Merge branch 'master' into temp_pr

Co-authored-by: Alexis Rolland <alexis@comfy.org>

.github/workflows/backport_release.yaml

@@ -1,519 +0,0 @@
-name: Backport Release
-
-on:
-  workflow_dispatch:
-    inputs:
-      commit:
-        description: 'Full 40-char SHA of the tip commit of the backport source branch (the PR head commit that passed tests). The branch is resolved from this SHA and must be unique.'
-        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: Resolve source branch from commit SHA
-        id: resolve
-        env:
-          SOURCE_COMMIT:  ${{ inputs.commit }}
-          DEFAULT_BRANCH: ${{ github.event.repository.default_branch }}
-        run: |
-          set -euo pipefail
-
-          # Require a full 40-char lowercase-hex SHA. Short SHAs are ambiguous
-          # and we will be comparing this value against API responses (PR head
-          # SHA, ref tips) that always return the full form.
-          if [[ ! "${SOURCE_COMMIT}" =~ ^[0-9a-f]{40}$ ]]; then
-            echo "::error::Input commit '${SOURCE_COMMIT}' is not a full 40-char lowercase hex SHA."
-            exit 1
-          fi
-
-          # Fetch all remote branches so we can search for which one(s) point
-          # at this SHA. `actions/checkout` with fetch-depth: 0 fetches full
-          # history of the checked-out ref but does not necessarily populate
-          # every refs/remotes/origin/*, so do it explicitly.
-          git fetch --prune origin '+refs/heads/*:refs/remotes/origin/*'
-
-          # Verify the commit actually exists in this repo's object DB.
-          if ! git cat-file -e "${SOURCE_COMMIT}^{commit}" 2>/dev/null; then
-            echo "::error::Commit ${SOURCE_COMMIT} was not found in the repository."
-            exit 1
-          fi
-
-          # Find every remote branch whose tip == SOURCE_COMMIT. Exactly one
-          # branch must point at it. If zero, the commit isn't anyone's tip
-          # (likely stale, force-pushed past, or never the PR head). If more
-          # than one, the (branch -> SHA) mapping is ambiguous and we refuse
-          # to guess — the operator must give us a unique branch to release.
-          mapfile -t matching_branches < <(
-            git for-each-ref \
-              --format='%(refname:strip=3)' \
-              --points-at="${SOURCE_COMMIT}" \
-              refs/remotes/origin/ \
-              | grep -vx 'HEAD' || true
-          )
-
-          if [[ "${#matching_branches[@]}" -eq 0 ]]; then
-            echo "::error::No branch on origin has ${SOURCE_COMMIT} as its tip."
-            echo "::error::Either the branch was updated after you copied this SHA, or this commit was never the head of a branch."
-            exit 1
-          fi
-
-          if [[ "${#matching_branches[@]}" -gt 1 ]]; then
-            echo "::error::More than one branch on origin has ${SOURCE_COMMIT} as its tip; cannot pick one:"
-            for b in "${matching_branches[@]}"; do
-              echo "::error::  - ${b}"
-            done
-            echo "::error::Refusing to proceed with an ambiguous source branch."
-            exit 1
-          fi
-
-          source_branch="${matching_branches[0]}"
-
-          if [[ "${source_branch}" == "${DEFAULT_BRANCH}" ]]; then
-            echo "::error::Source branch must not be the default branch ('${DEFAULT_BRANCH}')."
-            exit 1
-          fi
-
-          echo "Resolved commit ${SOURCE_COMMIT} to branch '${source_branch}'."
-          echo "source_branch=${source_branch}" >> "$GITHUB_OUTPUT"
-
-      - 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:   ${{ steps.resolve.outputs.source_branch }}
-          SOURCE_COMMIT:   ${{ inputs.commit }}
-          LATEST_TAG_SHA:  ${{ steps.latest.outputs.latest_sha }}
-          LATEST_TAG:      ${{ steps.latest.outputs.latest_tag }}
-        run: |
-          set -euo pipefail
-
-          # Use the user-provided SHA directly rather than re-resolving the branch
-          # tip — the resolve step already proved the branch tip equals SOURCE_COMMIT,
-          # and pinning to the SHA here makes the rest of the job TOCTOU-safe against
-          # someone pushing to the branch mid-run.
-          source_sha="${SOURCE_COMMIT}"
-
-          # Walking first-parent from the source tip must reach LATEST_TAG_SHA.
-          # We capture rev-list into a variable and grep against a here-string
-          # rather than piping `rev-list | grep -q`: under `set -o pipefail`,
-          # `grep -q` would exit on first match and SIGPIPE the still-streaming
-          # `rev-list`, propagating exit 141 as a spurious "not found".
-          first_parent_chain="$(git rev-list --first-parent "${source_sha}")"
-          if ! grep -Fxq "${LATEST_TAG_SHA}" <<< "${first_parent_chain}"; 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 open, named correctly, has latest commit, and checks pass
-        env:
-          GH_TOKEN:      ${{ steps.app-token.outputs.token }}
-          SOURCE_BRANCH: ${{ steps.resolve.outputs.source_branch }}
-          SOURCE_COMMIT: ${{ inputs.commit }}
-          NEW_VERSION:   ${{ steps.latest.outputs.new_version }}
-          REPO:          ${{ github.repository }}
-        run: |
-          set -euo pipefail
-
-          expected_title="ComfyUI backport release ${NEW_VERSION}"
-
-          # Find open PRs from this branch into master. The --state open filter
-          # is load-bearing: a closed/merged PR with passing checks must not be
-          # accepted as authorization for a new release.
-          pr_json="$(
-            gh pr list \
-              --repo "${REPO}" \
-              --state open \
-              --head "${SOURCE_BRANCH}" \
-              --base master \
-              --json number,title,headRefOid,state \
-              --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'. The PR must exist and be open."
-            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
-
-          # The PR's current head commit must equal the SHA the operator gave us.
-          # This is what closes the door on releasing stale code: if anyone has
-          # pushed to the branch since the operator validated tests passed, the
-          # PR head will have advanced past SOURCE_COMMIT and we abort. (The
-          # resolve step already proved the branch tip == SOURCE_COMMIT; this
-          # ties that same SHA to the PR that authorizes the release.)
-          if [[ "${pr_head_sha}" != "${SOURCE_COMMIT}" ]]; then
-            echo "::error::PR #${pr_number} head commit is ${pr_head_sha}, but the operator-provided commit is ${SOURCE_COMMIT}."
-            echo "::error::The PR has new commits since this release was authorized. Re-run with the new head SHA after verifying its checks."
-            exit 1
-          fi
-
-          echo "Found open PR #${pr_number} titled '${expected_title}' at head ${pr_head_sha} (matches operator-provided commit)."
-
-          # Verify all check runs on the head commit have completed successfully.
-          # A check is considered passing if conclusion is success, neutral, or skipped.
-          checks_json="$(
-            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 }}
-          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:  ${{ steps.resolve.outputs.source_branch }}
-          SOURCE_COMMIT:  ${{ inputs.commit }}
-          RELEASE_BRANCH: ${{ steps.latest.outputs.release_branch }}
-        run: |
-          set -euo pipefail
-
-          # --ff-only guarantees no merge commit is created. If a fast-forward is
-          # not possible (i.e. the release branch has commits the source branch
-          # doesn't), the merge will fail and we abort. Because we already validated
-          # that the source branch is rooted on the latest stable tag, and the
-          # release branch tip equals that same tag, this fast-forward should
-          # always succeed for a well-formed backport branch.
-          #
-          # We merge the operator-provided SHA, not the branch ref, so a push to
-          # the branch in the window between resolve and now cannot smuggle new
-          # commits into the release.
-          if ! git merge --ff-only "${SOURCE_COMMIT}"; then
-            echo "::error::Cannot fast-forward '${RELEASE_BRANCH}' to ${SOURCE_COMMIT} (tip of '${SOURCE_BRANCH}'). A merge commit would be required. Aborting."
-            exit 1
-          fi
-
-          echo "Fast-forwarded '${RELEASE_BRANCH}' to ${SOURCE_COMMIT} (tip of '${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: Delete remote source branch
-        env:
-          GH_TOKEN:        ${{ steps.app-token.outputs.token }}
-          REPO:            ${{ github.repository }}
-          SOURCE_BRANCH:   ${{ steps.resolve.outputs.source_branch }}
-          SOURCE_COMMIT:   ${{ inputs.commit }}
-          RELEASE_BRANCH:  ${{ steps.latest.outputs.release_branch }}
-          DEFAULT_BRANCH:  ${{ github.event.repository.default_branch }}
-        run: |
-          set -euo pipefail
-
-          # Belt-and-braces: the resolve step already refuses the default branch,
-          # but never delete the default or the release branch under any
-          # circumstances.
-          if [[ "${SOURCE_BRANCH}" == "${DEFAULT_BRANCH}" || "${SOURCE_BRANCH}" == "${RELEASE_BRANCH}" ]]; then
-            echo "::error::Refusing to delete '${SOURCE_BRANCH}' (matches default or release branch)."
-            exit 1
-          fi
-
-          # Delete the source branch on origin, but only if its tip is still the
-          # SHA we released from. If someone pushed new commits to it after we
-          # resolved it, leave it alone — those commits would be silently lost.
-          current_tip="$(git ls-remote origin "refs/heads/${SOURCE_BRANCH}" | awk '{print $1}')"
-          if [[ -z "${current_tip}" ]]; then
-            echo "Source branch '${SOURCE_BRANCH}' no longer exists on origin; nothing to delete."
-            exit 0
-          fi
-          if [[ "${current_tip}" != "${SOURCE_COMMIT}" ]]; then
-            echo "::warning::Source branch '${SOURCE_BRANCH}' tip (${current_tip}) no longer matches released commit (${SOURCE_COMMIT}). Leaving it in place."
-            exit 0
-          fi
-
-          git push origin --delete "refs/heads/${SOURCE_BRANCH}"
-          echo "Deleted remote branch '${SOURCE_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:  ${{ steps.resolve.outputs.source_branch }}
-          SOURCE_COMMIT:  ${{ inputs.commit }}
-        run: |
-          # SOURCE_BRANCH is empty if the resolve step never produced an output
-          # (e.g. the workflow failed in or before that step). Show a placeholder
-          # in that case so the summary table still renders cleanly.
-          source_branch_display="${SOURCE_BRANCH:-(unresolved)}"
-          {
-            echo "## Backport release"
-            echo ""
-            echo "| Field | Value |"
-            echo "|---|---|"
-            echo "| Source commit | \`${SOURCE_COMMIT}\` |"
-            echo "| Source branch | \`${source_branch_display}\` |"
-            echo "| Previous stable | \`${LATEST_TAG}\` |"
-            echo "| New version | \`${NEW_VERSION}\` |"
-            echo "| Release branch | \`${RELEASE_BRANCH}\` |"
-          } >> "$GITHUB_STEP_SUMMARY"

README.md

@@ -20,7 +20,7 @@
 [website-url]: https://www.comfy.org/
 <!-- Workaround to display total user from https://github.com/badges/shields/issues/4500#issuecomment-2060079995 -->
 [discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
-[discord-url]: https://discord.com/invite/comfyorg
+[discord-url]: https://www.comfy.org/discord
 [twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI
 [twitter-url]: https://x.com/ComfyUI
 

app/frontend_management.py

@@ -62,8 +62,6 @@ def get_comfy_package_versions():
 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
-    outdated_packages = []
-
     for pkg in get_comfy_package_versions():
         installed_str = pkg["installed"]
         required_str = pkg["required"]
@@ -75,26 +73,19 @@ def check_comfy_packages_versions():
             logging.error(f"Failed to check {pkg['name']} version: {e}")
             continue
         if outdated:
-            outdated_packages.append((pkg["name"], installed_str, required_str))
-        else:
-            logging.info("{} version: {}".format(pkg["name"], installed_str))
-
-    if outdated_packages:
-        package_warnings = "\n".join(
-            f"Installed {name} version {installed} is lower than the recommended version {required}."
-            for name, installed, required in outdated_packages
-        )
-        app.logger.log_startup_warning(
-            f"""
+            app.logger.log_startup_warning(
+                f"""
 ________________________________________________________________________
 WARNING WARNING WARNING WARNING WARNING
 
-{package_warnings}
+Installed {pkg["name"]} version {installed_str} is lower than the recommended version {required_str}.
 
 {get_missing_requirements_message()}
 ________________________________________________________________________
 """.strip()
-        )
+            )
+        else:
+            logging.info("{} version: {}".format(pkg["name"], installed_str))
 
 
 REQUEST_TIMEOUT = 10  # seconds

comfy/model_patcher.py

@@ -1613,16 +1613,6 @@ class ModelPatcherDynamic(ModelPatcher):
         #use all ModelPatcherDynamic this is ignored and its all done dynamically.
         return super().memory_required(input_shape=input_shape) * 1.3 + (1024 ** 3)
 
-    def restore_loaded_backups(self):
-        restored = self.model.model_loaded_weight_memory
-        for key in list(self.backup.keys()):
-            bk = self.backup.pop(key)
-            comfy.utils.set_attr_param(self.model, key, bk.weight)
-        for key in list(self.backup_buffers.keys()):
-            comfy.utils.set_attr_buffer(self.model, key, self.backup_buffers.pop(key))
-        self.model.model_loaded_weight_memory = 0
-        return restored
-
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False, dirty=False):
 
@@ -1639,7 +1629,7 @@ class ModelPatcherDynamic(ModelPatcher):
 
         num_patches = 0
         allocated_size = 0
-        self.restore_loaded_backups()
+        self.model.model_loaded_weight_memory = 0
 
         with self.use_ejected():
             self.unpatch_hooks()
@@ -1726,9 +1716,6 @@ class ModelPatcherDynamic(ModelPatcher):
                         force_load=True
 
                     if force_load:
-                        if hasattr(m, "_v"):
-                            comfy_aimdo.model_vbar.vbar_unpin(m._v)
-                            delattr(m, "_v")
                         force_load_param(self, "weight", device_to)
                         force_load_param(self, "bias", device_to)
                     else:
@@ -1786,7 +1773,13 @@ class ModelPatcherDynamic(ModelPatcher):
         freed = 0 if vbar is None else vbar.free_memory(memory_to_free)
 
         if freed < memory_to_free:
-            freed += self.restore_loaded_backups()
+            for key in list(self.backup.keys()):
+                bk = self.backup.pop(key)
+                comfy.utils.set_attr_param(self.model, key, bk.weight)
+            for key in list(self.backup_buffers.keys()):
+                comfy.utils.set_attr_buffer(self.model, key, self.backup_buffers.pop(key))
+            freed += self.model.model_loaded_weight_memory
+            self.model.model_loaded_weight_memory = 0
 
         return freed
 

comfy/utils.py

@@ -1019,11 +1019,10 @@ def bislerp(samples, width, height):
 
 def lanczos(samples, width, height):
     #the below API is strict and expects grayscale to be squeezed
-    if samples.ndim == 4:
-        samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)
+    samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)
     images = [Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
     images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
-    images = [torch.from_numpy(t).movedim(-1, 0) if (t := np.array(image).astype(np.float32) / 255.0).ndim == 3 else torch.from_numpy(t) for image in images]
+    images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
     result = torch.stack(images)
     return result.to(samples.device, samples.dtype)
 

comfy_api_nodes/apis/anthropic.py

@@ -35,19 +35,6 @@ class AnthropicMessage(BaseModel):
     content: list[AnthropicTextContent | AnthropicImageContent] = Field(...)
 
 
-class AnthropicThinkingConfig(BaseModel):
-    type: Literal["enabled", "disabled", "adaptive"] = Field(...)
-    budget_tokens: int | None = Field(
-        None, ge=1024,
-        description="Reasoning budget in tokens. Used when type is 'enabled'. Must be less than max_tokens.",
-    )
-
-
-class AnthropicOutputConfig(BaseModel):
-    """Used with `thinking.type='adaptive'` on models like Opus 4.7."""
-    effort: Literal["low", "medium", "high"] | None = Field(None)
-
-
 class AnthropicMessagesRequest(BaseModel):
     model: str = Field(...)
     messages: list[AnthropicMessage] = Field(...)
@@ -57,8 +44,6 @@ class AnthropicMessagesRequest(BaseModel):
     top_p: float | None = Field(None, ge=0.0, le=1.0)
     top_k: int | None = Field(None, ge=0)
     stop_sequences: list[str] | None = Field(None)
-    thinking: AnthropicThinkingConfig | None = Field(None)
-    output_config: AnthropicOutputConfig | None = Field(None)
 
 
 class AnthropicResponseTextBlock(BaseModel):
@@ -66,14 +51,6 @@ class AnthropicResponseTextBlock(BaseModel):
     text: str = Field(...)
 
