feat(Core): support partial graph execution

Co-authored-by: mattmillerai <7741082+mattmillerai@users.noreply.github.com>
Co-authored-by: Alexis Rolland <alexisrolland@hotmail.com>
Co-authored-by: Matt Miller <mattmiller@comfy.org>

.coderabbit.yaml

@@ -4,12 +4,12 @@ early_access: false
 tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
 
 reviews:
-  profile: "chill"
-  request_changes_workflow: false
+  profile: "assertive"
+  request_changes_workflow: true
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: false
+  review_details: true
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,6 +39,14 @@ reviews:
     - path: "**"
       instructions: |
         IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
+        Treat AGENTS.md as mandatory repository policy, not optional style guidance.
+        Flag PR changes that violate AGENTS.md even when the code is otherwise functional.
+        In particular, enforce architecture boundaries, dtype/device/memory rules,
+        interface contracts, import style, no unnecessary try/except blocks, no inline
+        imports, no outbound internet paths in core ComfyUI, and narrow scoped fixes.
+        Prefer direct findings over suggestions when a rule is violated. Only ignore
+        AGENTS.md when it clearly conflicts with a newer explicit maintainer instruction
+        in the PR.
         Do NOT flag pre-existing issues in code that was merely moved, re-indented,
         de-indented, or reformatted without logic changes. If code appears in the diff
         only due to whitespace or structural reformatting (e.g., removing a `with:` block),
@@ -123,5 +131,10 @@ chat:
 
 knowledge_base:
   opt_out: false
+  code_guidelines:
+    enabled: true
+    filePatterns:
+      - files: "AGENTS.md"
+        applyTo: "**"
   learnings:
     scope: "auto"

.github/workflows/ci-cursor-review.yml

@@ -0,0 +1,38 @@
+name: CI - Cursor Review
+
+# Thin caller for the shared reusable cursor-review workflow in
+# Comfy-Org/github-workflows. The review logic (panel matrix, judge
+# consolidation, prompts, extract/post/notify scripts) lives there as the
+# single source of truth, so this repo only carries the repo-specific diff
+# excludes.
+
+on:
+  pull_request:
+    types: [labeled, unlabeled]
+
+concurrency:
+  group: cursor-review-pr-${{ github.event.pull_request.number }}-${{ github.event.label.name }}
+  cancel-in-progress: true
+
+jobs:
+  cursor-review:
+    if: github.event.label.name == 'cursor-review'
+    permissions:
+      contents: read
+      pull-requests: write
+    # SHA-pinned per zizmor `unpinned-uses: hash-pin`. Bump this SHA to pick up
+    # upstream changes; keep `workflows_ref` matching so prompts/scripts load
+    # from the same commit as the workflow definition.
+    uses: Comfy-Org/github-workflows/.github/workflows/cursor-review.yml@047ca48febe3a6647608ed2e0c4331b491cb9d6a # github-workflows#9
+    with:
+      workflows_ref: 047ca48febe3a6647608ed2e0c4331b491cb9d6a
+      diff_excludes: >-
+        :!**/.claude/**
+        :!**/dist/**
+        :!**/vendor/**
+        :!**/*.generated.*
+        :!**/*.min.js
+        :!**/*.min.css
+    secrets:
+      CURSOR_API_KEY: ${{ secrets.CURSOR_API_KEY }}
+      SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}

.github/workflows/cla.yml

@@ -0,0 +1,93 @@
+name: CLA Assistant
+
+on:
+  issue_comment:
+    types: [created]
+  pull_request_target:
+    types: [opened, synchronize, closed]
+
+permissions:
+  actions: write
+  contents: read # 'read' is enough because signatures live in a REMOTE repo
+  pull-requests: write
+  statuses: write
+
+jobs:
+  cla-assistant:
+    runs-on: ubuntu-latest
+    steps:
+      # The CLA action normally requires every commit author in a PR to sign.
+      # We only want the PR author to sign, so we allowlist all other committers
+      # by computing them from the PR's commits and excluding the PR author.
+      - name: Build author-only allowlist
+        id: allowlist
+        if: >
+          github.event_name == 'pull_request_target' ||
+          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
+            github.event.comment.body == 'recheck' ||
+            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
+          ))
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
+          PR_AUTHOR: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
+          BASE_ALLOWLIST: action@github.com,actions-user,ampagent,claude,comfy-pr-bot,GitHub Action,github-actions,github-actions[bot],Glary Bot,Glary-Bot,*[bot]
+        # For each commit emit the GitHub login when the author/committer email resolves to a GitHub account
+        # otherwise fall back to the raw git name.
+        run: |
+          others=$(gh api "repos/${{ github.repository }}/pulls/${PR_NUMBER}/commits" --paginate \
+            --jq '.[] | (.author.login // .commit.author.name // empty), (.committer.login // .commit.committer.name // empty)' \
+            | sort -u | grep -vix "${PR_AUTHOR}" | paste -sd, -)
+          if [ -n "$others" ]; then
+            echo "allowlist=${BASE_ALLOWLIST},${others}" >> "$GITHUB_OUTPUT"
+          else
+            echo "allowlist=${BASE_ALLOWLIST}" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: CLA Assistant
+        # Run on PR events, on "recheck" comment, or when someone posts the signing phrase.
+        # IMPORTANT: this phrase must match `custom-pr-sign-comment` below.
+        if: >
+          github.event_name == 'pull_request_target' ||
+          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
+            github.event.comment.body == 'recheck' ||
+            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
+          ))
+        uses: contributor-assistant/github-action@ca4a40a7d1004f18d9960b404b97e5f30a505a08 # v2.6.1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          # PAT required to write to the centralized signatures repo.
+          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+        with:
+          # Where the CLA document lives (shown to contributors)
+          path-to-document: https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md
+
+          # Centralized signature storage
+          remote-organization-name: comfy-org
+          remote-repository-name: comfy-cla
+          path-to-signatures: signatures/cla.json
+          branch: main
+
+          # Only the PR author must sign: bots plus every non-author committer
+          # are allowlisted via the "Build author-only allowlist" step above.
+          # *[bot] is a catch-all for any GitHub App bot account.
+          allowlist: ${{ steps.allowlist.outputs.allowlist }}
+
+          # Custom PR comment messages
+          custom-notsigned-prcomment: |
+            🎉 Thank you for your contribution, we really appreciate it! 🎉
+
+            Like many open source projects, we require contributors to sign our [Contributor License Agreement (CLA)](https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md). A CLA makes the ownership of contributions explicit, so contributors and the project share a clear understanding of how the code can be used. By signing, you:
+
+            - Confirm that you own your contribution.
+            - Keep the right to reuse your own code.
+            - Grant us a copyright license to include and share it within our projects.
+
+            CLAs are standard practice across major open source projects including those under the Apache Software Foundation and the Linux Foundation. Ours is based on the Apache Software Foundation's CLA. Most importantly, it would enable us to relicense the project under a more permissive license in the future, giving the project and its community greater flexibility.
+
+            ✍ **To sign, please post a new comment on this PR with exactly the following text:** ✍
+
+          custom-pr-sign-comment: I have read and agree to the Contributor License Agreement
+
+          custom-allsigned-prcomment: |
+            ✅ All contributors have signed the CLA. Thank you! This PR is ready to be merged.

.github/workflows/test-ci.yml

@@ -22,10 +22,11 @@ jobs:
       fail-fast: false
       matrix:
         # os: [macos, linux, windows]
+        # os: [macos, linux]
         os: [linux]
-        python_version: ["3.14", "3.13", "3.12", "3.11"]
-        cuda_version: ["13.2"]
-        torch_version: ["specific"]
+        python_version: ["3.10", "3.11", "3.12"]
+        cuda_version: ["12.1"]
+        torch_version: ["stable"]
         include:
           # - os: macos
           #   runner_label: [self-hosted, macOS]
@@ -43,18 +44,33 @@ jobs:
         with:
           os: ${{ matrix.os }}
           python_version: ${{ matrix.python_version }}
-          cuda_version: ${{ matrix.cuda_version }}
           torch_version: ${{ matrix.torch_version }}
-          # Pin PyTorch 2.12 on CUDA 13.2 (cu132 wheel index). torchaudio is omitted
-          # on purpose: cu132 has no torchaudio build for these Python versions
-          # (it stops at 2.2.0/cp312). requirements.txt still lists torchaudio, so
-          # validate that leg once a runner exists — it may need to be made optional.
-          # NOTE: requires comfy-action to honor `torch_version: specific` on Linux
-          # (today macOS-only). Companion PR in comfy-org/comfy-action is required.
-          specific_torch_install: "pip install torch==2.12.* torchvision --index-url https://download.pytorch.org/whl/cu132"
           google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
           comfyui_flags: ${{ matrix.flags }}
 
+  # test-win-nightly:
+  #   strategy:
+  #     fail-fast: true
+  #     matrix:
+  #       os: [windows]
+  #       python_version: ["3.9", "3.10", "3.11", "3.12"]
+  #       cuda_version: ["12.1"]
+  #       torch_version: ["nightly"]
+  #       include:
+  #         - os: windows
+  #           runner_label: [self-hosted, Windows]
+  #           flags: ""
+  #   runs-on: ${{ matrix.runner_label }}
+  #   steps:
+  #     - name: Test Workflows
+  #       uses: comfy-org/comfy-action@main
+  #       with:
+  #         os: ${{ matrix.os }}
+  #         python_version: ${{ matrix.python_version }}
+  #         torch_version: ${{ matrix.torch_version }}
+  #         google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+  #         comfyui_flags: ${{ matrix.flags }}
+
   test-unix-nightly:
     strategy:
       fail-fast: false
@@ -62,7 +78,7 @@ jobs:
         # os: [macos, linux]
         os: [linux]
         python_version: ["3.11"]
-        cuda_version: ["13.2"]
+        cuda_version: ["12.1"]
         torch_version: ["nightly"]
         include:
           # - os: macos
@@ -78,7 +94,6 @@ jobs:
         with:
           os: ${{ matrix.os }}
           python_version: ${{ matrix.python_version }}
-          cuda_version: ${{ matrix.cuda_version }}
           torch_version: ${{ matrix.torch_version }}
           google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
           comfyui_flags: ${{ matrix.flags }}

AGENTS.md

@@ -0,0 +1,296 @@
+## Engineering Style
+
+- Keep changes small and direct. Most fixes should touch the narrowest code path
+  that explains the bug, performance issue, dtype issue, model-format issue, or
+  user-facing behavior.
+- Change the least amount of files possible. A change that touches many files is
+  more likely to be a bad change than a good one unless the broader scope is
+  directly required.
+- Prefer practical fixes over broad architecture work. Add abstractions only
+  when they remove real repeated logic or match an existing ComfyUI pattern.
+- Prefer fewer dependencies. Do not add new dependencies to ComfyUI unless they
+  are absolutely necessary.
+- Delete obsolete code aggressively when newer infrastructure makes it useless.
+  Remove dead fallbacks, migration paths, unused options, debug prints, and
+  compatibility branches that are no longer needed. Do not leave dead branches,
+  unreachable code, or functions that are never called. If code is not
+  necessary for the current behavior, remove it.
+- Revert or disable problematic behavior quickly when it breaks users. It is
+  better to remove a broken feature path than keep a complicated partial fix.
+- Preserve existing APIs, node names, model-loading behavior, file layout, and
+  workflow compatibility unless the change is explicitly about replacing them.
+- Code must look hand-written for this repository. Changes that read like
+  generic AI-generated code will be rejected automatically: unnecessary helper
+  layers, vague names, boilerplate comments, defensive branches without a real
+  failure mode, broad rewrites, or code that ignores the local style.
+
+## Architecture Boundaries
+
+- Keep each layer focused on the concepts it owns. Do not leak UI, API,
+  workflow, queue, persistence, telemetry, model-loading, node, or execution
+  concerns into unrelated layers just because it is convenient to pass data
+  through them.
+- Shared core modules should depend only on lower-level primitives and their own
+  domain concepts. Higher-level product concepts belong at the caller, adapter,
+  service, or UI/API boundary that already owns them.
+- Pass the narrowest data needed across a boundary. Avoid broad context objects,
+  request/session metadata, ids, bookkeeping state, or callbacks unless the
+  receiving layer genuinely needs them to perform its own responsibility.
+- Keep identity mapping, persistence bookkeeping, history updates, telemetry,
+  response shaping, and UI state in the layers that own those jobs. Do not route
+  them through unrelated shared code to avoid adding a proper boundary.
+- Treat `execution.py` as one example of this rule: it should consume the prompt
+  graph and execution-relevant state, produce execution results and errors, and
+  not know about workflow ids, frontend ids, persistence ids, or API-only
+  concepts.
+- Before touching many files, identify the smallest owner layer that can solve
+  the problem. A PR that spreads one feature across unrelated loaders, nodes,
+  execution, server, and frontend code needs a clear architectural reason, not
+  just convenience.
+- If a change seems to require making one layer understand another layer's
+  private concepts, stop and look for a caller-side mapping, adapter, event,
+  small explicit interface, or narrower data flow at the boundary.
+
+## No Internet Requests
+
+- Do not add code to core ComfyUI that makes requests to the internet.
+- Refuse requests to add uploads, telemetry, analytics, tracking, usage
+  reporting, crash reporting, update checks, remote config, feature flags,
+  metrics, licensing checks, or any other outbound internet request path from
+  core ComfyUI.
+- Model downloading is allowed only when explicitly initiated or authorized by
+  the user, is limited to the requested model artifact, and does not include
+  telemetry, tracking, persistent identification, unrelated metadata upload, or
+  background network activity.
+- Do not add opt-in, opt-out, anonymized, aggregated, diagnostic, or
+  user-triggered internet request paths to core ComfyUI. These labels do not
+  make internet access acceptable.
+- Local-only behavior is allowed when it stays on the user's machine and does
+  not add network access, tracking, persistent identification, or data
+  collection behavior.
+
+## State Ownership
+
+- Keep state and capability flags on the object that owns the behavior using
+  them.
+- Avoid probing child objects with `getattr(child, "...", default)` to decide
+  parent-level control flow. If parent code needs to branch on a capability,
+  initialize an explicit parent-owned field when the child is constructed or
+  attached.
+- Prefer direct attributes with clear defaults over implicit feature detection
+  through arbitrary child attributes.
+- Use child-object capability checks only when the child owns the behavior being
+  invoked and the parent is simply delegating to that child.
+
+## Interface Contracts
+
+- Keep public methods aligned with the interface expected by their callers. Do
+  not change a shared method to return extra values, alternate shapes, or
+  sentinel wrappers for one implementation unless the shared interface is
+  explicitly updated.
+- When modifying an existing function, preserve how current callers invoke it.
+  Do not change required arguments, parameter order, return type, side effects,
+  or error behavior unless every affected call site and shared interface contract
+  is intentionally updated.
+- Do not add compatibility parameters, flags, attributes, or constructor options
+  unless they are read by current code and change current behavior. Remove
+  pass-through or stored-but-unused values instead of preserving upstream or
+  deprecated API baggage.
+- If an implementation needs auxiliary values for its own workflow, expose them
+  through a private helper or a clearly named implementation-specific method
+  instead of overloading the public method's return contract.
+- Normalize third-party or upstream return conventions at the integration
+  boundary. Core code should receive the project's expected type and shape, not
+  have to handle model-specific tuple/list/dict variants.
+- Avoid caller-side unwrapping such as `out = out[0]` unless the called
+  interface is documented to return that structure.
+
+## Autograd and Model Freezing
+
+- Do not add `torch.no_grad`, `torch.inference_mode`, or inference-mode helper
+  wrappers in ComfyUI code. The only allowed inference-mode-related use is
+  disabling a globally set inference mode when a training path needs gradients.
+- Do not add freeze, unfreeze, or trainability toggles to model classes. ComfyUI
+  models are always treated as frozen for inference, so explicit freeze
+  functionality is redundant and should not be added.
+- Remove training-only behavior such as dropout from inference model code, but
+  preserve checkpoint and state-dict compatibility when doing so. If deleting a
+  module would change state-dict keys, module ordering, or checkpoint loading
+  behavior, replace it with a no-op such as `nn.Identity` instead of removing the
+  slot outright.
+
+## Python Style
+
+- Keep imports at module scope. Avoid inline imports unless they are already part
+  of an established optional-backend probe or are needed to avoid an import
+  cycle.
+- Do not add unnecessary `try`/`except` blocks. Use them for optional dependency,
+  platform, or backend capability detection only when the program has a useful
+  fallback. Prefer specific exception types when changing new code.
+- If a library version is pinned in `requirements.txt`, do not add code to
+  ComfyUI to handle older versions of that library.
+- Remove any workarounds for PyTorch versions that ComfyUI no longer officially
+  supports. Deprecated workarounds include catching an exception and rerunning
+  the same op with the input cast to float. If a workaround does not have a
+  comment naming the exact PyTorch version or versions that still need it,
+  remove it.
+- Let unsupported model formats, invalid quantization metadata, and bad states
+  fail with clear errors instead of silently producing lower quality output.
+- Match the existing local style in the file you edit. This codebase tolerates
+  long lines, simple helper functions, module-level state, and direct tensor
+  operations when they make the code easier to follow.
+- Keep comments sparse and useful. Strip useless comments that restate the code
+  or describe obvious behavior. Short TODOs are fine when they name the concrete
+  missing follow-up.
+
+## Model, Device, and Memory Behavior
+
+- Treat dtype, device placement, VRAM usage, and offloading behavior as core
+  correctness concerns. Check CPU, CUDA, ROCm, MPS, DirectML, XPU, NPU, and low
+  VRAM implications when touching shared execution or loading code.
+- Prefer native ComfyUI formats and existing quantization/offload helpers over
+  adding parallel code paths. Use `comfy.quant_ops`, `comfy.model_management`,
+  `comfy.memory_management`, `comfy.pinned_memory`, `comfy_aimdo`, and
+  `comfy-kitchen` helpers where they already solve the problem.
+- Use optimized comfy-kitchen ops in places where they improve performance
+  without changing the expected dtype, device, memory, or interface behavior.
+- All models should use the optimized attention function selected by ComfyUI.
+  Treat optimized backend functions, dispatch helpers, and capability-selected
+  callables as opaque. Higher-level code must not inspect function identity,
+  names, modules, or implementation details to decide behavior.
+- Apply the same opacity rule to similar patterns beyond attention: callers
+  should depend on the documented interface and result contract, not on which
+  backend implementation was selected underneath.
+- Do not use custom inference ops that only duplicate an existing op while
+  upcasting to float32, such as custom RMSNorm variants. Use the generic ComfyUI
+  ops and/or native torch ops instead.
+- If a model class `__init__` has an `operations` parameter, assume
+  `operations` is never `None`. Do not add fallback branches or default torch
+  ops for a missing `operations` object.
+- Do not add unnecessary parameters to model, model block, or model ops related
+  classes. Constructor and forward signatures should carry only values that are
+  actually needed by that object for inference.
+- Reuse existing model classes, blocks, ops, and helper modules when appropriate.
+  Before implementing a new version of a model component, search the existing
+  model code for a class or helper that already provides the behavior.
+- Model detection code that inspects linear weight shapes should only use the
+  first dimension. The second dimension may be half the original size for
+  NVFP4 or other 4-bit quantized models.
+- Avoid adding `einops` usage in core inference code. Use native torch tensor
+  ops such as `reshape`, `view`, `permute`, `transpose`, `flatten`, `unflatten`,
+  `unsqueeze`, and `squeeze` instead.
+- Do not use tensors as general-purpose Python data structures. Keep metadata,
+  bookkeeping, counters, flags, shape math, padding math, index planning, memory
+  estimates, and control-flow decisions in plain Python values unless the data
+  must participate directly in tensor computation. Do not create tensors for
+  structural metadata that is only used for Python-side control flow. Sequence
+  lengths, cumulative offsets, split indices, window counts, slice boundaries,
+  and repeat counts should be kept as Python ints/lists from the point they are
+  computed. Do not build them as CPU/GPU tensors and then cast, move, validate,
+  or convert them back to Python for `split`, `tensor_split`, indexing plans,
+  loops, or cache keys. Avoid creating temporary tensors just to use tensor
+  methods for scalar or structural calculations.
+- Avoid unnecessary casts and transfers. Preserve the intended compute dtype,
+  storage dtype, bias dtype, and original tensor shape metadata.
+- Keep model-native latent layout handling inside the model or latent-format
+  owner, not in helper nodes. Do not collapse, expand, pack, or unpack latent
+  dimensions in nodes or other caller-side adapters just to satisfy a model
+  forward; the model path should consume and return the native latent shape for
+  that model family.
+- Assume inputs to the main model forward are already in the compute dtype by
+  default, except integer inputs such as some model timestep tensors. Do not add
+  defensive or convenience casts in model code; it is better for invalid dtype
+  plumbing to error clearly than to hide it with unnecessary casts.
+- Raw model parameters that are not owned by an op and may be initialized in a
+  dtype different from the compute dtype should be cast at use in forward or
+  inference code with `comfy.ops.cast_to_input` or
+  `comfy.model_management.cast_to` to avoid dtype mismatches.
+- Model code should not care what dtype it is initialized in, and model
+  `__init__` methods should not contain workarounds for specific dtypes. Dtype
+  workaround code, such as making a model work with fp16 compute, belongs in the
+  execution or model-management layer that owns compute policy.
+- Model code should not perform unnecessary device-to-CPU or CPU-to-device
+  transfers. New allocations must be created on the correct device and dtype;
+  never allocate on CPU and then move to GPU, or allocate in one dtype and then
+  convert to another.
+- Model code itself should not perform memory management. Loading, unloading,
+  offloading, device movement, VRAM policy, cache lifetime, and cleanup belong
+  in the relevant model-management and execution layers, not inside model
+  implementations.
+- Do not add global, module-level, class-level, singleton, or model-owned stores
+  for tensors or other large memory that persist across executions. Temporary
+  caches must be scoped to a single execution or forward/encode/decode call:
+  allocate them in the owning top-level call, pass them explicitly through the
+  call stack, and let them be discarded when that call returns.
+- Follow the Wan VAE temporal cache pattern for temporary caches: create a local
+  cache such as `feat_map` for the encode/decode operation, pass it into the
+  blocks that need it, and do not retain it on the model or in global state.
+- In model init code, prefer `torch.empty` for parameter/buffer placeholders
+  that are populated from the model state dict instead of zero-initializing with
+  `torch.zeros` or similar. If an allocation is not loaded from the state dict
+  and is useless for inference, do not include it.
+- `nn.Parameter` tensors that are stored in and populated from the model state
+  dict should be initialized with `torch.empty`, not with zero, random, or
+  otherwise meaningful initialization.
+- Model initialization should describe module structure, not fabricate
+  checkpoint-owned tensor contents. Parameters and buffers that are loaded from
+  the state dict must not be manually initialized, reassigned, or filled with
+  fallback values unless that value is actually used when no checkpoint key
+  exists.
+- When slicing large tensors, copy the slice if the sliced tensor's lifetime
+  exceeds the current function scope. Do not keep a long-lived view into a large
+  backing tensor when a smaller copy would release memory sooner.
+- Use fused or compound torch operations such as `addcmul` when they naturally
+  match the math. Reducing Python and torch dispatch overhead is a valid
+  optimization when it does not obscure the code or change dtype/device
+  behavior.
+- Avoid caches that persist across different executions as much as possible.
+  Persistent caches are acceptable only when they use a very minimal amount of
+  memory and have a clear ownership and invalidation story.
+- When optimizing, favor small measurable changes: fewer allocations, fewer
+  device transfers, less peak memory, better batching, or use of a faster
+  existing backend op.
+
+## Nodes and User-Facing Behavior
+
+- Follow existing node conventions: `INPUT_TYPES`, `RETURN_TYPES`, `FUNCTION`,
+  `CATEGORY`, and registration through the local mapping used by that file.
+- Keep node changes backward compatible by default. Add inputs with sensible
+  defaults and avoid changing output types unless the request requires it.
+- Model implementations should add the minimal number of ComfyUI nodes required
+  to run the model. Reuse existing nodes as much as possible; adapting the model
+  to work with existing nodes is strongly preferred over creating new nodes.
+- Nodes should output only values they own. Do not add pass-through outputs for
+  workflow convenience unless the node is explicitly an output node. Existing
+  models, latents, conditioning, or other inputs should flow directly to the
+  next consumer instead of being re-emitted unchanged.
+- Nodes should expose only inputs they actually read to produce current
+  behavior. Do not add placeholder, pass-through, compatibility, or
+  workflow-shaping inputs that are ignored or could flow directly to another
+  node.
+- Node-level code must not patch model code directly. Any node behavior that
+  modifies, wraps, hooks, or changes model behavior must go through the model
+  patcher class instead of reaching into model internals.
+- The official mascot of ComfyUI is a very cute anime girl with massive fennec
+  ears, a big fluffy tail, long blonde wavy hair, and blue eyes. Feel free to
+  use her in ComfyUI materials, UI text, examples, tests, generated assets, or
+  comments, but do not disrespect her.
+- Warning and info messages should be short and actionable. Remove noisy or
+  misleading messages rather than adding more logging.
+- Documentation and README edits should be concise, factual, and tied to the
+  changed behavior.
+
+## Commit and Review Habits
+
+- If asked to write commit messages, use short direct subjects like the existing
+  history: `Fix ...`, `Add ...`, `Support ...`, `Remove ...`, `Update ...`,
+  `Make ...`, `Use ...`, `Disable ...`, `Bump ...`, or `Revert ...`.
+- Keep PR descriptions short and reviewable. State the problem, the behavioral
+  change, and the tests run; avoid long narrative explanations, implementation
+  diaries, or exhaustive file-by-file summaries unless the reviewer explicitly
+  needs that context.
+- Prefer one coherent behavioral change per commit. Dependency pins, tests, and
+  the code that needs them may be in the same commit when they are inseparable.
+- In reviews, prioritize real user impact: crashes, wrong dtype/device behavior,
+  memory regressions, broken model loading, workflow incompatibility, and noisy
+  or misleading user-facing output.

README.md

@@ -140,7 +140,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
    - Commits outside of the stable release tags may be very unstable and break many custom nodes.
    - Serves as the foundation for the desktop release
 
-2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
+2. **[Comfy Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
    - Builds a new release using the latest stable core version
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
@@ -229,7 +229,7 @@ Python 3.14 works but some custom nodes may have issues. The free threaded varia
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
-torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
+torch 2.5 is minimally supported but using a newer version is extremely recommended. Some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old. If your pytorch is more than 6 months old, please update it.
 
 ### Instructions:
 
@@ -309,7 +309,7 @@ After this you should have everything installed and can proceed to running Comfy
 
 #### Apple Mac silicon
 
-You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
+You can install ComfyUI in Apple Mac silicon (M1, M2, M3 or M4) with any recent macOS version.
 
 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
 1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
@@ -382,11 +382,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt
 
 ### AMD ROCm Tips
 
-You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
-
-```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
-
-You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
+You can try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
 
 # Notes
 

alembic_db/versions/0005_allow_case_sensitive_tags.py

@@ -0,0 +1,107 @@
+"""
+Allow case-sensitive tag names.
+
+Revision ID: 0005_allow_case_sensitive_tags
+Revises: 0004_drop_tag_type
+Create Date: 2026-06-16
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = "0005_allow_case_sensitive_tags"
+down_revision = "0004_drop_tag_type"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+    if bind.dialect.name == "sqlite":
+        # SQLite cannot ALTER/DROP CHECK constraints. Recreate the small tag
+        # vocabulary table without the lowercase constraint while preserving
+        # existing tag names.
+        op.execute("PRAGMA foreign_keys=OFF")
+        try:
+            op.execute(
+                "CREATE TABLE tags_new ("
+                "name VARCHAR(512) NOT NULL, "
+                "CONSTRAINT pk_tags PRIMARY KEY (name)"
+                ")"
+            )
+            op.execute("INSERT INTO tags_new(name) SELECT name FROM tags")
+            op.execute("DROP TABLE tags")
+            op.execute("ALTER TABLE tags_new RENAME TO tags")
+        finally:
+            op.execute("PRAGMA foreign_keys=ON")
+        return
+
+    op.drop_constraint("ck_tags_ck_tags_lowercase", "tags", type_="check")
+
+
+def downgrade() -> None:
+    # Existing mixed-case tags cannot satisfy the old constraint. Lowercase them
+    # before restoring it, merging duplicate vocabulary/link rows that collide.
+    bind = op.get_bind()
+
+    tag_names = [row[0] for row in bind.execute(sa.text("SELECT name FROM tags"))]
+    existing_names = set(tag_names)
+    lowercase_names = sorted({name.lower() for name in tag_names})
+    missing_lowercase_rows = [
+        {"name": name} for name in lowercase_names if name not in existing_names
+    ]
+    if missing_lowercase_rows:
+        bind.execute(sa.text("INSERT INTO tags(name) VALUES (:name)"), missing_lowercase_rows)
+
+    link_rows = bind.execute(
+        sa.text(
+            "SELECT asset_reference_id, tag_name, origin, added_at "
+            "FROM asset_reference_tags "
+            "ORDER BY asset_reference_id, tag_name"
+        )
+    ).mappings()
+    deduped_links = {}
+    for row in link_rows:
+        key = (row["asset_reference_id"], row["tag_name"].lower())
+        deduped_links.setdefault(
+            key,
+            {
+                "asset_reference_id": row["asset_reference_id"],
+                "tag_name": row["tag_name"].lower(),
+                "origin": row["origin"],
+                "added_at": row["added_at"],
+            },
+        )
+
+    op.execute("DELETE FROM asset_reference_tags")
+    if deduped_links:
+        bind.execute(
+            sa.text(
+                "INSERT INTO asset_reference_tags "
+                "(asset_reference_id, tag_name, origin, added_at) "
+                "VALUES (:asset_reference_id, :tag_name, :origin, :added_at)"
+            ),
+            list(deduped_links.values()),
+        )
+    op.execute("DELETE FROM tags WHERE name != lower(name)")
+
+    if bind.dialect.name == "sqlite":
+        op.execute("PRAGMA foreign_keys=OFF")
+        try:
+            op.execute(
+                "CREATE TABLE tags_new ("
+                "name VARCHAR(512) NOT NULL, "
+                "CONSTRAINT pk_tags PRIMARY KEY (name), "
+                "CONSTRAINT ck_tags_lowercase CHECK (name = lower(name))"
+                ")"
+            )
+            op.execute("INSERT INTO tags_new(name) SELECT name FROM tags")
+            op.execute("DROP TABLE tags")
+            op.execute("ALTER TABLE tags_new RENAME TO tags")
+        finally:
+            op.execute("PRAGMA foreign_keys=ON")
+        return
+
+    op.create_check_constraint(
+        "ck_tags_ck_tags_lowercase", "tags", "name = lower(name)"
+    )

alembic_db/versions/0006_add_loader_path.py

@@ -0,0 +1,30 @@
+"""
+Add loader_path column to asset_references.
+
+Stores the in-root loader path (path relative to the storage root with the
+top-level model category dropped) derived from file_path at scan/ingest time,
+so the assets API can return it without re-resolving against every registered
+model-folder base on every request.
+
+Revision ID: 0006_add_loader_path
+Revises: 0005_allow_case_sensitive_tags
+Create Date: 2026-07-02
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0006_add_loader_path"
+down_revision = "0005_allow_case_sensitive_tags"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("asset_references") as batch_op:
+        batch_op.add_column(sa.Column("loader_path", sa.Text(), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("asset_references") as batch_op:
+        batch_op.drop_column("loader_path")

app/assets/api/routes.py

@@ -40,6 +40,7 @@ from app.assets.services import (
     upload_from_temp_path,
 )
 from app.assets.services.cursor import InvalidCursorError
+from app.assets.services.path_utils import compute_display_name
 from app.assets.services.tagging import list_tag_histogram
 
 ROUTES = web.RouteTableDef()
@@ -161,11 +162,19 @@ def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResu
             preview_url = None
     else:
         preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata)
+    if result.ref.file_path:
+        display_name = compute_display_name(result.ref.file_path)
+        # In-root loader path (model category dropped): what model loaders consume.
+        loader_path = result.ref.loader_path
+    else:
+        display_name, loader_path = None, None
     asset_content_hash = result.asset.hash if result.asset else None
     return schemas_out.Asset(
         id=result.ref.id,
         name=result.ref.name,
         hash=asset_content_hash,
+        loader_path=loader_path,
+        display_name=display_name,
         asset_hash=asset_content_hash,
         size=int(result.asset.size_bytes) if result.asset else None,
         mime_type=result.asset.mime_type if result.asset else None,
@@ -306,12 +315,15 @@ async def download_asset_content(request: web.Request) -> web.Response:
             404, "FILE_NOT_FOUND", "Underlying file not found on disk."
         )
 
-    _DANGEROUS_MIME_TYPES = {
-        "text/html", "text/html-sandboxed", "application/xhtml+xml",
-        "text/javascript", "text/css",
-    }
-    if content_type in _DANGEROUS_MIME_TYPES:
+    # User-controlled asset content must never render inline in the app origin
+    # (stored XSS via SVG/HTML/XML). Force dangerous types to download and
+    # override any requested inline disposition. Centralised through
+    # folder_paths.is_dangerous_content_type so this can't drift from /view and
+    # /userdata (the previous inline set here omitted image/svg+xml and missed
+    # the charset/casing/+xml-dialect bypasses).
+    if folder_paths.is_dangerous_content_type(content_type):
         content_type = "application/octet-stream"
+        disposition = "attachment"
 
     safe_name = (filename or "").replace("\r", "").replace("\n", "")
     encoded = urllib.parse.quote(safe_name)
@@ -416,17 +428,6 @@ async def upload_asset(request: web.Request) -> web.Response:
             400, "INVALID_BODY", f"Validation failed: {ve.json()}"
         )
 
-    if spec.tags and spec.tags[0] == "models":
-        if (
-            len(spec.tags) < 2
-            or spec.tags[1] not in folder_paths.folder_names_and_paths
-        ):
-            delete_temp_file_if_exists(parsed.tmp_path)
-            category = spec.tags[1] if len(spec.tags) >= 2 else ""
-            return _build_error_response(
-                400, "INVALID_BODY", f"unknown models category '{category}'"
-            )
-
     try:
         # Fast path: hash exists, create AssetReference without writing anything
         if spec.hash and parsed.provided_hash_exists is True:
@@ -470,7 +471,7 @@ async def upload_asset(request: web.Request) -> web.Response:
         return _build_error_response(400, e.code, str(e))
     except ValueError as e:
         delete_temp_file_if_exists(parsed.tmp_path)
-        return _build_error_response(400, "BAD_REQUEST", str(e))
+        return _build_error_response(400, "INVALID_BODY", str(e))
     except HashMismatchError as e:
         delete_temp_file_if_exists(parsed.tmp_path)
         return _build_error_response(400, "HASH_MISMATCH", str(e))

app/assets/api/schemas_in.py

@@ -140,7 +140,7 @@ class CreateFromHashBody(BaseModel):
         if v is None:
             return []
         if isinstance(v, list):
-            out = [str(t).strip().lower() for t in v if str(t).strip()]
+            out = [str(t).strip() for t in v if str(t).strip()]
             seen = set()
             dedup = []
             for t in out:
@@ -149,7 +149,7 @@ class CreateFromHashBody(BaseModel):
                     dedup.append(t)
             return dedup
         if isinstance(v, str):
-            return [t.strip().lower() for t in v.split(",") if t.strip()]
+            return list(dict.fromkeys(t.strip() for t in v.split(",") if t.strip()))
         return []
 
 
@@ -206,7 +206,7 @@ class TagsListQuery(BaseModel):
         if v is None:
             return v
         v = v.strip()
-        return v.lower() or None
+        return v or None
 
 
 class TagsAdd(BaseModel):
@@ -220,7 +220,7 @@ class TagsAdd(BaseModel):
         for t in v:
             if not isinstance(t, str):
                 raise TypeError("tags must be strings")
-            tnorm = t.strip().lower()
+            tnorm = t.strip()
             if tnorm:
                 out.append(tnorm)
         seen = set()
@@ -239,8 +239,8 @@ class TagsRemove(TagsAdd):
 class UploadAssetSpec(BaseModel):
     """Upload Asset operation.
 
-    - tags: optional list; if provided, first is root ('models'|'input'|'output');
-            if root == 'models', second must be a valid category
+    - tags: labels plus one destination role ('models'|'input'|'output') for new bytes;
+            if role == 'models', exactly one model_type:<folder_name> tag is required
     - name: display name
     - user_metadata: arbitrary JSON object (optional)
     - hash: optional canonical 'blake3:<hex>' for validation / fast-path
@@ -309,7 +309,7 @@ class UploadAssetSpec(BaseModel):
         norm = []
         seen = set()
         for t in items:
-            tnorm = str(t).strip().lower()
+            tnorm = str(t).strip()
             if tnorm and tnorm not in seen:
                 seen.add(tnorm)
                 norm.append(tnorm)
@@ -335,14 +335,4 @@ class UploadAssetSpec(BaseModel):
 
     @model_validator(mode="after")
     def _validate_order(self):
-        if not self.tags:
-            raise ValueError("at least one tag is required for uploads")
-        root = self.tags[0]
-        if root not in {"models", "input", "output"}:
-            raise ValueError("first tag must be one of: models, input, output")
-        if root == "models":
-            if len(self.tags) < 2:
-                raise ValueError(
-                    "models uploads require a category tag as the second tag"
-                )
         return self

app/assets/api/schemas_out.py

@@ -9,8 +9,20 @@ class Asset(BaseModel):
     ``id`` here is the AssetReference id, not the content-addressed Asset id."""
 
     id: str
-    name: str
+    name: str = Field(
+        ...,
+        deprecated=True,
+        description="Reference label, often caller-provided or derived from the filename. Deprecated for storage path/display semantics; use `loader_path` and `display_name` when present.",
+    )
     hash: str | None = None
+    loader_path: str | None = Field(
+        default=None,
+        description="The value a loader consumes to load this asset. `None` when no loader can resolve the file.",
+    )
+    display_name: str | None = Field(
+        default=None,
+        description="Human-facing label for the asset. Not unique.",
+    )
     asset_hash: str | None = None
     size: int | None = None
     mime_type: str | None = None

app/assets/api/upload.py

@@ -140,7 +140,6 @@ async def parse_multipart_upload(
             provided_mime_type = ((await field.text()) or "").strip() or None
         elif fname == "preview_id":
             provided_preview_id = ((await field.text()) or "").strip() or None
-
     if not file_present and not (provided_hash and provided_hash_exists):
         raise UploadError(
             400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'."

app/assets/database/models.py

@@ -76,6 +76,8 @@ class AssetReference(Base):
 
     # Cache state fields (from former AssetCacheState)
     file_path: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # In-root loader path derived from file_path at scan/ingest time.
+    loader_path: Mapped[str | None] = mapped_column(Text, nullable=True)
     mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
     needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
     is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)

app/assets/database/queries/asset_reference.py

@@ -650,6 +650,7 @@ def upsert_reference(
     name: str,
     mtime_ns: int,
     owner_id: str = "",
+    loader_path: str | None = None,
 ) -> tuple[bool, bool]:
     """Upsert a reference by file_path. Returns (created, updated).
 
@@ -659,6 +660,7 @@ def upsert_reference(
     vals = {
         "asset_id": asset_id,
         "file_path": file_path,
+        "loader_path": loader_path,
         "name": name,
         "owner_id": owner_id,
         "mtime_ns": int(mtime_ns),
@@ -686,13 +688,14 @@ def upsert_reference(
                 AssetReference.asset_id != asset_id,
                 AssetReference.mtime_ns.is_(None),
                 AssetReference.mtime_ns != int(mtime_ns),
+                AssetReference.loader_path.is_distinct_from(loader_path),
                 AssetReference.is_missing == True,  # noqa: E712
                 AssetReference.deleted_at.isnot(None),
             )
         )
         .values(
-            asset_id=asset_id, mtime_ns=int(mtime_ns), is_missing=False,
-            deleted_at=None, updated_at=now,
+            asset_id=asset_id, mtime_ns=int(mtime_ns), loader_path=loader_path,
+            is_missing=False, deleted_at=None, updated_at=now,
         )
     )
     res2 = session.execute(upd)

app/assets/database/queries/tags.py

@@ -265,6 +265,8 @@ def list_tags_with_usage(
     order: str = "count_desc",
     owner_id: str = "",
 ) -> tuple[list[tuple[str, str, int]], int]:
+    prefix_filter = prefix.strip() if prefix else ""
+
     counts_sq = (
         select(
             AssetReferenceTag.tag_name.label("tag_name"),
@@ -293,9 +295,8 @@ def list_tags_with_usage(
         .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
     )
 
-    if prefix:
-        escaped, esc = escape_sql_like_string(prefix.strip().lower())
-        q = q.where(Tag.name.like(escaped + "%", escape=esc))
+    if prefix_filter:
+        q = q.where(func.substr(Tag.name, 1, len(prefix_filter)) == prefix_filter)
 
     if not include_zero:
         q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
@@ -306,9 +307,8 @@ def list_tags_with_usage(
         q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
 
     total_q = select(func.count()).select_from(Tag)
-    if prefix:
-        escaped, esc = escape_sql_like_string(prefix.strip().lower())
-        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
+    if prefix_filter:
+        total_q = total_q.where(func.substr(Tag.name, 1, len(prefix_filter)) == prefix_filter)
     if not include_zero:
         visible_tags_sq = (
             select(AssetReferenceTag.tag_name)

app/assets/helpers.py

@@ -41,10 +41,10 @@ def get_utc_now() -> datetime:
 def normalize_tags(tags: list[str] | None) -> list[str]:
     """
     Normalize a list of tags by:
-      - Stripping whitespace and converting to lowercase.
-      - Removing duplicates.
+      - Stripping whitespace.
+      - Removing exact duplicates while preserving order and case.
     """
-    return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or "").strip()))
+    return list(dict.fromkeys(t.strip() for t in (tags or []) if (t or "").strip()))
 
 
 def validate_blake3_hash(s: str) -> str:

app/assets/scanner.py

@@ -36,7 +36,7 @@ from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash
 from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.metadata_extract import extract_file_metadata
 from app.assets.services.path_utils import (
-    compute_relative_filename,
+    compute_loader_path,
     get_comfy_models_folders,
     get_name_and_tags_from_asset_path,
 )
@@ -63,7 +63,7 @@ RootType = Literal["models", "input", "output"]
 def get_prefixes_for_root(root: RootType) -> list[str]:
     if root == "models":
         bases: list[str] = []
-        for _bucket, paths in get_comfy_models_folders():
+        for _bucket, paths, _exts in get_comfy_models_folders():
             bases.extend(paths)
         return [os.path.abspath(p) for p in bases]
     if root == "input":
@@ -81,7 +81,7 @@ def get_all_known_prefixes() -> list[str]:
 
 def collect_models_files() -> list[str]:
     out: list[str] = []
-    for folder_name, bases in get_comfy_models_folders():
+    for folder_name, bases, _exts in get_comfy_models_folders():
         rel_files = folder_paths.get_filename_list(folder_name) or []
         for rel_path in rel_files:
             if not all(is_visible(part) for part in Path(rel_path).parts):
@@ -308,7 +308,7 @@ def build_asset_specs(
         if not stat_p.st_size:
             continue
         name, tags = get_name_and_tags_from_asset_path(abs_p)
-        rel_fname = compute_relative_filename(abs_p)
+        rel_fname = compute_loader_path(abs_p)
 
         # Extract metadata (tier 1: filesystem, tier 2: safetensors header)
         metadata = None
@@ -430,7 +430,7 @@ def enrich_asset(
         return new_level
 
     initial_mtime_ns = get_mtime_ns(stat_p)
-    rel_fname = compute_relative_filename(file_path)
+    rel_fname = compute_loader_path(file_path)
     mime_type: str | None = None
     metadata = None
 

app/assets/services/asset_management.py

@@ -38,7 +38,7 @@ from app.assets.database.queries import (
     update_reference_updated_at,
 )
 from app.assets.helpers import select_best_live_path
-from app.assets.services.path_utils import compute_relative_filename
+from app.assets.services.path_utils import compute_loader_path
 from app.assets.services.schemas import (
     AssetData,
     AssetDetailResult,
@@ -91,7 +91,7 @@ def update_asset_metadata(
             update_reference_name(session, reference_id=reference_id, name=name)
             touched = True
 
-        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
+        computed_filename = compute_loader_path(ref.file_path) if ref.file_path else None
 
         new_meta: dict | None = None
         if user_metadata is not None:

app/assets/services/bulk_ingest.py

@@ -56,6 +56,7 @@ class ReferenceRow(TypedDict):
     id: str
     asset_id: str
     file_path: str
+    loader_path: str | None
     mtime_ns: int
     owner_id: str
     name: str
@@ -134,6 +135,14 @@ def batch_insert_seed_assets(
 
     for spec in specs:
         absolute_path = os.path.abspath(spec["abs_path"])
+        existing_asset_id = path_to_asset_id.get(absolute_path)
+        if existing_asset_id is not None:
+            existing_tags = asset_id_to_ref_data[existing_asset_id]["tags"]
+            asset_id_to_ref_data[existing_asset_id]["tags"] = list(
+                dict.fromkeys([*existing_tags, *spec["tags"]])
+            )
+            continue
+
         asset_id = str(uuid.uuid4())
         reference_id = str(uuid.uuid4())
         absolute_path_list.append(absolute_path)
@@ -164,6 +173,8 @@ def batch_insert_seed_assets(
                 "id": reference_id,
                 "asset_id": asset_id,
                 "file_path": absolute_path,
+                # spec["fname"] is compute_loader_path(abs_path) from build_asset_specs.
+                "loader_path": spec["fname"],
                 "mtime_ns": spec["mtime_ns"],
                 "owner_id": owner_id,
                 "name": spec["info_name"],

app/assets/services/ingest.py

@@ -33,8 +33,9 @@ from app.assets.services.bulk_ingest import batch_insert_seed_assets
 from app.assets.services.file_utils import get_size_and_mtime_ns
 from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.path_utils import (
-    compute_relative_filename,
+    compute_loader_path,
     get_name_and_tags_from_asset_path,
+    get_path_derived_tags_from_path,
     resolve_destination_from_tags,
     validate_path_within_base,
 )
@@ -91,6 +92,7 @@ def _ingest_file_from_path(
             name=info_name or os.path.basename(locator),
             mtime_ns=mtime_ns,
             owner_id=owner_id,
+            loader_path=compute_loader_path(locator),
         )
 
         # Get the reference we just created/updated
@@ -101,17 +103,32 @@ def _ingest_file_from_path(
             if preview_id and ref.preview_id != preview_id:
                 ref.preview_id = preview_id
 
-            norm = normalize_tags(list(tags))
-            if norm:
+            try:
+                backend_tags = get_path_derived_tags_from_path(locator)
+            except ValueError:
+                backend_tags = []
+            caller_tags = normalize_tags(tags)
+            backend_tags = normalize_tags(backend_tags)
+            all_tags = normalize_tags([*caller_tags, *backend_tags])
+            if all_tags:
                 if require_existing_tags:
-                    validate_tags_exist(session, norm)
-                add_tags_to_reference(
-                    session,
-                    reference_id=reference_id,
-                    tags=norm,
-                    origin=tag_origin,
-                    create_if_missing=not require_existing_tags,
-                )
+                    validate_tags_exist(session, all_tags)
+                if backend_tags:
+                    add_tags_to_reference(
+                        session,
+                        reference_id=reference_id,
+                        tags=backend_tags,
+                        origin="automatic",
+                        create_if_missing=not require_existing_tags,
+                    )
+                if caller_tags:
+                    add_tags_to_reference(
+                        session,
+                        reference_id=reference_id,
+                        tags=caller_tags,
+                        origin=tag_origin,
+                        create_if_missing=not require_existing_tags,
+                    )
 
             _update_metadata_with_filename(
                 session,
@@ -228,7 +245,7 @@ def ingest_existing_file(
             "mtime_ns": mtime_ns,
             "info_name": name,
             "tags": tags,
-            "fname": os.path.basename(abs_path),
+            "fname": compute_loader_path(abs_path),
             "metadata": None,
             "hash": None,
             "mime_type": mime_type,
@@ -288,7 +305,7 @@ def _register_existing_asset(
             return result
 
         new_meta = dict(user_metadata)
-        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
+        computed_filename = compute_loader_path(ref.file_path) if ref.file_path else None
         if computed_filename:
             new_meta["filename"] = computed_filename
 
@@ -335,7 +352,7 @@ def _update_metadata_with_filename(
     current_metadata: dict | None,
     user_metadata: dict[str, Any],
 ) -> None:
-    computed_filename = compute_relative_filename(file_path) if file_path else None
+    computed_filename = compute_loader_path(file_path) if file_path else None
 
     current_meta = current_metadata or {}
     new_meta = dict(current_meta)
@@ -474,6 +491,10 @@ def upload_from_temp_path(
         existing = get_asset_by_hash(session, asset_hash=asset_hash)
 
     if existing is not None:
+        # Once content is already known, duplicate byte uploads are treated as
+        # reference-only creation. Request tags are labels only here: do not
+        # require upload destination tags, do not move bytes, and do not
+        # synthesize path-derived classification or uploaded provenance.
         with contextlib.suppress(Exception):
             if temp_path and os.path.exists(temp_path):
                 os.remove(temp_path)
@@ -535,7 +556,7 @@ def upload_from_temp_path(
         owner_id=owner_id,
         preview_id=preview_id,
         user_metadata=user_metadata or {},
-        tags=tags,
+        tags=[*(tags or []), "uploaded"],
         tag_origin="manual",
         require_existing_tags=False,
     )
@@ -569,15 +590,19 @@ def register_file_in_place(
 ) -> UploadResult:
     """Register an already-saved file in the asset database without moving it.
 
-    Tags are derived from the filesystem path (root category + subfolder names),
-    merged with any caller-provided tags, matching the behavior of the scanner.
+    This helper is used by upload paths that have already written bytes before
+    registering the file, so it records the same ``uploaded`` tag as the
+    multipart byte-upload path.
+
+    Tags are derived from trusted filesystem classification and merged with any
+    caller-provided tags, matching the behavior of the scanner.
     If the path is not under a known root, only the caller-provided tags are used.
     """
     try:
         _, path_tags = get_name_and_tags_from_asset_path(abs_path)
     except ValueError:
         path_tags = []
-    merged_tags = normalize_tags([*path_tags, *tags])
+    merged_tags = normalize_tags([*path_tags, *tags, "uploaded"])
 
     try:
         digest, _ = hashing.compute_blake3_hash(abs_path)

app/assets/services/path_utils.py

@@ -3,59 +3,66 @@ from pathlib import Path
 from typing import Literal
 
 import folder_paths
-from app.assets.helpers import normalize_tags
 
 
-_NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
+_NON_MODEL_FOLDER_NAMES = frozenset({"configs", "custom_nodes"})
+_KNOWN_SUBFOLDER_TAGS = frozenset({"3d", "pasted", "painter", "threed", "webcam"})
 
 
-def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
-    """Build list of (folder_name, base_paths[]) for all model locations.
+def get_comfy_models_folders() -> list[tuple[str, list[str], set[str]]]:
+    """Build list of (folder_name, base_paths[], extensions) for all model locations.
 
