanel": "Mostra il pannello laterale",
         "menu": "Menu",
+        "showGalleryPanel": "Mostra il pannello Galleria",
         "mode": "Modalità",
         "resetUI": "$t(accessibility.reset) l'Interfaccia Utente",
         "createIssue": "Segnala un problema",
         "about": "Informazioni",
-        "submitSupportTicket": "Invia ticket di supporto",
-        "toggleLeftPanel": "Attiva/disattiva il pannello sinistro (T)",
-        "toggleRightPanel": "Attiva/disattiva il pannello destro (G)",
-        "uploadImages": "Carica immagine(i)"
+        "submitSupportTicket": "Invia ticket di supporto"
     },
     "nodes": {
         "zoomOutNodes": "Rimpicciolire",
@@ -922,7 +856,7 @@
         "clearWorkflowDesc": "Cancellare questo flusso di lavoro e avviarne uno nuovo?",
         "clearWorkflow": "Cancella il flusso di lavoro",
         "clearWorkflowDesc2": "Il tuo flusso di lavoro attuale presenta modifiche non salvate.",
-        "viewMode": "Usa la vista lineare",
+        "viewMode": "Utilizzare nella vista lineare",
         "reorderLinearView": "Riordina la vista lineare",
         "editMode": "Modifica nell'editor del flusso di lavoro",
         "resetToDefaultValue": "Ripristina il valore predefinito",
@@ -940,10 +874,7 @@
         "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino ai valori predefiniti",
         "boardAccessError": "Impossibile trovare la bacheca {{board_id}}, ripristino ai valori predefiniti",
         "modelAccessError": "Impossibile trovare il modello {{key}}, ripristino ai valori predefiniti",
-        "saveToGallery": "Salva nella Galleria",
-        "noMatchingWorkflows": "Nessun flusso di lavoro corrispondente",
-        "noWorkflows": "Nessun flusso di lavoro",
-        "workflowHelpText": "Hai bisogno di aiuto? Consulta la nostra guida <LinkComponent>Introduzione ai flussi di lavoro</LinkComponent>."
+        "saveToGallery": "Salva nella Galleria"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -987,8 +918,7 @@
         "noBoards": "Nessuna bacheca {{boardType}}",
         "hideBoards": "Nascondi bacheche",
         "viewBoards": "Visualizza bacheche",
-        "deletedPrivateBoardsCannotbeRestored": "Le bacheche cancellate non possono essere ripristinate. Selezionando 'Cancella solo bacheca', le immagini verranno spostate nella bacheca \"Non categorizzato\" privata dell'autore dell'immagine.",
-        "updateBoardError": "Errore durante l'aggiornamento della bacheca"
+        "deletedPrivateBoardsCannotbeRestored": "Le bacheche cancellate non possono essere ripristinate. Selezionando 'Cancella solo bacheca', le immagini verranno spostate nella bacheca \"Non categorizzato\" privata dell'autore dell'immagine."
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1473,25 +1403,6 @@
             "paragraphs": [
                 "La struttura determina quanto l'immagine finale rispecchierà il layout dell'originale. Una struttura bassa permette cambiamenti significativi, mentre una struttura alta conserva la composizione e il layout originali."
             ]
-        },
-        "fluxDevLicense": {
-            "heading": "Licenza non commerciale",
-            "paragraphs": [
-                "I modelli FLUX.1 [dev] sono concessi in licenza con la licenza non commerciale FLUX [dev]. Per utilizzare questo tipo di modello per scopi commerciali in Invoke, visita il nostro sito Web per saperne di più."
-            ]
-        },
-        "optimizedDenoising": {
-            "heading": "Immagine-a-immagine ottimizzata",
-            "paragraphs": [
-                "Abilita 'Immagine-a-immagine ottimizzata' per una scala di riduzione del rumore più graduale per le trasformazioni da immagine a immagine e di inpainting con modelli Flux. Questa impostazione migliora la capacità di controllare la quantità di modifica applicata a un'immagine, ma può essere disattivata se preferisci usare la scala di riduzione rumore standard. Questa impostazione è ancora in fase di messa a punto ed è in stato beta."
-            ]
-        },
-        "paramGuidance": {
-            "heading": "Guida",
-            "paragraphs": [
-                "Controlla quanto il prompt influenza il processo di generazione.",
-                "Valori di guida elevati possono causare sovrasaturazione e una guida elevata o bassa può causare risultati di generazione distorti. La guida si applica solo ai modelli FLUX DEV."
-            ]
         }
     },
     "sdxl": {
@@ -1540,8 +1451,7 @@
         "parameterSet": "Parametro {{parameter}} impostato",
         "parsingFailed": "Analisi non riuscita",
         "recallParameter": "Richiama {{label}}",
-        "canvasV2Metadata": "Tela",
-        "guidance": "Guida"
+        "canvasV2Metadata": "Tela"
     },
     "hrf": {
         "enableHrf": "Abilita Correzione Alta Risoluzione",
@@ -1586,18 +1496,7 @@
         "convertGraph": "Converti grafico",
         "loadWorkflow": "$t(common.load) Flusso di lavoro",
         "autoLayout": "Disposizione automatica",
-        "loadFromGraph": "Carica il flusso di lavoro dal grafico",
-        "userWorkflows": "Flussi di lavoro utente",
-        "projectWorkflows": "Flussi di lavoro del progetto",
-        "defaultWorkflows": "Flussi di lavoro predefiniti",
-        "uploadAndSaveWorkflow": "Carica nella libreria",
-        "chooseWorkflowFromLibrary": "Scegli il flusso di lavoro dalla libreria",
-        "deleteWorkflow2": "Vuoi davvero eliminare questo flusso di lavoro? Questa operazione non può essere annullata.",
-        "edit": "Modifica",
-        "download": "Scarica",
-        "copyShareLink": "Copia Condividi Link",
-        "copyShareLinkForWorkflow": "Copia Condividi Link del Flusso di lavoro",
-        "delete": "Elimina"
+        "loadFromGraph": "Carica il flusso di lavoro dal grafico"
     },
     "accordions": {
         "compositing": {
@@ -1636,308 +1535,7 @@
         "addPositivePrompt": "Aggiungi $t(controlLayers.prompt)",
         "addNegativePrompt": "Aggiungi $t(controlLayers.negativePrompt)",
         "regionalGuidance": "Guida regionale",
-        "opacity": "Opacità",
-        "mergeVisible": "Fondi il visibile",
-        "mergeVisibleOk": "Livelli visibili uniti",
-        "deleteReferenceImage": "Elimina l'immagine di riferimento",
-        "referenceImage": "Immagine di riferimento",
-        "fitBboxToLayers": "Adatta il riquadro di delimitazione ai livelli",
-        "mergeVisibleError": "Errore durante l'unione dei livelli visibili",
-        "regionalReferenceImage": "Immagine di riferimento Regionale",
-        "newLayerFromImage": "Nuovo livello da immagine",
-        "newCanvasFromImage": "Nuova tela da immagine",
-        "globalReferenceImage": "Immagine di riferimento Globale",
-        "copyToClipboard": "Copia negli appunti",
-        "sendingToCanvas": "Effettua le generazioni nella Tela",
-        "clearHistory": "Cancella la cronologia",
-        "inpaintMask": "Maschera Inpaint",
-        "sendToGallery": "Invia alla Galleria",
-        "controlLayer": "Livello di Controllo",
-        "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
-        "rasterLayer_withCount_many": "Livelli Raster",
-        "rasterLayer_withCount_other": "Livelli Raster",
-        "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
-        "controlLayer_withCount_many": "Livelli di controllo",
-        "controlLayer_withCount_other": "Livelli di controllo",
-        "clipToBbox": "Ritaglia i tratti al riquadro",
-        "duplicate": "Duplica",
-        "width": "Larghezza",
-        "addControlLayer": "Aggiungi $t(controlLayers.controlLayer)",
-        "addInpaintMask": "Aggiungi $t(controlLayers.inpaintMask)",
-        "addRegionalGuidance": "Aggiungi $t(controlLayers.regionalGuidance)",
-        "sendToCanvasDesc": "Premendo Invoke il lavoro in corso viene visualizzato sulla tela.",
-        "addRasterLayer": "Aggiungi $t(controlLayers.rasterLayer)",
-        "clearCaches": "Svuota le cache",
-        "regionIsEmpty": "La regione selezionata è vuota",
-        "recalculateRects": "Ricalcola rettangoli",
-        "removeBookmark": "Rimuovi segnalibro",
-        "saveCanvasToGallery": "Salva la tela nella Galleria",
-        "regional": "Regionale",
-        "global": "Globale",
-        "canvas": "Tela",
-        "bookmark": "Segnalibro per cambio rapido",
-        "newRegionalReferenceImageOk": "Immagine di riferimento regionale creata",
-        "newRegionalReferenceImageError": "Problema nella creazione dell'immagine di riferimento regionale",
-        "newControlLayerOk": "Livello di controllo creato",
-        "bboxOverlay": "Mostra sovrapposizione riquadro",
-        "resetCanvas": "Reimposta la tela",
-        "outputOnlyMaskedRegions": "Solo regioni mascherate in uscita",
-        "enableAutoNegative": "Abilita Auto Negativo",
-        "disableAutoNegative": "Disabilita Auto Negativo",
-        "showHUD": "Mostra HUD",
-        "maskFill": "Riempimento maschera",
-        "addReferenceImage": "Aggiungi $t(controlLayers.referenceImage)",
-        "addGlobalReferenceImage": "Aggiungi $t(controlLayers.globalReferenceImage)",
-        "sendingToGallery": "Inviare generazioni alla Galleria",
-        "sendToGalleryDesc": "Premendo Invoke viene generata e salvata un'immagine unica nella tua galleria.",
-        "sendToCanvas": "Invia alla Tela",
-        "viewProgressInViewer": "Visualizza i progressi e i risultati nel <Btn>Visualizzatore immagini</Btn>.",
-        "viewProgressOnCanvas": "Visualizza i progressi e i risultati nella <Btn>Tela</Btn>.",
-        "saveBboxToGallery": "Salva il riquadro di delimitazione nella Galleria",
-        "cropLayerToBbox": "Ritaglia il livello al riquadro di delimitazione",
-        "savedToGalleryError": "Errore durante il salvataggio nella galleria",
-        "rasterLayer": "Livello Raster",
-        "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
-        "regionalGuidance_withCount_many": "Guide regionali",
-        "regionalGuidance_withCount_other": "Guide regionali",
-        "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
-        "inpaintMask_withCount_many": "Maschere Inpaint",
-        "inpaintMask_withCount_other": "Maschere Inpaint",
-        "savedToGalleryOk": "Salvato nella Galleria",
-        "newGlobalReferenceImageOk": "Immagine di riferimento globale creata",
-        "newGlobalReferenceImageError": "Problema nella creazione dell'immagine di riferimento globale",
-        "newControlLayerError": "Problema nella creazione del livello di controllo",
-        "newRasterLayerOk": "Livello raster creato",
-        "newRasterLayerError": "Problema nella creazione del livello raster",
-        "saveLayerToAssets": "Salva il livello nelle Risorse",
-        "pullBboxIntoLayerError": "Problema nel caricare il riquadro nel livello",
-        "pullBboxIntoReferenceImageOk": "Contenuto del riquadro inserito nell'immagine di riferimento",
-        "pullBboxIntoLayerOk": "Riquadro caricato nel livello",
-        "pullBboxIntoReferenceImageError": "Problema nell'inserimento del contenuto del riquadro nell'immagine di riferimento",
-        "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
-        "globalReferenceImage_withCount_many": "Immagini di riferimento Globali",
-        "globalReferenceImage_withCount_other": "Immagini di riferimento Globali",
-        "controlMode": {
-            "balanced": "Bilanciato",
-            "controlMode": "Modalità di controllo",
-            "prompt": "Prompt",
-            "control": "Controllo",
-            "megaControl": "Mega Controllo"
-        },
-        "negativePrompt": "Prompt Negativo",
-        "prompt": "Prompt Positivo",
-        "beginEndStepPercentShort": "Inizio/Fine %",
-        "stagingOnCanvas": "Genera immagini nella",
-        "ipAdapterMethod": {
-            "full": "Completo",
-            "style": "Solo Stile",
-            "composition": "Solo Composizione",
-            "ipAdapterMethod": "Metodo Adattatore IP"
-        },
-        "showingType": "Mostrare {{type}}",
-        "dynamicGrid": "Griglia dinamica",
-        "tool": {
-            "view": "Muovi",
-            "colorPicker": "Selettore Colore",
-            "rectangle": "Rettangolo",
-            "bbox": "Riquadro di delimitazione",
-            "move": "Sposta",
-            "brush": "Pennello",
-            "eraser": "Cancellino"
-        },
-        "filter": {
-            "apply": "Applica",
-            "reset": "Reimposta",
-            "process": "Elabora",
-            "cancel": "Annulla",
-            "autoProcess": "Processo automatico",
-            "filterType": "Tipo Filtro",
-            "filter": "Filtro",
-            "filters": "Filtri",
-            "mlsd_detection": {
-                "score_threshold": "Soglia di punteggio",
