Cube3D: reuse shared Flux RoPE (comfy-kitchen optimized kernel)

Replace cube's bespoke complex-number RoPE (torch.polar / view_as_complex) with
ComfyUI's shared Flux rotary embedding (comfy.ldm.flux.math):
  * precompute_freqs_cis now returns Flux's real rotation freqs via rope().
  * apply_rotary_emb applies them via apply_rope1, which at inference dispatches to
    comfy-kitchen's optimized apply_rope kernel (comfy.quant_ops.ck). q and k are
    still rotated separately to preserve the decode-time position asymmetry.

The pairing convention (adjacent dims) and rotation math are identical, so token
outputs are unchanged. The only numerical difference is that rope() computes the
rotation angles in fp64 before casting to fp32 (cube's original used fp32), so output
now matches upstream to fp32 rounding (~1e-6 on rotated q/k in a standalone check)
rather than bit-for-bit. Greedy argmax token selection is unaffected.

Deviation note: this is a deliberate, documented divergence from a strict upstream
port, taken to gain the shared optimized kernel. Needs GPU parity re-validation on the
2x4090 box (kosin-X570-AORUS-ULTRA) before merge.

Co-authored-by: Amp <amp@ampcode.com>
Amp-Thread-ID: https://ampcode.com/threads/T-019f013b-5892-71b9-af6b-c2ef28c67d2b

.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: "assertive"
-  request_changes_workflow: true
+  profile: "chill"
+  request_changes_workflow: false
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: true
+  review_details: false
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,14 +39,6 @@ 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),
@@ -131,10 +123,5 @@ chat:
 
 knowledge_base:
   opt_out: false
-  code_guidelines:
-    enabled: true
-    filePatterns:
-      - files: "AGENTS.md"
-        applyTo: "**"
   learnings:
     scope: "auto"

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

@@ -1,38 +0,0 @@
-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@964d5aad37cbfb57c5b23961d42c2fd85868bf1d # github-workflows main (964d5aa)
-    with:
-      workflows_ref: 964d5aad37cbfb57c5b23961d42c2fd85868bf1d
-      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

@@ -1,93 +0,0 @@
-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.

AGENTS.md

@@ -1,337 +0,0 @@
-## 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.
-- When compatibility is explicitly out of scope, remove compatibility-only
-  aliases, duplicate nodes, legacy entry points, and preset wrappers instead of
-  retaining parallel ways to perform the same operation.
-- 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.
-- Do not add a model-specific option to a shared helper when only one caller
-  needs it. Keep one-off behavior at the model integration boundary, or extend
-  the shared helper only when the option is a coherent reusable capability.
-- Implementations of shared model interfaces should accept the standard caller
-  contract without model-specific rejection branches for optional capabilities
-  they do not consume. Let supported behavior be determined by implementation
-  paths that actually use those inputs.
-- 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.
-- Prefer ComfyUI's shared optimized kernels and backend dispatchers over
-  handwritten implementations of the same operation. Remove duplicate local
-  kernels and adapt inputs to the shared operation's documented layout while
-  preserving the model's original math and output contract.
-- 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.
-- A model-detection signature must guard every state-dict key it dereferences.
-  Do not partially match a format and then raise an incidental `KeyError` while
-  extracting its configuration.
-- Order model-detection checks from established or more-specific signatures to
-  newer or broader signatures. Put a broad new detector near the generic
-  fallback when giving it higher precedence could steal another model family.
-- 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.
-- Do not cast the result of an optimized backend operation back to its input
-  dtype unless that backend's documented result contract requires normalization.
-  In particular, trust the selected optimized-attention implementation to honor
-  its dtype contract.
-- 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.
-- DiT models should accept latent dimensions that are not exact patch-size
-  multiples. Use `comfy.ldm.common_dit.pad_to_patch_size` on every patchified
-  target or reference input, then crop only the target output back to its
-  original dimensions.
-- Avoid defensive shape and configuration checks that merely replace the clear
-  failure from the tensor operation immediately below them. Add explicit
-  validation only when it provides materially better context at a real boundary
-  or prevents silent incorrect output.
-- 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.
-- Use `io.Autogrow` for a variable number of repeated inputs instead of a fixed
-  series of numbered optional sockets. Set its minimum to zero when the model
-  has a valid no-item path, and cap it only when the model has a real limit.
-- Mark inputs optional when execution has a valid path that does not read them.
-  If one optional input is needed only to process another optional input, do not
-  force users on the path that supplies neither to connect it.
-- Conditioning nodes should normally output conditioning only. Do not expose
-  input or intermediate images as convenience outputs for downstream sizing or
-  routing; use the existing image path or a dedicated image operation instead.
-- 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.

CODEOWNERS

@@ -1,6 +1,5 @@
 * @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 @kijai
 
 /CODEOWNERS @comfyanonymous
-/AGENTS.md @comfyanonymous
 /.ci/ @comfyanonymous
 /.github/ @comfyanonymous

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. **[Comfy Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
+2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
    - Builds a new release using the latest stable core version
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
@@ -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 2.5 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.
+torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
 
 ### Instructions:
 
@@ -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, M2, M3 or M4) with any recent macOS version.
+You can install ComfyUI in Apple Mac silicon (M1 or M2) 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.

alembic_db/versions/0005_allow_case_sensitive_tags.py

@@ -1,107 +0,0 @@
-"""
-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

@@ -1,30 +0,0 @@
-"""
-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

@@ -40,7 +40,6 @@ 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()
@@ -162,19 +161,11 @@ 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,
@@ -315,15 +306,12 @@ async def download_asset_content(request: web.Request) -> web.Response:
             404, "FILE_NOT_FOUND", "Underlying file not found on disk."
         )
 
-    # 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):
+    _DANGEROUS_MIME_TYPES = {
+        "text/html", "text/html-sandboxed", "application/xhtml+xml",
+        "text/javascript", "text/css",
+    }
+    if content_type in _DANGEROUS_MIME_TYPES:
         content_type = "application/octet-stream"
-        disposition = "attachment"
 
     safe_name = (filename or "").replace("\r", "").replace("\n", "")
     encoded = urllib.parse.quote(safe_name)
@@ -428,6 +416,17 @@ 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:
@@ -471,7 +470,7 @@ 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, "INVALID_BODY", str(e))
+        return _build_error_response(400, "BAD_REQUEST", 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() for t in v if str(t).strip()]
+            out = [str(t).strip().lower() 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 list(dict.fromkeys(t.strip() for t in v.split(",") if t.strip()))
+            return [t.strip().lower() 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 or None
+        return v.lower() 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()
+            tnorm = t.strip().lower()
             if tnorm:
                 out.append(tnorm)
         seen = set()
@@ -239,8 +239,8 @@ class TagsRemove(TagsAdd):
 class UploadAssetSpec(BaseModel):
     """Upload Asset operation.
 
-    - tags: labels plus one destination role ('models'|'input'|'output') for new bytes;
-            if role == 'models', exactly one model_type:<folder_name> tag is required
+    - tags: optional list; if provided, first is root ('models'|'input'|'output');
+            if root == 'models', second must be a valid category
     - 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()
+            tnorm = str(t).strip().lower()
             if tnorm and tnorm not in seen:
                 seen.add(tnorm)
                 norm.append(tnorm)
@@ -335,4 +335,14 @@ 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

@@ -9,20 +9,8 @@ class Asset(BaseModel):
     ``id`` here is the AssetReference id, not the content-addressed Asset id."""
 
     id: 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.",
-    )
+    name: str
     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

@@ -140,6 +140,7 @@ 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

@@ -76,8 +76,6 @@ 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

@@ -650,7 +650,6 @@ 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).
 
@@ -660,7 +659,6 @@ 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),
@@ -688,14 +686,13 @@ 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), loader_path=loader_path,
-            is_missing=False, deleted_at=None, updated_at=now,
+            asset_id=asset_id, mtime_ns=int(mtime_ns), is_missing=False,
+            deleted_at=None, updated_at=now,
         )
     )
     res2 = session.execute(upd)

app/assets/database/queries/tags.py

@@ -265,8 +265,6 @@ 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"),
@@ -295,8 +293,9 @@ def list_tags_with_usage(
         .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
     )
 
-    if prefix_filter:
-        q = q.where(func.substr(Tag.name, 1, len(prefix_filter)) == prefix_filter)
+    if prefix:
+        escaped, esc = escape_sql_like_string(prefix.strip().lower())
+        q = q.where(Tag.name.like(escaped + "%", escape=esc))
 
     if not include_zero:
         q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
@@ -307,8 +306,9 @@ 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_filter:
-        total_q = total_q.where(func.substr(Tag.name, 1, len(prefix_filter)) == prefix_filter)
+    if prefix:
+        escaped, esc = escape_sql_like_string(prefix.strip().lower())
+        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
     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.
-      - Removing exact duplicates while preserving order and case.
+      - Stripping whitespace and converting to lowercase.
+      - Removing duplicates.
     """
-    return list(dict.fromkeys(t.strip() for t in (tags or []) if (t or "").strip()))
+    return list(dict.fromkeys(t.strip().lower() 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_loader_path,
+    compute_relative_filename,
     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, _exts in get_comfy_models_folders():
+        for _bucket, paths 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, _exts in get_comfy_models_folders():
+    for folder_name, bases in get_comfy_models_folders():
         rel_files = folder_paths.get_filename_list(folder_name) or []
         for rel_path in rel_files:
             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_loader_path(abs_p)
+        rel_fname = compute_relative_filename(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_loader_path(file_path)
+    rel_fname = compute_relative_filename(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_loader_path
+from app.assets.services.path_utils import compute_relative_filename
 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_loader_path(ref.file_path) if ref.file_path else None
+        computed_filename = compute_relative_filename(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

@@ -56,7 +56,6 @@ class ReferenceRow(TypedDict):
     id: str
     asset_id: str
     file_path: str
-    loader_path: str | None
     mtime_ns: int
     owner_id: str
     name: str
@@ -135,14 +134,6 @@ 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)
@@ -173,8 +164,6 @@ 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

@@ -33,9 +33,8 @@ 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_loader_path,
+    compute_relative_filename,
     get_name_and_tags_from_asset_path,
-    get_path_derived_tags_from_path,
     resolve_destination_from_tags,
     validate_path_within_base,
 )
@@ -92,7 +91,6 @@ 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
@@ -103,32 +101,17 @@ def _ingest_file_from_path(
             if preview_id and ref.preview_id != preview_id:
                 ref.preview_id = preview_id
 
-            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:
+            norm = normalize_tags(list(tags))
+            if norm:
                 if 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,
-                    )
+                    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,
+                )
 
             _update_metadata_with_filename(
                 session,
@@ -245,7 +228,7 @@ def ingest_existing_file(
             "mtime_ns": mtime_ns,
             "info_name": name,
             "tags": tags,
-            "fname": compute_loader_path(abs_path),
+            "fname": os.path.basename(abs_path),
             "metadata": None,
             "hash": None,
             "mime_type": mime_type,
@@ -305,7 +288,7 @@ def _register_existing_asset(
             return result
 
         new_meta = dict(user_metadata)
-        computed_filename = compute_loader_path(ref.file_path) if ref.file_path else None
+        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
         if computed_filename:
             new_meta["filename"] = computed_filename
 
@@ -352,7 +335,7 @@ def _update_metadata_with_filename(
     current_metadata: dict | None,
     user_metadata: dict[str, Any],
 ) -> None:
-    computed_filename = compute_loader_path(file_path) if file_path else None
+    computed_filename = compute_relative_filename(file_path) if file_path else None
 
     current_meta = current_metadata or {}
     new_meta = dict(current_meta)
@@ -491,10 +474,6 @@ 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)
@@ -556,7 +535,7 @@ def upload_from_temp_path(
         owner_id=owner_id,
         preview_id=preview_id,
         user_metadata=user_metadata or {},
-        tags=[*(tags or []), "uploaded"],
+        tags=tags,
         tag_origin="manual",
         require_existing_tags=False,
     )
@@ -590,19 +569,15 @@ def register_file_in_place(
 ) -> UploadResult:
     """Register an already-saved file in the asset database without moving it.
 
-    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.
+    Tags are derived from the filesystem path (root category + subfolder names),
+    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, "uploaded"])
+    merged_tags = normalize_tags([*path_tags, *tags])
 
     try:
         digest, _ = hashing.compute_blake3_hash(abs_path)

app/assets/services/path_utils.py

@@ -3,66 +3,59 @@ from pathlib import Path
 from typing import Literal
 
 import folder_paths
+from app.assets.helpers import normalize_tags
 
 
-_NON_MODEL_FOLDER_NAMES = frozenset({"configs", "custom_nodes"})
-_KNOWN_SUBFOLDER_TAGS = frozenset({"3d", "pasted", "painter", "threed", "webcam"})
+_NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
 
 
-def get_comfy_models_folders() -> list[tuple[str, list[str], set[str]]]:
-    """Build list of (folder_name, base_paths[], extensions) for all model locations.
+def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
+    """Build list of (folder_name, base_paths[]) 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 configs and custom_nodes.
-
-    An empty extensions set means the category accepts any extension,
-    matching folder_paths.filter_files_extensions semantics.
+    but excludes non-model entries like custom_nodes.
     """
-    targets: list[tuple[str, list[str], set[str]]] = []
+    targets: list[tuple[str, list[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, set(exts)))
+            targets.append((name, paths))
     return targets
 
 
 def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """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]
+    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
+    if not tags:
+        raise ValueError("tags must not be empty")
+    root = tags[0].lower()
     if root == "models":
-        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()
-        }
+        if len(tags) < 2:
+            raise ValueError("at least two tags required for model asset")
         try:
-            bases = model_folder_paths[folder_name]
+            bases = folder_paths.folder_names_and_paths[tags[1]][0]
         except KeyError:
-            raise ValueError(f"unknown model category '{folder_name}'")
+            raise ValueError(f"unknown model category '{tags[1]}'")
         if not bases:
-            raise ValueError(f"no base path configured for category '{folder_name}'")
+            raise ValueError(f"no base path configured for category '{tags[1]}'")
         base_dir = os.path.abspath(bases[0])
+        raw_subdirs = tags[2:]
     elif root == "input":
         base_dir = os.path.abspath(folder_paths.get_input_directory())
-    else:
+        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")
 
-    return base_dir, []
+    return base_dir, raw_subdirs if raw_subdirs else []
 
 
 def validate_path_within_base(candidate: str, base: str) -> None:
@@ -72,79 +65,14 @@ def validate_path_within_base(candidate: str, base: str) -> None:
         raise ValueError("destination escapes base directory")
 
 
-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.
+def compute_relative_filename(file_path: str) -> str | None:
     """
-    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:
+    Return the model's path relative to the last well-known folder (the model category),
+    using forward slashes, eg:
       /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
       /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
 
-    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.
+    For non-model paths, returns None.
     """
     try:
         root_category, rel_path = get_asset_category_and_relative_path(file_path)
@@ -188,10 +116,9 @@ 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.
-        rel = os.path.relpath(
+        return 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())
@@ -209,14 +136,8 @@ 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, 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 bucket, bases in get_comfy_models_folders():
         for b in bases:
             base_abs = os.path.abspath(b)
             if not _check_is_within(fp_abs, base_abs):
@@ -228,111 +149,25 @@ def get_asset_category_and_relative_path(
     if best is not None:
         _, bucket, rel_inside = best
         combined = os.path.join(bucket, rel_inside)
-        normalized = os.path.relpath(os.path.join(os.sep, combined), os.sep)
-        return "models", normalized.replace(os.sep, "/")
+        return "models", os.path.relpath(os.path.join(os.sep, combined), 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: backend-derived tags from root/model classification and known input
-      subfolder layout conventions
+    - tags: [root_category] + parent folder names in order
 
     Raises:
         ValueError: path does not belong to any known root.
     """
-    return Path(file_path).name, get_path_derived_tags_from_path(file_path)
+    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])))

app/assets/services/schemas.py

@@ -25,7 +25,6 @@ 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
@@ -94,7 +93,6 @@ 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

@@ -35,11 +35,7 @@ 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),
-                    "extensions": sorted(folder_paths.folder_names_and_paths[folder][1]),
-                })
+                output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)})
             return web.json_response(output_folders)
 
         # NOTE: This is an experiment to replace `/models/{folder}`
@@ -54,45 +50,21 @@ 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.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)
+            full_filename = os.path.join(folder, filename)
 
             previews = self.get_model_previews(full_filename)
             default_preview = previews[0] if len(previews) > 0 else None
             if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
                 return web.Response(status=404)
 
-            # 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

@@ -6,7 +6,6 @@ import glob
 import shutil
 import logging
 import tempfile
-import mimetypes
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
@@ -337,20 +336,7 @@ class UserManager():
             if not isinstance(path, str):
                 return 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",
-            })
+            return web.FileResponse(path)
 
         @routes.post("/userdata/{file}")
         async def post_userdata(request):

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

@@ -1,569 +0,0 @@
-{
-  "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
-        },
-        {
-          "name": "resolution",
-          "type": "INT",
-          "widget": {
-            "name": "resolution"
-          },
-          "link": null
-        },
-        {
-          "name": "resize_method",
-          "type": "COMBO",
-          "widget": {
-            "name": "resize_method"
-          },
-          "link": null
-        },
-        {
-          "label": "output_type",
-          "name": "output",
-          "type": "COMFY_DYNAMICCOMBO_V3",
-          "widget": {
-            "name": "output"
-          },
-          "link": null
-        },
-        {
-          "label": "output_normalization",
-          "name": "output.normalization",
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blueprints/Text to Image (Anima).json

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-            "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

@@ -92,7 +92,6 @@ 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"
@@ -146,7 +145,6 @@ 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.")
@@ -226,7 +224,6 @@ 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.")
 
