ComfyUI 0.3.46

* [moonvalley] Update V2V node to match API specification

- Add exact resolution validation for supported resolutions (1920x1080, 1080x1920, 1152x1152, 1536x1152, 1152x1536)
- Change frame count validation from divisible by 32 to 16
- Add MP4 container format validation
- Remove internal parameters (steps, guidance_scale) from V2V inference params
- Update video duration handling to support only 5 seconds (auto-trim if longer)
- Add motion_intensity parameter (0-100) for Motion Transfer control type
- Add get_container_format() method to VideoInput classes

* update negative prompt

.ci/windows_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:

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -15,6 +15,14 @@ body:
         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.
+  - 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: true
   - type: textarea
     attributes:
       label: Expected Behavior

.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: true
     - type: textarea
       attributes:
             label: Your question

.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/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/stable-release.yml

@@ -102,5 +102,4 @@ jobs:
           file: ComfyUI_windows_portable_nvidia.7z
           tag: ${{ inputs.git_tag }}
           overwrite: true
-          prerelease: true
-          make_latest: false
+          draft: true

.github/workflows/windows_release_nightly_pytorch.yml

@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "128"
+        default: "129"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "2"
+        default: "5"
 #  push:
 #    branches:
 #      - master
@@ -53,6 +53,8 @@ jobs:
             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 --depth 1 https://github.com/comfyanonymous/taesd

CODEOWNERS

@@ -5,20 +5,20 @@
 # Inlined the team members for now.
 
 # Maintainers
-*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
 
 # Python web server
-/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
 
 # Node developers
-/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
-/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

README.md

@@ -6,6 +6,7 @@
 
 [![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]
@@ -20,6 +21,8 @@
 <!-- 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
@@ -52,7 +55,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Image Models
-   - SD1.x, SD2.x,
+   - 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/)
@@ -62,13 +65,19 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [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/)
+   - [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
+- 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)
 - 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/)
-   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
+   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/) and [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
+   - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -76,9 +85,10 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [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/)
@@ -89,13 +99,12 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - [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/)
 - 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).
 - [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/)
@@ -170,10 +179,6 @@ If you have trouble extracting it, right click the file -> properties -> unblock
 
 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)
 
@@ -235,7 +240,7 @@ Nvidia users should install stable pytorch using this command:
 
 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/cu128```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129```
 
 #### Troubleshooting
 
@@ -268,6 +273,8 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 #### DirectML (AMD Cards on Windows)
 
+This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
+
 ```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
 
 #### Ascend NPUs
@@ -287,6 +294,13 @@ For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a
 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`
 
+#### Iluvatar Corex
+
+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`
+
 # Running
 
 ```python main.py```

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"}

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 +1,14 @@
+from sqlalchemy.orm import declarative_base
+
+Base = declarative_base()
+
+
+def to_dict(obj):
+    fields = obj.__table__.columns.keys()
+    return {
+        field: (val.to_dict() if hasattr(val, "to_dict") else val)
+        for field in fields
+        if (val := getattr(obj, field))
+    }
+
+# TODO: Define models here

app/frontend_management.py

@@ -16,40 +16,61 @@ 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
 
-# The path to the requirements.txt file
-req_path = Path(__file__).parents[1] / "requirements.txt"
-
 
 def frontend_install_warning_message():
-    """The warning message to display when the frontend version is not up to date."""
-
-    extra = ""
-    if sys.flags.no_user_site:
-        extra = "-s "
     return f"""
-Please install the updated requirements.txt file by running:
-{sys.executable} {extra}-m pip install -r {req_path}
+{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.
-
-If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem
 """.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."""
 
-    def parse_version(version: str) -> tuple[int, int, int]:
-        return tuple(map(int, version.split(".")))
-
     try:
-        frontend_version_str = version("comfyui-frontend-package")
+        frontend_version_str = get_installed_frontend_version()
         frontend_version = parse_version(frontend_version_str)
-        with open(req_path, "r", encoding="utf-8") as f:
-            required_frontend = parse_version(f.readline().split("=")[-1])
+        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"""
@@ -121,9 +142,22 @@ class FrontEndProvider:
         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}"]:
@@ -164,6 +198,11 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None:
 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 default_frontend_path(cls) -> str:
         try:
@@ -205,6 +244,19 @@ comfyui-workflow-templates is not installed.
 """.strip()
             )
 
+    @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]:
         """
@@ -217,7 +269,7 @@ comfyui-workflow-templates is not installed.
         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+|latest)$"
+        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$"
         match_result = re.match(VERSION_PATTERN, value)
         if match_result is None:
             raise argparse.ArgumentTypeError(f"Invalid version string: {value}")

comfy/cli_args.py

@@ -49,7 +49,8 @@ parser.add_argument("--temp-directory", type=str, default=None, help="Set the Co
 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.")
@@ -144,6 +145,7 @@ class PerformanceFeature(enum.Enum):
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
 
 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.")
@@ -151,6 +153,7 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win
 
 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.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
@@ -203,6 +206,11 @@ parser.add_argument(
     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:'.")
+
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:

comfy/comfy_types/node_typing.py

@@ -37,6 +37,8 @@ class IO(StrEnum):
     CONTROL_NET = "CONTROL_NET"
     VAE = "VAE"
     MODEL = "MODEL"
+    LORA_MODEL = "LORA_MODEL"
+    LOSS_MAP = "LOSS_MAP"
     CLIP_VISION = "CLIP_VISION"
     CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT"
     STYLE_MODEL = "STYLE_MODEL"

comfy/conds.py

@@ -86,3 +86,45 @@ class CONDConstant(CONDRegular):
 
     def size(self):
         return [1]
+
+
+class CONDList(CONDRegular):
+    def __init__(self, cond):
+        self.cond = cond
+
+    def process_cond(self, batch_size, device, **kwargs):
+        out = []
+        for c in self.cond:
+            out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
+
+        return self._copy_with(out)
+
+    def can_concat(self, other):
+        if len(self.cond) != len(other.cond):
+            return False
+        for i in range(len(self.cond)):
+            if self.cond[i].shape != other.cond[i].shape:
+                return False
+
+        return True
+
+    def concat(self, others):
+        out = []
+        for i in range(len(self.cond)):
+            o = [self.cond[i]]
+            for x in others:
+                o.append(x.cond[i])
+            out.append(torch.cat(o))
+
+        return out
+
+    def size(self):  # hackish implementation to make the mem estimation work
+        o = 0
+        c = 1
+        for c in self.cond:
+            size = c.size()
+            o += math.prod(size)
+            if len(size) > 1:
+                c = size[1]
+
+        return [1, c, o // c]

comfy/controlnet.py

@@ -390,8 +390,9 @@ class ControlLora(ControlNet):
                 pass
 
         for k in self.control_weights:
-            if k not in {"lora_controlnet"}:
-                comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
+            if (k not in {"lora_controlnet"}):
+                if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k):
+                    comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
 
     def copy(self):
         c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)

comfy/gligen.py

@@ -1,55 +1,10 @@
 import math
 import torch
 from torch import nn
-from .ldm.modules.attention import CrossAttention
-from inspect import isfunction
+from .ldm.modules.attention import CrossAttention, FeedForward
 import comfy.ops
 ops = comfy.ops.manual_cast
 
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = ops.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * torch.nn.functional.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            ops.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            ops.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
 
 class GatedCrossAttentionDense(nn.Module):
     def __init__(self, query_dim, context_dim, n_heads, d_head):

comfy/k_diffusion/sa_solver.py

@@ -0,0 +1,121 @@
+# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019)
+# Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf
+# Codebase ref: https://github.com/scxue/SA-Solver
+
+import math
+from typing import Union, Callable
+import torch
+
+
+def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor:
+    """Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts.
+
+    Integral of exp((1 + tau^2) * x) * x^p dx
+        = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx,
+    with base case p=0 where integral equals product_terms[0].
+
+    where
+        product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2).
+
+    Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1).
+    Return coefficients used by the SA-Solver in data prediction mode.
+
+    Args:
+        s: Start time s.
+        t: End time t.
+        solver_order: Current order of the solver.
+        tau_t: Stochastic strength parameter in the SDE.
+
+    Returns:
+        Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,).
+    """
+    tau_mul = 1 + tau_t ** 2
+    h = t - s
+    p = torch.arange(solver_order, dtype=s.dtype, device=s.device)
+
+    # product_terms after factoring out exp((1 + tau^2) * t)
+    # Includes (1 + tau^2) factor from outside the integral
+    product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp())
+
+    # Lower triangular recursive coefficient matrix
+    # Accumulates recursive coefficients based on p / (1 + tau^2)
+    recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0)
+    log_factorial = (p + 1).lgamma()
+    recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0)
+    if tau_t > 0:
+        recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul))
+    signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0)
+    recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril()
+
+    return recursive_coeff_mat @ product_terms_factored
+
+
+def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor:
+    """Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details)."""
+    tau_mul = 1 + tau_t ** 2
+    h = lambda_t - lambda_s
+    alpha_t = sigma_next * lambda_t.exp()
+    if is_corrector_step:
+        # Simplified 1-step (order-2) corrector
+        b_1 = alpha_t * (0.5 * tau_mul * h)
+        b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1
+    else:
+        # Simplified 2-step predictor
+        b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s)
+        b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2
+    return torch.stack([b_2, b_1])
+
+
+def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor:
+    """Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18).
+
+    The solver order corresponds to the number of input lambdas (half-logSNR points).
+
+    Args:
+        sigma_next: Sigma at end time t.
+        curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,).
+        lambda_s: Lambda at start time s.
+        lambda_t: Lambda at end time t.
+        tau_t: Stochastic strength parameter in the SDE.
+        simple_order_2: Whether to enable the simple order-2 scheme.
+        is_corrector_step: Flag for corrector step in simple order-2 mode.
+
+    Returns:
+        b_i coefficients for the SA-Solver, shape (N,), where N is the solver order.
+    """
+    num_timesteps = curr_lambdas.shape[0]
+
+    if simple_order_2 and num_timesteps == 2:
+        return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step)
+
+    # Compute coefficients by solving a linear system from Lagrange basis interpolation
+    exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t)
+    vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T
+    lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs)
+
+    # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t)
+    # = sigma_t * exp(lambda_t) = alpha_t
+    # exp((1 + tau^2) * lambda_t) is extracted from the integral
+    alpha_t = sigma_next * lambda_t.exp()
+    return alpha_t * lagrange_integrals
+
+
+def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]:
+    """Return a function that controls the stochasticity of SA-Solver.
+
+    When eta = 0, SA-Solver runs as ODE. The official approach uses
+    time t to determine the SDE interval, while here we use sigma instead.
+
+    See:
+        https://github.com/scxue/SA-Solver/blob/main/README.md
+    """
+
+    def tau_func(sigma: Union[torch.Tensor, float]) -> float:
+        if eta <= 0:
+            return 0.0  # ODE
+
+        if isinstance(sigma, torch.Tensor):
+            sigma = sigma.item()
+        return eta if start_sigma >= sigma >= end_sigma else 0.0
+
+    return tau_func

comfy/k_diffusion/sampling.py

@@ -1,4 +1,5 @@
 import math
+from functools import partial
 
 from scipy import integrate
 import torch
@@ -8,6 +9,7 @@ from tqdm.auto import trange, tqdm
 
 from . import utils
 from . import deis
+from . import sa_solver
 import comfy.model_patcher
 import comfy.model_sampling
 
@@ -142,6 +144,33 @@ class BrownianTreeNoiseSampler:
         return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
 
 
+def sigma_to_half_log_snr(sigma, model_sampling):
+    """Convert sigma to half-logSNR log(alpha_t / sigma_t)."""
+    if isinstance(model_sampling, comfy.model_sampling.CONST):
+        # log((1 - t) / t) = log((1 - sigma) / sigma)
+        return sigma.logit().neg()
+    return sigma.log().neg()
+
+
+def half_log_snr_to_sigma(half_log_snr, model_sampling):
+    """Convert half-logSNR log(alpha_t / sigma_t) to sigma."""
+    if isinstance(model_sampling, comfy.model_sampling.CONST):
+        # 1 / (1 + exp(half_log_snr))
+        return half_log_snr.neg().sigmoid()
+    return half_log_snr.neg().exp()
+
+
+def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
+    """Adjust the first sigma to avoid invalid logSNR."""
+    if len(sigmas) <= 1:
+        return sigmas
+    if isinstance(model_sampling, comfy.model_sampling.CONST):
+        if sigmas[0] >= 1:
+            sigmas = sigmas.clone()
+            sigmas[0] = model_sampling.percent_to_sigma(percent_offset)
+    return sigmas
+
+
 @torch.no_grad()
 def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
@@ -384,9 +413,13 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o
             ds.pop(0)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        cur_order = min(i + 1, order)
-        coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
-        x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            cur_order = min(i + 1, order)
+            coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
+            x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
     return x
 
 
@@ -682,6 +715,7 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
         # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     """DPM-Solver++ (stochastic)."""
@@ -693,38 +727,49 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     seed = extra_args.get("seed", None)
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
+
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
+    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
         if sigmas[i + 1] == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigmas[i + 1] - sigmas[i]
-            x = x + d * dt
+            # Denoising step
+            x = denoised
         else:
             # DPM-Solver++
-            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-            h = t_next - t
-            s = t + h * r
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
+            lambda_s_1 = lambda_s + r * h
             fac = 1 / (2 * r)
 
+            sigma_s_1 = sigma_fn(lambda_s_1)
+
+            alpha_s = sigmas[i] * lambda_s.exp()
+            alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
+
             # Step 1
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
-            s_ = t_fn(sd)
-            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
-            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+            sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta)
+            lambda_s_1_ = sd.log().neg()
+            h_ = lambda_s_1_ - lambda_s
+            x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised
+            if eta > 0 and s_noise > 0:
+                x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su
+            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
             # Step 2
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
-            t_next_ = t_fn(sd)
+            sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta)
+            lambda_t_ = sd.log().neg()
+            h_ = lambda_t_ - lambda_s
             denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
-            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+            x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d
+            if eta > 0 and s_noise > 0:
+                x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su
     return x
 
 
@@ -753,6 +798,7 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
         old_denoised = denoised
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     """DPM-Solver++(2M) SDE."""
@@ -768,9 +814,12 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
     old_denoised = None
-    h_last = None
-    h = None
+    h, h_last = None, None
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -781,26 +830,29 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
             x = denoised
         else:
             # DPM-Solver++(2M) SDE
-            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = s - t
-            eta_h = eta * h
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
+            h_eta = h * (eta + 1)
 
-            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
 
             if old_denoised is not None:
                 r = h_last / h
                 if solver_type == 'heun':
-                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
+                    x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)
                 elif solver_type == 'midpoint':
-                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
+                    x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)
 
-            if eta:
-                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
+            if eta > 0 and s_noise > 0:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
 
         old_denoised = denoised
         h_last = h
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """DPM-Solver++(3M) SDE."""
@@ -814,6 +866,10 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
     denoised_1, denoised_2 = None, None
     h, h_1, h_2 = None, None, None
 
@@ -825,13 +881,16 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
             # Denoising step
             x = denoised
         else:
-            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = s - t
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
             h_eta = h * (eta + 1)
 
-            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
 
             if h_2 is not None:
+                # DPM-Solver++(3M) SDE
                 r0 = h_1 / h
                 r1 = h_2 / h
                 d1_0 = (denoised - denoised_1) / r0
@@ -840,20 +899,22 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
                 d2 = (d1_0 - d1_1) / (r0 + r1)
                 phi_2 = h_eta.neg().expm1() / h_eta + 1
                 phi_3 = phi_2 / h_eta - 0.5
-                x = x + phi_2 * d1 - phi_3 * d2
+                x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2
             elif h_1 is not None:
+                # DPM-Solver++(2M) SDE
                 r = h_1 / h
                 d = (denoised - denoised_1) / r
                 phi_2 = h_eta.neg().expm1() / h_eta + 1
-                x = x + phi_2 * d
+                x = x + (alpha_t * phi_2) * d
 
-            if eta:
+            if eta > 0 and s_noise > 0:
                 x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
 
         denoised_1, denoised_2 = denoised, denoised_1
         h_1, h_2 = h, h_1
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     if len(sigmas) <= 1:
@@ -863,6 +924,7 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
 
+
 @torch.no_grad()
 def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     if len(sigmas) <= 1:
@@ -872,6 +934,7 @@ def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
 
+
 @torch.no_grad()
 def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     if len(sigmas) <= 1:
@@ -1009,7 +1072,9 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
         d_cur = (x_cur - denoised) / t_cur
 
         order = min(max_order, i+1)
-        if order == 1:      # First Euler step.
+        if t_next == 0:     # Denoising step
+            x_next = denoised
+        elif order == 1:    # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
         elif order == 2:    # Use one history point.
             x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
@@ -1027,6 +1092,7 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
     return x_next
 
+
 #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
 #under Apache 2 license
 def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
@@ -1050,7 +1116,9 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
         d_cur = (x_cur - denoised) / t_cur
 
         order = min(max_order, i+1)
-        if order == 1:      # First Euler step.
+        if t_next == 0:     # Denoising step
+            x_next = denoised
+        elif order == 1:    # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
         elif order == 2:    # Use one history point.
             h_n = (t_next - t_cur)
@@ -1090,6 +1158,7 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
 
     return x_next
 
+
 #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
 #under Apache 2 license
 @torch.no_grad()
@@ -1140,39 +1209,22 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
     return x_next
 
-@torch.no_grad()
-def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    extra_args = {} if extra_args is None else extra_args
-
-    temp = [0]
-    def post_cfg_function(args):
-        temp[0] = args["uncond_denoised"]
-        return args["denoised"]
-
-    model_options = extra_args.get("model_options", {}).copy()
-    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
-
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        sigma_hat = sigmas[i]
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, temp[0])
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        # Euler method
-        x = denoised + d * sigmas[i + 1]
-    return x
 
 @torch.no_grad()
 def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with Euler method steps."""
+    """Ancestral sampling with Euler method steps (CFG++)."""
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
 
-    temp = [0]
+    model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+
+    uncond_denoised = None
+
     def post_cfg_function(args):
-        temp[0] = args["uncond_denoised"]
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
         return args["denoised"]
 
     model_options = extra_args.get("model_options", {}).copy()
@@ -1181,15 +1233,33 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        d = to_d(x, sigmas[i], temp[0])
-        # Euler method
-        x = denoised + d * sigma_down
-        if sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp()
+            alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp()
+            d = to_d(x, sigmas[i], alpha_s * uncond_denoised)   # to noise
+
+            # DDIM stochastic sampling
+            sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta)
+            sigma_down = alpha_t * sigma_down
+
+            # Euler method
+            x = alpha_t * denoised + sigma_down * d
+            if eta > 0 and s_noise > 0:
+                x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
+
+
+@torch.no_grad()
+def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """Euler method steps (CFG++)."""
+    return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None)
+
+
 @torch.no_grad()
 def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
@@ -1346,6 +1416,7 @@ def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=N
 def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
 
+
 @torch.no_grad()
 def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):
     """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
@@ -1372,31 +1443,32 @@ def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None,
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
         dt = sigmas[i + 1] - sigmas[i]
-        if i == 0:
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
             # Euler method
             if cfg_pp:
                 x = denoised + d * sigmas[i + 1]
             else:
                 x = x + d * dt
-        else:
-            # Gradient estimation
-            if cfg_pp:
+
+            if i >= 1:
+                # Gradient estimation
                 d_bar = (ge_gamma - 1) * (d - old_d)
-                x = denoised + d * sigmas[i + 1] + d_bar * dt
-            else:
-                d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
                 x = x + d_bar * dt
         old_d = d
     return x
 
+
 @torch.no_grad()
 def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
     return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True)
 
+
 @torch.no_grad()
-def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, noise_scaler=None, max_stage=3):
-    """
-    Extended Reverse-Time SDE solver (VE ER-SDE-Solver-3). Arxiv: https://arxiv.org/abs/2309.06169.
+def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3):
+    """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169.
     Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.
     """
     extra_args = {} if extra_args is None else extra_args
@@ -1404,12 +1476,18 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
-    def default_noise_scaler(sigma):
-        return sigma * ((sigma ** 0.3).exp() + 10.0)
-    noise_scaler = default_noise_scaler if noise_scaler is None else noise_scaler
+    def default_er_sde_noise_scaler(x):
+        return x * ((x ** 0.3).exp() + 10.0)
+
+    noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler
     num_integration_points = 200.0
     point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)
 
+    model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling)
+    er_lambdas = half_log_snrs.neg().exp()  # er_lambda_t = sigma_t / alpha_t
+
     old_denoised = None
     old_denoised_d = None
 
@@ -1420,41 +1498,45 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
         stage_used = min(max_stage, i + 1)
         if sigmas[i + 1] == 0:
             x = denoised
-        elif stage_used == 1:
-            r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
-            x = r * x + (1 - r) * denoised
         else:
-            r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
-            x = r * x + (1 - r) * denoised
+            er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1]
+            alpha_s = sigmas[i] / er_lambda_s
+            alpha_t = sigmas[i + 1] / er_lambda_t
+            r_alpha = alpha_t / alpha_s
+            r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s)
 
-            dt = sigmas[i + 1] - sigmas[i]
-            sigma_step_size = -dt / num_integration_points
-            sigma_pos = sigmas[i + 1] + point_indice * sigma_step_size
-            scaled_pos = noise_scaler(sigma_pos)
+            # Stage 1 Euler
+            x = r_alpha * r * x + alpha_t * (1 - r) * denoised
 
-            # Stage 2
-            s = torch.sum(1 / scaled_pos) * sigma_step_size
-            denoised_d = (denoised - old_denoised) / (sigmas[i] - sigmas[i - 1])
-            x = x + (dt + s * noise_scaler(sigmas[i + 1])) * denoised_d
+            if stage_used >= 2:
+                dt = er_lambda_t - er_lambda_s
+                lambda_step_size = -dt / num_integration_points
+                lambda_pos = er_lambda_t + point_indice * lambda_step_size
+                scaled_pos = noise_scaler(lambda_pos)
 
-            if stage_used >= 3:
-                # Stage 3
-                s_u = torch.sum((sigma_pos - sigmas[i]) / scaled_pos) * sigma_step_size
-                denoised_u = (denoised_d - old_denoised_d) / ((sigmas[i] - sigmas[i - 2]) / 2)
-                x = x + ((dt ** 2) / 2 + s_u * noise_scaler(sigmas[i + 1])) * denoised_u
-            old_denoised_d = denoised_d
+                # Stage 2
+                s = torch.sum(1 / scaled_pos) * lambda_step_size
+                denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1])
+                x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d
 
-        if s_noise != 0 and sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
+                if stage_used >= 3:
+                    # Stage 3
+                    s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size
+                    denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2)
+                    x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u
+                old_denoised_d = denoised_d
+
+            if s_noise > 0:
+                x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
         old_denoised = denoised
     return x
 
+
 @torch.no_grad()
 def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
-    '''
-    SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 2
-    Arxiv: https://arxiv.org/abs/2305.14267
-    '''
+    """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
+    arXiv: https://arxiv.org/abs/2305.14267
+    """
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@@ -1462,6 +1544,11 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
 
     inject_noise = eta > 0 and s_noise > 0
 
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
+    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
@@ -1469,80 +1556,206 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
         if sigmas[i + 1] == 0:
             x = denoised
         else:
-            t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = t_next - t
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
             h_eta = h * (eta + 1)
-            s = t + r * h
+            lambda_s_1 = lambda_s + r * h
             fac = 1 / (2 * r)
-            sigma_s = s.neg().exp()
+            sigma_s_1 = sigma_fn(lambda_s_1)
+
+            # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
+            alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
 
             coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1()
             if inject_noise:
+                # 0 < r < 1
                 noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt()
-                noise_coeff_2 = ((-2 * r * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
-                noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s), noise_sampler(sigma_s, sigmas[i + 1])
+                noise_coeff_2 = (-r * h * eta).exp() * (-2 * (1 - r) * h * eta).expm1().neg().sqrt()
+                noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigmas[i + 1])
 
             # Step 1
-            x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
-            if inject_noise:
-                x_2 = x_2 + sigma_s * (noise_coeff_1 * noise_1) * s_noise
-            denoised_2 = model(x_2, sigma_s * s_in, **extra_args)
-
-            # Step 2
-            denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (coeff_2 + 1) * x - coeff_2 * denoised_d
-            if inject_noise:
-                x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
-    return x
-
-@torch.no_grad()
-def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
-    '''
-    SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 3
-    Arxiv: https://arxiv.org/abs/2305.14267
-    '''
-    extra_args = {} if extra_args is None else extra_args
-    seed = extra_args.get("seed", None)
-    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-
-    inject_noise = eta > 0 and s_noise > 0
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            x = denoised
-        else:
-            t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = t_next - t
-            h_eta = h * (eta + 1)
-            s_1 = t + r_1 * h
-            s_2 = t + r_2 * h
-            sigma_s_1, sigma_s_2 = s_1.neg().exp(), s_2.neg().exp()
-
-            coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
-            if inject_noise:
-                noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
-                noise_coeff_2 = ((-2 * r_1 * h * eta).expm1() - (-2 * r_2 * h * eta).expm1()).sqrt()
-                noise_coeff_3 = ((-2 * r_2 * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
-                noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
-
-            # Step 1
-            x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
+            x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
             if inject_noise:
                 x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
             denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
             # Step 2
-            x_3 = (coeff_2 + 1) * x - coeff_2 * denoised + (r_2 / r_1) * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_2 * denoised_d
+            if inject_noise:
+                x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
+    return x
+
+
+@torch.no_grad()
+def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
+    """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3.
+    arXiv: https://arxiv.org/abs/2305.14267
+    """
+    extra_args = {} if extra_args is None else extra_args
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    inject_noise = eta > 0 and s_noise > 0
+
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
+    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            x = denoised
+        else:
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
+            h_eta = h * (eta + 1)
+            lambda_s_1 = lambda_s + r_1 * h
+            lambda_s_2 = lambda_s + r_2 * h
+            sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
+
+            # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
+            alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+            alpha_s_2 = sigma_s_2 * lambda_s_2.exp()
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+            coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
+            if inject_noise:
+                # 0 < r_1 < r_2 < 1
+                noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
+                noise_coeff_2 = (-r_1 * h * eta).exp() * (-2 * (r_2 - r_1) * h * eta).expm1().neg().sqrt()
+                noise_coeff_3 = (-r_2 * h * eta).exp() * (-2 * (1 - r_2) * h * eta).expm1().neg().sqrt()
+                noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
+
+            # Step 1
+            x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
+            if inject_noise:
+                x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
+            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+
+            # Step 2
+            x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * coeff_2 * denoised + (r_2 / r_1) * alpha_s_2 * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
             if inject_noise:
                 x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
             denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
 
             # Step 3
-            x = (coeff_3 + 1) * x - coeff_3 * denoised + (1. / r_2) * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_3 * denoised + (1. / r_2) * alpha_t * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
             if inject_noise:
                 x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise
     return x
+
+
+@torch.no_grad()
+def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False):
+    """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023)."""
+    if len(sigmas) <= 1:
+        return x
+    extra_args = {} if extra_args is None else extra_args
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
+
+    if tau_func is None:
+        # Use default interval for stochastic sampling
+        start_sigma = model_sampling.percent_to_sigma(0.2)
+        end_sigma = model_sampling.percent_to_sigma(0.8)
+        tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0)
+
+    max_used_order = max(predictor_order, corrector_order)
+    x_pred = x  # x: current state, x_pred: predicted next state
+
+    h = 0.0
+    tau_t = 0.0
+    noise = 0.0
+    pred_list = []
+
+    # Lower order near the end to improve stability
+    lower_order_to_end = sigmas[-1].item() == 0
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        # Evaluation
+        denoised = model(x_pred, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        pred_list.append(denoised)
+        pred_list = pred_list[-max_used_order:]
+
+        predictor_order_used = min(predictor_order, len(pred_list))
+        if i == 0 or (sigmas[i + 1] == 0 and not use_pece):
+            corrector_order_used = 0
+        else:
+            corrector_order_used = min(corrector_order, len(pred_list))
+
+        if lower_order_to_end:
+            predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i)
+            corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i)
+
+        # Corrector
+        if corrector_order_used == 0:
+            # Update by the predicted state
+            x = x_pred
+        else:
+            curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1]
+            b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
+                sigmas[i],
+                curr_lambdas,
+                lambdas[i - 1],
+                lambdas[i],
+                tau_t,
+                simple_order_2,
+                is_corrector_step=True,
+            )
+            pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1)    # (B, K, ...)
+            corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0]))  # (B, ...)
+            x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res
+
+            if tau_t > 0 and s_noise > 0:
+                # The noise from the previous predictor step
+                x = x + noise
+
+            if use_pece:
+                # Evaluate the corrected state
+                denoised = model(x, sigmas[i] * s_in, **extra_args)
+                pred_list[-1] = denoised
+
+        # Predictor
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            tau_t = tau_func(sigmas[i + 1])
+            curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
+            b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
+                sigmas[i + 1],
+                curr_lambdas,
+                lambdas[i],
+                lambdas[i + 1],
+                tau_t,
+                simple_order_2,
+                is_corrector_step=False,
+            )
+            pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1)    # (B, K, ...)
+            pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0]))  # (B, ...)
+            h = lambdas[i + 1] - lambdas[i]
+            x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res
+
+            if tau_t > 0 and s_noise > 0:
+                noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
+                x_pred = x_pred + noise
+    return x
+
+
+@torch.no_grad()
+def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
+    """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
+    return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)

comfy/latent_formats.py

@@ -457,6 +457,82 @@ class Wan21(LatentFormat):
         latents_std = self.latents_std.to(latent.device, latent.dtype)
         return latent * latents_std / self.scale_factor + latents_mean
 
+class Wan22(Wan21):
+    latent_channels = 48
+    latent_dimensions = 3
+
+    latent_rgb_factors = [
+            [ 0.0119,  0.0103,  0.0046],
+            [-0.1062, -0.0504,  0.0165],
+            [ 0.0140,  0.0409,  0.0491],
+            [-0.0813, -0.0677,  0.0607],
+            [ 0.0656,  0.0851,  0.0808],
+            [ 0.0264,  0.0463,  0.0912],
+            [ 0.0295,  0.0326,  0.0590],
+            [-0.0244, -0.0270,  0.0025],
+            [ 0.0443, -0.0102,  0.0288],
+            [-0.0465, -0.0090, -0.0205],
+            [ 0.0359,  0.0236,  0.0082],
+            [-0.0776,  0.0854,  0.1048],
+            [ 0.0564,  0.0264,  0.0561],
+            [ 0.0006,  0.0594,  0.0418],
+            [-0.0319, -0.0542, -0.0637],
+            [-0.0268,  0.0024,  0.0260],
+            [ 0.0539,  0.0265,  0.0358],
+            [-0.0359, -0.0312, -0.0287],
+            [-0.0285, -0.1032, -0.1237],
+            [ 0.1041,  0.0537,  0.0622],
+            [-0.0086, -0.0374, -0.0051],
+            [ 0.0390,  0.0670,  0.2863],
+            [ 0.0069,  0.0144,  0.0082],
+            [ 0.0006, -0.0167,  0.0079],
+            [ 0.0313, -0.0574, -0.0232],
+            [-0.1454, -0.0902, -0.0481],
+            [ 0.0714,  0.0827,  0.0447],
+            [-0.0304, -0.0574, -0.0196],
+            [ 0.0401,  0.0384,  0.0204],
+            [-0.0758, -0.0297, -0.0014],
+            [ 0.0568,  0.1307,  0.1372],
+            [-0.0055, -0.0310, -0.0380],
+            [ 0.0239, -0.0305,  0.0325],
+            [-0.0663, -0.0673, -0.0140],
+            [-0.0416, -0.0047, -0.0023],
+            [ 0.0166,  0.0112, -0.0093],
+            [-0.0211,  0.0011,  0.0331],
+            [ 0.1833,  0.1466,  0.2250],
+            [-0.0368,  0.0370,  0.0295],
+            [-0.3441, -0.3543, -0.2008],
+            [-0.0479, -0.0489, -0.0420],
+            [-0.0660, -0.0153,  0.0800],
+            [-0.0101,  0.0068,  0.0156],
+            [-0.0690, -0.0452, -0.0927],
+            [-0.0145,  0.0041,  0.0015],
+            [ 0.0421,  0.0451,  0.0373],
+            [ 0.0504, -0.0483, -0.0356],
+            [-0.0837,  0.0168,  0.0055]
+        ]
+
+    latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388]
+
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latents_mean = torch.tensor([
+                -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
+                -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
+                -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502,
+                -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230,
+                -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748,
+                0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667,
+            ]).view(1, self.latent_channels, 1, 1, 1)
+        self.latents_std = torch.tensor([
+                0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,
+                0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,
+                0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,
+                0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,
+                0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,
+                0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
+            ]).view(1, self.latent_channels, 1, 1, 1)
+
 class Hunyuan3Dv2(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1

comfy/ldm/chroma/layers.py

@@ -80,15 +80,13 @@ class DoubleStreamBlock(nn.Module):
         (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
 