     Includes every category registered in folder_names_and_paths,
     regardless of whether its paths are under the main models_dir,
-    but excludes non-model entries like custom_nodes.
+    but excludes non-model entries like configs and custom_nodes.
+
+    An empty extensions set means the category accepts any extension,
+    matching folder_paths.filter_files_extensions semantics.
     """
-    targets: list[tuple[str, list[str]]] = []
+    targets: list[tuple[str, list[str], set[str]]] = []
     for name, values in folder_paths.folder_names_and_paths.items():
         if name in _NON_MODEL_FOLDER_NAMES:
             continue
-        paths, _exts = values[0], values[1]
+        paths, exts = values[0], values[1]
         if paths:
-            targets.append((name, paths))
+            targets.append((name, paths, set(exts)))
     return targets
 
 
 def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
-    if not tags:
-        raise ValueError("tags must not be empty")
-    root = tags[0].lower()
+    """Validates and maps upload routing tags -> (base_dir, subdirs_for_fs).
+
+    The request tags are only used to choose the write destination. Extra tags
+    remain labels; they do not become path components or trusted classification.
+    """
+    destination_roles = [t for t in tags if t in {"input", "models", "output"}]
+    if len(destination_roles) != 1:
+        raise ValueError("uploads require exactly one destination role: input, models, or output")
+
+    root = destination_roles[0]
     if root == "models":
-        if len(tags) < 2:
-            raise ValueError("at least two tags required for model asset")
+        model_type_tags = [t for t in tags if t.startswith("model_type:")]
+        if len(model_type_tags) != 1:
+            raise ValueError("models uploads require exactly one model_type:<folder_name> tag")
+        folder_name = model_type_tags[0].split(":", 1)[1]
+        if not folder_name:
+            raise ValueError("models uploads require exactly one model_type:<folder_name> tag")
+        model_folder_paths = {
+            name: paths for name, paths, _exts in get_comfy_models_folders()
+        }
         try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
+            bases = model_folder_paths[folder_name]
         except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
+            raise ValueError(f"unknown model category '{folder_name}'")
         if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
+            raise ValueError(f"no base path configured for category '{folder_name}'")
         base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
     elif root == "input":
         base_dir = os.path.abspath(folder_paths.get_input_directory())
-        raw_subdirs = tags[1:]
-    elif root == "output":
-        base_dir = os.path.abspath(folder_paths.get_output_directory())
-        raw_subdirs = tags[1:]
     else:
-        raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'")
-    _sep_chars = frozenset(("/", "\\", os.sep))
-    for i in raw_subdirs:
-        if i in (".", "..") or _sep_chars & set(i):
-            raise ValueError("invalid path component in tags")
+        base_dir = os.path.abspath(folder_paths.get_output_directory())
 
-    return base_dir, raw_subdirs if raw_subdirs else []
+    return base_dir, []
 
 
 def validate_path_within_base(candidate: str, base: str) -> None:
@@ -65,14 +72,79 @@ def validate_path_within_base(candidate: str, base: str) -> None:
         raise ValueError("destination escapes base directory")
 
 
-def compute_relative_filename(file_path: str) -> str | None:
+def _compute_relative_path(child: str, parent: str) -> str:
+    rel = os.path.relpath(os.path.abspath(child), os.path.abspath(parent))
+    if rel == ".":
+        return ""
+    return rel.replace(os.sep, "/")
+
+
+def _is_relative_to(child: str, parent: str) -> bool:
+    return Path(os.path.abspath(child)).is_relative_to(os.path.abspath(parent))
+
+
+def compute_asset_response_paths(file_path: str) -> tuple[str, str | None] | None:
+    """Return (logical_path, display_name) for a file path.
+
+    ``logical_path`` is the internal namespaced storage locator (e.g.
+    ``models/checkpoints/foo/bar.safetensors``); ``display_name`` is the
+    human-facing label below that namespace, served on Asset responses. These
+    are storage locators, not model-loader namespaces. Registered model-folder
+    membership is represented by backend tags such as
+    ``model_type:<folder_name>``; these paths only use known storage roots.
     """
-    Return the model's path relative to the last well-known folder (the model category),
-    using forward slashes, eg:
+    fp_abs = os.path.abspath(file_path)
+    candidates: list[tuple[int, int, str, str]] = []
+
+    for order, (namespace, base) in enumerate(
+        (
+            ("input", folder_paths.get_input_directory()),
+            ("output", folder_paths.get_output_directory()),
+            ("temp", folder_paths.get_temp_directory()),
+            ("models", getattr(folder_paths, "models_dir", "")),
+        )
+    ):
+        if not base:
+            continue
+        base_abs = os.path.abspath(base)
+        if _is_relative_to(fp_abs, base_abs):
+            candidates.append((len(base_abs), -order, namespace, base_abs))
+
+    if not candidates:
+        return None
+
+    _base_len, _order, namespace, base = max(candidates)
+    rel = _compute_relative_path(fp_abs, base)
+    public_path = f"{namespace}/{rel}" if rel else namespace
+    return public_path, rel or None
+
+
+def compute_display_name(file_path: str) -> str | None:
+    """Return the asset's `display_name`, or None for unknown paths."""
+    result = compute_asset_response_paths(file_path)
+    return result[1] if result else None
+
+
+def compute_logical_path(file_path: str) -> str | None:
+    """Return the internal namespaced storage locator, or None for unknown paths."""
+    result = compute_asset_response_paths(file_path)
+    return result[0] if result else None
+
+
+def compute_loader_path(file_path: str) -> str | None:
+    """
+    Return the asset's in-root loader path: the path relative to the last
+    well-known folder (the model category), using forward slashes, eg:
       /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
       /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
 
-    For non-model paths, returns None.
+    This is the value model loaders consume (the model category is dropped). It
+    is persisted as ``AssetReference.loader_path`` and served as the public
+    Asset response `loader_path` field. The human-facing `display_name` comes
+    from compute_asset_response_paths().
+
+    For input/output/temp paths the full path relative to that root is returned.
+    For paths outside any known root, returns None.
     """
     try:
         root_category, rel_path = get_asset_category_and_relative_path(file_path)
@@ -116,9 +188,10 @@ def get_asset_category_and_relative_path(
     def _compute_relative(child: str, parent: str) -> str:
         # Normalize relative path, stripping any leading ".." components
         # by anchoring to root (os.sep) then computing relpath back from it.
-        return os.path.relpath(
+        rel = os.path.relpath(
             os.path.join(os.sep, os.path.relpath(child, parent)), os.sep
         )
+        return "" if rel == "." else rel.replace(os.sep, "/")
 
     # 1) input
     input_base = os.path.abspath(folder_paths.get_input_directory())
@@ -136,8 +209,14 @@ def get_asset_category_and_relative_path(
         return "temp", _compute_relative(fp_abs, temp_base)
 
     # 4) models (check deepest matching base to avoid ambiguity)
+    ext = os.path.splitext(fp_abs)[1].lower()
     best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
-    for bucket, bases in get_comfy_models_folders():
+    for bucket, bases, extensions in get_comfy_models_folders():
+        # A bucket only lists files within its extension set (empty set
+        # accepts any extension), so a bucket that cannot load the file
+        # must not contribute a loader path.
+        if extensions and ext not in extensions:
+            continue
         for b in bases:
             base_abs = os.path.abspath(b)
             if not _check_is_within(fp_abs, base_abs):
@@ -149,25 +228,111 @@ def get_asset_category_and_relative_path(
     if best is not None:
         _, bucket, rel_inside = best
         combined = os.path.join(bucket, rel_inside)
-        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
+        normalized = os.path.relpath(os.path.join(os.sep, combined), os.sep)
+        return "models", normalized.replace(os.sep, "/")
 
     raise ValueError(
         f"Path is not within input, output, temp, or configured model bases: {file_path}"
     )
 
 
+def get_backend_system_tags_from_path(path: str) -> list[str]:
+    """Return trusted backend tags derived from current filesystem facts.
+
+    The returned tags are only the backend-generated system tags: ``models``,
+    ``model_type:<folder_name>``, ``input``, ``output``, and ``temp``. Model
+    type tags are based on registered folder names, not path components.
+
+    A ``model_type:<folder_name>`` tag is only emitted when the file's
+    extension is accepted by that folder's registered extension set, so
+    categories sharing a base directory tag only the files they can
+    actually load. Files under a model base whose extension matches no
+    category still get the ``models`` tag.
+    """
+    fp_abs = os.path.abspath(path)
+    fp_path = Path(fp_abs)
+    tags: list[str] = []
+
+    def _add(tag: str) -> None:
+        if tag not in tags:
+            tags.append(tag)
+
+    for role, base in (
+        ("input", folder_paths.get_input_directory()),
+        ("output", folder_paths.get_output_directory()),
+        ("temp", folder_paths.get_temp_directory()),
+    ):
+        if fp_path.is_relative_to(os.path.abspath(base)):
+            _add(role)
+
+    ext = os.path.splitext(fp_abs)[1].lower()
+    model_types: list[str] = []
+    under_models_base = False
+    for folder_name, bases, extensions in get_comfy_models_folders():
+        for base in bases:
+            if fp_path.is_relative_to(os.path.abspath(base)):
+                under_models_base = True
+                # Empty set accepts any extension, matching
+                # folder_paths.filter_files_extensions semantics.
+                if not extensions or ext in extensions:
+                    model_types.append(folder_name)
+                break
+
+    if under_models_base:
+        _add("models")
+    for folder_name in model_types:
+        _add(f"model_type:{folder_name}")
+
+    if not tags:
+        raise ValueError(
+            f"Path is not within input, output, temp, or configured model bases: {path}"
+        )
+    return tags
+
+
+def get_known_subfolder_tags(subfolder: str | None) -> list[str]:
+    """Return tags for known UI/input subfolder names."""
+    if subfolder in _KNOWN_SUBFOLDER_TAGS:
+        return [subfolder]
+    return []
+
+
+def get_known_input_subfolder_tags_from_path(path: str) -> list[str]:
+    """Return known input-layout tags for files in canonical input subfolders.
+
+    These are compatibility tags for current UI-origin input directories such as
+    ``pasted`` and ``webcam``. They are intentionally narrow: only files directly
+    inside a known top-level input directory receive the matching tag.
+    """
+    fp_abs = os.path.abspath(path)
+    input_base = os.path.abspath(folder_paths.get_input_directory())
+    if not Path(fp_abs).is_relative_to(input_base):
+        return []
+
+    rel = os.path.relpath(fp_abs, input_base)
+    parts = Path(rel).parts
+    if len(parts) == 2:
+        return get_known_subfolder_tags(parts[0])
+    return []
+
+
+def get_path_derived_tags_from_path(path: str) -> list[str]:
+    """Return all backend-derived tags for an asset path."""
+    tags = get_backend_system_tags_from_path(path)
+    for tag in get_known_input_subfolder_tags_from_path(path):
+        if tag not in tags:
+            tags.append(tag)
+    return tags
+
+
 def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
     """Return (name, tags) derived from a filesystem path.
 
     - name: base filename with extension
-    - tags: [root_category] + parent folder names in order
+    - tags: backend-derived tags from root/model classification and known input
+      subfolder layout conventions
 
     Raises:
         ValueError: path does not belong to any known root.
     """
-    root_category, some_path = get_asset_category_and_relative_path(file_path)
-    p = Path(some_path)
-    parent_parts = [
-        part for part in p.parent.parts if part not in (".", "..", p.anchor)
-    ]
-    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
+    return Path(file_path).name, get_path_derived_tags_from_path(file_path)

app/assets/services/schemas.py

@@ -25,6 +25,7 @@ class ReferenceData:
     preview_id: str | None
     created_at: datetime
     updated_at: datetime
+    loader_path: str | None = None
     system_metadata: dict[str, Any] | None = None
     job_id: str | None = None
     last_access_time: datetime | None = None
@@ -93,6 +94,7 @@ def extract_reference_data(ref: AssetReference) -> ReferenceData:
         id=ref.id,
         name=ref.name,
         file_path=ref.file_path,
+        loader_path=ref.loader_path,
         user_metadata=ref.user_metadata,
         preview_id=ref.preview_id,
         system_metadata=ref.system_metadata,

app/model_manager.py

@@ -35,7 +35,11 @@ class ModelFileManager:
             for folder in model_types:
                 if folder in folder_black_list:
                     continue
-                output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)})
+                output_folders.append({
+                    "name": folder,
+                    "folders": folder_paths.get_folder_paths(folder),
+                    "extensions": sorted(folder_paths.folder_names_and_paths[folder][1]),
+                })
             return web.json_response(output_folders)
 
         # NOTE: This is an experiment to replace `/models/{folder}`
@@ -50,21 +54,45 @@ class ModelFileManager:
         @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}")
         async def get_model_preview(request):
             folder_name = request.match_info.get("folder", None)
-            path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
             if folder_name not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
+            # The "{filename:.*}" capture also matches the empty string, which
+            # would resolve to the folder itself; reject it explicitly.
+            if not filename:
+                return web.Response(status=400)
+
+            try:
+                path_index = int(request.match_info.get("path_index", None))
+            except (TypeError, ValueError):
+                return web.Response(status=400)
+
             folders = folder_paths.folder_names_and_paths[folder_name]
+            if path_index < 0 or path_index >= len(folders[0]):
+                return web.Response(status=404)
             folder = folders[0][path_index]
-            full_filename = os.path.join(folder, filename)
+            full_filename = os.path.normpath(os.path.join(folder, filename))
+
+            # Prevent path traversal: the requested file must stay within the
+            # configured model folder. `filename` is an unrestricted ".*" capture,
+            # so values like "../../../../etc/passwd" would otherwise escape it.
+            if not folder_paths.is_within_directory(folder, full_filename):
+                return web.Response(status=403)
 
             previews = self.get_model_previews(full_filename)
             default_preview = previews[0] if len(previews) > 0 else None
             if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
                 return web.Response(status=404)
 
+            # The preview is selected by a glob inside get_model_previews, so a
+            # companion file (e.g. "model.preview.png") could itself be a symlink
+            # resolving outside the model folder. Re-validate the file actually
+            # opened: is_within_directory realpaths it, catching symlink escape.
+            if isinstance(default_preview, str) and not folder_paths.is_within_directory(folder, default_preview):
+                return web.Response(status=403)
+
             try:
                 with Image.open(default_preview) as img:
                     img_bytes = BytesIO()

app/user_manager.py

@@ -6,6 +6,7 @@ import glob
 import shutil
 import logging
 import tempfile
+import mimetypes
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
@@ -336,7 +337,20 @@ class UserManager():
             if not isinstance(path, str):
                 return path
 
-            return web.FileResponse(path)
+            # User data files are arbitrary user-supplied content and are never
+            # meant to render inline. Disable MIME sniffing and force a download
+            # so uploaded markup/scripts can't execute in the app origin (stored
+            # XSS). Content-Disposition: attachment is the load-bearing guard;
+            # the content-type override and nosniff are defence in depth.
+            content_type = mimetypes.guess_type(path)[0] or 'application/octet-stream'
+            if folder_paths.is_dangerous_content_type(content_type):
+                content_type = 'application/octet-stream'
+
+            return web.FileResponse(path, headers={
+                "Content-Type": content_type,
+                "X-Content-Type-Options": "nosniff",
+                "Content-Disposition": "attachment",
+            })
 
         @routes.post("/userdata/{file}")
         async def post_userdata(request):

blueprints/Image Depth Estimation (Depth Anything 3).json

@@ -0,0 +1,569 @@
+{
+  "revision": 0,
+  "last_node_id": 89,
+  "last_link_id": 0,
+  "nodes": [
+    {
+      "id": 89,
+      "type": "85e595bd-af9e-40ee-85c5-b98bb15da47a",
+      "pos": [
+        320,
+        520
+      ],
+      "size": [
+        400,
+        360
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
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blueprints/Text to Image (Anima).json

@@ -1077,9 +1077,12 @@
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+        "category": "Image generation and editing/Text to image",
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       }
     ]
   },
-  "extra": {}
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+  }
 }

blueprints/Video Depth Estimation (Depth Anything 3).json

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+              1544,
+              184
+            ]
+          },
+          {
+            "id": "4c0e5484-d193-49c7-b107-92619628880a",
+            "name": "fps",
+            "type": "FLOAT",
+            "linkIds": [
+              113
+            ],
+            "pos": [
+              1544,
+              204
+            ]
+          }
+        ],
+        "widgets": [],
+        "nodes": [
+          {
+            "id": 92,
+            "type": "DA3Render",
+            "pos": [
+              740,
+              230
+            ],
+            "size": [
+              380,
+              130
+            ],
+            "flags": {},
+            "order": 0,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "da3_geometry",
+                "name": "da3_geometry",
+                "type": "DA3_GEOMETRY",
+                "link": 12
+              },
+              {
+                "localized_name": "output",
+                "name": "output",
+                "type": "COMFY_DYNAMICCOMBO_V3",
+                "widget": {
+                  "name": "output"
+                },
+                "link": 126
+              },
+              {
+                "localized_name": "output.normalization",
+                "name": "output.normalization",
+                "type": "COMBO",
+                "widget": {
+                  "name": "output.normalization"
+                },
+                "link": 127
+              },
+              {
+                "localized_name": "output.apply_sky_clip",
+                "name": "output.apply_sky_clip",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "output.apply_sky_clip"
+                },
+                "link": 128
+              },
+              {
+                "name": "geometry",
+                "type": "DA3_GEOMETRY",
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "IMAGE",
+                "name": "IMAGE",
+                "type": "IMAGE",
+                "slot_index": 0,
+                "links": [
+                  7
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "DA3Render",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.0"
+            },
+            "widgets_values": [
+              "depth",
+              "v2_style",
+              false
+            ]
+          },
+          {
+            "id": 93,
+            "type": "DA3Inference",
+            "pos": [
+              740,
+              -30
+            ],
+            "size": [
+              390,
+              130
+            ],
+            "flags": {},
+            "order": 1,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "da3_model",
+                "name": "da3_model",
+                "type": "DA3_MODEL",
+                "link": 107
+              },
+              {
+                "localized_name": "image",
+                "name": "image",
+                "type": "IMAGE",
+                "link": 111
+              },
+              {
+                "localized_name": "resolution",
+                "name": "resolution",
+                "type": "INT",
+                "widget": {
+                  "name": "resolution"
+                },
+                "link": 124
+              },
+              {
+                "localized_name": "resize_method",
+                "name": "resize_method",
+                "type": "COMBO",
+                "widget": {
+                  "name": "resize_method"
+                },
+                "link": 125
+              },
+              {
+                "localized_name": "mode",
+                "name": "mode",
+                "type": "COMFY_DYNAMICCOMBO_V3",
+                "widget": {
+                  "name": "mode"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "da3_geometry",
+                "name": "da3_geometry",
+                "type": "DA3_GEOMETRY",
+                "slot_index": 0,
+                "links": [
+                  12
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "DA3Inference",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.0"
+            },
+            "widgets_values": [
+              504,
+              "lower_bound_resize",
+              "mono"
+            ]
+          },
+          {
+            "id": 94,
+            "type": "LoadDA3Model",
+            "pos": [
+              50,
+              410
+            ],
+            "size": [
+              400,
+              140
+            ],
+            "flags": {},
+            "order": 2,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "model_name",
+                "name": "model_name",
+                "type": "COMBO",
+                "widget": {
+                  "name": "model_name"
+                },
+                "link": 129
+              },
+              {
+                "localized_name": "weight_dtype",
+                "name": "weight_dtype",
+                "type": "COMBO",
+                "widget": {
+                  "name": "weight_dtype"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "DA3_MODEL",
+                "name": "DA3_MODEL",
+                "type": "DA3_MODEL",
+                "links": [
+                  107
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "LoadDA3Model",
+              "cnr_id": "comfy-core",
+              "ver": "0.24.0",
+              "models": [
+                {
+                  "name": "depth_anything_3_mono_large.safetensors",
+                  "url": "https://huggingface.co/Comfy-Org/Depth-Anything-3/resolve/main/geometry_estimation/depth_anything_3_mono_large.safetensors",
+                  "directory": "geometry_estimation"
+                }
+              ]
+            },
+            "widgets_values": [
+              "depth_anything_3_mono_large.safetensors",
+              "default"
+            ]
+          },
+          {
+            "id": 95,
+            "type": "GetVideoComponents",
+            "pos": [
+              70,
+              -140
+            ],
+            "size": [
+              260,
+              120
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "video",
+                "name": "video",
+                "type": "VIDEO",
+                "link": 120
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "links": [
+                  111
+                ]
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "type": "AUDIO",
+                "links": [
+                  112
+                ]
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "links": [
+                  113
+                ]
+              },
+              {
+                "localized_name": "bit_depth",
+                "name": "bit_depth",
+                "type": "INT",
+                "links": null
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "GetVideoComponents",
+              "cnr_id": "comfy-core",
+              "ver": "0.24.0"
+            }
+          },
+          {
+            "id": 96,
+            "type": "Video Slice",
+            "pos": [
+              70,
+              -360
+            ],
+            "size": [
+              270,
+              170
+            ],
+            "flags": {},
+            "order": 4,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "video",
+                "name": "video",
+                "type": "VIDEO",
+                "link": 119
+              },
+              {
+                "localized_name": "start_time",
+                "name": "start_time",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "start_time"
+                },
+                "link": 121
+              },
+              {
+                "localized_name": "duration",
+                "name": "duration",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "duration"
+                },
+                "link": 122
+              },
+              {
+                "localized_name": "strict_duration",
+                "name": "strict_duration",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "strict_duration"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "VIDEO",
+                "name": "VIDEO",
+                "type": "VIDEO",
+                "links": [
+                  120
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "Video Slice",
+              "cnr_id": "comfy-core",
+              "ver": "0.24.0"
+            },
+            "widgets_values": [
+              0,
+              5,
+              false
+            ]
+          }
+        ],
+        "groups": [],
+        "links": [
+          {
+            "id": 12,
+            "origin_id": 93,
+            "origin_slot": 0,
+            "target_id": 92,
+            "target_slot": 0,
+            "type": "DA3_GEOMETRY"
+          },
+          {
+            "id": 7,
+            "origin_id": 92,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 107,
+            "origin_id": 94,
+            "origin_slot": 0,
+            "target_id": 93,
+            "target_slot": 0,
+            "type": "DA3_MODEL"
+          },
+          {
+            "id": 111,
+            "origin_id": 95,
+            "origin_slot": 0,
+            "target_id": 93,
+            "target_slot": 1,
+            "type": "IMAGE"
+          },
+          {
+            "id": 112,
+            "origin_id": 95,
+            "origin_slot": 1,
+            "target_id": -20,
+            "target_slot": 1,
+            "type": "AUDIO"
+          },
+          {
+            "id": 113,
+            "origin_id": 95,
+            "origin_slot": 2,
+            "target_id": -20,
+            "target_slot": 2,
+            "type": "FLOAT"
+          },
+          {
+            "id": 119,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 96,
+            "target_slot": 0,
+            "type": "VIDEO"
+          },
+          {
+            "id": 120,
+            "origin_id": 96,
+            "origin_slot": 0,
+            "target_id": 95,
+            "target_slot": 0,
+            "type": "VIDEO"
+          },
+          {
+            "id": 121,
+            "origin_id": -10,
+            "origin_slot": 1,
+            "target_id": 96,
+            "target_slot": 1,
+            "type": "FLOAT"
+          },
+          {
+            "id": 122,
+            "origin_id": -10,
+            "origin_slot": 2,
+            "target_id": 96,
+            "target_slot": 2,
+            "type": "FLOAT"
+          },
+          {
+            "id": 124,
+            "origin_id": -10,
+            "origin_slot": 3,
+            "target_id": 93,
+            "target_slot": 2,
+            "type": "INT"
+          },
+          {
+            "id": 125,
+            "origin_id": -10,
+            "origin_slot": 4,
+            "target_id": 93,
+            "target_slot": 3,
+            "type": "COMBO"
+          },
+          {
+            "id": 126,
+            "origin_id": -10,
+            "origin_slot": 5,
+            "target_id": 92,
+            "target_slot": 1,
+            "type": "COMFY_DYNAMICCOMBO_V3"
+          },
+          {
+            "id": 127,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 92,
+            "target_slot": 2,
+            "type": "COMBO"
+          },
+          {
+            "id": 128,
+            "origin_id": -10,
+            "origin_slot": 7,
+            "target_id": 92,
+            "target_slot": 3,
+            "type": "BOOLEAN"
+          },
+          {
+            "id": 129,
+            "origin_id": -10,
+            "origin_slot": 8,
+            "target_id": 94,
+            "target_slot": 0,
+            "type": "COMBO"
+          }
+        ],
+        "extra": {},
+        "category": "Conditioning & Preprocessors/Depth",
+        "description": "This subgraph processes a video input through Depth Anything 3 to produce temporally consistent depth maps for each frame, outputting a depth video. It is ideal for video content requiring spatial geometry estimation, such as 3D reconstruction, SLAM, or novel view synthesis from moving cameras. The model uses a plain transformer backbone trained with a depth-ray representation, supporting any number of views without requiring known camera poses."
+      }
+    ]
+  },
+  "extra": {
+    "BlueprintDescription": "This subgraph processes a video input through Depth Anything 3 to produce temporally consistent depth maps for each frame, outputting a depth video. It is ideal for video content requiring spatial geometry estimation, such as 3D reconstruction, SLAM, or novel view synthesis from moving cameras. The model uses a plain transformer backbone trained with a depth-ray representation, supporting any number of views without requiring known camera poses."
+  }
+}

comfy/cli_args.py

@@ -92,6 +92,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
+parser.add_argument("--disable-triton-backend", action="store_true", help="Force-disable the comfy-kitchen Triton backend, overriding the automatic ROCm/AMD default and --enable-triton-backend.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -115,6 +116,7 @@ cache_group.add_argument("--cache-ram", nargs='*', type=float, default=[], metav
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
+cache_group.add_argument("--high-ram", action="store_true", help="Can improve performance slightly on high RAM or on systems where pagefile use is preferred over model loading.")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
@@ -144,6 +146,7 @@ vram_group.add_argument("--novram", action="store_true", help="When lowvram isn'
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
 parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
+parser.add_argument("--vram-headroom", type=float, default=0, help="Set the amount of vram in GB for DynamicVRAM to maintain as extra headroom above default. ComfyUI will try and keep this much VRAM completely free and unused, even counting VRAM from other apps.")
 
 parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
 parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
@@ -223,6 +226,7 @@ parser.add_argument(
 )
 
 parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
+parser.add_argument("--models-directory", type=is_valid_directory, default=None, help="Set the ComfyUI models directory. Overrides the models folder in --base-directory.")
 
 parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
 
@@ -238,6 +242,7 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
+parser.add_argument("--enable-asset-hashing", action="store_true", help="Compute blake3 content hashes when scanning assets. Hashing enables future asset-portability features (deduplication, cross-machine model resolution) but adds startup cost and per-output cost on large models directories. Off by default; enable to opt in.")
 parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
 parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 
@@ -249,6 +254,9 @@ else:
 if args.cache_ram is not None and len(args.cache_ram) > 2:
     parser.error("--cache-ram accepts at most two values: active GB and inactive GB")
 
+if args.high_ram:
+    args.cache_classic = True
+
 if args.windows_standalone_build:
     args.auto_launch = True
 

comfy/comfy_api_env.py

@@ -0,0 +1,46 @@
+"""Runtime config the frontend reads from /features to follow --comfy-api-base.
+
+For a non-prod comfy.org backend (staging or an ephemeral preview env), "/features" exposes the api and
+platform base so the frontend talks to it without a rebuild, plus the Firebase environment it should use.
+Prod bases are left alone and keep their build-time defaults.
+"""
+
+from typing import Any
+from urllib.parse import urlparse
+
+from comfy.cli_args import args
+
+_STAGING_API_HOST = "stagingapi.comfy.org"
+_TESTENV_HOST_SUFFIX = ".testenvs.comfy.org"
+_STAGING_PLATFORM_BASE_URL = "https://stagingplatform.comfy.org"
+
+
+def _is_staging_tier(host: str) -> bool:
+    return host == _STAGING_API_HOST or host.endswith(_TESTENV_HOST_SUFFIX)
+
+
+def normalize_comfy_api_base(url: str) -> str:
+    """Rewrite a testenv's friendly main host to its comfy-api '-registry' sibling."""
+    parsed = urlparse(url)
+    host = parsed.hostname or ""
+    if not host.endswith(_TESTENV_HOST_SUFFIX):
+        return url
+    label = host[: -len(_TESTENV_HOST_SUFFIX)]
+    if label.endswith("-registry"):
+        return url
+    return f"{parsed.scheme or 'https'}://{label}-registry{_TESTENV_HOST_SUFFIX}"
+
+
+def environment_overrides_for_base(base_url: str) -> dict[str, Any] | None:
+    """The /features overrides for a staging-tier base, or None for prod."""
+    if not _is_staging_tier(urlparse(base_url).hostname or ""):
+        return None
+    return {
+        "comfy_api_base_url": normalize_comfy_api_base(base_url).rstrip("/"),
+        "comfy_platform_base_url": _STAGING_PLATFORM_BASE_URL,
+        "firebase_env": "dev",
+    }
+
+
+def get_environment_overrides() -> dict[str, Any] | None:
+    return environment_overrides_for_base(getattr(args, "comfy_api_base", "") or "")

comfy/context_windows.py

@@ -8,6 +8,8 @@ from abc import ABC, abstractmethod
 import logging
 import comfy.model_management
 import comfy.patcher_extension
+import comfy.utils
+import comfy.conds
 if TYPE_CHECKING:
     from comfy.model_base import BaseModel
     from comfy.model_patcher import ModelPatcher
@@ -51,12 +53,18 @@ class ContextHandlerABC(ABC):
 
 
 class IndexListContextWindow(ContextWindowABC):
-    def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0):
+    def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0, modality_windows: dict=None, context_overlap: int=0):
         self.index_list = index_list
         self.context_length = len(index_list)
+        self.context_overlap = context_overlap
         self.dim = dim
         self.total_frames = total_frames
         self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)
+        self.modality_windows = modality_windows  # dict of {mod_idx: IndexListContextWindow}
+        self.guide_frames_indices: list[int] = []
+        self.guide_overlap_info: list[tuple[int, int]] = []
+        self.guide_kf_local_positions: list[int] = []
+        self.guide_downscale_factors: list[int] = []
 
     def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:
         if dim is None:
@@ -85,6 +93,11 @@ class IndexListContextWindow(ContextWindowABC):
         region_idx = int(self.center_ratio * num_regions)
         return min(max(region_idx, 0), num_regions - 1)
 
+    def get_window_for_modality(self, modality_idx: int) -> 'IndexListContextWindow':
+        if modality_idx == 0:
+            return self
+        return self.modality_windows[modality_idx]
+
 
 class IndexListCallbacks:
     EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows"
@@ -148,6 +161,172 @@ def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, d
     return cond_value._copy_with(sliced)
 
 
+def compute_guide_overlap(guide_entries: list[dict], keyframe_idxs: torch.Tensor, temporal_downscale_ratio: int, window_index_list: list[int]):
+    """Compute which concatenated guide frames overlap with a context window.
+
+    Each guide's latent-space start is derived from its first token's pixel-t-start
+    in keyframe_idxs (shape (B, [t,h,w], num_tokens, [start, end])), divided by the
+    model's temporal_downscale_ratio.
+
+    Args:
+        guide_entries: list of guide_attention_entry dicts
+        keyframe_idxs: per-token pixel coords cond tensor for the modality
+        temporal_downscale_ratio: model's pixel-to-latent temporal compression ratio
+        window_index_list: the window's frame indices into the video portion
+
+    Returns:
+        suffix_indices: indices into the guide_frames tensor for frame selection
+        overlap_info: list of (entry_idx, overlap_count) for guide_attention_entries adjustment
+        kf_local_positions: window-local frame positions for keyframe_idxs regeneration
+        total_overlap: total number of overlapping guide frames
+    """
+    window_set = set(window_index_list)
+    window_list = list(window_index_list)
+    suffix_indices = []
+    overlap_info = []
+    kf_local_positions = []
+    suffix_base = 0
+    token_offset = 0
+
+    for entry_idx, entry in enumerate(guide_entries):
+        first_t_pixel = int(keyframe_idxs[0, 0, token_offset, 0].item())
+        latent_start = (first_t_pixel + temporal_downscale_ratio - 1) // temporal_downscale_ratio
+        guide_len = entry["latent_shape"][0]
+        entry_overlap = 0
+
+        for local_offset in range(guide_len):
+            video_pos = latent_start + local_offset
+            if video_pos in window_set:
+                suffix_indices.append(suffix_base + local_offset)
+                kf_local_positions.append(window_list.index(video_pos))
+                entry_overlap += 1
+
+        if entry_overlap > 0:
+            overlap_info.append((entry_idx, entry_overlap))
+        suffix_base += guide_len
+        token_offset += entry["pre_filter_count"]
+
+    return suffix_indices, overlap_info, kf_local_positions, len(suffix_indices)
+
+
+@dataclass
+class WindowingState:
+    """Per-modality context windowing state for each step,
+    built using IndexListContextHandler._build_window_state().
+    For non-multimodal models the lists are length 1
+    """
+    latents: list[torch.Tensor]                  # per-modality working latents (guide frames stripped)
+    guide_latents: list[torch.Tensor | None]     # per-modality guide frames stripped from latents
+    guide_entries: list[list[dict] | None]       # per-modality guide_attention_entry metadata
+    keyframe_idxs: list[torch.Tensor | None]     # per-modality keyframe_idxs tensor for guide latent_start derivation
+    latent_shapes: list | None                   # original packed shapes for unpack/pack (None if not multimodal)
+    dim: int = 0                                 # primary modality temporal dim for context windowing
+    is_multimodal: bool = False
+    temporal_downscale_ratio: int = 1            # model's pixel-to-latent temporal compression ratio
+
+    def prepare_window(self, window: IndexListContextWindow, model) -> IndexListContextWindow:
+        """Reformat window for multimodal contexts by deriving per-modality index lists.
+        Non-multimodal contexts return the input window unchanged.
+        """
+        if not self.is_multimodal:
+            return window
+
+        x = self.latents[0]
+        primary_total = self.latent_shapes[0][self.dim]
+        primary_overlap = window.context_overlap
+        map_shapes = self.latent_shapes
+        if x.size(self.dim) != primary_total:
+            map_shapes = list(self.latent_shapes)
+            video_shape = list(self.latent_shapes[0])
+            video_shape[self.dim] = x.size(self.dim)
+            map_shapes[0] = torch.Size(video_shape)
+        try:
+            per_modality_indices = model.map_context_window_to_modalities(
+                window.index_list, map_shapes, self.dim)
+        except AttributeError:
+            raise NotImplementedError(
+                f"{type(model).__name__} must implement map_context_window_to_modalities for multimodal context windows.")
+        modality_windows = {}
+        for mod_idx in range(1, len(self.latents)):
+            modality_total_frames = self.latents[mod_idx].shape[self.dim]
+            ratio = modality_total_frames / primary_total if primary_total > 0 else 1
+            modality_overlap = max(round(primary_overlap * ratio), 0)
+            modality_windows[mod_idx] = IndexListContextWindow(
+                per_modality_indices[mod_idx], dim=self.dim,
+                total_frames=modality_total_frames,
+                context_overlap=modality_overlap)
+        return IndexListContextWindow(
+            window.index_list, dim=self.dim, total_frames=x.shape[self.dim],
+            modality_windows=modality_windows, context_overlap=primary_overlap)
+
+    def slice_for_window(self, window: IndexListContextWindow, retain_index_list: list[int], device=None) -> tuple[list[torch.Tensor], list[int]]:
+        """Slice latents for a context window, injecting guide frames where applicable.
+        For multimodal contexts, uses the modality-specific windows derived in prepare_window().
+        """
+        sliced = []
+        guide_frame_counts = []
+        for idx in range(len(self.latents)):
+            modality_window = window.get_window_for_modality(idx)
+            retain = retain_index_list if idx == 0 else []
+            s = modality_window.get_tensor(self.latents[idx], device, retain_index_list=retain)
+            if self.guide_entries[idx] is not None:
+                s, ng = self._inject_guide_frames(s, modality_window, modality_idx=idx)
+            else:
+                ng = 0
+            sliced.append(s)
+            guide_frame_counts.append(ng)
+        return sliced, guide_frame_counts
+
+    def strip_guide_frames(self, out_per_modality: list[list[torch.Tensor]], guide_frame_counts: list[int], window: IndexListContextWindow):
+        """Strip injected guide frames from per-cond, per-modality outputs in place."""
+        for idx in range(len(self.latents)):
+            if guide_frame_counts[idx] > 0:
+                window_len = len(window.get_window_for_modality(idx).index_list)
+                for ci in range(len(out_per_modality)):
+                    out_per_modality[ci][idx] = out_per_modality[ci][idx].narrow(self.dim, 0, window_len)
+
+    def _inject_guide_frames(self, latent_slice: torch.Tensor, window: IndexListContextWindow, modality_idx: int = 0) -> tuple[torch.Tensor, int]:
+        guide_entries = self.guide_entries[modality_idx]
+        guide_frames = self.guide_latents[modality_idx]
+        keyframe_idxs = self.keyframe_idxs[modality_idx]
+        suffix_idx, overlap_info, kf_local_pos, guide_frame_count = compute_guide_overlap(
+            guide_entries, keyframe_idxs, self.temporal_downscale_ratio, window.index_list)
+        # Shift keyframe positions to account for causal_window_fix anchor occupying sub-pos 0.
+        anchor_idx = getattr(window, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            kf_local_pos = [p + 1 for p in kf_local_pos]
+        window.guide_frames_indices = suffix_idx
+        window.guide_overlap_info = overlap_info
+        window.guide_kf_local_positions = kf_local_pos
+
+        # Derive per-overlap-entry latent_downscale_factor from guide entry latent_shape vs guide frame spatial dims.
+        # guide_frames has full (post-dilation) spatial dims; entry["latent_shape"] has pre-dilation dims.
+        guide_downscale_factors = []
+        if guide_frame_count > 0:
+            full_H = guide_frames.shape[3]
+            for entry_idx, _ in overlap_info:
+                entry_H = guide_entries[entry_idx]["latent_shape"][1]
+                guide_downscale_factors.append(full_H // entry_H)
+        window.guide_downscale_factors = guide_downscale_factors
+
+        if guide_frame_count > 0:
+            idx = tuple([slice(None)] * self.dim + [suffix_idx])
+            return torch.cat([latent_slice, guide_frames[idx]], dim=self.dim), guide_frame_count
+        return latent_slice, 0
+
+    def patch_latent_shapes(self, sub_conds, new_shapes):
+        if not self.is_multimodal:
+            return
+
+        for cond_list in sub_conds:
+            if cond_list is None:
+                continue
+            for cond_dict in cond_list:
+                model_conds = cond_dict.get('model_conds', {})
+                if 'latent_shapes' in model_conds:
+                    model_conds['latent_shapes'] = comfy.conds.CONDConstant(new_shapes)
+
+
 @dataclass
 class ContextSchedule:
     name: str
@@ -162,7 +341,7 @@ ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_co
 class IndexListContextHandler(ContextHandlerABC):
     def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
                  closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False,
-                 causal_window_fix: bool=True):
+                 latent_retain_index_list: list[int]=[], causal_window_fix: bool=True):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -174,17 +353,118 @@ class IndexListContextHandler(ContextHandlerABC):
         self.freenoise = freenoise
         self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
         self.split_conds_to_windows = split_conds_to_windows
+        self.latent_retain_index_list = [int(x.strip()) for x in latent_retain_index_list.split(",")] if latent_retain_index_list else []
         self.causal_window_fix = causal_window_fix
 
         self.callbacks = {}
 
+    @staticmethod
+    def _get_latent_shapes(conds):
+        for cond_list in conds:
+            if cond_list is None:
+                continue
+            for cond_dict in cond_list:
+                model_conds = cond_dict.get('model_conds', {})
+                if 'latent_shapes' in model_conds:
+                    return model_conds['latent_shapes'].cond
+        return None
+
+    @staticmethod
+    def _get_guide_entries(conds):
+        for cond_list in conds:
+            if cond_list is None:
+                continue
+            for cond_dict in cond_list:
+                model_conds = cond_dict.get('model_conds', {})
+                entries = model_conds.get('guide_attention_entries')
+                if entries is not None and hasattr(entries, 'cond') and entries.cond:
+                    return entries.cond
+        return None
+
+    @staticmethod
+    def _get_keyframe_idxs(conds):
+        for cond_list in conds:
+            if cond_list is None:
+                continue
+            for cond_dict in cond_list:
+                model_conds = cond_dict.get('model_conds', {})
+                kf = model_conds.get('keyframe_idxs')
+                if kf is not None and hasattr(kf, 'cond') and kf.cond is not None:
+                    return kf.cond
+        return None
+
+    def _apply_freenoise(self, noise: torch.Tensor, conds: list[list[dict]], seed: int) -> torch.Tensor:
+        """Apply FreeNoise shuffling, scaling context length/overlap per-modality by frame ratio.
+        If guide frames are present on the primary modality, only the video portion is shuffled.
+        """
+        guide_entries = self._get_guide_entries(conds)
+        guide_count = sum(e["latent_shape"][0] for e in guide_entries) if guide_entries else 0
+
+        latent_shapes = self._get_latent_shapes(conds)
+        if latent_shapes is not None and len(latent_shapes) > 1:
+            modalities = comfy.utils.unpack_latents(noise, latent_shapes)
+            primary_total = latent_shapes[0][self.dim]
+            primary_video_count = modalities[0].size(self.dim) - guide_count
+            apply_freenoise(modalities[0].narrow(self.dim, 0, primary_video_count), self.dim, self.context_length, self.context_overlap, seed)
+            for i in range(1, len(modalities)):
+                mod_total = latent_shapes[i][self.dim]
+                ratio = mod_total / primary_total if primary_total > 0 else 1
+                mod_ctx_len = max(round(self.context_length * ratio), 1)
+                mod_ctx_overlap = max(round(self.context_overlap * ratio), 0)
+                modalities[i] = apply_freenoise(modalities[i], self.dim, mod_ctx_len, mod_ctx_overlap, seed)
+            noise, _ = comfy.utils.pack_latents(modalities)
+            return noise
+        video_count = noise.size(self.dim) - guide_count
+        apply_freenoise(noise.narrow(self.dim, 0, video_count), self.dim, self.context_length, self.context_overlap, seed)
+        return noise
+
+    def _build_window_state(self, x_in: torch.Tensor, conds: list[list[dict]], model: BaseModel) -> WindowingState:
+        """Build windowing state for the current step, including unpacking latents and extracting guide frame info from conds."""
+        latent_shapes = self._get_latent_shapes(conds)
+        is_multimodal = latent_shapes is not None and len(latent_shapes) > 1
+        unpacked_latents = comfy.utils.unpack_latents(x_in, latent_shapes) if is_multimodal else [x_in]
+
+        unpacked_latents_list = list(unpacked_latents)
+        guide_latents_list = [None] * len(unpacked_latents)
+        guide_entries_list = [None] * len(unpacked_latents)
+        keyframe_idxs_list = [None] * len(unpacked_latents)
+
+        extracted_guide_entries = self._get_guide_entries(conds)
+        extracted_keyframe_idxs = self._get_keyframe_idxs(conds)
+
+        # Strip guide frames (only from first modality for now)
+        if extracted_guide_entries is not None:
+            guide_count = sum(e["latent_shape"][0] for e in extracted_guide_entries)
+            if guide_count > 0:
+                x = unpacked_latents[0]
+                latent_count = x.size(self.dim) - guide_count
+                unpacked_latents_list[0] = x.narrow(self.dim, 0, latent_count)
+                guide_latents_list[0] = x.narrow(self.dim, latent_count, guide_count)
+                guide_entries_list[0] = extracted_guide_entries
+                keyframe_idxs_list[0] = extracted_keyframe_idxs
+
+
+        return WindowingState(
+            latents=unpacked_latents_list,
+            guide_latents=guide_latents_list,
+            guide_entries=guide_entries_list,
+            keyframe_idxs=keyframe_idxs_list,
+            latent_shapes=latent_shapes,
+            dim=self.dim,
+            is_multimodal=is_multimodal,
+            temporal_downscale_ratio=model.latent_format.temporal_downscale_ratio)
+
     def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
-        # for now, assume first dim is batch - should have stored on BaseModel in actual implementation
-        if x_in.size(self.dim) > self.context_length:
-            logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.")
+        window_state = self._build_window_state(x_in, conds, model) # build window_state to check frame counts, will be built again in execute
+        total_frame_count = window_state.latents[0].size(self.dim)
+        if total_frame_count > self.context_length:
+            logging.info(f"\nUsing context windows: Context length {self.context_length} with overlap {self.context_overlap} for {total_frame_count} frames.")
             if self.cond_retain_index_list:
                 logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
+            if self.latent_retain_index_list:
+                logging.info(f"Retaining original latent for indexes: {self.latent_retain_index_list}")
             return True
+        logging.info(f"\nNot using context windows since context length ({self.context_length}) exceeds input frames ({total_frame_count}).")
         return False
 
     def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase:
@@ -275,7 +555,9 @@ class IndexListContextHandler(ContextHandlerABC):
         return resized_cond
 
     def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
-        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
+        sample_sigmas = model_options["transformer_options"]["sample_sigmas"]
+        current_timestep = timestep[0].to(sample_sigmas.dtype)
+        mask = torch.isclose(sample_sigmas, current_timestep, rtol=0.0001)
         matches = torch.nonzero(mask)
         if torch.numel(matches) == 0:
             return  # substep from multi-step sampler: keep self._step from the last full step
@@ -284,54 +566,98 @@ class IndexListContextHandler(ContextHandlerABC):
     def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
         full_length = x_in.size(self.dim) # TODO: choose dim based on model
         context_windows = self.context_schedule.func(full_length, self, model_options)
-        context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows]
+        context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length, context_overlap=self.context_overlap) for window in context_windows]
         return context_windows
 
     def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
         self._model = model
         self.set_step(timestep, model_options)
-        context_windows = self.get_context_windows(model, x_in, model_options)
-        enumerated_context_windows = list(enumerate(context_windows))
 
-        conds_final = [torch.zeros_like(x_in) for _ in conds]
+        window_state = self._build_window_state(x_in, conds, model)
+        num_modalities = len(window_state.latents)
+
+        context_windows = self.get_context_windows(model, window_state.latents[0], model_options)
+        enumerated_context_windows = list(enumerate(context_windows))
+        total_windows = len(enumerated_context_windows)
+
+        # Initialize per-modality accumulators (length 1 for single-modality)
+        accum = [[torch.zeros_like(m) for _ in conds] for m in window_state.latents]
         if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-            counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
+            counts = [[torch.ones(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents]
         else:
-            counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
-        biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds]
+            counts = [[torch.zeros(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents]
+        biases = [[([0.0] * m.shape[self.dim]) for _ in conds] for m in window_state.latents]
 
         for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks):
             callback(self, model, x_in, conds, timestep, model_options)
 
+        # accumulate results from each context window
         for enum_window in enumerated_context_windows:
-            results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options)
+            results = self.evaluate_context_windows(
+                calc_cond_batch, model, x_in, conds, timestep, [enum_window],
+                model_options, window_state=window_state, total_windows=total_windows)
             for result in results:
-                self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep,
-                                            conds_final, counts_final, biases_final)
+                # result.sub_conds_out is per-cond, per-modality: list[list[Tensor]]
+                for mod_idx in range(num_modalities):
+                    mod_out = [result.sub_conds_out[ci][mod_idx] for ci in range(len(conds))]
+                    modality_window = result.window.get_window_for_modality(mod_idx)
+                    self.combine_context_window_results(
+                        window_state.latents[mod_idx], mod_out, result.sub_conds, modality_window,
+                        result.window_idx, total_windows, timestep,
+                        accum[mod_idx], counts[mod_idx], biases[mod_idx])
+
+        # fuse accumulated results into final conds
         try:
-            # finalize conds
-            if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-                # relative is already normalized, so return as is
-                del counts_final
-                return conds_final
-            else:
-                # normalize conds via division by context usage counts
-                for i in range(len(conds_final)):
-                    conds_final[i] /= counts_final[i]
-                del counts_final
-                return conds_final
+            result_out = []
+            for ci in range(len(conds)):
+                finalized = []
+                for mod_idx in range(num_modalities):
+                    if self.fuse_method.name != ContextFuseMethods.RELATIVE:
+                        accum[mod_idx][ci] /= counts[mod_idx][ci]
+                    f = accum[mod_idx][ci]
+
+                    # if guide frames were injected, append them to the end of the fused latents for the next step
+                    if window_state.guide_latents[mod_idx] is not None:
+                        f = torch.cat([f, window_state.guide_latents[mod_idx]], dim=self.dim)
+                    finalized.append(f)
+
+                # pack modalities together if needed
+                if window_state.is_multimodal and len(finalized) > 1:
+                    packed, _ = comfy.utils.pack_latents(finalized)
+                else:
+                    packed = finalized[0]
+
+                result_out.append(packed)
+            return result_out
         finally:
             for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks):
                 callback(self, model, x_in, conds, timestep, model_options)
 
-    def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]],
-                                model_options, device=None, first_device=None):
+    def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds,
+                                timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]],
+                                model_options, window_state: WindowingState, total_windows: int = None,
+                                device=None, first_device=None):
+        """Evaluate context windows and return per-cond, per-modality outputs in ContextResults.sub_conds_out
+
+        For each window:
+        1. Builds windows (for each modality if multimodal)
+        2. Slices window for each modality
+        3. Injects concatenated latent guide frames where present
+        4. Packs together if needed and calls model
+        5. Unpacks and strips any guides from outputs
+        """
+        x = window_state.latents[0]
+
         results: list[ContextResults] = []
         for window_idx, window in enumerated_context_windows:
             # allow processing to end between context window executions for faster Cancel
             comfy.model_management.throw_exception_if_processing_interrupted()
 
