Add job_ids filter for GET api assets (#13998)

Amp-Thread-ID: https://ampcode.com/threads/T-019e4ca5-b71a-7168-8f56-58b2325f34c3
Co-authored-by: Amp <amp@ampcode.com>

.ci/windows_amd_base_files/run_amd_gpu_enable_dynamic_vram.bat

@@ -1,2 +1,2 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --enable-dynamic-vram
 pause

.github/workflows/backport_release.yaml

@@ -0,0 +1,401 @@
+name: Backport Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      branch:
+        description: 'Source branch containing the backported commits (PR source branch into master)'
+        required: true
+        type: string
+
+permissions:
+  contents: read
+  pull-requests: read
+  checks: read
+
+jobs:
+  backport-release:
+    name: Create backport release
+    runs-on: ubuntu-latest
+    environment: backport release
+
+    steps:
+      - name: Generate GitHub App token
+        id: app-token
+        uses: actions/create-github-app-token@bcd2ba49218906704ab6c1aa796996da409d3eb1
+        with:
+          app-id: ${{ secrets.FEN_RELEASE_APP_ID }}
+          private-key: ${{ secrets.FEN_RELEASE_PRIVATE_KEY }}
+
+      - name: Checkout repository
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd
+        with:
+          token: ${{ steps.app-token.outputs.token }}
+          fetch-depth: 0
+          fetch-tags: true
+
+      - name: Configure git
+        run: |
+          git config user.name  "fen-release[bot]"
+          git config user.email "fen-release[bot]@users.noreply.github.com"
+
+      - name: Validate source branch exists
+        env:
+          SOURCE_BRANCH: ${{ inputs.branch }}
+        run: |
+          set -euo pipefail
+          git fetch origin "refs/heads/${SOURCE_BRANCH}:refs/remotes/origin/${SOURCE_BRANCH}"
+          if ! git show-ref --verify --quiet "refs/remotes/origin/${SOURCE_BRANCH}"; then
+            echo "::error::Source branch '${SOURCE_BRANCH}' not found on origin."
+            exit 1
+          fi
+
+      - name: Determine latest stable release
+        id: latest
+        env:
+          GH_TOKEN: ${{ steps.app-token.outputs.token }}
+        run: |
+          set -euo pipefail
+
+          # List all tags matching vMAJOR.MINOR.PATCH and pick the highest by numeric
+          # comparison of each component. We DO NOT use `sort -V` because it treats
+          # v0.19.99 as higher than v0.20.1.
+          latest_tag="$(
+            git tag --list 'v[0-9]*.[0-9]*.[0-9]*' \
+              | grep -E '^v[0-9]+\.[0-9]+\.[0-9]+$' \
+              | awk -F'[v.]' '{ printf "%010d %010d %010d %s\n", $2, $3, $4, $0 }' \
+              | sort -k1,1n -k2,2n -k3,3n \
+              | tail -n1 \
+              | awk '{print $4}'
+          )"
+
+          if [[ -z "${latest_tag}" ]]; then
+            echo "::error::No stable release tags (vMAJOR.MINOR.PATCH) were found."
+            exit 1
+          fi
+
+          # Parse components
+          ver="${latest_tag#v}"
+          major="${ver%%.*}"
+          rest="${ver#*.}"
+          minor="${rest%%.*}"
+          patch="${rest#*.}"
+
+          new_patch=$((patch + 1))
+          new_version="v${major}.${minor}.${new_patch}"
+          release_branch="release/v${major}.${minor}"
+
+          latest_sha="$(git rev-list -n 1 "refs/tags/${latest_tag}")"
+
+          echo "latest_tag=${latest_tag}"             >> "$GITHUB_OUTPUT"
+          echo "latest_sha=${latest_sha}"             >> "$GITHUB_OUTPUT"
+          echo "major=${major}"                       >> "$GITHUB_OUTPUT"
+          echo "minor=${minor}"                       >> "$GITHUB_OUTPUT"
+          echo "patch=${patch}"                       >> "$GITHUB_OUTPUT"
+          echo "new_version=${new_version}"           >> "$GITHUB_OUTPUT"
+          echo "new_version_no_v=${major}.${minor}.${new_patch}" >> "$GITHUB_OUTPUT"
+          echo "release_branch=${release_branch}"     >> "$GITHUB_OUTPUT"
+
+          echo "Latest stable release: ${latest_tag} (${latest_sha})"
+          echo "New version will be:   ${new_version}"
+          echo "Release branch:        ${release_branch}"
+
+      - name: Validate source branch is cut directly from the latest stable release
+        env:
+          SOURCE_BRANCH:   ${{ inputs.branch }}
+          LATEST_TAG_SHA:  ${{ steps.latest.outputs.latest_sha }}
+          LATEST_TAG:      ${{ steps.latest.outputs.latest_tag }}
+        run: |
+          set -euo pipefail
+
+          source_sha="$(git rev-parse "refs/remotes/origin/${SOURCE_BRANCH}")"
+
+          # The source branch must be cut directly off the latest stable tag.
+          # "Cut directly off" means: walking first-parent from the source tip
+          # eventually reaches LATEST_TAG_SHA. This rejects branches that were
+          # cut from master after the tag (which would carry unrelated commits),
+          # while accepting a branch rooted at the tag with N backport commits
+          # on top (each of which may itself be a merge — first-parent walks
+          # through the mainline of the branch).
+          if ! git rev-list --first-parent "${source_sha}" \
+                | grep -qx "${LATEST_TAG_SHA}"; then
+            echo "::error::Source branch '${SOURCE_BRANCH}' is not cut from '${LATEST_TAG}'."
+            echo "::error::Its first-parent history does not include ${LATEST_TAG_SHA}."
+            exit 1
+          fi
+
+          # Additionally, every commit added on top of the tag (the set we are
+          # about to publish) must itself be a descendant of the tag along
+          # first-parent — i.e. no sibling commits from master sneak in via a
+          # non-first-parent path. Enforce by requiring that the symmetric
+          # difference is empty in one direction: commits in source that are
+          # NOT first-parent-reachable from source starting at the tag.
+          # We do this by intersecting:
+          #   A = commits reachable from source but not from tag (full DAG)
+          #   B = commits on the first-parent chain from source down to tag
+          # and requiring A == B.
+          all_added="$(git rev-list "${LATEST_TAG_SHA}..${source_sha}" | sort)"
+          first_parent_added="$(
+            git rev-list --first-parent "${LATEST_TAG_SHA}..${source_sha}" | sort
+          )"
+
+          if [[ "${all_added}" != "${first_parent_added}" ]]; then
+            echo "::error::Source branch '${SOURCE_BRANCH}' contains commits not on its first-parent chain from '${LATEST_TAG}'."
+            echo "::error::This usually means the branch was cut from master (not from the tag) or contains a merge from master."
+            echo "Commits reachable but not on first-parent chain:"
+            comm -23 <(printf '%s\n' "${all_added}") <(printf '%s\n' "${first_parent_added}") \
+              | while read -r sha; do
+                  echo "  $(git log -1 --format='%h %s' "${sha}")"
+                done
+            exit 1
+          fi
+
+          added_count="$(printf '%s\n' "${all_added}" | grep -c . || true)"
+          echo "Source branch is cut directly from ${LATEST_TAG} with ${added_count} commit(s) on top."
+
+      - name: Validate PR exists, is named correctly, and checks pass
+        env:
+          GH_TOKEN:      ${{ steps.app-token.outputs.token }}
+          SOURCE_BRANCH: ${{ inputs.branch }}
+          NEW_VERSION:   ${{ steps.latest.outputs.new_version }}
+          REPO:          ${{ github.repository }}
+        run: |
+          set -euo pipefail
+
+          expected_title="ComfyUI backport release ${NEW_VERSION}"
+
+          # Find open PRs from this branch into master
+          pr_json="$(
+            gh pr list \
+              --repo "${REPO}" \
+              --state open \
+              --head "${SOURCE_BRANCH}" \
+              --base master \
+              --json number,title,headRefOid \
+              --limit 10
+          )"
+
+          pr_count="$(echo "${pr_json}" | jq 'length')"
+          if [[ "${pr_count}" -eq 0 ]]; then
+            echo "::error::No open PR found from '${SOURCE_BRANCH}' into 'master'."
+            exit 1
+          fi
+
+          # Pick the PR matching the expected title
+          pr_number="$(echo "${pr_json}" | jq -r --arg t "${expected_title}" '
+            map(select(.title == $t)) | .[0].number // empty
+          ')"
+          pr_head_sha="$(echo "${pr_json}" | jq -r --arg t "${expected_title}" '
+            map(select(.title == $t)) | .[0].headRefOid // empty
+          ')"
+
+          if [[ -z "${pr_number}" ]]; then
+            echo "::error::No open PR from '${SOURCE_BRANCH}' into 'master' is titled '${expected_title}'."
+            echo "Found PRs:"
+            echo "${pr_json}" | jq -r '.[] | "  #\(.number): \(.title)"'
+            exit 1
+          fi
+
+          echo "Found PR #${pr_number} titled '${expected_title}' (head ${pr_head_sha})."
+
+          # Verify all check runs on the head commit have completed successfully.
+          # A check is considered passing if conclusion is success, neutral, or skipped.
+          checks_json="$(
+            gh api \
+              --paginate \
+              "repos/${REPO}/commits/${pr_head_sha}/check-runs" \
+              --jq '.check_runs[] | {name: .name, status: .status, conclusion: .conclusion}'
+          )"
+
+          if [[ -z "${checks_json}" ]]; then
+            echo "::error::No check runs found on PR head commit ${pr_head_sha}."
+            exit 1
+          fi
+
+          echo "Check runs on ${pr_head_sha}:"
+          echo "${checks_json}" | jq -s '.'
+
+          failing="$(echo "${checks_json}" | jq -s '
+            map(select(
+              .status != "completed"
+              or (.conclusion as $c
+                  | ["success","neutral","skipped"]
+                  | index($c) | not)
+            ))
+          ')"
+
+          failing_count="$(echo "${failing}" | jq 'length')"
+          if [[ "${failing_count}" -gt 0 ]]; then
+            echo "::error::One or more checks have not passed on PR head commit ${pr_head_sha}:"
+            echo "${failing}" | jq -r '.[] | "  - \(.name): status=\(.status) conclusion=\(.conclusion)"'
+            exit 1
+          fi
+
+          echo "All checks have passed on ${pr_head_sha}."
+
+      - name: Prepare release branch
+        id: prepare
+        env:
+          GH_TOKEN:        ${{ steps.app-token.outputs.token }}
+          REPO:            ${{ github.repository }}
+          SOURCE_BRANCH:   ${{ inputs.branch }}
+          RELEASE_BRANCH:  ${{ steps.latest.outputs.release_branch }}
+          LATEST_TAG:      ${{ steps.latest.outputs.latest_tag }}
+          LATEST_TAG_SHA:  ${{ steps.latest.outputs.latest_sha }}
+          PATCH:           ${{ steps.latest.outputs.patch }}
+        run: |
+          set -euo pipefail
+
+          # Try to fetch the release branch. If patch == 0, it shouldn't exist yet
+          # and we'll create it from the latest stable tag. If patch > 0, it must
+          # already exist and its tip must equal the latest stable tag commit (i.e.
+          # the previous patch release).
+          if git ls-remote --exit-code --heads origin "${RELEASE_BRANCH}" >/dev/null 2>&1; then
+            echo "Release branch '${RELEASE_BRANCH}' already exists on origin."
+            git fetch origin "refs/heads/${RELEASE_BRANCH}:refs/remotes/origin/${RELEASE_BRANCH}"
+            git checkout -B "${RELEASE_BRANCH}" "refs/remotes/origin/${RELEASE_BRANCH}"
+
+            current_tip="$(git rev-parse HEAD)"
+            if [[ "${current_tip}" != "${LATEST_TAG_SHA}" ]]; then
+              echo "::error::Release branch '${RELEASE_BRANCH}' tip (${current_tip}) is not at the latest stable release '${LATEST_TAG}' (${LATEST_TAG_SHA})."
+              echo "::error::Refusing to release on top of a divergent branch."
+              exit 1
+            fi
+            echo "branch_existed=true" >> "$GITHUB_OUTPUT"
+          else
+            if [[ "${PATCH}" != "0" ]]; then
+              echo "::error::Release branch '${RELEASE_BRANCH}' does not exist on origin, but the latest stable release '${LATEST_TAG}' has patch=${PATCH} (>0). This is inconsistent."
+              exit 1
+            fi
+            echo "Release branch '${RELEASE_BRANCH}' does not exist. Creating from ${LATEST_TAG}."
+            git checkout -B "${RELEASE_BRANCH}" "refs/tags/${LATEST_TAG}"
+            echo "branch_existed=false" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Fast-forward merge source branch into release branch
+        env:
+          SOURCE_BRANCH:  ${{ inputs.branch }}
+          RELEASE_BRANCH: ${{ steps.latest.outputs.release_branch }}
+        run: |
+          set -euo pipefail
+
+          # --ff-only guarantees no merge commit is created. If a fast-forward is
+          # not possible (i.e. the release branch has commits the source branch
+          # doesn't), the merge will fail and we abort. Because we already validated
+          # that the source branch is rooted on the latest stable tag, and the
+          # release branch tip equals that same tag, this fast-forward should
+          # always succeed for a well-formed backport branch.
+          if ! git merge --ff-only "refs/remotes/origin/${SOURCE_BRANCH}"; then
+            echo "::error::Cannot fast-forward '${RELEASE_BRANCH}' to '${SOURCE_BRANCH}'. A merge commit would be required. Aborting."
+            exit 1
+          fi
+
+          echo "Fast-forwarded '${RELEASE_BRANCH}' to tip of '${SOURCE_BRANCH}'."
+
+      - name: Bump version files
+        env:
+          NEW_VERSION_NO_V: ${{ steps.latest.outputs.new_version_no_v }}
+        run: |
+          set -euo pipefail
+
+          if [[ ! -f comfyui_version.py ]]; then
+            echo "::error::comfyui_version.py not found in repo root."
+            exit 1
+          fi
+          if [[ ! -f pyproject.toml ]]; then
+            echo "::error::pyproject.toml not found in repo root."
+            exit 1
+          fi
+
+          # Replace the version string in comfyui_version.py.
+          # Expected format:  __version__ = "X.Y.Z"
+          python3 - "$NEW_VERSION_NO_V" <<'PY'
+          import re, sys, pathlib
+          new = sys.argv[1]
+
+          p = pathlib.Path("comfyui_version.py")
+          src = p.read_text()
+          new_src, n = re.subn(
+              r'(__version__\s*=\s*[\'"])[^\'"]+([\'"])',
+              lambda m: f'{m.group(1)}{new}{m.group(2)}',
+              src,
+              count=1,
+          )
+          if n != 1:
+              sys.exit("Could not find __version__ assignment in comfyui_version.py")
+          p.write_text(new_src)
+
+          p = pathlib.Path("pyproject.toml")
+          src = p.read_text()
+          # Replace the first `version = "..."` inside [project] or [tool.poetry].
+          new_src, n = re.subn(
+              r'(?m)^(version\s*=\s*")[^"]+(")',
+              lambda m: f'{m.group(1)}{new}{m.group(2)}',
+              src,
+              count=1,
+          )
+          if n != 1:
+              sys.exit("Could not find version assignment in pyproject.toml")
+          p.write_text(new_src)
+          PY
+
+          echo "Updated version to ${NEW_VERSION_NO_V} in comfyui_version.py and pyproject.toml."
+          git --no-pager diff -- comfyui_version.py pyproject.toml
+
+      - name: Commit version bump and tag release
+        env:
+          NEW_VERSION: ${{ steps.latest.outputs.new_version }}
+        run: |
+          set -euo pipefail
+
+          git add comfyui_version.py pyproject.toml
+          git commit -m "ComfyUI ${NEW_VERSION}"
+
+          if git rev-parse -q --verify "refs/tags/${NEW_VERSION}" >/dev/null; then
+            echo "::error::Tag ${NEW_VERSION} already exists locally."
+            exit 1
+          fi
+          git tag "${NEW_VERSION}"
+
+      - name: Verify tag does not already exist on origin
+        env:
+          NEW_VERSION: ${{ steps.latest.outputs.new_version }}
+        run: |
+          set -euo pipefail
+          if git ls-remote --exit-code --tags origin "refs/tags/${NEW_VERSION}" >/dev/null 2>&1; then
+            echo "::error::Tag ${NEW_VERSION} already exists on origin. Aborting."
+            exit 1
+          fi
+
+      - name: Push release branch and tag
+        env:
+          RELEASE_BRANCH: ${{ steps.latest.outputs.release_branch }}
+          NEW_VERSION:    ${{ steps.latest.outputs.new_version }}
+        run: |
+          set -euo pipefail
+
+          # Push the branch first, then the tag. Atomic-ish: if the branch push
+          # fails we never publish the tag.
+          git push origin "refs/heads/${RELEASE_BRANCH}:refs/heads/${RELEASE_BRANCH}"
+          git push origin "refs/tags/${NEW_VERSION}"
+
+          echo "Released ${NEW_VERSION} on ${RELEASE_BRANCH}."
+
+      - name: Summary
+        if: always()
+        env:
+          NEW_VERSION:    ${{ steps.latest.outputs.new_version }}
+          RELEASE_BRANCH: ${{ steps.latest.outputs.release_branch }}
+          LATEST_TAG:     ${{ steps.latest.outputs.latest_tag }}
+          SOURCE_BRANCH:  ${{ inputs.branch }}
+        run: |
+          {
+            echo "## Backport release"
+            echo ""
+            echo "| Field | Value |"
+            echo "|---|---|"
+            echo "| Source branch | \`${SOURCE_BRANCH}\` |"
+            echo "| Previous stable | \`${LATEST_TAG}\` |"
+            echo "| New version | \`${NEW_VERSION}\` |"
+            echo "| Release branch | \`${RELEASE_BRANCH}\` |"
+          } >> "$GITHUB_STEP_SUMMARY"

.github/workflows/openapi-lint.yml

@@ -0,0 +1,31 @@
+name: OpenAPI Lint
+
+on:
+  pull_request:
+    paths:
+      - 'openapi.yaml'
+      - '.spectral.yaml'
+      - '.github/workflows/openapi-lint.yml'
+
+permissions:
+  contents: read
+
+jobs:
+  spectral:
+    name: Run Spectral
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Install Spectral
+        run: npm install -g @stoplight/spectral-cli@6
+
+      - name: Lint openapi.yaml
+        run: spectral lint openapi.yaml --ruleset .spectral.yaml --fail-severity=error

.github/workflows/stable-release.yml

@@ -145,6 +145,8 @@ jobs:
           cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
+          echo 'local-portable' > ComfyUI/.comfy_environment
+
           cd ..
 
