Merge remote-tracking branch 'origin/main' into bria-clone

.github/workflows/build-container.yml

@@ -45,9 +45,6 @@ jobs:
     steps:
       - name: Free up more disk space on the runner
         # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
-        # the /mnt dir has 70GBs of free space
-        # /dev/sda1        74G   28K   70G   1% /mnt
-        # According to some online posts the /mnt is not always there, so checking before setting docker to use it
         run: |
           echo "----- Free space before cleanup"
           df -h
@@ -55,11 +52,6 @@ jobs:
           sudo rm -rf "$AGENT_TOOLSDIRECTORY"
           sudo swapoff /mnt/swapfile
           sudo rm -rf /mnt/swapfile
-          if [ -d /mnt ]; then
-            sudo chmod -R 777 /mnt
-            echo '{"data-root": "/mnt/docker-root"}' | sudo tee /etc/docker/daemon.json
-            sudo systemctl restart docker
-          fi
           echo "----- Free space after cleanup"
           df -h
 

.github/workflows/typegen-checks.yml

@@ -39,18 +39,6 @@ jobs:
       - name: checkout
         uses: actions/checkout@v4
 
-      - name: Free up more disk space on the runner
-        # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
-        run: |
-          echo "----- Free space before cleanup"
-          df -h
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
-          sudo swapoff /mnt/swapfile
-          sudo rm -rf /mnt/swapfile
-          echo "----- Free space after cleanup"
-          df -h
-
       - name: check for changed files
         if: ${{ inputs.always_run != true }}
         id: changed-files

docker/.env.sample

@@ -22,10 +22,6 @@
 ## GPU_DRIVER can be set to either `cuda` or `rocm` to enable GPU support in the container accordingly.
 # GPU_DRIVER=cuda #| rocm
 
-## If you are using ROCM, you will need to ensure that the render group within the container and the host system use the same group ID.
-## To obtain the group ID of the render group on the host system, run `getent group render` and grab the number.
-# RENDER_GROUP_ID=
-
 ## CONTAINER_UID can be set to the UID of the user on the host system that should own the files in the container.
 ## It is usually not necessary to change this. Use `id -u` on the host system to find the UID.
 # CONTAINER_UID=1000

docker/Dockerfile

@@ -43,6 +43,7 @@ ENV \
     UV_MANAGED_PYTHON=1 \
     UV_LINK_MODE=copy \
     UV_PROJECT_ENVIRONMENT=/opt/venv \
+    UV_INDEX="https://download.pytorch.org/whl/cu124" \
     INVOKEAI_ROOT=/invokeai \
     INVOKEAI_HOST=0.0.0.0 \
     INVOKEAI_PORT=9090 \
@@ -73,18 +74,20 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=uv.lock,target=uv.lock \
     # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
     --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    ulimit -n 30000 && \
-    uv sync --extra $GPU_DRIVER --frozen
-
-# Link amdgpu.ids for ROCm builds
-# contributed by https://github.com/Rubonnek
-RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids" && groupadd render
+    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
+    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
+    fi && \
+    uv sync --frozen
 
 # build patchmatch
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
 RUN python -c "from patchmatch import patch_match"
 
+# Link amdgpu.ids for ROCm builds
+# contributed by https://github.com/Rubonnek
+RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
+    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
@@ -102,6 +105,8 @@ COPY invokeai ${INVOKEAI_SRC}/invokeai
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
     --mount=type=bind,source=uv.lock,target=uv.lock \
-    ulimit -n 30000 && \
-    uv pip install -e .[$GPU_DRIVER]
+    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
+    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
+    fi && \
+    uv pip install -e .
 

docker/Dockerfile-rocm-full

@@ -1,136 +0,0 @@
-# syntax=docker/dockerfile:1.4
-
-#### Web UI ------------------------------------
-
-FROM docker.io/node:22-slim AS web-builder
-ENV PNPM_HOME="/pnpm"
-ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack use pnpm@8.x
-RUN corepack enable
-
-WORKDIR /build
-COPY invokeai/frontend/web/ ./
-RUN --mount=type=cache,target=/pnpm/store \
-    pnpm install --frozen-lockfile
-RUN npx vite build
-
-## Backend ---------------------------------------
-
-FROM library/ubuntu:24.04
-
-ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt \
-    --mount=type=cache,target=/var/lib/apt \
-    apt update && apt install -y --no-install-recommends \
-    ca-certificates \
-    git \
-    gosu \
-    libglib2.0-0 \
-    libgl1 \
-    libglx-mesa0 \
-    build-essential \
-    libopencv-dev \
-    libstdc++-10-dev \
-    wget
-
-ENV \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    VIRTUAL_ENV=/opt/venv \
-    INVOKEAI_SRC=/opt/invokeai \
-    PYTHON_VERSION=3.12 \
-    UV_PYTHON=3.12 \
-    UV_COMPILE_BYTECODE=1 \
-    UV_MANAGED_PYTHON=1 \
-    UV_LINK_MODE=copy \
-    UV_PROJECT_ENVIRONMENT=/opt/venv \
-    INVOKEAI_ROOT=/invokeai \
-    INVOKEAI_HOST=0.0.0.0 \
-    INVOKEAI_PORT=9090 \
-    PATH="/opt/venv/bin:$PATH" \
-    CONTAINER_UID=${CONTAINER_UID:-1000} \
-    CONTAINER_GID=${CONTAINER_GID:-1000}
-
-ARG GPU_DRIVER=cuda
-
-# Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
-
-# Install python & allow non-root user to use it by traversing the /root dir without read permissions
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv python install ${PYTHON_VERSION} && \
-    # chmod --recursive a+rX /root/.local/share/uv/python
-    chmod 711 /root
-
-WORKDIR ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    ulimit -n 30000 && \
-    uv sync --extra $GPU_DRIVER --frozen
-
-RUN --mount=type=cache,target=/var/cache/apt \
-    --mount=type=cache,target=/var/lib/apt \
-    if [ "$GPU_DRIVER" = "rocm" ]; then \
-    wget -O /tmp/amdgpu-install.deb \
-    https://repo.radeon.com/amdgpu-install/6.3.4/ubuntu/noble/amdgpu-install_6.3.60304-1_all.deb && \
-    apt install -y /tmp/amdgpu-install.deb && \
-    apt update && \
-    amdgpu-install --usecase=rocm -y && \
-    apt-get autoclean && \
-    apt clean && \
-    rm -rf /tmp/* /var/tmp/* && \
-    usermod -a -G render ubuntu && \
-    usermod -a -G video ubuntu && \
-    echo "\\n/opt/rocm/lib\\n/opt/rocm/lib64" >> /etc/ld.so.conf.d/rocm.conf && \
-    ldconfig && \
-    update-alternatives --auto rocm; \
-    fi
-
-## Heathen711: Leaving this for review input, will remove before merge
-# RUN --mount=type=cache,target=/var/cache/apt \
-#     --mount=type=cache,target=/var/lib/apt \
-#     if [ "$GPU_DRIVER" = "rocm" ]; then \
-#     groupadd render && \
-#     usermod -a -G render ubuntu && \
-#     usermod -a -G video ubuntu; \
-#     fi
-
-## Link amdgpu.ids for ROCm builds
-## contributed by https://github.com/Rubonnek
-# RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-#     ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
-
-# build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python -c "from patchmatch import patch_match"
-
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
-
-COPY docker/docker-entrypoint.sh ./
-ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
-CMD ["invokeai-web"]
-
-# --link requires buldkit w/ dockerfile syntax 1.4, does not work with podman
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# add sources last to minimize image changes on code changes
-COPY invokeai ${INVOKEAI_SRC}/invokeai
-
-# this should not increase image size because we've already installed dependencies
-# in a previous layer
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    ulimit -n 30000 && \
-    uv pip install -e .[$GPU_DRIVER]
-

docker/docker-compose.yml

@@ -47,9 +47,8 @@ services:
 
   invokeai-rocm:
     <<: *invokeai
-    environment:
-      - AMD_VISIBLE_DEVICES=all
-      - RENDER_GROUP_ID=${RENDER_GROUP_ID}
-    runtime: amd
+    devices:
+      - /dev/kfd:/dev/kfd
+      - /dev/dri:/dev/dri
     profiles:
       - rocm

docker/docker-entrypoint.sh

@@ -21,17 +21,6 @@ _=$(id ${USER} 2>&1) || useradd -u ${USER_ID} ${USER}
 # ensure the UID is correct
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
-## ROCM specific configuration
-# render group within the container must match the host render group
-# otherwise the container will not be able to access the host GPU.
-if [[ -v "RENDER_GROUP_ID" ]] && [[ ! -z "${RENDER_GROUP_ID}" ]]; then
-  # ensure the render group exists
-  groupmod -g ${RENDER_GROUP_ID} render
-  usermod -a -G render ${USER}
-  usermod -a -G video ${USER}
-fi
-
-
 ### Set the $PUBLIC_KEY env var to enable SSH access.
 # We do not install openssh-server in the image by default to avoid bloat.
 # but it is useful to have the full SSH server e.g. on Runpod.

docker/run.sh

@@ -13,7 +13,7 @@ run() {
 
   # parse .env file for build args
   build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
-  profile="$(awk -F '=' '/GPU_DRIVER=/ {print $2}' .env)"
+  profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
 
   # default to 'cuda' profile
   [[ -z "$profile" ]] && profile="cuda"
@@ -30,7 +30,7 @@ run() {
 
   printf "%s\n" "starting service $service_name"
   docker compose --profile "$profile" up -d "$service_name"
-  docker compose --profile "$profile" logs -f
+  docker compose logs -f
 }
 
 run

docs/contributing/MODEL_MANAGER.md

@@ -265,7 +265,7 @@ If the key is unrecognized, this call raises an
 
 #### exists(key) -> AnyModelConfig
 
-Returns True if a model with the given key exists in the database.
+Returns True if a model with the given key exists in the databsae.
 
 #### search_by_path(path) -> AnyModelConfig
 
@@ -718,7 +718,7 @@ When downloading remote models is implemented, additional
 configuration information, such as list of trigger terms, will be
 retrieved from the HuggingFace and Civitai model repositories.
 
-The probed values can be overridden by providing a dictionary in the
+The probed values can be overriden by providing a dictionary in the
 optional `config` argument passed to `import_model()`. You may provide
 overriding values for any of the model's configuration
 attributes. Here is an example of setting the
@@ -841,7 +841,7 @@ variable.
 
 #### installer.start(invoker)
 
-The `start` method is called by the API initialization routines when
+The `start` method is called by the API intialization routines when
 the API starts up. Its effect is to call `sync_to_config()` to
 synchronize the model record store database with what's currently on
 disk.

docs/contributing/contributors.md

@@ -16,7 +16,7 @@ We thank [all contributors](https://github.com/invoke-ai/InvokeAI/graphs/contrib
 - @psychedelicious (Spencer Mabrito) - Web Team Leader
 - @joshistoast (Josh Corbett) - Web Development
 - @cheerio (Mary Rogers) - Lead Engineer & Web App Development
-- @ebr (Eugene Brodsky) - Cloud/DevOps/Software engineer; your friendly neighbourhood cluster-autoscaler
+- @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
 - @sunija - Standalone version
 - @brandon (Brandon Rising) - Platform, Infrastructure, Backend Systems
 - @ryanjdick (Ryan Dick) - Machine Learning & Training

docs/installation/manual.md

@@ -69,34 +69,34 @@ The following commands vary depending on the version of Invoke being installed a
     - If you have an Nvidia 20xx series GPU or older, use `invokeai[xformers]`.
     - If you have an Nvidia 30xx series GPU or newer, or do not have an Nvidia GPU, use `invokeai`.
 
-7. Determine the torch backend to use for installation, if any. This is necessary to get the right version of torch installed. This is acheived by using [UV's built in torch support.](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection)
+7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
 
     === "Invoke v5.12 and later"
 
-        - If you are on Windows or Linux with an Nvidia GPU, use `--torch-backend=cu128`.
-        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
-        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.3`.
-        - **In all other cases, do not use a torch backend.**
+        - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu128`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
+        - **In all other cases, do not use an index.**
 
     === "Invoke v5.10.0 to v5.11.0"
 
-        - If you are on Windows or Linux with an Nvidia GPU, use `--torch-backend=cu126`.
-        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
-        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.2.4`.
+        - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu126`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v5.0.0 to v5.9.1"
 
-        - If you are on Windows with an Nvidia GPU, use `--torch-backend=cu124`.
-        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
-        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.1`.
+        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.1`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v4"
 
-        - If you are on Windows with an Nvidia GPU, use `--torch-backend=cu124`.
-        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
-        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm5.2`.
+        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm5.2`.
         - **In all other cases, do not use an index.**
 
 8. Install the `invokeai` package. Substitute the package specifier and version.
@@ -105,10 +105,10 @@ The following commands vary depending on the version of Invoke being installed a
     uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
     ```
 
-    If you determined you needed to use a torch backend in the previous step, you'll need to set the backend like this:
+    If you determined you needed to use a `PyPI` index URL in the previous step, you'll need to add `--index=<INDEX_URL>` like this:
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --torch-backend=<VERSION> --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/nodes/NODES.md

@@ -41,7 +41,7 @@ Nodes have a "Use Cache" option in their footer. This allows for performance imp
 
 There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
 
-### Create Latent Noise
+### Noise
 
 An initial noise tensor is necessary for the latent diffusion process. As a result, the Denoising node requires a noise node input.  
 

invokeai/app/api/dependencies.py

@@ -5,14 +5,11 @@ from logging import Logger
 
 import torch
 
-# from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
-# from invokeai.app.services.board_images.board_images_default import BoardImagesService
+from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
+from invokeai.app.services.board_images.board_images_default import BoardImagesService
 from invokeai.app.services.board_records.board_records_sqlite import SqliteBoardRecordStorage
-from invokeai.app.services.board_resource_records.board_resource_records_sqlite import SqliteBoardResourceRecordStorage
-from invokeai.app.services.board_resources.board_resources_default import BoardResourcesService
 from invokeai.app.services.boards.boards_default import BoardService
 from invokeai.app.services.bulk_download.bulk_download_default import BulkDownloadService
-from invokeai.app.services.client_state_persistence.client_state_persistence_sqlite import ClientStatePersistenceSqlite
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.download.download_default import DownloadQueueService
 from invokeai.app.services.events.events_fastapievents import FastAPIEventService
@@ -42,8 +39,6 @@ from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.app.services.style_preset_images.style_preset_images_disk import StylePresetImageFileStorageDisk
 from invokeai.app.services.style_preset_records.style_preset_records_sqlite import SqliteStylePresetRecordsStorage
 from invokeai.app.services.urls.urls_default import LocalUrlService
-from invokeai.app.services.video_records.video_records_sqlite import SqliteVideoRecordStorage
-from invokeai.app.services.videos.videos_default import VideoService
 from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_disk import WorkflowThumbnailFileStorageDisk
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
@@ -107,18 +102,14 @@ class ApiDependencies:
         configuration = config
         logger = logger
 
-        # board_image_records = SqliteBoardImageRecordStorage(db=db)
-        # board_images = BoardImagesService()
-        board_resource_records = SqliteBoardResourceRecordStorage(db=db)
-        board_resources = BoardResourcesService()
+        board_image_records = SqliteBoardImageRecordStorage(db=db)
+        board_images = BoardImagesService()
         board_records = SqliteBoardRecordStorage(db=db)
         boards = BoardService()
         events = FastAPIEventService(event_handler_id, loop=loop)
         bulk_download = BulkDownloadService()
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
-        video_records = SqliteVideoRecordStorage(db=db)
-        videos = VideoService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         tensors = ObjectSerializerForwardCache(
             ObjectSerializerDisk[torch.Tensor](
@@ -160,13 +151,10 @@ class ApiDependencies:
         style_preset_records = SqliteStylePresetRecordsStorage(db=db)
         style_preset_image_files = StylePresetImageFileStorageDisk(style_presets_folder / "images")
         workflow_thumbnails = WorkflowThumbnailFileStorageDisk(workflow_thumbnails_folder)
-        client_state_persistence = ClientStatePersistenceSqlite(db=db)
 
         services = InvocationServices(
-            # board_image_records=board_image_records,
-            # board_images=board_images,
-            board_resource_records=board_resource_records,
-            board_resources=board_resources,
+            board_image_records=board_image_records,
+            board_images=board_images,
             board_records=board_records,
             boards=boards,
             bulk_download=bulk_download,
@@ -187,15 +175,12 @@ class ApiDependencies:
             session_processor=session_processor,
             session_queue=session_queue,
             urls=urls,
-            videos=videos,
-            video_records=video_records,
             workflow_records=workflow_records,
             tensors=tensors,
             conditioning=conditioning,
             style_preset_records=style_preset_records,
             style_preset_image_files=style_preset_image_files,
             workflow_thumbnails=workflow_thumbnails,
-            client_state_persistence=client_state_persistence,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/routers/board_resources.py

@@ -1,93 +0,0 @@
-from fastapi import Body, HTTPException
-from fastapi.routing import APIRouter
-
-from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.board_resources.board_resources_common import ResourceType
-from invokeai.app.services.resources.resources_common import AddResourcesToBoardResult, RemoveResourcesFromBoardResult
-
-board_resources_router = APIRouter(prefix="/v1/board_resources", tags=["boards"])
-
-
-@board_resources_router.post(
-    "/batch",
-    operation_id="add_resources_to_board",
-    responses={
-        201: {"description": "Resources were added to board successfully"},
-    },
-    status_code=201,
-    response_model=AddResourcesToBoardResult,  # For now, using same response model
-)
-async def add_resources_to_board(
-    board_id: str = Body(description="The id of the board to add to"),
-    resource_ids: list[str] = Body(description="The ids of the resources to add", embed=True),
-    resource_type: ResourceType = Body(description="The type of resources"),
-) -> AddResourcesToBoardResult:
-    """Adds a list of resources to a board"""
-    try:
-        added_resources: set[str] = set()
-        affected_boards: set[str] = set()
-        for resource_id in resource_ids:
-            try:
-                if resource_type == ResourceType.IMAGE:
-                    old_board_id = ApiDependencies.invoker.services.images.get_dto(resource_id).board_id or "none"
-                else:
-                    old_board_id = ApiDependencies.invoker.services.videos.get_dto(resource_id).board_id or "none"
-
-                ApiDependencies.invoker.services.board_resources.add_resource_to_board(
-                    board_id=board_id,
-                    resource_id=resource_id,
-                    resource_type=resource_type,
-                )
-                added_resources.add(resource_id)
-                affected_boards.add(board_id)
-                affected_boards.add(old_board_id)
-
-            except Exception:
-                pass
-        return AddResourcesToBoardResult(
-            added_resources=list(added_resources),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to add resources to board")
-
-
-@board_resources_router.post(
-    "/batch/delete",
-    operation_id="remove_resources_from_board",
-    responses={
-        201: {"description": "Resources were removed from board successfully"},
-    },
-    status_code=201,
-    response_model=RemoveResourcesFromBoardResult,  # For now, using same response model
-)
-async def remove_resources_from_board(
-    resource_ids: list[str] = Body(description="The ids of the resources to remove", embed=True),
-    resource_type: ResourceType = Body(description="The type of resources", embed=True),
-) -> RemoveResourcesFromBoardResult:
-    """Removes a list of resources from their board, if they had one"""
-    try:
-        removed_resources: set[str] = set()
-        affected_boards: set[str] = set()
-        for resource_id in resource_ids:
-            try:
-                if resource_type == ResourceType.IMAGE:
-                    old_board_id = ApiDependencies.invoker.services.images.get_dto(resource_id).board_id or "none"
-                else:
-                    # For videos, we'll need to implement this once video service exists
-                    old_board_id = ApiDependencies.invoker.services.videos.get_dto(resource_id).board_id or "none"
-
-                ApiDependencies.invoker.services.board_resources.remove_resource_from_board(
-                    resource_id=resource_id, resource_type=resource_type
-                )
-                removed_resources.add(resource_id)
-                affected_boards.add("none")
-                affected_boards.add(old_board_id)
-            except Exception:
-                pass
-        return RemoveResourcesFromBoardResult(
-            removed_resources=list(removed_resources),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to remove resources from board")

invokeai/app/api/routers/boards.py

@@ -8,7 +8,6 @@ from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
 from invokeai.app.services.boards.boards_common import BoardDTO
 from invokeai.app.services.image_records.image_records_common import ImageCategory
-from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -17,10 +16,10 @@ boards_router = APIRouter(prefix="/v1/boards", tags=["boards"])
 
 class DeleteBoardResult(BaseModel):
     board_id: str = Field(description="The id of the board that was deleted.")
-    deleted_board_resources: list[str] = Field(
-        description="The resource ids of the board-resources relationships that were deleted."
+    deleted_board_images: list[str] = Field(
+        description="The image names of the board-images relationships that were deleted."
     )
-    deleted_resources: list[str] = Field(description="The names of the resources that were deleted.")
+    deleted_images: list[str] = Field(description="The names of the images that were deleted.")
 
 
 @boards_router.post(
@@ -83,34 +82,34 @@ async def update_board(
 @boards_router.delete("/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult)
 async def delete_board(
     board_id: str = Path(description="The id of board to delete"),
-    include_resources: Optional[bool] = Query(
-        description="Permanently delete all resources on the board", default=False
-    ),
+    include_images: Optional[bool] = Query(description="Permanently delete all images on the board", default=False),
 ) -> DeleteBoardResult:
     """Deletes a board"""
     try:
-        if include_resources is True:
-            deleted_resources = ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(
+        if include_images is True:
+            deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
                 board_id=board_id,
+                categories=None,
+                is_intermediate=None,
             )
             ApiDependencies.invoker.services.images.delete_images_on_board(board_id=board_id)
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
             return DeleteBoardResult(
                 board_id=board_id,
-                deleted_board_resources=[],
-                deleted_resources=deleted_resources,
+                deleted_board_images=[],
+                deleted_images=deleted_images,
             )
         else:
-            deleted_board_resources = (
-                ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(
-                    board_id=board_id,
-                )
+            deleted_board_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
+                board_id=board_id,
+                categories=None,
+                is_intermediate=None,
             )
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
             return DeleteBoardResult(
                 board_id=board_id,
-                deleted_board_resources=deleted_board_resources,
-                deleted_resources=[],
+                deleted_board_images=deleted_board_images,
+                deleted_images=[],
             )
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete board")
@@ -142,22 +141,20 @@ async def list_boards(
 
 
 @boards_router.get(
-    "/{board_id}/resource_ids",
-    operation_id="list_all_board_resource_ids",
-    response_model=list[ResourceIdentifier],
+    "/{board_id}/image_names",
+    operation_id="list_all_board_image_names",
+    response_model=list[str],
 )
-async def list_all_board_resource_ids(
+async def list_all_board_image_names(
     board_id: str = Path(description="The id of the board or 'none' for uncategorized images"),
-    resource_type: ResourceType = Query(default=ResourceType.IMAGE, description="The type of resource to include."),
     categories: list[ImageCategory] | None = Query(default=None, description="The categories of image to include."),
     is_intermediate: bool | None = Query(default=None, description="Whether to list intermediate images."),
-) -> list[ResourceIdentifier]:
+) -> list[str]:
     """Gets a list of images for a board"""
 
-    resources = ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(
+    image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
         board_id,
-        resource_type,
         categories,
         is_intermediate,
     )
-    return resources
+    return image_names

invokeai/app/api/routers/client_state.py

@@ -1,58 +0,0 @@
-from fastapi import Body, HTTPException, Path, Query
-from fastapi.routing import APIRouter
-
-from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.backend.util.logging import logging
-
-client_state_router = APIRouter(prefix="/v1/client_state", tags=["client_state"])
-
-
-@client_state_router.get(
-    "/{queue_id}/get_by_key",
-    operation_id="get_client_state_by_key",
-    response_model=str | None,
-)
-async def get_client_state_by_key(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    key: str = Query(..., description="Key to get"),
-) -> str | None:
-    """Gets the client state"""
-    try:
-        return ApiDependencies.invoker.services.client_state_persistence.get_by_key(queue_id, key)
-    except Exception as e:
-        logging.error(f"Error getting client state: {e}")
-        raise HTTPException(status_code=500, detail="Error setting client state")
-
-
-@client_state_router.post(
-    "/{queue_id}/set_by_key",
-    operation_id="set_client_state",
-    response_model=str,
-)
-async def set_client_state(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    key: str = Query(..., description="Key to set"),
-    value: str = Body(..., description="Stringified value to set"),
-) -> str:
-    """Sets the client state"""
-    try:
-        return ApiDependencies.invoker.services.client_state_persistence.set_by_key(queue_id, key, value)
-    except Exception as e:
-        logging.error(f"Error setting client state: {e}")
-        raise HTTPException(status_code=500, detail="Error setting client state")
-
-
-@client_state_router.post(
-    "/{queue_id}/delete",
-    operation_id="delete_client_state",
-    responses={204: {"description": "Client state deleted"}},
-)
-async def delete_client_state(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-) -> None:
-    """Deletes the client state"""
-    try:
-        ApiDependencies.invoker.services.client_state_persistence.delete(queue_id)
-    except Exception as e:
-        logging.error(f"Error deleting client state: {e}")
-        raise HTTPException(status_code=500, detail="Error deleting client state")

invokeai/app/api/routers/images.py

@@ -19,9 +19,13 @@ from invokeai.app.services.image_records.image_records_common import (
     ResourceOrigin,
 )
 from invokeai.app.services.images.images_common import (
+    DeleteImagesResult,
     ImageDTO,
     ImageUrlsDTO,
+    StarredImagesResult,
+    UnstarredImagesResult,
 )
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.controlnet_utils import heuristic_resize_fast
 from invokeai.backend.image_util.util import np_to_pil, pil_to_np
@@ -156,6 +160,31 @@ async def create_image_upload_entry(
     raise HTTPException(status_code=501, detail="Not implemented")
 
 
+@images_router.delete("/i/{image_name}", operation_id="delete_image", response_model=DeleteImagesResult)
+async def delete_image(
+    image_name: str = Path(description="The name of the image to delete"),
+) -> DeleteImagesResult:
+    """Deletes an image"""
+
+    deleted_images: set[str] = set()
+    affected_boards: set[str] = set()
+
+    try:
+        image_dto = ApiDependencies.invoker.services.images.get_dto(image_name)
+        board_id = image_dto.board_id or "none"
+        ApiDependencies.invoker.services.images.delete(image_name)
+        deleted_images.add(image_name)
+        affected_boards.add(board_id)
+    except Exception:
+        # TODO: Does this need any exception handling at all?
+        pass
+
+    return DeleteImagesResult(
+        deleted_images=list(deleted_images),
+        affected_boards=list(affected_boards),
+    )
+
+
 @images_router.delete("/intermediates", operation_id="clear_intermediates")
 async def clear_intermediates() -> int:
     """Clears all intermediates"""
@@ -338,6 +367,136 @@ async def get_image_urls(
         raise HTTPException(status_code=404)
 