-            # causal_window_fix: prepend a pre-window frame that will be stripped post-forward
+            # prepare the window accounting for multimodal windows
+            window = window_state.prepare_window(window, model)
+
+            # causal_window_fix: prepend a pre-window frame that will be stripped post-forward.
+            # Set anchor before slice_for_window so the latent slice and downstream cond slices both pick it up.
             anchor_applied = False
             if self.causal_window_fix:
                 anchor_idx = window.index_list[0] - 1
@@ -339,27 +665,46 @@ class IndexListContextHandler(ContextHandlerABC):
                     window.causal_anchor_index = anchor_idx
                     anchor_applied = True
 
+            # slice the window for each modality, injecting guide frames where applicable
+            sliced, guide_frame_counts_per_modality = window_state.slice_for_window(window, self.latent_retain_index_list, device)
+
             for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
                 callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
 
-            # update exposed params
+            logging.info(f"Context window {window_idx + 1}/{total_windows or len(enumerated_context_windows)}: frames {window.index_list[0]}-{window.index_list[-1]} of {x.shape[self.dim]}"
+                         + (f" (+{guide_frame_counts_per_modality[0]} guide frames)" if guide_frame_counts_per_modality[0] > 0 else "")
+                         )
+
+            # if multimodal, pack modalities together
+            if window_state.is_multimodal and len(sliced) > 1:
+                sub_x, sub_shapes = comfy.utils.pack_latents(sliced)
+            else:
+                sub_x, sub_shapes = sliced[0], [sliced[0].shape]
+
+            # get resized conds for window
             model_options["transformer_options"]["context_window"] = window
-            # get subsections of x, timestep, conds
-            sub_x = window.get_tensor(x_in, device)
-            sub_timestep = window.get_tensor(timestep, device, dim=0)
-            sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds]
+            sub_timestep = window.get_tensor(timestep, dim=0)
+            sub_conds = [self.get_resized_cond(cond, x, window) for cond in conds]
 
+            # if multimodal, patch latent_shapes in conds for correct unpacking in model
+            window_state.patch_latent_shapes(sub_conds, sub_shapes)
+
+            # call model on window
             sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options)
-            if device is not None:
-                for i in range(len(sub_conds_out)):
-                    sub_conds_out[i] = sub_conds_out[i].to(x_in.device)
 
-            # strip causal_window_fix anchor if applied
+            # unpack outputs
+            out_per_modality = [comfy.utils.unpack_latents(sub_conds_out[i], sub_shapes) for i in range(len(sub_conds_out))]
+
+            # strip causal_window_fix anchor from primary modality before guide strip so window_len math stays correct
             if anchor_applied:
-                for i in range(len(sub_conds_out)):
-                    sub_conds_out[i] = sub_conds_out[i].narrow(self.dim, 1, sub_conds_out[i].shape[self.dim] - 1)
+                for ci in range(len(out_per_modality)):
+                    t = out_per_modality[ci][0]
+                    out_per_modality[ci][0] = t.narrow(self.dim, 1, t.shape[self.dim] - 1)
 
-            results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
+            # strip injected guide frames
+            window_state.strip_guide_frames(out_per_modality, guide_frame_counts_per_modality, window)
+
+            results.append(ContextResults(window_idx, out_per_modality, sub_conds, window))
         return results
 
 
@@ -383,7 +728,7 @@ class IndexListContextHandler(ContextHandlerABC):
                     biases_final[i][idx] = bias_total + bias
         else:
             # add conds and counts based on weights of fuse method
-            weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep)
+            weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep, context_overlap=window.context_overlap)
             weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device)
             for i in range(len(sub_conds_out)):
                 window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor)
@@ -393,16 +738,22 @@ class IndexListContextHandler(ContextHandlerABC):
             callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final)
 
 
-def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs):
-    # limit noise_shape length to context_length for more accurate vram use estimation
+def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, conds, *args, **kwargs):
+    # Scale noise_shape to a single context window so VRAM estimation budgets per-window.
     model_options = kwargs.get("model_options", None)
     if model_options is None:
         raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.")
     handler: IndexListContextHandler = model_options.get("context_handler", None)
     if handler is not None:
         noise_shape = list(noise_shape)
-        noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)
-    return executor(model, noise_shape, *args, **kwargs)
+        is_packed = len(noise_shape) == 3 and noise_shape[1] == 1
+        if is_packed:
+            # TODO: latent_shapes cond isn't attached yet at this point, so we can't compute a
+            # per-window flat latent here. Skipping the clamp over-estimates but prevents immediate OOM.
+            pass
+        elif handler.dim < len(noise_shape) and noise_shape[handler.dim] > handler.context_length:
+            noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)
+    return executor(model, noise_shape, conds, *args, **kwargs)
 
 
 def create_prepare_sampling_wrapper(model: ModelPatcher):
@@ -422,11 +773,12 @@ def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, nois
         raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.")
     if not handler.freenoise:
         return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
-    noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"])
+
+    conds = [guider.conds.get('positive', guider.conds.get('negative', []))]
+    noise = handler._apply_freenoise(noise, conds, extra_args["seed"])
 
     return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
 
-
 def create_sampler_sample_wrapper(model: ModelPatcher):
     model.add_wrapper_with_key(
         comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
@@ -434,7 +786,6 @@ def create_sampler_sample_wrapper(model: ModelPatcher):
         _sampler_sample_wrapper
     )
 
-
 def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:
     total_dims = len(x_in.shape)
     weights_tensor = torch.Tensor(weights).to(device=device)
@@ -580,8 +931,9 @@ def get_matching_context_schedule(context_schedule: str) -> ContextSchedule:
     return ContextSchedule(context_schedule, func)
 
 
-def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None):
-    return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs)
+def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None, context_overlap: int=None):
+    context_overlap = handler.context_overlap if context_overlap is None else context_overlap
+    return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs, context_overlap=context_overlap)
 
 
 def create_weights_flat(length: int, **kwargs) -> list[float]:
@@ -599,18 +951,18 @@ def create_weights_pyramid(length: int, **kwargs) -> list[float]:
         weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))
     return weight_sequence
 
-def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs):
+def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], context_overlap: int, **kwargs):
     # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302
     # only expected overlap is given different weights
     weights_torch = torch.ones((length))
     # blend left-side on all except first window
     if min(idxs) > 0:
-        ramp_up = torch.linspace(1e-37, 1, handler.context_overlap)
-        weights_torch[:handler.context_overlap] = ramp_up
+        ramp_up = torch.linspace(1e-37, 1, context_overlap)
+        weights_torch[:context_overlap] = ramp_up
     # blend right-side on all except last window
     if max(idxs) < full_length-1:
-        ramp_down = torch.linspace(1, 1e-37, handler.context_overlap)
-        weights_torch[-handler.context_overlap:] = ramp_down
+        ramp_down = torch.linspace(1, 1e-37, context_overlap)
+        weights_torch[-context_overlap:] = ramp_down
     return weights_torch
 
 class ContextFuseMethods:

comfy/latent_formats.py

@@ -779,6 +779,10 @@ class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
 
+class SeedVR2(LatentFormat):
+    latent_channels = 16
+    latent_dimensions = 3
+
 class ACEAudio15(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1

comfy/ldm/ace/ace_step15.py

@@ -217,10 +217,7 @@ class AceStepAttention(nn.Module):
                 cos, sin = position_embeddings
                 query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
-        n_rep = self.num_heads // self.num_kv_heads
-        if n_rep > 1:
-            key_states = key_states.repeat_interleave(n_rep, dim=1)
-            value_states = value_states.repeat_interleave(n_rep, dim=1)
+        gqa_kwargs = {"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}
 
         attn_bias = None
         if self.sliding_window is not None and not self.is_cross_attention:
@@ -244,7 +241,7 @@ class AceStepAttention(nn.Module):
             else:
                 attn_bias = window_bias
 
-        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False)
+        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False, **gqa_kwargs)
         attn_output = self.o_proj(attn_output)
 
         return attn_output

comfy/ldm/audio/dit.py

@@ -425,19 +425,16 @@ class Attention(nn.Module):
         if n == 1 and causal:
             causal = False
 
-        if h != kv_h:
-            # Repeat interleave kv_heads to match q_heads
-            heads_per_kv_head = h // kv_h
-            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+        gqa_kwargs = {"enable_gqa": True} if h != kv_h else {}
 
         if self.differential:
             q, q_diff = q.unbind(dim=1)
             k, k_diff = k.unbind(dim=1)
-            out      = optimized_attention(q,      k,      v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
-            out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
+            out      = optimized_attention(q,      k,      v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
+            out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
             out = out - out_diff
         else:
-            out = optimized_attention(q, k, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
 
         out = self.to_out(out)
 

comfy/ldm/boogu/model.py

@@ -0,0 +1,318 @@
+# Boogu-Image-0.1 transformer
+# Architecture is an OmniGen2 derivative (see comfy/ldm/omnigen/omnigen2.py) with an
+# added dual-stream ("double_stream") stage before the single-stream layers, conditioned
+# by a Qwen3-VL multimodal LLM. Reuses the OmniGen2/Lumina building blocks and the Flux
+# RoPE core, the only new component is the double-stream block + the hybrid forward order.
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+import comfy.ldm.common_dit
+import comfy.ldm.omnigen.omnigen2
+from comfy.ldm.modules.attention import optimized_attention_masked
+from comfy.ldm.omnigen.omnigen2 import (
+    OmniGen2RotaryPosEmbed,
+    Lumina2CombinedTimestepCaptionEmbedding,
+    LuminaRMSNormZero,
+    LuminaLayerNormContinuous,
+    LuminaFeedForward,
+    Attention,
+    OmniGen2TransformerBlock,
+    apply_rotary_emb,
+)
+
+class BooguDoubleStreamProcessor(nn.Module):
+    # Joint attention over [instruct ; img] with separate per-stream q/k/v and output projections.
+    def __init__(self, dim, head_dim, heads, kv_heads, dtype=None, device=None, operations=None):
+        super().__init__()
+        query_dim = head_dim * heads
+        kv_dim = head_dim * kv_heads
+
+        self.img_to_q = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+        self.img_to_k = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+        self.img_to_v = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+
+        self.instruct_to_q = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+        self.instruct_to_k = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+        self.instruct_to_v = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device)
+
+        self.instruct_out = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+        self.img_out = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, attn, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask=None, transformer_options={}):
+        batch_size = img_hidden_states.shape[0]
+        L_instruct = instruct_hidden_states.shape[1]
+
+        img_q = self.img_to_q(img_hidden_states)
+        img_k = self.img_to_k(img_hidden_states)
+        img_v = self.img_to_v(img_hidden_states)
+
+        instruct_q = self.instruct_to_q(instruct_hidden_states)
+        instruct_k = self.instruct_to_k(instruct_hidden_states)
+        instruct_v = self.instruct_to_v(instruct_hidden_states)
+
+        # Concatenate instruction first, then image (matches reference processor order).
+        query = torch.cat([instruct_q, img_q], dim=1)
+        key = torch.cat([instruct_k, img_k], dim=1)
+        value = torch.cat([instruct_v, img_v], dim=1)
+
+        query = query.view(batch_size, -1, attn.heads, attn.dim_head)
+        key = key.view(batch_size, -1, attn.kv_heads, attn.dim_head)
+        value = value.view(batch_size, -1, attn.kv_heads, attn.dim_head)
+
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
+
+        if rotary_emb is not None:
+            query = apply_rotary_emb(query, rotary_emb)
+            key = apply_rotary_emb(key, rotary_emb)
+
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        gqa_kwargs = {"enable_gqa": True} if attn.kv_heads < attn.heads else {}
+        hidden_states = optimized_attention_masked(query, key, value, attn.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options, **gqa_kwargs)
+
+        # Split back to instruction/image, apply per-stream output projections, recombine.
+        instruct_hidden_states = self.instruct_out(hidden_states[:, :L_instruct])
+        img_hidden_states = self.img_out(hidden_states[:, L_instruct:])
+        hidden_states = torch.cat([instruct_hidden_states, img_hidden_states], dim=1)
+
+        hidden_states = attn.to_out[0](hidden_states)
+        return hidden_states
+
+
+class BooguJointAttention(nn.Module):
+    # Holds the shared q/k RMSNorm + final output projection
+    def __init__(self, dim, head_dim, heads, kv_heads, eps=1e-5, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = heads
+        self.kv_heads = kv_heads
+        self.dim_head = head_dim
+        self.scale = head_dim ** -0.5
+
+        self.norm_q = operations.RMSNorm(head_dim, eps=eps, dtype=dtype, device=device)
+        self.norm_k = operations.RMSNorm(head_dim, eps=eps, dtype=dtype, device=device)
+        self.to_out = nn.Sequential(
+            operations.Linear(heads * head_dim, dim, bias=False, dtype=dtype, device=device),
+            nn.Dropout(0.0),
+        )
+        self.processor = BooguDoubleStreamProcessor(dim, head_dim, heads, kv_heads, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask=None, transformer_options={}):
+        return self.processor(self, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask, transformer_options=transformer_options)
+
+
+class BooguDoubleStreamBlock(nn.Module):
+    # Dual-stream block: joint attention over [instruct ; img] + image self-attention, each stream with its own modulation/MLP.
+    def __init__(self, dim, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, dtype=None, device=None, operations=None):
+        super().__init__()
+        head_dim = dim // num_attention_heads
+
+        self.img_instruct_attn = BooguJointAttention(dim, head_dim, num_attention_heads, num_kv_heads, eps=1e-5, dtype=dtype, device=device, operations=operations)
+        self.img_self_attn = Attention(
+            query_dim=dim, dim_head=head_dim, heads=num_attention_heads, kv_heads=num_kv_heads,
+            eps=1e-5, bias=False, dtype=dtype, device=device, operations=operations,
+        )
+
+        self.img_feed_forward = LuminaFeedForward(dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations)
+        self.instruct_feed_forward = LuminaFeedForward(dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.img_norm2 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.img_norm3 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.instruct_norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.instruct_norm2 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+
+        self.img_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.img_self_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.img_ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.img_ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+
+        self.instruct_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.instruct_ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+        self.instruct_ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
+
+    def forward(self, img_hidden_states, instruct_hidden_states, joint_rotary_emb, img_rotary_emb, temb, joint_attention_mask=None, img_attention_mask=None, transformer_options={}):
+        L_instruct = instruct_hidden_states.shape[1]
+
+        img_norm1_out, img_gate_msa, img_scale_mlp, img_gate_mlp = self.img_norm1(img_hidden_states, temb)
+        img_norm2_out, img_shift_mlp, _, _ = self.img_norm2(img_hidden_states, temb)
+        img_norm3_out, img_gate_self, _, _ = self.img_norm3(img_hidden_states, temb)
+
+        instruct_norm1_out, instruct_gate_msa, instruct_scale_mlp, instruct_gate_mlp = self.instruct_norm1(instruct_hidden_states, temb)
+        instruct_norm2_out, instruct_shift_mlp, _, _ = self.instruct_norm2(instruct_hidden_states, temb)
+
+        joint_attn_out = self.img_instruct_attn(img_norm1_out, instruct_norm1_out, joint_rotary_emb, joint_attention_mask, transformer_options=transformer_options)
+        instruct_attn_out = joint_attn_out[:, :L_instruct]
+        img_attn_out = joint_attn_out[:, L_instruct:]
+
+        img_self_attn_out = self.img_self_attn(img_norm3_out, img_norm3_out, img_attention_mask, img_rotary_emb, transformer_options=transformer_options)
+
+        img_hidden_states = img_hidden_states + img_gate_msa.unsqueeze(1).tanh() * self.img_attn_norm(img_attn_out)
+        img_hidden_states = img_hidden_states + img_gate_self.unsqueeze(1).tanh() * self.img_self_attn_norm(img_self_attn_out)
+        img_mlp_input = (1 + img_scale_mlp.unsqueeze(1)) * img_norm2_out + img_shift_mlp.unsqueeze(1)
+        img_mlp_out = self.img_feed_forward(self.img_ffn_norm1(img_mlp_input))
+        img_hidden_states = img_hidden_states + img_gate_mlp.unsqueeze(1).tanh() * self.img_ffn_norm2(img_mlp_out)
+
+        instruct_hidden_states = instruct_hidden_states + instruct_gate_msa.unsqueeze(1).tanh() * self.instruct_attn_norm(instruct_attn_out)
+        instruct_mlp_input = (1 + instruct_scale_mlp.unsqueeze(1)) * instruct_norm2_out + instruct_shift_mlp.unsqueeze(1)
+        instruct_mlp_out = self.instruct_feed_forward(self.instruct_ffn_norm1(instruct_mlp_input))
+        instruct_hidden_states = instruct_hidden_states + instruct_gate_mlp.unsqueeze(1).tanh() * self.instruct_ffn_norm2(instruct_mlp_out)
+
+        return img_hidden_states, instruct_hidden_states
+
+
+class BooguTransformer2DModel(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 16,
+        out_channels: Optional[int] = None,
+        hidden_size: int = 3360,
+        num_layers: int = 32,
+        num_double_stream_layers: int = 8,
+        num_refiner_layers: int = 2,
+        num_attention_heads: int = 28,
+        num_kv_heads: int = 7,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        axes_dim_rope: Tuple[int, int, int] = (40, 40, 40),
+        axes_lens: Tuple[int, int, int] = (2048, 1664, 1664),
+        instruction_feat_dim: int = 4096,
+        timestep_scale: float = 1000.0,
+        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=instruction_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.double_stream_layers = nn.ModuleList([
+            BooguDoubleStreamBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, dtype=dtype, device=device, operations=operations)
+            for _ in range(num_double_stream_layers)
+        ])
+
+        self.single_stream_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))
+
+    # Patchify/refine helpers are identical to OmniGen2; reuse via bound methods.
+    flat_and_pad_to_seq = comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel.flat_and_pad_to_seq
+    img_patch_embed_and_refine = comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel.img_patch_embed_and_refine
+
+    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **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, transformer_options=transformer_options)
+
+        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,
+            transformer_options=transformer_options,
+        )
+
+        # Double-stream stage: the image self-attention only sees the [ref ; noise] tokens,
+        # which sit after the instruction tokens in the joint rope.
+        L_instruct = text_hidden_states.shape[1]
+        combined_img_rotary_emb = rotary_emb[:, L_instruct:]
+        for layer in self.double_stream_layers:
+            combined_img_hidden_states, text_hidden_states = layer(
+                combined_img_hidden_states, text_hidden_states,
+                rotary_emb, combined_img_rotary_emb, temb,
+                joint_attention_mask=None, img_attention_mask=None,
+                transformer_options=transformer_options,
+            )
+
+        hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
+
+        for layer in self.single_stream_layers:
+            hidden_states = layer(hidden_states, None, rotary_emb, temb, transformer_options=transformer_options)
+
+        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/cosmos/predict2.py

@@ -515,7 +515,7 @@ class Block(nn.Module):
             h=H,
             w=W,
         )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        x_B_T_H_W_D = torch.addcmul(x_B_T_H_W_D, gate_self_attn_B_T_1_1_D.to(residual_dtype), result_B_T_H_W_D.to(residual_dtype))
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
@@ -548,7 +548,7 @@ class Block(nn.Module):
             shift_cross_attn_B_T_1_1_D,
             transformer_options=transformer_options,
         )
-        x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D
+        x_B_T_H_W_D = torch.addcmul(x_B_T_H_W_D, gate_cross_attn_B_T_1_1_D.to(residual_dtype), result_B_T_H_W_D.to(residual_dtype))
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
@@ -557,7 +557,7 @@ class Block(nn.Module):
             shift_mlp_B_T_1_1_D,
         )
         result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        x_B_T_H_W_D = torch.addcmul(x_B_T_H_W_D, gate_mlp_B_T_1_1_D.to(residual_dtype), result_B_T_H_W_D.to(residual_dtype))
         return x_B_T_H_W_D
 
 

comfy/ldm/hidream_o1/attention.py

@@ -15,24 +15,24 @@ def make_two_pass_attention(ar_len: int, transformer_options=None):
     The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes.
     """
 
-    def two_pass_attention(q, k, v, heads, **kwargs):
+    def two_pass_attention(q, k, v, heads, enable_gqa=False, **kwargs):
         B, H, T, D = q.shape
 
         if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options, enable_gqa=enable_gqa)
         elif ar_len >= T:
-            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
+            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True, enable_gqa=enable_gqa)
         elif ar_len <= 0:
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options, enable_gqa=enable_gqa)
         else:
             out_ar = comfy.ops.scaled_dot_product_attention(
                 q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len],
-                attn_mask=None, dropout_p=0.0, is_causal=True,
+                attn_mask=None, dropout_p=0.0, is_causal=True, enable_gqa=enable_gqa,
             )
             out_gen = optimized_attention(
                 q[:, :, ar_len:], k, v, heads,
                 mask=None, skip_reshape=True, skip_output_reshape=True,
-                transformer_options=transformer_options,
+                transformer_options=transformer_options, enable_gqa=enable_gqa,
             )
             out = torch.cat([out_ar, out_gen], dim=2)
 

comfy/ldm/ideogram4/model.py

@@ -106,11 +106,11 @@ class Ideogram4EmbedScalar(nn.Module):
         self.mlp_in = operations.Linear(dim, dim, bias=True, dtype=dtype, device=device)
         self.mlp_out = operations.Linear(dim, dim, bias=True, dtype=dtype, device=device)
 
-    def forward(self, x):
+    def forward(self, x, dtype):
         x = x.to(torch.float32)
         scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min)
         emb = _sinusoidal_embedding(scaled, self.dim)
-        emb = emb.to(self.mlp_in.weight.dtype)
+        emb = emb.to(dtype)
         emb = F.silu(self.mlp_in(emb))
         return self.mlp_out(emb)
 
@@ -161,7 +161,7 @@ class Ideogram4Transformer(nn.Module):
         x = x * output_image_mask
         h = self.input_proj(x) * output_image_mask
 
-        t_cond = self.t_embedding(t)
+        t_cond = self.t_embedding(t, dtype=x.dtype)
         if t.dim() == 1:
             t_cond = t_cond.unsqueeze(1)
         adaln_input = F.silu(self.adaln_proj(t_cond))

comfy/ldm/krea2/model.py

@@ -0,0 +1,290 @@
+"""Krea 2 (K2) — single-stream MMDiT.
+
+Text tokens produced by a Qwen3-VL-4B 12-layer ``txtfusion`` adapter and patchified image tokens are
+concatenated into one sequence and run through ``layers`` shared transformer blocks with
+AdaLN-single modulation, GQA + per-head QK-norm + sigmoid-gated attention, SwiGLU MLP, and 3-axis RoPE.
+"""
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+import comfy.model_management
+import comfy.patcher_extension
+import comfy.ldm.common_dit
+from comfy.ldm.flux.layers import EmbedND, timestep_embedding
+from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.modules.attention import optimized_attention_masked
+
+
+class RMSNorm(nn.Module):
+    """RMSNorm with the reference ``(1 + scale)`` weight convention (scale stored zero-centered)."""
+
+    def __init__(self, features: int, eps: float = 1e-5, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.eps = eps
+        self.scale = nn.Parameter(torch.empty(features, device=device, dtype=dtype))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        dtype = x.dtype
+        weight = comfy.model_management.cast_to(self.scale, dtype=torch.float32, device=x.device) + 1.0
+        return F.rms_norm(x.float(), (x.shape[-1],), weight=weight, eps=self.eps).to(dtype)
+
+
+class QKNorm(nn.Module):
+    def __init__(self, dim: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.qnorm = RMSNorm(dim, device=device, dtype=dtype, operations=operations)
+        self.knorm = RMSNorm(dim, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, q, k):
+        return self.qnorm(q), self.knorm(k)
+
+
+class SwiGLU(nn.Module):
+    def __init__(self, features: int, multiplier: int, bias: bool = False, multiple: int = 128,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        mlpdim = int(2 * features / 3) * multiplier
+        mlpdim = multiple * ((mlpdim + multiple - 1) // multiple)
+        self.gate = operations.Linear(features, mlpdim, bias=bias, device=device, dtype=dtype)
+        self.up = operations.Linear(features, mlpdim, bias=bias, device=device, dtype=dtype)
+        self.down = operations.Linear(mlpdim, features, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.down(F.silu(self.gate(x)).mul_(self.up(x)))
+
+
+class Attention(nn.Module):
+    def __init__(self, dim: int, heads: int, kvheads: Optional[int] = None, bias: bool = False,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        self.heads = heads
+        self.kvheads = kvheads if kvheads is not None else heads
+        self.headdim = dim // self.heads
+        self.wq = operations.Linear(dim, self.headdim * self.heads, bias=bias, device=device, dtype=dtype)
+        self.wk = operations.Linear(dim, self.headdim * self.kvheads, bias=bias, device=device, dtype=dtype)
+        self.wv = operations.Linear(dim, self.headdim * self.kvheads, bias=bias, device=device, dtype=dtype)
+        self.gate = operations.Linear(dim, dim, bias=bias, device=device, dtype=dtype)
+        self.qknorm = QKNorm(self.headdim, device=device, dtype=dtype, operations=operations)
+        self.wo = operations.Linear(dim, dim, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x, freqs=None, mask=None, transformer_options={}):
+        q, k, v, gate = self.wq(x), self.wk(x), self.wv(x), self.gate(x)
+        q = rearrange(q, "B L (H D) -> B H L D", H=self.heads)
+        k = rearrange(k, "B L (H D) -> B H L D", H=self.kvheads)
+        v = rearrange(v, "B L (H D) -> B H L D", H=self.kvheads)
+        q, k = self.qknorm(q, k)
+        if freqs is not None:
+            q, k = apply_rope(q, k, freqs)
+        if self.kvheads != self.heads:
+            rep = self.heads // self.kvheads
+            k = k.repeat_interleave(rep, dim=1)
+            v = v.repeat_interleave(rep, dim=1)
+        out = optimized_attention_masked(q, k, v, self.heads, mask=mask, skip_reshape=True,
+                                         transformer_options=transformer_options)
+        return self.wo(out * F.sigmoid(gate))
+
+
+class SimpleModulation(nn.Module):
+    def __init__(self, dim: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.lin = nn.Parameter(torch.empty(2, dim, device=device, dtype=dtype))
+
+    def forward(self, vec):
+        out = vec + comfy.model_management.cast_to(self.lin, dtype=vec.dtype, device=vec.device).unsqueeze(0)
+        scale, shift = out.chunk(2, dim=1)
+        return scale, shift
+
+
+class DoubleSharedModulation(nn.Module):
+    def __init__(self, dim: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.lin = nn.Parameter(torch.empty(6 * dim, device=device, dtype=dtype))
+
+    def forward(self, vec):
+        out = vec + comfy.model_management.cast_to(self.lin, dtype=vec.dtype, device=vec.device)
+        return out.chunk(6, dim=-1)
+
+
+class TextFusionBlock(nn.Module):
+    def __init__(self, features, heads, multiplier, bias=False, kvheads=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.prenorm = RMSNorm(features, device=device, dtype=dtype, operations=operations)
+        self.postnorm = RMSNorm(features, device=device, dtype=dtype, operations=operations)
+        self.attn = Attention(features, heads, kvheads=kvheads, bias=bias, device=device, dtype=dtype, operations=operations)
+        self.mlp = SwiGLU(features, multiplier, bias, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x, mask=None, transformer_options={}):
+        x = x + self.attn(self.prenorm(x), mask=mask, transformer_options=transformer_options)
+        x = x + self.mlp(self.postnorm(x))
+        return x
+
+
+class TextFusionTransformer(nn.Module):
+    def __init__(self, num_txt_layers, txt_dim, heads, multiplier, bias=False, kvheads=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.layerwise_blocks = nn.ModuleList([
+            TextFusionBlock(txt_dim, heads, multiplier, bias, kvheads, device=device, dtype=dtype, operations=operations)
+            for _ in range(2)
+        ])
+        self.projector = operations.Linear(num_txt_layers, 1, bias=False, device=device, dtype=dtype)
+        self.refiner_blocks = nn.ModuleList([
+            TextFusionBlock(txt_dim, heads, multiplier, bias, kvheads, device=device, dtype=dtype, operations=operations)
+            for _ in range(2)
+        ])
+
+    def forward(self, x, mask=None, transformer_options={}):
+        b, l, n, d = x.shape
+        x = x.reshape(b * l, n, d)
+        for block in self.layerwise_blocks:
+            x = block(x.contiguous(), mask=None, transformer_options=transformer_options)
+        x = rearrange(x, "(b l) n d -> b l d n", b=b, l=l)
+        x = self.projector(x).squeeze(-1)
+        for block in self.refiner_blocks:
+            x = block(x, mask=mask, transformer_options=transformer_options)
+        return x
+
+
+class SingleStreamBlock(nn.Module):
+    def __init__(self, features, heads, multiplier, bias=False, kvheads=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.mod = DoubleSharedModulation(features, device=device, dtype=dtype, operations=operations)
+        self.prenorm = RMSNorm(features, device=device, dtype=dtype, operations=operations)
+        self.postnorm = RMSNorm(features, device=device, dtype=dtype, operations=operations)
+        self.attn = Attention(features, heads, kvheads=kvheads, bias=bias, device=device, dtype=dtype, operations=operations)
+        self.mlp = SwiGLU(features, multiplier, bias, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x, vec, freqs, mask=None, transformer_options={}):
+        prescale, preshift, pregate, postscale, postshift, postgate = self.mod(vec)
+        x = x + pregate * self.attn((1 + prescale) * self.prenorm(x) + preshift, freqs, mask, transformer_options=transformer_options)
+        x = x + postgate * self.mlp((1 + postscale) * self.postnorm(x) + postshift)
+        return x
+
+
+class LastLayer(nn.Module):
+    def __init__(self, features, patch, channels, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.norm = RMSNorm(features, device=device, dtype=dtype, operations=operations)
+        self.linear = operations.Linear(features, patch * patch * channels, bias=True, device=device, dtype=dtype)
+        self.modulation = SimpleModulation(features, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x, tvec):
+        scale, shift = self.modulation(tvec)
+        x = (1 + scale) * self.norm(x) + shift
+        return self.linear(x)
+
+
+class SingleStreamDiT(nn.Module):
+    def __init__(self, features=6144, tdim=256, txtdim=2560, heads=48, kvheads=12, multiplier=4,
+                 layers=28, patch=2, channels=16, bias=False, theta=1e3, txtlayers=12,
+                 txtheads=20, txtkvheads=20, image_model=None,
+                 device=None, dtype=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        self.patch = patch
+        self.channels = channels
+        self.tdim = tdim
+        self.heads = heads
+        self.txtdim = txtdim
+        self.txtlayers = txtlayers
+
+        headdim = features // heads
+        axes = [headdim - 12 * (headdim // 16), 6 * (headdim // 16), 6 * (headdim // 16)]
+        assert sum(axes) == headdim, f"axes {axes} sum != headdim {headdim}"
+        self.pe_embedder = EmbedND(dim=headdim, theta=int(theta), axes_dim=axes)
+
+        self.first = operations.Linear(channels * patch ** 2, features, bias=True, device=device, dtype=dtype)
+        self.blocks = nn.ModuleList([
+            SingleStreamBlock(features, heads, multiplier, bias, kvheads, device=device, dtype=dtype, operations=operations)
+            for _ in range(layers)
+        ])
+        self.tmlp = nn.Sequential(
+            operations.Linear(tdim, features, device=device, dtype=dtype),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(features, features, device=device, dtype=dtype),
+        )
+        self.txtfusion = TextFusionTransformer(txtlayers, txtdim, txtheads, multiplier, bias, txtkvheads,
+                                               device=device, dtype=dtype, operations=operations)
+        self.txtmlp = nn.Sequential(
+            RMSNorm(txtdim, device=device, dtype=dtype, operations=operations),
+            operations.Linear(txtdim, features, device=device, dtype=dtype),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(features, features, device=device, dtype=dtype),
+        )
+        self.last = LastLayer(features, patch, channels, device=device, dtype=dtype, operations=operations)
+        self.tproj = nn.Sequential(
+            nn.GELU(approximate="tanh"),
+            operations.Linear(features, features * 6, device=device, dtype=dtype),
+        )
+
+    def forward(self, x, timesteps, context, attention_mask=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options),
+        ).execute(x, timesteps, context, attention_mask, transformer_options, **kwargs)
+
+    def _forward(self, x, timesteps, context, attention_mask=None, transformer_options={}, **kwargs):
+        temporal = x.ndim == 5
+        if temporal:
+            b5, c5, t5, h5, w5 = x.shape
+            x = x.reshape(b5 * t5, c5, h5, w5)
+        bs, c, H_orig, W_orig = x.shape
+        patch = self.patch
+        # Pad the latent up to a multiple of patch (as Flux/Lumina/QwenImage do); crop back at the end.
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch, patch))
+        H, W = x.shape[-2], x.shape[-1]
+        h_, w_ = H // patch, W // patch
+
+        # context arrives as (B, seq, txtlayers*txtdim); reshape to (B, txtlayers, seq, txtdim).
+        context = self._unpack_context(context)
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch, pw=patch)
+        img = self.first(img)
+
+        t = self.tmlp(timestep_embedding(timesteps, self.tdim).unsqueeze(1).to(img.dtype))
+        tvec = self.tproj(t)
+
+        context = self.txtfusion(context, mask=None, transformer_options=transformer_options)
+        context = self.txtmlp(context)
+
+        txtlen, imglen = context.shape[1], img.shape[1]
+        combined = torch.cat((context, img), dim=1)
+
+        # Position ids: text at 0, image at (0, h_idx, w_idx).
+        device = combined.device
+        txtpos = torch.zeros(bs, txtlen, 3, device=device, dtype=torch.float32)
+        imgids = torch.zeros(h_, w_, 3, device=device, dtype=torch.float32)
+        imgids[..., 1] = torch.arange(h_, device=device, dtype=torch.float32)[:, None]
+        imgids[..., 2] = torch.arange(w_, device=device, dtype=torch.float32)[None, :]
+        imgpos = imgids.reshape(1, h_ * w_, 3).repeat(bs, 1, 1)
+        pos = torch.cat((txtpos, imgpos), dim=1)
+
+        freqs = self.pe_embedder(pos)
+
+        for block in self.blocks:
+            combined = block(combined, tvec, freqs, None, transformer_options=transformer_options)
+
+        final = self.last(combined, t)
+        out = final[:, txtlen:txtlen + imglen, :]
+        out = rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+                        h=h_, w=w_, ph=patch, pw=patch, c=self.channels)
+        out = out[:, :, :H_orig, :W_orig]  # crop padding back off
+        if temporal:
+            out = out.reshape(b5, t5, self.channels, H_orig, W_orig).movedim(1, 2)
+        return out
+
+    def _unpack_context(self, context):
+        # context: (B, seq, txtlayers*txtdim) -> (B, seq, txtlayers, txtdim).
+        b, seq, fused = context.shape
+        if fused != self.txtlayers * self.txtdim:
+            raise ValueError(
+                f"Krea2 expects conditioning with {self.txtlayers}x{self.txtdim}={self.txtlayers * self.txtdim} "
+                f"features (a {self.txtlayers}-layer Qwen3-VL stack) but got {fused}. "
+                f"Load the text encoder with CLIPLoader type 'krea2'."
+            )
+        return context.reshape(b, seq, self.txtlayers, self.txtdim)

comfy/ldm/lightricks/model.py

@@ -1085,7 +1085,7 @@ class LTXVModel(LTXBaseModel):
         )
 
         grid_mask = None
-        if keyframe_idxs is not None:
+        if keyframe_idxs is not None and keyframe_idxs.shape[2] > 0:
             additional_args.update({ "orig_patchified_shape": list(x.shape)})
             denoise_mask = self.patchifier.patchify(denoise_mask)[0]
             grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0]
@@ -1330,7 +1330,7 @@ class LTXVModel(LTXBaseModel):
         x = x * (1 + scale) + shift
         x = self.proj_out(x)
 
-        if keyframe_idxs is not None:
+        if keyframe_idxs is not None and keyframe_idxs.shape[2] > 0:
             grid_mask = kwargs["grid_mask"]
             orig_patchified_shape = kwargs["orig_patchified_shape"]
             full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device)

comfy/ldm/modules/attention.py

@@ -1,5 +1,6 @@
 import math
 import sys
+import inspect
 
 import torch
 import torch.nn.functional as F
@@ -14,16 +15,16 @@ from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
 
-TORCH_HAS_GQA = model_management.torch_version_numeric >= (2, 5)
-
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
 SAGE_ATTENTION_IS_AVAILABLE = False
+SAGE_ATTENTION_SUPPORTS_MASK = False
 try:
     from sageattention import sageattn
     SAGE_ATTENTION_IS_AVAILABLE = True
+    SAGE_ATTENTION_SUPPORTS_MASK = "attn_mask" in inspect.signature(sageattn).parameters
 except ImportError as e:
     if model_management.sage_attention_enabled():
         if e.name == "sageattention":
@@ -89,6 +90,44 @@ def default(val, d):
         return val
     return d
 
+def _gqa_repeat_factor(query_heads, key_heads, value_heads):
+    if key_heads != value_heads:
+        raise ValueError(f"Key/value head count mismatch for GQA: {key_heads} != {value_heads}")
+    if query_heads == key_heads:
+        return 1
+    if query_heads % key_heads != 0:
+        raise ValueError(f"Query heads must be divisible by key/value heads for GQA: {query_heads} vs {key_heads}")
+    return query_heads // key_heads
+
+def _repeat_kv_for_gqa(k, v, query_heads, head_dim):
+    n_rep = _gqa_repeat_factor(query_heads, k.shape[head_dim], v.shape[head_dim])
+    if n_rep > 1:
+        k = k.repeat_interleave(n_rep, dim=head_dim)
+        v = v.repeat_interleave(n_rep, dim=head_dim)
+    return k, v
+
+def _heads_from_dim(tensor, dim_head, name):
+    inner_dim = tensor.shape[-1]
+    if inner_dim % dim_head != 0:
+        raise ValueError(f"{name} inner dimension {inner_dim} is not divisible by head dimension {dim_head}")
+    return inner_dim // dim_head
+
+def _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, enable_gqa=False, expand_kv=True):
+    q = q.unsqueeze(3).reshape(b, -1, heads, dim_head)
+    if enable_gqa:
+        key_heads = _heads_from_dim(k, dim_head, "Key")
+        value_heads = _heads_from_dim(v, dim_head, "Value")
+    else:
+        key_heads = heads
+        value_heads = heads
+    k = k.unsqueeze(3).reshape(b, -1, key_heads, dim_head)
+    v = v.unsqueeze(3).reshape(b, -1, value_heads, dim_head)
+    if enable_gqa:
+        _gqa_repeat_factor(heads, key_heads, value_heads)
+        if expand_kv:
+            k, v = _repeat_kv_for_gqa(k, v, heads, -2)
+    return q, k, v
+
 
 # feedforward
 class GEGLU(nn.Module):
@@ -152,28 +191,19 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         b, _, dim_head = q.shape
         dim_head //= heads
 
-    if kwargs.get("enable_gqa", False) and q.shape[-3] != k.shape[-3]:
-        n_rep = q.shape[-3] // k.shape[-3]
-        k = k.repeat_interleave(n_rep, dim=-3)
-        v = v.repeat_interleave(n_rep, dim=-3)
-
     scale = kwargs.get("scale", dim_head ** -0.5)
 
     h = heads
     if skip_reshape:
-         q, k, v = map(
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
+        q, k, v = map(
             lambda t: t.reshape(b * heads, -1, dim_head),
             (q, k, v),
         )
     else:
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, -1, heads, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * heads, -1, dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        q, k, v = map(lambda t: t.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head).contiguous(), (q, k, v))
 
     # force cast to fp32 to avoid overflowing
     if attn_precision == torch.float32:
@@ -231,13 +261,16 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         query = query * (kwargs["scale"] * dim_head ** 0.5)
 
     if skip_reshape:
+        if kwargs.get("enable_gqa", False):
+            key, value = _repeat_kv_for_gqa(key, value, query.shape[-3], -3)
         query = query.reshape(b * heads, -1, dim_head)
         value = value.reshape(b * heads, -1, dim_head)
         key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
     else:
-        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+        query, key, value = _reshape_qkv_to_heads(query, key, value, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        query = query.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
 
 
     dtype = query.dtype
@@ -304,19 +337,15 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     scale = kwargs.get("scale", dim_head ** -0.5)
 
     if skip_reshape:
-         q, k, v = map(
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
+        q, k, v = map(
             lambda t: t.reshape(b * heads, -1, dim_head),
             (q, k, v),
         )
     else:
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, -1, heads, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * heads, -1, dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        q, k, v = map(lambda t: t.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head).contiguous(), (q, k, v))
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
@@ -438,7 +467,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             disabled_xformers = True
 
     if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
 
     if skip_reshape:
         # b h k d -> b k h d
@@ -446,13 +475,12 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             lambda t: t.permute(0, 2, 1, 3),
             (q, k, v),
         )
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-2], -2)
     # actually do the reshaping
     else:
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.reshape(b, -1, heads, dim_head),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
 
     if mask is not None:
         # add a singleton batch dimension
@@ -474,7 +502,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
         mask = mask_out[..., :mask.shape[-1]]
         mask = mask.expand(b, heads, -1, -1)
 
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask, scale=kwargs.get("scale", None))
 
     if skip_output_reshape:
         out = out.permute(0, 2, 1, 3)
@@ -498,10 +526,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
     else:
         b, _, dim_head = q.shape
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False), expand_kv=False)
+        q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v))
 
     if mask is not None:
         # add a batch dimension if there isn't already one
@@ -511,9 +537,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
-    # Pass through extra SDPA kwargs (scale, enable_gqa) if provided
-    # enable_gqa requires PyTorch 2.5+; older versions use manual KV expansion above
-    sdpa_keys = ("scale", "enable_gqa") if TORCH_HAS_GQA else ("scale",)
+    sdpa_keys = ("scale", "enable_gqa")
     sdpa_extra = {k: v for k, v in kwargs.items() if k in sdpa_keys}
 
     if SDP_BATCH_LIMIT >= b:
@@ -541,20 +565,19 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
-    if kwargs.get("low_precision_attention", True) is False:
+    if kwargs.get("low_precision_attention", True) is False or (mask is not None and not SAGE_ATTENTION_SUPPORTS_MASK):
         return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
 
     exception_fallback = False
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
     else:
         b, _, dim_head = q.shape
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
         tensor_layout = "NHD"
 
     if mask is not None:
@@ -565,8 +588,12 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
+    sage_kwargs = {"is_causal": False, "tensor_layout": tensor_layout, "sm_scale": kwargs.get("scale", None), "smooth_k": False}
+    if mask is not None:
+        sage_kwargs["attn_mask"] = mask
+
     try:
-        out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
+        out = sageattn(q, k, v, **sage_kwargs)
     except Exception as e:
         logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
         exception_fallback = True
@@ -616,7 +643,6 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 skip_output_reshape=skip_output_reshape,
                 **kwargs
             )
-        q_s, k_s, v_s = q, k, v
         N = q.shape[2]
         dim_head = D
     else:
@@ -642,11 +668,15 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 **kwargs
             )
 
-    if not skip_reshape:
-        q_s, k_s, v_s = map(
-            lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(),
-            (q, k, v),
-        )
+    if skip_reshape:
+        q_s = q
+        if kwargs.get("enable_gqa", False):
+            k_s, v_s = _repeat_kv_for_gqa(k, v, H, -3)
+        else:
+            k_s, v_s = k, v
+    else:
+        q_s, k_s, v_s = _reshape_qkv_to_heads(q, k, v, B, heads, dim_head, kwargs.get("enable_gqa", False))
+        q_s, k_s, v_s = map(lambda t: t.permute(0, 2, 1, 3).contiguous(), (q_s, k_s, v_s))
         B, H, L, D = q_s.shape
 
     try:
@@ -662,7 +692,7 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 q, k, v, heads,
                 mask=mask,
                 attn_precision=attn_precision,
-                skip_reshape=False,
+                skip_reshape=skip_reshape,
                 skip_output_reshape=skip_output_reshape,
                 **kwargs
             )
@@ -679,21 +709,22 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     return out
 
 try:
-    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    @torch.library.custom_op("comfy::flash_attn", mutates_args=())
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
-        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)
+                    dropout_p: float = 0.0, causal: bool = False, softmax_scale: float = -1.0) -> torch.Tensor:
+        softmax_scale_arg = None if softmax_scale == -1.0 else softmax_scale
+        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal, softmax_scale=softmax_scale_arg)
 
 
     @flash_attn_wrapper.register_fake
-    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
+    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False, softmax_scale=-1.0):
         # Output shape is the same as q
         return q.new_empty(q.shape)
 except AttributeError as error:
     FLASH_ATTN_ERROR = error
 
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+                    dropout_p: float = 0.0, causal: bool = False, softmax_scale: float = -1.0) -> torch.Tensor:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
 @wrap_attn
@@ -703,10 +734,8 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     else:
         b, _, dim_head = q.shape
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False), expand_kv=False)
+        q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v))
 
     if mask is not None:
         # add a batch dimension if there isn't already one
@@ -725,10 +754,16 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
             v.transpose(1, 2),
             dropout_p=0.0,
             causal=False,
+            softmax_scale=kwargs.get("scale", -1.0),
         ).transpose(1, 2)
     except Exception as e:
         logging.warning(f"Flash Attention failed, using default SDPA: {e}")
-        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        sdpa_extra = {}
+        if kwargs.get("enable_gqa", False):
+            sdpa_extra["enable_gqa"] = True
+        if "scale" in kwargs:
+            sdpa_extra["scale"] = kwargs["scale"]
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False, **sdpa_extra)
     if not skip_output_reshape:
         out = (
             out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -1209,5 +1244,3 @@ class SpatialVideoTransformer(SpatialTransformer):
             x = self.proj_out(x)
         out = x + x_in
         return out
-
-

comfy/ldm/modules/diffusionmodules/model.py

@@ -22,7 +22,7 @@ def torch_cat_if_needed(xl, dim):
     else:
         return None
 
-def get_timestep_embedding(timesteps, embedding_dim):
+def get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
     From Fairseq.
@@ -33,11 +33,13 @@ def get_timestep_embedding(timesteps, embedding_dim):
     assert len(timesteps.shape) == 1
 
     half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
+    emb = math.log(10000) / (half_dim - downscale_freq_shift)
     emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
     emb = emb.to(device=timesteps.device)
     emb = timesteps.float()[:, None] * emb[None, :]
     emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
     if embedding_dim % 2 == 1:  # zero pad
         emb = torch.nn.functional.pad(emb, (0,1,0,0))
     return emb

comfy/ldm/omnigen/omnigen2.py

@@ -8,6 +8,7 @@ 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.flux.math import apply_rope1
 from comfy.ldm.modules.attention import optimized_attention_masked
 import comfy.model_management
 import comfy.ldm.common_dit
@@ -17,13 +18,11 @@ 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)
+    return apply_rope1(x, freqs_cis)
 
 
 def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-    return F.silu(x) * y
+    return F.silu(x, inplace=True).mul_(y)
 
 
 class TimestepEmbedding(nn.Module):
@@ -142,11 +141,8 @@ class Attention(nn.Module):
         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, transformer_options=transformer_options)
+        gqa_kwargs = {"enable_gqa": True} if self.kv_heads < self.heads else {}
+        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options, **gqa_kwargs)
         hidden_states = self.to_out[0](hidden_states)
         return hidden_states
 

comfy/ldm/pixeldit/model.py

@@ -197,6 +197,9 @@ class PixDiT_T2I(nn.Module):
         """Hook for subclasses to inject per-block state into the patch stream (e.g. PiD's LQ gate)."""
         return s
 
+    def _pre_pixel_blocks(self, s, **kwargs):
+        return s
+
     def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
         H_orig, W_orig = x.shape[2], x.shape[3]
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
@@ -226,6 +229,7 @@ class PixDiT_T2I(nn.Module):
             s, y_emb = blk(s, y_emb, condition, pos_img, pos_txt, None, transformer_options=transformer_options)
         s = F.silu(t_emb + s)
 
+        s = self._pre_pixel_blocks(s, **kwargs)
         s_cond = s.view(B * L, self.hidden_size)
         x_pixels = self.pixel_embedder(x, patch_size=self.patch_size)
         for blk in self.pixel_blocks:

comfy/ldm/pixeldit/pid.py

@@ -13,15 +13,15 @@ from .model import PixDiT_T2I
 from .modules import precompute_freqs_cis_2d
 
 
-class SigmaAwareGatePerTokenPerDim(nn.Module):
+class SigmaAwareGate(nn.Module):
     """gate = sigmoid(content_proj(cat[x, lq]) - exp(log_alpha) * sigma); out = x + gate * lq.
 
     Trained init gives ~0.88 gate at sigma=0, ~0.05 at sigma=1.
     """
 
-    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, per_token: bool = False, dtype=None, device=None, operations=None):
         super().__init__()
-        self.content_proj = operations.Linear(dim * 2, dim, dtype=dtype, device=device)
+        self.content_proj = operations.Linear(dim * 2, 1 if per_token else dim, dtype=dtype, device=device)
         self.log_alpha = nn.Parameter(torch.empty((), dtype=dtype, device=device))
 
     def forward(self, x: torch.Tensor, lq: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
@@ -36,15 +36,15 @@ class SigmaAwareGatePerTokenPerDim(nn.Module):
 class ResBlock(nn.Module):
     """Pre-activation ResNet block: GN -> SiLU -> Conv -> GN -> SiLU -> Conv + skip."""
 