-                "distance_threshold": "Soglia di distanza",
-                "description": "Genera una mappa dei segmenti di linea dal livello selezionato utilizzando il modello di rilevamento dei segmenti di linea MLSD.",
-                "label": "Rilevamento segmenti di linea"
-            },
-            "content_shuffle": {
-                "label": "Mescola contenuto",
-                "scale_factor": "Fattore di scala",
-                "description": "Mescola il contenuto del livello selezionato, in modo simile all'effetto \"liquefa\"."
-            },
-            "mediapipe_face_detection": {
-                "min_confidence": "Confidenza minima",
-                "label": "Rilevamento del volto MediaPipe",
-                "max_faces": "Max volti",
-                "description": "Rileva i volti nel livello selezionato utilizzando il modello di rilevamento dei volti MediaPipe."
-            },
-            "dw_openpose_detection": {
-                "draw_face": "Disegna il volto",
-                "description": "Rileva le pose umane nel livello selezionato utilizzando il modello DW Openpose.",
-                "label": "Rilevamento DW Openpose",
-                "draw_hands": "Disegna le mani",
-                "draw_body": "Disegna il corpo"
-            },
-            "normal_map": {
-                "description": "Genera una mappa delle normali dal livello selezionato.",
-                "label": "Mappa delle normali"
-            },
-            "lineart_edge_detection": {
-                "label": "Rilevamento bordi Lineart",
-                "coarse": "Grossolano",
-                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi Lineart."
-            },
-            "depth_anything_depth_estimation": {
-                "model_size_small": "Piccolo",
-                "model_size_small_v2": "Piccolo v2",
-                "model_size": "Dimensioni modello",
-                "model_size_large": "Grande",
-                "model_size_base": "Base",
-                "description": "Genera una mappa di profondità dal livello selezionato utilizzando un modello Depth Anything."
-            },
-            "color_map": {
-                "label": "Mappa colore",
-                "description": "Crea una mappa dei colori dal livello selezionato.",
-                "tile_size": "Dimens. Piastrella"
-            },
-            "canny_edge_detection": {
-                "high_threshold": "Soglia superiore",
-                "low_threshold": "Soglia inferiore",
-                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando l'algoritmo di rilevamento dei bordi Canny.",
-                "label": "Rilevamento bordi Canny"
-            },
-            "spandrel_filter": {
-                "scale": "Scala di destinazione",
-                "autoScaleDesc": "Il modello selezionato verrà eseguito fino al raggiungimento della scala di destinazione.",
-                "description": "Esegue un modello immagine-a-immagine sul livello selezionato.",
-                "label": "Modello Immagine-a-Immagine",
-                "model": "Modello",
-                "autoScale": "Auto Scala"
-            },
-            "pidi_edge_detection": {
-                "quantize_edges": "Quantizza i bordi",
-                "scribble": "Scarabocchio",
-                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi PiDiNet.",
-                "label": "Rilevamento bordi PiDiNet"
-            },
-            "hed_edge_detection": {
-                "label": "Rilevamento bordi HED",
-                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi HED.",
-                "scribble": "Scarabocchio"
-            },
-            "lineart_anime_edge_detection": {
-                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi Lineart Anime.",
-                "label": "Rilevamento bordi Lineart Anime"
-            }
-        },
-        "controlLayers_withCount_hidden": "Livelli di controllo ({{count}} nascosti)",
-        "regionalGuidance_withCount_hidden": "Guida regionale ({{count}} nascosti)",
-        "fill": {
-            "grid": "Griglia",
-            "crosshatch": "Tratteggio incrociato",
-            "fillColor": "Colore di riempimento",
-            "fillStyle": "Stile riempimento",
-            "solid": "Solido",
-            "vertical": "Verticale",
-            "horizontal": "Orizzontale",
-            "diagonal": "Diagonale"
-        },
-        "rasterLayers_withCount_hidden": "Livelli raster ({{count}} nascosti)",
-        "inpaintMasks_withCount_hidden": "Maschere Inpaint ({{count}} nascoste)",
-        "regionalGuidance_withCount_visible": "Guide regionali ({{count}})",
-        "locked": "Bloccato",
-        "hidingType": "Nascondere {{type}}",
-        "logDebugInfo": "Registro Info Debug",
-        "inpaintMasks_withCount_visible": "Maschere Inpaint ({{count}})",
-        "layer_one": "Livello",
-        "layer_many": "Livelli",
-        "layer_other": "Livelli",
-        "disableTransparencyEffect": "Disabilita l'effetto trasparenza",
-        "controlLayers_withCount_visible": "Livelli di controllo ({{count}})",
-        "transparency": "Trasparenza",
-        "newCanvasSessionDesc": "Questo cancellerà la tela e tutte le impostazioni, eccetto la selezione del modello. Le generazioni saranno effettuate sulla tela.",
-        "rasterLayers_withCount_visible": "Livelli raster ({{count}})",
-        "globalReferenceImages_withCount_visible": "Immagini di riferimento Globali ({{count}})",
-        "globalReferenceImages_withCount_hidden": "Immagini di riferimento globali ({{count}} nascoste)",
-        "layer_withCount_one": "Livello ({{count}})",
-        "layer_withCount_many": "Livelli ({{count}})",
-        "layer_withCount_other": "Livelli ({{count}})",
-        "convertToControlLayer": "Converti in livello di controllo",
-        "convertToRasterLayer": "Converti in livello raster",
-        "unlocked": "Sbloccato",
-        "enableTransparencyEffect": "Abilita l'effetto trasparenza",
-        "replaceLayer": "Sostituisci livello",
-        "pullBboxIntoLayer": "Carica l'immagine delimitata nel riquadro",
-        "pullBboxIntoReferenceImage": "Carica l'immagine delimitata nel riquadro",
-        "showProgressOnCanvas": "Mostra i progressi sulla Tela",
-        "weight": "Peso",
-        "newGallerySession": "Nuova sessione Galleria",
-        "newGallerySessionDesc": "Questo cancellerà la tela e tutte le impostazioni, eccetto la selezione del modello. Le generazioni saranno inviate alla galleria.",
-        "newCanvasSession": "Nuova sessione Tela",
-        "deleteSelected": "Elimina selezione",
-        "settings": {
-            "isolatedFilteringPreview": "Anteprima del filtraggio isolata",
-            "isolatedStagingPreview": "Anteprima di generazione isolata",
-            "isolatedTransformingPreview": "Anteprima di trasformazione isolata",
-            "isolatedPreview": "Anteprima isolata",
-            "invertBrushSizeScrollDirection": "Inverti scorrimento per dimensione pennello",
-            "snapToGrid": {
-                "label": "Aggancia alla griglia",
-                "on": "Acceso",
-                "off": "Spento"
-            },
-            "pressureSensitivity": "Sensibilità alla pressione",
-            "preserveMask": {
-                "alert": "Preservare la regione mascherata",
-                "label": "Preserva la regione mascherata"
-            }
-        },
-        "transform": {
-            "reset": "Reimposta",
-            "fitToBbox": "Adatta al Riquadro",
-            "transform": "Trasforma",
-            "apply": "Applica",
-            "cancel": "Annulla",
-            "fitMode": "Adattamento",
-            "fitModeContain": "Contieni",
-            "fitModeFill": "Riempi",
-            "fitModeCover": "Copri"
-        },
-        "stagingArea": {
-            "next": "Successiva",
-            "discard": "Scarta",
-            "discardAll": "Scarta tutto",
-            "accept": "Accetta",
-            "saveToGallery": "Salva nella Galleria",
-            "previous": "Precedente",
-            "showResultsOn": "Risultati visualizzati",
-            "showResultsOff": "Risultati nascosti"
-        },
-        "HUD": {
-            "bbox": "Riquadro di delimitazione",
-            "entityStatus": {
-                "isHidden": "{{title}} è nascosto",
-                "isLocked": "{{title}} è bloccato",
-                "isTransforming": "{{title}} sta trasformando",
-                "isFiltering": "{{title}} sta filtrando",
-                "isEmpty": "{{title}} è vuoto",
-                "isDisabled": "{{title}} è disabilitato"
-            },
-            "scaledBbox": "Riquadro scalato"
-        },
-        "canvasContextMenu": {
-            "newControlLayer": "Nuovo Livello di Controllo",
-            "newRegionalReferenceImage": "Nuova immagine di riferimento Regionale",
-            "newGlobalReferenceImage": "Nuova immagine di riferimento Globale",
-            "bboxGroup": "Crea dal riquadro di delimitazione",
-            "saveBboxToGallery": "Salva il riquadro nella Galleria",
-            "cropCanvasToBbox": "Ritaglia la Tela al riquadro",
-            "canvasGroup": "Tela",
-            "newRasterLayer": "Nuovo Livello Raster",
-            "saveCanvasToGallery": "Salva la Tela nella Galleria",
-            "saveToGalleryGroup": "Salva nella Galleria"
-        },
-        "newImg2ImgCanvasFromImage": "Nuova Immagine da immagine"
+        "opacity": "Opacità"
     },
     "ui": {
         "tabs": {
@@ -1949,8 +1547,7 @@
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "queue": "Coda",
             "upscaling": "Amplia",
-            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
-            "gallery": "Galleria"
+            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
     },
     "upscaling": {
@@ -2020,49 +1617,5 @@
         "noTemplates": "Nessun modello",
         "acceptedColumnsKeys": "Colonne/chiavi accettate:",
         "promptTemplateCleared": "Modello di prompt cancellato"
-    },
-    "newUserExperience": {
-        "gettingStartedSeries": "Desideri maggiori informazioni? Consulta la nostra <LinkComponent>Getting Started Series</LinkComponent> per suggerimenti su come sfruttare appieno il potenziale di Invoke Studio.",
-        "toGetStarted": "Per iniziare, inserisci un prompt nella casella e fai clic su <StrongComponent>Invoke</StrongComponent> per generare la tua prima immagine. Seleziona un modello di prompt per migliorare i risultati. Puoi scegliere di salvare le tue immagini direttamente nella <StrongComponent>Galleria</StrongComponent> o modificarle nella <StrongComponent>Tela</StrongComponent>.",
-        "importModels": "Importa modelli",
-        "downloadStarterModels": "Scarica i modelli per iniziare",
-        "noModelsInstalled": "Sembra che tu non abbia installato alcun modello",
-        "toGetStartedLocal": "Per iniziare, assicurati di scaricare o importare i modelli necessari per eseguire Invoke. Quindi, inserisci un prompt nella casella e fai clic su <StrongComponent>Invoke</StrongComponent> per generare la tua prima immagine. Seleziona un modello di prompt per migliorare i risultati. Puoi scegliere di salvare le tue immagini direttamente nella <StrongComponent>Galleria</StrongComponent> o modificarle nella <StrongComponent>Tela</StrongComponent>."
-    },
-    "whatsNew": {
-        "canvasV2Announcement": {
-            "readReleaseNotes": "Leggi le Note di Rilascio",
-            "fluxSupport": "Supporto per la famiglia di modelli Flux",
-            "newCanvas": "Una nuova potente tela di controllo",
-            "watchReleaseVideo": "Guarda il video di rilascio",
-            "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
-            "newLayerTypes": "Nuovi tipi di livello per un miglior controllo"
-        },
-        "whatsNewInInvoke": "Novità in Invoke"
-    },
-    "system": {
-        "logLevel": {
-            "info": "Info",
-            "warn": "Avviso",
-            "fatal": "Fatale",
-            "error": "Errore",
-            "debug": "Debug",
-            "trace": "Traccia",
-            "logLevel": "Livello di registro"
-        },
-        "logNamespaces": {
-            "workflows": "Flussi di lavoro",
-            "generation": "Generazione",
-            "canvas": "Tela",
-            "config": "Configurazione",
-            "models": "Modelli",
-            "gallery": "Galleria",
-            "queue": "Coda",
-            "events": "Eventi",
-            "system": "Sistema",
-            "metadata": "Metadati",
-            "logNamespaces": "Elementi del registro"
-        },
-        "enableLogging": "Abilita la registrazione"
     }
 }