           "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable

.github/workflows/tag-dispatch-cloud.yml

@@ -0,0 +1,45 @@
+name: Tag Dispatch to Cloud
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  dispatch-cloud:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Send repository dispatch to cloud
+        env:
+          DISPATCH_TOKEN: ${{ secrets.CLOUD_REPO_DISPATCH_TOKEN }}
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          set -euo pipefail
+
+          if [ -z "${DISPATCH_TOKEN:-}" ]; then
+            echo "::error::CLOUD_REPO_DISPATCH_TOKEN is required but not set."
+            exit 1
+          fi
+
+          RELEASE_URL="https://github.com/${{ github.repository }}/releases/tag/${RELEASE_TAG}"
+
+          PAYLOAD="$(jq -n \
+            --arg release_tag "$RELEASE_TAG" \
+            --arg release_url "$RELEASE_URL" \
+            '{
+              event_type: "comfyui_tag_pushed",
+              client_payload: {
+                release_tag: $release_tag,
+                release_url: $release_url
+              }
+            }')"
+
+          curl -fsSL \
+            -X POST \
+            -H "Accept: application/vnd.github+json" \
+            -H "Content-Type: application/json" \
+            -H "Authorization: Bearer ${DISPATCH_TOKEN}" \
+            https://api.github.com/repos/Comfy-Org/cloud/dispatches \
+            -d "$PAYLOAD"
+
+          echo "✅ Dispatched ComfyUI tag ${RELEASE_TAG} to Comfy-Org/cloud"

.gitignore

@@ -23,3 +23,4 @@ web_custom_versions/
 .DS_Store
 filtered-openapi.yaml
 uv.lock
+.comfy_environment

.spectral.yaml

@@ -0,0 +1,100 @@
+extends:
+  - spectral:oas
+
+# Severity levels: error, warn, info, hint, off
+# Rules from the built-in "spectral:oas" ruleset are active by default.
+# Below we tune severity and add custom rules for our conventions.
+#
+# This ruleset mirrors Comfy-Org/cloud/.spectral.yaml so specs across the
+# organization are linted against a single consistent standard.
+
+rules:
+  # -----------------------------------------------------------------------
+  # Built-in rule severity overrides
+  # -----------------------------------------------------------------------
+  operation-operationId: error
+  operation-description: warn
+  operation-tag-defined: error
+  info-contact: off
+  info-description: warn
+  no-eval-in-markdown: error
+  no-$ref-siblings: error
+
+  # -----------------------------------------------------------------------
+  # Custom rules: naming conventions
+  # -----------------------------------------------------------------------
+
+  # Property names should be snake_case
+  property-name-snake-case:
+    description: Property names must be snake_case
+    severity: warn
+    given: "$.components.schemas.*.properties[*]~"
+    then:
+      function: pattern
+      functionOptions:
+        match: "^[a-z][a-z0-9]*(_[a-z0-9]+)*$"
+
+  # Operation IDs should be camelCase
+  operation-id-camel-case:
+    description: Operation IDs must be camelCase
+    severity: warn
+    given: "$.paths.*.*.operationId"
+    then:
+      function: pattern
+      functionOptions:
+        match: "^[a-z][a-zA-Z0-9]*$"
+
+  # -----------------------------------------------------------------------
+  # Custom rules: response conventions
+  # -----------------------------------------------------------------------
+
+  # Error responses (4xx, 5xx) should use a consistent shape
+  error-response-schema:
+    description: Error responses should reference a standard error schema
+    severity: hint
+    given: "$.paths.*.*.responses[?(@property >= '400' && @property < '600')].content['application/json'].schema"
+    then:
+      field: "$ref"
+      function: truthy
+
+  # All 2xx responses with JSON body should have a schema
+  response-schema-defined:
+    description: Success responses with JSON content should define a schema
+    severity: warn
+    given: "$.paths.*.*.responses[?(@property >= '200' && @property < '300')].content['application/json']"
+    then:
+      field: schema
+      function: truthy
+
+  # -----------------------------------------------------------------------
+  # Custom rules: best practices
+  # -----------------------------------------------------------------------
+
+  # Path parameters must have a description
+  path-param-description:
+    description: Path parameters should have a description
+    severity: warn
+    given:
+      - "$.paths.*.parameters[?(@.in == 'path')]"
+      - "$.paths.*.*.parameters[?(@.in == 'path')]"
+    then:
+      field: description
+      function: truthy
+
+  # Schemas should have a description
+  schema-description:
+    description: Component schemas should have a description
+    severity: hint
+    given: "$.components.schemas.*"
+    then:
+      field: description
+      function: truthy
+
+overrides:
+  # /ws uses HTTP 101 (Switching Protocols) — a legitimate response for a
+  # WebSocket upgrade, but not a 2xx, so operation-success-response fires
+  # as a false positive. OpenAPI 3.x has no native WebSocket support.
+  - files:
+      - "openapi.yaml#/paths/~1ws"
+    rules:
+      operation-success-response: off

CODEOWNERS

@@ -1,2 +1,5 @@
-# Admins
-* @comfyanonymous @kosinkadink @guill
+* @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 @kijai
+
+/CODEOWNERS @comfyanonymous
+/.ci/ @comfyanonymous
+/.github/ @comfyanonymous

README.md

@@ -1,7 +1,7 @@
 <div align="center">
 
 # ComfyUI
-**The most powerful and modular visual AI engine and application.**
+**The most powerful and modular AI engine for content creation.**
 
 
 [![Website][website-shield]][website-url]
@@ -31,10 +31,16 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
+<img width="1590" height="795" alt="ComfyUI Screenshot" src="https://github.com/user-attachments/assets/36e065e0-bfae-4456-8c7f-8369d5ea48a2" />
+<br>
 </div>
 
-ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
+ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...
+- ComfyUI natively supports the latest open-source state of the art models.
+- API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc.
+- It is available on Windows, Linux, and macOS, locally with our [desktop application](https://www.comfy.org/download), our [portable install](#installing) or on our [cloud](https://www.comfy.org/cloud).
+- The most sophisticated workflows can be exposed through a simple UI thanks to App Mode.
+- It integrates seamlessly into production pipelines with our API endpoints.
 
 ## Get Started
 
@@ -77,6 +83,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
    - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
    - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
    - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
+   - Ernie Image
 - Image Editing Models
    - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
    - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
@@ -126,7 +133,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
-   - Releases a new stable version (e.g., v0.7.0) roughly every week.
+   - Releases a new major stable version (e.g., v0.7.0) roughly every 2 weeks.
    - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
    - Minor versions will be used for releases off the master branch.
    - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
@@ -193,13 +200,15 @@ If you have trouble extracting it, right click the file -> properties -> unblock
 
 The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
 
-#### Alternative Downloads:
+#### All Official Portable Downloads:
 
 [Portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
 
-[Experimental portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
+[Portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
 
-[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
+[Portable for Nvidia GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) (supports 20 series and above).
+
+[Portable for Nvidia GPUs with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 
 #### How do I share models between another UI and ComfyUI?
 
@@ -420,6 +429,8 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 See also: [https://www.comfy.org/](https://www.comfy.org/)
 
+> _psst — we're hiring!_ Help build ComfyUI: [comfy.org/careers](https://www.comfy.org/careers)
+
 ## Frontend Development
 
 As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.

SECURITY.md

@@ -0,0 +1,44 @@
+# Security Policy
+
+## Scope
+
+ComfyUI is designed to run locally. By default, the server binds to `127.0.0.1`, meaning only the user's own machine can reach it. Our threat model assumes:
+
+- The user installed ComfyUI through a supported channel: the desktop application, the portable build, or a manual install following the README.
+- The user has not installed untrusted custom nodes. Custom nodes are arbitrary Python code and are trusted as much as any other software the user chooses to install.
+- Anyone with access to the ComfyUI URL is trusted (a direct consequence of the localhost-only default).
+- PyTorch and other dependencies are at the versions we ship or recommend in the README.
+
+A report is in scope only if it affects a user operating within this threat model.
+
+## What We Consider a Vulnerability
+
+We want to hear about issues where a **reasonable user** — someone who does not install random untrusted nodes and who reads UI prompts and warnings before clicking through them — can be harmed by ComfyUI itself.
+
+The clearest example: a workflow file that such a user might plausibly load and run, using only built-in nodes, that results in **untrusted code execution, arbitrary file read/write outside expected directories, or credential/data exfiltration**.
+
+When submitting a report, please include a clear description of *why this is a problem for a typical local ComfyUI user*. Reports without this context are difficult to act on.
+
+## What We Do Not Consider a Security Vulnerability
+
+Please report the following through our regular [GitHub issues](https://github.com/comfyanonymous/ComfyUI/issues) instead. Filing them as security reports will likely cause them to be deprioritized or closed.
+
+- **Issues requiring `--listen` or any non-default network exposure.** ComfyUI binds to localhost by default. If a remote attacker needs to reach the server for the attack to work, the user has chosen to expose it and is responsible for securing that deployment (firewall, reverse proxy, authentication, etc.). These are bugs, not vulnerabilities.
+- **`torch.load` and related deserialization issues in old PyTorch versions.** These are upstream PyTorch issues. Our distributions ship with — and our documentation recommends — recent PyTorch versions where these are addressed.
+- **Vulnerabilities that depend on outdated library versions** that we neither ship nor recommend (e.g., requiring PyTorch 2.6 or older).
+- **Issues that require a specific custom node to be installed.** Custom nodes are third-party code. Report these to the maintainer of that node.
+- **Crashes, hangs, or resource exhaustion from a loaded workflow.** Annoying, but not a security issue in our model. File a regular bug.
+- **Social-engineering scenarios** where the user is expected to ignore an explicit UI warning or prompt.
+
+## Reporting
+
+If you believe you have found an issue that falls within the scope above, please report it privately via GitHub's [Report a vulnerability](https://github.com/comfyanonymous/ComfyUI/security/advisories/new) feature rather than opening a public issue.
+
+Please include:
+
+1. A description of the vulnerability and the affected component.
+2. Reproduction steps, ideally with a minimal workflow file or proof-of-concept.
+3. The ComfyUI version, install method (desktop / portable / manual), and OS.
+4. An explanation of how this affects a typical local user as described in the threat model.
+
+We will acknowledge valid reports and coordinate a fix and disclosure timeline with you.

app/assets/api/routes.py

@@ -39,6 +39,8 @@ from app.assets.services import (
     update_asset_metadata,
     upload_from_temp_path,
 )
+from app.assets.services.cursor import InvalidCursorError
+from app.assets.services.path_utils import compute_paths_for_response
 from app.assets.services.tagging import list_tag_histogram
 
 ROUTES = web.RouteTableDef()
@@ -160,10 +162,19 @@ def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResu
             preview_url = None
     else:
         preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata)
+    asset_content_hash = result.asset.hash if result.asset else None
+    if result.ref.file_path:
+        paths = compute_paths_for_response(result.ref.file_path)
+        file_path, display_name = paths if paths else (None, None)
+    else:
+        file_path, display_name = None, None
     return schemas_out.Asset(
         id=result.ref.id,
         name=result.ref.name,
-        asset_hash=result.asset.hash if result.asset else None,
+        file_path=file_path,
+        display_name=display_name,
+        hash=asset_content_hash,
+        asset_hash=asset_content_hash,
         size=int(result.asset.size_bytes) if result.asset else None,
         mime_type=result.asset.mime_type if result.asset else None,
         tags=result.tags,
@@ -172,7 +183,7 @@ def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResu
         user_metadata=result.ref.user_metadata or {},
         metadata=result.ref.system_metadata,
         job_id=result.ref.job_id,
-        prompt_id=result.ref.job_id,  # deprecated: mirrors job_id for cloud compat
+        prompt_id=result.ref.job_id,  # deprecated alias of job_id, kept for compatibility
         created_at=result.ref.created_at,
         updated_at=result.ref.updated_at,
         last_access_time=result.ref.last_access_time,
@@ -209,24 +220,38 @@ async def list_assets_route(request: web.Request) -> web.Response:
     order_candidate = (q.order or "desc").lower()
     order = order_candidate if order_candidate in {"asc", "desc"} else "desc"
 
-    result = list_assets_page(
-        owner_id=USER_MANAGER.get_request_user_id(request),
-        include_tags=q.include_tags,
-        exclude_tags=q.exclude_tags,
-        name_contains=q.name_contains,
-        metadata_filter=q.metadata_filter,
-        limit=q.limit,
-        offset=q.offset,
-        sort=sort,
-        order=order,
-    )
+    try:
+        result = list_assets_page(
+            owner_id=USER_MANAGER.get_request_user_id(request),
+            include_tags=q.include_tags,
+            exclude_tags=q.exclude_tags,
+            name_contains=q.name_contains,
+            metadata_filter=q.metadata_filter,
+            job_ids=q.job_ids,
+            limit=q.limit,
+            offset=q.offset,
+            sort=sort,
+            order=order,
+            after=q.after,
+        )
+    except InvalidCursorError as e:
+        return _build_error_response(400, "INVALID_CURSOR", str(e))
 
     summaries = [_build_asset_response(item) for item in result.items]
 
+    # has_more semantics differ by mode:
+    #   - cursor mode: a non-empty next_cursor means there are more results.
+    #   - offset mode: derived from total - (offset + page size).
+    if q.after is not None:
+        has_more = result.next_cursor is not None
+    else:
+        has_more = (q.offset + len(summaries)) < result.total
+
     payload = schemas_out.AssetsList(
         assets=summaries,
         total=result.total,
-        has_more=(q.offset + len(summaries)) < result.total,
+        has_more=has_more,
+        next_cursor=result.next_cursor,
     )
     return web.json_response(payload.model_dump(mode="json", exclude_none=True))
 
@@ -401,12 +426,16 @@ async def upload_asset(request: web.Request) -> web.Response:
         )
 
     if spec.tags and spec.tags[0] == "models":
+        # tag[1] may be the standalone category ("checkpoints") or the
+        # slash-joined shape ("checkpoints/flux/...") that
+        # `get_name_and_tags_from_asset_path` and cloud both emit. Match
+        # `resolve_destination_from_tags` by extracting the first segment.
+        category = spec.tags[1].split("/", 1)[0] if len(spec.tags) >= 2 else ""
         if (
             len(spec.tags) < 2
-            or spec.tags[1] not in folder_paths.folder_names_and_paths
+            or category not in folder_paths.folder_names_and_paths
         ):
             delete_temp_file_if_exists(parsed.tmp_path)
-            category = spec.tags[1] if len(spec.tags) >= 2 else ""
             return _build_error_response(
                 400, "INVALID_BODY", f"unknown models category '{category}'"
             )

app/assets/api/schemas_in.py

@@ -1,4 +1,5 @@
 import json
+import uuid
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -53,12 +54,18 @@ class ListAssetsQuery(BaseModel):
     include_tags: list[str] = Field(default_factory=list)
     exclude_tags: list[str] = Field(default_factory=list)
     name_contains: str | None = None
+    job_ids: list[str] = Field(default_factory=list, max_length=500)
 
     # Accept either a JSON string (query param) or a dict
     metadata_filter: dict[str, Any] | None = None
 
     limit: conint(ge=1, le=500) = 20
     offset: conint(ge=0) = 0
+    # Opaque keyset cursor. When supplied, `offset` is ignored. Cursor pagination
+    # is supported for sort values `created_at`, `updated_at`, `name`, `size`.
+    # Supplying `after` together with `sort=last_access_time` returns
+    # 400 INVALID_CURSOR; that sort only supports offset/limit.
+    after: str | None = None
 
     sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = (
         "created_at"
@@ -81,6 +88,40 @@ class ListAssetsQuery(BaseModel):
             return out
         return v
 
+    @field_validator("job_ids", mode="before")
+    @classmethod
+    def _split_and_validate_job_ids(cls, v):
+        # Accept "uuid1,uuid2" or ["uuid1","uuid2"] or repeated query params.
+        # Each entry must parse as a UUID; canonicalized to lowercase hyphenated form.
+        if v is None:
+            return []
+        if isinstance(v, str):
+            raw = [t.strip() for t in v.split(",") if t.strip()]
+        elif isinstance(v, list):
+            raw = []
+            for item in v:
+                if not isinstance(item, str):
+                    raise ValueError(
+                        f"job_ids entries must be strings, got {type(item).__name__}"
+                    )
+                raw.extend([t.strip() for t in item.split(",") if t.strip()])
+        else:
+            raise ValueError(
+                f"job_ids must be a string or list of strings, got {type(v).__name__}"
+            )
+
+        out: list[str] = []
+        seen: set[str] = set()
+        for s in raw:
+            try:
+                canonical = str(uuid.UUID(s))
+            except ValueError as e:
+                raise ValueError(f"job_ids must be UUIDs: {s!r}") from e
+            if canonical not in seen:
+                seen.add(canonical)
+                out.append(canonical)
+        return out
+
     @field_validator("metadata_filter", mode="before")
     @classmethod
     def _parse_metadata_json(cls, v):

app/assets/api/schemas_out.py

@@ -9,7 +9,10 @@ class Asset(BaseModel):
     ``id`` here is the AssetReference id, not the content-addressed Asset id."""
 
     id: str
-    name: str
+    name: str = Field(..., deprecated=True)
+    file_path: str | None = None
+    display_name: str | None = None
+    hash: str | None = None
     asset_hash: str | None = None
     size: int | None = None
     mime_type: str | None = None
@@ -40,6 +43,8 @@ class AssetsList(BaseModel):
     assets: list[Asset]
     total: int
     has_more: bool
+    # Opaque cursor for the next page. Omitted when there are no more results.
+    next_cursor: str | None = None
 
 
 class TagUsage(BaseModel):

app/assets/database/queries/asset_reference.py

@@ -264,11 +264,21 @@ def list_references_page(
     include_tags: Sequence[str] | None = None,
     exclude_tags: Sequence[str] | None = None,
     metadata_filter: dict | None = None,
+    job_ids: Sequence[str] | None = None,
     sort: str | None = None,
     order: str | None = None,
+    after_cursor_value: object | None = None,
+    after_cursor_id: str | None = None,
 ) -> tuple[list[AssetReference], dict[str, list[str]], int]:
     """List references with pagination, filtering, and sorting.
 
