Rename UseValue to SetValue

* re-init

* Update model_multitalk.py

* whitespace...

* Update model_multitalk.py

* remove print

* this is redundant

* remove import

* Restore preview functionality

* Move block_idx to transformer_options

* Remove LoopingSamplerCustomAdvanced

* Remove looping functionality, keep extension functionality

* Update model_multitalk.py

* Handle ref_attn_mask with separate patch to avoid having to always return q and k from self_attn

* Chunk attention map calculation for multiple speakers to reduce peak VRAM usage

* Update model_multitalk.py

* Add ModelPatch type back

* Fix for latest upstream

* Use DynamicCombo for cleaner node

Basically just so that single_speaker mode hides mask inputs and 2nd audio input

* Update nodes_wan.py

.ci/update_windows/update.py

@@ -28,17 +28,17 @@ def pull(repo, remote_name='origin', branch='master'):
 
                 if repo.index.conflicts is not None:
                     for conflict in repo.index.conflicts:
-                        print('Conflicts found in:', conflict[0].path)
+                        print('Conflicts found in:', conflict[0].path)  # noqa: T201
                     raise AssertionError('Conflicts, ahhhhh!!')
 
                 user = repo.default_signature
                 tree = repo.index.write_tree()
-                commit = repo.create_commit('HEAD',
-                                            user,
-                                            user,
-                                            'Merge!',
-                                            tree,
-                                            [repo.head.target, remote_master_id])
+                repo.create_commit('HEAD',
+                                    user,
+                                    user,
+                                    'Merge!',
+                                    tree,
+                                    [repo.head.target, remote_master_id])
                 # We need to do this or git CLI will think we are still merging.
                 repo.state_cleanup()
             else:
@@ -49,26 +49,69 @@ repo_path = str(sys.argv[1])
 repo = pygit2.Repository(repo_path)
 ident = pygit2.Signature('comfyui', 'comfy@ui')
 try:
-    print("stashing current changes")
+    print("stashing current changes")  # noqa: T201
     repo.stash(ident)
 except KeyError:
-    print("nothing to stash")
+    print("nothing to stash")  # noqa: T201
+except:
+    print("Could not stash, cleaning index and trying again.")  # noqa: T201
+    repo.state_cleanup()
+    repo.index.read_tree(repo.head.peel().tree)
+    repo.index.write()
+    try:
+        repo.stash(ident)
+    except KeyError:
+        print("nothing to stash.")  # noqa: T201
+
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
-print("creating backup branch: {}".format(backup_branch_name))
+print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
 try:
     repo.branches.local.create(backup_branch_name, repo.head.peel())
 except:
     pass
 
-print("checking out master branch")
+print("checking out master branch")  # noqa: T201
 branch = repo.lookup_branch('master')
-ref = repo.lookup_reference(branch.name)
-repo.checkout(ref)
+if branch is None:
+    try:
+        ref = repo.lookup_reference('refs/remotes/origin/master')
+    except:
+        print("fetching.")  # noqa: T201
+        for remote in repo.remotes:
+            if remote.name == "origin":
+                remote.fetch()
+        ref = repo.lookup_reference('refs/remotes/origin/master')
+    repo.checkout(ref)
+    branch = repo.lookup_branch('master')
+    if branch is None:
+        repo.create_branch('master', repo.get(ref.target))
+else:
+    ref = repo.lookup_reference(branch.name)
+    repo.checkout(ref)
 
-print("pulling latest changes")
+print("pulling latest changes")  # noqa: T201
 pull(repo)
 
-print("Done!")
+if "--stable" in sys.argv:
+    def latest_tag(repo):
+        versions = []
+        for k in repo.references:
+            try:
+                prefix = "refs/tags/v"
+                if k.startswith(prefix):
+                    version = list(map(int, k[len(prefix):].split(".")))
+                    versions.append((version[0] * 10000000000 + version[1] * 100000 + version[2], k))
+            except:
+                pass
+        versions.sort()
+        if len(versions) > 0:
+            return versions[-1][1]
+        return None
+    latest_tag = latest_tag(repo)
+    if latest_tag is not None:
+        repo.checkout(latest_tag)
+
+print("Done!")  # noqa: T201
 
 self_update = True
 if len(sys.argv) > 2:
@@ -108,3 +151,14 @@ if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
         shutil.copy(repo_req_path, req_path)
     except:
         pass
+
+
+stable_update_script = os.path.join(repo_path, ".ci/update_windows/update_comfyui_stable.bat")
+stable_update_script_to = os.path.join(cur_path, "update_comfyui_stable.bat")
+
+try:
+    if not file_size(stable_update_script_to) > 10:
+        shutil.copy(stable_update_script, stable_update_script_to)
+except:
+    pass
+

.ci/update_windows/update_comfyui_stable.bat

@@ -0,0 +1,8 @@
+@echo off
+..\python_embeded\python.exe .\update.py ..\ComfyUI\ --stable
+if exist update_new.py (
+  move /y update_new.py update.py
+  echo Running updater again since it got updated.
+  ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update --stable
+)
+if "%~1"=="" pause

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -0,0 +1,28 @@
+As of the time of writing this you need this driver for best results:
+https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
+
+HOW TO RUN:
+
+If you have a AMD gpu:
+
+run_amd_gpu.bat
+
+If you have memory issues you can try disabling the smart memory management by running comfyui with:
+
+run_amd_gpu_disable_smart_memory.bat
+
+IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
+
+You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
+
+
+RECOMMENDED WAY TO UPDATE:
+To update the ComfyUI code: update\update_comfyui.bat
+
+
+TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
+In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
+Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
+
+
+

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
+pause

.ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat

@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast
+pause

.ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt

@@ -4,6 +4,9 @@ if you have a NVIDIA gpu:
 
 run_nvidia_gpu.bat
 
+if you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality):
+
+run_nvidia_gpu_fast_fp16_accumulation.bat
 
 
 To run it in slow CPU mode:
@@ -14,7 +17,7 @@ run_cpu.bat
 
 IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
 
-You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
+You can download the stable diffusion 1.5 one from: https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors
 
 
 RECOMMENDED WAY TO UPDATE:

.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat

@@ -0,0 +1,3 @@
+..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
+pause

.ci/windows_nvidia_base_files/run_nvidia_gpu.bat

@@ -0,0 +1,3 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
+pause

.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat

@@ -0,0 +1,3 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
+pause

.gitattributes

@@ -0,0 +1,3 @@
+/web/assets/** linguist-generated
+/web/** linguist-vendored
+comfy_api_nodes/apis/__init__.py linguist-generated

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -8,13 +8,23 @@ body:
         Before submitting a **Bug Report**, please ensure the following:
 
         - **1:** You are running the latest version of ComfyUI.
-        - **2:** You have looked at the existing bug reports and made sure this isn't already reported.
+        - **2:** You have your ComfyUI logs and relevant workflow on hand and will post them in this bug report.
         - **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing
-        `--disable-all-custom-nodes` command line argument.
+        `--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version.
         - **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact
         steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
 
-        If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+        ## Very Important
+
+        Please make sure that you post ALL your ComfyUI logs in the bug report. A bug report without logs will likely be ignored.
+  - type: checkboxes
+    id: custom-nodes-test
+    attributes:
+      label: Custom Node Testing
+      description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
+      options:
+        - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
+          required: false
   - type: textarea
     attributes:
       label: Expected Behavior

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +1,8 @@
 blank_issues_enabled: true
 contact_links:
+  - name: ComfyUI Frontend Issues
+    url: https://github.com/Comfy-Org/ComfyUI_frontend/issues
+    about: Issues related to the ComfyUI frontend (display issues, user interaction bugs), please go to the frontend repo to file the issue
   - name: ComfyUI Matrix Space
     url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
     about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).

.github/ISSUE_TEMPLATE/user-support.yml

@@ -11,6 +11,14 @@ body:
             **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
 
                 If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: checkboxes
+      id: custom-nodes-test
+      attributes:
+        label: Custom Node Testing
+        description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
+        options:
+          - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
+            required: false
     - type: textarea
       attributes:
             label: Your question

.github/PULL_REQUEST_TEMPLATE/api-node.md

@@ -0,0 +1,21 @@
+<!-- API_NODE_PR_CHECKLIST: do not remove -->
+
+## API Node PR Checklist
+
+### Scope
+- [ ] **Is API Node Change**
+
+### Pricing & Billing
+- [ ] **Need pricing update**
+- [ ] **No pricing update**
+
+If **Need pricing update**:
+- [ ] Metronome rate cards updated
+- [ ] Auto‑billing tests updated and passing
+
+### QA
+- [ ] **QA done**
+- [ ] **QA not required**
+
+### Comms
+- [ ] Informed **Kosinkadink**

.github/workflows/api-node-template.yml

@@ -0,0 +1,58 @@
+name: Append API Node PR template
+
+on:
+  pull_request_target:
+    types: [opened, reopened, synchronize, ready_for_review]
+    paths:
+      - 'comfy_api_nodes/**'   # only run if these files changed
+
+permissions:
+  contents: read
+  pull-requests: write
+
+jobs:
+  inject:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Ensure template exists and append to PR body
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const { owner, repo } = context.repo;
+            const number = context.payload.pull_request.number;
+            const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md';
+            const marker = '<!-- API_NODE_PR_CHECKLIST: do not remove -->';
+
+            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number });
+
+            let templateText;
+            try {
+              const res = await github.rest.repos.getContent({
+                owner,
+                repo,
+                path: templatePath,
+                ref: pr.base.ref
+              });
+              const buf = Buffer.from(res.data.content, res.data.encoding || 'base64');
+              templateText = buf.toString('utf8');
+            } catch (e) {
+              core.setFailed(`Required PR template not found at "${templatePath}" on ${pr.base.ref}. Please add it to the repo.`);
+              return;
+            }
+
+            // Enforce the presence of the marker inside the template (for idempotence)
+            if (!templateText.includes(marker)) {
+              core.setFailed(`Template at "${templatePath}" does not contain the required marker:\n${marker}\nAdd it so we can detect duplicates safely.`);
+              return;
+            }
+
+            // If the PR already contains the marker, do not append again.
+            const body = pr.body || '';
+            if (body.includes(marker)) {
+              core.info('Template already present in PR body; nothing to inject.');
+              return;
+            }
+
+            const newBody = (body ? body + '\n\n' : '') + templateText + '\n';
+            await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody });
+            core.notice('API Node template appended to PR description.');

.github/workflows/check-line-endings.yml

@@ -0,0 +1,40 @@
+name: Check for Windows Line Endings
+
+on:
+  pull_request:
+    branches: ['*'] # Trigger on all pull requests to any branch
+
+jobs:
+  check-line-endings:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0 # Fetch all history to compare changes
+
+      - name: Check for Windows line endings (CRLF)
+        run: |
+          # Get the list of changed files in the PR
+          CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }})
+
+          # Flag to track if CRLF is found
+          CRLF_FOUND=false
+
+          # Loop through each changed file
+          for FILE in $CHANGED_FILES; do
+            # Check if the file exists and is a text file
+            if [ -f "$FILE" ] && file "$FILE" | grep -q "text"; then
+              # Check for CRLF line endings
+              if grep -UP '\r$' "$FILE"; then
+                echo "Error: Windows line endings (CRLF) detected in $FILE"
+                CRLF_FOUND=true
+              fi
+            fi
+          done
+
+          # Exit with error if CRLF was found
+          if [ "$CRLF_FOUND" = true ]; then
+            exit 1
+          fi

.github/workflows/pullrequest-ci-run.yml

@@ -0,0 +1,53 @@
+# This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to ComfyUI, when the 'Run-CI-Test' label is added
+# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/
+name: Pull Request CI Workflow Runs
+on:
+    pull_request_target:
+        types: [labeled]
+
+jobs:
+  pr-test-stable:
+    if: ${{ github.event.label.name == 'Run-CI-Test' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos, linux, windows]
+        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        cuda_version: ["12.1"]
+        torch_version: ["stable"]
+        include:
+          - os: macos
+            runner_label: [self-hosted, macOS]
+            flags: "--use-pytorch-cross-attention"
+          - os: linux
+            runner_label: [self-hosted, Linux]
+            flags: ""
+          - os: windows
+            runner_label: [self-hosted, Windows]
+            flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}
+          use_prior_commit: 'true'
+  comment:
+    if: ${{ github.event.label.name == 'Run-CI-Test' }}
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    steps:
+      - uses: actions/github-script@v6
+        with:
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '(Automated Bot Message) CI Tests are running, you can view the results at https://ci.comfy.org/?branch=${{ github.event.pull_request.number }}%2Fmerge'
+            })

.github/workflows/release-stable-all.yml

@@ -0,0 +1,78 @@
+name: "Release Stable All Portable Versions"
+
+on:
+  workflow_dispatch:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+
+jobs:
+  release_nvidia_default:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA Default (cu130)"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu130"
+      python_minor: "13"
+      python_patch: "9"
+      rel_name: "nvidia"
+      rel_extra_name: ""
+      test_release: true
+    secrets: inherit
+
+  release_nvidia_cu128:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA cu128"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu128"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "nvidia"
+      rel_extra_name: "_cu128"
+      test_release: true
+    secrets: inherit
+
+  release_nvidia_cu126:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA cu126"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu126"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "nvidia"
+      rel_extra_name: "_cu126"
+      test_release: true
+    secrets: inherit
+
+  release_amd_rocm:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release AMD ROCm 7.1.1"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "rocm711"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "amd"
+      rel_extra_name: ""
+      test_release: false
+    secrets: inherit

.github/workflows/release-webhook.yml

@@ -0,0 +1,108 @@
+name: Release Webhook
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  send-webhook:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Send release webhook
+        env:
+          WEBHOOK_URL: ${{ secrets.RELEASE_GITHUB_WEBHOOK_URL }}
+          WEBHOOK_SECRET: ${{ secrets.RELEASE_GITHUB_WEBHOOK_SECRET }}
+        run: |
+          # Generate UUID for delivery ID
+          DELIVERY_ID=$(uuidgen)
+          HOOK_ID="release-webhook-$(date +%s)"
+          
+          # Create webhook payload matching GitHub release webhook format
+          PAYLOAD=$(cat <<EOF
+          {
+            "action": "published",
+            "release": {
+              "id": ${{ github.event.release.id }},
+              "node_id": "${{ github.event.release.node_id }}",
+              "url": "${{ github.event.release.url }}",
+              "html_url": "${{ github.event.release.html_url }}",
+              "assets_url": "${{ github.event.release.assets_url }}",
+              "upload_url": "${{ github.event.release.upload_url }}",
+              "tag_name": "${{ github.event.release.tag_name }}",
+              "target_commitish": "${{ github.event.release.target_commitish }}",
+              "name": ${{ toJSON(github.event.release.name) }},
+              "body": ${{ toJSON(github.event.release.body) }},
+              "draft": ${{ github.event.release.draft }},
+              "prerelease": ${{ github.event.release.prerelease }},
+              "created_at": "${{ github.event.release.created_at }}",
+              "published_at": "${{ github.event.release.published_at }}",
+              "author": {
+                "login": "${{ github.event.release.author.login }}",
+                "id": ${{ github.event.release.author.id }},
+                "node_id": "${{ github.event.release.author.node_id }}",
+                "avatar_url": "${{ github.event.release.author.avatar_url }}",
+                "url": "${{ github.event.release.author.url }}",
+                "html_url": "${{ github.event.release.author.html_url }}",
+                "type": "${{ github.event.release.author.type }}",
+                "site_admin": ${{ github.event.release.author.site_admin }}
+              },
+              "tarball_url": "${{ github.event.release.tarball_url }}",
+              "zipball_url": "${{ github.event.release.zipball_url }}",
+              "assets": ${{ toJSON(github.event.release.assets) }}
+            },
+            "repository": {
+              "id": ${{ github.event.repository.id }},
+              "node_id": "${{ github.event.repository.node_id }}",
+              "name": "${{ github.event.repository.name }}",
+              "full_name": "${{ github.event.repository.full_name }}",
+              "private": ${{ github.event.repository.private }},
+              "owner": {
+                "login": "${{ github.event.repository.owner.login }}",
+                "id": ${{ github.event.repository.owner.id }},
+                "node_id": "${{ github.event.repository.owner.node_id }}",
+                "avatar_url": "${{ github.event.repository.owner.avatar_url }}",
+                "url": "${{ github.event.repository.owner.url }}",
+                "html_url": "${{ github.event.repository.owner.html_url }}",
+                "type": "${{ github.event.repository.owner.type }}",
+                "site_admin": ${{ github.event.repository.owner.site_admin }}
+              },
+              "html_url": "${{ github.event.repository.html_url }}",
+              "clone_url": "${{ github.event.repository.clone_url }}",
+              "git_url": "${{ github.event.repository.git_url }}",
+              "ssh_url": "${{ github.event.repository.ssh_url }}",
+              "url": "${{ github.event.repository.url }}",
+              "created_at": "${{ github.event.repository.created_at }}",
+              "updated_at": "${{ github.event.repository.updated_at }}",
+              "pushed_at": "${{ github.event.repository.pushed_at }}",
+              "default_branch": "${{ github.event.repository.default_branch }}",
+              "fork": ${{ github.event.repository.fork }}
+            },
+            "sender": {
+              "login": "${{ github.event.sender.login }}",
+              "id": ${{ github.event.sender.id }},
+              "node_id": "${{ github.event.sender.node_id }}",
+              "avatar_url": "${{ github.event.sender.avatar_url }}",
+              "url": "${{ github.event.sender.url }}",
+              "html_url": "${{ github.event.sender.html_url }}",
+              "type": "${{ github.event.sender.type }}",
+              "site_admin": ${{ github.event.sender.site_admin }}
+            }
+          }
+          EOF
+          )
+          
+          # Generate HMAC-SHA256 signature
+          SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$WEBHOOK_SECRET" -hex | cut -d' ' -f2)
+          
+          # Send webhook with required headers
+          curl -X POST "$WEBHOOK_URL" \
+            -H "Content-Type: application/json" \
+            -H "X-GitHub-Event: release" \
+            -H "X-GitHub-Delivery: $DELIVERY_ID" \
+            -H "X-GitHub-Hook-ID: $HOOK_ID" \
+            -H "X-Hub-Signature-256: sha256=$SIGNATURE" \
+            -H "User-Agent: GitHub-Actions-Webhook/1.0" \
+            -d "$PAYLOAD" \
+            --fail --silent --show-error
+          
+          echo "✅ Release webhook sent successfully"

.github/workflows/ruff.yml

@@ -0,0 +1,48 @@
+name: Python Linting
+
+on: [push, pull_request]
+
+jobs:
+  ruff:
+    name: Run Ruff
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.x
+
+    - name: Install Ruff
+      run: pip install ruff
+
+    - name: Run Ruff
+      run: ruff check .
+
+  pylint:
+    name: Run Pylint
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+
+    - name: Install Pylint
+      run: pip install pylint
+
+    - name: Run Pylint
+      run: pylint comfy_api_nodes

.github/workflows/stable-release.yml

@@ -2,9 +2,78 @@
 name: "Release Stable Version"
 
 on:
-  push:
-    tags:
-      - 'v*'
+  workflow_call:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+      cache_tag:
+        description: 'Cached dependencies tag'
+        required: true
+        type: string
+        default: "cu129"
+      python_minor:
+        description: 'Python minor version'
+        required: true
+        type: string
+        default: "13"
+      python_patch:
+        description: 'Python patch version'
+        required: true
+        type: string
+        default: "6"
+      rel_name:
+        description: 'Release name'
+        required: true
+        type: string
+        default: "nvidia"
+      rel_extra_name:
+        description: 'Release extra name'
+        required: false
+        type: string
+        default: ""
+      test_release:
+        description: 'Test Release'
+        required: true
+        type: boolean
+        default: true
+  workflow_dispatch:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+      cache_tag:
+        description: 'Cached dependencies tag'
+        required: true
+        type: string
+        default: "cu129"
+      python_minor:
+        description: 'Python minor version'
+        required: true
+        type: string
+        default: "13"
+      python_patch:
+        description: 'Python patch version'
+        required: true
+        type: string
+        default: "6"
+      rel_name:
+        description: 'Release name'
+        required: true
+        type: string
+        default: "nvidia"
+      rel_extra_name:
+        description: 'Release extra name'
+        required: false
+        type: string
+        default: ""
+      test_release:
+        description: 'Test Release'
+        required: true
+        type: boolean
+        default: true
 
 jobs:
   package_comfy_windows:
@@ -13,70 +82,57 @@ jobs:
       packages: "write"
       pull-requests: "read"
     runs-on: windows-latest
-    strategy:
-      matrix:
-        python_version: [3.11.8]
-        cuda_version: [121]
     steps:
-      - name: Calculate Minor Version
-        shell: bash
-        run: |
-          # Extract the minor version from the Python version
-          MINOR_VERSION=$(echo "${{ matrix.python_version }}" | cut -d'.' -f2)
-          echo "MINOR_VERSION=$MINOR_VERSION" >> $GITHUB_ENV
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-        
       - uses: actions/checkout@v4
         with:
-          fetch-depth: 0
+          ref: ${{ inputs.git_tag }}
+          fetch-depth: 150
           persist-credentials: false
+      - uses: actions/cache/restore@v4
+        id: cache
+        with:
+          path: |
+            ${{ inputs.cache_tag }}_python_deps.tar
+            update_comfyui_and_python_dependencies.bat
+          key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
       - shell: bash
         run: |
-          echo "@echo off
-          call update_comfyui.bat nopause
-          echo -
-          echo This will try to update pytorch and all python dependencies.
-          echo -
-          echo If you just want to update normally, close this and run update_comfyui.bat instead.
-          echo -
-          pause
-          ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu${{ matrix.cuda_version }} -r ../ComfyUI/requirements.txt pygit2
-          pause" > update_comfyui_and_python_dependencies.bat
-
-          python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu${{ matrix.cuda_version }} -r requirements.txt pygit2 -w ./temp_wheel_dir
-          python -m pip install --no-cache-dir ./temp_wheel_dir/*
-          echo installed basic
-          ls -lah temp_wheel_dir
-          mv temp_wheel_dir cu${{ matrix.cuda_version }}_python_deps
-          mv cu${{ matrix.cuda_version }}_python_deps ../
+          mv ${{ inputs.cache_tag }}_python_deps.tar ../
           mv update_comfyui_and_python_dependencies.bat ../
           cd ..
+          tar xf ${{ inputs.cache_tag }}_python_deps.tar
           pwd
           ls
-          
+
+      - shell: bash
+        run: |
+          cd ..
           cp -r ComfyUI ComfyUI_copy
-          curl https://www.python.org/ftp/python/${{ matrix.python_version }}/python-${{ matrix.python_version }}-embed-amd64.zip -o python_embeded.zip
+          curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
           unzip python_embeded.zip -d python_embeded
           cd python_embeded
           echo ${{ env.MINOR_VERSION }}
-          echo 'import site' >> ./python3${{ env.MINOR_VERSION }}._pth
+          echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
           curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
           ./python.exe get-pip.py
-          ./python.exe --version
-          echo "Pip version:"
-          ./python.exe -m pip --version
+          ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
+
+          grep comfy ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
+          ./python.exe -s -m pip install -r requirements_comfyui.txt
+          rm requirements_comfyui.txt
+
+          sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
+
+          if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then
+            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
+            rm ./Lib/site-packages/torch/lib/libprotoc.lib
+            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
+          fi
 
-          set PATH=$PWD/Scripts:$PATH
-          echo $PATH
-          ./python.exe -s -m pip install ../cu${{ matrix.cuda_version }}_python_deps/*
-          sed -i '1i../ComfyUI' ./python3${{ env.MINOR_VERSION }}._pth
           cd ..
 
-          git clone https://github.com/comfyanonymous/taesd
-          cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
+          git clone --depth 1 https://github.com/comfyanonymous/taesd
+          cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/
 
           mkdir ComfyUI_windows_portable
           mv python_embeded ComfyUI_windows_portable
@@ -86,24 +142,29 @@ jobs:
 
           mkdir update
           cp -r ComfyUI/.ci/update_windows/* ./update/
-          cp -r ComfyUI/.ci/windows_base_files/* ./
+          cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
           cd ..
 