 
-class AnthropicResponseThinkingBlock(BaseModel):
-    type: Literal["thinking"] = "thinking"
-    thinking: str = Field(...)
-
-
-AnthropicResponseBlock = AnthropicResponseTextBlock | AnthropicResponseThinkingBlock
-
-
 class AnthropicCacheCreationUsage(BaseModel):
     ephemeral_5m_input_tokens: int | None = Field(None)
     ephemeral_1h_input_tokens: int | None = Field(None)
@@ -92,7 +69,7 @@ class AnthropicMessagesResponse(BaseModel):
     type: str | None = Field(None)
     role: str | None = Field(None)
     model: str | None = Field(None)
-    content: list[AnthropicResponseBlock] | None = Field(None)
+    content: list[AnthropicResponseTextBlock] | None = Field(None)
     stop_reason: str | None = Field(None)
     stop_sequence: str | None = Field(None)
     usage: AnthropicMessagesUsage | None = Field(None)

comfy_api_nodes/apis/openrouter.py

@@ -1,93 +0,0 @@
-"""Pydantic models for the OpenRouter chat completions API.
-
-See: https://openrouter.ai/docs/api/api-reference/chat/send-chat-completion-request
-"""
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-
-class OpenRouterTextContent(BaseModel):
-    type: Literal["text"] = "text"
-    text: str = Field(...)
-
-
-class OpenRouterImageUrl(BaseModel):
-    url: str = Field(...)
-
-
-class OpenRouterImageContent(BaseModel):
-    type: Literal["image_url"] = "image_url"
-    image_url: OpenRouterImageUrl = Field(...)
-
-
-class OpenRouterVideoUrl(BaseModel):
-    url: str = Field(...)
-
-
-class OpenRouterVideoContent(BaseModel):
-    type: Literal["video_url"] = "video_url"
-    video_url: OpenRouterVideoUrl = Field(...)
-
-
-OpenRouterContentBlock = OpenRouterTextContent | OpenRouterImageContent | OpenRouterVideoContent
-
-
-class OpenRouterMessage(BaseModel):
-    role: Literal["system", "user", "assistant"] = Field(...)
-    content: str | list[OpenRouterContentBlock] = Field(...)
-
-
-class OpenRouterReasoningConfig(BaseModel):
-    effort: str | None = Field(None)
-    exclude: bool | None = Field(None, description="If true, model reasons but reasoning is excluded from response.")
-
-
-class OpenRouterWebSearchOptions(BaseModel):
-    search_context_size: str | None = Field(None)
-
-
-class OpenRouterChatRequest(BaseModel):
-    model: str = Field(...)
-    messages: list[OpenRouterMessage] = Field(...)
-    seed: int | None = Field(None)
-    reasoning: OpenRouterReasoningConfig | None = Field(None)
-    web_search_options: OpenRouterWebSearchOptions | None = Field(None)
-    stream: bool = Field(False)
-
-
-class OpenRouterUsage(BaseModel):
-    prompt_tokens: int | None = Field(None)
-    completion_tokens: int | None = Field(None)
-    total_tokens: int | None = Field(None)
-    cost: float | None = Field(None, description="Server-side authoritative USD cost of the call.")
-
-
-class OpenRouterResponseMessage(BaseModel):
-    role: str | None = Field(None)
-    content: str | None = Field(None)
-    reasoning: str | None = Field(None)
-    refusal: str | None = Field(None)
-
-
-class OpenRouterChoice(BaseModel):
-    index: int | None = Field(None)
-    message: OpenRouterResponseMessage | None = Field(None)
-    finish_reason: str | None = Field(None)
-
-
-class OpenRouterError(BaseModel):
-    code: int | str | None = Field(None)
-    message: str | None = Field(None)
-    metadata: dict | None = Field(None)
-
-
-class OpenRouterChatResponse(BaseModel):
-    id: str | None = Field(None)
-    model: str | None = Field(None)
-    object: str | None = Field(None)
-    provider: str | None = Field(None)
-    choices: list[OpenRouterChoice] | None = Field(None)
-    usage: OpenRouterUsage | None = Field(None)
-    error: OpenRouterError | None = Field(None)

comfy_api_nodes/apis/rodin.py

@@ -1,5 +1,7 @@
-from enum import Enum
+from __future__ import annotations
 
+from enum import Enum
+from typing import Optional, List
 from pydantic import BaseModel, Field
 
 
@@ -9,76 +11,44 @@ class Rodin3DGenerateRequest(BaseModel):
     material: str = Field(..., description="The material type.")
     quality_override: int = Field(..., description="The poly count of the mesh.")
     mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
-    TAPose: bool | None = Field(None, description="")
-
-
-class Rodin3DGen25Request(BaseModel):
-
-    tier: str = Field(..., description="Gen-2.5 tier (e.g. Gen-2.5-High).")
-    prompt: str | None = Field(None, description="Required for Text-to-3D; ignored otherwise.")
-    seed: int | None = Field(None, description="0-65535.")
-    material: str | None = Field(None, description="PBR | Shaded | All | None.")
-    geometry_file_format: str | None = Field(None, description="glb | usdz | fbx | obj | stl.")
-    texture_mode: str | None = Field(None, description="legacy | extreme-low | low | medium | high.")
-    mesh_mode: str | None = Field(None, description="Raw (triangular) | Quad.")
-    quality_override: int | None = Field(None, description="Mesh face count override.")
-    geometry_instruct_mode: str | None = Field(None, description="faithful | creative.")
-    bbox_condition: list[int] | None = Field(None, description="Bounding box [Width(Y), Height(Z), Length(X)] in cm.")
-    height: int | None = Field(None, description="Approximate model height in cm.")
-    TAPose: bool | None = Field(None, description="T/A pose for human-like models.")
-    hd_texture: bool | None = Field(None, description="Enhanced texture quality.")
-    texture_delight: bool | None = Field(None, description="Remove baked lighting from textures.")
-    is_micro: bool | None = Field(None, description="Micro detail (Extreme-High only).")
-    use_original_alpha: bool | None = Field(None, description="Preserve image transparency.")
-    preview_render: bool | None = Field(None, description="Generate high-quality preview render.")
-    addons: list[str] | None = Field(None, description='Optional addons, e.g. ["HighPack"].')
-
+    TAPose: Optional[bool] = Field(None, description="")
 
 class GenerateJobsData(BaseModel):
-    uuids: list[str] = Field(..., description="str LIST")
+    uuids: List[str] = Field(..., description="str LIST")
     subscription_key: str = Field(..., description="subscription key")
 
-
 class Rodin3DGenerateResponse(BaseModel):
-    message: str | None = Field(None, description="Return message.")
-    prompt: str | None = Field(None, description="Generated Prompt from image.")
-    submit_time: str | None = Field(None, description="Submit Time")
-    uuid: str | None = Field(None, description="Task str")
-    jobs: GenerateJobsData | None = Field(None, description="Details of jobs")
-
+    message: Optional[str] = Field(None, description="Return message.")
+    prompt: Optional[str] = Field(None, description="Generated Prompt from image.")
+    submit_time: Optional[str] = Field(None, description="Submit Time")
+    uuid: Optional[str] = Field(None, description="Task str")
+    jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs")
 
 class JobStatus(str, Enum):
     """
     Status for jobs
     """
-
     Done = "Done"
     Failed = "Failed"
     Generating = "Generating"
     Waiting = "Waiting"
 
-
 class Rodin3DCheckStatusRequest(BaseModel):
     subscription_key: str = Field(..., description="subscription from generate endpoint")
 
-
 class JobItem(BaseModel):
     uuid: str = Field(..., description="uuid")
-    status: JobStatus = Field(..., description="Status Currently")
-
+    status: JobStatus = Field(...,description="Status Currently")
 
 class Rodin3DCheckStatusResponse(BaseModel):
-    jobs: list[JobItem] = Field(..., description="Job status List")
-
+    jobs: List[JobItem] = Field(..., description="Job status List")
 
 class Rodin3DDownloadRequest(BaseModel):
     task_uuid: str = Field(..., description="Task str")
 
-
 class RodinResourceItem(BaseModel):
     url: str = Field(..., description="Download Url")
     name: str = Field(..., description="File name with ext")
 
-
 class Rodin3DDownloadResponse(BaseModel):
-    items: list[RodinResourceItem] = Field(..., alias="list", description="Source List")
+    list: List[RodinResourceItem] = Field(..., description="Source List")

comfy_api_nodes/nodes_anthropic.py

@@ -9,11 +9,8 @@ from comfy_api_nodes.apis.anthropic import (
     AnthropicMessage,
     AnthropicMessagesRequest,
     AnthropicMessagesResponse,
-    AnthropicOutputConfig,
-    AnthropicResponseTextBlock,
     AnthropicRole,
     AnthropicTextContent,
-    AnthropicThinkingConfig,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -35,29 +32,15 @@ CLAUDE_MODELS: dict[str, str] = {
     "Haiku 4.5": "claude-haiku-4-5-20251001",
 }
 
-_THINKING_UNSUPPORTED = {"Haiku 4.5"}
-# Models that use the newer "adaptive" thinking mode (Opus 4.7 requires it; older models keep the explicit budget API).
-# Anthropic decides the actual budget when adaptive is used, based on the `output_config.effort` hint.
-_ADAPTIVE_THINKING_MODELS = {"Opus 4.7", "Opus 4.6", "Sonnet 4.6"}
 
-# Budget mode (Sonnet 4.5): effort -> reasoning budget in tokens. Must be < max_tokens.
-# Sized so even the "high" budget fits comfortably under the default max_tokens=32768.
-_REASONING_BUDGET: dict[str, int] = {
-    "low": 2048,
-    "medium": 8192,
-    "high": 16384,
-}
-_REASONING_EFFORTS = ["off", "low", "medium", "high"]
-
-
-def _claude_model_inputs(model_label: str):
-    inputs: list = [
+def _claude_model_inputs():
+    return [
         IO.Int.Input(
             "max_tokens",
-            default=32768,
-            min=4096,
-            max=64000,
-            tooltip="Maximum number of tokens to generate (includes reasoning tokens when enabled).",
+            default=16000,
+            min=32,
+            max=32000,
+            tooltip="Maximum number of tokens to generate before stopping.",
             advanced=True,
         ),
         IO.Float.Input(
@@ -66,24 +49,10 @@ def _claude_model_inputs(model_label: str):
             min=0.0,
             max=1.0,
             step=0.01,
-            tooltip=(
-                "Controls randomness. 0.0 is deterministic, 1.0 is most random. "
-                "Ignored for Opus 4.7 and any model when reasoning_effort is set."
-            ),
+            tooltip="Controls randomness. 0.0 is deterministic, 1.0 is most random. Ignored for Opus 4.7.",
             advanced=True,
         ),
     ]
-    if model_label not in _THINKING_UNSUPPORTED:
-        inputs.append(
-            IO.Combo.Input(
-                "reasoning_effort",
-                options=_REASONING_EFFORTS,
-                default="off",
-                tooltip="Extended thinking effort. 'off' disables reasoning.",
-                advanced=True,
-            )
-        )
-    return inputs
 
 
 def _model_price_per_million(model: str) -> tuple[float, float] | None:
@@ -126,11 +95,7 @@ def calculate_tokens_price(response: AnthropicMessagesResponse) -> float | None:
 def _get_text_from_response(response: AnthropicMessagesResponse) -> str:
     if not response.content:
         return ""
-    # Thinking blocks are silently dropped — we never want reasoning in the output.
-    return "\n".join(
-        block.text for block in response.content
-        if isinstance(block, AnthropicResponseTextBlock) and block.text
-    )
+    return "\n".join(block.text for block in response.content if block.text)
 
 
 async def _build_image_content_blocks(
@@ -168,10 +133,7 @@ class ClaudeNode(IO.ComfyNode):
                 ),
                 IO.DynamicCombo.Input(
                     "model",
-                    options=[
-                        IO.DynamicCombo.Option(label, _claude_model_inputs(label))
-                        for label in CLAUDE_MODELS
-                    ],
+                    options=[IO.DynamicCombo.Option(label, _claude_model_inputs()) for label in CLAUDE_MODELS],
                     tooltip="The Claude model used to generate the response.",
                 ),
                 IO.Int.Input(
@@ -245,29 +207,8 @@ class ClaudeNode(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_label = model["model"]
-        max_tokens = model.get("max_tokens", 32768)
-        reasoning_effort = model.get("reasoning_effort", "off")
-        thinking_enabled = reasoning_effort not in ("off", None) and model_label not in _THINKING_UNSUPPORTED
-
-        # Anthropic requires temperature to be unset (defaults to 1.0) when thinking is enabled.
-        # Opus 4.7 also rejects user-supplied temperature.
-        if thinking_enabled or model_label == "Opus 4.7":
-            temperature = None
-        else:
-            temperature = model.get("temperature", 1.0)
-
-        thinking_cfg: AnthropicThinkingConfig | None = None
-        output_cfg: AnthropicOutputConfig | None = None
-        if thinking_enabled:
-            if model_label in _ADAPTIVE_THINKING_MODELS:
-                # Adaptive mode - Anthropic chooses the budget based on effort hint
-                thinking_cfg = AnthropicThinkingConfig(type="adaptive")
-                output_cfg = AnthropicOutputConfig(effort=reasoning_effort)
-            else:
-                # Budget mode (Sonnet 4.5). Leave at least 1024 tokens for the actual response
-                budget = _REASONING_BUDGET[reasoning_effort]
-                budget = min(budget, max(1024, max_tokens - 1024))
-                thinking_cfg = AnthropicThinkingConfig(type="enabled", budget_tokens=budget)
+        max_tokens = model["max_tokens"]
+        temperature = None if model_label == "Opus 4.7" else model["temperature"]
 
         image_tensors: list[Input.Image] = [t for t in (images or {}).values() if t is not None]
         if sum(get_number_of_images(t) for t in image_tensors) > CLAUDE_MAX_IMAGES:
@@ -288,8 +229,6 @@ class ClaudeNode(IO.ComfyNode):
                 messages=[AnthropicMessage(role=AnthropicRole.user, content=content)],
                 system=system_prompt or None,
                 temperature=temperature,
-                thinking=thinking_cfg,
-                output_config=output_cfg,
             ),
             price_extractor=calculate_tokens_price,
         )

comfy_api_nodes/nodes_bytedance.py

@@ -43,16 +43,15 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
     download_url_to_video_output,
-    downscale_video_to_max_pixels,
     get_number_of_images,
     image_tensor_pair_to_batch,
     poll_op,
+    resize_video_to_pixel_budget,
     sync_op,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
-    upscale_video_to_min_pixels,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -111,13 +110,12 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: st
     max_px = limits.get("max")
     if min_px and pixels < min_px:
         raise ValueError(
-            f"Reference video {index} is too small: {w}x{h} = {pixels:,} total pixels. "
-            f"Minimum for this model is {min_px:,} total pixels."
+            f"Reference video {index} is too small: {w}x{h} = {pixels:,}px. " f"Minimum is {min_px:,}px for this model."
         )
     if max_px and pixels > max_px:
         raise ValueError(
-            f"Reference video {index} is too large: {w}x{h} = {pixels:,} total pixels. "
-            f"Maximum for this model is {max_px:,} total pixels. Try downscaling the video."
+            f"Reference video {index} is too large: {w}x{h} = {pixels:,}px. "
+            f"Maximum is {max_px:,}px for this model. Try downscaling the video."
         )
 