 
+@images_router.get(
+    "/",
+    operation_id="list_image_dtos",
+    response_model=OffsetPaginatedResults[ImageDTO],
+)
+async def list_image_dtos(
+    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
+    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
+    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
+    board_id: Optional[str] = Query(
+        default=None,
+        description="The board id to filter by. Use 'none' to find images without a board.",
+    ),
+    offset: int = Query(default=0, description="The page offset"),
+    limit: int = Query(default=10, description="The number of images per page"),
+    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
+    starred_first: bool = Query(default=True, description="Whether to sort by starred images first"),
+    search_term: Optional[str] = Query(default=None, description="The term to search for"),
+) -> OffsetPaginatedResults[ImageDTO]:
+    """Gets a list of image DTOs"""
+
+    image_dtos = ApiDependencies.invoker.services.images.get_many(
+        offset, limit, starred_first, order_dir, image_origin, categories, is_intermediate, board_id, search_term
+    )
+
+    return image_dtos
+
+
+@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesResult)
+async def delete_images_from_list(
+    image_names: list[str] = Body(description="The list of names of images to delete", embed=True),
+) -> DeleteImagesResult:
+    try:
+        deleted_images: set[str] = set()
+        affected_boards: set[str] = set()
+        for image_name in image_names:
+            try:
+                image_dto = ApiDependencies.invoker.services.images.get_dto(image_name)
+                board_id = image_dto.board_id or "none"
+                ApiDependencies.invoker.services.images.delete(image_name)
+                deleted_images.add(image_name)
+                affected_boards.add(board_id)
+            except Exception:
+                pass
+        return DeleteImagesResult(
+            deleted_images=list(deleted_images),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to delete images")
+
+
+@images_router.delete("/uncategorized", operation_id="delete_uncategorized_images", response_model=DeleteImagesResult)
+async def delete_uncategorized_images() -> DeleteImagesResult:
+    """Deletes all images that are uncategorized"""
+
+    image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
+        board_id="none", categories=None, is_intermediate=None
+    )
+
+    try:
+        deleted_images: set[str] = set()
+        affected_boards: set[str] = set()
+        for image_name in image_names:
+            try:
+                ApiDependencies.invoker.services.images.delete(image_name)
+                deleted_images.add(image_name)
+                affected_boards.add("none")
+            except Exception:
+                pass
+        return DeleteImagesResult(
+            deleted_images=list(deleted_images),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to delete images")
+
+
+class ImagesUpdatedFromListResult(BaseModel):
+    updated_image_names: list[str] = Field(description="The image names that were updated")
+
+
+@images_router.post("/star", operation_id="star_images_in_list", response_model=StarredImagesResult)
+async def star_images_in_list(
+    image_names: list[str] = Body(description="The list of names of images to star", embed=True),
+) -> StarredImagesResult:
+    try:
+        starred_images: set[str] = set()
+        affected_boards: set[str] = set()
+        for image_name in image_names:
+            try:
+                updated_image_dto = ApiDependencies.invoker.services.images.update(
+                    image_name, changes=ImageRecordChanges(starred=True)
+                )
+                starred_images.add(image_name)
+                affected_boards.add(updated_image_dto.board_id or "none")
+            except Exception:
+                pass
+        return StarredImagesResult(
+            starred_images=list(starred_images),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to star images")
+
+
+@images_router.post("/unstar", operation_id="unstar_images_in_list", response_model=UnstarredImagesResult)
+async def unstar_images_in_list(
+    image_names: list[str] = Body(description="The list of names of images to unstar", embed=True),
+) -> UnstarredImagesResult:
+    try:
+        unstarred_images: set[str] = set()
+        affected_boards: set[str] = set()
+        for image_name in image_names:
+            try:
+                updated_image_dto = ApiDependencies.invoker.services.images.update(
+                    image_name, changes=ImageRecordChanges(starred=False)
+                )
+                unstarred_images.add(image_name)
+                affected_boards.add(updated_image_dto.board_id or "none")
+            except Exception:
+                pass
+        return UnstarredImagesResult(
+            unstarred_images=list(unstarred_images),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to unstar images")
+
+
 class ImagesDownloaded(BaseModel):
     response: Optional[str] = Field(
         default=None, description="The message to display to the user when images begin downloading"

invokeai/app/api/routers/resources.py

@@ -1,159 +0,0 @@
-from fastapi import Body, HTTPException
-from fastapi.routing import APIRouter
-from pydantic import BaseModel, Field
-
-from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.image_records.image_records_common import (
-    ImageRecordChanges,
-)
-from invokeai.app.services.resources.resources_common import (
-    DeleteResourcesResult,
-    ResourceIdentifier,
-    ResourceType,
-    StarredResourcesResult,
-    UnstarredResourcesResult,
-)
-from invokeai.app.services.video_records.video_records_common import VideoRecordChanges
-
-# routes that act on both images and videos, possibly together
-resources_router = APIRouter(prefix="/v1/resources", tags=["resources"])
-
-
-@resources_router.post("/delete", operation_id="delete_resources_from_list", response_model=DeleteResourcesResult)
-async def delete_resources_from_list(
-    resources: list[ResourceIdentifier] = Body(description="The list of resources to delete", embed=True),
-) -> DeleteResourcesResult:
-    try:
-        deleted_resources: set[ResourceIdentifier] = set()
-        affected_boards: set[str] = set()
-        for resource in resources:
-            if resource.resource_type == ResourceType.IMAGE:
-                try:
-                    image_dto = ApiDependencies.invoker.services.images.get_dto(resource.resource_id)
-                    board_id = image_dto.board_id or "none"
-                    ApiDependencies.invoker.services.images.delete(resource.resource_id)
-                    deleted_resources.add(resource)
-                    affected_boards.add(board_id)
-                except Exception:
-                    pass
-            elif resource.resource_type == ResourceType.VIDEO:
-                try:
-                    video_dto = ApiDependencies.invoker.services.videos.get_dto(resource.resource_id)
-                    board_id = video_dto.board_id or "none"
-                    ApiDependencies.invoker.services.videos.delete(resource.resource_id)
-                    deleted_resources.add(resource)
-                    affected_boards.add(board_id)
-                except Exception:
-                    pass
-        return DeleteResourcesResult(
-            deleted_resources=list(deleted_resources),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to delete images")
-
-
-@resources_router.delete(
-    "/uncategorized", operation_id="delete_uncategorized_resources", response_model=DeleteResourcesResult
-)
-async def delete_uncategorized_resources() -> DeleteResourcesResult:
-    """Deletes all resources that are uncategorized"""
-
-    resources = ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(board_id="none")
-
-    try:
-        deleted_resources: set[ResourceIdentifier] = set()
-        affected_boards: set[str] = set()
-        for resource in resources:
-            if resource.resource_type == ResourceType.IMAGE:
-                try:
-                    ApiDependencies.invoker.services.images.delete(resource.resource_id)
-                    deleted_resources.add(resource)
-                    affected_boards.add("none")
-                except Exception:
-                    pass
-            elif resource.resource_type == ResourceType.VIDEO:
-                try:
-                    ApiDependencies.invoker.services.videos.delete(resource.resource_id)
-                    deleted_resources.add(resource)
-                    affected_boards.add("none")
-                except Exception:
-                    pass
-        return DeleteResourcesResult(
-            deleted_resources=list(deleted_resources),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to delete images")
-
-
-class ResourcesUpdatedFromListResult(BaseModel):
-    updated_resource: list[ResourceIdentifier] = Field(description="The resource ids that were updated")
-
-
-@resources_router.post("/star", operation_id="star_resources_in_list", response_model=StarredResourcesResult)
-async def star_resources_in_list(
-    resources: list[ResourceIdentifier] = Body(description="The list of resources to star", embed=True),
-) -> StarredResourcesResult:
-    try:
-        starred_resources: set[ResourceIdentifier] = set()
-        affected_boards: set[str] = set()
-        for resource in resources:
-            if resource.resource_type == ResourceType.IMAGE:
-                try:
-                    updated_resource_dto = ApiDependencies.invoker.services.images.update(
-                        resource.resource_id, changes=ImageRecordChanges(starred=True)
-                    )
-                    starred_resources.add(resource)
-                    affected_boards.add(updated_resource_dto.board_id or "none")
-                except Exception:
-                    pass
-            elif resource.resource_type == ResourceType.VIDEO:
-                try:
-                    updated_resource_dto = ApiDependencies.invoker.services.videos.update(
-                        resource.resource_id, changes=VideoRecordChanges(starred=True)
-                    )
-                    starred_resources.add(resource)
-                    affected_boards.add(updated_resource_dto.board_id or "none")
-                except Exception:
-                    pass
-        return StarredResourcesResult(
-            starred_resources=list(starred_resources),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to star images")
-
-
-@resources_router.post("/unstar", operation_id="unstar_resources_in_list", response_model=UnstarredResourcesResult)
-async def unstar_resources_in_list(
-    resources: list[ResourceIdentifier] = Body(description="The list of resources to unstar", embed=True),
-) -> UnstarredResourcesResult:
-    try:
-        unstarred_resources: set[ResourceIdentifier] = set()
-        affected_boards: set[str] = set()
-        for resource in resources:
-            if resource.resource_type == ResourceType.IMAGE:
-                try:
-                    updated_resource_dto = ApiDependencies.invoker.services.images.update(
-                        resource.resource_id, changes=ImageRecordChanges(starred=False)
-                    )
-                    unstarred_resources.add(resource)
-                    affected_boards.add(updated_resource_dto.board_id or "none")
-                except Exception:
-                    pass
-            elif resource.resource_type == ResourceType.VIDEO:
-                try:
-                    updated_resource_dto = ApiDependencies.invoker.services.videos.update(
-                        resource.resource_id, changes=VideoRecordChanges(starred=False)
-                    )
-                    unstarred_resources.add(resource)
-                    affected_boards.add(updated_resource_dto.board_id or "none")
-                except Exception:
-                    pass
-        return UnstarredResourcesResult(
-            unstarred_resources=list(unstarred_resources),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to unstar images")

invokeai/app/api/routers/videos.py

@@ -1,116 +0,0 @@
-from typing import Optional
-
-from fastapi import Body, Path, Query, Response
-from fastapi.routing import APIRouter
-
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.video_records.video_records_common import (
-    VideoNamesResult,
-    VideoRecordChanges,
-)
-from invokeai.app.services.videos.videos_common import (
-    VideoDTO,
-)
-
-videos_router = APIRouter(prefix="/v1/videos", tags=["videos"])
-
-
-# videos are immutable; set a high max-age
-VIDEO_MAX_AGE = 31536000
-
-
-@videos_router.get(
-    "/i/{video_id}",
-    operation_id="get_video_dto",
-    response_model=VideoDTO,
-)
-async def get_video_dto(
-    video_id: str = Path(description="The id of the video to get"),
-) -> VideoDTO:
-    """Gets a video's DTO"""
-
-    raise NotImplementedError("Not implemented")
-
-
-@videos_router.get(
-    "/",
-    operation_id="list_video_dtos",
-    response_model=OffsetPaginatedResults[VideoDTO],
-)
-async def list_video_dtos(
-    board_id: Optional[str] = Query(
-        default=None,
-        description="The board id to filter by. Use 'none' to find videos without a board.",
-    ),
-    offset: int = Query(default=0, description="The page offset"),
-    limit: int = Query(default=10, description="The number of videos per page"),
-    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
-    starred_first: bool = Query(default=True, description="Whether to sort by starred videos first"),
-    search_term: Optional[str] = Query(default=None, description="The term to search for"),
-) -> OffsetPaginatedResults[VideoDTO]:
-    """Gets a list of video DTOs"""
-
-    raise NotImplementedError("Not implemented")
-
-
-@videos_router.get("/ids", operation_id="get_video_ids")
-async def get_video_ids(
-    board_id: Optional[str] = Query(
-        default=None,
-        description="The board id to filter by. Use 'none' to find videos without a board.",
-    ),
-    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
-    starred_first: bool = Query(default=True, description="Whether to sort by starred videos first"),
-    search_term: Optional[str] = Query(default=None, description="The term to search for"),
-) -> VideoNamesResult:
-    """Gets ordered list of video names with metadata for optimistic updates"""
-
-    raise NotImplementedError("Not implemented")
-
-
-@videos_router.post(
-    "/videos_by_ids",
-    operation_id="get_videos_by_ids",
-    responses={200: {"model": list[VideoDTO]}},
-)
-async def get_videos_by_ids(
-    video_ids: list[str] = Body(embed=True, description="Object containing list of video ids to fetch DTOs for"),
-) -> list[VideoDTO]:
-    """Gets video DTOs for the specified video ids. Maintains order of input ids."""
-
-    raise NotImplementedError("Not implemented")
-
-
-@videos_router.patch(
-    "/i/{video_id}",
-    operation_id="update_video",
-    response_model=VideoDTO,
-)
-async def update_video(
-    video_id: str = Path(description="The id of the video to update"),
-    video_changes: VideoRecordChanges = Body(description="The changes to apply to the video"),
-) -> VideoDTO:
-    """Updates a video"""
-
-    raise NotImplementedError("Not implemented")
-
-
-@videos_router.get(
-    "/i/{video_id}/thumbnail",
-    operation_id="get_video_thumbnail",
-    response_class=Response,
-    responses={
-        200: {
-            "description": "Return the video thumbnail",
-            "content": {"image/webp": {}},
-        },
-        404: {"description": "Video not found"},
-    },
-)
-async def get_video_thumbnail(
-    video_id: str = Path(description="The id of video to get thumbnail for"),
-) -> Response:
-    """Gets a video thumbnail file"""
-
-    raise NotImplementedError("Not implemented")

invokeai/app/api_app.py

@@ -17,19 +17,15 @@ from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.api.routers import (
     app_info,
-    # board_images,
-    board_resources,
+    board_images,
     boards,
-    client_state,
     download_queue,
     images,
     model_manager,
     model_relationships,
-    resources,
     session_queue,
     style_presets,
     utilities,
-    videos,
     workflows,
 )
 from invokeai.app.api.sockets import SocketIO
@@ -128,16 +124,13 @@ app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(model_manager.model_manager_router, prefix="/api")
 app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
-app.include_router(videos.videos_router, prefix="/api")
-app.include_router(resources.resources_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
-app.include_router(board_resources.board_resources_router, prefix="/api")
+app.include_router(board_images.board_images_router, prefix="/api")
 app.include_router(model_relationships.model_relationships_router, prefix="/api")
 app.include_router(app_info.app_router, prefix="/api")
 app.include_router(session_queue.session_queue_router, prefix="/api")
 app.include_router(workflows.workflows_router, prefix="/api")
 app.include_router(style_presets.style_presets_router, prefix="/api")
-app.include_router(client_state.client_state_router, prefix="/api")
 
 app.openapi = get_openapi_func(app)
 
@@ -162,12 +155,6 @@ def overridden_redoc() -> HTMLResponse:
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
-if app_config.unsafe_disable_picklescan:
-    logger.warning(
-        "The unsafe_disable_picklescan option is enabled. This disables malware scanning while installing and"
-        "loading models, which may allow malicious code to be executed. Use at your own risk."
-    )
-
 try:
     app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
 except RuntimeError:

invokeai/app/invocations/bria_controlnet.py

@@ -0,0 +1,154 @@
+import cv2
+import numpy as np
+from PIL import Image
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    InputField,
+    OutputField,
+    UIType,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.bria.controlnet_aux.open_pose import Body, Face, Hand, OpenposeDetector
+from invokeai.backend.bria.controlnet_bria import BRIA_CONTROL_MODES
+from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
+from invokeai.invocation_api import Classification, ImageOutput
+
+DEPTH_SMALL_V2_URL = "depth-anything/Depth-Anything-V2-Small-hf"
+HF_LLLYASVIEL = "https://huggingface.co/lllyasviel/Annotators/resolve/main/"
+
+class BriaControlNetField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    model: ModelIdentifierField = Field(description="The ControlNet model to use")
+    mode: BRIA_CONTROL_MODES = Field(description="The mode of the ControlNet")
+    conditioning_scale: float = Field(description="The weight given to the ControlNet")
+
+@invocation_output("bria_controlnet_output")
+class BriaControlNetOutput(BaseInvocationOutput):
+    """Bria ControlNet info"""
+
+    control: BriaControlNetField = OutputField(description=FieldDescriptions.control)
+    preprocessed_images: ImageField = OutputField(description="The preprocessed control image")
+
+
+@invocation(
+    "bria_controlnet",
+    title="ControlNet - Bria",
+    tags=["controlnet", "bria"],
+    category="controlnet",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaControlNetInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Collect Bria ControlNet info to pass to denoiser node."""
+
+    control_image: ImageField = InputField(description="The control image")
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.BriaControlNetModel
+    )
+    control_mode: BRIA_CONTROL_MODES = InputField(
+        default="depth", description="The mode of the ControlNet"
+    )
+    control_weight: float = InputField(
+        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+    )
+
+    def invoke(self, context: InvocationContext) -> BriaControlNetOutput:
+        image_in = resize_img(context.images.get_pil(self.control_image.image_name))
+        if self.control_mode == "canny":
+            control_image = extract_canny(image_in)
+        elif self.control_mode == "depth":
+            control_image = extract_depth(image_in, context)
+        elif self.control_mode == "pose":
+            control_image = extract_openpose(image_in, context)
+        elif self.control_mode == "colorgrid":
+            control_image = tile(64, image_in)
+        elif self.control_mode == "recolor":
+            control_image = convert_to_grayscale(image_in)
+        elif self.control_mode == "tile":
+            control_image = tile(16, image_in)
+
+        control_image = resize_img(control_image)
+        image_dto = context.images.save(image=control_image)
+        image_output = ImageOutput.build(image_dto)
+        return BriaControlNetOutput(
+            preprocessed_images=image_output.image,
+            control=BriaControlNetField(
+                image=ImageField(image_name=image_dto.image_name),
+                model=self.control_model,
+                mode=self.control_mode,
+                conditioning_scale=self.control_weight,
+            ),
+        )
+
+
+RATIO_CONFIGS_1024 = {
+    0.6666666666666666: {"width": 832, "height": 1248},
+    0.7432432432432432: {"width": 880, "height": 1184},
+    0.8028169014084507: {"width": 912, "height": 1136},
+    1.0: {"width": 1024, "height": 1024},
+    1.2456140350877194: {"width": 1136, "height": 912},
+    1.3454545454545455: {"width": 1184, "height": 880},
+    1.4339622641509433: {"width": 1216, "height": 848},
+    1.5: {"width": 1248, "height": 832},
+    1.5490196078431373: {"width": 1264, "height": 816},
+    1.62: {"width": 1296, "height": 800},
+    1.7708333333333333: {"width": 1360, "height": 768},
+}
+
+def extract_depth(image: Image.Image, context: InvocationContext):
+    loaded_model = context.models.load_remote_model(DEPTH_SMALL_V2_URL, DepthAnythingPipeline.load_model)
+
+    with loaded_model as depth_anything_detector:
+        assert isinstance(depth_anything_detector, DepthAnythingPipeline)
+        depth_map = depth_anything_detector.generate_depth(image)
+    return depth_map
+
+def extract_openpose(image: Image.Image, context: InvocationContext):
+    body_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}body_pose_model.pth", Body)
+    hand_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}hand_pose_model.pth", Hand)
+    face_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}facenet.pth", Face)
+
+    with body_model as body_model, hand_model as hand_model, face_model as face_model:
+        open_pose_model = OpenposeDetector(body_model, hand_model, face_model)
+        processed_image_open_pose = open_pose_model(image, hand_and_face=True)
+
+    processed_image_open_pose = processed_image_open_pose.resize(image.size)
+    return processed_image_open_pose
+
+
+def extract_canny(input_image):
+    image = np.array(input_image)
+    image = cv2.Canny(image, 100, 200)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    canny_image = Image.fromarray(image)
+    return canny_image
+
+
+def convert_to_grayscale(image):
+    gray_image = image.convert('L').convert('RGB')
+    return gray_image
+
+def tile(downscale_factor, input_image):
+    control_image = input_image.resize((input_image.size[0] // downscale_factor, input_image.size[1] // downscale_factor)).resize(input_image.size, Image.Resampling.NEAREST)
+    return control_image
+
+def resize_img(control_image):
+    image_ratio = control_image.width / control_image.height
+    ratio = min(RATIO_CONFIGS_1024.keys(), key=lambda k: abs(k - image_ratio))
+    to_height = RATIO_CONFIGS_1024[ratio]["height"]
+    to_width = RATIO_CONFIGS_1024[ratio]["width"]
+    resized_image = control_image.resize((to_width, to_height), resample=Image.Resampling.LANCZOS)
+    return resized_image

invokeai/app/invocations/bria_decoder.py

@@ -0,0 +1,46 @@
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from PIL import Image
+
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.invocation_api import BaseInvocation, Classification, ImageOutput, invocation
+
+
+@invocation(
+    "bria_decoder",
+    title="Decoder - Bria",
+    tags=["image", "bria"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaDecoderInvocation(BaseInvocation):
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        latents = latents.view(1, 64, 64, 4, 2, 2).permute(0, 3, 1, 4, 2, 5).reshape(1, 4, 128, 128)
+
+        with context.models.load(self.vae.vae) as vae:
+            assert isinstance(vae, AutoencoderKL)
+            latents = (latents / vae.config.scaling_factor)
+            latents = latents.to(device=vae.device, dtype=vae.dtype)
+
+            decoded_output = vae.decode(latents)
+            image = decoded_output.sample
+
+        # Convert to numpy with proper gradient handling
+        image = ((image.clamp(-1, 1) + 1) / 2 * 255).cpu().detach().permute(0, 2, 3, 1).numpy().astype("uint8")[0]
+        img = Image.fromarray(image)
+        image_dto = context.images.save(image=img)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/bria_denoiser.py

@@ -0,0 +1,185 @@
+from typing import List, Tuple
+
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+
+from invokeai.app.invocations.bria_controlnet import BriaControlNetField
+from invokeai.app.invocations.fields import Input, InputField, LatentsField, OutputField
+from invokeai.app.invocations.model import SubModelType, T5EncoderField, TransformerField, VAEField
+from invokeai.app.invocations.primitives import BaseInvocationOutput, FieldDescriptions
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.bria.controlnet_bria import BriaControlModes, BriaMultiControlNetModel
+from invokeai.backend.bria.controlnet_utils import prepare_control_images
+from invokeai.backend.bria.pipeline_bria_controlnet import BriaControlNetPipeline
+from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
+from invokeai.invocation_api import BaseInvocation, Classification, invocation, invocation_output
+
+
+@invocation_output("bria_denoise_output")
+class BriaDenoiseInvocationOutput(BaseInvocationOutput):
+    latents: LatentsField = OutputField(description=FieldDescriptions.latents)
+
+
+@invocation(
+    "bria_denoise",
+    title="Denoise - Bria",
+    tags=["image", "bria"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaDenoiseInvocation(BaseInvocation):
+    num_steps: int = InputField(
+        default=30, title="Number of Steps", description="The number of steps to use for the denoiser"
+    )
+    guidance_scale: float = InputField(
+        default=5.0, title="Guidance Scale", description="The guidance scale to use for the denoiser"
+    )
+
+    transformer: TransformerField = InputField(
+        description="Bria model (Transformer) to load",
+        input=Input.Connection,
+        title="Transformer",
+    )
+    t5_encoder: T5EncoderField = InputField(
+        title="T5Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+        title="VAE",
+    )
+    latents: LatentsField = InputField(
+        description="Latents to denoise",
+        input=Input.Connection,
+        title="Latents",
+    )
+    latent_image_ids: LatentsField = InputField(
+        description="Latent Image IDs to denoise",
+        input=Input.Connection,
+        title="Latent Image IDs",
+    )
+    pos_embeds: LatentsField = InputField(
+        description="Positive Prompt Embeds",
+        input=Input.Connection,
+        title="Positive Prompt Embeds",
+    )
+    neg_embeds: LatentsField = InputField(
+        description="Negative Prompt Embeds",
+        input=Input.Connection,
+        title="Negative Prompt Embeds",
+    )
+    text_ids: LatentsField = InputField(
+        description="Text IDs",
+        input=Input.Connection,
+        title="Text IDs",
+    )
+    control: BriaControlNetField | list[BriaControlNetField] | None = InputField(
+        description="ControlNet",
+        input=Input.Connection,
+        title="ControlNet",
+        default = None,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> BriaDenoiseInvocationOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        pos_embeds = context.tensors.load(self.pos_embeds.latents_name)
+        neg_embeds = context.tensors.load(self.neg_embeds.latents_name)
+        text_ids = context.tensors.load(self.text_ids.latents_name)
+        latent_image_ids = context.tensors.load(self.latent_image_ids.latents_name)
+        scheduler_identifier = self.transformer.transformer.model_copy(update={"submodel_type": SubModelType.Scheduler})
+
+        device = None
+        dtype = None
+        with (
+            context.models.load(self.transformer.transformer) as transformer,
+            context.models.load(scheduler_identifier) as scheduler,
+            context.models.load(self.vae.vae) as vae,
+            context.models.load(self.t5_encoder.text_encoder) as t5_encoder,
+            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
+        ):
+            assert isinstance(transformer, BriaTransformer2DModel)
+            assert isinstance(scheduler, FlowMatchEulerDiscreteScheduler)
+            assert isinstance(vae, AutoencoderKL)
+            dtype = transformer.dtype
+            device = transformer.device
+            latents, pos_embeds, neg_embeds = (x.to(device, dtype) for x in (latents, pos_embeds, neg_embeds))
+
+            control_model, control_images, control_modes, control_scales = None, None, None, None
+            if self.control is not None:
+                control_model, control_images, control_modes, control_scales = self._prepare_multi_control(
+                    context=context,
+                    vae=vae,
+                    width=1024,
+                    height=1024,
+                    device=vae.device,
+                )
+
+            pipeline = BriaControlNetPipeline(
+                transformer=transformer,
+                scheduler=scheduler,
+                vae=vae,
+                text_encoder=t5_encoder,
+                tokenizer=t5_tokenizer,
+                controlnet=control_model,
+            )
+            pipeline.to(device=transformer.device, dtype=transformer.dtype)
+
+            latents = pipeline(
+                control_image=control_images,
+                control_mode=control_modes,
+                width=1024,
+                height=1024,
+                controlnet_conditioning_scale=control_scales,
+                num_inference_steps=self.num_steps,
+                max_sequence_length=128,
+                guidance_scale=self.guidance_scale,
+                latents=latents,
+                latent_image_ids=latent_image_ids,
+                text_ids=text_ids,
+                prompt_embeds=pos_embeds,
+                negative_prompt_embeds=neg_embeds,
+                output_type="latent",
+            )[0]
+
+        assert isinstance(latents, torch.Tensor)
+        saved_input_latents_tensor = context.tensors.save(latents)
+        latents_output = LatentsField(latents_name=saved_input_latents_tensor)
+        return BriaDenoiseInvocationOutput(latents=latents_output)
+
+
+    def _prepare_multi_control(
+        self,
+        context: InvocationContext,
+        vae: AutoencoderKL,
+        width: int,
+        height: int,
+        device: torch.device
+    ) -> Tuple[BriaMultiControlNetModel, List[torch.Tensor], List[torch.Tensor], List[float]]:
+
+        control = self.control if isinstance(self.control, list) else [self.control]
+        control_images, control_models, control_modes, control_scales = [], [], [], []
+        for controlnet in control:
+            if controlnet is not None:
+                control_models.append(context.models.load(controlnet.model).model)
+                control_modes.append(BriaControlModes[controlnet.mode].value)
+                control_scales.append(controlnet.conditioning_scale)
+                try:
+                    control_images.append(context.images.get_pil(controlnet.image.image_name))
+                except Exception:
+                    raise FileNotFoundError(f"Control image {controlnet.image.image_name} not found. Make sure not to delete the preprocessed image before finishing the pipeline.")
+
+        control_model = BriaMultiControlNetModel(control_models).to(device)
+        tensored_control_images, tensored_control_modes = prepare_control_images(
+            vae=vae,
+            control_images=control_images,
+            control_modes=control_modes,
+            width=width,
+            height=height,
+            device=device,
+            )
+        return control_model, tensored_control_images, tensored_control_modes, control_scales

invokeai/app/invocations/bria_latent_sampler.py

@@ -0,0 +1,76 @@
+import torch
+
+from invokeai.app.invocations.fields import Input, InputField, OutputField
+from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.primitives import (
+    BaseInvocationOutput,
+    FieldDescriptions,
+    LatentsField,
+)
+from invokeai.backend.bria.pipeline_bria_controlnet import prepare_latents
+from invokeai.invocation_api import (
+    BaseInvocation,
+    Classification,
+    InvocationContext,
+    invocation,
+    invocation_output,
+)
+
+
+@invocation_output("bria_latent_sampler_output")
+class BriaLatentSamplerInvocationOutput(BaseInvocationOutput):
+    """Base class for nodes that output a CogView text conditioning tensor."""
+
+    latents: LatentsField = OutputField(description=FieldDescriptions.cond)
+    latent_image_ids: LatentsField = OutputField(description=FieldDescriptions.cond)
+
+
+@invocation(
+    "bria_latent_sampler",
+    title="Latent Sampler - Bria",
+    tags=["image", "bria"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaLatentSamplerInvocation(BaseInvocation):
+    seed: int = InputField(
+        default=42,
+        title="Seed",
+        description="The seed to use for the latent sampler",
+    )
+    transformer: TransformerField = InputField(
+        description="Bria model (Transformer) to load",
+        input=Input.Connection,
+        title="Transformer",
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> BriaLatentSamplerInvocationOutput:
+        with context.models.load(self.transformer.transformer) as transformer:
+            device = transformer.device
+            dtype = transformer.dtype
+
+        height, width = 1024, 1024
+        generator = torch.Generator(device=device).manual_seed(self.seed)
+
+        num_channels_latents = 4
+        latents, latent_image_ids = prepare_latents(
+            batch_size=1,
+            num_channels_latents=num_channels_latents,
+            height=height,
+            width=width,
+            dtype=dtype,
+            device=device,
+            generator=generator,
+            )
+
+        saved_latents_tensor = context.tensors.save(latents)
+        saved_latent_image_ids_tensor = context.tensors.save(latent_image_ids)
+        latents_output = LatentsField(latents_name=saved_latents_tensor)
+        latent_image_ids_output = LatentsField(latents_name=saved_latent_image_ids_tensor)
+
+        return BriaLatentSamplerInvocationOutput(
+            latents=latents_output,
+            latent_image_ids=latent_image_ids_output,
+        )

invokeai/app/invocations/bria_model_loader.py

@@ -0,0 +1,58 @@
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import (
+    ModelIdentifierField,
+    SubModelType,
+    T5EncoderField,
+    TransformerField,
+    VAEField,
+)
+from invokeai.invocation_api import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    InvocationContext,
+    invocation,
+    invocation_output,
+)
+
+
+@invocation_output("bria_model_loader_output")
+class BriaModelLoaderOutput(BaseInvocationOutput):
+    """Bria base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
+@invocation(
+    "bria_model_loader",
+    title="Main Model - Bria",
+    tags=["model", "bria"],
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaModelLoaderInvocation(BaseInvocation):
+    """Loads a bria base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description="Bria model (Transformer) to load",
+        ui_type=UIType.BriaMainModel,
+        input=Input.Direct,
+    )
+
+    def invoke(self, context: InvocationContext) -> BriaModelLoaderOutput:
+        for key in [self.model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        return BriaModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[]),
+            vae=VAEField(vae=vae),
+        )

invokeai/app/invocations/bria_text_encoder.py

@@ -0,0 +1,93 @@
+from typing import Optional
+
+import torch
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from invokeai.app.invocations.model import T5EncoderField
+from invokeai.app.invocations.primitives import BaseInvocationOutput, FieldDescriptions, Input, OutputField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.bria.pipeline_bria_controlnet import encode_prompt
+from invokeai.invocation_api import (
+    BaseInvocation,
+    Classification,
+    InputField,
+    LatentsField,
+    invocation,
+    invocation_output,
+)
+
+
+@invocation_output("bria_text_encoder_output")
+class BriaTextEncoderInvocationOutput(BaseInvocationOutput):
+    """Base class for nodes that output a CogView text conditioning tensor."""
+
+    pos_embeds: LatentsField = OutputField(description=FieldDescriptions.cond)
+    neg_embeds: LatentsField = OutputField(description=FieldDescriptions.cond)
+    text_ids: LatentsField = OutputField(description=FieldDescriptions.cond)
+
+
+@invocation(
+    "bria_text_encoder",
+    title="Prompt - Bria",
+    tags=["prompt", "conditioning", "bria"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaTextEncoderInvocation(BaseInvocation):
+    prompt: str = InputField(
+        title="Prompt",
+        description="The prompt to encode",
+    )
+    negative_prompt: Optional[str] = InputField(
+        title="Negative Prompt",
+        description="The negative prompt to encode",
+        default="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate",
+    )
+    max_length: int = InputField(
+        default=128,
+        title="Max Length",
+        description="The maximum length of the prompt",
+    )
+    t5_encoder: T5EncoderField = InputField(
+        title="T5Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> BriaTextEncoderInvocationOutput:
+        t5_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
+        with (
+            t5_encoder_info as text_encoder,
+            t5_tokenizer_info as tokenizer,
+        ):
+            assert isinstance(tokenizer, T5TokenizerFast)
+            assert isinstance(text_encoder, T5EncoderModel)
+
+        (prompt_embeds, negative_prompt_embeds, text_ids) = encode_prompt(
+            prompt=self.prompt,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            negative_prompt=self.negative_prompt,
+            device=text_encoder.device,
+            num_images_per_prompt=1,
+            max_sequence_length=self.max_length,
+            lora_scale=1.0,
+        )
+
+        saved_pos_tensor = context.tensors.save(prompt_embeds)
+        saved_neg_tensor = context.tensors.save(negative_prompt_embeds)
+        saved_text_ids_tensor = context.tensors.save(text_ids)
+        pos_embeds_output = LatentsField(latents_name=saved_pos_tensor)
+        neg_embeds_output = LatentsField(latents_name=saved_neg_tensor)
+        text_ids_output = LatentsField(latents_name=saved_text_ids_tensor)
+        return BriaTextEncoderInvocationOutput(
+            pos_embeds=pos_embeds_output,
+            neg_embeds=neg_embeds_output,
+            text_ids=text_ids_output,
+        )

invokeai/app/invocations/cogview4_image_to_latents.py

@@ -36,19 +36,9 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to encode.")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
-    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoencoderKL) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        h = image_tensor.shape[-2]
-        w = image_tensor.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-        working_memory = h * w * element_size * scaling_constant
-        return int(working_memory)
-
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        with vae_info as vae:
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -72,12 +62,7 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         vae_info = context.models.load(self.vae.vae)
-        assert isinstance(vae_info.model, AutoencoderKL)
-
-        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
-        latents = self.vae_encode(
-            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
-        )
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/fields.py