+    When ``after_cursor_value``/``after_cursor_id`` are supplied the query uses
+    keyset pagination — ``offset`` is ignored and a WHERE clause selects rows
+    strictly after the given ``(sort_col, id)`` position in the active sort
+    direction. The cursor value must already be typed for the column
+    (datetime for time sorts, int for size, str for name); the caller decodes
+    the opaque cursor string and resolves to the typed value.
+
     Returns (references, tag_map, total_count).
     """
     base = (
@@ -284,6 +294,9 @@ def list_references_page(
         escaped, esc = escape_sql_like_string(name_contains)
         base = base.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc))
 
+    if job_ids:
+        base = base.where(AssetReference.job_id.in_(list(job_ids)))
+
     base = apply_tag_filters(base, include_tags, exclude_tags)
     base = apply_metadata_filter(base, metadata_filter)
 
@@ -297,9 +310,31 @@ def list_references_page(
         "size": Asset.size_bytes,
     }
     sort_col = sort_map.get(sort, AssetReference.created_at)
-    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
+    descending = order == "desc"
 
-    base = base.order_by(sort_exp).limit(limit).offset(offset)
+    # Keyset WHERE: (sort_col, id) strictly less-than / greater-than the cursor.
+    # Equivalent to: sort_col <op> v  OR  (sort_col = v AND id <op> cursor_id).
+    if after_cursor_value is not None and after_cursor_id is not None:
+        if descending:
+            keyset = sa.or_(
+                sort_col < after_cursor_value,
+                sa.and_(sort_col == after_cursor_value, AssetReference.id < after_cursor_id),
+            )
+        else:
+            keyset = sa.or_(
+                sort_col > after_cursor_value,
+                sa.and_(sort_col == after_cursor_value, AssetReference.id > after_cursor_id),
+            )
+        base = base.where(keyset)
+
+    # Secondary ORDER BY id (matching the primary direction) gives the keyset
+    # comparison a deterministic tiebreaker on duplicate sort_col values.
+    id_exp = AssetReference.id.desc() if descending else AssetReference.id.asc()
+    sort_exp = sort_col.desc() if descending else sort_col.asc()
+
+    base = base.order_by(sort_exp, id_exp).limit(limit)
+    if after_cursor_id is None:
+        base = base.offset(offset)
 
     count_stmt = (
         select(sa.func.count())
@@ -314,6 +349,8 @@ def list_references_page(
         count_stmt = count_stmt.where(
             AssetReference.name.ilike(f"%{escaped}%", escape=esc)
         )
+    if job_ids:
+        count_stmt = count_stmt.where(AssetReference.job_id.in_(list(job_ids)))
     count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
     count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
 
@@ -327,7 +364,12 @@ def list_references_page(
             select(AssetReferenceTag.asset_reference_id, Tag.name)
             .join(Tag, Tag.name == AssetReferenceTag.tag_name)
             .where(AssetReferenceTag.asset_reference_id.in_(id_list))
-            .order_by(AssetReferenceTag.tag_name.asc())
+            # Preserve insertion order so the structural first tag (the root
+            # category like "models") stays in position 0 and the path-derived
+            # sub-path tag stays in position 1, matching cloud's behavior.
+            # tag_name is a deterministic tiebreaker when multiple tags share
+            # an added_at (same-batch insert via set_reference_tags).
+            .order_by(AssetReferenceTag.added_at.asc(), AssetReferenceTag.tag_name.asc())
         )
         for ref_id, tag_name in rows.all():
             tag_map[ref_id].append(tag_name)
@@ -355,7 +397,8 @@ def fetch_reference_asset_and_tags(
             build_visible_owner_clause(owner_id),
         )
         .options(noload(AssetReference.tags))
-        .order_by(Tag.name.asc())
+        # See list_references_page for the rationale behind ordering by added_at.
+        .order_by(AssetReferenceTag.added_at.asc(), Tag.name.asc())
     )
 
     rows = session.execute(stmt).all()

app/assets/database/queries/tags.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass
+from datetime import datetime, timedelta
 from typing import Iterable, Sequence
 
 import sqlalchemy as sa
@@ -20,7 +21,12 @@ from app.assets.database.queries.common import (
     build_visible_owner_clause,
     iter_row_chunks,
 )
-from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags
+from app.assets.helpers import (
+    escape_sql_like_string,
+    expand_bucket_prefixes,
+    get_utc_now,
+    normalize_tags,
+)
 
 
 @dataclass(frozen=True)
@@ -44,6 +50,26 @@ class SetTagsResult:
     total: list[str]
 
 
+def _next_added_at_base(session: Session, reference_id: str) -> datetime:
+    """Return a timestamp strictly greater than any existing
+    `added_at` for this reference. On platforms where the wall clock
+    has insufficient resolution between back-to-back commits (notably
+    Windows), two write batches on the same reference can otherwise
+    share a microsecond — the `ORDER BY added_at, tag_name` retrieval
+    then falls back to the alphabetic tiebreaker and user-tier tags
+    sort ahead of path-tier tags they were meant to follow.
+    """
+    existing_max = session.execute(
+        sa.select(sa.func.max(AssetReferenceTag.added_at)).where(
+            AssetReferenceTag.asset_reference_id == reference_id
+        )
+    ).scalar()
+    now = get_utc_now()
+    if existing_max is None:
+        return now
+    return max(existing_max + timedelta(microseconds=1), now)
+
+
 def validate_tags_exist(session: Session, tags: list[str]) -> None:
     """Raise ValueError if any of the given tag names do not exist."""
     existing_tag_names = set(
@@ -77,7 +103,13 @@ def get_reference_tags(session: Session, reference_id: str) -> list[str]:
             session.execute(
                 select(AssetReferenceTag.tag_name)
                 .where(AssetReferenceTag.asset_reference_id == reference_id)
-                .order_by(AssetReferenceTag.tag_name.asc())
+                # Match the response-path ordering used by
+                # list_references_page / fetch_reference_asset_and_tags so
+                # upload responses and subsequent GETs agree on tag order.
+                .order_by(
+                    AssetReferenceTag.added_at.asc(),
+                    AssetReferenceTag.tag_name.asc(),
+                )
             )
         ).all()
     ]
@@ -89,7 +121,7 @@ def set_reference_tags(
     tags: Sequence[str],
     origin: str = "manual",
 ) -> SetTagsResult:
-    desired = normalize_tags(tags)
+    desired = expand_bucket_prefixes(normalize_tags(tags))
 
     current = set(get_reference_tags(session, reference_id))
 
@@ -98,15 +130,22 @@ def set_reference_tags(
 
     if to_add:
         ensure_tags_exist(session, to_add, tag_type="user")
+        # Stagger added_at by microsecond per tag so the retrieval ORDER BY
+        # added_at preserves input order. Per-tag get_utc_now() calls can
+        # collide at microsecond resolution on fast machines, dropping the
+        # query to the tag_name alphabetical tiebreaker — same fix as in
+        # batch_insert_seed_assets. Read max(existing) so this batch sorts
+        # strictly after any prior batch on the same reference.
+        base_ts = _next_added_at_base(session, reference_id)
         session.add_all(
             [
                 AssetReferenceTag(
                     asset_reference_id=reference_id,
                     tag_name=t,
                     origin=origin,
-                    added_at=get_utc_now(),
+                    added_at=base_ts + timedelta(microseconds=i),
                 )
-                for t in to_add
+                for i, t in enumerate(to_add)
             ]
         )
         session.flush()
@@ -136,7 +175,7 @@ def add_tags_to_reference(
         if not ref:
             raise ValueError(f"AssetReference {reference_id} not found")
 
-    norm = normalize_tags(tags)
+    norm = expand_bucket_prefixes(normalize_tags(tags))
     if not norm:
         total = get_reference_tags(session, reference_id=reference_id)
         return AddTagsResult(added=[], already_present=[], total_tags=total)
@@ -146,10 +185,17 @@ def add_tags_to_reference(
 
     current = set(get_reference_tags(session, reference_id))
 
+    # Preserve the caller's insertion order rather than alphabetizing —
+    # the retrieval ORDER BY added_at + microsecond stagger only meaningfully
+    # preserves insertion order if "the order we insert in" actually matches
+    # the caller's intent.
     want = set(norm)
-    to_add = sorted(want - current)
+    to_add = [t for t in norm if t not in current]
 
     if to_add:
+        # See set_reference_tags for the rationale behind the per-tag stagger
+        # and the max(existing) seed.
+        base_ts = _next_added_at_base(session, reference_id)
         with session.begin_nested() as nested:
             try:
                 session.add_all(
@@ -158,9 +204,9 @@ def add_tags_to_reference(
                             asset_reference_id=reference_id,
                             tag_name=t,
                             origin=origin,
-                            added_at=get_utc_now(),
+                            added_at=base_ts + timedelta(microseconds=i),
                         )
-                        for t in to_add
+                        for i, t in enumerate(to_add)
                     ]
                 )
                 session.flush()

app/assets/helpers.py

@@ -47,6 +47,50 @@ def normalize_tags(tags: list[str] | None) -> list[str]:
     return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or "").strip()))
 
 
+def _known_bucket_prefixes() -> set[str]:
+    """Lowercased model-category names eligible for standalone-prefix
+    expansion. Tags whose first slash segment matches one of these get
+    the bucket inserted as a separate token, so FE filters like
+    ``include_tags=models,checkpoints`` keep matching even when the
+    asset lives in a nested subfolder (`models/checkpoints/flux/foo`).
+
+    Bare user labels with slashes whose first segment is not a registered
+    bucket (e.g. ``my-org/team-a``) pass through unchanged.
+    """
+    try:
+        import folder_paths
+
+        return {
+            name.lower()
+            for name in folder_paths.folder_names_and_paths.keys()
+            if name != "custom_nodes"
+        }
+    except Exception:
+        return set()
+
+
+def expand_bucket_prefixes(tags: list[str]) -> list[str]:
+    """Insert standalone bucket tokens after any slash-joined tag whose
+    first segment is a registered model category. Preserves caller order
+    and is idempotent (existing bucket tokens are not duplicated).
+    """
+    if not tags:
+        return list(tags)
+    buckets = _known_bucket_prefixes()
+    if not buckets:
+        return list(tags)
+    seen = set(tags)
+    result: list[str] = []
+    for t in tags:
+        result.append(t)
+        if "/" in t:
+            prefix = t.split("/", 1)[0]
+            if prefix.lower() in buckets and prefix not in seen:
+                result.append(prefix)
+                seen.add(prefix)
+    return result
+
+
 def validate_blake3_hash(s: str) -> str:
     """Validate and normalize a blake3 hash string.
 

app/assets/scanner.py

@@ -33,6 +33,7 @@ from app.assets.services.file_utils import (
     verify_file_unchanged,
 )
 from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash
+from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.metadata_extract import extract_file_metadata
 from app.assets.services.path_utils import (
     compute_relative_filename,
@@ -506,6 +507,10 @@ def enrich_asset(
 
     if extract_metadata and metadata:
         system_metadata = metadata.to_user_metadata()
+        if mime_type and mime_type.startswith("image/"):
+            dims = extract_image_dimensions(file_path, mime_type=mime_type)
+            if dims:
+                system_metadata.update(dims)
         set_reference_system_metadata(session, reference_id, system_metadata)
 
     if full_hash:

app/assets/services/asset_management.py

@@ -1,8 +1,19 @@
 import contextlib
 import mimetypes
 import os
+from datetime import timezone
 from typing import Sequence
 
+from app.assets.services.cursor import (
+    CursorPayload,
+    InvalidCursorError,
+    decode_cursor,
+    decode_cursor_int,
+    decode_cursor_time,
+    encode_cursor,
+    encode_cursor_from_time,
+)
+
 
 from app.assets.database.models import Asset
 from app.assets.database.queries import (
@@ -242,17 +253,55 @@ def get_asset_by_hash(asset_hash: str) -> AssetData | None:
         return extract_asset_data(asset)
 
 
+# Sort fields that support cursor pagination. `last_access_time` is not
+# in this list — it falls back to offset/limit.
+_CURSOR_SORT_FIELDS = ("created_at", "updated_at", "name", "size")
+
+
 def list_assets_page(
     owner_id: str = "",
     include_tags: Sequence[str] | None = None,
     exclude_tags: Sequence[str] | None = None,
     name_contains: str | None = None,
     metadata_filter: dict | None = None,
+    job_ids: Sequence[str] | None = None,
     limit: int = 20,
     offset: int = 0,
     sort: str = "created_at",
     order: str = "desc",
+    after: str | None = None,
 ) -> ListAssetsResult:
+    """List assets with optional cursor pagination.
+
+    When ``after`` is supplied it overrides ``offset``. The cursor's sort field
+    must match ``sort`` and be in the cursor-supported allowlist; mismatches
+    raise InvalidCursorError so the handler can map to 400 INVALID_CURSOR.
+    """
+    cursor_value: object | None = None
+    cursor_id: str | None = None
+    # Mint next_cursor on every page where the sort is cursor-supported, not
+    # only when the request itself arrived with a cursor. Otherwise a first
+    # request (no `after`) returns next_cursor=None and the client can never
+    # enter cursor mode.
+    mint_cursor = sort in _CURSOR_SORT_FIELDS
+
+    if after is not None:
+        if sort not in _CURSOR_SORT_FIELDS:
+            raise InvalidCursorError(
+                f"cursor pagination is not supported for sort={sort!r}"
+            )
+        payload = decode_cursor(after, _CURSOR_SORT_FIELDS, expected_order=order)
+        if payload.sort_field != sort:
+            raise InvalidCursorError(
+                f"cursor sort field {payload.sort_field!r} does not match request sort {sort!r}"
+            )
+        cursor_value, cursor_id = _resolve_cursor_value(payload), payload.id
+
+    # Over-fetch by one row so we can distinguish "exactly `limit` rows total
+    # remaining" from "more rows past this page" without a second query. Drop
+    # the sentinel before returning.
+    fetch_limit = limit + 1 if mint_cursor else limit
+
     with create_session() as session:
         refs, tag_map, total = list_references_page(
             session,
@@ -261,12 +310,23 @@ def list_assets_page(
             exclude_tags=exclude_tags,
             name_contains=name_contains,
             metadata_filter=metadata_filter,
-            limit=limit,
+            job_ids=job_ids,
+            limit=fetch_limit,
             offset=offset,
             sort=sort,
             order=order,
+            after_cursor_value=cursor_value,
+            after_cursor_id=cursor_id,
         )
 
+        next_cursor: str | None = None
+        if mint_cursor and len(refs) > limit:
+            # There's at least one more row past this page — mint a cursor from
+            # the last row of the page (i.e. index `limit - 1`, since we
+            # over-fetched), and drop the sentinel.
+            next_cursor = _encode_next_cursor(refs[limit - 1], sort, order)
+            refs = refs[:limit]
+
         items: list[AssetSummaryData] = []
         for ref in refs:
             items.append(
@@ -277,7 +337,39 @@ def list_assets_page(
                 )
             )
 
-        return ListAssetsResult(items=items, total=total)
+        return ListAssetsResult(items=items, total=total, next_cursor=next_cursor)
+
+
+def _resolve_cursor_value(payload: CursorPayload) -> object:
+    """Map a decoded cursor payload to a column-typed Python value."""
+    if payload.sort_field in ("created_at", "updated_at"):
+        # DB stores naive UTC; strip tzinfo so the comparison binds against a
+        # `TIMESTAMP WITHOUT TIME ZONE` column without an offset shift.
+        return decode_cursor_time(payload).replace(tzinfo=None)
+    if payload.sort_field == "size":
+        return decode_cursor_int(payload)
+    return payload.value  # name, str-typed
+
+
+def _encode_next_cursor(ref, sort: str, order: str) -> str | None:
+    """Mint a cursor pointing at *ref* for the given sort dimension.
+
+    Returns None when the boundary row carries a NULL sort value (e.g. an asset
+    record whose size_bytes hasn't been backfilled). Continuing pagination
+    across a NULL boundary is undefined under keyset ordering — better to
+    truncate cleanly here than to mint a cursor that mis-positions.
+    """
+    if sort == "name":
+        return encode_cursor("name", ref.name, ref.id, order=order)
+    if sort == "size":
+        if ref.asset is None or ref.asset.size_bytes is None:
+            return None
+        return encode_cursor("size", str(ref.asset.size_bytes), ref.id, order=order)
+    # created_at / updated_at — DB datetimes are naive UTC; attach tz before encoding.
+    value = ref.created_at if sort == "created_at" else ref.updated_at
+    if value is None:
+        return None
+    return encode_cursor_from_time(sort, value.replace(tzinfo=timezone.utc), ref.id, order=order)
 
 
 def resolve_hash_to_path(

app/assets/services/bulk_ingest.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 import os
 import uuid
 from dataclasses import dataclass
-from datetime import datetime
+from datetime import datetime, timedelta
 from typing import TYPE_CHECKING, Any, TypedDict
 
 from sqlalchemy.orm import Session
@@ -13,13 +13,14 @@ from app.assets.database.queries import (
     bulk_insert_references_ignore_conflicts,
     bulk_insert_tags_and_meta,
     delete_assets_by_ids,
+    ensure_tags_exist,
     get_existing_asset_ids,
     get_reference_ids_by_ids,
     get_references_by_paths_and_asset_ids,
     get_unreferenced_unhashed_asset_ids,
     restore_references_by_paths,
 )
-from app.assets.helpers import get_utc_now
+from app.assets.helpers import expand_bucket_prefixes, get_utc_now
 
 if TYPE_CHECKING:
     from app.assets.services.metadata_extract import ExtractedMetadata
@@ -233,13 +234,20 @@ def batch_insert_seed_assets(
             if ref_id not in inserted_ref_ids:
                 continue
 