 
@@ -1678,14 +1676,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                     "first_frame_asset_id",
                     default="",
                     tooltip="Seedance asset_id to use as the first frame. "
-                    "Mutually exclusive with the first_frame image input.",
+                            "Mutually exclusive with the first_frame image input.",
                     optional=True,
                 ),
                 IO.String.Input(
                     "last_frame_asset_id",
                     default="",
                     tooltip="Seedance asset_id to use as the last frame. "
-                    "Mutually exclusive with the last_frame image input.",
+                            "Mutually exclusive with the last_frame image input.",
                     optional=True,
                 ),
                 IO.Int.Input(
@@ -1867,20 +1865,11 @@ def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16
         IO.Boolean.Input(
             "auto_downscale",
             default=False,
+            advanced=True,
             optional=True,
             tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
             "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
         ),
-        IO.Boolean.Input(
-            "auto_upscale",
-            default=False,
-            advanced=True,
-            optional=True,
-            tooltip="Automatically upscale reference videos that are below the model's minimum pixel count "
-            "for the selected resolution. Aspect ratio is preserved; videos already meeting the minimum are "
-            "untouched. Note: upscaling a low-resolution source does not add real detail and may produce "
-            "lower-quality generations.",
-        ),
         IO.Autogrow.Input(
             "reference_assets",
             template=IO.Autogrow.TemplateNames(
@@ -2041,13 +2030,7 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
             max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max")
             if max_px:
                 for key in reference_videos:
-                    reference_videos[key] = downscale_video_to_max_pixels(reference_videos[key], max_px)
-
-        if model.get("auto_upscale") and reference_videos:
-            min_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("min")
-            if min_px:
-                for key in reference_videos:
-                    reference_videos[key] = upscale_video_to_min_pixels(reference_videos[key], min_px)
+                    reference_videos[key] = resize_video_to_pixel_budget(reference_videos[key], max_px)
 
         total_video_duration = 0.0
         for i, key in enumerate(reference_videos, 1):

comfy_api_nodes/nodes_openrouter.py

@@ -1,374 +0,0 @@
-"""API Nodes for OpenRouter LLM chat completions."""
-
-from dataclasses import dataclass
-from typing import Literal
-
-from typing_extensions import override
-
-from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.openrouter import (
-    OpenRouterChatRequest,
-    OpenRouterChatResponse,
-    OpenRouterContentBlock,
-    OpenRouterImageContent,
-    OpenRouterImageUrl,
-    OpenRouterMessage,
-    OpenRouterReasoningConfig,
-    OpenRouterTextContent,
-    OpenRouterVideoContent,
-    OpenRouterVideoUrl,
-    OpenRouterWebSearchOptions,
-)
-from comfy_api_nodes.util import (
-    ApiEndpoint,
-    get_number_of_images,
-    sync_op,
-    upload_images_to_comfyapi,
-    upload_video_to_comfyapi,
-    validate_string,
-)
-
-OPENROUTER_CHAT_ENDPOINT = "/proxy/openrouter/api/v1/chat/completions"
-
-
-Profile = Literal["standard", "reasoning", "frontier_reasoning", "perplexity", "perplexity_reasoning"]
-
-
-@dataclass(frozen=True)
-class _ModelSpec:
-    slug: str  # exact OpenRouter model id
-    profile: Profile
-    price_in: float  # USD per token (prompt)
-    price_out: float  # USD per token (completion)
-    max_images: int = 0  # 0 = no image input; otherwise max URL-passed images supported
-    max_videos: int = 0  # 0 = no video input; otherwise max URL-passed videos supported
-
-
-MODELS: list[_ModelSpec] = [
-    _ModelSpec("anthropic/claude-opus-4.7", "frontier_reasoning", 0.000005, 0.000025, max_images=20),
-    _ModelSpec("openai/gpt-5.5-pro", "frontier_reasoning", 0.00003, 0.00018, max_images=20),
-    _ModelSpec("openai/gpt-5.5", "frontier_reasoning", 0.000005, 0.00003, max_images=20),
-    _ModelSpec("google/gemini-3.5-flash", "reasoning", 0.0000015, 0.000009, max_images=20, max_videos=4),
-    _ModelSpec("x-ai/grok-4.20", "reasoning", 0.00000125, 0.0000025, max_images=20),
-    _ModelSpec("x-ai/grok-4.3", "reasoning", 0.00000125, 0.0000025, max_images=20),
-    _ModelSpec("deepseek/deepseek-v4-pro", "reasoning", 0.000000435, 0.00000087),
-    _ModelSpec("deepseek/deepseek-v4-flash", "reasoning", 0.000000112, 0.000000224),
-    _ModelSpec("deepseek/deepseek-v3.2", "reasoning", 0.000000252, 0.000000378),
-    _ModelSpec("qwen/qwen3.6-max-preview", "reasoning", 0.00000104, 0.00000624),
-    _ModelSpec("qwen/qwen3.6-plus", "reasoning", 0.000000325, 0.00000195, max_images=10, max_videos=4),
-    _ModelSpec("qwen/qwen3.6-flash", "reasoning", 0.0000001875, 0.000001125, max_images=10, max_videos=4),
-    _ModelSpec("mistralai/mistral-large-2512", "standard", 0.0000005, 0.0000015, max_images=8),
-    _ModelSpec("mistralai/mistral-medium-3-5", "reasoning", 0.0000015, 0.0000075, max_images=8),
-    _ModelSpec("z-ai/glm-4.6", "reasoning", 0.00000043, 0.00000174),
-    _ModelSpec("z-ai/glm-5", "reasoning", 0.0000006, 0.00000192),
-    _ModelSpec("moonshotai/kimi-k2.6", "reasoning", 0.00000073, 0.00000349, max_images=10),
-    _ModelSpec("moonshotai/kimi-k2-thinking", "reasoning", 0.0000006, 0.0000025),
-    _ModelSpec("perplexity/sonar-pro", "perplexity", 0.000003, 0.000015),
-    _ModelSpec("perplexity/sonar-reasoning-pro", "perplexity_reasoning", 0.000002, 0.000008),
-    _ModelSpec("perplexity/sonar-deep-research", "perplexity_reasoning", 0.000002, 0.000008),
-]
-
-_MODELS_BY_SLUG: dict[str, _ModelSpec] = {m.slug: m for m in MODELS}
-_REASONING_EFFORTS = ["off", "low", "medium", "high"]
-_SEARCH_CONTEXT_SIZES = ["low", "medium", "high"]
-
-
-def _reasoning_extra_inputs() -> list:
-    return [
-        IO.Combo.Input(
-            "reasoning_effort",
-            options=_REASONING_EFFORTS,
-            default="off",
-            tooltip="Reasoning effort. 'off' disables reasoning entirely.",
-            advanced=True,
-        ),
-    ]
-
-
-def _perplexity_extra_inputs() -> list:
-    return [
-        IO.Combo.Input(
-            "search_context_size",
-            options=_SEARCH_CONTEXT_SIZES,
-            default="medium",
-            tooltip="How much web search context to retrieve. Larger = more grounded but slower/pricier.",
-            advanced=True,
-        ),
-    ]
-
-
-def _profile_inputs(profile: Profile) -> list:
-    if profile == "standard":
-        return []
-    if profile in ("reasoning", "frontier_reasoning"):
-        return _reasoning_extra_inputs()
-    if profile == "perplexity":
-        return _perplexity_extra_inputs()
-    if profile == "perplexity_reasoning":
-        return _perplexity_extra_inputs() + _reasoning_extra_inputs()
-    raise ValueError(f"Unknown profile: {profile}")
-
-
-def _media_inputs(spec: _ModelSpec) -> list:
-    extras: list = []
-    if spec.max_images > 0:
-        extras.append(
-            IO.Autogrow.Input(
-                "images",
-                template=IO.Autogrow.TemplateNames(
-                    IO.Image.Input("image"),
-                    names=[f"image_{i}" for i in range(1, spec.max_images + 1)],
-                    min=0,
-                ),
-                tooltip=f"Optional reference image(s) — up to {spec.max_images}. Sent as URLs.",
-            )
-        )
-    if spec.max_videos > 0:
-        extras.append(
-            IO.Autogrow.Input(
-                "videos",
-                template=IO.Autogrow.TemplateNames(
-                    IO.Video.Input("video"),
-                    names=[f"video_{i}" for i in range(1, spec.max_videos + 1)],
-                    min=0,
-                ),
-                tooltip=f"Optional reference video(s) — up to {spec.max_videos}. Sent as URLs.",
-            )
-        )
-    return extras
-
-
-def _inputs_for_model(spec: _ModelSpec) -> list:
-    return _profile_inputs(spec.profile) + _media_inputs(spec)
-
-
-def _build_model_options() -> list[IO.DynamicCombo.Option]:
-    return [IO.DynamicCombo.Option(spec.slug, _inputs_for_model(spec)) for spec in MODELS]
-
-
-def _calculate_price(response: OpenRouterChatResponse) -> float | None:
-    if response.usage and response.usage.cost is not None:
-        return float(response.usage.cost)
-    return None
-
-
-def _price_badge_jsonata() -> str:
-    rates_pairs = []
-    for spec in MODELS:
-        prompt_per_1k = spec.price_in * 1000
-        completion_per_1k = spec.price_out * 1000
-        rates_pairs.append(f'  "{spec.slug}": [{prompt_per_1k:.8g}, {completion_per_1k:.8g}]')
-    rates_block = ",\n".join(rates_pairs)
-    return (
-        "(\n"
-        "  $rates := {\n"
-        f"{rates_block}\n"
-        "  };\n"
-        "  $r := $lookup($rates, widgets.model);\n"
-        "  $r ? {\n"
-        '    "type": "list_usd",\n'
-        '    "usd": $r,\n'
-        '    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }\n'
-        '  } : {"type": "text", "text": "Token-based"}\n'
-        ")"
-    )
-
-
-async def _build_image_blocks(
-    cls: type[IO.ComfyNode], spec: _ModelSpec, images: list[Input.Image]
-) -> list[OpenRouterImageContent]:
-    urls = await upload_images_to_comfyapi(
-        cls,
-        images,
-        max_images=spec.max_images,
-        total_pixels=2048 * 2048,
-        mime_type="image/png",
-        wait_label="Uploading reference images",
-    )
-    return [OpenRouterImageContent(image_url=OpenRouterImageUrl(url=url)) for url in urls]
-
-
-async def _build_video_blocks(cls: type[IO.ComfyNode], videos: list[Input.Video]) -> list[OpenRouterVideoContent]:
-    blocks: list[OpenRouterVideoContent] = []
-    total = len(videos)
-    for idx, video in enumerate(videos):
-        label = "Uploading reference video"
-        if total > 1:
-            label = f"{label} ({idx + 1}/{total})"
-        url = await upload_video_to_comfyapi(cls, video, wait_label=label)
-        blocks.append(OpenRouterVideoContent(video_url=OpenRouterVideoUrl(url=url)))
-    return blocks
-
-
-def _user_message(prompt: str, media_blocks: list[OpenRouterContentBlock]) -> OpenRouterMessage:
-    if not media_blocks:
-        return OpenRouterMessage(role="user", content=prompt)
-    blocks: list[OpenRouterContentBlock] = list(media_blocks)
-    blocks.append(OpenRouterTextContent(text=prompt))
-    return OpenRouterMessage(role="user", content=blocks)
-
-
-def _build_messages(
-    system_prompt: str, prompt: str, media_blocks: list[OpenRouterContentBlock]
-) -> list[OpenRouterMessage]:
-    messages: list[OpenRouterMessage] = []
-    if system_prompt:
-        messages.append(OpenRouterMessage(role="system", content=system_prompt))
-    messages.append(_user_message(prompt, media_blocks))
-    return messages
-
-
-def _build_request(
-    slug: str,
-    system_prompt: str,
-    prompt: str,
-    media_blocks: list[OpenRouterContentBlock],
-    *,
-    seed: int,
-    reasoning_effort: str | None,
-    search_context_size: str | None,
-) -> OpenRouterChatRequest:
-    reasoning_cfg: OpenRouterReasoningConfig | None = None
-    if reasoning_effort and reasoning_effort != "off":
-        # exclude=True asks providers to reason internally but not return the trace
-        reasoning_cfg = OpenRouterReasoningConfig(effort=reasoning_effort, exclude=True)
-    web_search_cfg: OpenRouterWebSearchOptions | None = None
-    if search_context_size:
-        web_search_cfg = OpenRouterWebSearchOptions(search_context_size=search_context_size)
-    return OpenRouterChatRequest(
-        model=slug,
-        messages=_build_messages(system_prompt, prompt, media_blocks),
-        seed=seed if seed > 0 else None,
-        reasoning=reasoning_cfg,
-        web_search_options=web_search_cfg,
-    )
-
-
-def _extract_text(response: OpenRouterChatResponse) -> str:
-    if response.error:
-        code = response.error.code if response.error.code is not None else "unknown"
-        raise ValueError(f"OpenRouter error ({code}): {response.error.message or 'no message'}")
-    if not response.choices:
-        raise ValueError("Empty response from OpenRouter (no choices).")
-    message = response.choices[0].message
-    if not message:
-        raise ValueError("Empty response from OpenRouter (no message).")
-    if message.refusal:
-        raise ValueError(f"Model refused to respond: {message.refusal}")
-    return message.content or ""
-
-
-class OpenRouterLLMNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="OpenRouterLLMNode",
-            display_name="OpenRouter LLM",
-            category="api node/text/OpenRouter",
-            essentials_category="Text Generation",
-            description=(
-                "Generate text responses through OpenRouter. Routes to a curated set of popular "
-                "models from xAI, DeepSeek, Qwen, Mistral, Z.AI (GLM), Moonshot (Kimi), and "
-                "Perplexity Sonar."
-            ),
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text input to the model.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_build_model_options(),
-                    tooltip="The OpenRouter model used to generate the response.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed for sampling. Set to 0 to omit. Most models treat this as a hint only.",
-                ),
-                IO.String.Input(
-                    "system_prompt",
-                    multiline=True,
-                    default="",
-                    optional=True,
-                    advanced=True,
-                    tooltip="Foundational instructions that dictate the model's behavior.",
-                ),
-            ],
-            outputs=[IO.String.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
-                expr=_price_badge_jsonata(),
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        model: dict,
-        seed: int,
-        system_prompt: str = "",
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=True, min_length=1)
-        slug: str = model["model"]
-        spec = _MODELS_BY_SLUG.get(slug)
-        if spec is None:
-            raise ValueError(f"Unknown OpenRouter model: {slug}")
-
-        reasoning_effort: str | None = model.get("reasoning_effort")
-        search_context_size: str | None = model.get("search_context_size")
-
-        image_tensors: list[Input.Image] = [t for t in (model.get("images") or {}).values() if t is not None]
-        if image_tensors and sum(get_number_of_images(t) for t in image_tensors) > spec.max_images:
-            raise ValueError(f"Up to {spec.max_images} images are supported for {slug}.")
-        video_inputs: list[Input.Video] = [v for v in (model.get("videos") or {}).values() if v is not None]
-        if video_inputs and len(video_inputs) > spec.max_videos:
-            raise ValueError(f"Up to {spec.max_videos} videos are supported for {slug}.")
-
-        media_blocks: list[OpenRouterContentBlock] = []
-        if image_tensors:
-            media_blocks.extend(await _build_image_blocks(cls, spec, image_tensors))
-        if video_inputs:
-            media_blocks.extend(await _build_video_blocks(cls, video_inputs))
-
-        request = _build_request(
-            slug,
-            system_prompt,
-            prompt,
-            media_blocks,
-            seed=seed,
-            reasoning_effort=reasoning_effort,
-            search_context_size=search_context_size,
-        )
-
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path=OPENROUTER_CHAT_ENDPOINT, method="POST"),
-            response_model=OpenRouterChatResponse,
-            data=request,
-            price_extractor=_calculate_price,
-        )
-        return IO.NodeOutput(_extract_text(response))
-
-
-class OpenRouterExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [OpenRouterLLMNode]
-
-
-async def comfy_entrypoint() -> OpenRouterExtension:
-    return OpenRouterExtension()

comfy_api_nodes/nodes_rodin.py

@@ -5,37 +5,32 @@ Rodin API docs: https://developer.hyper3d.ai/
 
 """
 
