Add support for mask weights, and only mask the tokens associated with the prompts (not eh entire 77-token embedding).

* UI in MM to create trigger phrases

* add scheduler and vaePrecision to config

* UI for configuring default settings for models'

* hook MM default model settings up to API

* add button to set default settings in parameters

* pull out trigger phrases

* back-end for default settings

* lint

* remove log;
gi

* ruff

* ruff format

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>

.github/actions/install-frontend-deps/action.yml

@@ -1,33 +1,33 @@
-name: Install frontend dependencies
+name: install frontend dependencies
 description: Installs frontend dependencies with pnpm, with caching
 runs:
   using: 'composite'
   steps:
-    - name: Setup Node 18
+    - name: setup node 18
       uses: actions/setup-node@v4
       with:
         node-version: '18'
 
-    - name: Setup pnpm
+    - name: setup pnpm
       uses: pnpm/action-setup@v2
       with:
         version: 8
         run_install: false
 
-    - name: Get pnpm store directory
+    - name: get pnpm store directory
       shell: bash
       run: |
         echo "STORE_PATH=$(pnpm store path --silent)" >> $GITHUB_ENV
 
-    - uses: actions/cache@v3
-      name: Setup pnpm cache
+    - name: setup cache
+      uses: actions/cache@v4
       with:
         path: ${{ env.STORE_PATH }}
         key: ${{ runner.os }}-pnpm-store-${{ hashFiles('**/pnpm-lock.yaml') }}
         restore-keys: |
           ${{ runner.os }}-pnpm-store-
 
-    - name: Install frontend dependencies
+    - name: install frontend dependencies
       run: pnpm install --prefer-frozen-lockfile
       shell: bash
       working-directory: invokeai/frontend/web

.github/actions/install-python-deps/action.yml

@@ -1,11 +0,0 @@
-name: Install python dependencies
-description: Install python dependencies with pip, with caching
-runs:
-  using: 'composite'
-  steps:
-    - name: Setup python
-      uses: actions/setup-python@v5
-      with:
-        python-version: '3.10'
-        cache: pip
-        cache-dependency-path: pyproject.toml

.github/pr_labels.yml

@@ -1,59 +1,59 @@
-Root:
+root:
 - changed-files:
   - any-glob-to-any-file: '*'
 
-PythonDeps:
+python-deps:
 - changed-files:
   - any-glob-to-any-file: 'pyproject.toml'
 
-Python:
+python:
 - changed-files:
   - all-globs-to-any-file: 
     - 'invokeai/**'
     - '!invokeai/frontend/web/**'
 
-PythonTests:
+python-tests:
 - changed-files:
   - any-glob-to-any-file: 'tests/**'
 
-CICD:
+ci-cd:
 - changed-files:
   - any-glob-to-any-file: .github/**
 
-Docker:
+docker:
 - changed-files:
   - any-glob-to-any-file: docker/**
 
-Installer:
+installer:
 - changed-files:
   - any-glob-to-any-file: installer/**
 
-Documentation:
+docs:
 - changed-files:
   - any-glob-to-any-file: docs/**
 
-Invocations:
+invocations:
 - changed-files:
   - any-glob-to-any-file: 'invokeai/app/invocations/**'
 
-Backend:
+backend:
 - changed-files:
   - any-glob-to-any-file: 'invokeai/backend/**'
 
-Api:
+api:
 - changed-files:
   - any-glob-to-any-file: 'invokeai/app/api/**'
 
-Services:
+services:
 - changed-files:
   - any-glob-to-any-file: 'invokeai/app/services/**'
 
-FrontendDeps:
+frontend-deps:
 - changed-files:
   - any-glob-to-any-file:
     - '**/*/package.json'
     - '**/*/pnpm-lock.yaml'
 
-Frontend:
+frontend:
 - changed-files:
   - any-glob-to-any-file: 'invokeai/frontend/web/**'

.github/workflows/build-installer.yml

@@ -0,0 +1,45 @@
+# Builds and uploads the installer and python build artifacts.
+
+name: build installer
+
+on:
+  workflow_dispatch:
+  workflow_call:
+
+jobs:
+  build-installer:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5 # expected run time: <2 min
+    steps:
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: setup python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
+      - name: install pypa/build
+        run: pip install --upgrade build
+
+      - name: setup frontend
+        uses: ./.github/actions/install-frontend-deps
+
+      - name: create installer
+        id: create_installer
+        run: ./create_installer.sh
+        working-directory: installer
+
+      - name: upload python distribution artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: ${{ steps.create_installer.outputs.DIST_PATH }}
+
+      - name: upload installer artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ steps.create_installer.outputs.INSTALLER_FILENAME }}
+          path: ${{ steps.create_installer.outputs.INSTALLER_PATH }}

.github/workflows/check-frontend.yml

@@ -1,43 +0,0 @@
-# This workflow runs the frontend code quality checks.
-#
-# It may be triggered via dispatch, or by another workflow.
-
-name: 'Check: frontend'
-
-on:
-  workflow_dispatch:
-  workflow_call:
-
-defaults:
-  run:
-    working-directory: invokeai/frontend/web
-
-jobs:
-  check-frontend:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10 # expected run time: <2 min
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up frontend
-        uses: ./.github/actions/install-frontend-deps
-
-      - name: Run tsc check
-        run: 'pnpm run lint:tsc'
-        shell: bash
-
-      - name: Run dpdm check
-        run: 'pnpm run lint:dpdm'
-        shell: bash
-
-      - name: Run eslint check
-        run: 'pnpm run lint:eslint'
-        shell: bash
-
-      - name: Run prettier check
-        run: 'pnpm run lint:prettier'
-        shell: bash
-
-      - name: Run knip check
-        run: 'pnpm run lint:knip'
-        shell: bash

.github/workflows/check-pytest.yml

@@ -1,72 +0,0 @@
-# This workflow runs pytest on the codebase in a matrix of platforms.
-#
-# It may be triggered via dispatch, or by another workflow.
-
-name: 'Check: pytest'
-
-on:
-  workflow_dispatch:
-  workflow_call:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  matrix:
-    strategy:
-      matrix:
-        python-version:
-          - '3.10'
-        pytorch:
-          - linux-cuda-11_7
-          - linux-rocm-5_2
-          - linux-cpu
-          - macos-default
-          - windows-cpu
-        include:
-          - pytorch: linux-cuda-11_7
-            os: ubuntu-22.04
-            github-env: $GITHUB_ENV
-          - pytorch: linux-rocm-5_2
-            os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
-            github-env: $GITHUB_ENV
-          - pytorch: linux-cpu
-            os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/cpu'
-            github-env: $GITHUB_ENV
-          - pytorch: macos-default
-            os: macOS-12
-            github-env: $GITHUB_ENV
-          - pytorch: windows-cpu
-            os: windows-2022
-            github-env: $env:GITHUB_ENV
-    name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
-    runs-on: ${{ matrix.os }}
-    timeout-minutes: 30 # expected run time: <10 min, depending on platform
-    env:
-      PIP_USE_PEP517: '1'
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: set test prompt to main branch validation
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
-
-      - name: setup python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-          cache: pip
-          cache-dependency-path: pyproject.toml
-
-      - name: install invokeai
-        env:
-          PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
-        run: >
-          pip3 install
-          --editable=".[test]"
-
-      - name: run pytest
-        id: run-pytest
-        run: pytest

.github/workflows/check-python.yml

@@ -1,33 +0,0 @@
-# This workflow runs the python code quality checks.
-#
-# It may be triggered via dispatch, or by another workflow.
-#
-# TODO: Add mypy or pyright to the checks.
-
-name: 'Check: python'
-
-on:
-  workflow_dispatch:
-  workflow_call:
-
-jobs:
-  check-backend:
-    runs-on: ubuntu-latest
-    timeout-minutes: 5 # expected run time: <1 min
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install python dependencies
-        uses: ./.github/actions/install-python-deps
-
-      - name: Install ruff
-        run: pip install ruff
-        shell: bash
-
-      - name: Ruff check
-        run: ruff check --output-format=github .
-        shell: bash
-
-      - name: Ruff format
-        run: ruff format --check .
-        shell: bash

.github/workflows/frontend-checks.yml

@@ -0,0 +1,68 @@
+# Runs frontend code quality checks.
+#
+# Checks for changes to frontend files before running the checks.
+# When manually triggered or when called from another workflow, always runs the checks.
+
+name: 'frontend checks'
+
+on:
+  push:
+    branches:
+      - 'main'
+  pull_request:
+    types:
+      - 'ready_for_review'
+      - 'opened'
+      - 'synchronize'
+  merge_group:
+  workflow_dispatch:
+  workflow_call:
+
+defaults:
+  run:
+    working-directory: invokeai/frontend/web
+
+jobs:
+  frontend-checks:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10 # expected run time: <2 min
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: check for changed frontend files
+        if: ${{ github.event_name != 'workflow_dispatch' && github.event_name != 'workflow_call' }}
+        id: changed-files
+        uses: tj-actions/changed-files@v42
+        with:
+          files_yaml: |
+            frontend:
+              - 'invokeai/frontend/web/**'
+
+      - name: install dependencies
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        uses: ./.github/actions/install-frontend-deps
+
+      - name: tsc
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: 'pnpm lint:tsc'
+        shell: bash
+
+      - name: dpdm
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: 'pnpm lint:dpdm'
+        shell: bash
+
+      - name: eslint
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: 'pnpm lint:eslint'
+        shell: bash
+
+      - name: prettier
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: 'pnpm lint:prettier'
+        shell: bash
+
+      - name: knip
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: 'pnpm lint:knip'
+        shell: bash