         # prepare image for attention
-        img_modulated = self.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
         img_qkv = self.img_attn.qkv(img_modulated)
         img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
 
         # prepare txt for attention
-        txt_modulated = self.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
         txt_qkv = self.txt_attn.qkv(txt_modulated)
         txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
@@ -102,12 +100,12 @@ class DoubleStreamBlock(nn.Module):
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
-        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+        img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
+        img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
 
         # calculate the txt bloks
-        txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
+        txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
 
         if txt.dtype == torch.float16:
             txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
@@ -152,7 +150,7 @@ class SingleStreamBlock(nn.Module):
 
     def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
         mod = vec
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
         qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
@@ -162,7 +160,7 @@ class SingleStreamBlock(nn.Module):
         attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        x += mod.gate * output
+        x.addcmul_(mod.gate, output)
         if x.dtype == torch.float16:
             x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
         return x
@@ -178,6 +176,6 @@ class LastLayer(nn.Module):
         shift, scale = vec
         shift = shift.squeeze(1)
         scale = scale.squeeze(1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
         x = self.linear(x)
         return x

comfy/ldm/chroma/model.py

@@ -254,13 +254,12 @@ class Chroma(nn.Module):
 
     def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
-        patch_size = 2
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
 
-        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
 
-        h_len = ((h + (patch_size // 2)) // patch_size)
-        w_len = ((w + (patch_size // 2)) // patch_size)
+        h_len = ((h + (self.patch_size // 2)) // self.patch_size)
+        w_len = ((w + (self.patch_size // 2)) // self.patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
         img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
@@ -268,4 +267,4 @@ class Chroma(nn.Module):
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w]

comfy/ldm/cosmos/blocks.py

@@ -26,16 +26,6 @@ from torch import nn
 from comfy.ldm.modules.attention import optimized_attention
 
 
-def apply_rotary_pos_emb(
-    t: torch.Tensor,
-    freqs: torch.Tensor,
-) -> torch.Tensor:
-    t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float()
-    t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1]
-    t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t)
-    return t_out
-
-
 def get_normalization(name: str, channels: int, weight_args={}, operations=None):
     if name == "I":
         return nn.Identity()

comfy/ldm/cosmos/position_embedding.py

@@ -66,15 +66,16 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
         h_extrapolation_ratio: float = 1.0,
         w_extrapolation_ratio: float = 1.0,
         t_extrapolation_ratio: float = 1.0,
+        enable_fps_modulation: bool = True,
         device=None,
         **kwargs,  # used for compatibility with other positional embeddings; unused in this class
     ):
         del kwargs
         super().__init__()
-        self.register_buffer("seq", torch.arange(max(len_h, len_w, len_t), dtype=torch.float, device=device))
         self.base_fps = base_fps
         self.max_h = len_h
         self.max_w = len_w
+        self.enable_fps_modulation = enable_fps_modulation
 
         dim = head_dim
         dim_h = dim // 6 * 2
@@ -132,21 +133,19 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
         temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device))
 
         B, T, H, W, _ = B_T_H_W_C
+        seq = torch.arange(max(H, W, T), dtype=torch.float, device=device)
         uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max())
         assert (
             uniform_fps or B == 1 or T == 1
         ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1"
-        assert (
-            H <= self.max_h and W <= self.max_w
-        ), f"Input dimensions (H={H}, W={W}) exceed the maximum dimensions (max_h={self.max_h}, max_w={self.max_w})"
-        half_emb_h = torch.outer(self.seq[:H].to(device=device), h_spatial_freqs)
-        half_emb_w = torch.outer(self.seq[:W].to(device=device), w_spatial_freqs)
+        half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs)
+        half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs)
 
         # apply sequence scaling in temporal dimension
-        if fps is None:  # image case
-            half_emb_t = torch.outer(self.seq[:T].to(device=device), temporal_freqs)
+        if fps is None or self.enable_fps_modulation is False:  # image case
+            half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs)
         else:
-            half_emb_t = torch.outer(self.seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs)
+            half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs)
 
         half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1)
         half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1)

comfy/ldm/cosmos/predict2.py

@@ -0,0 +1,864 @@
+# original code from: https://github.com/nvidia-cosmos/cosmos-predict2
+
+import torch
+from torch import nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+import logging
+from typing import Callable, Optional, Tuple
+import math
+
+from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis
+from torchvision import transforms
+
+from comfy.ldm.modules.attention import optimized_attention
+
+def apply_rotary_pos_emb(
+    t: torch.Tensor,
+    freqs: torch.Tensor,
+) -> torch.Tensor:
+    t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float()
+    t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1]
+    t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t)
+    return t_out
+
+
+# ---------------------- Feed Forward Network -----------------------
+class GPT2FeedForward(nn.Module):
+    def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None:
+        super().__init__()
+        self.activation = nn.GELU()
+        self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype)
+        self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype)
+
+        self._layer_id = None
+        self._dim = d_model
+        self._hidden_dim = d_ff
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.layer1(x)
+
+        x = self.activation(x)
+        x = self.layer2(x)
+        return x
+
+
+def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor:
+    """Computes multi-head attention using PyTorch's native implementation.
+
+    This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
+    It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product
+    attention, and rearranges the output back to the original format.
+
+    The input tensor names use the following dimension conventions:
+
+    - B: batch size
+    - S: sequence length
+    - H: number of attention heads
+    - D: head dimension
+
+    Args:
+        q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim)
+        k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim)
+        v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim)
+
+    Returns:
+        Attention output tensor with shape (batch, seq_len, n_heads * head_dim)
+    """
+    in_q_shape = q_B_S_H_D.shape
+    in_k_shape = k_B_S_H_D.shape
+    q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])
+    k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
+    v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
+    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True)
+
+
+class Attention(nn.Module):
+    """
+    A flexible attention module supporting both self-attention and cross-attention mechanisms.
+
+    This module implements a multi-head attention layer that can operate in either self-attention
+    or cross-attention mode. The mode is determined by whether a context dimension is provided.
+    The implementation uses scaled dot-product attention and supports optional bias terms and
+    dropout regularization.
+
+    Args:
+        query_dim (int): The dimensionality of the query vectors.
+        context_dim (int, optional): The dimensionality of the context (key/value) vectors.
+            If None, the module operates in self-attention mode using query_dim. Default: None
+        n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8
+        head_dim (int, optional): The dimension of each attention head. Default: 64
+        dropout (float, optional): Dropout probability applied to the output. Default: 0.0
+        qkv_format (str, optional): Format specification for QKV tensors. Default: "bshd"
+        backend (str, optional): Backend to use for the attention operation. Default: "transformer_engine"
+
+    Examples:
+        >>> # Self-attention with 512 dimensions and 8 heads
+        >>> self_attn = Attention(query_dim=512)
+        >>> x = torch.randn(32, 16, 512)  # (batch_size, seq_len, dim)
+        >>> out = self_attn(x)  # (32, 16, 512)
+
+        >>> # Cross-attention
+        >>> cross_attn = Attention(query_dim=512, context_dim=256)
+        >>> query = torch.randn(32, 16, 512)
+        >>> context = torch.randn(32, 8, 256)
+        >>> out = cross_attn(query, context)  # (32, 16, 512)
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        context_dim: Optional[int] = None,
+        n_heads: int = 8,
+        head_dim: int = 64,
+        dropout: float = 0.0,
+        device=None,
+        dtype=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        logging.debug(
+            f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+            f"{n_heads} heads with a dimension of {head_dim}."
+        )
+        self.is_selfattn = context_dim is None  # self attention
+
+        context_dim = query_dim if context_dim is None else context_dim
+        inner_dim = head_dim * n_heads
+
+        self.n_heads = n_heads
+        self.head_dim = head_dim
+        self.query_dim = query_dim
+        self.context_dim = context_dim
+
+        self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
+        self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
+
+        self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+        self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
+
+        self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+        self.v_norm = nn.Identity()
+
+        self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype)
+        self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity()
+
+        self.attn_op = torch_attention_op
+
+        self._query_dim = query_dim
+        self._context_dim = context_dim
+        self._inner_dim = inner_dim
+
+    def compute_qkv(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        rope_emb: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        q = self.q_proj(x)
+        context = x if context is None else context
+        k = self.k_proj(context)
+        v = self.v_proj(context)
+        q, k, v = map(
+            lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim),
+            (q, k, v),
+        )
+
+        def apply_norm_and_rotary_pos_emb(
+            q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor]
+        ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+            v = self.v_norm(v)
+            if self.is_selfattn and rope_emb is not None:  # only apply to self-attention!
+                q = apply_rotary_pos_emb(q, rope_emb)
+                k = apply_rotary_pos_emb(k, rope_emb)
+            return q, k, v
+
+        q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb)
+
+        return q, k, v
+
+    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+        result = self.attn_op(q, k, v)  # [B, S, H, D]
+        return self.output_dropout(self.output_proj(result))
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        rope_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            x (Tensor): The query tensor of shape [B, Mq, K]
+            context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
+        """
+        q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
+        return self.compute_attention(q, k, v)
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int):
+        super().__init__()
+        self.num_channels = num_channels
+
+    def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor:
+        assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}"
+        timesteps = timesteps_B_T.flatten().float()
+        half_dim = self.num_channels // 2
+        exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device)
+        exponent = exponent / (half_dim - 0.0)
+
+        emb = torch.exp(exponent)
+        emb = timesteps[:, None].float() * emb[None, :]
+
+        sin_emb = torch.sin(emb)
+        cos_emb = torch.cos(emb)
+        emb = torch.cat([cos_emb, sin_emb], dim=-1)
+
+        return rearrange(emb, "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1])
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None):
+        super().__init__()
+        logging.debug(
+            f"Using AdaLN LoRA Flag:  {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility."
+        )
+        self.in_dim = in_features
+        self.out_dim = out_features
+        self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype)
+        self.activation = nn.SiLU()
+        self.use_adaln_lora = use_adaln_lora
+        if use_adaln_lora:
+            self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype)
+        else:
+            self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype)
+
+    def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        emb = self.linear_1(sample)
+        emb = self.activation(emb)
+        emb = self.linear_2(emb)
+
+        if self.use_adaln_lora:
+            adaln_lora_B_T_3D = emb
+            emb_B_T_D = sample
+        else:
+            adaln_lora_B_T_3D = None
+            emb_B_T_D = emb
+
+        return emb_B_T_D, adaln_lora_B_T_3D
+
+
+class PatchEmbed(nn.Module):
+    """
+    PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers,
+    depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions,
+    making it suitable for video and image processing tasks. It supports dividing the input into patches
+    and embedding each patch into a vector of size `out_channels`.
+
+    Parameters:
+    - spatial_patch_size (int): The size of each spatial patch.
+    - temporal_patch_size (int): The size of each temporal patch.
+    - in_channels (int): Number of input channels. Default: 3.
+    - out_channels (int): The dimension of the embedding vector for each patch. Default: 768.
+    - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True.
+    """
+
+    def __init__(
+        self,
+        spatial_patch_size: int,
+        temporal_patch_size: int,
+        in_channels: int = 3,
+        out_channels: int = 768,
+        device=None, dtype=None, operations=None
+    ):
+        super().__init__()
+        self.spatial_patch_size = spatial_patch_size
+        self.temporal_patch_size = temporal_patch_size
+
+        self.proj = nn.Sequential(
+            Rearrange(
+                "b c (t r) (h m) (w n) -> b t h w (c r m n)",
+                r=temporal_patch_size,
+                m=spatial_patch_size,
+                n=spatial_patch_size,
+            ),
+            operations.Linear(
+                in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype
+            ),
+        )
+        self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass of the PatchEmbed module.
+
+        Parameters:
+        - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where
+            B is the batch size,
+            C is the number of channels,
+            T is the temporal dimension,
+            H is the height, and
+            W is the width of the input.
+
+        Returns:
+        - torch.Tensor: The embedded patches as a tensor, with shape b t h w c.
+        """
+        assert x.dim() == 5
+        _, _, T, H, W = x.shape
+        assert (
+            H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0
+        ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}"
+        assert T % self.temporal_patch_size == 0
+        x = self.proj(x)
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of video DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        spatial_patch_size: int,
+        temporal_patch_size: int,
+        out_channels: int,
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        device=None, dtype=None, operations=None
+    ):
+        super().__init__()
+        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = operations.Linear(
+            hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
+        )
+        self.hidden_size = hidden_size
+        self.n_adaln_chunks = 2
+        self.use_adaln_lora = use_adaln_lora
+        self.adaln_lora_dim = adaln_lora_dim
+        if use_adaln_lora:
+            self.adaln_modulation = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype),
+                operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype),
+            )
+        else:
+            self.adaln_modulation = nn.Sequential(
+                nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype)
+            )
+
+    def forward(
+        self,
+        x_B_T_H_W_D: torch.Tensor,
+        emb_B_T_D: torch.Tensor,
+        adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
+    ):
+        if self.use_adaln_lora:
+            assert adaln_lora_B_T_3D is not None
+            shift_B_T_D, scale_B_T_D = (
+                self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size]
+            ).chunk(2, dim=-1)
+        else:
+            shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1)
+
+        shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d"), rearrange(
+            scale_B_T_D, "b t d -> b t 1 1 d"
+        )
+
+        def _fn(
+            _x_B_T_H_W_D: torch.Tensor,
+            _norm_layer: nn.Module,
+            _scale_B_T_1_1_D: torch.Tensor,
+            _shift_B_T_1_1_D: torch.Tensor,
+        ) -> torch.Tensor:
+            return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D
+
+        x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D)
+        x_B_T_H_W_O = self.linear(x_B_T_H_W_D)
+        return x_B_T_H_W_O
+
+
+class Block(nn.Module):
+    """
+    A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation.
+    Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation.
+
+    Parameters:
+        x_dim (int): Dimension of input features
+        context_dim (int): Dimension of context features for cross-attention
+        num_heads (int): Number of attention heads
+        mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False
+        adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256
+
+    The block applies the following sequence:
+    1. Self-attention with AdaLN modulation
+    2. Cross-attention with AdaLN modulation
+    3. MLP with AdaLN modulation
+
+    Each component uses skip connections and layer normalization.
+    """
+
+    def __init__(
+        self,
+        x_dim: int,
+        context_dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.x_dim = x_dim
+        self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)
+        self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations)
+
+        self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)
+        self.cross_attn = Attention(
+            x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations
+        )
+
+        self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)
+        self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations)
+
+        self.use_adaln_lora = use_adaln_lora
+        if self.use_adaln_lora:
+            self.adaln_modulation_self_attn = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),
+                operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),
+            )
+            self.adaln_modulation_cross_attn = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),
+                operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),
+            )
+            self.adaln_modulation_mlp = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),
+                operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),
+            )
+        else:
+            self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))
+            self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))
+            self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))
+
+    def forward(
+        self,
+        x_B_T_H_W_D: torch.Tensor,
+        emb_B_T_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+        adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
+        extra_per_block_pos_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if extra_per_block_pos_emb is not None:
+            x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
+
+        if self.use_adaln_lora:
+            shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = (
+                self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D
+            ).chunk(3, dim=-1)
+            shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = (
+                self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D
+            ).chunk(3, dim=-1)
+            shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = (
+                self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D
+            ).chunk(3, dim=-1)
+        else:
+            shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn(
+                emb_B_T_D
+            ).chunk(3, dim=-1)
+            shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn(
+                emb_B_T_D
+            ).chunk(3, dim=-1)
+            shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1)
+
+        # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting
+        shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d")
+        scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d")
+        gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d")
+
+        shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d")
+        scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d")
+        gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d")
+
+        shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d")
+        scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d")
+        gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d")
+
+        B, T, H, W, D = x_B_T_H_W_D.shape
+
+        def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D):
+            return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D
+
+        normalized_x_B_T_H_W_D = _fn(
+            x_B_T_H_W_D,
+            self.layer_norm_self_attn,
+            scale_self_attn_B_T_1_1_D,
+            shift_self_attn_B_T_1_1_D,
+        )
+        result_B_T_H_W_D = rearrange(
+            self.self_attn(
+                # normalized_x_B_T_HW_D,
+                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
+                None,
+                rope_emb=rope_emb_L_1_1_D,
+            ),
+            "b (t h w) d -> b t h w d",
+            t=T,
+            h=H,
+            w=W,
+        )
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
+
+        def _x_fn(
+            _x_B_T_H_W_D: torch.Tensor,
+            layer_norm_cross_attn: Callable,
+            _scale_cross_attn_B_T_1_1_D: torch.Tensor,
+            _shift_cross_attn_B_T_1_1_D: torch.Tensor,
+        ) -> torch.Tensor:
+            _normalized_x_B_T_H_W_D = _fn(
+                _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D
+            )
+            _result_B_T_H_W_D = rearrange(
+                self.cross_attn(
+                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
+                    crossattn_emb,
+                    rope_emb=rope_emb_L_1_1_D,
+                ),
+                "b (t h w) d -> b t h w d",
+                t=T,
+                h=H,
+                w=W,
+            )
+            return _result_B_T_H_W_D
+
+        result_B_T_H_W_D = _x_fn(
+            x_B_T_H_W_D,
+            self.layer_norm_cross_attn,
+            scale_cross_attn_B_T_1_1_D,
+            shift_cross_attn_B_T_1_1_D,
+        )
+        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
+
+        normalized_x_B_T_H_W_D = _fn(
+            x_B_T_H_W_D,
+            self.layer_norm_mlp,
+            scale_mlp_B_T_1_1_D,
+            shift_mlp_B_T_1_1_D,
+        )
+        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
+        return x_B_T_H_W_D
+
+
+class MiniTrainDIT(nn.Module):
+    """
+    A clean impl of DIT that can load and  reproduce the training results of the original DIT model in~(cosmos 1)
+    A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
+
+    Args:
+        max_img_h (int): Maximum height of the input images.
+        max_img_w (int): Maximum width of the input images.
+        max_frames (int): Maximum number of frames in the video sequence.
+        in_channels (int): Number of input channels (e.g., RGB channels for color images).
+        out_channels (int): Number of output channels.
+        patch_spatial (tuple): Spatial resolution of patches for input processing.
+        patch_temporal (int): Temporal resolution of patches for input processing.
+        concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.
+        model_channels (int): Base number of channels used throughout the model.
+        num_blocks (int): Number of transformer blocks.
+        num_heads (int): Number of heads in the multi-head attention layers.
+        mlp_ratio (float): Expansion ratio for MLP blocks.
+        crossattn_emb_channels (int): Number of embedding channels for cross-attention.
+        pos_emb_cls (str): Type of positional embeddings.
+        pos_emb_learnable (bool): Whether positional embeddings are learnable.
+        pos_emb_interpolation (str): Method for interpolating positional embeddings.
+        min_fps (int): Minimum frames per second.
+        max_fps (int): Maximum frames per second.
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA.
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA.
+        rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.
+        rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.
+        rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.
+        extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.
+        extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.
+        extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.
+        extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.
+    """
+
+    def __init__(
+        self,
+        max_img_h: int,
+        max_img_w: int,
+        max_frames: int,
+        in_channels: int,
+        out_channels: int,
+        patch_spatial: int,  # tuple,
+        patch_temporal: int,
+        concat_padding_mask: bool = True,
+        # attention settings
+        model_channels: int = 768,
+        num_blocks: int = 10,
+        num_heads: int = 16,
+        mlp_ratio: float = 4.0,
+        # cross attention settings
+        crossattn_emb_channels: int = 1024,
+        # positional embedding settings
+        pos_emb_cls: str = "sincos",
+        pos_emb_learnable: bool = False,
+        pos_emb_interpolation: str = "crop",
+        min_fps: int = 1,
+        max_fps: int = 30,
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        rope_h_extrapolation_ratio: float = 1.0,
+        rope_w_extrapolation_ratio: float = 1.0,
+        rope_t_extrapolation_ratio: float = 1.0,
+        extra_per_block_abs_pos_emb: bool = False,
+        extra_h_extrapolation_ratio: float = 1.0,
+        extra_w_extrapolation_ratio: float = 1.0,
+        extra_t_extrapolation_ratio: float = 1.0,
+        rope_enable_fps_modulation: bool = True,
+        image_model=None,
+        device=None,
+        dtype=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.dtype = dtype
+        self.max_img_h = max_img_h
+        self.max_img_w = max_img_w
+        self.max_frames = max_frames
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.patch_spatial = patch_spatial
+        self.patch_temporal = patch_temporal
+        self.num_heads = num_heads
+        self.num_blocks = num_blocks
+        self.model_channels = model_channels
+        self.concat_padding_mask = concat_padding_mask
+        # positional embedding settings
+        self.pos_emb_cls = pos_emb_cls
+        self.pos_emb_learnable = pos_emb_learnable
+        self.pos_emb_interpolation = pos_emb_interpolation
+        self.min_fps = min_fps
+        self.max_fps = max_fps
+        self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio
+        self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio
+        self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio
+        self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb
+        self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio
+        self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio
+        self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio
+        self.rope_enable_fps_modulation = rope_enable_fps_modulation
+
+        self.build_pos_embed(device=device, dtype=dtype)
+        self.use_adaln_lora = use_adaln_lora
+        self.adaln_lora_dim = adaln_lora_dim
+        self.t_embedder = nn.Sequential(
+            Timesteps(model_channels),
+            TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,),
+        )
+
+        in_channels = in_channels + 1 if concat_padding_mask else in_channels
+        self.x_embedder = PatchEmbed(
+            spatial_patch_size=patch_spatial,
+            temporal_patch_size=patch_temporal,
+            in_channels=in_channels,
+            out_channels=model_channels,
+            device=device, dtype=dtype, operations=operations,
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                Block(
+                    x_dim=model_channels,
+                    context_dim=crossattn_emb_channels,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    use_adaln_lora=use_adaln_lora,
+                    adaln_lora_dim=adaln_lora_dim,
+                    device=device, dtype=dtype, operations=operations,
+                )
+                for _ in range(num_blocks)
+            ]
+        )
+
+        self.final_layer = FinalLayer(
+            hidden_size=self.model_channels,
+            spatial_patch_size=self.patch_spatial,
+            temporal_patch_size=self.patch_temporal,
+            out_channels=self.out_channels,
+            use_adaln_lora=self.use_adaln_lora,
+            adaln_lora_dim=self.adaln_lora_dim,
+            device=device, dtype=dtype, operations=operations,
+        )
+
+        self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype)
+
+    def build_pos_embed(self, device=None, dtype=None) -> None:
+        if self.pos_emb_cls == "rope3d":
+            cls_type = VideoRopePosition3DEmb
+        else:
+            raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}")
+
+        logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}")
+        kwargs = dict(
+            model_channels=self.model_channels,
+            len_h=self.max_img_h // self.patch_spatial,
+            len_w=self.max_img_w // self.patch_spatial,
+            len_t=self.max_frames // self.patch_temporal,
+            max_fps=self.max_fps,
+            min_fps=self.min_fps,
+            is_learnable=self.pos_emb_learnable,
+            interpolation=self.pos_emb_interpolation,
+            head_dim=self.model_channels // self.num_heads,
+            h_extrapolation_ratio=self.rope_h_extrapolation_ratio,
+            w_extrapolation_ratio=self.rope_w_extrapolation_ratio,
+            t_extrapolation_ratio=self.rope_t_extrapolation_ratio,
+            enable_fps_modulation=self.rope_enable_fps_modulation,
+            device=device,
+        )
+        self.pos_embedder = cls_type(
+            **kwargs,  # type: ignore
+        )
+
+        if self.extra_per_block_abs_pos_emb:
+            kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio
+            kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio
+            kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio
+            kwargs["device"] = device
+            kwargs["dtype"] = dtype
+            self.extra_pos_embedder = LearnablePosEmbAxis(
+                **kwargs,  # type: ignore
+            )
+
+    def prepare_embedded_sequence(
+        self,
+        x_B_C_T_H_W: torch.Tensor,
+        fps: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
+        """
+        Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.
+
+        Args:
+            x_B_C_T_H_W (torch.Tensor): video
+            fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.
+                                    If None, a default value (`self.base_fps`) will be used.
+            padding_mask (Optional[torch.Tensor]): current it is not used
+
+        Returns:
+            Tuple[torch.Tensor, Optional[torch.Tensor]]:
+                - A tensor of shape (B, T, H, W, D) with the embedded sequence.
+                - An optional positional embedding tensor, returned only if the positional embedding class
+                (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.
+
+        Notes:
+            - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.
+            - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.
+            - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using
+                the `self.pos_embedder` with the shape [T, H, W].
+            - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the
+            `self.pos_embedder` with the fps tensor.
+            - Otherwise, the positional embeddings are generated without considering fps.
+        """
+        if self.concat_padding_mask:
+            if padding_mask is None:
+                padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)
+            else:
+                padding_mask = transforms.functional.resize(
+                    padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST
+                )
+            x_B_C_T_H_W = torch.cat(
+                [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1
+            )
+        x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
+
+        if self.extra_per_block_abs_pos_emb:
+            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)
+        else:
+            extra_pos_emb = None
+
+        if "rope" in self.pos_emb_cls.lower():
+            return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb
+        x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
+
+        return x_B_T_H_W_D, None, extra_pos_emb
+
+    def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor:
+        x_B_C_Tt_Hp_Wp = rearrange(
+            x_B_T_H_W_M,
+            "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)",
+            p1=self.patch_spatial,
+            p2=self.patch_spatial,
+            t=self.patch_temporal,
+        )
+        return x_B_C_Tt_Hp_Wp
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        context: torch.Tensor,
+        fps: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        x_B_C_T_H_W = x
+        timesteps_B_T = timesteps
+        crossattn_emb = context
+        """
+        Args:
+            x: (B, C, T, H, W) tensor of spatial-temp inputs
+            timesteps: (B, ) tensor of timesteps
+            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
+        """
+        x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(
+            x_B_C_T_H_W,
+            fps=fps,
+            padding_mask=padding_mask,
+        )
+
+        if timesteps_B_T.ndim == 1:
+            timesteps_B_T = timesteps_B_T.unsqueeze(1)
+        t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype))
+        t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D)
+
+        # for logging purpose
+        affline_scale_log_info = {}
+        affline_scale_log_info["t_embedding_B_T_D"] = t_embedding_B_T_D.detach()
+        self.affline_scale_log_info = affline_scale_log_info
+        self.affline_emb = t_embedding_B_T_D
+        self.crossattn_emb = crossattn_emb
+
+        if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+            assert (
+                x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
+            ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}"
+
+        block_kwargs = {
+            "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
+            "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
+            "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
+        }
+        for block in self.blocks:
+            x_B_T_H_W_D = block(
+                x_B_T_H_W_D,
+                t_embedding_B_T_D,
+                crossattn_emb,
+                **block_kwargs,
+            )
+
+        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
+        x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)
+        return x_B_C_Tt_Hp_Wp

comfy/ldm/flux/controlnet.py

@@ -121,6 +121,11 @@ class ControlNetFlux(Flux):
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
 
+        if y is None:
+            y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
+        else:
+            y = y[:, :self.params.vec_in_dim]
+
         # running on sequences img
         img = self.img_in(img)
 
@@ -174,7 +179,7 @@ class ControlNetFlux(Flux):
             out["output"] = out_output[:self.main_model_single]
         return out
 
-    def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
+    def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs):
         patch_size = 2
         if self.latent_input:
             hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))

comfy/ldm/flux/layers.py

@@ -118,7 +118,7 @@ class Modulation(nn.Module):
 def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
     if modulation_dims is None:
         if m_add is not None:
-            return tensor * m_mult + m_add
+            return torch.addcmul(m_add, tensor, m_mult)
         else:
             return tensor * m_mult
     else:

comfy/ldm/flux/model.py

@@ -101,6 +101,10 @@ class Flux(nn.Module):
         transformer_options={},
         attn_mask: Tensor = None,
     ) -> Tensor:
+
+        if y is None:
+            y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
+
         patches_replace = transformer_options.get("patches_replace", {})
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -155,6 +159,9 @@ class Flux(nn.Module):
                     if add is not None:
                         img += add
 
+        if img.dtype == torch.float16:
+            img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504)
+
         img = torch.cat((txt, img), 1)
 
         for i, block in enumerate(self.single_blocks):
@@ -188,20 +195,50 @@ class Flux(nn.Module):
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
+    def process_img(self, x, index=0, h_offset=0, w_offset=0):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
 
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
-
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
+
+        h_offset = ((h_offset + (patch_size // 2)) // patch_size)
+        w_offset = ((w_offset + (patch_size // 2)) // patch_size)
+
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
-        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+        img_ids[:, :, 0] = img_ids[:, :, 1] + index
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
+        bs, c, h_orig, w_orig = x.shape
+        patch_size = self.patch_size
+
+        h_len = ((h_orig + (patch_size // 2)) // patch_size)
+        w_len = ((w_orig + (patch_size // 2)) // patch_size)
+        img, img_ids = self.process_img(x)
+        img_tokens = img.shape[1]
+        if ref_latents is not None:
+            h = 0
+            w = 0
+            for ref in ref_latents:
+                h_offset = 0
+                w_offset = 0
+                if ref.shape[-2] + h > ref.shape[-1] + w:
+                    w_offset = w
+                else:
+                    h_offset = h
+
+                kontext, kontext_ids = self.process_img(ref, index=1, h_offset=h_offset, w_offset=w_offset)
+                img = torch.cat([img, kontext], dim=1)
+                img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+                h = max(h, ref.shape[-2] + h_offset)
+                w = max(w, ref.shape[-1] + w_offset)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+        out = out[:, :img_tokens]
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig]

comfy/ldm/lightricks/model.py

@@ -261,8 +261,8 @@ class CrossAttention(nn.Module):
         self.heads = heads
         self.dim_head = dim_head
 
-        self.q_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device)
-        self.k_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device)
+        self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)
+        self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)
 
         self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)
         self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -973,7 +973,7 @@ class VideoVAE(nn.Module):
             norm_layer=config.get("norm_layer", "group_norm"),
             causal=config.get("causal_decoder", False),
             timestep_conditioning=self.timestep_conditioning,
-            spatial_padding_mode=config.get("spatial_padding_mode", "zeros"),
+            spatial_padding_mode=config.get("spatial_padding_mode", "reflect"),
         )
 
         self.per_channel_statistics = processor()

comfy/ldm/models/autoencoder.py

@@ -11,7 +11,7 @@ from comfy.ldm.modules.ema import LitEma
 import comfy.ops
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
-    def __init__(self, sample: bool = True):
+    def __init__(self, sample: bool = False):
         super().__init__()
         self.sample = sample
 