-            for tag in ref_data["tags"]:
+            # Stagger added_at by microsecond per tag within a reference so
+            # the retrieval ORDER BY added_at preserves the input list order
+            # (the path-derived root category stays at position 0). Without
+            # this, every tag in a bulk-insert batch shares current_time and
+            # the tag_name tiebreaker sorts them alphabetically — putting the
+            # subpath tag ahead of "models" since "c"/"d"/"l" < "m".
+            ref_tags = expand_bucket_prefixes(ref_data["tags"])
+            for tag_idx, tag in enumerate(ref_tags):
                 tag_rows.append(
                     {
                         "asset_reference_id": ref_id,
                         "tag_name": tag,
                         "origin": "automatic",
-                        "added_at": current_time,
+                        "added_at": current_time + timedelta(microseconds=tag_idx),
                     }
                 )
 
@@ -261,6 +269,16 @@ def batch_insert_seed_assets(
                     }
                 )
 
+    if tag_rows:
+        # Bucket-prefix expansion may have introduced tags the caller did
+        # not register via the upstream tag_pool (e.g. `checkpoints` for a
+        # nested `checkpoints/flux/foo` path). Pre-register the full set so
+        # the AssetReferenceTag.tag_name FK is satisfied; the underlying
+        # insert is ON CONFLICT DO NOTHING so re-registration is idempotent.
+        ensure_tags_exist(
+            session, {row["tag_name"] for row in tag_rows}, tag_type="user"
+        )
+
     bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=metadata_rows)
 
     return BulkInsertResult(

app/assets/services/cursor.py

@@ -0,0 +1,225 @@
+"""Opaque keyset-pagination cursor for /api/assets.
+
+Payload JSON uses short keys to keep the encoded length small:
+
+    {"s": <sort_field>, "v": <value>, "id": <id>, "o": <order>}
+
+The `o` key binds the cursor to the sort direction it was minted under,
+so replaying a `desc` cursor against an `asc` request fails with
+``INVALID_CURSOR`` rather than silently walking the wrong direction.
+`o` is mandatory on every payload — a cursor without it is rejected as
+malformed.
+
+Encoding is base64url with no padding. JSON serialization escapes `<`,
+`>`, `&`, U+2028, and U+2029 in encoded string values so asset names
+containing those characters produce a stable, byte-identical wire form
+across any compatible implementation of the same payload format.
+
+Time values are serialized as Unix microseconds (UTC) — microsecond
+precision is sufficient to round-trip the timestamps stored by the
+database without rounding rows in the same millisecond bucket.
+"""
+from __future__ import annotations
+
+import base64
+import json
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Iterable, Optional
+
+
+class InvalidCursorError(ValueError):
+    """Raised on a malformed, oversized, or unsupported-sort-field cursor.
+
+    Map to a 400 response with code ``INVALID_CURSOR`` at the handler.
+    """
+
+
+# Wire-format length caps. Cursors are user-controlled, so caps protect the
+# decode path from oversized allocations and downstream SQL predicates from
+# unbounded strings.
+#
+# MAX_CURSOR_VALUE_LENGTH is 512 to fit the `AssetReference.name` column max
+# (`String(512)`) — otherwise a long-named asset would mint a cursor the same
+# server then refuses on the next request.
+MAX_ENCODED_CURSOR_LENGTH = 1024
+MAX_CURSOR_VALUE_LENGTH = 512
+MAX_CURSOR_ID_LENGTH = 128
+
+
+@dataclass(frozen=True)
+class CursorPayload:
+    sort_field: str
+    value: str
+    id: str
+    order: str
+
+
+_VALID_ORDERS = ("asc", "desc")
+
+
+def encode_cursor(sort_field: str, value: str, id: str, order: str = "desc") -> str:
+    """Encode a cursor payload as a base64url (no-padding) string.
+
+    `order` binds the cursor to the sort direction it was minted under so a
+    later request with a flipped `order` query parameter is rejected with
+    ``INVALID_CURSOR`` rather than silently walking the wrong direction.
+    """
+    if order not in _VALID_ORDERS:
+        raise InvalidCursorError(f"order must be one of {_VALID_ORDERS}, got {order!r}")
+    # Symmetric input validation: the encoder must reject anything the
+    # decoder rejects, or the same server will mint cursors it then 400s on
+    # the next request.
+    if not id:
+        raise InvalidCursorError("id must be non-empty")
+    if len(id) > MAX_CURSOR_ID_LENGTH:
+        raise InvalidCursorError("id exceeds maximum length")
+    if len(value) > MAX_CURSOR_VALUE_LENGTH:
+        raise InvalidCursorError("value exceeds maximum length")
+    payload = {"s": sort_field, "v": value, "id": id, "o": order}
+    raw = json.dumps(payload, separators=(",", ":"), ensure_ascii=False)
+    # Match the default JSON escaping of HTML-significant characters and JS
+    # line/paragraph separators (U+2028 / U+2029) so an asset name carrying
+    # any of them encodes to identical bytes across runtimes. None of these
+    # characters appear in JSON structural syntax, so a global replace on the
+    # serialized output can only touch encoded values. Use explicit \uXXXX
+    # escapes for U+2028 / U+2029 so the source survives any editor / git
+    # tooling that normalizes invisible separators.
+    raw = (
+        raw.replace("<", "\\u003c")
+           .replace(">", "\\u003e")
+           .replace("&", "\\u0026")
+           .replace("\u2028", "\\u2028")
+           .replace("\u2029", "\\u2029")
+    )
+    encoded = base64.urlsafe_b64encode(raw.encode("utf-8")).rstrip(b"=").decode("ascii")
+    # Final wire-size guard: the per-field caps above are char-counted, but the
+    # wire cap applies to the base64url of the UTF-8-encoded, escape-expanded
+    # payload. A value full of multibyte or HTML-significant characters (e.g.
+    # 512 \u00d7 "\u00e9" or 512 \u00d7 "<") inflates well past MAX_ENCODED_CURSOR_LENGTH even
+    # though it passes the char-count check. Refuse to mint a cursor the decoder
+    # on the next request would reject.
+    if len(encoded) > MAX_ENCODED_CURSOR_LENGTH:
+        raise InvalidCursorError("encoded cursor exceeds maximum length")
+    return encoded
+
+
+def encode_cursor_from_time(sort_field: str, t: datetime, id: str, order: str = "desc") -> str:
+    """Encode a time-typed cursor at Unix microsecond precision.
+
+    Accepts an aware datetime (any timezone) and normalizes to UTC. Naive
+    datetimes are rejected so callers can't accidentally encode the local
+    wall-clock value of a UTC-stored timestamp.
+    """
+    if t.tzinfo is None:
+        raise ValueError("encode_cursor_from_time requires an aware datetime")
+    micros = _datetime_to_unix_micros(t.astimezone(timezone.utc))
+    return encode_cursor(sort_field, str(micros), id, order=order)
+
+
+def decode_cursor(
+    cursor: str,
+    allowed_sort_fields: Iterable[str],
+    expected_order: str | None = None,
+) -> CursorPayload:
+    """Parse an opaque cursor.
+
+    ``allowed_sort_fields`` is the endpoint's accepted sort-field list — a
+    cursor carrying a field outside this set is rejected so a cursor minted
+    for one column can't be replayed against another (e.g. a ``created_at``
+    timestamp string compared against a ``name`` column).
+
+    ``expected_order`` (``"asc"``/``"desc"``), when supplied, must match the
+    payload's ``o`` field. ``o`` is required on every payload; a cursor
+    missing it is rejected as malformed.
+
+    Passing no allowed fields rejects every cursor.
+    """
+    if len(cursor) > MAX_ENCODED_CURSOR_LENGTH:
+        raise InvalidCursorError("cursor exceeds maximum length")
+
+    try:
+        # urlsafe_b64decode requires correct padding; we strip on encode, so
+        # restore the trailing '=' pad here.
+        padding = "=" * (-len(cursor) % 4)
+        raw = base64.urlsafe_b64decode(cursor + padding)
+    except (ValueError, base64.binascii.Error) as e:
+        raise InvalidCursorError(f"encoding: {e}") from e
+
+    try:
+        decoded = json.loads(raw)
+    except (json.JSONDecodeError, UnicodeDecodeError) as e:
+        raise InvalidCursorError(f"payload: {e}") from e
+
+    if not isinstance(decoded, dict):
+        raise InvalidCursorError("payload: expected object")
+
+    sort_field = decoded.get("s")
+    value = decoded.get("v")
+    id = decoded.get("id")
+    order = decoded.get("o")
+
+    if not isinstance(sort_field, str) or not isinstance(value, str) or not isinstance(id, str):
+        raise InvalidCursorError("payload: missing or non-string s/v/id")
+
+    if id == "":
+        raise InvalidCursorError("missing id")
+    if len(id) > MAX_CURSOR_ID_LENGTH:
+        raise InvalidCursorError("id exceeds maximum length")
+    if len(value) > MAX_CURSOR_VALUE_LENGTH:
+        raise InvalidCursorError("value exceeds maximum length")
+
+    if sort_field not in allowed_sort_fields:
+        raise InvalidCursorError(f"unsupported sort field {sort_field!r}")
+
+    if not isinstance(order, str):
+        raise InvalidCursorError("missing or non-string o")
+    if order not in _VALID_ORDERS:
+        raise InvalidCursorError(f"unsupported order {order!r}")
+    if expected_order is not None and order != expected_order:
+        raise InvalidCursorError(
+            f"cursor order {order!r} does not match request order {expected_order!r}"
+        )
+
+    return CursorPayload(sort_field=sort_field, value=value, id=id, order=order)
+
+
+def decode_cursor_time(payload: Optional[CursorPayload]) -> datetime:
+    """Parse a time-typed cursor value as Unix microseconds, returning UTC."""
+    if payload is None:
+        raise InvalidCursorError("nil cursor payload")
+    try:
+        micros = int(payload.value)
+    except ValueError as e:
+        raise InvalidCursorError(f"value is not a valid timestamp: {e}") from e
+    try:
+        return _unix_micros_to_datetime(micros)
+    except (OverflowError, OSError, ValueError) as e:
+        # Crafted out-of-range microseconds (e.g. > datetime.MAX_YEAR) blow up
+        # in fromtimestamp / datetime construction. Map to 400, not 500.
+        raise InvalidCursorError(f"value is out of representable range: {e}") from e
+
+
+def decode_cursor_int(payload: Optional[CursorPayload]) -> int:
+    """Parse a cursor value as a base-10 integer."""
+    if payload is None:
+        raise InvalidCursorError("nil cursor payload")
+    try:
+        return int(payload.value)
+    except ValueError as e:
+        raise InvalidCursorError(f"value is not a valid integer: {e}") from e
+
+
+_EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc)
+
+
+def _datetime_to_unix_micros(t: datetime) -> int:
+    """Convert an aware UTC datetime to Unix microseconds (integer math)."""
+    delta = t - _EPOCH
+    return (delta.days * 86_400 + delta.seconds) * 1_000_000 + delta.microseconds
+
+
+def _unix_micros_to_datetime(micros: int) -> datetime:
+    """Convert Unix microseconds to a UTC datetime, preserving precision."""
+    seconds, micro_remainder = divmod(micros, 1_000_000)
+    return datetime.fromtimestamp(seconds, tz=timezone.utc).replace(microsecond=micro_remainder)

app/assets/services/image_dimensions.py

@@ -0,0 +1,63 @@
+"""Image dimension extraction for asset ingest.
+
+Reads only the image header via Pillow to capture width/height cheaply,
+without a full pixel decode. Returns a metadata dict suitable for merging
+into ``AssetReference.system_metadata``.
+"""
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+def extract_image_dimensions(
+    file_path: str, mime_type: str | None = None
+) -> dict[str, Any] | None:
+    """Extract image dimensions for the file at ``file_path``.
+
+    Args:
+        file_path: Absolute path to a file on disk.
+        mime_type: Optional MIME type hint. When provided and not prefixed
+            with ``image/``, extraction is skipped without touching the file.
+
+    Returns:
+        ``{"kind": "image", "width": W, "height": H}`` when the file is a
+        recognizable image with positive dimensions, otherwise ``None``.
+
+    The dict shape is intended to be merged into ``system_metadata`` so the
+    asset response surfaces ``metadata.kind`` plus dimension fields for image
+    assets. Forward-compatible: future media kinds (e.g. ``"video"`` with
+    duration/fps) can extend this shape without schema changes.
+    """
+    if mime_type is not None and not mime_type.startswith("image/"):
+        return None
+
+    try:
+        from PIL import Image, UnidentifiedImageError
+    except ImportError:
+        logger.debug(
+            "Pillow not available; skipping image dimension extraction for %s",
+            file_path,
+        )
+        return None
+
+    try:
+        with Image.open(file_path) as img:
+            width, height = img.size
+    except (OSError, UnidentifiedImageError, ValueError) as exc:
+        logger.debug(
+            "Failed to read image dimensions from %s: %s", file_path, exc
+        )
+        return None
+
+    if (
+        not isinstance(width, int)
+        or not isinstance(height, int)
+        or width <= 0
+        or height <= 0
+    ):
+        return None
+
+    return {"kind": "image", "width": width, "height": height}

app/assets/services/ingest.py

@@ -17,9 +17,11 @@ from app.assets.database.queries import (
     get_reference_by_file_path,
     get_reference_tags,
     get_or_create_reference,
+    list_references_by_asset_id,
     reference_exists,
     remove_missing_tag_for_asset_id,
     set_reference_metadata,
+    set_reference_system_metadata,
     set_reference_tags,
     update_asset_hash_and_mime,
     upsert_asset,
@@ -29,6 +31,7 @@ from app.assets.database.queries import (
 from app.assets.helpers import get_utc_now, normalize_tags
 from app.assets.services.bulk_ingest import batch_insert_seed_assets
 from app.assets.services.file_utils import get_size_and_mtime_ns
+from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.path_utils import (
     compute_relative_filename,
     get_name_and_tags_from_asset_path,
@@ -118,6 +121,14 @@ def _ingest_file_from_path(
                 user_metadata=user_metadata,
             )
 
+            _maybe_store_image_dimensions(
+                session,
+                reference_id=reference_id,
+                file_path=locator,
+                mime_type=mime_type,
+                current_system_metadata=ref.system_metadata,
+            )
+
         try:
             remove_missing_tag_for_asset_id(session, asset_id=asset.id)
         except Exception:
@@ -288,6 +299,13 @@ def _register_existing_asset(
                 user_metadata=new_meta,
             )
 
+        _backfill_image_dimensions_from_siblings(
+            session,
+            asset_id=asset.id,
+            new_reference_id=ref.id,
+            current_system_metadata=ref.system_metadata,
+        )
+
         if tags is not None:
             set_reference_tags(
                 session,
@@ -334,6 +352,87 @@ def _update_metadata_with_filename(
         )
 
 
+_IMAGE_DIMENSION_KEYS = ("kind", "width", "height")
+
+
+def _maybe_store_image_dimensions(
+    session: Session,
+    reference_id: str,
+    file_path: str,
+    mime_type: str | None,
+    current_system_metadata: dict | None,
+) -> None:
+    """Populate ``kind``/``width``/``height`` on system_metadata for image refs.
+
+    Non-image MIME types are a no-op. Pre-existing keys (e.g. enricher-written
+    safetensors metadata, download provenance) are preserved by merge.
+    """
+    if not mime_type or not mime_type.startswith("image/"):
+        return
+
+    dims = extract_image_dimensions(file_path, mime_type=mime_type)
+    if not dims:
+        return
+
+    current = current_system_metadata or {}
+    merged = dict(current)
+    merged.update(dims)
+    if merged != current:
+        set_reference_system_metadata(
+            session,
+            reference_id=reference_id,
+            system_metadata=merged,
+        )
+
+
+def _backfill_image_dimensions_from_siblings(
+    session: Session,
+    asset_id: str,
+    new_reference_id: str,
+    current_system_metadata: dict | None,
+) -> None:
+    """Copy image dimension keys from any sibling reference of the same asset.
+
+    The from-hash path doesn't read the file bytes, so dimensions can't be
+    extracted there directly. When another reference of the same asset already
+    carries image dimensions, copy them onto the new reference so consumers
+    see consistent metadata regardless of how the asset was registered.
+
+    Best-effort: missing siblings, non-image siblings, or absent dimension
+    keys leave the target reference unchanged.
+    """
+    current = current_system_metadata or {}
+    if current.get("kind") == "image" and "width" in current and "height" in current:
+        return
+
+    for sibling in list_references_by_asset_id(session, asset_id):
+        if sibling.id == new_reference_id:
+            continue
+        meta = sibling.system_metadata or {}
+        if meta.get("kind") != "image":
+            continue
+        width = meta.get("width")
+        height = meta.get("height")
+        if (
+            type(width) is not int
+            or type(height) is not int
+            or width <= 0
+            or height <= 0
+        ):
+            continue
+        merged = dict(current)
+        merged["kind"] = "image"
+        merged["width"] = width
+        merged["height"] = height
+        if merged != current:
+            set_reference_system_metadata(
+                session,
+                reference_id=new_reference_id,
+                system_metadata=merged,
+            )
+        return
+
+
 def _sanitize_filename(name: str | None, fallback: str) -> str:
     n = os.path.basename((name or "").strip() or fallback)
     return n if n else fallback

app/assets/services/path_utils.py

@@ -3,11 +3,12 @@ from pathlib import Path
 from typing import Literal
 
 import folder_paths
-from app.assets.helpers import normalize_tags
 
 
 _NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
 
+RootCategory = Literal["input", "output", "temp", "models"]
+
 
 def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
     """Build list of (folder_name, base_paths[]) for all model locations.
@@ -27,27 +28,51 @@ def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
 
 
 def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
+    """Validates and maps tags -> (base_dir, subdirs_for_fs).
+
+    Accepts both the legacy one-tag-per-directory shape
+    (``["models", "diffusers", "Kolors", "text_encoder"]``) and the
+    slash-joined shape emitted by :func:`get_name_and_tags_from_asset_path`
+    (``["models", "diffusers/Kolors/text_encoder"]``). Hybrid shapes that
+    mix the two within a single call (e.g.
+    ``["models", "diffusers", "Kolors/text_encoder"]``) are also
+    accepted: each entry after ``tags[0]`` is split on ``/`` and
+    concatenated, so the two shapes — and any mix of them — resolve to
+    the same destination. The same safety checks are applied to each
+    component after expansion.
+    """
     if not tags:
         raise ValueError("tags must not be empty")
     root = tags[0].lower()
+
+    # Expand any slash-joined entries into individual path components so
+    # the rest of the function can treat both tag shapes uniformly. Each
+    # component is also stripped, so " a / b " behaves like ["a", "b"].
+    expanded: list[str] = []
+    for t in tags[1:]:
+        for part in str(t).split("/"):
+            part = part.strip()
+            if part:
+                expanded.append(part)
+
     if root == "models":
-        if len(tags) < 2:
+        if not expanded:
             raise ValueError("at least two tags required for model asset")
+        category = expanded[0]
         try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
+            bases = folder_paths.folder_names_and_paths[category][0]
         except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
+            raise ValueError(f"unknown model category '{category}'")
         if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
+            raise ValueError(f"no base path configured for category '{category}'")
         base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
+        raw_subdirs = expanded[1:]
     elif root == "input":
         base_dir = os.path.abspath(folder_paths.get_input_directory())
-        raw_subdirs = tags[1:]
+        raw_subdirs = expanded
     elif root == "output":
         base_dir = os.path.abspath(folder_paths.get_output_directory())
-        raw_subdirs = tags[1:]
+        raw_subdirs = expanded
     else:
         raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'")
     _sep_chars = frozenset(("/", "\\", os.sep))
@@ -65,35 +90,109 @@ def validate_path_within_base(candidate: str, base: str) -> None:
         raise ValueError("destination escapes base directory")
 
 
-def compute_relative_filename(file_path: str) -> str | None:
-    """
-    Return the model's path relative to the last well-known folder (the model category),
-    using forward slashes, eg:
-      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
-      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
+def compute_paths_for_response(
+    file_path: str,
+) -> tuple[str, str | None] | None:
+    """Compute (file_path, display_name) for an Asset response.
 