.github/workflows/frontend-tests.yml

@@ -0,0 +1,48 @@
+# Runs frontend tests.
+#
+# Checks for changes to frontend files before running the tests.
+# When manually triggered or called from another workflow, always runs the tests.
+
+name: 'frontend tests'
+
+on:
+  push:
+    branches:
+      - 'main'
+  pull_request:
+    types:
+      - 'ready_for_review'
+      - 'opened'
+      - 'synchronize'
+  merge_group:
+  workflow_dispatch:
+  workflow_call:
+
+defaults:
+  run:
+    working-directory: invokeai/frontend/web
+
+jobs:
+  frontend-tests:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10 # expected run time: <2 min
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: check for changed frontend files
+        if: ${{ github.event_name != 'workflow_dispatch' && github.event_name != 'workflow_call' }}
+        id: changed-files
+        uses: tj-actions/changed-files@v42
+        with:
+          files_yaml: |
+            frontend:
+              - 'invokeai/frontend/web/**'
+
+      - name: install dependencies
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        uses: ./.github/actions/install-frontend-deps
+
+      - name: vitest
+        if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: 'pnpm test:no-watch'
+        shell: bash

.github/workflows/label-pr.yml

@@ -1,6 +1,6 @@
-name: "Pull Request Labeler"
+name: 'label PRs'
 on:
-- pull_request_target
+  - pull_request_target
 
 jobs:
   labeler:
@@ -9,8 +9,10 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-      - name: Checkout
+      - name: checkout
         uses: actions/checkout@v4
-      - uses: actions/labeler@v5
+
+      - name: label PRs
+        uses: actions/labeler@v5
         with:
-          configuration-path: .github/pr_labels.yml
+          configuration-path: .github/pr_labels.yml

.github/workflows/mkdocs-material.yml

@@ -21,18 +21,29 @@ jobs:
       SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
 
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: setup python
+        uses: actions/setup-python@v5
         with:
           python-version: '3.10'
           cache: pip
           cache-dependency-path: pyproject.toml
-      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
-      - uses: actions/cache@v4
+
+      - name: set cache id
+        run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
+
+      - name: use cache
+        uses: actions/cache@v4
         with:
           key: mkdocs-material-${{ env.cache_id }}
           path: .cache
           restore-keys: |
             mkdocs-material-
-      - run: python -m pip install ".[docs]"
-      - run: mkdocs gh-deploy --force
+
+      - name: install dependencies
+        run: python -m pip install ".[docs]"
+
+      - name: build & deploy
+        run: mkdocs gh-deploy --force

.github/workflows/on-change-check-frontend.yml

@@ -1,39 +0,0 @@
-# This workflow runs of `check-frontend.yml` on push or pull request.
-#
-# The actual checks are in a separate workflow to support simpler workflow
-# composition without awkward or complicated conditionals.
-
-name: 'On change: run check-frontend'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-
-jobs:
-  check-changed-frontend-files:
-    if: github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    outputs:
-      frontend_any_changed: ${{ steps.changed-files.outputs.frontend_any_changed }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Check for changed frontend files
-        id: changed-files
-        uses: tj-actions/changed-files@v41
-        with:
-          files_yaml: |
-            frontend:
-              - 'invokeai/frontend/web/**'
-
-  run-check-frontend:
-    needs: check-changed-frontend-files
-    if: ${{ needs.check-changed-frontend-files.outputs.frontend_any_changed == 'true' }}
-    uses: ./.github/workflows/check-frontend.yml

.github/workflows/on-change-check-python.yml

@@ -1,42 +0,0 @@
-# This workflow runs of `check-python.yml` on push or pull request.
-#
-# The actual checks are in a separate workflow to support simpler workflow
-# composition without awkward or complicated conditionals.
-
-name: 'On change: run check-python'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-
-jobs:
-  check-changed-python-files:
-    if: github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    outputs:
-      python_any_changed: ${{ steps.changed-files.outputs.python_any_changed }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Check for changed python files
-        id: changed-files
-        uses: tj-actions/changed-files@v41
-        with:
-          files_yaml: |
-            python:
-              - 'pyproject.toml'
-              - 'invokeai/**'
-              - '!invokeai/frontend/web/**'
-              - 'tests/**'
-
-  run-check-python:
-    needs: check-changed-python-files
-    if: ${{ needs.check-changed-python-files.outputs.python_any_changed == 'true' }}
-    uses: ./.github/workflows/check-python.yml

.github/workflows/on-change-pytest.yml

@@ -1,42 +0,0 @@
-# This workflow runs of `check-pytest.yml` on push or pull request.
-#
-# The actual checks are in a separate workflow to support simpler workflow
-# composition without awkward or complicated conditionals.
-
-name: 'On change: run pytest'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-
-jobs:
-  check-changed-python-files:
-    if: github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    outputs:
-      python_any_changed: ${{ steps.changed-files.outputs.python_any_changed }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Check for changed python files
-        id: changed-files
-        uses: tj-actions/changed-files@v41
-        with:
-          files_yaml: |
-            python:
-              - 'pyproject.toml'
-              - 'invokeai/**'
-              - '!invokeai/frontend/web/**'
-              - 'tests/**'
-
-  run-pytest:
-    needs: check-changed-python-files
-    if: ${{ needs.check-changed-python-files.outputs.python_any_changed == 'true' }}
-    uses: ./.github/workflows/check-pytest.yml

.github/workflows/python-checks.yml

@@ -0,0 +1,64 @@
+# Runs python code quality checks.
+#
+# Checks for changes to python files before running the checks.
+# When manually triggered or called from another workflow, always runs the tests.
+#
+# TODO: Add mypy or pyright to the checks.
+
+name: 'python checks'
+
+on:
+  push:
+    branches:
+      - 'main'
+  pull_request:
+    types:
+      - 'ready_for_review'
+      - 'opened'
+      - 'synchronize'
+  merge_group:
+  workflow_dispatch:
+  workflow_call:
+
+jobs:
+  python-checks:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5 # expected run time: <1 min
+    steps:
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: check for changed python files
+        if: ${{ github.event_name != 'workflow_dispatch' && github.event_name != 'workflow_call' }}
+        id: changed-files
+        uses: tj-actions/changed-files@v42
+        with:
+          files_yaml: |
+            python:
+              - 'pyproject.toml'
+              - 'invokeai/**'
+              - '!invokeai/frontend/web/**'
+              - 'tests/**'
+
+      - name: setup python
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
+      - name: install ruff
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: pip install ruff
+        shell: bash
+
+      - name: ruff check
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: ruff check --output-format=github .
+        shell: bash
+
+      - name: ruff format
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: ruff format --check .
+        shell: bash

.github/workflows/python-tests.yml

@@ -0,0 +1,94 @@
+# Runs python tests on a matrix of python versions and platforms.
+#
+# Checks for changes to python files before running the tests.
+# When manually triggered or called from another workflow, always runs the tests.
+
+name: 'python tests'
+
+on:
+  push:
+    branches:
+      - 'main'
+  pull_request:
+    types:
+      - 'ready_for_review'
+      - 'opened'
+      - 'synchronize'
+  merge_group:
+  workflow_dispatch:
+  workflow_call:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  matrix:
+    strategy:
+      matrix:
+        python-version:
+          - '3.10'
+          - '3.11'
+        platform:
+          - linux-cuda-11_7
+          - linux-rocm-5_2
+          - linux-cpu
+          - macos-default
+          - windows-cpu
+        include:
+          - platform: linux-cuda-11_7
+            os: ubuntu-22.04
+            github-env: $GITHUB_ENV
+          - platform: linux-rocm-5_2
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
+            github-env: $GITHUB_ENV
+          - platform: linux-cpu
+            os: ubuntu-22.04
+            extra-index-url: 'https://download.pytorch.org/whl/cpu'
+            github-env: $GITHUB_ENV
+          - platform: macos-default
+            os: macOS-12
+            github-env: $GITHUB_ENV
+          - platform: windows-cpu
+            os: windows-2022
+            github-env: $env:GITHUB_ENV
+    name: 'py${{ matrix.python-version }}: ${{ matrix.platform }}'
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 15 # expected run time: 2-6 min, depending on platform
+    env:
+      PIP_USE_PEP517: '1'
+    steps:
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: check for changed python files
+        if: ${{ github.event_name != 'workflow_dispatch' && github.event_name != 'workflow_call' }}
+        id: changed-files
+        uses: tj-actions/changed-files@v42
+        with:
+          files_yaml: |
+            python:
+              - 'pyproject.toml'
+              - 'invokeai/**'
+              - '!invokeai/frontend/web/**'
+              - 'tests/**'
+
+      - name: setup python
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
+      - name: install dependencies
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        env:
+          PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
+        run: >
+          pip3 install --editable=".[test]"
+
+      - name: run pytest
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || github.event_name == 'workflow_dispatch' || github.event_name == 'workflow_call' }}
+        run: pytest

.github/workflows/release.yml

@@ -1,103 +1,96 @@
-name: Release
+# Main release workflow. Triggered on tag push or manual trigger.
+#
+# - Runs all code checks and tests
+# - Verifies the app version matches the tag version.
+# - Builds the installer and build, uploading them as artifacts.
+# - Publishes to TestPyPI and PyPI. Both are conditional on the previous steps passing and require a manual approval.
+#
+# See docs/RELEASE.md for more information on the release process.
+
+name: release
 
 on:
   push:
     tags:
       - 'v*'
   workflow_dispatch:
-    inputs:
-      skip_code_checks:
-        description: 'Skip code checks'
-        required: true
-        default: true
-        type: boolean
 
 jobs:
   check-version:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - name: checkout
+        uses: actions/checkout@v4
 