+from inspect import cleandoc
+import folder_paths as comfy_paths
+import os
 import logging
 import math
-import os
-from inspect import cleandoc
 from io import BytesIO
-from typing import Any
-
-import aiohttp
-from PIL import Image
 from typing_extensions import override
-
-import folder_paths as comfy_paths
-from comfy_api.latest import IO, ComfyExtension, Types
+from PIL import Image
 from comfy_api_nodes.apis.rodin import (
-    JobStatus,
+    Rodin3DGenerateRequest,
+    Rodin3DGenerateResponse,
     Rodin3DCheckStatusRequest,
     Rodin3DCheckStatusResponse,
     Rodin3DDownloadRequest,
     Rodin3DDownloadResponse,
-    Rodin3DGen25Request,
-    Rodin3DGenerateRequest,
-    Rodin3DGenerateResponse,
+    JobStatus,
 )
 from comfy_api_nodes.util import (
+    sync_op,
+    poll_op,
     ApiEndpoint,
     download_url_to_bytesio,
     download_url_to_file_3d,
-    poll_op,
-    sync_op,
-    validate_string,
 )
+from comfy_api.latest import ComfyExtension, IO, Types
+
 
 COMMON_PARAMETERS = [
     IO.Int.Input(
@@ -56,30 +51,40 @@ COMMON_PARAMETERS = [
 ]
 
 
-_QUALITY_MESH_OPTIONS: dict[str, tuple[str, int]] = {
-    "4K-Quad":       ("Quad", 4000),
-    "8K-Quad":       ("Quad", 8000),
-    "18K-Quad":      ("Quad", 18000),
-    "50K-Quad":      ("Quad", 50000),
-    "200K-Quad":     ("Quad", 200000),
-    "2K-Triangle":   ("Raw", 2000),
-    "20K-Triangle":  ("Raw", 20000),
-    "150K-Triangle": ("Raw", 150000),
-    "200K-Triangle": ("Raw", 200000),
-    "500K-Triangle": ("Raw", 500000),
-    "1M-Triangle":   ("Raw", 1000000),
-}
+def get_quality_mode(poly_count):
+    polycount = poly_count.split("-")
+    poly = polycount[1]
+    count = polycount[0]
+    if poly == "Triangle":
+        mesh_mode = "Raw"
+    elif poly == "Quad":
+        mesh_mode = "Quad"
+    else:
+        mesh_mode = "Quad"
+
+    if count == "4K":
+        quality_override = 4000
+    elif count == "8K":
+        quality_override = 8000
+    elif count == "18K":
+        quality_override = 18000
+    elif count == "50K":
+        quality_override = 50000
+    elif count == "2K":
+        quality_override = 2000
+    elif count == "20K":
+        quality_override = 20000
+    elif count == "150K":
+        quality_override = 150000
+    elif count == "500K":
+        quality_override = 500000
+    else:
+        quality_override = 18000
+
+    return mesh_mode, quality_override
 
 
-def get_quality_mode(poly_count: str) -> tuple[str, int]:
-    """Map a polygon-count preset like '18K-Quad' to (mesh_mode, quality_override).
-
-    Falls back to ('Quad', 18000) for unknown labels; legacy parity.
-    """
-    return _QUALITY_MESH_OPTIONS.get(poly_count, ("Quad", 18000))
-
-
-def tensor_to_filelike(tensor, max_pixels: int = 2048 * 2048):
+def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
     """
     Converts a PyTorch tensor to a file-like object.
 
@@ -91,8 +96,8 @@ def tensor_to_filelike(tensor, max_pixels: int = 2048 * 2048):
     - io.BytesIO: A file-like object containing the image data.
     """
     array = tensor.cpu().numpy()
-    array = (array * 255).astype("uint8")
-    image = Image.fromarray(array, "RGB")
+    array = (array * 255).astype('uint8')
+    image = Image.fromarray(array, 'RGB')
 
     original_width, original_height = image.size
     original_pixels = original_width * original_height
@@ -107,7 +112,7 @@ def tensor_to_filelike(tensor, max_pixels: int = 2048 * 2048):
         image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
 
     img_byte_arr = BytesIO()
-    image.save(img_byte_arr, format="PNG")  # PNG is used for lossless compression
+    image.save(img_byte_arr, format='PNG')  # PNG is used for lossless compression
     img_byte_arr.seek(0)
     return img_byte_arr
 
@@ -140,9 +145,11 @@ async def create_generate_task(
             TAPose=ta_pose,
         ),
         files=[
-            ("images", open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image))
-            for image in images
-            if image is not None
+            (
+                "images",
+                open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image)
+            )
+            for image in images if image is not None
         ],
         content_type="multipart/form-data",
     )
@@ -170,7 +177,6 @@ def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
         return "DONE"
     return "Generating"
 
-
 def extract_progress(response: Rodin3DCheckStatusResponse) -> int | None:
     if not response.jobs:
         return None
@@ -208,7 +214,7 @@ async def download_files(url_list, task_uuid: str) -> tuple[str | None, Types.Fi
     model_file_path = None
     file_3d = None
 
-    for i in url_list.items:
+    for i in url_list.list:
         file_path = os.path.join(save_path, i.name)
         if i.name.lower().endswith(".glb"):
             model_file_path = os.path.join(result_folder_name, i.name)
@@ -483,16 +489,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
                 IO.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
                 IO.Combo.Input(
                     "Polygon_count",
-                    options=[
-                        "4K-Quad",
-                        "8K-Quad",
-                        "18K-Quad",
-                        "50K-Quad",
-                        "2K-Triangle",
-                        "20K-Triangle",
-                        "150K-Triangle",
-                        "500K-Triangle",
-                    ],
+                    options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"],
                     default="500K-Triangle",
                     optional=True,
                 ),
@@ -545,566 +542,6 @@ class Rodin3D_Gen2(IO.ComfyNode):
         return IO.NodeOutput(model_path, file_3d)
 
 
-def _rodin_multipart_parser(data: dict[str, Any]) -> aiohttp.FormData:
-    """Convert a Rodin request dict to an aiohttp form, fixing bool/list serialization.
-
-    Booleans --> "true"/"false". Lists --> one field per element.
-    """
-    form = aiohttp.FormData(default_to_multipart=True)
-    for key, value in data.items():
-        if value is None:
-            continue
-        if isinstance(value, bool):
-            form.add_field(key, "true" if value else "false")
-        elif isinstance(value, list):
-            for item in value:
-                form.add_field(key, str(item))
-        elif isinstance(value, (bytes, bytearray)):
-            form.add_field(key, value)
-        else:
-            form.add_field(key, str(value))
-    return form
-
-
-async def _create_gen25_task(
-    cls: type[IO.ComfyNode],
-    request: Rodin3DGen25Request,
-    images: list | None,
-) -> tuple[str, str]:
-    """Submit a Gen-2.5 generate job; returns (task_uuid, subscription_key)."""
-
-    if images is not None and len(images) > 5:
-        raise ValueError("Rodin Gen-2.5 supports at most 5 input images.")
-
-    files = None
-    if images:
-        files = [
-            (
-                "images",
-                open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image),
-            )
-            for image in images
-            if image is not None
-        ]
-
-    response = await sync_op(
-        cls,
-        ApiEndpoint(path="/proxy/rodin/api/v2/rodin", method="POST"),
-        response_model=Rodin3DGenerateResponse,
-        data=request,
-        files=files,
-        content_type="multipart/form-data",
-        multipart_parser=_rodin_multipart_parser,
-    )
-
-    if not response.uuid or not response.jobs or not response.jobs.subscription_key:
-        raise RuntimeError(f"Rodin Gen-2.5 submit failed: message={response.message!r}")
-    return response.uuid, response.jobs.subscription_key
-
-
-_PREVIEWABLE_3D_EXTS = {".glb", ".obj", ".fbx", ".stl", ".gltf"}
-
-
-async def _download_gen25_files(
-    download_list: Rodin3DDownloadResponse,
-    task_uuid: str,
-    geometry_file_format: str,
-) -> Types.File3D | None:
-    """Download every file in the list; return the File3D matching the chosen format."""
-
-    folder_name = f"Rodin3D_Gen25_{task_uuid}"
-    save_dir = os.path.join(comfy_paths.get_output_directory(), folder_name)
-    os.makedirs(save_dir, exist_ok=True)
-
-    target_ext = f".{geometry_file_format.lower().lstrip('.')}"
-    file_3d: Types.File3D | None = None
-
-    for item in download_list.items:
-        file_path = os.path.join(save_dir, item.name)
-        ext = os.path.splitext(item.name.lower())[1]
-        # Prefer the file matching the user's chosen format; fall back below.
-        if file_3d is None and ext == target_ext and ext in _PREVIEWABLE_3D_EXTS:
-            file_3d = await download_url_to_file_3d(item.url, target_ext.lstrip("."))
-            with open(file_path, "wb") as f:
-                f.write(file_3d.get_bytes())
-            continue
-        await download_url_to_bytesio(item.url, file_path)
-
-    # If the chosen format wasn't found, surface any model file we did get.
-    if file_3d is None:
-        for item in download_list.items:
-            ext = os.path.splitext(item.name.lower())[1]
-            if ext in _PREVIEWABLE_3D_EXTS:
-                file_3d = await download_url_to_file_3d(item.url, ext.lstrip("."))
-                break
-    return file_3d
-
-
-_MODE_REGULAR = "Regular"
-_MODE_FAST = "Fast"
-_MODE_EXTREME_HIGH = "Extreme-High"
-
-_REGULAR_POLY_OPTIONS = [
-    "Default",
-    "4K-Quad",
-    "8K-Quad",
-    "18K-Quad",
-    "50K-Quad",
-    "2K-Triangle",
-    "20K-Triangle",
-    "150K-Triangle",
-    "500K-Triangle",
-    "1M-Triangle",
-]
-
-_TEXTURE_MODE_OPTIONS = ["Default", "legacy", "extreme-low", "low", "medium", "high"]
-_GEOMETRY_FORMAT_OPTIONS = ["glb", "fbx", "obj", "stl"]
-_MATERIAL_OPTIONS = ["PBR", "Shaded", "All", "None"]
-
-
-def _build_mode_input(name: str = "mode") -> IO.DynamicCombo.Input:
-    return IO.DynamicCombo.Input(
-        name,
-        options=[
-            IO.DynamicCombo.Option(
-                _MODE_REGULAR,
-                [
-                    IO.Combo.Input(
-                        "tier",
-                        options=["Gen-2.5-Low", "Gen-2.5-Medium", "Gen-2.5-High"],
-                        default="Gen-2.5-High",
-                        tooltip="Quality tier. Higher tiers produce higher-fidelity geometry.",
-                    ),
-                    IO.Combo.Input(
-                        "polygon_count",
-                        options=_REGULAR_POLY_OPTIONS,
-                        default="Default",
-                        tooltip="Preset face count. 'Default' uses the server's default for the selected tier.",
-                    ),
-                    IO.Boolean.Input(
-                        "creative",
-                        default=False,
-                        tooltip="Creative mode (Medium/High only). Enhances generative robustness.",
-                    ),
-                ],
-            ),
-            IO.DynamicCombo.Option(
-                _MODE_FAST,
-                [
-                    IO.Combo.Input(
-                        "tier",
-                        options=[
-                            "Gen-2.5-Extreme-Low",
-                            "Gen-2.5-Low",
-                            "Gen-2.5-Medium",
-                            "Gen-2.5-High",
-                        ],
-                        default="Gen-2.5-Low",
-                    ),
-                    IO.Int.Input(
-                        "mesh_faces",
-                        default=20000,
-                        min=1000,
-                        max=20000,
-                        display_mode=IO.NumberDisplay.number,
-                        tooltip="Mesh face count (1K-20K in Fast mode).",
-                    ),
-                ],
-            ),
-            IO.DynamicCombo.Option(
-                _MODE_EXTREME_HIGH,
-                [
-                    IO.Combo.Input("mesh_mode", options=["Raw", "Quad"], default="Raw"),
-                    IO.Int.Input(
-                        "mesh_faces",
-                        default=1000000,
-                        min=20000,
-                        max=2000000,
-                        display_mode=IO.NumberDisplay.number,
-                        tooltip=(
-                            "Mesh face count. Raw mode: 20K-2M. "
-                            "Quad mode: keep under 200K (upstream may reject higher values)."
-                        ),
-                    ),
-                    IO.Boolean.Input(
-                        "is_micro",
-                        default=False,
-                        tooltip="Enable micro detail (Extreme-High only).",
-                    ),
-                    IO.Boolean.Input(
-                        "creative",
-                        default=False,
-                        tooltip="Creative mode. Enhances generative robustness.",
-                    ),
-                ],
-            ),
-        ],
-        tooltip=(
-            "Generation mode. Regular = balanced. Fast = 1K-20K faces for rapid prototyping. "
-            "Extreme-High = 20K-2M faces with optional micro details."
-        ),
-    )
-
-
-def _build_common_inputs(*, include_image_only: bool) -> list:
-    inputs: list = [
-        IO.Combo.Input("material", options=_MATERIAL_OPTIONS, default="Shaded"),
-        IO.Combo.Input("geometry_file_format", options=_GEOMETRY_FORMAT_OPTIONS, default="glb"),
-        IO.Combo.Input(
-            "texture_mode",
-            options=_TEXTURE_MODE_OPTIONS,
-            default="Default",
-            optional=True,
-            tooltip="Texture quality preset. 'Default' uses the server's default for the selected tier.",
-        ),
-        IO.Int.Input(
-            "seed",
-            default=0,
-            min=0,
-            max=65535,
-            display_mode=IO.NumberDisplay.number,
-            control_after_generate=True,
-            optional=True,
-        ),
-        IO.Boolean.Input(
-            "TAPose", default=False, optional=True, advanced=True, tooltip="T/A pose for human-like models."
-        ),
-        IO.Boolean.Input(
-            "hd_texture", default=False, optional=True, advanced=True, tooltip="High-quality texture enhancement."
-        ),
-        IO.Boolean.Input(
-            "texture_delight",
-            default=False,
-            optional=True,
-            advanced=True,
-            tooltip="Remove baked lighting from textures.",
-        ),
-    ]
-    if include_image_only:
-        inputs.append(
-            IO.Boolean.Input(
-                "use_original_alpha",
-                default=False,
-                optional=True,
-                advanced=True,
-                tooltip="Preserve image transparency.",
-            )
-        )
-    inputs.extend(
-        [
-            IO.Boolean.Input(
-                "addon_highpack",
-                default=False,
-                optional=True,
-                advanced=True,
-                tooltip="HighPack addon: 4K textures and ~16x faces in Quad mode.",
-            ),
-            IO.Int.Input(
-                "bbox_width",
-                default=0,
-                min=0,
-                max=300,
-                display_mode=IO.NumberDisplay.number,
-                optional=True,
-                advanced=True,
-                tooltip="Bounding-box width (Y axis). Set to 0 with the others to skip bbox.",
-            ),
-            IO.Int.Input(
-                "bbox_height",
-                default=0,
-                min=0,
-                max=300,
-                display_mode=IO.NumberDisplay.number,
-                optional=True,
-                advanced=True,
-                tooltip="Bounding-box height (Z axis).",
-            ),
-            IO.Int.Input(
-                "bbox_length",
-                default=0,
-                min=0,
-                max=300,
-                display_mode=IO.NumberDisplay.number,
-                optional=True,
-                advanced=True,
-                tooltip="Bounding-box length (X axis).",
-            ),
-            IO.Int.Input(
-                "height_cm",
-                default=0,
-                min=0,
-                max=10000,
-                display_mode=IO.NumberDisplay.number,
-                optional=True,
-                advanced=True,
-                tooltip="Approximate model height in centimeters (0 to skip).",
-            ),
-        ]
-    )
-    return inputs
-
-
-_PRICE_EXPR = """
-(
-  $baseCredits := widgets.mode = "extreme-high" ? 1.0 : 0.5;
-  $addonCredits := widgets.addon_highpack ? 1.0 : 0.0;
-  $total := ($baseCredits * 1.5) + ($addonCredits * 0.8);
-  {"type":"usd","usd": $total}
-)
-"""
-
-
-def _resolve_mode_params(mode_input: dict) -> dict:
-    """Translate the DynamicCombo `mode` payload into Gen-2.5 request fields.
-
-    Returns a dict with: tier, quality_override, mesh_mode, geometry_instruct_mode, is_micro.
-    Missing keys mean "do not send" (so we don't override server defaults).
-    """
-    selected = mode_input["mode"]
-    out: dict = {}
-
-    if selected == _MODE_REGULAR:
-        out["tier"] = mode_input["tier"]
-        polygon = mode_input.get("polygon_count", "Default")
-        if polygon != "Default":
-            mesh_mode, faces = get_quality_mode(polygon)
-            out["mesh_mode"] = mesh_mode
-            out["quality_override"] = faces
-        if mode_input.get("creative"):
-            out["geometry_instruct_mode"] = "creative"
-
-    elif selected == _MODE_FAST:
-        out["tier"] = mode_input["tier"]
-        out["mesh_mode"] = "Raw"
-        out["quality_override"] = int(mode_input["mesh_faces"])
-
-    elif selected == _MODE_EXTREME_HIGH:
-        out["tier"] = "Gen-2.5-Extreme-High"
-        out["mesh_mode"] = mode_input["mesh_mode"]
-        out["quality_override"] = int(mode_input["mesh_faces"])
-        if mode_input.get("is_micro"):
-            out["is_micro"] = True
-        if mode_input.get("creative"):
-            out["geometry_instruct_mode"] = "creative"
-    return out
-
-
-def _build_request(
-    *,
-    mode_input: dict,
-    material: str,
-    geometry_file_format: str,
-    texture_mode: str,
-    seed: int,
-    TAPose: bool,
-    hd_texture: bool,
-    texture_delight: bool,
-    addon_highpack: bool,
-    bbox_width: int,
-    bbox_height: int,
-    bbox_length: int,
-    height_cm: int,
-    prompt: str | None = None,
-    use_original_alpha: bool = False,
-) -> Rodin3DGen25Request:
-    mode_params = _resolve_mode_params(mode_input)
-
-    bbox = None
-    if bbox_width and bbox_height and bbox_length:
-        bbox = [bbox_width, bbox_height, bbox_length]
-
-    return Rodin3DGen25Request(
-        tier=mode_params["tier"],
-        prompt=prompt or None,
-        seed=seed,
-        material=material,
-        geometry_file_format=geometry_file_format,
-        texture_mode=None if texture_mode == "Default" else texture_mode,
-        mesh_mode=mode_params.get("mesh_mode"),
-        quality_override=mode_params.get("quality_override"),
-        geometry_instruct_mode=mode_params.get("geometry_instruct_mode"),
-        bbox_condition=bbox,
-        height=height_cm or None,
-        TAPose=TAPose or None,
-        hd_texture=hd_texture or None,
-        texture_delight=texture_delight or None,
-        is_micro=mode_params.get("is_micro"),
-        use_original_alpha=use_original_alpha or None,
-        addons=["HighPack"] if addon_highpack else None,
-    )
-
-
-class Rodin3D_Gen25_Image(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="Rodin3D_Gen25_Image",
-            display_name="Rodin 3D Gen-2.5 - Image to 3D",
-            category="api node/3d/Rodin",
-            description=(
-                "Generate a 3D model from 1-5 reference images via Rodin Gen-2.5. "
-                "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
-            ),
-            inputs=[
-                IO.Autogrow.Input(
-                    "images",
-                    template=IO.Autogrow.TemplatePrefix(IO.Image.Input("image"), prefix="image", min=1, max=5),
-                    tooltip="1-5 images. The first image is used for materials when multi-view.",
-                ),
-                _build_mode_input(),
-                *_build_common_inputs(include_image_only=True),
-            ],
-            outputs=[IO.File3DAny.Output(display_name="model_file")],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["mode", "addon_highpack"]),
-                expr=_PRICE_EXPR,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        images: IO.Autogrow.Type,
-        mode: dict,
-        material: str,
-        geometry_file_format: str,
-        texture_mode: str,
-        seed: int,
-        TAPose: bool,
-        hd_texture: bool,
-        texture_delight: bool,
-        use_original_alpha: bool,
-        addon_highpack: bool,
-        bbox_width: int,
-        bbox_height: int,
-        bbox_length: int,
-        height_cm: int,
-    ) -> IO.NodeOutput:
-        image_tensors = [img for img in images.values() if img is not None]
-        if not image_tensors:
-            raise ValueError("Rodin Gen-2.5 Image-to-3D requires at least one image.")
-
-        # Flatten multi-image tensors into individual frames; the API accepts each as a separate part.
-        flat_images: list = []
-        for tensor in image_tensors:
-            if hasattr(tensor, "shape") and len(tensor.shape) == 4:
-                for i in range(tensor.shape[0]):
-                    flat_images.append(tensor[i])
-            else:
-                flat_images.append(tensor)
-
-        if len(flat_images) > 5:
-            raise ValueError(f"Rodin Gen-2.5 accepts at most 5 images; received {len(flat_images)}.")
-
-        request = _build_request(
-            mode_input=mode,
-            material=material,
-            geometry_file_format=geometry_file_format,
-            texture_mode=texture_mode,
-            seed=seed,
-            TAPose=TAPose,
-            hd_texture=hd_texture,
-            texture_delight=texture_delight,
-            addon_highpack=addon_highpack,
-            bbox_width=bbox_width,
-            bbox_height=bbox_height,
-            bbox_length=bbox_length,
-            height_cm=height_cm,
-            prompt=None,
-            use_original_alpha=use_original_alpha,
-        )
-
-        task_uuid, subscription_key = await _create_gen25_task(cls, request, flat_images)
-        await poll_for_task_status(subscription_key, cls)
-        download_list = await get_rodin_download_list(task_uuid, cls)
-        file_3d = await _download_gen25_files(download_list, task_uuid, geometry_file_format)
-        return IO.NodeOutput(file_3d)
-
-
-class Rodin3D_Gen25_Text(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="Rodin3D_Gen25_Text",
-            display_name="Rodin 3D Gen-2.5 - Text to 3D",
-            category="api node/3d/Rodin",
-            description=(
-                "Generate a 3D model from a text prompt via Rodin Gen-2.5. "
-                "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
-            ),
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text prompt for the 3D model.",
-                ),
-                _build_mode_input(),
-                *_build_common_inputs(include_image_only=False),
-            ],
-            outputs=[IO.File3DAny.Output(display_name="model_file")],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["mode", "addon_highpack"]),
-                expr=_PRICE_EXPR,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        mode: dict,
-        material: str,
-        geometry_file_format: str,
-        texture_mode: str,
-        seed: int,
-        TAPose: bool,
-        hd_texture: bool,
-        texture_delight: bool,
-        addon_highpack: bool,
-        bbox_width: int,
-        bbox_height: int,
-        bbox_length: int,
-        height_cm: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, field_name="prompt", min_length=1, max_length=2500)
-        request = _build_request(
-            mode_input=mode,
-            material=material,
-            geometry_file_format=geometry_file_format,
-            texture_mode=texture_mode,
-            seed=seed,
-            TAPose=TAPose,
-            hd_texture=hd_texture,
-            texture_delight=texture_delight,
-            addon_highpack=addon_highpack,
-            bbox_width=bbox_width,
-            bbox_height=bbox_height,
-            bbox_length=bbox_length,
-            height_cm=height_cm,
-            prompt=prompt,
-        )
-        task_uuid, subscription_key = await _create_gen25_task(cls, request, images=None)
-        await poll_for_task_status(subscription_key, cls)
-        download_list = await get_rodin_download_list(task_uuid, cls)
-        file_3d = await _download_gen25_files(download_list, task_uuid, geometry_file_format)
-        return IO.NodeOutput(file_3d)
-
-
 class Rodin3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1114,8 +551,6 @@ class Rodin3DExtension(ComfyExtension):
             Rodin3D_Smooth,
             Rodin3D_Sketch,
             Rodin3D_Gen2,
-            Rodin3D_Gen25_Image,
-            Rodin3D_Gen25_Text,
         ]
 