-          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
-          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
+          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
 
+      - shell: bash
+        if: ${{ inputs.test_release }}
+        run: |
+          cd ..
           cd ComfyUI_windows_portable
           python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
 
+          python_embeded/python.exe -s ./update/update.py ComfyUI/
+
           ls
 
       - name: Upload binaries to release
-        uses: svenstaro/upload-release-action@v2
+        uses: softprops/action-gh-release@v2
         with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ComfyUI_windows_portable_nvidia.7z
-          tag: ${{ github.ref }}
-          overwrite: true
-        
+          files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
+          tag_name: ${{ inputs.git_tag }}
+          draft: true
+          overwrite_files: true

.github/workflows/stale-issues.yml

@@ -0,0 +1,21 @@
+name: 'Close stale issues'
+on:
+  schedule:
+    # Run daily at 430 am PT
+    - cron: '30 11 * * *'
+permissions:
+  issues: write
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v9
+        with:
+          stale-issue-message: "This issue is being marked stale because it has not had any activity for 30 days. Reply below within 7 days if your issue still isn't solved, and it will be left open. Otherwise, the issue will be closed automatically."
+          days-before-stale: 30
+          days-before-close: 7
+          stale-issue-label: 'Stale'
+          only-labels: 'User Support'
+          exempt-all-assignees: true
+          exempt-all-milestones: true

.github/workflows/test-browser.yml

@@ -1,76 +0,0 @@
-# This is a temporary action during frontend TS migration.
-# This file should be removed after TS migration is completed.
-# The browser test is here to ensure TS repo is working the same way as the
-# current JS code.
-# If you are adding UI feature, please sync your changes to the TS repo:
-# huchenlei/ComfyUI_frontend and update test expectation files accordingly.
-name: Playwright Browser Tests CI
-
-on:
-  push:
-    branches: [ main, master ]
-  pull_request:
-    branches: [ main, master ]
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout ComfyUI
-      uses: actions/checkout@v4
-      with:
-        repository: "comfyanonymous/ComfyUI"
-        path: "ComfyUI"
-    - name: Checkout ComfyUI_frontend
-      uses: actions/checkout@v4
-      with:
-        repository: "huchenlei/ComfyUI_frontend"
-        path: "ComfyUI_frontend"
-        ref: "fcc54d803e5b6a9b08a462a1d94899318c96dcbb"
-    - uses: actions/setup-node@v3
-      with:
-        node-version: lts/*
-    - uses: actions/setup-python@v4
-      with:
-        python-version: '3.10'
-    - name: Install requirements
-      run: |
-        python -m pip install --upgrade pip
-        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install -r requirements.txt
-        pip install wait-for-it
-      working-directory: ComfyUI
-    - name: Start ComfyUI server
-      run: |
-        python main.py --cpu 2>&1 | tee console_output.log &
-        wait-for-it --service 127.0.0.1:8188 -t 600
-      working-directory: ComfyUI
-    - name: Install ComfyUI_frontend dependencies
-      run: |
-        npm ci
-      working-directory: ComfyUI_frontend
-    - name: Install Playwright Browsers
-      run: npx playwright install --with-deps
-      working-directory: ComfyUI_frontend
-    - name: Run Playwright tests
-      run: npx playwright test
-      working-directory: ComfyUI_frontend
-    - name: Check for unhandled exceptions in server log
-      run: |
-        if grep -qE "Exception|Error" console_output.log; then
-          echo "Unhandled exception/error found in server log."
-          exit 1
-        fi
-      working-directory: ComfyUI
-    - uses: actions/upload-artifact@v4
-      if: always()
-      with:
-        name: playwright-report
-        path: ComfyUI_frontend/playwright-report/
-        retention-days: 30
-    - uses: actions/upload-artifact@v4
-      if: always()
-      with:
-        name: console-output
-        path: ComfyUI/console_output.log
-        retention-days: 30

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -28,4 +28,4 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
+          pip install -r requirements.txt

.github/workflows/test-ci.yml

@@ -0,0 +1,99 @@
+# This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the master branch of ComfyUI
+# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/
+name: Full Comfy CI Workflow Runs
+on:
+  push:
+    branches:
+      - master
+      - release/**
+    paths-ignore:
+      - 'app/**'
+      - 'input/**'
+      - 'output/**'
+      - 'notebooks/**'
+      - 'script_examples/**'
+      - '.github/**'
+      - 'web/**'
+  workflow_dispatch:
+
+jobs:
+  test-stable:
+    strategy:
+      fail-fast: false
+      matrix:
+        # os: [macos, linux, windows]
+        # os: [macos, linux]
+        os: [linux]
+        python_version: ["3.10", "3.11", "3.12"]
+        cuda_version: ["12.1"]
+        torch_version: ["stable"]
+        include:
+          # - os: macos
+          #   runner_label: [self-hosted, macOS]
+          #   flags: "--use-pytorch-cross-attention"
+          - os: linux
+            runner_label: [self-hosted, Linux]
+            flags: ""
+          # - os: windows
+          #   runner_label: [self-hosted, Windows]
+          #   flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}
+
+  # test-win-nightly:
+  #   strategy:
+  #     fail-fast: true
+  #     matrix:
+  #       os: [windows]
+  #       python_version: ["3.9", "3.10", "3.11", "3.12"]
+  #       cuda_version: ["12.1"]
+  #       torch_version: ["nightly"]
+  #       include:
+  #         - os: windows
+  #           runner_label: [self-hosted, Windows]
+  #           flags: ""
+  #   runs-on: ${{ matrix.runner_label }}
+  #   steps:
+  #     - name: Test Workflows
+  #       uses: comfy-org/comfy-action@main
+  #       with:
+  #         os: ${{ matrix.os }}
+  #         python_version: ${{ matrix.python_version }}
+  #         torch_version: ${{ matrix.torch_version }}
+  #         google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+  #         comfyui_flags: ${{ matrix.flags }}
+
+  test-unix-nightly:
+    strategy:
+      fail-fast: false
+      matrix:
+        # os: [macos, linux]
+        os: [linux]
+        python_version: ["3.11"]
+        cuda_version: ["12.1"]
+        torch_version: ["nightly"]
+        include:
+          # - os: macos
+          #   runner_label: [self-hosted, macOS]
+          #   flags: "--use-pytorch-cross-attention"
+          - os: linux
+            runner_label: [self-hosted, Linux]
+            flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}

.github/workflows/test-execution.yml

@@ -0,0 +1,30 @@
+name: Execution Tests
+
+on:
+  push:
+    branches: [ main, master, release/** ]
+  pull_request:
+    branches: [ main, master, release/** ]
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python      
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+        pip install -r tests-unit/requirements.txt
+    - name: Run Execution Tests
+      run: |
+        python -m pytest tests/execution -v --skip-timing-checks

.github/workflows/test-launch.yml

@@ -0,0 +1,47 @@
+name: Test server launches without errors
+
+on:
+  push:
+    branches: [ main, master, release/** ]
+  pull_request:
+    branches: [ main, master, release/** ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout ComfyUI
+      uses: actions/checkout@v4
+      with:
+        repository: "Comfy-Org/ComfyUI"
+        path: "ComfyUI"
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+        pip install wait-for-it
+      working-directory: ComfyUI
+    - name: Start ComfyUI server
+      run: |
+        python main.py --cpu 2>&1 | tee console_output.log &
+        wait-for-it --service 127.0.0.1:8188 -t 30
+      working-directory: ComfyUI
+    - name: Check for unhandled exceptions in server log
+      run: |
+        grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" console_output.log | grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': False, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" > console_output_filtered.log
+        cat console_output_filtered.log
+        if grep -qE "Exception|Error" console_output_filtered.log; then
+          echo "Unhandled exception/error found in server log."
+          exit 1
+        fi
+      working-directory: ComfyUI
+    - uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: console-output
+        path: ComfyUI/console_output.log
+        retention-days: 30

.github/workflows/test-ui.yaml

@@ -1,26 +0,0 @@
-name: Tests CI
-
-on: [push, pull_request]
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-node@v3
-      with:
-        node-version: 18
-    - uses: actions/setup-python@v4
-      with: 
-        python-version: '3.10'
-    - name: Install requirements
-      run: |
-        python -m pip install --upgrade pip
-        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install -r requirements.txt
-    - name: Run Tests
-      run: | 
-        npm ci
-        npm run test:generate
-        npm test -- --verbose
-      working-directory: ./tests-ui

.github/workflows/test-unit.yml

@@ -0,0 +1,30 @@
+name: Unit Tests
+
+on:
+  push:
+    branches: [ main, master, release/** ]
+  pull_request:
+    branches: [ main, master, release/** ]
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-2022, macos-latest]
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python      
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+    - name: Run Unit Tests
+      run: |
+        pip install -r tests-unit/requirements.txt
+        python -m pytest tests-unit

.github/workflows/update-api-stubs.yml

@@ -0,0 +1,56 @@
+name: Generate Pydantic Stubs from api.comfy.org
+
+on:
+  schedule:
+    - cron: '0 0 * * 1'
+  workflow_dispatch:
+
+jobs:
+  generate-models:
+    runs-on: ubuntu-latest
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install 'datamodel-code-generator[http]'
+          npm install @redocly/cli
+      
+      - name: Download OpenAPI spec
+        run: |
+          curl -o openapi.yaml https://api.comfy.org/openapi
+      
+      - name: Filter OpenAPI spec with Redocly
+        run: |
+          npx @redocly/cli bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components
+      
+      - name: Generate API models
+        run: |
+          datamodel-codegen --use-subclass-enum --input filtered-openapi.yaml --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel
+      
+      - name: Check for changes
+        id: git-check
+        run: |
+          git diff --exit-code comfy_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT
+      
+      - name: Create Pull Request
+        if: steps.git-check.outputs.changes == 'true'
+        uses: peter-evans/create-pull-request@v5
+        with:
+          commit-message: 'chore: update API models from OpenAPI spec'
+          title: 'Update API models from api.comfy.org'
+          body: |
+            This PR updates the API models based on the latest api.comfy.org OpenAPI specification.
+            
+            Generated automatically by the a Github workflow.
+          branch: update-api-stubs
+          delete-branch: true
+          base: master

.github/workflows/update-ci-container.yml

@@ -0,0 +1,59 @@
+name: "CI: Update CI Container"
+
+on:
+  release:
+    types: [published]
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'ComfyUI version (e.g., v0.7.0)'
+        required: true
+        type: string
+
+jobs:
+  update-ci-container:
+    runs-on: ubuntu-latest
+    # Skip pre-releases unless manually triggered
+    if: github.event_name == 'workflow_dispatch' || !github.event.release.prerelease
+    steps:
+      - name: Get version
+        id: version
+        run: |
+          if [ "${{ github.event_name }}" = "release" ]; then
+            VERSION="${{ github.event.release.tag_name }}"
+          else
+            VERSION="${{ inputs.version }}"
+          fi
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
+
+      - name: Checkout comfyui-ci-container
+        uses: actions/checkout@v4
+        with:
+          repository: comfy-org/comfyui-ci-container
+          token: ${{ secrets.CI_CONTAINER_PAT }}
+
+      - name: Check current version
+        id: current
+        run: |
+          CURRENT=$(grep -oP 'ARG COMFYUI_VERSION=\K.*' Dockerfile || echo "unknown")
+          echo "current_version=$CURRENT" >> $GITHUB_OUTPUT
+
+      - name: Update Dockerfile
+        run: |
+          VERSION="${{ steps.version.outputs.version }}"
+          sed -i "s/^ARG COMFYUI_VERSION=.*/ARG COMFYUI_VERSION=${VERSION}/" Dockerfile
+
+      - name: Create Pull Request
+        id: create-pr
+        uses: peter-evans/create-pull-request@v7
+        with:
+          token: ${{ secrets.CI_CONTAINER_PAT }}
+          branch: automation/comfyui-${{ steps.version.outputs.version }}
+          title: "chore: bump ComfyUI to ${{ steps.version.outputs.version }}"
+          body: |
+            Updates ComfyUI version from `${{ steps.current.outputs.current_version }}` to `${{ steps.version.outputs.version }}`
+
+            **Triggered by:** ${{ github.event_name == 'release' && format('[Release {0}]({1})', github.event.release.tag_name, github.event.release.html_url) || 'Manual workflow dispatch' }}
+
+          labels: automation
+          commit-message: "chore: bump ComfyUI to ${{ steps.version.outputs.version }}"

.github/workflows/update-version.yml

@@ -0,0 +1,59 @@
+name: Update Version File
+
+on:
+  pull_request:
+    paths:
+      - "pyproject.toml"
+    branches:
+      - master
+      - release/**
+
+jobs:
+  update-version:
+    runs-on: ubuntu-latest
+    # Don't run on fork PRs
+    if: github.event.pull_request.head.repo.full_name == github.repository
+    permissions:
+      pull-requests: write
+      contents: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+
+      - name: Update comfyui_version.py
+        run: |
+          # Read version from pyproject.toml and update comfyui_version.py
+          python -c '
+          import tomllib
+
+          # Read version from pyproject.toml
+          with open("pyproject.toml", "rb") as f:
+              config = tomllib.load(f)
+              version = config["project"]["version"]
+
+          # Write version to comfyui_version.py
+          with open("comfyui_version.py", "w") as f:
+              f.write("# This file is automatically generated by the build process when version is\n")
+              f.write("# updated in pyproject.toml.\n")
+              f.write(f"__version__ = \"{version}\"\n")
+          '
+
+      - name: Commit changes
+        run: |
+          git config --local user.name "github-actions"
+          git config --local user.email "github-actions@github.com"
+          git fetch origin ${{ github.head_ref }}
+          git checkout -B ${{ github.head_ref }} origin/${{ github.head_ref }}
+          git add comfyui_version.py
+          git diff --quiet && git diff --staged --quiet || git commit -m "chore: Update comfyui_version.py to match pyproject.toml"
+          git push origin HEAD:${{ github.head_ref }}

.github/workflows/windows_release_dependencies.yml

@@ -8,23 +8,28 @@ on:
         required: false
         type: string
         default: ""
+      extra_dependencies:
+        description: 'extra dependencies'
+        required: false
+        type: string
+        default: ""
       cu:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "130"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "11"
+        default: "13"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master
@@ -51,7 +56,8 @@ jobs:
             ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > update_comfyui_and_python_dependencies.bat
 
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
+            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

.github/workflows/windows_release_dependencies_manual.yml

@@ -0,0 +1,64 @@
+name: "Windows Release dependencies Manual"
+
+on:
+  workflow_dispatch:
+    inputs:
+      torch_dependencies:
+        description: 'torch dependencies'
+        required: false
+        type: string
+        default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128"
+      cache_tag:
+        description: 'Cached dependencies tag'
+        required: true
+        type: string
+        default: "cu128"
+
+      python_minor:
+        description: 'python minor version'
+        required: true
+        type: string
+        default: "12"
+
+      python_patch:
+        description: 'python patch version'
+        required: true
+        type: string
+        default: "10"
+
+jobs:
+  build_dependencies:
+    runs-on: windows-latest
+    steps:
+        - uses: actions/checkout@v4
+        - uses: actions/setup-python@v5
+          with:
+            python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
+
+        - shell: bash
+          run: |
+            echo "@echo off
+            call update_comfyui.bat nopause
+            echo -
+            echo This will try to update pytorch and all python dependencies.
+            echo -
+            echo If you just want to update normally, close this and run update_comfyui.bat instead.
+            echo -
+            pause
+            ..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2
+            pause" > update_comfyui_and_python_dependencies.bat
+
+            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
+            python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
+            python -m pip install --no-cache-dir ./temp_wheel_dir/*
+            echo installed basic
+            ls -lah temp_wheel_dir
+            mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps
+            tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps
+
+        - uses: actions/cache/save@v4
+          with:
+            path: |
+              ${{ inputs.cache_tag }}_python_deps.tar
+              update_comfyui_and_python_dependencies.bat
+            key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}

.github/workflows/windows_release_nightly_pytorch.yml

@@ -7,19 +7,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "124"
+        default: "129"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "12"
+        default: "13"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "3"
+        default: "5"
 #  push:
 #    branches:
 #      - master
@@ -34,7 +34,7 @@ jobs:
     steps:
         - uses: actions/checkout@v4
           with:
-            fetch-depth: 0
+            fetch-depth: 30
             persist-credentials: false
         - uses: actions/setup-python@v5
           with:
@@ -49,14 +49,16 @@ jobs:
             echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
             curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
             ./python.exe get-pip.py
-            python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 numpy==1.26.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+            python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
+
+            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
             cd ..
 
-            git clone https://github.com/comfyanonymous/taesd
-            cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
+            git clone --depth 1 https://github.com/comfyanonymous/taesd
+            cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/
 
             mkdir ComfyUI_windows_portable_nightly_pytorch
             mv python_embeded ComfyUI_windows_portable_nightly_pytorch
@@ -66,14 +68,15 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
 
             echo "call update_comfyui.bat nopause
             ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > ./update/update_comfyui_and_python_dependencies.bat
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
             mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
 
             cd ComfyUI_windows_portable_nightly_pytorch

.github/workflows/windows_release_package.yml

@@ -7,19 +7,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "129"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "11"
+        default: "13"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "6"
 #  push:
 #    branches:
 #      - master
@@ -50,7 +50,7 @@ jobs:
 
         - uses: actions/checkout@v4
           with:
-            fetch-depth: 0
+            fetch-depth: 150
             persist-credentials: false
         - shell: bash
           run: |
@@ -64,10 +64,14 @@ jobs:
             ./python.exe get-pip.py
             ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
+
+            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
+            rm ./Lib/site-packages/torch/lib/libprotoc.lib
+            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
             cd ..
 
-            git clone https://github.com/comfyanonymous/taesd
-            cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
+            git clone --depth 1 https://github.com/comfyanonymous/taesd
+            cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/
 
             mkdir ComfyUI_windows_portable
             mv python_embeded ComfyUI_windows_portable
@@ -77,17 +81,19 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
             cp ../update_comfyui_and_python_dependencies.bat ./update/
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
             mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
 
             cd ComfyUI_windows_portable
             python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
 
+            python_embeded/python.exe -s ./update/update.py ComfyUI/
+
             ls
 
         - name: Upload binaries to release

.gitignore

@@ -12,9 +12,15 @@ extra_model_paths.yaml
 .vscode/
 .idea/
 venv/
+.venv/
 /web/extensions/*
 !/web/extensions/logging.js.example
 !/web/extensions/core/
 /tests-ui/data/object_info.json
 /user/
-*.log
+*.log
+web_custom_versions/
+.DS_Store
+openapi.yaml
+filtered-openapi.yaml
+uv.lock

CODEOWNERS

@@ -1 +1,2 @@
-*       @comfyanonymous
+# Admins
+* @comfyanonymous @kosinkadink @guill

QUANTIZATION.md

@@ -0,0 +1,168 @@
+# The Comfy guide to Quantization
+
+
+## How does quantization work?
+
+Quantization aims to map a high-precision value x_f to a lower precision format with minimal loss in accuracy. These smaller formats then serve to reduce the models memory footprint and increase throughput by using specialized hardware.
+
+When simply converting a value from FP16 to FP8 using the round-nearest method we might hit two issues:
+- The dynamic range of FP16 (-65,504, 65,504) far exceeds FP8 formats like E4M3 (-448, 448) or E5M2 (-57,344, 57,344), potentially resulting in clipped values
+- The original values are concentrated in a small range (e.g. -1,1) leaving many FP8-bits "unused"
+
+By using a scaling factor, we aim to map these values into the quantized-dtype range, making use of the full spectrum. One of the easiest approaches, and common, is using per-tensor absolute-maximum scaling.
+
+```
+absmax = max(abs(tensor))
+scale = amax / max_dynamic_range_low_precision
+
+# Quantization
+tensor_q = (tensor / scale).to(low_precision_dtype)
+
+# De-Quantization
+tensor_dq = tensor_q.to(fp16) * scale
+
+tensor_dq ~ tensor
+```
+
+Given that additional information (scaling factor) is needed to "interpret" the quantized values, we describe those as derived datatypes.
+
+
+## Quantization in Comfy
+
+```
+QuantizedTensor (torch.Tensor subclass)
+  ↓ __torch_dispatch__
+Two-Level Registry (generic + layout handlers)
+  ↓
+MixedPrecisionOps + Metadata Detection
+```
+
+### Representation
+
+To represent these derived datatypes, ComfyUI uses a subclass of torch.Tensor to implements these using the `QuantizedTensor` class found in `comfy/quant_ops.py`
+
+A `Layout` class defines how a specific quantization format behaves:
+- Required parameters
+- Quantize method
+- De-Quantize method
+
+```python
+from comfy.quant_ops import QuantizedLayout
+
+class MyLayout(QuantizedLayout):
+    @classmethod
+    def quantize(cls, tensor, **kwargs):
+        # Convert to quantized format
+        qdata = ...
+        params = {'scale': ..., 'orig_dtype': tensor.dtype}
+        return qdata, params
+    
+    @staticmethod
+    def dequantize(qdata, scale, orig_dtype, **kwargs):
+        return qdata.to(orig_dtype) * scale
+```
+
+To then run operations using these QuantizedTensors we use two registry systems to define supported operations. 
+The first is a **generic registry** that handles operations common to all quantized formats (e.g., `.to()`, `.clone()`, `.reshape()`).
+
+The second registry is layout-specific and allows to implement fast-paths like nn.Linear.
+```python
+from comfy.quant_ops import register_layout_op
+
+@register_layout_op(torch.ops.aten.linear.default, MyLayout)
+def my_linear(func, args, kwargs):
+    # Extract tensors, call optimized kernel
+    ...
+```
+When `torch.nn.functional.linear()` is called with QuantizedTensor arguments, `__torch_dispatch__` automatically routes to the registered implementation.
+For any unsupported operation, QuantizedTensor will fallback to call `dequantize` and dispatch using the high-precision implementation.
+
+
+### Mixed Precision
+
+The `MixedPrecisionOps` class (lines 542-648 in `comfy/ops.py`) enables per-layer quantization decisions, allowing different layers in a model to use different precisions. This is activated when a model config contains a `layer_quant_config` dictionary that specifies which layers should be quantized and how.
+
+**Architecture:**
+
+```python
+class MixedPrecisionOps(disable_weight_init):
+    _layer_quant_config = {}  # Maps layer names to quantization configs
+    _compute_dtype = torch.bfloat16  # Default compute / dequantize precision
+```
+
+**Key mechanism:**
+
+The custom `Linear._load_from_state_dict()` method inspects each layer during model loading:
+- If the layer name is **not** in `_layer_quant_config`: load weight as regular tensor in `_compute_dtype`
+- If the layer name **is** in `_layer_quant_config`: 
+  - Load weight as `QuantizedTensor` with the specified layout (e.g., `TensorCoreFP8Layout`)
+  - Load associated quantization parameters (scales, block_size, etc.)
+
+**Why it's needed:**
+
+Not all layers tolerate quantization equally. Sensitive operations like final projections can be kept in higher precision, while compute-heavy matmuls are quantized. This provides most of the performance benefits while maintaining quality.
+
+The system is selected in `pick_operations()` when `model_config.layer_quant_config` is present, making it the highest-priority operation mode.
+
+
+## Checkpoint Format
+
+Quantized checkpoints are stored as standard safetensors files with quantized weight tensors and associated scaling parameters, plus a `_quantization_metadata` JSON entry describing the quantization scheme.
+
+The quantized checkpoint will contain the same layers as the original checkpoint but:
+- The weights are stored as quantized values, sometimes using a different storage datatype. E.g. uint8 container for fp8.
+- For each quantized weight a number of additional scaling parameters are stored alongside depending on the recipe.
+- We store a metadata.json in the metadata of the final safetensor containing the `_quantization_metadata` describing which layers are quantized and what layout has been used.
+
+### Scaling Parameters details
+We define 4 possible scaling parameters that should cover most recipes in the near-future:
+- **weight_scale**: quantization scalers for the weights
+- **weight_scale_2**: global scalers in the context of double scaling
+- **pre_quant_scale**: scalers used for smoothing salient weights
+- **input_scale**: quantization scalers for the activations
+
+| Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale |
+|--------|---------------|--------------|----------------|-----------------|-------------|
+| float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) |
+
+You can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS).
+
+### Quantization Metadata
+
+The metadata stored alongside the checkpoint contains:
+- **format_version**: String to define a version of the standard
+- **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`. 
+
+Example:
+```json
+{
+  "_quantization_metadata": {
+    "format_version": "1.0",
+    "layers": {
+      "model.layers.0.mlp.up_proj": "float8_e4m3fn",
+      "model.layers.0.mlp.down_proj": "float8_e4m3fn",
+      "model.layers.1.mlp.up_proj": "float8_e4m3fn"
+    }
+  }
+}
+```
+
+
+## Creating Quantized Checkpoints
+
+To create compatible checkpoints, use any quantization tool provided the output follows the checkpoint format described above and uses a layout defined in `QUANT_ALGOS`.
+
+### Weight Quantization
+
+Weight quantization is straightforward - compute the scaling factor directly from the weight tensor using the absolute maximum method described earlier. Each layer's weights are quantized independently and stored with their corresponding `weight_scale` parameter.
+
+### Calibration (for Activation Quantization)
+
+Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_scale` parameters that cannot be determined from static weights alone. Since activation values depend on actual inputs, we use **post-training calibration (PTQ)**:
+
+1. **Collect statistics**: Run inference on N representative samples
+2. **Track activations**: Record the absolute maximum (`amax`) of inputs to each quantized layer
+3. **Compute scales**: Derive `input_scale` from collected statistics
+4. **Store in checkpoint**: Save `input_scale` parameters alongside weights
+
+The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters.

README.md

@@ -1,22 +1,98 @@
-ComfyUI
-=======
-The most powerful and modular stable diffusion GUI and backend.
------------
-![ComfyUI Screenshot](comfyui_screenshot.png)
+<div align="center">
 
-This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
-### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+# ComfyUI
+**The most powerful and modular visual AI engine and application.**
 
-### [Installing ComfyUI](#installing)
+
+[![Website][website-shield]][website-url]
+[![Dynamic JSON Badge][discord-shield]][discord-url]
+[![Twitter][twitter-shield]][twitter-url]
+[![Matrix][matrix-shield]][matrix-url]
+<br>
+[![][github-release-shield]][github-release-link]
+[![][github-release-date-shield]][github-release-link]
+[![][github-downloads-shield]][github-downloads-link]
+[![][github-downloads-latest-shield]][github-downloads-link]
+
+[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white
+[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
+[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat
+[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://www.comfy.org/discord
+[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI
+[twitter-url]: https://x.com/ComfyUI
+
+[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
+[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
+[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat
+[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat
+[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
+[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
+
+![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
+</div>
+
+ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
+
+## Get Started
+
+#### [Desktop Application](https://www.comfy.org/download)
+- The easiest way to get started.
+- Available on Windows & macOS.
+
+#### [Windows Portable Package](#installing)
+- Get the latest commits and completely portable.
+- Available on Windows.
+
+#### [Manual Install](#manual-install-windows-linux)
+Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
+
+## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- Image Models
+   - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
+   - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
+   - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
+   - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
+   - Pixart Alpha and Sigma
+   - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
+   - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
+   - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+   - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
+   - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
+   - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
+   - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
+   - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
+   - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
+- Image Editing Models
+   - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
+   - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
+   - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)
+   - [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model)
+- Video Models
+   - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
+   - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
+   - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
+   - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
+   - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
+   - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
+   - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)
+- Audio Models
+   - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+   - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- 3D Models
+   - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
-- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
+- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
-- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
+- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
+- Safe loading of ckpt, pt, pth, etc.. files.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
@@ -27,72 +103,119 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
 - [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
 - [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
-- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
 - [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
 - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
-- [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
-- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
-- Starts up very fast.
-- Works fully offline: will never download anything.
+- Works fully offline: core will never download anything unless you want to.
+- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview) disable with: `--disable-api-nodes`
 - [Config file](extra_model_paths.yaml.example) to set the search paths for models.
 