@@ -19,16 +19,12 @@ class DiagonalGaussianRegularizer(torch.nn.Module):
         yield from ()
 
     def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
-        log = dict()
         posterior = DiagonalGaussianDistribution(z)
         if self.sample:
             z = posterior.sample()
         else:
             z = posterior.mode()
-        kl_loss = posterior.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-        log["kl_loss"] = kl_loss
-        return z, log
+        return z, None
 
 
 class AbstractAutoencoder(torch.nn.Module):

comfy/ldm/modules/attention.py

@@ -753,7 +753,7 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n = p(n, extra_options)
 
-        x += n
+        x = n + x
         if "middle_patch" in transformer_patches:
             patch = transformer_patches["middle_patch"]
             for p in patch:
@@ -793,12 +793,12 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n = p(n, extra_options)
 
-        x += n
+        x = n + x
         if self.is_res:
             x_skip = x
         x = self.ff(self.norm3(x))
         if self.is_res:
-            x += x_skip
+            x = x_skip + x
 
         return x
 

comfy/ldm/modules/sub_quadratic_attention.py

@@ -31,7 +31,7 @@ def dynamic_slice(
     starts: List[int],
     sizes: List[int],
 ) -> Tensor:
-    slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
+    slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes))
     return x[slicing]
 
 class AttnChunk(NamedTuple):

comfy/ldm/omnigen/omnigen2.py

@@ -0,0 +1,469 @@
+# Original code: https://github.com/VectorSpaceLab/OmniGen2
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from comfy.ldm.lightricks.model import Timesteps
+from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.modules.attention import optimized_attention_masked
+import comfy.model_management
+import comfy.ldm.common_dit
+
+
+def apply_rotary_emb(x, freqs_cis):
+    if x.shape[1] == 0:
+        return x
+
+    t_ = x.reshape(*x.shape[:-1], -1, 1, 2)
+    t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
+    return t_out.reshape(*x.shape).to(dtype=x.dtype)
+
+
+def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    return F.silu(x) * y
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device)
+        self.act = nn.SiLU()
+        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device)
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        sample = self.linear_1(sample)
+        sample = self.act(sample)
+        sample = self.linear_2(sample)
+        return sample
+
+
+class LuminaRMSNormZero(nn.Module):
+    def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device)
+        self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        emb = self.linear(self.silu(emb))
+        scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
+        x = self.norm(x) * (1 + scale_msa[:, None])
+        return x, gate_msa, scale_mlp, gate_mlp
+
+
+class LuminaLayerNormContinuous(nn.Module):
+    def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device)
+        self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None
+
+    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
+        emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype))
+        x = self.norm(x) * (1 + emb)[:, None, :]
+        if self.linear_2 is not None:
+            x = self.linear_2(x)
+        return x
+
+
+class LuminaFeedForward(nn.Module):
+    def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)
+        self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device)
+        self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h1, h2 = self.linear_1(x), self.linear_3(x)
+        return self.linear_2(swiglu(h1, h2))
+
+
+class Lumina2CombinedTimestepCaptionEmbedding(nn.Module):
+    def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale)
+        self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations)
+        self.caption_embedder = nn.Sequential(
+            operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device),
+            operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:
+        timestep_proj = self.time_proj(timestep).to(dtype=dtype)
+        time_embed = self.timestep_embedder(timestep_proj)
+        caption_embed = self.caption_embedder(text_hidden_states)
+        return time_embed, caption_embed
+
+
+class Attention(nn.Module):
+    def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = heads
+        self.kv_heads = kv_heads
+        self.dim_head = dim_head
+        self.scale = dim_head ** -0.5
+
+        self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device)
+        self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
+        self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
+
+        self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
+        self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
+
+        self.to_out = nn.Sequential(
+            operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device),
+            nn.Dropout(0.0)
+        )
+
+    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        query = self.to_q(hidden_states)
+        key = self.to_k(encoder_hidden_states)
+        value = self.to_v(encoder_hidden_states)
+
+        query = query.view(batch_size, -1, self.heads, self.dim_head)
+        key = key.view(batch_size, -1, self.kv_heads, self.dim_head)
+        value = value.view(batch_size, -1, self.kv_heads, self.dim_head)
+
+        query = self.norm_q(query)
+        key = self.norm_k(key)
+
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        if self.kv_heads < self.heads:
+            key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
+            value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
+
+        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True)
+        hidden_states = self.to_out[0](hidden_states)
+        return hidden_states
+
+
+class OmniGen2TransformerBlock(nn.Module):
+    def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.modulation = modulation
+
+        self.attn = Attention(
+            query_dim=dim,
+            dim_head=dim // num_attention_heads,
+            heads=num_attention_heads,
+            kv_heads=num_kv_heads,
+            eps=1e-5,
+            bias=False,
+            dtype=dtype, device=device, operations=operations,
+        )
+
+        self.feed_forward = LuminaFeedForward(
+            dim=dim,
+            inner_dim=4 * dim,
+            multiple_of=multiple_of,
+            dtype=dtype, device=device, operations=operations
+        )
+
+        if modulation:
+            self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        else:
+            self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+
+        self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+
+    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if self.modulation:
+            norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
+            hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
+            mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
+            hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
+            hidden_states = hidden_states + self.norm2(attn_output)
+            mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
+            hidden_states = hidden_states + self.ffn_norm2(mlp_output)
+        return hidden_states
+
+
+class OmniGen2RotaryPosEmbed(nn.Module):
+    def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        self.axes_lens = axes_lens
+        self.patch_size = patch_size
+        self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim)
+
+    def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device):
+        p = self.patch_size
+
+        seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)]
+
+        max_seq_len = max(seq_lengths)
+        max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len])
+        max_img_len = max(l_effective_img_len)
+
+        position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device)
+
+        for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):
+            position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3")
+
+            pe_shift = cap_seq_len
+            pe_shift_len = cap_seq_len
+
+            if ref_img_sizes[i] is not None:
+                for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]):
+                    H, W = ref_img_size
+                    ref_H_tokens, ref_W_tokens = H // p, W // p
+
+                    row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten()
+                    col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten()
+                    position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift
+                    position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids
+                    position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids
+
+                    pe_shift += max(ref_H_tokens, ref_W_tokens)
+                    pe_shift_len += ref_img_len
+
+            H, W = img_sizes[i]
+            H_tokens, W_tokens = H // p, W // p
+
+            row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten()
+            col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten()
+
+            position_ids[i, pe_shift_len: seq_len, 0] = pe_shift
+            position_ids[i, pe_shift_len: seq_len, 1] = row_ids
+            position_ids[i, pe_shift_len: seq_len, 2] = col_ids
+
+        freqs_cis = self.rope_embedder(position_ids).movedim(1, 2)
+
+        cap_freqs_cis_shape = list(freqs_cis.shape)
+        cap_freqs_cis_shape[1] = encoder_seq_len
+        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        ref_img_freqs_cis_shape = list(freqs_cis.shape)
+        ref_img_freqs_cis_shape[1] = max_ref_img_len
+        ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        img_freqs_cis_shape = list(freqs_cis.shape)
+        img_freqs_cis_shape[1] = max_img_len
+        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)):
+            cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len]
+            ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)]
+            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len]
+
+        return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths
+
+
+class OmniGen2Transformer2DModel(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 16,
+        out_channels: Optional[int] = None,
+        hidden_size: int = 2304,
+        num_layers: int = 26,
+        num_refiner_layers: int = 2,
+        num_attention_heads: int = 24,
+        num_kv_heads: int = 8,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        axes_dim_rope: Tuple[int, int, int] = (32, 32, 32),
+        axes_lens: Tuple[int, int, int] = (300, 512, 512),
+        text_feat_dim: int = 1024,
+        timestep_scale: float = 1.0,
+        image_model=None,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.out_channels = out_channels or in_channels
+        self.hidden_size = hidden_size
+        self.dtype = dtype
+
+        self.rope_embedder = OmniGen2RotaryPosEmbed(
+            theta=10000,
+            axes_dim=axes_dim_rope,
+            axes_lens=axes_lens,
+            patch_size=patch_size,
+        )
+
+        self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
+        self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
+
+        self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding(
+            hidden_size=hidden_size,
+            text_feat_dim=text_feat_dim,
+            norm_eps=norm_eps,
+            timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
+        )
+
+        self.noise_refiner = nn.ModuleList([
+            OmniGen2TransformerBlock(
+                hidden_size, num_attention_heads, num_kv_heads,
+                multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
+            ) for _ in range(num_refiner_layers)
+        ])
+
+        self.ref_image_refiner = nn.ModuleList([
+            OmniGen2TransformerBlock(
+                hidden_size, num_attention_heads, num_kv_heads,
+                multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
+            ) for _ in range(num_refiner_layers)
+        ])
+
+        self.context_refiner = nn.ModuleList([
+            OmniGen2TransformerBlock(
+                hidden_size, num_attention_heads, num_kv_heads,
+                multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations
+            ) for _ in range(num_refiner_layers)
+        ])
+
+        self.layers = nn.ModuleList([
+            OmniGen2TransformerBlock(
+                hidden_size, num_attention_heads, num_kv_heads,
+                multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
+            ) for _ in range(num_layers)
+        ])
+
+        self.norm_out = LuminaLayerNormContinuous(
+            embedding_dim=hidden_size,
+            conditioning_embedding_dim=min(hidden_size, 1024),
+            elementwise_affine=False,
+            eps=1e-6,
+            out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations
+        )
+
+        self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype))
+
+    def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states):
+        batch_size = len(hidden_states)
+        p = self.patch_size
+
+        img_sizes = [(img.size(1), img.size(2)) for img in hidden_states]
+        l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes]
+
+        if ref_image_hidden_states is not None:
+            ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states))
+            ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size
+            l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes]
+        else:
+            ref_img_sizes = [None for _ in range(batch_size)]
+            l_effective_ref_img_len = [[0] for _ in range(batch_size)]
+
+        flat_ref_img_hidden_states = None
+        if ref_image_hidden_states is not None:
+            imgs = []
+            for ref_img in ref_image_hidden_states:
+                B, C, H, W = ref_img.size()
+                ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
+                imgs.append(ref_img)
+            flat_ref_img_hidden_states = torch.cat(imgs, dim=1)
+
+        img = hidden_states
+        B, C, H, W = img.size()
+        flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
+
+        return (
+            flat_hidden_states, flat_ref_img_hidden_states,
+            None, None,
+            l_effective_ref_img_len, l_effective_img_len,
+            ref_img_sizes, img_sizes,
+        )
+
+    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb):
+        batch_size = len(hidden_states)
+
+        hidden_states = self.x_embedder(hidden_states)
+        if ref_image_hidden_states is not None:
+            ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states)
+            image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device)
+
+            for i in range(batch_size):
+                shift = 0
+                for j, ref_img_len in enumerate(l_effective_ref_img_len[i]):
+                    ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j]
+                    shift += ref_img_len
+
+        for layer in self.noise_refiner:
+            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb)
+
+        if ref_image_hidden_states is not None:
+            for layer in self.ref_image_refiner:
+                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb)
+
+            hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)
+
+        return hidden_states
+
+    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs):
+        B, C, H, W = x.shape
+        hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        _, _, H_padded, W_padded = hidden_states.shape
+        timestep = 1.0 - timesteps
+        text_hidden_states = context
+        text_attention_mask = attention_mask
+        ref_image_hidden_states = ref_latents
+        device = hidden_states.device
+
+        temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype)
+
+        (
+            hidden_states, ref_image_hidden_states,
+            img_mask, ref_img_mask,
+            l_effective_ref_img_len, l_effective_img_len,
+            ref_img_sizes, img_sizes,
+        ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states)
+
+        (
+            context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb,
+            rotary_emb, encoder_seq_lengths, seq_lengths,
+        ) = self.rope_embedder(
+            hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0],
+            l_effective_ref_img_len, l_effective_img_len,
+            ref_img_sizes, img_sizes, device,
+        )
+
+        for layer in self.context_refiner:
+            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb)
+
+        img_len = hidden_states.shape[1]
+        combined_img_hidden_states = self.img_patch_embed_and_refine(
+            hidden_states, ref_image_hidden_states,
+            img_mask, ref_img_mask,
+            noise_rotary_emb, ref_img_rotary_emb,
+            l_effective_ref_img_len, l_effective_img_len,
+            temb,
+        )
+
+        hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
+        attention_mask = None
+
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
+
+        hidden_states = self.norm_out(hidden_states, temb)
+
+        p = self.patch_size
+        output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)',  h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W]
+
+        return -output

comfy/ldm/pixart/pixartms.py

@@ -1,256 +1,256 @@
-# Based on:
-# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
-# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
-import torch
-import torch.nn as nn
-
-from .blocks import (
-    t2i_modulate,
-    CaptionEmbedder,
-    AttentionKVCompress,
-    MultiHeadCrossAttention,
-    T2IFinalLayer,
-    SizeEmbedder,
-)
-from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
-
-
-def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
-    grid_h, grid_w = torch.meshgrid(
-        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
-        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
-        indexing='ij'
-    )
-    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
-    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
-    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
-    return emb
-
-class PixArtMSBlock(nn.Module):
-    """
-    A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
-    """
-    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
-                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.attn = AttentionKVCompress(
-            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
-            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
-        )
-        self.cross_attn = MultiHeadCrossAttention(
-            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
-        )
-        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        # to be compatible with lower version pytorch
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(
-            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
-            dtype=dtype, device=device, operations=operations
-        )
-        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
-
-    def forward(self, x, y, t, mask=None, HW=None, **kwargs):
-        B, N, C = x.shape
-
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)
-        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
-        x = x + self.cross_attn(x, y, mask)
-        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
-
-        return x
-
-
-### Core PixArt Model ###
-class PixArtMS(nn.Module):
-    """
-    Diffusion model with a Transformer backbone.
-    """
-    def __init__(
-            self,
-            input_size=32,
-            patch_size=2,
-            in_channels=4,
-            hidden_size=1152,
-            depth=28,
-            num_heads=16,
-            mlp_ratio=4.0,
-            class_dropout_prob=0.1,
-            learn_sigma=True,
-            pred_sigma=True,
-            drop_path: float = 0.,
-            caption_channels=4096,
-            pe_interpolation=None,
-            pe_precision=None,
-            config=None,
-            model_max_length=120,
-            micro_condition=True,
-            qk_norm=False,
-            kv_compress_config=None,
-            dtype=None,
-            device=None,
-            operations=None,
-            **kwargs,
-    ):
-        nn.Module.__init__(self)
-        self.dtype = dtype
-        self.pred_sigma = pred_sigma
-        self.in_channels = in_channels
-        self.out_channels = in_channels * 2 if pred_sigma else in_channels
-        self.patch_size = patch_size
-        self.num_heads = num_heads
-        self.pe_interpolation = pe_interpolation
-        self.pe_precision = pe_precision
-        self.hidden_size = hidden_size
-        self.depth = depth
-
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.t_block = nn.Sequential(
-            nn.SiLU(),
-            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
-        )
-        self.x_embedder = PatchEmbed(
-            patch_size=patch_size,
-            in_chans=in_channels,
-            embed_dim=hidden_size,
-            bias=True,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-        self.t_embedder = TimestepEmbedder(
-            hidden_size, dtype=dtype, device=device, operations=operations,
-        )
-        self.y_embedder = CaptionEmbedder(
-            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
-            act_layer=approx_gelu, token_num=model_max_length,
-            dtype=dtype, device=device, operations=operations,
-        )
-
-        self.micro_conditioning = micro_condition
-        if self.micro_conditioning:
-            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
-            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
-
-        # For fixed sin-cos embedding:
-        # num_patches = (input_size // patch_size) * (input_size // patch_size)
-        # self.base_size = input_size // self.patch_size
-        # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
-
-        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
-        if kv_compress_config is None:
-            kv_compress_config = {
-                'sampling': None,
-                'scale_factor': 1,
-                'kv_compress_layer': [],
-            }
-        self.blocks = nn.ModuleList([
-            PixArtMSBlock(
-                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
-                sampling=kv_compress_config['sampling'],
-                sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
-                qk_norm=qk_norm,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-            for i in range(depth)
-        ])
-        self.final_layer = T2IFinalLayer(
-            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
-        )
-
-    def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):
-        """
-        Original forward pass of PixArt.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N, 1, 120, C) conditioning
-        ar: (N, 1): aspect ratio
-        cs: (N ,2) size conditioning for height/width
-        """
-        B, C, H, W = x.shape
-        c_res = (H + W) // 2
-        pe_interpolation = self.pe_interpolation
-        if pe_interpolation is None or self.pe_precision is not None:
-            # calculate pe_interpolation on-the-fly
-            pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0)
-
-        pos_embed = get_2d_sincos_pos_embed_torch(
-            self.hidden_size,
-            h=(H // self.patch_size),
-            w=(W // self.patch_size),
-            pe_interpolation=pe_interpolation,
-            base_size=((round(c_res / 64) * 64) // self.patch_size),
-            device=x.device,
-            dtype=x.dtype,
-        ).unsqueeze(0)
-
-        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep, x.dtype)  # (N, D)
-
-        if self.micro_conditioning and (c_size is not None and c_ar is not None):
-            bs = x.shape[0]
-            c_size = self.csize_embedder(c_size, bs)  # (N, D)
-            c_ar = self.ar_embedder(c_ar, bs)  # (N, D)
-            t = t + torch.cat([c_size, c_ar], dim=1)
-
-        t0 = self.t_block(t)
-        y = self.y_embedder(y, self.training)  # (N, D)
-
-        if mask is not None:
-            if mask.shape[0] != y.shape[0]:
-                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
-            mask = mask.squeeze(1).squeeze(1)
-            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
-            y_lens = mask.sum(dim=1).tolist()
-        else:
-            y_lens = None
-            y = y.squeeze(1).view(1, -1, x.shape[-1])
-        for block in self.blocks:
-            x = block(x, y, t0, y_lens, (H, W), **kwargs)  # (N, T, D)
-
-        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x, H, W)  # (N, out_channels, H, W)
-
-        return x
-
-    def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
-        B, C, H, W = x.shape
-
-        # Fallback for missing microconds
-        if self.micro_conditioning:
-            if c_size is None:
-                c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
-
-            if c_ar is None:
-                c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
-
-        ## Still accepts the input w/o that dim but returns garbage
-        if len(context.shape) == 3:
-            context = context.unsqueeze(1)
-
-        ## run original forward pass
-        out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)
-
-        ## only return EPS
-        if self.pred_sigma:
-            return out[:, :self.in_channels]
-        return out
-
-    def unpatchify(self, x, h, w):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        h = h // self.patch_size
-        w = w // self.patch_size
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
-        return imgs
+# Based on:
+# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
+# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
+import torch
+import torch.nn as nn
+
+from .blocks import (
+    t2i_modulate,
+    CaptionEmbedder,
+    AttentionKVCompress,
+    MultiHeadCrossAttention,
+    T2IFinalLayer,
+    SizeEmbedder,
+)
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
+
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
+    grid_h, grid_w = torch.meshgrid(
+        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
+        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
+        indexing='ij'
+    )
+    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
+class PixArtMSBlock(nn.Module):
+    """
+    A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
+                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.attn = AttentionKVCompress(
+            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
+            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.cross_attn = MultiHeadCrossAttention(
+            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        # to be compatible with lower version pytorch
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(
+            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
+
+    def forward(self, x, y, t, mask=None, HW=None, **kwargs):
+        B, N, C = x.shape
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
+        x = x + self.cross_attn(x, y, mask)
+        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+
+        return x
+
+
+### Core PixArt Model ###
+class PixArtMS(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+            self,
+            input_size=32,
+            patch_size=2,
+            in_channels=4,
+            hidden_size=1152,
+            depth=28,
+            num_heads=16,
+            mlp_ratio=4.0,
+            class_dropout_prob=0.1,
+            learn_sigma=True,
+            pred_sigma=True,
+            drop_path: float = 0.,
+            caption_channels=4096,
+            pe_interpolation=None,
+            pe_precision=None,
+            config=None,
+            model_max_length=120,
+            micro_condition=True,
+            qk_norm=False,
+            kv_compress_config=None,
+            dtype=None,
+            device=None,
+            operations=None,
+            **kwargs,
+    ):
+        nn.Module.__init__(self)
+        self.dtype = dtype
+        self.pred_sigma = pred_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.pe_interpolation = pe_interpolation
+        self.pe_precision = pe_precision
+        self.hidden_size = hidden_size
+        self.depth = depth
+
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.t_block = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.x_embedder = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_channels,
+            embed_dim=hidden_size,
+            bias=True,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations,
+        )
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
+            act_layer=approx_gelu, token_num=model_max_length,
+            dtype=dtype, device=device, operations=operations,
+        )
+
+        self.micro_conditioning = micro_condition
+        if self.micro_conditioning:
+            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+
+        # For fixed sin-cos embedding:
+        # num_patches = (input_size // patch_size) * (input_size // patch_size)
+        # self.base_size = input_size // self.patch_size
+        # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+
+        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
+        if kv_compress_config is None:
+            kv_compress_config = {
+                'sampling': None,
+                'scale_factor': 1,
+                'kv_compress_layer': [],
+            }
+        self.blocks = nn.ModuleList([
+            PixArtMSBlock(
+                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
+                sampling=kv_compress_config['sampling'],
+                sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
+                qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            )
+            for i in range(depth)
+        ])
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
+
+    def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):
+        """
+        Original forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) conditioning
+        ar: (N, 1): aspect ratio
+        cs: (N ,2) size conditioning for height/width
+        """
+        B, C, H, W = x.shape
+        c_res = (H + W) // 2
+        pe_interpolation = self.pe_interpolation
+        if pe_interpolation is None or self.pe_precision is not None:
+            # calculate pe_interpolation on-the-fly
+            pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0)
+
+        pos_embed = get_2d_sincos_pos_embed_torch(
+            self.hidden_size,
+            h=(H // self.patch_size),
+            w=(W // self.patch_size),
+            pe_interpolation=pe_interpolation,
+            base_size=((round(c_res / 64) * 64) // self.patch_size),
+            device=x.device,
+            dtype=x.dtype,
+        ).unsqueeze(0)
+
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
+
+        if self.micro_conditioning and (c_size is not None and c_ar is not None):
+            bs = x.shape[0]
+            c_size = self.csize_embedder(c_size, bs)  # (N, D)
+            c_ar = self.ar_embedder(c_ar, bs)  # (N, D)
+            t = t + torch.cat([c_size, c_ar], dim=1)
+
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, D)
+
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = None
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+        for block in self.blocks:
+            x = block(x, y, t0, y_lens, (H, W), **kwargs)  # (N, T, D)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x, H, W)  # (N, out_channels, H, W)
+
+        return x
+
+    def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
+        B, C, H, W = x.shape
+
+        # Fallback for missing microconds
+        if self.micro_conditioning:
+            if c_size is None:
+                c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
+
+            if c_ar is None:
+                c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
+
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)
+
+        ## only return EPS
+        if self.pred_sigma:
+            return out[:, :self.in_channels]
+        return out
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        h = h // self.patch_size
+        w = w // self.patch_size
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs

comfy/ldm/wan/model.py

@@ -201,8 +201,10 @@ class WanAttentionBlock(nn.Module):
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
         # assert e.dtype == torch.float32
-
-        e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        if e.ndim < 4:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        else:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
         # assert e[0].dtype == torch.float32
 
         # self-attention
@@ -325,7 +327,10 @@ class Head(nn.Module):
             e(Tensor): Shape [B, C]
         """
         # assert e.dtype == torch.float32
-        e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
+        if e.ndim < 3:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
+        else:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2)
         x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
         return x
 
@@ -506,8 +511,9 @@ class WanModel(torch.nn.Module):
 
         # time embeddings
         e = self.time_embedding(
-            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
-        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
 
         # context
         context = self.text_embedding(context)
@@ -539,13 +545,20 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
         h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
         w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+
+        if time_dim_concat is not None:
+            time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
+            x = torch.cat([x, time_dim_concat], dim=2)
+            t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0])
+
         img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
         img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)

comfy/ldm/wan/vae.py

@@ -52,15 +52,6 @@ class RMS_norm(nn.Module):
             x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0)
 
 
-class Upsample(nn.Upsample):
-
-    def forward(self, x):
-        """
-        Fix bfloat16 support for nearest neighbor interpolation.
-        """
-        return super().forward(x.float()).type_as(x)
-
-
 class Resample(nn.Module):
 
     def __init__(self, dim, mode):
@@ -73,11 +64,11 @@ class Resample(nn.Module):
         # layers
         if mode == 'upsample2d':
             self.resample = nn.Sequential(
-                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
                 ops.Conv2d(dim, dim // 2, 3, padding=1))
         elif mode == 'upsample3d':
             self.resample = nn.Sequential(
-                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
                 ops.Conv2d(dim, dim // 2, 3, padding=1))
             self.time_conv = CausalConv3d(
                 dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
@@ -157,29 +148,6 @@ class Resample(nn.Module):
                     feat_idx[0] += 1
         return x
 
-    def init_weight(self, conv):
-        conv_weight = conv.weight
-        nn.init.zeros_(conv_weight)
-        c1, c2, t, h, w = conv_weight.size()
-        one_matrix = torch.eye(c1, c2)
-        init_matrix = one_matrix
-        nn.init.zeros_(conv_weight)
-        #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5
-        conv_weight.data[:, :, 1, 0, 0] = init_matrix  #* 0.5
-        conv.weight.data.copy_(conv_weight)
-        nn.init.zeros_(conv.bias.data)
-
-    def init_weight2(self, conv):
-        conv_weight = conv.weight.data
-        nn.init.zeros_(conv_weight)
-        c1, c2, t, h, w = conv_weight.size()
-        init_matrix = torch.eye(c1 // 2, c2)
-        #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2)
-        conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
-        conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
-        conv.weight.data.copy_(conv_weight)
-        nn.init.zeros_(conv.bias.data)
-
 
 class ResidualBlock(nn.Module):
 
@@ -494,12 +462,6 @@ class WanVAE(nn.Module):
         self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
                                  attn_scales, self.temperal_upsample, dropout)
 
-    def forward(self, x):
-        mu, log_var = self.encode(x)
-        z = self.reparameterize(mu, log_var)
-        x_recon = self.decode(z)
-        return x_recon, mu, log_var
-
     def encode(self, x):
         self.clear_cache()
         ## cache
@@ -545,18 +507,6 @@ class WanVAE(nn.Module):
         self.clear_cache()
         return out
 
-    def reparameterize(self, mu, log_var):
-        std = torch.exp(0.5 * log_var)
-        eps = torch.randn_like(std)
-        return eps * std + mu
-
-    def sample(self, imgs, deterministic=False):
-        mu, log_var = self.encode(imgs)
-        if deterministic:
-            return mu
-        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
-        return mu + std * torch.randn_like(std)
-
     def clear_cache(self):
         self._conv_num = count_conv3d(self.decoder)
         self._conv_idx = [0]