 

comfy_api_nodes/util/__init__.py

@@ -16,17 +16,16 @@ from .conversions import (
     convert_mask_to_image,
     downscale_image_tensor,
     downscale_image_tensor_by_max_side,
-    downscale_video_to_max_pixels,
     image_tensor_pair_to_batch,
     pil_to_bytesio,
     resize_mask_to_image,
+    resize_video_to_pixel_budget,
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
     text_filepath_to_base64_string,
     text_filepath_to_data_uri,
     trim_video,
-    upscale_video_to_min_pixels,
     video_to_base64_string,
 )
 from .download_helpers import (
@@ -89,17 +88,16 @@ __all__ = [
     "convert_mask_to_image",
     "downscale_image_tensor",
     "downscale_image_tensor_by_max_side",
-    "downscale_video_to_max_pixels",
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
     "resize_mask_to_image",
+    "resize_video_to_pixel_budget",
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",
     "text_filepath_to_base64_string",
     "text_filepath_to_data_uri",
     "trim_video",
-    "upscale_video_to_min_pixels",
     "video_to_base64_string",
     # Validation utilities
     "get_image_dimensions",

comfy_api_nodes/util/conversions.py

@@ -415,48 +415,14 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
         raise RuntimeError(f"Failed to trim video: {str(e)}") from e
 
 
-def downscale_video_to_max_pixels(video: Input.Video, max_pixels: int) -> Input.Video:
-    """Downscale a video to fit within ``max_pixels`` (w * h), preserving aspect ratio.
+def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video:
+    """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio.
 
     Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
     Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
     """
     src_w, src_h = video.get_dimensions()
-    scale_dims = _compute_downscale_dims(src_w, src_h, max_pixels)
-    if scale_dims is None:
-        return video
-    return _apply_video_scale(video, scale_dims)
-
-
-def _compute_upscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
-    """Return upscaled (w, h) with even dims meeting at least ``total_pixels``, or None if already large enough.
-
-    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
-    are rounded up to even values (many codecs require divisible-by-2). The result is guaranteed to be at
-    least ``total_pixels``.
-    """
-    pixels = src_w * src_h
-    if pixels >= total_pixels:
-        return None
-    scale = math.sqrt(total_pixels / pixels)
-    new_w = math.ceil(src_w * scale)
-    new_h = math.ceil(src_h * scale)
-    if new_w % 2:
-        new_w += 1
-    if new_h % 2:
-        new_h += 1
-    return new_w, new_h
-
-
-def upscale_video_to_min_pixels(video: Input.Video, min_pixels: int) -> Input.Video:
-    """Upscale a video to meet at least ``min_pixels`` (w * h), preserving aspect ratio.
-
-    Returns the original video object untouched when it already meets the minimum. Preserves frame rate,
-    duration, and audio. Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
-    Note: upscaling a low-resolution source does not add real detail; downstream model quality may suffer.
-    """
-    src_w, src_h = video.get_dimensions()
-    scale_dims = _compute_upscale_dims(src_w, src_h, min_pixels)
+    scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels)
     if scale_dims is None:
         return video
     return _apply_video_scale(video, scale_dims)

comfy_extras/nodes_audio.py

@@ -543,7 +543,7 @@ class AudioConcat(IO.ComfyNode):
         return IO.Schema(
             node_id="AudioConcat",
             search_aliases=["join audio", "combine audio", "append audio"],
-            display_name="Concatenate Audio",
+            display_name="Audio Concat",
             description="Concatenates the audio1 to audio2 in the specified direction.",
             category="audio",
             inputs=[
@@ -597,7 +597,7 @@ class AudioMerge(IO.ComfyNode):
         return IO.Schema(
             node_id="AudioMerge",
             search_aliases=["mix audio", "overlay audio", "layer audio"],
-            display_name="Merge Audio",
+            display_name="Audio Merge",
             description="Combine two audio tracks by overlaying their waveforms.",
             category="audio",
             inputs=[
@@ -667,9 +667,8 @@ class AudioAdjustVolume(IO.ComfyNode):
         return IO.Schema(
             node_id="AudioAdjustVolume",
             search_aliases=["audio gain", "loudness", "audio level"],
-            display_name="Adjust Audio Volume",
+            display_name="Audio Adjust Volume",
             category="audio",
-            description="Adjust the volume of the audio by a specified amount in decibels (dB).",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.Int.Input(

comfy_extras/nodes_dataset.py

@@ -47,10 +47,8 @@ class LoadImageDataSetFromFolderNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LoadImageDataSetFromFolder",
-            search_aliases=["load folder", "load from folder", "load dataset", "load images", "import dataset"],
-            display_name="Load Image (from Folder)",
-            category="image",
-            description="Load a dataset of images from a specified folder and return a list of images. Supported formats: PNG, JPG, JPEG, WEBP.",
+            display_name="Load Image Dataset from Folder",
+            category="dataset",
             is_experimental=True,
             inputs=[
                 io.Combo.Input(
@@ -86,16 +84,14 @@ class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LoadImageTextDataSetFromFolder",
-            search_aliases=["load folder", "load from folder", "load dataset", "load images", "import dataset"],
-            display_name="Load Image-Text (from Folder)",
-            category="image",
-            description="Load a dataset of pairs of images and text captions from a specified folder and return them as a list. Supported formats: PNG, JPG, JPEG, WEBP.",
+            display_name="Load Image and Text Dataset from Folder",
+            category="dataset",
             is_experimental=True,
             inputs=[
                 io.Combo.Input(
                     "folder",
                     options=folder_paths.get_input_subfolders(),
-                    tooltip="The folder to load images and text captions from.",
+                    tooltip="The folder to load images from.",
                 )
             ],
             outputs=[
@@ -210,10 +206,8 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SaveImageDataSetToFolder",
-            search_aliases=["save folder", "save to folder", "save dataset", "save images", "export dataset"],
-            display_name="Save Image (to Folder) (DEPRECATED)",
-            category="image",
-            description="Save a dataset of images to a specified folder. Supported formats: PNG.",
+            display_name="Save Image Dataset to Folder",
+            category="dataset",
             is_experimental=True,
             is_output_node=True,
             is_input_list=True,  # Receive images as list
@@ -232,7 +226,6 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
                 ),
             ],
             outputs=[],
-            is_deprecated=True,  # This node is redundant and superseded by existing Save Image nodes where the target folder can be specified in the filename_prefix
         )
 
     @classmethod
@@ -253,20 +246,14 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SaveImageTextDataSetToFolder",
-            search_aliases=["save folder", "save to folder", "save dataset", "save images", "save text", "export dataset"],
-            display_name="Save Image-Text (to Folder)",
-            category="image",
-            description="Save a dataset of pairs of images and text captions to a specified folder. Images are saved as PNG files and captions are saved as TXT files with the same filename_prefix.",
+            display_name="Save Image and Text Dataset to Folder",
+            category="dataset",
             is_experimental=True,
             is_output_node=True,
             is_input_list=True,  # Receive both images and texts as lists
             inputs=[
                 io.Image.Input("images", tooltip="List of images to save."),
-                io.String.Input("texts",
-                    optional=True,
-                    force_input=True,
-                    tooltip="List of text captions to save."
-                ),
+                io.String.Input("texts", tooltip="List of text captions to save."),
                 io.String.Input(
                     "folder_name",
                     default="dataset",
@@ -283,7 +270,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, images, folder_name, filename_prefix, texts=None):
+    def execute(cls, images, texts, folder_name, filename_prefix):
         # Extract scalar values
         folder_name = folder_name[0]
         filename_prefix = filename_prefix[0]
@@ -292,12 +279,11 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
         saved_files = save_images_to_folder(images, output_dir, filename_prefix)
 
         # Save captions
-        if texts:
-            for idx, (filename, caption) in enumerate(zip(saved_files, texts)):
-                caption_filename = filename.replace(".png", ".txt")
-                caption_path = os.path.join(output_dir, caption_filename)
-                with open(caption_path, "w", encoding="utf-8") as f:
-                    f.write(caption)
+        for idx, (filename, caption) in enumerate(zip(saved_files, texts)):
+            caption_filename = filename.replace(".png", ".txt")
+            caption_path = os.path.join(output_dir, caption_filename)
+            with open(caption_path, "w", encoding="utf-8") as f:
+                f.write(caption)
 
         logging.info(f"Saved {len(saved_files)} images and captions to {output_dir}.")
         return io.NodeOutput()
@@ -328,13 +314,11 @@ class ImageProcessingNode(io.ComfyNode):
 
     Child classes should set:
         node_id: Unique node identifier (required)
-        search_aliases: List of search aliases (optional)
         display_name: Display name (optional, defaults to node_id)
         description: Node description (optional)
         extra_inputs: List of additional io.Input objects beyond "images" (optional)
         is_group_process: None (auto-detect), True (group), or False (individual) (optional)
         is_output_list: True (list output) or False (single output) (optional, default True)
-        is_deprecated: True if the node is deprecated (optional, default False)
 