@@ -242,7 +239,6 @@ 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.")
 

comfy/comfy_api_env.py

@@ -1,46 +0,0 @@
-"""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

@@ -8,8 +8,6 @@ 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
@@ -53,18 +51,12 @@ class ContextHandlerABC(ABC):
 
 
 class IndexListContextWindow(ContextWindowABC):
-    def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0, modality_windows: dict=None, context_overlap: int=0):
+    def __init__(self, index_list: list[int], dim: int=0, total_frames: 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:
@@ -93,11 +85,6 @@ 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"
@@ -161,172 +148,6 @@ 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
@@ -341,7 +162,7 @@ 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,
-                 latent_retain_index_list: list[int]=[], causal_window_fix: bool=True):
+                 causal_window_fix: bool=True):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -353,118 +174,17 @@ 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:
-        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.")
+        # 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.")
             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:
@@ -555,9 +275,7 @@ class IndexListContextHandler(ContextHandlerABC):
         return resized_cond
 
     def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
-        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)
+        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], 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
@@ -566,98 +284,54 @@ 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, context_overlap=self.context_overlap) for window in context_windows]
+        context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) 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)
-
-        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)
+        context_windows = self.get_context_windows(model, x_in, 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]
+        conds_final = [torch.zeros_like(x_in) for _ in conds]
         if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-            counts = [[torch.ones(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents]
+            counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
         else:
-            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]
+            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]
 
         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, window_state=window_state, total_windows=total_windows)
+            results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options)
             for result in results:
-                # 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
+                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)
         try:
-            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
+            # 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
         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, 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]
-
+    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):
         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()
 
-            # 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.
+            # causal_window_fix: prepend a pre-window frame that will be stripped post-forward
             anchor_applied = False
             if self.causal_window_fix:
                 anchor_idx = window.index_list[0] - 1
@@ -665,46 +339,27 @@ 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)
 
-            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
+            # update exposed params
             model_options["transformer_options"]["context_window"] = window
-            sub_timestep = window.get_tensor(timestep, dim=0)
-            sub_conds = [self.get_resized_cond(cond, x, window) for cond in conds]
+            # 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]
 
-            # 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)
 
-            # 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
+            # strip causal_window_fix anchor if applied
             if anchor_applied:
-                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)
+                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)
 
-            # 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))
+            results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
         return results
 
 
@@ -728,7 +383,7 @@ 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, context_overlap=window.context_overlap)
+            weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep)
             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)
@@ -738,22 +393,16 @@ 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, conds, *args, **kwargs):
-    # Scale noise_shape to a single context window so VRAM estimation budgets per-window.
+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
     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)
-        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)
+        noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)
+    return executor(model, noise_shape, *args, **kwargs)
 
 
 def create_prepare_sampling_wrapper(model: ModelPatcher):
@@ -773,12 +422,11 @@ 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)
-
-    conds = [guider.conds.get('positive', guider.conds.get('negative', []))]
-    noise = handler._apply_freenoise(noise, conds, extra_args["seed"])
+    noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, 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,
@@ -786,6 +434,7 @@ 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)
@@ -931,9 +580,8 @@ 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, 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 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 create_weights_flat(length: int, **kwargs) -> list[float]:
@@ -951,18 +599,18 @@ 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], context_overlap: int, **kwargs):
+def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **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, context_overlap)
-        weights_torch[:context_overlap] = ramp_up
+        ramp_up = torch.linspace(1e-37, 1, handler.context_overlap)
+        weights_torch[:handler.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, context_overlap)
-        weights_torch[-context_overlap:] = ramp_down
+        ramp_down = torch.linspace(1, 1e-37, handler.context_overlap)
+        weights_torch[-handler.context_overlap:] = ramp_down
     return weights_torch
 
 class ContextFuseMethods:

comfy/k_diffusion/sampling.py

@@ -1955,3 +1955,120 @@ def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=No
         transformer_options.pop("ar_state", None)
 
     return output
+
+
+def _cube_process_logits(logits, top_p, generator):
+    """Token selection. top_p>=1 or <=0 -> greedy argmax (upstream default, deterministic)."""
+    if top_p is None or top_p >= 1.0 or top_p <= 0.0:
+        return torch.argmax(logits, dim=-1, keepdim=True)
+    sorted_logits, sorted_idx = logits.sort(dim=-1, descending=True)
+    remove = sorted_logits.softmax(dim=-1).cumsum(dim=-1) > top_p
+    remove[..., 0] = False
+    idx_remove = remove.scatter(-1, sorted_idx, remove)
+    logits = logits.masked_fill(idx_remove, float("-inf"))
+    probs = torch.softmax(logits, dim=-1)
+    return torch.multinomial(probs, num_samples=1, generator=generator)
+
+
+@torch.no_grad()
+def sample_cube(model, x, sigmas, extra_args=None, callback=None, disable=None, top_p=1.0):
+    """
+    Autoregressive sampler for Roblox Cube3D shape GPT (DualStreamRoformer).
+
+    Not a diffusion sampler: the noised input `x` and `sigmas` values are ignored;
+    only x's shape (batch, 1, num_tokens) is used. Generates a 1024-long sequence of VQ
+    token IDs from CLIP text conditioning, with upstream's linearly-decaying CFG and
+    optional top-p. Plugs into SamplerCustomAdvanced via the SamplerCube node.
+
+    Faithful to cube3d.inference.engine.Engine.run_gpt:
+      gamma_i = cfg * (T - i) / T ;  logits = (1+gamma)*cond - gamma*uncond
+      fp32 weights + bf16 autocast on cuda.
+    """
+    import comfy.model_management
+    extra_args = {} if extra_args is None else extra_args
+
+    guider = model.inner_model        # CFGGuider
+    base_model = guider.inner_model   # BaseModel (Cube3D)
+    cube = base_model.diffusion_model
+    cfg = getattr(guider, "cfg", 3.0)
+
+    def get_cond(name):
+        conds = guider.conds.get(name, None)
+        if not conds:
+            return None
+        return conds[0]["model_conds"]["c_crossattn"].cond
+
+    pos = get_cond("positive")
+    neg = get_cond("negative")
+    if pos is None:
+        raise ValueError("sample_cube requires positive conditioning (CLIP-L text embeds).")
+
+    device = x.device
+    weight_dtype = base_model.get_dtype()
+    T = x.shape[-1]  # sequence length; latent is (batch, 1, num_tokens)
+    batch = x.shape[0]
+    import comfy.utils
+    pos = comfy.utils.repeat_to_batch_size(pos, batch)
+    if neg is not None:
+        neg = comfy.utils.repeat_to_batch_size(neg, batch)
+    use_cfg = (cfg is not None) and (cfg > 0.0) and (neg is not None)
+    autocast_enabled = (device.type == "cuda")
+    cache_dtype = torch.bfloat16 if autocast_enabled else weight_dtype
+
+    def add_bbox(c):
+        if not getattr(cube, "use_bbox", False):
+            return c
+        bbox = torch.zeros((c.shape[0], 3), device=device, dtype=c.dtype)
+        return torch.cat([c, cube.bbox_proj(bbox).unsqueeze(1)], dim=1)
+
+    # Conditioning (text_proj + bbox_proj) is computed in the model's weight dtype
+    # OUTSIDE the bf16 autocast block, matching upstream cube's Engine.prepare_inputs
+    # (run_clip/encode_text run in full precision). The autocast only covers the
+    # autoregressive transformer forward, exactly like Engine.run_gpt.
+    cond = add_bbox(cube.encode_text(pos.to(device=device, dtype=weight_dtype)))
+    if use_cfg:
+        ucond = add_bbox(cube.encode_text(neg.to(device=device, dtype=weight_dtype)))
+        cond = torch.cat([cond, ucond], dim=0)
+
+    with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=autocast_enabled):
+        bos = torch.full((cond.shape[0], 1), cube.shape_bos_id, dtype=torch.long, device=device)
+        embed = cube.encode_token(bos)
+        Bp, input_seq_len, dim = embed.shape
+        embed_buffer = torch.zeros((Bp, input_seq_len + T, dim), dtype=embed.dtype, device=device)
+        embed_buffer[:, :input_seq_len, :].copy_(embed)
+
+        kv_cache = cube.init_kv_cache(Bp, cond.shape[1], T + 1, cache_dtype, device)
+
+        num_codes = cube.vocab_size - 3
+        seed = extra_args.get("seed", 0)
+        generator = None
+        if device.type != "mps":
+            generator = torch.Generator(device=device).manual_seed(int(seed))
+
+        output_ids = []
+        for i in trange(T, disable=disable):
+            comfy.model_management.throw_exception_if_processing_interrupted()
+            curr_pos_id = torch.tensor([i], dtype=torch.long, device=device)
+            logits = cube(embed_buffer, cond, kv_cache=kv_cache, curr_pos_id=curr_pos_id, decode=(i > 0))
+            logits = logits[:, 0, :num_codes]
+
+            if use_cfg:
+                cond_logits, uncond_logits = logits.float().chunk(2, dim=0)
+                gamma = cfg * (T - i) / T
+                logits = (1.0 + gamma) * cond_logits - gamma * uncond_logits
+            else:
+                logits = logits.float()
+
+            next_id = _cube_process_logits(logits, top_p, generator)
+            output_ids.append(next_id)
+
+            next_embed = cube.encode_token(next_id)
+            if use_cfg:
+                next_embed = torch.cat([next_embed, next_embed], dim=0)
+            embed_buffer[:, i + input_seq_len, :].copy_(next_embed.squeeze(1))
+
+            if callback is not None:
+                callback({"x": x, "i": i, "sigma": sigmas[0], "sigma_hat": sigmas[0], "denoised": x})
+
+    # (B, T) token IDs -> (B, 1, T) to keep the channels-first 1D latent layout.
+    return torch.cat(output_ids, dim=1).to(torch.float32).unsqueeze(1)

comfy/latent_formats.py

@@ -775,14 +775,20 @@ class Hunyuan3Dv2mini(LatentFormat):
     latent_dimensions = 1
     scale_factor = 1.0188137142395404
 
+class Cube3D(LatentFormat):
+    # Roblox Cube3D shape "latent" is a flat sequence of VQ token IDs (one scalar per
+    # position), so it maps to a channels-first 1D latent (B, 1, num_tokens), mirroring
+    # Hunyuan3Dv2's (B, C, L) convention. latent_channels=1 keeps fix_empty_latent_channels
+    # from truncating the token sequence. scale_factor=1.0 since IDs must pass through
+    # process_latent_in/out unchanged.
+    latent_channels = 1
+    latent_dimensions = 1
+    scale_factor = 1.0
+
 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

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

comfy/ldm/anima/lllite.py

@@ -1,278 +0,0 @@
-import re
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-import comfy.ops
-import comfy.utils
-
-
-MODULE_PATTERN = re.compile(r"lllite_dit_blocks_(\d+)_(self_attn_[qkv]_proj|cross_attn_q_proj|mlp_layer1)$")
-
-
-def _group_norm(channels, device=None, dtype=None, operations=None):
-    groups = 8
-    while groups > 1 and channels % groups != 0:
-        groups //= 2
-    return operations.GroupNorm(groups, channels, device=device, dtype=dtype)
-
-
-class AnimaLLLiteResBlock(nn.Module):
-    def __init__(self, channels, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.norm1 = _group_norm(channels, device=device, dtype=dtype, operations=operations)
-        self.conv1 = operations.Conv2d(channels, channels, kernel_size=3, padding=1, device=device, dtype=dtype)
-        self.norm2 = _group_norm(channels, device=device, dtype=dtype, operations=operations)
-        self.conv2 = operations.Conv2d(channels, channels, kernel_size=3, padding=1, device=device, dtype=dtype)
-
-    def forward(self, x):
-        h = self.conv1(F.silu(self.norm1(x)))
-        h = self.conv2(F.silu(self.norm2(h)))
-        return x + h
-
-
-class AnimaLLLiteASPP(nn.Module):
-    def __init__(self, channels, dilations, device=None, dtype=None, operations=None):
-        super().__init__()
-        branches = []
-        for dilation in dilations:
-            if dilation == 1:
-                conv = operations.Conv2d(channels, channels, kernel_size=1, device=device, dtype=dtype)
-            else:
-                conv = operations.Conv2d(channels, channels, kernel_size=3, padding=dilation, dilation=dilation, device=device, dtype=dtype)
-            branches.append(nn.Sequential(conv, _group_norm(channels, device=device, dtype=dtype, operations=operations), nn.SiLU()))
-        self.branches = nn.ModuleList(branches)
-        self.global_pool = nn.AdaptiveAvgPool2d(1)
-        self.global_conv = nn.Sequential(
-            operations.Conv2d(channels, channels, kernel_size=1, device=device, dtype=dtype),
-            _group_norm(channels, device=device, dtype=dtype, operations=operations),
-            nn.SiLU(),
-        )
-        self.proj = nn.Sequential(
-            operations.Conv2d(channels * (len(dilations) + 1), channels, kernel_size=1, device=device, dtype=dtype),
-            _group_norm(channels, device=device, dtype=dtype, operations=operations),
-            nn.SiLU(),
-        )
-
-    def forward(self, x):
-        height, width = x.shape[-2:]
-        outputs = [branch(x) for branch in self.branches]
-        pooled = self.global_conv(self.global_pool(x))
-        outputs.append(F.interpolate(pooled, size=(height, width), mode="bilinear", align_corners=False))
-        return self.proj(torch.cat(outputs, dim=1))
-
-
-class AnimaLLLiteConditioning(nn.Module):
-    def __init__(self, cond_in_channels, cond_dim, cond_emb_dim, cond_resblocks, aspp_dilations, device=None, dtype=None, operations=None):
-        super().__init__()
-        half_dim = cond_dim // 2
-        self.conv1 = operations.Conv2d(cond_in_channels, half_dim, kernel_size=4, stride=4, device=device, dtype=dtype)
-        self.norm1 = _group_norm(half_dim, device=device, dtype=dtype, operations=operations)
-        self.conv2 = operations.Conv2d(half_dim, half_dim, kernel_size=3, padding=1, device=device, dtype=dtype)
-        self.norm2 = _group_norm(half_dim, device=device, dtype=dtype, operations=operations)
-        self.conv3 = operations.Conv2d(half_dim, cond_dim, kernel_size=4, stride=4, device=device, dtype=dtype)
-        self.norm3 = _group_norm(cond_dim, device=device, dtype=dtype, operations=operations)
-        self.resblocks = nn.ModuleList([
-            AnimaLLLiteResBlock(cond_dim, device=device, dtype=dtype, operations=operations)
-            for _ in range(cond_resblocks)
-        ])
-        self.aspp = AnimaLLLiteASPP(cond_dim, aspp_dilations, device=device, dtype=dtype, operations=operations) if aspp_dilations else None
-        self.proj = operations.Conv2d(cond_dim, cond_emb_dim, kernel_size=1, device=device, dtype=dtype)
-        self.out_norm = operations.LayerNorm(cond_emb_dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = F.silu(self.norm1(self.conv1(x)))
-        x = F.silu(self.norm2(self.conv2(x)))
-        x = F.silu(self.norm3(self.conv3(x)))
-        for block in self.resblocks:
-            x = block(x)
-        if self.aspp is not None:
-            x = self.aspp(x)
-        x = self.proj(x).flatten(2).transpose(1, 2).contiguous()
-        return self.out_norm(x)
-
-
-class AnimaLLLiteModule(nn.Module):
-    def __init__(self, in_dim, cond_emb_dim, mlp_dim, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.down = operations.Linear(in_dim, mlp_dim, device=device, dtype=dtype)
-        self.mid = operations.Linear(mlp_dim + cond_emb_dim, mlp_dim, device=device, dtype=dtype)
-        self.cond_to_film = operations.Linear(cond_emb_dim, 2 * mlp_dim, device=device, dtype=dtype)
-        self.up = operations.Linear(mlp_dim, in_dim, device=device, dtype=dtype)
-        self.depth_embed = nn.Parameter(torch.empty(cond_emb_dim, device=device, dtype=dtype), requires_grad=False)
-
-    def forward(self, x, cond_emb, strength):
-        original_shape = x.shape
-        if x.ndim == 5:
-            x = x.flatten(1, 3)
-
-        if x.shape[0] != cond_emb.shape[0]:
-            if x.shape[0] % cond_emb.shape[0] != 0:
-                raise ValueError(f"Anima LLLite batch mismatch: model input batch {x.shape[0]}, control batch {cond_emb.shape[0]}")
-            cond_emb = cond_emb.repeat(x.shape[0] // cond_emb.shape[0], 1, 1)
-        if x.shape[1] != cond_emb.shape[1]:
-            raise ValueError(f"Anima LLLite sequence mismatch: model input has {x.shape[1]} tokens, control has {cond_emb.shape[1]}")
-
-        cond_local = cond_emb + comfy.ops.cast_to_input(self.depth_embed, cond_emb)
-        hidden = F.silu(self.down(x))
-        gamma, beta = self.cond_to_film(cond_local).chunk(2, dim=-1)
-        hidden = self.mid(torch.cat((cond_local, hidden), dim=-1))
-        hidden = F.silu(hidden * (1 + gamma) + beta)
-        x = x + self.up(hidden) * strength
-
-        if len(original_shape) == 5:
-            x = x.reshape(original_shape)
-        return x
-
-
-class AnimaLLLite(nn.Module):
-    def __init__(self, state_dict, metadata, device=None, dtype=None, operations=None):
-        super().__init__()
-        metadata = metadata or {}
-        version = metadata.get("lllite.version", "2")
-        if version != "2":
-            raise ValueError(f"Unsupported Anima LLLite version {version!r}; only named-key v2 checkpoints are supported")
-
-        module_names = sorted({key.split(".", 1)[0] for key in state_dict if key.startswith("lllite_dit_blocks_")})
-        if not module_names:
-            raise ValueError("Anima LLLite checkpoint has no lllite_dit_blocks_* modules")
-
-        cond_in_channels = state_dict["lllite_conditioning1.conv1.weight"].shape[1]
-        cond_dim = state_dict["lllite_conditioning1.conv3.weight"].shape[0]
-        cond_emb_dim = state_dict["lllite_conditioning1.proj.weight"].shape[0]
-        resblock_ids = {int(key.split(".")[2]) for key in state_dict if key.startswith("lllite_conditioning1.resblocks.")}
-        cond_resblocks = max(resblock_ids) + 1 if resblock_ids else 0
-        use_aspp = any(key.startswith("lllite_conditioning1.aspp.") for key in state_dict)
-        dilation_string = metadata.get("lllite.aspp_dilations", "1,2,4,8")
-        aspp_dilations = tuple(int(value) for value in dilation_string.split(",") if value.strip()) if use_aspp else ()
-
-        self.cond_in_channels = cond_in_channels
-        self.inpaint_masked_input = metadata.get("lllite.inpaint_masked_input", "false").lower() == "true"
-        self.lllite_conditioning1 = AnimaLLLiteConditioning(
-            cond_in_channels, cond_dim, cond_emb_dim, cond_resblocks, aspp_dilations,
-            device=device, dtype=dtype, operations=operations,
-        )
-
-        self.module_names = set()
-        self.block_count = 0
-        self.model_dim = None
-        for name in module_names:
-            match = MODULE_PATTERN.fullmatch(name)
-            if match is None:
-                raise ValueError(f"Unsupported Anima LLLite module name: {name}")
-            down_shape = state_dict[f"{name}.down.weight"].shape
-            mlp_dim, in_dim = down_shape
-            module_cond_dim = state_dict[f"{name}.cond_to_film.weight"].shape[1]
-            if module_cond_dim != cond_emb_dim:
-                raise ValueError(f"Anima LLLite conditioning dimension mismatch in {name}: {module_cond_dim} != {cond_emb_dim}")
-            if self.model_dim is None:
-                self.model_dim = in_dim
-            elif self.model_dim != in_dim:
-                raise ValueError(f"Anima LLLite model dimension mismatch in {name}: {in_dim} != {self.model_dim}")
-            self.add_module(name, AnimaLLLiteModule(in_dim, cond_emb_dim, mlp_dim, device=device, dtype=dtype, operations=operations))
-            self.module_names.add(name)
-            self.block_count = max(self.block_count, int(match.group(1)) + 1)
-
-    def encode_conditioning(self, image):
-        return self.lllite_conditioning1(image)
-
-    def apply(self, x, cond_emb, block_index, target, strength):
-        name = f"lllite_dit_blocks_{block_index}_{target}"
-        if name not in self.module_names:
-            return x
-        return self.get_submodule(name)(x, cond_emb, strength)
-
-
-class AnimaLLLitePatch:
-    def __init__(self, model_patch, image, mask, strength, sigma_start, sigma_end):
-        self.model_patch = model_patch
-        self.image = image
-        self.mask = mask
-        self.strength = strength
-        self.sigma_start = sigma_start
-        self.sigma_end = sigma_end
-
-    def __call__(self, args):
-        x = args["x"]
-        transformer_options = args["transformer_options"]
-        if self.strength == 0.0:
-            return args
-        sigmas = transformer_options.get("sigmas")
-        if sigmas is not None:
-            sigma = float(sigmas.max().item())
-            if not self.sigma_end <= sigma <= self.sigma_start:
-                return args
-        if x.shape[2] != 1:
-            raise ValueError(f"Anima LLLite only supports T=1, got T={x.shape[2]}")
-
-        target_height = x.shape[-2] * 8
-        target_width = x.shape[-1] * 8
-        image = comfy.utils.common_upscale(
-            self.image.movedim(-1, 1), target_width, target_height, "bicubic", crop="center"
-        ).clamp(0.0, 1.0)
-        image = image.to(device=x.device, dtype=x.dtype) * 2.0 - 1.0
-
-        if self.model_patch.model.cond_in_channels == 4:
-            mask = self.mask
-            if mask.ndim == 3:
-                mask = mask.unsqueeze(1)
-            if mask.ndim != 4 or mask.shape[1] != 1:
-                raise ValueError(f"Anima LLLite mask must have one channel, got shape {tuple(mask.shape)}")
-            mask = comfy.utils.common_upscale(
-                mask.float(), target_width, target_height, "nearest-exact", crop="center"
-            )
-            if mask.shape[0] != image.shape[0]:
-                if image.shape[0] % mask.shape[0] != 0:
-                    raise ValueError(
-                        f"Anima LLLite mask batch {mask.shape[0]} cannot be broadcast to image batch {image.shape[0]}"
-                    )
-                mask = mask.repeat(image.shape[0] // mask.shape[0], 1, 1, 1)
-            mask = (mask >= 0.5).to(device=x.device, dtype=x.dtype)
-            if self.model_patch.model.inpaint_masked_input:
-                image = image * (mask < 0.5).to(image.dtype)
-            image = torch.cat((image, mask * 2.0 - 1.0), dim=1)
-
-        cond_emb = self.model_patch.model.encode_conditioning(image)
-        transformer_options["model_patch_data"][self] = cond_emb
-        return args
-
-    def to(self, device_or_dtype):
-        return self
-
-    def models(self):
-        return [self.model_patch]
-
-
-class AnimaLLLiteAttentionPatch:
-    def __init__(self, patch, targets):
-        self.patch = patch
-        self.targets = targets
-
-    def __call__(self, q, k, v, pe=None, attn_mask=None, extra_options=None):
-        cond_emb = extra_options["model_patch_data"].get(self.patch)
-        if cond_emb is None:
-            return {"q": q, "k": k, "v": v, "pe": pe, "attn_mask": attn_mask}
-
-        block_index = extra_options["block_index"]
-        values = {"q": q, "k": k, "v": v}
-        for value_name, target in self.targets.items():
-            values[value_name] = self.patch.model_patch.model.apply(
-                values[value_name], cond_emb, block_index, target, self.patch.strength
-            )
-
-        return {"q": values["q"], "k": values["k"], "v": values["v"], "pe": pe, "attn_mask": attn_mask}
-
-
-class AnimaLLLiteMLPPatch:
-    def __init__(self, patch):
-        self.patch = patch
-
-    def __call__(self, args):
-        cond_emb = args["transformer_options"]["model_patch_data"].get(self.patch)
-        if cond_emb is None:
-            return args
-        args["x"] = self.patch.model_patch.model.apply(
-            args["x"], cond_emb, args["transformer_options"]["block_index"], "mlp_layer1", self.patch.strength
-        )
-        return args

comfy/ldm/audio/dit.py

@@ -425,16 +425,19 @@ class Attention(nn.Module):
         if n == 1 and causal:
             causal = False
 