-      - uses: samuelcolvin/check-python-version@v4
+      - name: check python version
+        uses: samuelcolvin/check-python-version@v4
         id: check-python-version
         with:
           version_file_path: invokeai/version/invokeai_version.py
 
-  check-frontend:
-    if: github.event.inputs.skip_code_checks != 'true'
-    uses: ./.github/workflows/check-frontend.yml
+  frontend-checks:
+    uses: ./.github/workflows/frontend-checks.yml
 
-  check-python:
-    if: github.event.inputs.skip_code_checks != 'true'
-    uses: ./.github/workflows/check-python.yml
+  frontend-tests:
+    uses: ./.github/workflows/frontend-tests.yml
 
-  check-pytest:
-    if: github.event.inputs.skip_code_checks != 'true'
-    uses: ./.github/workflows/check-pytest.yml
+  python-checks:
+    uses: ./.github/workflows/python-checks.yml
+
+  python-tests:
+    uses: ./.github/workflows/python-tests.yml
 
   build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install python dependencies
-        uses: ./.github/actions/install-python-deps
-
-      - name: Install pypa/build
-        run: pip install --upgrade build
-
-      - name: Setup frontend
-        uses: ./.github/actions/install-frontend-deps
-
-      - name: Run create_installer.sh
-        id: create_installer
-        run: ./create_installer.sh --skip_frontend_checks
-        working-directory: installer
-
-      - name: Upload python distribution artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: ${{ steps.create_installer.outputs.DIST_PATH }}
-
-      - name: Upload installer artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ steps.create_installer.outputs.INSTALLER_FILENAME }}
-          path: ${{ steps.create_installer.outputs.INSTALLER_PATH }}
+    uses: ./.github/workflows/build-installer.yml
 
   publish-testpypi:
     runs-on: ubuntu-latest
-    needs: [check-version, check-frontend, check-python, check-pytest, build]
-    if: github.event_name != 'workflow_dispatch'
+    timeout-minutes: 5 # expected run time: <1 min
+    needs:
+      [
+        check-version,
+        frontend-checks,
+        frontend-tests,
+        python-checks,
+        python-tests,
+        build,
+      ]
     environment:
       name: testpypi
       url: https://test.pypi.org/p/invokeai
     steps:
-      - name: Download distribution from build job
+      - name: download distribution from build job
         uses: actions/download-artifact@v4
         with:
           name: dist
           path: dist/
 
-      - name: Publish distribution to TestPyPI
+      - name: publish distribution to TestPyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           repository-url: https://test.pypi.org/legacy/
 
   publish-pypi:
     runs-on: ubuntu-latest
-    needs: [check-version, check-frontend, check-python, check-pytest, build]
-    if: github.event_name != 'workflow_dispatch'
+    timeout-minutes: 5 # expected run time: <1 min
+    needs:
+      [
+        check-version,
+        frontend-checks,
+        frontend-tests,
+        python-checks,
+        python-tests,
+        build,
+      ]
     environment:
       name: pypi
       url: https://pypi.org/p/invokeai
     steps:
-      - name: Download distribution from build job
+      - name: download distribution from build job
         uses: actions/download-artifact@v4
         with:
           name: dist
           path: dist/
 
-      - name: Publish distribution to PyPI
+      - name: publish distribution to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

docs/RELEASE.md

@@ -23,13 +23,13 @@ It is triggered on **tag push**, when the tag matches `v*`. It doesn't matter if
 
 Run `make tag-release` to tag the current commit and kick off the workflow.
 
-The release may also be run [manually].
+The release may also be dispatched [manually].
 
 ### Workflow Jobs and Process
 
 The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
 
-The publish jobs run if the 5 concurrent jobs all succeed and if/when the publish jobs are approved.
+The publish jobs require manual approval and are only run if the other jobs succeed.
 
 #### `check-version` Job
 
@@ -43,17 +43,16 @@ This job uses [samuelcolvin/check-python-version].
 
 #### Check and Test Jobs
 
-This is our test suite.
-
-- **`check-pytest`**: runs `pytest` on matrix of platforms
-- **`check-python`**: runs `ruff` (format and lint)
-- **`check-frontend`**: runs `prettier` (format), `eslint` (lint), `madge` (circular refs) and `tsc` (static type check)
+- **`python-tests`**: runs `pytest` on matrix of platforms
+- **`python-checks`**: runs `ruff` (format and lint)
+- **`frontend-tests`**: runs `vitest`
+- **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
 
 > **TODO** We should add `mypy` or `pyright` to the **`check-python`** job.
 
 > **TODO** We should add an end-to-end test job that generates an image.
 
-#### `build` Job
+#### `build-installer` Job
 
 This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
 
@@ -62,7 +61,7 @@ This sets up both python and frontend dependencies and builds the python package
 
 #### Sanity Check & Smoke Test
 
-At this point, the release workflow pauses (the remaining jobs all require approval).
+At this point, the release workflow pauses as the remaining publish jobs require approval.
 
 A maintainer should go to the **Summary** tab of the workflow, download the installer and test it. Ensure the app loads and generates.
 
@@ -70,7 +69,7 @@ A maintainer should go to the **Summary** tab of the workflow, download the inst
 
 #### PyPI Publish Jobs
 
-The publish jobs will skip if any of the previous jobs skip or fail.
+The publish jobs will run if any of the previous jobs fail.
 
 They use [GitHub environments], which are configured as [trusted publishers] on PyPI.
 
@@ -119,13 +118,17 @@ Once the release is published to PyPI, it's time to publish the GitHub release.
 
 > **TODO** Workflows can create a GitHub release from a template and upload release assets. One popular action to handle this is [ncipollo/release-action]. A future enhancement to the release process could set this up.
 
-## Manually Running the Release Workflow
+## Manual Build
 
-The release workflow can be run manually. This is useful to get an installer build and test it out without needing to push a tag.
+The `build installer` workflow can be dispatched manually. This is useful to test the installer for a given branch or tag.
 
-When run this way, you'll see **Skip code checks** checkbox. This allows the workflow to run without the time-consuming 3 code quality check jobs.
+No checks are run, it just builds.
 
-The publish jobs will skip if the workflow was run manually.
+## Manual Release
+
+The `release` workflow can be dispatched manually. You must dispatch the workflow from the right tag, else it will fail the version check.
+
+This functionality is available as a fallback in case something goes wonky. Typically, releases should be triggered via tag push as described above.
 
 [InvokeAI Releases Page]: https://github.com/invoke-ai/InvokeAI/releases
 [PyPI]: https://pypi.org/
@@ -136,4 +139,4 @@ The publish jobs will skip if the workflow was run manually.
 [GitHub environments]: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment
 [trusted publishers]: https://docs.pypi.org/trusted-publishers/
 [samuelcolvin/check-python-version]: https://github.com/samuelcolvin/check-python-version
-[manually]: #manually-running-the-release-workflow
+[manually]: #manual-release

docs/nodes/defaultNodes.md

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

invokeai/app/api/routers/model_manager.py

@@ -14,6 +14,7 @@ from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.services.model_install import ModelInstallJob
+from invokeai.app.services.model_metadata.metadata_store_base import ModelMetadataChanges
 from invokeai.app.services.model_records import (
     DuplicateModelException,
     InvalidModelException,
@@ -32,6 +33,7 @@ from invokeai.backend.model_manager.config import (
 )
 from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
+from invokeai.backend.model_manager.metadata.metadata_base import BaseMetadata
 from invokeai.backend.model_manager.search import ModelSearch
 
 from ..dependencies import ApiDependencies
@@ -243,6 +245,47 @@ async def get_model_metadata(
     return result
 
 
+@model_manager_router.patch(
+    "/i/{key}/metadata",
+    operation_id="update_model_metadata",
+    responses={
+        201: {
+            "description": "The model metadata was updated successfully",
+            "content": {"application/json": {"example": example_model_metadata}},
+        },
+        400: {"description": "Bad request"},
+    },
+)
+async def update_model_metadata(
+    key: str = Path(description="Key of the model repo metadata to fetch."),
+    changes: ModelMetadataChanges = Body(description="The changes"),
+) -> Optional[AnyModelRepoMetadata]:
+    """Updates or creates a model metadata object."""
+    record_store = ApiDependencies.invoker.services.model_manager.store
+    metadata_store = ApiDependencies.invoker.services.model_manager.store.metadata_store
+
+    try:
+        original_metadata = record_store.get_metadata(key)
+        if original_metadata:
+            if changes.default_settings:
+                original_metadata.default_settings = changes.default_settings
+
+            metadata_store.update_metadata(key, original_metadata)
+        else:
+            metadata_store.add_metadata(
+                key, BaseMetadata(name="", author="", default_settings=changes.default_settings)
+            )
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"An error occurred while updating the model metadata: {e}",
+        )
+
+    result: Optional[AnyModelRepoMetadata] = record_store.get_metadata(key)
+
+    return result
+
+
 @model_manager_router.get(
     "/tags",
     operation_id="list_tags",
@@ -451,6 +494,7 @@ async def add_model_record(
 )
 async def install_model(
     source: str = Query(description="Model source to install, can be a local path, repo_id, or remote URL"),
+    inplace: Optional[bool] = Query(description="Whether or not to install a local model in place", default=False),
     # TODO(MM2): Can we type this?
     config: Optional[Dict[str, Any]] = Body(
         description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
@@ -493,6 +537,7 @@ async def install_model(
             source=source,
             config=config,
             access_token=access_token,
+            inplace=bool(inplace),
         )
         logger.info(f"Started installation of {source}")
     except UnknownModelException as e:

invokeai/app/invocations/compel.py

@@ -5,7 +5,15 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    FieldDescriptions,
+    Input,
+    InputField,
+    MaskField,
+    OutputField,
+    UIComponent,
+)
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
@@ -36,7 +44,7 @@ from .model import ClipField
     title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.0.1",
+    version="1.2.0",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -51,6 +59,10 @@ class CompelInvocation(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
+    mask: Optional[MaskField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
+    )
+    mask_weight: float = InputField(default=1.0, description="")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -118,7 +130,13 @@ class CompelInvocation(BaseInvocation):
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+                mask=self.mask,
+                mask_weight=self.mask_weight,
+            )
+        )
 
 
 class SDXLPromptInvocationBase:
@@ -232,7 +250,7 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.1",
+    version="1.2.0",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -256,6 +274,11 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
+    mask: Optional[MaskField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
+    )
+    mask_weight: float = InputField(default=1.0, description="")
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         c1, c1_pooled, ec1 = self.run_clip_compel(
@@ -317,7 +340,13 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+                mask=self.mask,
+                mask_weight=self.mask_weight,
+            )
+        )
 