@@ -42,6 +42,8 @@ class UIType(str, Enum, metaclass=MetaEnum):
     MainModel = "MainModelField"
     CogView4MainModel = "CogView4MainModelField"
     FluxMainModel = "FluxMainModelField"
+    BriaMainModel = "BriaMainModelField"
+    BriaControlNetModel = "BriaControlNetModelField"
     SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"

invokeai/app/invocations/flux_denoise.py

@@ -63,7 +63,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="4.1.0",
+    version="4.0.0",
 )
 class FluxDenoiseInvocation(BaseInvocation):
     """Run denoising process with a FLUX transformer model."""
@@ -153,7 +153,7 @@ class FluxDenoiseInvocation(BaseInvocation):
         description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection
     )
 
-    kontext_conditioning: FluxKontextConditioningField | list[FluxKontextConditioningField] | None = InputField(
+    kontext_conditioning: Optional[FluxKontextConditioningField] = InputField(
         default=None,
         description="FLUX Kontext conditioning (reference image).",
         input=Input.Connection,
@@ -328,21 +328,6 @@ class FluxDenoiseInvocation(BaseInvocation):
             cfg_scale_end_step=self.cfg_scale_end_step,
         )
 
-        kontext_extension = None
-        if self.kontext_conditioning:
-            if not self.controlnet_vae:
-                raise ValueError("A VAE (e.g., controlnet_vae) must be provided to use Kontext conditioning.")
-
-            kontext_extension = KontextExtension(
-                context=context,
-                kontext_conditioning=self.kontext_conditioning
-                if isinstance(self.kontext_conditioning, list)
-                else [self.kontext_conditioning],
-                vae_field=self.controlnet_vae,
-                device=TorchDevice.choose_torch_device(),
-                dtype=inference_dtype,
-            )
-
         with ExitStack() as exit_stack:
             # Prepare ControlNet extensions.
             # Note: We do this before loading the transformer model to minimize peak memory (see implementation).
@@ -400,6 +385,19 @@ class FluxDenoiseInvocation(BaseInvocation):
                 dtype=inference_dtype,
             )
 
+            kontext_extension = None
+            if self.kontext_conditioning is not None:
+                if not self.controlnet_vae:
+                    raise ValueError("A VAE (e.g., controlnet_vae) must be provided to use Kontext conditioning.")
+
+                kontext_extension = KontextExtension(
+                    context=context,
+                    kontext_conditioning=self.kontext_conditioning,
+                    vae_field=self.controlnet_vae,
+                    device=TorchDevice.choose_torch_device(),
+                    dtype=inference_dtype,
+                )
+
             # Prepare Kontext conditioning if provided
             img_cond_seq = None
             img_cond_seq_ids = None

invokeai/app/invocations/flux_vae_encode.py

@@ -35,24 +35,14 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         input=Input.Connection,
     )
 
-    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoEncoder) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        h = image_tensor.shape[-2]
-        w = image_tensor.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-        working_memory = h * w * element_size * scaling_constant
-        return int(working_memory)
-
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
         # TODO(ryand): Expose seed parameter at the invocation level.
         # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
         # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
         # should be used for VAE encode sampling.
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
             vae_dtype = next(iter(vae.parameters())).dtype
             image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
@@ -70,10 +60,7 @@ class FluxVaeEncodeInvocation(BaseInvocation):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         context.util.signal_progress("Running VAE")
-        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
-        latents = self.vae_encode(
-            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
-        )
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/image.py

@@ -1347,96 +1347,3 @@ class PasteImageIntoBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoar
 
         image_dto = context.images.save(image=target_image)
         return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "flux_kontext_image_prep",
-    title="FLUX Kontext Image Prep",
-    tags=["image", "concatenate", "flux", "kontext"],
-    category="image",
-    version="1.0.0",
-)
-class FluxKontextConcatenateImagesInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Prepares an image or images for use with FLUX Kontext. The first/single image is resized to the nearest
-    preferred Kontext resolution. All other images are concatenated horizontally, maintaining their aspect ratio."""
-
-    images: list[ImageField] = InputField(
-        description="The images to concatenate",
-        min_length=1,
-        max_length=10,
-    )
-
-    use_preferred_resolution: bool = InputField(
-        default=True, description="Use FLUX preferred resolutions for the first image"
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
-
-        # Step 1: Load all images
-        pil_images = []
-        for image_field in self.images:
-            image = context.images.get_pil(image_field.image_name, mode="RGBA")
-            pil_images.append(image)
-
-        # Step 2: Determine target resolution for the first image
-        first_image = pil_images[0]
-        width, height = first_image.size
-
-        if self.use_preferred_resolution:
-            aspect_ratio = width / height
-
-            # Find the closest preferred resolution for the first image
-            _, target_width, target_height = min(
-                ((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS), key=lambda x: x[0]
-            )
-
-            # Apply BFL's scaling formula
-            scaled_height = 2 * int(target_height / 16)
-            final_height = 8 * scaled_height  # This will be consistent for all images
-            scaled_width = 2 * int(target_width / 16)
-            first_width = 8 * scaled_width
-        else:
-            # Use original dimensions of first image, ensuring divisibility by 16
-            final_height = 16 * (height // 16)
-            first_width = 16 * (width // 16)
-            # Ensure minimum dimensions
-            if final_height < 16:
-                final_height = 16
-            if first_width < 16:
-                first_width = 16
-
-        # Step 3: Process and resize all images with consistent height
-        processed_images = []
-        total_width = 0
-
-        for i, image in enumerate(pil_images):
-            if i == 0:
-                # First image uses the calculated dimensions
-                final_width = first_width
-            else:
-                # Subsequent images maintain aspect ratio with the same height
-                img_aspect_ratio = image.width / image.height
-                # Calculate width that maintains aspect ratio at the target height
-                calculated_width = int(final_height * img_aspect_ratio)
-                # Ensure width is divisible by 16 for proper VAE encoding
-                final_width = 16 * (calculated_width // 16)
-                # Ensure minimum width
-                if final_width < 16:
-                    final_width = 16
-
-            # Resize image to calculated dimensions
-            resized_image = image.resize((final_width, final_height), Image.Resampling.LANCZOS)
-            processed_images.append(resized_image)
-            total_width += final_width
-
-        # Step 4: Concatenate images horizontally
-        concatenated_image = Image.new("RGB", (total_width, final_height))
-        x_offset = 0
-        for img in processed_images:
-            concatenated_image.paste(img, (x_offset, 0))
-            x_offset += img.width
-
-        # Save the concatenated image
-        image_dto = context.images.save(image=concatenated_image)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/image_to_latents.py

@@ -52,48 +52,11 @@ class ImageToLatentsInvocation(BaseInvocation):
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
-    def _estimate_working_memory(
-        self, image_tensor: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
-    ) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        element_size = 4 if self.fp32 else 2
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-
-        if use_tiling:
-            tile_size = self.tile_size
-            if tile_size == 0:
-                tile_size = vae.tile_sample_min_size
-                assert isinstance(tile_size, int)
-            h = tile_size
-            w = tile_size
-            working_memory = h * w * element_size * scaling_constant
-
-            # We add 25% to the working memory estimate when tiling is enabled to account for factors like tile overlap
-            # and number of tiles. We could make this more precise in the future, but this should be good enough for
-            # most use cases.
-            working_memory = working_memory * 1.25
-        else:
-            h = image_tensor.shape[-2]
-            w = image_tensor.shape[-1]
-            working_memory = h * w * element_size * scaling_constant
-
-        if self.fp32:
-            # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
-            working_memory += 250 * 2**20
-
-        return int(working_memory)
-
     @staticmethod
     def vae_encode(
-        vae_info: LoadedModel,
-        upcast: bool,
-        tiled: bool,
-        image_tensor: torch.Tensor,
-        tile_size: int = 0,
-        estimated_working_memory: int = 0,
+        vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor, tile_size: int = 0
     ) -> torch.Tensor:
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info as vae:
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             orig_dtype = vae.dtype
             if upcast:
@@ -150,23 +113,14 @@ class ImageToLatentsInvocation(BaseInvocation):
         image = context.images.get_pil(self.image.image_name)
 
         vae_info = context.models.load(self.vae.vae)
-        assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        use_tiling = self.tiled or context.config.get().force_tiled_decode
-        estimated_working_memory = self._estimate_working_memory(image_tensor, use_tiling, vae_info.model)
-
         context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
-            vae_info=vae_info,
-            upcast=self.fp32,
-            tiled=self.tiled,
-            image_tensor=image_tensor,
-            tile_size=self.tile_size,
-            estimated_working_memory=estimated_working_memory,
+            vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
         )
 
         latents = latents.to("cpu")

invokeai/app/invocations/sd3_image_to_latents.py

@@ -32,19 +32,9 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to encode")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
-    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoencoderKL) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        h = image_tensor.shape[-2]
-        w = image_tensor.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-        working_memory = h * w * element_size * scaling_constant
-        return int(working_memory)
-
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        with vae_info as vae:
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -68,12 +58,7 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         vae_info = context.models.load(self.vae.vae)
-        assert isinstance(vae_info.model, AutoencoderKL)
-
-        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
-        latents = self.vae_encode(
-            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
-        )
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/services/board_resource_records/board_resource_records_base.py

@@ -1,57 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from invokeai.app.services.image_records.image_records_common import ImageCategory
-from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
-
-
-class BoardResourceRecordStorageBase(ABC):
-    """Abstract base class for the one-to-many board-resource relationship record storage."""
-
-    @abstractmethod
-    def add_resource_to_board(
-        self,
-        board_id: str,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        """Adds a resource to a board."""
-        pass
-
-    @abstractmethod
-    def remove_resource_from_board(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        """Removes a resource from a board."""
-        pass
-
-    @abstractmethod
-    def get_all_board_resource_ids_for_board(
-        self,
-        board_id: str,
-        resource_type: Optional[ResourceType] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> list[ResourceIdentifier]:
-        """Gets all board resources for a board, as a list of resource IDs."""
-        pass
-
-    @abstractmethod
-    def get_board_for_resource(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> Optional[str]:
-        """Gets a resource's board id, if it has one."""
-        pass
-
-    @abstractmethod
-    def get_resource_count_for_board(
-        self,
-        board_id: str,
-        resource_type: Optional[ResourceType] = None,
-    ) -> int:
-        """Gets the number of resources for a board."""
-        pass

invokeai/app/services/board_resource_records/board_resource_records_sqlite.py

@@ -1,153 +0,0 @@
-import sqlite3
-from typing import Optional, cast
-
-from invokeai.app.services.board_resource_records.board_resource_records_base import BoardResourceRecordStorageBase
-from invokeai.app.services.image_records.image_records_common import ImageCategory
-from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-
-
-class SqliteBoardResourceRecordStorage(BoardResourceRecordStorageBase):
-    def __init__(self, db: SqliteDatabase) -> None:
-        super().__init__()
-        self._db = db
-
-    def add_resource_to_board(
-        self,
-        board_id: str,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        with self._db.transaction() as cursor:
-            if resource_type == ResourceType.IMAGE:
-                cursor.execute(
-                    """--sql
-                    INSERT INTO board_images (board_id, image_name)
-                    VALUES (?, ?)
-                    ON CONFLICT (image_name) DO UPDATE SET board_id = ?;
-                    """,
-                    (board_id, resource_id, board_id),
-                )
-            elif resource_type == ResourceType.VIDEO:
-                raise NotImplementedError("Video resource type is not supported in OSS")
-
-    def remove_resource_from_board(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        with self._db.transaction() as cursor:
-            if resource_type == ResourceType.IMAGE:
-                cursor.execute(
-                    """--sql
-                    DELETE FROM board_images
-                    WHERE image_name = ?;
-                    """,
-                    (resource_id,),
-                )
-            elif resource_type == ResourceType.VIDEO:
-                raise NotImplementedError("Video resource type is not supported in OSS")
-
-    def get_all_board_resource_ids_for_board(
-        self,
-        board_id: str,
-        resource_type: Optional[ResourceType] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> list[ResourceIdentifier]:
-        image_name_results = []
-
-        with self._db.transaction() as cursor:
-            if resource_type == ResourceType.IMAGE or resource_type is None:
-                params: list[str | bool] = []
-
-                # Base query is a join between images and board_images
-                stmt = """
-                        SELECT images.image_name
-                        FROM images
-                        LEFT JOIN board_images ON board_images.image_name = images.image_name
-                        WHERE 1=1
-                        """
-
-                # Handle board_id filter
-                if board_id == "none":
-                    stmt += """--sql
-                        AND board_images.board_id IS NULL
-                        """
-                else:
-                    stmt += """--sql
-                        AND board_images.board_id = ?
-                        """
-                    params.append(board_id)
-
-                # Add the category filter
-                if categories is not None:
-                    # Convert the enum values to unique list of strings
-                    category_strings = [c.value for c in set(categories)]
-                    # Create the correct length of placeholders
-                    placeholders = ",".join("?" * len(category_strings))
-                    stmt += f"""--sql
-                        AND images.image_category IN ( {placeholders} )
-                        """
-
-                    # Unpack the included categories into the query params
-                    for c in category_strings:
-                        params.append(c)
-
-                # Add the is_intermediate filter
-                if is_intermediate is not None:
-                    stmt += """--sql
-                        AND images.is_intermediate = ?
-                        """
-                    params.append(is_intermediate)
-
-                # Put a ring on it
-                stmt += ";"
-
-                cursor.execute(stmt, params)
-
-                image_name_results = cast(list[sqlite3.Row], cursor.fetchall())
-
-            if resource_type == ResourceType.VIDEO or resource_type is None:
-                #    this is not actually a valid code path for OSS, just demonstrating that it could be
-                raise NotImplementedError("Video resource type is not supported in OSS")
-
-            return [
-                ResourceIdentifier(resource_id=image_name, resource_type=ResourceType.IMAGE)
-                for image_name in image_name_results
-            ]
-
-    def get_board_for_resource(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> Optional[str]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                    SELECT board_id
-                    FROM board_images
-                    WHERE image_name = ?;
-                    """,
-                (resource_id,),
-            )
-            result = cursor.fetchone()
-        if result is None:
-            return None
-        return cast(str, result[0])
-
-    def get_resource_count_for_board(self, board_id: str, resource_type: Optional[ResourceType] = None) -> int:
-        with self._db.transaction() as cursor:
-            # only images are supported in OSS
-            cursor.execute(
-                """--sql
-                    SELECT COUNT(*)
-                    FROM board_images
-                    INNER JOIN images ON board_images.image_name = images.image_name
-                    WHERE images.is_intermediate = FALSE
-                    AND board_images.board_id = ?;
-                    """,
-                (board_id,),
-            )
-            count = cast(int, cursor.fetchone()[0])
-        return count

invokeai/app/services/board_resources/board_resources_base.py

@@ -1,48 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from invokeai.app.services.image_records.image_records_common import ImageCategory
-from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
-
-
-class BoardResourcesServiceABC(ABC):
-    """High-level service for board-resource relationship management."""
-
-    @abstractmethod
-    def add_resource_to_board(
-        self,
-        board_id: str,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        """Adds a resource (image or video) to a board."""
-        pass
-
-    @abstractmethod
-    def remove_resource_from_board(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        """Removes a resource (image or video) from a board."""
-        pass
-
-    @abstractmethod
-    def get_all_board_resource_ids_for_board(
-        self,
-        board_id: str,
-        resource_type: Optional[ResourceType] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> list[ResourceIdentifier]:
-        """Gets all board resources for a board, as a list of resource IDs."""
-        pass
-
-    @abstractmethod
-    def get_board_for_resource(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> Optional[str]:
-        """Gets a resource's board id, if it has one."""
-        pass

invokeai/app/services/board_resources/board_resources_common.py

@@ -1,12 +0,0 @@
-from pydantic import Field
-
-from invokeai.app.services.resources.resources_common import ResourceType
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-
-
-class BoardResource(BaseModelExcludeNull):
-    """Represents a resource (image or video) associated with a board."""
-
-    board_id: str = Field(description="The id of the board")
-    resource_id: str = Field(description="The id of the resource (image_name or video_id)")
-    resource_type: ResourceType = Field(description="The type of resource")

invokeai/app/services/board_resources/board_resources_default.py

@@ -1,49 +0,0 @@
-from typing import Optional
-
-from invokeai.app.services.board_resources.board_resources_base import BoardResourcesServiceABC
-from invokeai.app.services.image_records.image_records_common import ImageCategory
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
-
-
-class BoardResourcesService(BoardResourcesServiceABC):
-    __invoker: Invoker
-
-    def start(self, invoker: Invoker) -> None:
-        self.__invoker = invoker
-
-    def add_resource_to_board(
-        self,
-        board_id: str,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        self.__invoker.services.board_resource_records.add_resource_to_board(board_id, resource_id, resource_type)
-
-    def remove_resource_from_board(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> None:
-        self.__invoker.services.board_resource_records.remove_resource_from_board(resource_id, resource_type)
-
-    def get_all_board_resource_ids_for_board(
-        self,
-        board_id: str,
-        resource_type: Optional[ResourceType] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> list[ResourceIdentifier]:
-        return self.__invoker.services.board_resource_records.get_all_board_resource_ids_for_board(
-            board_id,
-            resource_type,
-            categories,
-            is_intermediate,
-        )
-
-    def get_board_for_resource(
-        self,
-        resource_id: str,
-        resource_type: ResourceType,
-    ) -> Optional[str]:
-        return self.__invoker.services.board_resource_records.get_board_for_resource(resource_id, resource_type)

invokeai/app/services/boards/boards_common.py

@@ -12,17 +12,12 @@ class BoardDTO(BoardRecord):
     """The URL of the thumbnail of the most recent image in the board."""
     image_count: int = Field(description="The number of images in the board.")
     """The number of images in the board."""
-    video_count: int = Field(description="The number of videos in the board.")
-    """The number of videos in the board."""
 
 
-def board_record_to_dto(
-    board_record: BoardRecord, cover_image_name: Optional[str], image_count: int, video_count: int
-) -> BoardDTO:
+def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
     """Converts a board record to a board DTO."""
     return BoardDTO(
         **board_record.model_dump(exclude={"cover_image_name"}),
         cover_image_name=cover_image_name,
         image_count=image_count,
-        video_count=video_count,
     )

invokeai/app/services/boards/boards_default.py

@@ -2,7 +2,6 @@ from invokeai.app.services.board_records.board_records_common import BoardChange
 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.resources.resources_common import ResourceType
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -18,7 +17,7 @@ class BoardService(BoardServiceABC):
         board_name: str,
     ) -> BoardDTO:
         board_record = self.__invoker.services.board_records.save(board_name)
-        return board_record_to_dto(board_record, None, 0, 0)
+        return board_record_to_dto(board_record, None, 0)
 
     def get_dto(self, board_id: str) -> BoardDTO:
         board_record = self.__invoker.services.board_records.get(board_id)
@@ -27,13 +26,8 @@ class BoardService(BoardServiceABC):
             cover_image_name = cover_image.image_name
         else:
             cover_image_name = None
-        image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-            board_id, ResourceType.IMAGE
-        )
-        video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-            board_id, ResourceType.VIDEO
-        )
-        return board_record_to_dto(board_record, cover_image_name, image_count, video_count)
+        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
 
     def update(
         self,
@@ -47,13 +41,8 @@ class BoardService(BoardServiceABC):
         else:
             cover_image_name = None
 
-        image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-            board_id, ResourceType.IMAGE
-        )
-        video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-            board_id, ResourceType.VIDEO
-        )
-        return board_record_to_dto(board_record, cover_image_name, image_count, video_count)
+        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
 
     def delete(self, board_id: str) -> None:
         self.__invoker.services.board_records.delete(board_id)
@@ -69,7 +58,7 @@ class BoardService(BoardServiceABC):
         board_records = self.__invoker.services.board_records.get_many(
             order_by, direction, offset, limit, include_archived
         )
-        board_dtos: list[BoardDTO] = []
+        board_dtos = []
         for r in board_records.items:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
             if cover_image:
@@ -77,13 +66,8 @@ class BoardService(BoardServiceABC):
             else:
                 cover_image_name = None
 
-            image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-                r.board_id, ResourceType.IMAGE
-            )
-            video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-                r.board_id, ResourceType.VIDEO
-            )
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count, video_count))
+            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
 
         return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
@@ -91,7 +75,7 @@ class BoardService(BoardServiceABC):
         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)
-        board_dtos: list[BoardDTO] = []
+        board_dtos = []
         for r in board_records:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
             if cover_image:
@@ -99,12 +83,7 @@ class BoardService(BoardServiceABC):
             else:
                 cover_image_name = None
 
-            image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-                r.board_id, ResourceType.IMAGE
-            )
-            video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
-                r.board_id, ResourceType.VIDEO
-            )
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count, video_count))
+            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
 
         return board_dtos

invokeai/app/services/bulk_download/bulk_download_default.py

@@ -14,7 +14,6 @@ from invokeai.app.services.bulk_download.bulk_download_common import (
 from invokeai.app.services.image_records.image_records_common import ImageRecordNotFoundException
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.util.misc import uuid_string
 
 
@@ -64,11 +63,12 @@ class BulkDownloadService(BulkDownloadBase):
         return [self._invoker.services.images.get_dto(image_name) for image_name in image_names]
 
     def _board_handler(self, board_id: str) -> list[ImageDTO]:
-        resource_ids = self._invoker.services.board_resource_records.get_all_board_resource_ids_for_board(
+        image_names = self._invoker.services.board_image_records.get_all_board_image_names_for_board(
             board_id,
-            resource_type=ResourceType.IMAGE,
+            categories=None,
+            is_intermediate=None,
         )
-        return self._image_handler([resource_id.resource_id for resource_id in resource_ids])
+        return self._image_handler(image_names)
 
     def generate_item_id(self, board_id: Optional[str]) -> str:
         return uuid_string() if board_id is None else self._get_clean_board_name(board_id) + "_" + uuid_string()

invokeai/app/services/client_state_persistence/client_state_persistence_base.py

@@ -1,42 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class ClientStatePersistenceABC(ABC):
-    """
-    Base class for client persistence implementations.
-    This class defines the interface for persisting client data.
-    """
-
-    @abstractmethod
-    def set_by_key(self, queue_id: str, key: str, value: str) -> str:
-        """
-        Set a key-value pair for the client.
-
-        Args:
-            key (str): The key to set.
-            value (str): The value to set for the key.
-
-        Returns:
-            str: The value that was set.
-        """
-        pass
-
-    @abstractmethod
-    def get_by_key(self, queue_id: str, key: str) -> str | None:
-        """
-        Get the value for a specific key of the client.
-
-        Args:
-            key (str): The key to retrieve the value for.
-
-        Returns:
-            str | None: The value associated with the key, or None if the key does not exist.
-        """
-        pass
-
-    @abstractmethod
-    def delete(self, queue_id: str) -> None:
-        """
-        Delete all client state.
-        """
-        pass

invokeai/app/services/client_state_persistence/client_state_persistence_sqlite.py

@@ -1,65 +0,0 @@
-import json
-
-from invokeai.app.services.client_state_persistence.client_state_persistence_base import ClientStatePersistenceABC
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-
-
-class ClientStatePersistenceSqlite(ClientStatePersistenceABC):
-    """
-    Base class for client persistence implementations.
-    This class defines the interface for persisting client data.
-    """
-
-    def __init__(self, db: SqliteDatabase) -> None:
-        super().__init__()
-        self._db = db
-        self._default_row_id = 1
-
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-
-    def _get(self) -> dict[str, str] | None:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                f"""
-                SELECT data FROM client_state
-                WHERE id = {self._default_row_id}
-                """
-            )
-            row = cursor.fetchone()
-            if row is None:
-                return None
-            return json.loads(row[0])
-
-    def set_by_key(self, queue_id: str, key: str, value: str) -> str:
-        state = self._get() or {}
-        state.update({key: value})
-
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                f"""
-                INSERT INTO client_state (id, data)
-                VALUES ({self._default_row_id}, ?)
-                ON CONFLICT(id) DO UPDATE
-                  SET data = excluded.data;
-                """,
-                (json.dumps(state),),
-            )
-
-        return value
-
-    def get_by_key(self, queue_id: str, key: str) -> str | None:
-        state = self._get()
-        if state is None:
-            return None
-        return state.get(key, None)
-
-    def delete(self, queue_id: str) -> None:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                f"""
-                DELETE FROM client_state
-                WHERE id = {self._default_row_id}
-                """
-            )

invokeai/app/services/config/config_default.py

@@ -107,7 +107,6 @@ class InvokeAIAppConfig(BaseSettings):
         hashing_algorithm: Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `blake3_multi`, `blake3_single`, `random`, `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`
         remote_api_tokens: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
         scan_models_on_startup: Scan the models directory on startup, registering orphaned models. This is typically only used in conjunction with `use_memory_db` for testing purposes.
-        unsafe_disable_picklescan: UNSAFE. Disable the picklescan security check during model installation. Recommended only for development and testing purposes. This will allow arbitrary code execution during model installation, so should never be used in production.
     """
 
     _root: Optional[Path] = PrivateAttr(default=None)
@@ -197,7 +196,6 @@ class InvokeAIAppConfig(BaseSettings):
     hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3_single",  description="Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.")
     remote_api_tokens: Optional[list[URLRegexTokenPair]] = Field(default=None, description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.")
     scan_models_on_startup:        bool = Field(default=False,              description="Scan the models directory on startup, registering orphaned models. This is typically only used in conjunction with `use_memory_db` for testing purposes.")
-    unsafe_disable_picklescan:     bool = Field(default=False,              description="UNSAFE. Disable the picklescan security check during model installation. Recommended only for development and testing purposes. This will allow arbitrary code execution during model installation, so should never be used in production.")
 
     # fmt: on
 

invokeai/app/services/images/images_common.py

@@ -1,6 +1,6 @@
-from typing import Any, Literal, Optional
+from typing import Optional
 
-from pydantic import ConfigDict, Field
+from pydantic import BaseModel, Field
 
 from invokeai.app.services.image_records.image_records_common import ImageRecord
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@@ -17,24 +17,14 @@ class ImageUrlsDTO(BaseModelExcludeNull):
     """The URL of the image's thumbnail."""
 
 
-def make_type_required(s: dict[str, Any]):
-    if "required" in s:
-        s["required"].append("type")
-    else:
-        s["required"] = ["type"]
-
-
 class ImageDTO(ImageRecord, ImageUrlsDTO):
     """Deserialized image record, enriched for the frontend."""
 
-    type: Literal["image"] = Field(default="image")
     board_id: Optional[str] = Field(
         default=None, description="The id of the board the image belongs to, if one exists."
     )
     """The id of the board the image belongs to, if one exists."""
 