-        gqa_kwargs = {"enable_gqa": True} if h != kv_h else {}
+        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))
 
         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, **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      = 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 = out - out_diff
         else:
-            out = optimized_attention(q, k, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
+            out = optimized_attention(q, k, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
 
         out = self.to_out(out)
 

comfy/ldm/boogu/model.py

@@ -1,318 +0,0 @@
-# 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

@@ -14,7 +14,6 @@ from torchvision import transforms
 import comfy.patcher_extension
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.ldm.common_dit
-import comfy.ops
 import comfy.quant_ops
 
 
@@ -149,29 +148,11 @@ class Attention(nn.Module):
         x: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         rope_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        q = self.q_proj(x)
         context = x if context is None else context
-        q_input = x
-        k_input = context
-        v_input = context
-
-        transformer_patches = transformer_options.get("patches", {})
-        patch_name = "attn1_patch" if self.is_selfattn else "attn2_patch"
-        if patch_name in transformer_patches:
-            extra_options = transformer_options.copy()
-            extra_options["n_heads"] = self.n_heads
-            extra_options["dim_head"] = self.head_dim
-            for patch in transformer_patches[patch_name]:
-                out = patch(q_input, k_input, v_input, pe=rope_emb, attn_mask=None, extra_options=extra_options)
-                q_input = out.get("q", q_input)
-                k_input = out.get("k", k_input)
-                v_input = out.get("v", v_input)
-                rope_emb = out.get("pe", rope_emb)
-
-        q = self.q_proj(q_input)
-        k = self.k_proj(k_input)
-        v = self.v_proj(v_input)
+        k = self.k_proj(context)
+        v = self.v_proj(context)
         q, k, v = map(
             lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim),
             (q, k, v),
@@ -180,16 +161,11 @@ class Attention(nn.Module):
         def apply_norm_and_rotary_pos_emb(
             q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor]
         ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
             v = self.v_norm(v)
             if self.is_selfattn and rope_emb is not None:  # only apply to self-attention!
-                q_scale, _, q_offload_stream = comfy.ops.cast_bias_weight(self.q_norm, q, offloadable=True)
-                k_scale, _, k_offload_stream = comfy.ops.cast_bias_weight(self.k_norm, k, offloadable=True)
-                q, k = comfy.quant_ops.ck.rms_rope_split_half(q, k, rope_emb, q_scale, k_scale, self.q_norm.eps)
-                comfy.ops.uncast_bias_weight(self.q_norm, q_scale, None, q_offload_stream)
-                comfy.ops.uncast_bias_weight(self.k_norm, k_scale, None, k_offload_stream)
-            else:
-                q = self.q_norm(q)
-                k = self.k_norm(k)
+                q, k = comfy.quant_ops.ck.apply_rope_split_half(q, k, rope_emb)
             return q, k, v
 
         q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb)
@@ -212,7 +188,7 @@ class Attention(nn.Module):
             x (Tensor): The query tensor of shape [B, Mq, K]
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
-        q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb, transformer_options=transformer_options)
+        q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
         return self.compute_attention(q, k, v, transformer_options=transformer_options)
 
 
@@ -539,7 +515,7 @@ class Block(nn.Module):
             h=H,
             w=W,
         )
-        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))
+        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)
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
@@ -572,22 +548,16 @@ class Block(nn.Module):
             shift_cross_attn_B_T_1_1_D,
             transformer_options=transformer_options,
         )
-        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))
+        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
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
             self.layer_norm_mlp,
             scale_mlp_B_T_1_1_D,
             shift_mlp_B_T_1_1_D,
-        ).to(compute_dtype)
-        patches = transformer_options.get("patches", {})
-        if "mlp_patch" in patches:
-            args = {"x": normalized_x_B_T_H_W_D, "transformer_options": transformer_options}
-            for patch in patches["mlp_patch"]:
-                args = patch(args)
-            normalized_x_B_T_H_W_D = args["x"]
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
-        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))
+        )
+        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)
         return x_B_T_H_W_D
 
 
@@ -893,22 +863,11 @@ class MiniTrainDIT(nn.Module):
                 x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
             ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}"
 
-        transformer_options = kwargs.get("transformer_options", {})
-        patches = transformer_options.get("patches", {})
-        if "post_input" in patches:
-            transformer_options = transformer_options.copy()
-            transformer_options["model_patch_data"] = {}
-
-        if "post_input" in patches:
-            for patch in patches["post_input"]:
-                out = patch({"img": x_B_T_H_W_D, "x": x_B_C_T_H_W, "transformer_options": transformer_options})
-                x_B_T_H_W_D = out["img"]
-
         block_kwargs = {
             "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
             "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
-            "transformer_options": transformer_options,
+            "transformer_options": kwargs.get("transformer_options", {}),
         }
 
         # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream
@@ -918,8 +877,7 @@ class MiniTrainDIT(nn.Module):
         if x_B_T_H_W_D.dtype == torch.float16:
             x_B_T_H_W_D = x_B_T_H_W_D.float()
 
-        for block_index, block in enumerate(self.blocks):
-            transformer_options["block_index"] = block_index
+        for block in self.blocks:
             x_B_T_H_W_D = block(
                 x_B_T_H_W_D,
                 t_embedding_B_T_D,

comfy/ldm/cube/gpt.py

@@ -0,0 +1,436 @@
+"""
+Native port of Roblox/cube's shape GPT (DualStreamRoformer).
+
+Reference: https://github.com/Roblox/cube  (cube3d/model/gpt/dual_stream_roformer.py
+and cube3d/model/transformers/*).
+
+This is an autoregressive transformer over discrete VQ shape tokens, conditioned on
+CLIP text embeddings. It is NOT a diffusion model; it is driven by the dedicated
+`sample_cube` sampler (see comfy/k_diffusion/sampling.py), not KSampler.
+
+The forward pass is kept faithful to upstream so token IDs match:
+  * rope_theta = 10000
+  * per-head RMSNorm on Q and K
+  * dual-stream (MM-DiT style) joint attention; last dual block is cond_pre_only
+  * two separate RoPE frequency tensors (dual blocks offset cond tokens by S)
+  * SwiGLU MLP, non-affine LayerNorm upcast to fp32
+
+RoPE reuses ComfyUI's shared Flux rotary embedding (comfy.ldm.flux.math) so it
+benefits from comfy-kitchen's optimized apply_rope kernel; see the RoPE section below.
+"""
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ldm.flux.math
+
+
+# ---------------------------------------------------------------------------
+# Norms (faithful to cube3d/model/transformers/norm.py)
+# ---------------------------------------------------------------------------
+
+class CubeLayerNorm(nn.Module):
+    """Non-affine LayerNorm that upcasts to fp32 then back (matches cube)."""
+
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.dim = (dim,)
+        self.eps = eps
+
+    def forward(self, x):
+        y = F.layer_norm(x.float(), self.dim, None, None, self.eps)
+        return y.type_as(x)
+
+
+class CubeRMSNorm(nn.Module):
+    """Per-head RMSNorm with learnable weight, computed in fp32 (matches cube)."""
+
+    def __init__(self, dim, eps=1e-5, dtype=None, device=None):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
+
+    def forward(self, x):
+        xf = x.float()
+        out = xf * torch.rsqrt(xf.pow(2).mean(-1, keepdim=True) + self.eps)
+        return (out * self.weight).type_as(x)
+
+
+# ---------------------------------------------------------------------------
+# RoPE
+#
+# Reuses ComfyUI's shared Flux rotary embedding (comfy.ldm.flux.math):
+#   * rope() builds the real-valued rotation freqs (cos/-sin/sin/cos), shaped
+#     (B, L, head_dim/2, 2, 2);
+#   * apply_rope1() applies them and, at inference, dispatches to comfy-kitchen's
+#     optimized apply_rope kernel (comfy.quant_ops.ck).
+# This replaces cube's original complex-number RoPE (torch.polar / view_as_complex),
+# which was numerically equivalent but bypassed the kernel. The pairing convention is
+# identical (adjacent dims), so the rotation math is the same; the only difference is
+# rope() computes the angles in fp64 before casting to fp32, so outputs match upstream
+# to fp32 rounding rather than bit-for-bit.
+# ---------------------------------------------------------------------------
+
+def precompute_freqs_cis(dim, t, theta=10000.0):
+    # t: (B, L) integer position ids. Returns Flux-style real rotation freqs shaped
+    # (B, L, dim/2, 2, 2) for comfy.ldm.flux.math.apply_rope1.
+    return comfy.ldm.flux.math.rope(t, dim, theta)
+
+
+def apply_rotary_emb(x, freqs_cis, curr_pos_id=None):
+    # x: (B, num_heads, L, head_dim). Select the rotation freqs for x's positions, add
+    # the head-broadcast axis, then apply via the shared Flux/comfy-kitchen op. q (the
+    # new token[s]) and k (the full sequence) are rotated separately because their
+    # lengths/positions differ during decode.
+    if curr_pos_id is None:
+        freqs_cis = freqs_cis[:, -x.shape[2]:]
+    else:
+        freqs_cis = freqs_cis[:, curr_pos_id]
+    freqs_cis = freqs_cis.unsqueeze(1)
+    return comfy.ldm.flux.math.apply_rope1(x, freqs_cis)
+
+
+def sdpa_with_rope(q, k, v, freqs_cis, attn_mask=None, curr_pos_id=None, is_causal=False):
+    q = apply_rotary_emb(q, freqs_cis, curr_pos_id=curr_pos_id)
+    k = apply_rotary_emb(k, freqs_cis, curr_pos_id=None)
+    return F.scaled_dot_product_attention(
+        q, k, v, attn_mask=attn_mask, dropout_p=0.0,
+        is_causal=is_causal and attn_mask is None,
+    )
+
+
+# ---------------------------------------------------------------------------
+# KV cache
+# ---------------------------------------------------------------------------
+
+class Cache:
+    def __init__(self, key_states, value_states):
+        self.key_states = key_states
+        self.value_states = value_states
+
+    def update(self, curr_pos_id, k, v):
+        self.key_states.index_copy_(2, curr_pos_id, k)
+        self.value_states.index_copy_(2, curr_pos_id, v)
+
+
+# ---------------------------------------------------------------------------
+# Shared building blocks
+# ---------------------------------------------------------------------------
+
+class SwiGLUMLP(nn.Module):
+    def __init__(self, embed_dim, hidden_dim, bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.gate_proj = operations.Linear(embed_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
+        self.up_proj = operations.Linear(embed_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
+        self.down_proj = operations.Linear(hidden_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class SelfAttentionWithRotaryEmbedding(nn.Module):
+    def __init__(self, embed_dim, num_heads, bias=True, eps=1e-6, dtype=None, device=None, operations=None):
+        super().__init__()
+        assert embed_dim % num_heads == 0
+        self.num_heads = num_heads
+        head_dim = embed_dim // num_heads
+        self.c_qk = operations.Linear(embed_dim, 2 * embed_dim, bias=False, dtype=dtype, device=device)
+        self.c_v = operations.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.q_norm = CubeRMSNorm(head_dim, dtype=dtype, device=device)
+        self.k_norm = CubeRMSNorm(head_dim, dtype=dtype, device=device)
+
+    def forward(self, x, freqs_cis, attn_mask=None, is_causal=False, kv_cache=None, curr_pos_id=None, decode=False):
+        b, l, d = x.shape
+        q, k = self.c_qk(x).chunk(2, dim=-1)
+        v = self.c_v(x)
+        q = q.view(b, l, self.num_heads, -1).transpose(1, 2)
+        k = k.view(b, l, self.num_heads, -1).transpose(1, 2)
+        v = v.view(b, l, self.num_heads, -1).transpose(1, 2)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        if kv_cache is not None:
+            if not decode:
+                kv_cache.key_states[:, :, :k.shape[2], :].copy_(k)
+                kv_cache.value_states[:, :, :k.shape[2], :].copy_(v)
+            else:
+                kv_cache.update(curr_pos_id, k, v)
+            k = kv_cache.key_states
+            v = kv_cache.value_states
+        y = sdpa_with_rope(q, k, v, freqs_cis=freqs_cis, attn_mask=attn_mask,
+                           curr_pos_id=curr_pos_id if decode else None, is_causal=is_causal)
+        y = y.transpose(1, 2).contiguous().view(b, l, d)
+        return self.c_proj(y)
+
+
+class DecoderLayerWithRotaryEmbedding(nn.Module):
+    """Single-stream decoder layer (shape tokens only)."""
+
+    def __init__(self, embed_dim, num_heads, bias=True, eps=1e-6, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.ln_1 = CubeLayerNorm(embed_dim, eps=eps)
+        self.attn = SelfAttentionWithRotaryEmbedding(embed_dim, num_heads, bias=bias, eps=eps,
+                                                     dtype=dtype, device=device, operations=operations)
+        self.ln_2 = CubeLayerNorm(embed_dim, eps=eps)
+        self.mlp = SwiGLUMLP(embed_dim, embed_dim * 4, bias=bias, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x, freqs_cis, attn_mask=None, is_causal=True, kv_cache=None, curr_pos_id=None, decode=False):
+        x = x + self.attn(self.ln_1(x), freqs_cis=freqs_cis, attn_mask=attn_mask, is_causal=is_causal,
+                          kv_cache=kv_cache, curr_pos_id=curr_pos_id, decode=decode)
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+# ---------------------------------------------------------------------------
+# Dual-stream blocks (faithful to dual_stream_attention.py)
+# ---------------------------------------------------------------------------
+
+class DismantledPreAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, query=True, bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        assert embed_dim % num_heads == 0
+        self.query = query
+        head_dim = embed_dim // num_heads
+        if query:
+            self.c_qk = operations.Linear(embed_dim, 2 * embed_dim, bias=False, dtype=dtype, device=device)
+            self.q_norm = CubeRMSNorm(head_dim, dtype=dtype, device=device)
+        else:
+            self.c_k = operations.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.k_norm = CubeRMSNorm(head_dim, dtype=dtype, device=device)
+        self.c_v = operations.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.num_heads = num_heads
+
+    def _to_mha(self, x):
+        return x.view(*x.shape[:2], self.num_heads, -1).transpose(1, 2)
+
+    def forward(self, x):
+        if self.query:
+            q, k = self.c_qk(x).chunk(2, dim=-1)
+            q = self.q_norm(self._to_mha(q))
+        else:
+            q = None
+            k = self.c_k(x)
+        k = self.k_norm(self._to_mha(k))
+        v = self._to_mha(self.c_v(x))
+        return (q, k, v)
+
+
+class DismantledPostAttention(nn.Module):
+    def __init__(self, embed_dim, bias=True, eps=1e-6, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.c_proj = operations.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.ln_3 = CubeLayerNorm(embed_dim, eps=eps)
+        self.mlp = SwiGLUMLP(embed_dim, embed_dim * 4, bias=bias, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x, a):
+        x = x + self.c_proj(a)
+        x = x + self.mlp(self.ln_3(x))
+        return x
+
+
+class DualStreamAttentionWithRotaryEmbedding(nn.Module):
+    def __init__(self, embed_dim, num_heads, cond_pre_only=False, bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.cond_pre_only = cond_pre_only
+        self.pre_x = DismantledPreAttention(embed_dim, num_heads, query=True, bias=bias,
+                                            dtype=dtype, device=device, operations=operations)
+        self.pre_c = DismantledPreAttention(embed_dim, num_heads, query=not cond_pre_only, bias=bias,
+                                            dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x, c, freqs_cis, attn_mask=None, is_causal=False, kv_cache=None, curr_pos_id=None, decode=False):
+        if kv_cache is None or not decode:
+            qkv_c = self.pre_c(c)
+            qkv_x = self.pre_x(x)
+            if self.cond_pre_only:
+                q = qkv_x[0]
+            else:
+                q = torch.cat([qkv_c[0], qkv_x[0]], dim=2)
+            k = torch.cat([qkv_c[1], qkv_x[1]], dim=2)
+            v = torch.cat([qkv_c[2], qkv_x[2]], dim=2)
+        else:
+            is_causal = False
+            q, k, v = self.pre_x(x)
+
+        if kv_cache is not None:
+            if not decode:
+                kv_cache.key_states[:, :, :k.shape[2], :].copy_(k)
+                kv_cache.value_states[:, :, :k.shape[2], :].copy_(v)
+            else:
+                kv_cache.update(curr_pos_id, k, v)
+            k = kv_cache.key_states
+            v = kv_cache.value_states
+
+        if attn_mask is not None:
+            if decode:
+                attn_mask = attn_mask[..., curr_pos_id, :]
+            else:
+                attn_mask = attn_mask[..., -q.shape[2]:, :]
+
+        y = sdpa_with_rope(q, k, v, freqs_cis=freqs_cis, attn_mask=attn_mask,
+                           curr_pos_id=curr_pos_id if decode else None, is_causal=is_causal)
+        y = y.transpose(1, 2).contiguous().view(x.shape[0], -1, x.shape[2])
+
+        if y.shape[1] == x.shape[1]:
+            return y, None
+        y_c, y_x = torch.split(y, [c.shape[1], x.shape[1]], dim=1)
+        return y_x, y_c
+
+
+class DualStreamDecoderLayerWithRotaryEmbedding(nn.Module):
+    def __init__(self, embed_dim, num_heads, cond_pre_only=False, bias=True, eps=1e-6,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.ln_1 = CubeLayerNorm(embed_dim, eps=eps)
+        self.ln_2 = CubeLayerNorm(embed_dim, eps=eps)
+        self.attn = DualStreamAttentionWithRotaryEmbedding(embed_dim, num_heads, cond_pre_only=cond_pre_only,
+                                                           bias=bias, dtype=dtype, device=device, operations=operations)
+        self.post_1 = DismantledPostAttention(embed_dim, bias=bias, eps=eps, dtype=dtype, device=device, operations=operations)
+        if not cond_pre_only:
+            self.post_2 = DismantledPostAttention(embed_dim, bias=bias, eps=eps, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x, c, freqs_cis, attn_mask=None, is_causal=True, kv_cache=None, curr_pos_id=None, decode=False):
+        a_x, a_c = self.attn(
+            self.ln_1(x),
+            self.ln_2(c) if c is not None else None,
+            freqs_cis=freqs_cis, attn_mask=attn_mask, is_causal=is_causal,
+            kv_cache=kv_cache, curr_pos_id=curr_pos_id, decode=decode,
+        )
+        x = self.post_1(x, a_x)
+        if a_c is not None:
+            c = self.post_2(c, a_c)
+        else:
+            c = None
+        return x, c
+
+
+# ---------------------------------------------------------------------------
+# DualStreamRoformer
+# ---------------------------------------------------------------------------
+
+class DualStreamRoformer(nn.Module):
+    def __init__(
+        self,
+        n_layer=23,
+        n_single_layer=1,
+        rope_theta=10000,
+        n_head=12,
+        n_embd=1536,
+        bias=True,
+        eps=1e-6,
+        shape_model_vocab_size=16384,
+        shape_model_embed_dim=32,
+        text_model_embed_dim=768,
+        use_bbox=True,
+        image_model=None,  # detection key; unused
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.n_layer = n_layer
+        self.n_single_layer = n_single_layer
+        self.n_head = n_head
+        self.n_embd = n_embd
+        self.rope_theta = rope_theta
+        self.head_dim = n_embd // n_head
+
+        self.text_proj = operations.Linear(text_model_embed_dim, n_embd, bias=bias, dtype=dtype, device=device)
+        self.shape_proj = operations.Linear(shape_model_embed_dim, n_embd, bias=True, dtype=dtype, device=device)
+
+        self.vocab_size = shape_model_vocab_size
+        self.shape_bos_id = self.vocab_size
+        self.shape_eos_id = self.vocab_size + 1
+        self.padding_id = self.vocab_size + 2
+        self.vocab_size += 3
+
+        self.transformer = nn.ModuleDict(dict(
+            wte=operations.Embedding(self.vocab_size, n_embd, padding_idx=self.padding_id, dtype=dtype, device=device),
+            dual_blocks=nn.ModuleList([
+                DualStreamDecoderLayerWithRotaryEmbedding(
+                    n_embd, n_head, cond_pre_only=(i == n_layer - 1), bias=bias, eps=eps,
+                    dtype=dtype, device=device, operations=operations,
+                )
+                for i in range(n_layer)
+            ]),
+            single_blocks=nn.ModuleList([
+                DecoderLayerWithRotaryEmbedding(n_embd, n_head, bias=bias, eps=eps,
+                                                dtype=dtype, device=device, operations=operations)
+                for _ in range(n_single_layer)
+            ]),
+            ln_f=CubeLayerNorm(n_embd, eps=eps),
+        ))
+
+        self.lm_head = operations.Linear(n_embd, self.vocab_size, bias=False, dtype=dtype, device=device)
+
+        self.use_bbox = use_bbox
+        if use_bbox:
+            self.bbox_proj = operations.Linear(3, n_embd, bias=True, dtype=dtype, device=device)
+
+    def encode_text(self, text_embed):
+        return self.text_proj(text_embed)
+
+    def encode_token(self, tokens):
+        return self.transformer.wte(tokens)
+
+    def init_kv_cache(self, batch_size, cond_len, max_shape_tokens, dtype, device):
+        max_all = cond_len + max_shape_tokens
+        kv = [
+            Cache(
+                torch.zeros((batch_size, self.n_head, max_all, self.head_dim), dtype=dtype, device=device),
+                torch.zeros((batch_size, self.n_head, max_all, self.head_dim), dtype=dtype, device=device),
+            )
+            for _ in range(len(self.transformer.dual_blocks))
+        ]
+        kv += [
+            Cache(
+                torch.zeros((batch_size, self.n_head, max_shape_tokens, self.head_dim), dtype=dtype, device=device),
+                torch.zeros((batch_size, self.n_head, max_shape_tokens, self.head_dim), dtype=dtype, device=device),
+            )
+            for _ in range(len(self.transformer.single_blocks))
+        ]
+        return kv
+
+    def forward(self, embed, cond, kv_cache=None, curr_pos_id=None, decode=False):
+        b, l = embed.shape[:2]
+        s = cond.shape[1]
+        device = embed.device
+
+        attn_mask = torch.tril(torch.ones(s + l, s + l, dtype=torch.bool, device=device))
+
+        position_ids = torch.arange(l, dtype=torch.long, device=device).unsqueeze(0).expand(b, -1)
+        s_freqs_cis = precompute_freqs_cis(self.head_dim, position_ids, theta=self.rope_theta)
+
+        position_ids = torch.cat([
+            torch.zeros([b, s], dtype=torch.long, device=device),
+            position_ids,
+        ], dim=1)
+        d_freqs_cis = precompute_freqs_cis(self.head_dim, position_ids, theta=self.rope_theta)
+
+        if kv_cache is not None and decode:
+            embed = embed[:, curr_pos_id, :]
+
+        h = embed
+        c = cond
+        layer_idx = 0
+        for block in self.transformer.dual_blocks:
+            h, c = block(
+                h, c=c, freqs_cis=d_freqs_cis, attn_mask=attn_mask, is_causal=True,
+                kv_cache=kv_cache[layer_idx] if kv_cache is not None else None,
+                curr_pos_id=curr_pos_id + s if curr_pos_id is not None else None,
+                decode=decode,
+            )
+            layer_idx += 1
+        for block in self.transformer.single_blocks:
+            h = block(
+                h, freqs_cis=s_freqs_cis, attn_mask=None, is_causal=True,
+                kv_cache=kv_cache[layer_idx] if kv_cache is not None else None,
+                curr_pos_id=curr_pos_id, decode=decode,
+            )
+            layer_idx += 1
+
+        h = self.transformer.ln_f(h)
+        return self.lm_head(h)