 
 @invocation(
@@ -366,7 +395,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(conditioning=ConditioningField(conditioning_name=conditioning_name, mask_weight=1.0))
 
 
 @invocation_output("clip_skip_output")

invokeai/app/invocations/conditioning.py

@@ -6,25 +6,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
 )
 from invokeai.app.invocations.fields import InputField, WithMetadata
-from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, MaskField, MaskOutput
-
-
-@invocation(
-    "add_conditioning_mask",
-    title="Add Conditioning Mask",
-    tags=["conditioning"],
-    category="conditioning",
-    version="1.0.0",
-)
-class AddConditioningMaskInvocation(BaseInvocation):
-    """Add a mask to an existing conditioning tensor."""
-
-    conditioning: ConditioningField = InputField(description="The conditioning tensor to add a mask to.")
-    mask: MaskField = InputField(description="A mask to add to the conditioning tensor.")
-
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        self.conditioning.mask = self.mask
-        return ConditioningOutput(conditioning=self.conditioning)
+from invokeai.app.invocations.primitives import MaskField, MaskOutput
 
 
 @invocation(

invokeai/app/invocations/fields.py

@@ -236,6 +236,7 @@ class ConditioningField(BaseModel):
         description="The bool mask associated with this conditioning tensor. Excluded regions should be set to False, "
         "included regions should be set to True.",
     )
+    mask_weight: float = Field(description="")
 
 
 class MetadataField(RootModel):

invokeai/app/invocations/latent.py

@@ -51,7 +51,6 @@ from invokeai.app.invocations.primitives import (
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
@@ -181,6 +180,16 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
 
+@invocation_output("gradient_mask_output")
+class GradientMaskOutput(BaseInvocationOutput):
+    """Outputs a denoise mask and an image representing the total gradient of the mask."""
+
+    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+    expanded_mask_area: ImageField = OutputField(
+        description="Image representing the total gradient area of the mask. For paste-back purposes."
+    )
+
+
 @invocation(
     "create_gradient_mask",
     title="Create Gradient Mask",
@@ -201,38 +210,42 @@ class CreateGradientMaskInvocation(BaseInvocation):
     )
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
+    def invoke(self, context: InvocationContext) -> GradientMaskOutput:
         mask_image = context.images.get_pil(self.mask.image_name, mode="L")
-        if self.coherence_mode == "Box Blur":
-            blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
-        else:  # Gaussian Blur OR Staged
-            # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
-            blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
+        if self.edge_radius > 0:
+            if self.coherence_mode == "Box Blur":
+                blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
+            else:  # Gaussian Blur OR Staged
+                # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
+                blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
 
-        mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
-        blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
 
-        # redistribute blur so that the edges are 0 and blur out to 1
-        blur_tensor = (blur_tensor - 0.5) * 2
+            # redistribute blur so that the original edges are 0 and blur outwards to 1
+            blur_tensor = (blur_tensor - 0.5) * 2
 
-        threshold = 1 - self.minimum_denoise
+            threshold = 1 - self.minimum_denoise
+
+            if self.coherence_mode == "Staged":
+                # wherever the blur_tensor is less than fully masked, convert it to threshold
+                blur_tensor = torch.where((blur_tensor < 1) & (blur_tensor > 0), threshold, blur_tensor)
+            else:
+                # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
+                blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
 
-        if self.coherence_mode == "Staged":
-            # wherever the blur_tensor is masked to any degree, convert it to threshold
-            blur_tensor = torch.where((blur_tensor < 1), threshold, blur_tensor)
         else:
-            # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
-            blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
-
-        # multiply original mask to force actually masked regions to 0
-        blur_tensor = mask_tensor * blur_tensor
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
 
         mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
 
-        return DenoiseMaskOutput.build(
-            mask_name=mask_name,
-            masked_latents_name=None,
-            gradient=True,
+        # compute a [0, 1] mask from the blur_tensor
+        expanded_mask = torch.where((blur_tensor < 1), 0, 1)
+        expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
+        expanded_image_dto = context.images.save(expanded_mask_image)
+
+        return GradientMaskOutput(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=None, gradient=True),
+            expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
         )
 
 
@@ -359,35 +372,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError("cfg_scale must be greater than 1")
         return v
 
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-        base_model: BaseModelType,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.model_dump(),
-            source_node_id=source_node_id,
-            base_model=base_model,
-        )
-
     def _get_text_embeddings_and_masks(
         self,
-        cond_field: Union[ConditioningField, list[ConditioningField]],
+        cond_list: list[ConditioningField],
         context: InvocationContext,
         device: torch.device,
         dtype: torch.dtype,
     ) -> tuple[Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]], list[Optional[torch.Tensor]]]:
         """Get the text embeddings and masks from the input conditioning fields."""
-        # Normalize cond_field to a list.
-        cond_list = cond_field
-        if not isinstance(cond_list, list):
-            cond_list = [cond_list]
-
         text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
         text_embeddings_masks: list[Optional[torch.Tensor]] = []
         for cond in cond_list:
@@ -427,6 +419,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         self,
         text_conditionings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]],
         masks: Optional[list[Optional[torch.Tensor]]],
+        conditioning_fields: list[ConditioningField],
         latent_height: int,
         latent_width: int,
     ) -> tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[TextConditioningRegions]]:
@@ -447,7 +440,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
         embedding_ranges = []
         extra_conditioning = None
 
-        for text_embedding_info, mask in zip(text_conditionings, masks, strict=True):
+        for prompt_idx, text_embedding_info in enumerate(text_conditionings):
+            mask = masks[prompt_idx]
             if (
                 text_embedding_info.extra_conditioning is not None
                 and text_embedding_info.extra_conditioning.wants_cross_attention_control
@@ -472,9 +466,18 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             text_embedding.append(text_embedding_info.embeds)
             if not all_masks_are_none:
+                # embedding_ranges.append(
+                #     Range(
+                #         start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1]
+                #     )
+                # )
+                # HACK(ryand): Contrary to its name, tokens_count_including_eos_bos does not seem to include eos and bos
+                # in the count.
                 embedding_ranges.append(
                     Range(
-                        start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1]
+                        start=cur_text_embedding_len + 1,
+                        end=cur_text_embedding_len
+                        + text_embedding_info.extra_conditioning.tokens_count_including_eos_bos,
                     )
                 )
                 processed_masks.append(self._preprocess_regional_prompt_mask(mask, latent_height, latent_width))
@@ -486,7 +489,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         regions = None
         if not all_masks_are_none:
-            regions = TextConditioningRegions(masks=torch.cat(processed_masks, dim=1), ranges=embedding_ranges)
+            regions = TextConditioningRegions(
+                masks=torch.cat(processed_masks, dim=1),
+                ranges=embedding_ranges,
+                mask_weights=[x.mask_weight for x in conditioning_fields],
+            )
 
         if extra_conditioning is not None and len(text_conditionings) > 1:
             raise ValueError(
@@ -509,25 +516,36 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def get_conditioning_data(
         self,
         context: InvocationContext,
-        unet,
+        unet: UNet2DConditionModel,
         latent_height: int,
         latent_width: int,
     ) -> TextConditioningData:
+        # Normalize self.positive_conditioning and self.negative_conditioning to lists.
+        cond_list = self.positive_conditioning
+        if not isinstance(cond_list, list):
+            cond_list = [cond_list]
+        uncond_list = self.negative_conditioning
+        if not isinstance(uncond_list, list):
+            uncond_list = [uncond_list]
+
         cond_text_embeddings, cond_text_embedding_masks = self._get_text_embeddings_and_masks(
-            self.positive_conditioning, context, unet.device, unet.dtype
+            cond_list, context, unet.device, unet.dtype
         )
+
         uncond_text_embeddings, uncond_text_embedding_masks = self._get_text_embeddings_and_masks(
-            self.negative_conditioning, context, unet.device, unet.dtype
+            uncond_list, context, unet.device, unet.dtype
         )
         cond_text_embedding, cond_regions = self.concat_regional_text_embeddings(
             text_conditionings=cond_text_embeddings,
             masks=cond_text_embedding_masks,
+            conditioning_fields=cond_list,
             latent_height=latent_height,
             latent_width=latent_width,
         )
         uncond_text_embedding, uncond_regions = self.concat_regional_text_embeddings(
             text_conditionings=uncond_text_embeddings,
             masks=uncond_text_embedding_masks,
+            conditioning_fields=uncond_list,
             latent_height=latent_height,
             latent_width=latent_width,
         )

invokeai/app/invocations/primitives.py

@@ -427,10 +427,6 @@ class ConditioningOutput(BaseInvocationOutput):
 
     conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
 
-    @classmethod
-    def build(cls, conditioning_name: str) -> "ConditioningOutput":
-        return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
-
 