comfy/ldm/wan/vae2_2.py

@@ -0,0 +1,726 @@
+# original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from .vae import AttentionBlock, CausalConv3d, RMS_norm
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+CACHE_T = 2
+
+
+class Resample(nn.Module):
+
+    def __init__(self, dim, mode):
+        assert mode in (
+            "none",
+            "upsample2d",
+            "upsample3d",
+            "downsample2d",
+            "downsample3d",
+        )
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == "upsample2d":
+            self.resample = nn.Sequential(
+                nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
+                ops.Conv2d(dim, dim, 3, padding=1),
+            )
+        elif mode == "upsample3d":
+            self.resample = nn.Sequential(
+                nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
+                ops.Conv2d(dim, dim, 3, padding=1),
+                # ops.Conv2d(dim, dim//2, 3, padding=1)
+            )
+            self.time_conv = CausalConv3d(
+                dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
+        elif mode == "downsample2d":
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == "downsample3d":
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = CausalConv3d(
+                dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == "upsample3d":
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = "Rep"
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and
+                            feat_cache[idx] != "Rep"):
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat(
+                            [
+                                feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                    cache_x.device),
+                                cache_x,
+                            ],
+                            dim=2,
+                        )
+                    if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and
+                            feat_cache[idx] == "Rep"):
+                        cache_x = torch.cat(
+                            [
+                                torch.zeros_like(cache_x).to(cache_x.device),
+                                cache_x
+                            ],
+                            dim=2,
+                        )
+                    if feat_cache[idx] == "Rep":
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
+                                    3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = rearrange(x, "b c t h w -> (b t) c h w")
+        x = self.resample(x)
+        x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
+
+        if self.mode == "downsample3d":
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    x = self.time_conv(
+                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, in_dim, out_dim, dropout=0.0):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # layers
+        self.residual = nn.Sequential(
+            RMS_norm(in_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(in_dim, out_dim, 3, padding=1),
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            nn.Dropout(dropout),
+            CausalConv3d(out_dim, out_dim, 3, padding=1),
+        )
+        self.shortcut = (
+            CausalConv3d(in_dim, out_dim, 1)
+            if in_dim != out_dim else nn.Identity())
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        h = self.shortcut(x)
+        for layer in self.residual:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x + h
+
+
+def patchify(x, patch_size):
+    if patch_size == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b c f (h q) (w r) -> b (c r q) f h w",
+            q=patch_size,
+            r=patch_size,
+        )
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+
+    return x
+
+
+def unpatchify(x, patch_size):
+    if patch_size == 1:
+        return x
+
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b (c r q) f h w -> b c f (h q) (w r)",
+            q=patch_size,
+            r=patch_size,
+        )
+    return x
+
+
+class AvgDown3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert in_channels * self.factor % out_channels == 0
+        self.group_size = in_channels * self.factor // out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t
+        pad = (0, 0, 0, 0, pad_t, 0)
+        x = F.pad(x, pad)
+        B, C, T, H, W = x.shape
+        x = x.view(
+            B,
+            C,
+            T // self.factor_t,
+            self.factor_t,
+            H // self.factor_s,
+            self.factor_s,
+            W // self.factor_s,
+            self.factor_s,
+        )
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(
+            B,
+            C * self.factor,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.view(
+            B,
+            self.out_channels,
+            self.group_size,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.mean(dim=2)
+        return x
+
+
+class DupUp3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert out_channels * self.factor % in_channels == 0
+        self.repeats = out_channels * self.factor // in_channels
+
+    def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
+        x = x.repeat_interleave(self.repeats, dim=1)
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            self.factor_t,
+            self.factor_s,
+            self.factor_s,
+            x.size(2),
+            x.size(3),
+            x.size(4),
+        )
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            x.size(2) * self.factor_t,
+            x.size(4) * self.factor_s,
+            x.size(6) * self.factor_s,
+        )
+        if first_chunk:
+            x = x[:, :, self.factor_t - 1:, :, :]
+        return x
+
+
+class Down_ResidualBlock(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 dropout,
+                 mult,
+                 temperal_downsample=False,
+                 down_flag=False):
+        super().__init__()
+
+        # Shortcut path with downsample
+        self.avg_shortcut = AvgDown3D(
+            in_dim,
+            out_dim,
+            factor_t=2 if temperal_downsample else 1,
+            factor_s=2 if down_flag else 1,
+        )
+
+        # Main path with residual blocks and downsample
+        downsamples = []
+        for _ in range(mult):
+            downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+
+        # Add the final downsample block
+        if down_flag:
+            mode = "downsample3d" if temperal_downsample else "downsample2d"
+            downsamples.append(Resample(out_dim, mode=mode))
+
+        self.downsamples = nn.Sequential(*downsamples)
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        x_copy = x.clone()
+        for module in self.downsamples:
+            x = module(x, feat_cache, feat_idx)
+
+        return x + self.avg_shortcut(x_copy)
+
+
+class Up_ResidualBlock(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 dropout,
+                 mult,
+                 temperal_upsample=False,
+                 up_flag=False):
+        super().__init__()
+        # Shortcut path with upsample
+        if up_flag:
+            self.avg_shortcut = DupUp3D(
+                in_dim,
+                out_dim,
+                factor_t=2 if temperal_upsample else 1,
+                factor_s=2 if up_flag else 1,
+            )
+        else:
+            self.avg_shortcut = None
+
+        # Main path with residual blocks and upsample
+        upsamples = []
+        for _ in range(mult):
+            upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+
+        # Add the final upsample block
+        if up_flag:
+            mode = "upsample3d" if temperal_upsample else "upsample2d"
+            upsamples.append(Resample(out_dim, mode=mode))
+
+        self.upsamples = nn.Sequential(*upsamples)
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        x_main = x.clone()
+        for module in self.upsamples:
+            x_main = module(x_main, feat_cache, feat_idx)
+        if self.avg_shortcut is not None:
+            x_shortcut = self.avg_shortcut(x, first_chunk)
+            return x_main + x_shortcut
+        else:
+            return x_main
+
+
+class Encoder3d(nn.Module):
+
+    def __init__(
+        self,
+        dim=128,
+        z_dim=4,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_downsample=[True, True, False],
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(12, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            t_down_flag = (
+                temperal_downsample[i]
+                if i < len(temperal_downsample) else False)
+            downsamples.append(
+                Down_ResidualBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    dropout=dropout,
+                    mult=num_res_blocks,
+                    temperal_downsample=t_down_flag,
+                    down_flag=i != len(dim_mult) - 1,
+                ))
+            scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(out_dim, out_dim, dropout),
+            AttentionBlock(out_dim),
+            ResidualBlock(out_dim, out_dim, dropout),
+        )
+
+        # # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(out_dim, z_dim, 3, padding=1),
+        )
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device),
+                        cache_x,
+                    ],
+                    dim=2,
+                )
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+
+        return x
+
+
+class Decoder3d(nn.Module):
+
+    def __init__(
+        self,
+        dim=128,
+        z_dim=4,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_upsample=[False, True, True],
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(dims[0], dims[0], dropout),
+            AttentionBlock(dims[0]),
+            ResidualBlock(dims[0], dims[0], dropout),
+        )
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            t_up_flag = temperal_upsample[i] if i < len(
+                temperal_upsample) else False
+            upsamples.append(
+                Up_ResidualBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    dropout=dropout,
+                    mult=num_res_blocks + 1,
+                    temperal_upsample=t_up_flag,
+                    up_flag=i != len(dim_mult) - 1,
+                ))
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(out_dim, 12, 3, padding=1),
+        )
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device),
+                        cache_x,
+                    ],
+                    dim=2,
+                )
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx, first_chunk)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+def count_conv3d(model):
+    count = 0
+    for m in model.modules():
+        if isinstance(m, CausalConv3d):
+            count += 1
+    return count
+
+
+class WanVAE(nn.Module):
+
+    def __init__(
+        self,
+        dim=160,
+        dec_dim=256,
+        z_dim=16,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_downsample=[True, True, False],
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d(
+            dim,
+            z_dim * 2,
+            dim_mult,
+            num_res_blocks,
+            attn_scales,
+            self.temperal_downsample,
+            dropout,
+        )
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d(
+            dec_dim,
+            z_dim,
+            dim_mult,
+            num_res_blocks,
+            attn_scales,
+            self.temperal_upsample,
+            dropout,
+        )
+
+    def encode(self, x):
+        self.clear_cache()
+        x = patchify(x, patch_size=2)
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(
+                    x[:, :, :1, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx,
+                )
+            else:
+                out_ = self.encoder(
+                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx,
+                )
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        self.clear_cache()
+        return mu
+
+    def decode(self, z):
+        self.clear_cache()
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx,
+                    first_chunk=True,
+                )
+            else:
+                out_ = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx,
+                )
+                out = torch.cat([out, out_], 2)
+        out = unpatchify(out, patch_size=2)
+        self.clear_cache()
+        return out
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample(self, imgs, deterministic=False):
+        mu, log_var = self.encode(imgs)
+        if deterministic:
+            return mu
+        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
+        return mu + std * torch.randn_like(std)
+
+    def clear_cache(self):
+        self._conv_num = count_conv3d(self.decoder)
+        self._conv_idx = [0]
+        self._feat_map = [None] * self._conv_num
+        # cache encode
+        self._enc_conv_num = count_conv3d(self.encoder)
+        self._enc_conv_idx = [0]
+        self._enc_feat_map = [None] * self._enc_conv_num

comfy/lora.py

@@ -283,8 +283,9 @@ def model_lora_keys_unet(model, key_map={}):
         for k in sdk:
             if k.startswith("diffusion_model."):
                 if k.endswith(".weight"):
-                    key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
-                    key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format
+                    key_lora = k[len("diffusion_model."):-len(".weight")]
+                    key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format
+                    key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format
 
     if isinstance(model, comfy.model_base.ACEStep):
         for k in sdk:

comfy/model_base.py

@@ -34,12 +34,14 @@ import comfy.ldm.flux.model
 import comfy.ldm.lightricks.model
 import comfy.ldm.hunyuan_video.model
 import comfy.ldm.cosmos.model
+import comfy.ldm.cosmos.predict2
 import comfy.ldm.lumina.model
 import comfy.ldm.wan.model
 import comfy.ldm.hunyuan3d.model
 import comfy.ldm.hidream.model
 import comfy.ldm.chroma.model
 import comfy.ldm.ace.model
+import comfy.ldm.omnigen.omnigen2
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -48,6 +50,7 @@ import comfy.ops
 from enum import Enum
 from . import utils
 import comfy.latent_formats
+import comfy.model_sampling
 import math
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
@@ -63,38 +66,39 @@ class ModelType(Enum):
     V_PREDICTION_CONTINUOUS = 7
     FLUX = 8
     IMG_TO_IMG = 9
-
-
-from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
+    FLOW_COSMOS = 10
 
 
 def model_sampling(model_config, model_type):
-    s = ModelSamplingDiscrete
+    s = comfy.model_sampling.ModelSamplingDiscrete
 
     if model_type == ModelType.EPS:
-        c = EPS
+        c = comfy.model_sampling.EPS
     elif model_type == ModelType.V_PREDICTION:
-        c = V_PREDICTION
+        c = comfy.model_sampling.V_PREDICTION
     elif model_type == ModelType.V_PREDICTION_EDM:
-        c = V_PREDICTION
-        s = ModelSamplingContinuousEDM
+        c = comfy.model_sampling.V_PREDICTION
+        s = comfy.model_sampling.ModelSamplingContinuousEDM
     elif model_type == ModelType.FLOW:
         c = comfy.model_sampling.CONST
         s = comfy.model_sampling.ModelSamplingDiscreteFlow
     elif model_type == ModelType.STABLE_CASCADE:
-        c = EPS
-        s = StableCascadeSampling
+        c = comfy.model_sampling.EPS
+        s = comfy.model_sampling.StableCascadeSampling
     elif model_type == ModelType.EDM:
-        c = EDM
-        s = ModelSamplingContinuousEDM
+        c = comfy.model_sampling.EDM
+        s = comfy.model_sampling.ModelSamplingContinuousEDM
     elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
-        c = V_PREDICTION
-        s = ModelSamplingContinuousV
+        c = comfy.model_sampling.V_PREDICTION
+        s = comfy.model_sampling.ModelSamplingContinuousV
     elif model_type == ModelType.FLUX:
         c = comfy.model_sampling.CONST
         s = comfy.model_sampling.ModelSamplingFlux
     elif model_type == ModelType.IMG_TO_IMG:
         c = comfy.model_sampling.IMG_TO_IMG
+    elif model_type == ModelType.FLOW_COSMOS:
+        c = comfy.model_sampling.COSMOS_RFLOW
+        s = comfy.model_sampling.ModelSamplingCosmosRFlow
 
     class ModelSampling(s, c):
         pass
@@ -102,6 +106,13 @@ def model_sampling(model_config, model_type):
     return ModelSampling(model_config)
 
 
+def convert_tensor(extra, dtype):
+    if hasattr(extra, "dtype"):
+        if extra.dtype != torch.int and extra.dtype != torch.long:
+            extra = extra.to(dtype)
+    return extra
+
+
 class BaseModel(torch.nn.Module):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
         super().__init__()
@@ -165,9 +176,14 @@ class BaseModel(torch.nn.Module):
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
+
             if hasattr(extra, "dtype"):
-                if extra.dtype != torch.int and extra.dtype != torch.long:
-                    extra = extra.to(dtype)
+                extra = convert_tensor(extra, dtype)
+            elif isinstance(extra, list):
+                ex = []
+                for ext in extra:
+                    ex.append(convert_tensor(ext, dtype))
+                extra = ex
             extra_conds[o] = extra
 
         t = self.process_timestep(t, x=x, **extra_conds)
@@ -800,6 +816,7 @@ class PixArt(BaseModel):
 class Flux(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux):
         super().__init__(model_config, model_type, device=device, unet_model=unet_model)
+        self.memory_usage_factor_conds = ("ref_latents",)
 
     def concat_cond(self, **kwargs):
         try:
@@ -860,8 +877,23 @@ class Flux(BaseModel):
         guidance = kwargs.get("guidance", 3.5)
         if guidance is not None:
             out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            latents = []
+            for lat in ref_latents:
+                latents.append(self.process_latent_in(lat))
+            out['ref_latents'] = comfy.conds.CONDList(latents)
         return out
 
+    def extra_conds_shapes(self, **kwargs):
+        out = {}
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
+        return out
+
+
 class GenmoMochi(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)
@@ -986,6 +1018,45 @@ class CosmosVideo(BaseModel):
         latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
         return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)
 
+class CosmosPredict2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, image_to_video=False, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.predict2.MiniTrainDIT)
+        self.image_to_video = image_to_video
+        if self.image_to_video:
+            self.concat_keys = ("mask_inverted",)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if denoise_mask is not None:
+            out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
+
+        out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None))
+        return out
+
+    def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
+        if denoise_mask is None:
+            return timestep
+        if denoise_mask.ndim <= 4:
+            return timestep
+        condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True)
+        c_noise_B_1_T_1_1 = 0.0 * (1.0 - condition_video_mask_B_1_T_1_1) + timestep.reshape(timestep.shape[0], 1, 1, 1, 1) * condition_video_mask_B_1_T_1_1
+        out = c_noise_B_1_T_1_1.squeeze(dim=[1, 3, 4])
+        return out
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1))
+        sigma_noise_augmentation = 0 #TODO
+        if sigma_noise_augmentation != 0:
+            latent_image = latent_image + noise
+        latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
+        sigma = (sigma / (sigma + 1))
+        return latent_image / (1.0 - sigma)
+
 class Lumina2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT)
@@ -1026,8 +1097,9 @@ class WAN21(BaseModel):
                 image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16])
             image = utils.resize_to_batch_size(image, noise.shape[0])
 
-        if not self.image_to_video or extra_channels == image.shape[1]:
-            return image
+        if extra_channels != image.shape[1] + 4:
+            if not self.image_to_video or extra_channels == image.shape[1]:
+                return image
 
         if image.shape[1] > (extra_channels - 4):
             image = image[:, :(extra_channels - 4)]
@@ -1057,6 +1129,11 @@ class WAN21(BaseModel):
         clip_vision_output = kwargs.get("clip_vision_output", None)
         if clip_vision_output is not None:
             out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states)
+
+        time_dim_concat = kwargs.get("time_dim_concat", None)
+        if time_dim_concat is not None:
+            out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat))
+
         return out
 
 
@@ -1106,6 +1183,31 @@ class WAN21_Camera(WAN21):
             out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
         return out
 
+class WAN22(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if denoise_mask is not None:
+            out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
+        return out
+
+    def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
+        if denoise_mask is None:
+            return timestep
+        temp_ts = (torch.mean(denoise_mask[:, :, :, ::2, ::2], dim=1, keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
+        return temp_ts
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return latent_image
+
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)
@@ -1171,3 +1273,33 @@ class ACEStep(BaseModel):
         out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype))
         out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0))
         return out
+
+class Omnigen2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel)
+        self.memory_usage_factor_conds = ("ref_latents",)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+            out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            latents = []
+            for lat in ref_latents:
+                latents.append(self.process_latent_in(lat))
+            out['ref_latents'] = comfy.conds.CONDList(latents)
+        return out
+
+    def extra_conds_shapes(self, **kwargs):
+        out = {}
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
+        return out

comfy/model_detection.py

@@ -346,7 +346,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config = {}
         dit_config["image_model"] = "wan2.1"
         dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]
+        out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4
         dit_config["dim"] = dim
+        dit_config["out_dim"] = out_dim
         dit_config["num_heads"] = dim // 128
         dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
@@ -407,6 +409,78 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["text_emb_dim"] = 2048
         return dit_config
 
+    if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys:  # Cosmos predict2
+        dit_config = {}
+        dit_config["image_model"] = "cosmos_predict2"
+        dit_config["max_img_h"] = 240
+        dit_config["max_img_w"] = 240
+        dit_config["max_frames"] = 128
+        concat_padding_mask = True
+        dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
+        dit_config["out_channels"] = 16
+        dit_config["patch_spatial"] = 2
+        dit_config["patch_temporal"] = 1
+        dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0]
+        dit_config["concat_padding_mask"] = concat_padding_mask
+        dit_config["crossattn_emb_channels"] = 1024
+        dit_config["pos_emb_cls"] = "rope3d"
+        dit_config["pos_emb_learnable"] = True
+        dit_config["pos_emb_interpolation"] = "crop"
+        dit_config["min_fps"] = 1
+        dit_config["max_fps"] = 30
+
+        dit_config["use_adaln_lora"] = True
+        dit_config["adaln_lora_dim"] = 256
+        if dit_config["model_channels"] == 2048:
+            dit_config["num_blocks"] = 28
+            dit_config["num_heads"] = 16
+        elif dit_config["model_channels"] == 5120:
+            dit_config["num_blocks"] = 36
+            dit_config["num_heads"] = 40
+
+        if dit_config["in_channels"] == 16:
+            dit_config["extra_per_block_abs_pos_emb"] = False
+            dit_config["rope_h_extrapolation_ratio"] = 4.0
+            dit_config["rope_w_extrapolation_ratio"] = 4.0
+            dit_config["rope_t_extrapolation_ratio"] = 1.0
+        elif dit_config["in_channels"] == 17: # img to video
+            if dit_config["model_channels"] == 2048:
+                dit_config["extra_per_block_abs_pos_emb"] = False
+                dit_config["rope_h_extrapolation_ratio"] = 3.0
+                dit_config["rope_w_extrapolation_ratio"] = 3.0
+                dit_config["rope_t_extrapolation_ratio"] = 1.0
+            elif dit_config["model_channels"] == 5120:
+                dit_config["rope_h_extrapolation_ratio"] = 2.0
+                dit_config["rope_w_extrapolation_ratio"] = 2.0
+                dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334
+
+        dit_config["extra_h_extrapolation_ratio"] = 1.0
+        dit_config["extra_w_extrapolation_ratio"] = 1.0
+        dit_config["extra_t_extrapolation_ratio"] = 1.0
+        dit_config["rope_enable_fps_modulation"] = False
+
+        return dit_config
+
+    if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys:  # Omnigen2
+        dit_config = {}
+        dit_config["image_model"] = "omnigen2"
+        dit_config["axes_dim_rope"] = [40, 40, 40]
+        dit_config["axes_lens"] = [1024, 1664, 1664]
+        dit_config["ffn_dim_multiplier"] = None
+        dit_config["hidden_size"] = 2520
+        dit_config["in_channels"] = 16
+        dit_config["multiple_of"] = 256
+        dit_config["norm_eps"] = 1e-05
+        dit_config["num_attention_heads"] = 21
+        dit_config["num_kv_heads"] = 7
+        dit_config["num_layers"] = 32
+        dit_config["num_refiner_layers"] = 2
+        dit_config["out_channels"] = None
+        dit_config["patch_size"] = 2
+        dit_config["text_feat_dim"] = 2048
+        dit_config["timestep_scale"] = 1000.0
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 

comfy/model_management.py

@@ -101,7 +101,7 @@ if args.directml is not None:
     lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
 
 try:
-    import intel_extension_for_pytorch as ipex
+    import intel_extension_for_pytorch as ipex  # noqa: F401
     _ = torch.xpu.device_count()
     xpu_available = xpu_available or torch.xpu.is_available()
 except:
@@ -128,6 +128,11 @@ try:
 except:
     mlu_available = False
 
+try:
+    ixuca_available = hasattr(torch, "corex")
+except:
+    ixuca_available = False
+
 if args.cpu:
     cpu_state = CPUState.CPU
 
@@ -151,6 +156,12 @@ def is_mlu():
         return True
     return False
 
+def is_ixuca():
+    global ixuca_available
+    if ixuca_available:
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
@@ -186,8 +197,9 @@ def get_total_memory(dev=None, torch_total_too=False):
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
+            mem_total_xpu = torch.xpu.get_device_properties(dev).total_memory
             mem_total_torch = mem_reserved
-            mem_total = torch.xpu.get_device_properties(dev).total_memory
+            mem_total = mem_total_xpu
         elif is_ascend_npu():
             stats = torch.npu.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -288,21 +300,34 @@ try:
         if torch_version_numeric[0] >= 2:
             if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
-    if is_intel_xpu() or is_ascend_npu() or is_mlu():
+    if is_intel_xpu() or is_ascend_npu() or is_mlu() or is_ixuca():
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             ENABLE_PYTORCH_ATTENTION = True
 except:
     pass
 
 
+SUPPORT_FP8_OPS = args.supports_fp8_compute
 try:
     if is_amd():
+        try:
+            rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
+        except:
+            rocm_version = (6, -1)
         arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
         logging.info("AMD arch: {}".format(arch))
+        logging.info("ROCm version: {}".format(rocm_version))
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
-            if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7:  # works on 2.6 but doesn't actually seem to improve much
-                if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches
+            if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
+                if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                     ENABLE_PYTORCH_ATTENTION = True
+            if torch_version_numeric >= (2, 8):
+                if any((a in arch) for a in ["gfx1201"]):
+                    ENABLE_PYTORCH_ATTENTION = True
+        if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):
+            if any((a in arch) for a in ["gfx1201", "gfx942", "gfx950"]):  # TODO: more arches
+                SUPPORT_FP8_OPS = True
+
 except:
     pass
 
@@ -323,7 +348,7 @@ except:
     pass
 
 try:
-    if torch_version_numeric[0] == 2 and torch_version_numeric[1] >= 5:
+    if torch_version_numeric >= (2, 5):
         torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
 except:
     logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp")
@@ -367,6 +392,8 @@ def get_torch_device_name(device):
             except:
                 allocator_backend = ""
             return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend)
+        elif device.type == "xpu":
+            return "{} {}".format(device, torch.xpu.get_device_name(device))
         else:
             return "{}".format(device.type)
     elif is_intel_xpu():
@@ -866,6 +893,7 @@ def vae_dtype(device=None, allowed_dtypes=[]):
             return d
 
         # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32
+        # slowness still a problem on pytorch nightly 2.9.0.dev20250720+rocm6.4 tested on RDNA3
         if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device):
             return d
 
@@ -919,7 +947,7 @@ def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
     if is_intel_xpu():
-        return False
+        return True
     if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
         return False
     if directml_enabled:
@@ -958,6 +986,8 @@ def get_offload_stream(device):
         stream_counter = (stream_counter + 1) % len(ss)
         if is_device_cuda(device):
             ss[stream_counter].wait_stream(torch.cuda.current_stream())
+        elif is_device_xpu(device):
+            ss[stream_counter].wait_stream(torch.xpu.current_stream())
         stream_counters[device] = stream_counter
         return s
     elif is_device_cuda(device):
@@ -969,6 +999,15 @@ def get_offload_stream(device):
         stream_counter = (stream_counter + 1) % len(ss)
         stream_counters[device] = stream_counter
         return s
+    elif is_device_xpu(device):
+        ss = []
+        for k in range(NUM_STREAMS):
+            ss.append(torch.xpu.Stream(device=device, priority=0))
+        STREAMS[device] = ss
+        s = ss[stream_counter]
+        stream_counter = (stream_counter + 1) % len(ss)
+        stream_counters[device] = stream_counter
+        return s
     return None
 
 def sync_stream(device, stream):
@@ -976,6 +1015,8 @@ def sync_stream(device, stream):
         return
     if is_device_cuda(device):
         torch.cuda.current_stream().wait_stream(stream)
+    elif is_device_xpu(device):
+        torch.xpu.current_stream().wait_stream(stream)
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
     if device is None or weight.device == device:
@@ -1017,6 +1058,8 @@ def xformers_enabled():
         return False
     if is_mlu():
         return False
+    if is_ixuca():
+        return False
     if directml_enabled:
         return False
     return XFORMERS_IS_AVAILABLE
@@ -1042,7 +1085,7 @@ def pytorch_attention_flash_attention():
     global ENABLE_PYTORCH_ATTENTION
     if ENABLE_PYTORCH_ATTENTION:
         #TODO: more reliable way of checking for flash attention?
-        if is_nvidia(): #pytorch flash attention only works on Nvidia
+        if is_nvidia():
             return True
         if is_intel_xpu():
             return True
@@ -1052,13 +1095,15 @@ def pytorch_attention_flash_attention():
             return True
         if is_amd():
             return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
+        if is_ixuca():
+            return True
     return False
 
 def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
 
     macos_version = mac_version()
-    if macos_version is not None and ((14, 5) <= macos_version < (16,)):  # black image bug on recent versions of macOS
+    if macos_version is not None and ((14, 5) <= macos_version):  # black image bug on recent versions of macOS, I don't think it's ever getting fixed
         upcast = True
 
     if upcast:
@@ -1082,8 +1127,8 @@ def get_free_memory(dev=None, torch_free_too=False):
             stats = torch.xpu.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
             mem_reserved = stats['reserved_bytes.all.current']
-            mem_free_torch = mem_reserved - mem_active
             mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
+            mem_free_torch = mem_reserved - mem_active
             mem_free_total = mem_free_xpu + mem_free_torch
         elif is_ascend_npu():
             stats = torch.npu.memory_stats(dev)
@@ -1132,6 +1177,9 @@ def is_device_cpu(device):
 def is_device_mps(device):
     return is_device_type(device, 'mps')
 
+def is_device_xpu(device):
+    return is_device_type(device, 'xpu')
+
 def is_device_cuda(device):
     return is_device_type(device, 'cuda')
 
@@ -1163,7 +1211,10 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
         return False
 
     if is_intel_xpu():
-        return True
+        if torch_version_numeric < (2, 3):
+            return True
+        else:
+            return torch.xpu.get_device_properties(device).has_fp16
 
     if is_ascend_npu():
         return True
@@ -1171,6 +1222,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if is_mlu():
         return True
 
+    if is_ixuca():
+        return True
+
     if torch.version.hip:
         return True
 
@@ -1226,11 +1280,17 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         return False
 
     if is_intel_xpu():
-        return True
+        if torch_version_numeric < (2, 6):
+            return True
+        else:
+            return torch.xpu.get_device_capability(device)['has_bfloat16_conversions']
 
     if is_ascend_npu():
         return True
 
+    if is_ixuca():
+        return True
+
     if is_amd():
         arch = torch.cuda.get_device_properties(device).gcnArchName
         if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]):  # RDNA2 and older don't support bf16
@@ -1257,7 +1317,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     return False
 
 def supports_fp8_compute(device=None):
-    if args.supports_fp8_compute:
+    if SUPPORT_FP8_OPS:
         return True
 
     if not is_nvidia():
@@ -1271,15 +1331,22 @@ def supports_fp8_compute(device=None):
     if props.minor < 9:
         return False
 
-    if torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 3):
+    if torch_version_numeric < (2, 3):
         return False
 
     if WINDOWS:
-        if (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 4):
+        if torch_version_numeric < (2, 4):
             return False
 
     return True
 
+def extended_fp16_support():
+    # TODO: check why some models work with fp16 on newer torch versions but not on older
+    if torch_version_numeric < (2, 7):
+        return False
+
+    return True
+
 def soft_empty_cache(force=False):
     global cpu_state
     if cpu_state == CPUState.MPS:

comfy/model_patcher.py

@@ -17,23 +17,26 @@
 """
 
 from __future__ import annotations
-from typing import Optional, Callable
-import torch
+
+import collections
 import copy
 import inspect
 import logging
-import uuid
-import collections
 import math
+import uuid
+from typing import Callable, Optional
+
+import torch
 
-import comfy.utils
 import comfy.float
-import comfy.model_management
-import comfy.lora
 import comfy.hooks
+import comfy.lora
+import comfy.model_management
 import comfy.patcher_extension
-from comfy.patcher_extension import CallbacksMP, WrappersMP, PatcherInjection
+import comfy.utils
 from comfy.comfy_types import UnetWrapperFunction
+from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
+
 
 def string_to_seed(data):
     crc = 0xFFFFFFFF
@@ -376,6 +379,9 @@ class ModelPatcher:
     def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False):
         self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization)
 
+    def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond_batch_function):
+        self.model_options["sampler_calc_cond_batch_function"] = sampler_calc_cond_batch_function
+
     def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
 

comfy/model_sampling.py

@@ -77,6 +77,25 @@ class IMG_TO_IMG(X0):
     def calculate_input(self, sigma, noise):
         return noise
 
+class COSMOS_RFLOW:
+    def calculate_input(self, sigma, noise):
+        sigma = (sigma / (sigma + 1))
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        return noise * (1.0 - sigma)
+
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = (sigma / (sigma + 1))
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input * (1.0 - sigma) - model_output * sigma
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        noise = noise * sigma
+        noise += latent_image
+        return noise
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent
 
 class ModelSamplingDiscrete(torch.nn.Module):
     def __init__(self, model_config=None, zsnr=None):
@@ -350,3 +369,15 @@ class ModelSamplingFlux(torch.nn.Module):
         if percent >= 1.0:
             return 0.0
         return flux_time_shift(self.shift, 1.0, 1.0 - percent)
+
+
+class ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM):
+    def timestep(self, sigma):
+        return sigma / (sigma + 1)
+
+    def sigma(self, timestep):
+        sigma_max = self.sigma_max
+        if timestep >= (sigma_max / (sigma_max + 1)):
+            return sigma_max
+
+        return timestep / (1 - timestep)

comfy/ops.py

@@ -336,9 +336,12 @@ class fp8_ops(manual_cast):
             return None
 
         def forward_comfy_cast_weights(self, input):
-            out = fp8_linear(self, input)
-            if out is not None:
-                return out
+            try:
+                out = fp8_linear(self, input)
+                if out is not None:
+                    return out
+            except Exception as e:
+                logging.info("Exception during fp8 op: {}".format(e))
 
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.linear(input, weight, bias)

comfy/samplers.py

@@ -373,7 +373,11 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
         uncond_ = uncond
 
     conds = [cond, uncond_]
-    out = calc_cond_batch(model, conds, x, timestep, model_options)
+    if "sampler_calc_cond_batch_function" in model_options:
+        args = {"conds": conds, "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+        out = model_options["sampler_calc_cond_batch_function"](args)
+    else:
+        out = calc_cond_batch(model, conds, x, timestep, model_options)
 
     for fn in model_options.get("sampler_pre_cfg_function", []):
         args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
@@ -716,7 +720,7 @@ KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_c
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
-                  "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3"]
+                  "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
@@ -1039,13 +1043,13 @@ class SchedulerHandler(NamedTuple):
     use_ms: bool = True
 
 SCHEDULER_HANDLERS = {
-    "normal": SchedulerHandler(normal_scheduler),
+    "simple": SchedulerHandler(simple_scheduler),
+    "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)),
     "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False),
     "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False),
-    "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)),
-    "simple": SchedulerHandler(simple_scheduler),
     "ddim_uniform": SchedulerHandler(ddim_scheduler),
     "beta": SchedulerHandler(beta_scheduler),
+    "normal": SchedulerHandler(normal_scheduler),
     "linear_quadratic": SchedulerHandler(linear_quadratic_schedule),
     "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False),
 }

comfy/sd.py

@@ -14,10 +14,12 @@ import comfy.ldm.genmo.vae.model
 import comfy.ldm.lightricks.vae.causal_video_autoencoder
 import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
+import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import yaml
 import math
+import os
 
 import comfy.utils
 
@@ -44,6 +46,7 @@ import comfy.text_encoders.lumina2
 import comfy.text_encoders.wan
 import comfy.text_encoders.hidream
 import comfy.text_encoders.ace
+import comfy.text_encoders.omnigen2
 
 import comfy.model_patcher
 import comfy.lora
@@ -418,17 +421,30 @@ class VAE:
                 self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
             elif "decoder.middle.0.residual.0.gamma" in sd:
-                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
-                self.upscale_index_formula = (4, 8, 8)
-                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
-                self.downscale_index_formula = (4, 8, 8)
-                self.latent_dim = 3
-                self.latent_channels = 16
-                ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
-                self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
-                self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
-                self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
-                self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
+                if "decoder.upsamples.0.upsamples.0.residual.2.weight" in sd:  # Wan 2.2 VAE
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                    self.upscale_index_formula = (4, 16, 16)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                    self.downscale_index_formula = (4, 16, 16)
+                    self.latent_dim = 3
+                    self.latent_channels = 48
+                    ddconfig = {"dim": 160, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                    self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig)
+                    self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+                    self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
+                else:  # Wan 2.1 VAE
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                    self.upscale_index_formula = (4, 8, 8)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                    self.downscale_index_formula = (4, 8, 8)
+                    self.latent_dim = 3
+                    self.latent_channels = 16
+                    ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                    self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
+                    self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+                    self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
             elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
                 self.latent_dim = 1
                 ln_post = "geo_decoder.ln_post.weight" in sd
@@ -754,6 +770,7 @@ class CLIPType(Enum):
     HIDREAM = 14
     CHROMA = 15
     ACE = 16
+    OMNIGEN2 = 17
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -773,6 +790,7 @@ class TEModel(Enum):
     LLAMA3_8 = 7
     T5_XXL_OLD = 8
     GEMMA_2_2B = 9
+    QWEN25_3B = 10
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -793,6 +811,8 @@ def detect_te_model(sd):
         return TEModel.T5_BASE
     if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
         return TEModel.GEMMA_2_2B
+    if 'model.layers.0.self_attn.k_proj.bias' in sd:
+        return TEModel.QWEN25_3B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         return TEModel.LLAMA3_8
     return None
@@ -894,6 +914,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
             clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
+        elif te_model == TEModel.QWEN25_3B:
+            clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer
         else:
             # clip_l
             if clip_type == CLIPType.SD3:
@@ -969,6 +992,12 @@ def load_gligen(ckpt_path):
         model = model.half()
     return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
 