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

@@ -221,6 +221,8 @@
         "uploadImage": "画像をアップロード",
         "previousImage": "前の画像",
         "nextImage": "次の画像",
+        "showOptionsPanel": "サイドパネルを表示",
+        "showGalleryPanel": "ギャラリーパネルを表示",
         "menu": "メニュー",
         "createIssue": "問題を報告",
         "resetUI": "$t(accessibility.reset) UI",

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

@@ -92,7 +92,9 @@
         "mode": "모드",
         "menu": "메뉴",
         "uploadImage": "이미지 업로드",
-        "reset": "리셋"
+        "showGalleryPanel": "갤러리 패널 표시",
+        "reset": "리셋",
+        "showOptionsPanel": "사이드 패널 표시"
     },
     "modelManager": {
         "availableModels": "사용 가능한 모델",

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

@@ -326,7 +326,9 @@
         "uploadImage": "Upload afbeelding",
         "previousImage": "Vorige afbeelding",
         "nextImage": "Volgende afbeelding",
+        "showOptionsPanel": "Toon zijscherm",
         "menu": "Menu",
+        "showGalleryPanel": "Toon deelscherm Galerij",
         "about": "Over",
         "mode": "Modus",
         "resetUI": "$t(accessibility.reset) UI",

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

@@ -65,6 +65,7 @@
         "uploadImage": "Wgrywanie obrazu",
         "previousImage": "Poprzedni obraz",
         "nextImage": "Następny obraz",
+        "showOptionsPanel": "Pokaż panel opcji",
         "menu": "Menu"
     }
 }

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

@@ -104,6 +104,7 @@
         "invokeProgressBar": "Invocar barra de progresso",
         "reset": "Reiniciar",
         "nextImage": "Próxima imagem",
+        "showOptionsPanel": "Mostrar painel de opções",
         "uploadImage": "Enviar imagem",
         "previousImage": "Imagem Anterior",
         "menu": "Menu",
@@ -111,7 +112,8 @@
         "resetUI": "$t(accessibility.reset)UI",
         "createIssue": "Reportar Problema",
         "submitSupportTicket": "Submeter um ticket de Suporte",
-        "mode": "Modo"
+        "mode": "Modo",
+        "showGalleryPanel": "Mostrar Painel de Galeria"
     },
     "boards": {
         "selectedForAutoAdd": "Selecionado para Auto-Adicionar",