 @invocation_output("conditioning_collection_output")
 class ConditioningCollectionOutput(BaseInvocationOutput):

invokeai/app/services/model_install/model_install_default.py

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

invokeai/app/services/model_metadata/metadata_store_base.py

@@ -4,9 +4,25 @@ Storage for Model Metadata
 """
 
 from abc import ABC, abstractmethod
-from typing import List, Set, Tuple
+from typing import List, Optional, Set, Tuple
 
+from pydantic import Field
+
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
+from invokeai.backend.model_manager.metadata.metadata_base import ModelDefaultSettings
+
+
+class ModelMetadataChanges(BaseModelExcludeNull, extra="allow"):
+    """A set of changes to apply to model metadata.
+    Only limited changes are valid:
+      - `default_settings`: the user-configured default settings for this model
+    """
+
+    default_settings: Optional[ModelDefaultSettings] = Field(
+        default=None, description="The user-configured default settings for this model"
+    )
+    """The user-configured default settings for this model"""
 
 
 class ModelMetadataStoreBase(ABC):

invokeai/app/services/model_metadata/metadata_store_sql.py

@@ -179,44 +179,45 @@ class ModelMetadataStoreSQL(ModelMetadataStoreBase):
         )
         return {x[0] for x in self._cursor.fetchall()}
 
-    def _update_tags(self, model_key: str, tags: Set[str]) -> None:
+    def _update_tags(self, model_key: str, tags: Optional[Set[str]]) -> None:
         """Update tags for the model referenced by model_key."""
-        # remove previous tags from this model
-        self._cursor.execute(
-            """--sql
-            DELETE FROM model_tags
-            WHERE model_id=?;
-            """,
-            (model_key,),
-        )
+        if tags:
+            # remove previous tags from this model
+            self._cursor.execute(
+                """--sql
+                DELETE FROM model_tags
+                WHERE model_id=?;
+                """,
+                (model_key,),
+            )
 
-        for tag in tags:
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO tags (
-                  tag_text
-                  )
-                VALUES (?);
-                """,
-                (tag,),
-            )
-            self._cursor.execute(
-                """--sql
-                SELECT tag_id
-                FROM tags
-                WHERE tag_text = ?
-                LIMIT 1;
-                """,
-                (tag,),
-            )
-            tag_id = self._cursor.fetchone()[0]
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO model_tags (
-                   model_id,
-                   tag_id
-                  )
-                VALUES (?,?);
-                """,
-                (model_key, tag_id),
-            )
+            for tag in tags:
+                self._cursor.execute(
+                    """--sql
+                    INSERT OR IGNORE INTO tags (
+                    tag_text
+                    )
+                    VALUES (?);
+                    """,
+                    (tag,),
+                )
+                self._cursor.execute(
+                    """--sql
+                    SELECT tag_id
+                    FROM tags
+                    WHERE tag_text = ?
+                    LIMIT 1;
+                    """,
+                    (tag,),
+                )
+                tag_id = self._cursor.fetchone()[0]
+                self._cursor.execute(
+                    """--sql
+                    INSERT OR IGNORE INTO model_tags (
+                    model_id,
+                    tag_id
+                    )
+                    VALUES (?,?);
+                    """,
+                    (model_key, tag_id),
+                )

invokeai/app/services/session_processor/session_processor_default.py

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

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

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

invokeai/backend/model_manager/hash.py

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

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -25,6 +25,7 @@ from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 from typing_extensions import Annotated
 
+from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.backend.model_manager import ModelRepoVariant
 
 from ..util import select_hf_files
@@ -68,12 +69,24 @@ class RemoteModelFile(BaseModel):
     sha256: Optional[str] = Field(description="SHA256 hash of this model (not always available)", default=None)
 
 
+class ModelDefaultSettings(BaseModel):
+    vae: str | None
+    vae_precision: str | None
+    scheduler: SCHEDULER_NAME_VALUES | None
+    steps: int | None
+    cfg_scale: float | None
+    cfg_rescale_multiplier: float | None
+
+
 class ModelMetadataBase(BaseModel):
     """Base class for model metadata information."""
 
     name: str = Field(description="model's name")
     author: str = Field(description="model's author")
-    tags: Set[str] = Field(description="tags provided by model source")
+    tags: Optional[Set[str]] = Field(description="tags provided by model source", default=None)
+    default_settings: Optional[ModelDefaultSettings] = Field(
+        description="default settings for this model", default=None
+    )
 
 
 class BaseMetadata(ModelMetadataBase):

invokeai/backend/model_manager/probe.py

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

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -70,7 +70,12 @@ class Range:
 
 
 class TextConditioningRegions:
-    def __init__(self, masks: torch.Tensor, ranges: list[Range]):
+    def __init__(
+        self,
+        masks: torch.Tensor,
+        ranges: list[Range],
+        mask_weights: list[float],
+    ):
         # A binary mask indicating the regions of the image that the prompt should be applied to.
         # Shape: (1, num_prompts, height, width)
         # Dtype: torch.bool
@@ -80,7 +85,9 @@ class TextConditioningRegions:
         # ranges[i] contains the embedding range for the i'th prompt / mask.
         self.ranges = ranges
 
-        assert self.masks.shape[1] == len(self.ranges)
+        self.mask_weights = mask_weights
+
+        assert self.masks.shape[1] == len(self.ranges) == len(self.mask_weights)
 
 
 class TextConditioningData:

invokeai/backend/stable_diffusion/diffusion/custom_attention.py

@@ -1,3 +1,4 @@
+import math
 from typing import Optional
 
 import torch
@@ -58,6 +59,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         scale: float = 1.0,
         # For regional prompting:
         regional_prompt_data: Optional[RegionalPromptData] = None,
+        percent_through: Optional[float] = None,
         # For IP-Adapter:
         ip_adapter_image_prompt_embeds: Optional[list[torch.Tensor]] = None,
     ) -> torch.FloatTensor:
@@ -92,28 +94,25 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         # End unmodified block from AttnProcessor2_0.
 
         # Handle regional prompt attention masks.
-        if is_cross_attention and regional_prompt_data is not None:
+        if regional_prompt_data is not None:
+            assert percent_through is not None
             _, query_seq_len, _ = hidden_states.shape
-            prompt_region_attention_mask = regional_prompt_data.get_attn_mask(query_seq_len)
-            # TODO(ryand): Avoid redundant type/device conversion here.
-            prompt_region_attention_mask = prompt_region_attention_mask.to(
-                dtype=encoder_hidden_states.dtype, device=encoder_hidden_states.device
-            )
-            prompt_region_attention_mask[prompt_region_attention_mask < 0.5] = -10000.0
-            prompt_region_attention_mask[prompt_region_attention_mask >= 0.5] = 0.0
+            if is_cross_attention:
+                prompt_region_attention_mask = regional_prompt_data.get_cross_attn_mask(
+                    query_seq_len=query_seq_len, key_seq_len=sequence_length
+                )
+                # TODO(ryand): Avoid redundant type/device conversion here.
+                prompt_region_attention_mask = prompt_region_attention_mask.to(
+                    dtype=hidden_states.dtype, device=hidden_states.device
+                )
 
-            if attention_mask is None:
-                attention_mask = prompt_region_attention_mask
-            else:
-                attention_mask = prompt_region_attention_mask + attention_mask
-
-        # Start unmodified block from AttnProcessor2_0.
-        # vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+                attn_mask_weight = 1.0 * ((1 - percent_through) ** 5)
+            else:  # self-attention
+                prompt_region_attention_mask = regional_prompt_data.get_self_attn_mask(
+                    query_seq_len=query_seq_len,
+                    percent_through=percent_through,
+                )
+                attn_mask_weight = 0.3 * ((1 - percent_through) ** 5)
 
         if attn.group_norm is not None:
             hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
@@ -137,6 +136,40 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
         value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if regional_prompt_data is not None and percent_through < 0.3:
+            # Don't apply to uncond????
+
+            prompt_region_attention_mask = attn.prepare_attention_mask(
+                prompt_region_attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            prompt_region_attention_mask = prompt_region_attention_mask.view(
+                batch_size, attn.heads, -1, prompt_region_attention_mask.shape[-1]
+            )
+
+            scale_factor = 1 / math.sqrt(query.size(-1))
+            attn_weight = query @ key.transpose(-2, -1) * scale_factor
+            m_pos = attn_weight.max(dim=-1, keepdim=True)[0] - attn_weight
+            m_neg = attn_weight - attn_weight.min(dim=-1, keepdim=True)[0]
+
+            prompt_region_attention_mask = attn_mask_weight * (
+                m_pos * prompt_region_attention_mask - m_neg * (1.0 - prompt_region_attention_mask)
+            )
+
+            if attention_mask is None:
+                attention_mask = prompt_region_attention_mask
+            else:
+                attention_mask = prompt_region_attention_mask + attention_mask
+        else:
+            pass
+
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
         # TODO: add support for attn.scale when we move to Torch 2.1
         hidden_states = F.scaled_dot_product_attention(

invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py

@@ -7,58 +7,56 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 
 
 class RegionalPromptData:
-    def __init__(self, attn_masks_by_seq_len: dict[int, torch.Tensor]):
-        self._attn_masks_by_seq_len = attn_masks_by_seq_len
-
-    @classmethod
-    def from_regions(
-        cls,
+    def __init__(
+        self,
         regions: list[TextConditioningRegions],
-        key_seq_len: int,
-        # TODO(ryand): Pass in a list of downscale factors?
+        device: torch.device,
+        dtype: torch.dtype,
         max_downscale_factor: int = 8,
     ):
-        """Construct a `RegionalPromptData` object.
+        """Initialize a `RegionalPromptData` object.
 