-    For non-model paths, returns None.
+    `file_path` is a logical locator under the asset namespace: `<root>/<rel>`
+    for input/output/temp assets and `<root>/<bucket>/<rel>` for model assets.
+    `display_name` is the path below that root or model bucket, suitable for UI
+    labels. Returns None when the absolute path is not under a known asset root.
     """
     try:
-        root_category, rel_path = get_asset_category_and_relative_path(file_path)
+        root, bucket, rel = get_asset_root_bucket_and_filepath(file_path)
     except ValueError:
         return None
 
-    p = Path(rel_path)
-    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
-    if not parts:
-        return None
+    display_name = rel or None
+    if bucket is None:
+        response_file_path = f"{root}/{rel}" if rel else root
+    else:
+        response_file_path = f"{root}/{bucket}/{rel}" if rel else f"{root}/{bucket}"
+    return response_file_path, display_name
 
-    if root_category == "models":
-        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
-        inside = parts[1:] if len(parts) > 1 else [parts[0]]
-        return "/".join(inside)
-    return "/".join(parts)  # input/output: keep all parts
+
+def compute_display_name(file_path: str) -> str | None:
+    """Return the asset's `display_name`, or None for unknown paths."""
+    result = compute_paths_for_response(file_path)
+    return result[1] if result else None
+
+
+def compute_file_path(file_path: str) -> str | None:
+    """Return the asset's logical `file_path`, or None for unknown paths."""
+    result = compute_paths_for_response(file_path)
+    return result[0] if result else None
+
+
+def compute_relative_filename(file_path: str) -> str | None:
+    """
+    Return the path relative to the asset root or model category, using forward slashes, eg:
+      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
+      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
+      /.../input/sub/image.png -> "sub/image.png"
+
+    For unknown paths, returns None.
+    """
+    return compute_display_name(file_path)
+
+
+def get_asset_root_bucket_and_filepath(
+    file_path: str,
+) -> tuple[RootCategory, str | None, str]:
+    """Decompose an absolute path into (root, bucket, path-under-bucket).
+
+    `bucket` is only set for model assets. The returned relative path always
+    uses `/` separators and is empty when the path is exactly the matched root.
+
+    Raises:
+        ValueError: path does not belong to any known root.
+    """
+    fp_abs = os.path.abspath(file_path)
+
+    def _check_is_within(child: str, parent: str) -> bool:
+        return Path(child).is_relative_to(parent)
+
+    def _compute_relative(child: str, parent: str) -> str:
+        # Normalize relative path, stripping any leading ".." components
+        # by anchoring to root (os.sep) then computing relpath back from it.
+        rel = os.path.relpath(
+            os.path.join(os.sep, os.path.relpath(child, parent)), os.sep
+        )
+        return "" if rel == "." else rel.replace(os.sep, "/")
+
+    for root_tag, getter in (
+        ("input", folder_paths.get_input_directory),
+        ("output", folder_paths.get_output_directory),
+        ("temp", folder_paths.get_temp_directory),
+    ):
+        base = os.path.abspath(getter())
+        if _check_is_within(fp_abs, base):
+            return root_tag, None, _compute_relative(fp_abs, base)
+
+    # models: check deepest matching base to avoid ambiguity.
+    best: tuple[int, str, str] | None = None
+    for bucket, bases in get_comfy_models_folders():
+        for b in bases:
+            base_abs = os.path.abspath(b)
+            if not _check_is_within(fp_abs, base_abs):
+                continue
+            cand = (len(base_abs), bucket, _compute_relative(fp_abs, base_abs))
+            if best is None or cand[0] > best[0]:
+                best = cand
+
+    if best is not None:
+        _, bucket, rel_inside = best
+        return "models", bucket, rel_inside
+
+    raise ValueError(
+        f"Path is not within input, output, temp, or configured model bases: {file_path}"
+    )
 
 
 def get_asset_category_and_relative_path(
     file_path: str,
-) -> tuple[Literal["input", "output", "temp", "models"], str]:
+) -> tuple[RootCategory, str]:
     """Determine which root category a file path belongs to.
 
     Categories:
@@ -160,7 +259,21 @@ def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
     """Return (name, tags) derived from a filesystem path.
 
     - name: base filename with extension
-    - tags: [root_category] + parent folder names in order
+    - tags: [root_category] for paths with no parent subdirectories,
+      [root_category, slash_joined_subpath] otherwise. The parent subpath
+      (everything between the root category and the filename) is collapsed
+      into a single tag rather than emitted as one tag per directory, so
+      consumers can use ``tags[1]`` as a stable category identifier that
+      survives nested directory layouts (e.g. diffusers components).
+
+      The subpath is lowercased to match the canonicalization applied by
+      :func:`ensure_tags_exist`; without that, the
+      ``asset_reference_tags.tag_name`` FK to the lowercased ``tags.name``
+      would fail for any path containing uppercase letters. The root
+      category is lowercase by construction in
+      :func:`get_asset_category_and_relative_path`, so no separate cast
+      is applied here. Consumers that need to look up providers keyed on
+      original-case paths should normalize their lookup key to lowercase.
 
     Raises:
         ValueError: path does not belong to any known root.
@@ -170,4 +283,7 @@ def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
     parent_parts = [
         part for part in p.parent.parts if part not in (".", "..", p.anchor)
     ]
-    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
+    tags = [root_category]
+    if parent_parts:
+        tags.append("/".join(parent_parts).lower())
+    return p.name, list(dict.fromkeys(t.strip() for t in tags if t.strip()))

app/assets/services/schemas.py

@@ -71,6 +71,7 @@ class AssetSummaryData:
 class ListAssetsResult:
     items: list[AssetSummaryData]
     total: int
+    next_cursor: str | None = None
 
 
 @dataclass(frozen=True)

app/frontend_management.py

@@ -27,7 +27,7 @@ def frontend_install_warning_message():
     return f"""
 {get_missing_requirements_message()}
 
-This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+The ComfyUI frontend is shipped in a pip package so it needs to be updated separately from the ComfyUI code.
 """.strip()
 
 def parse_version(version: str) -> tuple[int, int, int]:
@@ -38,40 +38,54 @@ def is_valid_version(version: str) -> bool:
     pattern = r"^(\d+)\.(\d+)\.(\d+)$"
     return bool(re.match(pattern, version))
 
-def get_installed_frontend_version():
-    """Get the currently installed frontend package version."""
-    frontend_version_str = version("comfyui-frontend-package")
-    return frontend_version_str
-
-
 def get_required_frontend_version():
     return get_required_packages_versions().get("comfyui-frontend-package", None)
 
 
-def check_frontend_version():
-    """Check if the frontend version is up to date."""
+COMFY_PACKAGE_VERSIONS = []
+def get_comfy_package_versions():
+    """List installed/required versions for every comfy* package in requirements.txt."""
+    if COMFY_PACKAGE_VERSIONS:
+        return COMFY_PACKAGE_VERSIONS.copy()
+    out = COMFY_PACKAGE_VERSIONS
+    for name, required in (get_required_packages_versions() or {}).items():
+        if not name.startswith("comfy"):
+            continue
+        try:
+            installed = version(name)
+        except Exception:
+            installed = None
+        out.append({"name": name, "installed": installed, "required": required})
+    return out.copy()
 
-    try:
-        frontend_version_str = get_installed_frontend_version()
-        frontend_version = parse_version(frontend_version_str)
-        required_frontend_str = get_required_frontend_version()
-        required_frontend = parse_version(required_frontend_str)
-        if frontend_version < required_frontend:
+
+def check_comfy_packages_versions():
+    """Warn for every comfy* package whose installed version is below requirements.txt."""
+    from packaging.version import InvalidVersion, parse as parse_pep440
+    for pkg in get_comfy_package_versions():
+        installed_str = pkg["installed"]
+        required_str = pkg["required"]
+        if not installed_str or not required_str:
+            continue
+        try:
+            outdated = parse_pep440(installed_str) < parse_pep440(required_str)
+        except InvalidVersion as e:
+            logging.error(f"Failed to check {pkg['name']} version: {e}")
+            continue
+        if outdated:
             app.logger.log_startup_warning(
                 f"""
 ________________________________________________________________________
 WARNING WARNING WARNING WARNING WARNING
 
-Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
+Installed {pkg["name"]} version {installed_str} is lower than the recommended version {required_str}.
 
-{frontend_install_warning_message()}
+{get_missing_requirements_message()}
 ________________________________________________________________________
 """.strip()
             )
         else:
-            logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
-    except Exception as e:
-        logging.error(f"Failed to check frontend version: {e}")
+            logging.info("{} version: {}".format(pkg["name"], installed_str))
 
 
 REQUEST_TIMEOUT = 10  # seconds
@@ -201,6 +215,11 @@ class FrontendManager:
     def get_required_templates_version(cls) -> str:
         return get_required_packages_versions().get("comfyui-workflow-templates", None)
 
+    @classmethod
+    def get_comfy_package_versions(cls):
+        """List installed/required versions for every comfy* package in requirements.txt."""
+        return get_comfy_package_versions()
+
     @classmethod
     def default_frontend_path(cls) -> str:
         try:
@@ -341,7 +360,7 @@ comfyui-workflow-templates is not installed.
             main error source might be request timeout or invalid URL.
         """
         if version_string == DEFAULT_VERSION_STRING:
-            check_frontend_version()
+            check_comfy_packages_versions()
             return cls.default_frontend_path()
 
         repo_owner, repo_name, version = cls.parse_version_string(version_string)
@@ -403,7 +422,7 @@ comfyui-workflow-templates is not installed.
         except Exception as e:
             logging.error("Failed to initialize frontend: %s", e)
             logging.info("Falling back to the default frontend.")
-            check_frontend_version()
+            check_comfy_packages_versions()
             return cls.default_frontend_path()
     @classmethod
     def template_asset_handler(cls):

app/node_replace_manager.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import logging
+
 from aiohttp import web
 
 from typing import TYPE_CHECKING, TypedDict
@@ -31,8 +33,22 @@ class NodeReplaceManager:
         self._replacements: dict[str, list[NodeReplace]] = {}
 
     def register(self, node_replace: NodeReplace):
-        """Register a node replacement mapping."""
-        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
+        """Register a node replacement mapping.
+
+        Idempotent: if a replacement with the same (old_node_id, new_node_id)
+        is already registered, the duplicate is ignored. This prevents stale
+        entries from accumulating when custom nodes are reloaded in the same
+        process (e.g. via ComfyUI-Manager).
+        """
+        existing = self._replacements.setdefault(node_replace.old_node_id, [])
+        for entry in existing:
+            if entry.new_node_id == node_replace.new_node_id:
+                logging.debug(
+                    "Node replacement %s -> %s already registered, ignoring duplicate.",
+                    node_replace.old_node_id, node_replace.new_node_id,
+                )
+                return
+        existing.append(node_replace)
 
     def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
         """Get replacements for an old node ID."""

app/user_manager.py

@@ -28,8 +28,8 @@ def get_file_info(path: str, relative_to: str) -> FileInfo:
     return {
         "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
         "size": os.path.getsize(path),
-        "modified": os.path.getmtime(path),
-        "created": os.path.getctime(path)
+        "modified": int(os.path.getmtime(path) * 1000),
+        "created": int(os.path.getctime(path) * 1000),
     }
 
 

blueprints/Brightness and Contrast.json

@@ -431,9 +431,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts image brightness and contrast using a real-time GPU fragment shader."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Canny to Image (Z-Image-Turbo).json

@@ -162,7 +162,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Canny to Image (Z-Image-Turbo)",
+        "name": "Canny to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1553,7 +1553,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Canny to image"
+        "category": "Image generation and editing/Canny to image",
+        "description": "Generates an image from a Canny edge map using Z-Image-Turbo, with text conditioning."
       }
     ]
   },
@@ -1574,4 +1575,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Canny to Video (LTX 2.0).json

@@ -192,7 +192,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Canny to Video (LTX 2.0)",
+        "name": "Canny to Video (LTX 2.0)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -3600,7 +3600,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Canny to video"
+        "category": "Video generation and editing/Canny to video",
+        "description": "Generates video from Canny edge maps using LTX-2, with optional synchronized audio."
       }
     ]
   },
@@ -3616,4 +3617,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Chromatic Aberration.json

@@ -377,8 +377,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adds lens-style chromatic aberration (color fringing) using a real-time GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/Color Adjustment.json

@@ -596,7 +596,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts saturation, temperature, tint, and vibrance using a real-time GPU fragment shader."
       }
     ]
   }

blueprints/Color Balance.json

@@ -1129,7 +1129,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Balances colors across shadows, midtones, and highlights using a real-time GPU fragment shader."
       }
     ]
   }

blueprints/Color Curves.json

@@ -608,7 +608,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Fine-tunes tone and color with per-channel curve adjustments using a real-time GPU fragment shader."
       }
     ]
   }

blueprints/Crop Images 2x2.json

@@ -1609,7 +1609,8 @@
           }
         ],
         "extra": {},
-        "category": "Image Tools/Crop"
+        "category": "Image Tools/Crop",
+        "description": "Splits an image into a 2×2 grid of four equal tiles."
       }
     ]
   },

blueprints/Crop Images 3x3.json

@@ -2946,7 +2946,8 @@
           }
         ],
         "extra": {},
-        "category": "Image Tools/Crop"
+        "category": "Image Tools/Crop",
+        "description": "Splits an image into a 3×3 grid of nine equal tiles."
       }
     ]
   },

blueprints/Depth to Image (Z-Image-Turbo).json

@@ -1579,7 +1579,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Depth to image"
+        "category": "Image generation and editing/Depth to image",
+        "description": "Generates an image from a depth map using Z-Image-Turbo with text conditioning."
       },
       {
         "id": "458bdf3c-4b58-421c-af50-c9c663a4d74c",
@@ -2461,7 +2462,8 @@
             ]
           },
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
       }
     ]
   },

blueprints/Depth to Video (ltx 2.0).json

@@ -4233,7 +4233,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Depth to video"
+        "category": "Video generation and editing/Depth to video",
+        "description": "Generates depth-controlled video with LTX-2: motion and structure follow a depth-reference video alongside text prompting, optional first-frame image conditioning, with optional synchronized audio."
       },
       {
         "id": "38b60539-50a7-42f9-a5fe-bdeca26272e2",
@@ -5192,7 +5193,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
       }
     ]
   },

blueprints/Edge-Preserving Blur.json

@@ -450,9 +450,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Blur"
+        "category": "Image Tools/Blur",
+        "description": "Applies bilateral (edge-preserving) blur to soften images while retaining detail."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Film Grain.json

@@ -580,8 +580,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adds procedural film grain texture for a cinematic look via GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/First-Last-Frame to Video (LTX-2.3).json

@@ -3350,7 +3350,8 @@
           }
         ],
         "extra": {},
-        "category": "Video generation and editing/First-Last-Frame to Video"
+        "category": "Video generation and editing/First-Last-Frame to Video",
+        "description": "Generates a video interpolating between first and last keyframes using LTX-2.3."
       }
     ]
   },

blueprints/Frame Interpolation.json

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+          "name": "VIDEO_1",
+          "type": "VIDEO",
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+        "description": "Increases video frame rate by synthesizing intermediate frames with a frame interpolation model."
+      }
+    ]
+  },
+  "extra": {}
+}

blueprints/Get Any Video Frame.json

@@ -0,0 +1,485 @@
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+          "widget": {
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+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": []
+        }
+      ],
+      "title": "Get Any Video Frame",
+      "properties": {
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+                "type": "IMAGE",
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+                "name": "INT",
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+        "extra": {},
+        "category": "Video Tools",
+        "description": "Extracts one image frame from a video at a chosen index, with optional trim and FPS control."
+      }
+    ]
+  },
+  "extra": {
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+}

blueprints/Glow.json

@@ -575,8 +575,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adds a glow/bloom effect around bright image areas via GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/Hue and Saturation.json

@@ -752,8 +752,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts hue, saturation, and lightness of an image using a real-time GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/Image Blur.json