-    model_config = ConfigDict(json_schema_extra=make_type_required)
-
 
 def image_record_to_dto(
     image_record: ImageRecord,
@@ -49,3 +39,27 @@ def image_record_to_dto(
         thumbnail_url=thumbnail_url,
         board_id=board_id,
     )
+
+
+class ResultWithAffectedBoards(BaseModel):
+    affected_boards: list[str] = Field(description="The ids of boards affected by the delete operation")
+
+
+class DeleteImagesResult(ResultWithAffectedBoards):
+    deleted_images: list[str] = Field(description="The names of the images that were deleted")
+
+
+class StarredImagesResult(ResultWithAffectedBoards):
+    starred_images: list[str] = Field(description="The names of the images that were starred")
+
+
+class UnstarredImagesResult(ResultWithAffectedBoards):
+    unstarred_images: list[str] = Field(description="The names of the images that were unstarred")
+
+
+class AddImagesToBoardResult(ResultWithAffectedBoards):
+    added_images: list[str] = Field(description="The image names that were added to the board")
+
+
+class RemoveImagesFromBoardResult(ResultWithAffectedBoards):
+    removed_images: list[str] = Field(description="The image names that were removed from their board")

invokeai/app/services/images/images_default.py

@@ -23,7 +23,6 @@ from invokeai.app.services.image_records.image_records_common import (
 from invokeai.app.services.images.images_base import ImageServiceABC
 from invokeai.app.services.images.images_common import ImageDTO, image_record_to_dto
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -76,8 +75,8 @@ class ImageService(ImageServiceABC):
             )
             if board_id is not None:
                 try:
-                    self.__invoker.services.board_resource_records.add_resource_to_board(
-                        board_id=board_id, resource_id=image_name, resource_type=ResourceType.IMAGE
+                    self.__invoker.services.board_image_records.add_image_to_board(
+                        board_id=board_id, image_name=image_name
                     )
                 except Exception as e:
                     self.__invoker.services.logger.warning(f"Failed to add image to board {board_id}: {str(e)}")
@@ -143,9 +142,7 @@ class ImageService(ImageServiceABC):
                 image_record=image_record,
                 image_url=self.__invoker.services.urls.get_image_url(image_name),
                 thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
-                board_id=self.__invoker.services.board_resource_records.get_board_for_resource(
-                    image_name, ResourceType.IMAGE
-                ),
+                board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
             )
 
             return image_dto
@@ -237,9 +234,7 @@ class ImageService(ImageServiceABC):
                     image_record=r,
                     image_url=self.__invoker.services.urls.get_image_url(r.image_name),
                     thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
-                    board_id=self.__invoker.services.board_resource_records.get_board_for_resource(
-                        r.image_name, ResourceType.IMAGE
-                    ),
+                    board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
                 )
                 for r in results.items
             ]
@@ -271,9 +266,10 @@ class ImageService(ImageServiceABC):
 
     def delete_images_on_board(self, board_id: str):
         try:
-            image_names = self.__invoker.services.board_resource_records.get_all_board_resource_ids_for_board(
+            image_names = self.__invoker.services.board_image_records.get_all_board_image_names_for_board(
                 board_id,
-                resource_type=ResourceType.IMAGE,
+                categories=None,
+                is_intermediate=None,
             )
             for image_name in image_names:
                 self.__invoker.services.image_files.delete(image_name)

invokeai/app/services/invocation_services.py

@@ -12,14 +12,11 @@ if TYPE_CHECKING:
 
     import torch
 
-    # from invokeai.app.services.board_image_records.board_image_records_base import BoardImageRecordStorageBase
-    # from invokeai.app.services.board_images.board_images_base import BoardImagesServiceABC
+    from invokeai.app.services.board_image_records.board_image_records_base import BoardImageRecordStorageBase
+    from invokeai.app.services.board_images.board_images_base import BoardImagesServiceABC
     from invokeai.app.services.board_records.board_records_base import BoardRecordStorageBase
-    from invokeai.app.services.board_resource_records.board_resource_records_base import BoardResourceRecordStorageBase
-    from invokeai.app.services.board_resources.board_resources_base import BoardResourcesServiceABC
     from invokeai.app.services.boards.boards_base import BoardServiceABC
     from invokeai.app.services.bulk_download.bulk_download_base import BulkDownloadBase
-    from invokeai.app.services.client_state_persistence.client_state_persistence_base import ClientStatePersistenceABC
     from invokeai.app.services.config import InvokeAIAppConfig
     from invokeai.app.services.download import DownloadQueueServiceBase
     from invokeai.app.services.events.events_base import EventServiceBase
@@ -38,8 +35,6 @@ if TYPE_CHECKING:
     from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
     from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
     from invokeai.app.services.urls.urls_base import UrlServiceBase
-    from invokeai.app.services.video_records.video_records_base import VideoRecordStorageBase
-    from invokeai.app.services.videos.videos_base import VideoServiceABC
     from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
     from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_base import WorkflowThumbnailServiceBase
     from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
@@ -50,10 +45,8 @@ class InvocationServices:
 
     def __init__(
         self,
-        # board_images: "BoardImagesServiceABC",
-        # board_image_records: "BoardImageRecordStorageBase",
-        board_resources: "BoardResourcesServiceABC",
-        board_resource_records: "BoardResourceRecordStorageBase",
+        board_images: "BoardImagesServiceABC",
+        board_image_records: "BoardImageRecordStorageBase",
         boards: "BoardServiceABC",
         board_records: "BoardRecordStorageBase",
         bulk_download: "BulkDownloadBase",
@@ -74,20 +67,15 @@ class InvocationServices:
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
         urls: "UrlServiceBase",
-        videos: "VideoServiceABC",
-        video_records: "VideoRecordStorageBase",
         workflow_records: "WorkflowRecordsStorageBase",
         tensors: "ObjectSerializerBase[torch.Tensor]",
         conditioning: "ObjectSerializerBase[ConditioningFieldData]",
         style_preset_records: "StylePresetRecordsStorageBase",
         style_preset_image_files: "StylePresetImageFileStorageBase",
         workflow_thumbnails: "WorkflowThumbnailServiceBase",
-        client_state_persistence: "ClientStatePersistenceABC",
     ):
-        # self.board_images = board_images
-        # self.board_image_records = board_image_records
-        self.board_resources = board_resources
-        self.board_resource_records = board_resource_records
+        self.board_images = board_images
+        self.board_image_records = board_image_records
         self.boards = boards
         self.board_records = board_records
         self.bulk_download = bulk_download
@@ -108,12 +96,9 @@ class InvocationServices:
         self.invocation_cache = invocation_cache
         self.names = names
         self.urls = urls
-        self.videos = videos
-        self.video_records = video_records
         self.workflow_records = workflow_records
         self.tensors = tensors
         self.conditioning = conditioning
         self.style_preset_records = style_preset_records
         self.style_preset_image_files = style_preset_image_files
         self.workflow_thumbnails = workflow_thumbnails
-        self.client_state_persistence = client_state_persistence

invokeai/app/services/model_install/model_install_default.py

@@ -7,7 +7,7 @@ import threading
 import time
 from pathlib import Path
 from queue import Empty, Queue
-from shutil import move, rmtree
+from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
 
@@ -51,7 +51,6 @@ from invokeai.backend.model_manager.metadata import (
 from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
-from invokeai.backend.model_manager.util.lora_metadata_extractor import apply_lora_metadata
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice
@@ -186,15 +185,13 @@ class ModelInstallService(ModelInstallServiceBase):
         info: AnyModelConfig = self._probe(Path(model_path), config)  # type: ignore
 
         if preferred_name := config.name:
-            if Path(model_path).is_file():
-                # Careful! Don't use pathlib.Path(...).with_suffix - it can will strip everything after the first dot.
-                preferred_name = f"{preferred_name}{model_path.suffix}"
+            preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
 
         dest_path = (
             self.app_config.models_path / info.base.value / info.type.value / (preferred_name or model_path.name)
         )
         try:
-            new_path = self._move_model(model_path, dest_path)
+            new_path = self._copy_model(model_path, dest_path)
         except FileExistsError as excp:
             raise DuplicateModelException(
                 f"A model named {model_path.name} is already installed at {dest_path.as_posix()}"
@@ -619,17 +616,30 @@ class ModelInstallService(ModelInstallServiceBase):
         self.record_store.update_model(key, ModelRecordChanges(path=model.path))
         return model
 
+    def _copy_model(self, old_path: Path, new_path: Path) -> Path:
+        if old_path == new_path:
+            return old_path
+        new_path.parent.mkdir(parents=True, exist_ok=True)
+        if old_path.is_dir():
+            copytree(old_path, new_path)
+        else:
+            copyfile(old_path, new_path)
+        return new_path
+
     def _move_model(self, old_path: Path, new_path: Path) -> Path:
         if old_path == new_path:
             return old_path
 
-        if new_path.exists():
-            raise FileExistsError(f"Cannot move {old_path} to {new_path}: destination already exists")
-
         new_path.parent.mkdir(parents=True, exist_ok=True)
 
+        # if path already exists then we jigger the name to make it unique
+        counter: int = 1
+        while new_path.exists():
+            path = new_path.with_stem(new_path.stem + f"_{counter:02d}")
+            if not path.exists():
+                new_path = path
+            counter += 1
         move(old_path, new_path)
-
         return new_path
 
     def _probe(self, model_path: Path, config: Optional[ModelRecordChanges] = None):
@@ -657,10 +667,6 @@ class ModelInstallService(ModelInstallServiceBase):
 
         info = info or self._probe(model_path, config)
 
-        # Apply LoRA metadata if applicable
-        model_images_path = self.app_config.models_path / "model_images"
-        apply_lora_metadata(info, model_path.resolve(), model_images_path)
-
         model_path = model_path.resolve()
 
         # Models in the Invoke-managed models dir should use relative paths.

invokeai/app/services/model_load/model_load_default.py

@@ -87,21 +87,9 @@ class ModelLoadService(ModelLoadServiceBase):
         def torch_load_file(checkpoint: Path) -> AnyModel:
             scan_result = scan_file_path(checkpoint)
             if scan_result.infected_files != 0:
-                if self._app_config.unsafe_disable_picklescan:
-                    self._logger.warning(
-                        f"Model at {checkpoint} is potentially infected by malware, but picklescan is disabled. "
-                        "Proceeding with caution."
-                    )
-                else:
-                    raise Exception(f"The model at {checkpoint} is potentially infected by malware. Aborting load.")
+                raise Exception(f"The model at {checkpoint} is potentially infected by malware. Aborting load.")
             if scan_result.scan_err:
-                if self._app_config.unsafe_disable_picklescan:
-                    self._logger.warning(
-                        f"Error scanning model at {checkpoint} for malware, but picklescan is disabled. "
-                        "Proceeding with caution."
-                    )
-                else:
-                    raise Exception(f"Error scanning model at {checkpoint} for malware. Aborting load.")
+                raise Exception(f"Error scanning model at {checkpoint} for malware. Aborting load.")
 
             result = torch_load(checkpoint, map_location="cpu")
             return result

invokeai/app/services/names/names_common.py

@@ -0,0 +1,8 @@
+from enum import Enum, EnumMeta
+
+
+class ResourceType(str, Enum, metaclass=EnumMeta):
+    """Enum for resource types."""
+
+    IMAGE = "image"
+    LATENT = "latent"

invokeai/app/services/resources/resources_common.py

@@ -1,39 +0,0 @@
-from enum import Enum
-
-from pydantic import BaseModel, Field
-
-
-class ResourceType(str, Enum):
-    """The type of resource that can be associated with a board."""
-
-    IMAGE = "image"
-    VIDEO = "video"
-
-
-class ResourceIdentifier(BaseModel):
-    resource_id: str = Field(description="The id of the resource to delete")
-    resource_type: ResourceType = Field(description="The type of the resource to delete")
-
-
-class ResultWithAffectedBoards(BaseModel):
-    affected_boards: list[str] = Field(description="The ids of boards affected by the delete operation")
-
-
-class DeleteResourcesResult(ResultWithAffectedBoards):
-    deleted_resources: list[ResourceIdentifier] = Field(description="The ids of the resources that were deleted")
-
-
-class StarredResourcesResult(ResultWithAffectedBoards):
-    starred_resources: list[ResourceIdentifier] = Field(description="The resources that were starred")
-
-
-class UnstarredResourcesResult(ResultWithAffectedBoards):
-    unstarred_resources: list[ResourceIdentifier] = Field(description="The resources that were unstarred")
-
-
-class AddResourcesToBoardResult(ResultWithAffectedBoards):
-    added_resources: list[ResourceIdentifier] = Field(description="The resources that were added to the board")
-
-
-class RemoveResourcesFromBoardResult(ResultWithAffectedBoards):
-    removed_resources: list[ResourceIdentifier] = Field(description="The resources that were removed from their board")

invokeai/app/services/shared/invocation_context.py

@@ -16,7 +16,6 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.model_records.model_records_base import UnknownModelException
-from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.step_callback import diffusion_step_callback
@@ -113,7 +112,7 @@ class BoardsInterface(InvocationContextInterface):
             board_id: The ID of the board to add the image to.
             image_name: The name of the image to add to the board.
         """
-        return self._services.board_resources.add_resource_to_board(board_id, image_name, ResourceType.IMAGE)
+        return self._services.board_images.add_image_to_board(board_id, image_name)
 
     def get_all_image_names_for_board(self, board_id: str) -> list[str]:
         """Gets all image names for a board.
@@ -124,11 +123,11 @@ class BoardsInterface(InvocationContextInterface):
         Returns:
             A list of all image names for the board.
         """
-        resource_ids = self._services.board_resources.get_all_board_resource_ids_for_board(
+        return self._services.board_images.get_all_board_image_names_for_board(
             board_id,
-            resource_type=ResourceType.IMAGE,
+            categories=None,
+            is_intermediate=None,
         )
-        return [resource_id.resource_id for resource_id in resource_ids]
 
 
 class LoggerInterface(InvocationContextInterface):

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -23,7 +23,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_17 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_18 import build_migration_18
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_19 import build_migration_19
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_20 import build_migration_20
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_21 import build_migration_21
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -64,7 +63,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_18())
     migrator.register_migration(build_migration_19(app_config=config))
     migrator.register_migration(build_migration_20())
-    migrator.register_migration(build_migration_21())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_21.py

@@ -1,40 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration21Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        cursor.execute(
-            """
-            CREATE TABLE client_state (
-              id          INTEGER PRIMARY KEY CHECK(id = 1),
-              data        TEXT    NOT NULL, -- Frontend will handle the shape of this data
-              updated_at  DATETIME NOT NULL DEFAULT (CURRENT_TIMESTAMP)
-            );
-            """
-        )
-        cursor.execute(
-            """
-            CREATE TRIGGER tg_client_state_updated_at
-            AFTER UPDATE ON client_state
-            FOR EACH ROW
-            BEGIN
-              UPDATE client_state
-                SET updated_at = CURRENT_TIMESTAMP
-              WHERE id = OLD.id;
-            END;
-            """
-        )
-
-
-def build_migration_21() -> Migration:
-    """Builds the migration object for migrating from version 20 to version 21. This includes:
-    - Creating the `client_state` table.
-    - Adding a trigger to update the `updated_at` field on updates.
-    """
-    return Migration(
-        from_version=20,
-        to_version=21,
-        callback=Migration21Callback(),
-    )

invokeai/app/services/video_records/video_records_base.py

@@ -1,105 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from invokeai.app.invocations.fields import MetadataField
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.video_records.video_records_common import (
-    VideoNamesResult,
-    VideoRecord,
-    VideoRecordChanges,
-)
-
-
-class VideoRecordStorageBase(ABC):
-    """Low-level service responsible for interfacing with the video record store."""
-
-    @abstractmethod
-    def get(self, video_id: str) -> VideoRecord:
-        """Gets a video record."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
-        """Gets a video's metadata."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        video_id: str,
-        changes: VideoRecordChanges,
-    ) -> None:
-        """Updates a video record."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[VideoRecord]:
-        """Gets a page of video records."""
-        pass
-
-    @abstractmethod
-    def delete(self, video_id: str) -> None:
-        """Deletes a video record."""
-        pass
-
-    @abstractmethod
-    def delete_many(self, video_ids: list[str]) -> None:
-        """Deletes many video records."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        video_id: str,
-        width: int,
-        height: int,
-        duration: Optional[float] = None,
-        frame_rate: Optional[float] = None,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
-    ) -> VideoRecord:
-        """Saves a video record."""
-        pass
-
-    @abstractmethod
-    def get_workflow(self, video_id: str) -> Optional[str]:
-        """Gets a video's workflow."""
-        pass
-
-    @abstractmethod
-    def get_graph(self, video_id: str) -> Optional[str]:
-        """Gets a video's graph."""
-        pass
-
-    @abstractmethod
-    def get_video_names(
-        self,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> VideoNamesResult:
-        """Gets video names with metadata for optimistic updates."""
-        pass
-
-    @abstractmethod
-    def get_intermediates_count(self) -> int:
-        """Gets the count of intermediate videos."""
-        pass
-
-    @abstractmethod
-    def delete_intermediates(self) -> int:
-        """Deletes all intermediate videos and returns the count of deleted videos."""
-        pass

invokeai/app/services/video_records/video_records_common.py

@@ -1,52 +0,0 @@
-import datetime
-from typing import Optional, Union
-
-from pydantic import BaseModel, Field
-
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-
-VIDEO_DTO_COLS = ", ".join(
-    [
-        "videos." + c
-        for c in [
-            "id",
-            "width",
-            "height",
-            "created_at",
-            "updated_at",
-        ]
-    ]
-)
-
-
-class VideoRecord(BaseModelExcludeNull):
-    """Deserialized video record without metadata."""
-
-    id: str = Field(description="The unique id of the video.")
-    """The unique id of the video."""
-    width: int = Field(description="The width of the video in px.")
-    """The actual width of the video in px."""
-    height: int = Field(description="The height of the video in px.")
-    """The actual height of the video in px."""
-    created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the video.")
-    """The created timestamp of the video."""
-    updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the video.")
-    """The updated timestamp of the video."""
-
-
-class VideoRecordChanges(BaseModelExcludeNull):
-    """
-    A set of changes to apply to a video record.
-
-    Only limited changes are allowed:
-    - `starred` - Whether the video is starred.
-    """
-
-    starred: Optional[bool] = Field(default=None, description="Whether the video is starred.")
-    """The video's new `starred` state."""
-
-
-class VideoNamesResult(BaseModel):
-    """Result of fetching video names."""
-
-    video_ids: list[str] = Field(description="The video IDs")

invokeai/app/services/video_records/video_records_sqlite.py

@@ -1,86 +0,0 @@
-from typing import Optional
-
-from invokeai.app.invocations.fields import MetadataField
-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.services.video_records.video_records_base import VideoRecordStorageBase
-from invokeai.app.services.video_records.video_records_common import (
-    VideoNamesResult,
-    VideoRecord,
-    VideoRecordChanges,
-)
-
-
-class SqliteVideoRecordStorage(VideoRecordStorageBase):
-    def __init__(self, db: SqliteDatabase) -> None:
-        super().__init__()
-        self._db = db
-
-    def get(self, video_id: str) -> VideoRecord:
-        # For now, this is a placeholder that raises NotImplementedError
-        # In a real implementation, this would query the videos table
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def update(
-        self,
-        video_id: str,
-        changes: VideoRecordChanges,
-    ) -> None:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[VideoRecord]:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def delete(self, video_id: str) -> None:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def delete_many(self, video_ids: list[str]) -> None:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def save(
-        self,
-        video_id: str,
-        width: int,
-        height: int,
-        duration: Optional[float] = None,
-        frame_rate: Optional[float] = None,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
-    ) -> VideoRecord:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def get_workflow(self, video_id: str) -> Optional[str]:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def get_graph(self, video_id: str) -> Optional[str]:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def get_video_names(
-        self,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> VideoNamesResult:
-        raise NotImplementedError("Video record storage not yet implemented")
-
-    def get_intermediates_count(self) -> int:
-        return 0  # Placeholder implementation
-
-    def delete_intermediates(self) -> int:
-        return 0  # Placeholder implementation

invokeai/app/services/videos/videos_base.py

@@ -1,151 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable, Optional
-
-from invokeai.app.invocations.fields import MetadataField
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
-from invokeai.app.services.video_records.video_records_common import (
-    VideoNamesResult,
-    VideoRecord,
-    VideoRecordChanges,
-)
-from invokeai.app.services.videos.videos_common import VideoDTO
-
-
-class VideoServiceABC(ABC):
-    """High-level service for video management."""
-
-    _on_changed_callbacks: list[Callable[[VideoDTO], None]]
-    _on_deleted_callbacks: list[Callable[[str], None]]
-
-    def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
-
-    def on_changed(self, on_changed: Callable[[VideoDTO], None]) -> None:
-        """Register a callback for when a video is changed."""
-        self._on_changed_callbacks.append(on_changed)
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when a video is deleted."""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_changed(self, item: VideoDTO) -> None:
-        for callback in self._on_changed_callbacks:
-            callback(item)
-
-    def _on_deleted(self, item_id: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(item_id)
-
-    @abstractmethod
-    def create(
-        self,
-        video_id: str,
-        width: int,
-        height: int,
-        duration: Optional[float] = None,
-        frame_rate: Optional[float] = None,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
-        is_intermediate: Optional[bool] = False,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
-    ) -> VideoDTO:
-        """Creates a video record and returns its DTO."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        video_id: str,
-        changes: VideoRecordChanges,
-    ) -> VideoDTO:
-        """Updates a video record and returns its DTO."""
-        pass
-
-    @abstractmethod
-    def get_record(self, video_id: str) -> VideoRecord:
-        """Gets a video record."""
-        pass
-
-    @abstractmethod
-    def get_dto(self, video_id: str) -> VideoDTO:
-        """Gets a video DTO."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
-        """Gets a video's metadata."""
-        pass
-
-    @abstractmethod
-    def get_workflow(self, video_id: str) -> Optional[str]:
-        """Gets a video's workflow."""
-        pass
-
-    @abstractmethod
-    def get_graph(self, video_id: str) -> Optional[str]:
-        """Gets a video's graph."""
-        pass
-
-    @abstractmethod
-    def get_path(self, video_id: str, thumbnail: bool = False) -> str:
-        """Gets a video's path on disk."""
-        pass
-
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates a video path."""
-        pass
-
-    @abstractmethod
-    def get_url(self, video_id: str, thumbnail: bool = False) -> str:
-        """Gets a video's URL."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[VideoDTO]:
-        """Gets a page of video DTOs."""
-        pass
-
-    @abstractmethod
-    def delete(self, video_id: str):
-        """Deletes a video."""
-        pass
-
-    @abstractmethod
-    def delete_intermediates(self) -> int:
-        """Deletes all intermediate videos and returns the count."""
-        pass
-
-    @abstractmethod
-    def get_intermediates_count(self) -> int:
-        """Gets the count of intermediate videos."""
-        pass
-
-    @abstractmethod
-    def delete_videos_on_board(self, board_id: str):
-        """Deletes all videos on a board."""
-        pass
-
-    @abstractmethod
-    def get_video_names(
-        self,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> VideoNamesResult:
-        """Gets video names with metadata for optimistic updates."""
-        pass

invokeai/app/services/videos/videos_common.py

@@ -1,38 +0,0 @@
-from typing import Any, Literal, Optional
-
-from pydantic import BaseModel, ConfigDict, Field
-
-from invokeai.app.services.video_records.video_records_common import VideoRecord
-
-
-def make_type_required(s: dict[str, Any]):
-    if "required" in s:
-        s["required"].append("type")
-    else:
-        s["required"] = ["type"]
-
-
-class VideoDTO(BaseModel):
-    """Deserialized video record, enriched for the frontend."""
-
-    type: Literal["video"] = Field(default="video")
-    video_id: str = Field(description="The id of the board the video belongs to, if one exists.")
-    """The id of the board the video belongs to, if one exists."""
-    width: int = Field(description="The width of the video.")
-    height: int = Field(description="The height of the video.")
-    board_id: Optional[str] = Field(
-        default=None, description="The id of the board the video belongs to, if one exists."
-    )
-
-    model_config = ConfigDict(json_schema_extra=make_type_required)
-
-
-def video_record_to_dto(
-    video_record: VideoRecord,
-    board_id: Optional[str],
-) -> VideoDTO:
-    """Converts a VideoRecord to a VideoDTO."""
-    return VideoDTO(
-        **video_record.model_dump(),
-        board_id=board_id,
-    )

invokeai/app/services/videos/videos_default.py

@@ -1,103 +0,0 @@
-from typing import Optional
-
-from invokeai.app.invocations.fields import MetadataField
-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
-from invokeai.app.services.video_records.video_records_common import (
-    VideoNamesResult,
-    VideoRecord,
-    VideoRecordChanges,
-)
-from invokeai.app.services.videos.videos_base import VideoServiceABC
-from invokeai.app.services.videos.videos_common import VideoDTO
-
-
-class VideoService(VideoServiceABC):
-    __invoker: Invoker
-
-    def start(self, invoker: Invoker) -> None:
-        self.__invoker = invoker
-
-    def create(
-        self,
-        video_id: str,
-        width: int,
-        height: int,
-        duration: Optional[float] = None,
-        frame_rate: Optional[float] = None,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
-        is_intermediate: Optional[bool] = False,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
-    ) -> VideoDTO:
-        # For now, this is a placeholder implementation
-        raise NotImplementedError("Video service not yet implemented")
-
-    def update(
-        self,
-        video_id: str,
-        changes: VideoRecordChanges,
-    ) -> VideoDTO:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_record(self, video_id: str) -> VideoRecord:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_dto(self, video_id: str) -> VideoDTO:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_workflow(self, video_id: str) -> Optional[str]:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_graph(self, video_id: str) -> Optional[str]:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_path(self, video_id: str, thumbnail: bool = False) -> str:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def validate_path(self, path: str) -> bool:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_url(self, video_id: str, thumbnail: bool = False) -> str:
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[VideoDTO]:
-        # Return empty results for now
-        return OffsetPaginatedResults(items=[], offset=offset, limit=limit, total=0, has_more=False)
-
-    def delete(self, video_id: str):
-        raise NotImplementedError("Video service not yet implemented")
-
-    def delete_intermediates(self) -> int:
-        return 0  # Placeholder
-
-    def get_intermediates_count(self) -> int:
-        return 0  # Placeholder
-
-    def delete_videos_on_board(self, board_id: str):
-        raise NotImplementedError("Video service not yet implemented")
-
-    def get_video_names(
-        self,
-        starred_first: bool = True,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> VideoNamesResult:
-        # Return empty results for now
-        return VideoNamesResult(video_ids=[])

invokeai/backend/bria/bria_utils.py

@@ -0,0 +1,314 @@
+import math
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from diffusers.utils import logging
+from transformers import (
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def get_t5_prompt_embeds(
+    tokenizer: T5TokenizerFast,
+    text_encoder: T5EncoderModel,
+    prompt: Union[str, List[str], None] = None,
+    num_images_per_prompt: int = 1,
+    max_sequence_length: int = 128,
+    device: Optional[torch.device] = None,
+):
+    device = device or text_encoder.device
+
+    if prompt is None:
+        prompt = ""
+
+    prompt = [prompt] if isinstance(prompt, str) else prompt
+    batch_size = len(prompt)
+
+    text_inputs = tokenizer(
+        prompt,
+        # padding="max_length",
+        max_length=max_sequence_length,
+        truncation=True,
+        add_special_tokens=True,
+        return_tensors="pt",
+    )
+    text_input_ids = text_inputs.input_ids
+    untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+        removed_text = tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+        logger.warning(
+            "The following part of your input was truncated because `max_sequence_length` is set to "
+            f" {max_sequence_length} tokens: {removed_text}"
+        )
+
+    prompt_embeds = text_encoder(text_input_ids.to(device))[0]
+
+    # Concat zeros to max_sequence
+    b, seq_len, dim = prompt_embeds.shape
+    if seq_len < max_sequence_length:
+        padding = torch.zeros(
+            (b, max_sequence_length - seq_len, dim), dtype=prompt_embeds.dtype, device=prompt_embeds.device
+        )
+        prompt_embeds = torch.concat([prompt_embeds, padding], dim=1)
+
+    prompt_embeds = prompt_embeds.to(device=device)
+
+    _, seq_len, _ = prompt_embeds.shape
+
+    # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+    prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+    return prompt_embeds
+
+
+# in order the get the same sigmas as in training and sample from them
+def get_original_sigmas(num_train_timesteps=1000, num_inference_steps=1000):
+    timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
+    sigmas = timesteps / num_train_timesteps
+
+    inds = [int(ind) for ind in np.linspace(0, num_train_timesteps - 1, num_inference_steps)]
+    new_sigmas = sigmas[inds]
+    return new_sigmas
+
+
+def is_ng_none(negative_prompt):
+    return (
+        negative_prompt is None
+        or negative_prompt == ""
+        or (isinstance(negative_prompt, list) and negative_prompt[0] is None)
+        or (isinstance(negative_prompt, list) and negative_prompt[0] == "")
+    )
+
+
+class CudaTimerContext:
+    def __init__(self, times_arr):
+        self.times_arr = times_arr
+
+    def __enter__(self):
+        self.before_event = torch.cuda.Event(enable_timing=True)
+        self.after_event = torch.cuda.Event(enable_timing=True)
+        self.before_event.record()
+
+    def __exit__(self, type, value, traceback):
+        self.after_event.record()
+        torch.cuda.synchronize()
+        elapsed_time = self.before_event.elapsed_time(self.after_event) / 1000
+        self.times_arr.append(elapsed_time)
+
+
+def get_env_prefix():
+    env = os.environ.get("CLOUD_PROVIDER", "AWS").upper()
+    if env == "AWS":
+        return "SM_CHANNEL"
+    elif env == "AZURE":
+        return "AZUREML_DATAREFERENCE"
+
+    raise Exception(f"Env {env} not supported")
+
+
+def compute_density_for_timestep_sampling(
+    weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
+):
+    """Compute the density for sampling the timesteps when doing SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "logit_normal":
+        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
+        u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
+        u = torch.nn.functional.sigmoid(u)
+    elif weighting_scheme == "mode":
+        u = torch.rand(size=(batch_size,), device="cpu")
+        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
+    else:
+        u = torch.rand(size=(batch_size,), device="cpu")
+    return u
+
+
+def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
+    """Computes loss weighting scheme for SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "sigma_sqrt":
+        weighting = (sigmas**-2.0).float()
+    elif weighting_scheme == "cosmap":
+        bot = 1 - 2 * sigmas + 2 * sigmas**2
+        weighting = 2 / (math.pi * bot)
+    else:
+        weighting = torch.ones_like(sigmas)
+    return weighting
+
+
+def initialize_distributed():
+    # Initialize the process group for distributed training
+    dist.init_process_group("nccl")
+
+    # Get the current process's rank (ID) and the total number of processes (world size)
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+
+    print(f"Initialized distributed training: Rank {rank}/{world_size}")
+
+
+def get_clip_prompt_embeds(
+    text_encoder: CLIPTextModel,
+    text_encoder_2: CLIPTextModelWithProjection,
+    tokenizer: CLIPTokenizer,
+    tokenizer_2: CLIPTokenizer,
+    prompt: Union[str, List[str]] = None,
+    num_images_per_prompt: int = 1,
+    max_sequence_length: int = 77,
+    device: Optional[torch.device] = None,
+):
+    device = device or text_encoder.device
+    assert max_sequence_length == tokenizer.model_max_length
+    prompt = [prompt] if isinstance(prompt, str) else prompt
+
+    # Define tokenizers and text encoders
+    tokenizers = [tokenizer, tokenizer_2]
+    text_encoders = [text_encoder, text_encoder_2]
+
+    # textual inversion: process multi-vector tokens if necessary
+    prompt_embeds_list = []
+    prompts = [prompt, prompt]
+    for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders, strict=False):
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+        prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device), output_hidden_states=True)
+
+        # We are only ALWAYS interested in the pooled output of the final text encoder
+        pooled_prompt_embeds = prompt_embeds[0]
+        prompt_embeds = prompt_embeds.hidden_states[-2]
+
+        prompt_embeds_list.append(prompt_embeds)
+
+    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+    bs_embed, seq_len, _ = prompt_embeds.shape
+    # duplicate text embeddings for each generation per prompt, using mps friendly method
+    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+    pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+        bs_embed * num_images_per_prompt, -1
+    )
+
+    return prompt_embeds, pooled_prompt_embeds
+
+
+def get_1d_rotary_pos_embed(
+    dim: int,
+    pos: Union[np.ndarray, int],
+    theta: float = 10000.0,
+    use_real=False,
+    linear_factor=1.0,
+    ntk_factor=1.0,
+    repeat_interleave_real=True,
+    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
+):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
+    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
+    data type.
+
+    Args:
+        dim (`int`): Dimension of the frequency tensor.
+        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
+        theta (`float`, *optional*, defaults to 10000.0):
+            Scaling factor for frequency computation. Defaults to 10000.0.
+        use_real (`bool`, *optional*):
+            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+        linear_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for the context extrapolation. Defaults to 1.0.
+        ntk_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
+        repeat_interleave_real (`bool`, *optional*, defaults to `True`):
+            If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
+            Otherwise, they are concateanted with themselves.
+        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
+            the dtype of the frequency tensor.
+    Returns:
+        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
+    """
+    assert dim % 2 == 0
+
+    if isinstance(pos, int):
+        pos = torch.arange(pos)
+    if isinstance(pos, np.ndarray):
+        pos = torch.from_numpy(pos)  # type: ignore  # [S]
+
+    theta = theta * ntk_factor
+    freqs = (
+        1.0
+        / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
+        / linear_factor
+    )  # [D/2]
+    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
+    if use_real and repeat_interleave_real:
+        # flux, hunyuan-dit, cogvideox
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
+        return freqs_cos, freqs_sin
+    elif use_real:
+        # stable audio, allegro
+        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
+        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
+        return freqs_cos, freqs_sin
+    else:
+        # lumina
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+        return freqs_cis
+
+
+class FluxPosEmbed(torch.nn.Module):
+    # modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
+    def __init__(self, theta: int, axes_dim: List[int]):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: torch.Tensor) -> torch.Tensor:
+        n_axes = ids.shape[-1]
+        cos_out = []
+        sin_out = []
+        pos = ids.float()
+        is_mps = ids.device.type == "mps"
+        freqs_dtype = torch.float32 if is_mps else torch.float64
+        for i in range(n_axes):
+            cos, sin = get_1d_rotary_pos_embed(
+                self.axes_dim[i],
+                pos[:, i],
+                theta=self.theta,
+                repeat_interleave_real=True,
+                use_real=True,
+                freqs_dtype=freqs_dtype,
+            )
+            cos_out.append(cos)
+            sin_out.append(sin)
+        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
+        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
+        return freqs_cos, freqs_sin