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

@@ -93,9 +93,7 @@
         "placeholderSelectAModel": "Выбрать модель",
         "reset": "Сброс",
         "none": "Ничего",
-        "new": "Новый",
-        "ok": "Ok",
-        "close": "Закрыть"
+        "new": "Новый"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -159,11 +157,7 @@
         "move": "Двигать",
         "gallery": "Галерея",
         "openViewer": "Открыть просмотрщик",
-        "closeViewer": "Закрыть просмотрщик",
-        "imagesTab": "Изображения, созданные и сохраненные в Invoke.",
-        "assetsTab": "Файлы, которые вы загрузили для использования в своих проектах.",
-        "boardsSettings": "Настройки доски",
-        "imagesSettings": "Настройки галереи изображений"
+        "closeViewer": "Закрыть просмотрщик"
     },
     "hotkeys": {
         "searchHotkeys": "Поиск горячих клавиш",
@@ -233,118 +227,6 @@
             "selectBrushTool": {
                 "title": "Инструмент кисть",
                 "desc": "Выбирает кисть."
-            },
-            "selectBboxTool": {
-                "title": "Инструмент рамка",
-                "desc": "Выбрать инструмент «Ограничительная рамка»."
-            },
-            "incrementToolWidth": {
-                "desc": "Increment the brush or eraser tool width, whichever is selected.",
-                "title": "Increment Tool Width"
-            },
-            "selectColorPickerTool": {
-                "title": "Color Picker Tool",
-                "desc": "Select the color picker tool."
-            },
-            "prevEntity": {
-                "title": "Prev Layer",
-                "desc": "Select the previous layer in the list."
-            },
-            "filterSelected": {
-                "title": "Filter",
-                "desc": "Filter the selected layer. Only applies to Raster and Control layers."
-            },
-            "undo": {
-                "desc": "Отменяет последнее действие на холсте.",
-                "title": "Отменить"
-            },
-            "transformSelected": {
-                "title": "Transform",
-                "desc": "Transform the selected layer."
-            },
-            "setZoomTo400Percent": {
-                "title": "Zoom to 400%",
-                "desc": "Set the canvas zoom to 400%."
-            },
-            "setZoomTo200Percent": {
-                "title": "Zoom to 200%",
-                "desc": "Set the canvas zoom to 200%."
-            },
-            "deleteSelected": {
-                "desc": "Delete the selected layer.",
-                "title": "Delete Layer"
-            },
-            "resetSelected": {
-                "title": "Reset Layer",
-                "desc": "Reset the selected layer. Only applies to Inpaint Mask and Regional Guidance."
-            },
-            "redo": {
-                "desc": "Возвращает последнее отмененное действие.",
-                "title": "Вернуть"
-            },
-            "nextEntity": {
-                "title": "Next Layer",
-                "desc": "Select the next layer in the list."
-            },
-            "setFillToWhite": {
-                "title": "Set Color to White",
-                "desc": "Set the current tool color to white."
-            },
-            "applyFilter": {
-                "title": "Apply Filter",
-                "desc": "Apply the pending filter to the selected layer."
-            },
-            "cancelFilter": {
-                "title": "Cancel Filter",
-                "desc": "Cancel the pending filter."
-            },
-            "applyTransform": {
-                "desc": "Apply the pending transform to the selected layer.",
-                "title": "Apply Transform"
-            },
-            "cancelTransform": {
-                "title": "Cancel Transform",
-                "desc": "Cancel the pending transform."
-            },
-            "selectEraserTool": {
-                "title": "Eraser Tool",
-                "desc": "Select the eraser tool."
-            },
-            "fitLayersToCanvas": {
-                "desc": "Scale and position the view to fit all visible layers.",
-                "title": "Fit Layers to Canvas"
-            },
-            "decrementToolWidth": {
-                "title": "Decrement Tool Width",
-                "desc": "Decrement the brush or eraser tool width, whichever is selected."
-            },
-            "setZoomTo800Percent": {
-                "title": "Zoom to 800%",
-                "desc": "Set the canvas zoom to 800%."
-            },
-            "quickSwitch": {
-                "title": "Layer Quick Switch",
-                "desc": "Switch between the last two selected layers. If a layer is bookmarked, always switch between it and the last non-bookmarked layer."
-            },
-            "fitBboxToCanvas": {
-                "title": "Fit Bbox to Canvas",
-                "desc": "Scale and position the view to fit the bbox."
-            },
-            "setZoomTo100Percent": {
-                "title": "Zoom to 100%",
-                "desc": "Set the canvas zoom to 100%."
-            },
-            "selectMoveTool": {
-                "desc": "Select the move tool.",
-                "title": "Move Tool"
-            },
-            "selectRectTool": {
-                "title": "Rect Tool",
-                "desc": "Select the rect tool."
-            },
-            "selectViewTool": {
-                "title": "View Tool",
-                "desc": "Select the view tool."
             }
         },
         "hotkeys": "Горячие клавиши",
@@ -354,33 +236,11 @@
                 "desc": "Отменить последнее действие в рабочем процессе."
             },
             "deleteSelection": {
-                "desc": "Удалить выделенные узлы и ребра.",
-                "title": "Delete"
+                "desc": "Удалить выделенные узлы и ребра."
             },
             "redo": {
                 "title": "Вернуть",
                 "desc": "Вернуть последнее действие в рабочем процессе."
-            },
-            "copySelection": {
-                "title": "Copy",
-                "desc": "Copy selected nodes and edges."
-            },
-            "pasteSelection": {
-                "title": "Paste",
-                "desc": "Paste copied nodes and edges."
-            },
-            "addNode": {
-                "desc": "Open the add node menu.",
-                "title": "Add Node"
-            },
-            "title": "Workflows",
-            "pasteSelectionWithEdges": {
-                "title": "Paste with Edges",
-                "desc": "Paste copied nodes, edges, and all edges connected to copied nodes."
-            },
-            "selectAll": {
-                "desc": "Select all nodes and edges.",
-                "title": "Select All"
             }
         },
         "viewer": {
@@ -397,84 +257,12 @@
                 "title": "Восстановить все метаданные"
             },
             "swapImages": {
-                "desc": "Поменять местами сравниваемые изображения.",
-                "title": "Swap Comparison Images"
+                "desc": "Поменять местами сравниваемые изображения."
             },
             "title": "Просмотрщик изображений",
             "toggleViewer": {
                 "title": "Открыть/закрыть просмотрщик",
                 "desc": "Показать или скрыть просмотрщик изображений. Доступно только на вкладке «Холст»."
-            },
-            "recallSeed": {
-                "title": "Recall Seed",
-                "desc": "Recall the seed for the current image."
-            },
-            "recallPrompts": {
-                "desc": "Recall the positive and negative prompts for the current image.",
-                "title": "Recall Prompts"
-            },
-            "remix": {
-                "title": "Remix",
-                "desc": "Recall all metadata except for the seed for the current image."
-            },
-            "useSize": {
-                "desc": "Use the current image's size as the bbox size.",
-                "title": "Use Size"
-            },
-            "runPostprocessing": {
-                "title": "Run Postprocessing",
-                "desc": "Run the selected postprocessing on the current image."
-            },
-            "toggleMetadata": {
-                "title": "Show/Hide Metadata",
-                "desc": "Show or hide the current image's metadata overlay."
-            }
-        },
-        "gallery": {
-            "galleryNavRightAlt": {
-                "desc": "Same as Navigate Right, but selects the compare image, opening compare mode if it isn't already open.",
-                "title": "Navigate Right (Compare Image)"
-            },
-            "galleryNavRight": {
-                "desc": "Navigate right in the gallery grid, selecting that image. If at the last image of the row, go to the next row. If at the last image of the page, go to the next page.",
-                "title": "Navigate Right"
-            },
-            "galleryNavUp": {
-                "desc": "Navigate up in the gallery grid, selecting that image. If at the top of the page, go to the previous page.",
-                "title": "Navigate Up"
-            },
-            "galleryNavDown": {
-                "title": "Navigate Down",
-                "desc": "Navigate down in the gallery grid, selecting that image. If at the bottom of the page, go to the next page."
-            },
-            "galleryNavLeft": {
-                "title": "Navigate Left",
-                "desc": "Navigate left in the gallery grid, selecting that image. If at the first image of the row, go to the previous row. If at the first image of the page, go to the previous page."
-            },
-            "galleryNavDownAlt": {
-                "title": "Navigate Down (Compare Image)",
-                "desc": "Same as Navigate Down, but selects the compare image, opening compare mode if it isn't already open."
-            },
-            "galleryNavLeftAlt": {
-                "desc": "Same as Navigate Left, but selects the compare image, opening compare mode if it isn't already open.",
-                "title": "Navigate Left (Compare Image)"
-            },
-            "clearSelection": {
-                "desc": "Clear the current selection, if any.",
-                "title": "Clear Selection"