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
 
+## Release Process
+
+ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
+
+1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
+   - Releases a new stable version (e.g., v0.7.0) roughly every week.
+   - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
+   - Minor versions will be used for releases off the master branch.
+   - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
+   - Commits outside of the stable release tags may be very unstable and break many custom nodes.
+   - Serves as the foundation for the desktop release
+
+2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
+   - Builds a new release using the latest stable core version
+
+3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
+   - Weekly frontend updates are merged into the core repository
+   - Features are frozen for the upcoming core release
+   - Development continues for the next release cycle
+
 ## Shortcuts
 
 | Keybind                            | Explanation                                                                                                        |
 |------------------------------------|--------------------------------------------------------------------------------------------------------------------|
-| Ctrl + Enter                       | Queue up current graph for generation                                                                              |
-| Ctrl + Shift + Enter               | Queue up current graph as first for generation                                                                     |
-| Ctrl + Z/Ctrl + Y                  | Undo/Redo                                                                                                          |
-| Ctrl + S                           | Save workflow                                                                                                      |
-| Ctrl + O                           | Load workflow                                                                                                      |
-| Ctrl + A                           | Select all nodes                                                                                                   |
-| Alt + C                            | Collapse/uncollapse selected nodes                                                                                 |
-| Ctrl + M                           | Mute/unmute selected nodes                                                                                         |
-| Ctrl + B                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
-| Delete/Backspace                   | Delete selected nodes                                                                                              |
-| Ctrl + Backspace                   | Delete the current graph                                                                                           |
-| Space                              | Move the canvas around when held and moving the cursor                                                             |
-| Ctrl/Shift + Click                 | Add clicked node to selection                                                                                      |
-| Ctrl + C/Ctrl + V                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
-| Ctrl + C/Ctrl + Shift + V          | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
-| Shift + Drag                       | Move multiple selected nodes at the same time                                                                      |
-| Ctrl + D                           | Load default graph                                                                                                 |
-| Alt + `+`                          | Canvas Zoom in                                                                                                     |
-| Alt + `-`                          | Canvas Zoom out                                                                                                    |
-| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
-| Q                                  | Toggle visibility of the queue                                                                                     |
-| H                                  | Toggle visibility of history                                                                                       |
-| R                                  | Refresh graph                                                                                                      |
+| `Ctrl` + `Enter`                      | Queue up current graph for generation                                                                              |
+| `Ctrl` + `Shift` + `Enter`              | Queue up current graph as first for generation                                                                     |
+| `Ctrl` + `Alt` + `Enter`                | Cancel current generation                                                                                          |
+| `Ctrl` + `Z`/`Ctrl` + `Y`                 | Undo/Redo                                                                                                          |
+| `Ctrl` + `S`                          | Save workflow                                                                                                      |
+| `Ctrl` + `O`                          | Load workflow                                                                                                      |
+| `Ctrl` + `A`                          | Select all nodes                                                                                                   |
+| `Alt `+ `C`                           | Collapse/uncollapse selected nodes                                                                                 |
+| `Ctrl` + `M`                          | Mute/unmute selected nodes                                                                                         |
+| `Ctrl` + `B`                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
+| `Delete`/`Backspace`                   | Delete selected nodes                                                                                              |
+| `Ctrl` + `Backspace`                   | Delete the current graph                                                                                           |
+| `Space`                              | Move the canvas around when held and moving the cursor                                                             |
+| `Ctrl`/`Shift` + `Click`                 | Add clicked node to selection                                                                                      |
+| `Ctrl` + `C`/`Ctrl` + `V`                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
+| `Ctrl` + `C`/`Ctrl` + `Shift` + `V`          | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
+| `Shift` + `Drag`                       | Move multiple selected nodes at the same time                                                                      |
+| `Ctrl` + `D`                           | Load default graph                                                                                                 |
+| `Alt` + `+`                          | Canvas Zoom in                                                                                                     |
+| `Alt` + `-`                          | Canvas Zoom out                                                                                                    |
+| `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
+| `P`                                  | Pin/Unpin selected nodes                                                                                           |
+| `Ctrl` + `G`                           | Group selected nodes                                                                                               |
+| `Q`                                 | Toggle visibility of the queue                                                                                     |
+| `H`                                  | Toggle visibility of history                                                                                       |
+| `R`                                  | Refresh graph                                                                                                      |
+| `F`                                  | Show/Hide menu                                                                                                      |
+| `.`                                  | Fit view to selection (Whole graph when nothing is selected)                                                        |
 | Double-Click LMB                   | Open node quick search palette                                                                                     |
+| `Shift` + Drag                       | Move multiple wires at once                                                                                        |
+| `Ctrl` + `Alt` + LMB                   | Disconnect all wires from clicked slot                                                                             |
 
-Ctrl can also be replaced with Cmd instead for macOS users
+`Ctrl` can also be replaced with `Cmd` instead for macOS users
 
 # Installing
 
-## Windows
+## Windows Portable
 
 There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
 
-### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu121_or_cpu.7z)
+### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
 
-Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
+Simply download, extract with [7-Zip](https://7-zip.org) or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\models\
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
+The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
+
+#### Alternative Downloads:
+
+[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
+
+[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
+
+[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
+
 #### How do I share models between another UI and ComfyUI?
 
 See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
 
-## Jupyter Notebook
 
-To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
+## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)
+
+You can install and start ComfyUI using comfy-cli:
+```bash
+pip install comfy-cli
+comfy install
+```
 
 ## Manual Install (Windows, Linux)
 
+Python 3.14 works but you may encounter issues with the torch compile node. The free threaded variant is still missing some dependencies.
+
+Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
+
+torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
+
+### Instructions:
+
 Git clone this repo.
 
 Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
@@ -100,24 +223,54 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
 Put your VAE in: models/vae
 
 
-### AMD GPUs (Linux only)
+### AMD GPUs (Linux)
+
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
 
-This is the command to install the nightly with ROCm 6.0 which might have some performance improvements:
+This is the command to install the nightly with ROCm 7.1 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
+
+
+### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
+
+These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
+
+RDNA 3 (RX 7000 series):
+
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/```
+
+RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
+
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
+
+RDNA 4 (RX 9000 series):
+
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```
+
+### Intel GPUs (Windows and Linux)
+
+Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
+
+1. To install PyTorch xpu, use the following command:
+
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```
+
+This is the command to install the Pytorch xpu nightly which might have some performance improvements:
+
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
 
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```
 
-This is the command to install pytorch nightly instead which might have performance improvements:
+This is the command to install pytorch nightly instead which might have performance improvements.
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
 
 #### Troubleshooting
 
@@ -137,17 +290,6 @@ After this you should have everything installed and can proceed to running Comfy
 
 ### Others:
 
-#### Intel GPUs
-
-Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows:
-
-1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
-1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform.
-1. Install the packages for IPEX using the instructions provided in the Installation page for your platform.
-1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
-
-Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
-
 #### Apple Mac silicon
 
 You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
@@ -159,23 +301,55 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
 
-#### DirectML (AMD Cards on Windows)
+#### Ascend NPUs
 
-```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
+For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:
 
-### I already have another UI for Stable Diffusion installed do I really have to install all of these dependencies?
+1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
+2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
+3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
+4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
 
-You don't. If you have another UI installed and working with its own python venv you can use that venv to run ComfyUI. You can open up your favorite terminal and activate it:
+#### Cambricon MLUs
 
-```source path_to_other_sd_gui/venv/bin/activate```
+For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
 
-or on Windows:
+1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)
+2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
+3. Launch ComfyUI by running `python main.py`
 
-With Powershell: ```"path_to_other_sd_gui\venv\Scripts\Activate.ps1"```
+#### Iluvatar Corex
 
-With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"```
+For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
+
+1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
+2. Launch ComfyUI by running `python main.py`
+
+
+## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)
+
+**ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.
+
+### Setup
+
+1. Install the manager dependencies:
+   ```bash
+   pip install -r manager_requirements.txt
+   ```
+
+2. Enable the manager with the `--enable-manager` flag when running ComfyUI:
+   ```bash
+   python main.py --enable-manager
+   ```
+
+### Command Line Options
+
+| Flag | Description |
+|------|-------------|
+| `--enable-manager` | Enable ComfyUI-Manager |
+| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
+| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
 
-And then you can use that terminal to run ComfyUI without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI.
 
 # Running
 
@@ -189,6 +363,14 @@ For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.
 
 For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
 
+### AMD ROCm Tips
+
+You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
+
+```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
+
+You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
+
 # Notes
 
 Only parts of the graph that have an output with all the correct inputs will be executed.
@@ -212,25 +394,67 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
 
 Use ```--preview-method auto``` to enable previews.
 
-The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
 
 ## How to use TLS/SSL?
 Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
 
 Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
 
-> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. 
+> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
 <br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
 
 ## Support and dev channel
 
+[Discord](https://comfy.org/discord): Try the #help or #feedback channels.
+
 [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
 
 See also: [https://www.comfy.org/](https://www.comfy.org/)
 
+## Frontend Development
+
+As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.
+
+### Reporting Issues and Requesting Features
+
+For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently.
+
+### Using the Latest Frontend
+
+The new frontend is now the default for ComfyUI. However, please note:
+
+1. The frontend in the main ComfyUI repository is updated fortnightly.
+2. Daily releases are available in the separate frontend repository.
+
+To use the most up-to-date frontend version:
+
+1. For the latest daily release, launch ComfyUI with this command line argument:
+
+   ```
+   --front-end-version Comfy-Org/ComfyUI_frontend@latest
+   ```
+
+2. For a specific version, replace `latest` with the desired version number:
+
+   ```
+   --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
+   ```
+
+This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
+
+### Accessing the Legacy Frontend
+
+If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
+
+```
+--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
+```
+
+This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).
+
 # QA
 
 ### Which GPU should I buy for this?
 
 [See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
-

alembic.ini

@@ -0,0 +1,84 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts
+# Use forward slashes (/) also on windows to provide an os agnostic path
+script_location = alembic_db
+
+# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
+# Uncomment the line below if you want the files to be prepended with date and time
+# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
+# for all available tokens
+# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
+
+# sys.path path, will be prepended to sys.path if present.
+# defaults to the current working directory.
+prepend_sys_path = .
+
+# timezone to use when rendering the date within the migration file
+# as well as the filename.
+# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
+# Any required deps can installed by adding `alembic[tz]` to the pip requirements
+# string value is passed to ZoneInfo()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; This defaults
+# to alembic_db/versions.  When using multiple version
+# directories, initial revisions must be specified with --version-path.
+# The path separator used here should be the separator specified by "version_path_separator" below.
+# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions
+
+# version path separator; As mentioned above, this is the character used to split
+# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
+# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
+# Valid values for version_path_separator are:
+#
+# version_path_separator = :
+# version_path_separator = ;
+# version_path_separator = space
+# version_path_separator = newline
+#
+# Use os.pathsep. Default configuration used for new projects.
+version_path_separator = os
+
+# set to 'true' to search source files recursively
+# in each "version_locations" directory
+# new in Alembic version 1.10
+# recursive_version_locations = false
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+sqlalchemy.url = sqlite:///user/comfyui.db
+
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts.  See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks = black
+# black.type = console_scripts
+# black.entrypoint = black
+# black.options = -l 79 REVISION_SCRIPT_FILENAME
+
+# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
+# hooks = ruff
+# ruff.type = exec
+# ruff.executable = %(here)s/.venv/bin/ruff
+# ruff.options = check --fix REVISION_SCRIPT_FILENAME

alembic_db/README.md

@@ -0,0 +1,4 @@
+## Generate new revision
+
+1. Update models in `/app/database/models.py`
+2. Run `alembic revision --autogenerate -m "{your message}"`

alembic_db/env.py

@@ -0,0 +1,64 @@
+from sqlalchemy import engine_from_config
+from sqlalchemy import pool
+
+from alembic import context
+
+# this is the Alembic Config object, which provides
+# access to the values within the .ini file in use.
+config = context.config
+
+
+from app.database.models import Base
+target_metadata = Base.metadata
+
+# other values from the config, defined by the needs of env.py,
+# can be acquired:
+# my_important_option = config.get_main_option("my_important_option")
+# ... etc.
+
+
+def run_migrations_offline() -> None:
+    """Run migrations in 'offline' mode.
+    This configures the context with just a URL
+    and not an Engine, though an Engine is acceptable
+    here as well.  By skipping the Engine creation
+    we don't even need a DBAPI to be available.
+    Calls to context.execute() here emit the given string to the
+    script output.
+    """
+    url = config.get_main_option("sqlalchemy.url")
+    context.configure(
+        url=url,
+        target_metadata=target_metadata,
+        literal_binds=True,
+        dialect_opts={"paramstyle": "named"},
+    )
+
+    with context.begin_transaction():
+        context.run_migrations()
+
+
+def run_migrations_online() -> None:
+    """Run migrations in 'online' mode.
+    In this scenario we need to create an Engine
+    and associate a connection with the context.
+    """
+    connectable = engine_from_config(
+        config.get_section(config.config_ini_section, {}),
+        prefix="sqlalchemy.",
+        poolclass=pool.NullPool,
+    )
+
+    with connectable.connect() as connection:
+        context.configure(
+            connection=connection, target_metadata=target_metadata
+        )
+
+        with context.begin_transaction():
+            context.run_migrations()
+
+
+if context.is_offline_mode():
+    run_migrations_offline()
+else:
+    run_migrations_online()

alembic_db/script.py.mako

@@ -0,0 +1,28 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision: str = ${repr(up_revision)}
+down_revision: Union[str, None] = ${repr(down_revision)}
+branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
+depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    ${upgrades if upgrades else "pass"}
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    ${downgrades if downgrades else "pass"}

alembic_db/versions/0001_assets.py

@@ -0,0 +1,174 @@
+"""
+Initial assets schema
+Revision ID: 0001_assets
+Revises: None
+Create Date: 2025-12-10 00:00:00
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0001_assets"
+down_revision = None
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # ASSETS: content identity
+    op.create_table(
+        "assets",
+        sa.Column("id", sa.String(length=36), primary_key=True),
+        sa.Column("hash", sa.String(length=256), nullable=True),
+        sa.Column("size_bytes", sa.BigInteger(), nullable=False, server_default="0"),
+        sa.Column("mime_type", sa.String(length=255), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
+        sa.CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
+    )
+    op.create_index("uq_assets_hash", "assets", ["hash"], unique=True)
+    op.create_index("ix_assets_mime_type", "assets", ["mime_type"])
+
+    # ASSETS_INFO: user-visible references
+    op.create_table(
+        "assets_info",
+        sa.Column("id", sa.String(length=36), primary_key=True),
+        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
+        sa.Column("name", sa.String(length=512), nullable=False),
+        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False),
+        sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True),
+        sa.Column("user_metadata", sa.JSON(), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
+        sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
+    )
+    op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"])
+    op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"])
+    op.create_index("ix_assets_info_name", "assets_info", ["name"])
+    op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"])
+    op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"])
+    op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"])
+
+    # TAGS: normalized tag vocabulary
+    op.create_table(
+        "tags",
+        sa.Column("name", sa.String(length=512), primary_key=True),
+        sa.Column("tag_type", sa.String(length=32), nullable=False, server_default="user"),
+        sa.CheckConstraint("name = lower(name)", name="ck_tags_lowercase"),
+    )
+    op.create_index("ix_tags_tag_type", "tags", ["tag_type"])
+
+    # ASSET_INFO_TAGS: many-to-many for tags on AssetInfo
+    op.create_table(
+        "asset_info_tags",
+        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False),
+        sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"),
+        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
+        sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"),
+    )
+    op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"])
+    op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"])
+
+    # ASSET_CACHE_STATE: N:1 local cache rows per Asset
+    op.create_table(
+        "asset_cache_state",
+        sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
+        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("file_path", sa.Text(), nullable=False),  # absolute local path to cached file
+        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
+        sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")),
+        sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
+        sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    )
+    op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"])
+    op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"])
+
+    # ASSET_INFO_META: typed KV projection of user_metadata for filtering/sorting
+    op.create_table(
+        "asset_info_meta",
+        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("key", sa.String(length=256), nullable=False),
+        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("val_str", sa.String(length=2048), nullable=True),
+        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
+        sa.Column("val_bool", sa.Boolean(), nullable=True),
+        sa.Column("val_json", sa.JSON(), nullable=True),
+        sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"),
+    )
+    op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"])
+    op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"])
+    op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"])
+    op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"])
+
+    # Tags vocabulary
+    tags_table = sa.table(
+        "tags",
+        sa.column("name", sa.String(length=512)),
+        sa.column("tag_type", sa.String()),
+    )
+    op.bulk_insert(
+        tags_table,
+        [
+            {"name": "models", "tag_type": "system"},
+            {"name": "input", "tag_type": "system"},
+            {"name": "output", "tag_type": "system"},
+
+            {"name": "configs", "tag_type": "system"},
+            {"name": "checkpoints", "tag_type": "system"},
+            {"name": "loras", "tag_type": "system"},
+            {"name": "vae", "tag_type": "system"},
+            {"name": "text_encoders", "tag_type": "system"},
+            {"name": "diffusion_models", "tag_type": "system"},
+            {"name": "clip_vision", "tag_type": "system"},
+            {"name": "style_models", "tag_type": "system"},
+            {"name": "embeddings", "tag_type": "system"},
+            {"name": "diffusers", "tag_type": "system"},
+            {"name": "vae_approx", "tag_type": "system"},
+            {"name": "controlnet", "tag_type": "system"},
+            {"name": "gligen", "tag_type": "system"},
+            {"name": "upscale_models", "tag_type": "system"},
+            {"name": "hypernetworks", "tag_type": "system"},
+            {"name": "photomaker", "tag_type": "system"},
+            {"name": "classifiers", "tag_type": "system"},
+
+            {"name": "encoder", "tag_type": "system"},
+            {"name": "decoder", "tag_type": "system"},
+
+            {"name": "missing", "tag_type": "system"},
+            {"name": "rescan", "tag_type": "system"},
+        ],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta")
+    op.drop_table("asset_info_meta")
+
+    op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state")
+    op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state")
+    op.drop_constraint("uq_asset_cache_state_file_path", table_name="asset_cache_state")
+    op.drop_table("asset_cache_state")
+
+    op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags")
+    op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags")
+    op.drop_table("asset_info_tags")
+
+    op.drop_index("ix_tags_tag_type", table_name="tags")
+    op.drop_table("tags")
+
+    op.drop_constraint("uq_assets_info_asset_owner_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_owner_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_last_access_time", table_name="assets_info")
+    op.drop_index("ix_assets_info_created_at", table_name="assets_info")
+    op.drop_index("ix_assets_info_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_asset_id", table_name="assets_info")
+    op.drop_index("ix_assets_info_owner_id", table_name="assets_info")
+    op.drop_table("assets_info")
+
+    op.drop_index("uq_assets_hash", table_name="assets")
+    op.drop_index("ix_assets_mime_type", table_name="assets")
+    op.drop_table("assets")

api_server/routes/internal/README.md

@@ -0,0 +1,3 @@
+# ComfyUI Internal Routes
+
+All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.

api_server/routes/internal/internal_routes.py

@@ -0,0 +1,78 @@
+from aiohttp import web
+from typing import Optional
+from folder_paths import folder_names_and_paths, get_directory_by_type
+from api_server.services.terminal_service import TerminalService
+import app.logger
+import os
+
+class InternalRoutes:
+    '''
+    The top level web router for internal routes: /internal/*
+    The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
+    Check README.md for more information.
+    '''
+
+    def __init__(self, prompt_server):
+        self.routes: web.RouteTableDef = web.RouteTableDef()
+        self._app: Optional[web.Application] = None
+        self.prompt_server = prompt_server
+        self.terminal_service = TerminalService(prompt_server)
+
+    def setup_routes(self):
+        @self.routes.get('/logs')
+        async def get_logs(request):
+            return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
+
+        @self.routes.get('/logs/raw')
+        async def get_raw_logs(request):
+            self.terminal_service.update_size()
+            return web.json_response({
+                "entries": list(app.logger.get_logs()),
+                "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows}
+            })
+
+        @self.routes.patch('/logs/subscribe')
+        async def subscribe_logs(request):
+            json_data = await request.json()
+            client_id = json_data["clientId"]
+            enabled = json_data["enabled"]
+            if enabled:
+                self.terminal_service.subscribe(client_id)
+            else:
+                self.terminal_service.unsubscribe(client_id)
+
+            return web.Response(status=200)
+
+
+        @self.routes.get('/folder_paths')
+        async def get_folder_paths(request):
+            response = {}
+            for key in folder_names_and_paths:
+                response[key] = folder_names_and_paths[key][0]
+            return web.json_response(response)
+
+        @self.routes.get('/files/{directory_type}')
+        async def get_files(request: web.Request) -> web.Response:
+            directory_type = request.match_info['directory_type']
+            if directory_type not in ("output", "input", "temp"):
+                return web.json_response({"error": "Invalid directory type"}, status=400)
+
+            directory = get_directory_by_type(directory_type)
+
+            def is_visible_file(entry: os.DirEntry) -> bool:
+                """Filter out hidden files (e.g., .DS_Store on macOS)."""
+                return entry.is_file() and not entry.name.startswith('.')
+
+            sorted_files = sorted(
+                (entry for entry in os.scandir(directory) if is_visible_file(entry)),
+                key=lambda entry: -entry.stat().st_mtime
+            )
+            return web.json_response([entry.name for entry in sorted_files], status=200)
+
+
+    def get_app(self):
+        if self._app is None:
+            self._app = web.Application()
+            self.setup_routes()
+            self._app.add_routes(self.routes)
+        return self._app

api_server/services/terminal_service.py

@@ -0,0 +1,60 @@
+from app.logger import on_flush
+import os
+import shutil
+
+
+class TerminalService:
+    def __init__(self, server):
+        self.server = server
+        self.cols = None
+        self.rows = None
+        self.subscriptions = set()
+        on_flush(self.send_messages)
+
+    def get_terminal_size(self):
+        try:
+            size = os.get_terminal_size()
+            return (size.columns, size.lines)
+        except OSError:
+            try:
+                size = shutil.get_terminal_size()
+                return (size.columns, size.lines)
+            except OSError:
+                return (80, 24)  # fallback to 80x24
+
+    def update_size(self):
+        columns, lines = self.get_terminal_size()
+        changed = False
+
+        if columns != self.cols:
+            self.cols = columns
+            changed = True
+
+        if lines != self.rows:
+            self.rows = lines
+            changed = True
+
+        if changed:
+            return {"cols": self.cols, "rows": self.rows}
+
+        return None
+
+    def subscribe(self, client_id):
+        self.subscriptions.add(client_id)
+
+    def unsubscribe(self, client_id):
+        self.subscriptions.discard(client_id)
+
+    def send_messages(self, entries):
+        if not len(entries) or not len(self.subscriptions):
+            return
+
+        new_size = self.update_size()
+
+        for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
+            if client_id not in self.server.sockets:
+                # Automatically unsub if the socket has disconnected
+                self.unsubscribe(client_id)
+                continue
+
+            self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id)

api_server/utils/file_operations.py

@@ -0,0 +1,42 @@
+import os
+from typing import List, Union, TypedDict, Literal
+from typing_extensions import TypeGuard
+class FileInfo(TypedDict):
+    name: str
+    path: str
+    type: Literal["file"]
+    size: int
+
+class DirectoryInfo(TypedDict):
+    name: str
+    path: str
+    type: Literal["directory"]
+
+FileSystemItem = Union[FileInfo, DirectoryInfo]
+
+def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
+    return item["type"] == "file"
+
+class FileSystemOperations:
+    @staticmethod
+    def walk_directory(directory: str) -> List[FileSystemItem]:
+        file_list: List[FileSystemItem] = []
+        for root, dirs, files in os.walk(directory):
+            for name in files:
+                file_path = os.path.join(root, name)
+                relative_path = os.path.relpath(file_path, directory)
+                file_list.append({
+                    "name": name,
+                    "path": relative_path,
+                    "type": "file",
+                    "size": os.path.getsize(file_path)
+                })
+            for name in dirs:
+                dir_path = os.path.join(root, name)
+                relative_path = os.path.relpath(dir_path, directory)
+                file_list.append({
+                    "name": name,
+                    "path": relative_path,
+                    "type": "directory"
+                })
+        return file_list

app/app_settings.py

@@ -1,6 +1,7 @@
 import os
 import json
 from aiohttp import web
+import logging
 
 
 class AppSettings():
@@ -8,11 +9,21 @@ class AppSettings():
         self.user_manager = user_manager
 
     def get_settings(self, request):
-        file = self.user_manager.get_request_user_filepath(
-            request, "comfy.settings.json")
+        try:
+            file = self.user_manager.get_request_user_filepath(
+                request,
+                "comfy.settings.json"
+            )
+        except KeyError as e:
+            logging.error("User settings not found.")
+            raise web.HTTPUnauthorized() from e
         if os.path.isfile(file):
-            with open(file) as f:
-                return json.load(f)
+            try:
+                with open(file) as f:
+                    return json.load(f)
+            except:
+                logging.error(f"The user settings file is corrupted: {file}")
+                return {}
         else:
             return {}
 