     Child classes must implement ONE of:
         _process(cls, image, **kwargs) -> tensor  (for single-item processing)
@@ -342,13 +326,12 @@ class ImageProcessingNode(io.ComfyNode):
     """
 
     node_id = None
-    search_aliases = []
     display_name = None
     description = None
     extra_inputs = []
     is_group_process = None  # None = auto-detect, True/False = explicit
     is_output_list = None  # None = auto-detect based on processing mode
-    is_deprecated = False
+
     @classmethod
     def _detect_processing_mode(cls):
         """Detect whether this node uses group or individual processing.
@@ -419,10 +402,8 @@ class ImageProcessingNode(io.ComfyNode):
 
         return io.Schema(
             node_id=cls.node_id,
-            search_aliases=cls.search_aliases,
             display_name=cls.display_name or cls.node_id,
-            category=cls.category,
-            description=cls.description,
+            category="dataset/image",
             is_experimental=True,
             is_input_list=is_group,  # True for group, False for individual
             inputs=inputs,
@@ -491,13 +472,11 @@ class TextProcessingNode(io.ComfyNode):
 
     Child classes should set:
         node_id: Unique node identifier (required)
-        search_aliases: List of search aliases (optional)
         display_name: Display name (optional, defaults to node_id)
         description: Node description (optional)
         extra_inputs: List of additional io.Input objects beyond "texts" (optional)
         is_group_process: None (auto-detect), True (group), or False (individual) (optional)
         is_output_list: True (list output) or False (single output) (optional, default True)
-        is_deprecated: True if the node is deprecated (optional, default False)
 
     Child classes must implement ONE of:
         _process(cls, text, **kwargs) -> str  (for single-item processing)
@@ -505,13 +484,12 @@ class TextProcessingNode(io.ComfyNode):
     """
 
     node_id = None
-    search_aliases = []
     display_name = None
     description = None
     extra_inputs = []
     is_group_process = None  # None = auto-detect, True/False = explicit
     is_output_list = None  # None = auto-detect based on processing mode
-    is_deprecated = False
+
     @classmethod
     def _detect_processing_mode(cls):
         """Detect whether this node uses group or individual processing.
@@ -649,17 +627,15 @@ class TextProcessingNode(io.ComfyNode):
 
 class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
     node_id = "ResizeImagesByShorterEdge"
-    display_name = "Resize Images by Shorter Edge (DEPRECATED)"
-    category = "image/transform"
-    description = "Resize images so that the shorter edge matches the specified dimension while preserving aspect ratio."
-    is_deprecated = True  # This node is superseded by Resize Image/Mask with resize_type = scale shorter dimension
+    display_name = "Resize Images by Shorter Edge"
+    description = "Resize images so that the shorter edge matches the specified length while preserving aspect ratio."
     extra_inputs = [
         io.Int.Input(
             "shorter_edge",
             default=512,
             min=1,
             max=8192,
-            tooltip="Target dimension for the shorter edge.",
+            tooltip="Target length for the shorter edge.",
         ),
     ]
 
@@ -679,17 +655,15 @@ class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
 
 class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
     node_id = "ResizeImagesByLongerEdge"
-    display_name = "Resize Images by Longer Edge (DEPRECATED)"
-    category = "image/transform"
-    description = "Resize images so that the longer edge matches the specified dimension while preserving aspect ratio."
-    is_deprecated = True  # This node is superseded by Resize Image/Mask with resize_type = scale longer dimension
+    display_name = "Resize Images by Longer Edge"
+    description = "Resize images so that the longer edge matches the specified length while preserving aspect ratio."
     extra_inputs = [
         io.Int.Input(
             "longer_edge",
             default=1024,
             min=1,
             max=8192,
-            tooltip="Target dimension for the longer edge.",
+            tooltip="Target length for the longer edge.",
         ),
     ]
 
@@ -712,10 +686,8 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
 
 class CenterCropImagesNode(ImageProcessingNode):
     node_id = "CenterCropImages"
-    search_aliases=["crop", "cut", "trim"]
-    display_name="Crop Image (Center)"
-    category="image/transform"
-    description = "Center crop an image to the specified dimensions."
+    display_name = "Center Crop Images"
+    description = "Center crop all images to the specified dimensions."
     extra_inputs = [
         io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
         io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
@@ -734,11 +706,10 @@ class CenterCropImagesNode(ImageProcessingNode):
 
 class RandomCropImagesNode(ImageProcessingNode):
     node_id = "RandomCropImages"
-    search_aliases=["crop", "cut", "trim"]
-    display_name = "Crop Image (Random)"
-    category="image/transform"
-    description = "Randomly crop an image to the specified dimensions."
-
+    display_name = "Random Crop Images"
+    description = (
+        "Randomly crop all images to the specified dimensions (for data augmentation)."
+    )
     extra_inputs = [
         io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
         io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
@@ -763,9 +734,7 @@ class RandomCropImagesNode(ImageProcessingNode):
 
 class NormalizeImagesNode(ImageProcessingNode):
     node_id = "NormalizeImages"
-    search_aliases=["normalize", "normalize colors"]
-    display_name = "Normalize Image Colors"
-    category = "image/color"
+    display_name = "Normalize Images"
     description = "Normalize images using mean and standard deviation."
     extra_inputs = [
         io.Float.Input(
@@ -793,10 +762,8 @@ class NormalizeImagesNode(ImageProcessingNode):
 
 class AdjustBrightnessNode(ImageProcessingNode):
     node_id = "AdjustBrightness"
-    search_aliases=["brightness"]
     display_name = "Adjust Brightness"
-    category="image/adjustments"
-    description = "Adjust the brightness of an image."
+    description = "Adjust brightness of all images."
     extra_inputs = [
         io.Float.Input(
             "factor",
@@ -814,10 +781,8 @@ class AdjustBrightnessNode(ImageProcessingNode):
 
 class AdjustContrastNode(ImageProcessingNode):
     node_id = "AdjustContrast"
-    search_aliases=["contrast"]
     display_name = "Adjust Contrast"
-    category="image/adjustments"
-    description = "Adjust the contrast of an image."
+    description = "Adjust contrast of all images."
     extra_inputs = [
         io.Float.Input(
             "factor",
@@ -835,10 +800,8 @@ class AdjustContrastNode(ImageProcessingNode):
 
 class ShuffleDatasetNode(ImageProcessingNode):
     node_id = "ShuffleDataset"
-    search_aliases=["shuffle", "randomize", "mix"]
-    display_name = "Shuffle Images List"
-    category = "image/batch"
-    description = "Randomly shuffle the order of images in a list."
+    display_name = "Shuffle Image Dataset"
+    description = "Randomly shuffle the order of images in the dataset."
     is_group_process = True  # Requires full list to shuffle
     extra_inputs = [
         io.Int.Input(
@@ -860,15 +823,13 @@ class ShuffleImageTextDatasetNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ShuffleImageTextDataset",
-            search_aliases=["shuffle", "randomize", "mix"],
-            display_name = "Shuffle Pairs of Image-Text",
-            category = "image/batch",
-            description = "Randomly shuffle the order of pairs of image-text in a list.",
+            display_name="Shuffle Image-Text Dataset",
+            category="dataset/image",
             is_experimental=True,
             is_input_list=True,
             inputs=[
                 io.Image.Input("images", tooltip="List of images to shuffle."),
-                io.String.Input("texts", tooltip="List of texts to shuffle.", force_input=True),
+                io.String.Input("texts", tooltip="List of texts to shuffle."),
                 io.Int.Input(
                     "seed",
                     default=0,
@@ -904,11 +865,8 @@ class ShuffleImageTextDatasetNode(io.ComfyNode):
 
 class TextToLowercaseNode(TextProcessingNode):
     node_id = "TextToLowercase"
-    search_aliases=["lowercase"]
-    display_name = "Convert Text to Lowercase (DEPRECATED)"
-    category = "text"
-    description = "Convert text to lowercase."
-    is_deprecated = True  # This node is superseded by the Convert Text Case node
+    display_name = "Text to Lowercase"
+    description = "Convert all texts to lowercase."
 
     @classmethod
     def _process(cls, text):
@@ -917,11 +875,8 @@ class TextToLowercaseNode(TextProcessingNode):
 
 class TextToUppercaseNode(TextProcessingNode):
     node_id = "TextToUppercase"
-    search_aliases=["uppercase"]
-    display_name = "Convert Text to Uppercase (DEPRECATED)"
-    category = "text"
-    description = "Convert text to uppercase."
-    is_deprecated = True  # This node is superseded by the Convert Text Case node
+    display_name = "Text to Uppercase"
+    description = "Convert all texts to uppercase."
 
     @classmethod
     def _process(cls, text):
@@ -930,10 +885,8 @@ class TextToUppercaseNode(TextProcessingNode):
 
 class TruncateTextNode(TextProcessingNode):
     node_id = "TruncateText"
-    search_aliases=["truncate", "cut", "shorten"]
     display_name = "Truncate Text"
-    category = "text"
-    description = "Truncate text to a maximum length."
+    description = "Truncate all texts to a maximum length."
     extra_inputs = [
         io.Int.Input(
             "max_length", default=77, min=1, max=10000, tooltip="Maximum text length."
@@ -947,10 +900,8 @@ class TruncateTextNode(TextProcessingNode):
 
 class AddTextPrefixNode(TextProcessingNode):
     node_id = "AddTextPrefix"
-    display_name = "Add Text Prefix (DEPRECATED)"
-    category = "text"
+    display_name = "Add Text Prefix"
     description = "Add a prefix to all texts."
-    is_deprecated = True  # This node is superseded by the Concatenate Text node
     extra_inputs = [
         io.String.Input("prefix", default="", tooltip="Prefix to add."),
     ]
@@ -962,10 +913,8 @@ class AddTextPrefixNode(TextProcessingNode):
 
 class AddTextSuffixNode(TextProcessingNode):
     node_id = "AddTextSuffix"
-    display_name = "Add Text Suffix (DEPRECATED)"
-    category = "text"
+    display_name = "Add Text Suffix"
     description = "Add a suffix to all texts."
-    is_deprecated = True  # This node is superseded by the Concatenate Text node
     extra_inputs = [
         io.String.Input("suffix", default="", tooltip="Suffix to add."),
     ]
@@ -977,10 +926,8 @@ class AddTextSuffixNode(TextProcessingNode):
 
 class ReplaceTextNode(TextProcessingNode):
     node_id = "ReplaceText"
-    display_name = "Replace Text (DEPRECATED)"
-    category = "text"
+    display_name = "Replace Text"
     description = "Replace text in all texts."
-    is_deprecated = True  # This node is superseded by the other Replace Text node
     extra_inputs = [
         io.String.Input("find", default="", tooltip="Text to find."),
         io.String.Input("replace", default="", tooltip="Text to replace with."),
@@ -993,10 +940,8 @@ class ReplaceTextNode(TextProcessingNode):
 
 class StripWhitespaceNode(TextProcessingNode):
     node_id = "StripWhitespace"
-    display_name = "Strip Whitespace (DEPRECATED)"
-    category = "text"
+    display_name = "Strip Whitespace"
     description = "Strip leading and trailing whitespace from all texts."
-    is_deprecated = True  # This node is superseded by the Trim Text node
 
     @classmethod
     def _process(cls, text):
@@ -1007,13 +952,11 @@ class StripWhitespaceNode(TextProcessingNode):
 
 
 class ImageDeduplicationNode(ImageProcessingNode):
-    """Remove duplicate or very similar images from a list using perceptual hashing."""
+    """Remove duplicate or very similar images from the dataset using perceptual hashing."""
 
     node_id = "ImageDeduplication"
-    search_aliases=["deduplicate", "remove duplicates", "similarity filter"]
-    display_name = "Deduplicate Images"
-    category = "image/batch"
-    description = "Remove duplicate or very similar images from a list."
+    display_name = "Image Deduplication"
+    description = "Remove duplicate or very similar images from the dataset."
     is_group_process = True  # Requires full list to compare images
     extra_inputs = [
         io.Float.Input(
@@ -1083,9 +1026,7 @@ class ImageGridNode(ImageProcessingNode):
     """Combine multiple images into a single grid/collage."""
 
     node_id = "ImageGrid"
-    search_aliases=["grid", "collage", "combine"]
-    display_name = "Make Image Grid"
-    category="image/batch"
+    display_name = "Image Grid"
     description = "Arrange multiple images into a grid layout."
     is_group_process = True  # Requires full list to create grid
     is_output_list = False  # Outputs single grid image
@@ -1161,12 +1102,9 @@ class MergeImageListsNode(ImageProcessingNode):
     """Merge multiple image lists into a single list."""
 
     node_id = "MergeImageLists"
-    search_aliases=["list", "merge list", "make list"]
-    display_name = "Merge Image Lists (DEPRECATED)"
-    category = "image/batch"
+    display_name = "Merge Image Lists"
     description = "Concatenate multiple image lists into one."
     is_group_process = True  # Receives images as list
-    is_deprecated = True  # This node is superseded by the Create List node
 
     @classmethod
     def _group_process(cls, images):
@@ -1181,11 +1119,9 @@ class MergeTextListsNode(TextProcessingNode):
     """Merge multiple text lists into a single list."""
 
     node_id = "MergeTextLists"
-    display_name = "Merge Text Lists (DEPRECATED)"
-    category = "text"
+    display_name = "Merge Text Lists"
     description = "Concatenate multiple text lists into one."
     is_group_process = True  # Receives texts as list
-    is_deprecated = True  # This node is superseded by the Create List node
 
     @classmethod
     def _group_process(cls, texts):
@@ -1206,10 +1142,8 @@ class ResolutionBucket(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ResolutionBucket",
-            search_aliases=["bucket by resolution", "group by resolution", "batch by resolution"],
             display_name="Resolution Bucket",
-            category="training",
-            description="Group latents and conditionings into buckets",
+            category="dataset",
             is_experimental=True,
             is_input_list=True,
             inputs=[
@@ -1302,8 +1236,7 @@ class MakeTrainingDataset(io.ComfyNode):
             node_id="MakeTrainingDataset",
             search_aliases=["encode dataset"],
             display_name="Make Training Dataset",
-            category="training",
-            description="Encode images with VAE and texts with CLIP to create a training dataset of latents and conditionings.",
+            category="dataset",
             is_experimental=True,
             is_input_list=True,  # images and texts as lists
             inputs=[
@@ -1318,7 +1251,6 @@ class MakeTrainingDataset(io.ComfyNode):
                     "texts",
                     optional=True,
                     tooltip="List of text captions. Can be length n (matching images), 1 (repeated for all), or omitted (uses empty string).",
-                    force_input=True
                 ),
             ],
             outputs=[
@@ -1388,10 +1320,9 @@ class SaveTrainingDataset(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SaveTrainingDataset",
-            search_aliases=["export dataset", "save dataset"],
+            search_aliases=["export training data"],
             display_name="Save Training Dataset",
-            category="training",
-            description="Save encoded training dataset (latents + conditioning) to disk for efficient loading during training.",
+            category="dataset",
             is_experimental=True,
             is_output_node=True,
             is_input_list=True,  # Receive lists
@@ -1493,8 +1424,7 @@ class LoadTrainingDataset(io.ComfyNode):
             node_id="LoadTrainingDataset",
             search_aliases=["import dataset", "training data"],
             display_name="Load Training Dataset",
-            category="training",
-            description="Load encoded training dataset (latents + conditioning) from disk for use in training.",
+            category="dataset",
             is_experimental=True,
             inputs=[
                 io.String.Input(

comfy_extras/nodes_hunyuan3d.py

@@ -419,17 +419,15 @@ class VoxelToMeshBasic(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VoxelToMeshBasic",
-            display_name="Voxel to Mesh (Basic) (DEPRECATED)",
+            display_name="Voxel to Mesh (Basic)",
             category="3d",
-            description="Converts a voxel grid to a mesh.",
-            is_deprecated=True, # This node is superseded by the Voxel To Mesh node
             inputs=[
                 IO.Voxel.Input("voxel"),
                 IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
             ],
             outputs=[
                 IO.Mesh.Output(),
-            ],
+            ]
         )
 
     @classmethod
@@ -455,10 +453,9 @@ class VoxelToMesh(IO.ComfyNode):
             node_id="VoxelToMesh",
             display_name="Voxel to Mesh",
             category="3d",
-            description="Converts a voxel grid to a mesh.",
             inputs=[
                 IO.Voxel.Input("voxel"),
-                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"], advanced=True),
                 IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
             ],
             outputs=[

comfy_extras/nodes_images.py

@@ -3,15 +3,23 @@ from __future__ import annotations
 import nodes
 import folder_paths
 