-            },
-            "deleteSelection": {
-                "title": "Delete",
-                "desc": "Delete all selected images. By default, you will be prompted to confirm deletion. If the images are currently in use in the app, you will be warned."
-            },
-            "galleryNavUpAlt": {
-                "title": "Navigate Up (Compare Image)",
-                "desc": "Same as Navigate Up, but selects the compare image, opening compare mode if it isn't already open."
-            },
-            "title": "Gallery",
-            "selectAllOnPage": {
-                "title": "Select All On Page",
-                "desc": "Select all images on the current page."
             }
         }
     },
@@ -584,20 +372,7 @@
         "ipAdapters": "IP адаптеры",
         "starterModelsInModelManager": "Стартовые модели можно найти в Менеджере моделей",
         "learnMoreAboutSupportedModels": "Подробнее о поддерживаемых моделях",
-        "t5Encoder": "T5 энкодер",
-        "spandrelImageToImage": "Image to Image (Spandrel)",
-        "clipEmbed": "CLIP Embed",
-        "installingXModels_one": "Установка {{count}} модели",
-        "installingXModels_few": "Установка {{count}} моделей",
-        "installingXModels_many": "Установка {{count}} моделей",
-        "installingBundle": "Установка пакета",
-        "installingModel": "Установка модели",
-        "starterBundles": "Стартовые пакеты",
-        "skippingXDuplicates_one": ", пропуская {{count}} дубликат",
-        "skippingXDuplicates_few": ", пропуская {{count}} дубликата",
-        "skippingXDuplicates_many": ", пропуская {{count}} дубликатов",
-        "includesNModels": "Включает в себя {{n}} моделей и их зависимостей",
-        "starterBundleHelpText": "Легко установите все модели, необходимые для начала работы с базовой моделью, включая основную модель, сети управления, IP-адаптеры и многое другое. При выборе комплекта все уже установленные модели будут пропущены."
+        "t5Encoder": "T5 энкодер"
     },
     "parameters": {
         "images": "Изображения",
@@ -657,16 +432,12 @@
                 "rgNoRegion": "регион не выбран",
                 "rgNoPromptsOrIPAdapters": "нет текстовых запросов или IP-адаптеров",
                 "ipAdapterIncompatibleBaseModel": "несовместимая базовая модель IP-адаптера",
-                "ipAdapterNoImageSelected": "изображение IP-адаптера не выбрано",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, масштабированная ширина рамки {{width}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, высота рамки {{height}}",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, ширина рамки {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, масштабированная высота рамки {{height}}"
+                "ipAdapterNoImageSelected": "изображение IP-адаптера не выбрано"
             },
-            "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), ширина рамки {{width}}",
-            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), высота рамки {{height}}",
-            "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), масштабированная высота рамки {{height}}",
-            "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16) масштабированная ширина рамки {{width}}",
+            "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), ширина bbox {{width}}",
+            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), высота bbox {{height}}",
+            "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), масштабированная высота bbox {{height}}",
+            "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16) масштабированная ширина bbox {{width}}",
             "noFLUXVAEModelSelected": "Для генерации FLUX не выбрана модель VAE",
             "noT5EncoderModelSelected": "Для генерации FLUX не выбрана модель T5 энкодера",
             "canvasIsFiltering": "Холст фильтруется",
@@ -699,8 +470,7 @@
         "staged": "Инсценировка",
         "optimizedImageToImage": "Оптимизированное img2img",
         "sendToCanvas": "Отправить на холст",
-        "guidance": "Точность",
-        "boxBlur": "Box Blur"
+        "guidance": "Точность"
     },
     "settings": {
         "models": "Модели",
@@ -734,8 +504,7 @@
         "intermediatesClearedFailed": "Проблема очистки промежуточных",
         "reloadingIn": "Перезагрузка через",
         "informationalPopoversDisabled": "Информационные всплывающие окна отключены",
-        "informationalPopoversDisabledDesc": "Информационные всплывающие окна были отключены. Включите их в Настройках.",
-        "confirmOnNewSession": "Подтверждение нового сеанса"
+        "informationalPopoversDisabledDesc": "Информационные всплывающие окна были отключены. Включите их в Настройках."
     },
     "toast": {
         "uploadFailed": "Загрузка не удалась",
@@ -744,19 +513,19 @@
         "serverError": "Ошибка сервера",
         "connected": "Подключено к серверу",
         "canceled": "Обработка отменена",
-        "uploadFailedInvalidUploadDesc": "Это должны быть изображения PNG или JPEG.",
+        "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
         "parameterNotSet": "Параметр не задан",
         "parameterSet": "Параметр задан",
         "problemCopyingImage": "Не удается скопировать изображение",
         "baseModelChangedCleared_one": "Очищена или отключена {{count}} несовместимая подмодель",
-        "baseModelChangedCleared_few": "Очищено или отключено {{count}} несовместимых подмодели",
-        "baseModelChangedCleared_many": "Очищено или отключено {{count}} несовместимых подмоделей",
+        "baseModelChangedCleared_few": "Очищены или отключены {{count}} несовместимые подмодели",
+        "baseModelChangedCleared_many": "Очищены или отключены {{count}} несовместимых подмоделей",
         "loadedWithWarnings": "Рабочий процесс загружен с предупреждениями",
         "setControlImage": "Установить как контрольное изображение",
         "setNodeField": "Установить как поле узла",
         "invalidUpload": "Неверная загрузка",
         "imageUploaded": "Изображение загружено",
-        "addedToBoard": "Добавлено в активы доски {{name}}",
+        "addedToBoard": "Добавлено на доску",
         "workflowLoaded": "Рабочий процесс загружен",
         "problemDeletingWorkflow": "Проблема с удалением рабочего процесса",
         "modelAddedSimple": "Модель добавлена в очередь",
@@ -791,29 +560,22 @@
         "unableToLoadStylePreset": "Невозможно загрузить предустановку стиля",
         "layerCopiedToClipboard": "Слой скопирован в буфер обмена",
         "sentToUpscale": "Отправить на увеличение",
-        "layerSavedToAssets": "Слой сохранен в активах",
-        "linkCopied": "Ссылка скопирована",
-        "addedToUncategorized": "Добавлено в активы доски $t(boards.uncategorized)",
-        "imagesWillBeAddedTo": "Загруженные изображения будут добавлены в активы доски {{boardName}}.",
-        "uploadFailedInvalidUploadDesc_withCount_one": "Должно быть не более {{count}} изображения в формате PNG или JPEG.",
-        "uploadFailedInvalidUploadDesc_withCount_few": "Должно быть не более {{count}} изображений в формате PNG или JPEG.",
-        "uploadFailedInvalidUploadDesc_withCount_many": "Должно быть не более {{count}} изображений в формате PNG или JPEG."
+        "layerSavedToAssets": "Слой сохранен в активах"
     },
     "accessibility": {
         "uploadImage": "Загрузить изображение",
         "nextImage": "Следующее изображение",
         "previousImage": "Предыдущее изображение",
+        "showOptionsPanel": "Показать боковую панель",
         "invokeProgressBar": "Индикатор выполнения",
         "reset": "Сброс",
         "menu": "Меню",
+        "showGalleryPanel": "Показать панель галереи",
         "mode": "Режим",
         "resetUI": "$t(accessibility.reset) интерфейс",
         "createIssue": "Сообщить о проблеме",
         "about": "Об этом",
-        "submitSupportTicket": "Отправить тикет в службу поддержки",
-        "toggleRightPanel": "Переключить правую панель (G)",
-        "toggleLeftPanel": "Переключить левую панель (T)",
-        "uploadImages": "Загрузить изображения"
+        "submitSupportTicket": "Отправить тикет в службу поддержки"
     },
     "nodes": {
         "zoomInNodes": "Увеличьте масштаб",
@@ -951,10 +713,7 @@
         "imageAccessError": "Невозможно найти изображение {{image_name}}, сбрасываем на значение по умолчанию",
         "boardAccessError": "Невозможно найти доску {{board_id}}, сбрасываем на значение по умолчанию",
         "modelAccessError": "Невозможно найти модель {{key}}, сброс на модель по умолчанию",
-        "saveToGallery": "Сохранить в галерею",
-        "noWorkflows": "Нет рабочих процессов",
-        "noMatchingWorkflows": "Нет совпадающих рабочих процессов",
-        "workflowHelpText": "Нужна помощь? Ознакомьтесь с нашим руководством <LinkComponent>Getting Started with Workflows</LinkComponent>."
+        "saveToGallery": "Сохранить в галерею"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -973,16 +732,16 @@
         "loading": "Загрузка...",
         "clearSearch": "Очистить поиск",
         "deleteBoardOnly": "Удалить только доску",
-        "movingImagesToBoard_one": "Перемещение {{count}} изображения на доску:",
-        "movingImagesToBoard_few": "Перемещение {{count}} изображений на доску:",
-        "movingImagesToBoard_many": "Перемещение {{count}} изображений на доску:",
+        "movingImagesToBoard_one": "Перемещаем {{count}} изображение на доску:",
+        "movingImagesToBoard_few": "Перемещаем {{count}} изображения на доску:",
+        "movingImagesToBoard_many": "Перемещаем {{count}} изображений на доску:",
         "downloadBoard": "Скачать доску",
         "deleteBoard": "Удалить доску",
         "deleteBoardAndImages": "Удалить доску и изображения",
         "deletedBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в состояние без категории.",
-        "assetsWithCount_one": "{{count}} актив",
-        "assetsWithCount_few": "{{count}} актива",
-        "assetsWithCount_many": "{{count}} активов",
+        "assetsWithCount_one": "{{count}} ассет",
+        "assetsWithCount_few": "{{count}} ассета",
+        "assetsWithCount_many": "{{count}} ассетов",
         "imagesWithCount_one": "{{count}} изображение",
         "imagesWithCount_few": "{{count}} изображения",
         "imagesWithCount_many": "{{count}} изображений",