comfy/ldm/cube/marching_cubes.py

@@ -0,0 +1,379 @@
+"""Dependency-free marching cubes (classic Lorensen/Cline) in pure PyTorch.
+
+Vendored so Cube3D mesh extraction needs no scikit-image. This is the same
+algorithm family as upstream cube's default NVIDIA-warp backend (warp.MarchingCubes),
+so geometry is closer to the upstream default than skimage's Lewiner fallback.
+
+Output convention matches skimage.measure.marching_cubes: vertices are returned in
+array-index coordinates (axis 0, axis 1, axis 2 of the input volume), so the caller's
+`vertices / grid_size * bbox_size + bbox_min` transform applies unchanged.
+
+The standard 256-entry triangle table (Paul Bourke / Cory Bloyd) is used with the
+canonical corner and edge numbering:
+
+    corners (x, y, z):              edges (corner pairs):
+      0: (0,0,0)  1: (1,0,0)         0:0-1  1:1-2  2:2-3  3:3-0
+      2: (1,1,0)  3: (0,1,0)         4:4-5  5:5-6  6:6-7  7:7-4
+      4: (0,0,1)  5: (1,0,1)         8:0-4  9:1-5 10:2-6 11:3-7
+      6: (1,1,1)  7: (0,1,1)
+
+Here x maps to volume axis 0, y to axis 1, z to axis 2.
+"""
+
+import numpy as np
+import torch
+
+# Corner offsets in (axis0, axis1, axis2) for the 8 cube corners.
+_CORNERS = np.array([
+    [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0],
+    [0, 0, 1], [1, 0, 1], [1, 1, 1], [0, 1, 1],
+], dtype=np.int64)
+
+# The two corner indices that each of the 12 edges connects.
+_EDGE_CORNERS = np.array([
+    [0, 1], [1, 2], [2, 3], [3, 0],
+    [4, 5], [5, 6], [6, 7], [7, 4],
+    [0, 4], [1, 5], [2, 6], [3, 7],
+], dtype=np.int64)
+
+# Standard 256 x 16 triangle table. For cube configuration `i`, lists triples of
+# edge indices forming triangles, terminated by -1.
+_TRI_TABLE = [
+    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 1, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 8, 3, 9, 8, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 3, 1, 2, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 2, 10, 0, 2, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [2, 8, 3, 2, 10, 8, 10, 9, 8, -1, -1, -1, -1, -1, -1, -1],
+    [3, 11, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 11, 2, 8, 11, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 9, 0, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 11, 2, 1, 9, 11, 9, 8, 11, -1, -1, -1, -1, -1, -1, -1],
+    [3, 10, 1, 11, 10, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 10, 1, 0, 8, 10, 8, 11, 10, -1, -1, -1, -1, -1, -1, -1],
+    [3, 9, 0, 3, 11, 9, 11, 10, 9, -1, -1, -1, -1, -1, -1, -1],
+    [9, 8, 10, 10, 8, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 3, 0, 7, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 1, 9, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 1, 9, 4, 7, 1, 7, 3, 1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 10, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [3, 4, 7, 3, 0, 4, 1, 2, 10, -1, -1, -1, -1, -1, -1, -1],
+    [9, 2, 10, 9, 0, 2, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1],
+    [2, 10, 9, 2, 9, 7, 2, 7, 3, 7, 9, 4, -1, -1, -1, -1],
+    [8, 4, 7, 3, 11, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [11, 4, 7, 11, 2, 4, 2, 0, 4, -1, -1, -1, -1, -1, -1, -1],
+    [9, 0, 1, 8, 4, 7, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1],
+    [4, 7, 11, 9, 4, 11, 9, 11, 2, 9, 2, 1, -1, -1, -1, -1],
+    [3, 10, 1, 3, 11, 10, 7, 8, 4, -1, -1, -1, -1, -1, -1, -1],
+    [1, 11, 10, 1, 4, 11, 1, 0, 4, 7, 11, 4, -1, -1, -1, -1],
+    [4, 7, 8, 9, 0, 11, 9, 11, 10, 11, 0, 3, -1, -1, -1, -1],
+    [4, 7, 11, 4, 11, 9, 9, 11, 10, -1, -1, -1, -1, -1, -1, -1],
+    [9, 5, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 5, 4, 0, 8, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 5, 4, 1, 5, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [8, 5, 4, 8, 3, 5, 3, 1, 5, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 10, 9, 5, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [3, 0, 8, 1, 2, 10, 4, 9, 5, -1, -1, -1, -1, -1, -1, -1],
+    [5, 2, 10, 5, 4, 2, 4, 0, 2, -1, -1, -1, -1, -1, -1, -1],
+    [2, 10, 5, 3, 2, 5, 3, 5, 4, 3, 4, 8, -1, -1, -1, -1],
+    [9, 5, 4, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 11, 2, 0, 8, 11, 4, 9, 5, -1, -1, -1, -1, -1, -1, -1],
+    [0, 5, 4, 0, 1, 5, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1],
+    [2, 1, 5, 2, 5, 8, 2, 8, 11, 4, 8, 5, -1, -1, -1, -1],
+    [10, 3, 11, 10, 1, 3, 9, 5, 4, -1, -1, -1, -1, -1, -1, -1],
+    [4, 9, 5, 0, 8, 1, 8, 10, 1, 8, 11, 10, -1, -1, -1, -1],
+    [5, 4, 0, 5, 0, 11, 5, 11, 10, 11, 0, 3, -1, -1, -1, -1],
+    [5, 4, 8, 5, 8, 10, 10, 8, 11, -1, -1, -1, -1, -1, -1, -1],
+    [9, 7, 8, 5, 7, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 3, 0, 9, 5, 3, 5, 7, 3, -1, -1, -1, -1, -1, -1, -1],
+    [0, 7, 8, 0, 1, 7, 1, 5, 7, -1, -1, -1, -1, -1, -1, -1],
+    [1, 5, 3, 3, 5, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 7, 8, 9, 5, 7, 10, 1, 2, -1, -1, -1, -1, -1, -1, -1],
+    [10, 1, 2, 9, 5, 0, 5, 3, 0, 5, 7, 3, -1, -1, -1, -1],
+    [8, 0, 2, 8, 2, 5, 8, 5, 7, 10, 5, 2, -1, -1, -1, -1],
+    [2, 10, 5, 2, 5, 3, 3, 5, 7, -1, -1, -1, -1, -1, -1, -1],
+    [7, 9, 5, 7, 8, 9, 3, 11, 2, -1, -1, -1, -1, -1, -1, -1],
+    [9, 5, 7, 9, 7, 2, 9, 2, 0, 2, 7, 11, -1, -1, -1, -1],
+    [2, 3, 11, 0, 1, 8, 1, 7, 8, 1, 5, 7, -1, -1, -1, -1],
+    [11, 2, 1, 11, 1, 7, 7, 1, 5, -1, -1, -1, -1, -1, -1, -1],
+    [9, 5, 8, 8, 5, 7, 10, 1, 3, 10, 3, 11, -1, -1, -1, -1],
+    [5, 7, 0, 5, 0, 9, 7, 11, 0, 1, 0, 10, 11, 10, 0, -1],
+    [11, 10, 0, 11, 0, 3, 10, 5, 0, 8, 0, 7, 5, 7, 0, -1],
+    [11, 10, 5, 7, 11, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [10, 6, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 3, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 0, 1, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 8, 3, 1, 9, 8, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1],
+    [1, 6, 5, 2, 6, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 6, 5, 1, 2, 6, 3, 0, 8, -1, -1, -1, -1, -1, -1, -1],
+    [9, 6, 5, 9, 0, 6, 0, 2, 6, -1, -1, -1, -1, -1, -1, -1],
+    [5, 9, 8, 5, 8, 2, 5, 2, 6, 3, 2, 8, -1, -1, -1, -1],
+    [2, 3, 11, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [11, 0, 8, 11, 2, 0, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1],
+    [0, 1, 9, 2, 3, 11, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1],
+    [5, 10, 6, 1, 9, 2, 9, 11, 2, 9, 8, 11, -1, -1, -1, -1],
+    [6, 3, 11, 6, 5, 3, 5, 1, 3, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 11, 0, 11, 5, 0, 5, 1, 5, 11, 6, -1, -1, -1, -1],
+    [3, 11, 6, 0, 3, 6, 0, 6, 5, 0, 5, 9, -1, -1, -1, -1],
+    [6, 5, 9, 6, 9, 11, 11, 9, 8, -1, -1, -1, -1, -1, -1, -1],
+    [5, 10, 6, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 3, 0, 4, 7, 3, 6, 5, 10, -1, -1, -1, -1, -1, -1, -1],
+    [1, 9, 0, 5, 10, 6, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1],
+    [10, 6, 5, 1, 9, 7, 1, 7, 3, 7, 9, 4, -1, -1, -1, -1],
+    [6, 1, 2, 6, 5, 1, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 5, 5, 2, 6, 3, 0, 4, 3, 4, 7, -1, -1, -1, -1],
+    [8, 4, 7, 9, 0, 5, 0, 6, 5, 0, 2, 6, -1, -1, -1, -1],
+    [7, 3, 9, 7, 9, 4, 3, 2, 9, 5, 9, 6, 2, 6, 9, -1],
+    [3, 11, 2, 7, 8, 4, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1],
+    [5, 10, 6, 4, 7, 2, 4, 2, 0, 2, 7, 11, -1, -1, -1, -1],
+    [0, 1, 9, 4, 7, 8, 2, 3, 11, 5, 10, 6, -1, -1, -1, -1],
+    [9, 2, 1, 9, 11, 2, 9, 4, 11, 7, 11, 4, 5, 10, 6, -1],
+    [8, 4, 7, 3, 11, 5, 3, 5, 1, 5, 11, 6, -1, -1, -1, -1],
+    [5, 1, 11, 5, 11, 6, 1, 0, 11, 7, 11, 4, 0, 4, 11, -1],
+    [0, 5, 9, 0, 6, 5, 0, 3, 6, 11, 6, 3, 8, 4, 7, -1],
+    [6, 5, 9, 6, 9, 11, 4, 7, 9, 7, 11, 9, -1, -1, -1, -1],
+    [10, 4, 9, 6, 4, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 10, 6, 4, 9, 10, 0, 8, 3, -1, -1, -1, -1, -1, -1, -1],
+    [10, 0, 1, 10, 6, 0, 6, 4, 0, -1, -1, -1, -1, -1, -1, -1],
+    [8, 3, 1, 8, 1, 6, 8, 6, 4, 6, 1, 10, -1, -1, -1, -1],
+    [1, 4, 9, 1, 2, 4, 2, 6, 4, -1, -1, -1, -1, -1, -1, -1],
+    [3, 0, 8, 1, 2, 9, 2, 4, 9, 2, 6, 4, -1, -1, -1, -1],
+    [0, 2, 4, 4, 2, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [8, 3, 2, 8, 2, 4, 4, 2, 6, -1, -1, -1, -1, -1, -1, -1],
+    [10, 4, 9, 10, 6, 4, 11, 2, 3, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 2, 2, 8, 11, 4, 9, 10, 4, 10, 6, -1, -1, -1, -1],
+    [3, 11, 2, 0, 1, 6, 0, 6, 4, 6, 1, 10, -1, -1, -1, -1],
+    [6, 4, 1, 6, 1, 10, 4, 8, 1, 2, 1, 11, 8, 11, 1, -1],
+    [9, 6, 4, 9, 3, 6, 9, 1, 3, 11, 6, 3, -1, -1, -1, -1],
+    [8, 11, 1, 8, 1, 0, 11, 6, 1, 9, 1, 4, 6, 4, 1, -1],
+    [3, 11, 6, 3, 6, 0, 0, 6, 4, -1, -1, -1, -1, -1, -1, -1],
+    [6, 4, 8, 11, 6, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [7, 10, 6, 7, 8, 10, 8, 9, 10, -1, -1, -1, -1, -1, -1, -1],
+    [0, 7, 3, 0, 10, 7, 0, 9, 10, 6, 7, 10, -1, -1, -1, -1],
+    [10, 6, 7, 1, 10, 7, 1, 7, 8, 1, 8, 0, -1, -1, -1, -1],
+    [10, 6, 7, 10, 7, 1, 1, 7, 3, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 6, 1, 6, 8, 1, 8, 9, 8, 6, 7, -1, -1, -1, -1],
+    [2, 6, 9, 2, 9, 1, 6, 7, 9, 0, 9, 3, 7, 3, 9, -1],
+    [7, 8, 0, 7, 0, 6, 6, 0, 2, -1, -1, -1, -1, -1, -1, -1],
+    [7, 3, 2, 6, 7, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [2, 3, 11, 10, 6, 8, 10, 8, 9, 8, 6, 7, -1, -1, -1, -1],
+    [2, 0, 7, 2, 7, 11, 0, 9, 7, 6, 7, 10, 9, 10, 7, -1],
+    [1, 8, 0, 1, 7, 8, 1, 10, 7, 6, 7, 10, 2, 3, 11, -1],
+    [11, 2, 1, 11, 1, 7, 10, 6, 1, 6, 7, 1, -1, -1, -1, -1],
+    [8, 9, 6, 8, 6, 7, 9, 1, 6, 11, 6, 3, 1, 3, 6, -1],
+    [0, 9, 1, 11, 6, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [7, 8, 0, 7, 0, 6, 3, 11, 0, 11, 6, 0, -1, -1, -1, -1],
+    [7, 11, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [7, 6, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [3, 0, 8, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 1, 9, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [8, 1, 9, 8, 3, 1, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1],
+    [10, 1, 2, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 10, 3, 0, 8, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1],
+    [2, 9, 0, 2, 10, 9, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1],
+    [6, 11, 7, 2, 10, 3, 10, 8, 3, 10, 9, 8, -1, -1, -1, -1],
+    [7, 2, 3, 6, 2, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [7, 0, 8, 7, 6, 0, 6, 2, 0, -1, -1, -1, -1, -1, -1, -1],
+    [2, 7, 6, 2, 3, 7, 0, 1, 9, -1, -1, -1, -1, -1, -1, -1],
+    [1, 6, 2, 1, 8, 6, 1, 9, 8, 8, 7, 6, -1, -1, -1, -1],
+    [10, 7, 6, 10, 1, 7, 1, 3, 7, -1, -1, -1, -1, -1, -1, -1],
+    [10, 7, 6, 1, 7, 10, 1, 8, 7, 1, 0, 8, -1, -1, -1, -1],
+    [0, 3, 7, 0, 7, 10, 0, 10, 9, 6, 10, 7, -1, -1, -1, -1],
+    [7, 6, 10, 7, 10, 8, 8, 10, 9, -1, -1, -1, -1, -1, -1, -1],
+    [6, 8, 4, 11, 8, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [3, 6, 11, 3, 0, 6, 0, 4, 6, -1, -1, -1, -1, -1, -1, -1],
+    [8, 6, 11, 8, 4, 6, 9, 0, 1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 4, 6, 9, 6, 3, 9, 3, 1, 11, 3, 6, -1, -1, -1, -1],
+    [6, 8, 4, 6, 11, 8, 2, 10, 1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 10, 3, 0, 11, 0, 6, 11, 0, 4, 6, -1, -1, -1, -1],
+    [4, 11, 8, 4, 6, 11, 0, 2, 9, 2, 10, 9, -1, -1, -1, -1],
+    [10, 9, 3, 10, 3, 2, 9, 4, 3, 11, 3, 6, 4, 6, 3, -1],
+    [8, 2, 3, 8, 4, 2, 4, 6, 2, -1, -1, -1, -1, -1, -1, -1],
+    [0, 4, 2, 4, 6, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 9, 0, 2, 3, 4, 2, 4, 6, 4, 3, 8, -1, -1, -1, -1],
+    [1, 9, 4, 1, 4, 2, 2, 4, 6, -1, -1, -1, -1, -1, -1, -1],
+    [8, 1, 3, 8, 6, 1, 8, 4, 6, 6, 10, 1, -1, -1, -1, -1],
+    [10, 1, 0, 10, 0, 6, 6, 0, 4, -1, -1, -1, -1, -1, -1, -1],
+    [4, 6, 3, 4, 3, 8, 6, 10, 3, 0, 3, 9, 10, 9, 3, -1],
+    [10, 9, 4, 6, 10, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 9, 5, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 3, 4, 9, 5, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1],
+    [5, 0, 1, 5, 4, 0, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1],
+    [11, 7, 6, 8, 3, 4, 3, 5, 4, 3, 1, 5, -1, -1, -1, -1],
+    [9, 5, 4, 10, 1, 2, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1],
+    [6, 11, 7, 1, 2, 10, 0, 8, 3, 4, 9, 5, -1, -1, -1, -1],
+    [7, 6, 11, 5, 4, 10, 4, 2, 10, 4, 0, 2, -1, -1, -1, -1],
+    [3, 4, 8, 3, 5, 4, 3, 2, 5, 10, 5, 2, 11, 7, 6, -1],
+    [7, 2, 3, 7, 6, 2, 5, 4, 9, -1, -1, -1, -1, -1, -1, -1],
+    [9, 5, 4, 0, 8, 6, 0, 6, 2, 6, 8, 7, -1, -1, -1, -1],
+    [3, 6, 2, 3, 7, 6, 1, 5, 0, 5, 4, 0, -1, -1, -1, -1],
+    [6, 2, 8, 6, 8, 7, 2, 1, 8, 4, 8, 5, 1, 5, 8, -1],
+    [9, 5, 4, 10, 1, 6, 1, 7, 6, 1, 3, 7, -1, -1, -1, -1],
+    [1, 6, 10, 1, 7, 6, 1, 0, 7, 8, 7, 0, 9, 5, 4, -1],
+    [4, 0, 10, 4, 10, 5, 0, 3, 10, 6, 10, 7, 3, 7, 10, -1],
+    [7, 6, 10, 7, 10, 8, 5, 4, 10, 4, 8, 10, -1, -1, -1, -1],
+    [6, 9, 5, 6, 11, 9, 11, 8, 9, -1, -1, -1, -1, -1, -1, -1],
+    [3, 6, 11, 0, 6, 3, 0, 5, 6, 0, 9, 5, -1, -1, -1, -1],
+    [0, 11, 8, 0, 5, 11, 0, 1, 5, 5, 6, 11, -1, -1, -1, -1],
+    [6, 11, 3, 6, 3, 5, 5, 3, 1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 10, 9, 5, 11, 9, 11, 8, 11, 5, 6, -1, -1, -1, -1],
+    [0, 11, 3, 0, 6, 11, 0, 9, 6, 5, 6, 9, 1, 2, 10, -1],
+    [11, 8, 5, 11, 5, 6, 8, 0, 5, 10, 5, 2, 0, 2, 5, -1],
+    [6, 11, 3, 6, 3, 5, 2, 10, 3, 10, 5, 3, -1, -1, -1, -1],
+    [5, 8, 9, 5, 2, 8, 5, 6, 2, 3, 8, 2, -1, -1, -1, -1],
+    [9, 5, 6, 9, 6, 0, 0, 6, 2, -1, -1, -1, -1, -1, -1, -1],
+    [1, 5, 8, 1, 8, 0, 5, 6, 8, 3, 8, 2, 6, 2, 8, -1],
+    [1, 5, 6, 2, 1, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 3, 6, 1, 6, 10, 3, 8, 6, 5, 6, 9, 8, 9, 6, -1],
+    [10, 1, 0, 10, 0, 6, 9, 5, 0, 5, 6, 0, -1, -1, -1, -1],
+    [0, 3, 8, 5, 6, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [10, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [11, 5, 10, 7, 5, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [11, 5, 10, 11, 7, 5, 8, 3, 0, -1, -1, -1, -1, -1, -1, -1],
+    [5, 11, 7, 5, 10, 11, 1, 9, 0, -1, -1, -1, -1, -1, -1, -1],
+    [10, 7, 5, 10, 11, 7, 9, 8, 1, 8, 3, 1, -1, -1, -1, -1],
+    [11, 1, 2, 11, 7, 1, 7, 5, 1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 3, 1, 2, 7, 1, 7, 5, 7, 2, 11, -1, -1, -1, -1],
+    [9, 7, 5, 9, 2, 7, 9, 0, 2, 2, 11, 7, -1, -1, -1, -1],
+    [7, 5, 2, 7, 2, 11, 5, 9, 2, 3, 2, 8, 9, 8, 2, -1],
+    [2, 5, 10, 2, 3, 5, 3, 7, 5, -1, -1, -1, -1, -1, -1, -1],
+    [8, 2, 0, 8, 5, 2, 8, 7, 5, 10, 2, 5, -1, -1, -1, -1],
+    [9, 0, 1, 5, 10, 3, 5, 3, 7, 3, 10, 2, -1, -1, -1, -1],
+    [9, 8, 2, 9, 2, 1, 8, 7, 2, 10, 2, 5, 7, 5, 2, -1],
+    [1, 3, 5, 3, 7, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 7, 0, 7, 1, 1, 7, 5, -1, -1, -1, -1, -1, -1, -1],
+    [9, 0, 3, 9, 3, 5, 5, 3, 7, -1, -1, -1, -1, -1, -1, -1],
+    [9, 8, 7, 5, 9, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [5, 8, 4, 5, 10, 8, 10, 11, 8, -1, -1, -1, -1, -1, -1, -1],
+    [5, 0, 4, 5, 11, 0, 5, 10, 11, 11, 3, 0, -1, -1, -1, -1],
+    [0, 1, 9, 8, 4, 10, 8, 10, 11, 10, 4, 5, -1, -1, -1, -1],
+    [10, 11, 4, 10, 4, 5, 11, 3, 4, 9, 4, 1, 3, 1, 4, -1],
+    [2, 5, 1, 2, 8, 5, 2, 11, 8, 4, 5, 8, -1, -1, -1, -1],
+    [0, 4, 11, 0, 11, 3, 4, 5, 11, 2, 11, 1, 5, 1, 11, -1],
+    [0, 2, 5, 0, 5, 9, 2, 11, 5, 4, 5, 8, 11, 8, 5, -1],
+    [9, 4, 5, 2, 11, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [2, 5, 10, 3, 5, 2, 3, 4, 5, 3, 8, 4, -1, -1, -1, -1],
+    [5, 10, 2, 5, 2, 4, 4, 2, 0, -1, -1, -1, -1, -1, -1, -1],
+    [3, 10, 2, 3, 5, 10, 3, 8, 5, 4, 5, 8, 0, 1, 9, -1],
+    [5, 10, 2, 5, 2, 4, 1, 9, 2, 9, 4, 2, -1, -1, -1, -1],
+    [8, 4, 5, 8, 5, 3, 3, 5, 1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 4, 5, 1, 0, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [8, 4, 5, 8, 5, 3, 9, 0, 5, 0, 3, 5, -1, -1, -1, -1],
+    [9, 4, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 11, 7, 4, 9, 11, 9, 10, 11, -1, -1, -1, -1, -1, -1, -1],
+    [0, 8, 3, 4, 9, 7, 9, 11, 7, 9, 10, 11, -1, -1, -1, -1],
+    [1, 10, 11, 1, 11, 4, 1, 4, 0, 7, 4, 11, -1, -1, -1, -1],
+    [3, 1, 4, 3, 4, 8, 1, 10, 4, 7, 4, 11, 10, 11, 4, -1],
+    [4, 11, 7, 9, 11, 4, 9, 2, 11, 9, 1, 2, -1, -1, -1, -1],
+    [9, 7, 4, 9, 11, 7, 9, 1, 11, 2, 11, 1, 0, 8, 3, -1],
+    [11, 7, 4, 11, 4, 2, 2, 4, 0, -1, -1, -1, -1, -1, -1, -1],
+    [11, 7, 4, 11, 4, 2, 8, 3, 4, 3, 2, 4, -1, -1, -1, -1],
+    [2, 9, 10, 2, 7, 9, 2, 3, 7, 7, 4, 9, -1, -1, -1, -1],
+    [9, 10, 7, 9, 7, 4, 10, 2, 7, 8, 7, 0, 2, 0, 7, -1],
+    [3, 7, 10, 3, 10, 2, 7, 4, 10, 1, 10, 0, 4, 0, 10, -1],
+    [1, 10, 2, 8, 7, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 9, 1, 4, 1, 7, 7, 1, 3, -1, -1, -1, -1, -1, -1, -1],
+    [4, 9, 1, 4, 1, 7, 0, 8, 1, 8, 7, 1, -1, -1, -1, -1],
+    [4, 0, 3, 7, 4, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [4, 8, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [9, 10, 8, 10, 11, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [3, 0, 9, 3, 9, 11, 11, 9, 10, -1, -1, -1, -1, -1, -1, -1],
+    [0, 1, 10, 0, 10, 8, 8, 10, 11, -1, -1, -1, -1, -1, -1, -1],
+    [3, 1, 10, 11, 3, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 2, 11, 1, 11, 9, 9, 11, 8, -1, -1, -1, -1, -1, -1, -1],
+    [3, 0, 9, 3, 9, 11, 1, 2, 9, 2, 11, 9, -1, -1, -1, -1],
+    [0, 2, 11, 8, 0, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [3, 2, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [2, 3, 8, 2, 8, 10, 10, 8, 9, -1, -1, -1, -1, -1, -1, -1],
+    [9, 10, 2, 0, 9, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [2, 3, 8, 2, 8, 10, 0, 1, 8, 1, 10, 8, -1, -1, -1, -1],
+    [1, 10, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [1, 3, 8, 9, 1, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 9, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [0, 3, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+    [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+]
+
+
+@torch.no_grad()
+def marching_cubes(volume: torch.Tensor, level: float = 0.0):
+    """Extract an isosurface from a 3D scalar field.
+
+    Args:
+        volume: (D, H, W) float tensor. Inside is where ``volume < level`` (matches
+            the classic Lorensen convention and skimage's ``method="lorensen"``).
+        level: isosurface threshold.
+
+    Returns:
+        (vertices, faces): numpy arrays. ``vertices`` are float32 (N, 3) in array-index
+        coordinates (axis0, axis1, axis2); ``faces`` are int64 (M, 3).
+    """
+    assert volume.ndim == 3, "volume must be (D, H, W)"
+    device = volume.device
+    vol = volume.float()
+
+    tri_table = torch.tensor(_TRI_TABLE, dtype=torch.long, device=device)        # (256, 16)
+    edge_corners = torch.tensor(_EDGE_CORNERS, dtype=torch.long, device=device)  # (12, 2)
+    corners = torch.tensor(_CORNERS, dtype=torch.float32, device=device)         # (8, 3)
+
+    # Corner scalar values for every cell, shape (nc0, nc1, nc2, 8).
+    nc0, nc1, nc2 = vol.shape[0] - 1, vol.shape[1] - 1, vol.shape[2] - 1
+    if nc0 <= 0 or nc1 <= 0 or nc2 <= 0:
+        return (np.zeros((0, 3), dtype=np.float32), np.zeros((0, 3), dtype=np.int64))
+
+    corner_vals = torch.empty((nc0, nc1, nc2, 8), dtype=torch.float32, device=device)
+    for k in range(8):
+        o0, o1, o2 = _CORNERS[k]
+        corner_vals[..., k] = vol[o0:o0 + nc0, o1:o1 + nc1, o2:o2 + nc2]
+
+    # Cube configuration index: bit k set when corner k is inside (val < level).
+    inside = (corner_vals < level)
+    bits = torch.tensor([1 << k for k in range(8)], dtype=torch.long, device=device)
+    cube_index = (inside.long() * bits).sum(dim=-1)  # (nc0, nc1, nc2)
+
+    # Cells that actually intersect the surface.
+    active = (cube_index > 0) & (cube_index < 255)
+    if not active.any():
+        return (np.zeros((0, 3), dtype=np.float32), np.zeros((0, 3), dtype=np.int64))
+
+    idx0, idx1, idx2 = torch.where(active)              # (Nactive,)
+    cidx = cube_index[idx0, idx1, idx2]                 # (Nactive,)
+    cell_origin = torch.stack([idx0, idx1, idx2], dim=1).float()  # (Nactive, 3)
+    cell_vals = corner_vals[idx0, idx1, idx2]           # (Nactive, 8)
+
+    tris = tri_table[cidx]                              # (Nactive, 16)
+
+    # Each row holds up to 5 triangles (15 edge entries). Expand to (Nactive, 5, 3).
+    tri_edges = tris[:, :15].reshape(-1, 5, 3)          # edge indices, -1 = unused
+    valid_tri = tri_edges[..., 0] >= 0                  # (Nactive, 5)
+
+    cell_idx = torch.arange(cell_origin.shape[0], device=device).unsqueeze(1).expand(-1, 5)
+    cell_idx = cell_idx[valid_tri]                      # (T,)
+    edges = tri_edges[valid_tri]                        # (T, 3) edge index per triangle corner
+
+    # Interpolate a vertex on each referenced edge.
+    e_flat = edges.reshape(-1)                          # (T*3,)
+    cell_for_vert = cell_idx.unsqueeze(1).expand(-1, 3).reshape(-1)  # (T*3,)
+
+    ca = edge_corners[e_flat, 0]                        # (T*3,) corner index a
+    cb = edge_corners[e_flat, 1]                        # corner index b
+    va = cell_vals[cell_for_vert, ca]                   # scalar at corner a
+    vb = cell_vals[cell_for_vert, cb]
+    pa = cell_origin[cell_for_vert] + corners[ca]       # position of corner a (index space)
+    pb = cell_origin[cell_for_vert] + corners[cb]
+
+    denom = (vb - va)
+    t = torch.where(denom.abs() > 1e-12, (level - va) / denom, torch.zeros_like(denom))
+    t = t.clamp(0.0, 1.0).unsqueeze(1)
+    verts = pa + t * (pb - pa)                          # (T*3, 3) one vertex per triangle corner
+
+    # Weld shared vertices: a grid edge shared by adjacent cells interpolates to the exact
+    # same position (same corner values/positions), so exact dedup yields a clean indexed
+    # mesh like skimage/warp (one vertex per active edge).
+    uniq, inverse = torch.unique(verts, dim=0, return_inverse=True)
+    faces = inverse.reshape(-1, 3)
+
+    return (uniq.cpu().numpy().astype(np.float32), faces.cpu().numpy().astype(np.int64))