+def model_detection_error_hint(path, state_dict):
+    filename = os.path.basename(path)
+    if 'lora' in filename.lower():
+        return "\nHINT: This seems to be a Lora file and Lora files should be put in the lora folder and loaded with a lora loader node.."
+    return ""
+
 def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
     logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
     model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
@@ -997,7 +1026,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
     out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata)
     if out is None:
-        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
+        raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))
     return out
 
 def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None):
@@ -1081,7 +1110,28 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format
+def load_diffusion_model_state_dict(sd, model_options={}):
+    """
+    Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.
+
+    Args:
+        sd (dict): State dictionary containing model weights and configuration
+        model_options (dict, optional): Additional options for model loading. Supports:
+            - dtype: Override model data type
+            - custom_operations: Custom model operations
+            - fp8_optimizations: Enable FP8 optimizations
+
+    Returns:
+        ModelPatcher: A wrapped model instance that handles device management and weight loading.
+        Returns None if the model configuration cannot be detected.
+
+    The function:
+    1. Detects and handles different model formats (regular, diffusers, mmdit)
+    2. Configures model dtype based on parameters and device capabilities
+    3. Handles weight conversion and device placement
+    4. Manages model optimization settings
+    5. Loads weights and returns a device-managed model instance
+    """
     dtype = model_options.get("dtype", None)
 
     #Allow loading unets from checkpoint files
@@ -1139,7 +1189,7 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
     model.load_model_weights(new_sd, "")
     left_over = sd.keys()
     if len(left_over) > 0:
-        logging.info("left over keys in unet: {}".format(left_over))
+        logging.info("left over keys in diffusion model: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
 
@@ -1147,8 +1197,8 @@ def load_diffusion_model(unet_path, model_options={}):
     sd = comfy.utils.load_torch_file(unet_path)
     model = load_diffusion_model_state_dict(sd, model_options=model_options)
     if model is None:
-        logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
-        raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+        logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path))
+        raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd)))
     return model
 
 def load_unet(unet_path, dtype=None):

comfy/sd1_clip.py

@@ -462,7 +462,7 @@ class SDTokenizer:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
         self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length)
-        self.min_length = min_length
+        self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
         self.end_token = None
         self.min_padding = min_padding
 
@@ -482,7 +482,8 @@ class SDTokenizer:
             if end_token is not None:
                 self.end_token = end_token
             else:
-                self.end_token = empty[0]
+                if has_end_token:
+                    self.end_token = empty[0]
 
         if pad_token is not None:
             self.pad_token = pad_token

comfy/sd1_tokenizer/tokenizer_config.json

@@ -18,7 +18,7 @@
     "single_word": false
   },
   "errors": "replace",
-  "model_max_length": 77,
+  "model_max_length": 8192,
   "name_or_path": "openai/clip-vit-large-patch14",
   "pad_token": "<|endoftext|>",
   "special_tokens_map_file": "./special_tokens_map.json",

comfy/supported_models.py

@@ -18,6 +18,7 @@ import comfy.text_encoders.cosmos
 import comfy.text_encoders.lumina2
 import comfy.text_encoders.wan
 import comfy.text_encoders.ace
+import comfy.text_encoders.omnigen2
 
 from . import supported_models_base
 from . import latent_formats
@@ -908,6 +909,48 @@ class CosmosI2V(CosmosT2V):
         out = model_base.CosmosVideo(self, image_to_video=True, device=device)
         return out
 
+class CosmosT2IPredict2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "cosmos_predict2",
+        "in_channels": 16,
+    }
+
+    sampling_settings = {
+        "sigma_data": 1.0,
+        "sigma_max": 80.0,
+        "sigma_min": 0.002,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Wan21
+
+    memory_usage_factor = 1.0
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.CosmosPredict2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
+
+class CosmosI2VPredict2(CosmosT2IPredict2):
+    unet_config = {
+        "image_model": "cosmos_predict2",
+        "in_channels": 17,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.CosmosPredict2(self, image_to_video=True, device=device)
+        return out
+
 class Lumina2(supported_models_base.BASE):
     unet_config = {
         "image_model": "lumina2",
@@ -1016,6 +1059,19 @@ class WAN21_Vace(WAN21_T2V):
         out = model_base.WAN21_Vace(self, image_to_video=False, device=device)
         return out
 
+class WAN22_T2V(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "t2v",
+        "out_dim": 48,
+    }
+
+    latent_format = latent_formats.Wan22
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN22(self, image_to_video=True, device=device)
+        return out
+
 class Hunyuan3Dv2(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan3d2",
@@ -1139,6 +1195,41 @@ class ACEStep(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model)
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
+class Omnigen2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "omnigen2",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 2.6,
+    }
+
+    memory_usage_factor = 1.65 #TODO
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        if comfy.model_management.extended_fp16_support():
+            self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Omnigen2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
+
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
 
 models += [SVD_img2vid]

comfy/text_encoders/llama.py

@@ -24,6 +24,24 @@ class Llama2Config:
     head_dim = 128
     rms_norm_add = False
     mlp_activation = "silu"
+    qkv_bias = False
+
+@dataclass
+class Qwen25_3BConfig:
+    vocab_size: int = 151936
+    hidden_size: int = 2048
+    intermediate_size: int = 11008
+    num_hidden_layers: int = 36
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 2
+    max_position_embeddings: int = 128000
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 1000000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+    qkv_bias = True
 
 @dataclass
 class Gemma2_2B_Config:
@@ -40,6 +58,7 @@ class Gemma2_2B_Config:
     head_dim = 256
     rms_norm_add = True
     mlp_activation = "gelu_pytorch_tanh"
+    qkv_bias = False
 
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@@ -98,9 +117,9 @@ class Attention(nn.Module):
         self.inner_size = self.num_heads * self.head_dim
 
         ops = ops or nn
-        self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=False, device=device, dtype=dtype)
-        self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
-        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
+        self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
         self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
     def forward(
@@ -320,6 +339,14 @@ class Llama2(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+class Qwen25_3B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Qwen25_3BConfig(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
 
 class Gemma2_2B(BaseLlama, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):

comfy/text_encoders/long_clipl.json

@@ -1,25 +0,0 @@
-{
-  "_name_or_path": "openai/clip-vit-large-patch14",
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 49407,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 768,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 248,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "projection_dim": 768,
-  "torch_dtype": "float32",
-  "transformers_version": "4.24.0",
-  "vocab_size": 49408
-}

comfy/text_encoders/omnigen2.py

@@ -0,0 +1,44 @@
+from transformers import Qwen2Tokenizer
+from comfy import sd1_clip
+import comfy.text_encoders.llama
+import os
+
+
+class Qwen25_3BTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer)
+        self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n'
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs):
+        if llama_template is None:
+            llama_text = self.llama_template.format(text)
+        else:
+            llama_text = llama_template.format(text)
+        return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs)
+
+class Qwen25_3BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class Omnigen2Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_scaled_fp8=None):
+    class Omnigen2TEModel_(Omnigen2Model):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["scaled_fp8"] = llama_scaled_fp8
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Omnigen2TEModel_

comfy/text_encoders/pixart_t5.py

@@ -1,42 +1,42 @@
-import os
-
-from comfy import sd1_clip
-import comfy.text_encoders.t5
-import comfy.text_encoders.sd3_clip
-from comfy.sd1_clip import gen_empty_tokens
-
-from transformers import T5TokenizerFast
-
-class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def gen_empty_tokens(self, special_tokens, *args, **kwargs):
-        # PixArt expects the negative to be all pad tokens
-        special_tokens = special_tokens.copy()
-        special_tokens.pop("end")
-        return gen_empty_tokens(special_tokens, *args, **kwargs)
-
-class PixArtT5XXL(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
-
-class T5XXLTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding
-
-class PixArtTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
-
-def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
-    class PixArtTEModel_(PixArtT5XXL):
-        def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
-                model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
-            if dtype is None:
-                dtype = dtype_t5
-            super().__init__(device=device, dtype=dtype, model_options=model_options)
-    return PixArtTEModel_
+import os
+
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
+from comfy.sd1_clip import gen_empty_tokens
+
+from transformers import T5TokenizerFast
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def gen_empty_tokens(self, special_tokens, *args, **kwargs):
+        # PixArt expects the negative to be all pad tokens
+        special_tokens = special_tokens.copy()
+        special_tokens.pop("end")
+        return gen_empty_tokens(special_tokens, *args, **kwargs)
+
+class PixArtT5XXL(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding
+
+class PixArtTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+    class PixArtTEModel_(PixArtT5XXL):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+            if dtype is None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return PixArtTEModel_

comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json

@@ -0,0 +1,241 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<|img|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151666": {
+      "content": "<|endofimg|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151667": {
+      "content": "<|meta|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151668": {
+      "content": "<|endofmeta|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

comfy/text_encoders/t5.py

@@ -146,7 +146,7 @@ class T5Attention(torch.nn.Module):
         )
         values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype)  # shape (query_length, key_length, num_heads)
         values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
-        return values
+        return values.contiguous()
 
     def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
         q = self.q(x)

comfy/utils.py

@@ -31,6 +31,7 @@ from einops import rearrange
 from comfy.cli_args import args
 
 MMAP_TORCH_FILES = args.mmap_torch_files
+DISABLE_MMAP = args.disable_mmap
 
 ALWAYS_SAFE_LOAD = False
 if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated
@@ -58,7 +59,10 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
             with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
                 sd = {}
                 for k in f.keys():
-                    sd[k] = f.get_tensor(k)
+                    tensor = f.get_tensor(k)
+                    if DISABLE_MMAP:  # TODO: Not sure if this is the best way to bypass the mmap issues
+                        tensor = tensor.to(device=device, copy=True)
+                    sd[k] = tensor
                 if return_metadata:
                     metadata = f.metadata()
         except Exception as e:
@@ -77,6 +81,7 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
         if safe_load or ALWAYS_SAFE_LOAD:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)
         else:
+            logging.warning("WARNING: loading {} unsafely, upgrade your pytorch to 2.4 or newer to load this file safely.".format(ckpt))
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
         if "state_dict" in pl_sd:
             sd = pl_sd["state_dict"]
@@ -693,6 +698,26 @@ def resize_to_batch_size(tensor, batch_size):
 
     return output
 
+def resize_list_to_batch_size(l, batch_size):
+    in_batch_size = len(l)
+    if in_batch_size == batch_size or in_batch_size == 0:
+        return l
+
+    if batch_size <= 1:
+        return l[:batch_size]
+
+    output = []
+    if batch_size < in_batch_size:
+        scale = (in_batch_size - 1) / (batch_size - 1)
+        for i in range(batch_size):
+            output.append(l[min(round(i * scale), in_batch_size - 1)])
+    else:
+        scale = in_batch_size / batch_size
+        for i in range(batch_size):
+           output.append(l[min(math.floor((i + 0.5) * scale), in_batch_size - 1)])
+
+    return output
+
 def convert_sd_to(state_dict, dtype):
     keys = list(state_dict.keys())
     for k in keys:
@@ -997,11 +1022,12 @@ def set_progress_bar_global_hook(function):
     PROGRESS_BAR_HOOK = function
 
 class ProgressBar:
-    def __init__(self, total):
+    def __init__(self, total, node_id=None):
         global PROGRESS_BAR_HOOK
         self.total = total
         self.current = 0
         self.hook = PROGRESS_BAR_HOOK
+        self.node_id = node_id
 
     def update_absolute(self, value, total=None, preview=None):
         if total is not None:
@@ -1010,7 +1036,7 @@ class ProgressBar:
             value = self.total
         self.current = value
         if self.hook is not None:
-            self.hook(self.current, self.total, preview)
+            self.hook(self.current, self.total, preview, node_id=self.node_id)
 
     def update(self, value):
         self.update_absolute(self.current + value)

comfy/weight_adapter/__init__.py

@@ -1,4 +1,4 @@
-from .base import WeightAdapterBase
+from .base import WeightAdapterBase, WeightAdapterTrainBase
 from .lora import LoRAAdapter
 from .loha import LoHaAdapter
 from .lokr import LoKrAdapter
@@ -15,3 +15,20 @@ adapters: list[type[WeightAdapterBase]] = [
     OFTAdapter,
     BOFTAdapter,
 ]
+adapter_maps: dict[str, type[WeightAdapterBase]] = {
+    "LoRA": LoRAAdapter,
+    "LoHa": LoHaAdapter,
+    "LoKr": LoKrAdapter,
+    "OFT": OFTAdapter,
+    ## We disable not implemented algo for now
+    # "GLoRA": GLoRAAdapter,
+    # "BOFT": BOFTAdapter,
+}
+
+
+__all__ = [
+    "WeightAdapterBase",
+    "WeightAdapterTrainBase",
+    "adapters",
+    "adapter_maps",
+] + [a.__name__ for a in adapters]

comfy/weight_adapter/base.py

@@ -12,12 +12,20 @@ class WeightAdapterBase:
     weights: list[torch.Tensor]
 
     @classmethod
-    def load(cls, x: str, lora: dict[str, torch.Tensor]) -> Optional["WeightAdapterBase"]:
+    def load(cls, x: str, lora: dict[str, torch.Tensor], alpha: float, dora_scale: torch.Tensor) -> Optional["WeightAdapterBase"]:
         raise NotImplementedError
 
     def to_train(self) -> "WeightAdapterTrainBase":
         raise NotImplementedError
 
+    @classmethod
+    def create_train(cls, weight, *args) -> "WeightAdapterTrainBase":
+        """
+        weight: The original weight tensor to be modified.
+        *args: Additional arguments for configuration, such as rank, alpha etc.
+        """
+        raise NotImplementedError
+
     def calculate_weight(
         self,
         weight,
@@ -33,10 +41,22 @@ class WeightAdapterBase:
 
 
 class WeightAdapterTrainBase(nn.Module):
+    # We follow the scheme of PR #7032
     def __init__(self):
         super().__init__()
 
-    # [TODO] Collaborate with LoRA training PR #7032
+    def __call__(self, w):
+        """
+        w: The original weight tensor to be modified.
+        """
+        raise NotImplementedError
+
+    def passive_memory_usage(self):
+        raise NotImplementedError("passive_memory_usage is not implemented")
+
+    def move_to(self, device):
+        self.to(device)
+        return self.passive_memory_usage()
 
 
 def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function):
@@ -102,3 +122,54 @@ def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Ten
     padded_tensor[new_slices] = tensor[orig_slices]
 
     return padded_tensor
+
+
+def tucker_weight_from_conv(up, down, mid):
+    up = up.reshape(up.size(0), up.size(1))
+    down = down.reshape(down.size(0), down.size(1))
+    return torch.einsum("m n ..., i m, n j -> i j ...", mid, up, down)
+
+
+def tucker_weight(wa, wb, t):
+    temp = torch.einsum("i j ..., j r -> i r ...", t, wb)
+    return torch.einsum("i j ..., i r -> r j ...", temp, wa)
+
+
+def factorization(dimension: int, factor: int = -1) -> tuple[int, int]:
+    """
+    return a tuple of two value of input dimension decomposed by the number closest to factor
+    second value is higher or equal than first value.
+
+    examples)
+    factor
+        -1               2                4               8               16               ...
+    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
+    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
+    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
+    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
+    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
+    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
+    """
+
+    if factor > 0 and (dimension % factor) == 0 and dimension >= factor**2:
+        m = factor
+        n = dimension // factor
+        if m > n:
+            n, m = m, n
+        return m, n
+    if factor < 0:
+        factor = dimension
+    m, n = 1, dimension
+    length = m + n
+    while m < n:
+        new_m = m + 1
+        while dimension % new_m != 0:
+            new_m += 1
+        new_n = dimension // new_m
+        if new_m + new_n > length or new_m > factor:
+            break
+        else:
+            m, n = new_m, new_n
+    if m > n:
+        n, m = m, n
+    return m, n

comfy/weight_adapter/loha.py

@@ -3,7 +3,120 @@ from typing import Optional
 
 import torch
 import comfy.model_management
-from .base import WeightAdapterBase, weight_decompose
+from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose
+
+
+class HadaWeight(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, w1u, w1d, w2u, w2d, scale=torch.tensor(1)):
+        ctx.save_for_backward(w1d, w1u, w2d, w2u, scale)
+        diff_weight = ((w1u @ w1d) * (w2u @ w2d)) * scale
+        return diff_weight
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        (w1d, w1u, w2d, w2u, scale) = ctx.saved_tensors
+        grad_out = grad_out * scale
+        temp = grad_out * (w2u @ w2d)
+        grad_w1u = temp @ w1d.T
+        grad_w1d = w1u.T @ temp
+
+        temp = grad_out * (w1u @ w1d)
+        grad_w2u = temp @ w2d.T
+        grad_w2d = w2u.T @ temp
+
+        del temp
+        return grad_w1u, grad_w1d, grad_w2u, grad_w2d, None
+
+
+class HadaWeightTucker(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, t1, w1u, w1d, t2, w2u, w2d, scale=torch.tensor(1)):
+        ctx.save_for_backward(t1, w1d, w1u, t2, w2d, w2u, scale)
+
+        rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1d, w1u)
+        rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2d, w2u)
+
+        return rebuild1 * rebuild2 * scale
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        (t1, w1d, w1u, t2, w2d, w2u, scale) = ctx.saved_tensors
+        grad_out = grad_out * scale
+
+        temp = torch.einsum("i j ..., j r -> i r ...", t2, w2d)
+        rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w2u)
+
+        grad_w = rebuild * grad_out
+        del rebuild
+
+        grad_w1u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w)
+        grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w1u.T)
+        del grad_w, temp
+
+        grad_w1d = torch.einsum("i r ..., i j ... -> r j", t1, grad_temp)
+        grad_t1 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w1d.T)
+        del grad_temp
+
+        temp = torch.einsum("i j ..., j r -> i r ...", t1, w1d)
+        rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w1u)
+
+        grad_w = rebuild * grad_out
+        del rebuild
+
+        grad_w2u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w)
+        grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w2u.T)
+        del grad_w, temp
+
+        grad_w2d = torch.einsum("i r ..., i j ... -> r j", t2, grad_temp)
+        grad_t2 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w2d.T)
+        del grad_temp
+        return grad_t1, grad_w1u, grad_w1d, grad_t2, grad_w2u, grad_w2d, None
+
+
+class LohaDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        # Unpack weights tuple from LoHaAdapter
+        w1a, w1b, alpha, w2a, w2b, t1, t2, _ = weights
+
+        # Create trainable parameters
+        self.hada_w1_a = torch.nn.Parameter(w1a)
+        self.hada_w1_b = torch.nn.Parameter(w1b)
+        self.hada_w2_a = torch.nn.Parameter(w2a)
+        self.hada_w2_b = torch.nn.Parameter(w2b)
+
+        self.use_tucker = False
+        if t1 is not None and t2 is not None:
+            self.use_tucker = True
+            self.hada_t1 = torch.nn.Parameter(t1)
+            self.hada_t2 = torch.nn.Parameter(t2)
+        else:
+            # Keep the attributes for consistent access
+            self.hada_t1 = None
+            self.hada_t2 = None
+
+        # Store rank and non-trainable alpha
+        self.rank = w1b.shape[0]
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    def __call__(self, w):
+        org_dtype = w.dtype
+
+        scale = self.alpha / self.rank
+        if self.use_tucker:
+            diff_weight = HadaWeightTucker.apply(self.hada_t1, self.hada_w1_a, self.hada_w1_b, self.hada_t2, self.hada_w2_a, self.hada_w2_b, scale)
+        else:
+            diff_weight = HadaWeight.apply(self.hada_w1_a, self.hada_w1_b, self.hada_w2_a, self.hada_w2_b, scale)
+
+        # Add the scaled difference to the original weight
+        weight = w.to(diff_weight) + diff_weight.reshape(w.shape)
+
+        return weight.to(org_dtype)
+
+    def passive_memory_usage(self):
+        """Calculates memory usage of the trainable parameters."""
+        return sum(param.numel() * param.element_size() for param in self.parameters())
 
 
 class LoHaAdapter(WeightAdapterBase):
@@ -13,6 +126,25 @@ class LoHaAdapter(WeightAdapterBase):
         self.loaded_keys = loaded_keys
         self.weights = weights
 
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        in_dim = weight.shape[1:].numel()
+        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype)
+        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype)
+        torch.nn.init.normal_(mat1, 0.1)
+        torch.nn.init.constant_(mat2, 0.0)
+        mat3 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype)
+        mat4 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype)
+        torch.nn.init.normal_(mat3, 0.1)
+        torch.nn.init.normal_(mat4, 0.01)
+        return LohaDiff(
+            (mat1, mat2, alpha, mat3, mat4, None, None, None)
+        )
+
+    def to_train(self):
+        return LohaDiff(self.weights)
+
     @classmethod
     def load(
         cls,

comfy/weight_adapter/lokr.py

@@ -3,7 +3,77 @@ from typing import Optional
 
 import torch
 import comfy.model_management
-from .base import WeightAdapterBase, weight_decompose
+from .base import (
+    WeightAdapterBase,
+    WeightAdapterTrainBase,
+    weight_decompose,
+    factorization,
+)
+
+
+class LokrDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) = weights
+        self.use_tucker = False
+        if lokr_w1_a is not None:
+            _, rank_a = lokr_w1_a.shape[0], lokr_w1_a.shape[1]
+            rank_a, _ = lokr_w1_b.shape[0], lokr_w1_b.shape[1]
+            self.lokr_w1_a = torch.nn.Parameter(lokr_w1_a)
+            self.lokr_w1_b = torch.nn.Parameter(lokr_w1_b)
+            self.w1_rebuild = True
+            self.ranka = rank_a
+
+        if lokr_w2_a is not None:
+            _, rank_b = lokr_w2_a.shape[0], lokr_w2_a.shape[1]
+            rank_b, _ = lokr_w2_b.shape[0], lokr_w2_b.shape[1]
+            self.lokr_w2_a = torch.nn.Parameter(lokr_w2_a)
+            self.lokr_w2_b = torch.nn.Parameter(lokr_w2_b)
+            if lokr_t2 is not None:
+                self.use_tucker = True
+                self.lokr_t2 = torch.nn.Parameter(lokr_t2)
+            self.w2_rebuild = True
+            self.rankb = rank_b
+
+        if lokr_w1 is not None:
+            self.lokr_w1 = torch.nn.Parameter(lokr_w1)
+            self.w1_rebuild = False
+
+        if lokr_w2 is not None:
+            self.lokr_w2 = torch.nn.Parameter(lokr_w2)
+            self.w2_rebuild = False
+
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    @property
+    def w1(self):
+        if self.w1_rebuild:
+            return (self.lokr_w1_a @ self.lokr_w1_b) * (self.alpha / self.ranka)
+        else:
+            return self.lokr_w1
+
+    @property
+    def w2(self):
+        if self.w2_rebuild:
+            if self.use_tucker:
+                w2 = torch.einsum(
+                    'i j k l, j r, i p -> p r k l',
+                    self.lokr_t2,
+                    self.lokr_w2_b,
+                    self.lokr_w2_a
+                )
+            else:
+                w2 = self.lokr_w2_a @ self.lokr_w2_b
+            return w2 * (self.alpha / self.rankb)
+        else:
+            return self.lokr_w2
+
+    def __call__(self, w):
+        diff = torch.kron(self.w1, self.w2)
+        return w + diff.reshape(w.shape).to(w)
+
+    def passive_memory_usage(self):
+        return sum(param.numel() * param.element_size() for param in self.parameters())
 
 
 class LoKrAdapter(WeightAdapterBase):
@@ -13,6 +83,20 @@ class LoKrAdapter(WeightAdapterBase):
         self.loaded_keys = loaded_keys
         self.weights = weights
 
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        in_dim = weight.shape[1:].numel()
+        out1, out2 = factorization(out_dim, rank)
+        in1, in2 = factorization(in_dim, rank)
+        mat1 = torch.empty(out1, in1, device=weight.device, dtype=weight.dtype)
+        mat2 = torch.empty(out2, in2, device=weight.device, dtype=weight.dtype)
+        torch.nn.init.kaiming_uniform_(mat2, a=5**0.5)
+        torch.nn.init.constant_(mat1, 0.0)
+        return LokrDiff(
+            (mat1, mat2, alpha, None, None, None, None, None, None)
+        )
+
     @classmethod
     def load(
         cls,

comfy/weight_adapter/lora.py

@@ -3,7 +3,56 @@ from typing import Optional
 
 import torch
 import comfy.model_management
-from .base import WeightAdapterBase, weight_decompose, pad_tensor_to_shape
+from .base import (
+    WeightAdapterBase,
+    WeightAdapterTrainBase,
+    weight_decompose,
+    pad_tensor_to_shape,
+    tucker_weight_from_conv,
+)
+
+
+class LoraDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        mat1, mat2, alpha, mid, dora_scale, reshape = weights
+        out_dim, rank = mat1.shape[0], mat1.shape[1]
+        rank, in_dim = mat2.shape[0], mat2.shape[1]
+        if mid is not None:
+            convdim = mid.ndim - 2
+            layer = (
+                torch.nn.Conv1d,
+                torch.nn.Conv2d,
+                torch.nn.Conv3d
+            )[convdim]
+        else:
+            layer = torch.nn.Linear
+        self.lora_up = layer(rank, out_dim, bias=False)
+        self.lora_down = layer(in_dim, rank, bias=False)
+        self.lora_up.weight.data.copy_(mat1)
+        self.lora_down.weight.data.copy_(mat2)
+        if mid is not None:
+            self.lora_mid = layer(mid, rank, bias=False)
+            self.lora_mid.weight.data.copy_(mid)
+        else:
+            self.lora_mid = None
+        self.rank = rank
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    def __call__(self, w):
+        org_dtype = w.dtype
+        if self.lora_mid is None:
+            diff = self.lora_up.weight @ self.lora_down.weight
+        else:
+            diff = tucker_weight_from_conv(
+                self.lora_up.weight, self.lora_down.weight, self.lora_mid.weight
+            )
+        scale = self.alpha / self.rank
+        weight = w + scale * diff.reshape(w.shape)
+        return weight.to(org_dtype)
+
+    def passive_memory_usage(self):
+        return sum(param.numel() * param.element_size() for param in self.parameters())
 
 
 class LoRAAdapter(WeightAdapterBase):
@@ -13,6 +62,21 @@ class LoRAAdapter(WeightAdapterBase):
         self.loaded_keys = loaded_keys
         self.weights = weights
 
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        in_dim = weight.shape[1:].numel()
+        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype)
+        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype)
+        torch.nn.init.kaiming_uniform_(mat1, a=5**0.5)
+        torch.nn.init.constant_(mat2, 0.0)
+        return LoraDiff(
+            (mat1, mat2, alpha, None, None, None)
+        )
+
+    def to_train(self):
+        return LoraDiff(self.weights)
+
     @classmethod
     def load(
         cls,

comfy/weight_adapter/oft.py

@@ -3,7 +3,58 @@ from typing import Optional
 
 import torch
 import comfy.model_management
-from .base import WeightAdapterBase, weight_decompose
+from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose, factorization
+
+
+class OFTDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        # Unpack weights tuple from LoHaAdapter
+        blocks, rescale, alpha, _ = weights
+
+        # Create trainable parameters
+        self.oft_blocks = torch.nn.Parameter(blocks)
+        if rescale is not None:
+            self.rescale = torch.nn.Parameter(rescale)
+            self.rescaled = True
+        else:
+            self.rescaled = False
+        self.block_num, self.block_size, _ = blocks.shape
+        self.constraint = float(alpha)
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    def __call__(self, w):
+        org_dtype = w.dtype
+        I = torch.eye(self.block_size, device=self.oft_blocks.device)
+
+        ## generate r
+        # for Q = -Q^T
+        q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
+        normed_q = q
+        if self.constraint:
+            q_norm = torch.norm(q) + 1e-8
+            if q_norm > self.constraint:
+                normed_q = q * self.constraint / q_norm
+        # use float() to prevent unsupported type
+        r = (I + normed_q) @ (I - normed_q).float().inverse()
+
+        ## Apply chunked matmul on weight
+        _, *shape = w.shape
+        org_weight = w.to(dtype=r.dtype)
+        org_weight = org_weight.unflatten(0, (self.block_num, self.block_size))
+        # Init R=0, so add I on it to ensure the output of step0 is original model output
+        weight = torch.einsum(
+            "k n m, k n ... -> k m ...",
+            r,
+            org_weight,
+        ).flatten(0, 1)
+        if self.rescaled:
+            weight = self.rescale * weight
+        return weight.to(org_dtype)
+
+    def passive_memory_usage(self):
+        """Calculates memory usage of the trainable parameters."""
+        return sum(param.numel() * param.element_size() for param in self.parameters())
 
 
 class OFTAdapter(WeightAdapterBase):
@@ -13,6 +64,18 @@ class OFTAdapter(WeightAdapterBase):
         self.loaded_keys = loaded_keys
         self.weights = weights
 
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        block_size, block_num = factorization(out_dim, rank)
+        block = torch.zeros(block_num, block_size, block_size, device=weight.device, dtype=weight.dtype)
+        return OFTDiff(
+            (block, None, alpha, None)
+        )
+
+    def to_train(self):
+        return OFTDiff(self.weights)
+
     @classmethod
     def load(
         cls,
@@ -60,6 +123,8 @@ class OFTAdapter(WeightAdapterBase):
         blocks = v[0]
         rescale = v[1]
         alpha = v[2]
+        if alpha is None:
+            alpha = 0
         dora_scale = v[3]
 
         blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype)

comfy_api/feature_flags.py

@@ -0,0 +1,69 @@
+"""
+Feature flags module for ComfyUI WebSocket protocol negotiation.
+
+This module handles capability negotiation between frontend and backend,
+allowing graceful protocol evolution while maintaining backward compatibility.
+"""
+
+from typing import Any, Dict
+
+from comfy.cli_args import args
+
+# Default server capabilities
+SERVER_FEATURE_FLAGS: Dict[str, Any] = {
+    "supports_preview_metadata": True,
+    "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
+}
+
+
+def get_connection_feature(
+    sockets_metadata: Dict[str, Dict[str, Any]],
+    sid: str,
+    feature_name: str,
+    default: Any = False
+) -> Any:
+    """
+    Get a feature flag value for a specific connection.
+
+    Args:
+        sockets_metadata: Dictionary of socket metadata
+        sid: Session ID of the connection
+        feature_name: Name of the feature to check
+        default: Default value if feature not found
+
+    Returns:
+        Feature value or default if not found
+    """
+    if sid not in sockets_metadata:
+        return default
+
+    return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default)
+
+
+def supports_feature(
+    sockets_metadata: Dict[str, Dict[str, Any]],
+    sid: str,
+    feature_name: str
+) -> bool:
+    """
+    Check if a connection supports a specific feature.
+
+    Args:
+        sockets_metadata: Dictionary of socket metadata
+        sid: Session ID of the connection
+        feature_name: Name of the feature to check
+
+    Returns:
+        Boolean indicating if feature is supported
+    """
+    return get_connection_feature(sockets_metadata, sid, feature_name, False) is True
+
+
+def get_server_features() -> Dict[str, Any]:
+    """
+    Get the server's feature flags.
+
+    Returns:
+        Dictionary of server feature flags
+    """
+    return SERVER_FEATURE_FLAGS.copy()

comfy_api/input/video_types.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import Optional, Union
+import io
+import av
 from comfy_api.util import VideoContainer, VideoCodec, VideoComponents
 
 class VideoInput(ABC):
@@ -31,6 +33,22 @@ class VideoInput(ABC):
         """
         pass
 
+    def get_stream_source(self) -> Union[str, io.BytesIO]:
+        """
+        Get a streamable source for the video. This allows processing without
+        loading the entire video into memory.
+
+        Returns:
+            Either a file path (str) or a BytesIO object that can be opened with av.
+
+        Default implementation creates a BytesIO buffer, but subclasses should
+        override this for better performance when possible.
+        """
+        buffer = io.BytesIO()
+        self.save_to(buffer)
+        buffer.seek(0)
+        return buffer
+
     # Provide a default implementation, but subclasses can provide optimized versions
     # if possible.
     def get_dimensions(self) -> tuple[int, int]:
@@ -53,3 +71,15 @@ class VideoInput(ABC):
         components = self.get_components()
         frame_count = components.images.shape[0]
         return float(frame_count / components.frame_rate)
+
+    def get_container_format(self) -> str:
+        """
+        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
+
+        Returns:
+            Container format as string
+        """
+        # Default implementation - subclasses should override for better performance
+        source = self.get_stream_source()
+        with av.open(source, mode="r") as container:
+            return container.format.name

comfy_api/input_impl/video_types.py

@@ -64,6 +64,15 @@ class VideoFromFile(VideoInput):
         """
         self.__file = file
 
+    def get_stream_source(self) -> str | io.BytesIO:
+        """
+        Return the underlying file source for efficient streaming.
+        This avoids unnecessary memory copies when the source is already a file path.
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        return self.__file
+
     def get_dimensions(self) -> tuple[int, int]:
         """
         Returns the dimensions of the video input.
@@ -112,6 +121,18 @@ class VideoFromFile(VideoInput):
 
         raise ValueError(f"Could not determine duration for file '{self.__file}'")
 
+    def get_container_format(self) -> str:
+        """
+        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
+
+        Returns:
+            Container format as string
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode='r') as container:
+            return container.format.name
+
     def get_components_internal(self, container: InputContainer) -> VideoComponents:
         # Get video frames
         frames = []

comfy_api_nodes/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction 
 
-Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview#api-nodes).
+Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview).
 