@@ -998,8 +757,7 @@
         "hideBoards": "Скрыть доски",
         "viewBoards": "Просмотреть доски",
         "noBoards": "Нет досок {{boardType}}",
-        "deletedPrivateBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в приватное состояние без категории для создателя изображения.",
-        "updateBoardError": "Ошибка обновления доски"
+        "deletedPrivateBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в приватное состояние без категории для создателя изображения."
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1430,8 +1188,7 @@
         "recallParameter": "Отозвать {{label}}",
         "allPrompts": "Все запросы",
         "imageDimensions": "Размеры изображения",
-        "canvasV2Metadata": "Холст",
-        "guidance": "Точность"
+        "canvasV2Metadata": "Холст"
     },
     "queue": {
         "status": "Статус",
@@ -1580,15 +1337,7 @@
         "autoLayout": "Автоматическое расположение",
         "userWorkflows": "Пользовательские рабочие процессы",
         "projectWorkflows": "Рабочие процессы проекта",
-        "defaultWorkflows": "Стандартные рабочие процессы",
-        "deleteWorkflow2": "Вы уверены, что хотите удалить этот рабочий процесс? Это нельзя отменить.",
-        "chooseWorkflowFromLibrary": "Выбрать рабочий процесс из библиотеки",
-        "uploadAndSaveWorkflow": "Загрузить в библиотеку",
-        "edit": "Редактировать",
-        "download": "Скачать",
-        "copyShareLink": "Скопировать ссылку на общий доступ",
-        "copyShareLinkForWorkflow": "Скопировать ссылку на общий доступ для рабочего процесса",
-        "delete": "Удалить"
+        "defaultWorkflows": "Стандартные рабочие процессы"
     },
     "hrf": {
         "enableHrf": "Включить исправление высокого разрешения",
@@ -1645,15 +1394,15 @@
         "autoNegative": "Авто негатив",
         "deletePrompt": "Удалить запрос",
         "rectangle": "Прямоугольник",
-        "addNegativePrompt": "Добавить $t(controlLayers.negativePrompt)",
+        "addNegativePrompt": "Добавить $t(common.negativePrompt)",
         "regionalGuidance": "Региональная точность",
         "opacity": "Непрозрачность",
         "addLayer": "Добавить слой",
         "moveToFront": "На передний план",
-        "addPositivePrompt": "Добавить $t(controlLayers.prompt)",
+        "addPositivePrompt": "Добавить $t(common.positivePrompt)",
         "regional": "Региональный",
         "bookmark": "Закладка для быстрого переключения",
-        "fitBboxToLayers": "Подогнать рамку к слоям",
+        "fitBboxToLayers": "Подогнать Bbox к слоям",
         "mergeVisibleOk": "Объединенные видимые слои",
         "mergeVisibleError": "Ошибка объединения видимых слоев",
         "clearHistory": "Очистить историю",
@@ -1662,7 +1411,7 @@
         "saveLayerToAssets": "Сохранить слой в активы",
         "clearCaches": "Очистить кэши",
         "recalculateRects": "Пересчитать прямоугольники",
-        "saveBboxToGallery": "Сохранить рамку в галерею",
+        "saveBboxToGallery": "Сохранить Bbox в галерею",
         "resetCanvas": "Сбросить холст",
         "canvas": "Холст",
         "global": "Глобальный",
@@ -1674,284 +1423,15 @@
         "newRasterLayerOk": "Создан растровый слой",
         "newRasterLayerError": "Ошибка создания растрового слоя",
         "newGlobalReferenceImageOk": "Создано глобальное эталонное изображение",
-        "bboxOverlay": "Показать наложение ограничительной рамки",
+        "bboxOverlay": "Показать наложение Bbox",
         "saveCanvasToGallery": "Сохранить холст в галерею",
-        "pullBboxIntoReferenceImageOk": "рамка перенесена в эталонное изображение",
-        "pullBboxIntoReferenceImageError": "Ошибка переноса рамки в эталонное изображение",
+        "pullBboxIntoReferenceImageOk": "Bbox перенесен в эталонное изображение",
+        "pullBboxIntoReferenceImageError": "Ошибка переноса BBox в эталонное изображение",
         "regionIsEmpty": "Выбранный регион пуст",
         "savedToGalleryOk": "Сохранено в галерею",
         "savedToGalleryError": "Ошибка сохранения в галерею",
-        "pullBboxIntoLayerOk": "Рамка перенесена в слой",
-        "pullBboxIntoLayerError": "Проблема с переносом рамки в слой",
-        "newLayerFromImage": "Новый слой из изображения",
-        "filter": {
-            "lineart_anime_edge_detection": {
-                "label": "Обнаружение краев Lineart Anime",
-                "description": "Создает карту краев выбранного слоя с помощью модели обнаружения краев Lineart Anime."
-            },
-            "hed_edge_detection": {
-                "scribble": "Штрих",
-                "label": "обнаружение границ HED",
-                "description": "Создает карту границ из выбранного слоя с использованием модели обнаружения границ HED."
-            },
-            "mlsd_detection": {
-                "description": "Генерирует карту сегментов линий из выбранного слоя с помощью модели обнаружения сегментов линий MLSD.",
-                "score_threshold": "Пороговый балл",
-                "distance_threshold": "Порог расстояния",
-                "label": "Обнаружение сегментов линии"
-            },
-            "canny_edge_detection": {
-                "low_threshold": "Низкий порог",
-                "high_threshold": "Высокий порог",
-                "label": "Обнаружение краев",
-                "description": "Создает карту краев выбранного слоя с помощью алгоритма обнаружения краев Canny."
-            },
-            "color_map": {
-                "description": "Создайте цветовую карту из выбранного слоя.",
-                "label": "Цветная карта",
-                "tile_size": "Размер плитки"
-            },
-            "depth_anything_depth_estimation": {
-                "model_size_base": "Базовая",
-                "model_size_large": "Большая",
-                "label": "Анализ глубины",
-                "model_size_small": "Маленькая",
-                "model_size_small_v2": "Маленькая v2",
-                "description": "Создает карту глубины из выбранного слоя с использованием модели Depth Anything.",
-                "model_size": "Размер модели"
-            },
-            "mediapipe_face_detection": {
-                "min_confidence": "Минимальная уверенность",
-                "label": "Распознавание лиц MediaPipe",
-                "description": "Обнаруживает лица в выбранном слое с помощью модели обнаружения лиц MediaPipe.",
-                "max_faces": "Максимум лиц"
-            },
-            "lineart_edge_detection": {
-                "label": "Обнаружение краев Lineart",
-                "description": "Создает карту краев выбранного слоя с помощью модели обнаружения краев Lineart.",
-                "coarse": "Грубый"
-            },
-            "filterType": "Тип фильтра",
-            "autoProcess": "Автообработка",
-            "reset": "Сбросить",
-            "content_shuffle": {
-                "scale_factor": "Коэффициент",
-                "label": "Перетасовка контента",
-                "description": "Перемешивает содержимое выбранного слоя, аналогично эффекту «сжижения»."
-            },
-            "dw_openpose_detection": {
-                "label": "Обнаружение DW Openpose",
-                "draw_hands": "Рисовать руки",
-                "description": "Обнаруживает позы человека в выбранном слое с помощью модели DW Openpose.",
-                "draw_face": "Рисовать лицо",
-                "draw_body": "Рисовать тело"
-            },
-            "normal_map": {
-                "label": "Карта нормалей",
-                "description": "Создает карту нормалей для выбранного слоя."
-            },
-            "spandrel_filter": {
-                "model": "Модель",
-                "label": "Модель img2img",
-                "autoScale": "Авто масштабирование",
-                "scale": "Целевой масштаб",
-                "description": "Запустить модель изображения к изображению на выбранном слое.",
-                "autoScaleDesc": "Выбранная модель будет работать до тех пор, пока не будет достигнут целевой масштаб."
-            },
-            "pidi_edge_detection": {
-                "scribble": "Штрих",
-                "description": "Генерирует карту краев из выбранного слоя с помощью модели обнаружения краев PiDiNet.",
-                "label": "Обнаружение краев PiDiNet",
-                "quantize_edges": "Квантизация краев"
-            },
-            "process": "Обработать",
-            "apply": "Применить",
-            "cancel": "Отменить",
-            "filter": "Фильтр",
-            "filters": "Фильтры"
-        },
-        "HUD": {
-            "entityStatus": {
-                "isHidden": "{{title}} скрыт",
-                "isLocked": "{{title}} заблокирован",
-                "isDisabled": "{{title}} отключен",
-                "isEmpty": "{{title}} пуст",
-                "isFiltering": "{{title}} фильтруется",
-                "isTransforming": "{{title}} трансформируется"
-            },
-            "scaledBbox": "Масштабированная рамка",
-            "bbox": "Ограничительная рамка"
-        },
-        "canvasContextMenu": {
-            "saveBboxToGallery": "Сохранить рамку в галерею",
-            "newGlobalReferenceImage": "Новое глобальное эталонное изображение",
-            "bboxGroup": "Сохдать из рамки",
-            "canvasGroup": "Холст",
-            "newControlLayer": "Новый контрольный слой",
-            "newRasterLayer": "Новый растровый слой",
-            "saveToGalleryGroup": "Сохранить в галерею",
-            "saveCanvasToGallery": "Сохранить холст в галерею",
-            "cropCanvasToBbox": "Обрезать холст по рамке",
-            "newRegionalReferenceImage": "Новое региональное эталонное изображение"
-        },
-        "fill": {
-            "solid": "Сплошной",
-            "fillStyle": "Стиль заполнения",
-            "fillColor": "Цвет заполнения",
-            "grid": "Сетка",
-            "horizontal": "Горизонтальная",
-            "diagonal": "Диагональная",
-            "crosshatch": "Штриховка",
-            "vertical": "Вертикальная"
-        },
-        "showHUD": "Показать HUD",
-        "copyToClipboard": "Копировать в буфер обмена",
-        "ipAdapterMethod": {
-            "composition": "Только композиция",
-            "style": "Только стиль",
-            "ipAdapterMethod": "Метод IP адаптера",
-            "full": "Полный"
-        },
