chore: bump version to v5.10.0dev2

Doing a dev build so I can test the launcher.

.dockerignore

@@ -1,9 +1,11 @@
 *
 !invokeai
 !pyproject.toml
+!uv.lock
 !docker/docker-entrypoint.sh
 !LICENSE
 
+**/dist
 **/node_modules
 **/__pycache__
-**/*.egg-info
+**/*.egg-info

.github/workflows/build-container.yml

@@ -97,6 +97,8 @@ jobs:
           context: .
           file: docker/Dockerfile
           platforms: ${{ env.PLATFORMS }}
+          build-args: |
+            GPU_DRIVER=${{ matrix.gpu-driver }}
           push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}

.github/workflows/build-installer.yml

@@ -17,7 +17,7 @@ jobs:
       - name: setup python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.12'
           cache: pip
           cache-dependency-path: pyproject.toml
 

.nvmrc

@@ -1 +1 @@
-v22.12.0
+v22.14.0

docker/Dockerfile

@@ -1,77 +1,6 @@
 # syntax=docker/dockerfile:1.4
 
-## Builder stage
-
-FROM library/ubuntu:24.04 AS builder
-
-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,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt update && apt-get install -y \
-        build-essential \
-        git
-
-# Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
-
-ENV VIRTUAL_ENV=/opt/venv
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV PYTHON_VERSION=3.11
-ENV UV_PYTHON=3.11
-ENV UV_COMPILE_BYTECODE=1
-ENV UV_LINK_MODE=copy
-ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
-ENV UV_INDEX="https://download.pytorch.org/whl/cu124"
-
-ARG GPU_DRIVER=cuda
-# unused but available
-ARG BUILDPLATFORM
-
-# Switch to the `ubuntu` user to work around dependency issues with uv-installed python
-RUN mkdir -p ${VIRTUAL_ENV} && \
-    mkdir -p ${INVOKEAI_SRC} && \
-    chmod -R a+w /opt && \
-    mkdir ~ubuntu/.cache && chown ubuntu: ~ubuntu/.cache
-USER ubuntu
-
-# Install python
-RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
-  uv python install ${PYTHON_VERSION}
-
-WORKDIR ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    if [ "$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/rocm6.1"; \
-    fi && \
-    uv sync --no-install-project
-
-# Now that the bulk of the dependencies have been installed, copy in the project files that change more frequently.
-COPY invokeai invokeai
-COPY pyproject.toml .
-
-RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    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/rocm6.1"; \
-    fi && \
-    uv sync
-
-
-#### Build the Web UI ------------------------------------
+#### Web UI ------------------------------------
 
 FROM docker.io/node:22-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
@@ -85,69 +14,89 @@ RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
 RUN npx vite build
 
-#### Runtime stage ---------------------------------------
+## Backend ---------------------------------------
 
-FROM library/ubuntu:24.04 AS runtime
+FROM library/ubuntu:24.04
 
 ARG DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
+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
 
-RUN apt update && apt install -y --no-install-recommends \
-        git \
-        curl \
-        vim \
-        tmux \
-        ncdu \
-        iotop \
-        bzip2 \
-        gosu \
-        magic-wormhole \
-        libglib2.0-0 \
-        libgl1 \
-        libglx-mesa0 \
-        build-essential \
-        libopencv-dev \
-        libstdc++-10-dev &&\
-    apt-get clean && apt-get autoclean
+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 \
+    UV_INDEX="https://download.pytorch.org/whl/cu124" \
+    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}
 
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv
-ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
-ENV PYTHON_VERSION=3.11
-ENV INVOKEAI_ROOT=/invokeai
-ENV INVOKEAI_HOST=0.0.0.0
-ENV INVOKEAI_PORT=9090
-ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
-ENV CONTAINER_UID=${CONTAINER_UID:-1000}
-ENV CONTAINER_GID=${CONTAINER_GID:-1000}
+ARG GPU_DRIVER=cuda
 
 # Install `uv` for package management
-# and install python for the ubuntu user (expected to exist on ubuntu >=24.x)
-# this is too tiny to optimize with multi-stage builds, but maybe we'll come back to it
-COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
-USER ubuntu
-RUN uv python install ${PYTHON_VERSION}
-USER root
+COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
 
-# --link requires buldkit w/ dockerfile syntax 1.4
-COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
-COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# 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"
+# 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 \
+    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 ./
 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

docs/contributing/dev-environment.md

@@ -41,7 +41,7 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
      With the modifications made, the install command should look something like this:
 
       ```sh
-      uv pip install -e ".[dev,test,docs,xformers]" --python 3.11 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
+      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
       ```
 
 6. At this point, you should have Invoke installed, a venv set up and activated, and the server running. But you will see a warning in the terminal that no UI was found. If you go to the URL for the server, you won't get a UI.