 ## Development
 

comfy_api_nodes/apis/__init__.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  filtered-openapi.yaml
-#   timestamp: 2025-05-19T21:38:55+00:00
+#   timestamp: 2025-07-06T09:47:31+00:00
 
 from __future__ import annotations
 
@@ -1355,6 +1355,158 @@ class ModelResponseProperties(BaseModel):
     )
 
 
+class Keyframes(BaseModel):
+    image_url: Optional[str] = None
+
+
+class MoonvalleyPromptResponse(BaseModel):
+    error: Optional[Dict[str, Any]] = None
+    frame_conditioning: Optional[Dict[str, Any]] = None
+    id: Optional[str] = None
+    inference_params: Optional[Dict[str, Any]] = None
+    meta: Optional[Dict[str, Any]] = None
+    model_params: Optional[Dict[str, Any]] = None
+    output_url: Optional[str] = None
+    prompt_text: Optional[str] = None
+    status: Optional[str] = None
+
+
+class MoonvalleyTextToVideoInferenceParams(BaseModel):
+    add_quality_guidance: Optional[bool] = Field(
+        True, description='Whether to add quality guidance'
+    )
+    caching_coefficient: Optional[float] = Field(
+        0.3, description='Caching coefficient for optimization'
+    )
+    caching_cooldown: Optional[int] = Field(
+        3, description='Number of caching cooldown steps'
+    )
+    caching_warmup: Optional[int] = Field(
+        3, description='Number of caching warmup steps'
+    )
+    clip_value: Optional[float] = Field(
+        3, description='CLIP value for generation control'
+    )
+    conditioning_frame_index: Optional[int] = Field(
+        0, description='Index of the conditioning frame'
+    )
+    cooldown_steps: Optional[int] = Field(
+        None, description='Number of cooldown steps (calculated based on num_frames)'
+    )
+    fps: Optional[int] = Field(
+        24, description='Frames per second of the generated video'
+    )
+    guidance_scale: Optional[float] = Field(
+        12.5, description='Guidance scale for generation control'
+    )
+    height: Optional[int] = Field(
+        1080, description='Height of the generated video in pixels'
+    )
+    negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
+    num_frames: Optional[int] = Field(64, description='Number of frames to generate')
+    seed: Optional[int] = Field(
+        None, description='Random seed for generation (default: random)'
+    )
+    shift_value: Optional[float] = Field(
+        3, description='Shift value for generation control'
+    )
+    steps: Optional[int] = Field(80, description='Number of denoising steps')
+    use_guidance_schedule: Optional[bool] = Field(
+        True, description='Whether to use guidance scheduling'
+    )
+    use_negative_prompts: Optional[bool] = Field(
+        False, description='Whether to use negative prompts'
+    )
+    use_timestep_transform: Optional[bool] = Field(
+        True, description='Whether to use timestep transformation'
+    )
+    warmup_steps: Optional[int] = Field(
+        None, description='Number of warmup steps (calculated based on num_frames)'
+    )
+    width: Optional[int] = Field(
+        1920, description='Width of the generated video in pixels'
+    )
+
+
+class MoonvalleyTextToVideoRequest(BaseModel):
+    image_url: Optional[str] = None
+    inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None
+    prompt_text: Optional[str] = None
+    webhook_url: Optional[str] = None
+
+
+class MoonvalleyUploadFileRequest(BaseModel):
+    file: Optional[StrictBytes] = None
+
+
+class MoonvalleyUploadFileResponse(BaseModel):
+    access_url: Optional[str] = None
+
+
+class MoonvalleyVideoToVideoInferenceParams(BaseModel):
+    add_quality_guidance: Optional[bool] = Field(
+        True, description='Whether to add quality guidance'
+    )
+    caching_coefficient: Optional[float] = Field(
+        0.3, description='Caching coefficient for optimization'
+    )
+    caching_cooldown: Optional[int] = Field(
+        3, description='Number of caching cooldown steps'
+    )
+    caching_warmup: Optional[int] = Field(
+        3, description='Number of caching warmup steps'
+    )
+    clip_value: Optional[float] = Field(
+        3, description='CLIP value for generation control'
+    )
+    conditioning_frame_index: Optional[int] = Field(
+        0, description='Index of the conditioning frame'
+    )
+    cooldown_steps: Optional[int] = Field(
+        None, description='Number of cooldown steps (calculated based on num_frames)'
+    )
+    guidance_scale: Optional[float] = Field(
+        12.5, description='Guidance scale for generation control'
+    )
+    negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
+    seed: Optional[int] = Field(
+        None, description='Random seed for generation (default: random)'
+    )
+    shift_value: Optional[float] = Field(
+        3, description='Shift value for generation control'
+    )
+    steps: Optional[int] = Field(80, description='Number of denoising steps')
+    use_guidance_schedule: Optional[bool] = Field(
+        True, description='Whether to use guidance scheduling'
+    )
+    use_negative_prompts: Optional[bool] = Field(
+        False, description='Whether to use negative prompts'
+    )
+    use_timestep_transform: Optional[bool] = Field(
+        True, description='Whether to use timestep transformation'
+    )
+    warmup_steps: Optional[int] = Field(
+        None, description='Number of warmup steps (calculated based on num_frames)'
+    )
+
+
+class ControlType(str, Enum):
+    motion_control = 'motion_control'
+    pose_control = 'pose_control'
+
+
+class MoonvalleyVideoToVideoRequest(BaseModel):
+    control_type: ControlType = Field(
+        ..., description='Supported types for video control'
+    )
+    inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None
+    prompt_text: str = Field(..., description='Describes the video to generate')
+    video_url: str = Field(..., description='Url to control video')
+    webhook_url: Optional[str] = Field(
+        None, description='Optional webhook URL for notifications'
+    )
+
+
 class Moderation(str, Enum):
     low = 'low'
     auto = 'auto'
@@ -3107,6 +3259,23 @@ class LumaUpscaleVideoGenerationRequest(BaseModel):
     resolution: Optional[LumaVideoModelOutputResolution] = None
 
 
+class MoonvalleyImageToVideoRequest(MoonvalleyTextToVideoRequest):
+    keyframes: Optional[Dict[str, Keyframes]] = None
+
+
+class MoonvalleyResizeVideoRequest(MoonvalleyVideoToVideoRequest):
+    frame_position: Optional[List[int]] = Field(None, max_length=2, min_length=2)
+    frame_resolution: Optional[List[int]] = Field(None, max_length=2, min_length=2)
+    scale: Optional[List[int]] = Field(None, max_length=2, min_length=2)
+
+
+class MoonvalleyTextToImageRequest(BaseModel):
+    image_url: Optional[str] = None
+    inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None
+    prompt_text: Optional[str] = None
+    webhook_url: Optional[str] = None
+
+
 class OutputContent(RootModel[Union[OutputTextContent, OutputAudioContent]]):
     root: Union[OutputTextContent, OutputAudioContent]
 

comfy_api_nodes/apis/bfl_api.py

@@ -108,6 +108,24 @@ class BFLFluxProGenerateRequest(BaseModel):
     # )
 
 
+class BFLFluxKontextProGenerateRequest(BaseModel):
+    prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
+    input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
+    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
+    guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
+    steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
+    safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
+        2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
+    )
+    aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
+    prompt_upsampling: Optional[bool] = Field(
+        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
+    )
+
+
 class BFLFluxProUltraGenerateRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt for image generation.')
     prompt_upsampling: Optional[bool] = Field(

comfy_api_nodes/apis/client.py

@@ -327,7 +327,9 @@ class ApiClient:
             ApiServerError: If the API server is unreachable but internet is working
             Exception: For other request failures
         """
-        url = urljoin(self.base_url, path)
+        # Use urljoin but ensure path is relative to avoid absolute path behavior
+        relative_path = path.lstrip('/')
+        url = urljoin(self.base_url, relative_path)
         self.check_auth(self.auth_token, self.comfy_api_key)
         # Combine default headers with any provided headers
         request_headers = self.get_headers()

comfy_api_nodes/nodes_bfl.py

@@ -1,6 +1,6 @@
 import io
 from inspect import cleandoc
-from typing import Union
+from typing import Union, Optional
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api_nodes.apis.bfl_api import (
     BFLStatus,
@@ -9,6 +9,7 @@ from comfy_api_nodes.apis.bfl_api import (
     BFLFluxCannyImageRequest,
     BFLFluxDepthImageRequest,
     BFLFluxProGenerateRequest,
+    BFLFluxKontextProGenerateRequest,
     BFLFluxProUltraGenerateRequest,
     BFLFluxProGenerateResponse,
 )
@@ -269,6 +270,145 @@ class FluxProUltraImageNode(ComfyNodeABC):
         return (output_image,)
 
 
+class FluxKontextProImageNode(ComfyNodeABC):
+    """
+    Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.
+    """
+
+    MINIMUM_RATIO = 1 / 4
+    MAXIMUM_RATIO = 4 / 1
+    MINIMUM_RATIO_STR = "1:4"
+    MAXIMUM_RATIO_STR = "4:1"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Prompt for the image generation - specify what and how to edit.",
+                    },
+                ),
+                "aspect_ratio": (
+                    IO.STRING,
+                    {
+                        "default": "16:9",
+                        "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
+                    },
+                ),
+                "guidance": (
+                    IO.FLOAT,
+                    {
+                        "default": 3.0,
+                        "min": 0.1,
+                        "max": 99.0,
+                        "step": 0.1,
+                        "tooltip": "Guidance strength for the image generation process"
+                    },
+                ),
+                "steps": (
+                    IO.INT,
+                    {
+                        "default": 50,
+                        "min": 1,
+                        "max": 150,
+                        "tooltip": "Number of steps for the image generation process"
+                    },
+                ),
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 1234,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": True,
+                        "tooltip": "The random seed used for creating the noise.",
+                    },
+                ),
+                "prompt_upsampling": (
+                    IO.BOOLEAN,
+                    {
+                        "default": False,
+                        "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    },
+                ),
+            },
+            "optional": {
+                "input_image": (IO.IMAGE,),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = (IO.IMAGE,)
+    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    FUNCTION = "api_call"
+    API_NODE = True
+    CATEGORY = "api node/image/BFL"
+
+    BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate"
+
+    def api_call(
+        self,
+        prompt: str,
+        aspect_ratio: str,
+        guidance: float,
+        steps: int,
+        input_image: Optional[torch.Tensor]=None,
+        seed=0,
+        prompt_upsampling=False,
+        unique_id: Union[str, None] = None,
+        **kwargs,
+    ):
+        aspect_ratio = validate_aspect_ratio(
+            aspect_ratio,
+            minimum_ratio=self.MINIMUM_RATIO,
+            maximum_ratio=self.MAXIMUM_RATIO,
+            minimum_ratio_str=self.MINIMUM_RATIO_STR,
+            maximum_ratio_str=self.MAXIMUM_RATIO_STR,
+        )
+        if input_image is None:
+            validate_string(prompt, strip_whitespace=False)
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=self.BFL_PATH,
+                method=HttpMethod.POST,
+                request_model=BFLFluxKontextProGenerateRequest,
+                response_model=BFLFluxProGenerateResponse,
+            ),
+            request=BFLFluxKontextProGenerateRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                guidance=round(guidance, 1),
+                steps=steps,
+                seed=seed,
+                aspect_ratio=aspect_ratio,
+                input_image=(
+                    input_image
+                    if input_image is None
+                    else convert_image_to_base64(input_image)
+                )
+            ),
+            auth_kwargs=kwargs,
+        )
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+        return (output_image,)
+
+
+class FluxKontextMaxImageNode(FluxKontextProImageNode):
+    """
+    Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio.
+    """
+
+    DESCRIPTION = cleandoc(__doc__ or "")
+    BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
+
 
 class FluxProImageNode(ComfyNodeABC):
     """
@@ -914,6 +1054,8 @@ class FluxProDepthNode(ComfyNodeABC):
 NODE_CLASS_MAPPINGS = {
     "FluxProUltraImageNode": FluxProUltraImageNode,
     # "FluxProImageNode": FluxProImageNode,
+    "FluxKontextProImageNode": FluxKontextProImageNode,
+    "FluxKontextMaxImageNode": FluxKontextMaxImageNode,
     "FluxProExpandNode": FluxProExpandNode,
     "FluxProFillNode": FluxProFillNode,
     "FluxProCannyNode": FluxProCannyNode,
@@ -924,6 +1066,8 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
     # "FluxProImageNode": "Flux 1.1 [pro] Image",
+    "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image",
+    "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image",
     "FluxProExpandNode": "Flux.1 Expand Image",
     "FluxProFillNode": "Flux.1 Fill Image",
     "FluxProCannyNode": "Flux.1 Canny Control Image",

comfy_api_nodes/nodes_gemini.py

@@ -406,7 +406,7 @@ class GeminiInputFiles(ComfyNodeABC):
 
     def create_file_part(self, file_path: str) -> GeminiPart:
         mime_type = (
-            GeminiMimeType.pdf
+            GeminiMimeType.application_pdf
             if file_path.endswith(".pdf")
             else GeminiMimeType.text_plain
         )

comfy_api_nodes/nodes_ideogram.py

@@ -324,7 +324,7 @@ class IdeogramV1(ComfyNodeABC):
 
     RETURN_TYPES = (IO.IMAGE,)
     FUNCTION = "api_call"
-    CATEGORY = "api node/image/Ideogram/v1"
+    CATEGORY = "api node/image/Ideogram"
     DESCRIPTION = cleandoc(__doc__ or "")
     API_NODE = True
 
@@ -483,7 +483,7 @@ class IdeogramV2(ComfyNodeABC):
 
     RETURN_TYPES = (IO.IMAGE,)
     FUNCTION = "api_call"
-    CATEGORY = "api node/image/Ideogram/v2"
+    CATEGORY = "api node/image/Ideogram"
     DESCRIPTION = cleandoc(__doc__ or "")
     API_NODE = True
 
@@ -649,7 +649,7 @@ class IdeogramV3(ComfyNodeABC):
 
     RETURN_TYPES = (IO.IMAGE,)
     FUNCTION = "api_call"
-    CATEGORY = "api node/image/Ideogram/v3"
+    CATEGORY = "api node/image/Ideogram"
     DESCRIPTION = cleandoc(__doc__ or "")
     API_NODE = True
 

comfy_api_nodes/nodes_kling.py

@@ -132,6 +132,8 @@ def poll_until_finished(
         result_url_extractor=result_url_extractor,
         estimated_duration=estimated_duration,
         node_id=node_id,
+        poll_interval=16.0,
+        max_poll_attempts=256,
     ).execute()
 
 

comfy_api_nodes/nodes_moonvalley.py

@@ -0,0 +1,743 @@
+import logging
+from typing import Any, Callable, Optional, TypeVar
+import random
+import torch
+from comfy_api_nodes.util.validation_utils import (
+    get_image_dimensions,
+    validate_image_dimensions,
+)
+
+
+from comfy_api_nodes.apis import (
+    MoonvalleyTextToVideoRequest,
+    MoonvalleyTextToVideoInferenceParams,
+    MoonvalleyVideoToVideoInferenceParams,
+    MoonvalleyVideoToVideoRequest,
+    MoonvalleyPromptResponse,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+    download_url_to_video_output,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+
+from comfy_api.input.video_types import VideoInput
+from comfy.comfy_types.node_typing import IO
+from comfy_api.input_impl import VideoFromFile
+import av
+import io
+
+API_UPLOADS_ENDPOINT = "/proxy/moonvalley/uploads"
+API_PROMPTS_ENDPOINT = "/proxy/moonvalley/prompts"
+API_VIDEO2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/video-to-video"
+API_TXT2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/text-to-video"
+API_IMG2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/image-to-video"
+
+MIN_WIDTH = 300
+MIN_HEIGHT = 300
+
+MAX_WIDTH = 10000
+MAX_HEIGHT = 10000
+
+MIN_VID_WIDTH = 300
+MIN_VID_HEIGHT = 300
+
+MAX_VID_WIDTH = 10000
+MAX_VID_HEIGHT = 10000
+
+MAX_VIDEO_SIZE = 1024 * 1024 * 1024  # 1 GB max for in-memory video processing
+
+MOONVALLEY_MAREY_MAX_PROMPT_LENGTH = 5000
+R = TypeVar("R")
+
+
+class MoonvalleyApiError(Exception):
+    """Base exception for Moonvalley API errors."""
+
+    pass
+
+
+def is_valid_task_creation_response(response: MoonvalleyPromptResponse) -> bool:
+    """Verifies that the initial response contains a task ID."""
+    return bool(response.id)
+
+
+def validate_task_creation_response(response) -> None:
+    if not is_valid_task_creation_response(response):
+        error_msg = f"Moonvalley Marey API: Initial request failed. Code: {response.code}, Message: {response.message}, Data: {response}"
+        logging.error(error_msg)
+        raise MoonvalleyApiError(error_msg)
+
+
+def get_video_from_response(response):
+    video = response.output_url
+    logging.info(
+        "Moonvalley Marey API: Task %s succeeded. Video URL: %s", response.id, video
+    )
+    return video
+
+
+def get_video_url_from_response(response) -> Optional[str]:
+    """Returns the first video url from the Moonvalley video generation task result.
+    Will not raise an error if the response is not valid.
+    """
+    if response:
+        return str(get_video_from_response(response))
+    else:
+        return None
+
+
+def poll_until_finished(
+    auth_kwargs: dict[str, str],
+    api_endpoint: ApiEndpoint[Any, R],
+    result_url_extractor: Optional[Callable[[R], str]] = None,
+    node_id: Optional[str] = None,
+) -> R:
+    """Polls the Moonvalley API endpoint until the task reaches a terminal state, then returns the response."""
+    return PollingOperation(
+        poll_endpoint=api_endpoint,
+        completed_statuses=[
+            "completed",
+        ],
+        max_poll_attempts=240,  # 64 minutes with 16s interval
+        poll_interval=16.0,
+        failed_statuses=["error"],
+        status_extractor=lambda response: (
+            response.status if response and response.status else None
+        ),
+        auth_kwargs=auth_kwargs,
+        result_url_extractor=result_url_extractor,
+        node_id=node_id,
+    ).execute()
+
+
+def validate_prompts(
+    prompt: str, negative_prompt: str, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH
+):
+    """Verifies that the prompt isn't empty and that neither prompt is too long."""
+    if not prompt:
+        raise ValueError("Positive prompt is empty")
+    if len(prompt) > max_length:
+        raise ValueError(f"Positive prompt is too long: {len(prompt)} characters")
+    if negative_prompt and len(negative_prompt) > max_length:
+        raise ValueError(
+            f"Negative prompt is too long: {len(negative_prompt)} characters"
+        )
+    return True
+
+
+def validate_input_media(width, height, with_frame_conditioning, num_frames_in=None):
+    # inference validation
+    # T = num_frames
+    # in all cases, the following must be true: T divisible by 16 and H,W by 8. in addition...
+    # with image conditioning: H*W must be divisible by 8192
+    # without image conditioning: T divisible by 32
+    if num_frames_in and not num_frames_in % 16 == 0:
+        return False, ("The input video total frame count must be divisible by 16!")
+
+    if height % 8 != 0 or width % 8 != 0:
+        return False, (
+            f"Height ({height}) and width ({width}) must be " "divisible by 8"
+        )
+
+    if with_frame_conditioning:
+        if (height * width) % 8192 != 0:
+            return False, (
+                f"Height * width ({height * width}) must be "
+                "divisible by 8192 for frame conditioning"
+            )
+    else:
+        if num_frames_in and not num_frames_in % 32 == 0:
+            return False, ("The input video total frame count must be divisible by 32!")
+
+
+def validate_input_image(
+    image: torch.Tensor, with_frame_conditioning: bool = False
+) -> None:
+    """
+    Validates the input image adheres to the expectations of the API:
+    - The image resolution should not be less than 300*300px
+    - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
+
+    """
+    height, width = get_image_dimensions(image)
+    validate_input_media(width, height, with_frame_conditioning)
+    validate_image_dimensions(
+        image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH
+    )
+
+
+def validate_video_to_video_input(video: VideoInput) -> VideoInput:
+    """
+    Validates and processes video input for Moonvalley Video-to-Video generation.
+
+    Args:
+        video: Input video to validate
+
+    Returns:
+        Validated and potentially trimmed video
+
+    Raises:
+        ValueError: If video doesn't meet requirements
+        MoonvalleyApiError: If video duration is too short
+    """
+    width, height = _get_video_dimensions(video)
+    _validate_video_dimensions(width, height)
+    _validate_container_format(video)
+
+    return _validate_and_trim_duration(video)
+
+
+def _get_video_dimensions(video: VideoInput) -> tuple[int, int]:
+    """Extracts video dimensions with error handling."""
+    try:
+        return video.get_dimensions()
+    except Exception as e:
+        logging.error("Error getting dimensions of video: %s", e)
+        raise ValueError(f"Cannot get video dimensions: {e}") from e
+
+
+def _validate_video_dimensions(width: int, height: int) -> None:
+    """Validates video dimensions meet Moonvalley V2V requirements."""
+    supported_resolutions = {
+        (1920, 1080), (1080, 1920), (1152, 1152),
+        (1536, 1152), (1152, 1536)
+    }
+
+    if (width, height) not in supported_resolutions:
+        supported_list = ', '.join([f'{w}x{h}' for w, h in sorted(supported_resolutions)])
+        raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}")
+
+
+def _validate_container_format(video: VideoInput) -> None:
+    """Validates video container format is MP4."""
+    container_format = video.get_container_format()
+    if container_format not in ['mp4', 'mov,mp4,m4a,3gp,3g2,mj2']:
+        raise ValueError(f"Only MP4 container format supported. Got: {container_format}")
+
+
+def _validate_and_trim_duration(video: VideoInput) -> VideoInput:
+    """Validates video duration and trims to 5 seconds if needed."""
+    duration = video.get_duration()
+    _validate_minimum_duration(duration)
+    return _trim_if_too_long(video, duration)
+
+
+def _validate_minimum_duration(duration: float) -> None:
+    """Ensures video is at least 5 seconds long."""
+    if duration < 5:
+        raise MoonvalleyApiError("Input video must be at least 5 seconds long.")
+
+
+def _trim_if_too_long(video: VideoInput, duration: float) -> VideoInput:
+    """Trims video to 5 seconds if longer."""
+    if duration > 5:
+        return trim_video(video, 5)
+    return video
+
+
+
+def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
+    """
+    Returns a new VideoInput object trimmed from the beginning to the specified duration,
+    using av to avoid loading entire video into memory.
+
+    Args:
+        video: Input video to trim
+        duration_sec: Duration in seconds to keep from the beginning
+
+    Returns:
+        VideoFromFile object that owns the output buffer
+    """
+    output_buffer = io.BytesIO()
+
+    input_container = None
+    output_container = None
+
+    try:
+        # Get the stream source - this avoids loading entire video into memory
+        # when the source is already a file path
+        input_source = video.get_stream_source()
+
+        # Open containers
+        input_container = av.open(input_source, mode="r")
+        output_container = av.open(output_buffer, mode="w", format="mp4")
+
+        # Set up output streams for re-encoding
+        video_stream = None
+        audio_stream = None
+
+        for stream in input_container.streams:
+            logging.info(f"Found stream: type={stream.type}, class={type(stream)}")
+            if isinstance(stream, av.VideoStream):
+                # Create output video stream with same parameters
+                video_stream = output_container.add_stream(
+                    "h264", rate=stream.average_rate
+                )
+                video_stream.width = stream.width
+                video_stream.height = stream.height
+                video_stream.pix_fmt = "yuv420p"
+                logging.info(
+                    f"Added video stream: {stream.width}x{stream.height} @ {stream.average_rate}fps"
+                )
+            elif isinstance(stream, av.AudioStream):
+                # Create output audio stream with same parameters
+                audio_stream = output_container.add_stream(
+                    "aac", rate=stream.sample_rate
+                )
+                audio_stream.sample_rate = stream.sample_rate
+                audio_stream.layout = stream.layout
+                logging.info(
+                    f"Added audio stream: {stream.sample_rate}Hz, {stream.channels} channels"
+                )
+
+        # Calculate target frame count that's divisible by 16
+        fps = input_container.streams.video[0].average_rate
+        estimated_frames = int(duration_sec * fps)
+        target_frames = (estimated_frames // 16) * 16  # Round down to nearest multiple of 16
+
+        if target_frames == 0:
+            raise ValueError("Video too short: need at least 16 frames for Moonvalley")
+
+        frame_count = 0
+        audio_frame_count = 0
+
+        # Decode and re-encode video frames
+        if video_stream:
+            for frame in input_container.decode(video=0):
+                if frame_count >= target_frames:
+                    break
+
+                # Re-encode frame
+                for packet in video_stream.encode(frame):
+                    output_container.mux(packet)
+                frame_count += 1
+
+            # Flush encoder
+            for packet in video_stream.encode():
+                output_container.mux(packet)
+
+            logging.info(
+                f"Encoded {frame_count} video frames (target: {target_frames})"
+            )
+
+        # Decode and re-encode audio frames
+        if audio_stream:
+            input_container.seek(0)  # Reset to beginning for audio
+            for frame in input_container.decode(audio=0):
+                if frame.time >= duration_sec:
+                    break
+
+                # Re-encode frame
+                for packet in audio_stream.encode(frame):
+                    output_container.mux(packet)
+                audio_frame_count += 1
+
+            # Flush encoder
+            for packet in audio_stream.encode():
+                output_container.mux(packet)
+
+            logging.info(f"Encoded {audio_frame_count} audio frames")
+
+        # Close containers
+        output_container.close()
+        input_container.close()
+
+        # Return as VideoFromFile using the buffer
+        output_buffer.seek(0)
+        return VideoFromFile(output_buffer)
+
+    except Exception as e:
+        # Clean up on error
+        if input_container is not None:
+            input_container.close()
+        if output_container is not None:
+            output_container.close()
+        raise RuntimeError(f"Failed to trim video: {str(e)}") from e
+
+
+# --- BaseMoonvalleyVideoNode ---
+class BaseMoonvalleyVideoNode:
+    def parseWidthHeightFromRes(self, resolution: str):
+        # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict
+        res_map = {
+            "16:9 (1920 x 1080)": {"width": 1920, "height": 1080},
+            "9:16 (1080 x 1920)": {"width": 1080, "height": 1920},
+            "1:1 (1152 x 1152)": {"width": 1152, "height": 1152},
+            "4:3 (1536 x 1152)": {"width": 1536, "height": 1152},
+            "3:4 (1152 x 1536)": {"width": 1152, "height": 1536},
+            "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
+        }
+        if resolution in res_map:
+            return res_map[resolution]
+        else:
+            # Default to 1920x1080 if unknown
+            return {"width": 1920, "height": 1080}
+
+    def parseControlParameter(self, value):
+        control_map = {
+            "Motion Transfer": "motion_control",
+            "Canny": "canny_control",
+            "Pose Transfer": "pose_control",
+            "Depth": "depth_control",
+        }
+        if value in control_map:
+            return control_map[value]
+        else:
+            return control_map["Motion Transfer"]
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> MoonvalleyPromptResponse:
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{API_PROMPTS_ENDPOINT}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=MoonvalleyPromptResponse,
+            ),
+            result_url_extractor=get_video_url_from_response,
+            node_id=node_id,
+        )
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING,
+                    MoonvalleyTextToVideoRequest,
+                    "prompt_text",
+                    multiline=True,
+                ),
+                "negative_prompt": model_field_to_node_input(
+                    IO.STRING,
+                    MoonvalleyTextToVideoInferenceParams,
+                    "negative_prompt",
+                    multiline=True,
+                    default="low-poly, flat shader, bad rigging, stiff animation, uncanny eyes, low-quality textures, looping glitch, cheap effect, overbloom, bloom spam, default lighting, game asset, stiff face, ugly specular, AI artifacts",
+                ),
+                "resolution": (
+                    IO.COMBO,
+                    {
+                        "options": [
+                            "16:9 (1920 x 1080)",
+                            "9:16 (1080 x 1920)",
+                            "1:1 (1152 x 1152)",
+                            "4:3 (1440 x 1080)",
+                            "3:4 (1080 x 1440)",
+                            "21:9 (2560 x 1080)",
+                        ],
+                        "default": "16:9 (1920 x 1080)",
+                        "tooltip": "Resolution of the output video",
+                    },
+                ),
+                # "length": (IO.COMBO,{"options":['5s','10s'], "default": '5s'}),
+                "prompt_adherence": model_field_to_node_input(
+                    IO.FLOAT,
+                    MoonvalleyTextToVideoInferenceParams,
+                    "guidance_scale",
+                    default=7.0,
+                    step=1,
+                    min=1,
+                    max=20,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    MoonvalleyTextToVideoInferenceParams,
+                    "seed",
+                    default=random.randint(0, 2**32 - 1),
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    display="number",
+                    tooltip="Random seed value",
+                    control_after_generate=True,
+                ),
+                "steps": model_field_to_node_input(
+                    IO.INT,
+                    MoonvalleyTextToVideoInferenceParams,
+                    "steps",
+                    default=100,
+                    min=1,
+                    max=100,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+            "optional": {
+                "image": model_field_to_node_input(
+                    IO.IMAGE,
+                    MoonvalleyTextToVideoRequest,
+                    "image_url",
+                    tooltip="The reference image used to generate the video",
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "generate"
+    CATEGORY = "api node/video/Moonvalley Marey"
+    API_NODE = True
+
+    def generate(self, **kwargs):
+        return None
+
+
+# --- MoonvalleyImg2VideoNode ---
+class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return super().INPUT_TYPES()
+
+    RETURN_TYPES = ("VIDEO",)
+    RETURN_NAMES = ("video",)
+    DESCRIPTION = "Moonvalley Marey Image to Video Node"
+
+    def generate(
+        self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
+    ):
+        image = kwargs.get("image", None)
+        if image is None:
+            raise MoonvalleyApiError("image is required")
+
+        validate_input_image(image, True)
+        validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
+        width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
+
+        inference_params = MoonvalleyTextToVideoInferenceParams(
+            negative_prompt=negative_prompt,
+            steps=kwargs.get("steps"),
+            seed=kwargs.get("seed"),
+            guidance_scale=kwargs.get("prompt_adherence"),
+            num_frames=128,
+            width=width_height.get("width"),
+            height=width_height.get("height"),
+            use_negative_prompts=True,
+        )
+        """Upload image to comfy backend to have a URL available for further processing"""
+        # Get MIME type from tensor - assuming PNG format for image tensors
+        mime_type = "image/png"
+
+        image_url = upload_images_to_comfyapi(
+            image, max_images=1, auth_kwargs=kwargs, mime_type=mime_type
+        )[0]
+
+        request = MoonvalleyTextToVideoRequest(
+            image_url=image_url, prompt_text=prompt, inference_params=inference_params
+        )
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=API_IMG2VIDEO_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=MoonvalleyTextToVideoRequest,
+                response_model=MoonvalleyPromptResponse,
+            ),
+            request=request,
+            auth_kwargs=kwargs,
+        )
+        task_creation_response = initial_operation.execute()
+        validate_task_creation_response(task_creation_response)
+        task_id = task_creation_response.id
+
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
+        video = download_url_to_video_output(final_response.output_url)
+        return (video,)
+
+
+# --- MoonvalleyVid2VidNode ---
+class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
+    def __init__(self):
+        super().__init__()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, MoonvalleyVideoToVideoRequest, "prompt_text",
+                    multiline=True
+                ),
+                "negative_prompt": model_field_to_node_input(
+                    IO.STRING,
+                    MoonvalleyVideoToVideoInferenceParams,
+                    "negative_prompt",
+                    multiline=True,
+                    default="low-poly, flat shader, bad rigging, stiff animation, uncanny eyes, low-quality textures, looping glitch, cheap effect, overbloom, bloom spam, default lighting, game asset, stiff face, ugly specular, AI artifacts"
+                ),
+                "seed": model_field_to_node_input(IO.INT,MoonvalleyVideoToVideoInferenceParams, "seed", default=random.randint(0, 2**32 - 1), min=0, max=4294967295, step=1, display="number", tooltip="Random seed value", control_after_generate=True),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+            "optional": {
+                "video": (IO.VIDEO, {"default": "", "multiline": False, "tooltip": "The reference video used to generate the output video. Must be at least 5 seconds long. Videos longer than 5s will be automatically trimmed. Only MP4 format supported."}),
+                "control_type": (
+                    ["Motion Transfer", "Pose Transfer"],
+                    {"default": "Motion Transfer"},
+                ),
+                "motion_intensity": (
+                    "INT",
+                    {
+                        "default": 100,
+                        "step": 1,
+                        "min": 0,
+                        "max": 100,
+                        "tooltip": "Only used if control_type is 'Motion Transfer'",
+                    },
+                )
+            }
+        }
+
+    RETURN_TYPES = ("VIDEO",)
+    RETURN_NAMES = ("video",)
+
+    def generate(
+        self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
+    ):
+        video = kwargs.get("video")
+
+        if not video:
+            raise MoonvalleyApiError("video is required")
+
+        video_url = ""
+        if video:
+            validated_video = validate_video_to_video_input(video)
+            video_url = upload_video_to_comfyapi(validated_video, auth_kwargs=kwargs)
+
+        control_type = kwargs.get("control_type")
+        motion_intensity = kwargs.get("motion_intensity")
+
+        """Validate prompts and inference input"""
+        validate_prompts(prompt, negative_prompt)
+
+        # Only include motion_intensity for Motion Transfer
+        control_params = {}
+        if control_type == "Motion Transfer" and motion_intensity is not None:
+            control_params['motion_intensity'] = motion_intensity
+
+        inference_params=MoonvalleyVideoToVideoInferenceParams(
+            negative_prompt=negative_prompt,
+            seed=kwargs.get("seed"),
+            control_params=control_params
+        )
+
+        control = self.parseControlParameter(control_type)
+
+        request = MoonvalleyVideoToVideoRequest(
+            control_type=control,
+            video_url=video_url,
+            prompt_text=prompt,
+            inference_params=inference_params,
+        )
+
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=API_VIDEO2VIDEO_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=MoonvalleyVideoToVideoRequest,
+                response_model=MoonvalleyPromptResponse,
+            ),
+            request=request,
+            auth_kwargs=kwargs,
+        )
+        task_creation_response = initial_operation.execute()
+        validate_task_creation_response(task_creation_response)
+        task_id = task_creation_response.id
+
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
+
+        video = download_url_to_video_output(final_response.output_url)
+
+        return (video,)
+
+
+# --- MoonvalleyTxt2VideoNode ---
+class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode):
+    def __init__(self):
+        super().__init__()
+
+    RETURN_TYPES = ("VIDEO",)
+    RETURN_NAMES = ("video",)
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        input_types = super().INPUT_TYPES()
+        # Remove image-specific parameters
+        for param in ["image"]:
+            if param in input_types["optional"]:
+                del input_types["optional"][param]
+        return input_types
+
+    def generate(
+        self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
+    ):
+        validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
+        width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
+
+        inference_params=MoonvalleyTextToVideoInferenceParams(
+                    negative_prompt=negative_prompt,
+                    steps=kwargs.get("steps"),
+                    seed=kwargs.get("seed"),
+                    guidance_scale=kwargs.get("prompt_adherence"),
+                    num_frames=128,
+                    width=width_height.get("width"),
+                    height=width_height.get("height"),
+                )
+        request = MoonvalleyTextToVideoRequest(
+            prompt_text=prompt, inference_params=inference_params
+        )
+
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=API_TXT2VIDEO_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=MoonvalleyTextToVideoRequest,
+                response_model=MoonvalleyPromptResponse,
+            ),
+            request=request,
+            auth_kwargs=kwargs,
+        )
+        task_creation_response = initial_operation.execute()
+        validate_task_creation_response(task_creation_response)
+        task_id = task_creation_response.id
+
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
+
+        video = download_url_to_video_output(final_response.output_url)
+        return (video,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "MoonvalleyImg2VideoNode": MoonvalleyImg2VideoNode,
+    "MoonvalleyTxt2VideoNode": MoonvalleyTxt2VideoNode,
+    "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode,
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MoonvalleyImg2VideoNode": "Moonvalley Marey Image to Video",
+    "MoonvalleyTxt2VideoNode": "Moonvalley Marey Text to Video",
+    "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video",
+}