invokeai/backend/bria/controlnet_aux/__init__.py

@@ -0,0 +1,6 @@
+__version__ = "0.0.9"
+
+from invokeai.backend.bria.controlnet_aux.canny import CannyDetector as CannyDetector
+from invokeai.backend.bria.controlnet_aux.open_pose import OpenposeDetector as OpenposeDetector
+
+__all__ = ["CannyDetector", "OpenposeDetector"]

invokeai/backend/bria/controlnet_aux/canny/__init__.py

@@ -0,0 +1,39 @@
+import warnings
+
+import cv2
+import numpy as np
+from PIL import Image
+
+from invokeai.backend.bria.controlnet_aux.util import HWC3, resize_image
+
+
+class CannyDetector:
+    def __call__(self, input_image=None, low_threshold=100, high_threshold=200, detect_resolution=512, image_resolution=512, output_type=None, **kwargs):
+        if "img" in kwargs:
+            warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning, stacklevel=2)
+            input_image = kwargs.pop("img")
+
+        if input_image is None:
+            raise ValueError("input_image must be defined.")
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+            output_type = output_type or "pil"
+        else:
+            output_type = output_type or "np"
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+
+        detected_map = cv2.Canny(input_image, low_threshold, high_threshold)
+        detected_map = HWC3(detected_map)
+
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map

invokeai/backend/bria/controlnet_aux/open_pose/LICENSE

@@ -0,0 +1,108 @@
+OPENPOSE: MULTIPERSON KEYPOINT DETECTION
+SOFTWARE LICENSE AGREEMENT
+ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+
+BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+
+This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Carnegie Mellon University (called "Licensor" in this Agreement).  All rights not specifically granted to you in this Agreement are reserved for Licensor. 
+
+RESERVATION OF OWNERSHIP AND GRANT OF LICENSE: 
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+
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+
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+
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+
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+************************************************************************
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+
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+ 
+1.	Caffe, version 1.0.0, (https://github.com/BVLC/caffe/)
+
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+
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+Copyright (c) 2014-2017 The Regents of the University of California (Regents)
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+
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+modification, are permitted provided that the following conditions are met: 
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+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer. 
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
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+
+************END OF THIRD-PARTY SOFTWARE NOTICES AND INFORMATION**********

invokeai/backend/bria/controlnet_aux/open_pose/__init__.py

@@ -0,0 +1,233 @@
+# Openpose
+# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
+# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
+# 3rd Edited by ControlNet
+# 4th Edited by ControlNet (added face and correct hands)
+# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs)
+# This preprocessor is licensed by CMU for non-commercial use only.
+
+
+import os
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import warnings
+from typing import List, NamedTuple, Tuple, Union
+
+import cv2
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+from invokeai.backend.bria.controlnet_aux.open_pose.body import Body, BodyResult, Keypoint
+from invokeai.backend.bria.controlnet_aux.open_pose.face import Face
+from invokeai.backend.bria.controlnet_aux.open_pose.hand import Hand
+from invokeai.backend.bria.controlnet_aux.util import HWC3, resize_image
+
+HandResult = List[Keypoint]
+FaceResult = List[Keypoint]
+
+class PoseResult(NamedTuple):
+    body: BodyResult
+    left_hand: Union[HandResult, None]
+    right_hand: Union[HandResult, None]
+    face: Union[FaceResult, None]
+
+def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True):
+    """
+    Draw the detected poses on an empty canvas.
+
+    Args:
+        poses (List[PoseResult]): A list of PoseResult objects containing the detected poses.
+        H (int): The height of the canvas.
+        W (int): The width of the canvas.
+        draw_body (bool, optional): Whether to draw body keypoints. Defaults to True.
+        draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True.
+        draw_face (bool, optional): Whether to draw face keypoints. Defaults to True.
+
+    Returns:
+        numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses.
+    """
+    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
+
+    for pose in poses:
+        if draw_body:
+            canvas = util.draw_bodypose(canvas, pose.body.keypoints)
+
+        if draw_hand:
+            canvas = util.draw_handpose(canvas, pose.left_hand)
+            canvas = util.draw_handpose(canvas, pose.right_hand)
+
+        if draw_face:
+            canvas = util.draw_facepose(canvas, pose.face)
+
+    return canvas
+
+
+class OpenposeDetector:
+    """
+    A class for detecting human poses in images using the Openpose model.
+
+    Attributes:
+        model_dir (str): Path to the directory where the pose models are stored.
+    """
+    def __init__(self, body_estimation, hand_estimation=None, face_estimation=None):
+        self.body_estimation = body_estimation
+        self.hand_estimation = hand_estimation
+        self.face_estimation = face_estimation
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path, filename=None, hand_filename=None, face_filename=None, cache_dir=None, local_files_only=False):
+
+        if pretrained_model_or_path == "lllyasviel/ControlNet":
+            filename = filename or "annotator/ckpts/body_pose_model.pth"
+            hand_filename = hand_filename or "annotator/ckpts/hand_pose_model.pth"
+            face_filename = face_filename or "facenet.pth"
+
+            face_pretrained_model_or_path = "lllyasviel/Annotators"
+        else:
+            filename = filename or "body_pose_model.pth"
+            hand_filename = hand_filename or "hand_pose_model.pth"
+            face_filename = face_filename or "facenet.pth"
+
+            face_pretrained_model_or_path = pretrained_model_or_path
+
+        if os.path.isdir(pretrained_model_or_path):
+            body_model_path = os.path.join(pretrained_model_or_path, filename)
+            hand_model_path = os.path.join(pretrained_model_or_path, hand_filename)
+            face_model_path = os.path.join(face_pretrained_model_or_path, face_filename)
+        else:
+            body_model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
+            hand_model_path = hf_hub_download(pretrained_model_or_path, hand_filename, cache_dir=cache_dir, local_files_only=local_files_only)
+            face_model_path = hf_hub_download(face_pretrained_model_or_path, face_filename, cache_dir=cache_dir, local_files_only=local_files_only)
+
+        body_estimation = Body(body_model_path)
+        hand_estimation = Hand(hand_model_path)
+        face_estimation = Face(face_model_path)
+
+        return cls(body_estimation, hand_estimation, face_estimation)
+
+    def to(self, device):
+        self.body_estimation.to(device)
+        self.hand_estimation.to(device)
+        self.face_estimation.to(device)
+        return self
+
+    def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]:
+        left_hand = None
+        right_hand = None
+        H, W, _ = oriImg.shape
+        for x, y, w, is_left in util.handDetect(body, oriImg):
+            peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]).astype(np.float32)
+            if peaks.ndim == 2 and peaks.shape[1] == 2:
+                peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
+                peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
+
+                hand_result = [
+                    Keypoint(x=peak[0], y=peak[1])
+                    for peak in peaks
+                ]
+
+                if is_left:
+                    left_hand = hand_result
+                else:
+                    right_hand = hand_result
+
+        return left_hand, right_hand
+
+    def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]:
+        face = util.faceDetect(body, oriImg)
+        if face is None:
+            return None
+
+        x, y, w = face
+        H, W, _ = oriImg.shape
+        heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :])
+        peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32)
+        if peaks.ndim == 2 and peaks.shape[1] == 2:
+            peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
+            peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
+            return [
+                Keypoint(x=peak[0], y=peak[1])
+                for peak in peaks
+            ]
+
+        return None
+
+    def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]:
+        """
+        Detect poses in the given image.
+            Args:
+                oriImg (numpy.ndarray): The input image for pose detection.
+                include_hand (bool, optional): Whether to include hand detection. Defaults to False.
+                include_face (bool, optional): Whether to include face detection. Defaults to False.
+
+        Returns:
+            List[PoseResult]: A list of PoseResult objects containing the detected poses.
+        """
+        oriImg = oriImg[:, :, ::-1].copy()
+        H, W, C = oriImg.shape
+        with torch.no_grad():
+            candidate, subset = self.body_estimation(oriImg)
+            bodies = self.body_estimation.format_body_result(candidate, subset)
+
+            results = []
+            for body in bodies:
+                left_hand, right_hand, face = (None,) * 3
+                if include_hand:
+                    left_hand, right_hand = self.detect_hands(body, oriImg)
+                if include_face:
+                    face = self.detect_face(body, oriImg)
+
+                results.append(PoseResult(BodyResult(
+                    keypoints=[
+                        Keypoint(
+                            x=keypoint.x / float(W),
+                            y=keypoint.y / float(H)
+                        ) if keypoint is not None else None
+                        for keypoint in body.keypoints
+                    ],
+                    total_score=body.total_score,
+                    total_parts=body.total_parts
+                ), left_hand, right_hand, face))
+
+            return results
+
+    def __call__(self, input_image, detect_resolution=512, image_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", **kwargs):
+        if hand_and_face is not None:
+            warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning, stacklevel=2)
+            include_hand = hand_and_face
+            include_face = hand_and_face
+
+        if "return_pil" in kwargs:
+            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning, stacklevel=2)
+            output_type = "pil" if kwargs["return_pil"] else "np"
+        if type(output_type) is bool:
+            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions", stacklevel=2)
+            if output_type:
+                output_type = "pil"
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+        H, W, C = input_image.shape
+
+        poses = self.detect_poses(input_image, include_hand, include_face)
+        canvas = draw_poses(poses, H, W, draw_body=include_body, draw_hand=include_hand, draw_face=include_face)
+
+        detected_map = canvas
+        detected_map = HWC3(detected_map)
+
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map

invokeai/backend/bria/controlnet_aux/open_pose/body.py

@@ -0,0 +1,259 @@
+import math
+from typing import List, NamedTuple, Union
+
+import numpy as np
+import torch
+from scipy.ndimage.filters import gaussian_filter
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+from invokeai.backend.bria.controlnet_aux.open_pose.model import bodypose_model
+
+
+class Keypoint(NamedTuple):
+    x: float
+    y: float
+    score: float = 1.0
+    id: int = -1
+
+
+class BodyResult(NamedTuple):
+    # Note: Using `Union` instead of `|` operator as the ladder is a Python
+    # 3.10 feature.
+    # Annotator code should be Python 3.8 Compatible, as controlnet repo uses
+    # Python 3.8 environment.
+    # https://github.com/lllyasviel/ControlNet/blob/d3284fcd0972c510635a4f5abe2eeb71dc0de524/environment.yaml#L6
+    keypoints: List[Union[Keypoint, None]]
+    total_score: float
+    total_parts: int
+
+
+class Body(object):
+    def __init__(self, model_path):
+        self.model = bodypose_model()
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, oriImg):
+        device = next(iter(self.model.parameters())).device
+        # scale_search = [0.5, 1.0, 1.5, 2.0]
+        scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre1 = 0.1
+        thre2 = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
+        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale)
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            data = data.to(device)
+            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+            with torch.no_grad():
+                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
+            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
+            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1]))
+
+            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
+            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
+            paf = util.smart_resize_k(paf, fx=stride, fy=stride)
+            paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1]))
+
+            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
+            paf_avg += + paf / len(multiplier)
+
+        all_peaks = []
+        peak_counter = 0
+
+        for part in range(18):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+
+            map_left = np.zeros(one_heatmap.shape)
+            map_left[1:, :] = one_heatmap[:-1, :]
+            map_right = np.zeros(one_heatmap.shape)
+            map_right[:-1, :] = one_heatmap[1:, :]
+            map_up = np.zeros(one_heatmap.shape)
+            map_up[:, 1:] = one_heatmap[:, :-1]
+            map_down = np.zeros(one_heatmap.shape)
+            map_down[:, :-1] = one_heatmap[:, 1:]
+
+            peaks_binary = np.logical_and.reduce(
+                (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
+            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0], strict=False))  # note reverse
+            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
+            peak_id = range(peak_counter, peak_counter + len(peaks))
+            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
+
+            all_peaks.append(peaks_with_score_and_id)
+            peak_counter += len(peaks)
+
+        # find connection in the specified sequence, center 29 is in the position 15
+        limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+                   [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+                   [1, 16], [16, 18], [3, 17], [6, 18]]
+        # the middle joints heatmap correpondence
+        mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
+                  [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
+                  [55, 56], [37, 38], [45, 46]]
+
+        connection_all = []
+        special_k = []
+        mid_num = 10
+
+        for k in range(len(mapIdx)):
+            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
+            candA = all_peaks[limbSeq[k][0] - 1]
+            candB = all_peaks[limbSeq[k][1] - 1]
+            nA = len(candA)
+            nB = len(candB)
+            indexA, indexB = limbSeq[k]
+            if (nA != 0 and nB != 0):
+                connection_candidate = []
+                for i in range(nA):
+                    for j in range(nB):
+                        vec = np.subtract(candB[j][:2], candA[i][:2])
+                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
+                        norm = max(0.001, norm)
+                        vec = np.divide(vec, norm)
+
+                        startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
+                                            np.linspace(candA[i][1], candB[j][1], num=mid_num), strict=False))
+
+                        vec_x = np.array([score_mid[int(round(startend[i][1])), int(round(startend[i][0])), 0] \
+                                          for i in range(len(startend))])
+                        vec_y = np.array([score_mid[int(round(startend[i][1])), int(round(startend[i][0])), 1] \
+                                          for i in range(len(startend))])
+
+                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
+                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
+                            0.5 * oriImg.shape[0] / norm - 1, 0)
+                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
+                        criterion2 = score_with_dist_prior > 0
+                        if criterion1 and criterion2:
+                            connection_candidate.append(
+                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
+
+                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
+                connection = np.zeros((0, 5))
+                for c in range(len(connection_candidate)):
+                    i, j, s = connection_candidate[c][0:3]
+                    if (i not in connection[:, 3] and j not in connection[:, 4]):
+                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
+                        if (len(connection) >= min(nA, nB)):
+                            break
+
+                connection_all.append(connection)
+            else:
+                special_k.append(k)
+                connection_all.append([])
+
+        # last number in each row is the total parts number of that person
+        # the second last number in each row is the score of the overall configuration
+        subset = -1 * np.ones((0, 20))
+        candidate = np.array([item for sublist in all_peaks for item in sublist])
+
+        for k in range(len(mapIdx)):
+            if k not in special_k:
+                partAs = connection_all[k][:, 0]
+                partBs = connection_all[k][:, 1]
+                indexA, indexB = np.array(limbSeq[k]) - 1
+
+                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
+                    found = 0
+                    subset_idx = [-1, -1]
+                    for j in range(len(subset)):  # 1:size(subset,1):
+                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
+                            subset_idx[found] = j
+                            found += 1
+
+                    if found == 1:
+                        j = subset_idx[0]
+                        if subset[j][indexB] != partBs[i]:
+                            subset[j][indexB] = partBs[i]
+                            subset[j][-1] += 1
+                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+                    elif found == 2:  # if found 2 and disjoint, merge them
+                        j1, j2 = subset_idx
+                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
+                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
+                            subset[j1][:-2] += (subset[j2][:-2] + 1)
+                            subset[j1][-2:] += subset[j2][-2:]
+                            subset[j1][-2] += connection_all[k][i][2]
+                            subset = np.delete(subset, j2, 0)
+                        else:  # as like found == 1
+                            subset[j1][indexB] = partBs[i]
+                            subset[j1][-1] += 1
+                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+
+                    # if find no partA in the subset, create a new subset
+                    elif not found and k < 17:
+                        row = -1 * np.ones(20)
+                        row[indexA] = partAs[i]
+                        row[indexB] = partBs[i]
+                        row[-1] = 2
+                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
+                        subset = np.vstack([subset, row])
+        # delete some rows of subset which has few parts occur
+        deleteIdx = []
+        for i in range(len(subset)):
+            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
+                deleteIdx.append(i)
+        subset = np.delete(subset, deleteIdx, axis=0)
+
+        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
+        # candidate: x, y, score, id
+        return candidate, subset
+
+    @staticmethod
+    def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]:
+        """
+        Format the body results from the candidate and subset arrays into a list of BodyResult objects.
+
+        Args:
+            candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id
+                for each body part.
+            subset (np.ndarray): An array of subsets containing indices to the candidate array for each
+                person detected. The last two columns of each row hold the total score and total parts
+                of the person.
+
+        Returns:
+            List[BodyResult]: A list of BodyResult objects, where each object represents a person with
+                detected keypoints, total score, and total parts.
+        """
+        return [
+            BodyResult(
+                keypoints=[
+                    Keypoint(
+                        x=candidate[candidate_index][0],
+                        y=candidate[candidate_index][1],
+                        score=candidate[candidate_index][2],
+                        id=candidate[candidate_index][3]
+                    ) if candidate_index != -1 else None
+                    for candidate_index in person[:18].astype(int)
+                ],
+                total_score=person[18],
+                total_parts=person[19]
+            )
+            for person in subset
+        ]

invokeai/backend/bria/controlnet_aux/open_pose/face.py

@@ -0,0 +1,364 @@
+import logging
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.nn import Conv2d, MaxPool2d, Module, ReLU, init
+from torchvision.transforms import ToPILImage, ToTensor
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+
+
+class FaceNet(Module):
+    """Model the cascading heatmaps. """
+    def __init__(self):
+        super(FaceNet, self).__init__()
+        # cnn to make feature map
+        self.relu = ReLU()
+        self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2)
+        self.conv1_1 = Conv2d(in_channels=3, out_channels=64,
+                              kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = Conv2d(
+            in_channels=64, out_channels=64, kernel_size=3, stride=1,
+            padding=1)
+        self.conv2_1 = Conv2d(
+            in_channels=64, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+        self.conv2_2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_1 = Conv2d(
+            in_channels=128, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_2 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_3 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_4 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_1 = Conv2d(
+            in_channels=256, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_2 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_3 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_4 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_1 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_2 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_3_CPM = Conv2d(
+            in_channels=512, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+
+        # stage1
+        self.conv6_1_CPM = Conv2d(
+            in_channels=128, out_channels=512, kernel_size=1, stride=1,
+            padding=0)
+        self.conv6_2_CPM = Conv2d(
+            in_channels=512, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage2
+        self.Mconv1_stage2 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage2 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage3
+        self.Mconv1_stage3 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage3 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage4
+        self.Mconv1_stage4 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage4 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage5
+        self.Mconv1_stage5 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage5 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage6
+        self.Mconv1_stage6 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage6 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        for m in self.modules():
+            if isinstance(m, Conv2d):
+                init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        """Return a list of heatmaps."""
+        heatmaps = []
+
+        h = self.relu(self.conv1_1(x))
+        h = self.relu(self.conv1_2(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv2_1(h))
+        h = self.relu(self.conv2_2(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv3_1(h))
+        h = self.relu(self.conv3_2(h))
+        h = self.relu(self.conv3_3(h))
+        h = self.relu(self.conv3_4(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv4_1(h))
+        h = self.relu(self.conv4_2(h))
+        h = self.relu(self.conv4_3(h))
+        h = self.relu(self.conv4_4(h))
+        h = self.relu(self.conv5_1(h))
+        h = self.relu(self.conv5_2(h))
+        h = self.relu(self.conv5_3_CPM(h))
+        feature_map = h
+
+        # stage1
+        h = self.relu(self.conv6_1_CPM(h))
+        h = self.conv6_2_CPM(h)
+        heatmaps.append(h)
+
+        # stage2
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage2(h))
+        h = self.relu(self.Mconv2_stage2(h))
+        h = self.relu(self.Mconv3_stage2(h))
+        h = self.relu(self.Mconv4_stage2(h))
+        h = self.relu(self.Mconv5_stage2(h))
+        h = self.relu(self.Mconv6_stage2(h))
+        h = self.Mconv7_stage2(h)
+        heatmaps.append(h)
+
+        # stage3
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage3(h))
+        h = self.relu(self.Mconv2_stage3(h))
+        h = self.relu(self.Mconv3_stage3(h))
+        h = self.relu(self.Mconv4_stage3(h))
+        h = self.relu(self.Mconv5_stage3(h))
+        h = self.relu(self.Mconv6_stage3(h))
+        h = self.Mconv7_stage3(h)
+        heatmaps.append(h)
+
+        # stage4
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage4(h))
+        h = self.relu(self.Mconv2_stage4(h))
+        h = self.relu(self.Mconv3_stage4(h))
+        h = self.relu(self.Mconv4_stage4(h))
+        h = self.relu(self.Mconv5_stage4(h))
+        h = self.relu(self.Mconv6_stage4(h))
+        h = self.Mconv7_stage4(h)
+        heatmaps.append(h)
+
+        # stage5
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage5(h))
+        h = self.relu(self.Mconv2_stage5(h))
+        h = self.relu(self.Mconv3_stage5(h))
+        h = self.relu(self.Mconv4_stage5(h))
+        h = self.relu(self.Mconv5_stage5(h))
+        h = self.relu(self.Mconv6_stage5(h))
+        h = self.Mconv7_stage5(h)
+        heatmaps.append(h)
+
+        # stage6
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage6(h))
+        h = self.relu(self.Mconv2_stage6(h))
+        h = self.relu(self.Mconv3_stage6(h))
+        h = self.relu(self.Mconv4_stage6(h))
+        h = self.relu(self.Mconv5_stage6(h))
+        h = self.relu(self.Mconv6_stage6(h))
+        h = self.Mconv7_stage6(h)
+        heatmaps.append(h)
+
+        return heatmaps
+
+
+LOG = logging.getLogger(__name__)
+TOTEN = ToTensor()
+TOPIL = ToPILImage()
+
+
+params = {
+    'gaussian_sigma': 2.5,
+    'inference_img_size': 736,  # 368, 736, 1312
+    'heatmap_peak_thresh': 0.1,
+    'crop_scale': 1.5,
+    'line_indices': [
+        [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
+        [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13],
+        [13, 14], [14, 15], [15, 16],
+        [17, 18], [18, 19], [19, 20], [20, 21],
+        [22, 23], [23, 24], [24, 25], [25, 26],
+        [27, 28], [28, 29], [29, 30],
+        [31, 32], [32, 33], [33, 34], [34, 35],
+        [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36],
+        [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42],
+        [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54],
+        [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48],
+        [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66],
+        [66, 67], [67, 60]
+    ],
+}
+
+
+class Face(object):
+    """
+    The OpenPose face landmark detector model.
+
+    Args:
+        inference_size: set the size of the inference image size, suggested:
+            368, 736, 1312, default 736
+        gaussian_sigma: blur the heatmaps, default 2.5
+        heatmap_peak_thresh: return landmark if over threshold, default 0.1
+
+    """
+    def __init__(self, face_model_path,
+                 inference_size=None,
+                 gaussian_sigma=None,
+                 heatmap_peak_thresh=None):
+        self.inference_size = inference_size or params["inference_img_size"]
+        self.sigma = gaussian_sigma or params['gaussian_sigma']
+        self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"]
+        self.model = FaceNet()
+        self.model.load_state_dict(torch.load(face_model_path))
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, face_img):
+        device = next(iter(self.model.parameters())).device
+        H, W, C = face_img.shape
+
+        w_size = 384
+        x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5
+
+        x_data = x_data.to(device)
+
+        with torch.no_grad():
+            hs = self.model(x_data[None, ...])
+            heatmaps = F.interpolate(
+                hs[-1],
+                (H, W),
+                mode='bilinear', align_corners=True).cpu().numpy()[0]
+        return heatmaps
+
+    def compute_peaks_from_heatmaps(self, heatmaps):
+        all_peaks = []
+        for part in range(heatmaps.shape[0]):
+            map_ori = heatmaps[part].copy()
+            binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8)
+
+            if np.sum(binary) == 0:
+                continue
+
+            positions = np.where(binary > 0.5)
+            intensities = map_ori[positions]
+            mi = np.argmax(intensities)
+            y, x = positions[0][mi], positions[1][mi]
+            all_peaks.append([x, y])
+
+        return np.array(all_peaks)

invokeai/backend/bria/controlnet_aux/open_pose/hand.py

@@ -0,0 +1,90 @@
+import cv2
+import numpy as np
+import torch
+from scipy.ndimage.filters import gaussian_filter
+from skimage.measure import label
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+from invokeai.backend.bria.controlnet_aux.open_pose.model import handpose_model
+
+
+class Hand(object):
+    def __init__(self, model_path):
+        self.model = handpose_model()
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, oriImgRaw):
+        device = next(iter(self.model.parameters())).device
+        scale_search = [0.5, 1.0, 1.5, 2.0]
+        # scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre = 0.05
+        multiplier = [x * boxsize for x in scale_search]
+
+        wsize = 128
+        heatmap_avg = np.zeros((wsize, wsize, 22))
+
+        Hr, Wr, Cr = oriImgRaw.shape
+
+        oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8)
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = util.smart_resize(oriImg, (scale, scale))
+
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            data = data.to(device)
+
+            with torch.no_grad():
+                output = self.model(data).cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            heatmap = np.transpose(np.squeeze(output), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = util.smart_resize(heatmap, (wsize, wsize))
+
+            heatmap_avg += heatmap / len(multiplier)
+
+        all_peaks = []
+        for part in range(21):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
+
+            if np.sum(binary) == 0:
+                all_peaks.append([0, 0])
+                continue
+            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
+            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
+            label_img[label_img != max_index] = 0
+            map_ori[label_img == 0] = 0
+
+            y, x = util.npmax(map_ori)
+            y = int(float(y) * float(Hr) / float(wsize))
+            x = int(float(x) * float(Wr) / float(wsize))
+            all_peaks.append([x, y])
+        return np.array(all_peaks)
+
+if __name__ == "__main__":
+    hand_estimation = Hand('../model/hand_pose_model.pth')
+
+    # test_image = '../images/hand.jpg'
+    test_image = '../images/hand.jpg'
+    oriImg = cv2.imread(test_image)  # B,G,R order
+    peaks = hand_estimation(oriImg)
+    canvas = util.draw_handpose(oriImg, peaks, True)
+    cv2.imshow('', canvas)
+    cv2.waitKey(0)