+import av
 import json
+
 import os
 import re
 import math
+import numpy as np
+import struct
 import torch
+
+import zlib
 import comfy.utils
+from fractions import Fraction
 
 from server import PromptServer
 from comfy_api.latest import ComfyExtension, IO, UI
+from comfy.cli_args import args
 from typing_extensions import override
 
 SVG = IO.SVG.Type  # TODO: temporary solution for backward compatibility, will be removed later.
@@ -55,10 +63,9 @@ class ImageCropV2(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCropV2",
-            search_aliases=["crop", "cut", "trim"],
+            search_aliases=["trim"],
             display_name="Crop Image",
             category="image/transform",
-            description = "Crop an image to the specified dimensions.",
             essentials_category="Image Tools",
             has_intermediate_output=True,
             inputs=[
@@ -835,6 +842,405 @@ class ImageMergeTileList(IO.ComfyNode):
         return IO.NodeOutput(merged_image)
 
 
+# ---------------------------------------------------------------------------
+# Format specifications
+# ---------------------------------------------------------------------------
+
+# Maps (file_format, bit_depth, has_alpha) -> (numpy dtype scale, av pixel format,
+# stream pix_fmt). Keeps the encode path declarative instead of branchy.
+_FORMAT_SPECS = {
+    ("png", "8-bit", False):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgb24",     "stream_fmt": "rgb24"},
+    ("png", "8-bit", True):   {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgba",      "stream_fmt": "rgba"},
+    ("png", "16-bit", False): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgb48le",   "stream_fmt": "rgb48be"},
+    ("png", "16-bit", True):  {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgba64le",  "stream_fmt": "rgba64be"},
+    ("exr", "32-bit float", False): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrpf32le",  "stream_fmt": "gbrpf32le"},
+    ("exr", "32-bit float", True):  {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrapf32le", "stream_fmt": "gbrapf32le"},
+}
+
+
+# ---------------------------------------------------------------------------
+# Color transforms
+# ---------------------------------------------------------------------------
+
+def srgb_to_linear(t: torch.Tensor) -> torch.Tensor:
+    """Inverse sRGB EOTF (IEC 61966-2-1). Operates on RGB channels only;
+    alpha (if present as the 4th channel) is passed through unchanged."""
+    if t.shape[-1] == 4:
+        rgb, alpha = t[..., :3], t[..., 3:]
+        return torch.cat([srgb_to_linear(rgb), alpha], dim=-1)
+
+    # Piecewise: linear toe below 0.04045, gamma curve above.
+    low = t / 12.92
+    high = ((t.clamp(min=0.0) + 0.055) / 1.055) ** 2.4
+    return torch.where(t <= 0.04045, low, high)
+
+
+# HLG OETF constants from BT.2100 Table 5.
+_HLG_A = 0.17883277
+_HLG_B = 0.28466892
+_HLG_C = 0.55991072928   # = 0.5 - a*ln(4*a)
+
+
+def hlg_to_linear(t: torch.Tensor) -> torch.Tensor:
+    """Inverse HLG OETF (BT.2100). Maps a non-linear HLG signal in [0, 1] to
+    *scene*-linear light in [0, 1]. Per BT.2100 Note 5a, this is the correct
+    transform when converting HLG to a linear scene-light representation
+    (rather than display-light, which would also involve the HLG OOTF).
+
+    Operates on RGB channels only; alpha is passed through unchanged."""
+    if t.shape[-1] == 4:
+        rgb, alpha = t[..., :3], t[..., 3:]
+        return torch.cat([hlg_to_linear(rgb), alpha], dim=-1)
+
+    # Piecewise: sqrt branch below 0.5, log branch above.
+    # Clamp inside the log branch so negative / out-of-range values don't blow up;
+    # values above 1.0 are allowed and extrapolate naturally.
+    low = (t ** 2) / 3.0
+    high = (torch.exp((t.clamp(min=_HLG_C) - _HLG_C) / _HLG_A) + _HLG_B) / 12.0
+    return torch.where(t <= 0.5, low, high)
+
+
+# ---------------------------------------------------------------------------
+# Metadata injection
+# ---------------------------------------------------------------------------
+
+_PNG_SIGNATURE = b"\x89PNG\r\n\x1a\n"
+
+
+def _png_chunk(chunk_type: bytes, data: bytes) -> bytes:
+    """Build a single PNG chunk: length | type | data | CRC32(type+data)."""
+    crc = zlib.crc32(chunk_type + data) & 0xFFFFFFFF
+    return struct.pack(">I", len(data)) + chunk_type + data + struct.pack(">I", crc)
+
+
+def _png_text_chunk(keyword: str, text: str) -> bytes:
+    """tEXt chunk: latin-1 keyword + NUL + latin-1 text."""
+    payload = keyword.encode("latin-1") + b"\x00" + text.encode("latin-1", errors="replace")
+    return _png_chunk(b"tEXt", payload)
+
+
+def inject_png_metadata(png_bytes: bytes, prompt: dict | None, extra_pnginfo: dict | None) -> bytes:
+    """Insert ComfyUI prompt/workflow as tEXt chunks right after IHDR."""
+    if not png_bytes.startswith(_PNG_SIGNATURE):
+        return png_bytes
+
+    chunks: list[bytes] = []
+    if prompt is not None:
+        chunks.append(_png_text_chunk("prompt", json.dumps(prompt)))
+    if extra_pnginfo:
+        for key, value in extra_pnginfo.items():
+            chunks.append(_png_text_chunk(key, json.dumps(value)))
+    if not chunks:
+        return png_bytes
+
+    # IHDR is always the first chunk; insert ours immediately after it.
+    ihdr_length = struct.unpack(">I", png_bytes[8:12])[0]
+    ihdr_end = 8 + 8 + ihdr_length + 4  # signature + (len+type) + data + crc
+    return png_bytes[:ihdr_end] + b"".join(chunks) + png_bytes[ihdr_end:]
+
+
+# Standard chromaticities (CIE 1931 xy) for the colorspaces this node writes.
+# Each tuple is (Rx, Ry, Gx, Gy, Bx, By, Wx, Wy). All share D65 white point.
+_CHROMATICITIES = {
+    # ITU-R BT.709 / sRGB primaries
+    "Rec.709":  (0.6400, 0.3300, 0.3000, 0.6000, 0.1500, 0.0600, 0.3127, 0.3290),
+    # ITU-R BT.2020 (UHDTV / wide-gamut HDR) primaries
+    "Rec.2020": (0.7080, 0.2920, 0.1700, 0.7970, 0.1310, 0.0460, 0.3127, 0.3290),
+}
+
+
+def _pack_chromaticities(primaries: tuple) -> bytes:
+    """Serialize 8 chromaticity floats into the EXR `chromaticities` payload."""
+    return struct.pack("<8f", *primaries)
+
+
+def _exr_attribute(name: str, attr_type: str, value: bytes) -> bytes:
+    """Serialize one EXR header attribute: name\\0 type\\0 size:int32 value."""
+    return (
+        name.encode("utf-8") + b"\x00"
+        + attr_type.encode("utf-8") + b"\x00"
+        + struct.pack("<i", len(value))
+        + value
+    )
+
+
+def inject_exr_metadata(
+    exr_bytes: bytes,
+    prompt: dict | None,
+    extra_pnginfo: dict | None,
+    colorspace: str | None = None,
+) -> bytes:
+    """Insert ComfyUI metadata and color-space info into an EXR header.
+
+    Color: EXR pixels are linear by convention. The standard way to describe
+    their RGB→XYZ relationship is the `chromaticities` attribute. We pick the
+    primaries that match what the user told us their input was:
+
+      colorspace="sRGB" → Rec. 709 / sRGB primaries (D65)
+      colorspace="HDR"  → Rec. 2020 / BT.2100 primaries (D65)
+
+    Pixels are always converted to linear scene light upstream (sRGB EOTF
+    inverse for sRGB; HLG OETF inverse for HDR), so the file content is
+    scene-linear in the indicated gamut. OpenEXR has no standard transfer-
+    function attribute (the OpenEXR TSC has discussed adding one but it
+    doesn't exist), so we don't invent one — `chromaticities` plus the EXR
+    linear-by-convention rule fully specifies the color.
+
+    Prompt/workflow: written as plain `string` attributes using the same keys
+    (`prompt`, `workflow`, ...) that Comfy uses for PNG tEXt chunks, so the
+    same readers can pull them out symmetrically.
+
+    Implementation note: the chunk-offset table that follows the header stores
+    *absolute* byte offsets into the file. Inserting N bytes into the header
+    means every offset must be incremented by N or the file becomes unreadable.
+    """
+    if len(exr_bytes) < 8 or exr_bytes[:4] != b"\x76\x2f\x31\x01":
+        return exr_bytes
+
+    new_blob = b""
+    if prompt is not None:
+        new_blob += _exr_attribute("prompt", "string", json.dumps(prompt).encode("utf-8"))
+    if extra_pnginfo:
+        for key, value in extra_pnginfo.items():
+            new_blob += _exr_attribute(key, "string", json.dumps(value).encode("utf-8"))
+    if colorspace is not None:
+        # Map each colorspace option to the RGB primaries the linear pixels
+        # are now in. "sRGB" and "linear" both produce Rec. 709 linear; "HDR"
+        # (HLG-encoded Rec. 2020 input) produces Rec. 2020 linear.
+        primaries_name = {
+            "sRGB":   "Rec.709",
+            "linear": "Rec.709",
+            "HDR":    "Rec.2020",
+        }.get(colorspace, "Rec.709")
+        new_blob += _exr_attribute(
+            "chromaticities",
+            "chromaticities",
+            _pack_chromaticities(_CHROMATICITIES[primaries_name]),
+        )
+    if not new_blob:
+        return exr_bytes
+
+    # Walk header attributes to find the terminating null byte, and pick up
+    # dataWindow + compression so we know how many chunks the offset table has.
+    pos = 8  # past magic (4) + version (4)
+    data_window = None
+    compression = 0
+    while pos < len(exr_bytes) and exr_bytes[pos] != 0:
+        name_end = exr_bytes.index(b"\x00", pos)
+        attr_name = exr_bytes[pos:name_end].decode("latin-1", errors="replace")
+        type_end = exr_bytes.index(b"\x00", name_end + 1)
+        attr_type = exr_bytes[name_end + 1:type_end].decode("latin-1", errors="replace")
+        size = struct.unpack("<i", exr_bytes[type_end + 1:type_end + 5])[0]
+        value_start = type_end + 5
+        value = exr_bytes[value_start:value_start + size]
+
+        if attr_name == "dataWindow" and attr_type == "box2i":
+            data_window = struct.unpack("<iiii", value)  # xMin, yMin, xMax, yMax
+        elif attr_name == "compression" and attr_type == "compression":
+            compression = value[0]
+
+        pos = value_start + size
+
+    if data_window is None:
+        return exr_bytes  # required attribute missing — don't risk corrupting
+
+    # Scanlines per chunk by compression, from the OpenEXR spec.
+    scanlines_per_block = {
+        0: 1,   # NO_COMPRESSION
+        1: 1,   # RLE
+        2: 1,   # ZIPS
+        3: 16,  # ZIP
+        4: 32,  # PIZ
+        5: 16,  # PXR24
+        6: 32,  # B44
+        7: 32,  # B44A
+        8: 256, # DWAA
+        9: 256, # DWAB
+    }.get(compression, 1)
+
+    _, y_min, _, y_max = data_window
+    height = y_max - y_min + 1
+    num_chunks = (height + scanlines_per_block - 1) // scanlines_per_block
+
+    header_end = pos  # position of the terminating null byte
+    table_start = header_end + 1
+    pixel_start = table_start + num_chunks * 8
+    delta = len(new_blob)
+
+    old_offsets = struct.unpack(f"<{num_chunks}Q", exr_bytes[table_start:pixel_start])
+    new_table = struct.pack(f"<{num_chunks}Q", *(o + delta for o in old_offsets))
+
+    return (
+        exr_bytes[:header_end]                # header attributes
+        + new_blob                            # our new attributes
+        + exr_bytes[header_end:table_start]   # terminating null byte
+        + new_table                           # shifted offset table
+        + exr_bytes[pixel_start:]             # pixel data, untouched
+    )
+
+
+# ---------------------------------------------------------------------------
+# Encoding
+# ---------------------------------------------------------------------------
+
+def _encode_image(
+    img_tensor: torch.Tensor,
+    file_format: str,
+    bit_depth: str,
+    colorspace: str,
+) -> bytes:
+    """Encode a single HxWxC tensor to PNG or EXR bytes in memory.
+
+    For EXR the input is interpreted according to `colorspace` and converted
+    to scene-linear (EXR's convention) before writing:
+
+      "sRGB"   → input is sRGB-encoded Rec. 709; apply inverse sRGB EOTF.
+      "HDR"    → input is HLG-encoded Rec. 2020 (BT.2100); apply inverse HLG
+                 OETF to get scene-linear, per BT.2100 Note 5a.
+      "linear" → input is already scene-linear (Rec. 709 primaries); write
+                 through unchanged. Use this for renderer/compositor output.
+
+    For PNG, colorspace selection does not modify pixels — PNG is delivered
+    sRGB-encoded and there is no PNG path for wide-gamut HDR in this node.
+    """
+    height, width, num_channels = img_tensor.shape
+    has_alpha = num_channels == 4
+
+    spec = _FORMAT_SPECS[(file_format, bit_depth, has_alpha)]
+
+    if spec["dtype"] == np.float32:
+        # EXR path: preserve full range, no clamp.
+        if colorspace == "sRGB":
+            img_tensor = srgb_to_linear(img_tensor)
+        elif colorspace == "HDR":
+            img_tensor = hlg_to_linear(img_tensor)
+        img_np = img_tensor.cpu().numpy().astype(np.float32)
+    else:
+        # PNG path: quantize to integer range.
+        scaled = (img_tensor * spec["scale"]).clamp(0, spec["scale"])
+        img_np = scaled.to(torch.int32).cpu().numpy().astype(spec["dtype"])
+
+    # Encode directly via CodecContext. PyAV's `image2` muxer does NOT write to
+    # BytesIO (it expects a real file path), so we bypass the container entirely.
+    # For single-frame PNG/EXR the raw codec output IS the file.
+    codec = av.CodecContext.create(file_format, "w")
+    codec.width = width
+    codec.height = height
+    codec.pix_fmt = spec["stream_fmt"]
+    codec.time_base = Fraction(1, 1)
+
+    frame = av.VideoFrame.from_ndarray(img_np, format=spec["frame_fmt"])
+    if spec["frame_fmt"] != spec["stream_fmt"]:
+        frame = frame.reformat(format=spec["stream_fmt"])
+    frame.pts = 0
+    frame.time_base = codec.time_base
+
+    packets = list(codec.encode(frame)) + list(codec.encode(None))  # flush with None
+    return b"".join(bytes(p) for p in packets)
+
+
+# ---------------------------------------------------------------------------
+# Node
+# ---------------------------------------------------------------------------
+
+class SaveImageAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveImageAdvanced",
+            search_aliases=["save", "save image", "export image", "output image", "write image"],
+            display_name="Save Image (Advanced)",
+            description="Saves the input images to your ComfyUI output directory.",
+            category="image",
+            essentials_category="Basics",
+            inputs=[
+                IO.Image.Input("images", tooltip="The images to save."),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="ComfyUI",
+                    tooltip=(
+                        "The prefix for the file to save. May include formatting tokens "
+                        "such as %date:yyyy-MM-dd% or %Empty Latent Image.width%."
+                    ),
+                ),
+                IO.DynamicCombo.Input(
+                    "format",
+                    options=[
+                        IO.DynamicCombo.Option("png", [
+                            IO.Combo.Input("bit_depth", options=["8-bit", "16-bit"],
+                                           default="8-bit", advanced=True),
+                            IO.Combo.Input("input_color_space", options=["sRGB"],
+                                           default="sRGB", advanced=True),
+                        ]),
+                        IO.DynamicCombo.Option("exr", [
+                            IO.Combo.Input("bit_depth", options=["32-bit float"],
+                                           default="32-bit float", advanced=True),
+                            IO.Combo.Input(
+                                "input_color_space",
+                                options=["sRGB", "HDR", "linear"],
+                                default="sRGB",
+                                advanced=True,
+                                tooltip=(
+                                    "Colorspace of the input tensor. The EXR is "
+                                    "always written as scene-linear in the matching "
+                                    "gamut.\n"
+                                    "  'sRGB'   — input is sRGB-encoded Rec.709; "
+                                    "the inverse sRGB EOTF is applied.\n"
+                                    "  'HDR'    — input is HLG-encoded Rec.2020 "
+                                    "(BT.2100); the inverse HLG OETF is applied "
+                                    "to get scene-linear light.\n"
+                                    "  'linear' — input is already scene-linear "
+                                    "(Rec.709 primaries); written through unchanged. "
+                                    "Use this for renderer/compositor output."
+                                ),
+                            ),
+                        ]),
+                    ],
+                    tooltip="The file format in which to save the image.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, images, filename_prefix: str, format: dict) -> IO.NodeOutput:
+        file_format = format["format"]
+        bit_depth = format["bit_depth"]
+        colorspace = format.get("input_color_space", "sRGB")
+
+        output_dir = folder_paths.get_output_directory()
+        full_output_folder, filename, counter, subfolder, filename_prefix = (
+            folder_paths.get_save_image_path(
+                filename_prefix, output_dir, images[0].shape[1], images[0].shape[0]
+            )
+        )
+
+        prompt = cls.hidden.prompt
+        extra_pnginfo = cls.hidden.extra_pnginfo
+        write_metadata = not args.disable_metadata
+
+        results = []
+        for batch_number, image in enumerate(images):
+            encoded = _encode_image(image, file_format, bit_depth, colorspace)
+
+            if write_metadata:
+                if file_format == "png":
+                    encoded = inject_png_metadata(encoded, prompt, extra_pnginfo)
+                elif file_format == "exr":
+                    encoded = inject_exr_metadata(encoded, prompt, extra_pnginfo, colorspace)
+
+            name = filename.replace("%batch_num%", str(batch_number))
+            file = f"{name}_{counter:05}.{file_format}"
+            with open(os.path.join(full_output_folder, file), "wb") as f:
+                f.write(encoded)
+
+            results.append({"filename": file, "subfolder": subfolder, "type": "output"})
+            counter += 1
+
+        return IO.NodeOutput(ui={"images": results})
+
+
 class ImagesExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -847,6 +1253,7 @@ class ImagesExtension(ComfyExtension):
             ImageAddNoise,
             SaveAnimatedWEBP,
             SaveAnimatedPNG,
+            SaveImageAdvanced,
             SaveSVGNode,
             ImageStitch,
             ResizeAndPadImage,