comfy/ldm/cube/vae.py

@@ -0,0 +1,364 @@
+"""
+Native port of Roblox/cube's shape tokenizer decode path (OneDAutoEncoder).
+
+Reference: https://github.com/Roblox/cube  (cube3d/model/autoencoder/*).
+
+Only the DECODE path is ported (token IDs -> latents -> occupancy grid -> mesh);
+the point-cloud encoder is not needed for text-to-3D generation. Encoder weights in
+the checkpoint are loaded with strict=False and ignored.
+
+Module/parameter names mirror upstream so the checkpoint loads directly:
+  embedder.weight
+  bottleneck.block.{codebook, cb_weight, cb_bias, c_in, c_x, c_out, ...}
+  decoder.{positional_encodings, blocks.N...}
+  occupancy_decoder.{query_in, attn_out, ln_f, c_head}
+"""
+
+import logging
+import math
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+
+# ---------------------------------------------------------------------------
+# Norms
+# ---------------------------------------------------------------------------
+
+class CubeLayerNorm(nn.Module):
+    """LayerNorm upcasting to fp32. affine=False by default (no params)."""
+
+    def __init__(self, dim, eps=1e-6, elementwise_affine=False, dtype=None, device=None):
+        super().__init__()
+        self.dim = (dim,)
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
+            self.bias = nn.Parameter(torch.zeros(dim, dtype=dtype, device=device))
+        else:
+            self.weight = None
+            self.bias = None
+
+    def forward(self, x):
+        w = self.weight.float() if self.weight is not None else None
+        b = self.bias.float() if self.bias is not None else None
+        y = F.layer_norm(x.float(), self.dim, w, b, self.eps)
+        return y.type_as(x)
+
+
+class CubeRMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5, elementwise_affine=True, dtype=None, device=None):
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=device))
+        else:
+            self.register_buffer("weight", torch.ones(dim), persistent=False)
+
+    def forward(self, x):
+        xf = x.float()
+        out = xf * torch.rsqrt(xf.pow(2).mean(-1, keepdim=True) + self.eps)
+        return (out * self.weight.float()).type_as(x)
+
+
+# ---------------------------------------------------------------------------
+# Fourier embedder
+# ---------------------------------------------------------------------------
+
+class PhaseModulatedFourierEmbedder(nn.Module):
+    def __init__(self, num_freqs, input_dim=3, dtype=None, device=None):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty(input_dim, num_freqs, dtype=dtype, device=device))
+        carrier = (num_freqs / 8) ** torch.linspace(1, 0, num_freqs)
+        carrier = (carrier + torch.linspace(0, 1, num_freqs)) * 2 * math.pi
+        self.register_buffer("carrier", carrier, persistent=False)
+        self.out_dim = input_dim * (num_freqs * 2 + 1)
+
+    def forward(self, x):
+        m = x.float().unsqueeze(-1)
+        w = self.weight.float()
+        carrier = self.carrier.float()
+        fm = (m * w).view(*x.shape[:-1], -1)
+        pm = (m * 0.5 * math.pi + carrier).view(*x.shape[:-1], -1)
+        return torch.cat([x, fm.cos() + pm.cos(), fm.sin() + pm.sin()], dim=-1).type_as(x)
+
+
+# ---------------------------------------------------------------------------
+# Attention building blocks
+# ---------------------------------------------------------------------------
+
+class MLP(nn.Module):
+    def __init__(self, embed_dim, hidden_dim, bias=True, dtype=None, device=None):
+        super().__init__()
+        self.up_proj = ops.Linear(embed_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
+        self.down_proj = ops.Linear(hidden_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.act_fn = nn.GELU(approximate="none")
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.up_proj(x)))
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, bias=True, eps=1e-6, dtype=None, device=None):
+        super().__init__()
+        assert embed_dim % num_heads == 0
+        self.num_heads = num_heads
+        head_dim = embed_dim // num_heads
+        self.c_qk = ops.Linear(embed_dim, 2 * embed_dim, bias=bias, dtype=dtype, device=device)
+        self.c_v = ops.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.c_proj = ops.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.q_norm = CubeRMSNorm(head_dim, dtype=dtype, device=device)
+        self.k_norm = CubeRMSNorm(head_dim, dtype=dtype, device=device)
+
+    def forward(self, x, attn_mask=None, is_causal=False):
+        b, l, d = x.shape
+        q, k = self.c_qk(x).chunk(2, dim=-1)
+        v = self.c_v(x)
+        q = self.q_norm(q.view(b, l, self.num_heads, -1).transpose(1, 2))
+        k = self.k_norm(k.view(b, l, self.num_heads, -1).transpose(1, 2))
+        v = v.view(b, l, self.num_heads, -1).transpose(1, 2)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=0.0,
+                                           is_causal=is_causal and attn_mask is None)
+        y = y.transpose(1, 2).contiguous().view(b, l, d)
+        return self.c_proj(y)
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, q_dim=None, kv_dim=None, bias=True, dtype=None, device=None):
+        super().__init__()
+        assert embed_dim % num_heads == 0
+        q_dim = q_dim or embed_dim
+        kv_dim = kv_dim or embed_dim
+        self.c_q = ops.Linear(q_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.c_k = ops.Linear(kv_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.c_v = ops.Linear(kv_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.c_proj = ops.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.num_heads = num_heads
+
+    def forward(self, x, c, attn_mask=None):
+        q, k, v = self.c_q(x), self.c_k(c), self.c_v(c)
+        b, l, d = q.shape
+        s = k.shape[1]
+        q = q.view(b, l, self.num_heads, -1).transpose(1, 2)
+        k = k.view(b, s, self.num_heads, -1).transpose(1, 2)
+        v = v.view(b, s, self.num_heads, -1).transpose(1, 2)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=0.0)
+        y = y.transpose(1, 2).contiguous().view(b, l, d)
+        return self.c_proj(y)
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, embed_dim, num_heads, bias=True, eps=1e-6, dtype=None, device=None):
+        super().__init__()
+        self.ln_1 = CubeLayerNorm(embed_dim, eps=eps)
+        self.attn = SelfAttention(embed_dim, num_heads, bias=bias, eps=eps, dtype=dtype, device=device)
+        self.ln_2 = CubeLayerNorm(embed_dim, eps=eps)
+        self.mlp = MLP(embed_dim, embed_dim * 4, bias=bias, dtype=dtype, device=device)
+
+    def forward(self, x, attn_mask=None, is_causal=False):
+        x = x + self.attn(self.ln_1(x), attn_mask=attn_mask, is_causal=is_causal)
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class EncoderCrossAttentionLayer(nn.Module):
+    def __init__(self, embed_dim, num_heads, q_dim=None, kv_dim=None, bias=True, eps=1e-6, dtype=None, device=None):
+        super().__init__()
+        q_dim = q_dim or embed_dim
+        kv_dim = kv_dim or embed_dim
+        self.attn = CrossAttention(embed_dim, num_heads, q_dim=q_dim, kv_dim=kv_dim, bias=bias, dtype=dtype, device=device)
+        self.ln_1 = CubeLayerNorm(q_dim, eps=eps)
+        self.ln_2 = CubeLayerNorm(kv_dim, eps=eps)
+        self.ln_f = CubeLayerNorm(embed_dim, eps=eps)
+        self.mlp = MLP(embed_dim, embed_dim * 4, bias=bias, dtype=dtype, device=device)
+
+    def forward(self, x, c, attn_mask=None):
+        x = x + self.attn(self.ln_1(x), self.ln_2(c), attn_mask=attn_mask)
+        x = x + self.mlp(self.ln_f(x))
+        return x
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim, embed_dim, bias=True, dtype=None, device=None):
+        super().__init__()
+        self.in_layer = ops.Linear(in_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+        self.silu = nn.SiLU()
+        self.out_layer = ops.Linear(embed_dim, embed_dim, bias=bias, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+# ---------------------------------------------------------------------------
+# Spherical VQ (decode-only parts)
+# ---------------------------------------------------------------------------
+
+class SphericalVectorQuantizer(nn.Module):
+    def __init__(self, embed_dim, num_codes, width=None, dtype=None, device=None):
+        super().__init__()
+        self.num_codes = num_codes
+        self.codebook = ops.Embedding(num_codes, embed_dim, dtype=dtype, device=device)
+        width = width or embed_dim
+        if width != embed_dim:
+            self.c_in = ops.Linear(width, embed_dim, dtype=dtype, device=device)
+            self.c_x = ops.Linear(width, embed_dim, dtype=dtype, device=device)
+            self.c_out = ops.Linear(embed_dim, width, dtype=dtype, device=device)
+        else:
+            self.c_in = self.c_out = self.c_x = nn.Identity()
+        self.norm = CubeRMSNorm(embed_dim, elementwise_affine=False, dtype=dtype, device=device)
+        # "kl" codebook regularization (released config)
+        self.cb_weight = nn.Parameter(torch.ones([embed_dim], dtype=dtype, device=device))
+        self.cb_bias = nn.Parameter(torch.zeros([embed_dim], dtype=dtype, device=device))
+
+    def cb_norm(self, x):
+        return x * self.cb_weight + self.cb_bias
+
+    def get_codebook(self):
+        return self.norm(self.cb_norm(self.codebook.weight))
+
+    def lookup_codebook(self, q):
+        z_q = F.embedding(q, self.get_codebook())
+        return self.c_out(z_q)
+
+
+class OneDBottleNeck(nn.Module):
+    def __init__(self, block):
+        super().__init__()
+        self.block = block
+
+
+# ---------------------------------------------------------------------------
+# Decoders
+# ---------------------------------------------------------------------------
+
+class OneDDecoder(nn.Module):
+    def __init__(self, num_latents, width, num_heads, num_layers, eps=1e-6, dtype=None, device=None):
+        super().__init__()
+        self.register_buffer("query", torch.empty([0, width]), persistent=False)
+        self.positional_encodings = nn.Parameter(torch.empty(num_latents, width, dtype=dtype, device=device))
+        self.blocks = nn.ModuleList([
+            EncoderLayer(width, num_heads, eps=eps, dtype=dtype, device=device)
+            for _ in range(num_layers)
+        ])
+
+    def forward(self, z):
+        h = z + self.positional_encodings[:z.shape[1]].unsqueeze(0).to(z.dtype)
+        for block in self.blocks:
+            h = block(h)
+        return h
+
+
+class OneDOccupancyDecoder(nn.Module):
+    def __init__(self, embedder, out_features, width, num_heads, eps=1e-6, dtype=None, device=None):
+        super().__init__()
+        self.embedder = embedder
+        self.query_in = MLPEmbedder(embedder.out_dim, width, dtype=dtype, device=device)
+        self.attn_out = EncoderCrossAttentionLayer(width, num_heads, dtype=dtype, device=device)
+        self.ln_f = CubeLayerNorm(width, eps=eps, elementwise_affine=True, dtype=dtype, device=device)
+        self.c_head = ops.Linear(width, out_features, dtype=dtype, device=device)
+
+    def forward(self, queries, latents):
+        x = self.query_in(self.embedder(queries))
+        x = self.attn_out(x, latents)
+        return self.c_head(self.ln_f(x))
+
+
+# ---------------------------------------------------------------------------
+# Top-level shape VAE
+# ---------------------------------------------------------------------------
+
+def generate_dense_grid_points(bbox_min, bbox_max, resolution_base, indexing="ij"):
+    length = bbox_max - bbox_min
+    num_cells = np.exp2(resolution_base)
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    xs, ys, zs = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1).reshape(-1, 3)
+    grid_size = [int(num_cells) + 1] * 3
+    return xyz, grid_size, length
+
+
+class CubeShapeVAE(nn.Module):
+    """Decode-only OneDAutoEncoder. Encoder weights load with strict=False (ignored)."""
+
+    # Fixed query bounds for the occupancy grid (upstream default).
+    decode_bounds = (-1.05, -1.05, -1.05, 1.05, 1.05, 1.05)
+
+    def __init__(self, num_encoder_latents=1024, embed_dim=32, width=768, num_heads=12,
+                 num_freqs=128, num_decoder_layers=24, num_codes=16384, out_dim=1, eps=1e-6,
+                 dtype=None, device=None):
+        super().__init__()
+        self.cfg_num_encoder_latents = num_encoder_latents
+        self.cfg_num_codes = num_codes
+        self.embedder = PhaseModulatedFourierEmbedder(num_freqs=num_freqs, input_dim=3, dtype=dtype, device=device)
+        self.bottleneck = OneDBottleNeck(
+            SphericalVectorQuantizer(embed_dim, num_codes, width, dtype=dtype, device=device)
+        )
+        self.decoder = OneDDecoder(num_encoder_latents, width, num_heads, num_decoder_layers,
+                                   eps=eps, dtype=dtype, device=device)
+        self.occupancy_decoder = OneDOccupancyDecoder(self.embedder, out_dim, width, num_heads,
+                                                      eps=eps, dtype=dtype, device=device)
+
+    @torch.no_grad()
+    def decode(self, samples, resolution_base=8.0, chunk_size=100_000, **kwargs):
+        """Token IDs -> occupancy grid logits. Entry point for comfy.sd.VAE.decode, which
+        manages model loading/device/dtype. `samples` arrive as (B, 1, num_tokens) in the
+        VAE working dtype on the load device. VAE.decode applies a trailing movedim(1, -1),
+        so pre-invert it here to hand the node grid logits as (B, gx, gy, gz)."""
+        ids = samples.reshape(samples.shape[0], -1)[:, :self.cfg_num_encoder_latents]
+        ids = ids.round().long().clamp(0, self.cfg_num_codes - 1)
+        latents = self.decode_indices(ids)
+        grid_logits, _, _, _ = self.extract_geometry(
+            latents, bounds=self.decode_bounds, resolution_base=resolution_base, chunk_size=chunk_size)
+        return grid_logits.movedim(-1, 1)
+
+    @torch.no_grad()
+    def decode_indices(self, shape_ids):
+        z_q = self.bottleneck.block.lookup_codebook(shape_ids)
+        return self.decoder(z_q)
+
+    @torch.no_grad()
+    def query(self, queries, latents):
+        return self.occupancy_decoder(queries, latents).squeeze(-1)
+
+    @torch.no_grad()
+    def extract_geometry(self, latents, bounds=(-1.05, -1.05, -1.05, 1.05, 1.05, 1.05),
+                         resolution_base=8.0, chunk_size=100_000):
+        bbox_min = np.array(bounds[0:3])
+        bbox_max = np.array(bounds[3:6])
+        bbox_size = bbox_max - bbox_min
+
+        xyz, grid_size, _ = generate_dense_grid_points(bbox_min, bbox_max, resolution_base, indexing="ij")
+        xyz = torch.from_numpy(xyz)
+        batch_size = latents.shape[0]
+        batch_logits = []
+        for start in range(0, xyz.shape[0], chunk_size):
+            queries = xyz[start:start + chunk_size, :]
+            n = queries.shape[0]
+            if start > 0 and n < chunk_size:
+                queries = F.pad(queries, [0, 0, 0, chunk_size - n])
+            bq = queries.unsqueeze(0).expand(batch_size, -1, -1).to(latents)
+            batch_logits.append(self.query(bq, latents)[:, :n])
+
+        grid_logits = torch.cat(batch_logits, dim=1).detach().view(
+            batch_size, grid_size[0], grid_size[1], grid_size[2]).float()
+        return grid_logits, grid_size, bbox_size, bbox_min
+
+
+def grid_logits_to_mesh(grid_logit, grid_size, bbox_size, bbox_min, level=0.0):
+    """Occupancy-logit grid -> mesh, using the vendored dependency-free marching cubes
+    (classic Lorensen, same family as upstream cube's default warp backend). Vertices are
+    rescaled from grid-index space into the bbox, matching upstream's transform."""
+    from comfy.ldm.cube.marching_cubes import marching_cubes
+    vertices, faces = marching_cubes(grid_logit, level)
+    vertices = vertices / np.array(grid_size) * bbox_size + bbox_min
+    # The vendored Lorensen table already emits outward-facing winding for this
+    # occupancy convention, so (unlike the upstream skimage path) no face flip is needed.
+    return vertices.astype(np.float32), np.ascontiguousarray(faces)

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, enable_gqa=False, **kwargs):
+    def two_pass_attention(q, k, v, heads, **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, enable_gqa=enable_gqa)
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
         elif ar_len >= T:
-            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True, enable_gqa=enable_gqa)
+            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
         elif ar_len <= 0:
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options, enable_gqa=enable_gqa)
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
         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, enable_gqa=enable_gqa,
+                attn_mask=None, dropout_p=0.0, is_causal=True,
             )
             out_gen = optimized_attention(
                 q[:, :, ar_len:], k, v, heads,
                 mask=None, skip_reshape=True, skip_output_reshape=True,
-                transformer_options=transformer_options, enable_gqa=enable_gqa,
+                transformer_options=transformer_options,
             )
             out = torch.cat([out_ar, out_gen], dim=2)
 

comfy/ldm/joyimage/model.py

@@ -1,445 +0,0 @@
-# https://github.com/jdopensource/JoyAI-Image-Edit (Apache 2.0)
-import math
-from typing import Optional, Tuple
-
-import comfy_kitchen
-import torch
-import torch.nn as nn
-
-import comfy.ldm.common_dit
-import comfy.ops
-import comfy.patcher_extension
-from comfy.ldm.lightricks.model import GELU_approx, PixArtAlphaTextProjection, TimestepEmbedding, Timesteps
-from comfy.ldm.modules.attention import optimized_attention
-
-
-class JoyImageModulate(nn.Module):
-    def __init__(self, hidden_size: int, factor: int, dtype=None, device=None):
-        super().__init__()
-        self.factor = factor
-        self.modulate_table = nn.Parameter(
-            torch.empty(1, factor, hidden_size, dtype=dtype, device=device)
-        )
-
-    def forward(self, x: torch.Tensor) -> list:
-        if x.ndim != 3:
-            x = x.unsqueeze(1)
-        table = comfy.ops.cast_to_input(self.modulate_table, x)
-        return [o.squeeze(1) for o in (table + x).chunk(self.factor, dim=1)]
-
-
-class JoyImageFeedForward(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        inner_dim: int,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.net = nn.ModuleList([
-            GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations),
-            nn.Identity(),
-            operations.Linear(inner_dim, dim, bias=True, dtype=dtype, device=device),
-        ])
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        for module in self.net:
-            x = module(x)
-        return x
-
-
-class JoyImageAttention(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        eps: float = 1e-6,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.num_attention_heads = num_attention_heads
-        inner_dim = num_attention_heads * attention_head_dim
-
-        self.img_attn_qkv = operations.Linear(dim, inner_dim * 3, bias=True, dtype=dtype, device=device)
-        self.img_attn_q_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
-        self.img_attn_k_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
-        self.img_attn_proj = operations.Linear(inner_dim, dim, bias=True, dtype=dtype, device=device)
-
-        self.txt_attn_qkv = operations.Linear(dim, inner_dim * 3, bias=True, dtype=dtype, device=device)
-        self.txt_attn_q_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
-        self.txt_attn_k_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
-        self.txt_attn_proj = operations.Linear(inner_dim, dim, bias=True, dtype=dtype, device=device)
-
-    def forward(
-        self,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        image_rotary_emb: torch.Tensor,
-        transformer_options=None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        heads = self.num_attention_heads
-
-        img_q, img_k, img_v = self.img_attn_qkv(img).chunk(3, dim=-1)
-        txt_q, txt_k, txt_v = self.txt_attn_qkv(txt).chunk(3, dim=-1)
-
-        img_q = img_q.unflatten(-1, (heads, -1))
-        img_k = img_k.unflatten(-1, (heads, -1))
-        img_v = img_v.unflatten(-1, (heads, -1))
-        txt_q = txt_q.unflatten(-1, (heads, -1))
-        txt_k = txt_k.unflatten(-1, (heads, -1))
-        txt_v = txt_v.unflatten(-1, (heads, -1))
-
-        img_q = self.img_attn_q_norm(img_q)
-        img_k = self.img_attn_k_norm(img_k)
-        txt_q = self.txt_attn_q_norm(txt_q)
-        txt_k = self.txt_attn_k_norm(txt_k)
-
-        img_q, img_k = comfy_kitchen.apply_rope(img_q, img_k, image_rotary_emb)
-
-        joint_q = torch.cat([img_q, txt_q], dim=1)
-        joint_k = torch.cat([img_k, txt_k], dim=1)
-        joint_v = torch.cat([img_v, txt_v], dim=1)
-
-        joint_q = joint_q.flatten(2, 3)
-        joint_k = joint_k.flatten(2, 3)
-        joint_v = joint_v.flatten(2, 3)
-
-        joint_out = optimized_attention(joint_q, joint_k, joint_v, heads=heads, transformer_options=transformer_options)
-
-        seq_img = img.shape[1]
-        img_out = joint_out[:, :seq_img, :]
-        txt_out = joint_out[:, seq_img:, :]
-
-        img_out = self.img_attn_proj(img_out)
-        txt_out = self.txt_attn_proj(txt_out)
-        return img_out, txt_out
-
-
-class JoyImageTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        mlp_width_ratio: float = 4.0,
-        eps: float = 1e-6,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        mlp_hidden_dim = int(dim * mlp_width_ratio)
-
-        self.img_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device)
-        self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.img_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
-
-        self.txt_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device)
-        self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.txt_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
-
-        self.attn = JoyImageAttention(
-            dim=dim,
-            num_attention_heads=num_attention_heads,
-            attention_head_dim=attention_head_dim,
-            eps=eps,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: torch.Tensor,
-        transformer_options=None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        (
-            img_mod1_shift,
-            img_mod1_scale,
-            img_mod1_gate,
-            img_mod2_shift,
-            img_mod2_scale,
-            img_mod2_gate,
-        ) = self.img_mod(temb)
-        (
-            txt_mod1_shift,
-            txt_mod1_scale,
-            txt_mod1_gate,
-            txt_mod2_shift,
-            txt_mod2_scale,
-            txt_mod2_gate,
-        ) = self.txt_mod(temb)
-
-        img_normed = self.img_norm1(hidden_states)
-        txt_normed = self.txt_norm1(encoder_hidden_states)
-        img_modulated = img_normed * (1 + img_mod1_scale.unsqueeze(1)) + img_mod1_shift.unsqueeze(1)
-        txt_modulated = txt_normed * (1 + txt_mod1_scale.unsqueeze(1)) + txt_mod1_shift.unsqueeze(1)
-
-        img_attn, txt_attn = self.attn(img_modulated, txt_modulated, image_rotary_emb, transformer_options=transformer_options)
-
-        hidden_states = hidden_states + img_attn * img_mod1_gate.unsqueeze(1)
-        encoder_hidden_states = encoder_hidden_states + txt_attn * txt_mod1_gate.unsqueeze(1)
-
-        img_ffn_normed = self.img_norm2(hidden_states)
-        txt_ffn_normed = self.txt_norm2(encoder_hidden_states)
-        img_ffn_input = img_ffn_normed * (1 + img_mod2_scale.unsqueeze(1)) + img_mod2_shift.unsqueeze(1)
-        txt_ffn_input = txt_ffn_normed * (1 + txt_mod2_scale.unsqueeze(1)) + txt_mod2_shift.unsqueeze(1)
-        hidden_states = hidden_states + self.img_mlp(img_ffn_input) * img_mod2_gate.unsqueeze(1)
-        encoder_hidden_states = encoder_hidden_states + self.txt_mlp(txt_ffn_input) * txt_mod2_gate.unsqueeze(1)
-
-        return hidden_states, encoder_hidden_states
-
-
-class JoyImageTimeTextImageEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        time_freq_dim: int,
-        time_proj_dim: int,
-        text_embed_dim: int,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.time_embedder = TimestepEmbedding(
-            in_channels=time_freq_dim,
-            time_embed_dim=dim,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-        self.act_fn = nn.SiLU()
-        self.time_proj = operations.Linear(dim, time_proj_dim, bias=True, dtype=dtype, device=device)
-        self.text_embedder = PixArtAlphaTextProjection(
-            text_embed_dim, dim, act_fn="gelu_tanh", dtype=dtype, device=device, operations=operations,
-        )
-
-    def forward(self, timestep: torch.Tensor, encoder_hidden_states: torch.Tensor):
-        timestep = self.timesteps_proj(timestep)
-        temb = self.time_embedder(timestep.to(dtype=encoder_hidden_states.dtype)).type_as(encoder_hidden_states)
-        timestep_proj = self.time_proj(self.act_fn(temb))
-        encoder_hidden_states = self.text_embedder(encoder_hidden_states)
-        return temb, timestep_proj, encoder_hidden_states
-
-
-class JoyImageTransformer3DModel(nn.Module):
-    def __init__(
-        self,
-        patch_size: list = [1, 2, 2],
-        in_channels: int = 16,
-        out_channels: Optional[int] = None,
-        hidden_size: int = 3072,
-        num_attention_heads: int = 24,
-        text_dim: int = 4096,
-        mlp_width_ratio: float = 4.0,
-        num_layers: int = 20,
-        rope_dim_list: list = [16, 56, 56],
-        theta: int = 256,
-        image_model=None,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.dtype = dtype
-        self.out_channels = out_channels or in_channels
-        self.patch_size = list(patch_size)
-        self.rope_dim_list = list(rope_dim_list)
-        self.theta = theta
-
-        attention_head_dim = hidden_size // num_attention_heads
-
-        self.img_in = operations.Conv3d(
-            in_channels,
-            hidden_size,
-            kernel_size=tuple(self.patch_size),
-            stride=tuple(self.patch_size),
-            dtype=dtype,
-            device=device,
-        )
-
-        self.condition_embedder = JoyImageTimeTextImageEmbedding(
-            dim=hidden_size,
-            time_freq_dim=256,
-            time_proj_dim=hidden_size * 6,
-            text_embed_dim=text_dim,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.double_blocks = nn.ModuleList([
-            JoyImageTransformerBlock(
-                dim=hidden_size,
-                num_attention_heads=num_attention_heads,
-                attention_head_dim=attention_head_dim,
-                mlp_width_ratio=mlp_width_ratio,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-            for _ in range(num_layers)
-        ])
-
-        self.norm_out = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.proj_out = operations.Linear(
-            hidden_size,
-            self.out_channels * math.prod(self.patch_size),
-            bias=True,
-            dtype=dtype,
-            device=device,
-        )
-
-    def _get_rotary_pos_embed_for_range(
-        self,
-        start: Tuple[int, int, int],
-        stop: Tuple[int, int, int],
-        device=None,
-    ) -> torch.Tensor:
-        # 3D RoPE for the patch grid range [start, stop) over (t, h, w). Token order after
-        # reshape(-1) is (t, h, w), matching the img_in Conv3d flatten.
-        rope_dim_list = self.rope_dim_list
-
-        grids = [torch.arange(start[i], stop[i], dtype=torch.float32, device=device) for i in range(3)]
-        mesh = torch.stack(torch.meshgrid(*grids, indexing="ij"), dim=0)
-
-        angles_parts = []
-        for i, dim in enumerate(rope_dim_list):
-            pos = mesh[i].reshape(-1)
-            freqs = 1.0 / (self.theta ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device)[: (dim // 2)] / dim))
-            angles_parts.append(torch.outer(pos, freqs))
-
-        angles = torch.cat(angles_parts, dim=1)
-        cos = angles.cos()
-        sin = angles.sin()
-        return torch.stack((cos, -sin, sin, cos), dim=-1).unflatten(-1, (2, 2))
-
-    def get_rotary_pos_embed_for_components(
-        self,
-        component_sizes,
-        device=None,
-    ) -> torch.Tensor:
-        # Per-component 3D RoPE. component_sizes is a list of (t, h, w) patch grid sizes in
-        # sequence order [target, ref0, ref1, ...]; h/w restart at 0 for each component while t
-        # continues from the running offset, giving every image its own temporal position band.
-        freqs_parts = []
-        t_offset = 0
-        for (t, h, w) in component_sizes:
-            freqs = self._get_rotary_pos_embed_for_range(
-                start=(t_offset, 0, 0),
-                stop=(t_offset + t, h, w),
-                device=device,
-            )
-            freqs_parts.append(freqs)
-            t_offset += t
-        return torch.cat(freqs_parts, dim=0).unsqueeze(0).unsqueeze(2)
-
-    def unpatchify(self, x: torch.Tensor, t: int, h: int, w: int) -> torch.Tensor:
-        c = self.out_channels
-        pt, ph, pw = self.patch_size
-        x = x.reshape(x.shape[0], t, h, w, pt, ph, pw, c)
-        x = x.permute(0, 7, 1, 4, 2, 5, 3, 6)
-        return x.reshape(x.shape[0], c, t * pt, h * ph, w * pw)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor = None,
-        ref_latents=None,
-        control=None,
-        transformer_options=None,
-        **kwargs,
-    ) -> torch.Tensor:
-        transformer_options = {} if transformer_options is None else transformer_options.copy()
-        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(hidden_states, timestep, context, ref_latents, transformer_options, **kwargs)
-
-    def _forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        ref_latents=None,
-        transformer_options=None,
-        **kwargs,
-    ) -> torch.Tensor:
-        pt, ph, pw = self.patch_size
-        _, _, ot, oh, ow = hidden_states.shape
-
-        components = [hidden_states, *(ref_latents or [])]
-        component_sizes = []
-        img_tokens = []
-        for comp in components:
-            comp = comfy.ldm.common_dit.pad_to_patch_size(comp, self.patch_size)
-            _, _, ct, ch, cw = comp.shape
-            component_sizes.append((ct // pt, ch // ph, cw // pw))
-            tokens = self.img_in(comp).flatten(2).transpose(1, 2)  # (B, n_i, D)
-            img_tokens.append(tokens)
-
-        img = torch.cat(img_tokens, dim=1)
-
-        _, vec, txt = self.condition_embedder(timestep, context)
-        vec = vec.unflatten(1, (6, -1))
-
-        image_rotary_emb = self.get_rotary_pos_embed_for_components(
-            component_sizes,
-            device=hidden_states.device,
-        )
-
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.double_blocks)
-        transformer_options["block_type"] = "double"
-        for i, block in enumerate(self.double_blocks):
-            transformer_options["block_index"] = i
-            if ("double_block", i) in blocks_replace:
-                def block_wrap(args):
-                    out = {}
-                    out["img"], out["txt"] = block(
-                        hidden_states=args["img"],
-                        encoder_hidden_states=args["txt"],
-                        temb=args["vec"],
-                        image_rotary_emb=args["pe"],
-                        transformer_options=args.get("transformer_options"),
-                    )
-                    return out
-
-                out = blocks_replace[("double_block", i)]({"img": img,
-                                                           "txt": txt,
-                                                           "vec": vec,
-                                                           "pe": image_rotary_emb,
-                                                           "transformer_options": transformer_options},
-                                                          {"original_block": block_wrap})
-                txt = out["txt"]
-                img = out["img"]
-            else:
-                img, txt = block(
-                    hidden_states=img,
-                    encoder_hidden_states=txt,
-                    temb=vec,
-                    image_rotary_emb=image_rotary_emb,
-                    transformer_options=transformer_options,
-                )
-
-        tt, th, tw = component_sizes[0]
-        target_tokens = tt * th * tw
-        img = img[:, :target_tokens, :]
-        img = self.proj_out(self.norm_out(img))
-        img = self.unpatchify(img, tt, th, tw)
-        return img[:, :, :ot, :oh, :ow]