@@ -51,4 +62,4 @@ class AppSettings():
             settings = self.get_settings(request)
             settings[setting_id] = await request.json()
             self.save_settings(request, settings)
-            return web.Response(status=200)
+            return web.Response(status=200)

app/assets/api/routes.py

@@ -0,0 +1,102 @@
+import logging
+import uuid
+from aiohttp import web
+
+from pydantic import ValidationError
+
+import app.assets.manager as manager
+from app import user_manager
+from app.assets.api import schemas_in
+from app.assets.helpers import get_query_dict
+
+ROUTES = web.RouteTableDef()
+USER_MANAGER: user_manager.UserManager | None = None
+
+# UUID regex (canonical hyphenated form, case-insensitive)
+UUID_RE = r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
+
+def register_assets_system(app: web.Application, user_manager_instance: user_manager.UserManager) -> None:
+    global USER_MANAGER
+    USER_MANAGER = user_manager_instance
+    app.add_routes(ROUTES)
+
+def _error_response(status: int, code: str, message: str, details: dict | None = None) -> web.Response:
+    return web.json_response({"error": {"code": code, "message": message, "details": details or {}}}, status=status)
+
+
+def _validation_error_response(code: str, ve: ValidationError) -> web.Response:
+    return _error_response(400, code, "Validation failed.", {"errors": ve.json()})
+
+
+@ROUTES.get("/api/assets")
+async def list_assets(request: web.Request) -> web.Response:
+    """
+    GET request to list assets.
+    """
+    query_dict = get_query_dict(request)
+    try:
+        q = schemas_in.ListAssetsQuery.model_validate(query_dict)
+    except ValidationError as ve:
+        return _validation_error_response("INVALID_QUERY", ve)
+
+    payload = manager.list_assets(
+        include_tags=q.include_tags,
+        exclude_tags=q.exclude_tags,
+        name_contains=q.name_contains,
+        metadata_filter=q.metadata_filter,
+        limit=q.limit,
+        offset=q.offset,
+        sort=q.sort,
+        order=q.order,
+        owner_id=USER_MANAGER.get_request_user_id(request),
+    )
+    return web.json_response(payload.model_dump(mode="json"))
+
+
+@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}")
+async def get_asset(request: web.Request) -> web.Response:
+    """
+    GET request to get an asset's info as JSON.
+    """
+    asset_info_id = str(uuid.UUID(request.match_info["id"]))
+    try:
+        result = manager.get_asset(
+            asset_info_id=asset_info_id,
+            owner_id=USER_MANAGER.get_request_user_id(request),
+        )
+    except ValueError as e:
+        return _error_response(404, "ASSET_NOT_FOUND", str(e), {"id": asset_info_id})
+    except Exception:
+        logging.exception(
+            "get_asset failed for asset_info_id=%s, owner_id=%s",
+            asset_info_id,
+            USER_MANAGER.get_request_user_id(request),
+        )
+        return _error_response(500, "INTERNAL", "Unexpected server error.")
+    return web.json_response(result.model_dump(mode="json"), status=200)
+
+
+@ROUTES.get("/api/tags")
+async def get_tags(request: web.Request) -> web.Response:
+    """
+    GET request to list all tags based on query parameters.
+    """
+    query_map = dict(request.rel_url.query)
+
+    try:
+        query = schemas_in.TagsListQuery.model_validate(query_map)
+    except ValidationError as e:
+        return web.json_response(
+            {"error": {"code": "INVALID_QUERY", "message": "Invalid query parameters", "details": e.errors()}},
+            status=400,
+        )
+
+    result = manager.list_tags(
+        prefix=query.prefix,
+        limit=query.limit,
+        offset=query.offset,
+        order=query.order,
+        include_zero=query.include_zero,
+        owner_id=USER_MANAGER.get_request_user_id(request),
+    )
+    return web.json_response(result.model_dump(mode="json"))

app/assets/api/schemas_in.py

@@ -0,0 +1,94 @@
+import json
+import uuid
+from typing import Any, Literal
+
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    conint,
+    field_validator,
+)
+
+
+class ListAssetsQuery(BaseModel):
+    include_tags: list[str] = Field(default_factory=list)
+    exclude_tags: list[str] = Field(default_factory=list)
+    name_contains: str | None = None
+
+    # Accept either a JSON string (query param) or a dict
+    metadata_filter: dict[str, Any] | None = None
+
+    limit: conint(ge=1, le=500) = 20
+    offset: conint(ge=0) = 0
+
+    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = "created_at"
+    order: Literal["asc", "desc"] = "desc"
+
+    @field_validator("include_tags", "exclude_tags", mode="before")
+    @classmethod
+    def _split_csv_tags(cls, v):
+        # Accept "a,b,c" or ["a","b"] (we are liberal in what we accept)
+        if v is None:
+            return []
+        if isinstance(v, str):
+            return [t.strip() for t in v.split(",") if t.strip()]
+        if isinstance(v, list):
+            out: list[str] = []
+            for item in v:
+                if isinstance(item, str):
+                    out.extend([t.strip() for t in item.split(",") if t.strip()])
+            return out
+        return v
+
+    @field_validator("metadata_filter", mode="before")
+    @classmethod
+    def _parse_metadata_json(cls, v):
+        if v is None or isinstance(v, dict):
+            return v
+        if isinstance(v, str) and v.strip():
+            try:
+                parsed = json.loads(v)
+            except Exception as e:
+                raise ValueError(f"metadata_filter must be JSON: {e}") from e
+            if not isinstance(parsed, dict):
+                raise ValueError("metadata_filter must be a JSON object")
+            return parsed
+        return None
+
+
+class TagsListQuery(BaseModel):
+    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
+
+    prefix: str | None = Field(None, min_length=1, max_length=256)
+    limit: int = Field(100, ge=1, le=1000)
+    offset: int = Field(0, ge=0, le=10_000_000)
+    order: Literal["count_desc", "name_asc"] = "count_desc"
+    include_zero: bool = True
+
+    @field_validator("prefix")
+    @classmethod
+    def normalize_prefix(cls, v: str | None) -> str | None:
+        if v is None:
+            return v
+        v = v.strip()
+        return v.lower() or None
+
+
+class SetPreviewBody(BaseModel):
+    """Set or clear the preview for an AssetInfo. Provide an Asset.id or null."""
+    preview_id: str | None = None
+
+    @field_validator("preview_id", mode="before")
+    @classmethod
+    def _norm_uuid(cls, v):
+        if v is None:
+            return None
+        s = str(v).strip()
+        if not s:
+            return None
+        try:
+            uuid.UUID(s)
+        except Exception:
+            raise ValueError("preview_id must be a UUID")
+        return s

app/assets/api/schemas_out.py

@@ -0,0 +1,60 @@
+from datetime import datetime
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field, field_serializer
+
+
+class AssetSummary(BaseModel):
+    id: str
+    name: str
+    asset_hash: str | None = None
+    size: int | None = None
+    mime_type: str | None = None
+    tags: list[str] = Field(default_factory=list)
+    preview_url: str | None = None
+    created_at: datetime | None = None
+    updated_at: datetime | None = None
+    last_access_time: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+    @field_serializer("created_at", "updated_at", "last_access_time")
+    def _ser_dt(self, v: datetime | None, _info):
+        return v.isoformat() if v else None
+
+
+class AssetsList(BaseModel):
+    assets: list[AssetSummary]
+    total: int
+    has_more: bool
+
+
+class AssetDetail(BaseModel):
+    id: str
+    name: str
+    asset_hash: str | None = None
+    size: int | None = None
+    mime_type: str | None = None
+    tags: list[str] = Field(default_factory=list)
+    user_metadata: dict[str, Any] = Field(default_factory=dict)
+    preview_id: str | None = None
+    created_at: datetime | None = None
+    last_access_time: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+    @field_serializer("created_at", "last_access_time")
+    def _ser_dt(self, v: datetime | None, _info):
+        return v.isoformat() if v else None
+
+
+class TagUsage(BaseModel):
+    name: str
+    count: int
+    type: str
+
+
+class TagsList(BaseModel):
+    tags: list[TagUsage] = Field(default_factory=list)
+    total: int
+    has_more: bool

app/assets/database/bulk_ops.py

@@ -0,0 +1,204 @@
+import os
+import uuid
+import sqlalchemy
+from typing import Iterable
+from sqlalchemy.orm import Session
+from sqlalchemy.dialects import sqlite
+
+from app.assets.helpers import utcnow
+from app.assets.database.models import Asset, AssetCacheState, AssetInfo, AssetInfoTag, AssetInfoMeta
+
+MAX_BIND_PARAMS = 800
+
+def _chunk_rows(rows: list[dict], cols_per_row: int, max_bind_params: int) -> Iterable[list[dict]]:
+    if not rows:
+        return []
+    rows_per_stmt = max(1, max_bind_params // max(1, cols_per_row))
+    for i in range(0, len(rows), rows_per_stmt):
+        yield rows[i:i + rows_per_stmt]
+
+def _iter_chunks(seq, n: int):
+    for i in range(0, len(seq), n):
+        yield seq[i:i + n]
+
+def _rows_per_stmt(cols: int) -> int:
+    return max(1, MAX_BIND_PARAMS // max(1, cols))
+
+
+def seed_from_paths_batch(
+    session: Session,
+    *,
+    specs: list[dict],
+    owner_id: str = "",
+) -> dict:
+    """Each spec is a dict with keys:
+      - abs_path: str
+      - size_bytes: int
+      - mtime_ns: int
+      - info_name: str
+      - tags: list[str]
+      - fname: Optional[str]
+    """
+    if not specs:
+        return {"inserted_infos": 0, "won_states": 0, "lost_states": 0}
+
+    now = utcnow()
+    asset_rows: list[dict] = []
+    state_rows: list[dict] = []
+    path_to_asset: dict[str, str] = {}
+    asset_to_info: dict[str, dict] = {}  # asset_id -> prepared info row
+    path_list: list[str] = []
+
+    for sp in specs:
+        ap = os.path.abspath(sp["abs_path"])
+        aid = str(uuid.uuid4())
+        iid = str(uuid.uuid4())
+        path_list.append(ap)
+        path_to_asset[ap] = aid
+
+        asset_rows.append(
+            {
+                "id": aid,
+                "hash": None,
+                "size_bytes": sp["size_bytes"],
+                "mime_type": None,
+                "created_at": now,
+            }
+        )
+        state_rows.append(
+            {
+                "asset_id": aid,
+                "file_path": ap,
+                "mtime_ns": sp["mtime_ns"],
+            }
+        )
+        asset_to_info[aid] = {
+            "id": iid,
+            "owner_id": owner_id,
+            "name": sp["info_name"],
+            "asset_id": aid,
+            "preview_id": None,
+            "user_metadata": {"filename": sp["fname"]} if sp["fname"] else None,
+            "created_at": now,
+            "updated_at": now,
+            "last_access_time": now,
+            "_tags": sp["tags"],
+            "_filename": sp["fname"],
+        }
+
+    # insert all seed Assets (hash=NULL)
+    ins_asset = sqlite.insert(Asset)
+    for chunk in _iter_chunks(asset_rows, _rows_per_stmt(5)):
+        session.execute(ins_asset, chunk)
+
+    # try to claim AssetCacheState (file_path)
+    # Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
+    ins_state = (
+        sqlite.insert(AssetCacheState)
+        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
+    )
+    for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
+        session.execute(ins_state, chunk)
+
+    # Query to find which of our paths won (were actually inserted)
+    winners_by_path: set[str] = set()
+    for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
+        result = session.execute(
+            sqlalchemy.select(AssetCacheState.file_path)
+            .where(AssetCacheState.file_path.in_(chunk))
+            .where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
+        )
+        winners_by_path.update(result.scalars().all())
+
+    all_paths_set = set(path_list)
+    losers_by_path = all_paths_set - winners_by_path
+    lost_assets = [path_to_asset[p] for p in losers_by_path]
+    if lost_assets:  # losers get their Asset removed
+        for id_chunk in _iter_chunks(lost_assets, MAX_BIND_PARAMS):
+            session.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(id_chunk)))
+
+    if not winners_by_path:
+        return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
+
+    # insert AssetInfo only for winners
+    # Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
+    winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
+    ins_info = (
+        sqlite.insert(AssetInfo)
+        .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
+    )
+    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
+        session.execute(ins_info, chunk)
+
+    # Query to find which info rows were actually inserted (by matching our generated IDs)
+    all_info_ids = [row["id"] for row in winner_info_rows]
+    inserted_info_ids: set[str] = set()
+    for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
+        )
+        inserted_info_ids.update(result.scalars().all())
+
+    # build and insert tag + meta rows for the AssetInfo
+    tag_rows: list[dict] = []
+    meta_rows: list[dict] = []
+    if inserted_info_ids:
+        for row in winner_info_rows:
+            iid = row["id"]
+            if iid not in inserted_info_ids:
+                continue
+            for t in row["_tags"]:
+                tag_rows.append({
+                    "asset_info_id": iid,
+                    "tag_name": t,
+                    "origin": "automatic",
+                    "added_at": now,
+                })
+            if row["_filename"]:
+                meta_rows.append(
+                    {
+                        "asset_info_id": iid,
+                        "key": "filename",
+                        "ordinal": 0,
+                        "val_str": row["_filename"],
+                        "val_num": None,
+                        "val_bool": None,
+                        "val_json": None,
+                    }
+                )
+
+    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=meta_rows, max_bind_params=MAX_BIND_PARAMS)
+    return {
+        "inserted_infos": len(inserted_info_ids),
+        "won_states": len(winners_by_path),
+        "lost_states": len(losers_by_path),
+    }
+
+
+def bulk_insert_tags_and_meta(
+    session: Session,
+    *,
+    tag_rows: list[dict],
+    meta_rows: list[dict],
+    max_bind_params: int,
+) -> None:
+    """Batch insert into asset_info_tags and asset_info_meta with ON CONFLICT DO NOTHING.
+    - tag_rows keys: asset_info_id, tag_name, origin, added_at
+    - meta_rows keys: asset_info_id, key, ordinal, val_str, val_num, val_bool, val_json
+    """
+    if tag_rows:
+        ins_links = (
+            sqlite.insert(AssetInfoTag)
+            .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
+        )
+        for chunk in _chunk_rows(tag_rows, cols_per_row=4, max_bind_params=max_bind_params):
+            session.execute(ins_links, chunk)
+    if meta_rows:
+        ins_meta = (
+            sqlite.insert(AssetInfoMeta)
+            .on_conflict_do_nothing(
+                index_elements=[AssetInfoMeta.asset_info_id, AssetInfoMeta.key, AssetInfoMeta.ordinal]
+            )
+        )
+        for chunk in _chunk_rows(meta_rows, cols_per_row=7, max_bind_params=max_bind_params):
+            session.execute(ins_meta, chunk)

app/assets/database/models.py

@@ -0,0 +1,233 @@
+from __future__ import annotations
+
+import uuid
+from datetime import datetime
+
+from typing import Any
+from sqlalchemy import (
+    JSON,
+    BigInteger,
+    Boolean,
+    CheckConstraint,
+    DateTime,
+    ForeignKey,
+    Index,
+    Integer,
+    Numeric,
+    String,
+    Text,
+    UniqueConstraint,
+)
+from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
+
+from app.assets.helpers import utcnow
+from app.database.models import to_dict, Base
+
+
+class Asset(Base):
+    __tablename__ = "assets"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
+    size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
+    mime_type: Mapped[str | None] = mapped_column(String(255))
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=utcnow
+    )
+
+    infos: Mapped[list[AssetInfo]] = relationship(
+        "AssetInfo",
+        back_populates="asset",
+        primaryjoin=lambda: Asset.id == foreign(AssetInfo.asset_id),
+        foreign_keys=lambda: [AssetInfo.asset_id],
+        cascade="all,delete-orphan",
+        passive_deletes=True,
+    )
+
+    preview_of: Mapped[list[AssetInfo]] = relationship(
+        "AssetInfo",
+        back_populates="preview_asset",
+        primaryjoin=lambda: Asset.id == foreign(AssetInfo.preview_id),
+        foreign_keys=lambda: [AssetInfo.preview_id],
+        viewonly=True,
+    )
+
+    cache_states: Mapped[list[AssetCacheState]] = relationship(
+        back_populates="asset",
+        cascade="all, delete-orphan",
+        passive_deletes=True,
+    )
+
+    __table_args__ = (
+        Index("uq_assets_hash", "hash", unique=True),
+        Index("ix_assets_mime_type", "mime_type"),
+        CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        return to_dict(self, include_none=include_none)
+
+    def __repr__(self) -> str:
+        return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
+
+
+class AssetCacheState(Base):
+    __tablename__ = "asset_cache_state"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False)
+    file_path: Mapped[str] = mapped_column(Text, nullable=False)
+    mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
+    needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+
+    asset: Mapped[Asset] = relationship(back_populates="cache_states")
+
+    __table_args__ = (
+        Index("ix_asset_cache_state_file_path", "file_path"),
+        Index("ix_asset_cache_state_asset_id", "asset_id"),
+        CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
+        UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        return to_dict(self, include_none=include_none)
+
+    def __repr__(self) -> str:
+        return f"<AssetCacheState id={self.id} asset_id={self.asset_id} path={self.file_path!r}>"
+
+
+class AssetInfo(Base):
+    __tablename__ = "assets_info"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
+    name: Mapped[str] = mapped_column(String(512), nullable=False)
+    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False)
+    preview_id: Mapped[str | None] = mapped_column(String(36), ForeignKey("assets.id", ondelete="SET NULL"))
+    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(JSON(none_as_null=True))
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    last_access_time: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+
+    asset: Mapped[Asset] = relationship(
+        "Asset",
+        back_populates="infos",
+        foreign_keys=[asset_id],
+        lazy="selectin",
+    )
+    preview_asset: Mapped[Asset | None] = relationship(
+        "Asset",
+        back_populates="preview_of",
+        foreign_keys=[preview_id],
+    )
+
+    metadata_entries: Mapped[list[AssetInfoMeta]] = relationship(
+        back_populates="asset_info",
+        cascade="all,delete-orphan",
+        passive_deletes=True,
+    )
+
+    tag_links: Mapped[list[AssetInfoTag]] = relationship(
+        back_populates="asset_info",
+        cascade="all,delete-orphan",
+        passive_deletes=True,
+        overlaps="tags,asset_infos",
+    )
+
+    tags: Mapped[list[Tag]] = relationship(
+        secondary="asset_info_tags",
+        back_populates="asset_infos",
+        lazy="selectin",
+        viewonly=True,
+        overlaps="tag_links,asset_info_links,asset_infos,tag",
+    )
+
+    __table_args__ = (
+        UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
+        Index("ix_assets_info_owner_name", "owner_id", "name"),
+        Index("ix_assets_info_owner_id", "owner_id"),
+        Index("ix_assets_info_asset_id", "asset_id"),
+        Index("ix_assets_info_name", "name"),
+        Index("ix_assets_info_created_at", "created_at"),
+        Index("ix_assets_info_last_access_time", "last_access_time"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        data = to_dict(self, include_none=include_none)
+        data["tags"] = [t.name for t in self.tags]
+        return data
+
+    def __repr__(self) -> str:
+        return f"<AssetInfo id={self.id} name={self.name!r} asset_id={self.asset_id}>"
+
+
+class AssetInfoMeta(Base):
+    __tablename__ = "asset_info_meta"
+
+    asset_info_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
+    )
+    key: Mapped[str] = mapped_column(String(256), primary_key=True)
+    ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
+
+    val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True)
+    val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True)
+    val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
+    val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
+
+    asset_info: Mapped[AssetInfo] = relationship(back_populates="metadata_entries")
+
+    __table_args__ = (
+        Index("ix_asset_info_meta_key", "key"),
+        Index("ix_asset_info_meta_key_val_str", "key", "val_str"),
+        Index("ix_asset_info_meta_key_val_num", "key", "val_num"),
+        Index("ix_asset_info_meta_key_val_bool", "key", "val_bool"),
+    )
+
+
+class AssetInfoTag(Base):
+    __tablename__ = "asset_info_tags"
+
+    asset_info_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
+    )
+    tag_name: Mapped[str] = mapped_column(
+        String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
+    )
+    origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
+    added_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=utcnow
+    )
+
+    asset_info: Mapped[AssetInfo] = relationship(back_populates="tag_links")
+    tag: Mapped[Tag] = relationship(back_populates="asset_info_links")
+
+    __table_args__ = (
+        Index("ix_asset_info_tags_tag_name", "tag_name"),
+        Index("ix_asset_info_tags_asset_info_id", "asset_info_id"),
+    )
+
+
+class Tag(Base):
+    __tablename__ = "tags"
+
+    name: Mapped[str] = mapped_column(String(512), primary_key=True)
+    tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
+
+    asset_info_links: Mapped[list[AssetInfoTag]] = relationship(
+        back_populates="tag",
+        overlaps="asset_infos,tags",
+    )
+    asset_infos: Mapped[list[AssetInfo]] = relationship(
+        secondary="asset_info_tags",
+        back_populates="tags",
+        viewonly=True,
+        overlaps="asset_info_links,tag_links,tags,asset_info",
+    )
+
+    __table_args__ = (
+        Index("ix_tags_tag_type", "tag_type"),
+    )
+
+    def __repr__(self) -> str:
+        return f"<Tag {self.name}>"

app/assets/database/queries.py

@@ -0,0 +1,267 @@
+import sqlalchemy as sa
+from collections import defaultdict
+from sqlalchemy import select, exists, func
+from sqlalchemy.orm import Session, contains_eager, noload
+from app.assets.database.models import Asset, AssetInfo, AssetInfoMeta, AssetInfoTag, Tag
+from app.assets.helpers import escape_like_prefix, normalize_tags
+from typing import Sequence
+
+
+def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
+    """Build owner visibility predicate for reads. Owner-less rows are visible to everyone."""
+    owner_id = (owner_id or "").strip()
+    if owner_id == "":
+        return AssetInfo.owner_id == ""
+    return AssetInfo.owner_id.in_(["", owner_id])
+
+
+def apply_tag_filters(
+    stmt: sa.sql.Select,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+) -> sa.sql.Select:
+    """include_tags: every tag must be present; exclude_tags: none may be present."""
+    include_tags = normalize_tags(include_tags)
+    exclude_tags = normalize_tags(exclude_tags)
+
+    if include_tags:
+        for tag_name in include_tags:
+            stmt = stmt.where(
+                exists().where(
+                    (AssetInfoTag.asset_info_id == AssetInfo.id)
+                    & (AssetInfoTag.tag_name == tag_name)
+                )
+            )
+
+    if exclude_tags:
+        stmt = stmt.where(
+            ~exists().where(
+                (AssetInfoTag.asset_info_id == AssetInfo.id)
+                & (AssetInfoTag.tag_name.in_(exclude_tags))
+            )
+        )
+    return stmt
+
+def apply_metadata_filter(
+    stmt: sa.sql.Select,
+    metadata_filter: dict | None = None,
+) -> sa.sql.Select:
+    """Apply filters using asset_info_meta projection table."""
+    if not metadata_filter:
+        return stmt
+
+    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
+        return sa.exists().where(
+            AssetInfoMeta.asset_info_id == AssetInfo.id,
+            AssetInfoMeta.key == key,
+            *preds,
+        )
+
+    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
+        if value is None:
+            no_row_for_key = sa.not_(
+                sa.exists().where(
+                    AssetInfoMeta.asset_info_id == AssetInfo.id,
+                    AssetInfoMeta.key == key,
+                )
+            )
+            null_row = _exists_for_pred(
+                key,
+                AssetInfoMeta.val_json.is_(None),
+                AssetInfoMeta.val_str.is_(None),
+                AssetInfoMeta.val_num.is_(None),
+                AssetInfoMeta.val_bool.is_(None),
+            )
+            return sa.or_(no_row_for_key, null_row)
+
+        if isinstance(value, bool):
+            return _exists_for_pred(key, AssetInfoMeta.val_bool == bool(value))
+        if isinstance(value, (int, float)):
+            from decimal import Decimal
+            num = value if isinstance(value, Decimal) else Decimal(str(value))
+            return _exists_for_pred(key, AssetInfoMeta.val_num == num)
+        if isinstance(value, str):
+            return _exists_for_pred(key, AssetInfoMeta.val_str == value)
+        return _exists_for_pred(key, AssetInfoMeta.val_json == value)
+
+    for k, v in metadata_filter.items():
+        if isinstance(v, list):
+            ors = [_exists_clause_for_value(k, elem) for elem in v]
+            if ors:
+                stmt = stmt.where(sa.or_(*ors))
+        else:
+            stmt = stmt.where(_exists_clause_for_value(k, v))
+    return stmt
+
+
+def asset_exists_by_hash(session: Session, asset_hash: str) -> bool:
+    """
+    Check if an asset with a given hash exists in database.
+    """
+    row = (
+        session.execute(
+            select(sa.literal(True)).select_from(Asset).where(Asset.hash == asset_hash).limit(1)
+        )
+    ).first()
+    return row is not None
+
+def get_asset_info_by_id(session: Session, asset_info_id: str) -> AssetInfo | None:
+    return session.get(AssetInfo, asset_info_id)
+
+def list_asset_infos_page(
+    session: Session,
+    owner_id: str = "",
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+) -> tuple[list[AssetInfo], dict[str, list[str]], int]:
+    base = (
+        select(AssetInfo)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .options(contains_eager(AssetInfo.asset), noload(AssetInfo.tags))
+        .where(visible_owner_clause(owner_id))
+    )
+
+    if name_contains:
+        escaped, esc = escape_like_prefix(name_contains)
+        base = base.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
+
+    base = apply_tag_filters(base, include_tags, exclude_tags)
+    base = apply_metadata_filter(base, metadata_filter)
+
+    sort = (sort or "created_at").lower()
+    order = (order or "desc").lower()
+    sort_map = {
+        "name": AssetInfo.name,
+        "created_at": AssetInfo.created_at,
+        "updated_at": AssetInfo.updated_at,
+        "last_access_time": AssetInfo.last_access_time,
+        "size": Asset.size_bytes,
+    }
+    sort_col = sort_map.get(sort, AssetInfo.created_at)
+    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
+
+    base = base.order_by(sort_exp).limit(limit).offset(offset)
+
+    count_stmt = (
+        select(sa.func.count())
+        .select_from(AssetInfo)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .where(visible_owner_clause(owner_id))
+    )
+    if name_contains:
+        escaped, esc = escape_like_prefix(name_contains)
+        count_stmt = count_stmt.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
+    count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
+    count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
+
+    total = int((session.execute(count_stmt)).scalar_one() or 0)
+
+    infos = (session.execute(base)).unique().scalars().all()
+
+    id_list: list[str] = [i.id for i in infos]
+    tag_map: dict[str, list[str]] = defaultdict(list)
+    if id_list:
+        rows = session.execute(
+            select(AssetInfoTag.asset_info_id, Tag.name)
+            .join(Tag, Tag.name == AssetInfoTag.tag_name)
+            .where(AssetInfoTag.asset_info_id.in_(id_list))
+        )
+        for aid, tag_name in rows.all():
+            tag_map[aid].append(tag_name)
+
+    return infos, tag_map, total
+
+def fetch_asset_info_asset_and_tags(
+    session: Session,
+    asset_info_id: str,
+    owner_id: str = "",
+) -> tuple[AssetInfo, Asset, list[str]] | None:
+    stmt = (
+        select(AssetInfo, Asset, Tag.name)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .join(AssetInfoTag, AssetInfoTag.asset_info_id == AssetInfo.id, isouter=True)
+        .join(Tag, Tag.name == AssetInfoTag.tag_name, isouter=True)
+        .where(
+            AssetInfo.id == asset_info_id,
+            visible_owner_clause(owner_id),
+        )
+        .options(noload(AssetInfo.tags))
+        .order_by(Tag.name.asc())
+    )
+
+    rows = (session.execute(stmt)).all()
+    if not rows:
+        return None
+
+    first_info, first_asset, _ = rows[0]
+    tags: list[str] = []
+    seen: set[str] = set()
+    for _info, _asset, tag_name in rows:
+        if tag_name and tag_name not in seen:
+            seen.add(tag_name)
+            tags.append(tag_name)
+    return first_info, first_asset, tags
+
+def list_tags_with_usage(
+    session: Session,
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    include_zero: bool = True,
+    order: str = "count_desc",
+    owner_id: str = "",
+) -> tuple[list[tuple[str, str, int]], int]:
+    counts_sq = (
+        select(
+            AssetInfoTag.tag_name.label("tag_name"),
+            func.count(AssetInfoTag.asset_info_id).label("cnt"),
+        )
+        .select_from(AssetInfoTag)
+        .join(AssetInfo, AssetInfo.id == AssetInfoTag.asset_info_id)
+        .where(visible_owner_clause(owner_id))
+        .group_by(AssetInfoTag.tag_name)
+        .subquery()
+    )
+
+    q = (
+        select(
+            Tag.name,
+            Tag.tag_type,
+            func.coalesce(counts_sq.c.cnt, 0).label("count"),
+        )
+        .select_from(Tag)
+        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
+    )
+
+    if prefix:
+        escaped, esc = escape_like_prefix(prefix.strip().lower())
+        q = q.where(Tag.name.like(escaped + "%", escape=esc))
+
+    if not include_zero:
+        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
+
+    if order == "name_asc":
+        q = q.order_by(Tag.name.asc())
+    else:
+        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
+
+    total_q = select(func.count()).select_from(Tag)
+    if prefix:
+        escaped, esc = escape_like_prefix(prefix.strip().lower())
+        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
+    if not include_zero:
+        total_q = total_q.where(
+            Tag.name.in_(select(AssetInfoTag.tag_name).group_by(AssetInfoTag.tag_name))
+        )
+
+    rows = (session.execute(q.limit(limit).offset(offset))).all()
+    total = (session.execute(total_q)).scalar_one()
+
+    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
+    return rows_norm, int(total or 0)