comfy_extras/nodes_logic.py

@@ -8,82 +8,6 @@ from comfy_api.latest import _io
 MISSING = object()
 
 
-class NotNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="ComfyNotNode",
-            display_name="Not",
-            category="utils/logic",
-            description="Logical NOT operation. Returns true if the value is falsy. Uses Python's rules for truthiness.",
-            search_aliases=["invert", "toggle", "negate", "flip boolean"],
-            inputs=[
-                io.AnyType.Input("value"),
-            ],
-            outputs=[
-                io.Boolean.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, value) -> io.NodeOutput:
-        return io.NodeOutput(not value)
-
-
-class AndNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        template = io.Autogrow.TemplatePrefix(
-            input=io.AnyType.Input("value"),
-            prefix="value",
-            min=1,
-        )
-        return io.Schema(
-            node_id="ComfyAndNode",
-            display_name="And",
-            category="utils/logic",
-            description="Logical AND operation. Returns true if all of the values are truthy. Uses Python's rules for truthiness.",
-            search_aliases=["all", "every"],
-            inputs=[
-                io.Autogrow.Input("values", template=template),
-            ],
-            outputs=[
-                io.Boolean.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, values: io.Autogrow.Type) -> io.NodeOutput:
-        return io.NodeOutput(all(values.values()))
-
-
-class OrNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        template = io.Autogrow.TemplatePrefix(
-            input=io.AnyType.Input("value"),
-            prefix="value",
-            min=1,
-        )
-        return io.Schema(
-            node_id="ComfyOrNode",
-            display_name="Or",
-            category="utils/logic",
-            description="Logical OR operation. Returns true if any of the values are truthy. Uses Python's rules for truthiness.",
-            search_aliases=["any", "some"],
-            inputs=[
-                io.Autogrow.Input("values", template=template),
-            ],
-            outputs=[
-                io.Boolean.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, values: io.Autogrow.Type) -> io.NodeOutput:
-        return io.NodeOutput(any(values.values()))
-
-
 class SwitchNode(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -91,7 +15,7 @@ class SwitchNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfySwitchNode",
             display_name="Switch",
-            category="utils/logic",
+            category="logic",
             is_experimental=True,
             inputs=[
                 io.Boolean.Input("switch"),
@@ -122,7 +46,7 @@ class SoftSwitchNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfySoftSwitchNode",
             display_name="Soft Switch",
-            category="utils/logic",
+            category="logic",
             is_experimental=True,
             inputs=[
                 io.Boolean.Input("switch"),
@@ -212,7 +136,7 @@ class DCTestNode(io.ComfyNode):
         return io.Schema(
             node_id="DCTestNode",
             display_name="DCTest",
-            category="utils/logic",
+            category="logic",
             is_output_node=True,
             inputs=[io.DynamicCombo.Input("combo", options=[
                 io.DynamicCombo.Option("option1", [io.String.Input("string")]),
@@ -250,7 +174,7 @@ class AutogrowNamesTestNode(io.ComfyNode):
         return io.Schema(
             node_id="AutogrowNamesTestNode",
             display_name="AutogrowNamesTest",
-            category="utils/logic",
+            category="logic",
             inputs=[
                 _io.Autogrow.Input("autogrow", template=template)
             ],
@@ -270,7 +194,7 @@ class AutogrowPrefixTestNode(io.ComfyNode):
         return io.Schema(
             node_id="AutogrowPrefixTestNode",
             display_name="AutogrowPrefixTest",
-            category="utils/logic",
+            category="logic",
             inputs=[
                 _io.Autogrow.Input("autogrow", template=template)
             ],
@@ -289,7 +213,7 @@ class ComboOutputTestNode(io.ComfyNode):
         return io.Schema(
             node_id="ComboOptionTestNode",
             display_name="ComboOptionTest",
-            category="utils/logic",
+            category="logic",
             inputs=[io.Combo.Input("combo", options=["option1", "option2", "option3"]),
                     io.Combo.Input("combo2", options=["option4", "option5", "option6"])],
             outputs=[io.Combo.Output(), io.Combo.Output()],
@@ -306,7 +230,7 @@ class ConvertStringToComboNode(io.ComfyNode):
             node_id="ConvertStringToComboNode",
             search_aliases=["string to dropdown", "text to combo"],
             display_name="Convert String to Combo",
-            category="utils/logic",
+            category="logic",
             inputs=[io.String.Input("string")],
             outputs=[io.Combo.Output()],
         )
@@ -322,7 +246,7 @@ class InvertBooleanNode(io.ComfyNode):
             node_id="InvertBooleanNode",
             search_aliases=["not", "toggle", "negate", "flip boolean"],
             display_name="Invert Boolean",
-            category="utils/logic",
+            category="logic",
             inputs=[io.Boolean.Input("boolean")],
             outputs=[io.Boolean.Output()],
         )
@@ -337,9 +261,6 @@ class LogicExtension(ComfyExtension):
         return [
             SwitchNode,
             CustomComboNode,
-            NotNode,
-            AndNode,
-            OrNode,
             # SoftSwitchNode,
             # ConvertStringToComboNode,
             # DCTestNode,

comfy_extras/nodes_lt_audio.py

@@ -11,8 +11,8 @@ class LTXVAudioVAELoader(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="LTXVAudioVAELoader",
-            display_name="Load LTXV Audio VAE",
-            category="loaders",
+            display_name="LTXV Audio VAE Loader",
+            category="audio",
             inputs=[
                 io.Combo.Input(
                     "ckpt_name",
@@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio):
         return io.Schema(
             node_id="LTXVAudioVAEEncode",
             display_name="LTXV Audio VAE Encode",
-            category="latent/audio",
+            category="audio",
             inputs=[
                 io.Audio.Input("audio", tooltip="The audio to be encoded."),
                 io.Vae.Input(
@@ -63,7 +63,7 @@ class LTXVAudioVAEDecode(io.ComfyNode):
         return io.Schema(
             node_id="LTXVAudioVAEDecode",
             display_name="LTXV Audio VAE Decode",
-            category="latent/audio",
+            category="audio",
             inputs=[
                 io.Latent.Input("samples", tooltip="The latent to be decoded."),
                 io.Vae.Input(

comfy_extras/nodes_math.py

@@ -70,7 +70,7 @@ class MathExpressionNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfyMathExpression",
             display_name="Math Expression",
-            category="utils",
+            category="logic",
             search_aliases=[
                 "expression", "formula", "calculate", "calculator",
                 "eval", "math",

comfy_extras/nodes_mediapipe.py

@@ -28,7 +28,7 @@ from comfy_extras.mediapipe.face_landmarker import FaceLandmarker
 from comfy_extras.mediapipe.face_geometry import transformation_matrix_from_detection
 
 
-FaceDetectionType = io.Custom("FACE_DETECTION_MODEL")
+FaceLandmarkerType = io.Custom("FACE_LANDMARKER")
 FaceLandmarksType = io.Custom("FACE_LANDMARKS")
 
 _CANONICAL_KEYS = ("canonical_vertices", "procrustes_indices", "procrustes_weights")
@@ -204,19 +204,18 @@ class LoadMediaPipeFaceLandmarker(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LoadMediaPipeFaceLandmarker",
-            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
-            display_name="Load Face Detection Model (MediaPipe)",
+            display_name="Load MediaPipe Face Landmarker",
             category="loaders",
             inputs=[
-                io.Combo.Input("model_name", options=folder_paths.get_filename_list("detection"),
-                               tooltip="Face detection model from models/detection/."),
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("mediapipe"),
+                               tooltip="Face Landmarker safetensors from models/mediapipe/."),
             ],
-            outputs=[FaceDetectionType.Output()],
+            outputs=[FaceLandmarkerType.Output()],
         )
 
     @classmethod
     def execute(cls, model_name) -> io.NodeOutput:
-        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("detection", model_name), safe_load=True)
+        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("mediapipe", model_name), safe_load=True)
         wrapper = FaceLandmarkerModel(sd)
         return io.NodeOutput(wrapper)
 
@@ -235,12 +234,10 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceLandmarker",
-            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
-            display_name="Detect Face Landmarks (MediaPipe)",
+            display_name="MediaPipe Face Landmarker",
             category="image/detection",
-            description="Detects facial landmarks using MediaPipe model.",
             inputs=[
-                FaceDetectionType.Input("face_detection_model"),
+                FaceLandmarkerType.Input("face_landmarker"),
                 io.Image.Input("image"),
                 io.Combo.Input("detector_variant", options=["short", "full", "both"], default="short",
                                tooltip="Face detector range. 'short' is tuned for close-up faces "
@@ -264,9 +261,9 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, face_detection_model, image, detector_variant, num_faces, min_confidence,
+    def execute(cls, face_landmarker, image, detector_variant, num_faces, min_confidence,
                 missing_frame_fallback) -> io.NodeOutput:
-        canonical = face_detection_model.canonical_data
+        canonical = face_landmarker.canonical_data
         img_np = _image_to_uint8(image)
         B, H, W = img_np.shape[:3]
         chunk = 16
@@ -279,7 +276,7 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
             with tqdm(total=B, desc=f"MediaPipe Face Landmarker ({variant})") as tq:
                 for i in range(0, B, chunk):
                     end = min(i + chunk, B)
-                    res.extend(face_detection_model.detect_batch(
+                    res.extend(face_landmarker.detect_batch(
                         [img_np[bi] for bi in range(i, end)],
                         num_faces=int(num_faces),
                         score_thresh=float(min_confidence),
@@ -309,7 +306,7 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
                 per_bb.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1, "label": "face", "score": float(f["score"])})
             bboxes.append(per_bb)
         return io.NodeOutput({"frames": frames, "image_size": (H, W),
-                              "connection_sets": face_detection_model.connection_sets}, bboxes)
+                              "connection_sets": face_landmarker.connection_sets}, bboxes)
 
 
 # Topology keys unioned by the 'all' connections preset (contour parts + irises + nose).
@@ -335,10 +332,8 @@ class MediaPipeFaceMeshVisualize(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceMeshVisualize",
-            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection", "visualize"],
-            display_name="Visualize Face Landmarks (MediaPipe)",
+            display_name="MediaPipe Face Mesh Visualize",
             category="image/detection",
-            description="Draws face landmarks mesh on the input image.",
             inputs=[
                 FaceLandmarksType.Input("face_landmarks"),
                 io.Image.Input("image", optional=True, tooltip="If not connected, a black canvas will be used."),
@@ -448,10 +443,8 @@ class MediaPipeFaceMask(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceMask",
-            search_aliases=["face", "facial", "mediapipe", "face mask", "blazeface", "face detection", "visualize"],
-            display_name="Draw Face Mask (MediaPipe)",
+            display_name="MediaPipe Face Mask",
             category="image/detection",
-            description="Draws a mask from face landmarks.",
             inputs=[
                 FaceLandmarksType.Input("face_landmarks"),
                 io.DynamicCombo.Input(

comfy_extras/nodes_moge.py

@@ -103,10 +103,8 @@ class MoGePanoramaInference(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGePanoramaInference",
-            search_aliases=["moge", "panorama", "depth", "geometry", "depth estimation", "geometry estimation"],
-            display_name="Run MoGe Panorama Inference",
+            display_name="MoGe Panorama Inference",
             category="image/geometry_estimation",
-            description="Run MoGe on an equirectangular panorama by splitting it into 12 perspective views, running inference on each, and merging the results into a single depth map.",
             inputs=[
                 MoGeModelType.Input("moge_model"),
                 io.Image.Input("image", tooltip="Equirectangular panorama (any aspect)."),
@@ -224,9 +222,7 @@ class MoGeInference(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGeInference",
-            search_aliases=["moge", "depth", "geometry", "depth estimation", "geometry estimation"],
-            display_name="Run MoGe Inference",
-            description="Run MoGe on a single image to estimate depth and geometry.",
+            display_name="MoGe Inference",
             category="image/geometry_estimation",
             inputs=[
                 MoGeModelType.Input("moge_model"),
@@ -281,9 +277,7 @@ class MoGeRender(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGeRender",
-            search_aliases=["moge", "render", "geometry", "depth", "normal"],
-            display_name="Render MoGe Geometry",
-            description="Render a depth map or normal map from geometry data",
+            display_name="MoGe Render",
             category="image/geometry_estimation",
             inputs=[
                 MoGeGeometry.Input("moge_geometry"),
@@ -348,9 +342,7 @@ class MoGePointMapToMesh(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGePointMapToMesh",
-            search_aliases=["moge", "mesh", "geometry", "point map"],
-            display_name="Convert MoGe Point Map to Mesh",
-            description="Convert a MoGe point map into a 3D mesh.",
+            display_name="MoGe Point Map to Mesh",
             category="image/geometry_estimation",
             inputs=[
                 MoGeGeometry.Input("moge_geometry"),

comfy_extras/nodes_toolkit.py

@@ -14,7 +14,7 @@ class CreateList(io.ComfyNode):
         return io.Schema(
             node_id="CreateList",
             display_name="Create List",
-            category="utils",
+            category="logic",
             is_input_list=True,
             search_aliases=["Image Iterator", "Text Iterator", "Iterator"],
             inputs=[io.Autogrow.Input("inputs", template=template_autogrow)],

folder_paths.py

@@ -60,7 +60,7 @@ folder_names_and_paths["geometry_estimation"] = ([os.path.join(models_dir, "geom
 
 folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)
 
-folder_names_and_paths["detection"] = ([os.path.join(models_dir, "detection")], supported_pt_extensions)
+folder_names_and_paths["mediapipe"] = ([os.path.join(models_dir, "mediapipe")], supported_pt_extensions)
 
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.43.18
-comfyui-workflow-templates==0.9.82
+comfyui-workflow-templates==0.9.79
 comfyui-embedded-docs==0.5.0
 torch
 torchsde