comfy_api_nodes/nodes_pika.py

@@ -6,40 +6,42 @@ Pika API docs: https://pika-827374fb.mintlify.app/api-reference
 from __future__ import annotations
 
 import io
-from typing import Optional, TypeVar
 import logging
-import torch
+from typing import Optional, TypeVar
+
 import numpy as np
+import torch
+
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput
+from comfy_api_nodes.apinode_utils import (
+    download_url_to_video_output,
+    tensor_to_bytesio,
+)
 from comfy_api_nodes.apis import (
-    PikaBodyGenerate22T2vGenerate22T2vPost,
-    PikaGenerateResponse,
-    PikaBodyGenerate22I2vGenerate22I2vPost,
-    PikaVideoResponse,
-    PikaBodyGenerate22C2vGenerate22PikascenesPost,
     IngredientsMode,
-    PikaDurationEnum,
-    PikaResolutionEnum,
-    PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
-    PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
-    PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+    PikaBodyGenerate22C2vGenerate22PikascenesPost,
+    PikaBodyGenerate22I2vGenerate22I2vPost,
     PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
+    PikaBodyGenerate22T2vGenerate22T2vPost,
+    PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+    PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+    PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+    PikaDurationEnum,
     Pikaffect,
+    PikaGenerateResponse,
+    PikaResolutionEnum,
+    PikaVideoResponse,
 )
 from comfy_api_nodes.apis.client import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
     EmptyRequest,
-)
-from comfy_api_nodes.apinode_utils import (
-    tensor_to_bytesio,
-    download_url_to_video_output,
+    HttpMethod,
+    PollingOperation,
+    SynchronousOperation,
 )
 from comfy_api_nodes.mapper_utils import model_field_to_node_input
-from comfy_api.input_impl.video_types import VideoInput, VideoContainer, VideoCodec
-from comfy_api.input_impl import VideoFromFile
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
 
 R = TypeVar("R")
 
@@ -204,6 +206,7 @@ class PikaImageToVideoV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -457,7 +460,7 @@ class PikAdditionsNode(PikaNodeBase):
             },
         }
 
-    DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you’d like to add to create a seamlessly integrated result."
+    DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result."
 
     def api_call(
         self,

comfy_config/config_parser.py

@@ -0,0 +1,152 @@
+import os
+from pathlib import Path
+from typing import Optional
+
+from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource
+
+from comfy_config.types import (
+    ComfyConfig,
+    ProjectConfig,
+    PyProjectConfig,
+    PyProjectSettings
+)
+
+def validate_and_extract_os_classifiers(classifiers: list) -> list:
+    os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")]
+    if not os_classifiers:
+        return []
+
+    os_values = [c[len("Operating System :: ") :] for c in os_classifiers]
+    valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"}
+
+    for os_value in os_values:
+        if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes):
+            return []
+
+    return os_values
+
+
+def validate_and_extract_accelerator_classifiers(classifiers: list) -> list:
+    accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")]
+    if not accelerator_classifiers:
+        return []
+
+    accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers]
+
+    valid_accelerators = {
+        "GPU :: NVIDIA CUDA",
+        "GPU :: AMD ROCm",
+        "GPU :: Intel Arc",
+        "NPU :: Huawei Ascend",
+        "GPU :: Apple Metal",
+    }
+
+    for accelerator_value in accelerator_values:
+        if accelerator_value not in valid_accelerators:
+            return []
+
+    return accelerator_values
+
+
+"""
+Extract configuration from a custom node directory's pyproject.toml file or a Python file.
+
+This function reads and parses the pyproject.toml file in the specified directory
+to extract project and ComfyUI-specific configuration information. If no
+pyproject.toml file is found, it creates a minimal configuration using the
+folder name as the project name. If a Python file is provided, it uses the
+file name (without extension) as the project name.
+
+Args:
+    path (str): Path to the directory containing the pyproject.toml file, or
+               path to a .py file. If pyproject.toml doesn't exist in a directory,
+               the folder name will be used as the default project name. If a .py
+               file is provided, the filename (without .py extension) will be used
+               as the project name.
+
+Returns:
+    Optional[PyProjectConfig]: A PyProjectConfig object containing:
+        - project: Basic project information (name, version, dependencies, etc.)
+        - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.)
+        Returns None if configuration extraction fails or if the provided file
+        is not a Python file.
+
+Notes:
+    - If pyproject.toml is missing in a directory, creates a default config with folder name
+    - If a .py file is provided, creates a default config with filename (without extension)
+    - Returns None for non-Python files
+
+Example:
+    >>> from comfy_config import config_parser
+    >>> # For directory
+    >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__))
+    >>> project_config = config_parser.extract_node_configuration(custom_node_dir)
+    >>> print(project_config.project.name)  # "my_custom_node" or name from pyproject.toml
+    >>>
+    >>> # For single-file Python node file
+    >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py"
+    >>> project_config = config_parser.extract_node_configuration(py_file_path)
+    >>> print(project_config.project.name)  # "my_node"
+"""
+def extract_node_configuration(path) -> Optional[PyProjectConfig]:
+    if os.path.isfile(path):
+        file_path = Path(path)
+
+        if file_path.suffix.lower() != '.py':
+            return None
+
+        project_name = file_path.stem
+        project = ProjectConfig(name=project_name)
+        comfy = ComfyConfig()
+        return PyProjectConfig(project=project, tool_comfy=comfy)
+
+    folder_name = os.path.basename(path)
+    toml_path = Path(path) / "pyproject.toml"
+
+    if not toml_path.exists():
+        project = ProjectConfig(name=folder_name)
+        comfy = ComfyConfig()
+        return PyProjectConfig(project=project, tool_comfy=comfy)
+
+    raw_settings = load_pyproject_settings(toml_path)
+
+    project_data = raw_settings.project
+
+    tool_data = raw_settings.tool
+    comfy_data = tool_data.get("comfy", {}) if tool_data else {}
+
+    dependencies = project_data.get("dependencies", [])
+    supported_comfyui_frontend_version = ""
+    for dep in dependencies:
+        if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"):
+            supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package")
+            break
+
+    supported_comfyui_version = comfy_data.get("requires-comfyui", "")
+
+    classifiers = project_data.get('classifiers', [])
+    supported_os = validate_and_extract_os_classifiers(classifiers)
+    supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers)
+
+    project_data['supported_os'] = supported_os
+    project_data['supported_accelerators'] = supported_accelerators
+    project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version
+    project_data['supported_comfyui_version'] = supported_comfyui_version
+
+    return PyProjectConfig(project=project_data, tool_comfy=comfy_data)
+
+
+def load_pyproject_settings(toml_path: Path) -> PyProjectSettings:
+    class PyProjectLoader(PyProjectSettings):
+        @classmethod
+        def settings_customise_sources(
+            cls,
+            settings_cls,
+            init_settings: PydanticBaseSettingsSource,
+            env_settings: PydanticBaseSettingsSource,
+            dotenv_settings: PydanticBaseSettingsSource,
+            file_secret_settings: PydanticBaseSettingsSource,
+        ):
+            return (TomlConfigSettingsSource(settings_cls, toml_path),)
+
+    return PyProjectLoader()

comfy_config/types.py

@@ -0,0 +1,97 @@
+from pydantic import BaseModel, Field, field_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import List, Optional
+
+# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes
+# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py.
+# Any changes to one must be reflected in the other to maintain consistency.
+
+class NodeVersion(BaseModel):
+    changelog: str
+    dependencies: List[str]
+    deprecated: bool
+    id: str
+    version: str
+    download_url: str
+
+
+class Node(BaseModel):
+    id: str
+    name: str
+    description: str
+    author: Optional[str] = None
+    license: Optional[str] = None
+    icon: Optional[str] = None
+    repository: Optional[str] = None
+    tags: List[str] = Field(default_factory=list)
+    latest_version: Optional[NodeVersion] = None
+
+
+class PublishNodeVersionResponse(BaseModel):
+    node_version: NodeVersion
+    signedUrl: str
+
+
+class URLs(BaseModel):
+    homepage: str = Field(default="", alias="Homepage")
+    documentation: str = Field(default="", alias="Documentation")
+    repository: str = Field(default="", alias="Repository")
+    issues: str = Field(default="", alias="Issues")
+
+
+class Model(BaseModel):
+    location: str
+    model_url: str
+
+
+class ComfyConfig(BaseModel):
+    publisher_id: str = Field(default="", alias="PublisherId")
+    display_name: str = Field(default="", alias="DisplayName")
+    icon: str = Field(default="", alias="Icon")
+    models: List[Model] = Field(default_factory=list, alias="Models")
+    includes: List[str] = Field(default_factory=list)
+    web: Optional[str] = None
+    banner_url: str = ""
+
+class License(BaseModel):
+    file: str = ""
+    text: str = ""
+
+
+class ProjectConfig(BaseModel):
+    name: str = ""
+    description: str = ""
+    version: str = "1.0.0"
+    requires_python: str = Field(default=">= 3.9", alias="requires-python")
+    dependencies: List[str] = Field(default_factory=list)
+    license: License = Field(default_factory=License)
+    urls: URLs = Field(default_factory=URLs)
+    supported_os: List[str] = Field(default_factory=list)
+    supported_accelerators: List[str] = Field(default_factory=list)
+    supported_comfyui_version: str = ""
+    supported_comfyui_frontend_version: str = ""
+
+    @field_validator('license', mode='before')
+    @classmethod
+    def validate_license(cls, v):
+        if isinstance(v, str):
+            return License(text=v)
+        elif isinstance(v, dict):
+            return License(**v)
+        elif isinstance(v, License):
+            return v
+        else:
+            return License()
+
+
+class PyProjectConfig(BaseModel):
+    project: ProjectConfig = Field(default_factory=ProjectConfig)
+    tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig)
+
+
+class PyProjectSettings(BaseSettings):
+    project: dict = Field(default_factory=dict)
+
+    tool: dict = Field(default_factory=dict)
+
+    model_config = SettingsConfigDict(extra='allow')

comfy_execution/caching.py

@@ -1,6 +1,7 @@
 import itertools
 from typing import Sequence, Mapping, Dict
 from comfy_execution.graph import DynamicPrompt
+from abc import ABC, abstractmethod
 
 import nodes
 
@@ -16,12 +17,13 @@ def include_unique_id_in_input(class_type: str) -> bool:
     NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values()
     return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
 
-class CacheKeySet:
+class CacheKeySet(ABC):
     def __init__(self, dynprompt, node_ids, is_changed_cache):
         self.keys = {}
         self.subcache_keys = {}
 
-    def add_keys(self, node_ids):
+    @abstractmethod
+    async def add_keys(self, node_ids):
         raise NotImplementedError()
 
     def all_node_ids(self):
@@ -60,9 +62,8 @@ class CacheKeySetID(CacheKeySet):
     def __init__(self, dynprompt, node_ids, is_changed_cache):
         super().__init__(dynprompt, node_ids, is_changed_cache)
         self.dynprompt = dynprompt
-        self.add_keys(node_ids)
 
-    def add_keys(self, node_ids):
+    async def add_keys(self, node_ids):
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
@@ -77,37 +78,36 @@ class CacheKeySetInputSignature(CacheKeySet):
         super().__init__(dynprompt, node_ids, is_changed_cache)
         self.dynprompt = dynprompt
         self.is_changed_cache = is_changed_cache
-        self.add_keys(node_ids)
 
     def include_node_id_in_input(self) -> bool:
         return False
 
-    def add_keys(self, node_ids):
+    async def add_keys(self, node_ids):
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
             if not self.dynprompt.has_node(node_id):
                 continue
             node = self.dynprompt.get_node(node_id)
-            self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id)
+            self.keys[node_id] = await self.get_node_signature(self.dynprompt, node_id)
             self.subcache_keys[node_id] = (node_id, node["class_type"])
 
-    def get_node_signature(self, dynprompt, node_id):
+    async def get_node_signature(self, dynprompt, node_id):
         signature = []
         ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
-        signature.append(self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
+        signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
         for ancestor_id in ancestors:
-            signature.append(self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
+            signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
         return to_hashable(signature)
 
-    def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+    async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
         if not dynprompt.has_node(node_id):
             # This node doesn't exist -- we can't cache it.
             return [float("NaN")]
         node = dynprompt.get_node(node_id)
         class_type = node["class_type"]
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-        signature = [class_type, self.is_changed_cache.get(node_id)]
+        signature = [class_type, await self.is_changed_cache.get(node_id)]
         if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
             signature.append(node_id)
         inputs = node["inputs"]
@@ -150,9 +150,10 @@ class BasicCache:
         self.cache = {}
         self.subcaches = {}
 
-    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
         self.dynprompt = dynprompt
         self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache)
+        await self.cache_key_set.add_keys(node_ids)
         self.is_changed_cache = is_changed_cache
         self.initialized = True
 
@@ -201,13 +202,13 @@ class BasicCache:
         else:
             return None
 
-    def _ensure_subcache(self, node_id, children_ids):
+    async def _ensure_subcache(self, node_id, children_ids):
         subcache_key = self.cache_key_set.get_subcache_key(node_id)
         subcache = self.subcaches.get(subcache_key, None)
         if subcache is None:
             subcache = BasicCache(self.key_class)
             self.subcaches[subcache_key] = subcache
-        subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
+        await subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
         return subcache
 
     def _get_subcache(self, node_id):
@@ -259,10 +260,10 @@ class HierarchicalCache(BasicCache):
         assert cache is not None
         cache._set_immediate(node_id, value)
 
-    def ensure_subcache_for(self, node_id, children_ids):
+    async def ensure_subcache_for(self, node_id, children_ids):
         cache = self._get_cache_for(node_id)
         assert cache is not None
-        return cache._ensure_subcache(node_id, children_ids)
+        return await cache._ensure_subcache(node_id, children_ids)
 
 class LRUCache(BasicCache):
     def __init__(self, key_class, max_size=100):
@@ -273,8 +274,8 @@ class LRUCache(BasicCache):
         self.used_generation = {}
         self.children = {}
 
-    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
-        super().set_prompt(dynprompt, node_ids, is_changed_cache)
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        await super().set_prompt(dynprompt, node_ids, is_changed_cache)
         self.generation += 1
         for node_id in node_ids:
             self._mark_used(node_id)
@@ -303,11 +304,11 @@ class LRUCache(BasicCache):
         self._mark_used(node_id)
         return self._set_immediate(node_id, value)
 
-    def ensure_subcache_for(self, node_id, children_ids):
+    async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
-        super()._ensure_subcache(node_id, children_ids)
+        await super()._ensure_subcache(node_id, children_ids)
 
-        self.cache_key_set.add_keys(children_ids)
+        await self.cache_key_set.add_keys(children_ids)
         self._mark_used(node_id)
         cache_key = self.cache_key_set.get_data_key(node_id)
         self.children[cache_key] = []
@@ -337,7 +338,7 @@ class DependencyAwareCache(BasicCache):
         self.ancestors = {}    # Maps node_id -> set of ancestor node_ids
         self.executed_nodes = set()  # Tracks nodes that have been executed
 
-    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
         """
         Clear the entire cache and rebuild the dependency graph.
 
@@ -354,7 +355,7 @@ class DependencyAwareCache(BasicCache):
         self.executed_nodes.clear()
 
         # Call the parent method to initialize the cache with the new prompt
-        super().set_prompt(dynprompt, node_ids, is_changed_cache)
+        await super().set_prompt(dynprompt, node_ids, is_changed_cache)
 
         # Rebuild the dependency graph
         self._build_dependency_graph(dynprompt, node_ids)
@@ -405,7 +406,7 @@ class DependencyAwareCache(BasicCache):
         """
         return self._get_immediate(node_id)
 
-    def ensure_subcache_for(self, node_id, children_ids):
+    async def ensure_subcache_for(self, node_id, children_ids):
         """
         Ensure a subcache exists for a node and update dependencies.
 
@@ -416,7 +417,7 @@ class DependencyAwareCache(BasicCache):
         Returns:
             The subcache object for the node.
         """
-        subcache = super()._ensure_subcache(node_id, children_ids)
+        subcache = await super()._ensure_subcache(node_id, children_ids)
         for child_id in children_ids:
             self.descendants[node_id].add(child_id)
             self.ancestors[child_id].add(node_id)

comfy_execution/graph.py

@@ -2,6 +2,8 @@ from __future__ import annotations
 from typing import Type, Literal
 
 import nodes
+import asyncio
+import inspect
 from comfy_execution.graph_utils import is_link
 from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
 
@@ -100,6 +102,8 @@ class TopologicalSort:
         self.pendingNodes = {}
         self.blockCount = {} # Number of nodes this node is directly blocked by
         self.blocking = {} # Which nodes are blocked by this node
+        self.externalBlocks = 0
+        self.unblockedEvent = asyncio.Event()
 
     def get_input_info(self, unique_id, input_name):
         class_type = self.dynprompt.get_node(unique_id)["class_type"]
@@ -153,6 +157,16 @@ class TopologicalSort:
         for link in links:
             self.add_strong_link(*link)
 
+    def add_external_block(self, node_id):
+        assert node_id in self.blockCount, "Can't add external block to a node that isn't pending"
+        self.externalBlocks += 1
+        self.blockCount[node_id] += 1
+        def unblock():
+            self.externalBlocks -= 1
+            self.blockCount[node_id] -= 1
+            self.unblockedEvent.set()
+        return unblock
+
     def is_cached(self, node_id):
         return False
 
@@ -181,11 +195,16 @@ class ExecutionList(TopologicalSort):
     def is_cached(self, node_id):
         return self.output_cache.get(node_id) is not None
 
-    def stage_node_execution(self):
+    async def stage_node_execution(self):
         assert self.staged_node_id is None
         if self.is_empty():
             return None, None, None
         available = self.get_ready_nodes()
+        while len(available) == 0 and self.externalBlocks > 0:
+            # Wait for an external block to be released
+            await self.unblockedEvent.wait()
+            self.unblockedEvent.clear()
+            available = self.get_ready_nodes()
         if len(available) == 0:
             cycled_nodes = self.get_nodes_in_cycle()
             # Because cycles composed entirely of static nodes are caught during initial validation,
@@ -221,8 +240,15 @@ class ExecutionList(TopologicalSort):
                 return True
             return False
 
+        # If an available node is async, do that first.
+        # This will execute the asynchronous function earlier, reducing the overall time.
+        def is_async(node_id):
+            class_type = self.dynprompt.get_node(node_id)["class_type"]
+            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+            return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
+
         for node_id in node_list:
-            if is_output(node_id):
+            if is_output(node_id) or is_async(node_id):
                 return node_id
 
         #This should handle the VAEDecode -> preview case

comfy_execution/progress.py

@@ -0,0 +1,347 @@
+from typing import TypedDict, Dict, Optional
+from typing_extensions import override
+from PIL import Image
+from enum import Enum
+from abc import ABC
+from tqdm import tqdm
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy_execution.graph import DynamicPrompt
+from protocol import BinaryEventTypes
+from comfy_api import feature_flags
+
+
+class NodeState(Enum):
+    Pending = "pending"
+    Running = "running"
+    Finished = "finished"
+    Error = "error"
+
+
+class NodeProgressState(TypedDict):
+    """
+    A class to represent the state of a node's progress.
+    """
+
+    state: NodeState
+    value: float
+    max: float
+
+
+class ProgressHandler(ABC):
+    """
+    Abstract base class for progress handlers.
+    Progress handlers receive progress updates and display them in various ways.
+    """
+
+    def __init__(self, name: str):
+        self.name = name
+        self.enabled = True
+
+    def set_registry(self, registry: "ProgressRegistry"):
+        pass
+
+    def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        """Called when a node starts processing"""
+        pass
+
+    def update_handler(
+        self,
+        node_id: str,
+        value: float,
+        max_value: float,
+        state: NodeProgressState,
+        prompt_id: str,
+        image: Optional[Image.Image] = None,
+    ):
+        """Called when a node's progress is updated"""
+        pass
+
+    def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        """Called when a node finishes processing"""
+        pass
+
+    def reset(self):
+        """Called when the progress registry is reset"""
+        pass
+
+    def enable(self):
+        """Enable this handler"""
+        self.enabled = True
+
+    def disable(self):
+        """Disable this handler"""
+        self.enabled = False
+
+
+class CLIProgressHandler(ProgressHandler):
+    """
+    Handler that displays progress using tqdm progress bars in the CLI.
+    """
+
+    def __init__(self):
+        super().__init__("cli")
+        self.progress_bars: Dict[str, tqdm] = {}
+
+    @override
+    def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Create a new tqdm progress bar
+        if node_id not in self.progress_bars:
+            self.progress_bars[node_id] = tqdm(
+                total=state["max"],
+                desc=f"Node {node_id}",
+                unit="steps",
+                leave=True,
+                position=len(self.progress_bars),
+            )
+
+    @override
+    def update_handler(
+        self,
+        node_id: str,
+        value: float,
+        max_value: float,
+        state: NodeProgressState,
+        prompt_id: str,
+        image: Optional[Image.Image] = None,
+    ):
+        # Handle case where start_handler wasn't called
+        if node_id not in self.progress_bars:
+            self.progress_bars[node_id] = tqdm(
+                total=max_value,
+                desc=f"Node {node_id}",
+                unit="steps",
+                leave=True,
+                position=len(self.progress_bars),
+            )
+            self.progress_bars[node_id].update(value)
+        else:
+            # Update existing progress bar
+            if max_value != self.progress_bars[node_id].total:
+                self.progress_bars[node_id].total = max_value
+            # Calculate the update amount (difference from current position)
+            current_position = self.progress_bars[node_id].n
+            update_amount = value - current_position
+            if update_amount > 0:
+                self.progress_bars[node_id].update(update_amount)
+
+    @override
+    def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Complete and close the progress bar if it exists
+        if node_id in self.progress_bars:
+            # Ensure the bar shows 100% completion
+            remaining = state["max"] - self.progress_bars[node_id].n
+            if remaining > 0:
+                self.progress_bars[node_id].update(remaining)
+            self.progress_bars[node_id].close()
+            del self.progress_bars[node_id]
+
+    @override
+    def reset(self):
+        # Close all progress bars
+        for bar in self.progress_bars.values():
+            bar.close()
+        self.progress_bars.clear()
+
+
+class WebUIProgressHandler(ProgressHandler):
+    """
+    Handler that sends progress updates to the WebUI via WebSockets.
+    """
+
+    def __init__(self, server_instance):
+        super().__init__("webui")
+        self.server_instance = server_instance
+
+    def set_registry(self, registry: "ProgressRegistry"):
+        self.registry = registry
+
+    def _send_progress_state(self, prompt_id: str, nodes: Dict[str, NodeProgressState]):
+        """Send the current progress state to the client"""
+        if self.server_instance is None:
+            return
+
+        # Only send info for non-pending nodes
+        active_nodes = {
+            node_id: {
+                "value": state["value"],
+                "max": state["max"],
+                "state": state["state"].value,
+                "node_id": node_id,
+                "prompt_id": prompt_id,
+                "display_node_id": self.registry.dynprompt.get_display_node_id(node_id),
+                "parent_node_id": self.registry.dynprompt.get_parent_node_id(node_id),
+                "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+            }
+            for node_id, state in nodes.items()
+            if state["state"] != NodeState.Pending
+        }
+
+        # Send a combined progress_state message with all node states
+        self.server_instance.send_sync(
+            "progress_state", {"prompt_id": prompt_id, "nodes": active_nodes}
+        )
+
+    @override
+    def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Send progress state of all nodes
+        if self.registry:
+            self._send_progress_state(prompt_id, self.registry.nodes)
+
+    @override
+    def update_handler(
+        self,
+        node_id: str,
+        value: float,
+        max_value: float,
+        state: NodeProgressState,
+        prompt_id: str,
+        image: Optional[Image.Image] = None,
+    ):
+        # Send progress state of all nodes
+        if self.registry:
+            self._send_progress_state(prompt_id, self.registry.nodes)
+        if image:
+            # Only send new format if client supports it
+            if feature_flags.supports_feature(
+                self.server_instance.sockets_metadata,
+                self.server_instance.client_id,
+                "supports_preview_metadata",
+            ):
+                metadata = {
+                    "node_id": node_id,
+                    "prompt_id": prompt_id,
+                    "display_node_id": self.registry.dynprompt.get_display_node_id(
+                        node_id
+                    ),
+                    "parent_node_id": self.registry.dynprompt.get_parent_node_id(
+                        node_id
+                    ),
+                    "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+                }
+                self.server_instance.send_sync(
+                    BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA,
+                    (image, metadata),
+                    self.server_instance.client_id,
+                )
+
+    @override
+    def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Send progress state of all nodes
+        if self.registry:
+            self._send_progress_state(prompt_id, self.registry.nodes)
+
+
+class ProgressRegistry:
+    """
+    Registry that maintains node progress state and notifies registered handlers.
+    """
+
+    def __init__(self, prompt_id: str, dynprompt: "DynamicPrompt"):
+        self.prompt_id = prompt_id
+        self.dynprompt = dynprompt
+        self.nodes: Dict[str, NodeProgressState] = {}
+        self.handlers: Dict[str, ProgressHandler] = {}
+
+    def register_handler(self, handler: ProgressHandler) -> None:
+        """Register a progress handler"""
+        self.handlers[handler.name] = handler
+
+    def unregister_handler(self, handler_name: str) -> None:
+        """Unregister a progress handler"""
+        if handler_name in self.handlers:
+            # Allow handler to clean up resources
+            self.handlers[handler_name].reset()
+            del self.handlers[handler_name]
+
+    def enable_handler(self, handler_name: str) -> None:
+        """Enable a progress handler"""
+        if handler_name in self.handlers:
+            self.handlers[handler_name].enable()
+
+    def disable_handler(self, handler_name: str) -> None:
+        """Disable a progress handler"""
+        if handler_name in self.handlers:
+            self.handlers[handler_name].disable()
+
+    def ensure_entry(self, node_id: str) -> NodeProgressState:
+        """Ensure a node entry exists"""
+        if node_id not in self.nodes:
+            self.nodes[node_id] = NodeProgressState(
+                state=NodeState.Pending, value=0, max=1
+            )
+        return self.nodes[node_id]
+
+    def start_progress(self, node_id: str) -> None:
+        """Start progress tracking for a node"""
+        entry = self.ensure_entry(node_id)
+        entry["state"] = NodeState.Running
+        entry["value"] = 0.0
+        entry["max"] = 1.0
+
+        # Notify all enabled handlers
+        for handler in self.handlers.values():
+            if handler.enabled:
+                handler.start_handler(node_id, entry, self.prompt_id)
+
+    def update_progress(
+        self, node_id: str, value: float, max_value: float, image: Optional[Image.Image]
+    ) -> None:
+        """Update progress for a node"""
+        entry = self.ensure_entry(node_id)
+        entry["state"] = NodeState.Running
+        entry["value"] = value
+        entry["max"] = max_value
+
+        # Notify all enabled handlers
+        for handler in self.handlers.values():
+            if handler.enabled:
+                handler.update_handler(
+                    node_id, value, max_value, entry, self.prompt_id, image
+                )
+
+    def finish_progress(self, node_id: str) -> None:
+        """Finish progress tracking for a node"""
+        entry = self.ensure_entry(node_id)
+        entry["state"] = NodeState.Finished
+        entry["value"] = entry["max"]
+
+        # Notify all enabled handlers
+        for handler in self.handlers.values():
+            if handler.enabled:
+                handler.finish_handler(node_id, entry, self.prompt_id)
+
+    def reset_handlers(self) -> None:
+        """Reset all handlers"""
+        for handler in self.handlers.values():
+            handler.reset()
+
+# Global registry instance
+global_progress_registry: ProgressRegistry = None
+
+def reset_progress_state(prompt_id: str, dynprompt: "DynamicPrompt") -> None:
+    global global_progress_registry
+
+    # Reset existing handlers if registry exists
+    if global_progress_registry is not None:
+        global_progress_registry.reset_handlers()
+
+    # Create new registry
+    global_progress_registry = ProgressRegistry(prompt_id, dynprompt)
+
+
+def add_progress_handler(handler: ProgressHandler) -> None:
+    registry = get_progress_state()
+    handler.set_registry(registry)
+    registry.register_handler(handler)
+
+
+def get_progress_state() -> ProgressRegistry:
+    global global_progress_registry
+    if global_progress_registry is None:
+        from comfy_execution.graph import DynamicPrompt
+
+        global_progress_registry = ProgressRegistry(
+            prompt_id="", dynprompt=DynamicPrompt({})
+        )
+    return global_progress_registry

comfy_execution/utils.py

@@ -0,0 +1,46 @@
+import contextvars
+from typing import Optional, NamedTuple
+
+class ExecutionContext(NamedTuple):
+    """
+    Context information about the currently executing node.
+
+    Attributes:
+        node_id: The ID of the currently executing node
+        list_index: The index in a list being processed (for operations on batches/lists)
+    """
+    prompt_id: str
+    node_id: str
+    list_index: Optional[int]
+
+current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
+
+def get_executing_context() -> Optional[ExecutionContext]:
+    return current_executing_context.get(None)
+
+class CurrentNodeContext:
+    """
+    Context manager for setting the current executing node context.
+
+    Sets the current_executing_context on enter and resets it on exit.
+
+    Example:
+        with CurrentNodeContext(node_id="123", list_index=0):
+            # Code that should run with the current node context set
+            process_image()
+    """
+    def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
+        self.context = ExecutionContext(
+            prompt_id= prompt_id,
+            node_id= node_id,
+            list_index= list_index
+        )
+        self.token = None
+
+    def __enter__(self):
+        self.token = current_executing_context.set(self.context)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.token is not None:
+            current_executing_context.reset(self.token)

comfy_extras/nodes_audio.py

@@ -133,14 +133,6 @@ def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=Non
             if sample_rate != audio["sample_rate"]:
                 waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
 