-    def __init__(self, channels: int, num_groups: int = 4, dtype=None, device=None, operations=None):
+    def __init__(self, channels: int, num_groups: int = 4, conv_padding_mode: str = "zeros", dtype=None, device=None, operations=None):
         super().__init__()
         self.block = nn.Sequential(
             operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(channels, channels, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
             operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(channels, channels, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -62,9 +62,13 @@ class LQProjection2D(nn.Module):
         patch_size: int = 16,
         sr_scale: int = 4,
         latent_spatial_down_factor: int = 8,
+        latent_unpatchify_factor: int = 1,
         num_res_blocks: int = 4,
         num_outputs: int = 7,
         interval: int = 2,
+        conv_padding_mode: str = "zeros",
+        gate_per_token: bool = False,
+        pit_output: bool = False,
         dtype=None, device=None, operations=None,
     ):
         super().__init__()
@@ -74,34 +78,38 @@ class LQProjection2D(nn.Module):
         self.patch_size = patch_size
         self.sr_scale = sr_scale
         self.latent_spatial_down_factor = latent_spatial_down_factor
+        self.latent_unpatchify_factor = latent_unpatchify_factor
         self.num_outputs = num_outputs
         self.interval = interval
 
-        z_to_patch_ratio = (sr_scale * latent_spatial_down_factor) / patch_size
+        effective_latent_channels = latent_channels // (latent_unpatchify_factor * latent_unpatchify_factor)
+        effective_spatial_down_factor = latent_spatial_down_factor // latent_unpatchify_factor
+        z_to_patch_ratio = (sr_scale * effective_spatial_down_factor) / patch_size
         self.z_to_patch_ratio = z_to_patch_ratio
         if z_to_patch_ratio >= 1:
             self.latent_fold_factor = 0
-            latent_proj_in_ch = latent_channels
+            latent_proj_in_ch = effective_latent_channels
         else:
             fold_factor = int(1 / z_to_patch_ratio)
             assert fold_factor * z_to_patch_ratio == 1.0
             self.latent_fold_factor = fold_factor
-            latent_proj_in_ch = latent_channels * fold_factor * fold_factor
+            latent_proj_in_ch = effective_latent_channels * fold_factor * fold_factor
 
         layers = [
-            operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
         ]
         for _ in range(num_res_blocks):
-            layers.append(ResBlock(hidden_dim, dtype=dtype, device=device, operations=operations))
+            layers.append(ResBlock(hidden_dim, conv_padding_mode=conv_padding_mode, dtype=dtype, device=device, operations=operations))
         self.latent_proj = nn.Sequential(*layers)
 
         self.output_heads = nn.ModuleList(
             [operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) for _ in range(num_outputs)]
         )
+        self.pit_head = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) if pit_output else None
         self.gate_modules = nn.ModuleList(
-            [SigmaAwareGatePerTokenPerDim(out_dim, dtype=dtype, device=device, operations=operations)
+            [SigmaAwareGate(out_dim, per_token=gate_per_token, dtype=dtype, device=device, operations=operations)
              for _ in range(num_outputs)]
         )
 
@@ -115,6 +123,11 @@ class LQProjection2D(nn.Module):
         return self.gate_modules[out_idx](x, lq_feature, sigma)
 
     def _align_latent_to_patch_grid(self, lq_latent: torch.Tensor, pH: int, pW: int) -> torch.Tensor:
+        f = self.latent_unpatchify_factor
+        if f > 1:
+            B, C, H, W = lq_latent.shape
+            lq_latent = lq_latent.reshape(B, C // (f * f), f, f, H, W)
+            lq_latent = lq_latent.permute(0, 1, 4, 2, 5, 3).reshape(B, C // (f * f), H * f, W * f)
         B, z_dim = lq_latent.shape[:2]
         if self.z_to_patch_ratio >= 1:
             if lq_latent.shape[2] != pH or lq_latent.shape[3] != pW:
@@ -134,7 +147,10 @@ class LQProjection2D(nn.Module):
         feat = self._align_latent_to_patch_grid(lq_latent, target_pH, target_pW)
         B, C, H, W = feat.shape
         tokens = feat.permute(0, 2, 3, 1).contiguous().view(B, H * W, C)
-        return [head(tokens) for head in self.output_heads]
+        outputs = [head(tokens) for head in self.output_heads]
+        if self.pit_head is not None:
+            outputs.append(self.pit_head(tokens))
+        return outputs
 
 
 class PidNet(PixDiT_T2I):
@@ -148,6 +164,10 @@ class PidNet(PixDiT_T2I):
         lq_interval: int = 2,
         sr_scale: int = 4,
         latent_spatial_down_factor: int = 8,
+        lq_latent_unpatchify_factor: int = 1,
+        lq_conv_padding_mode: str = "zeros",
+        lq_gate_per_token: bool = False,
+        pit_lq_inject: bool = False,
         rope_ref_h: int = 1024, # NTK ref resolution in PIXEL units: 1024px / patch=16 -> grid_ref=64.
         rope_ref_w: int = 1024,
         image_model=None,
@@ -165,6 +185,8 @@ class PidNet(PixDiT_T2I):
         for blk in self.pixel_blocks:
             blk._rope_fn = _pit_rope_fn
 
+        self.pit_lq_inject = pit_lq_inject
+
         num_lq_outputs = (self.patch_depth + lq_interval - 1) // lq_interval
         self.lq_proj = LQProjection2D(
             latent_channels=lq_latent_channels,
@@ -173,13 +195,20 @@ class PidNet(PixDiT_T2I):
             patch_size=self.patch_size,
             sr_scale=sr_scale,
             latent_spatial_down_factor=latent_spatial_down_factor,
+            latent_unpatchify_factor=lq_latent_unpatchify_factor,
             num_res_blocks=lq_num_res_blocks,
             num_outputs=num_lq_outputs,
             interval=lq_interval,
+            conv_padding_mode=lq_conv_padding_mode,
+            gate_per_token=lq_gate_per_token,
+            pit_output=pit_lq_inject,
             dtype=dtype,
             device=device,
             operations=operations,
         )
+        self.pit_lq_gate = SigmaAwareGate(
+            self.hidden_size, per_token=lq_gate_per_token, dtype=dtype, device=device, operations=operations
+        ) if pit_lq_inject else None
 
     def _fetch_patch_pos(self, height, width, device, dtype, **rope_opts):
         return precompute_freqs_cis_2d(
@@ -197,6 +226,11 @@ class PidNet(PixDiT_T2I):
             return s
         return self.lq_proj.gate(s, pid_lq_features[out_idx], pid_degrade_sigma, out_idx)
 
+    def _pre_pixel_blocks(self, s, pid_pit_lq_feature=None, pid_degrade_sigma=None, **kwargs):
+        if pid_pit_lq_feature is None:
+            return s
+        return self.pit_lq_gate(s, pid_pit_lq_feature, pid_degrade_sigma)
+
     def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, lq_latent=None, degrade_sigma=None, **kwargs):
         if lq_latent is None:
             raise ValueError("PidNet requires lq_latent — attach via PiDConditioning")
@@ -216,12 +250,14 @@ class PidNet(PixDiT_T2I):
             degrade_sigma = degrade_sigma.expand(B).contiguous()
 
         lq_features = self.lq_proj(lq_latent=lq_latent.to(x), target_pH=Hs, target_pW=Ws)
+        pit_lq_feature = lq_features.pop() if self.pit_lq_inject else None
 
         return super()._forward(
             x, timesteps,
             context=context, attention_mask=attention_mask,
             transformer_options=transformer_options,
             pid_lq_features=lq_features,
+            pid_pit_lq_feature=pit_lq_feature,
             pid_degrade_sigma=degrade_sigma,
             **kwargs,
         )

comfy/ldm/seedvr/attention.py

@@ -0,0 +1,51 @@
+import torch
+
+from comfy.ldm.modules import attention as _attention
+
+
+def _var_attention_qkv(q, k, v, heads, skip_reshape):
+    if skip_reshape:
+        return q, k, v, q.shape[-1]
+    total_tokens, embed_dim = q.shape
+    head_dim = embed_dim // heads
+    return (
+        q.view(total_tokens, heads, head_dim),
+        k.view(k.shape[0], heads, head_dim),
+        v.view(v.shape[0], heads, head_dim),
+        head_dim,
+    )
+
+
+def _var_attention_output(out, heads, head_dim, skip_output_reshape):
+    if skip_output_reshape:
+        return out
+    return out.reshape(-1, heads * head_dim)
+
+
+def var_attention_optimized_split(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, *args, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    q, k, v, head_dim = _var_attention_qkv(q, k, v, heads, skip_reshape)
+
+    q_split_indices = cu_seqlens_q[1:-1]
+    k_split_indices = cu_seqlens_k[1:-1]
+    if k.shape[0] != v.shape[0]:
+        raise ValueError("cu_seqlens_k does not match v token count")
+
+    q_splits = torch.tensor_split(q, q_split_indices, dim=0)
+    k_splits = torch.tensor_split(k, k_split_indices, dim=0)
+    v_splits = torch.tensor_split(v, k_split_indices, dim=0)
+    if len(q_splits) != len(k_splits) or len(q_splits) != len(v_splits):
+        raise ValueError("cu_seqlens_q and cu_seqlens_k must describe the same sequence count")
+
+    out = []
+    for q_i, k_i, v_i in zip(q_splits, k_splits, v_splits):
+        q_i = q_i.permute(1, 0, 2).unsqueeze(0)
+        k_i = k_i.permute(1, 0, 2).unsqueeze(0)
+        v_i = v_i.permute(1, 0, 2).unsqueeze(0)
+        out_i = _attention.optimized_attention(q_i, k_i, v_i, heads, skip_reshape=True, skip_output_reshape=True)
+        out.append(out_i.squeeze(0).permute(1, 0, 2))
+
+    out = torch.cat(out, dim=0)
+    return _var_attention_output(out, heads, head_dim, skip_output_reshape)
+
+
+optimized_var_attention = var_attention_optimized_split

comfy/ldm/seedvr/color_fix.py

@@ -0,0 +1,301 @@
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+from comfy.ldm.seedvr.constants import (
+    CIELAB_DELTA,
+    CIELAB_KAPPA,
+    D65_WHITE_X,
+    D65_WHITE_Z,
+    WAVELET_DECOMP_LEVELS,
+)
+
+
+def wavelet_blur(image: Tensor, radius):
+    max_safe_radius = max(1, min(image.shape[-2:]) // 8)
+    if radius > max_safe_radius:
+        radius = max_safe_radius
+
+    num_channels = image.shape[1]
+
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125,  0.25,  0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    kernel = kernel[None, None].repeat(num_channels, 1, 1, 1)
+
+    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
+    output = F.conv2d(image, kernel, groups=num_channels, dilation=radius)
+
+    return output
+
+def wavelet_decomposition(image: Tensor, levels: int = WAVELET_DECOMP_LEVELS):
+    high_freq = torch.zeros_like(image)
+
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq.add_(image).sub_(low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat: Tensor, style_feat: Tensor) -> Tensor:
+
+    if content_feat.shape != style_feat.shape:
+        if len(content_feat.shape) >= 3:
+            style_feat = F.interpolate(
+                style_feat,
+                size=content_feat.shape[-2:],
+                mode='bilinear',
+                align_corners=False
+            )
+
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+
+    if content_high_freq.shape != style_low_freq.shape:
+        style_low_freq = F.interpolate(
+            style_low_freq,
+            size=content_high_freq.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    content_high_freq.add_(style_low_freq)
+
+    return content_high_freq.clamp_(-1.0, 1.0)
+
+def _histogram_matching_channel(source: Tensor, reference: Tensor) -> Tensor:
+    original_shape = source.shape
+
+    source_flat = source.flatten()
+    reference_flat = reference.flatten()
+
+    source_sorted, source_indices = torch.sort(source_flat)
+    reference_sorted, _ = torch.sort(reference_flat)
+    del reference_flat
+
+    n_source = len(source_sorted)
+    n_reference = len(reference_sorted)
+
+    if n_source == n_reference:
+        matched_sorted = reference_sorted
+    else:
+        source_quantiles = torch.linspace(0, 1, n_source, device=source.device)
+        ref_indices = (source_quantiles * (n_reference - 1)).long()
+        ref_indices.clamp_(0, n_reference - 1)
+        matched_sorted = reference_sorted[ref_indices]
+        del source_quantiles, ref_indices, reference_sorted
+
+    del source_sorted, source_flat
+
+    inverse_indices = torch.argsort(source_indices)
+    del source_indices
+    matched_flat = matched_sorted[inverse_indices]
+    del matched_sorted, inverse_indices
+
+    return matched_flat.reshape(original_shape)
+
+def _lab_to_rgb_batch(lab: Tensor, matrix_inv: Tensor, epsilon: float, kappa: float) -> Tensor:
+    L, a, b = lab[:, 0], lab[:, 1], lab[:, 2]
+
+    fy = (L + 16.0) / 116.0
+    fx = a.div(500.0).add_(fy)
+    fz = fy - b / 200.0
+    del L, a, b
+
+    x = torch.where(
+        fx > epsilon,
+        torch.pow(fx, 3.0),
+        fx.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    y = torch.where(
+        fy > epsilon,
+        torch.pow(fy, 3.0),
+        fy.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    z = torch.where(
+        fz > epsilon,
+        torch.pow(fz, 3.0),
+        fz.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    del fx, fy, fz
+
+    x.mul_(D65_WHITE_X)
+    z.mul_(D65_WHITE_Z)
+
+    xyz = torch.stack([x, y, z], dim=1)
+    del x, y, z
+
+    B, _, H, W = xyz.shape
+    xyz_flat = xyz.permute(0, 2, 3, 1).reshape(-1, 3)
+    del xyz
+
+    xyz_flat = xyz_flat.to(dtype=matrix_inv.dtype)
+    rgb_linear_flat = torch.matmul(xyz_flat, matrix_inv.T)
+    del xyz_flat
+
+    rgb_linear = rgb_linear_flat.reshape(B, H, W, 3).permute(0, 3, 1, 2)
+    del rgb_linear_flat
+
+    mask = rgb_linear > 0.0031308
+    rgb = torch.where(
+        mask,
+        torch.pow(torch.clamp(rgb_linear, min=0.0), 1.0 / 2.4).mul_(1.055).sub_(0.055),
+        rgb_linear * 12.92
+    )
+    del mask, rgb_linear
+
+    return torch.clamp(rgb, 0.0, 1.0)
+
+def _rgb_to_lab_batch(rgb: Tensor, matrix: Tensor, epsilon: float, kappa: float) -> Tensor:
+    mask = rgb > 0.04045
+    rgb_linear = torch.where(
+        mask,
+        torch.pow((rgb + 0.055) / 1.055, 2.4),
+        rgb / 12.92
+    )
+    del mask
+
+    B, _, H, W = rgb_linear.shape
+    rgb_flat = rgb_linear.permute(0, 2, 3, 1).reshape(-1, 3)
+    del rgb_linear
+
+    rgb_flat = rgb_flat.to(dtype=matrix.dtype)
+    xyz_flat = torch.matmul(rgb_flat, matrix.T)
+    del rgb_flat
+
+    xyz = xyz_flat.reshape(B, H, W, 3).permute(0, 3, 1, 2)
+    del xyz_flat
+
+    xyz[:, 0].div_(D65_WHITE_X)
+    xyz[:, 2].div_(D65_WHITE_Z)
+
+    epsilon_cubed = epsilon ** 3
+    mask = xyz > epsilon_cubed
+    f_xyz = torch.where(
+        mask,
+        torch.pow(xyz, 1.0 / 3.0),
+        xyz.mul(kappa).add_(16.0).div_(116.0)
+    )
+    del xyz, mask
+
+    L = f_xyz[:, 1].mul(116.0).sub_(16.0)
+    a = (f_xyz[:, 0] - f_xyz[:, 1]).mul_(500.0)
+    b = (f_xyz[:, 1] - f_xyz[:, 2]).mul_(200.0)
+    del f_xyz
+
+    return torch.stack([L, a, b], dim=1)
+
+def lab_color_transfer(
+    content_feat: Tensor,
+    style_feat: Tensor,
+    luminance_weight: float = 0.8
+) -> Tensor:
+    content_feat = wavelet_reconstruction(content_feat, style_feat)
+
+    if content_feat.shape != style_feat.shape:
+        style_feat = F.interpolate(
+            style_feat,
+            size=content_feat.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    device = content_feat.device
+    original_dtype = content_feat.dtype
+    content_feat = content_feat.float()
+    style_feat = style_feat.float()
+
+    rgb_to_xyz_matrix = torch.tensor([
+        [0.4124564, 0.3575761, 0.1804375],
+        [0.2126729, 0.7151522, 0.0721750],
+        [0.0193339, 0.1191920, 0.9503041]
+    ], dtype=torch.float32, device=device)
+
+    xyz_to_rgb_matrix = torch.tensor([
+        [ 3.2404542, -1.5371385, -0.4985314],
+        [-0.9692660,  1.8760108,  0.0415560],
+        [ 0.0556434, -0.2040259,  1.0572252]
+    ], dtype=torch.float32, device=device)
+
+    epsilon = CIELAB_DELTA
+    kappa = CIELAB_KAPPA
+
+    content_feat.add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
+    style_feat.add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
+
+    content_lab = _rgb_to_lab_batch(content_feat, rgb_to_xyz_matrix, epsilon, kappa)
+    del content_feat
+
+    style_lab = _rgb_to_lab_batch(style_feat, rgb_to_xyz_matrix, epsilon, kappa)
+    del style_feat, rgb_to_xyz_matrix
+
+    matched_a = _histogram_matching_channel(content_lab[:, 1], style_lab[:, 1])
+    matched_b = _histogram_matching_channel(content_lab[:, 2], style_lab[:, 2])
+
+    if luminance_weight < 1.0:
+        matched_L = _histogram_matching_channel(content_lab[:, 0], style_lab[:, 0])
+        result_L = content_lab[:, 0].mul(luminance_weight).add_(matched_L.mul(1.0 - luminance_weight))
+        del matched_L
+    else:
+        result_L = content_lab[:, 0]
+
+    del content_lab, style_lab
+
+    result_lab = torch.stack([result_L, matched_a, matched_b], dim=1)
+    del result_L, matched_a, matched_b
+
+    result_rgb = _lab_to_rgb_batch(result_lab, xyz_to_rgb_matrix, epsilon, kappa)
+    del result_lab, xyz_to_rgb_matrix
+
+    result = result_rgb.mul_(2.0).sub_(1.0)
+    del result_rgb
+
+    result = result.to(original_dtype)
+
+    return result
+
+
+def wavelet_color_transfer(content_feat: Tensor, style_feat: Tensor) -> Tensor:
+    return wavelet_reconstruction(content_feat, style_feat)
+
+
+def adain_color_transfer(content_feat: Tensor, style_feat: Tensor, eps: float = 1e-5) -> Tensor:
+    if content_feat.shape != style_feat.shape:
+        style_feat = F.interpolate(
+            style_feat,
+            size=content_feat.shape[-2:],
+            mode='bilinear',
+            align_corners=False,
+        )
+
+    original_dtype = content_feat.dtype
+    content_feat = content_feat.float()
+    style_feat = style_feat.float()
+
+    b, c = content_feat.shape[:2]
+    content_flat = content_feat.reshape(b, c, -1)
+    style_flat = style_feat.reshape(b, c, -1)
+
+    content_mean = content_flat.mean(dim=2).reshape(b, c, 1, 1)
+    content_std = (content_flat.var(dim=2, correction=0) + eps).sqrt().reshape(b, c, 1, 1)
+    style_mean = style_flat.mean(dim=2).reshape(b, c, 1, 1)
+    style_std = (style_flat.var(dim=2, correction=0) + eps).sqrt().reshape(b, c, 1, 1)
+    del content_flat, style_flat
+
+    normalized = (content_feat - content_mean) / content_std
+    del content_mean, content_std
+    result = normalized * style_std + style_mean
+    del normalized, style_mean, style_std
+
+    result = result.clamp_(-1.0, 1.0)
+    if result.dtype != original_dtype:
+        result = result.to(original_dtype)
+    return result

comfy/ldm/seedvr/constants.py

@@ -0,0 +1,48 @@
+"""SeedVR2 constants."""
+
+# Temporal chunk-size law: the sampler's activation wall is linear in
+# T_latent * pixel area (17-cell resolution sweep + T bisection, RTX 5090, 3b fp16):
+#   max_latent_frames = (free_GiB - RESERVED - K*SIGMA) / (GIB_PER_MPX_FRAME * megapixels)
+# RESERVED covers model staging plus fixed CUDA/torch overhead; SIGMA is the measured
+# run-to-run spread of the wall; K=4 trades ~10% smaller chunks for ~1e-5 OOM odds.
+SEEDVR2_CHUNK_GIB_PER_MPX_FRAME = 0.55
+SEEDVR2_CHUNK_RESERVED_GIB = 8.5
+SEEDVR2_CHUNK_SIGMA_GIB = 0.55
+SEEDVR2_CHUNK_SIGMA_K = 4
+
+SEEDVR2_7B_VID_DIM = 3072
+SEEDVR2_OOM_BACKOFF_DIVISOR = 2
+SEEDVR2_DTYPE_BYTES_FLOOR = 4
+SEEDVR2_7B_MLP_CHUNK = 8192
+SEEDVR2_ROPE_PARTIAL_CHUNK_TOKENS = 4096  # partial-RoPE application token-chunk.
+SEEDVR2_LATENT_CHANNELS = 16
+
+SEEDVR2_COLOR_MEM_HEADROOM = 0.75
+SEEDVR2_LAB_SCALE_MULTIPLIER = 13
+SEEDVR2_WAVELET_SCALE_MULTIPLIER = 10  # per-frame byte multiplier, wavelet path.
+SEEDVR2_ADAIN_SCALE_MULTIPLIER = 6
+
+BYTEDANCE_VAE_SCALING_FACTOR = 0.9152   # configs_3b/main.yaml:57.
+BYTEDANCE_VAE_SHIFTING_FACTOR = 0.0
+BYTEDANCE_VAE_CONV_MEM_GIB = 0.5
+BYTEDANCE_VAE_NORM_MEM_GIB = 0.5
+BYTEDANCE_LOGVAR_CLAMP_MIN = -30.0      # video_vae_v3/modules/types.py:28.
+BYTEDANCE_LOGVAR_CLAMP_MAX = 20.0       # video_vae_v3/modules/types.py:28.
+BYTEDANCE_GN_CHUNKS_FP16 = 4            # causal_inflation_lib.py:351 (GroupNorm chunk count, fp16).
+BYTEDANCE_GN_CHUNKS_FP32 = 2            # causal_inflation_lib.py:351 (GroupNorm chunk count, fp32).
+BYTEDANCE_BLOCK_OUT_CHANNELS = (128, 256, 512, 512)  # s8_c16_t4_inflation_sd3.yaml:7-11.
+BYTEDANCE_SLICING_SAMPLE_MIN = 4        # s8_c16_t4_inflation_sd3.yaml:22 (slicing_sample_min_size).
+BYTEDANCE_VAE_TEMPORAL_DOWNSAMPLE = 4   # infer.py:230 (temporal_downsample_factor); the 4n+1 factor.
+BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE = 8    # infer.py:231 (spatial_downsample_factor).
+BYTEDANCE_720P_REF_AREA = 45 * 80       # dit_v2/window.py:32 (720p reference area for window scaling).
+BYTEDANCE_MAX_TEMPORAL_WINDOW = 30      # dit_v2/window.py:35 (max temporal window frames).
+BYTEDANCE_ROPE_MAX_FREQ = 256           # dit_v2/rope.py:31 (pixel-RoPE max frequency).
+BYTEDANCE_SINUSOIDAL_DIM = 256          # dit_3b/nadit.py:120 (timestep sinusoidal embed dim).
+
+ROPE_THETA = 10000   # RoPE base; Su et al., "RoFormer", arXiv:2104.09864.
+
+CIELAB_DELTA = 6.0 / 29.0          # CIE 15 (delta).
+CIELAB_KAPPA = (29.0 / 3.0) ** 3   # CIE 15 (kappa).
+D65_WHITE_X = 0.95047              # CIE D65 standard illuminant Xn (Yn = 1).
+D65_WHITE_Z = 1.08883              # CIE D65 standard illuminant Zn.
+WAVELET_DECOMP_LEVELS = 5          # wavelet color-fix decomposition depth (GIMP/Krita; StableSR).

comfy/ldm/wan/model.py

@@ -1665,7 +1665,7 @@ class SCAILWanModel(WanModel):
 
         # embeddings
         x = self.patch_embedding(x.float()).to(x.dtype)
-        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream
+        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream (one identity mask frame per reference, then video)
             x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         transformer_options["grid_sizes"] = grid_sizes
@@ -1728,22 +1728,25 @@ class SCAILWanModel(WanModel):
 
     # ref_mask_flag is a scalar bool (CONDConstant, SCAIL-2 only). False => replacement mode,
     # which places ref/pose via H/W rope shifts instead of the animation-mode temporal offset.
+    # reference_latent may stack several frames: the last is the primary reference adjacent to the video, the earlier frames are additional references.
     def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, ref_mask_flag=None, transformer_options={}):
+        ref_t_patches = 0
+        if reference_latent is not None:
+            ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
+
         if ref_mask_flag is not None and not bool(ref_mask_flag):
             REF_ROPE_H = 120.0
             POSE_ROPE_W = 120.0
 
-            ref_t_patches = 0
-            if reference_latent is not None:
-                ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
             main_t_patches = t - ref_t_patches
+            video_t_start = max(ref_t_patches - 1, 0)
 
             parts = []
             if ref_t_patches > 0:
                 ref_tf = {"rope_options": {"shift_y": REF_ROPE_H, "shift_x": 0.0, "scale_y": 1.0, "scale_x": 1.0}}
                 parts.append(super().rope_encode(ref_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=ref_tf))
             if main_t_patches > 0:
-                parts.append(super().rope_encode(main_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=transformer_options))
+                parts.append(super().rope_encode(main_t_patches, h, w, t_start=video_t_start, device=device, dtype=dtype, transformer_options=transformer_options))
 
             if pose_latents is not None:
                 F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
@@ -1752,7 +1755,7 @@ class SCAILWanModel(WanModel):
                 h_shift = (h_scale - 1) / 2
                 w_shift = (w_scale - 1) / 2
                 pose_tf = {"rope_options": {"shift_y": h_shift, "shift_x": POSE_ROPE_W + w_shift, "scale_y": h_scale, "scale_x": w_scale}}
-                parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=0, device=device, dtype=dtype, transformer_options=pose_tf))
+                parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=video_t_start, device=device, dtype=dtype, transformer_options=pose_tf))
 
             return torch.cat(parts, dim=1)
 
@@ -1761,10 +1764,6 @@ class SCAILWanModel(WanModel):
         if pose_latents is None:
             return main_freqs
 
-        ref_t_patches = 0
-        if reference_latent is not None:
-            ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
-
         F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
 
         # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames

comfy/lora.py

@@ -326,6 +326,17 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["transformer.{}".format(key_lora)] = k
                 key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format
 
+    if isinstance(model, comfy.model_base.Krea2):
+        diffusers_keys = comfy.utils.krea2_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = k[:-len(".weight")]
+                key_map["diffusion_model.{}".format(key_lora)] = to
+                key_map["transformer.{}".format(key_lora)] = to
+                key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
+                key_map[key_lora] = to
+
     if isinstance(model, comfy.model_base.Lumina2):
         diffusers_keys = comfy.utils.z_image_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
         for k in diffusers_keys:

comfy/model_base.py

@@ -21,6 +21,7 @@ import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
 import comfy.ldm.lightricks.av_model
+import comfy.ldm.lightricks.symmetric_patchifier
 import comfy.context_windows
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from comfy.ldm.cascade.stage_c import StageC
@@ -54,8 +55,11 @@ import comfy.ldm.pixeldit.model
 import comfy.ldm.pixeldit.pid
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
+import comfy.ldm.seedvr.model
+import comfy.ldm.boogu.model
 import comfy.ldm.qwen_image.model
 import comfy.ldm.ideogram4.model
+import comfy.ldm.krea2.model
 import comfy.ldm.kandinsky5.model
 import comfy.ldm.anima.model
 import comfy.ldm.ace.ace_step15
@@ -929,6 +933,17 @@ class HunyuanDiT(BaseModel):
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
 
+class SeedVR2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.seedvr.model.NaDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        condition = kwargs.get("condition", None)
+        if condition is not None:
+            out["condition"] = comfy.conds.CONDRegular(condition)
+        return out
+
 class PixArt(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)
@@ -1203,6 +1218,127 @@ class LTXAV(BaseModel):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return latent_image
 
+    def map_context_window_to_modalities(self, primary_indices, latent_shapes, dim):
+        result = [primary_indices]
+        if len(latent_shapes) < 2:
+            return result
+
+        video_total = latent_shapes[0][dim]
+
+        for i in range(1, len(latent_shapes)):
+            mod_total = latent_shapes[i][dim]
+            # Map each primary index to its proportional range of modality indices and
+            # concatenate in order. Preserves wrapped/strided geometry so the modality
+            # attends to the same temporal regions as the primary window.
+            mod_indices = []
+            seen = set()
+            for v_idx in primary_indices:
+                a_start = min(int(round(v_idx * mod_total / video_total)), mod_total - 1)
+                a_end = min(int(round((v_idx + 1) * mod_total / video_total)), mod_total)
+                if a_end <= a_start:
+                    a_end = a_start + 1
+                for a in range(a_start, a_end):
+                    if a not in seen:
+                        seen.add(a)
+                        mod_indices.append(a)
+            result.append(mod_indices)
+
+        return result
+
+    @staticmethod
+    def _get_guide_entries(conds):
+        for cond_list in conds:
+            if cond_list is None:
+                continue
+            for cond_dict in cond_list:
+                model_conds = cond_dict.get('model_conds', {})
+                entries = model_conds.get('guide_attention_entries')
+                if entries is not None and hasattr(entries, 'cond') and entries.cond:
+                    return entries.cond
+        return None
+
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        # Audio denoise mask — slice using audio modality window
+        if cond_key == "audio_denoise_mask" and hasattr(window, 'modality_windows') and window.modality_windows:
+            audio_window = window.modality_windows.get(1)
+            if audio_window is not None and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+                sliced = audio_window.get_tensor(cond_value.cond, device, dim=2)
+                return cond_value._copy_with(sliced)
+
+        # Video denoise mask — split into video + guide portions, slice each
+        if cond_key == "denoise_mask" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+            cond_tensor = cond_value.cond
+            guide_count = cond_tensor.size(window.dim) - x_in.size(window.dim)
+            if guide_count > 0:
+                T_video = x_in.size(window.dim)
+                video_mask = cond_tensor.narrow(window.dim, 0, T_video)
+                guide_mask = cond_tensor.narrow(window.dim, T_video, guide_count)
+                sliced_video = window.get_tensor(video_mask, device, retain_index_list=retain_index_list)
+                suffix_indices = window.guide_frames_indices
+                if suffix_indices:
+                    idx = tuple([slice(None)] * window.dim + [suffix_indices])
+                    sliced_guide = guide_mask[idx].to(device)
+                    return cond_value._copy_with(torch.cat([sliced_video, sliced_guide], dim=window.dim))
+                else:
+                    return cond_value._copy_with(sliced_video)
+
+        # Keyframe indices — regenerate pixel coords for window, select guide positions
+        if cond_key == "keyframe_idxs":
+            kf_local_pos = window.guide_kf_local_positions
+            if not kf_local_pos:
+                return cond_value._copy_with(cond_value.cond[:, :, :0, :])  # empty
+            H, W = x_in.shape[3], x_in.shape[4]
+            window_len = len(window.index_list)
+            # account for causal_window_fix anchor in coord space size
+            anchor_idx = getattr(window, 'causal_anchor_index', None)
+            if anchor_idx is not None and anchor_idx >= 0:
+                window_len += 1
+            patchifier = self.diffusion_model.patchifier
+            latent_coords = patchifier.get_latent_coords(window_len, H, W, 1, cond_value.cond.device)
+            scale_factors = self.diffusion_model.vae_scale_factors
+            pixel_coords = comfy.ldm.lightricks.symmetric_patchifier.latent_to_pixel_coords(
+                latent_coords,
+                scale_factors,
+                causal_fix=self.diffusion_model.causal_temporal_positioning)
+            tokens = []
+            for pos in kf_local_pos:
+                tokens.extend(range(pos * H * W, (pos + 1) * H * W))
+            pixel_coords = pixel_coords[:, :, tokens, :]
+
+            # Adjust spatial end positions for dilated (downscaled) guides.
+            # Each guide entry may have a different downscale factor; expand the
+            # per-entry factor to cover all tokens belonging to that entry.
+            downscale_factors = window.guide_downscale_factors
+            overlap_info = window.guide_overlap_info
+            if downscale_factors:
+                per_token_factor = []
+                for (entry_idx, overlap_count), dsf in zip(overlap_info, downscale_factors):
+                    per_token_factor.extend([dsf] * (overlap_count * H * W))
+                factor_tensor = torch.tensor(per_token_factor, device=pixel_coords.device, dtype=pixel_coords.dtype)
+                spatial_end_offset = (factor_tensor.unsqueeze(0).unsqueeze(0).unsqueeze(-1) - 1) * torch.tensor(
+                    scale_factors[1:], device=pixel_coords.device, dtype=pixel_coords.dtype,
+                ).view(1, -1, 1, 1)
+                pixel_coords[:, 1:, :, 1:] += spatial_end_offset
+
+            B = cond_value.cond.shape[0]
+            if B > 1:
+                pixel_coords = pixel_coords.expand(B, -1, -1, -1)
+            return cond_value._copy_with(pixel_coords)
+
+        # Guide attention entries — adjust per-guide counts based on window overlap
+        if cond_key == "guide_attention_entries":
+            overlap_info = window.guide_overlap_info
+            H, W = x_in.shape[3], x_in.shape[4]
+            new_entries = []
+            for entry_idx, overlap_count in overlap_info:
+                e = cond_value.cond[entry_idx]
+                new_entries.append({**e,
+                    "pre_filter_count": overlap_count * H * W,
+                    "latent_shape": [overlap_count, H, W]})
+            return cond_value._copy_with(new_entries)
+
+        return None
+
 class HunyuanVideo(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)
@@ -1747,10 +1883,14 @@ class WAN21_SCAIL(WAN21):
 
         reference_latents = kwargs.get("reference_latents", None)
         if reference_latents is not None:
-            ref_latent = self.process_latent_in(reference_latents[-1])
-            ref_mask = torch.ones_like(ref_latent[:, :4])
-            ref_latent = torch.cat([ref_latent, ref_mask], dim=1)
-            out['reference_latent'] = comfy.conds.CONDRegular(ref_latent)
+            # SCAIL-2 multi-reference: reference_latents[0] is the primary ref, [1:] are additional
+            # references. Stack as [additional..., primary] so the primary stays adjacent to the video.
+            ordered = list(reference_latents[1:]) + list(reference_latents[:1])
+            stacked = []
+            for lat in ordered:
+                lat = self.process_latent_in(lat)
+                stacked.append(torch.cat([lat, torch.ones_like(lat[:, :4])], dim=1))
+            out['reference_latent'] = comfy.conds.CONDRegular(torch.cat(stacked, dim=2))
 
         pose_latents = kwargs.get("pose_video_latent", None)
         if pose_latents is not None:
@@ -1792,6 +1932,7 @@ class WAN21_SCAIL2(WAN21_SCAIL):
         if driving_mask_28ch is not None:
             out['sam_latents'] = comfy.conds.CONDRegular(driving_mask_28ch.movedim(1, 2).contiguous())
 
+        # ref_mask_28ch holds one identity mask per stacked reference frame (additional refs first, then the primary ref), followed by zeros over the video frames.
         ref_mask_28ch = kwargs.get("ref_mask_28ch", None)
         if ref_mask_28ch is not None:
             out['ref_mask_latents'] = comfy.conds.CONDRegular(ref_mask_28ch.movedim(1, 2).contiguous())
@@ -1819,10 +1960,11 @@ class WAN21_SCAIL2(WAN21_SCAIL):
             # Return sliced view omitting retain_index_list
             return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=0)
         if cond_key == "ref_mask_latents" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-            # The ref mask is just a single frame padded with frames of zeros, so just grab the first frames for all windows
+            # The ref mask is N leading ref frames padded with frames of zeros, so just grab the first frames for all windows
             full_ref_mask = cond_value.cond
             video_frame_count = x_in.shape[2]
-            if full_ref_mask.shape[2] != video_frame_count + 1:
+            ref_frame_count = full_ref_mask.shape[2] - video_frame_count
+            if ref_frame_count < 1:
                 return None
             window_length = len(window.index_list)
 
@@ -1831,7 +1973,7 @@ class WAN21_SCAIL2(WAN21_SCAIL):
             if anchor_index is not None and anchor_index >= 0:
                 window_length += 1
 
-            window_ref_mask = full_ref_mask[:, :, :window_length + 1].to(device)
+            window_ref_mask = full_ref_mask[:, :, :window_length + ref_frame_count].to(device)
             return cond_value._copy_with(window_ref_mask)
 
         return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
@@ -2097,6 +2239,11 @@ class Omnigen2(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
         return out
 
+class Boogu(Omnigen2):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super(Omnigen2, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.boogu.model.BooguTransformer2DModel)
+        self.memory_usage_factor_conds = ("ref_latents",)
+
 class QwenImage(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel)
@@ -2144,6 +2291,17 @@ class Ideogram4(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
 
+class Krea2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.krea2.model.SingleStreamDiT)
+
+    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)
+        return out
+
 class HunyuanImage21(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)

comfy/model_detection.py

@@ -470,15 +470,46 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
     # PiD (Pixel Diffusion Decoder). Must check BEFORE plain PixelDiT_T2I.
     _lq_w_key = '{}lq_proj.latent_proj.0.weight'.format(key_prefix)
     if _lq_w_key in state_dict_keys:
-        in_ch = int(state_dict[_lq_w_key].shape[1])
+        latent_proj_in_channels = int(state_dict[_lq_w_key].shape[1])
+        hidden_dim = int(state_dict[_lq_w_key].shape[0])
         _gate_prefix = '{}lq_proj.gate_modules.'.format(key_prefix)
         num_gates = len({k[len(_gate_prefix):].split('.')[0]
                          for k in state_dict_keys if k.startswith(_gate_prefix)})
+        pid_v1_5 = '{}lq_proj.pit_head.weight'.format(key_prefix) in state_dict_keys
         dit_config = {"image_model": "pid",
-                      "lq_latent_channels": in_ch,
-                      "latent_spatial_down_factor": 16 if in_ch >= 64 else 8}
+                      "lq_hidden_dim": hidden_dim}
         if num_gates > 0:
             dit_config["lq_interval"] = (14 + num_gates - 1) // num_gates
+        if pid_v1_5:
+            pid_v1_5_variants = {
+                16: {  # Flux and QwenImage
+                    "lq_latent_channels": 16,
+                    "latent_spatial_down_factor": 8,
+                    "lq_latent_unpatchify_factor": 1,
+                },
+                32: {  # Flux2 after 2x latent unpatchify
+                    "lq_latent_channels": 128,
+                    "latent_spatial_down_factor": 16,
+                    "lq_latent_unpatchify_factor": 2,
+                },
+            }
+            variant = pid_v1_5_variants.get(latent_proj_in_channels)
+            if variant is None:
+                raise ValueError(f"Unsupported PiD v1.5 latent projection with {latent_proj_in_channels} input channels")
+            gate_weight = state_dict['{}lq_proj.gate_modules.0.content_proj.weight'.format(key_prefix)]
+            dit_config.update(variant)
+            dit_config.update({
+                "lq_conv_padding_mode": "replicate",
+                "lq_gate_per_token": gate_weight.shape[0] == 1,
+                "pit_lq_inject": True,
+                "rope_ref_h": 2048,
+                "rope_ref_w": 2048,
+            })
+        else:
+            dit_config.update({
+                "lq_latent_channels": latent_proj_in_channels,
+                "latent_spatial_down_factor": 16 if latent_proj_in_channels >= 64 else 8,
+            })
         return dit_config
 
     if '{}core.pixel_embedder.proj.weight'.format(key_prefix) in state_dict_keys:  # PixelDiT T2I
@@ -598,6 +629,44 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    seedvr2_7b_separate_key = "{}blocks.35.mlp.vid.proj_out.weight".format(key_prefix)
+    if seedvr2_7b_separate_key in state_dict_keys and state_dict[seedvr2_7b_separate_key].shape[0] == 3072: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint uses separate vid/txt MMModule keys in every block.
+        dit_config["mm_layers"] = 36
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "normal"
+        return dit_config
+    if "{}blocks.35.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint uses shared all.* MMModule keys after the initial blocks.
+        dit_config["mm_layers"] = 10
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "swiglu"
+        return dit_config
+    if "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 2560
+        dit_config["heads"] = 20
+        dit_config["num_layers"] = 32
+        dit_config["norm_eps"] = 1.0e-05
+        dit_config["mlp_type"] = "swiglu"
+        dit_config["vid_out_norm"] = True
+        return dit_config
+
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
         dit_config = {}
         dit_config["image_model"] = "wan2.1"
@@ -761,6 +830,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    if '{}double_stream_layers.0.img_instruct_attn.processor.img_to_q.weight'.format(key_prefix) in state_dict_keys:  # Boogu-Image (OmniGen2 derivative + dual-stream stage)
+        dit_config = {}
+        dit_config["image_model"] = "boogu"
+        dit_config["hidden_size"] = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[0]
+        dit_config["num_layers"] = count_blocks(state_dict_keys, '{}single_stream_layers.'.format(key_prefix) + '{}.')
+        dit_config["num_double_stream_layers"] = count_blocks(state_dict_keys, '{}double_stream_layers.'.format(key_prefix) + '{}.')
+        dit_config["num_refiner_layers"] = count_blocks(state_dict_keys, '{}noise_refiner.'.format(key_prefix) + '{}.')
+        dit_config["instruction_feat_dim"] = state_dict['{}time_caption_embed.caption_embedder.0.weight'.format(key_prefix)].shape[0]
+        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"
@@ -824,6 +903,21 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
         return dit_config
 
+    if '{}txtfusion.projector.weight'.format(key_prefix) in state_dict_keys:  # Krea 2 (K2)
+        dit_config = {}
+        dit_config["image_model"] = "krea2"
+        head_dim = 128
+        first_w = state_dict['{}first.weight'.format(key_prefix)]  # (features, channels*patch^2)
+        dit_config["features"] = first_w.shape[0]
+        dit_config["channels"] = first_w.shape[1] // (2 * 2)  # patch=2
+        dit_config["patch"] = 2
+        dit_config["layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+        dit_config["heads"] = state_dict['{}blocks.0.attn.wq.weight'.format(key_prefix)].shape[0] // head_dim
+        dit_config["kvheads"] = state_dict['{}blocks.0.attn.wk.weight'.format(key_prefix)].shape[0] // head_dim
+        dit_config["txtlayers"] = state_dict['{}txtfusion.projector.weight'.format(key_prefix)].shape[1]
+        dit_config["txtdim"] = state_dict['{}txtfusion.layerwise_blocks.0.prenorm.scale'.format(key_prefix)].shape[0]
+        return dit_config
+
     if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5
         dit_config = {}
         model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
@@ -1094,9 +1188,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     return unet_config
 
-def model_config_from_unet_config(unet_config, state_dict=None):
+
+def model_config_from_unet_config(unet_config, state_dict=None, unet_key_prefix=""):
     for model_config in comfy.supported_models.models:
-        if model_config.matches(unet_config, state_dict):
+        if model_config.matches(unet_config, state_dict, unet_key_prefix=unet_key_prefix):
             return model_config(unet_config)
 
     logging.error("no match {}".format(unet_config))
@@ -1106,7 +1201,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)
     if unet_config is None:
         return None
-    model_config = model_config_from_unet_config(unet_config, state_dict)
+    model_config = model_config_from_unet_config(unet_config, state_dict, unet_key_prefix)
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 

comfy/model_management.py

@@ -616,6 +616,8 @@ PIN_PRESSURE_HYSTERESIS = 256 * 1024 * 1024
 #Freeing registerables on pressure does imply a GPU sync, so go big on
 #the hysteresis so each expensive sync gives us back a good chunk.
 REGISTERABLE_PIN_HYSTERESIS = 2048 * 1024 * 1024
+WINDOWS_PIN_EVICTION_SWAP_PERCENT = 5.0
+WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE = 512 * 1024 ** 2
 
 def module_size(module):
     module_mem = 0
@@ -642,7 +644,18 @@ def free_pins(size, evict_active=False):
             size -= freed
     return freed_total
 
+def should_free_pins_for_ram_pressure(shortfall):
+    if shortfall <= 0:
+        return False
+    if not WINDOWS:
+        return True
+    if psutil.virtual_memory().available < WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE:
+        return True
+    return psutil.swap_memory().percent >= WINDOWS_PIN_EVICTION_SWAP_PERCENT
+
 def ensure_pin_budget(size, evict_active=False):
+    if args.high_ram:
+        return True
     if args.fast_disk:
         shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY
     else:
@@ -1496,6 +1509,8 @@ if not args.disable_pinned_memory:
 PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"])
 
 def pinned_hostbuf_size(size):
+    if args.high_ram:
+        return max(0, int(size * 2))
     return max(0, int(min(size, MAX_PINNED_MEMORY) * 2))
 
 def discard_cuda_async_error():

comfy/ops.py

@@ -174,13 +174,15 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
                     elif xfer_dest2 is not None:
                         xfer_source.prepare(xfer_dest2, stream, copy=True, commit=False)
                         return
+                    else:
+                        return
                 comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=stream, r2=xfer_dest2)
 
         def handle_pin(m, pin, source, dest, subset="weights", size=None):
             if pin is not None:
                 cast_maybe_lowvram_patch([pin], dest, offload_stream)
                 return
-            if signature is None:
+            if signature is None or args.high_ram:
                 comfy.pinned_memory.pin_memory(m, subset=subset, size=size)
                 pin = comfy.pinned_memory.get_pin(m, subset=subset)
             cast_maybe_lowvram_patch(source, pin, offload_stream, xfer_dest2=dest)
@@ -256,7 +258,7 @@ def resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, w
             if (want_requant and len(fns) == 0 or update_weight):
                 seed = comfy.utils.string_to_seed(s.seed_key)
                 if isinstance(orig, QuantizedTensor):
-                    y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
+                    y = orig.requantize_from_float(x, scale="recalculate", stochastic_rounding=seed)
                 else:
                     y = comfy.float.stochastic_rounding(x, orig.dtype, seed=seed)
             if want_requant and len(fns) == 0:
@@ -1089,6 +1091,34 @@ def _load_quantized_module(module, super_load, state_dict, prefix, local_metadat
             if ts is None or bs is None:
                 raise ValueError(f"Missing NVFP4 scales for layer {layer_name}")
             scales = {"scale": ts, "block_scale": bs}
+        elif module.quant_format == "int8_tensorwise":
+            scale = pop_scale("weight_scale")
+            if scale is None:
+                raise ValueError(f"Missing INT8 weight scale for layer {layer_name}")
+            scales = {"scale": scale}
+            params_conf = layer_conf.get("params", {})
+            if not isinstance(params_conf, dict):
+                params_conf = {}
+            if layer_conf.get("convrot", params_conf.get("convrot", False)):
+                scales["convrot"] = True
+                scales["convrot_groupsize"] = int(
+                    layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
+                )
+        elif module.quant_format == "convrot_w4a4":
+            scale = pop_scale("weight_scale")
+            if scale is None:
+                raise ValueError(f"Missing ConvRot W4A4 weight scale for layer {layer_name}")
+            params_conf = layer_conf.get("params", {})
+            if not isinstance(params_conf, dict):
+                params_conf = {}
+            scales = {
+                "scale": scale,
+                "convrot_groupsize": int(
+                    layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
+                ),
+                "quant_group_size": 64,
+                "linear_dtype": layer_conf.get("linear_dtype", params_conf.get("linear_dtype", "int4")),
+            }
         else:
             raise ValueError(f"Unsupported quantization format: {module.quant_format}")
 
@@ -1131,6 +1161,15 @@ def _quantized_weight_state_dict(module, sd, prefix, extra_quant_conf=None, extr
         quant_conf = {"format": module.quant_format}
         if getattr(module, '_full_precision_mm_config', False):
             quant_conf["full_precision_matrix_mult"] = True
+        params = getattr(module.weight, "_params", None)
+        if module.quant_format == "int8_tensorwise" and getattr(params, "convrot", False):
+            quant_conf["convrot"] = True
+            quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
+        elif module.quant_format == "convrot_w4a4":
+            quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
+            linear_dtype = getattr(params, "linear_dtype", "int4")
+            if linear_dtype != "int4":
+                quant_conf["linear_dtype"] = linear_dtype
         if extra_quant_conf:
             quant_conf.update(extra_quant_conf)
         sd[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8)
@@ -1183,8 +1222,33 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
             def _forward(self, input, weight, bias):
                 return torch.nn.functional.linear(input, weight, bias)
 
-            def forward_comfy_cast_weights(self, input, compute_dtype=None, want_requant=False):
-                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True, compute_dtype=compute_dtype, want_requant=want_requant)
+            def forward_comfy_cast_weights(
+                self,
+                input,
+                compute_dtype=None,
+                want_requant=False,
+                weight_only_quant=False,
+            ):
+                if weight_only_quant:
+                    weight, bias, offload_stream = cast_bias_weight(
+                        self,
+                        input=None,
+                        dtype=self.weight.dtype,
+                        device=input.device,
+                        bias_dtype=input.dtype,
+                        offloadable=True,
+                        compute_dtype=compute_dtype,
+                        want_requant=True,
+                    )
+                    weight = weight.to(dtype=input.dtype)
+                else:
+                    weight, bias, offload_stream = cast_bias_weight(
+                        self,
+                        input,
+                        offloadable=True,
+                        compute_dtype=compute_dtype,
+                        want_requant=want_requant,
+                    )
                 x = self._forward(input, weight, bias)
                 uncast_bias_weight(self, weight, bias, offload_stream)
                 return x
@@ -1193,7 +1257,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 run_every_op()
 
                 input_shape = input.shape
-                reshaped_3d = False
+                reshaped_nd = False
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
@@ -1203,9 +1267,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     not getattr(self, 'comfy_force_cast_weights', False) and
                     len(self.weight_function) == 0 and len(self.bias_function) == 0
                 )
+                quantize_input = QUANT_ALGOS.get(getattr(self, 'quant_format', None), {}).get("quantize_input", True)
 
                 # Training path: quantized forward with compute_dtype backward via autograd function
-                if (input.requires_grad and _use_quantized):
+                if (input.requires_grad and _use_quantized and quantize_input):
 
                     weight, bias, offload_stream = cast_bias_weight(
                         self,
@@ -1227,25 +1292,31 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     return output
 
                 # Inference path (unchanged)
-                if _use_quantized:
+                if _use_quantized and quantize_input:
 
-                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
+                    # Reshape >=3D tensors to 2D for quantization (needed for NVFP4 and others)
+                    input_reshaped = input.reshape(-1, input_shape[-1]) if input.ndim >= 3 else input
 
                     # Fall back to non-quantized for non-2D tensors
                     if input_reshaped.ndim == 2:
-                        reshaped_3d = input.ndim == 3
+                        reshaped_nd = input.ndim >= 3
                         # dtype is now implicit in the layout class
                         scale = getattr(self, 'input_scale', None)
                         if scale is not None:
                             scale = comfy.model_management.cast_to_device(scale, input.device, None)
                         input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
 