app/assets/database/tags.py

@@ -0,0 +1,62 @@
+from typing import Iterable
+
+import sqlalchemy
+from sqlalchemy.orm import Session
+from sqlalchemy.dialects import sqlite
+
+from app.assets.helpers import normalize_tags, utcnow
+from app.assets.database.models import Tag, AssetInfoTag, AssetInfo
+
+
+def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
+    wanted = normalize_tags(list(names))
+    if not wanted:
+        return
+    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
+    ins = (
+            sqlite.insert(Tag)
+            .values(rows)
+            .on_conflict_do_nothing(index_elements=[Tag.name])
+        )
+    return session.execute(ins)
+
+def add_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+    origin: str = "automatic",
+) -> None:
+    select_rows = (
+        sqlalchemy.select(
+            AssetInfo.id.label("asset_info_id"),
+            sqlalchemy.literal("missing").label("tag_name"),
+            sqlalchemy.literal(origin).label("origin"),
+            sqlalchemy.literal(utcnow()).label("added_at"),
+        )
+        .where(AssetInfo.asset_id == asset_id)
+        .where(
+            sqlalchemy.not_(
+                sqlalchemy.exists().where((AssetInfoTag.asset_info_id == AssetInfo.id) & (AssetInfoTag.tag_name == "missing"))
+            )
+        )
+    )
+    session.execute(
+        sqlite.insert(AssetInfoTag)
+        .from_select(
+            ["asset_info_id", "tag_name", "origin", "added_at"],
+            select_rows,
+        )
+        .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
+    )
+
+def remove_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+) -> None:
+    session.execute(
+        sqlalchemy.delete(AssetInfoTag).where(
+            AssetInfoTag.asset_info_id.in_(sqlalchemy.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
+            AssetInfoTag.tag_name == "missing",
+        )
+    )

app/assets/hashing.py

@@ -0,0 +1,75 @@
+from blake3 import blake3
+from typing import IO
+import os
+import asyncio
+
+
+DEFAULT_CHUNK = 8 * 1024 *1024 # 8MB
+
+# NOTE: this allows hashing different representations of a file-like object
+def blake3_hash(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+) -> str:
+    """
+    Returns a BLAKE3 hex digest for ``fp``, which may be:
+      - a filename (str/bytes) or PathLike
+      - an open binary file object
+    If ``fp`` is a file object, it must be opened in **binary** mode and support
+    ``read``, ``seek``, and ``tell``. The function will seek to the start before
+    reading and will attempt to restore the original position afterward.
+    """
+    # duck typing to check if input is a file-like object
+    if hasattr(fp, "read"):
+        return _hash_file_obj(fp, chunk_size)
+
+    with open(os.fspath(fp), "rb") as f:
+        return _hash_file_obj(f, chunk_size)
+
+
+async def blake3_hash_async(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+) -> str:
+    """Async wrapper for ``blake3_hash_sync``.
+    Uses a worker thread so the event loop remains responsive.
+    """
+    # If it is a path, open inside the worker thread to keep I/O off the loop.
+    if hasattr(fp, "read"):
+        return await asyncio.to_thread(blake3_hash, fp, chunk_size)
+
+    def _worker() -> str:
+        with open(os.fspath(fp), "rb") as f:
+            return _hash_file_obj(f, chunk_size)
+
+    return await asyncio.to_thread(_worker)
+
+
+def _hash_file_obj(file_obj: IO, chunk_size: int = DEFAULT_CHUNK) -> str:
+    """
+    Hash an already-open binary file object by streaming in chunks.
+    - Seeks to the beginning before reading (if supported).
+    - Restores the original position afterward (if tell/seek are supported).
+    """
+    if chunk_size <= 0:
+        chunk_size = DEFAULT_CHUNK
+
+    # in case file object is already open and not at the beginning, track so can be restored after hashing
+    orig_pos = file_obj.tell()
+
+    try:
+        # seek to the beginning before reading
+        if orig_pos != 0:
+            file_obj.seek(0)
+
+        h = blake3()
+        while True:
+            chunk = file_obj.read(chunk_size)
+            if not chunk:
+                break
+            h.update(chunk)
+        return h.hexdigest()
+    finally:
+        # restore original position in file object, if needed
+        if orig_pos != 0:
+            file_obj.seek(orig_pos)

app/assets/helpers.py

@@ -0,0 +1,217 @@
+import contextlib
+import os
+from aiohttp import web
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Literal, Any
+
+import folder_paths
+
+
+RootType = Literal["models", "input", "output"]
+ALLOWED_ROOTS: tuple[RootType, ...] = ("models", "input", "output")
+
+def get_query_dict(request: web.Request) -> dict[str, Any]:
+    """
+    Gets a dictionary of query parameters from the request.
+
+    'request.query' is a MultiMapping[str], needs to be converted to a dictionary to be validated by Pydantic.
+    """
+    query_dict = {
+        key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key)
+        for key in request.query.keys()
+    }
+    return query_dict
+
+def list_tree(base_dir: str) -> list[str]:
+    out: list[str] = []
+    base_abs = os.path.abspath(base_dir)
+    if not os.path.isdir(base_abs):
+        return out
+    for dirpath, _subdirs, filenames in os.walk(base_abs, topdown=True, followlinks=False):
+        for name in filenames:
+            out.append(os.path.abspath(os.path.join(dirpath, name)))
+    return out
+
+def prefixes_for_root(root: RootType) -> list[str]:
+    if root == "models":
+        bases: list[str] = []
+        for _bucket, paths in get_comfy_models_folders():
+            bases.extend(paths)
+        return [os.path.abspath(p) for p in bases]
+    if root == "input":
+        return [os.path.abspath(folder_paths.get_input_directory())]
+    if root == "output":
+        return [os.path.abspath(folder_paths.get_output_directory())]
+    return []
+
+def escape_like_prefix(s: str, escape: str = "!") -> tuple[str, str]:
+    """Escapes %, _ and the escape char itself in a LIKE prefix.
+    Returns (escaped_prefix, escape_char). Caller should append '%' and pass escape=escape_char to .like().
+    """
+    s = s.replace(escape, escape + escape)  # escape the escape char first
+    s = s.replace("%", escape + "%").replace("_", escape + "_")  # escape LIKE wildcards
+    return s, escape
+
+def fast_asset_file_check(
+    *,
+    mtime_db: int | None,
+    size_db: int | None,
+    stat_result: os.stat_result,
+) -> bool:
+    if mtime_db is None:
+        return False
+    actual_mtime_ns = getattr(stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000))
+    if int(mtime_db) != int(actual_mtime_ns):
+        return False
+    sz = int(size_db or 0)
+    if sz > 0:
+        return int(stat_result.st_size) == sz
+    return True
+
+def utcnow() -> datetime:
+    """Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC."""
+    return datetime.now(timezone.utc).replace(tzinfo=None)
+
+def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
+    """Build a list of (folder_name, base_paths[]) categories that are configured for model locations.
+
+    We trust `folder_paths.folder_names_and_paths` and include a category if
+    *any* of its base paths lies under the Comfy `models_dir`.
+    """
+    targets: list[tuple[str, list[str]]] = []
+    models_root = os.path.abspath(folder_paths.models_dir)
+    for name, values in folder_paths.folder_names_and_paths.items():
+        paths, _exts = values[0], values[1]  # NOTE: this prevents nodepacks that hackily edit folder_... from breaking ComfyUI
+        if any(os.path.abspath(p).startswith(models_root + os.sep) for p in paths):
+            targets.append((name, paths))
+    return targets
+
+def compute_relative_filename(file_path: str) -> str | None:
+    """
+    Return the model's path relative to the last well-known folder (the model category),
+    using forward slashes, eg:
+      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
+      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
+
+    For non-model paths, returns None.
+    NOTE: this is a temporary helper, used only for initializing metadata["filename"] field.
+    """
+    try:
+        root_category, rel_path = get_relative_to_root_category_path_of_asset(file_path)
+    except ValueError:
+        return None
+
+    p = Path(rel_path)
+    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
+    if not parts:
+        return None
+
+    if root_category == "models":
+        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
+        inside = parts[1:] if len(parts) > 1 else [parts[0]]
+        return "/".join(inside)
+    return "/".join(parts)  # input/output: keep all parts
+
+
+def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
+    """Given an absolute or relative file path, determine which root category the path belongs to:
+      - 'input' if the file resides under `folder_paths.get_input_directory()`
+      - 'output' if the file resides under `folder_paths.get_output_directory()`
+      - 'models' if the file resides under any base path of categories returned by `get_comfy_models_folders()`
+
+    Returns:
+        (root_category, relative_path_inside_that_root)
+        For 'models', the relative path is prefixed with the category name:
+            e.g. ('models', 'vae/test/sub/ae.safetensors')
+
+    Raises:
+        ValueError: if the path does not belong to input, output, or configured model bases.
+    """
+    fp_abs = os.path.abspath(file_path)
+
+    def _is_within(child: str, parent: str) -> bool:
+        try:
+            return os.path.commonpath([child, parent]) == parent
+        except Exception:
+            return False
+
+    def _rel(child: str, parent: str) -> str:
+        return os.path.relpath(os.path.join(os.sep, os.path.relpath(child, parent)), os.sep)
+
+    # 1) input
+    input_base = os.path.abspath(folder_paths.get_input_directory())
+    if _is_within(fp_abs, input_base):
+        return "input", _rel(fp_abs, input_base)
+
+    # 2) output
+    output_base = os.path.abspath(folder_paths.get_output_directory())
+    if _is_within(fp_abs, output_base):
+        return "output", _rel(fp_abs, output_base)
+
+    # 3) models (check deepest matching base to avoid ambiguity)
+    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
+    for bucket, bases in get_comfy_models_folders():
+        for b in bases:
+            base_abs = os.path.abspath(b)
+            if not _is_within(fp_abs, base_abs):
+                continue
+            cand = (len(base_abs), bucket, _rel(fp_abs, base_abs))
+            if best is None or cand[0] > best[0]:
+                best = cand
+
+    if best is not None:
+        _, bucket, rel_inside = best
+        combined = os.path.join(bucket, rel_inside)
+        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
+
+    raise ValueError(f"Path is not within input, output, or configured model bases: {file_path}")
+
+def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
+    """Return a tuple (name, tags) derived from a filesystem path.
+
+    Semantics:
+      - Root category is determined by `get_relative_to_root_category_path_of_asset`.
+      - The returned `name` is the base filename with extension from the relative path.
+      - The returned `tags` are:
+            [root_category] + parent folders of the relative path (in order)
+        For 'models', this means:
+            file '/.../ModelsDir/vae/test_tag/ae.safetensors'
+            -> root_category='models', some_path='vae/test_tag/ae.safetensors'
+            -> name='ae.safetensors', tags=['models', 'vae', 'test_tag']
+
+    Raises:
+        ValueError: if the path does not belong to input, output, or configured model bases.
+    """
+    root_category, some_path = get_relative_to_root_category_path_of_asset(file_path)
+    p = Path(some_path)
+    parent_parts = [part for part in p.parent.parts if part not in (".", "..", p.anchor)]
+    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
+
+def normalize_tags(tags: list[str] | None) -> list[str]:
+    """
+    Normalize a list of tags by:
+      - Stripping whitespace and converting to lowercase.
+      - Removing duplicates.
+    """
+    return [t.strip().lower() for t in (tags or []) if (t or "").strip()]
+
+def collect_models_files() -> list[str]:
+    out: list[str] = []
+    for folder_name, bases in get_comfy_models_folders():
+        rel_files = folder_paths.get_filename_list(folder_name) or []
+        for rel_path in rel_files:
+            abs_path = folder_paths.get_full_path(folder_name, rel_path)
+            if not abs_path:
+                continue
+            abs_path = os.path.abspath(abs_path)
+            allowed = False
+            for b in bases:
+                base_abs = os.path.abspath(b)
+                with contextlib.suppress(Exception):
+                    if os.path.commonpath([abs_path, base_abs]) == base_abs:
+                        allowed = True
+                        break
+            if allowed:
+                out.append(abs_path)
+    return out

app/assets/manager.py

@@ -0,0 +1,123 @@
+from typing import Sequence
+
+from app.database.db import create_session
+from app.assets.api import schemas_out
+from app.assets.database.queries import (
+    asset_exists_by_hash,
+    fetch_asset_info_asset_and_tags,
+    list_asset_infos_page,
+    list_tags_with_usage,
+)
+
+
+def _safe_sort_field(requested: str | None) -> str:
+    if not requested:
+        return "created_at"
+    v = requested.lower()
+    if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
+        return v
+    return "created_at"
+
+
+def asset_exists(asset_hash: str) -> bool:
+    with create_session() as session:
+        return asset_exists_by_hash(session, asset_hash=asset_hash)
+
+def list_assets(
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+    owner_id: str = "",
+) -> schemas_out.AssetsList:
+    sort = _safe_sort_field(sort)
+    order = "desc" if (order or "desc").lower() not in {"asc", "desc"} else order.lower()
+
+    with create_session() as session:
+        infos, tag_map, total = list_asset_infos_page(
+            session,
+            owner_id=owner_id,
+            include_tags=include_tags,
+            exclude_tags=exclude_tags,
+            name_contains=name_contains,
+            metadata_filter=metadata_filter,
+            limit=limit,
+            offset=offset,
+            sort=sort,
+            order=order,
+        )
+
+    summaries: list[schemas_out.AssetSummary] = []
+    for info in infos:
+        asset = info.asset
+        tags = tag_map.get(info.id, [])
+        summaries.append(
+            schemas_out.AssetSummary(
+                id=info.id,
+                name=info.name,
+                asset_hash=asset.hash if asset else None,
+                size=int(asset.size_bytes) if asset else None,
+                mime_type=asset.mime_type if asset else None,
+                tags=tags,
+                preview_url=f"/api/assets/{info.id}/content",
+                created_at=info.created_at,
+                updated_at=info.updated_at,
+                last_access_time=info.last_access_time,
+            )
+        )
+
+    return schemas_out.AssetsList(
+        assets=summaries,
+        total=total,
+        has_more=(offset + len(summaries)) < total,
+    )
+
+def get_asset(asset_info_id: str, owner_id: str = "") -> schemas_out.AssetDetail:
+    with create_session() as session:
+        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
+        if not res:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        info, asset, tag_names = res
+        preview_id = info.preview_id
+
+    return schemas_out.AssetDetail(
+        id=info.id,
+        name=info.name,
+        asset_hash=asset.hash if asset else None,
+        size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
+        mime_type=asset.mime_type if asset else None,
+        tags=tag_names,
+        user_metadata=info.user_metadata or {},
+        preview_id=preview_id,
+        created_at=info.created_at,
+        last_access_time=info.last_access_time,
+    )
+
+def list_tags(
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    order: str = "count_desc",
+    include_zero: bool = True,
+    owner_id: str = "",
+) -> schemas_out.TagsList:
+    limit = max(1, min(1000, limit))
+    offset = max(0, offset)
+
+    with create_session() as session:
+        rows, total = list_tags_with_usage(
+            session,
+            prefix=prefix,
+            limit=limit,
+            offset=offset,
+            include_zero=include_zero,
+            order=order,
+            owner_id=owner_id,
+        )
+
+    tags = [schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows]
+    return schemas_out.TagsList(tags=tags, total=total, has_more=(offset + len(tags)) < total)

app/assets/scanner.py

@@ -0,0 +1,229 @@
+import contextlib
+import time
+import logging
+import os
+import sqlalchemy
+
+import folder_paths
+from app.database.db import create_session, dependencies_available
+from app.assets.helpers import (
+    collect_models_files, compute_relative_filename, fast_asset_file_check, get_name_and_tags_from_asset_path,
+    list_tree,prefixes_for_root, escape_like_prefix,
+    RootType
+)
+from app.assets.database.tags import add_missing_tag_for_asset_id, ensure_tags_exist, remove_missing_tag_for_asset_id
+from app.assets.database.bulk_ops import seed_from_paths_batch
+from app.assets.database.models import Asset, AssetCacheState, AssetInfo
+
+
+def seed_assets(roots: tuple[RootType, ...], enable_logging: bool = False) -> None:
+    """
+    Scan the given roots and seed the assets into the database.
+    """
+    if not dependencies_available():
+        if enable_logging:
+            logging.warning("Database dependencies not available, skipping assets scan")
+        return
+    t_start = time.perf_counter()
+    created = 0
+    skipped_existing = 0
+    paths: list[str] = []
+    try:
+        existing_paths: set[str] = set()
+        for r in roots:
+            try:
+                survivors: set[str] = _fast_db_consistency_pass(r, collect_existing_paths=True, update_missing_tags=True)
+                if survivors:
+                    existing_paths.update(survivors)
+            except Exception as e:
+                logging.exception("fast DB scan failed for %s: %s", r, e)
+
+        if "models" in roots:
+            paths.extend(collect_models_files())
+        if "input" in roots:
+            paths.extend(list_tree(folder_paths.get_input_directory()))
+        if "output" in roots:
+            paths.extend(list_tree(folder_paths.get_output_directory()))
+
+        specs: list[dict] = []
+        tag_pool: set[str] = set()
+        for p in paths:
+            abs_p = os.path.abspath(p)
+            if abs_p in existing_paths:
+                skipped_existing += 1
+                continue
+            try:
+                stat_p = os.stat(abs_p, follow_symlinks=False)
+            except OSError:
+                continue
+            # skip empty files
+            if not stat_p.st_size:
+                continue
+            name, tags = get_name_and_tags_from_asset_path(abs_p)
+            specs.append(
+                {
+                    "abs_path": abs_p,
+                    "size_bytes": stat_p.st_size,
+                    "mtime_ns": getattr(stat_p, "st_mtime_ns", int(stat_p.st_mtime * 1_000_000_000)),
+                    "info_name": name,
+                    "tags": tags,
+                    "fname": compute_relative_filename(abs_p),
+                }
+            )
+            for t in tags:
+                tag_pool.add(t)
+        # if no file specs, nothing to do
+        if not specs:
+            return
+        with create_session() as sess:
+            if tag_pool:
+                ensure_tags_exist(sess, tag_pool, tag_type="user")
+
+            result = seed_from_paths_batch(sess, specs=specs, owner_id="")
+            created += result["inserted_infos"]
+            sess.commit()
+    finally:
+        if enable_logging:
+            logging.info(
+                "Assets scan(roots=%s) completed in %.3fs (created=%d, skipped_existing=%d, total_seen=%d)",
+                roots,
+                time.perf_counter() - t_start,
+                created,
+                skipped_existing,
+                len(paths),
+            )
+
+
+def _fast_db_consistency_pass(
+    root: RootType,
+    *,
+    collect_existing_paths: bool = False,
+    update_missing_tags: bool = False,
+) -> set[str] | None:
+    """Fast DB+FS pass for a root:
+      - Toggle needs_verify per state using fast check
+      - For hashed assets with at least one fast-ok state in this root: delete stale missing states
+      - For seed assets with all states missing: delete Asset and its AssetInfos
+      - Optionally add/remove 'missing' tags based on fast-ok in this root
+      - Optionally return surviving absolute paths
+    """
+    prefixes = prefixes_for_root(root)
+    if not prefixes:
+        return set() if collect_existing_paths else None
+
+    conds = []
+    for p in prefixes:
+        base = os.path.abspath(p)
+        if not base.endswith(os.sep):
+            base += os.sep
+        escaped, esc = escape_like_prefix(base)
+        conds.append(AssetCacheState.file_path.like(escaped + "%", escape=esc))
+
+    with create_session() as sess:
+        rows = (
+            sess.execute(
+                sqlalchemy.select(
+                    AssetCacheState.id,
+                    AssetCacheState.file_path,
+                    AssetCacheState.mtime_ns,
+                    AssetCacheState.needs_verify,
+                    AssetCacheState.asset_id,
+                    Asset.hash,
+                    Asset.size_bytes,
+                )
+                .join(Asset, Asset.id == AssetCacheState.asset_id)
+                .where(sqlalchemy.or_(*conds))
+                .order_by(AssetCacheState.asset_id.asc(), AssetCacheState.id.asc())
+            )
+        ).all()
+
+        by_asset: dict[str, dict] = {}
+        for sid, fp, mtime_db, needs_verify, aid, a_hash, a_size in rows:
+            acc = by_asset.get(aid)
+            if acc is None:
+                acc = {"hash": a_hash, "size_db": int(a_size or 0), "states": []}
+                by_asset[aid] = acc
+
+            fast_ok = False
+            try:
+                exists = True
+                fast_ok = fast_asset_file_check(
+                    mtime_db=mtime_db,
+                    size_db=acc["size_db"],
+                    stat_result=os.stat(fp, follow_symlinks=True),
+                )
+            except FileNotFoundError:
+                exists = False
+            except OSError:
+                exists = False
+
+            acc["states"].append({
+                "sid": sid,
+                "fp": fp,
+                "exists": exists,
+                "fast_ok": fast_ok,
+                "needs_verify": bool(needs_verify),
+            })
+
+        to_set_verify: list[int] = []
+        to_clear_verify: list[int] = []
+        stale_state_ids: list[int] = []
+        survivors: set[str] = set()
+
+        for aid, acc in by_asset.items():
+            a_hash = acc["hash"]
+            states = acc["states"]
+            any_fast_ok = any(s["fast_ok"] for s in states)
+            all_missing = all(not s["exists"] for s in states)
+
+            for s in states:
+                if not s["exists"]:
+                    continue
+                if s["fast_ok"] and s["needs_verify"]:
+                    to_clear_verify.append(s["sid"])
+                if not s["fast_ok"] and not s["needs_verify"]:
+                    to_set_verify.append(s["sid"])
+
+            if a_hash is None:
+                if states and all_missing:  # remove seed Asset completely, if no valid AssetCache exists
+                    sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id == aid))
+                    asset = sess.get(Asset, aid)
+                    if asset:
+                        sess.delete(asset)
+                else:
+                    for s in states:
+                        if s["exists"]:
+                            survivors.add(os.path.abspath(s["fp"]))
+                continue
+
+            if any_fast_ok:  # if Asset has at least one valid AssetCache record, remove any invalid AssetCache records
+                for s in states:
+                    if not s["exists"]:
+                        stale_state_ids.append(s["sid"])
+                if update_missing_tags:
+                    with contextlib.suppress(Exception):
+                        remove_missing_tag_for_asset_id(sess, asset_id=aid)
+            elif update_missing_tags:
+                with contextlib.suppress(Exception):
+                    add_missing_tag_for_asset_id(sess, asset_id=aid, origin="automatic")
+
+            for s in states:
+                if s["exists"]:
+                    survivors.add(os.path.abspath(s["fp"]))
+
+        if stale_state_ids:
+            sess.execute(sqlalchemy.delete(AssetCacheState).where(AssetCacheState.id.in_(stale_state_ids)))
+        if to_set_verify:
+            sess.execute(
+                sqlalchemy.update(AssetCacheState)
+                .where(AssetCacheState.id.in_(to_set_verify))
+                .values(needs_verify=True)
+            )
+        if to_clear_verify:
+            sess.execute(
+                sqlalchemy.update(AssetCacheState)
+                .where(AssetCacheState.id.in_(to_clear_verify))
+                .values(needs_verify=False)
+            )
+        sess.commit()
+        return survivors if collect_existing_paths else None

app/custom_node_manager.py

@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+import os
+import folder_paths
+import glob
+from aiohttp import web
+import json
+import logging
+from functools import lru_cache
+
+from utils.json_util import merge_json_recursive
+
+
+# Extra locale files to load into main.json
+EXTRA_LOCALE_FILES = [
+    "nodeDefs.json",
+    "commands.json",
+    "settings.json",
+]
+
+
+def safe_load_json_file(file_path: str) -> dict:
+    if not os.path.exists(file_path):
+        return {}
+
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except json.JSONDecodeError:
+        logging.error(f"Error loading {file_path}")
+        return {}
+
+
+class CustomNodeManager:
+    @lru_cache(maxsize=1)
+    def build_translations(self):
+        """Load all custom nodes translations during initialization. Translations are
+        expected to be loaded from `locales/` folder.
+
+        The folder structure is expected to be the following:
+        - custom_nodes/
+            - custom_node_1/
+                - locales/
+                    - en/
+                        - main.json
+                        - commands.json
+                        - settings.json
+
+        returned translations are expected to be in the following format:
+        {
+            "en": {
+                "nodeDefs": {...},
+                "commands": {...},
+                "settings": {...},
+                ...{other main.json keys}
+            }
+        }
+        """
+
+        translations = {}
+
+        for folder in folder_paths.get_folder_paths("custom_nodes"):
+            # Sort glob results for deterministic ordering
+            for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
+                locales_dir = os.path.join(custom_node_dir, "locales")
+                if not os.path.exists(locales_dir):
+                    continue
+
+                for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
+                    lang_code = os.path.basename(os.path.dirname(lang_dir))
+
+                    if lang_code not in translations:
+                        translations[lang_code] = {}
+
+                    # Load main.json
+                    main_file = os.path.join(lang_dir, "main.json")
+                    node_translations = safe_load_json_file(main_file)
+
+                    # Load extra locale files
+                    for extra_file in EXTRA_LOCALE_FILES:
+                        extra_file_path = os.path.join(lang_dir, extra_file)
+                        key = extra_file.split(".")[0]
+                        json_data = safe_load_json_file(extra_file_path)
+                        if json_data:
+                            node_translations[key] = json_data
+
+                    if node_translations:
+                        translations[lang_code] = merge_json_recursive(
+                            translations[lang_code], node_translations
+                        )
+
+        return translations
+
+    def add_routes(self, routes, webapp, loadedModules):
+
+        example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"]
+
+        @routes.get("/workflow_templates")
+        async def get_workflow_templates(request):
+            """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted."""
+
+            files = []
+
+            for folder in folder_paths.get_folder_paths("custom_nodes"):
+                for folder_name in example_workflow_folder_names:
+                    pattern = os.path.join(folder, f"*/{folder_name}/*.json")
+                    matched_files = glob.glob(pattern)
+                    files.extend(matched_files)
+
+            workflow_templates_dict = (
+                {}
+            )  # custom_nodes folder name -> example workflow names
+            for file in files:
+                custom_nodes_name = os.path.basename(
+                    os.path.dirname(os.path.dirname(file))
+                )
+                workflow_name = os.path.splitext(os.path.basename(file))[0]
+                workflow_templates_dict.setdefault(custom_nodes_name, []).append(
+                    workflow_name
+                )
+            return web.json_response(workflow_templates_dict)
+
+        # Serve workflow templates from custom nodes.
+        for module_name, module_dir in loadedModules:
+            for folder_name in example_workflow_folder_names:
+                workflows_dir = os.path.join(module_dir, folder_name)
+
+                if os.path.exists(workflows_dir):
+                    if folder_name != "example_workflows":
+                        logging.debug(
+                            "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'",
+                            folder_name, module_name)
+
+                    webapp.add_routes(
+                        [
+                            web.static(
+                                "/api/workflow_templates/" + module_name, workflows_dir
+                            )
+                        ]
+                    )
+
+        @routes.get("/i18n")
+        async def get_i18n(request):
+            """Returns translations from all custom nodes' locales folders."""
+            return web.json_response(self.build_translations())