invokeai/backend/bria/controlnet_aux/open_pose/model.py

@@ -0,0 +1,217 @@
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+
+def make_layers(block, no_relu_layers):
+    layers = []
+    for layer_name, v in block.items():
+        if 'pool' in layer_name:
+            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
+                                    padding=v[2])
+            layers.append((layer_name, layer))
+        else:
+            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
+                               kernel_size=v[2], stride=v[3],
+                               padding=v[4])
+            layers.append((layer_name, conv2d))
+            if layer_name not in no_relu_layers:
+                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
+
+    return nn.Sequential(OrderedDict(layers))
+
+class bodypose_model(nn.Module):
+    def __init__(self):
+        super(bodypose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
+                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
+                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
+                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
+        blocks = {}
+        block0 = OrderedDict([
+                      ('conv1_1', [3, 64, 3, 1, 1]),
+                      ('conv1_2', [64, 64, 3, 1, 1]),
+                      ('pool1_stage1', [2, 2, 0]),
+                      ('conv2_1', [64, 128, 3, 1, 1]),
+                      ('conv2_2', [128, 128, 3, 1, 1]),
+                      ('pool2_stage1', [2, 2, 0]),
+                      ('conv3_1', [128, 256, 3, 1, 1]),
+                      ('conv3_2', [256, 256, 3, 1, 1]),
+                      ('conv3_3', [256, 256, 3, 1, 1]),
+                      ('conv3_4', [256, 256, 3, 1, 1]),
+                      ('pool3_stage1', [2, 2, 0]),
+                      ('conv4_1', [256, 512, 3, 1, 1]),
+                      ('conv4_2', [512, 512, 3, 1, 1]),
+                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
+                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
+                  ])
+
+
+        # Stage 1
+        block1_1 = OrderedDict([
+                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
+                    ])
+
+        block1_2 = OrderedDict([
+                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
+                    ])
+        blocks['block1_1'] = block1_1
+        blocks['block1_2'] = block1_2
+
+        self.model0 = make_layers(block0, no_relu_layers)
+
+        # Stages 2 - 6
+        for i in range(2, 7):
+            blocks['block%d_1' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
+                ])
+
+            blocks['block%d_2' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_1 = blocks['block1_1']
+        self.model2_1 = blocks['block2_1']
+        self.model3_1 = blocks['block3_1']
+        self.model4_1 = blocks['block4_1']
+        self.model5_1 = blocks['block5_1']
+        self.model6_1 = blocks['block6_1']
+
+        self.model1_2 = blocks['block1_2']
+        self.model2_2 = blocks['block2_2']
+        self.model3_2 = blocks['block3_2']
+        self.model4_2 = blocks['block4_2']
+        self.model5_2 = blocks['block5_2']
+        self.model6_2 = blocks['block6_2']
+
+
+    def forward(self, x):
+
+        out1 = self.model0(x)
+
+        out1_1 = self.model1_1(out1)
+        out1_2 = self.model1_2(out1)
+        out2 = torch.cat([out1_1, out1_2, out1], 1)
+
+        out2_1 = self.model2_1(out2)
+        out2_2 = self.model2_2(out2)
+        out3 = torch.cat([out2_1, out2_2, out1], 1)
+
+        out3_1 = self.model3_1(out3)
+        out3_2 = self.model3_2(out3)
+        out4 = torch.cat([out3_1, out3_2, out1], 1)
+
+        out4_1 = self.model4_1(out4)
+        out4_2 = self.model4_2(out4)
+        out5 = torch.cat([out4_1, out4_2, out1], 1)
+
+        out5_1 = self.model5_1(out5)
+        out5_2 = self.model5_2(out5)
+        out6 = torch.cat([out5_1, out5_2, out1], 1)
+
+        out6_1 = self.model6_1(out6)
+        out6_2 = self.model6_2(out6)
+
+        return out6_1, out6_2
+
+class handpose_model(nn.Module):
+    def __init__(self):
+        super(handpose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
+                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
+        # stage 1
+        block1_0 = OrderedDict([
+                ('conv1_1', [3, 64, 3, 1, 1]),
+                ('conv1_2', [64, 64, 3, 1, 1]),
+                ('pool1_stage1', [2, 2, 0]),
+                ('conv2_1', [64, 128, 3, 1, 1]),
+                ('conv2_2', [128, 128, 3, 1, 1]),
+                ('pool2_stage1', [2, 2, 0]),
+                ('conv3_1', [128, 256, 3, 1, 1]),
+                ('conv3_2', [256, 256, 3, 1, 1]),
+                ('conv3_3', [256, 256, 3, 1, 1]),
+                ('conv3_4', [256, 256, 3, 1, 1]),
+                ('pool3_stage1', [2, 2, 0]),
+                ('conv4_1', [256, 512, 3, 1, 1]),
+                ('conv4_2', [512, 512, 3, 1, 1]),
+                ('conv4_3', [512, 512, 3, 1, 1]),
+                ('conv4_4', [512, 512, 3, 1, 1]),
+                ('conv5_1', [512, 512, 3, 1, 1]),
+                ('conv5_2', [512, 512, 3, 1, 1]),
+                ('conv5_3_CPM', [512, 128, 3, 1, 1])
+            ])
+
+        block1_1 = OrderedDict([
+            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
+            ('conv6_2_CPM', [512, 22, 1, 1, 0])
+        ])
+
+        blocks = {}
+        blocks['block1_0'] = block1_0
+        blocks['block1_1'] = block1_1
+
+        # stage 2-6
+        for i in range(2, 7):
+            blocks['block%d' % i] = OrderedDict([
+                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_0 = blocks['block1_0']
+        self.model1_1 = blocks['block1_1']
+        self.model2 = blocks['block2']
+        self.model3 = blocks['block3']
+        self.model4 = blocks['block4']
+        self.model5 = blocks['block5']
+        self.model6 = blocks['block6']
+
+    def forward(self, x):
+        out1_0 = self.model1_0(x)
+        out1_1 = self.model1_1(out1_0)
+        concat_stage2 = torch.cat([out1_1, out1_0], 1)
+        out_stage2 = self.model2(concat_stage2)
+        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
+        out_stage3 = self.model3(concat_stage3)
+        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
+        out_stage4 = self.model4(concat_stage4)
+        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
+        out_stage5 = self.model5(concat_stage5)
+        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
+        out_stage6 = self.model6(concat_stage6)
+        return out_stage6

invokeai/backend/bria/controlnet_aux/open_pose/util.py

@@ -0,0 +1,388 @@
+import math
+from typing import List, Tuple, Union
+
+import cv2
+import numpy as np
+
+from invokeai.backend.bria.controlnet_aux.open_pose.body import BodyResult, Keypoint
+
+eps = 0.01
+
+
+def smart_resize(x, s):
+    Ht, Wt = s
+    if x.ndim == 2:
+        Ho, Wo = x.shape
+        Co = 1
+    else:
+        Ho, Wo, Co = x.shape
+    if Co == 3 or Co == 1:
+        k = float(Ht + Wt) / float(Ho + Wo)
+        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
+    else:
+        return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
+
+
+def smart_resize_k(x, fx, fy):
+    if x.ndim == 2:
+        Ho, Wo = x.shape
+        Co = 1
+    else:
+        Ho, Wo, Co = x.shape
+    Ht, Wt = Ho * fy, Wo * fx
+    if Co == 3 or Co == 1:
+        k = float(Ht + Wt) / float(Ho + Wo)
+        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
+    else:
+        return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
+
+
+def padRightDownCorner(img, stride, padValue):
+    h = img.shape[0]
+    w = img.shape[1]
+
+    pad = 4 * [None]
+    pad[0] = 0 # up
+    pad[1] = 0 # left
+    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
+    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
+
+    img_padded = img
+    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
+    img_padded = np.concatenate((pad_up, img_padded), axis=0)
+    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
+    img_padded = np.concatenate((pad_left, img_padded), axis=1)
+    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
+    img_padded = np.concatenate((img_padded, pad_down), axis=0)
+    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
+    img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+    return img_padded, pad
+
+
+def transfer(model, model_weights):
+    transfered_model_weights = {}
+    for weights_name in model.state_dict().keys():
+        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
+    return transfered_model_weights
+
+
+def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
+    """
+    Draw keypoints and limbs representing body pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
+        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    H, W, C = canvas.shape
+    stickwidth = 4
+
+    limbSeq = [
+        [2, 3], [2, 6], [3, 4], [4, 5],
+        [6, 7], [7, 8], [2, 9], [9, 10],
+        [10, 11], [2, 12], [12, 13], [13, 14],
+        [2, 1], [1, 15], [15, 17], [1, 16],
+        [16, 18],
+    ]
+
+    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
+
+    for (k1_index, k2_index), color in zip(limbSeq, colors, strict=False):
+        keypoint1 = keypoints[k1_index - 1]
+        keypoint2 = keypoints[k2_index - 1]
+
+        if keypoint1 is None or keypoint2 is None:
+            continue
+
+        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
+        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])
+
+    for keypoint, color in zip(keypoints, colors, strict=False):
+        if keypoint is None:
+            continue
+
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+
+    return canvas
+
+
+def draw_handpose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
+    import matplotlib
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    if not keypoints:
+        return canvas
+
+    H, W, C = canvas.shape
+
+    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
+             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
+
+    for ie, (e1, e2) in enumerate(edges):
+        k1 = keypoints[e1]
+        k2 = keypoints[e2]
+        if k1 is None or k2 is None:
+            continue
+
+        x1 = int(k1.x * W)
+        y1 = int(k1.y * H)
+        x2 = int(k2.x * W)
+        y2 = int(k2.y * H)
+        if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+            cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2)
+
+    for keypoint in keypoints:
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+    return canvas
+
+
+def draw_facepose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
+    """
+    Draw keypoints representing face pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the face pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the face keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn face pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    if not keypoints:
+        return canvas
+
+    H, W, C = canvas.shape
+    for keypoint in keypoints:
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
+    return canvas
+
+
+# detect hand according to body pose keypoints
+# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
+def handDetect(body: BodyResult, oriImg) -> List[Tuple[int, int, int, bool]]:
+    """
+    Detect hands in the input body pose keypoints and calculate the bounding box for each hand.
+
+    Args:
+        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
+        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
+
+    Returns:
+        List[Tuple[int, int, int, bool]]: A list of tuples, each containing the coordinates (x, y) of the top-left
+                                          corner of the bounding box, the width (height) of the bounding box, and
+                                          a boolean flag indicating whether the hand is a left hand (True) or a
+                                          right hand (False).
+
+    Notes:
+        - The width and height of the bounding boxes are equal since the network requires squared input.
+        - The minimum bounding box size is 20 pixels.
+    """
+    ratioWristElbow = 0.33
+    detect_result = []
+    image_height, image_width = oriImg.shape[0:2]
+
+    keypoints = body.keypoints
+    # right hand: wrist 4, elbow 3, shoulder 2
+    # left hand: wrist 7, elbow 6, shoulder 5
+    left_shoulder = keypoints[5]
+    left_elbow = keypoints[6]
+    left_wrist = keypoints[7]
+    right_shoulder = keypoints[2]
+    right_elbow = keypoints[3]
+    right_wrist = keypoints[4]
+
+    # if any of three not detected
+    has_left = all(keypoint is not None for keypoint in (left_shoulder, left_elbow, left_wrist))
+    has_right = all(keypoint is not None for keypoint in (right_shoulder, right_elbow, right_wrist))
+    if not (has_left or has_right):
+        return []
+
+    hands = []
+    #left hand
+    if has_left:
+        hands.append([
+            left_shoulder.x, left_shoulder.y,
+            left_elbow.x, left_elbow.y,
+            left_wrist.x, left_wrist.y,
+            True
+        ])
+    # right hand
+    if has_right:
+        hands.append([
+            right_shoulder.x, right_shoulder.y,
+            right_elbow.x, right_elbow.y,
+            right_wrist.x, right_wrist.y,
+            False
+        ])
+
+    for x1, y1, x2, y2, x3, y3, is_left in hands:
+        # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
+        # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
+        # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
+        # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
+        # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
+        # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
+        x = x3 + ratioWristElbow * (x3 - x2)
+        y = y3 + ratioWristElbow * (y3 - y2)
+        distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
+        distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+        width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
+        # x-y refers to the center --> offset to topLeft point
+        # handRectangle.x -= handRectangle.width / 2.f;
+        # handRectangle.y -= handRectangle.height / 2.f;
+        x -= width / 2
+        y -= width / 2  # width = height
+        # overflow the image
+        if x < 0:
+            x = 0
+        if y < 0:
+            y = 0
+        width1 = width
+        width2 = width
+        if x + width > image_width:
+            width1 = image_width - x
+        if y + width > image_height:
+            width2 = image_height - y
+        width = min(width1, width2)
+        # the max hand box value is 20 pixels
+        if width >= 20:
+            detect_result.append((int(x), int(y), int(width), is_left))
+
+    '''
+    return value: [[x, y, w, True if left hand else False]].
+    width=height since the network require squared input.
+    x, y is the coordinate of top left.
+    '''
+    return detect_result
+
+
+# Written by Lvmin
+def faceDetect(body: BodyResult, oriImg) -> Union[Tuple[int, int, int], None]:
+    """
+    Detect the face in the input body pose keypoints and calculate the bounding box for the face.
+
+    Args:
+        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
+        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
+
+    Returns:
+        Tuple[int, int, int] | None: A tuple containing the coordinates (x, y) of the top-left corner of the
+                                   bounding box and the width (height) of the bounding box, or None if the
+                                   face is not detected or the bounding box width is less than 20 pixels.
+
+    Notes:
+        - The width and height of the bounding box are equal.
+        - The minimum bounding box size is 20 pixels.
+    """
+    # left right eye ear 14 15 16 17
+    image_height, image_width = oriImg.shape[0:2]
+
+    keypoints = body.keypoints
+    head = keypoints[0]
+    left_eye = keypoints[14]
+    right_eye = keypoints[15]
+    left_ear = keypoints[16]
+    right_ear = keypoints[17]
+
+    if head is None or all(keypoint is None for keypoint in (left_eye, right_eye, left_ear, right_ear)):
+        return None
+
+    width = 0.0
+    x0, y0 = head.x, head.y
+
+    if left_eye is not None:
+        x1, y1 = left_eye.x, left_eye.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 3.0)
+
+    if right_eye is not None:
+        x1, y1 = right_eye.x, right_eye.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 3.0)
+
+    if left_ear is not None:
+        x1, y1 = left_ear.x, left_ear.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 1.5)
+
+    if right_ear is not None:
+        x1, y1 = right_ear.x, right_ear.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 1.5)
+
+    x, y = x0, y0
+
+    x -= width
+    y -= width
+
+    if x < 0:
+        x = 0
+
+    if y < 0:
+        y = 0
+
+    width1 = width * 2
+    width2 = width * 2
+
+    if x + width > image_width:
+        width1 = image_width - x
+
+    if y + width > image_height:
+        width2 = image_height - y
+
+    width = min(width1, width2)
+
+    if width >= 20:
+        return int(x), int(y), int(width)
+    else:
+        return None
+
+
+# get max index of 2d array
+def npmax(array):
+    arrayindex = array.argmax(1)
+    arrayvalue = array.max(1)
+    i = arrayvalue.argmax()
+    j = arrayindex[i]
+    return i, j

invokeai/backend/bria/controlnet_aux/util.py

@@ -0,0 +1,146 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+
+annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
+
+
+def HWC3(x):
+    assert x.dtype == np.uint8
+    if x.ndim == 2:
+        x = x[:, :, None]
+    assert x.ndim == 3
+    H, W, C = x.shape
+    assert C == 1 or C == 3 or C == 4
+    if C == 3:
+        return x
+    if C == 1:
+        return np.concatenate([x, x, x], axis=2)
+    if C == 4:
+        color = x[:, :, 0:3].astype(np.float32)
+        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+        y = color * alpha + 255.0 * (1.0 - alpha)
+        y = y.clip(0, 255).astype(np.uint8)
+        return y
+
+
+def make_noise_disk(H, W, C, F):
+    noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C))
+    noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC)
+    noise = noise[F: F + H, F: F + W]
+    noise -= np.min(noise)
+    noise /= np.max(noise)
+    if C == 1:
+        noise = noise[:, :, None]
+    return noise
+
+
+def nms(x, t, s):
+    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
+
+    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
+    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
+    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
+    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
+
+    y = np.zeros_like(x)
+
+    for f in [f1, f2, f3, f4]:
+        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
+
+    z = np.zeros_like(y, dtype=np.uint8)
+    z[y > t] = 255
+    return z
+
+def min_max_norm(x):
+    x -= np.min(x)
+    x /= np.maximum(np.max(x), 1e-5)
+    return x
+
+
+def safe_step(x, step=2):
+    y = x.astype(np.float32) * float(step + 1)
+    y = y.astype(np.int32).astype(np.float32) / float(step)
+    return y
+
+
+def img2mask(img, H, W, low=10, high=90):
+    assert img.ndim == 3 or img.ndim == 2
+    assert img.dtype == np.uint8
+
+    if img.ndim == 3:
+        y = img[:, :, random.randrange(0, img.shape[2])]
+    else:
+        y = img
+
+    y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC)
+
+    if random.uniform(0, 1) < 0.5:
+        y = 255 - y
+
+    return y < np.percentile(y, random.randrange(low, high))
+
+
+def resize_image(input_image, resolution):
+    H, W, C = input_image.shape
+    H = float(H)
+    W = float(W)
+    k = float(resolution) / min(H, W)
+    H *= k
+    W *= k
+    H = int(np.round(H / 64.0)) * 64
+    W = int(np.round(W / 64.0)) * 64
+    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
+    return img
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+            [102, 255, 0], [92, 0, 255]]
+

invokeai/backend/bria/controlnet_bria.py

@@ -0,0 +1,547 @@
+# type: ignore
+# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.controlnet import zero_module
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.outputs import BaseOutput
+
+from invokeai.backend.bria.transformer_bria import (
+    EmbedND,
+    FluxSingleTransformerBlock,
+    FluxTransformerBlock,
+    TimestepProjEmbeddings,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+BRIA_CONTROL_MODES = Literal["depth", "canny", "colorgrid", "recolor", "tile", "pose"]
+class BriaControlModes(Enum):
+    depth = 0
+    canny = 1
+    colorgrid = 2
+    recolor = 3
+    tile = 4
+    pose = 5
+
+
+@dataclass
+class BriaControlNetOutput(BaseOutput):
+    controlnet_block_samples: Tuple[torch.Tensor]
+    controlnet_single_block_samples: Tuple[torch.Tensor]
+
+
+class BriaControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Optional[List[int]] = None,
+        num_mode: int = None,
+        rope_theta: int = 10000,
+        time_theta: int = 10000,
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        # self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+        axes_dims_rope = [16, 56, 56] if axes_dims_rope is None else axes_dims_rope
+        self.pos_embed = EmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
+
+        # text_time_guidance_cls = (
+        # CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
+        # )
+        # self.time_text_embed = text_time_guidance_cls(
+        # embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
+        # )
+        self.time_embed = TimestepProjEmbeddings(
+            embedding_dim=self.inner_dim, time_theta=time_theta
+        )
+
+        self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
+        self.x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for i in range(num_single_layers)
+            ]
+        )
+
+        # controlnet_blocks
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.transformer_blocks)):
+            self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(len(self.single_transformer_blocks)):
+            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
+
+        self.union = num_mode is not None and num_mode > 0
+        if self.union:
+            self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
+
+        self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
+
+        self.gradient_checkpointing = False
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self):
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor):
+        r"""
+        Sets the attention processor to use to compute attention.
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    @classmethod
+    def from_transformer(
+        cls,
+        transformer,
+        num_layers: int = 4,
+        num_single_layers: int = 10,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        load_weights_from_transformer=True,
+    ):
+        config = transformer.config
+        config["num_layers"] = num_layers
+        config["num_single_layers"] = num_single_layers
+        config["attention_head_dim"] = attention_head_dim
+        config["num_attention_heads"] = num_attention_heads
+
+        controlnet = cls(**config)
+
+        if load_weights_from_transformer:
+            controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
+            controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
+            controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
+            controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
+            controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
+            controlnet.single_transformer_blocks.load_state_dict(
+                transformer.single_transformer_blocks.state_dict(), strict=False
+            )
+
+            controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
+
+        return controlnet
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        controlnet_mode: torch.Tensor = None,
+        conditioning_scale: float = 1.0,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            controlnet_cond (`torch.Tensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            controlnet_mode (`torch.Tensor`):
+                The mode tensor of shape `(batch_size, 1)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if guidance is not None:
+            print("guidance is not supported in BriaControlNetModel")
+        if pooled_projections is not None:
+            print("pooled_projections is not supported in BriaControlNetModel")
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        # Convert controlnet_cond to the same dtype as the model weights
+        controlnet_cond = controlnet_cond.to(dtype=self.controlnet_x_embedder.weight.dtype)
+
+        # add
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
+
+        timestep = timestep.to(hidden_states.dtype)  # Original code was * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype)  # Original code was * 1000
+        else:
+            guidance = None
+
+        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
+
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            logger.warning(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            logger.warning(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        if self.union:
+            # union mode
+            if controlnet_mode is None:
+                raise ValueError("`controlnet_mode` cannot be `None` when applying ControlNet-Union")
+
+            # Validate controlnet_mode values are within the valid range
+            if torch.any(controlnet_mode < 0) or torch.any(controlnet_mode >= self.num_mode):
+                raise ValueError(f"`controlnet_mode` values must be in range [0, {self.num_mode-1}], but got values outside this range")
+
+            # union mode emb
+            controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
+            if controlnet_mode_emb.shape[0] < encoder_hidden_states.shape[0]: # duplicate mode emb for each batch
+                controlnet_mode_emb = controlnet_mode_emb.expand(encoder_hidden_states.shape[0], 1, encoder_hidden_states.shape[2])
+            encoder_hidden_states = torch.cat([controlnet_mode_emb, encoder_hidden_states], dim=1)
+
+        txt_ids = torch.cat((txt_ids[0:1, :], txt_ids), dim=0)
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+
+        block_samples = ()
+        for _, block in enumerate(self.transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+            block_samples = block_samples + (hidden_states,)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        single_block_samples = ()
+        for _, block in enumerate(self.single_transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+            single_block_samples = single_block_samples + (hidden_states[:, encoder_hidden_states.shape[1] :],)
+
+        # controlnet block
+        controlnet_block_samples = ()
+        for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks, strict=False):
+            block_sample = controlnet_block(block_sample)
+            controlnet_block_samples = controlnet_block_samples + (block_sample,)
+
+        controlnet_single_block_samples = ()
+        for single_block_sample, controlnet_block in zip(single_block_samples, self.controlnet_single_blocks, strict=False):
+            single_block_sample = controlnet_block(single_block_sample)
+            controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample,)
+
+        # scaling
+        controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
+        controlnet_single_block_samples = [sample * conditioning_scale for sample in controlnet_single_block_samples]
+
+        controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
+        controlnet_single_block_samples = (
+            None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
+        )
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (controlnet_block_samples, controlnet_single_block_samples)
+
+        return BriaControlNetOutput(
+            controlnet_block_samples=controlnet_block_samples,
+            controlnet_single_block_samples=controlnet_single_block_samples,
+        )
+
+
+class BriaMultiControlNetModel(ModelMixin):
+    r"""
+    `BriaMultiControlNetModel` wrapper class for Multi-BriaControlNetModel
+    This module is a wrapper for multiple instances of the `BriaControlNetModel`. The `forward()` API is designed to be
+    compatible with `BriaControlNetModel`.
+    Args:
+        controlnets (`List[BriaControlNetModel]`):
+            Provides additional conditioning to the unet during the denoising process. You must set multiple
+            `BriaControlNetModel` as a list.
+    """
+
+    def __init__(self, controlnets):
+        super().__init__()
+        self.nets = nn.ModuleList(controlnets)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        controlnet_cond: List[torch.tensor],
+        controlnet_mode: List[torch.tensor],
+        conditioning_scale: List[float],
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[BriaControlNetOutput, Tuple]:
+        # ControlNet-Union with multiple conditions
+        # only load one ControlNet for saving memories
+        if len(self.nets) == 1 and self.nets[0].union:
+            controlnet = self.nets[0]
+
+            for i, (image, mode, scale) in enumerate(zip(controlnet_cond, controlnet_mode, conditioning_scale, strict=False)):
+                block_samples, single_block_samples = controlnet(
+                    hidden_states=hidden_states,
+                    controlnet_cond=image,
+                    controlnet_mode=mode[:, None],
+                    conditioning_scale=scale,
+                    timestep=timestep,
+                    guidance=guidance,
+                    pooled_projections=pooled_projections,
+                    encoder_hidden_states=encoder_hidden_states,
+                    txt_ids=txt_ids,
+                    img_ids=img_ids,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                    return_dict=return_dict,
+                )
+
+                # merge samples
+                if i == 0:
+                    control_block_samples = block_samples
+                    control_single_block_samples = single_block_samples
+                else:
+                    control_block_samples = [
+                        control_block_sample + block_sample
+                        for control_block_sample, block_sample in zip(control_block_samples, block_samples, strict=False)
+                    ]
+
+                    control_single_block_samples = [
+                        control_single_block_sample + block_sample
+                        for control_single_block_sample, block_sample in zip(
+                            control_single_block_samples, single_block_samples, strict=False
+                        )
+                    ]
+
+        # Regular Multi-ControlNets
+        # load all ControlNets into memories
+        else:
+            for i, (image, mode, scale, controlnet) in enumerate(
+                zip(controlnet_cond, controlnet_mode, conditioning_scale, self.nets, strict=False)
+            ):
+                block_samples, single_block_samples = controlnet(
+                    hidden_states=hidden_states,
+                    controlnet_cond=image,
+                    controlnet_mode=mode[:, None],
+                    conditioning_scale=scale,
+                    timestep=timestep,
+                    guidance=guidance,
+                    pooled_projections=pooled_projections,
+                    encoder_hidden_states=encoder_hidden_states,
+                    txt_ids=txt_ids,
+                    img_ids=img_ids,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                    return_dict=return_dict,
+                )
+
+                # merge samples
+                if i == 0:
+                    control_block_samples = block_samples
+                    control_single_block_samples = single_block_samples
+                else:
+                    if block_samples is not None and control_block_samples is not None:
+                        control_block_samples = [
+                            control_block_sample + block_sample
+                            for control_block_sample, block_sample in zip(control_block_samples, block_samples, strict=False)
+                        ]
+                    if single_block_samples is not None and control_single_block_samples is not None:
+                        control_single_block_samples = [
+                            control_single_block_sample + block_sample
+                            for control_single_block_sample, block_sample in zip(
+                                control_single_block_samples, single_block_samples, strict=False
+                            )
+                        ]
+
+        return control_block_samples, control_single_block_samples

invokeai/backend/bria/controlnet_utils.py

@@ -0,0 +1,67 @@
+from typing import List, Tuple
+
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from PIL import Image
+
+
+@torch.no_grad()
+def prepare_control_images(
+    vae: AutoencoderKL,
+    control_images: list[Image.Image],
+    control_modes: list[int],
+    width: int,
+    height: int,
+    device: torch.device,
+) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+
+    tensored_control_images = []
+    tensored_control_modes = []
+    for idx, control_image_ in enumerate(control_images):
+        tensored_control_image = _prepare_image(
+            image=control_image_,
+            width=width,
+            height=height,
+            device=device,
+            dtype=vae.dtype,
+        )
+        height, width = tensored_control_image.shape[-2:]
+
+        # vae encode
+        tensored_control_image = vae.encode(tensored_control_image).latent_dist.sample()
+        tensored_control_image = (tensored_control_image) * vae.config.scaling_factor
+
+        # pack
+        height_control_image, width_control_image = tensored_control_image.shape[2:]
+        tensored_control_image = _pack_latents(
+            tensored_control_image,
+            height_control_image,
+            width_control_image,
+        )
+        tensored_control_images.append(tensored_control_image)
+        tensored_control_modes.append(torch.tensor(control_modes[idx]).expand(
+            tensored_control_image.shape[0]).to(device, dtype=torch.long))
+
+    return tensored_control_images, tensored_control_modes
+
+def _prepare_image(
+    image: Image.Image,
+    width: int,
+    height: int,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    image = image.convert("RGB")
+    image = VaeImageProcessor(vae_scale_factor=16).preprocess(image, height=height, width=width)
+    image = image.repeat_interleave(1, dim=0)
+    image = image.to(device=device, dtype=dtype)
+    return image
+
+def _pack_latents(latents, height, width):
+    latents = latents.view(1, 4, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 2, 4, 1, 3, 5)
+    latents = latents.reshape(1, (height // 2) * (width // 2), 16)
+
+    return latents
+