         Args:
             regions (list[TextConditioningRegions]): regions[i] contains the prompt regions for the i'th sample in the
                 batch.
-            key_seq_len (int): The sequence length of the expected prompt embeddings (which act as the key in the
-                cross-attention layers). This is most likely equal to the max embedding range end, but we pass it
-                explicitly to be sure.
+            device (torch.device): The device to use for the attention masks.
+            dtype (torch.dtype): The data type to use for the attention masks.
+            max_downscale_factor: Spatial masks will be prepared for downscale factors from 1 to max_downscale_factor
+                in steps of 2x.
         """
-        attn_masks_by_seq_len = {}
+        self._regions = regions
+        self._device = device
+        self._dtype = dtype
+        # self._spatial_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query
+        # sequence length of s.
+        self._spatial_masks_by_seq_len: list[dict[int, torch.Tensor]] = self._prepare_spatial_masks(
+            regions, max_downscale_factor
+        )
+        self._negative_cross_attn_mask_score = 0.0
+        self._size_weight = 1.0
+
+    def _prepare_spatial_masks(
+        self, regions: list[TextConditioningRegions], max_downscale_factor: int = 8
+    ) -> list[dict[int, torch.Tensor]]:
+        """Prepare the spatial masks for all downscaling factors."""
+        # batch_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query sequence length
+        # of s.
+        batch_sample_masks_by_seq_len: list[dict[int, torch.Tensor]] = []
 
-        # batch_attn_mask_by_seq_len[b][s] contains the attention mask for the b'th batch sample with a query sequence
-        # length of s.
-        batch_attn_masks_by_seq_len: list[dict[int, torch.Tensor]] = []
         for batch_sample_regions in regions:
-            batch_attn_masks_by_seq_len.append({})
+            batch_sample_masks_by_seq_len.append({})
 
             # Convert the bool masks to float masks so that max pooling can be applied.
-            batch_masks = batch_sample_regions.masks.to(dtype=torch.float32)
+            batch_sample_masks = batch_sample_regions.masks.to(device=self._device, dtype=self._dtype)
 
             # Downsample the spatial dimensions by factors of 2 until max_downscale_factor is reached.
             downscale_factor = 1
             while downscale_factor <= max_downscale_factor:
-                _, num_prompts, h, w = batch_masks.shape
+                b, _num_prompts, h, w = batch_sample_masks.shape
+                assert b == 1
                 query_seq_len = h * w
 
-                # Flatten the spatial dimensions of the mask by reshaping to (1, num_prompts, query_seq_len, 1).
-                batch_query_masks = batch_masks.reshape((1, num_prompts, -1, 1))
-
-                # Create a cross-attention mask for each prompt that selects the corresponding embeddings from
-                # `encoder_hidden_states`.
-                # attn_mask shape: (batch_size, query_seq_len, key_seq_len)
-                # TODO(ryand): What device / dtype should this be?
-                attn_mask = torch.zeros((1, query_seq_len, key_seq_len))
-
-                for prompt_idx, embedding_range in enumerate(batch_sample_regions.ranges):
-                    attn_mask[0, :, embedding_range.start : embedding_range.end] = batch_query_masks[
-                        :, prompt_idx, :, :
-                    ]
-
-                batch_attn_masks_by_seq_len[-1][query_seq_len] = attn_mask
+                batch_sample_masks_by_seq_len[-1][query_seq_len] = batch_sample_masks
 
                 downscale_factor *= 2
                 if downscale_factor <= max_downscale_factor:
@@ -66,23 +64,17 @@ class RegionalPromptData:
                     # regions to be lost entirely.
                     # TODO(ryand): In the future, we may want to experiment with other downsampling methods, and could
                     # potentially use a weighted mask rather than a binary mask.
-                    batch_masks = F.max_pool2d(batch_masks, kernel_size=2, stride=2)
+                    batch_sample_masks = F.max_pool2d(batch_sample_masks, kernel_size=2, stride=2)
 
-        # Merge the batch_attn_masks_by_seq_len into a single attn_masks_by_seq_len.
-        for query_seq_len in batch_attn_masks_by_seq_len[0].keys():
-            attn_masks_by_seq_len[query_seq_len] = torch.cat(
-                [batch_attn_masks_by_seq_len[i][query_seq_len] for i in range(len(batch_attn_masks_by_seq_len))]
-            )
+        return batch_sample_masks_by_seq_len
 
-        return cls(attn_masks_by_seq_len)
+    def get_cross_attn_mask(self, query_seq_len: int, key_seq_len: int) -> torch.Tensor:
+        """Get the cross-attention mask for the given query sequence length.
 
-    def get_attn_mask(self, query_seq_len: int) -> torch.Tensor:
-        """Get the attention mask for the given query sequence length (i.e. downscaling level).
-
-        This is called during cross-attention, where query_seq_len is the length of the flattened spatial features, so
-        it changes at each downscaling level in the model.
-
-        key_seq_len is the length of the expected prompt embeddings.
+        Args:
+            query_seq_len: The length of the flattened spatial features at the current downscaling level.
+            key_seq_len (int): The sequence length of the prompt embeddings (which act as the key in the cross-attention
+                layers). This is most likely equal to the max embedding range end, but we pass it explicitly to be sure.
 
         Returns:
             torch.Tensor: The masks.
@@ -90,4 +82,83 @@ class RegionalPromptData:
                 dtype: float
                 The mask is a binary mask with values of 0.0 and 1.0.
         """
-        return self._attn_masks_by_seq_len[query_seq_len]
+        batch_size = len(self._spatial_masks_by_seq_len)
+        batch_spatial_masks = [self._spatial_masks_by_seq_len[b][query_seq_len] for b in range(batch_size)]
+
+        # Create an empty attention mask with the correct shape.
+        attn_mask = torch.zeros((batch_size, query_seq_len, key_seq_len), dtype=self._dtype, device=self._device)
+
+        for batch_idx in range(batch_size):
+            batch_sample_spatial_masks = batch_spatial_masks[batch_idx]
+            batch_sample_regions = self._regions[batch_idx]
+
+            # Flatten the spatial dimensions of the mask by reshaping to (1, num_prompts, query_seq_len, 1).
+            _, num_prompts, _, _ = batch_sample_spatial_masks.shape
+            batch_sample_query_masks = batch_sample_spatial_masks.view((1, num_prompts, query_seq_len, 1))
+
+            for prompt_idx, embedding_range in enumerate(batch_sample_regions.ranges):
+                batch_sample_query_scores = batch_sample_query_masks[0, prompt_idx, :, :]
+                size = batch_sample_query_scores.sum() / batch_sample_query_scores.numel()
+                mask_weight = batch_sample_regions.mask_weights[prompt_idx]
+                # size = size.to(dtype=batch_sample_query_scores.dtype)
+                # batch_sample_query_mask = batch_sample_query_scores > 0.5
+                # batch_sample_query_scores[batch_sample_query_mask] = 1.0 * (1.0 - size)
+                # batch_sample_query_scores[~batch_sample_query_mask] = 0.0
+                attn_mask[batch_idx, :, embedding_range.start : embedding_range.end] = batch_sample_query_scores * (
+                    mask_weight + self._size_weight * (1 - size)
+                )
+
+        return attn_mask
+
+    def get_self_attn_mask(self, query_seq_len: int, percent_through: float) -> torch.Tensor:
+        """Get the self-attention mask for the given query sequence length.
+
+        Args:
+            query_seq_len: The length of the flattened spatial features at the current downscaling level.
+
+        Returns:
+            torch.Tensor: The masks.
+                shape: (batch_size, query_seq_len, query_seq_len).
+                dtype: float
+                The mask is a binary mask with values of 0.0 and 1.0.
+        """
+        batch_size = len(self._spatial_masks_by_seq_len)
+        batch_spatial_masks = [self._spatial_masks_by_seq_len[b][query_seq_len] for b in range(batch_size)]
+
+        # Create an empty attention mask with the correct shape.
+        attn_mask = torch.zeros((batch_size, query_seq_len, query_seq_len), dtype=self._dtype, device=self._device)
+
+        for batch_idx in range(batch_size):
+            batch_sample_spatial_masks = batch_spatial_masks[batch_idx]
+            batch_sample_regions = self._regions[batch_idx]
+
+            # Flatten the spatial dimensions of the mask by reshaping to (1, num_prompts, query_seq_len, 1).
+            _, num_prompts, _, _ = batch_sample_spatial_masks.shape
+            batch_sample_query_masks = batch_sample_spatial_masks.view((1, num_prompts, query_seq_len, 1))
+
+            for prompt_idx in range(num_prompts):
+                prompt_query_mask = batch_sample_query_masks[0, prompt_idx, :, 0]  # Shape: (query_seq_len,)
+                size = prompt_query_mask.sum() / prompt_query_mask.numel()
+                size = size.to(dtype=prompt_query_mask.dtype)
+                mask_weight = batch_sample_regions.mask_weights[prompt_idx]
+                # Multiply a (1, query_seq_len) mask by a (query_seq_len, 1) mask to get a (query_seq_len,
+                # query_seq_len) mask.
+                # TODO(ryand): Is += really the best option here? Maybe elementwise max is better?
+                attn_mask[batch_idx, :, :] = torch.maximum(
+                    attn_mask[batch_idx, :, :],
+                    prompt_query_mask.unsqueeze(0)
+                    * prompt_query_mask.unsqueeze(1)
+                    * (mask_weight + self._size_weight * (1 - size)),
+                )
+
+            # if attn_mask[batch_idx].max() < 0.01:
+            #     attn_mask[batch_idx, ...] = 1.0
+
+        # attn_mask[attn_mask > 0.5] = 1.0
+        # attn_mask[attn_mask <= 0.5] = 0.0
+        # attn_mask_min = attn_mask[batch_idx].min()
+
+        # # Adjust so that the minimum value is 0.0 regardless of whether all pixels are covered or not.
+        # if abs(attn_mask_min) > 0.0001:
+        #     attn_mask[batch_idx] = attn_mask[batch_idx] - attn_mask_min
+        return attn_mask