-        # Create in-memory WAV buffer
-        wav_buffer = io.BytesIO()
-        torchaudio.save(wav_buffer, waveform, sample_rate, format="WAV")
-        wav_buffer.seek(0)  # Rewind for reading
-
-        # Use PyAV to convert and add metadata
-        input_container = av.open(wav_buffer)
-
         # Create output with specified format
         output_buffer = io.BytesIO()
         output_container = av.open(output_buffer, mode='w', format=format)
@@ -150,7 +142,6 @@ def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=Non
             output_container.metadata[key] = value
 
         # Set up the output stream with appropriate properties
-        input_container.streams.audio[0]
         if format == "opus":
             out_stream = output_container.add_stream("libopus", rate=sample_rate)
             if quality == "64k":
@@ -175,18 +166,16 @@ def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=Non
         else: #format == "flac":
             out_stream = output_container.add_stream("flac", rate=sample_rate)
 
-
-        # Copy frames from input to output
-        for frame in input_container.decode(audio=0):
-            frame.pts = None  # Let PyAV handle timestamps
-            output_container.mux(out_stream.encode(frame))
+        frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[0] == 1 else 'stereo')
+        frame.sample_rate = sample_rate
+        frame.pts = 0
+        output_container.mux(out_stream.encode(frame))
 
         # Flush encoder
         output_container.mux(out_stream.encode(None))
 
         # Close containers
         output_container.close()
-        input_container.close()
 
         # Write the output to file
         output_buffer.seek(0)
@@ -289,6 +278,42 @@ class PreviewAudio(SaveAudio):
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
+def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
+    """Convert audio to float 32 bits PCM format."""
+    if wav.dtype.is_floating_point:
+        return wav
+    elif wav.dtype == torch.int16:
+        return wav.float() / (2 ** 15)
+    elif wav.dtype == torch.int32:
+        return wav.float() / (2 ** 31)
+    raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
+
+def load(filepath: str) -> tuple[torch.Tensor, int]:
+    with av.open(filepath) as af:
+        if not af.streams.audio:
+            raise ValueError("No audio stream found in the file.")
+
+        stream = af.streams.audio[0]
+        sr = stream.codec_context.sample_rate
+        n_channels = stream.channels
+
+        frames = []
+        length = 0
+        for frame in af.decode(streams=stream.index):
+            buf = torch.from_numpy(frame.to_ndarray())
+            if buf.shape[0] != n_channels:
+                buf = buf.view(-1, n_channels).t()
+
+            frames.append(buf)
+            length += buf.shape[1]
+
+        if not frames:
+            raise ValueError("No audio frames decoded.")
+
+        wav = torch.cat(frames, dim=1)
+        wav = f32_pcm(wav)
+        return wav, sr
+
 class LoadAudio:
     @classmethod
     def INPUT_TYPES(s):
@@ -303,7 +328,7 @@ class LoadAudio:
 
     def load(self, audio):
         audio_path = folder_paths.get_annotated_filepath(audio)
-        waveform, sample_rate = torchaudio.load(audio_path)
+        waveform, sample_rate = load(audio_path)
         audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
         return (audio, )
 

comfy_extras/nodes_cfg.py

@@ -40,6 +40,33 @@ class CFGZeroStar:
         m.set_model_sampler_post_cfg_function(cfg_zero_star)
         return (m, )
 
+class CFGNorm:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                            }}
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("patched_model",)
+    FUNCTION = "patch"
+    CATEGORY = "advanced/guidance"
+    EXPERIMENTAL = True
+
+    def patch(self, model, strength):
+        m = model.clone()
+        def cfg_norm(args):
+            cond_p = args['cond_denoised']
+            pred_text_ = args["denoised"]
+
+            norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
+            norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
+            scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
+            return pred_text_ * scale * strength
+
+        m.set_model_sampler_post_cfg_function(cfg_norm)
+        return (m, )
+
 NODE_CLASS_MAPPINGS = {
-    "CFGZeroStar": CFGZeroStar
+    "CFGZeroStar": CFGZeroStar,
+    "CFGNorm": CFGNorm,
 }

comfy_extras/nodes_cosmos.py

@@ -2,6 +2,7 @@ import nodes
 import torch
 import comfy.model_management
 import comfy.utils
+import comfy.latent_formats
 
 
 class EmptyCosmosLatentVideo:
@@ -75,8 +76,53 @@ class CosmosImageToVideoLatent:
         out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
         return (out_latent,)
 
+class CosmosPredict2ImageToVideoLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vae": ("VAE", ),
+                             "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 93, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"start_image": ("IMAGE", ),
+                             "end_image": ("IMAGE", ),
+                }}
+
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+        latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is None and end_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return (out_latent,)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        if end_image is not None:
+            latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+            latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+            mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+        out_latent = {}
+        latent_format = comfy.latent_formats.Wan21()
+        latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return (out_latent,)
 
 NODE_CLASS_MAPPINGS = {
     "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
     "CosmosImageToVideoLatent": CosmosImageToVideoLatent,
+    "CosmosPredict2ImageToVideoLatent": CosmosPredict2ImageToVideoLatent,
 }

comfy_extras/nodes_custom_sampler.py

@@ -2,6 +2,8 @@ import math
 import comfy.samplers
 import comfy.sample
 from comfy.k_diffusion import sampling as k_diffusion_sampling
+from comfy.k_diffusion import sa_solver
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
 import latent_preview
 import torch
 import comfy.utils
@@ -299,6 +301,35 @@ class ExtendIntermediateSigmas:
 
         return (extended_sigmas,)
 
+
+class SamplingPercentToSigma:
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "model": (IO.MODEL, {}),
+                "sampling_percent": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001}),
+                "return_actual_sigma": (IO.BOOLEAN, {"default": False, "tooltip": "Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."}),
+            }
+        }
+
+    RETURN_TYPES = (IO.FLOAT,)
+    RETURN_NAMES = ("sigma_value",)
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "get_sigma"
+
+    def get_sigma(self, model, sampling_percent, return_actual_sigma):
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_val = model_sampling.percent_to_sigma(sampling_percent)
+        if return_actual_sigma:
+            if sampling_percent == 0.0:
+                sigma_val = model_sampling.sigma_max.item()
+            elif sampling_percent == 1.0:
+                sigma_val = model_sampling.sigma_min.item()
+        return (sigma_val,)
+
+
 class KSamplerSelect:
     @classmethod
     def INPUT_TYPES(s):
@@ -480,6 +511,89 @@ class SamplerDPMAdaptative:
                                                               "s_noise":s_noise })
         return (sampler, )
 
+
+class SamplerER_SDE(ComfyNodeABC):
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "solver_type": (IO.COMBO, {"options": ["ER-SDE", "Reverse-time SDE", "ODE"]}),
+                "max_stage": (IO.INT, {"default": 3, "min": 1, "max": 3}),
+                "eta": (
+                    IO.FLOAT,
+                    {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False, "tooltip": "Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."},
+                ),
+                "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False}),
+            }
+        }
+
+    RETURN_TYPES = (IO.SAMPLER,)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, solver_type, max_stage, eta, s_noise):
+        if solver_type == "ODE" or (solver_type == "Reverse-time SDE" and eta == 0):
+            eta = 0
+            s_noise = 0
+
+        def reverse_time_sde_noise_scaler(x):
+            return x ** (eta + 1)
+
+        if solver_type == "ER-SDE":
+            # Use the default one in sample_er_sde()
+            noise_scaler = None
+        else:
+            noise_scaler = reverse_time_sde_noise_scaler
+
+        sampler_name = "er_sde"
+        sampler = comfy.samplers.ksampler(sampler_name, {"s_noise": s_noise, "noise_scaler": noise_scaler, "max_stage": max_stage})
+        return (sampler,)
+
+
+class SamplerSASolver(ComfyNodeABC):
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "model": (IO.MODEL, {}),
+                "eta": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False},),
+                "sde_start_percent": (IO.FLOAT, {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001},),
+                "sde_end_percent": (IO.FLOAT, {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.001},),
+                "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False},),
+                "predictor_order": (IO.INT, {"default": 3, "min": 1, "max": 6}),
+                "corrector_order": (IO.INT, {"default": 4, "min": 0, "max": 6}),
+                "use_pece": (IO.BOOLEAN, {}),
+                "simple_order_2": (IO.BOOLEAN, {}),
+            }
+        }
+
+    RETURN_TYPES = (IO.SAMPLER,)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2):
+        model_sampling = model.get_model_object("model_sampling")
+        start_sigma = model_sampling.percent_to_sigma(sde_start_percent)
+        end_sigma = model_sampling.percent_to_sigma(sde_end_percent)
+        tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=eta)
+
+        sampler_name = "sa_solver"
+        sampler = comfy.samplers.ksampler(
+            sampler_name,
+            {
+                "tau_func": tau_func,
+                "s_noise": s_noise,
+                "predictor_order": predictor_order,
+                "corrector_order": corrector_order,
+                "use_pece": use_pece,
+                "simple_order_2": simple_order_2,
+            },
+        )
+        return (sampler,)
+
+
 class Noise_EmptyNoise:
     def __init__(self):
         self.seed = 0
@@ -598,9 +712,10 @@ class CFGGuider:
         return (guider,)
 
 class Guider_DualCFG(comfy.samplers.CFGGuider):
-    def set_cfg(self, cfg1, cfg2):
+    def set_cfg(self, cfg1, cfg2, nested=False):
         self.cfg1 = cfg1
         self.cfg2 = cfg2
+        self.nested = nested
 
     def set_conds(self, positive, middle, negative):
         middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"})
@@ -609,9 +724,21 @@ class Guider_DualCFG(comfy.samplers.CFGGuider):
     def predict_noise(self, x, timestep, model_options={}, seed=None):
         negative_cond = self.conds.get("negative", None)
         middle_cond = self.conds.get("middle", None)
+        positive_cond = self.conds.get("positive", None)
 
-        out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options)
-        return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
+        if self.nested:
+            out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+            pred_text = comfy.samplers.cfg_function(self.inner_model, out[2], out[1], self.cfg1, x, timestep, model_options=model_options, cond=positive_cond, uncond=middle_cond)
+            return out[0] + self.cfg2 * (pred_text - out[0])
+        else:
+            if model_options.get("disable_cfg1_optimization", False) == False:
+                if math.isclose(self.cfg2, 1.0):
+                    negative_cond = None
+                    if math.isclose(self.cfg1, 1.0):
+                        middle_cond = None
+
+            out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+            return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
 
 class DualCFGGuider:
     @classmethod
@@ -623,6 +750,7 @@ class DualCFGGuider:
                     "negative": ("CONDITIONING", ),
                     "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
                     "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "style": (["regular", "nested"],),
                      }
                 }
 
@@ -631,10 +759,10 @@ class DualCFGGuider:
     FUNCTION = "get_guider"
     CATEGORY = "sampling/custom_sampling/guiders"
 
-    def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative):
+    def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style):
         guider = Guider_DualCFG(model)
         guider.set_conds(cond1, cond2, negative)
-        guider.set_cfg(cfg_conds, cfg_cond2_negative)
+        guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested"))
         return (guider,)
 
 class DisableNoise:
@@ -781,11 +909,14 @@ NODE_CLASS_MAPPINGS = {
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
     "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral,
     "SamplerDPMAdaptative": SamplerDPMAdaptative,
+    "SamplerER_SDE": SamplerER_SDE,
+    "SamplerSASolver": SamplerSASolver,
     "SplitSigmas": SplitSigmas,
     "SplitSigmasDenoise": SplitSigmasDenoise,
     "FlipSigmas": FlipSigmas,
     "SetFirstSigma": SetFirstSigma,
     "ExtendIntermediateSigmas": ExtendIntermediateSigmas,
+    "SamplingPercentToSigma": SamplingPercentToSigma,
 
     "CFGGuider": CFGGuider,
     "DualCFGGuider": DualCFGGuider,

comfy_extras/nodes_edit_model.py

@@ -0,0 +1,26 @@
+import node_helpers
+
+
+class ReferenceLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                            },
+                "optional": {"latent": ("LATENT", ),}
+               }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "advanced/conditioning/edit_models"
+    DESCRIPTION = "This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images."
+
+    def append(self, conditioning, latent=None):
+        if latent is not None:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True)
+        return (conditioning, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "ReferenceLatent": ReferenceLatent,
+}

comfy_extras/nodes_flux.py

@@ -1,4 +1,5 @@
 import node_helpers
+import comfy.utils
 
 class CLIPTextEncodeFlux:
     @classmethod
@@ -56,8 +57,52 @@ class FluxDisableGuidance:
         return (c, )
 
 
+PREFERED_KONTEXT_RESOLUTIONS = [
+    (672, 1568),
+    (688, 1504),
+    (720, 1456),
+    (752, 1392),
+    (800, 1328),
+    (832, 1248),
+    (880, 1184),
+    (944, 1104),
+    (1024, 1024),
+    (1104, 944),
+    (1184, 880),
+    (1248, 832),
+    (1328, 800),
+    (1392, 752),
+    (1456, 720),
+    (1504, 688),
+    (1568, 672),
+]
+
+
+class FluxKontextImageScale:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image": ("IMAGE", ),
+                            },
+               }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "scale"
+
+    CATEGORY = "advanced/conditioning/flux"
+    DESCRIPTION = "This node resizes the image to one that is more optimal for flux kontext."
+
+    def scale(self, image):
+        width = image.shape[2]
+        height = image.shape[1]
+        aspect_ratio = width / height
+        _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
+        image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
+        return (image, )
+
+
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
     "FluxGuidance": FluxGuidance,
     "FluxDisableGuidance": FluxDisableGuidance,
+    "FluxKontextImageScale": FluxKontextImageScale,
 }

comfy_extras/nodes_fresca.py

@@ -71,8 +71,11 @@ class FreSca:
     DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
     def patch(self, model, scale_low, scale_high, freq_cutoff):
         def custom_cfg_function(args):
-            cond = args["conds_out"][0]
-            uncond = args["conds_out"][1]
+            conds_out = args["conds_out"]
+            if len(conds_out) <= 1 or None in args["conds"][:2]:
+                return conds_out
+            cond = conds_out[0]
+            uncond = conds_out[1]
 
             guidance = cond - uncond
             filtered_guidance = Fourier_filter(
@@ -83,7 +86,7 @@ class FreSca:
             )
             filtered_cond = filtered_guidance + uncond
 
-            return [filtered_cond, uncond]
+            return [filtered_cond, uncond] + conds_out[2:]
 
         m = model.clone()
         m.set_model_sampler_pre_cfg_function(custom_cfg_function)

comfy_extras/nodes_images.py

@@ -14,8 +14,10 @@ import re
 from io import BytesIO
 from inspect import cleandoc
 import torch
+import comfy.utils
 
-from comfy.comfy_types import FileLocator
+from comfy.comfy_types import FileLocator, IO
+from server import PromptServer
 
 MAX_RESOLUTION = nodes.MAX_RESOLUTION
 
@@ -229,6 +231,246 @@ class SVG:
             all_svgs_list.extend(svg_item.data)
         return SVG(all_svgs_list)
 
+
+class ImageStitch:
+    """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image1": ("IMAGE",),
+                "direction": (["right", "down", "left", "up"], {"default": "right"}),
+                "match_image_size": ("BOOLEAN", {"default": True}),
+                "spacing_width": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 1024, "step": 2},
+                ),
+                "spacing_color": (
+                    ["white", "black", "red", "green", "blue"],
+                    {"default": "white"},
+                ),
+            },
+            "optional": {
+                "image2": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stitch"
+    CATEGORY = "image/transform"
+    DESCRIPTION = """
+Stitches image2 to image1 in the specified direction.
+If image2 is not provided, returns image1 unchanged.
+Optional spacing can be added between images.
+"""
+
+    def stitch(
+        self,
+        image1,
+        direction,
+        match_image_size,
+        spacing_width,
+        spacing_color,
+        image2=None,
+    ):
+        if image2 is None:
+            return (image1,)
+
+        # Handle batch size differences
+        if image1.shape[0] != image2.shape[0]:
+            max_batch = max(image1.shape[0], image2.shape[0])
+            if image1.shape[0] < max_batch:
+                image1 = torch.cat(
+                    [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)]
+                )
+            if image2.shape[0] < max_batch:
+                image2 = torch.cat(
+                    [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)]
+                )
+
+        # Match image sizes if requested
+        if match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+            aspect_ratio = w2 / h2
+
+            if direction in ["left", "right"]:
+                target_h, target_w = h1, int(h1 * aspect_ratio)
+            else:  # up, down
+                target_w, target_h = w1, int(w1 / aspect_ratio)
+
+            image2 = comfy.utils.common_upscale(
+                image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
+            ).movedim(1, -1)
+
+        color_map = {
+            "white": 1.0,
+            "black": 0.0,
+            "red": (1.0, 0.0, 0.0),
+            "green": (0.0, 1.0, 0.0),
+            "blue": (0.0, 0.0, 1.0),
+        }
+
+        color_val = color_map[spacing_color]
+
+        # When not matching sizes, pad to align non-concat dimensions
+        if not match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+            pad_value = 0.0
+            if not isinstance(color_val, tuple):
+                pad_value = color_val
+
+            if direction in ["left", "right"]:
+                # For horizontal concat, pad heights to match
+                if h1 != h2:
+                    target_h = max(h1, h2)
+                    if h1 < target_h:
+                        pad_h = target_h - h1
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
+                    if h2 < target_h:
+                        pad_h = target_h - h2
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
+            else:  # up, down
+                # For vertical concat, pad widths to match
+                if w1 != w2:
+                    target_w = max(w1, w2)
+                    if w1 < target_w:
+                        pad_w = target_w - w1
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
+                    if w2 < target_w:
+                        pad_w = target_w - w2
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
+
+        # Ensure same number of channels
+        if image1.shape[-1] != image2.shape[-1]:
+            max_channels = max(image1.shape[-1], image2.shape[-1])
+            if image1.shape[-1] < max_channels:
+                image1 = torch.cat(
+                    [
+                        image1,
+                        torch.ones(
+                            *image1.shape[:-1],
+                            max_channels - image1.shape[-1],
+                            device=image1.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+            if image2.shape[-1] < max_channels:
+                image2 = torch.cat(
+                    [
+                        image2,
+                        torch.ones(
+                            *image2.shape[:-1],
+                            max_channels - image2.shape[-1],
+                            device=image2.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+
+        # Add spacing if specified
+        if spacing_width > 0:
+            spacing_width = spacing_width + (spacing_width % 2)  # Ensure even
+
+            if direction in ["left", "right"]:
+                spacing_shape = (
+                    image1.shape[0],
+                    max(image1.shape[1], image2.shape[1]),
+                    spacing_width,
+                    image1.shape[-1],
+                )
+            else:
+                spacing_shape = (
+                    image1.shape[0],
+                    spacing_width,
+                    max(image1.shape[2], image2.shape[2]),
+                    image1.shape[-1],
+                )
+
+            spacing = torch.full(spacing_shape, 0.0, device=image1.device)
+            if isinstance(color_val, tuple):
+                for i, c in enumerate(color_val):
+                    if i < spacing.shape[-1]:
+                        spacing[..., i] = c
+                if spacing.shape[-1] == 4:  # Add alpha
+                    spacing[..., 3] = 1.0
+            else:
+                spacing[..., : min(3, spacing.shape[-1])] = color_val
+                if spacing.shape[-1] == 4:
+                    spacing[..., 3] = 1.0
+
+        # Concatenate images
+        images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
+        if spacing_width > 0:
+            images.insert(1, spacing)
+
+        concat_dim = 2 if direction in ["left", "right"] else 1
+        return (torch.cat(images, dim=concat_dim),)
+
+class ResizeAndPadImage:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "target_width": ("INT", {
+                    "default": 512,
+                    "min": 1,
+                    "max": MAX_RESOLUTION,
+                    "step": 1
+                }),
+                "target_height": ("INT", {
+                    "default": 512,
+                    "min": 1,
+                    "max": MAX_RESOLUTION,
+                    "step": 1
+                }),
+                "padding_color": (["white", "black"],),
+                "interpolation": (["area", "bicubic", "nearest-exact", "bilinear", "lanczos"],),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "resize_and_pad"
+    CATEGORY = "image/transform"
+
+    def resize_and_pad(self, image, target_width, target_height, padding_color, interpolation):
+        batch_size, orig_height, orig_width, channels = image.shape
+
+        scale_w = target_width / orig_width
+        scale_h = target_height / orig_height
+        scale = min(scale_w, scale_h)
+
+        new_width = int(orig_width * scale)
+        new_height = int(orig_height * scale)
+
+        image_permuted = image.permute(0, 3, 1, 2)
+
+        resized = comfy.utils.common_upscale(image_permuted, new_width, new_height, interpolation, "disabled")
+
+        pad_value = 0.0 if padding_color == "black" else 1.0
+        padded = torch.full(
+            (batch_size, channels, target_height, target_width),
+            pad_value,
+            dtype=image.dtype,
+            device=image.device
+        )
+
+        y_offset = (target_height - new_height) // 2
+        x_offset = (target_width - new_width) // 2
+
+        padded[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized
+
+        output = padded.permute(0, 2, 3, 1)
+        return (output,)
+
 class SaveSVGNode:
     """
     Save SVG files on disk.
@@ -310,6 +552,80 @@ class SaveSVGNode:
             counter += 1
         return { "ui": { "images": results } }
 
+class GetImageSize:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": (IO.IMAGE,),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+            }
+        }
+
+    RETURN_TYPES = (IO.INT, IO.INT, IO.INT)
+    RETURN_NAMES = ("width", "height", "batch_size")
+    FUNCTION = "get_size"
+
+    CATEGORY = "image"
+    DESCRIPTION = """Returns width and height of the image, and passes it through unchanged."""
+
+    def get_size(self, image, unique_id=None) -> tuple[int, int]:
+        height = image.shape[1]
+        width = image.shape[2]
+        batch_size = image.shape[0]
+
+        # Send progress text to display size on the node
+        if unique_id:
+            PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id)
+
+        return width, height, batch_size
+
+class ImageRotate:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": (IO.IMAGE,),
+                              "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],),
+                              }}
+    RETURN_TYPES = (IO.IMAGE,)
+    FUNCTION = "rotate"
+
+    CATEGORY = "image/transform"
+
+    def rotate(self, image, rotation):
+        rotate_by = 0
+        if rotation.startswith("90"):
+            rotate_by = 1
+        elif rotation.startswith("180"):
+            rotate_by = 2
+        elif rotation.startswith("270"):
+            rotate_by = 3
+
+        image = torch.rot90(image, k=rotate_by, dims=[2, 1])
+        return (image,)
+
+class ImageFlip:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": (IO.IMAGE,),
+                              "flip_method": (["x-axis: vertically", "y-axis: horizontally"],),
+                              }}
+    RETURN_TYPES = (IO.IMAGE,)
+    FUNCTION = "flip"
+
+    CATEGORY = "image/transform"
+
+    def flip(self, image, flip_method):
+        if flip_method.startswith("x"):
+            image = torch.flip(image, dims=[1])
+        elif flip_method.startswith("y"):
+            image = torch.flip(image, dims=[2])
+
+        return (image,)
+
+
 NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
     "RepeatImageBatch": RepeatImageBatch,
@@ -318,4 +634,9 @@ NODE_CLASS_MAPPINGS = {
     "SaveAnimatedWEBP": SaveAnimatedWEBP,
     "SaveAnimatedPNG": SaveAnimatedPNG,
     "SaveSVGNode": SaveSVGNode,
+    "ImageStitch": ImageStitch,
+    "ResizeAndPadImage": ResizeAndPadImage,
+    "GetImageSize": GetImageSize,
+    "ImageRotate": ImageRotate,
+    "ImageFlip": ImageFlip,
 }

comfy_extras/nodes_load_3d.py

@@ -5,6 +5,8 @@ import os
 from comfy.comfy_types import IO
 from comfy_api.input_impl import VideoFromFile
 
+from pathlib import Path
+
 
 def normalize_path(path):
     return path.replace('\\', '/')
@@ -16,7 +18,14 @@ class Load3D():
 
         os.makedirs(input_dir, exist_ok=True)
 
-        files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.fbx', '.stl'))]
+        input_path = Path(input_dir)
+        base_path = Path(folder_paths.get_input_directory())
+
+        files = [
+            normalize_path(str(file_path.relative_to(base_path)))
+            for file_path in input_path.rglob("*")
+            if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
+        ]
 
         return {"required": {
             "model_file": (sorted(files), {"file_upload": True}),
@@ -61,7 +70,14 @@ class Load3DAnimation():
 
         os.makedirs(input_dir, exist_ok=True)
 
-        files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.fbx'))]
+        input_path = Path(input_dir)
+        base_path = Path(folder_paths.get_input_directory())
+
+        files = [
+            normalize_path(str(file_path.relative_to(base_path)))
+            for file_path in input_path.rglob("*")
+            if file_path.suffix.lower() in {'.gltf', '.glb', '.fbx'}
+        ]
 
         return {"required": {
             "model_file": (sorted(files), {"file_upload": True}),

comfy_extras/nodes_lt.py

@@ -134,8 +134,8 @@ class LTXVAddGuide:
         _, num_keyframes = get_keyframe_idxs(cond)
         latent_count = latent_length - num_keyframes
         frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0)
-        if guide_length > 1:
-            frame_idx = frame_idx // time_scale_factor * time_scale_factor # frame index must be divisible by 8
+        if guide_length > 1 and frame_idx != 0:
+            frame_idx = (frame_idx - 1) // time_scale_factor * time_scale_factor + 1 # frame index - 1 must be divisible by 8 or frame_idx == 0
 
         latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor
 
@@ -144,7 +144,7 @@ class LTXVAddGuide:
     def add_keyframe_index(self, cond, frame_idx, guiding_latent, scale_factors):
         keyframe_idxs, _ = get_keyframe_idxs(cond)
         _, latent_coords = self._patchifier.patchify(guiding_latent)
-        pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, True)
+        pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=frame_idx == 0)  # we need the causal fix only if we're placing the new latents at index 0
         pixel_coords[:, 0] += frame_idx
         if keyframe_idxs is None:
             keyframe_idxs = pixel_coords

comfy_extras/nodes_mask.py

@@ -152,7 +152,7 @@ class ImageColorToMask:
     def image_to_mask(self, image, color):
         temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
         temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
-        mask = torch.where(temp == color, 255, 0).float()
+        mask = torch.where(temp == color, 1.0, 0).float()
         return (mask,)
 
 class SolidMask:
@@ -247,7 +247,7 @@ class MaskComposite:
         visible_width, visible_height = (right - left, bottom - top,)
 
         source_portion = source[:, :visible_height, :visible_width]
-        destination_portion = destination[:, top:bottom, left:right]
+        destination_portion = output[:, top:bottom, left:right]
 
         if operation == "multiply":
             output[:, top:bottom, left:right] = destination_portion * source_portion

comfy_extras/nodes_model_advanced.py

@@ -189,7 +189,7 @@ class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps"],),
+                              "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],),
                               "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               }}
@@ -202,6 +202,7 @@ class ModelSamplingContinuousEDM:
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
 
+        sampling_base = comfy.model_sampling.ModelSamplingContinuousEDM
         latent_format = None
         sigma_data = 1.0
         if sampling == "eps":
@@ -215,8 +216,11 @@ class ModelSamplingContinuousEDM:
             sampling_type = comfy.model_sampling.EDM
             sigma_data = 0.5
             latent_format = comfy.latent_formats.SDXL_Playground_2_5()
+        elif sampling == "cosmos_rflow":
+            sampling_type = comfy.model_sampling.COSMOS_RFLOW
+            sampling_base = comfy.model_sampling.ModelSamplingCosmosRFlow
 
-        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass
 
         model_sampling = ModelSamplingAdvanced(model.model.model_config)