docs/installation/manual.md

@@ -43,10 +43,10 @@ The following commands vary depending on the version of Invoke being installed a
 3. Create a virtual environment in that directory:
 
     ```sh
-    uv venv --relocatable --prompt invoke --python 3.11 --python-preference only-managed .venv
+    uv venv --relocatable --prompt invoke --python 3.12 --python-preference only-managed .venv
     ```
 
-    This command creates a portable virtual environment at `.venv` complete with a portable python 3.11. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
+    This command creates a portable virtual environment at `.venv` complete with a portable python 3.12. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
 
 4. Activate the virtual environment:
 
@@ -88,13 +88,13 @@ The following commands vary depending on the version of Invoke being installed a
 8. Install the `invokeai` package. Substitute the package specifier and version.
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --python-preference only-managed --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
     ```
 
     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 3.11 --python-preference only-managed --index=<INDEX_URL> --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/installation/requirements.md

@@ -41,7 +41,7 @@ The requirements below are rough guidelines for best performance. GPUs with less
 
     You don't need to do this if you are installing with the [Invoke Launcher](./quick_start.md).
 
-Invoke requires python 3.10 or 3.11. If you don't already have one of these versions installed, we suggest installing 3.11, as it will be supported for longer.
+Invoke requires python 3.10 through 3.12. If you don't already have one of these versions installed, we suggest installing 3.12, as it will be supported for longer.
 
 Check that your system has an up-to-date Python installed by running `python3 --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).
 
@@ -49,19 +49,19 @@ Check that your system has an up-to-date Python installed by running `python3 --
 
     === "Windows"
 
-        - Install python 3.11 with [an official installer].
+        - Install python with [an official installer].
         - The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox.
         - You may need to install [Microsoft Visual C++ Redistributable].
 
     === "macOS"
 
-        - Install python 3.11 with [an official installer].
+        - Install python with [an official installer].
         - If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): `/Applications/Python\ 3.10/Install\ Certificates.command`
         - If you haven't already, you will need to install the XCode CLI Tools by running `xcode-select --install` in a terminal.
 
     === "Linux"
 
-        - Installing python varies depending on your system. On Ubuntu, you can use the [deadsnakes PPA](https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa).
+        - Installing python varies depending on your system. We recommend [using `uv` to manage your python installation](https://docs.astral.sh/uv/concepts/python-versions/#installing-a-python-version).
         - You'll need to install `libglib2.0-0` and `libgl1-mesa-glx` for OpenCV to work. For example, on a Debian system: `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
 
 ## Drivers

invokeai/app/api/dependencies.py

@@ -37,7 +37,13 @@ from invokeai.app.services.style_preset_records.style_preset_records_sqlite impo
 from invokeai.app.services.urls.urls_default import LocalUrlService
 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 ConditioningFieldData
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    BasicConditioningInfo,
+    ConditioningFieldData,
+    FLUXConditioningInfo,
+    SD3ConditioningInfo,
+    SDXLConditioningInfo,
+)
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -101,10 +107,25 @@ class ApiDependencies:
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         tensors = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
+            ObjectSerializerDisk[torch.Tensor](
+                output_folder / "tensors",
+                safe_globals=[torch.Tensor],
+                ephemeral=True,
+            ),
+            max_cache_size=0,
         )
         conditioning = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
+            ObjectSerializerDisk[ConditioningFieldData](
+                output_folder / "conditioning",
+                safe_globals=[
+                    ConditioningFieldData,
+                    BasicConditioningInfo,
+                    SDXLConditioningInfo,
+                    FLUXConditioningInfo,
+                    SD3ConditioningInfo,
+                ],
+                ephemeral=True,
+            ),
         )
         download_queue_service = DownloadQueueService(app_config=configuration, event_bus=events)
         model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -21,10 +21,16 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
     """Disk-backed storage for arbitrary python objects. Serialization is handled by `torch.save` and `torch.load`.
 
     :param output_dir: The folder where the serialized objects will be stored
+    :param safe_globals: A list of types to be added to the safe globals for torch serialization
     :param ephemeral: If True, objects will be stored in a temporary directory inside the given output_dir and cleaned up on exit
     """
 
-    def __init__(self, output_dir: Path, ephemeral: bool = False):
+    def __init__(
+        self,
+        output_dir: Path,
+        safe_globals: list[type],
+        ephemeral: bool = False,
+    ) -> None:
         super().__init__()
         self._ephemeral = ephemeral
         self._base_output_dir = output_dir
@@ -42,6 +48,8 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
         self._output_dir = Path(self._tempdir.name) if self._tempdir else self._base_output_dir
         self.__obj_class_name: Optional[str] = None
 