invokeai/backend/bria/pipeline_bria.py

@@ -0,0 +1,640 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import diffusers
+import numpy as np
+import torch
+from diffusers import AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FluxLoraLoaderMixin
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
+from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import StableDiffusion3Pipeline
+
+        >>> pipe = StableDiffusion3Pipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
+        ... )
+        >>> pipe.to("cuda")
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> image = pipe(prompt).images[0]
+        >>> image.save("sd3.png")
+        ```
+"""
+
+T5_PRECISION = torch.float16
+
+"""
+Based on FluxPipeline with several changes:
+- no pooled embeddings
+- We use zero padding for prompts
+- No guidance embedding since this is not a distilled version
+"""
+class BriaPipeline(FluxPipeline):
+    r"""
+    Args:
+        transformer ([`SD3Transformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. Stable Diffusion 3 uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
+            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`T5TokenizerFast`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+    """
+
+    def __init__(
+        self,
+        transformer: BriaTransformer2DModel,
+        scheduler: Union[FlowMatchEulerDiscreteScheduler,KarrasDiffusionSchedulers],
+        vae: AutoencoderKL,
+        text_encoder: T5EncoderModel,
+        tokenizer: T5TokenizerFast
+    ):
+        self.register_modules(
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+        )
+
+        # TODO - why different than offical flux (-1)
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.default_sample_size = 64 # due to patchify=> 128,128 => res of 1k,1k
+
+        # T5 is senstive to precision so we use the precision used for precompute and cast as needed
+
+        if self.vae.config.shift_factor is None:
+            self.vae.config.shift_factor=0
+            self.vae.to(dtype=torch.float32)
+
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 128,
+        lora_scale: Optional[float] = None,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = get_t5_prompt_embeds(
+                self.tokenizer,
+                self.text_encoder,
+                prompt=prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            ).to(dtype=self.transformer.dtype)
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            if not is_ng_none(negative_prompt):
+                negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+                if prompt is not None and type(prompt) is not type(negative_prompt):
+                    raise TypeError(
+                        f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                        f" {type(prompt)}."
+                    )
+                elif batch_size != len(negative_prompt):
+                    raise ValueError(
+                        f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                        f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                        " the batch size of `prompt`."
+                    )
+
+                negative_prompt_embeds = get_t5_prompt_embeds(
+                    self.tokenizer,
+                    self.text_encoder,
+                    prompt=negative_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    max_sequence_length=max_sequence_length,
+                    device=device,
+                ).to(dtype=self.transformer.dtype)
+            else:
+                negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+
+        if self.text_encoder is not None:
+            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
+        text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+        text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
+
+        return prompt_embeds, negative_prompt_embeds, text_ids
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 30,
+        timesteps: List[int] = None,
+        guidance_scale: float = 5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
+        max_sequence_length: int = 128,
+        clip_value:Union[None,float] = None,
+        normalize:bool = False
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+          Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        callback_on_step_end_tensor_inputs = ["latents"] if callback_on_step_end_tensor_inputs is None else callback_on_step_end_tensor_inputs
+        self.check_inputs(
+            prompt=prompt,
+            height=height,
+            width=width,
+            prompt_embeds=prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            text_ids
+        ) = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4 # due to patch=2, we devide by 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        if  isinstance(self.scheduler,FlowMatchEulerDiscreteScheduler) and self.scheduler.config['use_dynamic_shifting']:
+            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+            image_seq_len = latents.shape[1] # Shift by height - Why just height?
+            print(f"Using dynamic shift in pipeline with sequence length {image_seq_len}")
+
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler,
+                num_inference_steps,
+                device,
+                timesteps,
+                sigmas,
+                mu=mu,
+            )
+        else:
+            # 4. Prepare timesteps
+            # Sample from training sigmas
+            if isinstance(self.scheduler,DDIMScheduler) or isinstance(self.scheduler,EulerAncestralDiscreteScheduler):
+                timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None, None)
+            else:
+                sigmas = get_original_sigmas(num_train_timesteps=self.scheduler.config.num_train_timesteps,num_inference_steps=num_inference_steps)
+                timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps,sigmas=sigmas)
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # Supprot different diffusers versions
+        if diffusers.__version__>='0.32.0':
+            latent_image_ids=latent_image_ids[0]
+            text_ids=text_ids[0]
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # This is predicts "v" from flow-matching or eps from diffusion
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    cfg_noise_pred_text = noise_pred_text.std()
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if normalize:
+                    noise_pred = noise_pred * (0.7 *(cfg_noise_pred_text/noise_pred.std())) + 0.3 * noise_pred
+
+                if clip_value:
+                    assert clip_value>0
+                    noise_pred = noise_pred.clip(-clip_value,clip_value)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents.to(dtype=torch.float32) / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(dtype=self.vae.dtype), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+
+        if max_sequence_length is not None and max_sequence_length > 512:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
+
+    def to(self, *args, **kwargs):
+        DiffusionPipeline.to(self, *args, **kwargs)
+        # T5 is senstive to precision so we use the precision used for precompute and cast as needed
+        self.text_encoder = self.text_encoder.to(dtype=T5_PRECISION)
+        for block in self.text_encoder.encoder.block:
+            block.layer[-1].DenseReluDense.wo.to(dtype=torch.float32)
+
+        if self.vae.config.shift_factor == 0 and self.vae.dtype!=torch.float32:
+            self.vae.to(dtype=torch.float32)
+
+
+        return self
+
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // self.vae_scale_factor)
+        width = 2 * (int(width) // self.vae_scale_factor )
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is not None:
+            latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+            return latents.to(device=device, dtype=dtype), latent_image_ids
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+
+        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+
+        return latents, latent_image_ids
+
+    @staticmethod
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        height = height // vae_scale_factor
+        width = width // vae_scale_factor
+
+        latents = latents.view(batch_size, height, width, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2)
+
+        return latents
+
+    @staticmethod
+    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+        latent_image_ids = torch.zeros(height, width, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+
+        return latent_image_ids.to(device=device, dtype=dtype)
+
+
+
+
+
+
+

invokeai/backend/bria/pipeline_bria_controlnet.py

@@ -0,0 +1,666 @@
+# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import diffusers
+import numpy as np
+import torch
+from diffusers import AutoencoderKL  # Waiting for diffusers udpdate
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
+from diffusers.utils import USE_PEFT_BACKEND, logging
+from diffusers.utils.peft_utils import scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
+from invokeai.backend.bria.controlnet_bria import BriaControlNetModel
+from invokeai.backend.bria.pipeline_bria import BriaPipeline
+from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class BriaControlNetPipeline(BriaPipeline):
+    r"""
+    Args:
+        transformer ([`SD3Transformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. Stable Diffusion 3 uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
+            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`T5TokenizerFast`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
+
+    def __init__(  # EYAL - removed clip text encoder + tokenizer
+        self,
+        transformer: BriaTransformer2DModel,
+        scheduler: Union[FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers],
+        vae: AutoencoderKL,
+        text_encoder: T5EncoderModel,
+        tokenizer: T5TokenizerFast,
+        controlnet: BriaControlNetModel,
+    ):
+        super().__init__(
+            transformer=transformer, scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer
+        )
+        self.register_modules(controlnet=controlnet)
+
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        if isinstance(image, torch.Tensor):
+            pass
+        else:
+            image = self.image_processor.preprocess(image, height=height, width=width)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    def prepare_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
+        num_channels_latents = self.transformer.config.in_channels // 4
+        control_image = self.prepare_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=self.vae.dtype,
+        )
+        height, width = control_image.shape[-2:]
+
+        # vae encode
+        control_image = self.vae.encode(control_image).latent_dist.sample()
+        control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+        # pack
+        height_control_image, width_control_image = control_image.shape[2:]
+        control_image = self._pack_latents(
+            control_image,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height_control_image,
+            width_control_image,
+        )
+
+        # Here we ensure that `control_mode` has the same length as the control_image.
+        if control_mode is not None:
+            if not isinstance(control_mode, int):
+                raise ValueError(" For `BriaControlNet`, `control_mode` should be an `int` or `None`")
+            control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
+            control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1)
+
+        return control_image, control_mode
+
+    def prepare_multi_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
+        num_channels_latents = self.transformer.config.in_channels // 4
+        control_images = []
+        for _, control_image_ in enumerate(control_image):
+            control_image_ = self.prepare_image(
+                image=control_image_,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=self.vae.dtype,
+            )
+            height, width = control_image_.shape[-2:]
+
+            # vae encode
+            control_image_ = self.vae.encode(control_image_).latent_dist.sample()
+            control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+            # pack
+            height_control_image, width_control_image = control_image_.shape[2:]
+            control_image_ = self._pack_latents(
+                control_image_,
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height_control_image,
+                width_control_image,
+            )
+            control_images.append(control_image_)
+
+        control_image = control_images
+
+        # Here we ensure that `control_mode` has the same length as the control_image.
+        if isinstance(control_mode, list) and len(control_mode) != len(control_image):
+            raise ValueError(
+                "For Multi-ControlNet, `control_mode` must be a list of the same "
+                + " length as the number of controlnets (control images) specified"
+            )
+        if not isinstance(control_mode, list):
+            control_mode = [control_mode] * len(control_image)
+        # set control mode
+        control_modes = []
+        for cmode in control_mode:
+            if cmode is None:
+                cmode = -1
+            control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long)
+            control_modes.append(control_mode)
+        control_mode = control_modes
+
+        return control_image, control_mode
+
+    def get_controlnet_keep(self, timesteps, control_guidance_start, control_guidance_end):
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end, strict=False)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, BriaControlNetModel) else keeps)
+        return controlnet_keep
+
+    def get_control_start_end(self, control_guidance_start, control_guidance_end):
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = 1  # TODO - why is this 1?
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
+        return control_guidance_start, control_guidance_end
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 30,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        control_image: Optional[PipelineImageInput] = None,
+        control_mode: Optional[Union[int, List[int]]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        latent_image_ids: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        text_ids: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
+        max_sequence_length: int = 128,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+        Examples:
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+        control_guidance_start, control_guidance_end = self.get_control_start_end(
+            control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end
+        )
+
+        # 1. Check inputs. Raise error if not correct
+        callback_on_step_end_tensor_inputs = ["latents"] if callback_on_step_end_tensor_inputs is None else callback_on_step_end_tensor_inputs
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        device = self._execution_device
+
+
+        # 4. Prepare timesteps
+        if  isinstance(self.scheduler,FlowMatchEulerDiscreteScheduler) and self.scheduler.config['use_dynamic_shifting']:
+            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+
+
+            # Determine image sequence length
+            if control_image is not None:
+                if isinstance(control_image, list):
+                    image_seq_len = control_image[0].shape[1]
+                else:
+                    image_seq_len = control_image.shape[1]
+            else:
+                # Use latents sequence length when no control image is provided
+                image_seq_len = latents.shape[1]
+
+            print(f"Using dynamic shift in pipeline with sequence length {image_seq_len}")
+
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler,
+                num_inference_steps,
+                device,
+                timesteps=None,
+                sigmas=sigmas,
+                mu=mu,
+            )
+        else:
+            # 5. Prepare timesteps
+            sigmas = get_original_sigmas(
+                num_train_timesteps=self.scheduler.config.num_train_timesteps, num_inference_steps=num_inference_steps
+            )
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas
+            )
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Create tensor stating which controlnets to keep
+        if control_image is not None:
+            controlnet_keep = self.get_controlnet_keep(
+                timesteps=timesteps,
+                control_guidance_start=control_guidance_start,
+                control_guidance_end=control_guidance_end,
+            )
+
+        if diffusers.__version__>='0.32.0':
+            latent_image_ids=latent_image_ids[0]
+            text_ids=text_ids[0]
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # EYAL - added the CFG loop
+        # 7. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                # if type(self.scheduler) != FlowMatchEulerDiscreteScheduler:
+                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # Handling ControlNet
+                if control_image is not None:
+                    if isinstance(controlnet_keep[i], list):
+                        if isinstance(controlnet_conditioning_scale, list):
+                            cond_scale = controlnet_conditioning_scale
+                        else:
+                            cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i], strict=False)]
+                    else:
+                        controlnet_cond_scale = controlnet_conditioning_scale
+                        if isinstance(controlnet_cond_scale, list):
+                            controlnet_cond_scale = controlnet_cond_scale[0]
+                        cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
+                    controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
+                        hidden_states=latents,
+                        controlnet_cond=control_image,
+                        controlnet_mode=control_mode,
+                        conditioning_scale=cond_scale,
+                        timestep=timestep,
+                        # guidance=guidance,
+                        # pooled_projections=pooled_prompt_embeds,
+                        encoder_hidden_states=prompt_embeds,
+                        txt_ids=text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                    )
+                else:
+                    controlnet_block_samples, controlnet_single_block_samples = None, None
+
+                # This is predicts "v" from flow-matching
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    controlnet_block_samples=controlnet_block_samples,
+                    controlnet_single_block_samples=controlnet_single_block_samples,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(dtype=self.vae.dtype), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+
+def encode_prompt(
+        prompt: Union[str, List[str]],
+        tokenizer: T5TokenizerFast,
+        text_encoder: T5EncoderModel,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 128,
+        lora_scale: Optional[float] = None,
+    ):
+    r"""
+
+    Args:
+        prompt (`str` or `List[str]`, *optional*):
+            prompt to be encoded
+        device: (`torch.device`):
+            torch device
+        num_images_per_prompt (`int`):
+            number of images that should be generated per prompt
+        do_classifier_free_guidance (`bool`):
+            whether to use classifier free guidance or not
+        negative_prompt (`str` or `List[str]`, *optional*):
+            The prompt or prompts not to guide the image generation. If not defined, one has to pass
+            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+            less than `1`).
+        prompt_embeds (`torch.FloatTensor`, *optional*):
+            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+            provided, text embeddings will be generated from `prompt` input argument.
+        negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+            Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+            weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+            argument.
+    """
+    device = device or torch.device("cuda")
+
+    # set lora scale so that monkey patched LoRA
+    # function of text encoder can correctly access it
+    # dynamically adjust the LoRA scale
+    if text_encoder is not None and USE_PEFT_BACKEND:
+        scale_lora_layers(text_encoder, lora_scale)
+
+    prompt = [prompt] if isinstance(prompt, str) else prompt
+    if prompt is not None:
+        batch_size = len(prompt)
+    else:
+        batch_size = prompt_embeds.shape[0]
+
+    dtype = text_encoder.dtype if text_encoder is not None else torch.float32
+    if prompt_embeds is None:
+        prompt_embeds = get_t5_prompt_embeds(
+            tokenizer,
+            text_encoder,
+            prompt=prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        ).to(dtype=dtype)
+
+    if negative_prompt_embeds is None:
+        if not is_ng_none(negative_prompt):
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = get_t5_prompt_embeds(
+                tokenizer,
+                text_encoder,
+                prompt=negative_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            ).to(dtype=dtype)
+        else:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+
+    if text_encoder is not None:
+        if USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(text_encoder, lora_scale)
+
+    text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+    text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
+
+    return prompt_embeds, negative_prompt_embeds, text_ids
+
+
+def prepare_latents(
+    batch_size: int,
+    num_channels_latents: int,
+    height: int,
+    width: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    generator: torch.Generator,
+    latents: Optional[torch.FloatTensor] = None,
+):
+    # VAE applies 8x compression on images but we must also account for packing which requires
+    # latent height and width to be divisible by 2.
+    vae_scale_factor = 16
+    height = 2 * (int(height) // vae_scale_factor)
+    width = 2 * (int(width) // vae_scale_factor )
+
+    shape = (batch_size, num_channels_latents, height, width)
+
+    if latents is not None:
+        latent_image_ids = _prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+        return latents.to(device=device, dtype=dtype), latent_image_ids
+
+    if isinstance(generator, list) and len(generator) != batch_size:
+        raise ValueError(
+            f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+            f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+        )
+
+    latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+    latents = _pack_latents(latents, batch_size, num_channels_latents, height, width)
+
+    latent_image_ids = _prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+
+    return latents, latent_image_ids
+
+
+def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+    latent_image_ids = torch.zeros(height, width, 3)
+    latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+    latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+
+    latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+    latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1, 1)
+    latent_image_ids = latent_image_ids.reshape(
+        batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+    )
+
+    return latent_image_ids.to(device=device, dtype=dtype)
+
+
+
+
+def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 2, 4, 1, 3, 5)
+    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+    return latents

invokeai/backend/bria/transformer_bria.py

@@ -0,0 +1,322 @@
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.embeddings import TimestepEmbedding, get_timestep_embedding
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormContinuous
+from diffusers.models.transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+
+from invokeai.backend.bria.bria_utils import FluxPosEmbed as EmbedND
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class Timesteps(nn.Module):
+    def __init__(
+        self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1, time_theta=10000
+    ):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+        self.time_theta = time_theta
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+            max_period=self.time_theta,
+        )
+        return t_emb
+
+
+class TimestepProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, time_theta):
+        super().__init__()
+
+        self.time_proj = Timesteps(
+            num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, time_theta=time_theta
+        )
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timestep, dtype):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=dtype))  # (N, D)
+        return timesteps_emb
+
+
+"""
+Based on FluxPipeline with several changes:
+- no pooled embeddings
+- We use zero padding for prompts
+- No guidance embedding since this is not a distilled version
+"""
+
+
+class BriaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
+    """
+    The Transformer model introduced in Flux.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Parameters:
+        patch_size (`int`): Patch size to turn the input data into small patches.
+        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
+        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
+        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
+        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
+        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
+        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
+        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = None,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Optional[List[int]] = None,
+        rope_theta=10000,
+        time_theta=10000,
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+
+        axes_dims_rope = [16, 56, 56] if axes_dims_rope is None else axes_dims_rope
+        self.pos_embed = EmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
+
+        self.time_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim, time_theta=time_theta)
+
+        # if pooled_projection_dim:
+        #     self.pooled_text_embed = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim=self.inner_dim, act_fn="silu")
+
+        if guidance_embeds:
+            self.guidance_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim)
+
+        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
+        self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_single_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype)
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype)
+        else:
+            guidance = None
+
+        # temb = (
+        #     self.time_text_embed(timestep, pooled_projections)
+        #     if guidance is None
+        #     else self.time_text_embed(timestep, guidance, pooled_projections)
+        # )
+
+        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
+
+        # if pooled_projections:
+        #     temb+=self.pooled_text_embed(pooled_projections)
+
+        if guidance:
+            temb += self.guidance_embed(guidance, dtype=hidden_states.dtype)
+
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if len(txt_ids.shape) == 2:
+            ids = torch.cat((txt_ids, img_ids), dim=0)
+        else:
+            ids = torch.cat((txt_ids, img_ids), dim=1)
+        image_rotary_emb = self.pos_embed(ids)
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

invokeai/backend/flux/denoise.py

@@ -112,7 +112,7 @@ def denoise(
         )
 
         # Slice prediction to only include the main image tokens
-        if img_cond_seq is not None:
+        if img_input_ids is not None:
             pred = pred[:, :original_seq_len]
 
         step_cfg_scale = cfg_scale[step_index]
@@ -125,26 +125,9 @@ def denoise(
             if neg_regional_prompting_extension is None:
                 raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
 
-            # For negative prediction with Kontext, we need to include the reference images
-            # to maintain consistency between positive and negative passes. Without this,
-            # CFG would create artifacts as the attention mechanism would see different
-            # spatial structures in each pass
-            neg_img_input = img
-            neg_img_input_ids = img_ids
-
-            # Add channel-wise conditioning for negative pass if present
-            if img_cond is not None:
-                neg_img_input = torch.cat((neg_img_input, img_cond), dim=-1)
-
-            # Add sequence-wise conditioning (Kontext) for negative pass
-            # This ensures reference images are processed consistently
-            if img_cond_seq is not None:
-                neg_img_input = torch.cat((neg_img_input, img_cond_seq), dim=1)
-                neg_img_input_ids = torch.cat((neg_img_input_ids, img_cond_seq_ids), dim=1)
-
             neg_pred = model(
-                img=neg_img_input,
-                img_ids=neg_img_input_ids,
+                img=img,
+                img_ids=img_ids,
                 txt=neg_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
                 txt_ids=neg_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
                 y=neg_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
@@ -157,10 +140,6 @@ def denoise(
                 ip_adapter_extensions=neg_ip_adapter_extensions,
                 regional_prompting_extension=neg_regional_prompting_extension,
             )
-
-            # Slice negative prediction to match main image tokens
-            if img_cond_seq is not None:
-                neg_pred = neg_pred[:, :original_seq_len]
             pred = neg_pred + step_cfg_scale * (pred - neg_pred)
 
         preview_img = img - t_curr * pred

invokeai/backend/flux/extensions/kontext_extension.py

@@ -1,14 +1,15 @@
+import einops
+import numpy as np
 import torch
-import torch.nn.functional as F
-import torchvision.transforms as T
 from einops import repeat
+from PIL import Image
 
 from invokeai.app.invocations.fields import FluxKontextConditioningField
+from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.sampling_utils import pack
-from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
 
 
 def generate_img_ids_with_offset(
@@ -18,10 +19,8 @@ def generate_img_ids_with_offset(
     device: torch.device,
     dtype: torch.dtype,
     idx_offset: int = 0,
-    h_offset: int = 0,
-    w_offset: int = 0,
 ) -> torch.Tensor:
-    """Generate tensor of image position ids with optional index and spatial offsets.
+    """Generate tensor of image position ids with an optional offset.
 
     Args:
         latent_height (int): Height of image in latent space (after packing, this becomes h//2).
@@ -29,9 +28,7 @@ def generate_img_ids_with_offset(
         batch_size (int): Number of images in the batch.
         device (torch.device): Device to create tensors on.
         dtype (torch.dtype): Data type for the tensors.
-        idx_offset (int): Offset to add to the first dimension of the image ids (default: 0).
-        h_offset (int): Spatial offset for height/y-coordinates in latent space (default: 0).
-        w_offset (int): Spatial offset for width/x-coordinates in latent space (default: 0).
+        idx_offset (int): Offset to add to the first dimension of the image ids.
 
     Returns:
         torch.Tensor: Image position ids with shape [batch_size, (latent_height//2 * latent_width//2), 3].
@@ -45,10 +42,6 @@ def generate_img_ids_with_offset(
     packed_height = latent_height // 2
     packed_width = latent_width // 2
 
-    # Convert spatial offsets from latent space to packed space
-    packed_h_offset = h_offset // 2
-    packed_w_offset = w_offset // 2
-
     # Create base tensor for position IDs with shape [packed_height, packed_width, 3]
     # The 3 channels represent: [batch_offset, y_position, x_position]
     img_ids = torch.zeros(packed_height, packed_width, 3, device=device, dtype=dtype)
@@ -56,13 +49,13 @@ def generate_img_ids_with_offset(
     # Set the batch offset for all positions
     img_ids[..., 0] = idx_offset
 
-    # Create y-coordinate indices (vertical positions) with spatial offset
-    y_indices = torch.arange(packed_height, device=device, dtype=dtype) + packed_h_offset
+    # Create y-coordinate indices (vertical positions)
+    y_indices = torch.arange(packed_height, device=device, dtype=dtype)
     # Broadcast y_indices to match the spatial dimensions [packed_height, 1]
     img_ids[..., 1] = y_indices[:, None]
 
-    # Create x-coordinate indices (horizontal positions) with spatial offset
-    x_indices = torch.arange(packed_width, device=device, dtype=dtype) + packed_w_offset
+    # Create x-coordinate indices (horizontal positions)
+    x_indices = torch.arange(packed_width, device=device, dtype=dtype)
     # Broadcast x_indices to match the spatial dimensions [1, packed_width]
     img_ids[..., 2] = x_indices[None, :]
 
@@ -80,14 +73,14 @@ class KontextExtension:
 
     def __init__(
         self,
-        kontext_conditioning: list[FluxKontextConditioningField],
+        kontext_conditioning: FluxKontextConditioningField,
         context: InvocationContext,
         vae_field: VAEField,
         device: torch.device,
         dtype: torch.dtype,
     ):
         """
-        Initializes the KontextExtension, pre-processing the reference images
+        Initializes the KontextExtension, pre-processing the reference image
         into latents and positional IDs.
         """
         self._context = context
@@ -100,116 +93,54 @@ class KontextExtension:
         self.kontext_latents, self.kontext_ids = self._prepare_kontext()
 
     def _prepare_kontext(self) -> tuple[torch.Tensor, torch.Tensor]:
-        """Encodes the reference images and prepares their concatenated latents and IDs with spatial tiling."""
-        all_latents = []
-        all_ids = []
+        """Encodes the reference image and prepares its latents and IDs."""
+        image = self._context.images.get_pil(self.kontext_conditioning.image.image_name)
 
-        # Track cumulative dimensions for spatial tiling
-        # These track the running extent of the virtual canvas in latent space
-        canvas_h = 0  # Running canvas height
-        canvas_w = 0  # Running canvas width
+        # Calculate aspect ratio of input image
+        width, height = image.size
+        aspect_ratio = width / height
 
+        # Find the closest preferred resolution by aspect ratio
+        _, target_width, target_height = min(
+            ((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS), key=lambda x: x[0]
+        )
+
+        # Apply BFL's scaling formula
+        # This ensures compatibility with the model's training
+        scaled_width = 2 * int(target_width / 16)
+        scaled_height = 2 * int(target_height / 16)
+
+        # Resize to the exact resolution used during training
+        image = image.convert("RGB")
+        final_width = 8 * scaled_width
+        final_height = 8 * scaled_height
+        image = image.resize((final_width, final_height), Image.Resampling.LANCZOS)
+
+        # Convert to tensor with same normalization as BFL
+        image_np = np.array(image)
+        image_tensor = torch.from_numpy(image_np).float() / 127.5 - 1.0
+        image_tensor = einops.rearrange(image_tensor, "h w c -> 1 c h w")
+        image_tensor = image_tensor.to(self._device)
+
+        # Continue with VAE encoding
         vae_info = self._context.models.load(self._vae_field.vae)
+        kontext_latents_unpacked = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
-        for idx, kontext_field in enumerate(self.kontext_conditioning):