-                output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor))
+                weight_only_quant = _use_quantized and not quantize_input and isinstance(self.weight, QuantizedTensor)
+                output = self.forward_comfy_cast_weights(
+                    input,
+                    compute_dtype,
+                    want_requant=isinstance(input, QuantizedTensor),
+                    weight_only_quant=weight_only_quant,
+                )
 
-                # Reshape output back to 3D if input was 3D
-                if reshaped_3d:
-                    output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
+                # Reshape output back to original rank if input was >2D
+                if reshaped_nd:
+                    output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
 
                 return output
 
@@ -1257,8 +1328,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
 
             def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
                 if getattr(self, 'layout_type', None) is not None:
-                    # dtype is now implicit in the layout class
-                    weight = QuantizedTensor.from_float(weight, self.layout_type, scale="recalculate", stochastic_rounding=seed, inplace_ops=True).to(self.weight.dtype)
+                    weight = self.weight.requantize_from_float(weight, scale="recalculate", stochastic_rounding=seed, inplace_ops=True).to(self.weight.dtype)
                 else:
                     weight = weight.to(self.weight.dtype)
                 if return_weight:
@@ -1380,6 +1450,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 }
                 if hasattr(params, "block_scale"): # NVFP4
                     kwargs["block_scale"] = params.block_scale[i]
+                if hasattr(params, "quant_group_size"):
+                    kwargs["quant_group_size"] = params.quant_group_size
+                if hasattr(params, "convrot_groupsize"):
+                    kwargs["convrot_groupsize"] = params.convrot_groupsize
+                if hasattr(params, "linear_dtype"):
+                    kwargs["linear_dtype"] = params.linear_dtype
                 return QuantizedTensor(weight._qdata[i], weight._layout_cls, type(params)(**kwargs))
 
             def state_dict(self, *args, destination=None, prefix="", **kwargs):

comfy/quant_ops.py

@@ -3,6 +3,22 @@ import logging
 
 from comfy.cli_args import args
 
+
+def _rocm_kitchen_arch_supported():
+    """comfy-kitchen's INT8 Triton kernels compile tl.dot to matrix-core instructions.
+    RDNA3/3.5/4 (gfx11xx/gfx12xx) have WMMA and CDNA (gfx9xx) has MFMA; RDNA1/RDNA2
+    (gfx10xx) have neither, so the INT8 path hangs the GPU there. Gates the automatic
+    ROCm default so those cards stay on the eager fallback (an explicit
+    --enable-triton-backend still forces it on any arch)."""
+    try:
+        arch = torch.cuda.get_device_properties(torch.cuda.current_device()).gcnArchName.split(":")[0]
+    except Exception:
+        return False
+    if arch.startswith(("gfx11", "gfx12")):
+        return True
+    return arch in ("gfx908", "gfx90a", "gfx940", "gfx941", "gfx942", "gfx950")
+
+
 try:
     import comfy_kitchen as ck
     from comfy_kitchen.tensor import (
@@ -10,6 +26,8 @@ try:
         QuantizedLayout,
         TensorCoreFP8Layout as _CKFp8Layout,
         TensorCoreNVFP4Layout as _CKNvfp4Layout,
+        TensorCoreConvRotW4A4Layout as _CKTensorCoreConvRotW4A4Layout,
+        TensorWiseINT8Layout as _CKTensorWiseINT8Layout,
         register_layout_op,
         register_layout_class,
         get_layout_class,
@@ -23,10 +41,22 @@ try:
             ck.registry.disable("cuda")
             logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
-    if args.enable_triton_backend:
+    # On ROCm/AMD the CUDA backend is unavailable, so Triton is the only accelerated
+    # comfy-kitchen backend. Enable it by default there, but only on Triton >= 3.7 AND a
+    # matrix-core GPU (RDNA3+ WMMA gfx11xx/gfx12xx, CDNA MFMA gfx9xx). RDNA1/RDNA2
+    # (gfx10xx) have no WMMA -> the INT8 tl.dot path hangs the GPU, so they stay eager.
+    # older Triton lacks libdevice.rint on the HIP backend and hard-crashes the INT8 path.
+    if args.disable_triton_backend:
+        ck.registry.disable("triton")
+    elif args.enable_triton_backend: # or (torch.version.hip is not None and _rocm_kitchen_arch_supported()):
         try:
             import triton
-            logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
+            triton_version = tuple(int(v) for v in triton.__version__.split(".")[:2])
+            if args.enable_triton_backend or triton_version >= (3, 7):
+                logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
+            else:
+                logging.info("Triton %s is too old for the ROCm INT8 path (needs >= 3.7); comfy-kitchen triton backend disabled.", triton.__version__)
+                ck.registry.disable("triton")
         except ImportError as e:
             logging.error(f"Failed to import triton, Error: {e}, the comfy-kitchen triton backend will not be available.")
             ck.registry.disable("triton")
@@ -47,6 +77,12 @@ except ImportError as e:
     class _CKNvfp4Layout:
         pass
 
+    class _CKTensorWiseINT8Layout:
+        pass
+
+    class _CKTensorCoreConvRotW4A4Layout:
+        pass
+
     def register_layout_class(name, cls):
         pass
 
@@ -174,6 +210,8 @@ class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):
 
 # Backward compatibility alias - default to E4M3
 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout
+TensorWiseINT8Layout = _CKTensorWiseINT8Layout
+TensorCoreConvRotW4A4Layout = _CKTensorCoreConvRotW4A4Layout
 
 
 # ==============================================================================
@@ -184,6 +222,8 @@ register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout)
 register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
 register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
+register_layout_class("TensorWiseINT8Layout", _CKTensorWiseINT8Layout)
+register_layout_class("TensorCoreConvRotW4A4Layout", _CKTensorCoreConvRotW4A4Layout)
 if _CK_MXFP8_AVAILABLE:
     register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 
@@ -214,6 +254,20 @@ if _CK_MXFP8_AVAILABLE:
         "group_size": 32,
     }
 
+QUANT_ALGOS["int8_tensorwise"] = {
+    "storage_t": torch.int8,
+    "parameters": {"weight_scale"},
+    "comfy_tensor_layout": "TensorWiseINT8Layout",
+    "quantize_input": False,
+}
+
+QUANT_ALGOS["convrot_w4a4"] = {
+    "storage_t": torch.int8,
+    "parameters": {"weight_scale"},
+    "comfy_tensor_layout": "TensorCoreConvRotW4A4Layout",
+    "quantize_input": False,
+}
+
 
 # ==============================================================================
 # Re-exports for backward compatibility
@@ -226,6 +280,8 @@ __all__ = [
     "TensorCoreFP8E4M3Layout",
     "TensorCoreFP8E5M2Layout",
     "TensorCoreNVFP4Layout",
+    "TensorCoreConvRotW4A4Layout",
+    "TensorWiseINT8Layout",
     "QUANT_ALGOS",
     "register_layout_op",
 ]

comfy/sd.py

@@ -16,6 +16,7 @@ import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
+import comfy.ldm.seedvr.vae
 import comfy.ldm.triposplat.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.cogvideo.vae
@@ -58,6 +59,7 @@ import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.z_image
+import comfy.text_encoders.krea2
 import comfy.text_encoders.ideogram4
 import comfy.text_encoders.ovis
 import comfy.text_encoders.kandinsky5
@@ -67,6 +69,8 @@ import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
 import comfy.text_encoders.qwen35
+import comfy.text_encoders.qwen3vl
+import comfy.text_encoders.boogu
 import comfy.text_encoders.ernie
 import comfy.text_encoders.gemma4
 import comfy.text_encoders.cogvideo
@@ -465,9 +469,13 @@ class CLIP:
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
 
+    def is_dynamic(self):
+        return self.patcher.is_dynamic()
+
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
-        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+        is_seedvr2_vae = "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd
+        if not is_seedvr2_vae and 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
         if model_management.is_amd():
@@ -494,6 +502,8 @@ class VAE:
         self.upscale_index_formula = None
         self.extra_1d_channel = None
         self.crop_input = True
+        self.handles_tiling = False
+        self.format_encoded = None
 
         self.audio_sample_rate = 44100
 
@@ -540,6 +550,22 @@ class VAE:
                 self.first_stage_model = StageC_coder()
                 self.downscale_ratio = 32
                 self.latent_channels = 16
+            elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
+                self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
+                self.latent_channels = comfy.ldm.seedvr.vae.SEEDVR2_LATENT_CHANNELS
+                self.latent_dim = 3
+                self.disable_offload = True
+                self.memory_used_decode = lambda shape, dtype: self.first_stage_model.comfy_memory_used_decode(shape)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[2], 5) * shape[3] * shape[4] * 64) * model_management.dtype_size(dtype)
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                self.handles_tiling = True
+                self.format_encoded = self.first_stage_model.comfy_format_encoded
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.process_input = lambda image: image * 2.0 - 1.0
+                self.crop_input = False
             elif "decoder.conv_in.weight" in sd:
                 if sd['decoder.conv_in.weight'].shape[1] == 64:
                     ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
@@ -1006,6 +1032,10 @@ class VAE:
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
+    def _decode_tiled_owned(self, samples, **kwargs):
+        out = self.first_stage_model.decode_tiled(samples.to(self.vae_dtype).to(self.device), **kwargs)
+        return self.process_output(out.to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
@@ -1042,6 +1072,25 @@ class VAE:
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
+    def _encode_tiled_owned(self, pixel_samples, **kwargs):
+        x = self.process_input(pixel_samples).to(self.vae_dtype).to(self.device)
+        out = self.first_stage_model.encode_tiled(x, **kwargs)
+        return out.to(device=self.output_device, dtype=self.vae_output_dtype())
+
+    def _owned_tiled_args(self, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        args = {}
+        if tile_x is not None:
+            args["tile_x"] = tile_x
+        if tile_y is not None:
+            args["tile_y"] = tile_y
+        if overlap is not None:
+            args["overlap"] = overlap
+        if tile_t is not None:
+            args["tile_t"] = tile_t
+        if overlap_t is not None:
+            args["overlap_t"] = overlap_t
+        return args
+
     def decode(self, samples_in, vae_options={}):
         self.throw_exception_if_invalid()
         pixel_samples = None
@@ -1089,11 +1138,19 @@ class VAE:
                 if dims == 1 or self.extra_1d_channel is not None:
                     pixel_samples = self.decode_tiled_1d(samples_in)
                 elif dims == 2:
-                    pixel_samples = self.decode_tiled_(samples_in)
+                    if self.handles_tiling:
+                        tile = 256 // self.spacial_compression_decode()
+                        overlap = tile // 4
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_(samples_in)
                 elif dims == 3:
                     tile = 256 // self.spacial_compression_decode()
                     overlap = tile // 4
-                    pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if self.handles_tiling:
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -1112,7 +1169,9 @@ class VAE:
             args["overlap"] = overlap
 
         with model_management.cuda_device_context(self.device):
-            if dims == 1 or self.extra_1d_channel is not None:
+            if self.handles_tiling and dims in (2, 3):
+                output = self._decode_tiled_owned(samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
+            elif dims == 1 or self.extra_1d_channel is not None:
                 args.pop("tile_y")
                 output = self.decode_tiled_1d(samples, **args)
             elif dims == 2:
@@ -1173,12 +1232,17 @@ class VAE:
                 if self.latent_dim == 3:
                     tile = 256
                     overlap = tile // 4
-                    samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if self.handles_tiling:
+                        samples = self._encode_tiled_owned(pixel_samples, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
                 elif self.latent_dim == 1 or self.extra_1d_channel is not None:
                     samples = self.encode_tiled_1d(pixel_samples)
                 else:
                     samples = self.encode_tiled_(pixel_samples)
 
+        if self.format_encoded is not None:
+            samples = self.format_encoded(samples)
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
@@ -1186,7 +1250,7 @@ class VAE:
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
-        if dims == 3:
+        if dims == 3 and pixel_samples.ndim < 5:
             if not self.not_video:
                 pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
             else:
@@ -1210,21 +1274,27 @@ class VAE:
             elif dims == 2:
                 samples = self.encode_tiled_(pixel_samples, **args)
             elif dims == 3:
-                if tile_t is not None:
-                    tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                if self.handles_tiling:
+                    samples = self._encode_tiled_owned(pixel_samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
                 else:
-                    tile_t_latent = 9999
-                args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
+                    if tile_t is not None:
+                        tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                    else:
+                        tile_t_latent = 9999
+                    args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
 
-                if overlap_t is None:
-                    args["overlap"] = (1, overlap, overlap)
-                else:
-                    args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)
-                maximum = pixel_samples.shape[2]
-                maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
+                    spatial_overlap = overlap if overlap is not None else 64
+                    if overlap_t is None:
+                        args["overlap"] = (1, spatial_overlap, spatial_overlap)
+                    else:
+                        args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), spatial_overlap, spatial_overlap)
+                    maximum = pixel_samples.shape[2]
+                    maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
 
-                samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
+                    samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
 
+        if self.format_encoded is not None:
+            samples = self.format_encoded(samples)
         return samples
 
     def get_sd(self):
@@ -1248,6 +1318,11 @@ class VAE:
         except:
             return None
 
+    def is_dynamic(self):
+        # A VAE built from a state dict with no detectable VAE weights returns early
+        # from __init__ ("No VAE weights detected") before self.patcher is assigned.
+        patcher = getattr(self, "patcher", None)
+        return patcher is not None and patcher.is_dynamic()
 
 class StyleModel:
     def __init__(self, model, device="cpu"):
@@ -1300,6 +1375,8 @@ class CLIPType(Enum):
     LENS = 28
     PIXELDIT = 29
     IDEOGRAM4 = 30
+    BOOGU = 31
+    KREA2 = 32
 
 
 
@@ -1353,6 +1430,8 @@ class TEModel(Enum):
     GEMMA_4_31B = 31
     T5_GEMMA = 32
     GPT_OSS_20B = 33
+    QWEN3VL_4B = 34
+    QWEN3VL_8B = 35
 
 
 def detect_te_model(sd):
@@ -1414,6 +1493,8 @@ def detect_te_model(sd):
         if weight.shape[0] == 5120:
             return TEModel.QWEN35_27B
         return TEModel.QWEN35_2B
+    if "model.visual.deepstack_merger_list.0.norm.weight" in sd:  # DeepStack is unique to Qwen3-VL
+        return TEModel.QWEN3VL_4B if sd["model.visual.merger.linear_fc2.weight"].shape[0] == 2560 else TEModel.QWEN3VL_8B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         weight = sd['model.layers.0.post_attention_layernorm.weight']
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
@@ -1612,6 +1693,28 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
             clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
             clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
+        elif te_model in (TEModel.QWEN3VL_4B, TEModel.QWEN3VL_8B):
+            if clip_type == CLIPType.IDEOGRAM4 and te_model == TEModel.QWEN3VL_8B:  # Ideogram4 reuses the full Qwen3-VL-8B (13-layer tap for conditioning + multimodal generate).
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer
+            elif clip_type == CLIPType.BOOGU and te_model == TEModel.QWEN3VL_8B:  # Boogu-Image: full Qwen3-VL-8B, last hidden state, no-think template.
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                clip_target.clip = comfy.text_encoders.boogu.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.boogu.BooguTokenizer
+            elif clip_type == CLIPType.KREA2 and te_model == TEModel.QWEN3VL_4B:  # Krea2: full Qwen3-VL-4B (12-layer tap for conditioning + multimodal generate).
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                clip_target.clip = comfy.text_encoders.krea2.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.krea2.Krea2Tokenizer
+            elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2):  # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused.
+                klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b"
+                clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type)
+                clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B if te_model == TEModel.QWEN3VL_8B else comfy.text_encoders.flux.KleinTokenizer
+            else:
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model]
+                clip_target.clip = comfy.text_encoders.qwen3vl.te(**llama_detect(clip_data), model_type=qwen3vl_type)
+                clip_target.tokenizer = comfy.text_encoders.qwen3vl.tokenizer(model_type=qwen3vl_type)
         elif te_model == TEModel.QWEN3_06B:
             clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer
@@ -1859,7 +1962,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype, device=load_device)
 
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
@@ -2000,7 +2103,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype, device=load_device)
 
     if custom_operations is not None:
         model_config.custom_operations = custom_operations

comfy/sd1_clip.py

@@ -543,18 +543,24 @@ class SDTokenizer:
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding, the cleaned embedding name, and any leftover string, embedding can be None.
         '''
         split_embed = embedding_name.split()
         embedding_name = split_embed[0]
         leftover = ' '.join(split_embed[1:])
+
+        match = re.search(r'[<\[]', embedding_name)
+        if match is not None:
+            leftover = embedding_name[match.start():] + (" " + leftover if leftover else "")
+            embedding_name = embedding_name[:match.start()]
+
         embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
         if embed is None:
             stripped = embedding_name.strip(',')
             if len(stripped) < len(embedding_name):
                 embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, leftover)
+                return (embed, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, embedding_name, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -585,7 +591,7 @@ class SDTokenizer:
         tokens = []
         for weighted_segment, weight in parsed_weights:
             to_tokenize = unescape_important(weighted_segment)
-            split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
+            split = re.split(r'(?<=\s){}'.format(re.escape(self.embedding_identifier)), to_tokenize)
             to_tokenize = [split[0]]
             for i in range(1, len(split)):
                 to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
@@ -595,7 +601,7 @@ class SDTokenizer:
                 # if we find an embedding, deal with the embedding
                 if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
                     embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, leftover = self._try_get_embedding(embedding_name)
+                    embed, embedding_name, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
                         logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
                     else:

comfy/supported_models.py

@@ -25,6 +25,8 @@ import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.z_image
 import comfy.text_encoders.ideogram4
+import comfy.text_encoders.boogu
+import comfy.text_encoders.krea2
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
@@ -1683,6 +1685,40 @@ class Chroma(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))
 
+class SeedVR2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "seedvr2"
+    }
+    unet_extra_config = {}
+    required_keys = {
+        "{}positive_conditioning",
+        "{}negative_conditioning",
+    }
+    latent_format = comfy.latent_formats.SeedVR2
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    sampling_settings = {
+        "shift": 1.0,
+    }
+
+    def set_inference_dtype(self, dtype, manual_cast_dtype, device=None):
+        if (
+            dtype == torch.float16
+            and manual_cast_dtype is None
+            and comfy.model_management.should_use_bf16(device)
+        ):
+            manual_cast_dtype = torch.bfloat16
+        super().set_inference_dtype(dtype, manual_cast_dtype, device=device)
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SeedVR2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return None
+
 class ChromaRadiance(Chroma):
     unet_config = {
         "image_model": "chroma_radiance",
@@ -1758,6 +1794,27 @@ class Omnigen2(supported_models_base.BASE):
         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))
 
+class Boogu(Omnigen2):
+    unet_config = {
+        "image_model": "boogu",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 3.16,
+    }
+
+    memory_usage_factor = 2.15
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Boogu(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, "{}qwen3vl_8b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.boogu.BooguTokenizer, comfy.text_encoders.boogu.te(**hunyuan_detect))
+
 class Ideogram4(supported_models_base.BASE):
     unet_config = {
         "image_model": "ideogram4",
@@ -1796,6 +1853,35 @@ class Ideogram4(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3vl_8b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.ideogram4.Ideogram4Tokenizer, comfy.text_encoders.ideogram4.te(**hunyuan_detect))
 
+
+class Krea2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "krea2",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 1.15,
+    }
+
+    memory_usage_factor = 2.2
+
+    latent_format = latent_formats.Wan21
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Krea2(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, "{}qwen3vl_4b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.krea2.Krea2Tokenizer, comfy.text_encoders.krea2.te(**hunyuan_detect))
+
 class QwenImage(supported_models_base.BASE):
     unet_config = {
         "image_model": "qwen_image",
@@ -2296,12 +2382,15 @@ models = [
     HiDream,
     HiDreamO1,
     Chroma,
+    SeedVR2,
     ChromaRadiance,
     ACEStep,
     ACEStep15,
     Omnigen2,
+    Boogu,
     QwenImage,
     Ideogram4,
+    Krea2,
     Flux2,
     Lens,
     Kandinsky5Image,

comfy/supported_models_base.py

@@ -54,13 +54,13 @@ class BASE:
     optimizations = {"fp8": False}
 
     @classmethod
-    def matches(s, unet_config, state_dict=None):
+    def matches(s, unet_config, state_dict=None, unet_key_prefix=""):
         for k in s.unet_config:
             if k not in unet_config or s.unet_config[k] != unet_config[k]:
                 return False
         if state_dict is not None:
             for k in s.required_keys:
-                if k not in state_dict:
+                if k.format(unet_key_prefix) not in state_dict:
                     return False
         return True
 
@@ -115,7 +115,7 @@ class BASE:
         replace_prefix = {"": self.vae_key_prefix[0]}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def set_inference_dtype(self, dtype, manual_cast_dtype):
+    def set_inference_dtype(self, dtype, manual_cast_dtype, device=None):
         self.unet_config['dtype'] = dtype
         self.manual_cast_dtype = manual_cast_dtype
 

comfy/text_encoders/boogu.py

@@ -0,0 +1,58 @@
+"""Boogu-Image text encoder: full Qwen3-VL-8B, last hidden state (4096-dim).
+
+Boogu uses the final hidden state of Qwen3-VL as the per-token instruction feature
+(num_instruction_feature_layers=1, reduce_type=mean -> just the last layer).
+The model itself is the standard Qwen3-VL TE, only the chat template differs
+(a fixed system prompt and no <think> block).
+"""
+
+import comfy.text_encoders.qwen3vl
+from comfy import sd1_clip
+
+
+# System prompts from the reference pipeline (pipeline_boogu.py).
+# T2I (non-empty instruction, no image) uses the helpful-assistant prompt
+# everything else (the CFG negative / "drop" condition, and any image case) uses the TI2I "describe" prompt.
+BOOGU_T2I_SYSTEM = "You are a helpful assistant that generates high-quality images based on user instructions. The instructions are as follows."
+BOOGU_DROP_SYSTEM = "Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate."
+
+
+class BooguTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b")
+        # apply_chat_template without add_generation_prompt
+        self.llama_template = "<|im_start|>system\n" + BOOGU_T2I_SYSTEM + "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
+        self.llama_template_images = "<|im_start|>system\n" + BOOGU_DROP_SYSTEM + "<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n"
+        # Reference SYSTEM_PROMPT_DROP: used for the empty negative/uncond instruction.
+        self.llama_template_drop = "<|im_start|>system\n" + BOOGU_DROP_SYSTEM + "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
+        if llama_template is None and len(images) == 0 and text.strip() == "":
+            llama_template = self.llama_template_drop
+        # Boogu conditions on the no-think template; thinking=True drops the empty <think> block qwen3vl adds by default.
+        return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
+
+
+class BooguQwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
+    def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"):
+        super().__init__(device=device, dtype=dtype, attention_mask=attention_mask, model_options=model_options, model_type=model_type)
+        # apply the final RMSNorm to the tapped last layer
+        self.layer_norm_hidden_state = True
+
+
+class BooguTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        clip_model = lambda **kw: BooguQwen3VLClipModel(**kw, model_type="qwen3vl_8b")
+        super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=clip_model, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class BooguTEModel_(BooguTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return BooguTEModel_

comfy/text_encoders/gemma4.py

@@ -1088,7 +1088,7 @@ class Gemma4_Tokenizer():
             h, w = samples.shape[2], samples.shape[3]
             patch_size = 16
             pooling_k = 3
-            max_soft_tokens = 70 if is_video else 280  # video uses smaller token budget per frame
+            max_soft_tokens = kwargs.get("max_soft_tokens", 70 if is_video else 280)
             max_patches = max_soft_tokens * pooling_k * pooling_k
             target_px = max_patches * patch_size * patch_size
             factor = (target_px / (h * w)) ** 0.5

comfy/text_encoders/gpt_oss.py

@@ -12,7 +12,7 @@ import torch.nn.functional as F
 
 import comfy.ops
 from comfy import sd1_clip
-from comfy.ldm.modules.attention import TORCH_HAS_GQA, optimized_attention_for_device
+from comfy.ldm.modules.attention import optimized_attention_for_device
 from comfy.text_encoders.llama import RMSNorm, apply_rope
 
 
@@ -110,10 +110,6 @@ def _attention_with_sinks(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, sin
     putting the sink logit in the mask at that column.
     """
 
-    if num_kv_groups > 1 and not TORCH_HAS_GQA:
-        k = k.repeat_interleave(num_kv_groups, dim=1)
-        v = v.repeat_interleave(num_kv_groups, dim=1)
-
     B, _, S_q, D = q.shape
     H_kv = k.shape[1]
     S_kv = k.shape[-2]

comfy/text_encoders/ideogram4.py

@@ -9,6 +9,7 @@ import os
 from transformers import Qwen2Tokenizer
 
 import comfy.text_encoders.llama
+import comfy.text_encoders.qwen3vl
 from comfy import sd1_clip
 
 # Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1.
@@ -77,3 +78,43 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
                 model_options["quantization_metadata"] = llama_quantization_metadata
             super().__init__(device=device, dtype=dtype, model_options=model_options)
     return Ideogram4TEModel_
+
+
+# Full Qwen3-VL-8B variant with vision
+
+class Ideogram4Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
+    def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None, dtype=dtype,
+                         attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_8b")
+
+
+class Ideogram4Qwen3VLTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Ideogram4Qwen3VLClipModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        b, n, seq, h = out.shape  # (B, n_taps=13, seq, 4096), ascending layer order.
+        out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n)  # (B, seq, 4096*13 = 53248).
+        return out, pooled, extra
+
+
+class Ideogram4Qwen3VLTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b")
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
+        # Ideogram 4 conditions on the no-think template; default thinking=True drops the empty think block qwen3vl adds.
+        return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
+
+
+def te_qwen3vl(dtype_llama=None, llama_quantization_metadata=None):
+    class Ideogram4Qwen3VLTEModel_(Ideogram4Qwen3VLTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Ideogram4Qwen3VLTEModel_

comfy/text_encoders/krea2.py

@@ -0,0 +1,84 @@
+"""Krea 2 (K2) text encoder: Qwen3-VL-4B, 12-layer tap.
+
+K2 conditions on a stack of hidden states from 12 layers of Qwen3-VL-4B
+(reference taps ``hidden_states[2,5,8,...,35]``), kept as a ``(B, 12, seq, 2560)`` tensor and
+consumed by the DiT's internal ``txtfusion`` adapter. Comfy carries conditioning as a 3D tensor,
+so the 12-layer stack is flattened to ``(B, seq, 12*2560)`` here and unpacked inside the model.
+"""
+
+import numbers
+
+import torch
+
+import comfy.text_encoders.qwen3vl
+from comfy import sd1_clip
+
+# tap k == hidden_states[k] (no offset).
+KREA2_TAP_LAYERS = [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35]
+
+# Identical system template to Qwen-Image; Krea2 strips the system+user-opening prefix.
+KREA2_TEMPLATE = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+
+
+class Krea2Tokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_4b")
+        self.llama_template = KREA2_TEMPLATE  # conditioning template; image text-gen uses qwen3vl's default image template.
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
+        # Krea2 conditions on the no-think template; thinking=True drops the empty <think> block qwen3vl adds.
+        return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
+
+
+class Krea2Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
+    def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=KREA2_TAP_LAYERS, layer_idx=None, dtype=dtype,
+                         attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_4b")
+
+
+class Krea2TEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3vl_4b", clip_model=Krea2Qwen3VLClipModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs, template_end=-1):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)  # out: (B, 12, seq, 2560)
+        tok_pairs = token_weight_pairs["qwen3vl_4b"][0]
+
+        # Strip the system + user-opening prefix
+        count_im_start = 0
+        if template_end == -1:
+            for i, v in enumerate(tok_pairs):
+                elem = v[0]
+                if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral):
+                    if elem == 151644 and count_im_start < 2:
+                        template_end = i
+                        count_im_start += 1
+            if out.shape[2] > (template_end + 3):
+                if tok_pairs[template_end + 1][0] == 872:      # "user"
+                    if tok_pairs[template_end + 2][0] == 198:   # "\n"
+                        template_end += 3
+
+        out = out[:, :, template_end:]
+
+        b, n, seq, h = out.shape
+        # Flatten the 12-layer axis into the feature dim: (B, seq, 12*2560). Unpacked in the model.
+        out = out.permute(0, 2, 1, 3).reshape(b, seq, n * h)
+
+        if "attention_mask" in extra:
+            extra["attention_mask"] = extra["attention_mask"][:, template_end:]
+            if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
+                extra.pop("attention_mask")
+
+        return out, pooled, extra
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class Krea2TEModel_(Krea2TEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Krea2TEModel_

comfy/text_encoders/llama.py

@@ -251,6 +251,19 @@ class Qwen3_8BConfig:
     lm_head: bool = True
     stop_tokens = [151643, 151645]
 
+@dataclass
+class Qwen3VL_8BConfig(Qwen3_8BConfig):
+    max_position_embeddings: int = 262144
+    rope_theta: float = 5000000.0
+    rope_dims = [24, 20, 20]
+    interleaved_mrope = True
+
+@dataclass
+class Qwen3VL_4BConfig(Qwen3VL_8BConfig):
+    hidden_size: int = 2560
+    intermediate_size: int = 9728
+    lm_head: bool = False  # 4B ties word embeddings
+
 @dataclass
 class Ovis25_2BConfig:
     vocab_size: int = 151936
@@ -537,10 +550,8 @@ class Attention(nn.Module):
                 xv = xv[:, :, -sliding_window:]
                 attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None
 
-        xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-        xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-
-        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+        gqa_kwargs = {"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}
+        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True, **gqa_kwargs)
         return self.o_proj(output), present_key_value
 
 class MLP(nn.Module):
@@ -703,7 +714,8 @@ class Llama2_(nn.Module):
                                     interleaved_mrope=getattr(self.config, "interleaved_mrope", False),
                                     device=device)
 
-    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None):
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True,
+                dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None,deepstack_embeds=None, visual_pos_masks=None):
         if embeds is not None:
             x = embeds
         else:
@@ -767,6 +779,10 @@ class Llama2_(nn.Module):
             if current_kv is not None:
                 next_key_values.append(current_kv)
 
+            # DeepStack: add per-layer visual features into the first len() decoder layers at image positions (Qwen3-VL)
+            if deepstack_embeds is not None and i < len(deepstack_embeds):
+                x[visual_pos_masks] = x[visual_pos_masks] + deepstack_embeds[i].to(x)
+
             if i == intermediate_output:
                 intermediate = x.clone()
 
@@ -860,7 +876,7 @@ class BaseGenerate:
                                     torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
         return past_key_values
 
-    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None):
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None, position_ids=None, deepstack_embeds=None, visual_pos_masks=None):
         device = embeds.device
 
         if stop_tokens is None:
@@ -884,10 +900,18 @@ class BaseGenerate:
         generated_token_ids = []
         pbar = comfy.utils.ProgressBar(max_length)
 
+        # MRoPE: prefill uses explicit 3D position_ids, decode continues from the last position
+        next_pos = int(position_ids[:, -1].max()) + 1 if position_ids is not None else None
+
         # Generation loop
         current_input_ids = initial_input_ids
         for step in tqdm(range(max_length), desc="Generating tokens"):
-            x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids)
+            # DeepStack visual features are injected on the prefill only; gemma4's forward lacks these kwargs.
+            extra = {}
+            if step == 0 and deepstack_embeds is not None:
+                extra["deepstack_embeds"] = deepstack_embeds
+                extra["visual_pos_masks"] = visual_pos_masks
+            x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids, position_ids=position_ids, **extra)
             logits = self.logits(x)[:, -1]
             next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty)
             token_id = next_token[0].item()
@@ -895,6 +919,9 @@ class BaseGenerate:
 
             embeds = self.model.embed_tokens(next_token).to(execution_dtype)
             current_input_ids = next_token if initial_input_ids is not None else None
+            if next_pos is not None:  # advance MRoPE position for the next (decode) step
+                position_ids = torch.tensor([[next_pos]], device=device)
+                next_pos += 1
             pbar.update(1)
 
             if token_id in stop_tokens:
@@ -908,22 +935,41 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if repetition_penalty != 1.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
-
-        if presence_penalty is not None and presence_penalty != 0.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] -= presence_penalty
+        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
+            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
+            token_logits = logits[:, token_ids]
+            if repetition_penalty != 1.0:
+                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
+            if presence_penalty is not None and presence_penalty != 0.0:
+                token_logits = token_logits - presence_penalty
+            logits[:, token_ids] = token_logits
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+            top_k = min(top_k, logits.shape[-1])
+            logits, top_indices = torch.topk(logits, top_k)
+
+            if min_p > 0.0:
+                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+                min_threshold = min_p * top_probs
+                indices_to_remove = probs_before_filter < min_threshold
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 0] = False
+                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
+            return top_indices.gather(1, next_token)
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy/text_encoders/qwen35.py

@@ -3,7 +3,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 from dataclasses import dataclass, field
 import os
-import math
 
 import comfy.model_management
 from comfy.ldm.modules.attention import optimized_attention_for_device
@@ -367,12 +366,8 @@ class GatedAttention(nn.Module):
                     xv = torch.cat((past_value[:, :, :index], xv), dim=2)
                 present_key_value = (xk, xv, index + num_tokens)
 
-        # Expand KV heads for GQA
-        if self.num_heads != self.num_kv_heads:
-            xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-            xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-
-        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+        gqa_kwargs = {"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}
+        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True, **gqa_kwargs)
         output = output * gate.sigmoid()
 
         return self.o_proj(output), present_key_value
@@ -563,6 +558,8 @@ class Qwen35VisionModel(nn.Module):
             for _ in range(config["depth"])
         ])
         self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
+        self.deepstack_visual_indexes = [] # DeepStack, per-layer visual features (Qwen3-VL)
+        self.deepstack_merger_list = None
 
     def rot_pos_emb(self, grid_thw):
         merge_size = self.spatial_merge_size
@@ -664,9 +661,14 @@ class Qwen35VisionModel(nn.Module):
         ).cumsum(dim=0, dtype=torch.int32)
         cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
         optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
-        for blk in self.blocks:
+        deepstack_features = []
+        for layer_num, blk in enumerate(self.blocks):
             x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
+            if self.deepstack_merger_list is not None and layer_num in self.deepstack_visual_indexes:
+                deepstack_features.append(self.deepstack_merger_list[self.deepstack_visual_indexes.index(layer_num)](x))
         merged = self.merger(x)
+        if self.deepstack_merger_list is not None:
+            return merged, deepstack_features
         return merged
 
 # Model Wrapper
@@ -690,30 +692,7 @@ class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module):
         return None, None
 
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None):
-        grid = None
-        position_ids = None
-        offset = 0
-        for e in embeds_info:
-            if e.get("type") == "image":
-                grid = e.get("extra", None)
-                start = e.get("index")
-                if position_ids is None:
-                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
-                    position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
-                end = e.get("size") + start
-                len_max = int(grid.max()) // 2
-                start_next = len_max + start
-                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
-                position_ids[0, start:end] = start + offset
-                max_d = int(grid[0][1]) // 2
-                position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
-                max_d = int(grid[0][2]) // 2
-                position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
-                offset += len_max - (end - start)
-
-        if grid is None:
-            position_ids = None
-
+        position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, embeds.shape[1], embeds.device)
         return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values)
 
     def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):

comfy/text_encoders/qwen3vl.py

@@ -0,0 +1,214 @@
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import Qwen2Tokenizer
+
+from comfy import sd1_clip
+import comfy.text_encoders.qwen_vl
+from .qwen35 import Qwen35VisionModel
+from .llama import BaseLlama, BaseQwen3, BaseGenerate, Llama2_, Qwen3VL_4BConfig, Qwen3VL_8BConfig
+
+
+QWEN3VL_VISION = {
+    "qwen3vl_4b": dict(hidden_size=1024, intermediate_size=4096, depth=24, deepstack_visual_indexes=[5, 11, 17]),
+    "qwen3vl_8b": dict(hidden_size=1152, intermediate_size=4304, depth=27, deepstack_visual_indexes=[8, 16, 24]),
+}
+QWEN3VL_VISION_COMMON = dict(num_heads=16, patch_size=16, temporal_patch_size=2, in_channels=3,
+                             spatial_merge_size=2, num_position_embeddings=2304)
+
+QWEN3VL_CONFIGS = {"qwen3vl_4b": Qwen3VL_4BConfig, "qwen3vl_8b": Qwen3VL_8BConfig}
+
+
+class Qwen3VLDeepstackMerger(nn.Module):
+    # DeepStack merger: postshuffle LayerNorm (applied after spatial merge), unlike the main merger.
+    def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.merge_dim = hidden_size * (spatial_merge_size ** 2)
+        self.norm = ops.LayerNorm(self.merge_dim, eps=1e-6, device=device, dtype=dtype)
+        self.linear_fc1 = ops.Linear(self.merge_dim, self.merge_dim, device=device, dtype=dtype)
+        self.linear_fc2 = ops.Linear(self.merge_dim, out_hidden_size, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.norm(x.view(-1, self.merge_dim))
+        return self.linear_fc2(F.gelu(self.linear_fc1(x)))
+
+
+class Qwen3VLVisionModel(Qwen35VisionModel):
+    # Qwen3.5 vision + DeepStack
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__(config, device=device, dtype=dtype, ops=ops)
+        self.deepstack_visual_indexes = config["deepstack_visual_indexes"]
+        self.deepstack_merger_list = nn.ModuleList([
+            Qwen3VLDeepstackMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
+            for _ in self.deepstack_visual_indexes
+        ])
+
+
+class Qwen3VL(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
+    model_type = "qwen3vl_8b"
+
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = QWEN3VL_CONFIGS[self.model_type](**config_dict)
+        self.num_layers = config.num_hidden_layers
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        vision_config = {**QWEN3VL_VISION_COMMON, **QWEN3VL_VISION[self.model_type], "out_hidden_size": config.hidden_size}
+        self.visual = Qwen3VLVisionModel(vision_config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            # Qwen3-VL normalizes to [-1, 1] (mean/std 0.5), unlike Qwen2.5-VL's CLIP normalization.
+            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
+            merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid)
+            return merged, {"grid": grid, "deepstack": deepstack}
+        return None, None
+
+    def build_image_inputs(self, embeds, embeds_info):
+        # Returns (position_ids, visual_pos_masks, deepstack) for the prompt
+        images = sorted([e for e in embeds_info if e.get("type") == "image"], key=lambda e: e["index"])
+        if len(images) == 0:
+            return None, None, None
+
+        device = embeds.device
+        seq = embeds.shape[1]
+        position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, seq, device)
+
+        # DeepStack: mask of image positions + per-vision-layer features to inject there.
+        visual_pos_masks = torch.zeros((1, seq), dtype=torch.bool, device=device)
+        deepstack = None
+        for e in images:
+            start = e["index"]
+            end = e["size"] + start
+            visual_pos_masks[0, start:end] = True
+            ds = e["extra"]["deepstack"]
+            if deepstack is None:
+                deepstack = [d for d in ds]
+            else:
+                deepstack = [torch.cat([deepstack[i], ds[i]], dim=0) for i in range(len(ds))]
+        return position_ids, visual_pos_masks, deepstack
+
+    def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], **kwargs):
+        position_ids = kwargs.pop("position_ids", None)
+        visual_pos_masks = kwargs.pop("visual_pos_masks", None)
+        deepstack_embeds = kwargs.pop("deepstack_embeds", None)
+        if embeds is not None and position_ids is None:
+            position_ids, visual_pos_masks, deepstack_embeds = self.build_image_inputs(embeds, embeds_info)
+        return self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            embeds=embeds,
+            num_tokens=num_tokens,
+            intermediate_output=intermediate_output,
+            final_layer_norm_intermediate=final_layer_norm_intermediate,
+            dtype=dtype,
+            position_ids=position_ids,
+            embeds_info=embeds_info,
+            visual_pos_masks=visual_pos_masks,
+            deepstack_embeds=deepstack_embeds,
+            **kwargs,
+        )
+
+
+def _make_qwen3vl_model(model_type):
+    class Qwen3VL_(Qwen3VL):
+        pass
+    Qwen3VL_.model_type = model_type
+    return Qwen3VL_
+
+
+class Qwen3VLClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"):
+        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=_make_qwen3vl_model(model_type), enable_attention_masks=attention_mask,
+                         return_attention_masks=attention_mask, model_options=model_options)
+
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0):
+        if isinstance(tokens, dict):
+            tokens = next(iter(tokens.values()))
+        tokens_only = [[t[0] for t in b] for b in tokens]
+        embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
+        position_ids, visual_pos_masks, deepstack = self.transformer.build_image_inputs(embeds, embeds_info)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed,
+                                         presence_penalty=presence_penalty, position_ids=position_ids,
+                                         visual_pos_masks=visual_pos_masks, deepstack_embeds=deepstack)
+
+
+class Qwen3VLTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen3vl_8b"):
+        clip_model = lambda **kw: Qwen3VLClipModel(**kw, model_type=model_type)
+        super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options)
+
+
+class Qwen3VLSDTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=4096, embedding_key="qwen3vl_8b"):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, 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 Qwen3VLTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen3vl_8b"):
+        embedding_size = 2560 if model_type == "qwen3vl_4b" else 4096
+        tokenizer = lambda *a, **kw: Qwen3VLSDTokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type)
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer)
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs):
+        image = kwargs.get("image", None)
+        if image is not None and len(images) == 0:
+            images = [image[i:i + 1] for i in range(image.shape[0])]
+
+        skip_template = text.startswith('<|im_start|>')
+        if prevent_empty_text and text == '':
+            text = ' '
+
+        if skip_template:
+            llama_text = text
+        else:
+            if llama_template is not None:
+                template = llama_template
+            elif len(images) == 0:
+                template = self.llama_template
+            else:
+                template = self.llama_template_images
+                if len(images) > 1:
+                    vision_block = "<|vision_start|><|image_pad|><|vision_end|>"
+                    template = template.replace(vision_block, vision_block * len(images), 1)
+            llama_text = template.format(text)
+            if not thinking:  # Qwen3 convention: empty think block suppresses reasoning
+                llama_text += "<think>\n\n</think>\n\n"
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        key_name = next(iter(tokens))
+        embed_count = 0
+        for r in tokens[key_name]:
+            for i in range(len(r)):
+                if isinstance(r[i][0], (int, float)) and r[i][0] == 151655:  # <|image_pad|>
+                    if len(images) > embed_count:
+                        r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
+                        embed_count += 1
+        return tokens
+
+
+def tokenizer(model_type="qwen3vl_8b"):
+    class Qwen3VLTokenizer_(Qwen3VLTokenizer):
+        def __init__(self, embedding_directory=None, tokenizer_data={}):
+            super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type)
+    return Qwen3VLTokenizer_
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen3vl_8b"):
+    class Qwen3VLTEModel_(Qwen3VLTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type)
+    return Qwen3VLTEModel_

comfy/text_encoders/qwen_vl.py

@@ -88,6 +88,32 @@ def process_qwen2vl_images(
     return flatten_patches, image_grid_thw
 
 
+def qwen2vl_mrope_position_ids(embeds_info, seq_len, device):
+    # (3, seq_len) T/H/W MRoPE position ids: text runs sequentially, each image span gets its grid positions.
+    # Returns None when there are no image embeds. `extra` is the image grid_thw, or a dict carrying it under "grid".
+    position_ids = None
+    offset = 0
+    for e in embeds_info:
+        if e.get("type") == "image":
+            extra = e.get("extra", None)
+            grid = extra["grid"] if isinstance(extra, dict) else extra
+            start = e.get("index")
+            if position_ids is None:
+                position_ids = torch.zeros((3, seq_len), device=device)
+                position_ids[:, :start] = torch.arange(0, start, device=device)
+            end = e.get("size") + start
+            len_max = int(grid.max()) // 2
+            start_next = len_max + start
+            position_ids[:, end:] = torch.arange(start_next + offset, start_next + (seq_len - end) + offset, device=device)
+            position_ids[0, start:end] = start + offset
+            max_d = int(grid[0][1]) // 2
+            position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
+            max_d = int(grid[0][2]) // 2
+            position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
+            offset += len_max - (end - start)
+    return position_ids
+
+
 class VisionPatchEmbed(nn.Module):
     def __init__(
         self,

comfy/utils.py

@@ -818,6 +818,44 @@ def z_image_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+def krea2_to_diffusers(mmdit_config, output_prefix=""):
+    n_layers = mmdit_config.get("layers", 0)
+    n_txt_layerwise = 2  # TextFusionTransformer hardcodes 2 layerwise + 2 refiner blocks
+    n_txt_refiner = 2
+    key_map = {}
+
+    def add_block(prefix_to, prefix_from):
+        block_map = {
+            "attn.to_q": "attn.wq", "attn.to_k": "attn.wk", "attn.to_v": "attn.wv",
+            "attn.to_gate": "attn.gate", "attn.to_out.0": "attn.wo",
+            "attn.to_out": "attn.wo",  # some tools drop the ".0" on to_out
+            "ff.gate": "mlp.gate", "ff.up": "mlp.up", "ff.down": "mlp.down",
+        }
+        for d, c in block_map.items():
+            key_map["{}.{}.weight".format(prefix_to, d)] = "{}{}.{}.weight".format(output_prefix, prefix_from, c)
+
+    for i in range(n_layers):
+        add_block("transformer_blocks.{}".format(i), "blocks.{}".format(i))
+    for i in range(n_txt_layerwise):
+        add_block("text_fusion.layerwise_blocks.{}".format(i), "txtfusion.layerwise_blocks.{}".format(i))
+    for i in range(n_txt_refiner):
+        add_block("text_fusion.refiner_blocks.{}".format(i), "txtfusion.refiner_blocks.{}".format(i))
+
+    MAP_BASIC = [
+        ("img_in", "first"),
+        ("time_embed.linear_1", "tmlp.0"),
+        ("time_embed.linear_2", "tmlp.2"),
+        ("time_mod_proj", "tproj.1"),
+        ("txt_in.linear_1", "txtmlp.1"),
+        ("txt_in.linear_2", "txtmlp.3"),
+        ("text_fusion.projector", "txtfusion.projector"),
+        ("final_layer.linear", "last.linear"),
+    ]
+    for d, c in MAP_BASIC:
+        key_map["{}.weight".format(d)] = "{}{}.weight".format(output_prefix, c)
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)

comfy_api/feature_flags.py

@@ -25,6 +25,11 @@ CLI_FEATURE_FLAG_REGISTRY: dict[str, FeatureFlagInfo] = {
         "default": False,
         "description": "Show the sign-in button in the frontend even when not signed in",
     },
+    "enable_telemetry": {
+        "type": "bool",
+        "default": False,
+        "description": "Signal the frontend that telemetry collection is enabled",
+    },
 }
 
 
@@ -95,6 +100,8 @@ def _parse_cli_feature_flags() -> dict[str, Any]:
 # Default server capabilities
 _CORE_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": True,
+    "supports_node_failure_policy": True,
+    "supports_model_type_tags": True,
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
     "extension": {"manager": {"supports_v4": True}},
     "node_replacements": True,

comfy_api/latest/_input/video_types.py

@@ -27,10 +27,13 @@ class VideoInput(ABC):
         path: Union[str, IO[bytes]],
         format: VideoContainer = VideoContainer.AUTO,
         codec: VideoCodec = VideoCodec.AUTO,
-        metadata: Optional[dict] = None
+        metadata: Optional[dict] = None,
+        bit_depth: int | None = None,
     ):
         """
         Abstract method to save the video input to a file.
+
+        bit_depth selects the encoded bit depth; None keeps the video's native depth.
         """
         pass
 
@@ -83,6 +86,14 @@ class VideoInput(ABC):
         components = self.get_components()
         return components.images.shape[2], components.images.shape[1]
 
+    def get_bit_depth(self) -> int:
+        """
+        Returns the bit depth of the video (e.g. 8 or 10).
+
+        Default implementation returns 8; subclasses report their real depth.
+        """
+        return 8
+
     def get_duration(self) -> float:
         """
         Returns the duration of the video in seconds.

comfy_api/latest/_input_impl/video_types.py

@@ -52,6 +52,12 @@ def get_open_write_kwargs(
     return open_kwargs
 
 
+def video_stream_bit_depth(stream) -> int:
+    if stream is None or stream.format is None or not stream.format.components:
+        return 8
+    return max(component.bits for component in stream.format.components)
+
+
 class VideoFromFile(VideoInput):
     """
     Class representing video input from a file.
@@ -97,6 +103,13 @@ class VideoFromFile(VideoInput):
                     return stream.width, stream.height
         raise ValueError(f"No video stream found in file '{self.__file}'")
 
+    def get_bit_depth(self) -> int:
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)  # Reset the BytesIO object to the beginning
+        with av.open(self.__file, mode="r") as container:
+            video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None
+            return video_stream_bit_depth(video_stream)
+
     def get_duration(self) -> float:
         """
         Returns the duration of the video in seconds.
@@ -257,6 +270,7 @@ class VideoFromFile(VideoInput):
 
         image_format = 'gbrpf32le'
         process_image_format = lambda a: a
+        align_graph = None
         audio = None
 
         streams = [video_stream]
@@ -267,11 +281,18 @@ class VideoFromFile(VideoInput):
         video_done = False
         audio_done = True
 
-        if len(container.streams.audio):
-            audio_stream = container.streams.audio[-1]
+        # Use the last decodable audio stream. Streams FFmpeg has no decoder for have no codec context,
+        # and decoding their packets crashes the process. (e.g. APAC spatial-audio track in iPhone)
+        audio_stream = next(
+            (s for s in reversed(container.streams.audio) if s.codec_context is not None),
+            None,
+        )
+        if audio_stream is not None:
             streams += [audio_stream]
             resampler = av.audio.resampler.AudioResampler(format='fltp')
             audio_done = False
+        elif len(container.streams.audio):
+            logging.warning("No decodable audio stream found in video; ignoring audio.")
 
         for packet in container.demux(*streams):
             if video_done and audio_done:
@@ -310,7 +331,28 @@ class VideoFromFile(VideoInput):
 