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import math
+import time
 from contextlib import contextmanager
 from typing import Any, Callable, Optional, Union
 
@@ -200,9 +201,9 @@ class InvokeAIDiffuserComponent:
         mid_block_additional_residual: Optional[torch.Tensor] = None,  # for ControlNet
         down_intrablock_additional_residuals: Optional[torch.Tensor] = None,  # for T2I-Adapter
     ):
+        percent_through = step_index / total_step_count
         cross_attention_control_types_to_do = []
         if self.cross_attention_control_context is not None:
-            percent_through = step_index / total_step_count
             cross_attention_control_types_to_do = (
                 self.cross_attention_control_context.get_active_cross_attention_control_types_for_step(percent_through)
             )
@@ -219,6 +220,7 @@ class InvokeAIDiffuserComponent:
                 sigma=timestep,
                 conditioning_data=conditioning_data,
                 ip_adapter_conditioning=ip_adapter_conditioning,
+                percent_through=percent_through,
                 cross_attention_control_types_to_do=cross_attention_control_types_to_do,
                 down_block_additional_residuals=down_block_additional_residuals,
                 mid_block_additional_residual=mid_block_additional_residual,
@@ -232,6 +234,7 @@ class InvokeAIDiffuserComponent:
                 x=sample,
                 sigma=timestep,
                 conditioning_data=conditioning_data,
+                percent_through=percent_through,
                 ip_adapter_conditioning=ip_adapter_conditioning,
                 down_block_additional_residuals=down_block_additional_residuals,
                 mid_block_additional_residual=mid_block_additional_residual,
@@ -293,6 +296,7 @@ class InvokeAIDiffuserComponent:
         sigma,
         conditioning_data: TextConditioningData,
         ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]],
+        percent_through: float,
         down_block_additional_residuals: Optional[torch.Tensor] = None,  # for ControlNet
         mid_block_additional_residual: Optional[torch.Tensor] = None,  # for ControlNet
         down_intrablock_additional_residuals: Optional[torch.Tensor] = None,  # for T2I-Adapter
@@ -326,8 +330,8 @@ class InvokeAIDiffuserComponent:
         )
 
         if conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None:
-            # TODO(ryand): We currently call from_regions(...) for every denoising step. The text conditionings and
-            # masks are not changing from step-to-step, so this really only needs to be done once. While this seems
+            # TODO(ryand): We currently initialize RegionalPromptData for every denoising step. The text conditionings
+            # and masks are not changing from step-to-step, so this really only needs to be done once. While this seems
             # painfully inefficient, the time spent is typically negligible compared to the forward inference pass of
             # the UNet. The main reason that this hasn't been moved up to eliminate redundancy is that it is slightly
             # awkward to handle both standard conditioning and sequential conditioning further up the stack.
@@ -342,13 +346,15 @@ class InvokeAIDiffuserComponent:
                     r = TextConditioningRegions(
                         masks=torch.ones((1, 1, h, w), dtype=torch.bool),
                         ranges=[Range(start=0, end=c.embeds.shape[1])],
+                        mask_weights=[0.0],
                     )
                 regions.append(r)
 
-            _, key_seq_len, _ = both_conditionings.shape
-            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData.from_regions(
-                regions=regions, key_seq_len=key_seq_len
+            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
+                regions=regions, device=x.device, dtype=x.dtype
             )
+            cross_attention_kwargs["percent_through"] = percent_through
+            time.sleep(1.0)
 
         both_results = self.model_forward_callback(
             x_twice,
@@ -371,6 +377,7 @@ class InvokeAIDiffuserComponent:
         conditioning_data: TextConditioningData,
         ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]],
         cross_attention_control_types_to_do: list[CrossAttentionType],
+        percent_through: float,
         down_block_additional_residuals: Optional[torch.Tensor] = None,  # for ControlNet
         mid_block_additional_residual: Optional[torch.Tensor] = None,  # for ControlNet
         down_intrablock_additional_residuals: Optional[torch.Tensor] = None,  # for T2I-Adapter
@@ -441,10 +448,10 @@ class InvokeAIDiffuserComponent:
 
         # Prepare prompt regions for the unconditioned pass.
         if conditioning_data.uncond_regions is not None:
-            _, key_seq_len, _ = conditioning_data.uncond_text.embeds.shape
-            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData.from_regions(
-                regions=[conditioning_data.uncond_regions], key_seq_len=key_seq_len
+            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
+                regions=[conditioning_data.uncond_regions], device=x.device, dtype=x.dtype
             )
+            cross_attention_kwargs["percent_through"] = percent_through
 
         # Run unconditioned UNet denoising (i.e. negative prompt).
         unconditioned_next_x = self.model_forward_callback(
@@ -487,10 +494,10 @@ class InvokeAIDiffuserComponent:
 
         # Prepare prompt regions for the conditioned pass.
         if conditioning_data.cond_regions is not None:
-            _, key_seq_len, _ = conditioning_data.cond_text.embeds.shape
-            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData.from_regions(
-                regions=[conditioning_data.cond_regions], key_seq_len=key_seq_len
+            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
+                regions=[conditioning_data.cond_regions], device=x.device, dtype=x.dtype
             )
+            cross_attention_kwargs["percent_through"] = percent_through
 
         # Run conditioned UNet denoising (i.e. positive prompt).
         conditioned_next_x = self.model_forward_callback(

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

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

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

@@ -78,6 +78,7 @@
         "aboutDesc": "Using Invoke for work? Check out:",
         "aboutHeading": "Own Your Creative Power",
         "accept": "Accept",
+        "add": "Add",
         "advanced": "Advanced",
         "advancedOptions": "Advanced Options",
         "ai": "ai",
@@ -734,6 +735,8 @@
         "customConfig": "Custom Config",
         "customConfigFileLocation": "Custom Config File Location",
         "customSaveLocation": "Custom Save Location",
+        "defaultSettings": "Default Settings",
+        "defaultSettingsSaved": "Default Settings Saved",
         "delete": "Delete",
         "deleteConfig": "Delete Config",
         "deleteModel": "Delete Model",
@@ -768,6 +771,7 @@
         "mergedModelName": "Merged Model Name",
         "mergedModelSaveLocation": "Save Location",
         "mergeModels": "Merge Models",
+        "metadata": "Metadata",
         "model": "Model",
         "modelAdded": "Model Added",
         "modelConversionFailed": "Model Conversion Failed",
@@ -839,9 +843,12 @@
         "statusConverting": "Converting",
         "syncModels": "Sync Models",
         "syncModelsDesc": "If your models are out of sync with the backend, you can refresh them up using this option. This is generally handy in cases where you add models to the InvokeAI root folder or autoimport directory after the application has booted.",
+        "triggerPhrases": "Trigger Phrases",
+        "typePhraseHere": "Type phrase here",
         "upcastAttention": "Upcast Attention",
         "updateModel": "Update Model",
         "useCustomConfig": "Use Custom Config",
+        "useDefaultSettings": "Use Default Settings",
         "v1": "v1",
         "v2_768": "v2 (768px)",
         "v2_base": "v2 (512px)",
@@ -860,6 +867,7 @@
     "models": {
         "addLora": "Add LoRA",
         "allLoRAsAdded": "All LoRAs added",
+        "concepts": "Concepts",
         "loraAlreadyAdded": "LoRA already added",
         "esrganModel": "ESRGAN Model",
         "loading": "loading",

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPane.tsx

@@ -8,7 +8,7 @@ export const ModelPane = () => {
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
   return (
     <Box layerStyle="first" p={2} borderRadius="base" w="50%" h="full">
-      {selectedModelKey ? <Model /> : <ImportModels />}
+      {selectedModelKey ? <Model key={selectedModelKey} /> : <ImportModels />}
     </Box>
   );
 };

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Metadata/ModelMetadata.tsx

@@ -0,0 +1,18 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { skipToken } from '@reduxjs/toolkit/query';
+import { useAppSelector } from 'app/store/storeHooks';
+import DataViewer from 'features/gallery/components/ImageMetadataViewer/DataViewer';
+import { useGetModelMetadataQuery } from 'services/api/endpoints/models';
+
+export const ModelMetadata = () => {
+  const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+  const { data: metadata } = useGetModelMetadataQuery(selectedModelKey ?? skipToken);
+
+  return (
+    <>
+      <Flex flexDir="column" height="full" gap="3">
+        <DataViewer label="metadata" data={metadata || {}} />
+      </Flex>
+    </>
+  );
+};

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

@@ -1,9 +1,58 @@
+import { Box, Flex, Heading, Tab, TabList, TabPanel, TabPanels, Tabs, Text } from '@invoke-ai/ui-library';
+import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
+import { useTranslation } from 'react-i18next';
+import { useGetModelConfigQuery } from 'services/api/endpoints/models';
 