-            image = self._context.images.get_pil(kontext_field.image.image_name)
+        # Extract tensor dimensions
+        batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
 
-            # Convert to RGB
-            image = image.convert("RGB")
+        # Pack the latents and generate IDs
+        kontext_latents_packed = pack(kontext_latents_unpacked).to(self._device, self._dtype)
+        kontext_ids = generate_img_ids_with_offset(
+            latent_height=latent_height,
+            latent_width=latent_width,
+            batch_size=batch_size,
+            device=self._device,
+            dtype=self._dtype,
+            idx_offset=1,
+        )
 
-            # Convert to tensor using torchvision transforms for consistency
-            transformation = T.Compose(
-                [
-                    T.ToTensor(),  # Converts PIL image to tensor and scales to [0, 1]
-                ]
-            )
-            image_tensor = transformation(image)
-            # Convert from [0, 1] to [-1, 1] range expected by VAE
-            image_tensor = image_tensor * 2.0 - 1.0
-            image_tensor = image_tensor.unsqueeze(0)  # Add batch dimension
-            image_tensor = image_tensor.to(self._device)
-
-            # Continue with VAE encoding
-            # Don't sample from the distribution for reference images - use the mean (matching ComfyUI)
-            # Estimate working memory for encode operation (50% of decode memory requirements)
-            img_h = image_tensor.shape[-2]
-            img_w = image_tensor.shape[-1]
-            element_size = next(vae_info.model.parameters()).element_size()
-            scaling_constant = 1100  # 50% of decode scaling constant (2200)
-            estimated_working_memory = int(img_h * img_w * element_size * scaling_constant)
-
-            with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
-                assert isinstance(vae, AutoEncoder)
-                vae_dtype = next(iter(vae.parameters())).dtype
-                image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
-                # Use sample=False to get the distribution mean without noise
-                kontext_latents_unpacked = vae.encode(image_tensor, sample=False)
-                TorchDevice.empty_cache()
-
-            # Extract tensor dimensions
-            batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
-
-            # Pad latents to be compatible with patch_size=2
-            # This ensures dimensions are even for the pack() function
-            pad_h = (2 - latent_height % 2) % 2
-            pad_w = (2 - latent_width % 2) % 2
-            if pad_h > 0 or pad_w > 0:
-                kontext_latents_unpacked = F.pad(kontext_latents_unpacked, (0, pad_w, 0, pad_h), mode="circular")
-                # Update dimensions after padding
-                _, _, latent_height, latent_width = kontext_latents_unpacked.shape
-
-            # Pack the latents
-            kontext_latents_packed = pack(kontext_latents_unpacked).to(self._device, self._dtype)
-
-            # Determine spatial offsets for this reference image
-            h_offset = 0
-            w_offset = 0
-
-            if idx > 0:  # First image starts at (0, 0)
-                # Calculate potential canvas dimensions for each tiling option
-                # Option 1: Tile vertically (below existing content)
-                potential_h_vertical = canvas_h + latent_height
-
-                # Option 2: Tile horizontally (to the right of existing content)
-                potential_w_horizontal = canvas_w + latent_width
-
-                # Choose arrangement that minimizes the maximum dimension
-                # This keeps the canvas closer to square, optimizing attention computation
-                if potential_h_vertical > potential_w_horizontal:
-                    # Tile horizontally (to the right of existing images)
-                    w_offset = canvas_w
-                    canvas_w = canvas_w + latent_width
-                    canvas_h = max(canvas_h, latent_height)
-                else:
-                    # Tile vertically (below existing images)
-                    h_offset = canvas_h
-                    canvas_h = canvas_h + latent_height
-                    canvas_w = max(canvas_w, latent_width)
-            else:
-                # First image - just set canvas dimensions
-                canvas_h = latent_height
-                canvas_w = latent_width
-
-            # Generate IDs with both index offset and spatial offsets
-            kontext_ids = generate_img_ids_with_offset(
-                latent_height=latent_height,
-                latent_width=latent_width,
-                batch_size=batch_size,
-                device=self._device,
-                dtype=self._dtype,
-                idx_offset=1,  # All reference images use index=1 (matching ComfyUI implementation)
-                h_offset=h_offset,
-                w_offset=w_offset,
-            )
-
-            all_latents.append(kontext_latents_packed)
-            all_ids.append(kontext_ids)
-
-        # Concatenate all latents and IDs along the sequence dimension
-        concatenated_latents = torch.cat(all_latents, dim=1)  # Concatenate along sequence dimension
-        concatenated_ids = torch.cat(all_ids, dim=1)  # Concatenate along sequence dimension
-
-        return concatenated_latents, concatenated_ids
+        return kontext_latents_packed, kontext_ids
 
     def ensure_batch_size(self, target_batch_size: int) -> None:
         """Ensures the kontext latents and IDs match the target batch size by repeating if necessary."""

invokeai/backend/model_manager/legacy_probe.py

@@ -9,7 +9,6 @@ import spandrel
 import torch
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_instantx_controlnet,
@@ -126,6 +125,8 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
+        "BriaPipeline": ModelType.Main,
+        "BriaTransformer2DModel": ModelType.ControlNet,
         "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
@@ -494,21 +495,9 @@ class ModelProbe(object):
         # scan model
         scan_result = pscan.scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
-            if get_config().unsafe_disable_picklescan:
-                logger.warning(
-                    f"The model {model_name} is potentially infected by malware, but picklescan is disabled. "
-                    "Proceeding with caution."
-                )
-            else:
-                raise RuntimeError(f"The model {model_name} is potentially infected by malware. Aborting import.")
+            raise Exception(f"The model {model_name} is potentially infected by malware. Aborting import.")
         if scan_result.scan_err:
-            if get_config().unsafe_disable_picklescan:
-                logger.warning(
-                    f"Error scanning the model at {model_name} for malware, but picklescan is disabled. "
-                    "Proceeding with caution."
-                )
-            else:
-                raise RuntimeError(f"Error scanning the model at {model_name} for malware. Aborting import.")
+            raise Exception(f"Error scanning model {model_name} for malware. Aborting import.")
 
 
 # Probing utilities
@@ -874,6 +863,8 @@ class PipelineFolderProbe(FolderProbeBase):
                 return BaseModelType.StableDiffusion3
             elif transformer_conf["_class_name"] == "CogView4Transformer2DModel":
                 return BaseModelType.CogView4
+            elif transformer_conf["_class_name"] == "BriaTransformer2DModel":
+                return BaseModelType.Bria
             else:
                 raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
@@ -1023,6 +1014,9 @@ class ControlNetFolderProbe(FolderProbeBase):
         if config.get("_class_name", None) == "FluxControlNetModel":
             return BaseModelType.Flux
 
+        if config.get("_class_name", None) == "BriaTransformer2DModel":
+            return BaseModelType.Bria
+
         # no obvious way to distinguish between sd2-base and sd2-768
         dimension = config["cross_attention_dim"]
         if dimension == 768:

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

@@ -0,0 +1,95 @@
+from pathlib import Path
+from typing import Optional
+
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    CheckpointConfigBase,
+    ControlNetCheckpointConfig,
+    ControlNetDiffusersConfig,
+    DiffusersConfigBase,
+)
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Bria, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
+class BriaControlNetDiffusersModel(GenericDiffusersLoader):
+    """Class to load Bria control net models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, ControlNetCheckpointConfig):
+            raise NotImplementedError("CheckpointConfigBase is not implemented for Bria models.")
+
+        model_path = Path(config.path)
+        load_class = self.get_hf_load_class(model_path)
+        repo_variant = config.repo_variant if isinstance(config, ControlNetDiffusersConfig) else None
+        variant = repo_variant.value if repo_variant else None
+        model_path = model_path
+
+        dtype = self._torch_dtype
+
+        try:
+            result: AnyModel = load_class.from_pretrained(
+                model_path,
+                torch_dtype=dtype,
+                variant=variant,
+                use_safetensors=False,
+            )
+        except OSError as e:
+            if variant and "no file named" in str(
+                e
+            ):  # try without the variant, just in case user's preferences changed
+                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
+            else:
+                raise e
+
+        return result
+
+@ModelLoaderRegistry.register(base=BaseModelType.Bria, type=ModelType.Main, format=ModelFormat.Diffusers)
+class BriaDiffusersModel(GenericDiffusersLoader):
+    """Class to load Bria main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, CheckpointConfigBase):
+            raise NotImplementedError("CheckpointConfigBase is not implemented for Bria models.")
+
+        if submodel_type is None:
+            raise Exception("A submodel type must be provided when loading main pipelines.")
+
+        model_path = Path(config.path)
+        load_class = self.get_hf_load_class(model_path, submodel_type)
+        repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
+        variant = repo_variant.value if repo_variant else None
+        model_path = model_path / submodel_type.value
+
+        dtype = self._torch_dtype
+        try:
+            result: AnyModel = load_class.from_pretrained(
+                model_path,
+                torch_dtype=dtype,
+                variant=variant,
+            )
+        except OSError as e:
+            if variant and "no file named" in str(
+                e
+            ):  # try without the variant, just in case user's preferences changed
+                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
+            else:
+                raise e
+
+        return result

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

@@ -80,7 +80,13 @@ class GenericDiffusersLoader(ModelLoader):
             "transformers",
             "invokeai.backend.quantization.fast_quantized_transformers_model",
             "invokeai.backend.quantization.fast_quantized_diffusion_model",
+            "transformer_bria",
         ]:
+            if module == "transformer_bria":
+                module = "invokeai.backend.bria.transformer_bria"
+            elif class_name == "BriaTransformer2DModel":
+                class_name = "BriaControlNetModel"
+                module = "invokeai.backend.bria.controlnet_bria"
             res_type = sys.modules[module]
         else:
             res_type = sys.modules["diffusers"].pipelines

invokeai/backend/model_manager/load/model_util.py

@@ -12,6 +12,9 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
 
+from invokeai.backend.bria.controlnet_aux.open_pose.body import Body
+from invokeai.backend.bria.controlnet_aux.open_pose.face import Face
+from invokeai.backend.bria.controlnet_aux.open_pose.hand import Hand
 from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
@@ -62,6 +65,8 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         else:
             # If neither is available, return 0
             return 0
+    elif isinstance(model, (Body, Hand, Face)):
+        return calc_module_size(model.model)
     elif isinstance(
         model,
         (

invokeai/backend/model_manager/model_on_disk.py

@@ -6,17 +6,13 @@ import torch
 from picklescan.scanner import scan_file_path
 from safetensors import safe_open
 
-from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
-from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 StateDict: TypeAlias = dict[str | int, Any]  # When are the keys int?
 
-logger = InvokeAILogger.get_logger()
-
 
 class ModelOnDisk:
     """A utility class representing a model stored on disk."""
@@ -83,24 +79,8 @@ class ModelOnDisk:
         with SilenceWarnings():
             if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
                 scan_result = scan_file_path(path)
-                if scan_result.infected_files != 0:
-                    if get_config().unsafe_disable_picklescan:
-                        logger.warning(
-                            f"The model {path.stem} is potentially infected by malware, but picklescan is disabled. "
-                            "Proceeding with caution."
-                        )
-                    else:
-                        raise RuntimeError(
-                            f"The model {path.stem} is potentially infected by malware. Aborting import."
-                        )
-                if scan_result.scan_err:
-                    if get_config().unsafe_disable_picklescan:
-                        logger.warning(
-                            f"Error scanning the model at {path.stem} for malware, but picklescan is disabled. "
-                            "Proceeding with caution."
-                        )
-                    else:
-                        raise RuntimeError(f"Error scanning the model at {path.stem} for malware. Aborting import.")
+                if scan_result.infected_files != 0 or scan_result.scan_err:
+                    raise RuntimeError(f"The model {path.stem} is potentially infected by malware. Aborting import.")
                 checkpoint = torch.load(path, map_location="cpu")
                 assert isinstance(checkpoint, dict)
             elif path.suffix.endswith(".gguf"):

invokeai/backend/model_manager/starter_models.py

@@ -149,29 +149,13 @@ flux_kontext = StarterModel(
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
 flux_kontext_quantized = StarterModel(
-    name="FLUX.1 Kontext dev (quantized)",
+    name="FLUX.1 Kontext dev (Quantized)",
     base=BaseModelType.Flux,
     source="https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF/resolve/main/flux1-kontext-dev-Q4_K_M.gguf",
     description="FLUX.1 Kontext dev quantized (q4_k_m). Total size with dependencies: ~14GB",
     type=ModelType.Main,
     dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
 )
-flux_krea = StarterModel(
-    name="FLUX.1 Krea dev",
-    base=BaseModelType.Flux,
-    source="https://huggingface.co/InvokeAI/FLUX.1-Krea-dev/resolve/main/flux1-krea-dev.safetensors",
-    description="FLUX.1 Krea dev. Total size with dependencies: ~33GB",
-    type=ModelType.Main,
-    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-)
-flux_krea_quantized = StarterModel(
-    name="FLUX.1 Krea dev (quantized)",
-    base=BaseModelType.Flux,
-    source="https://huggingface.co/InvokeAI/FLUX.1-Krea-dev-GGUF/resolve/main/flux1-krea-dev-Q4_K_M.gguf",
-    description="FLUX.1 Krea dev quantized (q4_k_m). Total size with dependencies: ~14GB",
-    type=ModelType.Main,
-    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-)
 sd35_medium = StarterModel(
     name="SD3.5 Medium",
     base=BaseModelType.StableDiffusion3,
@@ -596,14 +580,13 @@ t2i_sketch_sdxl = StarterModel(
 )
 # endregion
 # region SpandrelImageToImage
-animesharp_v4_rcan = StarterModel(
-    name="2x-AnimeSharpV4_RCAN",
+realesrgan_anime = StarterModel(
+    name="RealESRGAN_x4plus_anime_6B",
     base=BaseModelType.Any,
-    source="https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV4/2x-AnimeSharpV4_RCAN.safetensors",
-    description="A 2x upscaling model (optimized for anime images).",
+    source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+    description="A Real-ESRGAN 4x upscaling model (optimized for anime images).",
     type=ModelType.SpandrelImageToImage,
 )
-
 realesrgan_x4 = StarterModel(
     name="RealESRGAN_x4plus",
     base=BaseModelType.Any,
@@ -749,7 +732,7 @@ STARTER_MODELS: list[StarterModel] = [
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
-    animesharp_v4_rcan,
+    realesrgan_anime,
     realesrgan_x2,
     swinir,
     t5_base_encoder,
@@ -760,8 +743,6 @@ STARTER_MODELS: list[StarterModel] = [
     llava_onevision,
     flux_fill,
     cogview4,
-    flux_krea,
-    flux_krea_quantized,
 ]
 
 sd1_bundle: list[StarterModel] = [
@@ -813,7 +794,6 @@ flux_bundle: list[StarterModel] = [
     flux_redux,
     flux_fill,
     flux_kontext_quantized,
-    flux_krea_quantized,
 ]
 
 STARTER_BUNDLES: dict[str, StarterModelBundle] = {

invokeai/backend/model_manager/taxonomy.py

@@ -30,6 +30,7 @@ class BaseModelType(str, Enum):
     Imagen4 = "imagen4"
     ChatGPT4o = "chatgpt-4o"
     FluxKontext = "flux-kontext"
+    Bria = "bria"
 
 
 class ModelType(str, Enum):

invokeai/backend/model_manager/util/lora_metadata_extractor.py

@@ -1,145 +0,0 @@
-"""Utility functions for extracting metadata from LoRA model files."""
-
-import json
-import logging
-from pathlib import Path
-from typing import Any, Dict, Optional, Set, Tuple
-
-from PIL import Image
-
-from invokeai.app.util.thumbnails import make_thumbnail
-from invokeai.backend.model_manager.config import AnyModelConfig, ModelType
-
-logger = logging.getLogger(__name__)
-
-
-def extract_lora_metadata(
-    model_path: Path, model_key: str, model_images_path: Path
-) -> Tuple[Optional[str], Optional[Set[str]]]:
-    """
-    Extract metadata for a LoRA model from associated JSON and image files.
-
-    Args:
-        model_path: Path to the LoRA model file
-        model_key: Unique key for the model
-        model_images_path: Path to the model images directory
-
-    Returns:
-        Tuple of (description, trigger_phrases)
-    """
-    model_stem = model_path.stem
-    model_dir = model_path.parent
-
-    # Find and process preview image
-    _process_preview_image(model_stem, model_dir, model_key, model_images_path)
-
-    # Extract metadata from JSON
-    description, trigger_phrases = _extract_json_metadata(model_stem, model_dir)
-
-    return description, trigger_phrases
-
-
-def _process_preview_image(model_stem: str, model_dir: Path, model_key: str, model_images_path: Path) -> bool:
-    """Find and process a preview image for the model, saving it to the model images store."""
-    image_extensions = [".png", ".jpg", ".jpeg", ".webp"]
-
-    for ext in image_extensions:
-        image_path = model_dir / f"{model_stem}{ext}"
-        if image_path.exists():
-            try:
-                # Open the image
-                with Image.open(image_path) as img:
-                    # Create thumbnail and save to model images directory
-                    thumbnail = make_thumbnail(img, 256)
-                    thumbnail_path = model_images_path / f"{model_key}.webp"
-                    thumbnail.save(thumbnail_path, format="webp")
-
-                logger.info(f"Processed preview image {image_path.name} for model {model_key}")
-                return True
-
-            except Exception as e:
-                logger.warning(f"Failed to process preview image {image_path.name}: {e}")
-                return False
-
-    return False
-
-
-def _extract_json_metadata(model_stem: str, model_dir: Path) -> Tuple[Optional[str], Optional[Set[str]]]:
-    """Extract metadata from a JSON file with the same name as the model."""
-    json_path = model_dir / f"{model_stem}.json"
-
-    if not json_path.exists():
-        return None, None
-
-    try:
-        with open(json_path, "r", encoding="utf-8") as f:
-            metadata = json.load(f)
-
-        # Extract description
-        description = _build_description(metadata)
-
-        # Extract trigger phrases
-        trigger_phrases = _extract_trigger_phrases(metadata)
-
-        if description or trigger_phrases:
-            logger.info(f"Applied metadata from {json_path.name}")
-
-        return description, trigger_phrases
-
-    except (json.JSONDecodeError, IOError, Exception) as e:
-        logger.warning(f"Failed to read metadata from {json_path}: {e}")
-        return None, None
-
-
-def _build_description(metadata: Dict[str, Any]) -> Optional[str]:
-    """Build a description from metadata fields."""
-    description_parts = []
-
-    if description := metadata.get("description"):
-        description_parts.append(str(description).strip())
-
-    if notes := metadata.get("notes"):
-        description_parts.append(str(notes).strip())
-
-    return " | ".join(description_parts) if description_parts else None
-
-
-def _extract_trigger_phrases(metadata: Dict[str, Any]) -> Optional[Set[str]]:
-    """Extract trigger phrases from metadata."""
-    if not (activation_text := metadata.get("activation text")):
-        return None
-
-    activation_text = str(activation_text).strip()
-    if not activation_text:
-        return None
-
-    # Split on commas and clean up each phrase
-    phrases = [phrase.strip() for phrase in activation_text.split(",") if phrase.strip()]
-
-    return set(phrases) if phrases else None
-
-
-def apply_lora_metadata(info: AnyModelConfig, model_path: Path, model_images_path: Path) -> None:
-    """
-    Apply extracted metadata to a LoRA model configuration.
-
-    Args:
-        info: The model configuration to update
-        model_path: Path to the LoRA model file
-        model_images_path: Path to the model images directory
-    """
-    # Only process LoRA models
-    if info.type != ModelType.LoRA:
-        return
-
-    # Extract and apply metadata
-    description, trigger_phrases = extract_lora_metadata(model_path, info.key, model_images_path)
-
-    # We don't set cover_image path in the config anymore since images are stored
-    # separately in the model images store by model key
-
-    if description:
-        info.description = description
-
-    if trigger_phrases:
-        info.trigger_phrases = trigger_phrases

invokeai/backend/model_manager/util/model_util.py

@@ -8,12 +8,8 @@ import picklescan.scanner as pscan
 import safetensors
 import torch
 
-from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.model_manager.taxonomy import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
-from invokeai.backend.util.logging import InvokeAILogger
-
-logger = InvokeAILogger.get_logger()
 
 
 def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
@@ -63,21 +59,9 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> Dict[str,
         if scan:
             scan_result = pscan.scan_file_path(path)
             if scan_result.infected_files != 0:
-                if get_config().unsafe_disable_picklescan:
-                    logger.warning(
-                        f"The model {path} is potentially infected by malware, but picklescan is disabled. "
-                        "Proceeding with caution."
-                    )
-                else:
-                    raise RuntimeError(f"The model {path} is potentially infected by malware. Aborting import.")
+                raise Exception(f"The model at {path} is potentially infected by malware. Aborting import.")
             if scan_result.scan_err:
-                if get_config().unsafe_disable_picklescan:
-                    logger.warning(
-                        f"Error scanning the model at {path} for malware, but picklescan is disabled. "
-                        "Proceeding with caution."
-                    )
-                else:
-                    raise RuntimeError(f"Error scanning the model at {path} for malware. Aborting import.")
+                raise Exception(f"Error scanning model at {path} for malware. Aborting import.")
 
         checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint

invokeai/backend/quantization/scripts/load_flux_model_bnb_llm_int8.py

@@ -20,7 +20,7 @@ def main():
         "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
     )
 
-    with log_time("Initialize FLUX transformer on meta device"):
+    with log_time("Intialize FLUX transformer on meta device"):
         # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
         p = params["flux-schnell"]
 

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

@@ -33,7 +33,7 @@ def main():
     )
 
     # inference_dtype = torch.bfloat16
-    with log_time("Initialize FLUX transformer on meta device"):
+    with log_time("Intialize FLUX transformer on meta device"):
         # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
         p = params["flux-schnell"]
 

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

@@ -27,7 +27,7 @@ def main():
     """
     model_path = Path("/data/misc/text_encoder_2")
 
-    with log_time("Initialize T5 on meta device"):
+    with log_time("Intialize T5 on meta device"):
         model_config = AutoConfig.from_pretrained(model_path)
         with accelerate.init_empty_weights():
             model = AutoModelForTextEncoding.from_config(model_config)

invokeai/frontend/web/.gitignore

@@ -44,5 +44,4 @@ yalc.lock
 
 # vitest
 tsconfig.vitest-temp.json
-coverage/
-*.tgz
+coverage/

invokeai/frontend/web/.storybook/preview.tsx

@@ -26,7 +26,7 @@ i18n.use(initReactI18next).init({
   returnNull: false,
 });
 
-const store = createStore();
+const store = createStore(undefined, false);
 $store.set(store);
 $baseUrl.set('http://localhost:9090');
 

invokeai/frontend/web/eslint.config.mjs

@@ -197,10 +197,6 @@ export default [
               importNames: ['isEqual'],
               message: 'Please use objectEquals from @observ33r/object-equals instead.',
             },
-            {
-              name: 'zod/v3',
-              message: 'Import from zod instead.',
-            },
           ],
         },
       ],

invokeai/frontend/web/knip.ts

@@ -17,7 +17,6 @@ const config: KnipConfig = {
     'src/app/store/use-debounced-app-selector.ts',
   ],
   ignoreBinaries: ['only-allow'],
-  ignoreDependencies: ['magic-string'],
   paths: {
     'public/*': ['public/*'],
   },

invokeai/frontend/web/package.json

@@ -63,7 +63,7 @@
     "framer-motion": "^11.10.0",
     "i18next": "^25.3.2",
     "i18next-http-backend": "^3.0.2",
-    "idb-keyval": "6.2.1",
+    "idb-keyval": "6.2.2",
     "jsondiffpatch": "^0.7.3",
     "konva": "^9.3.22",
     "linkify-react": "^4.3.1",
@@ -103,7 +103,7 @@
     "use-debounce": "^10.0.5",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^4.0.10",
+    "zod": "^4.0.5",
     "zod-validation-error": "^3.5.2"
   },
   "peerDependencies": {
@@ -139,7 +139,6 @@
     "eslint-plugin-unused-imports": "^4.1.4",
     "globals": "^16.3.0",
     "knip": "^5.61.3",
-    "magic-string": "^0.30.17",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",

invokeai/frontend/web/pnpm-lock.yaml

@@ -81,8 +81,8 @@ importers:
         specifier: ^3.0.2
         version: 3.0.2
       idb-keyval:
-        specifier: 6.2.1
-        version: 6.2.1
+        specifier: 6.2.2
+        version: 6.2.2
       jsondiffpatch:
         specifier: ^0.7.3
         version: 0.7.3
@@ -201,11 +201,11 @@ importers:
         specifier: ^11.1.0
         version: 11.1.0
       zod:
-        specifier: ^4.0.10
-        version: 4.0.10
+        specifier: ^4.0.5
+        version: 4.0.5
       zod-validation-error:
         specifier: ^3.5.2
-        version: 3.5.3(zod@4.0.10)
+        version: 3.5.3(zod@4.0.5)
     devDependencies:
       '@eslint/js':
         specifier: ^9.31.0
@@ -291,9 +291,6 @@ importers:
       knip:
         specifier: ^5.61.3
         version: 5.61.3(@types/node@22.16.0)(typescript@5.8.3)
-      magic-string:
-        specifier: ^0.30.17
-        version: 0.30.17
       openapi-types:
         specifier: ^12.1.3
         version: 12.1.3
@@ -414,10 +411,6 @@ packages:
     resolution: {integrity: sha512-vbavdySgbTTrmFE+EsiqUTzlOr5bzlnJtUv9PynGCAKvfQqjIXbvFdumPM/GxMDfyuGMJaJAU6TO4zc1Jf1i8Q==}
     engines: {node: '>=6.9.0'}
 
-  '@babel/runtime@7.28.2':
-    resolution: {integrity: sha512-KHp2IflsnGywDjBWDkR9iEqiWSpc8GIi0lgTT3mOElT0PP1tG26P4tmFI2YvAdzgq9RGyoHZQEIEdZy6Ec5xCA==}
-    engines: {node: '>=6.9.0'}
-
   '@babel/template@7.27.2':
     resolution: {integrity: sha512-LPDZ85aEJyYSd18/DkjNh4/y1ntkE5KwUHWTiqgRxruuZL2F1yuHligVHLvcHY2vMHXttKFpJn6LwfI7cw7ODw==}
     engines: {node: '>=6.9.0'}
@@ -2778,8 +2771,8 @@ packages:
       typescript:
         optional: true
 
-  idb-keyval@6.2.1:
-    resolution: {integrity: sha512-8Sb3veuYCyrZL+VBt9LJfZjLUPWVvqn8tG28VqYNFCo43KHcKuq+b4EiXGeuaLAQWL2YmyDgMp2aSpH9JHsEQg==}
+  idb-keyval@6.2.2:
+    resolution: {integrity: sha512-yjD9nARJ/jb1g+CvD0tlhUHOrJ9Sy0P8T9MF3YaLlHnSRpwPfpTX0XIvpmw3gAJUmEu3FiICLBDPXVwyEvrleg==}
 
   ieee754@1.2.1:
     resolution: {integrity: sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA==}
@@ -4518,8 +4511,8 @@ packages:
   zod@3.25.76:
     resolution: {integrity: sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==}
 
-  zod@4.0.10:
-    resolution: {integrity: sha512-3vB+UU3/VmLL2lvwcY/4RV2i9z/YU0DTV/tDuYjrwmx5WeJ7hwy+rGEEx8glHp6Yxw7ibRbKSaIFBgReRPe5KA==}
+  zod@4.0.5:
+    resolution: {integrity: sha512-/5UuuRPStvHXu7RS+gmvRf4NXrNxpSllGwDnCBcJZtQsKrviYXm54yDGV2KYNLT5kq0lHGcl7lqWJLgSaG+tgA==}
 
   zustand@4.5.7:
     resolution: {integrity: sha512-CHOUy7mu3lbD6o6LJLfllpjkzhHXSBlX8B9+qPddUsIfeF5S/UZ5q0kmCsnRqT1UHFQZchNFDDzMbQsuesHWlw==}
@@ -4640,8 +4633,6 @@ snapshots:
 
   '@babel/runtime@7.27.6': {}
 
-  '@babel/runtime@7.28.2': {}
-
   '@babel/template@7.27.2':
     dependencies:
       '@babel/code-frame': 7.27.1
@@ -5745,7 +5736,7 @@ snapshots:
   '@testing-library/dom@10.4.0':
     dependencies:
       '@babel/code-frame': 7.27.1
-      '@babel/runtime': 7.28.2
+      '@babel/runtime': 7.27.6
       '@types/aria-query': 5.0.4
       aria-query: 5.3.0
       chalk: 4.1.2
@@ -7275,7 +7266,7 @@ snapshots:
     optionalDependencies:
       typescript: 5.8.3
 
-  idb-keyval@6.2.1: {}
+  idb-keyval@6.2.2: {}
 
   ieee754@1.2.1: {}
 
@@ -9071,13 +9062,13 @@ snapshots:
     dependencies:
       zod: 3.25.76
 
-  zod-validation-error@3.5.3(zod@4.0.10):
+  zod-validation-error@3.5.3(zod@4.0.5):
     dependencies:
-      zod: 4.0.10
+      zod: 4.0.5
 
   zod@3.25.76: {}
 
-  zod@4.0.10: {}
+  zod@4.0.5: {}
 
   zustand@4.5.7(@types/react@18.3.23)(immer@10.1.1)(react@18.3.1):
     dependencies:

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

@@ -711,8 +711,7 @@
         "gaussianBlur": "Gaußsche Unschärfe",
         "sendToUpscale": "An Hochskalieren senden",
         "useCpuNoise": "CPU-Rauschen verwenden",
-        "sendToCanvas": "An Leinwand senden",
-        "disabledNoRasterContent": "Deaktiviert (kein Rasterinhalt)"
+        "sendToCanvas": "An Leinwand senden"
     },
     "settings": {
         "displayInProgress": "Zwischenbilder anzeigen",
@@ -790,10 +789,7 @@
         "pasteSuccess": "Eingefügt in {{destination}}",
         "pasteFailed": "Einfügen fehlgeschlagen",
         "unableToCopy": "Kopieren nicht möglich",
-        "unableToCopyDesc_theseSteps": "diese Schritte",
-        "noRasterLayers": "Keine Rasterebenen gefunden",
-        "noActiveRasterLayers": "Keine aktiven Rasterebenen",
-        "noVisibleRasterLayers": "Keine sichtbaren Rasterebenen"
+        "unableToCopyDesc_theseSteps": "diese Schritte"
     },
     "accessibility": {
         "uploadImage": "Bild hochladen",
@@ -851,10 +847,7 @@
         "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.",
-        "updateBoardError": "Fehler beim Aktualisieren des Ordners",
-        "uncategorizedImages": "Nicht kategorisierte Bilder",
-        "deleteAllUncategorizedImages": "Alle nicht kategorisierten Bilder löschen",
-        "deletedImagesCannotBeRestored": "Gelöschte Bilder können nicht wiederhergestellt werden."
+        "updateBoardError": "Fehler beim Aktualisieren des Ordners"
     },
     "queue": {
         "status": "Status",
@@ -1201,9 +1194,6 @@
                 "Die Kantengröße des Kohärenzdurchlaufs."
             ],
             "heading": "Kantengröße"
-        },
-        "rasterLayer": {
-            "heading": "Rasterebene"
         }
     },
     "invocationCache": {
@@ -1441,10 +1431,7 @@
         "autoLayout": "Auto Layout",
         "copyShareLink": "Teilen-Link kopieren",
         "download": "Herunterladen",
-        "convertGraph": "Graph konvertieren",
-        "filterByTags": "Nach Tags filtern",
-        "yourWorkflows": "Ihre Arbeitsabläufe",
-        "recentlyOpened": "Kürzlich geöffnet"
+        "convertGraph": "Graph konvertieren"
     },
     "sdxl": {
         "concatPromptStyle": "Verknüpfen von Prompt & Stil",
@@ -1457,19 +1444,12 @@
     "prompt": {
         "noMatchingTriggers": "Keine passenden Trigger",
         "addPromptTrigger": "Prompt-Trigger hinzufügen",
-        "compatibleEmbeddings": "Kompatible Einbettungen",
-        "replace": "Ersetzen",
-        "insert": "Einfügen",
-        "discard": "Verwerfen",
-        "generateFromImage": "Prompt aus Bild generieren",
-        "expandCurrentPrompt": "Aktuelle Prompt erweitern",
-        "uploadImageForPromptGeneration": "Bild zur Prompt-Generierung hochladen",
-        "expandingPrompt": "Prompt wird erweitert...",
-        "resultTitle": "Prompt-Erweiterung abgeschlossen"
+        "compatibleEmbeddings": "Kompatible Einbettungen"
     },
     "ui": {
         "tabs": {
             "queue": "Warteschlange",
+            "generation": "Erzeugung",
             "gallery": "Galerie",
             "models": "Modelle",
             "upscaling": "Hochskalierung",
@@ -1593,30 +1573,30 @@
             "newGlobalReferenceImage": "Neues globales Referenzbild",
             "newRegionalReferenceImage": "Neues regionales Referenzbild",
             "newControlLayer": "Neue Kontroll-Ebene",
-            "newRasterLayer": "Neue Rasterebene"
+            "newRasterLayer": "Neue Raster-Ebene"
         },
         "rectangle": "Rechteck",
         "saveCanvasToGallery": "Leinwand in Galerie speichern",
-        "newRasterLayerError": "Problem beim Erstellen einer Rasterebene",
+        "newRasterLayerError": "Problem beim Erstellen einer Raster-Ebene",
         "saveLayerToAssets": "Ebene in Galerie speichern",
         "deleteReferenceImage": "Referenzbild löschen",
         "referenceImage": "Referenzbild",
         "opacity": "Opazität",
         "removeBookmark": "Lesezeichen entfernen",
-        "rasterLayer": "Rasterebene",
-        "rasterLayers_withCount_visible": "Rasterebenen ({{count}})",
+        "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": "Rasterebene erstellt",
+        "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": "Rasterebenen ({{count}} ausgeblendet)",
+        "rasterLayers_withCount_hidden": "Raster-Ebenen ({{count}} ausgeblendet)",
         "transparency": "Transparenz",
         "canvas": "Leinwand",
         "global": "Global",
@@ -1702,14 +1682,7 @@
             "filterType": "Filtertyp",
             "filter": "Filter"
         },
-        "bookmark": "Lesezeichen für Schnell-Umschalten",
-        "asRasterLayer": "Als $t(controlLayers.rasterLayer)",
-        "asRasterLayerResize": "Als $t(controlLayers.rasterLayer) (Größe anpassen)",
-        "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
-        "rasterLayer_withCount_other": "Rasterebenen",
-        "newRasterLayer": "Neue $t(controlLayers.rasterLayer)",
-        "showNonRasterLayers": "Nicht-Rasterebenen anzeigen (Umschalt+H)",
-        "hideNonRasterLayers": "Nicht-Rasterebenen ausblenden (Umschalt+H)"
+        "bookmark": "Lesezeichen für Schnell-Umschalten"
     },
     "upsell": {
         "shareAccess": "Zugang teilen",