                             checked_alpha = True
 
-                        img = frame.to_ndarray(format=image_format)  # shape: (H, W, 4)
+                        # Fix non-deterministic video decode when the video width is not a multiple of 32
+                        # For non-yuvj pixel formats: most H.264/H.265 video and static images (e.g. lossy WebP via LoadImage)
+                        # Pad both axes to a multiple of 32 and smear the border so the alignment padding never bleeds into the cropped edges
+                        if image_format in ('gbrpf32le', 'gbrapf32le') and frame.width % 32 != 0:
+                            if align_graph is None:
+                                pad_w = ((frame.width + 31) // 32) * 32
+                                pad_h = ((frame.height + 31) // 32) * 32
+                                g = av.filter.Graph()
+                                g_src = g.add_buffer(width=frame.width, height=frame.height,
+                                                     format=frame.format.name, time_base=video_stream.time_base)
+                                g_pad = g.add('pad', f'{pad_w}:{pad_h}:0:0')
+                                g_fill = g.add('fillborders', f'left=0:right={pad_w - frame.width}:top=0:bottom={pad_h - frame.height}:mode=smear')
+                                g_sink = g.add('buffersink')
+                                g_src.link_to(g_pad)
+                                g_pad.link_to(g_fill)
+                                g_fill.link_to(g_sink)
+                                g.configure()
+                                align_graph = (g, g_src, g_sink)
+                            align_graph[1].push(frame)
+                            img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:frame.height, :frame.width])
+                        else:
+                            img = frame.to_ndarray(format=image_format)
                         if frame.rotation != 0:
                             k = int(round(frame.rotation // 90))
                             img = np.rot90(img, k=k, axes=(0, 1)).copy()
@@ -377,25 +419,32 @@ class VideoFromFile(VideoInput):
         format: VideoContainer = VideoContainer.AUTO,
         codec: VideoCodec = VideoCodec.AUTO,
         metadata: Optional[dict] = None,
+        bit_depth: int | None = None,
     ):
         if isinstance(self.__file, io.BytesIO):
             self.__file.seek(0)  # Reset the BytesIO object to the beginning
         with av.open(self.__file, mode='r') as container:
             container_format = container.format.name
-            video_encoding = container.streams.video[0].codec.name if len(container.streams.video) > 0 else None
+            video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None
+            video_encoding = video_stream.codec.name if video_stream is not None else None
+            source_bit_depth = video_stream_bit_depth(video_stream)
             reuse_streams = True
             if format != VideoContainer.AUTO and format not in container_format.split(","):
                 reuse_streams = False
             if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None:
                 reuse_streams = False
+            if bit_depth is not None and video_encoding is not None and bit_depth != source_bit_depth:
+                reuse_streams = False
             if self.__start_time or self.__duration:
                 reuse_streams = False
 
             if not reuse_streams:
+                if bit_depth is None:
+                    bit_depth = source_bit_depth
                 components = self.get_components_internal(container)
                 video = VideoFromComponents(components)
                 return video.save_to(
-                    path, format=format, codec=codec, metadata=metadata
+                    path, format=format, codec=codec, metadata=metadata, bit_depth=bit_depth,
                 )
 
             streams = container.streams
@@ -415,10 +464,13 @@ class VideoFromFile(VideoInput):
                         else:
                             output_container.metadata[key] = json.dumps(value)
 
-                # Add streams to the new container
+                # Add streams to the new container. Streams with no codec context cannot be used as an output template.
                 stream_map = {}
                 for stream in streams:
                     if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)):
+                        if stream.codec_context is None:
+                            logging.warning("Skipping %s stream %d with unsupported codec", stream.type, stream.index)
+                            continue
                         out_stream = output_container.add_stream_from_template(template=stream, opaque=True)
                         stream_map[stream] = out_stream
 
@@ -451,8 +503,10 @@ class VideoFromComponents(VideoInput):
     Class representing video input from tensors.
     """
 
-    def __init__(self, components: VideoComponents):
+    def __init__(self, components: VideoComponents, bit_depth: int = 8):
         self.__components = components
+        # Tensor components have no inherent bit depth; this is the depth used when encoding.
+        self.__bit_depth = bit_depth
 
     def get_components(self) -> VideoComponents:
         return VideoComponents(
@@ -461,18 +515,26 @@ class VideoFromComponents(VideoInput):
             frame_rate=self.__components.frame_rate,
         )
 
+    def get_bit_depth(self) -> int:
+        return self.__bit_depth
+
     def save_to(
         self,
         path: str,
         format: VideoContainer = VideoContainer.AUTO,
         codec: VideoCodec = VideoCodec.AUTO,
         metadata: Optional[dict] = None,
+        bit_depth: int | None = None,
     ):
         """Save the video to a file path or BytesIO buffer."""
         if format != VideoContainer.AUTO and format != VideoContainer.MP4:
             raise ValueError("Only MP4 format is supported for now")
         if codec != VideoCodec.AUTO and codec != VideoCodec.H264:
             raise ValueError("Only H264 codec is supported for now")
+        # None means "use the depth this video was created with" (CreateVideo's choice).
+        if bit_depth is None:
+            bit_depth = self.__bit_depth
+        is_10bit = bit_depth >= 10
         extra_kwargs = {}
         if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
             extra_kwargs["format"] = format.value
@@ -488,10 +550,11 @@ class VideoFromComponents(VideoInput):
 
             frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000)
             # Create a video stream
+            pix_fmt = "yuv420p10le" if is_10bit else "yuv420p"
             video_stream = output.add_stream('h264', rate=frame_rate)
             video_stream.width = self.__components.images.shape[2]
             video_stream.height = self.__components.images.shape[1]
-            video_stream.pix_fmt = 'yuv420p'
+            video_stream.pix_fmt = pix_fmt
 
             # Create an audio stream
             audio_sample_rate = 1
@@ -505,9 +568,14 @@ class VideoFromComponents(VideoInput):
 
             # Encode video
             for i, frame in enumerate(self.__components.images):
-                img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3)
-                frame = av.VideoFrame.from_ndarray(img, format='rgb24')
-                frame = frame.reformat(format='yuv420p')  # Convert to YUV420P as required by h264
+                if is_10bit:
+                    # 16-bit RGB keeps float precision through the conversion to 10-bit YUV.
+                    img = (frame.float() * 65535).clamp(0, 65535).cpu().numpy().astype(np.uint16)  # shape: (H, W, 3)
+                    frame = av.VideoFrame.from_ndarray(img, format="rgb48le")
+                else:
+                    img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3)
+                    frame = av.VideoFrame.from_ndarray(img, format='rgb24')
+                frame = frame.reformat(format=pix_fmt)
                 packet = video_stream.encode(frame)
                 output.mux(packet)
 

comfy_api/latest/_io.py

@@ -891,6 +891,14 @@ class Tracks(ComfyTypeIO):
         track_visibility: torch.Tensor
     Type = TrackDict
 
+@comfytype(io_type="DICT")
+class Dict(ComfyTypeIO):
+    Type = dict
+
+@comfytype(io_type="ARRAY")
+class Array(ComfyTypeIO):
+    Type = list
+
 @comfytype(io_type="COMFY_MULTITYPED_V3")
 class MultiType:
     Type = Any
@@ -1279,6 +1287,19 @@ class Color(ComfyTypeIO):
       def as_dict(self):
           return super().as_dict()
 
+
+@comfytype(io_type="COLORS")
+class Colors(ComfyTypeIO):
+    Type = list[Color.Type]
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[str]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = []
+
+
 @comfytype(io_type="BOUNDING_BOX")
 class BoundingBox(ComfyTypeIO):
     class BoundingBoxDict(TypedDict):
@@ -1326,6 +1347,20 @@ class Curve(ComfyTypeIO):
             return d
 
 
+@comfytype(io_type="BOUNDING_BOXES")
+class BoundingBoxes(ComfyTypeIO):
+    class BoundingBoxWithMetadata(BoundingBox.BoundingBoxDict):
+        metadata: dict
+    Type = list[BoundingBoxWithMetadata]
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[dict]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = []
+
+
 @comfytype(io_type="HISTOGRAM")
 class Histogram(ComfyTypeIO):
     """A histogram represented as a list of bin counts."""
@@ -2376,6 +2411,8 @@ __all__ = [
     "AnyType",
     "MultiType",
     "Tracks",
+    "Dict",
+    "Array",
     "Color",
     # Dynamic Types
     "MatchType",
@@ -2394,6 +2431,8 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
+    "BoundingBoxes",
+    "Colors",
     "Curve",
     "Histogram",
     "Range",

comfy_api_nodes/apis/bytedance.py

@@ -1,4 +1,4 @@
-from typing import Literal
+from typing import Any, Literal
 
 from pydantic import BaseModel, Field
 
@@ -17,6 +17,10 @@ class Seedream4Options(BaseModel):
     max_images: int = Field(15)
 
 
+class Seedream5OptimizePromptOptions(BaseModel):
+    thinking: Literal["auto", "enabled", "disabled"] = Field(...)
+
+
 class Seedream4TaskCreationRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)
@@ -24,10 +28,11 @@ class Seedream4TaskCreationRequest(BaseModel):
     image: list[str] | None = Field(None, description="Image URLs")
     size: str = Field(...)
     seed: int = Field(..., ge=0, le=2147483647)
-    sequential_image_generation: str = Field("disabled")
-    sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
+    sequential_image_generation: str | None = Field("disabled")
+    sequential_image_generation_options: Seedream4Options | None = Field(Seedream4Options(max_images=15))
     watermark: bool = Field(False)
     output_format: str | None = None
+    optimize_prompt_options: Seedream5OptimizePromptOptions | None = None
 
 
 class ImageTaskCreationResponse(BaseModel):
@@ -163,15 +168,31 @@ class SeedanceVirtualLibraryCreateAssetRequest(BaseModel):
     asset_type: str | None = Field(None, description="BytePlus asset type. Defaults to Image server-side when omitted.")
 
 
-# Dollars per 1K tokens, keyed by (model_id, has_video_input).
+# Dollars per 1K tokens, keyed by (model_id, has_video_input, resolution).
 SEEDANCE2_PRICE_PER_1K_TOKENS = {
-    ("dreamina-seedance-2-0-260128", False): 0.007,
-    ("dreamina-seedance-2-0-260128", True): 0.0043,
-    ("dreamina-seedance-2-0-fast-260128", False): 0.0056,
-    ("dreamina-seedance-2-0-fast-260128", True): 0.0033,
+    ("dreamina-seedance-2-0-260128", False, "480p"): 0.007,
+    ("dreamina-seedance-2-0-260128", True, "480p"): 0.0043,
+    ("dreamina-seedance-2-0-260128", False, "720p"): 0.007,
+    ("dreamina-seedance-2-0-260128", True, "720p"): 0.0043,
+    ("dreamina-seedance-2-0-260128", False, "1080p"): 0.0077,
+    ("dreamina-seedance-2-0-260128", True, "1080p"): 0.0047,
+    ("dreamina-seedance-2-0-260128", False, "4k"): 0.004,
+    ("dreamina-seedance-2-0-260128", True, "4k"): 0.0024,
+    ("dreamina-seedance-2-0-fast-260128", False, "480p"): 0.0056,
+    ("dreamina-seedance-2-0-fast-260128", True, "480p"): 0.0033,
+    ("dreamina-seedance-2-0-fast-260128", False, "720p"): 0.0056,
+    ("dreamina-seedance-2-0-fast-260128", True, "720p"): 0.0033,
+    ("dreamina-seedance-2-0-mini", False, "480p"): 0.0035,
+    ("dreamina-seedance-2-0-mini", True, "480p"): 0.0021,
+    ("dreamina-seedance-2-0-mini", False, "720p"): 0.0035,
+    ("dreamina-seedance-2-0-mini", True, "720p"): 0.0021,
 }
 
 
+def seedance2_price_per_1k_tokens(model_id: str, has_video_input: bool, resolution: str) -> float | None:
+    return SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input, resolution))
+
+
 RECOMMENDED_PRESETS = [
     ("1024x1024 (1:1)", 1024, 1024),
     ("864x1152 (3:4)", 864, 1152),
@@ -245,6 +266,19 @@ _PRESETS_SEEDREAM_4K = [
 
 _CUSTOM_PRESET = [("Custom", None, None)]
 
+_PRESETS_SEEDREAM_2K_PRO = [
+    ("(2K) 2048x2048 (1:1)", 2048, 2048),
+    ("(2K) 1728x2304 (3:4)", 1728, 2304),
+    ("(2K) 2304x1728 (4:3)", 2304, 1728),
+    # ("(2K) 2848x1600 (16:9)", 2848, 1600),  # 4,556,800 px - temporarily unavailable
+    # ("(2K) 1600x2848 (9:16)", 1600, 2848),  # 4,556,800 px - temporarily unavailable
+    ("(2K) 1664x2496 (2:3)", 1664, 2496),
+    ("(2K) 2496x1664 (3:2)", 2496, 1664),
+    # ("(2K) 3136x1344 (21:9)", 3136, 1344),  # 4,214,784 px - temporarily unavailable
+]
+RECOMMENDED_PRESETS_SEEDREAM_5_PRO = (
+    _PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K_PRO + _CUSTOM_PRESET
+)
 RECOMMENDED_PRESETS_SEEDREAM_5_LITE = (
     _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_3K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
 )
@@ -266,6 +300,10 @@ SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
         "480p": {"min": 409_600, "max": 927_408},
         "720p": {"min": 409_600, "max": 927_408},
     },
+    "dreamina-seedance-2-0-mini": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+    },
 }
 
 # The time in this dictionary are given for 10 seconds duration.
@@ -296,3 +334,36 @@ VIDEO_TASKS_EXECUTION_TIME = {
         "1080p": 150,
     },
 }
+
+
+class SeedAudioConfig(BaseModel):
+    format: str = Field(default="mp3")
+    sample_rate: int = Field(default=24000)
+    speech_rate: int = Field(default=0)
+    loudness_rate: int = Field(default=0)
+    pitch_rate: int = Field(default=0)
+
+
+class SeedAudioReference(BaseModel):
+    speaker: str | None = Field(default=None)
+    audio_data: str | None = Field(default=None)
+    audio_url: str | None = Field(default=None)
+    image_data: str | None = Field(default=None)
+    image_url: str | None = Field(default=None)
+
+
+class SeedAudioRequest(BaseModel):
+    model: str = Field(default="seed-audio-1.0")
+    text_prompt: str = Field(...)
+    references: list[SeedAudioReference] | None = Field(default=None)
+    audio_config: SeedAudioConfig = Field(default_factory=SeedAudioConfig)
+    watermark: dict[str, Any] = Field(default_factory=dict)
+
+
+class SeedAudioResponse(BaseModel):
+    audio: str | None = Field(default=None)
+    url: str | None = Field(default=None)
+    duration: float | None = Field(default=None)
+    original_duration: float | None = Field(default=None)
+    code: int | None = Field(default=None)
+    message: str | None = Field(default=None)

comfy_api_nodes/apis/gemini.py

@@ -121,6 +121,7 @@ class GeminiGenerationConfig(BaseModel):
     topK: int | None = Field(None, ge=1)
     topP: float | None = Field(None, ge=0.0, le=1.0)
     thinkingConfig: GeminiThinkingConfig | None = Field(None)
+    responseModalities: list[str] | None = Field(None)
 
 
 class GeminiImageOutputOptions(BaseModel):

comfy_api_nodes/apis/hunyuan3d.py

@@ -77,6 +77,7 @@ class To3DUVTaskRequest(BaseModel):
 
 class To3DPartTaskRequest(BaseModel):
     File: TaskFile3DInput = Field(...)
+    EnableStagedGeneration: bool | None = Field(None)
 
 
 class TextureEditImageInfo(BaseModel):

comfy_api_nodes/apis/ideogram.py

@@ -33,53 +33,6 @@ class IdeogramColorPalette(
     )
 
 
-class ImageRequest(BaseModel):
-    aspect_ratio: Optional[str] = Field(
-        None,
-        description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.",
-    )
-    color_palette: Optional[Dict[str, Any]] = Field(
-        None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.'
-    )
-    magic_prompt_option: Optional[str] = Field(
-        None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')."
-    )
-    model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')")
-    negative_prompt: Optional[str] = Field(
-        None,
-        description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.',
-    )
-    num_images: Optional[int] = Field(
-        1,
-        description='Optional. Number of images to generate (1-8). Defaults to 1.',
-        ge=1,
-        le=8,
-    )
-    prompt: str = Field(
-        ..., description='Required. The prompt to use to generate the image.'
-    )
-    resolution: Optional[str] = Field(
-        None,
-        description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.",
-    )
-    seed: Optional[int] = Field(
-        None,
-        description='Optional. A number between 0 and 2147483647.',
-        ge=0,
-        le=2147483647,
-    )
-    style_type: Optional[str] = Field(
-        None,
-        description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.",
-    )
-
-
-class IdeogramGenerateRequest(BaseModel):
-    image_request: ImageRequest = Field(
-        ..., description='The image generation request parameters.'
-    )
-
-
 class Datum(BaseModel):
     is_image_safe: Optional[bool] = Field(
         None, description='Indicates whether the image is considered safe.'
@@ -113,20 +66,6 @@ class StyleCode(RootModel[str]):
     root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$')
 
 
-class Datum1(BaseModel):
-    is_image_safe: Optional[bool] = None
-    prompt: Optional[str] = None
-    resolution: Optional[str] = None
-    seed: Optional[int] = None
-    style_type: Optional[str] = None
-    url: Optional[str] = None
-
-
-class IdeogramV3IdeogramResponse(BaseModel):
-    created: Optional[datetime] = None
-    data: Optional[List[Datum1]] = None
-
-
 class RenderingSpeed1(str, Enum):
     TURBO = 'TURBO'
     DEFAULT = 'DEFAULT'

comfy_api_nodes/apis/kling.py

@@ -149,3 +149,59 @@ class MotionControlRequest(BaseModel):
     character_orientation: str = Field(...)
     mode: str = Field(..., description="'pro' or 'std'")
     model_name: str = Field(...)
+
+
+class Kling3TurboSettings(BaseModel):
+    resolution: str = Field("720p", description="'720p' or '1080p'")
+    aspect_ratio: str | None = Field(None, description="'16:9'/'9:16'/'1:1'; text-to-video only")
+    duration: int = Field(5, description="3-15 second")
+
+
+class Kling3TurboText2VideoRequest(BaseModel):
+    prompt: str = Field(..., description="<=3072 chars; may use multi-shot 'shot n, m, words; ...'")
+    settings: Kling3TurboSettings | None = Field(None)
+
+
+class Kling3TurboContent(BaseModel):
+    type: str = Field(..., description="'prompt' or 'first_frame'")
+    text: str | None = Field(None, description="for type=prompt; <=2500 chars")
+    url: str | None = Field(None, description="for type=first_frame")
+
+
+class Kling3TurboImage2VideoRequest(BaseModel):
+    contents: list[Kling3TurboContent] = Field(..., description="prompt + first_frame materials")
+    settings: Kling3TurboSettings | None = Field(None)
+
+
+class Kling3TurboCreateData(BaseModel):
+    id: str | None = Field(None, description="Task ID")
+    status: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class Kling3TurboCreateResponse(BaseModel):
+    code: int | None = Field(None)
+    message: str | None = Field(None)
+    request_id: str | None = Field(None)
+    data: Kling3TurboCreateData | None = Field(None)
+
+
+class Kling3TurboOutput(BaseModel):
+    type: str | None = Field(None, description="'video', 'image', 'audio', ...")
+    id: str | None = Field(None)
+    url: str | None = Field(None)
+    duration: str | None = Field(None)
+
+
+class Kling3TurboTaskData(BaseModel):
+    id: str | None = Field(None)
+    status: str | None = Field(None, description="submitted | processing | succeeded | failed")
+    message: str | None = Field(None)
+    outputs: list[Kling3TurboOutput] | None = Field(None)
+
+
+class Kling3TurboQueryResponse(BaseModel):
+    code: int | None = Field(None)
+    message: str | None = Field(None)
+    request_id: str | None = Field(None)
+    data: list[Kling3TurboTaskData] | None = Field(None)

comfy_api_nodes/apis/luma.py

@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, confloat
 class LumaIO:
     LUMA_REF = "LUMA_REF"
     LUMA_CONCEPTS = "LUMA_CONCEPTS"
+    LUMA_RAY32_KEYFRAME = "LUMA_RAY32_KEYFRAME"
 
 
 class LumaReference:
@@ -20,13 +21,14 @@ class LumaReference:
     def create_api_model(self, download_url: str):
         return LumaImageRef(url=download_url, weight=self.weight)
 
+
 class LumaReferenceChain:
-    def __init__(self, first_ref: LumaReference=None):
+    def __init__(self, first_ref: LumaReference = None):
         self.refs: list[LumaReference] = []
         if first_ref:
             self.refs.append(first_ref)
 
-    def add(self, luma_ref: LumaReference=None):
+    def add(self, luma_ref: LumaReference = None):
         self.refs.append(luma_ref)
 
     def create_api_model(self, download_urls: list[str], max_refs=4):
@@ -124,7 +126,7 @@ def get_luma_concepts(include_none=False):
         "pull_out",
         "aerial",
         "crane_up",
-        "eye_level"
+        "eye_level",
     ]
 
 
@@ -162,8 +164,8 @@ class LumaVideoModelOutputDuration(str, Enum):
 
 
 class LumaGenerationType(str, Enum):
-    video = 'video'
-    image = 'image'
+    video = "video"
+    image = "image"
 
 
 class LumaState(str, Enum):
@@ -174,86 +176,109 @@ class LumaState(str, Enum):
 
 
 class LumaAssets(BaseModel):
-    video: Optional[str] = Field(None, description='The URL of the video')
-    image: Optional[str] = Field(None, description='The URL of the image')
-    progress_video: Optional[str] = Field(None, description='The URL of the progress video')
+    video: Optional[str] = Field(None, description="The URL of the video")
+    image: Optional[str] = Field(None, description="The URL of the image")
+    progress_video: Optional[str] = Field(None, description="The URL of the progress video")
 
 
 class LumaImageRef(BaseModel):
     """Used for image gen"""
-    url: str = Field(..., description='The URL of the image reference')
-    weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference')
+
+    url: str = Field(..., description="The URL of the image reference")
+    weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference")
 
 
 class LumaImageReference(BaseModel):
     """Used for video gen"""
-    type: Optional[str] = Field('image', description='Input type, defaults to image')
-    url: str = Field(..., description='The URL of the image')
+
+    type: Optional[str] = Field("image", description="Input type, defaults to image")
+    url: str = Field(..., description="The URL of the image")
 
 
 class LumaModifyImageRef(BaseModel):
-    url: str = Field(..., description='The URL of the image reference')
-    weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference')
+    url: str = Field(..., description="The URL of the image reference")
+    weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference")
 
 
 class LumaCharacterRef(BaseModel):
-    identity0: LumaImageIdentity = Field(..., description='The image identity object')
+    identity0: LumaImageIdentity = Field(..., description="The image identity object")
 
 
 class LumaImageIdentity(BaseModel):
-    images: list[str] = Field(..., description='The URLs of the image identity')
+    images: list[str] = Field(..., description="The URLs of the image identity")
 
 
 class LumaGenerationReference(BaseModel):
-    type: str = Field('generation', description='Input type, defaults to generation')
-    id: str = Field(..., description='The ID of the generation')
+    type: str = Field("generation", description="Input type, defaults to generation")
+    id: str = Field(..., description="The ID of the generation")
 
 
 class LumaKeyframes(BaseModel):
-    frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='')
-    frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='')
+    frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
+    frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
 
 
 class LumaConceptObject(BaseModel):
-    key: str = Field(..., description='Camera Concept name')
+    key: str = Field(..., description="Camera Concept name")
 
 
 class LumaImageGenerationRequest(BaseModel):
-    prompt: str = Field(..., description='The prompt of the generation')
-    model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation')
-    aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation')
-    image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects')
-    style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects')
-    character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object')
-    modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object')
+    prompt: str = Field(..., description="The prompt of the generation")
+    model: LumaImageModel = Field(LumaImageModel.photon_1, description="The image model used for the generation")
+    aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9)
+    image_ref: Optional[list[LumaImageRef]] = Field(None, description="List of image reference objects")
+    style_ref: Optional[list[LumaImageRef]] = Field(None, description="List of style reference objects")
+    character_ref: Optional[LumaCharacterRef] = Field(None, description="The image identity object")
+    modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description="The modify image reference object")
 
 
 class LumaGenerationRequest(BaseModel):
-    prompt: str = Field(..., description='The prompt of the generation')
-    model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation')
-    duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation')
-    aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation')
-    resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation')
-    loop: Optional[bool] = Field(None, description='Whether to loop the video')
-    keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation')
-    concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation')
+    prompt: str = Field(..., description="The prompt of the generation")
+    model: LumaVideoModel = Field(LumaVideoModel.ray_2, description="The video model used for the generation")
+    duration: Optional[LumaVideoModelOutputDuration] = Field(None, description="The duration of the generation")
+    aspect_ratio: Optional[LumaAspectRatio] = Field(None, description="The aspect ratio of the generation")
+    resolution: Optional[LumaVideoOutputResolution] = Field(None, description="The resolution of the generation")
+    loop: Optional[bool] = Field(None, description="Whether to loop the video")
+    keyframes: Optional[LumaKeyframes] = Field(None, description="The keyframes of the generation")
+    concepts: Optional[list[LumaConceptObject]] = Field(None, description="Camera Concepts to apply to generation")
 
 
 class LumaGeneration(BaseModel):
-    id: str = Field(..., description='The ID of the generation')
-    generation_type: LumaGenerationType = Field(..., description='Generation type, image or video')
-    state: LumaState = Field(..., description='The state of the generation')
-    failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation')
-    created_at: str = Field(..., description='The date and time when the generation was created')
-    assets: Optional[LumaAssets] = Field(None, description='The assets of the generation')
-    model: str = Field(..., description='The model used for the generation')
-    request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation")
+    id: str = Field(..., description="The ID of the generation")
+    generation_type: LumaGenerationType = Field(..., description="Generation type, image or video")
+    state: LumaState = Field(..., description="The state of the generation")
+    failure_reason: Optional[str] = Field(None, description="The reason for the state of the generation")
+    created_at: str = Field(..., description="The date and time when the generation was created")
+    assets: Optional[LumaAssets] = Field(None, description="The assets of the generation")
+    model: str = Field(..., description="The model used for the generation")
+    request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(...)
 
 
 class Luma2ImageRef(BaseModel):
     url: str | None = None
     data: str | None = None
     media_type: str | None = None
+    generation_id: str | None = Field(None, description="reference a prior generation (extend / source reuse)")
+
+
+class Luma2VideoEdit(BaseModel):
+    """Edit controls for Ray 3.2 ``video_edit`` generations."""
+
+    auto_controls: bool | None = Field(None, description="derive a conditioning schedule from the source (recommended)")
+    strength: str | None = Field(None, description="'adhere_1' .. 'reimagine_3'; constrained by IO.Combo")
+
+
+class Luma2VideoOptions(BaseModel):
+    """Ray 3.2 ``video`` output settings (text / image / keyframe / edit / extend)."""
+
+    resolution: str | None = Field(None, description="360p | 540p | 720p | 1080p")
+    duration: str | None = Field(None, description="5s | 10s")
+    loop: bool | None = Field(None)
+    start_frame: Luma2ImageRef | None = Field(None)
+    end_frame: Luma2ImageRef | None = Field(None)
+    keyframes: list[Luma2ImageRef] | None = Field(None)
+    keyframe_indexes: list[int] | None = Field(None)
+    edit: Luma2VideoEdit | None = Field(None)
 
 
 class Luma2GenerationRequest(BaseModel):
@@ -266,6 +291,7 @@ class Luma2GenerationRequest(BaseModel):
     web_search: bool | None = None
     image_ref: list[Luma2ImageRef] | None = None
     source: Luma2ImageRef | None = None
+    video: Luma2VideoOptions | None = Field(None)
 
 
 class Luma2Generation(BaseModel):
@@ -277,3 +303,31 @@ class Luma2Generation(BaseModel):
     output: list[LumaImageReference] | None = None
     failure_reason: str | None = None
     failure_code: str | None = None
+
+
+# --- Ray 3.2 multi-keyframe chain ---
+
+LUMA_KEYFRAME_MODE_FRACTION = "fraction"  # value in [0.0, 1.0] of the output video duration
+LUMA_KEYFRAME_MODE_SECONDS = "seconds"  # absolute time, in seconds, from the start of the output
+
+
+class LumaRay32KeyframeItem:
+    """One guide image anchored at a position on the Ray 3.2 output timeline."""
+
+    def __init__(self, image: torch.Tensor, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # LUMA_KEYFRAME_MODE_FRACTION | LUMA_KEYFRAME_MODE_SECONDS
+        self.value = value
+
+
+class LumaRay32KeyframeChain:
+    def __init__(self):
+        self.items: list[LumaRay32KeyframeItem] = []
+
+    def add(self, item: LumaRay32KeyframeItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "LumaRay32KeyframeChain":
+        c = LumaRay32KeyframeChain()
+        c.items = list(self.items)
+        return c

comfy_api_nodes/apis/runway.py

@@ -67,15 +67,6 @@ class RunwayImageToVideoResponse(BaseModel):
     id: Optional[str] = Field(None, description='Task ID')
 
 
-class RunwayTaskStatusEnum(str, Enum):
-    SUCCEEDED = 'SUCCEEDED'
-    RUNNING = 'RUNNING'
-    FAILED = 'FAILED'
-    PENDING = 'PENDING'
-    CANCELLED = 'CANCELLED'
-    THROTTLED = 'THROTTLED'
-
-
 class RunwayTaskStatusResponse(BaseModel):
     createdAt: datetime = Field(..., description='Task creation timestamp')
     id: str = Field(..., description='Task ID')
@@ -86,7 +77,7 @@ class RunwayTaskStatusResponse(BaseModel):
         ge=0.0,
         le=1.0,
     )
-    status: RunwayTaskStatusEnum
+    status: str = Field(..., description="SUCCEEDED, RUNNING, FAILED, PENDING, CANCELLED or THROTTLED")
 
 
 class Model4(str, Enum):
@@ -125,3 +116,144 @@ class RunwayTextToImageRequest(BaseModel):
 
 class RunwayTextToImageResponse(BaseModel):
     id: Optional[str] = Field(None, description='Task ID')
+
+
+class RunwayAleph2IO:
+    """Custom socket types for chaining Aleph2 guidance images."""
+
+    KEYFRAME = "RUNWAY_ALEPH2_KEYFRAME"
+    PROMPT_IMAGE = "RUNWAY_ALEPH2_PROMPT_IMAGE"
+
+
+# Keyframe timing modes (anchored to the INPUT video). Stored on the chain item and used to
+# choose the request model below. The values match the Aleph2 keyframe union field names.
+KEYFRAME_MODE_SECONDS = "seconds"  # absolute time, in seconds, from the start of the input video
+KEYFRAME_MODE_AT = "at"  # fraction [0.0, 1.0] of the input video duration
+
+# Prompt-image position modes (anchored to the OUTPUT video). Values match the Aleph2 position `type`.
+PROMPT_IMAGE_MODE_TIMESTAMP = "timestamp"  # absolute time, in seconds, from the start of the output video
+PROMPT_IMAGE_MODE_POSITION = "position"  # fraction [0.0, 1.0] of the output video duration
+
+
+class RunwayAleph2KeyframeItem:
+    """A guidance image anchored to a point of the INPUT video (one Aleph2 ``keyframe``)."""
+
+    def __init__(self, image, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # KEYFRAME_MODE_SECONDS | KEYFRAME_MODE_AT
+        self.value = value
+
+
+class RunwayAleph2KeyframeChain:
+    """An ordered collection of keyframes, built by chaining Runway Aleph2 Keyframe nodes."""
+
+    def __init__(self):
+        self.items: list[RunwayAleph2KeyframeItem] = []
+
+    def add(self, item: RunwayAleph2KeyframeItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "RunwayAleph2KeyframeChain":
+        c = RunwayAleph2KeyframeChain()
+        c.items = list(self.items)
+        return c
+
+
+class RunwayAleph2PromptImageItem:
+    """A guidance image anchored to a point of the OUTPUT video (one Aleph2 ``promptImage``)."""
+
+    def __init__(self, image, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # PROMPT_IMAGE_MODE_TIMESTAMP | PROMPT_IMAGE_MODE_POSITION
+        self.value = value
+
+
+class RunwayAleph2PromptImageChain:
+    """An ordered collection of prompt images, built by chaining Runway Aleph2 Prompt Image nodes."""
+
+    def __init__(self):
+        self.items: list[RunwayAleph2PromptImageItem] = []
+
+    def add(self, item: RunwayAleph2PromptImageItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "RunwayAleph2PromptImageChain":
+        c = RunwayAleph2PromptImageChain()
+        c.items = list(self.items)
+        return c
+
+
+class RunwayAleph2KeyframeSeconds(BaseModel):
+    seconds: float = Field(
+        ...,
+        description="Absolute timestamp in seconds from the start of the input video when this guidance image should apply.",
+        ge=0.0,
+    )
+    uri: str = Field(...)
+
+
+class RunwayAleph2KeyframeAt(BaseModel):
+    at: float = Field(
+        ...,
+        description="Position as a fraction [0.0, 1.0] of the input video duration.",
+        ge=0.0,
+        le=1.0,
+    )
+    uri: str = Field(...)
+
+
+class RunwayAleph2TimestampPosition(BaseModel):
+    type: str = Field(default="timestamp")
+    timestampSeconds: float = Field(
+        ...,
+        description="Absolute timestamp in seconds from the start of the output video.",
+        ge=0.0,
+    )
+
+
+class RunwayAleph2RelativePosition(BaseModel):
+    type: str = Field(default="position")
+    positionPercentage: float = Field(
+        ...,
+        description="Position as a fraction [0.0, 1.0] of the total output video duration.",
+        ge=0.0,
+        le=1.0,
+    )
+
+
+class RunwayAleph2PromptImage(BaseModel):
+    position: RunwayAleph2TimestampPosition | RunwayAleph2RelativePosition
+    uri: str = Field(...)
+
+
+class RunwayAleph2ContentModeration(BaseModel):
+    publicFigureThreshold: str = Field(
+        ...,
+        description='When set to "low", the content moderation system is less strict about '
+        'recognizable public figures. One of "auto" or "low".',
+    )
+
+
+class RunwayAleph2Request(BaseModel):
+    model: str = Field(default="aleph2")
+    promptText: str = Field(
+        ...,
+        description="A non-empty string describing what should appear in the output.",
+        min_length=1,
+        max_length=1000,
+    )
+    videoUri: str = Field(...)
+    seed: int = Field(..., description="Random seed for generation", ge=0, le=4294967295)
+    contentModeration: RunwayAleph2ContentModeration = Field(...)
+    keyframes: list[RunwayAleph2KeyframeSeconds | RunwayAleph2KeyframeAt] | None = Field(
+        None,
+        description="Timed guidance images placed at specific points in the input video. Up to 5.",
+    )
+    promptImage: list[RunwayAleph2PromptImage] | None = Field(
+        None,
+        description="Up to 5 image keyframes for guiding the edit at specific points in the output video.",
+    )
+
+
+class RunwayAleph2Response(BaseModel):
+    id: str | None = Field(None, description="Task ID")

comfy_api_nodes/apis/stability.py

@@ -1,147 +0,0 @@
-from enum import Enum
-from typing import Optional
-
-from pydantic import BaseModel, Field, confloat
-
-
-class StabilityFormat(str, Enum):
-    png = 'png'
-    jpeg = 'jpeg'
-    webp = 'webp'
-
-
-class StabilityAspectRatio(str, Enum):
-    ratio_1_1 = "1:1"
-    ratio_16_9 = "16:9"
-    ratio_9_16 = "9:16"
-    ratio_3_2 = "3:2"
-    ratio_2_3 = "2:3"
-    ratio_5_4 = "5:4"
-    ratio_4_5 = "4:5"
-    ratio_21_9 = "21:9"
-    ratio_9_21 = "9:21"
-
-
-def get_stability_style_presets(include_none=True):
-    presets = []
-    if include_none:
-        presets.append("None")
-    return presets + [x.value for x in StabilityStylePreset]
-
-
-class StabilityStylePreset(str, Enum):
-    _3d_model = "3d-model"
-    analog_film = "analog-film"
-    anime = "anime"
-    cinematic = "cinematic"
-    comic_book = "comic-book"
-    digital_art = "digital-art"
-    enhance = "enhance"
-    fantasy_art = "fantasy-art"
-    isometric = "isometric"
-    line_art = "line-art"
-    low_poly = "low-poly"
-    modeling_compound = "modeling-compound"
-    neon_punk = "neon-punk"
-    origami = "origami"
-    photographic = "photographic"
-    pixel_art = "pixel-art"
-    tile_texture = "tile-texture"
-
-
-class Stability_SD3_5_Model(str, Enum):
-    sd3_5_large = "sd3.5-large"
-    # sd3_5_large_turbo = "sd3.5-large-turbo"
-    sd3_5_medium = "sd3.5-medium"
-
-
-class Stability_SD3_5_GenerationMode(str, Enum):
-    text_to_image = "text-to-image"
-    image_to_image = "image-to-image"
-
-
-class StabilityStable3_5Request(BaseModel):
-    model: str = Field(...)
-    mode: str = Field(...)
-    prompt: str = Field(...)
-    negative_prompt: Optional[str] = Field(None)
-    aspect_ratio: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-    output_format: Optional[str] = Field(StabilityFormat.png.value)
-    image: Optional[str] = Field(None)
-    style_preset: Optional[str] = Field(None)
-    cfg_scale: float = Field(...)
-    strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None)
-
-
-class StabilityUpscaleConservativeRequest(BaseModel):
-    prompt: str = Field(...)
-    negative_prompt: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-    output_format: Optional[str] = Field(StabilityFormat.png.value)
-    image: Optional[str] = Field(None)
-    creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None)
-
-
-class StabilityUpscaleCreativeRequest(BaseModel):
-    prompt: str = Field(...)
-    negative_prompt: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-    output_format: Optional[str] = Field(StabilityFormat.png.value)
-    image: Optional[str] = Field(None)
-    creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None)
-    style_preset: Optional[str] = Field(None)
-
-
-class StabilityStableUltraRequest(BaseModel):
-    prompt: str = Field(...)
-    negative_prompt: Optional[str] = Field(None)
-    aspect_ratio: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-    output_format: Optional[str] = Field(StabilityFormat.png.value)
-    image: Optional[str] = Field(None)
-    style_preset: Optional[str] = Field(None)
-    strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None)
-
-
-class StabilityStableUltraResponse(BaseModel):
-    image: Optional[str] = Field(None)
-    finish_reason: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-
-
-class StabilityResultsGetResponse(BaseModel):
-    image: Optional[str] = Field(None)
-    finish_reason: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-    id: Optional[str] = Field(None)
-    name: Optional[str] = Field(None)
-    errors: Optional[list[str]] = Field(None)
-    status: Optional[str] = Field(None)
-    result: Optional[str] = Field(None)
-
-
-class StabilityAsyncResponse(BaseModel):
-    id: Optional[str] = Field(None)
-
-
-class StabilityTextToAudioRequest(BaseModel):
-    model: str = Field(...)
-    prompt: str = Field(...)
-    duration: int = Field(190, ge=1, le=190)
-    seed: int = Field(0, ge=0, le=4294967294)
-    steps: int = Field(8, ge=4, le=8)
-    output_format: str = Field("wav")
-
-
-class StabilityAudioToAudioRequest(StabilityTextToAudioRequest):
-    strength: float = Field(0.01, ge=0.01, le=1.0)
-
-
-class StabilityAudioInpaintRequest(StabilityTextToAudioRequest):
-    mask_start: int = Field(30, ge=0, le=190)
-    mask_end: int = Field(190, ge=0, le=190)
-
-
-class StabilityAudioResponse(BaseModel):
-    audio: Optional[str] = Field(None)

comfy_api_nodes/apis/tripo.py

@@ -208,6 +208,10 @@ class TripoMultiviewToModelRequest(BaseModel):
     quad: bool | None = Field(False, description="Whether to apply quad to the generated model")
 
 
+class TripoTexturePrompt(BaseModel):
+    text: str | None = Field(None, description="Text guidance for texture generation")
+
+
 class TripoTextureModelRequest(BaseModel):
     type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description="Type of task")
     original_model_task_id: str = Field(..., description="The task ID of the original model")
@@ -219,6 +223,11 @@ class TripoTextureModelRequest(BaseModel):
     texture_alignment: TripoTextureAlignment | None = Field(
         TripoTextureAlignment.ORIGINAL_IMAGE, description="The texture alignment method"
     )
+    texture_prompt: TripoTexturePrompt | None = Field(
+        None,
+        description="Optional guidance for texturing. Required in practice for imported models, "
+        "which carry no source image to infer texture from.",
+    )
 
 
 class TripoRefineModelRequest(BaseModel):
@@ -307,6 +316,17 @@ class TripoP1MultiviewToModelRequest(TripoP1CommonRequest):
     orientation: str | None = None
 
 
+class TripoImportModelRequest(BaseModel):
+    """Request for the comfy-api composite import endpoint (/proxy/tripo/v2/openapi/import).
+
+    The model file is uploaded to ComfyUI API storage first; the backend downloads it from
+    `url`, re-uploads it to Tripo's storage and creates the import_model task server-side.
+    """
+
+    url: str = Field(..., description="ComfyUI API storage download URL of the model file")
+    format: str = Field(..., description='File format: "glb", "fbx", "obj" or "stl"')
+
+
 class TripoTaskOutput(BaseModel):
     model: str | None = Field(None, description="URL to the model")
     base_model: str | None = Field(None, description="URL to the base model")

comfy_api_nodes/nodes_bytedance.py

@@ -1,3 +1,4 @@
+import base64
 import hashlib
 import logging
 import math
@@ -15,12 +16,16 @@ from comfy_api_nodes.apis.bytedance import (
     RECOMMENDED_PRESETS_SEEDREAM_4_0,
     RECOMMENDED_PRESETS_SEEDREAM_4_5,
     RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
-    SEEDANCE2_PRICE_PER_1K_TOKENS,
+    RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
     SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
     VIDEO_TASKS_EXECUTION_TIME,
     GetAssetResponse,
     Image2VideoTaskCreationRequest,
     ImageTaskCreationResponse,
+    SeedAudioConfig,
+    SeedAudioReference,
+    SeedAudioRequest,
+    SeedAudioResponse,
     Seedance2TaskCreationRequest,
     SeedanceCreateAssetRequest,
     SeedanceCreateAssetResponse,
@@ -29,6 +34,7 @@ from comfy_api_nodes.apis.bytedance import (
     SeedanceVirtualLibraryCreateAssetRequest,
     Seedream4Options,
     Seedream4TaskCreationRequest,
+    Seedream5OptimizePromptOptions,
     TaskAudioContent,
     TaskAudioContentUrl,
     TaskCreationResponse,
@@ -40,9 +46,12 @@ from comfy_api_nodes.apis.bytedance import (
     TaskVideoContentUrl,
     Text2ImageTaskCreationRequest,
     Text2VideoTaskCreationRequest,
+    seedance2_price_per_1k_tokens,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_input_to_mp3,
     download_url_to_image_tensor,
     download_url_to_video_output,
     downscale_image_tensor_by_max_side,
@@ -51,11 +60,14 @@ from comfy_api_nodes.util import (
     image_tensor_pair_to_batch,
     poll_op,
     sync_op,
+    tensor_to_base64_string,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
+    upscale_image_tensor_to_min_pixels,
     upscale_video_to_min_pixels,
+    validate_audio_duration,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -70,12 +82,14 @@ _VERIFICATION_POLL_TIMEOUT_SEC = 120
 _VERIFICATION_POLL_INTERVAL_SEC = 3
 
 SEEDREAM_MODELS = {
+    "seedream 5.0 pro": "seedream-5-0-pro-260628",
     "seedream 5.0 lite": "seedream-5-0-260128",
     "seedream-4-5-251128": "seedream-4-5-251128",
     "seedream-4-0-250828": "seedream-4-0-250828",
 }
 
 SEEDREAM_PRESETS = {
+    "seedream-5-0-pro-260628": RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
     "seedream-5-0-260128": RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
     "seedream-4-5-251128": RECOMMENDED_PRESETS_SEEDREAM_4_5,
     "seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0,
@@ -89,6 +103,7 @@ BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT = "/proxy/byteplus-seedance2/api/v3/cont
 SEEDANCE_MODELS = {
     "Seedance 2.0": "dreamina-seedance-2-0-260128",
     "Seedance 2.0 Fast": "dreamina-seedance-2-0-fast-260128",
+    "Seedance 2.0 Mini": "dreamina-seedance-2-0-mini",
 }
 
 DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"}
@@ -141,7 +156,7 @@ SEEDANCE2_RATIO_WH = {
     "9:16": (9, 16),
     "21:9": (21, 9),
 }
-SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080}
+SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080, "4k": 2160}
 
 
 def _seedance2_target_dims(resolution: str, ratio: str, image: torch.Tensor) -> tuple[int, int]:
@@ -377,9 +392,9 @@ async def _seedance_virtual_library_upload_video_asset(
     return f"asset://{create_resp.asset_id}"
 
 
-def _seedance2_price_extractor(model_id: str, has_video_input: bool):
+def _seedance2_price_extractor(model_id: str, has_video_input: bool, resolution: str):
     """Returns a price_extractor closure for Seedance 2.0 poll_op."""
-    rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input))
+    rate = seedance2_price_per_1k_tokens(model_id, has_video_input, resolution)
     if rate is None:
         return None
 
@@ -732,8 +747,15 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
 
 
-def _seedream_model_inputs(*, max_ref_images: int, presets: list):
-    return [
+def _seedream_model_inputs(
+    *,
+    max_ref_images: int,
+    presets: list,
+    max_width: int = 6240,
+    max_height: int = 4992,
+    supports_batch: bool = True,
+):
+    inputs = [
         IO.Combo.Input(
             "size_preset",
             options=[label for label, _, _ in presets],
@@ -743,7 +765,7 @@ def _seedream_model_inputs(*, max_ref_images: int, presets: list):
             "width",
             default=2048,
             min=1024,
-            max=6240,
+            max=max_width,
             step=2,
             tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
         ),
@@ -751,22 +773,27 @@ def _seedream_model_inputs(*, max_ref_images: int, presets: list):
             "height",
             default=2048,
             min=1024,
-            max=4992,
+            max=max_height,
             step=2,
             tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
         ),
-        IO.Int.Input(
-            "max_images",
-            default=1,
-            min=1,
-            max=max_ref_images,
-            step=1,
-            display_mode=IO.NumberDisplay.number,
-            tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
-            "With >1, the model generates between 1 and max_images related images "
-            "(e.g., story scenes, character variations). "
-            "Total images (input + generated) cannot exceed 15.",
-        ),
+    ]
+    if supports_batch:
+        inputs.append(
+            IO.Int.Input(
+                "max_images",
+                default=1,
+                min=1,
+                max=max_ref_images,
+                step=1,
+                display_mode=IO.NumberDisplay.number,
+                tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
+                "With >1, the model generates between 1 and max_images related images "
+                "(e.g., story scenes, character variations). "
+                "Total images (input + generated) cannot exceed 15.",
+            )
+        )
+    inputs.append(
         IO.Autogrow.Input(
             "images",
             template=IO.Autogrow.TemplateNames(
@@ -776,14 +803,18 @@ def _seedream_model_inputs(*, max_ref_images: int, presets: list):
             ),
             tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. "
             f"Up to {max_ref_images} images.",
-        ),
-        IO.Boolean.Input(
-            "fail_on_partial",
-            default=False,
-            tooltip="If enabled, abort execution if any requested images are missing or return an error.",
-            advanced=True,
-        ),
-    ]
+        )
+    )
+    if supports_batch:
+        inputs.append(
+            IO.Boolean.Input(
+                "fail_on_partial",
+                default=False,
+                tooltip="If enabled, abort execution if any requested images are missing or return an error.",
+                advanced=True,
+            )
+        )
+    return inputs
 
 
 class ByteDanceSeedreamNodeV2(IO.ComfyNode):
@@ -805,6 +836,16 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
+                        IO.DynamicCombo.Option(
+                            "seedream 5.0 pro",
+                            _seedream_model_inputs(
+                                max_ref_images=10,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+                                max_width=3136,
+                                max_height=2496,
+                                supports_batch=False,
+                            ),
+                        ),
                         IO.DynamicCombo.Option(
                             "seedream 5.0 lite",
                             _seedream_model_inputs(max_ref_images=14, presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE),
@@ -835,6 +876,17 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     advanced=True,
                 ),
+                IO.Boolean.Input(
+                    "thinking",
+                    default=True,
+                    tooltip=(
+                        "Enable the model's prompt-optimization reasoning ('thinking') for better adherence. "
+                        "Can substantially increase generation time — notably on Seedream 5.0 Pro. "
+                        "Can only be disabled for text-to-image (not when reference images are provided)."
+                    ),
+                    optional=True,
+                    advanced=True,
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -846,15 +898,27 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.size_preset", "model.width", "model.height"]
+                ),
                 expr="""
                 (
-                  $price := $contains(widgets.model, "5.0 lite") ? 0.035 :
-                            $contains(widgets.model, "4-5") ? 0.04 : 0.03;
+                  $sp := $lookup(widgets, "model.size_preset");
+                  $px := $lookup(widgets, "model.width") * $lookup(widgets, "model.height");
+                  $isPro := $contains(widgets.model, "5.0 pro");
+                  $price := $isPro
+                              ? (
+                                  $contains($sp, "custom")
+                                    ? ($px <= 2360000 ? 0.045 : 0.09)
+                                    : ($contains($sp, "1k") ? 0.045 : 0.09)
+                                )
+                              : $contains(widgets.model, "5.0 lite") ? 0.035
+                              : $contains(widgets.model, "4-5") ? 0.04
+                              : 0.03;
                   {
-                    "type":"usd",
+                    "type": "usd",
                     "usd": $price,
-                    "format": { "suffix":" x images/Run", "approximate": true }
+                    "format": { "suffix": $isPro ? "/Image" : " x images/Run", "approximate": true }
                   }
                 )
                 """,
@@ -868,10 +932,12 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
         model: dict,
         seed: int = 0,
         watermark: bool = False,
+        thinking: bool = True,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_id = SEEDREAM_MODELS[model["model"]]
         presets = SEEDREAM_PRESETS[model_id]
+        is_pro = "seedream-5-0-pro" in model_id
 
         size_preset = model.get("size_preset", presets[0][0])
         width = model.get("width", 2048)
@@ -891,19 +957,29 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
 
         out_num_pixels = w * h
         mp_provided = out_num_pixels / 1_000_000.0
-        if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3686400:
-            raise ValueError(
-                f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
-            )
-        if "seedream-4-0" in model_id and out_num_pixels < 921600:
-            raise ValueError(
-                f"Minimum image resolution that the selected model can generate is 0.92MP, "
-                f"but {mp_provided:.2f}MP provided."
-            )
-        if out_num_pixels > 16_777_216:
-            raise ValueError(
-                f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
-            )
+        if is_pro:
+            if out_num_pixels < 921_600:
+                raise ValueError(
+                    f"Minimum image resolution for the selected model is 0.92MP, but {mp_provided:.2f}MP provided."
+                )
+            if out_num_pixels > 4_194_304:
+                raise ValueError(
+                    f"Maximum image resolution for the selected model is 4.19MP, but {mp_provided:.2f}MP provided."
+                )
+        else:
+            if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3_686_400:
+                raise ValueError(
+                    f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
+                )
+            if "seedream-4-0" in model_id and out_num_pixels < 921_600:
+                raise ValueError(
+                    f"Minimum image resolution that the selected model can generate is 0.92MP, "
+                    f"but {mp_provided:.2f}MP provided."
+                )
+            if out_num_pixels > 16_777_216:
+                raise ValueError(
+                    f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
+                )
 
         image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
         n_input_images = sum(get_number_of_images(t) for t in image_tensors)
@@ -916,6 +992,10 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
             raise ValueError(
                 "The maximum number of generated images plus the number of reference images cannot exceed 15."
             )
+        if not thinking and n_input_images > 0:
+            raise ValueError(
+                "'thinking' can only be disabled for text-to-image; enable it when using reference images."
+            )
 
         reference_images_urls: list[str] = []
         if image_tensors:
@@ -929,6 +1009,9 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 wait_label="Uploading reference images",
             )
 