-        "addReferenceImage": "Добавить $t(controlLayers.referenceImage)",
-        "inpaintMask": "Маска перерисовки",
-        "sendToGalleryDesc": "При нажатии кнопки Invoke создается изображение и сохраняется в вашей галерее.",
-        "sendToCanvas": "Отправить на холст",
-        "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
-        "regionalGuidance_withCount_few": "Региональных точности",
-        "regionalGuidance_withCount_many": "Региональных точностей",
-        "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
-        "controlLayer_withCount_few": "Контрольных слоя",
-        "controlLayer_withCount_many": "Контрольных слоев",
-        "newCanvasFromImage": "Новый холст из изображения",
-        "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
-        "inpaintMask_withCount_few": "Маски перерисовки",
-        "inpaintMask_withCount_many": "Масок перерисовки",
-        "globalReferenceImages_withCount_visible": "Глобальные эталонные изображения ({{count}})",
-        "controlMode": {
-            "prompt": "Запрос",
-            "controlMode": "Режим контроля",
-            "megaControl": "Мега контроль",
-            "balanced": "Сбалансированный",
-            "control": "Контроль"
-        },
-        "settings": {
-            "isolatedPreview": "Изолированный предпросмотр",
-            "isolatedTransformingPreview": "Изолированный предпросмотр преобразования",
-            "invertBrushSizeScrollDirection": "Инвертировать прокрутку для размера кисти",
-            "snapToGrid": {
-                "label": "Привязка к сетке",
-                "on": "Вкл",
-                "off": "Выкл"
-            },
-            "isolatedFilteringPreview": "Изолированный предпросмотр фильтрации",
-            "pressureSensitivity": "Чувствительность к давлению",
-            "isolatedStagingPreview": "Изолированный предпросмотр на промежуточной стадии",
-            "preserveMask": {
-                "label": "Сохранить замаскированную область",
-                "alert": "Сохранение замаскированной области"
-            }
-        },
-        "stagingArea": {
-            "discardAll": "Отбросить все",
-            "discard": "Отбросить",
-            "accept": "Принять",
-            "previous": "Предыдущий",
-            "next": "Следующий",
-            "saveToGallery": "Сохранить в галерею",
-            "showResultsOn": "Показать результаты",
-            "showResultsOff": "Скрыть результаты"
-        },
-        "pullBboxIntoReferenceImage": "Поместить рамку в эталонное изображение",
-        "enableAutoNegative": "Включить авто негатив",
-        "maskFill": "Заполнение маски",
-        "viewProgressInViewer": "Просматривайте прогресс и результаты в <Btn>Просмотрщике изображений</Btn>.",
-        "convertToRasterLayer": "Конвертировать в растровый слой",
-        "tool": {
-            "move": "Двигать",
-            "bbox": "Ограничительная рамка",
-            "view": "Смотреть",
-            "brush": "Кисть",
-            "eraser": "Ластик",
-            "rectangle": "Прямоугольник",
-            "colorPicker": "Подборщик цветов"
-        },
-        "rasterLayer": "Растровый слой",
-        "sendingToCanvas": "Постановка генераций на холст",
-        "rasterLayers_withCount_visible": "Растровые слои ({{count}})",
-        "regionalGuidance_withCount_hidden": "Региональная точность ({{count}} скрыто)",
-        "enableTransparencyEffect": "Включить эффект прозрачности",
-        "hidingType": "Скрыть {{type}}",
-        "addRegionalGuidance": "Добавить $t(controlLayers.regionalGuidance)",
-        "sendingToGallery": "Отправка генераций в галерею",
-        "viewProgressOnCanvas": "Просматривайте прогресс и результаты этапов на <Btn>Холсте</Btn>.",
-        "controlLayers_withCount_hidden": "Контрольные слои ({{count}} скрыто)",
-        "rasterLayers_withCount_hidden": "Растровые слои ({{count}} скрыто)",
-        "deleteSelected": "Удалить выбранное",
-        "stagingOnCanvas": "Постановка изображений на",
-        "pullBboxIntoLayer": "Поместить рамку в слой",
-        "locked": "Заблокировано",
-        "replaceLayer": "Заменить слой",
-        "width": "Ширина",
-        "controlLayer": "Слой управления",
-        "addRasterLayer": "Добавить $t(controlLayers.rasterLayer)",
-        "addControlLayer": "Добавить $t(controlLayers.controlLayer)",
-        "addInpaintMask": "Добавить $t(controlLayers.inpaintMask)",
-        "inpaintMasks_withCount_hidden": "Маски перерисовки ({{count}} скрыто)",
-        "regionalGuidance_withCount_visible": "Региональная точность ({{count}})",
-        "newGallerySessionDesc": "Это очистит холст и все настройки, кроме выбранной модели. Генерации будут отправлены в галерею.",
-        "newCanvasSession": "Новая сессия холста",
-        "newCanvasSessionDesc": "Это очистит холст и все настройки, кроме выбора модели. Генерации будут размещены на холсте.",
-        "cropLayerToBbox": "Обрезать слой по ограничительной рамке",
-        "clipToBbox": "Обрезка штрихов в рамке",
-        "outputOnlyMaskedRegions": "Вывод только маскированных областей",
-        "duplicate": "Дублировать",
-        "inpaintMasks_withCount_visible": "Маски перерисовки ({{count}})",
-        "layer_one": "Слой",
-        "layer_few": "Слоя",
-        "layer_many": "Слоев",
-        "prompt": "Запрос",
-        "negativePrompt": "Исключающий запрос",
-        "beginEndStepPercentShort": "Начало/конец %",
-        "transform": {
-            "transform": "Трансформировать",
-            "fitToBbox": "Вместить в рамку",
-            "reset": "Сбросить",
-            "apply": "Применить",
-            "cancel": "Отменить",
-            "fitModeContain": "Уместить",
-            "fitMode": "Режим подгонки",
-            "fitModeFill": "Заполнить"
-        },
-        "disableAutoNegative": "Отключить авто негатив",
-        "deleteReferenceImage": "Удалить эталонное изображение",
-        "controlLayers_withCount_visible": "Контрольные слои ({{count}})",
-        "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
-        "rasterLayer_withCount_few": "Растровых слоя",
-        "rasterLayer_withCount_many": "Растровых слоев",
-        "transparency": "Прозрачность",
-        "weight": "Вес",
-        "newGallerySession": "Новая сессия галереи",
-        "sendToCanvasDesc": "Нажатие кнопки Invoke отображает вашу текущую работу на холсте.",
-        "globalReferenceImages_withCount_hidden": "Глобальные эталонные изображения ({{count}} скрыто)",
-        "convertToControlLayer": "Конвертировать в контрольный слой",
-        "layer_withCount_one": "Слой ({{count}})",
-        "layer_withCount_few": "Слои ({{count}})",
-        "layer_withCount_many": "Слои ({{count}})",
-        "disableTransparencyEffect": "Отключить эффект прозрачности",
-        "showingType": "Показать {{type}}",
-        "dynamicGrid": "Динамическая сетка",
-        "logDebugInfo": "Писать отладочную информацию",
-        "unlocked": "Разблокировано",
-        "showProgressOnCanvas": "Показать прогресс на холсте",
-        "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
-        "globalReferenceImage_withCount_few": "Глобальных эталонных изображения",
-        "globalReferenceImage_withCount_many": "Глобальных эталонных изображений",
-        "regionalReferenceImage": "Региональное эталонное изображение",
-        "globalReferenceImage": "Глобальное эталонное изображение",
-        "sendToGallery": "Отправить в галерею",
-        "referenceImage": "Эталонное изображение",
-        "addGlobalReferenceImage": "Добавить $t(controlLayers.globalReferenceImage)",
-        "newImg2ImgCanvasFromImage": "Новое img2img из изображения"
+        "pullBboxIntoLayerOk": "Bbox перенесен в слой",
+        "pullBboxIntoLayerError": "Проблема с переносом BBox в слой"
     },
     "ui": {
         "tabs": {
@@ -1963,8 +1443,7 @@
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "queue": "Очередь",
             "upscaling": "Увеличение",
-            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
-            "gallery": "Галерея"
+            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
     },
     "upscaling": {
@@ -2036,45 +1515,5 @@
         "professional": "Профессионал",
         "professionalUpsell": "Доступно в профессиональной версии Invoke. Нажмите здесь или посетите invoke.com/pricing для получения более подробной информации.",
         "shareAccess": "Поделиться доступом"
-    },
-    "system": {
-        "logNamespaces": {
-            "canvas": "Холст",
-            "config": "Конфигурация",
-            "generation": "Генерация",
-            "workflows": "Рабочие процессы",
-            "gallery": "Галерея",
-            "models": "Модели",
-            "logNamespaces": "Пространства имен логов",
-            "events": "События",
-            "system": "Система",
-            "queue": "Очередь",
-            "metadata": "Метаданные"
-        },
-        "enableLogging": "Включить логи",
-        "logLevel": {
-            "logLevel": "Уровень логов",
-            "fatal": "Фатальное",
-            "debug": "Отладка",
-            "info": "Инфо",
-            "warn": "Предупреждение",
-            "error": "Ошибки",
-            "trace": "Трассировка"
-        }
-    },
-    "whatsNew": {
-        "canvasV2Announcement": {
-            "newLayerTypes": "Новые типы слоев для еще большего контроля",
-            "readReleaseNotes": "Прочитать информацию о выпуске",
-            "watchReleaseVideo": "Смотреть видео о выпуске",
-            "fluxSupport": "Поддержка семейства моделей Flux",
-            "newCanvas": "Новый мощный холст управления",
-            "watchUiUpdatesOverview": "Обзор обновлений пользовательского интерфейса"
-        },
-        "whatsNewInInvoke": "Что нового в Invoke"
-    },
-    "newUserExperience": {
-        "toGetStarted": "Чтобы начать работу, введите в поле запрос и нажмите <StrongComponent>Invoke</StrongComponent>, чтобы сгенерировать первое изображение. Выберите шаблон запроса, чтобы улучшить результаты. Вы можете сохранить изображения непосредственно в <StrongComponent>Галерею</StrongComponent> или отредактировать их на <StrongComponent>Холсте</StrongComponent>.",
-        "gettingStartedSeries": "Хотите получить больше рекомендаций? Ознакомьтесь с нашей серией <LinkComponent>Getting Started Series</LinkComponent> для получения советов по раскрытию всего потенциала Invoke Studio."
     }
 }