+        torch.serialization.add_safe_globals(safe_globals) if safe_globals else None
+
     def load(self, name: str) -> T:
         file_path = self._get_path(name)
         try:

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -69,6 +69,9 @@ class SD3ConditioningInfo:
 
 @dataclass
 class ConditioningFieldData:
+    # If you change this class, adding more types, you _must_ update the instantiation of ObjectSerializerDisk in
+    # invokeai/app/api/dependencies.py, adding the types to the list of safe globals. If you do not, torch will be
+    # unable to deserialize the object and will raise an error.
     conditionings: (
         List[BasicConditioningInfo]
         | List[SDXLConditioningInfo]

invokeai/frontend/web/package.json

@@ -162,5 +162,6 @@
   },
   "engines": {
     "pnpm": "8"
-  }
+  },
+  "packageManager": "pnpm@8.15.9+sha512.499434c9d8fdd1a2794ebf4552b3b25c0a633abcee5bb15e7b5de90f32f47b513aca98cd5cfd001c31f0db454bc3804edccd578501e4ca293a6816166bbd9f81"
 }

invokeai/frontend/web/src/features/nodes/types/openapi.ts

@@ -79,4 +79,4 @@ export const isInvocationOutputSchemaObject = (
 
 export const isInvocationFieldSchema = (
   obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject
-): obj is InvocationFieldSchema => !('$ref' in obj);
+): obj is InvocationFieldSchema => 'field_kind' in obj;

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.9.1"
+__version__ = "5.10.0dev2"

pins.json

@@ -0,0 +1,14 @@
+{
+  "python": "3.11",
+  "torchIndexUrl": {
+    "win32": {
+      "cuda": "https://download.pytorch.org/whl/cu126"
+    },
+    "linux": {
+      "cpu": "https://download.pytorch.org/whl/cpu",
+      "rocm": "https://download.pytorch.org/whl/rocm6.2.4",
+      "cuda": "https://download.pytorch.org/whl/cu126"
+    },
+    "darwin": {}
+  }
+}

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "InvokeAI"
 description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
-requires-python = ">=3.10, <3.12"
+requires-python = ">=3.10, <3.13"
 readme = { content-type = "text/markdown", file = "README.md" }
 keywords = ["stable-diffusion", "AI"]
 dynamic = ["version"]
@@ -33,12 +33,12 @@ classifiers = [
 ]
 dependencies = [
   # Core generation dependencies, pinned for reproducible builds.
-  "accelerate==1.0.1",
-  "bitsandbytes==0.45.0; sys_platform!='darwin'",
+  "accelerate",
+  "bitsandbytes; sys_platform!='darwin'",
   "clip_anytorch==2.6.0",       # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
   "compel==2.0.2",
-  "controlnet-aux",
-  "diffusers[torch]==0.31.0",
+  "controlnet-aux==0.0.7",
+  "diffusers[torch]",
   "gguf==0.10.0",
   "invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "mediapipe==0.10.14",         # needed for "mediapipeface" controlnet model
@@ -46,26 +46,27 @@ dependencies = [
   "onnx==1.16.1",
   "onnxruntime==1.19.2",
   "opencv-python==4.9.0.80",
-  "pytorch-lightning==2.1.3",
-  "safetensors==0.4.3",
+  "pytorch-lightning",
+  "safetensors",
   # sentencepiece is required to load T5TokenizerFast (used by FLUX).
   "sentencepiece==0.2.0",
   "spandrel==0.3.4",
   "timm~=1.0.0",
-  "torch<2.5.0",                # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]`
+  "torch~=2.6.0",                # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]`
   "torchmetrics",
   "torchsde",
   "torchvision",
-  "transformers==4.46.3",
+  "timm~=1.0.0",                 # controlnet-aux depends on a version of timm that breaks LLaVA. explicitly pinning `timm` here, which results in a downgrade of `controlnet-aux`. https://github.com/huggingface/controlnet_aux/pull/101. If this poses a problem, we need to decide between newer `controlnet_aux` and LLaVA, OR we can fork `controlnet_aux` and update the pin.
+  "transformers",
 
   # Core application dependencies, pinned for reproducible builds.
-  "fastapi-events==0.11.1",
-  "fastapi==0.111.0",
-  "huggingface-hub==0.26.1",
-  "pydantic-settings==2.2.1",
-  "pydantic==2.7.2",
-  "python-socketio==5.11.1",
-  "uvicorn[standard]==0.28.0",
+  "fastapi-events",
+  "fastapi",
+  "huggingface-hub",
+  "pydantic-settings",
+  "pydantic",
+  "python-socketio",
+  "uvicorn[standard]",
 