+        optimize_prompt_options = None
+        if n_input_images == 0:
+            optimize_prompt_options = Seedream5OptimizePromptOptions(thinking="enabled" if thinking else "disabled")
         response = await sync_op(
             cls,
             ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
@@ -939,9 +1022,10 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 image=reference_images_urls,
                 size=f"{w}x{h}",
                 seed=seed,
-                sequential_image_generation=sequential_image_generation,
-                sequential_image_generation_options=Seedream4Options(max_images=max_images),
+                sequential_image_generation=None if is_pro else sequential_image_generation,
+                sequential_image_generation_options=None if is_pro else Seedream4Options(max_images=max_images),
                 watermark=watermark,
+                optimize_prompt_options=optimize_prompt_options,
             ),
         )
         if len(response.data) == 1:
@@ -1621,10 +1705,12 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
-                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p"])),
+                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p", "4k"])),
                         IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])),
+                        IO.DynamicCombo.Option("Seedance 2.0 Mini", _seedance2_text_inputs(["480p", "720p"])),
                     ],
-                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
+                    tooltip="Seedance 2.0 for maximum quality; Fast for speed optimization; "
+                    "Mini for the fastest, lowest-cost generation.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -1660,11 +1746,16 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
                   $rate480 := 10044;
                   $rate720 := 21600;
                   $rate1080 := 48800;
+                  $rate4k := 195200;
                   $m := widgets.model;
-                  $pricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001;
                   $res := $lookup(widgets, "model.resolution");
                   $dur := $lookup(widgets, "model.duration");
-                  $rate := $res = "1080p" ? $rate1080 :
+                  $pricePer1K := $res = "4k"    ? 0.00572 :
+                                 $res = "1080p" ? 0.011011 :
+                                 $contains($m, "mini") ? 0.005005 :
+                                 $contains($m, "fast") ? 0.008008 : 0.01001;
+                  $rate := $res = "4k"    ? $rate4k :
+                           $res = "1080p" ? $rate1080 :
                            $res = "720p"  ? $rate720 :
                                             $rate480;
                   $cost := $dur * $rate * $pricePer1K / 1000;
@@ -1703,7 +1794,7 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
             ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{initial_response.id}"),
             response_model=TaskStatusResponse,
             status_extractor=lambda r: r.status,
-            price_extractor=_seedance2_price_extractor(model_id, has_video_input=False),
+            price_extractor=_seedance2_price_extractor(model_id, has_video_input=False, resolution=model["resolution"]),
             poll_interval=9,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
@@ -1724,14 +1815,19 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Seedance 2.0",
-                            _seedance2_text_inputs(["480p", "720p", "1080p"], default_ratio="adaptive"),
+                            _seedance2_text_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
                         ),
                         IO.DynamicCombo.Option(
                             "Seedance 2.0 Fast",
                             _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
                         ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Mini",
+                            _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
                     ],
-                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
+                    tooltip="Seedance 2.0 for maximum quality; Fast for speed optimization; "
+                    "Mini for the fastest, lowest-cost generation.",
                 ),
                 IO.Image.Input(
                     "first_frame",
@@ -1791,11 +1887,16 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                   $rate480 := 10044;
                   $rate720 := 21600;
                   $rate1080 := 48800;
+                  $rate4k := 195200;
                   $m := widgets.model;
-                  $pricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001;
                   $res := $lookup(widgets, "model.resolution");
                   $dur := $lookup(widgets, "model.duration");
-                  $rate := $res = "1080p" ? $rate1080 :
+                  $pricePer1K := $res = "4k"    ? 0.00572 :
+                                 $res = "1080p" ? 0.011011 :
+                                 $contains($m, "mini") ? 0.005005 :
+                                 $contains($m, "fast") ? 0.008008 : 0.01001;
+                  $rate := $res = "4k"    ? $rate4k :
+                           $res = "1080p" ? $rate1080 :
                            $res = "720p"  ? $rate720 :
                                             $rate480;
                   $cost := $dur * $rate * $pricePer1K / 1000;
@@ -1913,7 +2014,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
             ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{initial_response.id}"),
             response_model=TaskStatusResponse,
             status_extractor=lambda r: r.status,
-            price_extractor=_seedance2_price_extractor(model_id, has_video_input=False),
+            price_extractor=_seedance2_price_extractor(model_id, has_video_input=False, resolution=model["resolution"]),
             poll_interval=9,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
@@ -2010,14 +2111,19 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Seedance 2.0",
-                            _seedance2_reference_inputs(["480p", "720p", "1080p"], default_ratio="adaptive"),
+                            _seedance2_reference_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
                         ),
                         IO.DynamicCombo.Option(
                             "Seedance 2.0 Fast",
                             _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
                         ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Mini",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
                     ],
-                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
+                    tooltip="Seedance 2.0 for maximum quality; Fast for speed optimization; "
+                    "Mini for the fastest, lowest-cost generation.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -2056,13 +2162,21 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                   $rate480 := 10044;
                   $rate720 := 21600;
                   $rate1080 := 48800;
+                  $rate4k := 195200;
                   $m := widgets.model;
                   $hasVideo := $lookup(inputGroups, "model.reference_videos") > 0;
-                  $noVideoPricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001;
-                  $videoPricePer1K := $contains($m, "fast") ? 0.004719 : 0.006149;
                   $res := $lookup(widgets, "model.resolution");
                   $dur := $lookup(widgets, "model.duration");
-                  $rate := $res = "1080p" ? $rate1080 :
+                  $noVideoPricePer1K := $res = "4k"    ? 0.00572 :
+                                        $res = "1080p" ? 0.011011 :
+                                        $contains($m, "mini") ? 0.005005 :
+                                        $contains($m, "fast") ? 0.008008 : 0.01001;
+                  $videoPricePer1K := $res = "4k"    ? 0.003432 :
+                                      $res = "1080p" ? 0.006721 :
+                                      $contains($m, "mini") ? 0.003003 :
+                                      $contains($m, "fast") ? 0.004719 : 0.006149;
+                  $rate := $res = "4k"    ? $rate4k :
+                           $res = "1080p" ? $rate1080 :
                            $res = "720p"  ? $rate720 :
                                             $rate480;
                   $noVideoCost := $dur * $rate * $noVideoPricePer1K / 1000;
@@ -2258,7 +2372,9 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
             ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{initial_response.id}"),
             response_model=TaskStatusResponse,
             status_extractor=lambda r: r.status,
-            price_extractor=_seedance2_price_extractor(model_id, has_video_input=has_video_input),
+            price_extractor=_seedance2_price_extractor(
+                model_id, has_video_input=has_video_input, resolution=model["resolution"]
+            ),
             poll_interval=9,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
@@ -2441,6 +2557,311 @@ class ByteDanceCreateVideoAsset(IO.ComfyNode):
         return IO.NodeOutput(asset_id, resolved_group)
 
 
+MODE_TEXT = "text only"
+MODE_AUDIO = "audio reference"
+MODE_IMAGE = "image reference"
+MODE_SPEAKER = "preset voice"
+
+# (speaker_id, display_label) for built-in TTS 2.0 voices; resolvable ids are account-scoped.
+SEED_AUDIO_PRESET_VOICES: list[tuple[str, str]] = [
+    ("zh_female_vv_uranus_bigtts", "Vivi (Female, multilingual)"),
+    ("zh_female_xiaohe_uranus_bigtts", "Mindy (Female, multilingual)"),
+    ("en_female_stokie_uranus_bigtts", "Stokie (Female, English)"),
+    ("en_female_dacey_uranus_bigtts", "Dacey (Female, English)"),
+    ("en_male_tim_uranus_bigtts", "Tim (Male, English)"),
+    ("zh_male_m191_uranus_bigtts", "Kian (Male, multilingual)"),
+    ("zh_male_taocheng_uranus_bigtts", "Cedric (Male, multilingual)"),
+    ("zh_male_sophie_uranus_bigtts", "Sophie (Female, multilingual)"),
+    ("zh_female_yingyujiaoxue_uranus_bigtts", "Jean (Female, multilingual)"),
+    ("zh_male_dayi_uranus_bigtts", "Magnus (Male, multilingual)"),
+    ("zh_female_mizai_uranus_bigtts", "Mabel (Female, multilingual)"),
+    ("zh_female_jitangnv_uranus_bigtts", "Nadia (Female, multilingual)"),
+    ("zh_female_meilinvyou_uranus_bigtts", "Opal (Female, multilingual)"),
+    ("zh_female_liuchangnv_uranus_bigtts", "Pearl (Female, multilingual)"),
+    ("zh_male_ruyayichen_uranus_bigtts", "Quentin (Male, multilingual)"),
+    ("zh_female_vivo_uranus_bigtts", "Vienna (Female, multilingual)"),
+    ("zh_female_xiaoai_uranus_bigtts", "Alina (Female, multilingual)"),
+    ("zh_female_cancan_uranus_bigtts", "Corinne (Female, multilingual)"),
+    ("zh_female_tianmeixiaoyuan_uranus_bigtts", "Esther (Female, multilingual)"),
+    ("zh_female_tianmeitaozi_uranus_bigtts", "Freya (Female, multilingual)"),
+    ("zh_female_shuangkuaisisi_uranus_bigtts", "Gigi (Female, multilingual)"),
+    ("zh_female_peiqi_uranus_bigtts", "Holly (Female, multilingual)"),
+    ("zh_female_xiaoxue_uranus_bigtts", "Lyla (Female, multilingual)"),
+    ("zh_female_yuanqi_uranus_bigtts", "Daisy (Female, multilingual)"),
+    ("zh_female_kefunvsheng_uranus_bigtts", "Tracy (Female, multilingual)"),
+    ("zh_male_shaonianzixin_uranus_bigtts", "Jess (Male, multilingual)"),
+    ("zh_female_linjianvhai_uranus_bigtts", "Pinky (Female, multilingual)"),
+    ("zh_female_kiwi_uranus_bigtts", "Sweety (Female, multilingual)"),
+    ("zh_female_sajiaoxuemei_uranus_bigtts", "Sandy (Female, multilingual)"),
+    ("de_male_seven_uranus_bigtts", "Sven (Male, German)"),
+    ("jp_female_minimi_uranus_bigtts", "Minimi (Female, Japanese)"),
+    ("fr_male_usseau_uranus_bigtts", "Usseau (Male, French)"),
+    ("es_male_felipe_uranus_bigtts", "Felipe (Male, Spanish)"),
+    ("id_male_han_uranus_bigtts", "Han (Male, Indonesian)"),
+    ("pt_male_martins_uranus_bigtts", "Martins (Male, Portuguese)"),
+    ("it_male_enzo_uranus_bigtts", "Enzo (Male, Italian)"),
+    ("kr_male_shane_uranus_bigtts", "Shane (Male, Korean)"),
+    ("zh_male_liufei_uranus_bigtts", "Felix (Male, Chinese)"),
+    ("zh_female_qingxinnvsheng_uranus_bigtts", "Celeste (Female, Chinese)"),
+    ("zh_male_sunwukong_uranus_bigtts", "Monkey King (Male, Chinese)"),
+]
+SEED_AUDIO_VOICE_OPTIONS = [label for _, label in SEED_AUDIO_PRESET_VOICES]
+SEED_AUDIO_VOICE_MAP = {label: speaker_id for speaker_id, label in SEED_AUDIO_PRESET_VOICES}
+
+_AUDIO_TAG_RE = re.compile(r"@Audio(\d+)", re.IGNORECASE)
+
+
+def max_audio_tag(prompt: str) -> int:
+    """Highest N referenced as @AudioN in the prompt (0 if none)."""
+    nums = [int(m) for m in _AUDIO_TAG_RE.findall(prompt or "")]
+    return max(nums) if nums else 0
+
+
+def connected_audio_indices(reference_mode: dict) -> list[int]:
+    """Indices (1-based) of connected reference_audio sockets, in order."""
+    return [
+        i
+        for i in range(1, 3 + 1)
+        if reference_mode.get(f"reference_audio_{i}") is not None
+    ]
+
+
+def validate_seed_audio_inputs(
+    text_prompt: str,
+    mode: str,
+    audio_indices: list[int],
+    has_image: bool,
+    preset_voice: str | None = None,
+) -> None:
+    validate_string(text_prompt, field_name="text_prompt", min_length=1, max_length=3000)
+    max_tag = max_audio_tag(text_prompt)
+
+    if mode == MODE_TEXT:
+        if max_tag:
+            raise ValueError(
+                f"The prompt references @Audio{max_tag}, but reference mode is '{MODE_TEXT}'. "
+                f"Switch to '{MODE_AUDIO}' and connect the reference clip(s)."
+            )
+    elif mode == MODE_AUDIO:
+        if not audio_indices:
+            raise ValueError(
+                f"Reference mode '{MODE_AUDIO}' requires at least one reference_audio input "
+                f"(or switch to '{MODE_TEXT}')."
+            )
+        if audio_indices != list(range(1, len(audio_indices) + 1)):
+            raise ValueError(
+                "Connect reference_audio inputs in order without gaps: reference_audio_1, then _2, then _3."
+            )
+        if max_tag > len(audio_indices):
+            raise ValueError(
+                f"The prompt references @Audio{max_tag}, but only {len(audio_indices)} "
+                f"reference audio(s) are connected."
+            )
+    elif mode == MODE_IMAGE:
+        if not has_image:
+            raise ValueError(f"Reference mode '{MODE_IMAGE}' requires a reference_image input.")
+        if max_tag:
+            raise ValueError(
+                f"@AudioN tags are not used in '{MODE_IMAGE}' mode; the prompt should contain "
+                f"only the text to synthesize."
+            )
+    elif mode == MODE_SPEAKER:
+        if not preset_voice or preset_voice not in SEED_AUDIO_VOICE_MAP:
+            raise ValueError(f"Reference mode '{MODE_SPEAKER}' requires selecting a preset voice.")
+        if max_tag > 1:
+            raise ValueError(
+                f"'{MODE_SPEAKER}' mode uses a single voice, so @Audio{max_tag} is out of range. "
+                f"Remove the @AudioN tags — the whole prompt is read in the selected voice."
+            )
+    else:
+        raise ValueError(f"Unknown reference mode: {mode!r}")
+
+
+class ByteDanceSeedAudioNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceSeedAudio",
+            display_name="ByteDance Seed Audio 1.0",
+            category="partner/audio/ByteDance",
+            description=(
+                "Generate speech, music, sound effects and multi-speaker dialogue from a single prompt "
+                "with ByteDance Seed Audio 1.0. Describe the voice(s), emotion, ambience, background music "
+                "and sound effects in the prompt, and include the lines to speak. Optionally pick a built-in "
+                "preset voice, clone voices from up to 3 reference clips (tagged @Audio1-3 in the prompt), "
+                "or derive a voice from a character image. Up to 2 minutes of audio per run."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "text_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip=(
+                        "Describe the voice(s), emotion, pacing, ambience, background music and sound "
+                        "effects, and include the lines to speak (name characters inline for dialogue). "
+                        "In 'audio reference' mode, refer to connected clips by order as @Audio1, @Audio2, "
+                        "@Audio3. Maximum 3000 characters."
+                    ),
+                ),
+                IO.DynamicCombo.Input(
+                    "reference_mode",
+                    options=[
+                        IO.DynamicCombo.Option(MODE_TEXT, []),
+                        IO.DynamicCombo.Option(
+                            MODE_AUDIO,
+                            [
+                                IO.Audio.Input(
+                                    "reference_audio_1",
+                                    optional=True,
+                                    tooltip="Reference clip for voice cloning, tagged @Audio1 in the prompt. "
+                                    "Up to 30s.",
+                                ),
+                                IO.Audio.Input(
+                                    "reference_audio_2",
+                                    optional=True,
+                                    tooltip="Reference clip tagged @Audio2 in the prompt. Up to 30s.",
+                                ),
+                                IO.Audio.Input(
+                                    "reference_audio_3",
+                                    optional=True,
+                                    tooltip="Reference clip tagged @Audio3 in the prompt. Up to 30s.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            MODE_IMAGE,
+                            [
+                                IO.Image.Input(
+                                    "reference_image",
+                                    optional=True,
+                                    tooltip="A single character image; the model derives a voice from it. "
+                                    "Cannot be combined with reference audio.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            MODE_SPEAKER,
+                            [
+                                IO.Combo.Input(
+                                    "preset_voice",
+                                    options=SEED_AUDIO_VOICE_OPTIONS,
+                                    default=SEED_AUDIO_VOICE_OPTIONS[0],
+                                    tooltip="A built-in TTS 2.0 voice that reads the prompt. No reference "
+                                    "clip needed, and @AudioN tags are not used in this mode.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip=(
+                        "How to condition the voice: 'text only' (describe everything in the prompt), "
+                        "'audio reference' (clone up to 3 voices, tagged @Audio1-3), 'image reference' "
+                        "(derive a voice from one character image), or 'preset voice' (pick a built-in "
+                        "named voice that reads the prompt)."
+                    ),
+                ),
+                IO.Combo.Input(
+                    "sample_rate",
+                    options=["8000", "16000", "24000", "32000", "44100", "48000"],
+                    default="24000",
+                    tooltip="Output sample rate in Hz.",
+                ),
+                IO.Int.Input(
+                    "speech_rate",
+                    default=0,
+                    min=-50,
+                    max=100,
+                    tooltip="Speaking speed. 0 = normal, 100 = 2.0x, -50 = 0.5x.",
+                ),
+                IO.Int.Input(
+                    "loudness_rate",
+                    default=0,
+                    min=-50,
+                    max=100,
+                    tooltip="Loudness. 0 = normal, 100 = 2.0x, -50 = 0.5x.",
+                ),
+                IO.Int.Input(
+                    "pitch_rate",
+                    default=0,
+                    min=-12,
+                    max=12,
+                    tooltip="Pitch shift in semitones (-12 to 12).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.2145, "format":{"suffix":"/minute","approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text_prompt: str,
+        reference_mode: dict,
+        sample_rate: str,
+        speech_rate: int,
+        loudness_rate: int,
+        pitch_rate: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        mode = reference_mode["reference_mode"]
+        audio_indices = connected_audio_indices(reference_mode)
+        image = reference_mode.get("reference_image")
+        preset_voice = reference_mode.get("preset_voice")
+        validate_seed_audio_inputs(text_prompt, mode, audio_indices, image is not None, preset_voice)
+
+        references: list[SeedAudioReference] | None = None
+        if mode == MODE_AUDIO:
+            references = []
+            for i in audio_indices:
+                clip = reference_mode[f"reference_audio_{i}"]
+                validate_audio_duration(clip, max_duration=30.0)
+                mp3_bytes = audio_input_to_mp3(clip).getvalue()
+                references.append(SeedAudioReference(audio_data=base64.b64encode(mp3_bytes).decode("utf-8")))
+        elif mode == MODE_IMAGE:
+            image = upscale_image_tensor_to_min_pixels(image, 160_000)
+            references = [SeedAudioReference(image_data=tensor_to_base64_string(image, mime_type="image/png"))]
+        elif mode == MODE_SPEAKER:
+            references = [SeedAudioReference(speaker=SEED_AUDIO_VOICE_MAP[preset_voice])]
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/byteplus/api/v3/tts/create", method="POST"),
+            response_model=SeedAudioResponse,
+            data=SeedAudioRequest(
+                text_prompt=text_prompt,
+                references=references,
+                audio_config=SeedAudioConfig(
+                    sample_rate=int(sample_rate),
+                    speech_rate=speech_rate,
+                    loudness_rate=loudness_rate,
+                    pitch_rate=pitch_rate,
+                ),
+            ),
+        )
+        if not response.audio:
+            raise Exception(
+                f"Seed Audio returned no audio (code={response.code}): {response.message}"
+            )
+        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response.audio)))
+
+
 class ByteDanceExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -2457,6 +2878,7 @@ class ByteDanceExtension(ComfyExtension):
             ByteDance2ReferenceNode,
             ByteDanceCreateImageAsset,
             ByteDanceCreateVideoAsset,
+            ByteDanceSeedAudioNode,
         ]
 
 

comfy_api_nodes/nodes_gemini.py

@@ -5,7 +5,6 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer
 
 import base64
 import os
-from enum import Enum
 from fnmatch import fnmatch
 from io import BytesIO
 from typing import Any, Literal
@@ -14,7 +13,7 @@ import torch
 from typing_extensions import override
 
 import folder_paths
-from comfy_api.latest import IO, ComfyExtension, Input, Types
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl, Types
 from comfy_api_nodes.apis.gemini import (
     GeminiContent,
     GeminiFileData,
@@ -38,6 +37,7 @@ from comfy_api_nodes.util import (
     audio_to_base64_string,
     bytesio_to_image_tensor,
     download_url_to_image_tensor,
+    download_url_to_video_output,
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
@@ -46,6 +46,7 @@ from comfy_api_nodes.util import (
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
     validate_string,
+    validate_video_duration,
     video_to_base64_string,
 )
 
@@ -78,15 +79,6 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
 )
 
 
-class GeminiImageModel(str, Enum):
-    """
-    Gemini Image Model Names allowed by comfy-api
-    """
-
-    gemini_2_5_flash_image_preview = "gemini-2.5-flash-image-preview"
-    gemini_2_5_flash_image = "gemini-2.5-flash-image"
-
-
 async def create_image_parts(
     cls: type[IO.ComfyNode],
     images: Input.Image | list[Input.Image],
@@ -239,25 +231,38 @@ async def get_image_from_response(response: GeminiGenerateContentResponse, thoug
     return torch.cat(image_tensors, dim=0)
 
 
+async def get_video_from_response(
+    response: GeminiGenerateContentResponse, cls: type[IO.ComfyNode] | None = None
+) -> InputImpl.VideoFromFile:
+    parts = get_parts_by_type(response, "video/*")
+    for part in parts:
+        if part.inlineData and part.inlineData.data:
+            return InputImpl.VideoFromFile(BytesIO(base64.b64decode(part.inlineData.data)))
+        if part.fileData and part.fileData.fileUri:
+            return await download_url_to_video_output(part.fileData.fileUri, cls=cls)
+    model_message = get_text_from_response(response).strip()
+    if model_message:
+        raise ValueError(f"Gemini did not generate a video. Model response: {model_message}")
+    raise ValueError(
+        "Gemini did not generate a video. Try rephrasing your prompt, "
+        "shortening the requested duration, or reducing the number of input images/videos."
+    )
+
+
 def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | None:
     if not response.modelVersion:
         return None
     # Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing
-    if response.modelVersion in ("gemini-2.5-pro-preview-05-06", "gemini-2.5-pro"):
+    output_video_tokens_price = 0.0
+    if response.modelVersion == "gemini-2.5-pro":
         input_tokens_price = 1.25
         output_text_tokens_price = 10.0
         output_image_tokens_price = 0.0
-    elif response.modelVersion in (
-        "gemini-2.5-flash-preview-04-17",
-        "gemini-2.5-flash",
-    ):
+    elif response.modelVersion == "gemini-2.5-flash":
         input_tokens_price = 0.30
         output_text_tokens_price = 2.50
         output_image_tokens_price = 0.0
-    elif response.modelVersion in (
-        "gemini-2.5-flash-image-preview",
-        "gemini-2.5-flash-image",
-    ):
+    elif response.modelVersion == "gemini-2.5-flash-image":
         input_tokens_price = 0.30
         output_text_tokens_price = 2.50
         output_image_tokens_price = 30.0
@@ -265,18 +270,27 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 0.0
-    elif response.modelVersion == "gemini-3.1-flash-lite-preview":
+    elif response.modelVersion in ("gemini-3.1-flash-lite-preview", "gemini-3.1-flash-lite"):
         input_tokens_price = 0.25
         output_text_tokens_price = 1.50
         output_image_tokens_price = 0.0
-    elif response.modelVersion == "gemini-3-pro-image-preview":
+    elif response.modelVersion in ("gemini-3-pro-image-preview", "gemini-3-pro-image"):
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 120.0
-    elif response.modelVersion == "gemini-3.1-flash-image-preview":
+    elif response.modelVersion in ("gemini-3.1-flash-image-preview", "gemini-3.1-flash-image"):
         input_tokens_price = 0.5
         output_text_tokens_price = 3.0
         output_image_tokens_price = 60.0
+    elif response.modelVersion == "gemini-3.1-flash-lite-image":
+        input_tokens_price = 0.25
+        output_text_tokens_price = 1.50
+        output_image_tokens_price = 30.0
+    elif response.modelVersion == "gemini-omni-flash-preview":
+        input_tokens_price = 2.145
+        output_text_tokens_price = 12.87
+        output_image_tokens_price = 0.0
+        output_video_tokens_price = 25.025
     else:
         return None
     final_price = response.usageMetadata.promptTokenCount * input_tokens_price
@@ -284,6 +298,8 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         for i in response.usageMetadata.candidatesTokensDetails:
             if i.modality == Modality.IMAGE:
                 final_price += output_image_tokens_price * i.tokenCount  # for Nano Banana models
+            elif i.modality == Modality.VIDEO:
+                final_price += output_video_tokens_price * i.tokenCount  # for Omni Flash
             else:
                 final_price += output_text_tokens_price * i.tokenCount
     if response.usageMetadata.thoughtsTokenCount:
@@ -455,8 +471,6 @@ class GeminiNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "model",
                     options=[
-                        "gemini-2.5-pro-preview-05-06",
-                        "gemini-2.5-flash-preview-04-17",
                         "gemini-2.5-pro",
                         "gemini-2.5-flash",
                         "gemini-3-pro-preview",
@@ -904,8 +918,7 @@ class GeminiImage(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=GeminiImageModel,
-                    default=GeminiImageModel.gemini_2_5_flash_image,
+                    options=["gemini-2.5-flash-image"],
                     tooltip="The Gemini model to use for generating responses.",
                 ),
                 IO.Int.Input(
@@ -1120,7 +1133,9 @@ class GeminiImage2(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
-            model = "gemini-3.1-flash-image-preview"
+            model = "gemini-3.1-flash-image"
+        elif model == "gemini-3-pro-image-preview":
+            model = "gemini-3-pro-image"
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -1321,7 +1336,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
         )
 
 
-def _nano_banana_2_v2_model_inputs():
+def _nano_banana_2_v2_model_inputs(resolutions: list[str]):
     return [
         IO.Combo.Input(
             "aspect_ratio",
@@ -1348,8 +1363,8 @@ def _nano_banana_2_v2_model_inputs():
         ),
         IO.Combo.Input(
             "resolution",
-            options=["1K", "2K", "4K"],
-            tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+            options=resolutions,
+            tooltip="Target output resolution.",
         ),
         IO.Combo.Input(
             "thinking_level",
@@ -1395,7 +1410,11 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Nano Banana 2 (Gemini 3.1 Flash Image)",
-                            _nano_banana_2_v2_model_inputs(),
+                            _nano_banana_2_v2_model_inputs(resolutions=["1K", "2K", "4K"]),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Nano Banana 2 Lite",
+                            _nano_banana_2_v2_model_inputs(resolutions=["1K"]),
                         ),
                     ],
                 ),
@@ -1464,9 +1483,13 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
                 expr="""
                 (
-                  $r := $lookup(widgets, "model.resolution");
-                  $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
-                  {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                  $contains(widgets.model, "lite")
+                    ? {"type":"usd","usd": 0.034, "format":{"suffix":"/Image","approximate":true}}
+                    : (
+                        $r := $lookup(widgets, "model.resolution");
+                        $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+                        {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                      )
                 )
                 """,
             ),
@@ -1486,7 +1509,9 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_choice = model["model"]
         if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
-            model_id = "gemini-3.1-flash-image-preview"
+            model_id = "gemini-3.1-flash-image"
+        elif model_choice == "Nano Banana 2 Lite":
+            model_id = "gemini-3.1-flash-lite-image"
         else:
             model_id = model_choice
 
@@ -1536,6 +1561,149 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         )
 
 
+OMNI_MAX_IMAGES = 14
+OMNI_MAX_VIDEOS = 3
+
+OMNI_MODELS: dict[str, str] = {
+    "Omni Flash": "gemini-omni-flash-preview",
+}
+
+
+def _omni_flash_inputs() -> list[Input]:
+    """Per-model inputs for the Omni video DynamicCombo (prompt + reference media + sampling)."""
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Describe the video to generate. Specify the length and aspect ratio directly in the "
+            'prompt, e.g. "a 6-second clip in 16:9". Length may be 3-10 seconds; the aspect ratio must be '
+            "16:9 (landscape) or 9:16 (portrait). The output is 720p, 24 FPS, with audio.",
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images.",
+        ),
+        IO.Autogrow.Input(
+            "videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
+            f"each up to 10 seconds long.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more varied.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+    ]
+
+
+class GeminiVideoOmni(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiVideoOmni",
+            display_name="Google Gemini Omni (Video)",
+            category="partner/video/Gemini",
+            essentials_category="Video Generation",
+            description="Generate a video with audio from a text prompt using Google's Gemini Omni Flash model. "
+            "Optionally provide reference images and/or videos to guide or edit the result. Describe the desired "
+            "length (3-10s) and aspect ratio (16:9 or 9:16) directly in the prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Omni Flash", _omni_flash_inputs()),
+                    ],
+                    tooltip="The Gemini video model used to generate the video.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr='{"type":"usd","usd":0.146,"format":{"suffix":"/second","approximate":true}}'
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, model: dict, seed: int) -> IO.NodeOutput:
+        prompt = model.get("prompt") or ""
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = OMNI_MODELS[model["model"]]
+
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        videos = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if sum(get_number_of_images(t) for t in images) > OMNI_MAX_IMAGES:
+            raise ValueError(f"The current maximum number of supported images is {OMNI_MAX_IMAGES}.")
+        if len(videos) > OMNI_MAX_VIDEOS:
+            raise ValueError(f"The current maximum number of supported videos is {OMNI_MAX_VIDEOS}.")
+        for video in videos:
+            validate_video_duration(video, max_duration=10)
+
+        parts: list[GeminiPart] = []
+        if images or videos:
+            parts.extend(await build_gemini_media_parts(cls, images, [], videos))
+        parts.append(GeminiPart(text=prompt))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
+            data=GeminiGenerateContentRequest(
+                contents=[GeminiContent(role=GeminiRole.user, parts=parts)],
+                generationConfig=GeminiGenerationConfig(
+                    responseModalities=["TEXT", "VIDEO"],
+                    temperature=model.get("temperature", 1.0),
+                    topP=model.get("top_p", 0.95),
+                ),
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(
+            await get_video_from_response(response, cls=cls),
+            get_text_from_response(response),
+        )
+
+
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1546,6 +1714,7 @@ class GeminiExtension(ComfyExtension):
             GeminiImage2,
             GeminiNanoBanana2,
             GeminiNanoBanana2V2,
+            GeminiVideoOmni,
             GeminiInputFiles,
         ]
 

comfy_api_nodes/nodes_grok.py

@@ -30,7 +30,7 @@ from comfy_api_nodes.util import (
 
 
 _GROK_VIDEO_MODEL_API_IDS = {
-    "grok-imagine-video-1.5": "grok-imagine-video-1.5-preview",
+    "grok-imagine-video-1.5": "grok-imagine-video-1.5",
 }
 
 
@@ -521,8 +521,8 @@ class GrokVideoNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "resolution",
-                    options=["480p", "720p"],
-                    tooltip="The resolution of the output video.",
+                    options=["480p", "720p", "1080p"],
+                    tooltip="The resolution of the output video. 1080p is only available for grok-imagine-video-1.5.",
                 ),
                 IO.Combo.Input(
                     "aspect_ratio",
@@ -570,11 +570,12 @@ class GrokVideoNode(IO.ComfyNode):
                 (
                   $is15 := $contains(widgets.model, "1.5");
                   $rate := $is15
-                    ? (widgets.resolution = "720p" ? 0.2002 : 0.1144)
+                    ? (widgets.resolution = "1080p" ? 0.25 : (widgets.resolution = "720p" ? 0.14 : 0.08))
                     : (widgets.resolution = "720p" ? 0.07 : 0.05);
-                  $imgCost := $is15 ? 0.0143 : 0.002;
+                  $imgCost := $is15 ? 0.01 : 0.002;
                   $base := $rate * widgets.duration;
-                  {"type":"usd","usd": inputs.image.connected ? $base + $imgCost : $base}
+                  $total := inputs.image.connected ? $base + $imgCost : $base;
+                  {"type":"usd","usd": $is15 ? $total * 1.43 : $total}
                 )
                 """,
             ),
@@ -593,6 +594,8 @@ class GrokVideoNode(IO.ComfyNode):
     ) -> IO.NodeOutput:
         if image is None and model == "grok-imagine-video-1.5":
             raise ValueError(f"The '{model}' model requires an input image; connect one to the 'image' input.")
+        if resolution == "1080p" and model != "grok-imagine-video-1.5":
+            raise ValueError(f"1080p resolution is only available for grok-imagine-video-1.5, not '{model}'.")
         image_url = None
         if image is not None:
             if get_number_of_images(image) != 1:

comfy_api_nodes/nodes_hunyuan3d.py

@@ -642,6 +642,7 @@ class Tencent3DPartNode(IO.ComfyNode):
             response_model=To3DProTaskCreateResponse,
             data=To3DPartTaskRequest(
                 File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                EnableStagedGeneration=True,
             ),
             is_rate_limited=_is_tencent_rate_limited,
         )

comfy_api_nodes/nodes_ideogram.py

@@ -5,9 +5,7 @@ from PIL import Image
 import numpy as np
 import torch
 from comfy_api_nodes.apis.ideogram import (
-    IdeogramGenerateRequest,
     IdeogramGenerateResponse,
-    ImageRequest,
     IdeogramV3Request,
     IdeogramV3EditRequest,
     IdeogramV4Request,
@@ -21,101 +19,6 @@ from comfy_api_nodes.util import (
     validate_string,
 )
 
-V1_V1_RES_MAP = {
-  "Auto":"AUTO",
-  "512 x 1536":"RESOLUTION_512_1536",
-  "576 x 1408":"RESOLUTION_576_1408",
-  "576 x 1472":"RESOLUTION_576_1472",
-  "576 x 1536":"RESOLUTION_576_1536",
-  "640 x 1024":"RESOLUTION_640_1024",
-  "640 x 1344":"RESOLUTION_640_1344",
-  "640 x 1408":"RESOLUTION_640_1408",
-  "640 x 1472":"RESOLUTION_640_1472",
-  "640 x 1536":"RESOLUTION_640_1536",
-  "704 x 1152":"RESOLUTION_704_1152",
-  "704 x 1216":"RESOLUTION_704_1216",
-  "704 x 1280":"RESOLUTION_704_1280",
-  "704 x 1344":"RESOLUTION_704_1344",
-  "704 x 1408":"RESOLUTION_704_1408",
-  "704 x 1472":"RESOLUTION_704_1472",
-  "720 x 1280":"RESOLUTION_720_1280",
-  "736 x 1312":"RESOLUTION_736_1312",
-  "768 x 1024":"RESOLUTION_768_1024",
-  "768 x 1088":"RESOLUTION_768_1088",
-  "768 x 1152":"RESOLUTION_768_1152",
-  "768 x 1216":"RESOLUTION_768_1216",
-  "768 x 1232":"RESOLUTION_768_1232",
-  "768 x 1280":"RESOLUTION_768_1280",
-  "768 x 1344":"RESOLUTION_768_1344",
-  "832 x 960":"RESOLUTION_832_960",
-  "832 x 1024":"RESOLUTION_832_1024",
-  "832 x 1088":"RESOLUTION_832_1088",
-  "832 x 1152":"RESOLUTION_832_1152",
-  "832 x 1216":"RESOLUTION_832_1216",
-  "832 x 1248":"RESOLUTION_832_1248",
-  "864 x 1152":"RESOLUTION_864_1152",
-  "896 x 960":"RESOLUTION_896_960",
-  "896 x 1024":"RESOLUTION_896_1024",
-  "896 x 1088":"RESOLUTION_896_1088",
-  "896 x 1120":"RESOLUTION_896_1120",
-  "896 x 1152":"RESOLUTION_896_1152",
-  "960 x 832":"RESOLUTION_960_832",
-  "960 x 896":"RESOLUTION_960_896",
-  "960 x 1024":"RESOLUTION_960_1024",
-  "960 x 1088":"RESOLUTION_960_1088",
-  "1024 x 640":"RESOLUTION_1024_640",
-  "1024 x 768":"RESOLUTION_1024_768",
-  "1024 x 832":"RESOLUTION_1024_832",
-  "1024 x 896":"RESOLUTION_1024_896",
-  "1024 x 960":"RESOLUTION_1024_960",
-  "1024 x 1024":"RESOLUTION_1024_1024",
-  "1088 x 768":"RESOLUTION_1088_768",
-  "1088 x 832":"RESOLUTION_1088_832",
-  "1088 x 896":"RESOLUTION_1088_896",
-  "1088 x 960":"RESOLUTION_1088_960",
-  "1120 x 896":"RESOLUTION_1120_896",
-  "1152 x 704":"RESOLUTION_1152_704",
-  "1152 x 768":"RESOLUTION_1152_768",
-  "1152 x 832":"RESOLUTION_1152_832",
-  "1152 x 864":"RESOLUTION_1152_864",
-  "1152 x 896":"RESOLUTION_1152_896",
-  "1216 x 704":"RESOLUTION_1216_704",
-  "1216 x 768":"RESOLUTION_1216_768",
-  "1216 x 832":"RESOLUTION_1216_832",
-  "1232 x 768":"RESOLUTION_1232_768",
-  "1248 x 832":"RESOLUTION_1248_832",
-  "1280 x 704":"RESOLUTION_1280_704",
-  "1280 x 720":"RESOLUTION_1280_720",
-  "1280 x 768":"RESOLUTION_1280_768",
-  "1280 x 800":"RESOLUTION_1280_800",
-  "1312 x 736":"RESOLUTION_1312_736",
-  "1344 x 640":"RESOLUTION_1344_640",
-  "1344 x 704":"RESOLUTION_1344_704",
-  "1344 x 768":"RESOLUTION_1344_768",
-  "1408 x 576":"RESOLUTION_1408_576",
-  "1408 x 640":"RESOLUTION_1408_640",
-  "1408 x 704":"RESOLUTION_1408_704",
-  "1472 x 576":"RESOLUTION_1472_576",
-  "1472 x 640":"RESOLUTION_1472_640",
-  "1472 x 704":"RESOLUTION_1472_704",
-  "1536 x 512":"RESOLUTION_1536_512",
-  "1536 x 576":"RESOLUTION_1536_576",
-  "1536 x 640":"RESOLUTION_1536_640",
-}
-
-V1_V2_RATIO_MAP = {
-  "1:1":"ASPECT_1_1",
-  "4:3":"ASPECT_4_3",
-  "3:4":"ASPECT_3_4",
-  "16:9":"ASPECT_16_9",
-  "9:16":"ASPECT_9_16",
-  "2:1":"ASPECT_2_1",
-  "1:2":"ASPECT_1_2",
-  "3:2":"ASPECT_3_2",
-  "2:3":"ASPECT_2_3",
-  "4:5":"ASPECT_4_5",
-  "5:4":"ASPECT_5_4",
-}
 
 V3_RATIO_MAP = {
     "1:3":"1x3",
@@ -229,298 +132,6 @@ async def download_and_process_images(image_urls):
     return stacked_tensors
 
 
-class IdeogramV1(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="IdeogramV1",
-            display_name="Ideogram V1",
-            category="partner/image/Ideogram",
-            description="Generates images using the Ideogram V1 model.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Prompt for the image generation",
-                ),
-                IO.Boolean.Input(
-                    "turbo",
-                    default=False,
-                    tooltip="Whether to use turbo mode (faster generation, potentially lower quality)",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=list(V1_V2_RATIO_MAP.keys()),
-                    default="1:1",
-                    tooltip="The aspect ratio for image generation.",
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "magic_prompt_option",
-                    options=["AUTO", "ON", "OFF"],
-                    default="AUTO",
-                    tooltip="Determine if MagicPrompt should be used in generation",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    control_after_generate=True,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Description of what to exclude from the image",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "num_images",
-                    default=1,
-                    min=1,
-                    max=8,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["num_images", "turbo"]),
-                expr="""
-                (
-                  $n := widgets.num_images;
-                  $base := (widgets.turbo = true) ? 0.0286 : 0.0858;
-                  {"type":"usd","usd": $round($base * $n, 2)}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt,
-        turbo=False,
-        aspect_ratio="1:1",
-        magic_prompt_option="AUTO",
-        seed=0,
-        negative_prompt="",
-        num_images=1,
-    ):
-        # Determine the model based on turbo setting
-        aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
-        model = "V_1_TURBO" if turbo else "V_1"
-
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/ideogram/generate", method="POST"),
-            response_model=IdeogramGenerateResponse,
-            data=IdeogramGenerateRequest(
-                image_request=ImageRequest(
-                    prompt=prompt,
-                    model=model,
-                    num_images=num_images,
-                    seed=seed,
-                    aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
-                    magic_prompt_option=(magic_prompt_option if magic_prompt_option != "AUTO" else None),
-                    negative_prompt=negative_prompt if negative_prompt else None,
-                )
-            ),
-            max_retries=1,
-        )
-
-        if not response.data or len(response.data) == 0:
-            raise Exception("No images were generated in the response")
-
-        image_urls = [image_data.url for image_data in response.data if image_data.url]
-        if not image_urls:
-            raise Exception("No image URLs were generated in the response")
-        return IO.NodeOutput(await download_and_process_images(image_urls))
-
-
-class IdeogramV2(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="IdeogramV2",
-            display_name="Ideogram V2",
-            category="partner/image/Ideogram",
-            description="Generates images using the Ideogram V2 model.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Prompt for the image generation",
-                ),
-                IO.Boolean.Input(
-                    "turbo",
-                    default=False,
-                    tooltip="Whether to use turbo mode (faster generation, potentially lower quality)",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=list(V1_V2_RATIO_MAP.keys()),
-                    default="1:1",
-                    tooltip="The aspect ratio for image generation. Ignored if resolution is not set to AUTO.",
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "resolution",
-                    options=list(V1_V1_RES_MAP.keys()),
-                    default="Auto",
-                    tooltip="The resolution for image generation. "
-                            "If not set to AUTO, this overrides the aspect_ratio setting.",
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "magic_prompt_option",
-                    options=["AUTO", "ON", "OFF"],
-                    default="AUTO",
-                    tooltip="Determine if MagicPrompt should be used in generation",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    control_after_generate=True,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "style_type",
-                    options=["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"],
-                    default="NONE",
-                    tooltip="Style type for generation (V2 only)",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Description of what to exclude from the image",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "num_images",
-                    default=1,
-                    min=1,
-                    max=8,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-                #"color_palette": (
-                #    IO.STRING,
-                #    {
-                #        "multiline": False,
-                #        "default": "",
-                #        "tooltip": "Color palette preset name or hex colors with weights",
-                #    },
-                #),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["num_images", "turbo"]),
-                expr="""
-                (
-                  $n := widgets.num_images;
-                  $base := (widgets.turbo = true) ? 0.0715 : 0.1144;
-                  {"type":"usd","usd": $round($base * $n, 2)}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt,
-        turbo=False,
-        aspect_ratio="1:1",
-        resolution="Auto",
-        magic_prompt_option="AUTO",
-        seed=0,
-        style_type="NONE",
-        negative_prompt="",
-        num_images=1,
-        color_palette="",
-    ):
-        aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
-        resolution = V1_V1_RES_MAP.get(resolution, None)
-        # Determine the model based on turbo setting
-        model = "V_2_TURBO" if turbo else "V_2"
-
-        # Handle resolution vs aspect_ratio logic
-        # If resolution is not AUTO, it overrides aspect_ratio
-        final_resolution = None
-        final_aspect_ratio = None
-
-        if resolution != "AUTO":
-            final_resolution = resolution
-        else:
-            final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None
-
-        response = await sync_op(
-            cls,
-            endpoint=ApiEndpoint(path="/proxy/ideogram/generate", method="POST"),
-            response_model=IdeogramGenerateResponse,
-            data=IdeogramGenerateRequest(
-                image_request=ImageRequest(
-                    prompt=prompt,
-                    model=model,
-                    num_images=num_images,
-                    seed=seed,
-                    aspect_ratio=final_aspect_ratio,
-                    resolution=final_resolution,
-                    magic_prompt_option=(magic_prompt_option if magic_prompt_option != "AUTO" else None),
-                    style_type=style_type if style_type != "NONE" else None,
-                    negative_prompt=negative_prompt if negative_prompt else None,
-                    color_palette=color_palette if color_palette else None,
-                )
-            ),
-            max_retries=1,
-        )
-        if not response.data or len(response.data) == 0:
-            raise Exception("No images were generated in the response")
-
-        image_urls = [image_data.url for image_data in response.data if image_data.url]
-        if not image_urls:
-            raise Exception("No image URLs were generated in the response")
-        return IO.NodeOutput(await download_and_process_images(image_urls))
-
-
 class IdeogramV3(IO.ComfyNode):
 
     @classmethod
@@ -917,8 +528,6 @@ class IdeogramExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
-            IdeogramV1,
-            IdeogramV2,
             IdeogramV3,
             IdeogramV4,
         ]

comfy_api_nodes/nodes_kling.py

@@ -60,6 +60,12 @@ from comfy_api_nodes.apis.kling import (
     OmniProImageRequest,
     OmniProReferences2VideoRequest,
     OmniProText2VideoRequest,
+    Kling3TurboSettings,
+    Kling3TurboText2VideoRequest,
+    Kling3TurboContent,
+    Kling3TurboImage2VideoRequest,
+    Kling3TurboCreateResponse,
+    Kling3TurboQueryResponse,
     TaskStatusResponse,
     TextToVideoWithAudioRequest,
 )
@@ -2847,6 +2853,67 @@ class MotionControl(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
 
 
+def build_turbo_shot_prompt(multi_prompt: list[MultiPromptEntry]) -> str:
+    """Render storyboard entries into the Turbo multi-shot prompt 'shot n, m, words; ...'."""
+    return "; ".join(f"shot {i}, {int(e.duration)}, {e.prompt}" for i, e in enumerate(multi_prompt, 1)) + ";"
+
+
+def _turbo_video_url(response: Kling3TurboQueryResponse) -> str:
+    """Extract the result video URL from a /tasks response (data[].outputs[] where type == 'video')."""
+    task = response.data[0] if response.data else None
+    if task and task.outputs:
+        for output in task.outputs:
+            if output.type == "video" and output.url:
+                return output.url
+    raise RuntimeError(f"Kling 3.0 Turbo task finished without a video output: {response.model_dump()}")
+
+
+async def execute_kling_turbo(
+    cls: type[IO.ComfyNode],
+    *,
+    prompt: str,
+    resolution: str,
+    aspect_ratio: str,
+    duration: int,
+    start_frame: torch.Tensor | None,
+) -> IO.NodeOutput:
+    """Create + poll a Kling 3.0 Turbo task. Image-to-video when start_frame is given, else text-to-video."""
+    if start_frame is not None:
+        validate_image_dimensions(start_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+        contents = [Kling3TurboContent(type="first_frame", url=tensor_to_base64_string(start_frame))]
+        if prompt:
+            contents.insert(0, Kling3TurboContent(type="prompt", text=prompt))
+        create = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/image-to-video/kling-3.0-turbo", method="POST"),
+            response_model=Kling3TurboCreateResponse,
+            data=Kling3TurboImage2VideoRequest(
+                contents=contents,
+                settings=Kling3TurboSettings(resolution=resolution, duration=duration),  # i2v: no aspect_ratio
+            ),
+        )
+    else:
+        create = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/text-to-video/kling-3.0-turbo", method="POST"),
+            response_model=Kling3TurboCreateResponse,
+            data=Kling3TurboText2VideoRequest(
+                prompt=prompt,
+                settings=Kling3TurboSettings(resolution=resolution, aspect_ratio=aspect_ratio, duration=duration),
+            ),
+        )
+    if not (create.data and create.data.id):
+        raise RuntimeError(f"Kling 3.0 Turbo create failed. Code: {create.code}, Message: {create.message}")
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/kling/tasks", query_params={"task_ids": create.data.id}),
+        response_model=Kling3TurboQueryResponse,
+        status_extractor=lambda r: (r.data[0].status if r.data else None),
+    )
+    return IO.NodeOutput(await download_url_to_video_output(_turbo_video_url(final_response)))
+
+
 class KlingVideoNode(IO.ComfyNode):
 