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

@@ -4,7 +4,8 @@
         "invokeProgressBar": "Invoke förloppsmätare",
         "nextImage": "Nästa bild",
         "reset": "Starta om",
-        "previousImage": "Föregående bild"
+        "previousImage": "Föregående bild",
+        "showOptionsPanel": "Visa inställningspanelen"
     },
     "common": {
         "hotkeysLabel": "Snabbtangenter",

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

@@ -2,6 +2,7 @@
     "accessibility": {
         "invokeProgressBar": "Invoke durum çubuğu",
         "nextImage": "Sonraki Görsel",
+        "showOptionsPanel": "Yan Paneli Göster",
         "reset": "Resetle",
         "uploadImage": "Görsel Yükle",
         "previousImage": "Önceki Görsel",
@@ -9,6 +10,7 @@
         "about": "Hakkında",
         "mode": "Kip",
         "resetUI": "$t(accessibility.reset)Arayüz",
+        "showGalleryPanel": "Galeri Panelini Göster",
         "createIssue": "Sorun Bildir"
     },
     "common": {

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

@@ -114,6 +114,7 @@
         "reset": "Скинути",
         "uploadImage": "Завантажити зображення",
         "previousImage": "Попереднє зображення",
+        "showOptionsPanel": "Показати опції",
         "menu": "Меню"
     }
 }