app/database/db.py

@@ -0,0 +1,112 @@
+import logging
+import os
+import shutil
+from app.logger import log_startup_warning
+from utils.install_util import get_missing_requirements_message
+from comfy.cli_args import args
+
+_DB_AVAILABLE = False
+Session = None
+
+
+try:
+    from alembic import command
+    from alembic.config import Config
+    from alembic.runtime.migration import MigrationContext
+    from alembic.script import ScriptDirectory
+    from sqlalchemy import create_engine
+    from sqlalchemy.orm import sessionmaker
+
+    _DB_AVAILABLE = True
+except ImportError as e:
+    log_startup_warning(
+        f"""
+------------------------------------------------------------------------
+Error importing dependencies: {e}
+{get_missing_requirements_message()}
+This error is happening because ComfyUI now uses a local sqlite database.
+------------------------------------------------------------------------
+""".strip()
+    )
+
+
+def dependencies_available():
+    """
+    Temporary function to check if the dependencies are available
+    """
+    return _DB_AVAILABLE
+
+
+def can_create_session():
+    """
+    Temporary function to check if the database is available to create a session
+    During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created
+    """
+    return dependencies_available() and Session is not None
+
+
+def get_alembic_config():
+    root_path = os.path.join(os.path.dirname(__file__), "../..")
+    config_path = os.path.abspath(os.path.join(root_path, "alembic.ini"))
+    scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db"))
+
+    config = Config(config_path)
+    config.set_main_option("script_location", scripts_path)
+    config.set_main_option("sqlalchemy.url", args.database_url)
+
+    return config
+
+
+def get_db_path():
+    url = args.database_url
+    if url.startswith("sqlite:///"):
+        return url.split("///")[1]
+    else:
+        raise ValueError(f"Unsupported database URL '{url}'.")
+
+
+def init_db():
+    db_url = args.database_url
+    logging.debug(f"Database URL: {db_url}")
+    db_path = get_db_path()
+    db_exists = os.path.exists(db_path)
+
+    config = get_alembic_config()
+
+    # Check if we need to upgrade
+    engine = create_engine(db_url)
+    conn = engine.connect()
+
+    context = MigrationContext.configure(conn)
+    current_rev = context.get_current_revision()
+
+    script = ScriptDirectory.from_config(config)
+    target_rev = script.get_current_head()
+
+    if target_rev is None:
+        logging.warning("No target revision found.")
+    elif current_rev != target_rev:
+        # Backup the database pre upgrade
+        backup_path = db_path + ".bkp"
+        if db_exists:
+            shutil.copy(db_path, backup_path)
+        else:
+            backup_path = None
+
+        try:
+            command.upgrade(config, target_rev)
+            logging.info(f"Database upgraded from {current_rev} to {target_rev}")
+        except Exception as e:
+            if backup_path:
+                # Restore the database from backup if upgrade fails
+                shutil.copy(backup_path, db_path)
+                os.remove(backup_path)
+            logging.exception("Error upgrading database: ")
+            raise e
+
+    global Session
+    Session = sessionmaker(bind=engine)
+
+
+def create_session():
+    return Session()

app/database/models.py

@@ -0,0 +1,21 @@
+from typing import Any
+from datetime import datetime
+from sqlalchemy.orm import DeclarativeBase
+
+class Base(DeclarativeBase):
+    pass
+
+def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:
+    fields = obj.__table__.columns.keys()
+    out: dict[str, Any] = {}
+    for field in fields:
+        val = getattr(obj, field)
+        if val is None and not include_none:
+            continue
+        if isinstance(val, datetime):
+            out[field] = val.isoformat()
+        else:
+            out[field] = val
+    return out
+
+# TODO: Define models here

app/frontend_management.py

@@ -0,0 +1,457 @@
+from __future__ import annotations
+import argparse
+import logging
+import os
+import re
+import sys
+import tempfile
+import zipfile
+import importlib
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import Dict, TypedDict, Optional
+from aiohttp import web
+from importlib.metadata import version
+
+import requests
+from typing_extensions import NotRequired
+
+from utils.install_util import get_missing_requirements_message, requirements_path
+
+from comfy.cli_args import DEFAULT_VERSION_STRING
+import app.logger
+
+
+def frontend_install_warning_message():
+    return f"""
+{get_missing_requirements_message()}
+
+This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+""".strip()
+
+def parse_version(version: str) -> tuple[int, int, int]:
+        return tuple(map(int, version.split(".")))
+
+def is_valid_version(version: str) -> bool:
+    """Validate if a string is a valid semantic version (X.Y.Z format)."""
+    pattern = r"^(\d+)\.(\d+)\.(\d+)$"
+    return bool(re.match(pattern, version))
+
+def get_installed_frontend_version():
+    """Get the currently installed frontend package version."""
+    frontend_version_str = version("comfyui-frontend-package")
+    return frontend_version_str
+
+
+def get_required_frontend_version():
+    """Get the required frontend version from requirements.txt."""
+    try:
+        with open(requirements_path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if line.startswith("comfyui-frontend-package=="):
+                    version_str = line.split("==")[-1]
+                    if not is_valid_version(version_str):
+                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
+                        return None
+                    return version_str
+            logging.error("comfyui-frontend-package not found in requirements.txt")
+            return None
+    except FileNotFoundError:
+        logging.error("requirements.txt not found. Cannot determine required frontend version.")
+        return None
+    except Exception as e:
+        logging.error(f"Error reading requirements.txt: {e}")
+        return None
+
+
+def check_frontend_version():
+    """Check if the frontend version is up to date."""
+
+    try:
+        frontend_version_str = get_installed_frontend_version()
+        frontend_version = parse_version(frontend_version_str)
+        required_frontend_str = get_required_frontend_version()
+        required_frontend = parse_version(required_frontend_str)
+        if frontend_version < required_frontend:
+            app.logger.log_startup_warning(
+                f"""
+________________________________________________________________________
+WARNING WARNING WARNING WARNING WARNING
+
+Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
+
+{frontend_install_warning_message()}
+________________________________________________________________________
+""".strip()
+            )
+        else:
+            logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
+    except Exception as e:
+        logging.error(f"Failed to check frontend version: {e}")
+
+
+REQUEST_TIMEOUT = 10  # seconds
+
+
+class Asset(TypedDict):
+    url: str
+
+
+class Release(TypedDict):
+    id: int
+    tag_name: str
+    name: str
+    prerelease: bool
+    created_at: str
+    published_at: str
+    body: str
+    assets: NotRequired[list[Asset]]
+
+
+@dataclass
+class FrontEndProvider:
+    owner: str
+    repo: str
+
+    @property
+    def folder_name(self) -> str:
+        return f"{self.owner}_{self.repo}"
+
+    @property
+    def release_url(self) -> str:
+        return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases"
+
+    @cached_property
+    def all_releases(self) -> list[Release]:
+        releases = []
+        api_url = self.release_url
+        while api_url:
+            response = requests.get(api_url, timeout=REQUEST_TIMEOUT)
+            response.raise_for_status()  # Raises an HTTPError if the response was an error
+            releases.extend(response.json())
+            # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly.
+            if "next" in response.links:
+                api_url = response.links["next"]["url"]
+            else:
+                api_url = None
+        return releases
+
+    @cached_property
+    def latest_release(self) -> Release:
+        latest_release_url = f"{self.release_url}/latest"
+        response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT)
+        response.raise_for_status()  # Raises an HTTPError if the response was an error
+        return response.json()
+
+    @cached_property
+    def latest_prerelease(self) -> Release:
+        """Get the latest pre-release version - even if it's older than the latest release"""
+        release = [release for release in self.all_releases if release["prerelease"]]
+
+        if not release:
+            raise ValueError("No pre-releases found")
+
+        # GitHub returns releases in reverse chronological order, so first is latest
+        return release[0]
+
+    def get_release(self, version: str) -> Release:
+        if version == "latest":
+            return self.latest_release
+        elif version == "prerelease":
+            return self.latest_prerelease
+        else:
+            for release in self.all_releases:
+                if release["tag_name"] in [version, f"v{version}"]:
+                    return release
+            raise ValueError(f"Version {version} not found in releases")
+
+
+def download_release_asset_zip(release: Release, destination_path: str) -> None:
+    """Download dist.zip from github release."""
+    asset_url = None
+    for asset in release.get("assets", []):
+        if asset["name"] == "dist.zip":
+            asset_url = asset["url"]
+            break
+
+    if not asset_url:
+        raise ValueError("dist.zip not found in the release assets")
+
+    # Use a temporary file to download the zip content
+    with tempfile.TemporaryFile() as tmp_file:
+        headers = {"Accept": "application/octet-stream"}
+        response = requests.get(
+            asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT
+        )
+        response.raise_for_status()  # Ensure we got a successful response
+
+        # Write the content to the temporary file
+        tmp_file.write(response.content)
+
+        # Go back to the beginning of the temporary file
+        tmp_file.seek(0)
+
+        # Extract the zip file content to the destination path
+        with zipfile.ZipFile(tmp_file, "r") as zip_ref:
+            zip_ref.extractall(destination_path)
+
+
+class FrontendManager:
+    CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
+
+    @classmethod
+    def get_required_frontend_version(cls) -> str:
+        """Get the required frontend package version."""
+        return get_required_frontend_version()
+
+    @classmethod
+    def get_installed_templates_version(cls) -> str:
+        """Get the currently installed workflow templates package version."""
+        try:
+            templates_version_str = version("comfyui-workflow-templates")
+            return templates_version_str
+        except Exception:
+            return None
+
+    @classmethod
+    def get_required_templates_version(cls) -> str:
+        """Get the required workflow templates version from requirements.txt."""
+        try:
+            with open(requirements_path, "r", encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    if line.startswith("comfyui-workflow-templates=="):
+                        version_str = line.split("==")[-1]
+                        if not is_valid_version(version_str):
+                            logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
+                            return None
+                        return version_str
+                logging.error("comfyui-workflow-templates not found in requirements.txt")
+                return None
+        except FileNotFoundError:
+            logging.error("requirements.txt not found. Cannot determine required templates version.")
+            return None
+        except Exception as e:
+            logging.error(f"Error reading requirements.txt: {e}")
+            return None
+
+    @classmethod
+    def default_frontend_path(cls) -> str:
+        try:
+            import comfyui_frontend_package
+
+            return str(importlib.resources.files(comfyui_frontend_package) / "static")
+        except ImportError:
+            logging.error(
+                f"""
+********** ERROR ***********
+
+comfyui-frontend-package is not installed.
+
+{frontend_install_warning_message()}
+
+********** ERROR ***********
+""".strip()
+            )
+            sys.exit(-1)
+
+    @classmethod
+    def template_asset_map(cls) -> Optional[Dict[str, str]]:
+        """Return a mapping of template asset names to their absolute paths."""
+        try:
+            from comfyui_workflow_templates import (
+                get_asset_path,
+                iter_templates,
+            )
+        except ImportError:
+            logging.error(
+                f"""
+********** ERROR ***********
+
+comfyui-workflow-templates is not installed.
+
+{frontend_install_warning_message()}
+
+********** ERROR ***********
+""".strip()
+            )
+            return None
+
+        try:
+            template_entries = list(iter_templates())
+        except Exception as exc:
+            logging.error(f"Failed to enumerate workflow templates: {exc}")
+            return None
+
+        asset_map: Dict[str, str] = {}
+        try:
+            for entry in template_entries:
+                for asset in entry.assets:
+                    asset_map[asset.filename] = get_asset_path(
+                        entry.template_id, asset.filename
+                    )
+        except Exception as exc:
+            logging.error(f"Failed to resolve template asset paths: {exc}")
+            return None
+
+        if not asset_map:
+            logging.error("No workflow template assets found. Did the packages install correctly?")
+            return None
+
+        return asset_map
+
+
+    @classmethod
+    def legacy_templates_path(cls) -> Optional[str]:
+        """Return the legacy templates directory shipped inside the meta package."""
+        try:
+            import comfyui_workflow_templates
+
+            return str(
+                importlib.resources.files(comfyui_workflow_templates) / "templates"
+            )
+        except ImportError:
+            logging.error(
+                f"""
+********** ERROR ***********
+
+comfyui-workflow-templates is not installed.
+
+{frontend_install_warning_message()}
+
+********** ERROR ***********
+""".strip()
+            )
+            return None
+
+    @classmethod
+    def embedded_docs_path(cls) -> str:
+        """Get the path to embedded documentation"""
+        try:
+            import comfyui_embedded_docs
+
+            return str(
+                importlib.resources.files(comfyui_embedded_docs) / "docs"
+            )
+        except ImportError:
+            logging.info("comfyui-embedded-docs package not found")
+            return None
+
+    @classmethod
+    def parse_version_string(cls, value: str) -> tuple[str, str, str]:
+        """
+        Args:
+            value (str): The version string to parse.
+
+        Returns:
+            tuple[str, str]: A tuple containing provider name and version.
+
+        Raises:
+            argparse.ArgumentTypeError: If the version string is invalid.
+        """
+        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$"
+        match_result = re.match(VERSION_PATTERN, value)
+        if match_result is None:
+            raise argparse.ArgumentTypeError(f"Invalid version string: {value}")
+
+        return match_result.group(1), match_result.group(2), match_result.group(3)
+
+    @classmethod
+    def init_frontend_unsafe(
+        cls, version_string: str, provider: Optional[FrontEndProvider] = None
+    ) -> str:
+        """
+        Initializes the frontend for the specified version.
+
+        Args:
+            version_string (str): The version string.
+            provider (FrontEndProvider, optional): The provider to use. Defaults to None.
+
+        Returns:
+            str: The path to the initialized frontend.
+
+        Raises:
+            Exception: If there is an error during the initialization process.
+            main error source might be request timeout or invalid URL.
+        """
+        if version_string == DEFAULT_VERSION_STRING:
+            check_frontend_version()
+            return cls.default_frontend_path()
+
+        repo_owner, repo_name, version = cls.parse_version_string(version_string)
+
+        if version.startswith("v"):
+            expected_path = str(
+                Path(cls.CUSTOM_FRONTENDS_ROOT)
+                / f"{repo_owner}_{repo_name}"
+                / version.lstrip("v")
+            )
+            if os.path.exists(expected_path):
+                logging.info(
+                    f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}"
+                )
+                return expected_path
+
+        logging.info(
+            f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..."
+        )
+
+        provider = provider or FrontEndProvider(repo_owner, repo_name)
+        release = provider.get_release(version)
+
+        semantic_version = release["tag_name"].lstrip("v")
+        web_root = str(
+            Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version
+        )
+        if not os.path.exists(web_root):
+            try:
+                os.makedirs(web_root, exist_ok=True)
+                logging.info(
+                    "Downloading frontend(%s) version(%s) to (%s)",
+                    provider.folder_name,
+                    semantic_version,
+                    web_root,
+                )
+                logging.debug(release)
+                download_release_asset_zip(release, destination_path=web_root)
+            finally:
+                # Clean up the directory if it is empty, i.e. the download failed
+                if not os.listdir(web_root):
+                    os.rmdir(web_root)
+
+        return web_root
+
+    @classmethod
+    def init_frontend(cls, version_string: str) -> str:
+        """
+        Initializes the frontend with the specified version string.
+
+        Args:
+            version_string (str): The version string to initialize the frontend with.
+
+        Returns:
+            str: The path of the initialized frontend.
+        """
+        try:
+            return cls.init_frontend_unsafe(version_string)
+        except Exception as e:
+            logging.error("Failed to initialize frontend: %s", e)
+            logging.info("Falling back to the default frontend.")
+            check_frontend_version()
+            return cls.default_frontend_path()
+    @classmethod
+    def template_asset_handler(cls):
+        assets = cls.template_asset_map()
+        if not assets:
+            return None
+
+        async def serve_template(request: web.Request) -> web.StreamResponse:
+            rel_path = request.match_info.get("path", "")
+            target = assets.get(rel_path)
+            if target is None:
+                raise web.HTTPNotFound()
+            return web.FileResponse(target)
+
+        return serve_template

app/logger.py

@@ -0,0 +1,98 @@
+from collections import deque
+from datetime import datetime
+import io
+import logging
+import sys
+import threading
+
+logs = None
+stdout_interceptor = None
+stderr_interceptor = None
+
+
+class LogInterceptor(io.TextIOWrapper):
+    def __init__(self, stream,  *args, **kwargs):
+        buffer = stream.buffer
+        encoding = stream.encoding
+        super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering)
+        self._lock = threading.Lock()
+        self._flush_callbacks = []
+        self._logs_since_flush = []
+
+    def write(self, data):
+        entry = {"t": datetime.now().isoformat(), "m": data}
+        with self._lock:
+            self._logs_since_flush.append(entry)
+
+            # Simple handling for cr to overwrite the last output if it isnt a full line
+            # else logs just get full of progress messages
+            if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"):
+                logs.pop()
+            logs.append(entry)
+        super().write(data)
+
+    def flush(self):
+        super().flush()
+        for cb in self._flush_callbacks:
+            cb(self._logs_since_flush)
+            self._logs_since_flush = []
+
+    def on_flush(self, callback):
+        self._flush_callbacks.append(callback)
+
+
+def get_logs():
+    return logs
+
+
+def on_flush(callback):
+    if stdout_interceptor is not None:
+        stdout_interceptor.on_flush(callback)
+    if stderr_interceptor is not None:
+        stderr_interceptor.on_flush(callback)
+
+def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False):
+    global logs
+    if logs:
+        return
+
+    # Override output streams and log to buffer
+    logs = deque(maxlen=capacity)
+
+    global stdout_interceptor
+    global stderr_interceptor
+    stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout)
+    stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr)
+
+    # Setup default global logger
+    logger = logging.getLogger()
+    logger.setLevel(log_level)
+
+    stream_handler = logging.StreamHandler()
+    stream_handler.setFormatter(logging.Formatter("%(message)s"))
+
+    if use_stdout:
+        # Only errors and critical to stderr
+        stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR)
+
+        # Lesser to stdout
+        stdout_handler = logging.StreamHandler(sys.stdout)
+        stdout_handler.setFormatter(logging.Formatter("%(message)s"))
+        stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR)
+        logger.addHandler(stdout_handler)
+
+    logger.addHandler(stream_handler)
+
+
+STARTUP_WARNINGS = []
+
+
+def log_startup_warning(msg):
+    logging.warning(msg)
+    STARTUP_WARNINGS.append(msg)
+
+
+def print_startup_warnings():
+    for s in STARTUP_WARNINGS:
+        logging.warning(s)
+    STARTUP_WARNINGS.clear()

app/model_manager.py

@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+import os
+import base64
+import json
+import time
+import logging
+import folder_paths
+import glob
+import comfy.utils
+from aiohttp import web
+from PIL import Image
+from io import BytesIO
+from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types
+
+
+class ModelFileManager:
+    def __init__(self) -> None:
+        self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}
+
+    def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None:
+        return self.cache.get(key, default)
+
+    def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]):
+        self.cache[key] = value
+
+    def clear_cache(self):
+        self.cache.clear()
+
+    def add_routes(self, routes):
+        # NOTE: This is an experiment to replace `/models`
+        @routes.get("/experiment/models")
+        async def get_model_folders(request):
+            model_types = list(folder_paths.folder_names_and_paths.keys())
+            folder_black_list = ["configs", "custom_nodes"]
+            output_folders: list[dict] = []
+            for folder in model_types:
+                if folder in folder_black_list:
+                    continue
+                output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)})
+            return web.json_response(output_folders)
+
+        # NOTE: This is an experiment to replace `/models/{folder}`
+        @routes.get("/experiment/models/{folder}")
+        async def get_all_models(request):
+            folder = request.match_info.get("folder", None)
+            if folder not in folder_paths.folder_names_and_paths:
+                return web.Response(status=404)
+            files = self.get_model_file_list(folder)
+            return web.json_response(files)
+
+        @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}")
+        async def get_model_preview(request):
+            folder_name = request.match_info.get("folder", None)
+            path_index = int(request.match_info.get("path_index", None))
+            filename = request.match_info.get("filename", None)
+
+            if folder_name not in folder_paths.folder_names_and_paths:
+                return web.Response(status=404)
+
+            folders = folder_paths.folder_names_and_paths[folder_name]
+            folder = folders[0][path_index]
+            full_filename = os.path.join(folder, filename)
+
+            previews = self.get_model_previews(full_filename)
+            default_preview = previews[0] if len(previews) > 0 else None
+            if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
+                return web.Response(status=404)
+
+            try:
+                with Image.open(default_preview) as img:
+                    img_bytes = BytesIO()
+                    img.save(img_bytes, format="WEBP")
+                    img_bytes.seek(0)
+                    return web.Response(body=img_bytes.getvalue(), content_type="image/webp")
+            except:
+                return web.Response(status=404)
+
+    def get_model_file_list(self, folder_name: str):
+        folder_name = map_legacy(folder_name)
+        folders = folder_paths.folder_names_and_paths[folder_name]
+        output_list: list[dict] = []
+
+        for index, folder in enumerate(folders[0]):
+            if not os.path.isdir(folder):
+                continue
+            out = self.cache_model_file_list_(folder)
+            if out is None:
+                out = self.recursive_search_models_(folder, index)
+                self.set_cache(folder, out)
+            output_list.extend(out[0])
+
+        return output_list
+
+    def cache_model_file_list_(self, folder: str):
+        model_file_list_cache = self.get_cache(folder)
+
+        if model_file_list_cache is None:
+            return None
+        if not os.path.isdir(folder):
+            return None
+        if os.path.getmtime(folder) != model_file_list_cache[1]:
+            return None
+        for x in model_file_list_cache[1]:
+            time_modified = model_file_list_cache[1][x]
+            folder = x
+            if os.path.getmtime(folder) != time_modified:
+                return None
+
+        return model_file_list_cache
+
+    def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]:
+        if not os.path.isdir(directory):
+            return [], {}, time.perf_counter()
+
+        excluded_dir_names = [".git"]
+        # TODO use settings
+        include_hidden_files = False
+
+        result: list[str] = []
+        dirs: dict[str, float] = {}
+
+        for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
+            subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
+            if not include_hidden_files:
+                subdirs[:] = [d for d in subdirs if not d.startswith(".")]
+                filenames = [f for f in filenames if not f.startswith(".")]
+
+            filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions)
+
+            for file_name in filenames:
+                try:
+                    full_path = os.path.join(dirpath, file_name)
+                    relative_path = os.path.relpath(full_path, directory)
+
+                    # Get file metadata
+                    file_info = {
+                        "name": relative_path,
+                        "pathIndex": pathIndex,
+                        "modified": os.path.getmtime(full_path),  # Add modification time
+                        "created": os.path.getctime(full_path),   # Add creation time
+                        "size": os.path.getsize(full_path)        # Add file size
+                    }
+                    result.append(file_info)
+
+                except Exception as e:
+                    logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
+                    continue
+
+            for d in subdirs:
+                path: str = os.path.join(dirpath, d)
+                try:
+                    dirs[path] = os.path.getmtime(path)
+                except FileNotFoundError:
+                    logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
+                    continue
+
+        return result, dirs, time.perf_counter()
+
+    def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
+        dirname = os.path.dirname(filepath)
+
+        if not os.path.exists(dirname):
+            return []
+
+        basename = os.path.splitext(filepath)[0]
+        match_files = glob.glob(f"{basename}.*", recursive=False)
+        image_files = filter_files_content_types(match_files, "image")
+        safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None)
+        safetensors_metadata = {}
+
+        result: list[str | BytesIO] = []
+
+        for filename in image_files:
+            _basename = os.path.splitext(filename)[0]
+            if _basename == basename:
+                result.append(filename)
+            if _basename == f"{basename}.preview":
+                result.append(filename)
+
+        if safetensors_file:
+            safetensors_filepath = os.path.join(dirname, safetensors_file)
+            header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024)
+            if header:
+                safetensors_metadata = json.loads(header)
+        safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None)
+        if safetensors_images:
+            safetensors_images = json.loads(safetensors_images)
+            for image in safetensors_images:
+                result.append(BytesIO(base64.b64decode(image)))
+
+        return result
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.clear_cache()

app/node_replace_manager.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from aiohttp import web
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy_api.latest._node_replace import NodeReplace
+
+REGISTERED_NODE_REPLACEMENTS: dict[str, list[NodeReplace]] = {}
+
+def register_node_replacement(node_replace: NodeReplace):
+    REGISTERED_NODE_REPLACEMENTS.setdefault(node_replace.old_node_id, []).append(node_replace)
+
+def registered_as_dict():
+    return {
+        k: [v.as_dict() for v in v_list] for k, v_list in REGISTERED_NODE_REPLACEMENTS.items()
+    }
+
+class NodeReplaceManager:
+    def add_routes(self, routes):
+        @routes.get("/node_replacements")
+        async def get_node_replacements(request):
+            return web.json_response(registered_as_dict())