     @classmethod
@@ -2884,7 +2951,11 @@ class KlingVideoNode(IO.ComfyNode):
                     ],
                     tooltip="Generate a series of video segments with individual prompts and durations.",
                 ),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=True,
+                    tooltip="'kling-3.0-turbo' always generates native audio, so the audio toggle is ignored.",
+                ),
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
@@ -2899,6 +2970,17 @@ class KlingVideoNode(IO.ComfyNode):
                                 ),
                             ],
                         ),
+                        IO.DynamicCombo.Option(
+                            "kling-3.0-turbo",
+                            [
+                                IO.Combo.Input("resolution", options=["1080p", "720p"], default="720p"),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "1:1"],
+                                    tooltip="Ignored in image-to-video mode.",
+                                ),
+                            ],
+                        ),
                     ],
                     tooltip="Model and generation settings.",
                 ),
@@ -2930,6 +3012,7 @@ class KlingVideoNode(IO.ComfyNode):
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(
                     widgets=[
+                        "model",
                         "model.resolution",
                         "generate_audio",
                         "multi_shot",
@@ -2944,14 +3027,7 @@ class KlingVideoNode(IO.ComfyNode):
                 ),
                 expr="""
                 (
-                  $rates := {
-                    "4k": {"off": 0.42, "on": 0.42},
-                    "1080p": {"off": 0.112, "on": 0.168},
-                    "720p": {"off": 0.084, "on": 0.126}
-                  };
                   $res := $lookup(widgets, "model.resolution");
-                  $audio := widgets.generate_audio ? "on" : "off";
-                  $rate := $lookup($lookup($rates, $res), $audio);
                   $ms := widgets.multi_shot;
                   $isSb := $ms != "disabled";
                   $n := $isSb ? $number($substring($ms, 0, 1)) : 0;
@@ -2962,7 +3038,18 @@ class KlingVideoNode(IO.ComfyNode):
                   $d5 := $n >= 5 ? $lookup(widgets, "multi_shot.storyboard_5_duration") : 0;
                   $d6 := $n >= 6 ? $lookup(widgets, "multi_shot.storyboard_6_duration") : 0;
                   $dur := $isSb ? $d1 + $d2 + $d3 + $d4 + $d5 + $d6 : $lookup(widgets, "multi_shot.duration");
-                  {"type":"usd","usd": $rate * $dur}
+                  widgets.model = "kling-3.0-turbo"
+                    ? {"type":"usd","usd": ($res = "1080p" ? 0.14 : 0.112) * $dur}
+                    : (
+                        $rates := {
+                          "4k": {"off": 0.42, "on": 0.42},
+                          "1080p": {"off": 0.112, "on": 0.168},
+                          "720p": {"off": 0.084, "on": 0.126}
+                        };
+                        $audio := widgets.generate_audio ? "on" : "off";
+                        $rate := $lookup($lookup($rates, $res), $audio);
+                        {"type":"usd","usd": $rate * $dur}
+                      )
                 )
                 """,
             ),
@@ -3015,6 +3102,17 @@ class KlingVideoNode(IO.ComfyNode):
             duration = multi_shot["duration"]
             validate_string(multi_shot["prompt"], min_length=1, max_length=2500)
 
+        if model["model"] == "kling-3.0-turbo":
+            turbo_prompt = build_turbo_shot_prompt(multi_prompt_list) if custom_multi_shot else multi_shot["prompt"]
+            return await execute_kling_turbo(
+                cls,
+                prompt=turbo_prompt,
+                resolution=model["resolution"],
+                aspect_ratio=model["aspect_ratio"],
+                duration=duration,
+                start_frame=start_frame,
+            )
+
         if start_frame is not None:
             validate_image_dimensions(start_frame, min_width=300, min_height=300)
             validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))

comfy_api_nodes/nodes_luma.py

@@ -3,9 +3,13 @@ from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.luma import (
+    LUMA_KEYFRAME_MODE_FRACTION,
+    LUMA_KEYFRAME_MODE_SECONDS,
     Luma2Generation,
     Luma2GenerationRequest,
     Luma2ImageRef,
+    Luma2VideoEdit,
+    Luma2VideoOptions,
     LumaAspectRatio,
     LumaCharacterRef,
     LumaConceptChain,
@@ -18,6 +22,8 @@ from comfy_api_nodes.apis.luma import (
     LumaIO,
     LumaKeyframes,
     LumaModifyImageRef,
+    LumaRay32KeyframeChain,
+    LumaRay32KeyframeItem,
     LumaReference,
     LumaReferenceChain,
     LumaVideoModel,
@@ -33,6 +39,7 @@ from comfy_api_nodes.util import (
     sync_op,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
     validate_string,
 )
 
@@ -692,7 +699,10 @@ async def _luma2_upload_image_refs(
 async def _luma2_submit_and_poll(
     cls: type[IO.ComfyNode],
     request: Luma2GenerationRequest,
-) -> Input.Image:
+    *,
+    estimated_duration: int | None = None,
+) -> Luma2Generation:
+    """Submit a Luma Agents generation and poll until done; returns the completed generation."""
     initial = await sync_op(
         cls,
         ApiEndpoint(path="/proxy/luma_2/generations", method="POST"),
@@ -700,21 +710,21 @@ async def _luma2_submit_and_poll(
         data=request,
     )
     if not initial.id:
-        raise RuntimeError("Luma 2 API did not return a generation id.")
+        raise RuntimeError("Luma API did not return a generation id.")
     final = await poll_op(
         cls,
         ApiEndpoint(path=f"/proxy/luma_2/generations/{initial.id}", method="GET"),
         response_model=Luma2Generation,
         status_extractor=lambda r: r.state,
         progress_extractor=lambda r: None,
+        estimated_duration=estimated_duration,
     )
-    if not final.output:
+    if not final.output or not final.output[0].url:
         msg = final.failure_reason or "no output returned"
-        raise RuntimeError(f"Luma 2 generation failed: {msg}")
-    url = final.output[0].url
-    if not url:
-        raise RuntimeError("Luma 2 generation completed without an output URL.")
-    return await download_url_to_image_tensor(url)
+        if final.failure_code:
+            msg = f"{msg} [{final.failure_code}]"
+        raise RuntimeError(f"Luma generation failed: {msg}")
+    return final
 
 
 class LumaImageNode(IO.ComfyNode):
@@ -843,7 +853,8 @@ class LumaImageNode(IO.ComfyNode):
             web_search=model["web_search"],
             image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=9),
         )
-        return IO.NodeOutput(await _luma2_submit_and_poll(cls, request))
+        final = await _luma2_submit_and_poll(cls, request)
+        return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
 
 
 class LumaImageEditNode(IO.ComfyNode):
@@ -929,7 +940,533 @@ class LumaImageEditNode(IO.ComfyNode):
             web_search=model["web_search"],
             image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=8),
         )
-        return IO.NodeOutput(await _luma2_submit_and_poll(cls, request))
+        final = await _luma2_submit_and_poll(cls, request)
+        return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
+
+
+_BADGE_RAY32_VIDEO = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]),
+    expr="""
+    (
+      $p := {
+        "360p": {"5s": 0.06, "10s": 0.18},
+        "540p": {"5s": 0.15, "10s": 0.45},
+        "720p": {"5s": 0.3, "10s": 0.9},
+        "1080p": {"5s": 1.2, "10s": 3.6}
+      };
+      {"type": "usd", "usd": $lookup($lookup($p, widgets.resolution), widgets.duration)}
+    )
+    """,
+)
+
+_BADGE_RAY32_VIDEO_5S = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {"360p": 0.06, "540p": 0.15, "720p": 0.3, "1080p": 1.2};
+      {"type": "usd", "usd": $lookup($p, widgets.resolution)}
+    )
+    """,
+)
+
+_BADGE_RAY32_EDIT = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {
+        "360p": {"min": 0.54, "max": 1.08},
+        "540p": {"min": 0.72, "max": 1.44},
+        "720p": {"min": 1.08, "max": 2.16},
+        "1080p": {"min": 2.16, "max": 4.32}
+      };
+      $r := $lookup($p, widgets.resolution);
+      {"type": "range_usd", "min_usd": $r.min, "max_usd": $r.max, "format": {"note": "(by source length)"}}
+    )
+    """,
+)
+
+_BADGE_RAY32_REFRAME = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {"360p": 0.03, "540p": 0.06, "720p": 0.12, "1080p": 0.36};
+      {"type": "usd", "usd": $lookup($p, widgets.resolution), "format": {"suffix": "/second"}}
+    )
+    """,
+)
+
+
+def _ray32_seed_input() -> IO.Input:
+    return IO.Int.Input(
+        "seed",
+        default=0,
+        min=0,
+        max=0xFFFFFFFFFFFFFFFF,
+        control_after_generate=True,
+        tooltip="Seed to determine if node should re-run; results are nondeterministic regardless of seed.",
+    )
+
+
+async def _ray32_generate(cls: type[IO.ComfyNode], request: Luma2GenerationRequest) -> IO.NodeOutput:
+    """Run a ray-3.2 generation and return (video, generation_id)."""
+    final = await _luma2_submit_and_poll(cls, request, estimated_duration=120)
+    video = await download_url_to_video_output(final.output[0].url)
+    return IO.NodeOutput(video, final.id or "")
+
+
+class LumaRay32TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32TextToVideoNode",
+            display_name="Luma Ray 3.2 Text to Video",
+            category="partner/video/Luma",
+            description="Generate a video from a text prompt using Luma's Ray 3.2 model.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input("duration", options=["5s", "10s"]),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                    tooltip="Make the video loop seamlessly. Only available with 5s duration.",
+                ),
+                _ray32_seed_input(),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls, prompt: str, aspect_ratio: str, resolution: str, duration: str, loop: bool, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        if loop and duration == "10s":
+            raise ValueError("Looping is only available with 5s duration on Ray 3.2.")
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video",
+            aspect_ratio=aspect_ratio,
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, loop=loop or None),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32ImageToVideoNode",
+            display_name="Luma Ray 3.2 Image to Video",
+            category="partner/video/Luma",
+            description="Generate a video from a start and/or end frame using Luma's Ray 3.2 model. "
+            "Image-anchored generations are always 5 seconds.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                    tooltip="Make the video loop seamlessly. Not available when an end_frame is set.",
+                ),
+                _ray32_seed_input(),
+                IO.Image.Input("start_frame", optional=True, tooltip="First frame of the generated video."),
+                IO.Image.Input("end_frame", optional=True, tooltip="Last frame of the generated video."),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO_5S,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        loop: bool,
+        seed: int,
+        start_frame: torch.Tensor | None = None,
+        end_frame: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        if start_frame is None and end_frame is None:
+            raise ValueError("Provide at least one of start_frame / end_frame.")
+        if loop and end_frame is not None:
+            raise ValueError("Looping is not available when an end_frame is set.")
+        video = Luma2VideoOptions(resolution=resolution, duration="5s", loop=loop or None)
+        if start_frame is not None:
+            url = await upload_image_to_comfyapi(cls, start_frame, mime_type="image/png")
+            video.start_frame = Luma2ImageRef(url=url)
+        if end_frame is not None:
+            url = await upload_image_to_comfyapi(cls, end_frame, mime_type="image/png")
+            video.end_frame = Luma2ImageRef(url=url)
+        request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32KeyframeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32KeyframeNode",
+            display_name="Luma Ray 3.2 Keyframe",
+            category="partner/video/Luma",
+            description="Anchor a guide image to a position on the Ray 3.2 output video timeline. Connect this to "
+            "the 'keyframes' input of the Luma Ray 3.2 Keyframes to Video node; chain several together via the "
+            "optional 'keyframes' input below.",
+            inputs=[
+                IO.Image.Input("image", tooltip="Guide image to place at the chosen moment of the output video."),
+                IO.DynamicCombo.Input(
+                    "position",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Fraction of duration (0.0-1.0)",
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the output video this image applies " "(0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Absolute time (seconds)",
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=10.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from the start of the output video where this "
+                                    "image applies.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the output video's timeline.",
+                ),
+                IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Optional earlier keyframes to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Output(display_name="keyframes")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: torch.Tensor,
+        position: dict,
+        keyframes: LumaRay32KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = keyframes.clone() if keyframes is not None else LumaRay32KeyframeChain()
+        if position["position"] == "Absolute time (seconds)":
+            mode, value = LUMA_KEYFRAME_MODE_SECONDS, float(position["seconds"])
+        else:
+            mode, value = LUMA_KEYFRAME_MODE_FRACTION, float(position["fraction"])
+        chain.add(LumaRay32KeyframeItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class LumaRay32KeyframesToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32KeyframesToVideoNode",
+            display_name="Luma Ray 3.2 Keyframes to Video",
+            category="partner/video/Luma",
+            description="Generate a video that interpolates through a sequence of guide images, each anchored to a "
+            "position on the timeline, using Luma Ray 3.2. Build the sequence with Luma Ray 3.2 Keyframe nodes "
+            "(at least 2).",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input("duration", options=["5s", "10s"]),
+                _ray32_seed_input(),
+                IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
+                    "keyframes",
+                    tooltip="Keyframe sequence from Luma Ray 3.2 Keyframe nodes (at least 2).",
+                ),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        duration: str,
+        seed: int,
+        keyframes: LumaRay32KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        items = keyframes.items if keyframes is not None else []
+        if len(items) < 2:
+            raise ValueError(
+                "Connect at least 2 Luma Ray 3.2 Keyframe nodes "
+                "(use Luma Ray 3.2 Image to Video for a single start/end frame)."
+            )
+        if len(items) > 64:
+            raise ValueError(f"Ray 3.2 supports at most 64 keyframes; got {len(items)}.")
+        maxframe = 120 if duration == "5s" else 240
+        duration_seconds = maxframe / 24  # 5.0 or 10.0
+        # Resolve each keyframe to an output-frame index, then order by position
+        # (so the user can chain keyframes in any order — the position is what places them)
+        placed: list[tuple[int, torch.Tensor]] = []
+        for item in items:
+            if item.mode == LUMA_KEYFRAME_MODE_SECONDS:
+                if item.value > duration_seconds:
+                    raise ValueError(
+                        f"Keyframe position {item.value:g}s is past the end of the {duration} video; "
+                        f"use 0-{duration_seconds:g}s (or switch the keyframe to fraction mode)."
+                    )
+                idx = round(item.value * 24)
+            else:
+                idx = round(item.value * maxframe)
+            placed.append((max(0, min(maxframe, idx)), item.image))
+        placed.sort(key=lambda p: p[0])
+        indexes = [idx for idx, _ in placed]
+        for a, b in zip(indexes, indexes[1:]):
+            if a == b:
+                raise ValueError(
+                    f"Two keyframes resolve to the same output frame ({a}) for a {duration} video "
+                    f"(valid range 0-{maxframe}); give each keyframe a distinct position."
+                )
+        refs: list[Luma2ImageRef] = []
+        for _, image in placed:
+            url = await upload_image_to_comfyapi(cls, image, mime_type="image/png")
+            refs.append(Luma2ImageRef(url=url))
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video",
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, keyframes=refs, keyframe_indexes=indexes),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32VideoEditNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32VideoEditNode",
+            display_name="Luma Ray 3.2 Video Edit",
+            category="partner/video/Luma",
+            description="Re-render an existing video under a new prompt using Luma Ray 3.2 (restyle, relight, add "
+            "or remove elements) while keeping the original motion. Source video up to 18 seconds; the edited "
+            "video keeps the source's length.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Source video to edit. Up to 18 seconds."),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Describes the desired edit."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input(
+                    "strength",
+                    options=[
+                        "auto",
+                        "adhere_1",
+                        "adhere_2",
+                        "adhere_3",
+                        "flex_1",
+                        "flex_2",
+                        "flex_3",
+                        "reimagine_1",
+                        "reimagine_2",
+                        "reimagine_3",
+                    ],
+                    default="auto",
+                    tooltip="How strongly to preserve vs. reimagine the source. 'auto' lets Ray 3.2 choose; "
+                    "adhere_* preserves the most, flex_* is balanced, reimagine_* changes the most.",
+                ),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_EDIT,
+        )
+
+    @classmethod
+    async def execute(
+        cls, video: Input.Video, prompt: str, resolution: str, strength: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        try:
+            duration = "5s" if video.get_duration() <= 5.0 else "10s"
+        except Exception:
+            duration = "10s"
+        source_url = await upload_video_to_comfyapi(cls, video, max_duration=18)
+        edit = Luma2VideoEdit(auto_controls=True) if strength == "auto" else Luma2VideoEdit(strength=strength)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video_edit",
+            source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, edit=edit),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32VideoReframeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32VideoReframeNode",
+            display_name="Luma Ray 3.2 Video Reframe",
+            category="partner/video/Luma",
+            description="Change the aspect ratio of an existing video, using Luma Ray 3.2 to fill the newly "
+            "exposed canvas areas. Source video up to 30 seconds. Billed per second of output.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Source video to reframe. Up to 30 seconds."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes how the newly exposed canvas areas should be filled.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_REFRAME,
+        )
+
+    @classmethod
+    async def execute(
+        cls, video: Input.Video, prompt: str, aspect_ratio: str, resolution: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
+        if resolution == "1080p" and aspect_ratio in {"9:16", "3:4"}:
+            raise ValueError("1080p is not available for vertical aspect ratios (9:16, 3:4) when reframing.")
+        source_url = await upload_video_to_comfyapi(cls, video, max_duration=30)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video_reframe",
+            aspect_ratio=aspect_ratio,
+            source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
+            video=Luma2VideoOptions(resolution=resolution),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32ExtendVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32ExtendVideoNode",
+            display_name="Luma Ray 3.2 Extend Video",
+            category="partner/video/Luma",
+            description="Extend a previous Ray 3.2 generation forward (continue after it) or backward (lead-in "
+            "before it). Connect the generation_id output of a prior Luma Ray 3.2 node."
+            " Extensions are always 5 seconds.",
+            inputs=[
+                IO.String.Input(
+                    "source_generation_id",
+                    default="",
+                    tooltip="generation_id of the prior Ray 3.2 video to extend."
+                    " Connect the generation_id output of another Luma Ray 3.2 node.",
+                ),
+                IO.DynamicCombo.Input(
+                    "direction",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Forward (continue after)",
+                            [
+                                IO.Boolean.Input(
+                                    "loop",
+                                    default=False,
+                                    tooltip="Loop the extended video seamlessly (forward extend only).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Backward (lead-in before)", []),
+                    ],
+                    tooltip="Forward continues after the prior clip; backward is prepended before it.",
+                ),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the new content."),
+                IO.Combo.Input("resolution", options=["540p", "720p", "1080p"], default="720p"),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO_5S,
+        )
+
+    @classmethod
+    async def execute(
+        cls, source_generation_id: str, direction: dict, prompt: str, resolution: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
+        gen_id = (source_generation_id or "").strip()
+        if not gen_id:
+            raise ValueError(
+                "source_generation_id is required (connect the generation_id output of a prior Luma Ray 3.2 node)."
+            )
+        video = Luma2VideoOptions(resolution=resolution, duration="5s")
+        ref = Luma2ImageRef(generation_id=gen_id)
+        if direction["direction"] == "Forward (continue after)":
+            video.start_frame = ref
+            if direction.get("loop"):
+                video.loop = True
+        else:
+            video.end_frame = ref
+        request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
+        return await _ray32_generate(cls, request)
 
 
 class LumaExtension(ComfyExtension):
@@ -944,6 +1481,13 @@ class LumaExtension(ComfyExtension):
             LumaConceptsNode,
             LumaImageNode,
             LumaImageEditNode,
+            LumaRay32TextToVideoNode,
+            LumaRay32ImageToVideoNode,
+            LumaRay32KeyframeNode,
+            LumaRay32KeyframesToVideoNode,
+            LumaRay32VideoEditNode,
+            LumaRay32VideoReframeNode,
+            LumaRay32ExtendVideoNode,
         ]
 
 

comfy_api_nodes/nodes_runway.py

@@ -30,13 +30,33 @@ from comfy_api_nodes.apis.runway import (
     Model4,
     ReferenceImage,
     RunwayTextToImageAspectRatioEnum,
+    RunwayAleph2IO,
+    RunwayAleph2KeyframeChain,
+    RunwayAleph2KeyframeItem,
+    RunwayAleph2PromptImageChain,
+    RunwayAleph2PromptImageItem,
+    RunwayAleph2Request,
+    RunwayAleph2Response,
+    RunwayAleph2KeyframeSeconds,
+    RunwayAleph2KeyframeAt,
+    RunwayAleph2PromptImage,
+    RunwayAleph2TimestampPosition,
+    RunwayAleph2RelativePosition,
+    RunwayAleph2ContentModeration,
+    KEYFRAME_MODE_SECONDS,
+    KEYFRAME_MODE_AT,
+    PROMPT_IMAGE_MODE_TIMESTAMP,
+    PROMPT_IMAGE_MODE_POSITION,
 )
 from comfy_api_nodes.util import (
     image_tensor_pair_to_batch,
     validate_string,
     validate_image_dimensions,
     validate_image_aspect_ratio,
+    validate_video_duration,
     upload_images_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
     download_url_to_video_output,
     download_url_to_image_tensor,
     ApiEndpoint,
@@ -45,6 +65,7 @@ from comfy_api_nodes.util import (
 )
 
 PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
+PATH_VIDEO_TO_VIDEO = "/proxy/runway/video_to_video"
 PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
 PATH_GET_TASK_STATUS = "/proxy/runway/tasks"
 
@@ -53,12 +74,6 @@ AVERAGE_DURATION_FLF_SECONDS = 256
 AVERAGE_DURATION_T2I_SECONDS = 41
 
 
-class RunwayApiError(Exception):
-    """Base exception for Runway API errors."""
-
-    pass
-
-
 class RunwayGen4TurboAspectRatio(str, Enum):
     """Aspect ratios supported for Image to Video API when using gen4_turbo model."""
 
@@ -84,14 +99,6 @@ def get_video_url_from_task_status(response: TaskStatusResponse) -> str | None:
     return None
 
 
-def extract_progress_from_task_status(
-    response: TaskStatusResponse,
-) -> float | None:
-    if hasattr(response, "progress") and response.progress is not None:
-        return response.progress * 100
-    return None
-
-
 def get_image_url_from_task_status(response: TaskStatusResponse) -> str | None:
     """Returns the image URL from the task status response if it exists."""
     if hasattr(response, "output") and len(response.output) > 0:
@@ -102,14 +109,13 @@ def get_image_url_from_task_status(response: TaskStatusResponse) -> str | None:
 async def get_response(
     cls: type[IO.ComfyNode], task_id: str, estimated_duration: int | None = None
 ) -> TaskStatusResponse:
-    """Poll the task status until it is finished then get the response."""
     return await poll_op(
         cls,
         ApiEndpoint(path=f"{PATH_GET_TASK_STATUS}/{task_id}"),
         response_model=TaskStatusResponse,
-        status_extractor=lambda r: r.status.value,
+        status_extractor=lambda r: r.status,
         estimated_duration=estimated_duration,
-        progress_extractor=extract_progress_from_task_status,
+        progress_extractor=lambda r: r.progress * 100 if r.progress is not None else None,
     )
 
 
@@ -127,7 +133,7 @@ async def generate_video(
 
     final_response = await get_response(cls, initial_response.id, estimated_duration)
     if not final_response.output:
-        raise RunwayApiError("Runway task succeeded but no video data found in response.")
+        raise ValueError("Runway task succeeded but no video data found in response.")
 
     video_url = get_video_url_from_task_status(final_response)
     return await download_url_to_video_output(video_url)
@@ -410,7 +416,7 @@ class RunwayFirstLastFrameNode(IO.ComfyNode):
             mime_type="image/png",
         )
         if len(download_urls) != 2:
-            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+            raise ValueError("Failed to upload one or more images to comfy api.")
 
         return IO.NodeOutput(
             await generate_video(
@@ -514,11 +520,321 @@ class RunwayTextToImageNode(IO.ComfyNode):
             estimated_duration=AVERAGE_DURATION_T2I_SECONDS,
         )
         if not final_response.output:
-            raise RunwayApiError("Runway task succeeded but no image data found in response.")
+            raise ValueError("Runway task succeeded but no image data found in response.")
 
         return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_task_status(final_response)))
 
 
+_TIMING_ABSOLUTE = "Absolute time (seconds)"
+_TIMING_FRACTION = "Fraction of duration (0.0-1.0)"
+
+
+class RunwayAleph2KeyframeNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayAleph2KeyframeNode",
+            display_name="Runway Aleph2 Keyframe",
+            category="partner/video/Runway",
+            description="Anchor a guidance image to a moment of the input (source) video, so Aleph2 "
+            "steers the edit at that point of your footage. Connect this to the 'keyframes' input of "
+            "the Runway Aleph2 Video to Video node; chain several together (up to 5) via the optional "
+            "'keyframes' input below.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="The guidance image to apply at the chosen moment of the input video.",
+                ),
+                IO.DynamicCombo.Input(
+                    "timing",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            _TIMING_ABSOLUTE,
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from start of the input video where this image applies.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            _TIMING_FRACTION,
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the input video this image applies, "
+                                    "as a fraction of its duration (0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the input video's timeline.",
+                ),
+                IO.Custom(RunwayAleph2IO.KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Optional earlier keyframes to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(RunwayAleph2IO.KEYFRAME).Output(display_name="keyframes")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: Input.Image,
+        timing: dict,
+        keyframes: RunwayAleph2KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = keyframes.clone() if keyframes is not None else RunwayAleph2KeyframeChain()
+        if timing["timing"] == _TIMING_ABSOLUTE:
+            mode, value = KEYFRAME_MODE_SECONDS, float(timing["seconds"])
+        else:
+            mode, value = KEYFRAME_MODE_AT, float(timing["fraction"])
+        chain.add(RunwayAleph2KeyframeItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class RunwayAleph2PromptImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayAleph2PromptImageNode",
+            display_name="Runway Aleph2 Prompt Image",
+            category="partner/video/Runway",
+            description="Anchor a guidance image to a moment of the output (result) video, to guide what "
+            "the edited video looks like at that point. Connect this to the 'prompt_images' input of the "
+            "Runway Aleph2 Video to Video node; chain several together (up to 5) via the optional "
+            "'prompt_images' input below.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="The guidance image to place at the chosen moment of the output video.",
+                ),
+                IO.DynamicCombo.Input(
+                    "position",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            _TIMING_ABSOLUTE,
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from start of the output video where this image applies.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            _TIMING_FRACTION,
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the output video this image applies, "
+                                    "as a fraction of its duration (0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the output video's timeline.",
+                ),
+                IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Input(
+                    "prompt_images",
+                    optional=True,
+                    tooltip="Optional earlier prompt images to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Output(display_name="prompt_images")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: Input.Image,
+        position: dict,
+        prompt_images: RunwayAleph2PromptImageChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = prompt_images.clone() if prompt_images is not None else RunwayAleph2PromptImageChain()
+        if position["position"] == _TIMING_ABSOLUTE:
+            mode, value = PROMPT_IMAGE_MODE_TIMESTAMP, float(position["seconds"])
+        else:
+            mode, value = PROMPT_IMAGE_MODE_POSITION, float(position["fraction"])
+        chain.add(RunwayAleph2PromptImageItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class RunwayAleph2VideoToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayAleph2VideoToVideoNode",
+            display_name="Runway Aleph2 Video to Video",
+            category="partner/video/Runway",
+            description="Edit a video with a text prompt using Runway's Aleph2 model. Aleph2 transforms "
+            "your footage (restyle, relight, add or remove elements, change the viewpoint) while keeping "
+            "the original motion and timing; the output resolution matches the input video, which must be "
+            "2-30 seconds at 30 fps or lower. Optionally steer the edit with either keyframes (anchored to "
+            "the input video) or prompt images (anchored to the output video) - use one or the other, not both.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes what should appear in the output (1-1000 characters).",
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="Input video to edit. Must be 2-30 seconds at 30 fps or lower.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Random seed for generation",
+                ),
+                IO.Combo.Input(
+                    "public_figure_threshold",
+                    options=["auto", "low"],
+                    default="low",
+                    tooltip="Content moderation for recognizable public figures.",
+                ),
+                IO.Custom(RunwayAleph2IO.KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Guidance images anchored to the input video, from Aleph2 Keyframe nodes (up to 5). "
+                    "Use keyframes or prompt images, not both.",
+                ),
+                IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Input(
+                    "prompt_images",
+                    optional=True,
+                    tooltip="Guidance images anchored to the output video, from Aleph2 Prompt Image nodes (up to 5). "
+                    "Use keyframes or prompt images, not both.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.4004, "format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        video: Input.Video,
+        seed: int,
+        public_figure_threshold: str = "low",
+        keyframes: RunwayAleph2KeyframeChain | None = None,
+        prompt_images: RunwayAleph2PromptImageChain | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=1000)
+        validate_video_duration(
+            video,
+            min_duration=2.0,
+            max_duration=30.0,
+        )
+        try:
+            fps = float(video.get_frame_rate())
+        except Exception:
+            fps = None
+        if fps is not None and fps > 30.0 + 0.01:
+            raise ValueError(f"Input video frame rate ({fps:.2f} fps) exceeds Aleph2's maximum of 30 fps.")
+
+        if (keyframes and keyframes.items) and (prompt_images and prompt_images.items):
+            raise ValueError("Aleph2 accepts either keyframes or prompt images, not both.")
+
+        video_duration: float | None = None
+        try:
+            video_duration = video.get_duration()
+        except Exception:
+            video_duration = None
+
+        def _check_seconds(value: float, label: str) -> None:
+            if video_duration is not None and value > video_duration + 0.0001:
+                raise ValueError(f"{label} {value:.2f}s exceeds the input video duration ({video_duration:.2f}s).")
+
+        video_url = await upload_video_to_comfyapi(cls, video)
+
+        keyframe_models: list[RunwayAleph2KeyframeSeconds | RunwayAleph2KeyframeAt] = []
+        if keyframes is not None:
+            if len(keyframes.items) > 5:
+                raise ValueError("Aleph2 supports at most 5 keyframes.")
+            for item in keyframes.items:
+                image_url = await upload_image_to_comfyapi(cls, item.image, mime_type="image/png")
+                if item.mode == KEYFRAME_MODE_SECONDS:
+                    _check_seconds(item.value, "Keyframe timestamp")
+                    keyframe_models.append(RunwayAleph2KeyframeSeconds(seconds=item.value, uri=image_url))
+                else:
+                    keyframe_models.append(RunwayAleph2KeyframeAt(at=item.value, uri=image_url))
+
+        prompt_image_models: list[RunwayAleph2PromptImage] = []
+        if prompt_images is not None:
+            if len(prompt_images.items) > 5:
+                raise ValueError("Aleph2 supports at most 5 prompt images.")
+            for item in prompt_images.items:
+                image_url = await upload_image_to_comfyapi(cls, item.image, mime_type="image/png")
+                position: RunwayAleph2TimestampPosition | RunwayAleph2RelativePosition
+                if item.mode == PROMPT_IMAGE_MODE_TIMESTAMP:
+                    _check_seconds(item.value, "Prompt image timestamp")
+                    position = RunwayAleph2TimestampPosition(timestampSeconds=item.value)
+                else:
+                    position = RunwayAleph2RelativePosition(positionPercentage=item.value)
+                prompt_image_models.append(RunwayAleph2PromptImage(position=position, uri=image_url))
+
+        initial_response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=PATH_VIDEO_TO_VIDEO, method="POST"),
+            response_model=RunwayAleph2Response,
+            data=RunwayAleph2Request(
+                promptText=prompt,
+                videoUri=video_url,
+                seed=seed,
+                contentModeration=RunwayAleph2ContentModeration(publicFigureThreshold=public_figure_threshold),
+                keyframes=keyframe_models or None,
+                promptImage=prompt_image_models or None,
+            ),
+        )
+
+        final_response = await get_response(cls, initial_response.id)
+        if not final_response.output:
+            raise ValueError("Runway task succeeded but no video data found in response.")
+
+        return IO.NodeOutput(await download_url_to_video_output(get_video_url_from_task_status(final_response)))
+
+
 class RunwayExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -527,6 +843,9 @@ class RunwayExtension(ComfyExtension):
             RunwayImageToVideoNodeGen3a,
             RunwayImageToVideoNodeGen4,
             RunwayTextToImageNode,
+            RunwayAleph2VideoToVideoNode,
+            RunwayAleph2KeyframeNode,
+            RunwayAleph2PromptImageNode,
         ]
 
 

comfy_api_nodes/nodes_sonilo.py

@@ -100,8 +100,7 @@ class SoniloTextToMusic(IO.ComfyNode):
             node_id="SoniloTextToMusic",
             display_name="Sonilo Text to Music",
             category="partner/audio/Sonilo",
-            description="Generate music from a text prompt using Sonilo's AI model. "
-            "Leave duration at 0 to let the model infer it from the prompt.",
+            description="Generate music from a text prompt using Sonilo's AI model.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -111,11 +110,10 @@ class SoniloTextToMusic(IO.ComfyNode):
                 ),
                 IO.Int.Input(
                     "duration",
-                    default=0,
-                    min=0,
+                    default=30,
+                    min=1,
                     max=360,
-                    tooltip="Target duration in seconds. Set to 0 to let the model "
-                    "infer the duration from the prompt. Maximum: 6 minutes.",
+                    tooltip="Target duration in seconds. Maximum: 6 minutes.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -136,13 +134,7 @@ class SoniloTextToMusic(IO.ComfyNode):
             is_api_node=True,
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
-                expr="""
-                (
-                  widgets.duration > 0
-                    ? {"type":"usd","usd": 0.005 * widgets.duration}
-                    : {"type":"usd","usd": 0.005, "format":{"suffix":"/second"}}
-                )
-                """,
+                expr='{"type":"usd","usd": 0.0025 * widgets.duration}',
             ),
         )
 
@@ -150,14 +142,13 @@ class SoniloTextToMusic(IO.ComfyNode):
     async def execute(
         cls,
         prompt: str,
-        duration: int = 0,
+        duration: int = 1,
         seed: int = 0,
     ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=True, min_length=1)
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=1000)
         form = aiohttp.FormData()
         form.add_field("prompt", prompt)
-        if duration > 0:
-            form.add_field("duration", str(duration))
+        form.add_field("duration", str(duration))
         audio_bytes = await _stream_sonilo_music(
             cls,
             ApiEndpoint(path="/proxy/sonilo/t2m/generate", method="POST"),

comfy_api_nodes/nodes_stability.py

@@ -1,932 +0,0 @@
-from inspect import cleandoc
-from typing import Optional
-from typing_extensions import override
-
-from comfy_api.latest import ComfyExtension, Input, IO
-from comfy_api_nodes.apis.stability import (
-    StabilityUpscaleConservativeRequest,
-    StabilityUpscaleCreativeRequest,
-    StabilityAsyncResponse,
-    StabilityResultsGetResponse,
-    StabilityStable3_5Request,
-    StabilityStableUltraRequest,
-    StabilityStableUltraResponse,
-    StabilityAspectRatio,
-    Stability_SD3_5_Model,
-    Stability_SD3_5_GenerationMode,
-    get_stability_style_presets,
-    StabilityTextToAudioRequest,
-    StabilityAudioToAudioRequest,
-    StabilityAudioInpaintRequest,
-    StabilityAudioResponse,
-)
-from comfy_api_nodes.util import (
-    validate_audio_duration,
-    validate_string,
-    audio_input_to_mp3,
-    bytesio_to_image_tensor,
-    tensor_to_bytesio,
-    audio_bytes_to_audio_input,
-    sync_op,
-    poll_op,
-    ApiEndpoint,
-)
-
-import torch
-import base64
-from io import BytesIO
-from enum import Enum
-
-
-class StabilityPollStatus(str, Enum):
-    finished = "finished"
-    in_progress = "in_progress"
-    failed = "failed"
-
-
-def get_async_dummy_status(x: StabilityResultsGetResponse):
-    if x.name is not None or x.errors is not None:
-        return StabilityPollStatus.failed
-    elif x.finish_reason is not None:
-        return StabilityPollStatus.finished
-    return StabilityPollStatus.in_progress
-
-
-class StabilityStableImageUltraNode(IO.ComfyNode):
-    """
-    Generates images synchronously based on prompt and resolution.
-    """
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityStableImageUltraNode",
-            display_name="Stability AI Stable Image Ultra",
-            category="partner/image/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines" +
-                                    "elements, colors, and subjects will lead to better results. " +
-                                    "To control the weight of a given word use the format `(word:weight)`," +
-                                    "where `word` is the word you'd like to control the weight of and `weight`" +
-                                    "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" +
-                                    "would convey a sky that was blue and green, but more green than blue.",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=StabilityAspectRatio,
-                    default=StabilityAspectRatio.ratio_1_1,
-                    tooltip="Aspect ratio of generated image.",
-                ),
-                IO.Combo.Input(
-                    "style_preset",
-                    options=get_stability_style_presets(),
-                    tooltip="Optional desired style of generated image.",
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for creating the noise.",
-                ),
-                IO.Image.Input(
-                    "image",
-                    optional=True,
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    default="",
-                    tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.",
-                    force_input=True,
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Float.Input(
-                    "image_denoise",
-                    default=0.5,
-                    min=0.0,
-                    max=1.0,
-                    step=0.01,
-                    tooltip="Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
-                    optional=True,
-                ),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.08}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        aspect_ratio: str,
-        style_preset: str,
-        seed: int,
-        image: Optional[torch.Tensor] = None,
-        negative_prompt: str = "",
-        image_denoise: Optional[float] = 0.5,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False)
-        # prepare image binary if image present
-        image_binary = None
-        if image is not None:
-            image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
-        else:
-            image_denoise = None
-
-        if not negative_prompt:
-            negative_prompt = None
-        if style_preset == "None":
-            style_preset = None
-
-        files = {
-            "image": image_binary
-        }
-
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/generate/ultra", method="POST"),
-            response_model=StabilityStableUltraResponse,
-            data=StabilityStableUltraRequest(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                aspect_ratio=aspect_ratio,
-                seed=seed,
-                strength=image_denoise,
-                style_preset=style_preset,
-            ),
-            files=files,
-            content_type="multipart/form-data",
-        )
-
-        if response_api.finish_reason != "SUCCESS":
-            raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.")
-
-        image_data = base64.b64decode(response_api.image)
-        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
-
-        return IO.NodeOutput(returned_image)
-
-
-class StabilityStableImageSD_3_5Node(IO.ComfyNode):
-    """
-    Generates images synchronously based on prompt and resolution.
-    """
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityStableImageSD_3_5Node",
-            display_name="Stability AI Stable Diffusion 3.5 Image",
-            category="partner/image/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
-                ),
-                IO.Combo.Input(
-                    "model",
-                    options=Stability_SD3_5_Model,
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=StabilityAspectRatio,
-                    default=StabilityAspectRatio.ratio_1_1,
-                    tooltip="Aspect ratio of generated image.",
-                ),
-                IO.Combo.Input(
-                    "style_preset",
-                    options=get_stability_style_presets(),
-                    tooltip="Optional desired style of generated image.",
-                    advanced=True,
-                ),
-                IO.Float.Input(
-                    "cfg_scale",
-                    default=4.0,
-                    min=1.0,
-                    max=10.0,
-                    step=0.1,
-                    tooltip="How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for creating the noise.",
-                ),
-                IO.Image.Input(
-                    "image",
-                    optional=True,
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    default="",
-                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
-                    force_input=True,
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Float.Input(
-                    "image_denoise",
-                    default=0.5,
-                    min=0.0,
-                    max=1.0,
-                    step=0.01,
-                    tooltip="Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
-                    optional=True,
-                ),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
-                expr="""
-                (
-                  $contains(widgets.model,"large")
-                    ? {"type":"usd","usd":0.065}
-                    : {"type":"usd","usd":0.035}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        model: str,
-        prompt: str,
-        aspect_ratio: str,
-        style_preset: str,
-        seed: int,
-        cfg_scale: float,
-        image: Optional[torch.Tensor] = None,
-        negative_prompt: str = "",
-        image_denoise: Optional[float] = 0.5,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False)
-        # prepare image binary if image present
-        image_binary = None
-        mode = Stability_SD3_5_GenerationMode.text_to_image
-        if image is not None:
-            image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
-            mode = Stability_SD3_5_GenerationMode.image_to_image
-            aspect_ratio = None
-        else:
-            image_denoise = None
-
-        if not negative_prompt:
-            negative_prompt = None
-        if style_preset == "None":
-            style_preset = None
-
-        files = {
-            "image": image_binary
-        }
-
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/generate/sd3", method="POST"),
-            response_model=StabilityStableUltraResponse,
-            data=StabilityStable3_5Request(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                aspect_ratio=aspect_ratio,
-                seed=seed,
-                strength=image_denoise,
-                style_preset=style_preset,
-                cfg_scale=cfg_scale,
-                model=model,
-                mode=mode,
-            ),
-            files=files,
-            content_type="multipart/form-data",
-        )
-
-        if response_api.finish_reason != "SUCCESS":
-            raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.")
-
-        image_data = base64.b64decode(response_api.image)
-        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
-
-        return IO.NodeOutput(returned_image)
-
-
-class StabilityUpscaleConservativeNode(IO.ComfyNode):
-    """
-    Upscale image with minimal alterations to 4K resolution.
-    """
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityUpscaleConservativeNode",
-            display_name="Stability AI Upscale Conservative",
-            category="partner/image/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.Image.Input("image"),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
-                ),
-                IO.Float.Input(
-                    "creativity",
-                    default=0.35,
-                    min=0.2,
-                    max=0.5,
-                    step=0.01,
-                    tooltip="Controls the likelihood of creating additional details not heavily conditioned by the init image.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for creating the noise.",
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    default="",
-                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
-                    force_input=True,
-                    optional=True,
-                    advanced=True,
-                ),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.4}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: torch.Tensor,
-        prompt: str,
-        creativity: float,
-        seed: int,
-        negative_prompt: str = "",
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False)
-        image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
-
-        if not negative_prompt:
-            negative_prompt = None
-
-        files = {
-            "image": image_binary
-        }
-
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/conservative", method="POST"),
-            response_model=StabilityStableUltraResponse,
-            data=StabilityUpscaleConservativeRequest(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                creativity=round(creativity,2),
-                seed=seed,
-            ),
-            files=files,
-            content_type="multipart/form-data",
-        )
-
-        if response_api.finish_reason != "SUCCESS":
-            raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.")
-
-        image_data = base64.b64decode(response_api.image)
-        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
-
-        return IO.NodeOutput(returned_image)
-
-
-class StabilityUpscaleCreativeNode(IO.ComfyNode):
-    """
-    Upscale image with minimal alterations to 4K resolution.
-    """
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityUpscaleCreativeNode",
-            display_name="Stability AI Upscale Creative",
-            category="partner/image/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.Image.Input("image"),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.",
-                ),
-                IO.Float.Input(
-                    "creativity",
-                    default=0.3,
-                    min=0.1,
-                    max=0.5,
-                    step=0.01,
-                    tooltip="Controls the likelihood of creating additional details not heavily conditioned by the init image.",
-                ),
-                IO.Combo.Input(
-                    "style_preset",
-                    options=get_stability_style_presets(),
-                    tooltip="Optional desired style of generated image.",
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for creating the noise.",
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    default="",
-                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
-                    force_input=True,
-                    optional=True,
-                    advanced=True,
-                ),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.6}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: torch.Tensor,
-        prompt: str,
-        creativity: float,
-        style_preset: str,
-        seed: int,
-        negative_prompt: str = "",
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False)
-        image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
-
-        if not negative_prompt:
-            negative_prompt = None
-        if style_preset == "None":
-            style_preset = None
-
-        files = {
-            "image": image_binary
-        }
-
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/creative", method="POST"),
-            response_model=StabilityAsyncResponse,
-            data=StabilityUpscaleCreativeRequest(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                creativity=round(creativity,2),
-                style_preset=style_preset,
-                seed=seed,
-            ),
-            files=files,
-            content_type="multipart/form-data",
-        )
-
-        response_poll = await poll_op(
-            cls,
-            ApiEndpoint(path=f"/proxy/stability/v2beta/results/{response_api.id}"),
-            response_model=StabilityResultsGetResponse,
-            poll_interval=3,
-            status_extractor=lambda x: get_async_dummy_status(x),
-        )
-
-        if response_poll.finish_reason != "SUCCESS":
-            raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.")
-
-        image_data = base64.b64decode(response_poll.result)
-        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
-
-        return IO.NodeOutput(returned_image)
-
-
-class StabilityUpscaleFastNode(IO.ComfyNode):
-    """
-    Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images.
-    """
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityUpscaleFastNode",
-            display_name="Stability AI Upscale Fast",
-            category="partner/image/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.Image.Input("image"),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.02}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
-        image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read()
-
-        files = {
-            "image": image_binary
-        }
-
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/stable-image/upscale/fast", method="POST"),
-            response_model=StabilityStableUltraResponse,
-            files=files,
-            content_type="multipart/form-data",
-        )
-
-        if response_api.finish_reason != "SUCCESS":
-            raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.")
-
-        image_data = base64.b64decode(response_api.image)
-        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
-
-        return IO.NodeOutput(returned_image)
-
-
-class StabilityTextToAudio(IO.ComfyNode):
-    """Generates high-quality music and sound effects from text descriptions."""
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityTextToAudio",
-            display_name="Stability AI Text To Audio",
-            category="partner/audio/Stability AI",
-            essentials_category="Audio",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=["stable-audio-2.5"],
-                ),
-                IO.String.Input("prompt", multiline=True, default=""),
-                IO.Int.Input(
-                    "duration",
-                    default=190,
-                    min=1,
-                    max=190,
-                    step=1,
-                    tooltip="Controls the duration in seconds of the generated audio.",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for generation.",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "steps",
-                    default=8,
-                    min=4,
-                    max=8,
-                    step=1,
-                    tooltip="Controls the number of sampling steps.",
-                    optional=True,
-                    advanced=True,
-                ),
-            ],
-            outputs=[
-                IO.Audio.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.2}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(cls, model: str, prompt: str, duration: int, seed: int, steps: int) -> IO.NodeOutput:
-        validate_string(prompt, max_length=10000)
-        payload = StabilityTextToAudioRequest(prompt=prompt, model=model, duration=duration, seed=seed, steps=steps)
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/text-to-audio", method="POST"),
-            response_model=StabilityAudioResponse,
-            data=payload,
-            content_type="multipart/form-data",
-        )
-        if not response_api.audio:
-            raise ValueError("No audio file was received in response.")
-        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
-
-
-class StabilityAudioToAudio(IO.ComfyNode):
-    """Transforms existing audio samples into new high-quality compositions using text instructions."""
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityAudioToAudio",
-            display_name="Stability AI Audio To Audio",
-            category="partner/audio/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=["stable-audio-2.5"],
-                ),
-                IO.String.Input("prompt", multiline=True, default=""),
-                IO.Audio.Input("audio", tooltip="Audio must be between 6 and 190 seconds long."),
-                IO.Int.Input(
-                    "duration",
-                    default=190,
-                    min=1,
-                    max=190,
-                    step=1,
-                    tooltip="Controls the duration in seconds of the generated audio.",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for generation.",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "steps",
-                    default=8,
-                    min=4,
-                    max=8,
-                    step=1,
-                    tooltip="Controls the number of sampling steps.",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Float.Input(
-                    "strength",
-                    default=1,
-                    min=0.01,
-                    max=1.0,
-                    step=0.01,
-                    display_mode=IO.NumberDisplay.slider,
-                    tooltip="Parameter controls how much influence the audio parameter has on the generated audio.",
-                    optional=True,
-                ),
-            ],
-            outputs=[
-                IO.Audio.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.2}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls, model: str, prompt: str, audio: Input.Audio, duration: int, seed: int, steps: int, strength: float
-    ) -> IO.NodeOutput:
-        validate_string(prompt, max_length=10000)
-        validate_audio_duration(audio, 6, 190)
-        payload = StabilityAudioToAudioRequest(
-            prompt=prompt, model=model, duration=duration, seed=seed, steps=steps, strength=strength
-        )
-        response_api = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/audio-to-audio", method="POST"),
-            response_model=StabilityAudioResponse,
-            data=payload,
-            content_type="multipart/form-data",
-            files={"audio": audio_input_to_mp3(audio)},
-        )
-        if not response_api.audio:
-            raise ValueError("No audio file was received in response.")
-        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
-
-
-class StabilityAudioInpaint(IO.ComfyNode):
-    """Transforms part of existing audio sample using text instructions."""
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="StabilityAudioInpaint",
-            display_name="Stability AI Audio Inpaint",
-            category="partner/audio/Stability AI",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=["stable-audio-2.5"],
-                ),
-                IO.String.Input("prompt", multiline=True, default=""),
-                IO.Audio.Input("audio", tooltip="Audio must be between 6 and 190 seconds long."),
-                IO.Int.Input(
-                    "duration",
-                    default=190,
-                    min=1,
-                    max=190,
-                    step=1,
-                    tooltip="Controls the duration in seconds of the generated audio.",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=4294967294,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="The random seed used for generation.",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "steps",
-                    default=8,
-                    min=4,
-                    max=8,
-                    step=1,
-                    tooltip="Controls the number of sampling steps.",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "mask_start",
-                    default=30,
-                    min=0,
-                    max=190,
-                    step=1,
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "mask_end",
-                    default=190,
-                    min=0,
-                    max=190,
-                    step=1,
-                    optional=True,
-                    advanced=True,
-                ),
-            ],
-            outputs=[
-                IO.Audio.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.2}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        model: str,
-        prompt: str,
-        audio: Input.Audio,
-        duration: int,
-        seed: int,
-        steps: int,
-        mask_start: int,
-        mask_end: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, max_length=10000)
-        if mask_end <= mask_start:
-            raise ValueError(f"Value of mask_end({mask_end}) should be greater then mask_start({mask_start})")
-        validate_audio_duration(audio, 6, 190)
-
-        payload = StabilityAudioInpaintRequest(
-            prompt=prompt,
-            model=model,
-            duration=duration,
-            seed=seed,
-            steps=steps,
-            mask_start=mask_start,
-            mask_end=mask_end,
-        )
-        response_api = await sync_op(
-            cls,
-            endpoint=ApiEndpoint(path="/proxy/stability/v2beta/audio/stable-audio-2/inpaint", method="POST"),
-            response_model=StabilityAudioResponse,
-            data=payload,
-            content_type="multipart/form-data",
-            files={"audio": audio_input_to_mp3(audio)},
-        )
-        if not response_api.audio:
-            raise ValueError("No audio file was received in response.")
-        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio)))
-
-
-class StabilityExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [
-            StabilityStableImageUltraNode,
-            StabilityStableImageSD_3_5Node,
-            StabilityUpscaleConservativeNode,
-            StabilityUpscaleCreativeNode,
-            StabilityUpscaleFastNode,
-            StabilityTextToAudio,
-            StabilityAudioToAudio,
-            StabilityAudioInpaint,
-        ]
-
-
-async def comfy_entrypoint() -> StabilityExtension:
-    return StabilityExtension()