comfy/ldm/krea2/model.py

@@ -1,391 +0,0 @@
-"""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
-import comfy.utils
-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={}):
-        transformer_patches = transformer_options.get("patches", {})
-        extra_options = transformer_options.copy()
-        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 "block_index" in transformer_options and "attn1_patch" in transformer_patches:
-            for p in transformer_patches["attn1_patch"]:
-                out = p(q, k, v, pe=freqs, attn_mask=mask, extra_options=extra_options)
-                q, k, v = out.get("q", q), out.get("k", k), out.get("v", v)
-                freqs, mask = out.get("pe", freqs), out.get("attn_mask", mask)
-
-        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)
-
-        if "block_index" in transformer_options and "attn1_output_patch" in transformer_patches:
-            for p in transformer_patches["attn1_output_patch"]:
-                out = p(out, extra_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, timestep_zero_index=None, transformer_options={}):
-        prescale, preshift, pregate, postscale, postshift, postgate = self.mod(vec)
-        if timestep_zero_index is not None:
-            bs = x.shape[0]
-            ref_prescale = prescale[bs:]
-            ref_preshift = preshift[bs:]
-            ref_pregate = pregate[bs:]
-            ref_postscale = postscale[bs:]
-            ref_postshift = postshift[bs:]
-            ref_postgate = postgate[bs:]
-            prescale = prescale[:bs]
-            preshift = preshift[:bs]
-            pregate = pregate[:bs]
-            postscale = postscale[:bs]
-            postshift = postshift[:bs]
-            postgate = postgate[:bs]
-
-            pre = self.prenorm(x)
-            pre[:, :timestep_zero_index].mul_(1 + prescale).add_(preshift)
-            pre[:, timestep_zero_index:].mul_(1 + ref_prescale).add_(ref_preshift)
-            attn = self.attn(pre, freqs, mask, transformer_options=transformer_options)
-            del pre
-            attn[:, :timestep_zero_index].mul_(pregate)
-            attn[:, timestep_zero_index:].mul_(ref_pregate)
-            x = x + attn
-            del attn
-
-            post = self.postnorm(x)
-            post[:, :timestep_zero_index].mul_(1 + postscale).add_(postshift)
-            post[:, timestep_zero_index:].mul_(1 + ref_postscale).add_(ref_postshift)
-            mlp = self.mlp(post)
-            del post
-            mlp[:, :timestep_zero_index].mul_(postgate)
-            mlp[:, timestep_zero_index:].mul_(ref_postgate)
-            x = x + mlp
-            del mlp
-            return x
-
-        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, default_ref_method=None, 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
-        self.default_ref_method = default_ref_method
-
-        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, ref_latents=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, ref_latents, transformer_options, **kwargs)
-
-    def process_img(self, x, index=0):
-        patch = self.patch
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch, patch))
-        h, w = x.shape[-2] // patch, x.shape[-1] // patch
-        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch, pw=patch)
-
-        img_ids = torch.zeros(h, w, 3, device=x.device, dtype=torch.float32)
-        img_ids[..., 0] = index
-        img_ids[..., 1] = torch.arange(h, device=x.device, dtype=torch.float32)[:, None]
-        img_ids[..., 2] = torch.arange(w, device=x.device, dtype=torch.float32)[None, :]
-        return img, img_ids.reshape(1, h * w, 3).repeat(x.shape[0], 1, 1), h, w
-
-    def _forward(self, x, timesteps, context, attention_mask=None, ref_latents=None, transformer_options={}, **kwargs):
-        transformer_options = transformer_options.copy()
-        temporal = x.ndim == 5
-        if temporal:
-            b5, c5, t5, h5, w5 = x.shape
-            x = x.reshape(b5 * t5, c5, h5, w5)
-        bs, _, h_orig, w_orig = x.shape
-        patch = self.patch
-
-        # context arrives as (B, seq, txtlayers*txtdim); reshape to (B, txtlayers, seq, txtdim).
-        context = self._unpack_context(context)
-
-        img, imgpos, h_, w_ = self.process_img(x)
-        img_tokens = img.shape[1]
-        timestep_zero_index = None
-        ref_method = kwargs.get("ref_latents_method", self.default_ref_method)
-        if ref_method is not None and ref_latents is not None and len(ref_latents) > 0:
-            ref_tokens = []
-            ref_pos = []
-            ref_num_tokens = []
-            for index, ref in enumerate(ref_latents, 1):
-                if ref.ndim == 5:
-                    rb, rc, rt, rh5, rw5 = ref.shape
-                    ref = ref.reshape(rb * rt, rc, rh5, rw5)
-                ref = comfy.utils.repeat_to_batch_size(ref, bs)
-                kontext, kontext_ids, _, _ = self.process_img(ref, index=index)
-                ref_tokens.append(kontext)
-                ref_pos.append(kontext_ids)
-                ref_num_tokens.append(kontext.shape[1])
-            img = torch.cat([img] + ref_tokens, dim=1)
-            imgpos = torch.cat([imgpos] + ref_pos, dim=1)
-            del ref_tokens, ref_pos
-            if ref_method == "index_timestep_zero":
-                timestep_zero_index = img_tokens
-            transformer_options["reference_image_num_tokens"] = ref_num_tokens
-
-        img = self.first(img)
-
-        t = self.tmlp(timestep_embedding(timesteps, self.tdim).unsqueeze(1).to(img.dtype))
-        tvec = self.tproj(t)
-        if timestep_zero_index is not None:
-            t0 = self.tmlp(timestep_embedding(torch.zeros_like(timesteps), self.tdim).unsqueeze(1).to(img.dtype))
-            tvec = torch.cat((tvec, self.tproj(t0)), dim=0)
-
-        context = self.txtfusion(context, mask=None, transformer_options=transformer_options)
-        context = self.txtmlp(context)
-
-        txtlen = context.shape[1]
-        device = context.device
-        txtpos = torch.zeros(bs, txtlen, 3, device=device, dtype=torch.float32)
-
-        patches = transformer_options.get("patches", {})
-        if "post_input" in patches:
-            for p in patches["post_input"]:
-                out = p({"img": img, "txt": context, "img_ids": imgpos, "txt_ids": txtpos, "transformer_options": transformer_options})
-                img, context = out["img"], out["txt"]
-                imgpos, txtpos = out["img_ids"], out["txt_ids"]
-
-        combined = torch.cat((context, img), dim=1)
-        del context, img
-        if timestep_zero_index is not None:
-            timestep_zero_index += txtlen
-
-        # Position ids: text at 0, image at (0, h_idx, w_idx).
-        pos = torch.cat((txtpos, imgpos), dim=1)
-        del txtpos, imgpos
-
-        freqs = self.pe_embedder(pos)
-        del pos
-
-        transformer_options["total_blocks"] = len(self.blocks)
-        transformer_options["block_type"] = "single"
-        transformer_options["img_slice"] = [txtlen, combined.shape[1]]
-        for i, block in enumerate(self.blocks):
-            transformer_options["block_index"] = i
-            combined = block(combined, tvec, freqs, None, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
-
-        final = self.last(combined, t)
-        del combined
-        out = final[:, txtlen:txtlen + img_tokens, :]
-        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 and keyframe_idxs.shape[2] > 0:
+        if keyframe_idxs is not None:
             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 and keyframe_idxs.shape[2] > 0:
+        if keyframe_idxs is not None:
             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

@@ -1,6 +1,5 @@
 import math
 import sys
-import inspect
 
 import torch
 import torch.nn.functional as F
@@ -15,16 +14,16 @@ 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":
@@ -90,44 +89,6 @@ 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):
@@ -191,19 +152,28 @@ 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:
-        if kwargs.get("enable_gqa", False):
-            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
-        q, k, v = map(
+         q, k, v = map(
             lambda t: t.reshape(b * heads, -1, dim_head),
             (q, k, v),
         )
     else:
-        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))
+        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),
+        )
 
     # force cast to fp32 to avoid overflowing
     if attn_precision == torch.float32:
@@ -261,16 +231,13 @@ 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, 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)
+        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)
 
 
     dtype = query.dtype
@@ -337,15 +304,19 @@ 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:
-        if kwargs.get("enable_gqa", False):
-            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
-        q, k, v = map(
+         q, k, v = map(
             lambda t: t.reshape(b * heads, -1, dim_head),
             (q, k, v),
         )
     else:
-        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))
+        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),
+        )
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
@@ -467,7 +438,7 @@ 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, skip_output_reshape=skip_output_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
 
     if skip_reshape:
         # b h k d -> b k h d
@@ -475,12 +446,13 @@ 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 = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
 
     if mask is not None:
         # add a singleton batch dimension
@@ -502,7 +474,7 @@ 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, scale=kwargs.get("scale", None))
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
     if skip_output_reshape:
         out = out.permute(0, 2, 1, 3)
@@ -526,8 +498,10 @@ 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 = _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))
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
 
     if mask is not None:
         # add a batch dimension if there isn't already one
@@ -537,7 +511,9 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
-    sdpa_keys = ("scale", "enable_gqa")
+    # 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_extra = {k: v for k, v in kwargs.items() if k in sdpa_keys}
 
     if SDP_BATCH_LIMIT >= b:
@@ -565,19 +541,20 @@ 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 or (mask is not None and not SAGE_ATTENTION_SUPPORTS_MASK):
+    if kwargs.get("low_precision_attention", True) is False:
         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 = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head),
+            (q, k, v),
+        )
         tensor_layout = "NHD"
 
     if mask is not None:
@@ -588,12 +565,8 @@ 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, **sage_kwargs)
+        out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
     except Exception as e:
         logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
         exception_fallback = True
@@ -643,6 +616,7 @@ 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:
@@ -668,15 +642,11 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 **kwargs
             )
 
-    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))
+    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),
+        )
         B, H, L, D = q_s.shape
 
     try:
@@ -692,7 +662,7 @@ 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=skip_reshape,
+                skip_reshape=False,
                 skip_output_reshape=skip_output_reshape,
                 **kwargs
             )
@@ -709,22 +679,21 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     return out
 
 try:
-    @torch.library.custom_op("comfy::flash_attn", mutates_args=())
+    @torch.library.custom_op("flash_attention::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, 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)
+                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)
 
 
     @flash_attn_wrapper.register_fake
-    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False, softmax_scale=-1.0):
+    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
         # 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, softmax_scale: float = -1.0) -> torch.Tensor:
+                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
 @wrap_attn
@@ -734,8 +703,10 @@ 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 = _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))
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
 
     if mask is not None:
         # add a batch dimension if there isn't already one
@@ -754,16 +725,10 @@ 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}")
-        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)
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
     if not skip_output_reshape:
         out = (
             out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -1244,3 +1209,5 @@ 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, flip_sin_to_cos=False, downscale_freq_shift=1):
+def get_timestep_embedding(timesteps, embedding_dim):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
     From Fairseq.
@@ -33,13 +33,11 @@ def get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False, down
     assert len(timesteps.shape) == 1
 
     half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - downscale_freq_shift)
+    emb = math.log(10000) / (half_dim - 1)
     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

@@ -22,7 +22,7 @@ def apply_rotary_emb(x, freqs_cis):
 
 
 def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-    return F.silu(x, inplace=True).mul_(y)
+    return F.silu(x) * y
 
 
 class TimestepEmbedding(nn.Module):
@@ -141,8 +141,11 @@ class Attention(nn.Module):
         key = key.transpose(1, 2)
         value = value.transpose(1, 2)
 
-        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)
+        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)
         hidden_states = self.to_out[0](hidden_states)
         return hidden_states
 

comfy/ldm/pixeldit/model.py

@@ -197,9 +197,6 @@ 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))
@@ -229,7 +226,6 @@ 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 SigmaAwareGate(nn.Module):
+class SigmaAwareGatePerTokenPerDim(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, per_token: bool = False, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
         super().__init__()
-        self.content_proj = operations.Linear(dim * 2, 1 if per_token else dim, dtype=dtype, device=device)
+        self.content_proj = operations.Linear(dim * 2, 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 SigmaAwareGate(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, conv_padding_mode: str = "zeros", dtype=None, device=None, operations=None):
+    def __init__(self, channels: int, num_groups: int = 4, 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, padding_mode=conv_padding_mode, dtype=dtype, device=device),
+            operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
             operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(channels, channels, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
+            operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -62,13 +62,9 @@ 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__()
@@ -78,38 +74,34 @@ 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
 
-        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
+        z_to_patch_ratio = (sr_scale * latent_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 = effective_latent_channels
+            latent_proj_in_ch = 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 = effective_latent_channels * fold_factor * fold_factor
+            latent_proj_in_ch = latent_channels * fold_factor * fold_factor
 
         layers = [
-            operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
+            operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
+            operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
         ]
         for _ in range(num_res_blocks):
-            layers.append(ResBlock(hidden_dim, conv_padding_mode=conv_padding_mode, dtype=dtype, device=device, operations=operations))
+            layers.append(ResBlock(hidden_dim, 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(
-            [SigmaAwareGate(out_dim, per_token=gate_per_token, dtype=dtype, device=device, operations=operations)
+            [SigmaAwareGatePerTokenPerDim(out_dim, dtype=dtype, device=device, operations=operations)
              for _ in range(num_outputs)]
         )
 
@@ -123,11 +115,6 @@ 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:
@@ -147,10 +134,7 @@ 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)
-        outputs = [head(tokens) for head in self.output_heads]
-        if self.pit_head is not None:
-            outputs.append(self.pit_head(tokens))
-        return outputs
+        return [head(tokens) for head in self.output_heads]
 
 
 class PidNet(PixDiT_T2I):
@@ -164,10 +148,6 @@ 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,
@@ -185,8 +165,6 @@ 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,
@@ -195,20 +173,13 @@ 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(
@@ -226,11 +197,6 @@ 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")
@@ -250,14 +216,12 @@ 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

@@ -1,51 +0,0 @@
-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

@@ -1,301 +0,0 @@
-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

@@ -1,48 +0,0 @@
-"""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

@@ -552,7 +552,6 @@ class WanModel(torch.nn.Module):
                 List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
         """
         # embeddings
-        x_input = x
         x = self.patch_embedding(x.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         transformer_options["grid_sizes"] = grid_sizes
@@ -565,13 +564,11 @@ class WanModel(torch.nn.Module):
         e0 = self.time_projection(e).unflatten(2, (6, self.dim))
 
         full_ref = None
-        img_offset = 0
         if self.ref_conv is not None:
             full_ref = kwargs.get("reference_latent", None)
             if full_ref is not None:
                 full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
                 x = torch.concat((full_ref, x), dim=1)
-                img_offset = full_ref.shape[1]
 
         # In-context reference (Bernini)
         context_latents = kwargs.get("context_latents", None)
@@ -592,7 +589,6 @@ class WanModel(torch.nn.Module):
             context_img_len = clip_fea.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -608,11 +604,6 @@ class WanModel(torch.nn.Module):
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": img_offset, "transformer_options": transformer_options})
-                    x = out["img"]
-
         # head
         x = self.head(x, e)
 
@@ -786,7 +777,6 @@ class VaceWanModel(WanModel):
         **kwargs,
     ):
         # embeddings
-        x_input = x
         x = self.patch_embedding(x.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         transformer_options["grid_sizes"] = grid_sizes
@@ -817,7 +807,6 @@ class VaceWanModel(WanModel):
         x_orig = x
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -833,11 +822,6 @@ class VaceWanModel(WanModel):
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": 0, "transformer_options": transformer_options})
-                    x = out["img"]
-
             ii = self.vace_layers_mapping.get(i, None)
             if ii is not None:
                 for iii in range(len(c)):
@@ -903,7 +887,6 @@ class CameraWanModel(WanModel):
         **kwargs,
     ):
         # embeddings
-        x_input = x
         x = self.patch_embedding(x.float()).to(x.dtype)
         if self.control_adapter is not None and camera_conditions is not None:
             x = x + self.control_adapter(camera_conditions).to(x.dtype)
@@ -926,7 +909,6 @@ class CameraWanModel(WanModel):
             context_img_len = clip_fea.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -942,11 +924,6 @@ class CameraWanModel(WanModel):
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": 0, "transformer_options": transformer_options})
-                    x = out["img"]
-
         # head
         x = self.head(x, e)
 
@@ -1358,7 +1335,6 @@ class WanModel_S2V(WanModel):
 
         # embeddings
         bs, _, time, height, width = x.shape
-        x_input = x
         x = self.patch_embedding(x.float()).to(x.dtype)
         if control_video is not None:
             x = x + self.cond_encoder(control_video)
@@ -1403,7 +1379,6 @@ class WanModel_S2V(WanModel):
         context = self.text_embedding(context)
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -1418,12 +1393,6 @@ class WanModel_S2V(WanModel):
                 x = out["img"]
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, transformer_options=transformer_options)
-
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": 0, "transformer_options": transformer_options})
-                    x = out["img"]
-
             if audio_emb is not None:
                 x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len)
         # head
@@ -1630,7 +1599,6 @@ class HumoWanModel(WanModel):
         bs, _, time, height, width = x.shape
 
         # embeddings
-        x_input = x
         x = self.patch_embedding(x.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         x = x.flatten(2).transpose(1, 2)
@@ -1662,7 +1630,6 @@ class HumoWanModel(WanModel):
             audio = None
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -1678,11 +1645,6 @@ class HumoWanModel(WanModel):
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, audio=audio, transformer_options=transformer_options)
 
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": 0, "transformer_options": transformer_options})
-                    x = out["img"]
-
         # head
         x = self.head(x, e)
 
@@ -1698,18 +1660,12 @@ class SCAILWanModel(WanModel):
 
     def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, ref_mask_latents=None, sam_latents=None, **kwargs):
 
-        x_input = x
-
-        img_offset = 0
         if reference_latent is not None:
             x = torch.cat((reference_latent, x), dim=2)
-            img_offset = (reference_latent.shape[2] // self.patch_size[0]) * \
-                         (reference_latent.shape[3] // self.patch_size[1]) * \
-                         (reference_latent.shape[4] // self.patch_size[2])
 