@@ -374,7 +374,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Blur"
+        "category": "Image Tools/Blur",
+        "description": "Applies Gaussian, Box, or Radial blur to soften images and create stylized depth or motion effects."
       }
     ]
   }

blueprints/Image Captioning (gemini).json

@@ -310,7 +310,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Image Captioning"
+        "category": "Text generation/Image Captioning",
+        "description": "Generates descriptive captions for images using Google's Gemini multimodal LLM."
       }
     ]
   }

blueprints/Image Channels.json

@@ -315,8 +315,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Manipulates individual RGBA channels for masking, compositing, and channel effects."
       }
     ]
   }
-}
+}

blueprints/Image Edit (Flux.2 Klein 4B).json

@@ -1472,7 +1472,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Edit image"
+        "category": "Image generation and editing/Edit image",
+        "description": "Edits an input image via text instructions using FLUX.2 [klein] 4B."
       },
       {
         "id": "6007e698-2ebd-4917-84d8-299b35d7b7ab",
@@ -1821,7 +1822,8 @@
         ],
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-        }
+        },
+        "description": "Applies reference image conditioning for style/identity transfer (Flux.2 Klein 4B)."
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     ]
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@@ -1837,4 +1839,4 @@
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+}

blueprints/Image Edit (LongCat Image Edit).json

@@ -1417,7 +1417,8 @@
           }
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         "extra": {},
-        "category": "Image generation and editing/Edit image"
+        "category": "Image generation and editing/Edit image",
+        "description": "Edits images via text instructions using LongCat Image Edit, an instruction-following image editing diffusion model."
       }
     ]
   },

blueprints/Image Edit (Qwen 2511).json

@@ -132,7 +132,7 @@
         },
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         "config": {},
-        "name": "local-Image Edit (Qwen 2511)",
+        "name": "Image Edit (Qwen 2511)",
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@@ -1468,7 +1468,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
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-        "category": "Image generation and editing/Edit image"
+        "category": "Image generation and editing/Edit image",
+        "description": "Edits images via text instructions using Qwen-Image-Edit-2511 with improved character consistency and integrated LoRA."
       }
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@@ -1489,4 +1490,4 @@
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blueprints/Image Inpainting (Flux.1 Fill Dev).json

@@ -1188,7 +1188,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Inpaint image"
+        "category": "Image generation and editing/Inpaint image",
+        "description": "Inpaints masked image regions using Flux.1 fill [dev], Black Forest Labs' inpainting/outpainting model."
       }
     ]
   },
@@ -1202,4 +1203,4 @@
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blueprints/Image Inpainting (Qwen-image).json

@@ -1548,7 +1548,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Inpaint image"
+        "category": "Image generation and editing/Inpaint image",
+        "description": "Inpaints masked regions using Qwen-Image, extending its multilingual text rendering to inpainting tasks."
       },
       {
         "id": "56a1f603-fbd2-40ed-94ef-c9ecbd96aca8",
@@ -1907,7 +1908,8 @@
         ],
         "extra": {
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-        }
+        },
+        "description": "Expands and softens mask edges to reduce visible seams after image processing."
       }
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   },

blueprints/Image Levels.json

@@ -742,9 +742,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts black point, white point, and gamma for tonal range control via GPU shader."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Image Outpainting (Qwen-Image).json

@@ -1919,7 +1919,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Outpaint image"
+        "category": "Image generation and editing/Outpaint image",
+        "description": "Outpaints beyond image boundaries using Qwen-Image's outpainting capabilities."
       },
       {
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@@ -2278,7 +2279,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Expands and softens mask edges to reduce visible seams after image processing."
       },
       {
         "id": "2a4b2cc0-db37-4302-a067-da392f38f06b",
@@ -2733,7 +2735,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Scales both image and mask together while preserving alignment for editing workflows."
       }
     ]
   },

blueprints/Image Segmentation (SAM3).json

@@ -0,0 +1,714 @@
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+        },
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+          "type": "INT",
+          "widget": {
+            "name": "refine_iterations"
+          },
+          "link": null
+        },
+        {
+          "name": "individual_masks",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "individual_masks"
+          },
+          "link": null
+        },
+        {
+          "name": "ckpt_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "ckpt_name"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "masks",
+          "name": "masks",
+          "type": "MASK",
+          "links": []
+        },
+        {
+          "localized_name": "bboxes",
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+}

blueprints/Image Upscale(Z-image-Turbo).json

@@ -141,7 +141,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image Upscale(Z-image-Turbo)",
+        "name": "Image Upscale (Z-image-Turbo)",
         "inputNode": {
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           "bounding": [
@@ -1302,7 +1302,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Enhance"
+        "category": "Image generation and editing/Enhance",
+        "description": "Upscales images to higher resolution using Z-Image-Turbo."
       }
     ]
   },

blueprints/Image to Depth Map (Lotus).json

@@ -99,7 +99,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image to Depth Map (Lotus)",
+        "name": "Image to Depth Map (Lotus)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -948,7 +948,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Depth to image"
+        "category": "Image generation and editing/Depth to image",
+        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
       }
     ]
   },
@@ -964,4 +965,4 @@
     "workflowRendererVersion": "LG"
   },
   "version": 0.4
-}
+}

blueprints/Image to Layers(Qwen-Image-Layered).json

@@ -1586,7 +1586,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Image to layers"
+        "category": "Image generation and editing/Image to layers",
+        "description": "Decomposes an image into variable-resolution RGBA layers for independent editing using Qwen-Image-Layered."
       }
     ]
   },

blueprints/Image to Model (Hunyuan3d 2.1).json

@@ -72,7 +72,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image to Model (Hunyuan3d 2.1)",
+        "name": "Image to 3D Model (Hunyuan3d 2.1)",
         "inputNode": {
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           "bounding": [
@@ -765,7 +765,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "3D/Image to 3D Model"
+        "category": "3D/Image to 3D Model",
+        "description": "Generates 3D mesh models from a single input image using Hunyuan3D 2.0/2.1."
       }
     ]
   },

blueprints/Image to Video (LTX-2.3).json

@@ -4223,7 +4223,8 @@
         "extra": {
           "workflowRendererVersion": "Vue-corrected"
         },
-        "category": "Video generation and editing/Image to video"
+        "category": "Video generation and editing/Image to video",
+        "description": "Generates video from a single input image using LTX-2.3."
       }
     ]
   },

blueprints/Image to Video (Wan 2.2).json

@@ -206,7 +206,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image to Video (Wan 2.2)",
+        "name": "Image to Video (Wan 2.2)",
         "inputNode": {
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           "bounding": [
@@ -2027,7 +2027,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Image to video"
+        "category": "Video generation and editing/Image to video",
+        "description": "Image-to-video with Wan 2.2 using a start image plus text prompt to extend motion from the still frame."
       }
     ]
   },

blueprints/Pose to Image (Z-Image-Turbo).json

@@ -134,7 +134,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Pose to Image (Z-Image-Turbo)",
+        "name": "Pose to Image (Z-Image-Turbo)",
         "inputNode": {
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@@ -1298,7 +1298,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Pose to image"
+        "category": "Image generation and editing/Pose to image",
+        "description": "Generates an image from pose keypoints using Z-Image-Turbo with text conditioning."
       }
     ]
   },
@@ -1319,4 +1320,4 @@
     }
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   "version": 0.4
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+}

blueprints/Pose to Video (LTX 2.0).json

@@ -3870,7 +3870,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Pose to video"
+        "category": "Video generation and editing/Pose to video",
+        "description": "Generates video from pose reference frames using LTX-2, with optional synchronized audio."
       }
     ]
   },

blueprints/Prompt Enhance.json

@@ -270,9 +270,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Prompt enhance"
+        "category": "Text generation/Prompt enhance",
+        "description": "Expands short text prompts into detailed descriptions using a text generation model for better generation quality."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Remove Background (BiRefNet).json

@@ -0,0 +1,397 @@
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+          "widget": {
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+          "type": "IMAGE",
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+        "name": "Remove Background (BiRefNet)",
+        "description": "Removes or replaces image backgrounds using BiRefNet segmentation and alpha compositing.",
+        "inputNode": {
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+            "properties": {
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+                "widget": {
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+                "name": "bg_model",
+                "type": "BACKGROUND_REMOVAL",
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+              {
+                "localized_name": "alpha",
+                "name": "alpha",
+                "type": "MASK",
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+            "outputs": [
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+                "localized_name": "IMAGE",
+                "name": "IMAGE",
+                "type": "IMAGE",
+                "links": [
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+              }
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+        ],
+        "extra": {},
+        "category": "Image generation and editing/Background Removal"
+      }
+    ]
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+  "extra": {}
+}

blueprints/Sharpen.json

@@ -302,8 +302,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Sharpen"
+        "category": "Image Tools/Sharpen",
+        "description": "Sharpens image details using a GPU fragment shader for enhanced clarity."
       }
     ]
   }
-}
+}

blueprints/Text to Audio (ACE-Step 1.5).json

@@ -222,7 +222,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Text to Audio (ACE-Step 1.5)",
+        "name": "Text to Audio (ACE-Step 1.5)",
         "inputNode": {
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           "bounding": [
@@ -1502,7 +1502,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Audio/Music generation"
+        "category": "Audio/Music generation",
+        "description": "Generates audio/music from text prompts using ACE-Step 1.5, a diffusion-based audio generation model."
       }
     ]
   },
@@ -1518,4 +1519,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Text to Image (Flux.1 Dev).json

@@ -1029,7 +1029,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from prompts using FLUX.1 [dev]: a 12B rectified-flow MMDiT with dual CLIP plus T5-XXL text encoders and guidance-distilled sampling for sharp prompt following versus classic DDPM diffusion."
       }
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@@ -1043,4 +1044,4 @@
     },
     "ue_links": []
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-}
+}

blueprints/Text to Image (Flux.1 Krea Dev).json

@@ -1023,7 +1023,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "FLUX.1 Krea [dev] (Black Forest Labs × Krea): open-weight 12B rectified-flow text-to-image drop-in alongside FLUX.1 [dev], tuned away from overcooked saturation toward more natural diversity in people, realism, and style while keeping ecosystem compatibility."
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@@ -1037,4 +1038,4 @@
     },
     "ue_links": []
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+}

blueprints/Text to Image (NetaYume Lumina).json

@@ -1104,7 +1104,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from text prompts using NetaYume Lumina, fine-tuned from Neta Lumina for anime-style and illustration generation."
       },
       {
         "id": "a07fdf06-1bda-4dac-bdbd-63ee8ebca1c9",
@@ -1458,11 +1459,12 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Encodes a negative text prompt via CLIP for classifier-free guidance in anime-style generation (NetaYume Lumina)."
       }
     ]
   },
   "extra": {
     "ue_links": []
   }
-}
+}

blueprints/Text to Image (Qwen-Image 2512).json

@@ -1941,7 +1941,8 @@
         "extra": {
           "workflowRendererVersion": "Vue-corrected"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from text prompts using Qwen-Image-2512, with enhanced human realism and finer natural detail over the base version."
       }
     ]
   },

blueprints/Text to Image (Qwen-Image).json

@@ -1873,7 +1873,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from text prompts using Qwen-Image, Alibaba's 20B MMDiT model with excellent multilingual text rendering."
       }
     ]
   },

blueprints/Text to Image (Z-Image-Turbo).json

@@ -1,22 +1,21 @@
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   "revision": 0,
-  "last_node_id": 34,
-  "last_link_id": 40,
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   "nodes": [
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-      "type": "dfe9eb32-97c0-43a5-90d5-4fd37768d91b",
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+      "order": 1,
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@@ -44,6 +43,22 @@
           },
           "link": null
         },
+        {
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
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+        },
+        {
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+          "widget": {
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+          },
+          "link": null
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         {
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@@ -80,15 +95,15 @@
       "properties": {
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             "height"
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@@ -97,19 +112,23 @@
           ],
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             "3",
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+            "steps"
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             "unet_name"
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             "clip_name"
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             "vae_name"
+          ],
+          [
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+            "control_after_generate"
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@@ -122,48 +141,40 @@
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-        "qwen_3_4b.safetensors",
-        "ae.safetensors"
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+      "widgets_values": [],
+      "title": "Text to Image (Z-Image-Turbo)"
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   "links": [],
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           "lastRerouteId": 0
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         "revision": 0,
         "config": {},
-        "name": "local-Text to Image (Z-Image-Turbo)",
+        "name": "Text to Image (Z-Image-Turbo)",
         "inputNode": {
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           "bounding": [
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             120,
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             120,
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@@ -178,8 +189,8 @@
             ],
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@@ -190,8 +201,8 @@
               35
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@@ -202,44 +213,68 @@
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           {
-            "id": "23087d15-8412-4fbd-b71e-9b6d7ef76de1",
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           {
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@@ -253,8 +288,8 @@
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@@ -264,15 +299,15 @@
             "id": 30,
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@@ -282,7 +317,7 @@
                 "widget": {
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-                "link": 39
+                "link": 74
               },
               {
                 "localized_name": "type",
@@ -315,9 +350,9 @@
               }
             ],
             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.3.73",
-              "Node name for S&R": "CLIPLoader",
               "models": [
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@@ -343,15 +378,15 @@
             "id": 29,
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@@ -361,7 +396,7 @@
                 "widget": {
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-                "link": 40
+                "link": 75
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             "outputs": [
@@ -375,9 +410,9 @@
               }
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             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.3.73",
-              "Node name for S&R": "VAELoader",
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@@ -401,12 +436,12 @@
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@@ -430,9 +465,9 @@
               }
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             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.3.73",
-              "Node name for S&R": "ConditioningZeroOut",
               "enableTabs": false,
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@@ -440,22 +475,21 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
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@@ -483,9 +517,9 @@
               }
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             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.3.64",
-              "Node name for S&R": "VAEDecode",
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@@ -493,22 +527,21 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
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@@ -518,7 +551,7 @@
                 "widget": {
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+                "link": 73
               },
               {
                 "localized_name": "weight_dtype",
@@ -541,9 +574,9 @@
               }
             ],
             "properties": {
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               "cnr_id": "comfy-core",
               "ver": "0.3.73",
-              "Node name for S&R": "UNETLoader",
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@@ -568,15 +601,15 @@
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@@ -607,9 +640,9 @@
               }
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             "properties": {
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@@ -626,15 +659,15 @@
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@@ -677,9 +710,9 @@
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               "tabWidth": 65,
               "tabXOffset": 10,
@@ -694,19 +727,77 @@
               1
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+                "type": "FLOAT",
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+                "name": "MODEL",
+                "type": "MODEL",
+                "slot_index": 0,
+                "links": [
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@@ -800,9 +891,9 @@
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blueprints/Text to Video (LTX-2.3).json

@@ -4286,7 +4286,8 @@
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@@ -1572,7 +1572,8 @@
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blueprints/Unsharp Mask.json

@@ -434,8 +434,9 @@
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blueprints/Video Captioning (Gemini).json

@@ -307,7 +307,8 @@
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blueprints/Video Inpaint(Wan2.1 VACE).json

@@ -165,7 +165,7 @@
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+              "hasSecondTab": false,
+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65,
+              "models": [
+                {
+                  "name": "sam3.1_multiplex_fp16.safetensors",
+                  "url": "https://huggingface.co/Comfy-Org/sam3.1/resolve/main/checkpoints/sam3.1_multiplex_fp16.safetensors",
+                  "directory": "checkpoints"
+                }
+              ]
+            },
+            "widgets_values": [
+              "sam3.1_multiplex_fp16.safetensors"
+            ]
+          },
+          {
+            "id": 128,
+            "type": "GetVideoComponents",
+            "pos": [
+              -1910,
+              -3540
+            ],
+            "size": [
+              230,
+              120
+            ],
+            "flags": {},
+            "order": 4,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "video",
+                "name": "video",
+                "type": "VIDEO",
+                "link": 252
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "links": [
+                  253
+                ]
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "type": "AUDIO",
+                "links": [
+                  259
+                ]
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "links": [
+                  260
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "GetVideoComponents",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.3",
+              "enableTabs": false,
+              "tabWidth": 65,
+              "tabXOffset": 10,
+              "hasSecondTab": false,
+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65
+            }
+          },
+          {
+            "id": 129,
+            "type": "Note",
+            "pos": [
+              -1980,
+              -2790
+            ],
+            "size": [
+              370,
+              250
+            ],
+            "flags": {},
+            "order": 0,
+            "mode": 0,
+            "inputs": [],
+            "outputs": [],
+            "title": "Note: Prompt format",
+            "properties": {},
+            "widgets_values": [
+              "Max tokens for this model is only 32, to separately prompt multiple subjects you can separate prompts with comma, and set the max amount of objects detected for each prompt with :N\n\nFor example above test prompt finds 2 cakes, one apron, 4 window panels"
+            ],
+            "color": "#432",
+            "bgcolor": "#653"
+          }
+        ],
+        "groups": [],
+        "links": [
+          {
+            "id": 237,
+            "origin_id": 127,
+            "origin_slot": 0,
+            "target_id": 126,
+            "target_slot": 0,
+            "type": "MODEL"
+          },
+          {
+            "id": 200,
+            "origin_id": 125,
+            "origin_slot": 0,
+            "target_id": 126,
+            "target_slot": 2,
+            "type": "CONDITIONING"
+          },
+          {
+            "id": 240,
+            "origin_id": 127,
+            "origin_slot": 1,
+            "target_id": 125,
+            "target_slot": 0,
+            "type": "CLIP"
+          },
+          {
+            "id": 231,
+            "origin_id": 126,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "MASK"
+          },
+          {
+            "id": 232,
+            "origin_id": 126,
+            "origin_slot": 1,
+            "target_id": -20,
+            "target_slot": 1,
+            "type": "BOUNDING_BOX"
+          },
+          {
+            "id": 252,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 128,
+            "target_slot": 0,
+            "type": "VIDEO"
+          },
+          {
+            "id": 253,
+            "origin_id": 128,
+            "origin_slot": 0,
+            "target_id": 126,
+            "target_slot": 1,
+            "type": "IMAGE"
+          },
+          {
+            "id": 254,
+            "origin_id": -10,
+            "origin_slot": 1,
+            "target_id": 125,
+            "target_slot": 1,
+            "type": "STRING"
+          },
+          {
+            "id": 255,
+            "origin_id": -10,
+            "origin_slot": 2,
+            "target_id": 126,
+            "target_slot": 3,
+            "type": "BOUNDING_BOX"
+          },
+          {
+            "id": 256,
+            "origin_id": -10,
+            "origin_slot": 3,
+            "target_id": 126,
+            "target_slot": 4,
+            "type": "STRING"
+          },
+          {
+            "id": 257,
+            "origin_id": -10,
+            "origin_slot": 4,
+            "target_id": 126,
+            "target_slot": 5,
+            "type": "STRING"
+          },
+          {
+            "id": 259,
+            "origin_id": 128,
+            "origin_slot": 1,
+            "target_id": -20,
+            "target_slot": 2,
+            "type": "AUDIO"
+          },
+          {
+            "id": 260,
+            "origin_id": 128,
+            "origin_slot": 2,
+            "target_id": -20,
+            "target_slot": 3,
+            "type": "FLOAT"
+          },
+          {
+            "id": 261,
+            "origin_id": -10,
+            "origin_slot": 5,
+            "target_id": 126,
+            "target_slot": 6,
+            "type": "FLOAT"
+          },
+          {
+            "id": 262,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 126,
+            "target_slot": 7,
+            "type": "INT"
+          },
+          {
+            "id": 263,
+            "origin_id": -10,
+            "origin_slot": 7,
+            "target_id": 126,
+            "target_slot": 8,
+            "type": "BOOLEAN"
+          },
+          {
+            "id": 273,
+            "origin_id": -10,
+            "origin_slot": 8,
+            "target_id": 127,
+            "target_slot": 0,
+            "type": "COMBO"
+          }
+        ],
+        "extra": {},
+        "category": "Video Tools",
+        "description": "Segments video into temporally consistent masks using Meta SAM3 from text or interactive prompts."
+      }
+    ]
+  },
+  "extra": {}
+}