+import { ModelMetadata } from './Metadata/ModelMetadata';
+import { ModelAttrView } from './ModelAttrView';
 import { ModelEdit } from './ModelEdit';
 import { ModelView } from './ModelView';
 
 export const Model = () => {
+  const { t } = useTranslation();
   const selectedModelMode = useAppSelector((s) => s.modelmanagerV2.selectedModelMode);
-  return selectedModelMode === 'view' ? <ModelView /> : <ModelEdit />;
+  const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+  const { data, isLoading } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
+
+  if (isLoading) {
+    return <Text>{t('common.loading')}</Text>;
+  }
+
+  if (!data) {
+    return <Text>{t('common.somethingWentWrong')}</Text>;
+  }
+
+  return (
+    <>
+      <Flex flexDir="column" gap={1} p={2}>
+        <Heading as="h2" fontSize="lg">
+          {data.name}
+        </Heading>
+
+        {data.source && (
+          <Text variant="subtext">
+            {t('modelManager.source')}: {data?.source}
+          </Text>
+        )}
+        <Box mt="4">
+          <ModelAttrView label="Description" value={data.description} />
+        </Box>
+      </Flex>
+
+      <Tabs mt="4" h="100%">
+        <TabList>
+          <Tab>{t('modelManager.settings')}</Tab>
+          <Tab>{t('modelManager.metadata')}</Tab>
+        </TabList>
+
+        <TabPanels h="100%">
+          <TabPanel>{selectedModelMode === 'view' ? <ModelView /> : <ModelEdit />}</TabPanel>
+          <TabPanel h="full">
+            <ModelMetadata />
+          </TabPanel>
+        </TabPanels>
+      </Tabs>
+    </>
+  );
 };

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

@@ -1,12 +1,11 @@
-import { Box, Button, Flex, Heading, Text } from '@invoke-ai/ui-library';
+import { Box, Button, Flex, Text } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import DataViewer from 'features/gallery/components/ImageMetadataViewer/DataViewer';
 import { setSelectedModelMode } from 'features/modelManagerV2/store/modelManagerV2Slice';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { IoPencil } from 'react-icons/io5';
-import { useGetModelConfigQuery, useGetModelMetadataQuery } from 'services/api/endpoints/models';
+import { useGetModelConfigQuery } from 'services/api/endpoints/models';
 import type {
   CheckpointModelConfig,
   ControlNetModelConfig,
@@ -18,6 +17,7 @@ import type {
   VAEModelConfig,
 } from 'services/api/types';
 
+import { DefaultSettings } from './DefaultSettings';
 import { ModelAttrView } from './ModelAttrView';
 import { ModelConvert } from './ModelConvert';
 
@@ -26,7 +26,6 @@ export const ModelView = () => {
   const dispatch = useAppDispatch();
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
   const { data, isLoading } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
-  const { data: metadata } = useGetModelMetadataQuery(selectedModelKey ?? skipToken);
 
   const modelData = useMemo(() => {
     if (!data) {
@@ -73,85 +72,56 @@ export const ModelView = () => {
     return <Text>{t('common.somethingWentWrong')}</Text>;
   }
   return (
-    <Flex flexDir="column" h="full">
-      <Flex w="full" justifyContent="space-between">
-        <Flex flexDir="column" gap={1} p={2}>
-          <Heading as="h2" fontSize="lg">
-            {modelData.name}
-          </Heading>
-
-          {modelData.source && (
-            <Text variant="subtext">
-              {t('modelManager.source')}: {modelData.source}
-            </Text>
-          )}
-        </Flex>
-        <Flex gap={2}>
+    <Flex flexDir="column" h="full" gap="2">
+      <Box layerStyle="second" borderRadius="base" p={3}>
+        <Flex gap="2" justifyContent="flex-end" w="full">
           <Button size="sm" leftIcon={<IoPencil />} colorScheme="invokeYellow" onClick={handleEditModel}>
             {t('modelManager.edit')}
           </Button>
+
           {modelData.type === 'main' && modelData.format === 'checkpoint' && <ModelConvert model={modelData} />}
         </Flex>
-      </Flex>
-
-      <Flex flexDir="column" p={2} gap={3}>
-        <Flex>
-          <ModelAttrView label="Description" value={modelData.description} />
-        </Flex>
-        <Heading as="h3" fontSize="md" mt="4">
-          {t('modelManager.modelSettings')}
-        </Heading>
-        <Box layerStyle="second" borderRadius="base" p={3}>
-          <Flex flexDir="column" gap={3}>
-            <Flex gap={2}>
-              <ModelAttrView label={t('modelManager.baseModel')} value={modelData.base} />
-              <ModelAttrView label={t('modelManager.modelType')} value={modelData.type} />
-            </Flex>
-            <Flex gap={2}>
-              <ModelAttrView label={t('common.format')} value={modelData.format} />
-              <ModelAttrView label={t('modelManager.path')} value={modelData.path} />
-            </Flex>
-            {modelData.type === 'main' && (
-              <>
-                <Flex gap={2}>
-                  {modelData.format === 'diffusers' && (
-                    <ModelAttrView label={t('modelManager.repoVariant')} value={modelData.repo_variant} />
-                  )}
-                  {modelData.format === 'checkpoint' && (
-                    <ModelAttrView label={t('modelManager.pathToConfig')} value={modelData.config} />
-                  )}
-
-                  <ModelAttrView label={t('modelManager.variant')} value={modelData.variant} />
-                </Flex>
-                <Flex gap={2}>
-                  <ModelAttrView label={t('modelManager.predictionType')} value={modelData.prediction_type} />
-                  <ModelAttrView label={t('modelManager.upcastAttention')} value={`${modelData.upcast_attention}`} />
-                </Flex>
-                <Flex gap={2}>
-                  <ModelAttrView label={t('modelManager.ztsnrTraining')} value={`${modelData.ztsnr_training}`} />
-                  <ModelAttrView label={t('modelManager.vae')} value={modelData.vae} />
-                </Flex>
-              </>
-            )}
-            {modelData.type === 'ip_adapter' && (
+        <Flex flexDir="column" gap={3}>
+          <Flex gap={2}>
+            <ModelAttrView label={t('modelManager.baseModel')} value={modelData.base} />
+            <ModelAttrView label={t('modelManager.modelType')} value={modelData.type} />
+          </Flex>
+          <Flex gap={2}>
+            <ModelAttrView label={t('common.format')} value={modelData.format} />
+            <ModelAttrView label={t('modelManager.path')} value={modelData.path} />
+          </Flex>
+          {modelData.type === 'main' && (
+            <>
               <Flex gap={2}>
-                <ModelAttrView label={t('modelManager.imageEncoderModelId')} value={modelData.image_encoder_model_id} />
-              </Flex>
-            )}
-          </Flex>
-        </Box>
-      </Flex>
+                {modelData.format === 'diffusers' && (
+                  <ModelAttrView label={t('modelManager.repoVariant')} value={modelData.repo_variant} />
+                )}
+                {modelData.format === 'checkpoint' && (
+                  <ModelAttrView label={t('modelManager.pathToConfig')} value={modelData.config} />
+                )}
 
-      {metadata && (
-        <>
-          <Heading as="h3" fontSize="md" mt="4">
-            {t('modelManager.modelMetadata')}
-          </Heading>
-          <Flex h="full" w="full" p={2}>
-            <DataViewer label="metadata" data={metadata} />
-          </Flex>
-        </>
-      )}
+                <ModelAttrView label={t('modelManager.variant')} value={modelData.variant} />
+              </Flex>
+              <Flex gap={2}>
+                <ModelAttrView label={t('modelManager.predictionType')} value={modelData.prediction_type} />
+                <ModelAttrView label={t('modelManager.upcastAttention')} value={`${modelData.upcast_attention}`} />
+              </Flex>
+              <Flex gap={2}>
+                <ModelAttrView label={t('modelManager.ztsnrTraining')} value={`${modelData.ztsnr_training}`} />
+                <ModelAttrView label={t('modelManager.vae')} value={modelData.vae} />
+              </Flex>
+            </>
+          )}
+          {modelData.type === 'ip_adapter' && (
+            <Flex gap={2}>
+              <ModelAttrView label={t('modelManager.imageEncoderModelId')} value={modelData.image_encoder_model_id} />
+            </Flex>
+          )}
+        </Flex>
+      </Box>
+      <Box layerStyle="second" borderRadius="base" p={3}>
+        <DefaultSettings />
+      </Box>
     </Flex>
   );
 };

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -24,7 +24,15 @@ export type UpdateModelArg = {
   body: paths['/api/v2/models/i/{key}']['patch']['requestBody']['content']['application/json'];
 };
 
+type UpdateModelMetadataArg = {
+  key: paths['/api/v2/models/i/{key}/metadata']['patch']['parameters']['path']['key'];
+  body: paths['/api/v2/models/i/{key}/metadata']['patch']['requestBody']['content']['application/json'];
+};
+
 type UpdateModelResponse = paths['/api/v2/models/i/{key}']['patch']['responses']['200']['content']['application/json'];
+type UpdateModelMetadataResponse =
+  paths['/api/v2/models/i/{key}/metadata']['patch']['responses']['200']['content']['application/json'];
+
 type GetModelConfigResponse = paths['/api/v2/models/i/{key}']['get']['responses']['200']['content']['application/json'];
 
 type GetModelMetadataResponse =
@@ -172,6 +180,16 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: ['Model'],
     }),
+    updateModelMetadata: build.mutation<UpdateModelMetadataResponse, UpdateModelMetadataArg>({
+      query: ({ key, body }) => {
+        return {
+          url: buildModelsUrl(`i/${key}/metadata`),
+          method: 'PATCH',
+          body: body,
+        };
+      },
+      invalidatesTags: ['Model'],
+    }),
     installModel: build.mutation<InstallModelResponse, InstallModelArg>({
       query: ({ source, config, access_token }) => {
         return {
@@ -351,6 +369,7 @@ export const {
   useGetModelMetadataQuery,
   useDeleteModelImportMutation,
   usePruneModelImportsMutation,
+  useUpdateModelMetadataMutation,
 } = modelsApi;
 
 const upsertModelConfigs = (

pyproject.toml

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

tests/app/services/model_install/test_model_install.py

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

tests/test_model_hash.py

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