app/subgraph_manager.py

@@ -0,0 +1,132 @@
+from __future__ import annotations
+
+from typing import TypedDict
+import os
+import folder_paths
+import glob
+from aiohttp import web
+import hashlib
+
+
+class Source:
+    custom_node = "custom_node"
+    templates = "templates"
+
+class SubgraphEntry(TypedDict):
+    source: str
+    """
+    Source of subgraph - custom_nodes vs templates.
+    """
+    path: str
+    """
+    Relative path of the subgraph file.
+    For custom nodes, will be the relative directory like <custom_node_dir>/subgraphs/<name>.json
+    """
+    name: str
+    """
+    Name of subgraph file.
+    """
+    info: CustomNodeSubgraphEntryInfo
+    """
+    Additional info about subgraph; in the case of custom_nodes, will contain nodepack name
+    """
+    data: str
+
+class CustomNodeSubgraphEntryInfo(TypedDict):
+    node_pack: str
+    """Node pack name."""
+
+class SubgraphManager:
+    def __init__(self):
+        self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None
+        self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None
+
+    def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]:
+        """Create a subgraph entry from a file path. Expects normalized path (forward slashes)."""
+        entry_id = hashlib.sha256(f"{source}{file}".encode()).hexdigest()
+        entry: SubgraphEntry = {
+            "source": source,
+            "name": os.path.splitext(os.path.basename(file))[0],
+            "path": file,
+            "info": {"node_pack": node_pack},
+        }
+        return entry_id, entry
+
+    async def load_entry_data(self, entry: SubgraphEntry):
+        with open(entry['path'], 'r') as f:
+            entry['data'] = f.read()
+        return entry
+
+    async def sanitize_entry(self, entry: SubgraphEntry | None, remove_data=False) -> SubgraphEntry | None:
+        if entry is None:
+            return None
+        entry = entry.copy()
+        entry.pop('path', None)
+        if remove_data:
+            entry.pop('data', None)
+        return entry
+
+    async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=False) -> dict[str, SubgraphEntry]:
+        entries = entries.copy()
+        for key in list(entries.keys()):
+            entries[key] = await self.sanitize_entry(entries[key], remove_data)
+        return entries
+
+    async def get_custom_node_subgraphs(self, loadedModules, force_reload=False):
+        """Load subgraphs from custom nodes."""
+        if not force_reload and self.cached_custom_node_subgraphs is not None:
+            return self.cached_custom_node_subgraphs
+
+        subgraphs_dict: dict[SubgraphEntry] = {}
+        for folder in folder_paths.get_folder_paths("custom_nodes"):
+            pattern = os.path.join(folder, "*/subgraphs/*.json")
+            for file in glob.glob(pattern):
+                file = file.replace('\\', '/')
+                node_pack = "custom_nodes." + file.split('/')[-3]
+                entry_id, entry = self._create_entry(file, Source.custom_node, node_pack)
+                subgraphs_dict[entry_id] = entry
+
+        self.cached_custom_node_subgraphs = subgraphs_dict
+        return subgraphs_dict
+
+    async def get_blueprint_subgraphs(self, force_reload=False):
+        """Load subgraphs from the blueprints directory."""
+        if not force_reload and self.cached_blueprint_subgraphs is not None:
+            return self.cached_blueprint_subgraphs
+
+        subgraphs_dict: dict[SubgraphEntry] = {}
+        blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')
+
+        if os.path.exists(blueprints_dir):
+            for file in glob.glob(os.path.join(blueprints_dir, "*.json")):
+                file = file.replace('\\', '/')
+                entry_id, entry = self._create_entry(file, Source.templates, "comfyui")
+                subgraphs_dict[entry_id] = entry
+
+        self.cached_blueprint_subgraphs = subgraphs_dict
+        return subgraphs_dict
+
+    async def get_all_subgraphs(self, loadedModules, force_reload=False):
+        """Get all subgraphs from all sources (custom nodes and blueprints)."""
+        custom_node_subgraphs = await self.get_custom_node_subgraphs(loadedModules, force_reload)
+        blueprint_subgraphs = await self.get_blueprint_subgraphs(force_reload)
+        return {**custom_node_subgraphs, **blueprint_subgraphs}
+
+    async def get_subgraph(self, id: str, loadedModules):
+        """Get a specific subgraph by ID from any source."""
+        entry = (await self.get_all_subgraphs(loadedModules)).get(id)
+        if entry is not None and entry.get('data') is None:
+            await self.load_entry_data(entry)
+        return entry
+
+    def add_routes(self, routes, loadedModules):
+        @routes.get("/global_subgraphs")
+        async def get_global_subgraphs(request):
+            subgraphs_dict = await self.get_all_subgraphs(loadedModules)
+            return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True))
+
+        @routes.get("/global_subgraphs/{id}")
+        async def get_global_subgraph(request):
+            id = request.match_info.get("id", None)
+            subgraph = await self.get_subgraph(id, loadedModules)
+            return web.json_response(await self.sanitize_entry(subgraph))

app/user_manager.py

@@ -1,42 +1,67 @@
+from __future__ import annotations
 import json
 import os
 import re
 import uuid
 import glob
 import shutil
+import logging
 from aiohttp import web
+from urllib import parse
 from comfy.cli_args import args
-from folder_paths import user_directory
+import folder_paths
 from .app_settings import AppSettings
+from typing import TypedDict
 
 default_user = "default"
-users_file = os.path.join(user_directory, "users.json")
+
+
+class FileInfo(TypedDict):
+    path: str
+    size: int
+    modified: int
+    created: int
+
+
+def get_file_info(path: str, relative_to: str) -> FileInfo:
+    return {
+        "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
+        "size": os.path.getsize(path),
+        "modified": os.path.getmtime(path),
+        "created": os.path.getctime(path)
+    }
 
 
 class UserManager():
     def __init__(self):
-        global user_directory
+        user_directory = folder_paths.get_user_directory()
 
         self.settings = AppSettings(self)
         if not os.path.exists(user_directory):
-            os.mkdir(user_directory)
+            os.makedirs(user_directory, exist_ok=True)
             if not args.multi_user:
-                print("****** User settings have been changed to be stored on the server instead of browser storage. ******")
-                print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
+                logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******")
+                logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
 
         if args.multi_user:
-            if os.path.isfile(users_file):
-                with open(users_file) as f:
+            if os.path.isfile(self.get_users_file()):
+                with open(self.get_users_file()) as f:
                     self.users = json.load(f)
             else:
                 self.users = {}
         else:
             self.users = {"default": "default"}
 
+    def get_users_file(self):
+        return os.path.join(folder_paths.get_user_directory(), "users.json")
+
     def get_request_user_id(self, request):
         user = "default"
         if args.multi_user and "comfy-user" in request.headers:
             user = request.headers["comfy-user"]
+            # Block System Users (use same error message to prevent probing)
+            if user.startswith(folder_paths.SYSTEM_USER_PREFIX):
+                raise KeyError("Unknown user: " + user)
 
         if user not in self.users:
             raise KeyError("Unknown user: " + user)
@@ -44,21 +69,26 @@ class UserManager():
         return user
 
     def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
-        global user_directory
-
         if type == "userdata":
-            root_dir = user_directory
+            root_dir = folder_paths.get_user_directory()
         else:
             raise KeyError("Unknown filepath type:" + type)
 
         user = self.get_request_user_id(request)
-        path = user_root = os.path.abspath(os.path.join(root_dir, user))
+        user_root = folder_paths.get_public_user_directory(user)
+        if user_root is None:
+            return None
+        path = user_root
 
         # prevent leaving /{type}
         if os.path.commonpath((root_dir, user_root)) != root_dir:
             return None
 
         if file is not None:
+            # Check if filename is url encoded
+            if "%" in file:
+                file = parse.unquote(file)
+
             # prevent leaving /{type}/{user}
             path = os.path.abspath(os.path.join(user_root, file))
             if os.path.commonpath((user_root, path)) != user_root:
@@ -75,13 +105,16 @@ class UserManager():
         name = name.strip()
         if not name:
             raise ValueError("username not provided")
+        if name.startswith(folder_paths.SYSTEM_USER_PREFIX):
+            raise ValueError("System User prefix not allowed")
         user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
+        if user_id.startswith(folder_paths.SYSTEM_USER_PREFIX):
+            raise ValueError("System User prefix not allowed")
         user_id = user_id + "_" + str(uuid.uuid4())
 
         self.users[user_id] = name
 
-        global users_file
-        with open(users_file, "w") as f:
+        with open(self.get_users_file(), "w") as f:
             json.dump(self.users, f)
 
         return user_id
@@ -107,30 +140,179 @@ class UserManager():
             if username in self.users.values():
                 return web.json_response({"error": "Duplicate username."}, status=400)
 
-            user_id = self.add_user(username)
+            try:
+                user_id = self.add_user(username)
+            except ValueError as e:
+                return web.json_response({"error": str(e)}, status=400)
             return web.json_response(user_id)
 
         @routes.get("/userdata")
         async def listuserdata(request):
+            """
+            List user data files in a specified directory.
+
+            This endpoint allows listing files in a user's data directory, with options for recursion,
+            full file information, and path splitting.
+
+            Query Parameters:
+            - dir (required): The directory to list files from.
+            - recurse (optional): If "true", recursively list files in subdirectories.
+            - full_info (optional): If "true", return detailed file information (path, size, modified time).
+            - split (optional): If "true", split file paths into components (only applies when full_info is false).
+
+            Returns:
+            - 400: If 'dir' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 404: If the requested directory does not exist.
+            - 200: JSON response with the list of files or file information.
+
+            The response format depends on the query parameters:
+            - Default: List of relative file paths.
+            - full_info=true: List of dictionaries with file details.
+            - split=true (and full_info=false): List of lists, each containing path components.
+            """
             directory = request.rel_url.query.get('dir', '')
             if not directory:
-                return web.Response(status=400)
-                
+                return web.Response(status=400, text="Directory not provided")
+
             path = self.get_request_user_filepath(request, directory)
             if not path:
-                return web.Response(status=403)
-            
+                return web.Response(status=403, text="Invalid directory")
+
             if not os.path.exists(path):
-                return web.Response(status=404)
-            
+                return web.Response(status=404, text="Directory not found")
+
             recurse = request.rel_url.query.get('recurse', '').lower() == "true"
-            results = glob.glob(os.path.join(
-                glob.escape(path), '**/*'), recursive=recurse)
-            results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)]
-            
+            full_info = request.rel_url.query.get('full_info', '').lower() == "true"
             split_path = request.rel_url.query.get('split', '').lower() == "true"
-            if split_path:
-                results = [[x] + x.split(os.sep) for x in results]
+
+            # Use different patterns based on whether we're recursing or not
+            if recurse:
+                pattern = os.path.join(glob.escape(path), '**', '*')
+            else:
+                pattern = os.path.join(glob.escape(path), '*')
+
+            def process_full_path(full_path: str) -> FileInfo | str | list[str]:
+                if full_info:
+                    return get_file_info(full_path, path)
+
+                rel_path = os.path.relpath(full_path, path).replace(os.sep, '/')
+                if split_path:
+                    return [rel_path] + rel_path.split('/')
+
+                return rel_path
+
+            results = [
+                process_full_path(full_path)
+                for full_path in glob.glob(pattern, recursive=recurse)
+                if os.path.isfile(full_path)
+            ]
+
+            return web.json_response(results)
+
+        @routes.get("/v2/userdata")
+        async def list_userdata_v2(request):
+            """
+            List files and directories in a user's data directory.
+
+            This endpoint provides a structured listing of contents within a specified
+            subdirectory of the user's data storage.
+
+            Query Parameters:
+            - path (optional): The relative path within the user's data directory
+                               to list. Defaults to the root ('').
+
+            Returns:
+            - 400: If the requested path is invalid, outside the user's data directory, or is not a directory.
+            - 404: If the requested path does not exist.
+            - 403: If the user is invalid.
+            - 500: If there is an error reading the directory contents.
+            - 200: JSON response containing a list of file and directory objects.
+                   Each object includes:
+                   - name: The name of the file or directory.
+                   - type: 'file' or 'directory'.
+                   - path: The relative path from the user's data root.
+                   - size (for files): The size in bytes.
+                   - modified (for files): The last modified timestamp (Unix epoch).
+            """
+            requested_rel_path = request.rel_url.query.get('path', '')
+
+            # URL-decode the path parameter
+            try:
+                requested_rel_path = parse.unquote(requested_rel_path)
+            except Exception as e:
+                logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}")
+                return web.Response(status=400, text="Invalid characters in path parameter")
+
+
+            # Check user validity and get the absolute path for the requested directory
+            try:
+                 base_user_path = self.get_request_user_filepath(request, None, create_dir=False)
+
+                 if requested_rel_path:
+                     target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False)
+                 else:
+                     target_abs_path = base_user_path
+
+            except KeyError as e:
+                 # Invalid user detected by get_request_user_id inside get_request_user_filepath
+                 logging.warning(f"Access denied for user: {e}")
+                 return web.Response(status=403, text="Invalid user specified in request")
+
+
+            if not target_abs_path:
+                 # Path traversal or other issue detected by get_request_user_filepath
+                 return web.Response(status=400, text="Invalid path requested")
+
+            # Handle cases where the user directory or target path doesn't exist
+            if not os.path.exists(target_abs_path):
+                # Check if it's the base user directory that's missing (new user case)
+                if target_abs_path == base_user_path:
+                    # It's okay if the base user directory doesn't exist yet, return empty list
+                     return web.json_response([])
+                else:
+                    # A specific subdirectory was requested but doesn't exist
+                     return web.Response(status=404, text="Requested path not found")
+
+            if not os.path.isdir(target_abs_path):
+                 return web.Response(status=400, text="Requested path is not a directory")
+
+            results = []
+            try:
+                for root, dirs, files in os.walk(target_abs_path, topdown=True):
+                    # Process directories
+                    for dir_name in dirs:
+                        dir_path = os.path.join(root, dir_name)
+                        rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/')
+                        results.append({
+                            "name": dir_name,
+                            "path": rel_path,
+                            "type": "directory"
+                        })
+
+                    # Process files
+                    for file_name in files:
+                        file_path = os.path.join(root, file_name)
+                        rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/')
+                        entry_info = {
+                            "name": file_name,
+                            "path": rel_path,
+                            "type": "file"
+                        }
+                        try:
+                            stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk
+                            entry_info["size"] = stats.st_size
+                            entry_info["modified"] = stats.st_mtime
+                        except OSError as stat_error:
+                            logging.warning(f"Could not stat file {file_path}: {stat_error}")
+                            pass # Include file with available info
+                        results.append(entry_info)
+            except OSError as e:
+                logging.error(f"Error listing directory {target_abs_path}: {e}")
+                return web.Response(status=500, text="Error reading directory contents")
+
+            # Sort results alphabetically, directories first then files
+            results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower()))
 
             return web.json_response(results)
 
@@ -138,14 +320,14 @@ class UserManager():
             file = request.match_info.get(param, None)
             if not file:
                 return web.Response(status=400)
-                
+
             path = self.get_request_user_filepath(request, file)
             if not path:
                 return web.Response(status=403)
-            
+
             if check_exists and not os.path.exists(path):
                 return web.Response(status=404)
-            
+
             return path
 
         @routes.get("/userdata/{file}")
@@ -153,25 +335,63 @@ class UserManager():
             path = get_user_data_path(request, check_exists=True)
             if not isinstance(path, str):
                 return path
-            
+
             return web.FileResponse(path)
 
         @routes.post("/userdata/{file}")
         async def post_userdata(request):
+            """
+            Upload or update a user data file.
+
+            This endpoint handles file uploads to a user's data directory, with options for
+            controlling overwrite behavior and response format.
+
+            Query Parameters:
+            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+                                  If "false", returns only the relative file path.
+
+            Path Parameters:
+            - file: The target file path (URL encoded if necessary).
+
+            Returns:
+            - 400: If 'file' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 409: If overwrite=false and the file already exists.
+            - 200: JSON response with either:
+                  - Full file information (if full_info=true)
+                  - Relative file path (if full_info=false)
+
+            The request body should contain the raw file content to be written.
+            """
             path = get_user_data_path(request)
             if not isinstance(path, str):
                 return path
-            
-            overwrite = request.query["overwrite"] != "false"
+
+            overwrite = request.query.get("overwrite", 'true') != "false"
+            full_info = request.query.get('full_info', 'false').lower() == "true"
+
             if not overwrite and os.path.exists(path):
-                return web.Response(status=409)
+                return web.Response(status=409, text="File already exists")
 
-            body = await request.read()
+            try:
+                body = await request.read()
+
+                with open(path, "wb") as f:
+                    f.write(body)
+            except OSError as e:
+                logging.warning(f"Error saving file '{path}': {e}")
+                return web.Response(
+                    status=400,
+                    reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|"
+                )
+
+            user_path = self.get_request_user_filepath(request, None)
+            if full_info:
+                resp = get_file_info(path, user_path)
+            else:
+                resp = os.path.relpath(path, user_path)
 
-            with open(path, "wb") as f:
-                f.write(body)
-                
-            resp = os.path.relpath(path, self.get_request_user_filepath(request, None))
             return web.json_response(resp)
 
         @routes.delete("/userdata/{file}")
@@ -181,25 +401,56 @@ class UserManager():
                 return path
 
             os.remove(path)
-                
+
             return web.Response(status=204)
 
         @routes.post("/userdata/{file}/move/{dest}")
         async def move_userdata(request):
+            """
+            Move or rename a user data file.
+
+            This endpoint handles moving or renaming files within a user's data directory, with options for
+            controlling overwrite behavior and response format.
+
+            Path Parameters:
+            - file: The source file path (URL encoded if necessary)
+            - dest: The destination file path (URL encoded if necessary)
+
+            Query Parameters:
+            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+                                  If "false", returns only the relative file path.
+
+            Returns:
+            - 400: If either 'file' or 'dest' parameter is missing
+            - 403: If either requested path is not allowed
+            - 404: If the source file does not exist
+            - 409: If overwrite=false and the destination file already exists
+            - 200: JSON response with either:
+                  - Full file information (if full_info=true)
+                  - Relative file path (if full_info=false)
+            """
             source = get_user_data_path(request, check_exists=True)
             if not isinstance(source, str):
                 return source
-            
-            dest = get_user_data_path(request, check_exists=False, param="dest")
-            if not isinstance(source, str):
-                return dest
-            
-            overwrite = request.query["overwrite"] != "false"
-            if not overwrite and os.path.exists(dest):
-                return web.Response(status=409)
 
-            print(f"moving '{source}' -> '{dest}'")
+            dest = get_user_data_path(request, check_exists=False, param="dest")
+            if not isinstance(dest, str):
+                return dest
+
+            overwrite = request.query.get("overwrite", 'true') != "false"
+            full_info = request.query.get('full_info', 'false').lower() == "true"
+
+            if not overwrite and os.path.exists(dest):
+                return web.Response(status=409, text="File already exists")
+
+            logging.info(f"moving '{source}' -> '{dest}'")
             shutil.move(source, dest)
-                
-            resp = os.path.relpath(dest, self.get_request_user_filepath(request, None))
+
+            user_path = self.get_request_user_filepath(request, None)
+            if full_info:
+                resp = get_file_info(dest, user_path)
+            else:
+                resp = os.path.relpath(dest, user_path)
+
             return web.json_response(resp)

comfy/audio_encoders/audio_encoders.py

@@ -0,0 +1,91 @@
+from .wav2vec2 import Wav2Vec2Model
+from .whisper import WhisperLargeV3
+import comfy.model_management
+import comfy.ops
+import comfy.utils
+import logging
+import torchaudio
+
+
+class AudioEncoderModel():
+    def __init__(self, config):
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+        model_type = config.pop("model_type")
+        model_config = dict(config)
+        model_config.update({
+            "dtype": self.dtype,
+            "device": offload_device,
+            "operations": comfy.ops.manual_cast
+        })
+
+        if model_type == "wav2vec2":
+            self.model = Wav2Vec2Model(**model_config)
+        elif model_type == "whisper3":
+            self.model = WhisperLargeV3(**model_config)
+        self.model.eval()
+        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+        self.model_sample_rate = 16000
+
+    def load_sd(self, sd):
+        return self.model.load_state_dict(sd, strict=False)
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def encode_audio(self, audio, sample_rate):
+        comfy.model_management.load_model_gpu(self.patcher)
+        audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate)
+        out, all_layers = self.model(audio.to(self.load_device))
+        outputs = {}
+        outputs["encoded_audio"] = out
+        outputs["encoded_audio_all_layers"] = all_layers
+        outputs["audio_samples"] = audio.shape[2]
+        return outputs
+
+
+def load_audio_encoder_from_sd(sd, prefix=""):
+    sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""})
+    if "encoder.layer_norm.bias" in sd: #wav2vec2
+        embed_dim = sd["encoder.layer_norm.bias"].shape[0]
+        if embed_dim == 1024:# large
+            config = {
+                "model_type": "wav2vec2",
+                "embed_dim": 1024,
+                "num_heads": 16,
+                "num_layers": 24,
+                "conv_norm": True,
+                "conv_bias": True,
+                "do_normalize": True,
+                "do_stable_layer_norm": True
+                }
+        elif embed_dim == 768: # base
+            config = {
+                "model_type": "wav2vec2",
+                "embed_dim": 768,
+                "num_heads": 12,
+                "num_layers": 12,
+                "conv_norm": False,
+                "conv_bias": False,
+                "do_normalize": False, # chinese-wav2vec2-base has this False
+                "do_stable_layer_norm": False
+            }
+        else:
+            raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim))
+    elif "model.encoder.embed_positions.weight" in sd:
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""})
+        config = {
+            "model_type": "whisper3",
+        }
+    else:
+        raise RuntimeError("ERROR: audio encoder not supported.")
+
+    audio_encoder = AudioEncoderModel(config)
+    m, u = audio_encoder.load_sd(sd)
+    if len(m) > 0:
+        logging.warning("missing audio encoder: {}".format(m))
+    if len(u) > 0:
+        logging.warning("unexpected audio encoder: {}".format(u))
+
+    return audio_encoder

comfy/audio_encoders/wav2vec2.py

@@ -0,0 +1,252 @@
+import torch
+import torch.nn as nn
+from comfy.ldm.modules.attention import optimized_attention_masked
+
+
+class LayerNormConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+        self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))
+
+class LayerGroupNormConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+        self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(self.layer_norm(x))
+
+class ConvNoNorm(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(x)
+
+
+class ConvFeatureEncoder(nn.Module):
+    def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        if conv_norm:
+            self.conv_layers = nn.ModuleList([
+                LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+            ])
+        else:
+            self.conv_layers = nn.ModuleList([
+                LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+            ])
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+
+        for conv in self.conv_layers:
+            x = conv(x)
+
+        return x.transpose(1, 2)
+
+
+class FeatureProjection(nn.Module):
+    def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype)
+        self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.layer_norm(x)
+        x = self.projection(x)
+        return x
+
+
+class PositionalConvEmbedding(nn.Module):
+    def __init__(self, embed_dim=768, kernel_size=128, groups=16):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            embed_dim,
+            embed_dim,
+            kernel_size=kernel_size,
+            padding=kernel_size // 2,
+            groups=groups,
+        )
+        self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2)
+        self.activation = nn.GELU()
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        x = self.conv(x)[:, :, :-1]
+        x = self.activation(x)
+        x = x.transpose(1, 2)
+        return x
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        embed_dim=768,
+        num_heads=12,
+        num_layers=12,
+        mlp_ratio=4.0,
+        do_stable_layer_norm=True,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+
+        self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim)
+        self.layers = nn.ModuleList([
+            TransformerEncoderLayer(
+                embed_dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                do_stable_layer_norm=do_stable_layer_norm,
+                device=device, dtype=dtype, operations=operations
+            )
+            for _ in range(num_layers)
+        ])
+
+        self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)
+        self.do_stable_layer_norm = do_stable_layer_norm
+
+    def forward(self, x, mask=None):
+        x = x + self.pos_conv_embed(x)
+        all_x = ()
+        if not self.do_stable_layer_norm:
+            x = self.layer_norm(x)
+        for layer in self.layers:
+            all_x += (x,)
+            x = layer(x, mask)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
+        all_x += (x,)
+        return x, all_x
+
+
+class Attention(nn.Module):
+    def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+
+        self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
+        self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
+        self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
+        self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x, mask=None):
+        assert (mask is None)  # TODO?
+        q = self.q_proj(x)
+        k = self.k_proj(x)
+        v = self.v_proj(x)
+
+        out = optimized_attention_masked(q, k, v, self.num_heads)
+        return self.out_proj(out)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype)
+        self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.intermediate_dense(x)
+        x = torch.nn.functional.gelu(x)
+        x = self.output_dense(x)
+        return x
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        embed_dim=768,
+        num_heads=12,
+        mlp_ratio=4.0,
+        do_stable_layer_norm=True,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+
+        self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations)
+
+        self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
+        self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
+        self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
+        self.do_stable_layer_norm = do_stable_layer_norm
+
+    def forward(self, x, mask=None):
+        residual = x
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
+        x = self.attention(x, mask=mask)
+        x = residual + x
+        if not self.do_stable_layer_norm:
+            x = self.layer_norm(x)
+            return self.final_layer_norm(x + self.feed_forward(x))
+        else:
+            return x + self.feed_forward(self.final_layer_norm(x))
+
+
+class Wav2Vec2Model(nn.Module):
+    """Complete Wav2Vec 2.0 model."""
+
+    def __init__(
+        self,
+        embed_dim=1024,
+        final_dim=256,
+        num_heads=16,
+        num_layers=24,
+        conv_norm=True,
+        conv_bias=True,
+        do_normalize=True,
+        do_stable_layer_norm=True,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+
+        conv_dim = 512
+        self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations)
+        self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)
+
+        self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))
+        self.do_normalize = do_normalize
+
+        self.encoder = TransformerEncoder(
+            embed_dim=embed_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            do_stable_layer_norm=do_stable_layer_norm,
+            device=device, dtype=dtype, operations=operations
+        )
+
+    def forward(self, x, mask_time_indices=None, return_dict=False):
+        x = torch.mean(x, dim=1)
+
+        if self.do_normalize:
+            x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
+
+        features = self.feature_extractor(x)
+        features = self.feature_projection(features)
+        batch_size, seq_len, _ = features.shape
+
+        x, all_x = self.encoder(features)
+        return x, all_x

comfy/audio_encoders/whisper.py

@@ -0,0 +1,186 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from typing import Optional
+from comfy.ldm.modules.attention import optimized_attention_masked
+import comfy.ops
+
+class WhisperFeatureExtractor(nn.Module):
+    def __init__(self, n_mels=128, device=None):
+        super().__init__()
+        self.sample_rate = 16000
+        self.n_fft = 400
+        self.hop_length = 160
+        self.n_mels = n_mels
+        self.chunk_length = 30
+        self.n_samples = 480000
+
+        self.mel_spectrogram = torchaudio.transforms.MelSpectrogram(
+            sample_rate=self.sample_rate,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            n_mels=self.n_mels,
+            f_min=0,
+            f_max=8000,
+            norm="slaney",
+            mel_scale="slaney",
+        ).to(device)
+
+    def __call__(self, audio):
+        audio = torch.mean(audio, dim=1)
+        batch_size = audio.shape[0]
+        processed_audio = []
+
+        for i in range(batch_size):
+            aud = audio[i]
+            if aud.shape[0] > self.n_samples:
+                aud = aud[:self.n_samples]
+            elif aud.shape[0] < self.n_samples:
+                aud = F.pad(aud, (0, self.n_samples - aud.shape[0]))
+            processed_audio.append(aud)
+
+        audio = torch.stack(processed_audio)
+
+        mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device)
+
+        log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0)
+        log_mel_spec = (log_mel_spec + 4.0) / 4.0
+
+        return log_mel_spec
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        assert d_model % n_heads == 0
+
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.d_k = d_model // n_heads
+
+        self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device)
+        self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        batch_size, seq_len, _ = query.shape
+
+        q = self.q_proj(query)
+        k = self.k_proj(key)
+        v = self.v_proj(value)
+
+        attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations)
+        self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
+
+        self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device)
+        self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device)
+        self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        residual = x
+        x = self.self_attn_layer_norm(x)
+        x = self.self_attn(x, x, x, attention_mask)
+        x = residual + x
+
+        residual = x
+        x = self.final_layer_norm(x)
+        x = self.fc1(x)
+        x = F.gelu(x)
+        x = self.fc2(x)
+        x = residual + x
+
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(
+        self,
+        n_mels: int = 128,
+        n_ctx: int = 1500,
+        n_state: int = 1280,
+        n_head: int = 20,
+        n_layer: int = 32,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+
+        self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device)
+        self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device)
+
+        self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device)
+
+        self.layers = nn.ModuleList([
+            EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations)
+            for _ in range(n_layer)
+        ])
+
+        self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+
+        x = x.transpose(1, 2)
+
+        x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x)
+
+        all_x = ()
+        for layer in self.layers:
+            all_x += (x,)
+            x = layer(x)
+
+        x = self.layer_norm(x)
+        all_x += (x,)
+        return x, all_x
+
+
+class WhisperLargeV3(nn.Module):
+    def __init__(
+        self,
+        n_mels: int = 128,
+        n_audio_ctx: int = 1500,
+        n_audio_state: int = 1280,
+        n_audio_head: int = 20,
+        n_audio_layer: int = 32,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+
+        self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device)
+
+        self.encoder = AudioEncoder(
+            n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer,
+            dtype=dtype, device=device, operations=operations
+        )
+
+    def forward(self, audio):
+        mel = self.feature_extractor(audio)
+        x, all_x = self.encoder(mel)
+        return x, all_x

comfy/cldm/cldm.py

@@ -2,17 +2,16 @@
 #and modified
 
 import torch
-import torch as th
 import torch.nn as nn
 
 from ..ldm.modules.diffusionmodules.util import (
-    zero_module,
     timestep_embedding,
 )
 