   # Auxiliary dependencies, pinned only if necessary.
   "albumentations",
@@ -90,11 +91,8 @@ dependencies = [
   "pyreadline3",
   "python-multipart",
   "requests",
-  "rich~=13.3",
   "scikit-image",
   "semver~=3.0.1",
-  "test-tube",
-  "windows-curses; sys_platform=='win32'",
   "humanize==4.12.1",
 ]
 

tests/test_object_serializer_disk.py

@@ -21,16 +21,18 @@ def count_files(path: Path):
 
 @pytest.fixture
 def obj_serializer(tmp_path: Path):
-    return ObjectSerializerDisk[MockDataclass](tmp_path)
+    return ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass])
 
 
 @pytest.fixture
 def fwd_cache(tmp_path: Path):
-    return ObjectSerializerForwardCache(ObjectSerializerDisk[MockDataclass](tmp_path), max_cache_size=2)
+    return ObjectSerializerForwardCache(
+        ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass]), max_cache_size=2
+    )
 
 
 def test_obj_serializer_disk_initializes(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass])
     assert obj_serializer._output_dir == tmp_path
 
 
@@ -70,7 +72,7 @@ def test_obj_serializer_disk_deletes(obj_serializer: ObjectSerializerDisk[MockDa
 
 
 def test_obj_serializer_ephemeral_creates_tempdir(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     assert isinstance(obj_serializer._tempdir, tempfile.TemporaryDirectory)
     assert obj_serializer._base_output_dir == tmp_path
     assert obj_serializer._output_dir != tmp_path
@@ -78,21 +80,21 @@ def test_obj_serializer_ephemeral_creates_tempdir(tmp_path: Path):
 
 
 def test_obj_serializer_ephemeral_deletes_tempdir(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     tempdir_path = obj_serializer._output_dir
     del obj_serializer
     assert not tempdir_path.exists()
 
 
 def test_obj_serializer_ephemeral_deletes_tempdir_on_stop(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     tempdir_path = obj_serializer._output_dir
     obj_serializer.stop(None)  # pyright: ignore [reportArgumentType]
     assert not tempdir_path.exists()
 
 
 def test_obj_serializer_ephemeral_writes_to_tempdir(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     obj_1 = MockDataclass(foo="bar")
     obj_1_name = obj_serializer.save(obj_1)
     assert Path(obj_serializer._output_dir, obj_1_name).exists()
@@ -102,19 +104,19 @@ def test_obj_serializer_ephemeral_writes_to_tempdir(tmp_path: Path):
 def test_obj_serializer_ephemeral_deletes_dangling_tempdirs_on_init(tmp_path: Path):
     tempdir = tmp_path / "tmpdir"
     tempdir.mkdir()
-    ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     assert not tempdir.exists()
 
 
 def test_obj_serializer_does_not_delete_tempdirs_on_init(tmp_path: Path):
     tempdir = tmp_path / "tmpdir"
     tempdir.mkdir()
-    ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=False)
+    ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=False)
     assert tempdir.exists()
 
 
 def test_obj_serializer_disk_different_types(tmp_path: Path):
-    obj_serializer_1 = ObjectSerializerDisk[MockDataclass](tmp_path)
+    obj_serializer_1 = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass])
     obj_1 = MockDataclass(foo="bar")
     obj_1_name = obj_serializer_1.save(obj_1)
     obj_1_loaded = obj_serializer_1.load(obj_1_name)
@@ -123,19 +125,19 @@ def test_obj_serializer_disk_different_types(tmp_path: Path):
     assert obj_1_loaded.foo == "bar"
     assert obj_1_name.startswith("MockDataclass_")
 
-    obj_serializer_2 = ObjectSerializerDisk[int](tmp_path)
+    obj_serializer_2 = ObjectSerializerDisk[int](tmp_path, safe_globals=[int])
     obj_2_name = obj_serializer_2.save(9001)
     assert obj_serializer_2._obj_class_name == "int"
     assert obj_serializer_2.load(obj_2_name) == 9001
     assert obj_2_name.startswith("int_")
 
-    obj_serializer_3 = ObjectSerializerDisk[str](tmp_path)
+    obj_serializer_3 = ObjectSerializerDisk[str](tmp_path, safe_globals=[str])
     obj_3_name = obj_serializer_3.save("foo")
     assert obj_serializer_3._obj_class_name == "str"
     assert obj_serializer_3.load(obj_3_name) == "foo"
     assert obj_3_name.startswith("str_")
 
-    obj_serializer_4 = ObjectSerializerDisk[torch.Tensor](tmp_path)
+    obj_serializer_4 = ObjectSerializerDisk[torch.Tensor](tmp_path, safe_globals=[torch.Tensor])
     obj_4_name = obj_serializer_4.save(torch.tensor([1, 2, 3]))
     obj_4_loaded = obj_serializer_4.load(obj_4_name)
     assert obj_serializer_4._obj_class_name == "Tensor"