blueprints/Video Stitch.json

@@ -1,21 +1,21 @@
 {
   "revision": 0,
-  "last_node_id": 84,
+  "last_node_id": 85,
   "last_link_id": 0,
   "nodes": [
     {
-      "id": 84,
-      "type": "8e8aa94a-647e-436d-8440-8ee4691864de",
+      "id": 85,
+      "type": "637913e7-0206-46ba-8ded-70ae3a7c2e19",
       "pos": [
-        -6100,
-        2620
+        -880,
+        -2260
       ],
       "size": [
         290,
         160
       ],
       "flags": {},
-      "order": 0,
+      "order": 2,
       "mode": 0,
       "inputs": [
         {
@@ -76,31 +76,26 @@
       "properties": {
         "proxyWidgets": [
           [
-            "-1",
+            "79",
             "direction"
           ],
           [
-            "-1",
+            "79",
             "match_image_size"
           ],
           [
-            "-1",
+            "79",
             "spacing_width"
           ],
           [
-            "-1",
+            "79",
             "spacing_color"
           ]
         ],
         "cnr_id": "comfy-core",
         "ver": "0.13.0"
       },
-      "widgets_values": [
-        "right",
-        true,
-        0,
-        "white"
-      ],
+      "widgets_values": [],
       "title": "Video Stitch"
     }
   ],
@@ -109,12 +104,12 @@
   "definitions": {
     "subgraphs": [
       {
-        "id": "8e8aa94a-647e-436d-8440-8ee4691864de",
+        "id": "637913e7-0206-46ba-8ded-70ae3a7c2e19",
         "version": 1,
         "state": {
           "lastGroupId": 1,
-          "lastNodeId": 84,
-          "lastLinkId": 262,
+          "lastNodeId": 97,
+          "lastLinkId": 282,
           "lastRerouteId": 0
         },
         "revision": 0,
@@ -123,8 +118,8 @@
         "inputNode": {
           "id": -10,
           "bounding": [
-            -6580,
-            2649,
+            -6810,
+            2580,
             143.55859375,
             160
           ]
@@ -132,8 +127,8 @@
         "outputNode": {
           "id": -20,
           "bounding": [
-            -5720,
-            2659,
+            -4770,
+            2600,
             120,
             60
           ]
@@ -149,8 +144,8 @@
             "localized_name": "video",
             "label": "Before Video",
             "pos": [
-              -6456.44140625,
-              2669
+              -6686.44140625,
+              2600
             ]
           },
           {
@@ -163,8 +158,8 @@
             "localized_name": "video_1",
             "label": "After Video",
             "pos": [
-              -6456.44140625,
-              2689
+              -6686.44140625,
+              2620
             ]
           },
           {
@@ -175,8 +170,8 @@
               259
             ],
             "pos": [
-              -6456.44140625,
-              2709
+              -6686.44140625,
+              2640
             ]
           },
           {
@@ -187,8 +182,8 @@
               260
             ],
             "pos": [
-              -6456.44140625,
-              2729
+              -6686.44140625,
+              2660
             ]
           },
           {
@@ -199,8 +194,8 @@
               261
             ],
             "pos": [
-              -6456.44140625,
-              2749
+              -6686.44140625,
+              2680
             ]
           },
           {
@@ -211,8 +206,8 @@
               262
             ],
             "pos": [
-              -6456.44140625,
-              2769
+              -6686.44140625,
+              2700
             ]
           }
         ],
@@ -226,8 +221,8 @@
             ],
             "localized_name": "VIDEO",
             "pos": [
-              -5700,
-              2679
+              -4750,
+              2620
             ]
           }
         ],
@@ -238,11 +233,11 @@
             "type": "GetVideoComponents",
             "pos": [
               -6390,
-              2560
+              2600
             ],
             "size": [
-              193.530859375,
-              66
+              230,
+              120
             ],
             "flags": {},
             "order": 1,
@@ -278,9 +273,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "GetVideoComponents",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "GetVideoComponents"
+              "ver": "0.13.0"
             }
           },
           {
@@ -291,8 +286,8 @@
               2420
             ],
             "size": [
-              193.530859375,
-              66
+              230,
+              120
             ],
             "flags": {},
             "order": 0,
@@ -332,21 +327,254 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "GetVideoComponents",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "GetVideoComponents"
+              "ver": "0.13.0"
             }
           },
+          {
+            "id": 90,
+            "type": "GetImageSize",
+            "pos": [
+              -6390,
+              3030
+            ],
+            "size": [
+              230,
+              120
+            ],
+            "flags": {},
+            "order": 4,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "image",
+                "name": "image",
+                "type": "IMAGE",
+                "link": 266
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "width",
+                "name": "width",
+                "type": "INT",
+                "links": [
+                  274
+                ]
+              },
+              {
+                "localized_name": "height",
+                "name": "height",
+                "type": "INT",
+                "links": [
+                  276
+                ]
+              },
+              {
+                "localized_name": "batch_size",
+                "name": "batch_size",
+                "type": "INT",
+                "links": null
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "GetImageSize"
+            }
+          },
+          {
+            "id": 80,
+            "type": "CreateVideo",
+            "pos": [
+              -5190,
+              2420
+            ],
+            "size": [
+              270,
+              130
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "link": 282
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "shape": 7,
+                "type": "AUDIO",
+                "link": 251
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "fps"
+                },
+                "link": 252
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "VIDEO",
+                "name": "VIDEO",
+                "type": "VIDEO",
+                "links": [
+                  255
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "CreateVideo",
+              "cnr_id": "comfy-core",
+              "ver": "0.13.0"
+            },
+            "widgets_values": [
+              30
+            ]
+          },
+          {
+            "id": 95,
+            "type": "ComfyMathExpression",
+            "pos": [
+              -6040,
+              3020
+            ],
+            "size": [
+              400,
+              200
+            ],
+            "flags": {},
+            "order": 5,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "a",
+                "localized_name": "values.a",
+                "name": "values.a",
+                "type": "FLOAT,INT",
+                "link": 274
+              },
+              {
+                "label": "b",
+                "localized_name": "values.b",
+                "name": "values.b",
+                "shape": 7,
+                "type": "FLOAT,INT",
+                "link": null
+              },
+              {
+                "localized_name": "expression",
+                "name": "expression",
+                "type": "STRING",
+                "widget": {
+                  "name": "expression"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "FLOAT",
+                "name": "FLOAT",
+                "type": "FLOAT",
+                "links": null
+              },
+              {
+                "localized_name": "INT",
+                "name": "INT",
+                "type": "INT",
+                "links": [
+                  279
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "ComfyMathExpression"
+            },
+            "widgets_values": [
+              "a & ~1"
+            ]
+          },
+          {
+            "id": 96,
+            "type": "ComfyMathExpression",
+            "pos": [
+              -6040,
+              3290
+            ],
+            "size": [
+              400,
+              200
+            ],
+            "flags": {},
+            "order": 6,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "a",
+                "localized_name": "values.a",
+                "name": "values.a",
+                "type": "FLOAT,INT",
+                "link": 276
+              },
+              {
+                "label": "b",
+                "localized_name": "values.b",
+                "name": "values.b",
+                "shape": 7,
+                "type": "FLOAT,INT",
+                "link": null
+              },
+              {
+                "localized_name": "expression",
+                "name": "expression",
+                "type": "STRING",
+                "widget": {
+                  "name": "expression"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "FLOAT",
+                "name": "FLOAT",
+                "type": "FLOAT",
+                "links": null
+              },
+              {
+                "localized_name": "INT",
+                "name": "INT",
+                "type": "INT",
+                "links": [
+                  280
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "ComfyMathExpression"
+            },
+            "widgets_values": [
+              "a & ~1"
+            ]
+          },
           {
             "id": 79,
             "type": "ImageStitch",
             "pos": [
               -6390,
-              2700
+              2780
             ],
             "size": [
               270,
-              150
+              160
             ],
             "flags": {},
             "order": 2,
@@ -408,14 +636,15 @@
                 "name": "IMAGE",
                 "type": "IMAGE",
                 "links": [
-                  250
+                  266,
+                  281
                 ]
               }
             ],
             "properties": {
+              "Node name for S&R": "ImageStitch",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "ImageStitch"
+              "ver": "0.13.0"
             },
             "widgets_values": [
               "right",
@@ -425,60 +654,91 @@
             ]
           },
           {
-            "id": 80,
-            "type": "CreateVideo",
+            "id": 97,
+            "type": "ResizeImageMaskNode",
             "pos": [
-              -6040,
-              2610
+              -5560,
+              2790
             ],
             "size": [
               270,
-              78
+              160
             ],
             "flags": {},
-            "order": 3,
+            "order": 7,
             "mode": 0,
             "inputs": [
               {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 250
+                "localized_name": "input",
+                "name": "input",
+                "type": "IMAGE,MASK",
+                "link": 281
               },
               {
-                "localized_name": "audio",
-                "name": "audio",
-                "shape": 7,
-                "type": "AUDIO",
-                "link": 251
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
+                "localized_name": "resize_type",
+                "name": "resize_type",
+                "type": "COMFY_DYNAMICCOMBO_V3",
                 "widget": {
-                  "name": "fps"
+                  "name": "resize_type"
                 },
-                "link": 252
+                "link": null
+              },
+              {
+                "localized_name": "width",
+                "name": "resize_type.width",
+                "type": "INT",
+                "widget": {
+                  "name": "resize_type.width"
+                },
+                "link": 279
+              },
+              {
+                "localized_name": "height",
+                "name": "resize_type.height",
+                "type": "INT",
+                "widget": {
+                  "name": "resize_type.height"
+                },
+                "link": 280
+              },
+              {
+                "localized_name": "crop",
+                "name": "resize_type.crop",
+                "type": "COMBO",
+                "widget": {
+                  "name": "resize_type.crop"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "scale_method",
+                "name": "scale_method",
+                "type": "COMBO",
+                "widget": {
+                  "name": "scale_method"
+                },
+                "link": null
               }
             ],
             "outputs": [
               {
-                "localized_name": "VIDEO",
-                "name": "VIDEO",
-                "type": "VIDEO",
+                "localized_name": "resized",
+                "name": "resized",
+                "type": "*",
                 "links": [
-                  255
+                  282
                 ]
               }
             ],
             "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "CreateVideo"
+              "Node name for S&R": "ResizeImageMaskNode"
             },
             "widgets_values": [
-              30
+              "scale dimensions",
+              512,
+              512,
+              "center",
+              "area"
             ]
           }
         ],
@@ -500,14 +760,6 @@
             "target_slot": 1,
             "type": "IMAGE"
           },
-          {
-            "id": 250,
-            "origin_id": 79,
-            "origin_slot": 0,
-            "target_id": 80,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
           {
             "id": 251,
             "origin_id": 77,
@@ -579,13 +831,71 @@
             "target_id": 79,
             "target_slot": 5,
             "type": "COMBO"
+          },
+          {
+            "id": 266,
+            "origin_id": 79,
+            "origin_slot": 0,
+            "target_id": 90,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 274,
+            "origin_id": 90,
+            "origin_slot": 0,
+            "target_id": 95,
+            "target_slot": 0,
+            "type": "INT"
+          },
+          {
+            "id": 276,
+            "origin_id": 90,
+            "origin_slot": 1,
+            "target_id": 96,
+            "target_slot": 0,
+            "type": "INT"
+          },
+          {
+            "id": 279,
+            "origin_id": 95,
+            "origin_slot": 1,
+            "target_id": 97,
+            "target_slot": 2,
+            "type": "INT"
+          },
+          {
+            "id": 280,
+            "origin_id": 96,
+            "origin_slot": 1,
+            "target_id": 97,
+            "target_slot": 3,
+            "type": "INT"
+          },
+          {
+            "id": 281,
+            "origin_id": 79,
+            "origin_slot": 0,
+            "target_id": 97,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 282,
+            "origin_id": 97,
+            "origin_slot": 0,
+            "target_id": 80,
+            "target_slot": 0,
+            "type": "IMAGE"
           }
         ],
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video Tools/Stitch videos"
+        "category": "Video Tools/Stitch videos",
+        "description": "Stitches multiple video clips into a single sequential video file."
       }
     ]
-  }
-}
+  },
+  "extra": {}
+}

blueprints/Video Upscale(GAN x4).json

@@ -412,9 +412,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Enhance video"
+        "category": "Video generation and editing/Enhance video",
+        "description": "Upscales video to 4× resolution using a GAN-based upscaling model."
       }
     ]
   },
   "extra": {}
-}
+}

comfy/background_removal/birefnet.json

@@ -0,0 +1,7 @@
+{
+    "model_type": "birefnet",
+    "image_std": [1.0, 1.0, 1.0],
+    "image_mean": [0.0, 0.0, 0.0],
+    "image_size": 1024,
+    "resize_to_original": true
+}