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
+from .control_types import UNION_CONTROLNET_TYPES
 from collections import OrderedDict
 import comfy.ops
 from comfy.ldm.modules.attention import optimized_attention
@@ -161,7 +160,6 @@ class ControlNet(nn.Module):
             if isinstance(self.num_classes, int):
                 self.label_emb = nn.Embedding(num_classes, time_embed_dim)
             elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
                 self.label_emb = nn.Linear(1, time_embed_dim)
             elif self.num_classes == "sequential":
                 assert adm_in_channels is not None
@@ -361,7 +359,7 @@ class ControlNet(nn.Module):
             controlnet_cond = self.input_hint_block(hint[idx], emb, context)
             feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
             if idx < len(control_type):
-                feat_seq += self.task_embedding[control_type[idx]]
+                feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)
 
             inputs.append(feat_seq.unsqueeze(1))
             condition_list.append(controlnet_cond)
@@ -390,6 +388,18 @@ class ControlNet(nn.Module):
         if self.control_add_embedding is not None: #Union Controlnet
             control_type = kwargs.get("control_type", [])
 
+            if any([c >= self.num_control_type for c in control_type]):
+                max_type = max(control_type)
+                max_type_name = {
+                    v: k for k, v in UNION_CONTROLNET_TYPES.items()
+                }[max_type]
+                raise ValueError(
+                    f"Control type {max_type_name}({max_type}) is out of range for the number of control types" +
+                    f"({self.num_control_type}) supported.\n" +
+                    "Please consider using the ProMax ControlNet Union model.\n" +
+                    "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main"
+                )
+
             emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
             if len(control_type) > 0:
                 if len(hint.shape) < 5:
@@ -402,9 +412,9 @@ class ControlNet(nn.Module):
         out_output = []
         out_middle = []
 
-        hs = []
         if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
+            if y is None:
+                raise ValueError("y is None, did you try using a controlnet for SDXL on SD1?")
             emb = emb + self.label_emb(y)
 
         h = x

comfy/cldm/control_types.py

@@ -0,0 +1,10 @@
+UNION_CONTROLNET_TYPES = {
+    "openpose": 0,
+    "depth": 1,
+    "hed/pidi/scribble/ted": 2,
+    "canny/lineart/anime_lineart/mlsd": 3,
+    "normal": 4,
+    "segment": 5,
+    "tile": 6,
+    "repaint": 7,
+}

comfy/cldm/dit_embedder.py

@@ -0,0 +1,120 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch
+
+
+class ControlNetEmbedder(nn.Module):
+
+    def __init__(
+        self,
+        img_size: int,
+        patch_size: int,
+        in_chans: int,
+        attention_head_dim: int,
+        num_attention_heads: int,
+        adm_in_channels: int,
+        num_layers: int,
+        main_model_double: int,
+        double_y_emb: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        pos_embed_max_size: Optional[int] = None,
+        operations = None,
+    ):
+        super().__init__()
+        self.main_model_double = main_model_double
+        self.dtype = dtype
+        self.hidden_size = num_attention_heads * attention_head_dim
+        self.patch_size = patch_size
+        self.x_embedder = PatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=self.hidden_size,
+            strict_img_size=pos_embed_max_size is None,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+        )
+
+        self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        self.double_y_emb = double_y_emb
+        if self.double_y_emb:
+            self.orig_y_embedder = VectorEmbedder(
+                adm_in_channels, self.hidden_size, dtype, device, operations=operations
+            )
+            self.y_embedder = VectorEmbedder(
+                self.hidden_size, self.hidden_size, dtype, device, operations=operations
+            )
+        else:
+            self.y_embedder = VectorEmbedder(
+                adm_in_channels, self.hidden_size, dtype, device, operations=operations
+            )
+
+        self.transformer_blocks = nn.ModuleList(
+            DismantledBlock(
+                hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True,
+                dtype=dtype, device=device, operations=operations
+            )
+            for _ in range(num_layers)
+        )
+
+        # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features
+        # TODO double check this logic when 8b
+        self.use_y_embedder = True
+
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.transformer_blocks)):
+            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+            self.controlnet_blocks.append(controlnet_block)
+
+        self.pos_embed_input = PatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=self.hidden_size,
+            strict_img_size=False,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        hint = None,
+    ) -> Tuple[Tensor, List[Tensor]]:
+        x_shape = list(x.shape)
+        x = self.x_embedder(x)
+        if not self.double_y_emb:
+            h = (x_shape[-2] + 1) // self.patch_size
+            w = (x_shape[-1] + 1) // self.patch_size
+            x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device)
+        c = self.t_embedder(timesteps, dtype=x.dtype)
+        if y is not None and self.y_embedder is not None:
+            if self.double_y_emb:
+                y = self.orig_y_embedder(y)
+            y = self.y_embedder(y)
+            c = c + y
+
+        x = x + self.pos_embed_input(hint)
+
+        block_out = ()
+
+        repeat = math.ceil(self.main_model_double / len(self.transformer_blocks))
+        for i in range(len(self.transformer_blocks)):
+            out = self.transformer_blocks[i](x, c)
+            if not self.double_y_emb:
+                x = out
+            block_out += (self.controlnet_blocks[i](out),) * repeat
+
+        return {"output": block_out}

comfy/cldm/mmdit.py

@@ -1,11 +1,12 @@
 import torch
-from typing import Dict, Optional
+from typing import Optional
 import comfy.ldm.modules.diffusionmodules.mmdit
 
 class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
     def __init__(
         self,
         num_blocks = None,
+        control_latent_channels = None,
         dtype = None,
         device = None,
         operations = None,
@@ -17,10 +18,13 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
         for _ in range(len(self.joint_blocks)):
             self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
 
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+
         self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
             None,
             self.patch_size,
-            self.in_channels,
+            control_latent_channels,
             self.hidden_size,
             bias=True,
             strict_img_size=False,

comfy/cli_args.py

@@ -1,7 +1,9 @@
 import argparse
 import enum
+import os
 import comfy.options
 
+
 class EnumAction(argparse.Action):
     """
     Argparse action for handling Enums
@@ -33,20 +35,22 @@ class EnumAction(argparse.Action):
 
 parser = argparse.ArgumentParser()
 
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
+parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
 parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
 parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
 parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
 parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
 parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
 
+parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.")
 parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.")
+parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
-parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
+parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.")
+parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
 cm_group = parser.add_mutually_exclusive_group()
 cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
 cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
@@ -57,10 +61,13 @@ fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If
 fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
 
 fpunet_group = parser.add_mutually_exclusive_group()
-fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
-fpunet_group.add_argument("--fp16-unet", action="store_true", help="Store unet weights in fp16.")
+fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.")
+fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.")
+fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.")
+fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16")
 fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
 fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")
+fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.")
 
 fpvae_group = parser.add_mutually_exclusive_group()
 fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
@@ -74,12 +81,15 @@ fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Stor
 fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
 fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
+fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
 
 parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")
+parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
+parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -87,12 +97,29 @@ class LatentPreviewMethod(enum.Enum):
     Latent2RGB = "latent2rgb"
     TAESD = "taesd"
 
+    @classmethod
+    def from_string(cls, value: str):
+        for member in cls:
+            if member.value == value:
+                return member
+        return None
+
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
+parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
+
+cache_group = parser.add_mutually_exclusive_group()
+cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
+cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
+cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
+cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB")
+
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
 attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
 attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
+attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.")
+attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.")
 
 parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
 
@@ -101,6 +128,12 @@ upcast.add_argument("--force-upcast-attention", action="store_true", help="Force
 upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
 
 
+parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.")
+manager_group = parser.add_mutually_exclusive_group()
+manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.")
+manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager")
+
+
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
@@ -109,22 +142,97 @@ vram_group.add_argument("--lowvram", action="store_true", help="Split the unet i
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
+parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
+
+parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
+parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
+
+parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
+
+parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
 
 parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
 parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
 
+class PerformanceFeature(enum.Enum):
+    Fp16Accumulation = "fp16_accumulation"
+    Fp8MatrixMultiplication = "fp8_matrix_mult"
+    CublasOps = "cublas_ops"
+    AutoTune = "autotune"
+
+parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
+
+parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.")
+
+parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
+parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")
+
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
 parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
 parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
 
 parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
 parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
+parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.")
+parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes. Also prevents the frontend from communicating with the internet.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
-parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
+parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
+parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
 
 
+# The default built-in provider hosted under web/
+DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
+
+parser.add_argument(
+    "--front-end-version",
+    type=str,
+    default=DEFAULT_VERSION_STRING,
+    help="""
+    Specifies the version of the frontend to be used. This command needs internet connectivity to query and
+    download available frontend implementations from GitHub releases.
+
+    The version string should be in the format of:
+    [repoOwner]/[repoName]@[version]
+    where version is one of: "latest" or a valid version number (e.g. "1.0.0")
+    """,
+)
+
+def is_valid_directory(path: str) -> str:
+    """Validate if the given path is a directory, and check permissions."""
+    if not os.path.exists(path):
+        raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.")
+    if not os.path.isdir(path):
+        raise argparse.ArgumentTypeError(f"'{path}' is not a directory.")
+    if not os.access(path, os.R_OK):
+        raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.")
+    return path
+
+parser.add_argument(
+    "--front-end-root",
+    type=is_valid_directory,
+    default=None,
+    help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
+)
+
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
+
+parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
+
+parser.add_argument(
+    "--comfy-api-base",
+    type=str,
+    default="https://api.comfy.org",
+    help="Set the base URL for the ComfyUI API.  (default: https://api.comfy.org)",
+)
+
+database_default_path = os.path.abspath(
+    os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
+)
+parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
+parser.add_argument("--disable-assets-autoscan", action="store_true", help="Disable asset scanning on startup for database synchronization.")
+
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:
@@ -136,9 +244,16 @@ if args.windows_standalone_build:
 if args.disable_auto_launch:
     args.auto_launch = False
 
-import logging
-logging_level = logging.INFO
-if args.verbose:
-    logging_level = logging.DEBUG
+if args.force_fp16:
+    args.fp16_unet = True
 
-logging.basicConfig(format="%(message)s", level=logging_level)
+
+# '--fast' is not provided, use an empty set
+if args.fast is None:
+    args.fast = set()
+# '--fast' is provided with an empty list, enable all optimizations
+elif args.fast == []:
+    args.fast = set(PerformanceFeature)
+# '--fast' is provided with a list of performance features, use that list
+else:
+    args.fast = set(args.fast)

comfy/clip_config_bigg.json

@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1280,
   "initializer_factor": 1.0,

comfy/clip_model.py

@@ -1,5 +1,59 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+import math
+
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+    image = image[:, :, :, :3] if image.shape[3] > 3 else image
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
+    image = image.movedim(-1, 1)
+    if not (image.shape[2] == size and image.shape[3] == size):
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        h = (image.shape[2] - size)//2
+        w = (image.shape[3] - size)//2
+        image = image[:,:,h:h+size,w:w+size]
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+
+def siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, eps=1e-5):
+    def scale_dim(size, scale):
+        scaled = math.ceil(size * scale / patch_size) * patch_size
+        return max(patch_size, int(scaled))
+
+    # Binary search for optimal scale
+    lo, hi = eps / 10, 100.0
+    while hi - lo >= eps:
+        mid = (lo + hi) / 2
+        h, w = scale_dim(oh, mid), scale_dim(ow, mid)
+        if (h // patch_size) * (w // patch_size) <= max_num_patches:
+            lo = mid
+        else:
+            hi = mid
+
+    return scale_dim(oh, lo), scale_dim(ow, lo)
+
+def siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True):
+    if size > 0:
+        return clip_preprocess(image, size=size, mean=mean, std=std, crop=crop)
+
+    image = image[:, :, :, :3] if image.shape[3] > 3 else image
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
+    image = image.movedim(-1, 1)
+
+    b, c, h, w = image.shape
+    h, w = siglip2_flex_calc_resolution(h, w, patch_size, num_patches)
+
+    image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear", antialias=True)
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3, 1, 1])) / std.view([3, 1, 1])
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -22,6 +76,7 @@ class CLIPAttention(torch.nn.Module):
 
 ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
                "gelu": torch.nn.functional.gelu,
+               "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
 }
 
 class CLIPMLP(torch.nn.Module):
@@ -59,8 +114,12 @@ class CLIPEncoder(torch.nn.Module):
     def forward(self, x, mask=None, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
+        all_intermediate = None
         if intermediate_output is not None:
-            if intermediate_output < 0:
+            if intermediate_output == "all":
+                all_intermediate = []
+                intermediate_output = None
+            elif intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
         intermediate = None
@@ -68,16 +127,22 @@ class CLIPEncoder(torch.nn.Module):
             x = l(x, mask, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
+            if all_intermediate is not None:
+                all_intermediate.append(x.unsqueeze(1).clone())
+
+        if all_intermediate is not None:
+            intermediate = torch.cat(all_intermediate, dim=1)
+
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
+    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
         super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens):
-        return self.token_embedding(input_tokens) + self.position_embedding.weight
+    def forward(self, input_tokens, dtype=torch.float32):
+        return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
 
 
 class CLIPTextModel_(torch.nn.Module):
@@ -87,20 +152,27 @@ class CLIPTextModel_(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        num_positions = config_dict["max_position_embeddings"]
+        self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
+        self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations)
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
+    def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]):
+        if embeds is not None:
+            x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device)
+        else:
+            x = self.embeddings(input_tokens, dtype=dtype)
+
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+        causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1)
 
-        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
         if mask is not None:
             mask += causal_mask
         else:
@@ -111,7 +183,10 @@ class CLIPTextModel_(torch.nn.Module):
         if i is not None and final_layer_norm_intermediate:
             i = self.final_layer_norm(i)
 
-        pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
+        if num_tokens is not None:
+            pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))]
+        else:
+            pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
         return x, i, pooled_output
 
 class CLIPTextModel(torch.nn.Module):
@@ -121,7 +196,6 @@ class CLIPTextModel(torch.nn.Module):
         self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
         embed_dim = config_dict["hidden_size"]
         self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
-        self.text_projection.weight.copy_(torch.eye(embed_dim))
         self.dtype = dtype
 
     def get_input_embeddings(self):
@@ -135,29 +209,58 @@ class CLIPTextModel(torch.nn.Module):
         out = self.text_projection(x[2])
         return (x[0], x[1], out, x[2])
 
+def siglip2_pos_embed(embed_weight, embeds, orig_shape):
+    embed_weight_len = round(embed_weight.shape[0] ** 0.5)
+    embed_weight = comfy.ops.cast_to_input(embed_weight, embeds).movedim(1, 0).reshape(1, -1, embed_weight_len, embed_weight_len)
+    embed_weight = torch.nn.functional.interpolate(embed_weight, size=orig_shape, mode="bilinear", align_corners=False, antialias=True)
+    embed_weight = embed_weight.reshape(-1, embed_weight.shape[-2] * embed_weight.shape[-1]).movedim(0, 1)
+    return embeds + embed_weight
+
+class Siglip2Embeddings(torch.nn.Module):
+    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", num_patches=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.patch_embedding = operations.Linear(num_channels * patch_size * patch_size, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
+        self.patch_size = patch_size
+
+    def forward(self, pixel_values):
+        b, c, h, w = pixel_values.shape
+        img = pixel_values.movedim(1, -1).reshape(b, h // self.patch_size, self.patch_size, w // self.patch_size, self.patch_size, c)
+        img = img.permute(0, 1, 3, 2, 4, 5)
+        img = img.reshape(b, img.shape[1] * img.shape[2], -1)
+        img = self.patch_embedding(img)
+        return siglip2_pos_embed(self.position_embedding.weight, img, (h // self.patch_size, w // self.patch_size))
 
 class CLIPVisionEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None):
+    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
         super().__init__()
-        self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
+
+        num_patches = (image_size // patch_size) ** 2
+        if model_type == "siglip_vision_model":
+            self.class_embedding = None
+            patch_bias = True
+        else:
+            num_patches = num_patches + 1
+            self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
+            patch_bias = False
 
         self.patch_embedding = operations.Conv2d(
             in_channels=num_channels,
             out_channels=embed_dim,
             kernel_size=patch_size,
             stride=patch_size,
-            bias=False,
+            bias=patch_bias,
             dtype=dtype,
             device=device
         )
 
-        num_patches = (image_size // patch_size) ** 2
-        num_positions = num_patches + 1
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)
+        if self.class_embedding is not None:
+            embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1)
+        return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
 
 
 class CLIPVision(torch.nn.Module):
@@ -168,9 +271,18 @@ class CLIPVision(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        model_type = config_dict["model_type"]
 
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations)
-        self.pre_layrnorm = operations.LayerNorm(embed_dim)
+        if model_type in ["siglip2_vision_model"]:
+            self.embeddings = Siglip2Embeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, num_patches=config_dict.get("num_patches", None), dtype=dtype, device=device, operations=operations)
+        else:
+            self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
+        if model_type in ["siglip_vision_model", "siglip2_vision_model"]:
+            self.pre_layrnorm = lambda a: a
+            self.output_layernorm = True
+        else:
+            self.pre_layrnorm = operations.LayerNorm(embed_dim)
+            self.output_layernorm = False
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.post_layernorm = operations.LayerNorm(embed_dim)
 
@@ -179,16 +291,41 @@ class CLIPVision(torch.nn.Module):
         x = self.pre_layrnorm(x)
         #TODO: attention_mask?
         x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)
-        pooled_output = self.post_layernorm(x[:, 0, :])
+        if self.output_layernorm:
+            x = self.post_layernorm(x)
+            pooled_output = x
+        else:
+            pooled_output = self.post_layernorm(x[:, 0, :])
         return x, i, pooled_output
 
+class LlavaProjector(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, dtype, device, operations):
+        super().__init__()
+        self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype)
+        self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:])))
+
 class CLIPVisionModelProjection(torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         self.vision_model = CLIPVision(config_dict, dtype, device, operations)
-        self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
+        if "projection_dim" in config_dict:
+            self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
+        else:
+            self.visual_projection = lambda a: a
+
+        if "llava3" == config_dict.get("projector_type", None):
+            self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations)
+        else:
+            self.multi_modal_projector = None
 
     def forward(self, *args, **kwargs):
         x = self.vision_model(*args, **kwargs)
         out = self.visual_projection(x[2])
-        return (x[0], x[1], out)
+        projected = None
+        if self.multi_modal_projector is not None:
+            projected = self.multi_modal_projector(x[1])
+
+        return (x[0], x[1], out, projected)

comfy/clip_vision.py

@@ -1,6 +1,5 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
-import torch
 import json
 import logging
 
@@ -9,6 +8,7 @@ import comfy.model_patcher
 import comfy.model_management
 import comfy.utils
 import comfy.clip_model
+import comfy.image_encoders.dino2
 
 class Output:
     def __getitem__(self, key):
@@ -16,28 +16,35 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224):
-    mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype)
-    std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        scale = (size / min(image.shape[2], image.shape[3]))
-        image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+clip_preprocess = comfy.clip_model.clip_preprocess  # Prevent some stuff from breaking, TODO: remove eventually
+
+IMAGE_ENCODERS = {
+    "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
+    "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
+    "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection,
+    "dinov2": comfy.image_encoders.dino2.Dinov2Model,
+}
 
 class ClipVisionModel():
     def __init__(self, json_config):
         with open(json_config) as f:
             config = json.load(f)
 
+        self.image_size = config.get("image_size", 224)
+        self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
+        self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
+        self.model_type = config.get("model_type", "clip_vision_model")
+        self.config = config.copy()
+        model_class = IMAGE_ENCODERS.get(self.model_type)
+        if self.model_type == "siglip_vision_model":
+            self.return_all_hidden_states = True
+        else:
+            self.return_all_hidden_states = False
+
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast)
+        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
         self.model.eval()
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
@@ -48,15 +55,26 @@ class ClipVisionModel():
     def get_sd(self):
         return self.model.state_dict()
 
-    def encode_image(self, image):
+    def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device)).float()
-        out = self.model(pixel_values=pixel_values, intermediate_output=-2)
+        if self.model_type == "siglip2_vision_model":
+            pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get("patch_size", 16), num_patches=self.config.get("num_patches", 256), mean=self.image_mean, std=self.image_std, crop=crop).float()
+        else:
+            pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()
         outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
         outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
-        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+        outputs["image_sizes"] = [pixel_values.shape[1:]] * pixel_values.shape[0]
+        if self.return_all_hidden_states:
+            all_hs = out[1].to(comfy.model_management.intermediate_device())
+            outputs["penultimate_hidden_states"] = all_hs[:, -2]
+            outputs["all_hidden_states"] = all_hs
+        else:
+            outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+
+        outputs["mm_projected"] = out[3]
         return outputs
 
 def convert_to_transformers(sd, prefix):
@@ -93,7 +111,29 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
     elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
+        embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0]
+        if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
+            patch_embedding_shape = sd["vision_model.embeddings.patch_embedding.weight"].shape
+            if len(patch_embedding_shape) == 2:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip2_base_naflex.json")
+            else:
+                if embed_shape == 729:
+                    json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
+                elif embed_shape == 1024:
+                    json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
+        elif embed_shape == 577:
+            if "multi_modal_projector.linear_1.bias" in sd:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json")
+            else:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
+        else:
+            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
+
+    # Dinov2
+    elif 'encoder.layer.39.layer_scale2.lambda1' in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
+    elif 'encoder.layer.23.layer_scale2.lambda1' in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json")
     else:
         return None
 
@@ -105,8 +145,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     keys = list(sd.keys())
     for k in keys:
         if k not in u:
-            t = sd.pop(k)
-            del t
+            sd.pop(k)
     return clip
 
 def load(ckpt_path):

comfy/clip_vision_config_vitl_336.json

@@ -0,0 +1,18 @@
+{
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 336,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-5,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "float32"
+}

comfy/clip_vision_config_vitl_336_llava.json

@@ -0,0 +1,19 @@
+{
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 336,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-5,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "projector_type": "llava3",
+  "torch_dtype": "float32"
+}

comfy/clip_vision_siglip2_base_naflex.json

@@ -0,0 +1,14 @@
+{
+  "num_channels": 3,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "image_size": -1,
+  "intermediate_size": 4304,
+  "model_type": "siglip2_vision_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 27,
+  "patch_size": 16,
+  "num_patches": 256,
+  "image_mean": [0.5, 0.5, 0.5],
+  "image_std": [0.5, 0.5, 0.5]
+}

comfy/clip_vision_siglip_384.json

@@ -0,0 +1,13 @@
+{
+  "num_channels": 3,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "image_size": 384,
+  "intermediate_size": 4304,
+  "model_type": "siglip_vision_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 27,
+  "patch_size": 14,
+  "image_mean": [0.5, 0.5, 0.5],
+  "image_std": [0.5, 0.5, 0.5]
+}

comfy/clip_vision_siglip_512.json

@@ -0,0 +1,13 @@
+{
+  "num_channels": 3,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "image_size": 512,
+  "intermediate_size": 4304,
+  "model_type": "siglip_vision_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 27,
+  "patch_size": 16,
+  "image_mean": [0.5, 0.5, 0.5],
+  "image_std": [0.5, 0.5, 0.5]
+}

comfy/comfy_types/README.md

@@ -0,0 +1,43 @@
+# Comfy Typing
+## Type hinting for ComfyUI Node development
+
+This module provides type hinting and concrete convenience types for node developers.
+If cloned to the custom_nodes directory of ComfyUI, types can be imported using:
+
+```python
+from comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin
+
+class ExampleNode(ComfyNodeABC):
+    @classmethod
+    def INPUT_TYPES(s) -> InputTypeDict:
+        return {"required": {}}
+```
+
+Full example is in [examples/example_nodes.py](examples/example_nodes.py).
+
+# Types
+A few primary types are documented below.  More complete information is available via the docstrings on each type.
+
+## `IO`
+
+A string enum of built-in and a few custom data types.  Includes the following special types and their requisite plumbing:
+
+- `ANY`: `"*"`
+- `NUMBER`: `"FLOAT,INT"`
+- `PRIMITIVE`: `"STRING,FLOAT,INT,BOOLEAN"`
+
+## `ComfyNodeABC`
+
+An abstract base class for nodes, offering type-hinting / autocomplete, and somewhat-alright docstrings.
+
+### Type hinting for `INPUT_TYPES`
+
+![INPUT_TYPES auto-completion in Visual Studio Code](examples/input_types.png)
+
+### `INPUT_TYPES` return dict
+
+![INPUT_TYPES return value type hinting in Visual Studio Code](examples/required_hint.png)
+
+### Options for individual inputs
+
+![INPUT_TYPES return value option auto-completion in Visual Studio Code](examples/input_options.png)

comfy/comfy_types/__init__.py

@@ -1,5 +1,6 @@
 import torch
 from typing import Callable, Protocol, TypedDict, Optional, List
+from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator
 
 
 class UnetApplyFunction(Protocol):
@@ -30,3 +31,16 @@ class UnetParams(TypedDict):
 
 
 UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]
+
+
+__all__ = [
+    "UnetWrapperFunction",
+    UnetApplyConds.__name__,
+    UnetParams.__name__,
+    UnetApplyFunction.__name__,
+    IO.__name__,
+    InputTypeDict.__name__,
+    ComfyNodeABC.__name__,
+    CheckLazyMixin.__name__,
+    FileLocator.__name__,
+]

comfy/comfy_types/examples/example_nodes.py

@@ -0,0 +1,28 @@
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
+from inspect import cleandoc
+
+
+class ExampleNode(ComfyNodeABC):
+    """An example node that just adds 1 to an input integer.
+
+    * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc).
+    * This node is intended as an example for developers only.
+    """
+
+    DESCRIPTION = cleandoc(__doc__)
+    CATEGORY = "examples"
+
+    @classmethod
+    def INPUT_TYPES(s) -> InputTypeDict:
+        return {
+            "required": {
+                "input_int": (IO.INT, {"defaultInput": True}),
+            }
+        }
+
+    RETURN_TYPES = (IO.INT,)
+    RETURN_NAMES = ("input_plus_one",)
+    FUNCTION = "execute"
+
+    def execute(self, input_int: int):
+        return (input_int + 1,)