comfy/background_removal/birefnet.py

@@ -0,0 +1,689 @@
+import torch
+import comfy.ops
+import numpy as np
+import torch.nn as nn
+from functools import partial
+import torch.nn.functional as F
+from torchvision.ops import deform_conv2d
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+CXT = [3072, 1536, 768, 384][1:][::-1][-3:]
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, device=None, dtype=None, operations=None):
+        super().__init__()
+
+        self.dim = dim
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.q = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype)
+        self.kv = operations.Linear(dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+
+        x = optimized_attention(
+            q, k, v, heads=self.num_heads, skip_output_reshape=True, skip_reshape=True
+        ).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+
+        return x
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = operations.Linear(in_features, hidden_features, device=device, dtype=dtype)
+        self.act = nn.GELU()
+        self.fc2 = operations.Linear(hidden_features, out_features, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+def window_partition(x, window_size):
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, device=None, dtype=None, operations=None):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads, device=device, dtype=dtype))
+
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 norm_layer=nn.LayerNorm, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+
+        self.norm1 = norm_layer(dim, device=device, dtype=dtype)
+        self.attn = WindowAttention(
+            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, device=device, dtype=dtype, operations=operations)
+
+        self.norm2 = norm_layer(dim, device=device, dtype=dtype)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, device=device, dtype=dtype, operations=operations)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        B, L, C = x.shape
+        H, W = self.H, self.W
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        x_windows = window_partition(shifted_x, self.window_size)
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
+
+        attn_windows = self.attn(x_windows, mask=attn_mask)
+
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
+
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    def __init__(self, dim, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.dim = dim
+        self.reduction = operations.Linear(4 * dim, 2 * dim, bias=False, device=device, dtype=dtype)
+        self.norm = operations.LayerNorm(4 * dim, device=device, dtype=dtype)
+
+    def forward(self, x, H, W):
+        B, L, C = x.shape
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(
+                dim=dim,
+                num_heads=num_heads,
+                window_size=window_size,
+                shift_size=0 if (i % 2 == 0) else window_size // 2,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                norm_layer=norm_layer,
+                device=device, dtype=dtype, operations=operations)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, device=device, dtype=dtype, operations=operations)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        patch_size = (patch_size, patch_size)
+        self.patch_size = patch_size
+
+        self.in_channels = in_channels
+        self.embed_dim = embed_dim
+
+        self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_channels=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+
+        norm_layer = partial(operations.LayerNorm, device=device, dtype=dtype)
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
+            device=device, dtype=dtype, operations=operations,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2 ** i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                norm_layer=norm_layer,
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                device=device, dtype=dtype, operations=operations)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+
+        outs = []
+        x = x.flatten(2).transpose(1, 2)
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+
+        return tuple(outs)
+
+class DeformableConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1,
+                 bias=False, device=None, dtype=None, operations=None):
+
+        super(DeformableConv2d, self).__init__()
+
+        kernel_size = kernel_size if type(kernel_size) is tuple else (kernel_size, kernel_size)
+        self.stride = stride if type(stride) is tuple else (stride, stride)
+        self.padding = padding
+
+        self.offset_conv = operations.Conv2d(in_channels,
+                                     2 * kernel_size[0] * kernel_size[1],
+                                     kernel_size=kernel_size,
+                                     stride=stride,
+                                     padding=self.padding,
+                                     bias=True, device=device, dtype=dtype)
+
+        self.modulator_conv = operations.Conv2d(in_channels,
+                                     1 * kernel_size[0] * kernel_size[1],
+                                     kernel_size=kernel_size,
+                                     stride=stride,
+                                     padding=self.padding,
+                                     bias=True, device=device, dtype=dtype)
+
+        self.regular_conv = operations.Conv2d(in_channels,
+                                      out_channels=out_channels,
+                                      kernel_size=kernel_size,
+                                      stride=stride,
+                                      padding=self.padding,
+                                      bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        offset = self.offset_conv(x)
+        modulator = 2. * torch.sigmoid(self.modulator_conv(x))
+        weight, bias, offload_info = comfy.ops.cast_bias_weight(self.regular_conv, x, offloadable=True)
+
+        x = deform_conv2d(
+            input=x,
+            offset=offset,
+            weight=weight,
+            bias=None,
+            padding=self.padding,
+            mask=modulator,
+            stride=self.stride,
+        )
+        comfy.ops.uncast_bias_weight(self.regular_conv, weight, bias, offload_info)
+        return x
+
+class BasicDecBlk(nn.Module):
+    def __init__(self, in_channels=64, out_channels=64, inter_channels=64, device=None, dtype=None, operations=None):
+        super(BasicDecBlk, self).__init__()
+        inter_channels = 64
+        self.conv_in = operations.Conv2d(in_channels, inter_channels, 3, 1, padding=1, device=device, dtype=dtype)
+        self.relu_in = nn.ReLU(inplace=True)
+        self.dec_att = ASPPDeformable(in_channels=inter_channels, device=device, dtype=dtype, operations=operations)
+        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, padding=1, device=device, dtype=dtype)
+        self.bn_in = operations.BatchNorm2d(inter_channels, device=device, dtype=dtype)
+        self.bn_out = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        x = self.bn_in(x)
+        x = self.relu_in(x)
+        x = self.dec_att(x)
+        x = self.conv_out(x)
+        x = self.bn_out(x)
+        return x
+
+
+class BasicLatBlk(nn.Module):
+    def __init__(self, in_channels=64, out_channels=64, device=None, dtype=None, operations=None):
+        super(BasicLatBlk, self).__init__()
+        self.conv = operations.Conv2d(in_channels, out_channels, 1, 1, 0, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+class _ASPPModuleDeformable(nn.Module):
+    def __init__(self, in_channels, planes, kernel_size, padding, device, dtype, operations):
+        super(_ASPPModuleDeformable, self).__init__()
+        self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
+                                            stride=1, padding=padding, bias=False, device=device, dtype=dtype, operations=operations)
+        self.bn = operations.BatchNorm2d(planes, device=device, dtype=dtype)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.atrous_conv(x)
+        x = self.bn(x)
+
+        return self.relu(x)
+
+
+class ASPPDeformable(nn.Module):
+    def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7], device=None, dtype=None, operations=None):
+        super(ASPPDeformable, self).__init__()
+        self.down_scale = 1
+        if out_channels is None:
+            out_channels = in_channels
+        self.in_channelster = 256 // self.down_scale
+
+        self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0, device=device, dtype=dtype, operations=operations)
+        self.aspp_deforms = nn.ModuleList([
+            _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2), device=device, dtype=dtype, operations=operations)
+              for conv_size in parallel_block_sizes
+        ])
+
+        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
+                                             operations.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False, device=device, dtype=dtype),
+                                             operations.BatchNorm2d(self.in_channelster, device=device, dtype=dtype),
+                                             nn.ReLU(inplace=True))
+        self.conv1 = operations.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False, device=device, dtype=dtype)
+        self.bn1 = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x1 = self.aspp1(x)
+        x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
+        x5 = self.global_avg_pool(x)
+        x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
+        x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        return x
+
+class BiRefNet(nn.Module):
+    def __init__(self, config=None, dtype=None, device=None, operations=None):
+        super(BiRefNet, self).__init__()
+        self.bb = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12, device=device, dtype=dtype, operations=operations)
+
+        channels = [1536, 768, 384, 192]
+        channels = [c * 2 for c in channels]
+        self.cxt = channels[1:][::-1][-3:]
+        self.squeeze_module = nn.Sequential(*[
+            BasicDecBlk(channels[0]+sum(self.cxt), channels[0], device=device, dtype=dtype, operations=operations)
+            for _ in range(1)
+        ])
+
+        self.decoder = Decoder(channels, device=device, dtype=dtype, operations=operations)
+
+    def forward_enc(self, x):
+        x1, x2, x3, x4 = self.bb(x)
+        B, C, H, W = x.shape
+        x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
+        x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x4 = torch.cat(
+            (
+                *[
+                    F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                    F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                    F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                ][-len(CXT):],
+                x4
+            ),
+            dim=1
+        )
+        return (x1, x2, x3, x4)
+
+    def forward_ori(self, x):
+        (x1, x2, x3, x4) = self.forward_enc(x)
+        x4 = self.squeeze_module(x4)
+        features = [x, x1, x2, x3, x4]
+        scaled_preds = self.decoder(features)
+        return scaled_preds
+
+    def forward(self, pixel_values, intermediate_output=None):
+        scaled_preds = self.forward_ori(pixel_values)
+        return scaled_preds
+
+
+class Decoder(nn.Module):
+    def __init__(self, channels, device, dtype, operations):
+        super(Decoder, self).__init__()
+        # factory kwargs
+        fk = {"device":device, "dtype":dtype, "operations":operations}
+        DecoderBlock = partial(BasicDecBlk, **fk)
+        LateralBlock = partial(BasicLatBlk, **fk)
+        DBlock = partial(SimpleConvs, **fk)
+
+        self.split = True
+        N_dec_ipt = 64
+        ic = 64
+        ipt_cha_opt = 1
+        self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
+
+        self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[1])
+        self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[2])
+        self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt]), channels[3])
+        self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt]), channels[3]//2)
+
+        fk = {"device":device, "dtype":dtype}
+
+        self.conv_out1 = nn.Sequential(operations.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt]), 1, 1, 1, 0, **fk))
+
+        self.lateral_block4 = LateralBlock(channels[1], channels[1])
+        self.lateral_block3 = LateralBlock(channels[2], channels[2])
+        self.lateral_block2 = LateralBlock(channels[3], channels[3])
+
+        self.conv_ms_spvn_4 = operations.Conv2d(channels[1], 1, 1, 1, 0, **fk)
+        self.conv_ms_spvn_3 = operations.Conv2d(channels[2], 1, 1, 1, 0, **fk)
+        self.conv_ms_spvn_2 = operations.Conv2d(channels[3], 1, 1, 1, 0, **fk)
+
+        _N = 16
+
+        self.gdt_convs_4 = nn.Sequential(operations.Conv2d(channels[0] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+        self.gdt_convs_3 = nn.Sequential(operations.Conv2d(channels[1] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+        self.gdt_convs_2 = nn.Sequential(operations.Conv2d(channels[2] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+
+        [setattr(self, f"gdt_convs_pred_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
+        [setattr(self, f"gdt_convs_attn_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
+
+    def get_patches_batch(self, x, p):
+        _size_h, _size_w = p.shape[2:]
+        patches_batch = []
+        for idx in range(x.shape[0]):
+            columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
+            patches_x = []
+            for column_x in columns_x:
+                patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
+            patch_sample = torch.cat(patches_x, dim=1)
+            patches_batch.append(patch_sample)
+        return torch.cat(patches_batch, dim=0)
+
+    def forward(self, features):
+        x, x1, x2, x3, x4 = features
+
+        patches_batch = self.get_patches_batch(x, x4) if self.split else x
+        x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p4 = self.decoder_block4(x4)
+        p4_gdt = self.gdt_convs_4(p4)
+        gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
+        p4 = p4 * gdt_attn_4
+        _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
+        _p3 = _p4 + self.lateral_block4(x3)
+
+        patches_batch = self.get_patches_batch(x, _p3) if self.split else x
+        _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p3 = self.decoder_block3(_p3)
+
+        p3_gdt = self.gdt_convs_3(p3)
+        gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
+        p3 = p3 * gdt_attn_3
+        _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
+        _p2 = _p3 + self.lateral_block3(x2)
+
+        patches_batch = self.get_patches_batch(x, _p2) if self.split else x
+        _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p2 = self.decoder_block2(_p2)
+
+        p2_gdt = self.gdt_convs_2(p2)
+        gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
+        p2 = p2 * gdt_attn_2
+
+        _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
+        _p1 = _p2 + self.lateral_block2(x1)
+
+        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
+        _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
+        _p1 = self.decoder_block1(_p1)
+        _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
+
+        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
+        _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p1_out = self.conv_out1(_p1)
+        return p1_out
+
+
+class SimpleConvs(nn.Module):
+    def __init__(
+        self, in_channels: int, out_channels: int, inter_channels=64, device=None, dtype=None, operations=None
+    ) -> None:
+        super().__init__()
+        self.conv1 = operations.Conv2d(in_channels, inter_channels, 3, 1, 1, device=device, dtype=dtype)
+        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, 1, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.conv_out(self.conv1(x))

comfy/bg_removal_model.py

@@ -0,0 +1,85 @@
+from .utils import load_torch_file
+import os
+import json
+import torch
+import logging
+
+import comfy.ops
+import comfy.model_patcher
+import comfy.model_management
+import comfy.clip_model
+import comfy.background_removal.birefnet
+
+BG_REMOVAL_MODELS = {
+    "birefnet": comfy.background_removal.birefnet.BiRefNet
+}
+
+class BackgroundRemovalModel():
+    def __init__(self, json_config):
+        with open(json_config) as f:
+            config = json.load(f)
+
+        self.image_size = config.get("image_size", 1024)
+        self.image_mean = config.get("image_mean", [0.0, 0.0, 0.0])
+        self.image_std = config.get("image_std", [1.0, 1.0, 1.0])
+        self.model_type = config.get("model_type", "birefnet")
+        self.config = config.copy()
+        model_class = BG_REMOVAL_MODELS.get(self.model_type)
+
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
+        self.model.eval()
+
+        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+
+    def load_sd(self, sd):
+        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def encode_image(self, image):
+        comfy.model_management.load_model_gpu(self.patcher)
+        H, W = image.shape[1], image.shape[2]
+        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=False)
+
+        if pixel_values.shape[0] > 1:
+            out = torch.cat([
+                self.model(pixel_values=pixel_values[i:i+1])
+                for i in range(pixel_values.shape[0])
+            ], dim=0)
+        else:
+            out = self.model(pixel_values=pixel_values)
+        out = torch.nn.functional.interpolate(out, size=(H, W), mode="bicubic", antialias=False)
+
+        mask = out.sigmoid().to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(0)
+        if mask.shape[1] != 1:
+            mask = mask.movedim(-1, 1)
+
+        return mask
+
+
+def load_background_removal_model(sd):
+    if "bb.layers.1.blocks.0.attn.relative_position_index" in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "background_removal"), "birefnet.json")
+    else:
+        return None
+
+    bg_model = BackgroundRemovalModel(json_config)
+    m, u = bg_model.load_sd(sd)
+    if len(m) > 0:
+        logging.warning("missing background removal: {}".format(m))
+    u = set(u)
+    keys = list(sd.keys())
+    for k in keys:
+        if k not in u:
+            sd.pop(k)
+    return bg_model
+
+def load(ckpt_path):
+    sd = load_torch_file(ckpt_path)
+    return load_background_removal_model(sd)

comfy/cli_args.py

@@ -90,8 +90,8 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
+parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -110,13 +110,11 @@ parser.add_argument("--preview-method", type=LatentPreviewMethod, default=Latent
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
 
-CACHE_RAM_AUTO_GB = -1.0
-
 cache_group = parser.add_mutually_exclusive_group()
+cache_group.add_argument("--cache-ram", nargs='*', type=float, default=[], metavar="GB", help="Use RAM pressure caching with the specified headroom thresholds. This is the default caching mode. The first value sets the active-cache threshold; the optional second value sets the inactive-cache/pin threshold. Defaults when no values are provided: active 25%% of system RAM (min 4GB, max 32GB), inactive 75%% of system RAM (min 12GB, max 96GB).")
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-cache_group.add_argument("--cache-ram", nargs='?', const=CACHE_RAM_AUTO_GB, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threshold the cache removes large items to free RAM. Default (when no value is provided): 25%% of system RAM (min 4GB, max 32GB).")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
@@ -141,8 +139,7 @@ manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", he
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
-vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
-vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
+vram_group.add_argument("--lowvram", action="store_true", help="Doesn't do anything if dynamic vram is enabled. If dynamic vram isn't being used this option makes the text encoders run on the CPU.")
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
@@ -238,12 +235,17 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
+parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
+parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:
     args = parser.parse_args([])
 
+if args.cache_ram is not None and len(args.cache_ram) > 2:
+    parser.error("--cache-ram accepts at most two values: active GB and inactive GB")
+
 if args.windows_standalone_build:
     args.auto_launch = True
 

comfy/context_windows.py

@@ -63,7 +63,11 @@ class IndexListContextWindow(ContextWindowABC):
             dim = self.dim
         if dim == 0 and full.shape[dim] == 1:
             return full
-        idx = tuple([slice(None)] * dim + [self.index_list])
+        indices = self.index_list
+        anchor_idx = getattr(self, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            indices = [anchor_idx] + list(indices)
+        idx = tuple([slice(None)] * dim + [indices])
         window = full[idx]
         if retain_index_list:
             idx = tuple([slice(None)] * dim + [retain_index_list])
@@ -113,7 +117,14 @@ def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, d
 
     # skip leading latent positions that have no corresponding conditioning (e.g. reference frames)
     if temporal_offset > 0:
-        indices = [i - temporal_offset for i in window.index_list[temporal_offset:]]
+        anchor_idx = getattr(window, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            # anchor occupies one of the no-cond positions, so skip one fewer from window.index_list
+            skip_count = temporal_offset - 1
+        else:
+            skip_count = temporal_offset
+
+        indices = [i - temporal_offset for i in window.index_list[skip_count:]]
         indices = [i for i in indices if 0 <= i]
     else:
         indices = list(window.index_list)
@@ -150,7 +161,8 @@ class ContextFuseMethod:
 ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
 class IndexListContextHandler(ContextHandlerABC):
     def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
-                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):
+                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False,
+                 causal_window_fix: bool=True):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -162,6 +174,7 @@ class IndexListContextHandler(ContextHandlerABC):
         self.freenoise = freenoise
         self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
         self.split_conds_to_windows = split_conds_to_windows
+        self.causal_window_fix = causal_window_fix
 
         self.callbacks = {}
 
@@ -318,6 +331,14 @@ class IndexListContextHandler(ContextHandlerABC):
             # allow processing to end between context window executions for faster Cancel
             comfy.model_management.throw_exception_if_processing_interrupted()
 
+            # causal_window_fix: prepend a pre-window frame that will be stripped post-forward
+            anchor_applied = False
+            if self.causal_window_fix:
+                anchor_idx = window.index_list[0] - 1
+                if 0 <= anchor_idx < x_in.size(self.dim):
+                    window.causal_anchor_index = anchor_idx
+                    anchor_applied = True
+
             for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
                 callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
 
@@ -332,6 +353,12 @@ class IndexListContextHandler(ContextHandlerABC):
             if device is not None:
                 for i in range(len(sub_conds_out)):
                     sub_conds_out[i] = sub_conds_out[i].to(x_in.device)
+
+            # strip causal_window_fix anchor if applied
+            if anchor_applied:
+                for i in range(len(sub_conds_out)):
+                    sub_conds_out[i] = sub_conds_out[i].narrow(self.dim, 1, sub_conds_out[i].shape[self.dim] - 1)
+
             results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
         return results
 

comfy/deploy_environment.py

@@ -0,0 +1,34 @@
+import functools
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_DEPLOY_ENV = "local-git"
+_ENV_FILENAME = ".comfy_environment"
+
+# Resolve the ComfyUI install directory (the parent of this `comfy/` package).
+# We deliberately avoid `folder_paths.base_path` here because that is overridden
+# by the `--base-directory` CLI arg to a user-supplied path, whereas the
+# `.comfy_environment` marker is written by launchers/installers next to the
+# ComfyUI install itself.
+_COMFY_INSTALL_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+
+@functools.cache
+def get_deploy_environment() -> str:
+    env_file = os.path.join(_COMFY_INSTALL_DIR, _ENV_FILENAME)
+    try:
+        with open(env_file, encoding="utf-8") as f:
+            # Cap the read so a malformed or maliciously crafted file (e.g.
+            # a single huge line with no newline) can't blow up memory.
+            first_line = f.readline(128).strip()
+            value = "".join(c for c in first_line if 32 <= ord(c) < 127)
+            if value:
+                return value
+    except FileNotFoundError:
+        pass
+    except Exception as e:
+        logger.error("Failed to read %s: %s", env_file, e)
+
+    return _DEFAULT_DEPLOY_ENV

comfy/hooks.py

@@ -93,7 +93,7 @@ class Hook:
         self.hook_scope = hook_scope
         '''Scope of where this hook should apply in terms of the conds used in sampling run.'''
         self.custom_should_register = default_should_register
-        '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
+        '''Can be overridden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
 
     @property
     def strength(self):

comfy/image_encoders/dino2.py

@@ -106,6 +106,7 @@ class Dino2Encoder(torch.nn.Module):
 class Dino2PatchEmbeddings(torch.nn.Module):
     def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None):
         super().__init__()
+        self.patch_size = patch_size
         self.projection = operations.Conv2d(
             in_channels=num_channels,
             out_channels=dim,
@@ -125,17 +126,37 @@ class Dino2Embeddings(torch.nn.Module):
         super().__init__()
         patch_size = 14
         image_size = 518
+        self.patch_size = patch_size
 
         self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
         self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
-        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device))
+        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key.
         self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
 
+    def interpolate_pos_encoding(self, x, h_pixels, w_pixels):
+        pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32)
+
+        class_pos = pos_embed[:, 0:1]
+        patch_pos = pos_embed[:, 1:]
+        N = patch_pos.shape[1]
+        M = int(N ** 0.5)
+        h0 = h_pixels // self.patch_size
+        w0 = w_pixels // self.patch_size
+        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)  # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
+
+        patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2)
+        patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False)
+        patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2)
+        return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype)
+
     def forward(self, pixel_values):
         x = self.patch_embeddings(pixel_values)
-        # TODO: mask_token?
         x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1)
-        x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
+        if x.shape[1] - 1 == self.position_embeddings.shape[1] - 1:
+            x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
+        else:
+            h, w = pixel_values.shape[-2:]
+            x = x + self.interpolate_pos_encoding(x, h, w)
         return x
 
 
@@ -158,3 +179,21 @@ class Dinov2Model(torch.nn.Module):
         x = self.layernorm(x)
         pooled_output = x[:, 0, :]
         return x, i, pooled_output, None
+
+    def get_intermediate_layers(self, pixel_values, indices, apply_norm=True):
+        x = self.embeddings(pixel_values)
+        optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
+        n_layers = len(self.encoder.layer)
+        resolved = [(i if i >= 0 else n_layers + i) for i in indices]
+        target = set(resolved)
+        max_idx = max(resolved)
+        n_skip = 1  # skip cls token
+        cache = {}
+        for i, layer in enumerate(self.encoder.layer):
+            x = layer(x, optimized_attention)
+            if i in target:
+                normed = self.layernorm(x) if apply_norm else x
+                cache[i] = (normed[:, n_skip:], normed[:, 0])
+            if i >= max_idx:
+                break
+        return [cache[i] for i in resolved]