         # embeddings
         x = self.patch_embedding(x.float()).to(x.dtype)
-        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream (one identity mask frame per reference, then video)
+        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream
             x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         transformer_options["grid_sizes"] = grid_sizes
@@ -1741,7 +1697,6 @@ class SCAILWanModel(WanModel):
             context_img_len = clip_fea.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -1757,11 +1712,6 @@ class SCAILWanModel(WanModel):
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": img_offset, "transformer_options": transformer_options})
-                    x = out["img"]
-
         # head
         x = self.head(x, e)
 
@@ -1778,25 +1728,22 @@ 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=video_t_start, device=device, dtype=dtype, transformer_options=transformer_options))
+                parts.append(super().rope_encode(main_t_patches, h, w, t_start=0, 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]
@@ -1805,7 +1752,7 @@ 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=video_t_start, device=device, dtype=dtype, transformer_options=pose_tf))
+                parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=0, device=device, dtype=dtype, transformer_options=pose_tf))
 
             return torch.cat(parts, dim=1)
 
@@ -1814,6 +1761,10 @@ 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/ldm/wan/model_animate.py

@@ -493,7 +493,6 @@ class AnimateWanModel(WanModel):
         **kwargs,
     ):
         # embeddings
-        x_input = x
         x = self.patch_embedding(x.float()).to(x.dtype)
         x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
         grid_sizes = x.shape[2:]
@@ -506,13 +505,11 @@ class AnimateWanModel(WanModel):
         e0 = self.time_projection(e).unflatten(2, (6, self.dim))
 
         full_ref = None
-        img_offset = 0
         if self.ref_conv is not None:
             full_ref = kwargs.get("reference_latent", None)
             if full_ref is not None:
                 full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
                 x = torch.concat((full_ref, x), dim=1)
-                img_offset = full_ref.shape[1]
 
         # context
         context = self.text_embedding(context)
@@ -525,7 +522,6 @@ class AnimateWanModel(WanModel):
             context_img_len = clip_fea.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -541,11 +537,6 @@ class AnimateWanModel(WanModel):
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": img_offset, "transformer_options": transformer_options})
-                    x = out["img"]
-
             if i % 5 == 0 and motion_vec is not None:
                 x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec)
 

comfy/ldm/wan/model_wandancer.py

@@ -111,7 +111,6 @@ class WanDancerModel(WanModel):
 
     def forward_orig(self, x, t, context, clip_fea=None, clip_fea_ref=None, freqs=None, audio_embed=None, fps=30, audio_inject_scale=1.0, transformer_options={}, **kwargs):
         # embeddings
-        x_input = x
         if int(fps + 0.5) != 30:
             x = self.patch_embedding_global(x.float()).to(x.dtype)
         else:
@@ -129,13 +128,11 @@ class WanDancerModel(WanModel):
         e0 = self.time_projection(e).unflatten(2, (6, self.dim))
 
         full_ref = None
-        img_offset = 0
         if self.ref_conv is not None: # model has the weight, but this wasn't used in the original pipeline
             full_ref = kwargs.get("reference_latent", None)
             if full_ref is not None:
                 full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
                 x = torch.concat((full_ref, x), dim=1)
-                img_offset = full_ref.shape[1]
 
         # context
         context = self.text_embedding(context)
@@ -166,7 +163,6 @@ class WanDancerModel(WanModel):
             context_img_len += clip_fea_ref.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.blocks)
         transformer_options["block_type"] = "double"
@@ -181,12 +177,6 @@ class WanDancerModel(WanModel):
                 x = out["img"]
             else:
                 x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
-
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": x, "x": x_input, "vec": e, "block_index": i, "img_offset": img_offset, "transformer_options": transformer_options})
-                    x = out["img"]
-
             if audio_emb is not None:
                 x = self.music_injector(x, i, audio_emb, audio_emb_global=None, seq_len=seq_len, scale=audio_inject_scale)
 

comfy/ldm/wan/uni3c.py

@@ -1,149 +0,0 @@
-# Uni3C controlnet for Wan 2.1: https://github.com/ewrfcas/Uni3C
-# Converted from the original diffusers based implementation.
-import torch
-import torch.nn as nn
-
-from comfy.ldm.flux.layers import EmbedND
-from .model import WanSelfAttention
-
-
-class Uni3CLayerNormZero(nn.Module):
-    def __init__(
-        self,
-        conditioning_dim,
-        embedding_dim,
-        eps=1e-5,
-        device=None, dtype=None, operations=None
-    ):
-        super().__init__()
-        self.silu = nn.SiLU()
-        self.linear = operations.Linear(conditioning_dim, 3 * embedding_dim, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(embedding_dim, eps=eps, elementwise_affine=True, device=device, dtype=dtype)
-
-    def forward(self, x, temb):
-        shift, scale, gate = self.linear(self.silu(temb)).chunk(3, dim=1)
-        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
-        return x, gate[:, None, :]
-
-
-class Uni3CAttentionBlock(nn.Module):
-    def __init__(
-        self,
-        dim,
-        ffn_dim,
-        num_heads,
-        time_embed_dim=5120,
-        eps=1e-6,
-        device=None, dtype=None, operations=None
-    ):
-        super().__init__()
-        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
-        self.norm1 = Uni3CLayerNormZero(time_embed_dim, dim, device=device, dtype=dtype, operations=operations)
-        self.self_attn = WanSelfAttention(dim, num_heads, qk_norm=True, eps=eps, operation_settings=operation_settings)
-        self.norm2 = Uni3CLayerNormZero(time_embed_dim, dim, device=device, dtype=dtype, operations=operations)
-        self.ffn = nn.Sequential(
-            operations.Linear(dim, ffn_dim, device=device, dtype=dtype), nn.GELU(approximate='tanh'),
-            operations.Linear(ffn_dim, dim, device=device, dtype=dtype))
-
-    def forward(self, x, temb, freqs):
-        norm_x, gate_msa = self.norm1(x, temb)
-        x = x + gate_msa * self.self_attn(norm_x, freqs)
-        norm_x, gate_ff = self.norm2(x, temb)
-        x = x + gate_ff * self.ffn(norm_x)
-        return x
-
-
-class MaskCamEmbed(nn.Module):
-    def __init__(
-        self,
-        add_channels=7,
-        mid_channels=256,
-        conv_out_dim=5120,
-        device=None, dtype=None, operations=None
-    ):
-        super().__init__()
-        self.mask_padding = [0, 0, 0, 0, 3, 0]  # first frame conditioning
-        self.mask_proj = nn.Sequential(
-            operations.Conv3d(add_channels, mid_channels, kernel_size=(4, 8, 8), stride=(4, 8, 8), device=device, dtype=dtype),
-            operations.GroupNorm(mid_channels // 8, mid_channels, device=device, dtype=dtype),
-            nn.SiLU())
-        self.mask_zero_proj = operations.Conv3d(mid_channels, conv_out_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2), device=device, dtype=dtype)
-
-    def forward(self, add_inputs):
-        add_padded = torch.nn.functional.pad(add_inputs, self.mask_padding, mode="constant", value=0)
-        add_embeds = self.mask_proj(add_padded)
-        add_embeds = self.mask_zero_proj(add_embeds)
-        add_embeds = add_embeds.flatten(2).transpose(1, 2)
-        return add_embeds
-
-
-class WanUni3CControlnet(nn.Module):
-    def __init__(
-        self,
-        in_channels=36,
-        conv_out_dim=5120,
-        dim=1024,
-        ffn_dim=8192,
-        num_heads=16,
-        num_layers=20,
-        time_embed_dim=5120,
-        out_proj_dim=5120,
-        add_channels=7,
-        mid_channels=256,
-        device=None, dtype=None, operations=None
-    ):
-        super().__init__()
-        patch_size = (1, 2, 2)
-        self.num_layers = num_layers
-
-        self.controlnet_patch_embedding = operations.Conv3d(
-            in_channels, conv_out_dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)
-        self.controlnet_mask_embedding = MaskCamEmbed(add_channels, mid_channels, conv_out_dim, device=device, dtype=dtype, operations=operations)
-
-        if conv_out_dim != dim:
-            self.proj_in = operations.Linear(conv_out_dim, dim, device=device, dtype=dtype)
-        else:
-            self.proj_in = nn.Identity()
-
-        self.controlnet_blocks = nn.ModuleList([
-            Uni3CAttentionBlock(dim, ffn_dim, num_heads, time_embed_dim, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)])
-        self.proj_out = nn.ModuleList([
-            operations.Linear(dim, out_proj_dim, device=device, dtype=dtype)
-            for _ in range(num_layers)])
-
-        head_dim = dim // num_heads
-        self.rope_embedder = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim - 4 * (head_dim // 6), 2 * (head_dim // 6), 2 * (head_dim // 6)])
-
-    def rope_encode(self, t_len, h_len, w_len, device=None, dtype=None):
-        img_ids = torch.zeros((t_len, h_len, w_len, 3), device=device, dtype=dtype)
-        img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.arange(t_len, device=device, dtype=dtype).reshape(-1, 1, 1)
-        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.arange(h_len, device=device, dtype=dtype).reshape(1, -1, 1)
-        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.arange(w_len, device=device, dtype=dtype).reshape(1, 1, -1)
-        img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
-        freqs = self.rope_embedder(img_ids).movedim(1, 2)
-        return freqs
-
-    def process_input(self, control_input, render_mask=None, camera_embedding=None):
-        # render_mask/camera_embedding are the checkpoint's extra conditioning path, not wired up yet
-        hidden = self.controlnet_patch_embedding(control_input.float()).to(control_input.dtype)
-        t_len, h_len, w_len = hidden.shape[2:]
-        freqs = self.rope_encode(t_len, h_len, w_len, device=hidden.device, dtype=hidden.dtype)
-        hidden = hidden.flatten(2).transpose(1, 2)
-
-        add_inputs = None
-        if camera_embedding is not None and render_mask is not None:
-            add_inputs = torch.cat([render_mask, camera_embedding], dim=1)
-        elif render_mask is not None:
-            add_inputs = render_mask
-
-        if add_inputs is not None:
-            hidden = hidden + self.controlnet_mask_embedding(add_inputs.to(hidden.dtype))
-
-        hidden = self.proj_in(hidden)
-        return hidden, freqs
-
-    def forward_block(self, block_index, hidden, temb, freqs):
-        hidden = self.controlnet_blocks[block_index](hidden, temb, freqs)
-        residual = self.proj_out[block_index](hidden)
-        return hidden, residual

comfy/lora.py

@@ -326,17 +326,6 @@ 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

@@ -21,7 +21,6 @@ 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
@@ -45,6 +44,7 @@ import comfy.ldm.lumina.model
 import comfy.ldm.wan.model
 import comfy.ldm.wan.model_animate
 import comfy.ldm.wan.ar_model
+import comfy.ldm.cube.gpt
 import comfy.ldm.wan.model_wandancer
 import comfy.ldm.hunyuan3d.model
 import comfy.ldm.triposplat.model
@@ -55,12 +55,8 @@ 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.joyimage.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
@@ -934,17 +930,6 @@ 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)
@@ -1219,127 +1204,6 @@ 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)
@@ -1884,14 +1748,10 @@ class WAN21_SCAIL(WAN21):
 
         reference_latents = kwargs.get("reference_latents", None)
         if reference_latents is not None:
-            # 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))
+            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)
 
         pose_latents = kwargs.get("pose_video_latent", None)
         if pose_latents is not None:
@@ -1933,7 +1793,6 @@ 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())
@@ -1961,11 +1820,10 @@ 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 N leading ref frames padded with frames of zeros, so just grab the first frames for all windows
+            # The ref mask is just a single frame 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]
-            ref_frame_count = full_ref_mask.shape[2] - video_frame_count
-            if ref_frame_count < 1:
+            if full_ref_mask.shape[2] != video_frame_count + 1:
                 return None
             window_length = len(window.index_list)
 
@@ -1974,7 +1832,7 @@ 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 + ref_frame_count].to(device)
+            window_ref_mask = full_ref_mask[:, :, :window_length + 1].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)
@@ -2024,11 +1882,11 @@ class WAN22_WanDancer(WAN21):
 
         fps = kwargs.get("fps", None)
         if fps is not None:
-            out['fps'] = comfy.conds.CONDConstant(fps)
+            out['fps'] = comfy.conds.CONDRegular(torch.FloatTensor([fps]))
 
         audio_inject_scale = kwargs.get("audio_inject_scale", None)
         if audio_inject_scale is not None:
-            out['audio_inject_scale'] = comfy.conds.CONDConstant(audio_inject_scale)
+            out['audio_inject_scale'] = comfy.conds.CONDRegular(torch.FloatTensor([audio_inject_scale]))
         return out
 
 class Hunyuan3Dv2(BaseModel):
@@ -2046,6 +1904,26 @@ class Hunyuan3Dv2(BaseModel):
             out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
+class Cube3D(BaseModel):
+    """Roblox Cube3D shape GPT (autoregressive). Generation goes through the
+    dedicated `cube` sampler (SamplerCustomAdvanced), never KSampler/apply_model."""
+    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cube.gpt.DualStreamRoformer)
+
+    def _apply_model(self, *args, **kwargs):
+        raise RuntimeError(
+            "Cube3D is an autoregressive token model. Use the 'cube' sampler "
+            "(SamplerCube + SamplerCustomAdvanced), not KSampler."
+        )
+
+    def extra_conds(self, **kwargs):
+        out = {}
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
+
 class Hunyuan3Dv2_1(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3dv2_1.hunyuandit.HunYuanDiTPlain)
@@ -2227,7 +2105,10 @@ class Omnigen2(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         ref_latents = kwargs.get("reference_latents", None)
         if ref_latents is not None:
-            out['ref_latents'] = comfy.conds.CONDList([self.process_latent_in(lat) for lat in ref_latents])
+            latents = []
+            for lat in ref_latents:
+                latents.append(self.process_latent_in(lat))
+            out['ref_latents'] = comfy.conds.CONDList(latents)
         return out
 
     def extra_conds_shapes(self, **kwargs):
@@ -2237,11 +2118,6 @@ 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)
@@ -2274,28 +2150,6 @@ class QwenImage(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
         return out
 
-class JoyImage(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.joyimage.model.JoyImageTransformer3DModel)
-        self.memory_usage_factor_conds = ("ref_latents",)
-
-    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)
-        ref_latents = kwargs.get("reference_latents", None)
-        if ref_latents is not None:
-            out['ref_latents'] = comfy.conds.CONDList([self.process_latent_in(lat) for lat in ref_latents])
-        return out
-
-    def extra_conds_shapes(self, **kwargs):
-        out = {}
-        ref_latents = kwargs.get("reference_latents", None)
-        if ref_latents is not None:
-            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
-        return out
-
 class Ideogram4(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ideogram4.model.Ideogram4Transformer2DModel)
@@ -2311,35 +2165,6 @@ 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)
-        self.memory_usage_factor_conds = ("ref_latents",)
-
-    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)
-        ref_latents = kwargs.get("reference_latents", None)
-        if ref_latents is not None:
-            latents = []
-            for lat in ref_latents:
-                latents.append(self.process_latent_in(lat))
-            out['ref_latents'] = comfy.conds.CONDList(latents)
-
-            ref_latents_method = kwargs.get("reference_latents_method", None)
-            if ref_latents_method is not None:
-                out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method)
-        return out
-
-    def extra_conds_shapes(self, **kwargs):
-        out = {}
-        ref_latents = kwargs.get("reference_latents", None)
-        if ref_latents is not None:
-            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
-        return out
-
 class 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

@@ -470,46 +470,15 @@ 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:
-        latent_proj_in_channels = int(state_dict[_lq_w_key].shape[1])
-        hidden_dim = int(state_dict[_lq_w_key].shape[0])
+        in_ch = int(state_dict[_lq_w_key].shape[1])
         _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_hidden_dim": hidden_dim}
+                      "lq_latent_channels": in_ch,
+                      "latent_spatial_down_factor": 16 if in_ch >= 64 else 8}
         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
@@ -629,44 +598,6 @@ 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"
@@ -723,6 +654,23 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    if '{}shape_proj.weight'.format(key_prefix) in state_dict_keys and '{}lm_head.weight'.format(key_prefix) in state_dict_keys:  # Roblox Cube3D shape GPT
+        dit_config = {}
+        dit_config["image_model"] = "cube3d"
+        n_embd = state_dict['{}transformer.wte.weight'.format(key_prefix)].shape[1]
+        dit_config["n_embd"] = n_embd
+        dit_config["shape_model_vocab_size"] = state_dict['{}transformer.wte.weight'.format(key_prefix)].shape[0] - 3
+        dit_config["n_layer"] = count_blocks(state_dict_keys, '{}transformer.dual_blocks.'.format(key_prefix) + '{}.')
+        dit_config["n_single_layer"] = count_blocks(state_dict_keys, '{}transformer.single_blocks.'.format(key_prefix) + '{}.')
+        head_dim = state_dict['{}transformer.dual_blocks.0.attn.pre_x.q_norm.weight'.format(key_prefix)].shape[0]
+        dit_config["n_head"] = n_embd // head_dim
+        dit_config["shape_model_embed_dim"] = state_dict['{}shape_proj.weight'.format(key_prefix)].shape[1]
+        dit_config["text_model_embed_dim"] = state_dict['{}text_proj.weight'.format(key_prefix)].shape[1]
+        dit_config["use_bbox"] = '{}bbox_proj.weight'.format(key_prefix) in state_dict_keys
+        dit_config["bias"] = '{}text_proj.bias'.format(key_prefix) in state_dict_keys
+        dit_config["rope_theta"] = 10000  # not stored in the state dict; upstream's fixed constant
+        return dit_config
+
     if '{}latent_in.weight'.format(key_prefix) in state_dict_keys:  # Hunyuan 3D
         in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape
         dit_config = {}
@@ -830,16 +778,6 @@ 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"
@@ -903,21 +841,6 @@ 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]
@@ -1058,25 +981,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["image_model"] = "SAM31"
             return dit_config
 
-    if (
-        '{}double_blocks.0.attn.img_attn_qkv.weight'.format(key_prefix) in state_dict_keys
-        and '{}double_blocks.0.attn.img_attn_q_norm.weight'.format(key_prefix) in state_dict_keys
-        and '{}condition_embedder.time_embedder.linear_1.weight'.format(key_prefix) in state_dict_keys
-        and '{}img_in.weight'.format(key_prefix) in state_dict_keys
-        and len(state_dict['{}img_in.weight'.format(key_prefix)].shape) == 5
-    ):
-        img_in = state_dict['{}img_in.weight'.format(key_prefix)]
-        head_dim = state_dict['{}double_blocks.0.attn.img_attn_q_norm.weight'.format(key_prefix)].shape[0]
-        return {
-            "image_model": "joyimage",
-            "in_channels": img_in.shape[1],
-            "hidden_size": img_in.shape[0],
-            "patch_size": list(img_in.shape[2:]),
-            "num_layers": count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.'),
-            "num_attention_heads": img_in.shape[0] // head_dim,
-            "text_dim": 4096,
-        }
-
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -1207,10 +1111,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     return unet_config
 
-
-def model_config_from_unet_config(unet_config, state_dict=None, unet_key_prefix=""):
+def model_config_from_unet_config(unet_config, state_dict=None):
     for model_config in comfy.supported_models.models:
-        if model_config.matches(unet_config, state_dict, unet_key_prefix=unet_key_prefix):
+        if model_config.matches(unet_config, state_dict):
             return model_config(unet_config)
 
     logging.error("no match {}".format(unet_config))
@@ -1220,7 +1123,7 @@ 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, unet_key_prefix)
+    model_config = model_config_from_unet_config(unet_config, state_dict)
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 

comfy/model_management.py

@@ -473,7 +473,7 @@ except:
 
 SUPPORT_FP8_OPS = args.supports_fp8_compute
 
-AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1035", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]
+AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]
 AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'
 
 try:
@@ -616,8 +616,6 @@ 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
@@ -644,15 +642,6 @@ 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

comfy/ops.py

@@ -174,8 +174,6 @@ 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):
@@ -258,7 +256,7 @@ 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 = orig.requantize_from_float(x, scale="recalculate", stochastic_rounding=seed)
+                    y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
                 else:
                     y = comfy.float.stochastic_rounding(x, orig.dtype, seed=seed)
             if want_requant and len(fns) == 0:
@@ -1091,34 +1089,6 @@ 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}")
 
@@ -1161,15 +1131,6 @@ 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)
@@ -1222,33 +1183,8 @@ 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_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,
-                    )
+            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)
                 x = self._forward(input, weight, bias)
                 uncast_bias_weight(self, weight, bias, offload_stream)
                 return x
@@ -1257,7 +1193,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 run_every_op()
 
                 input_shape = input.shape
-                reshaped_nd = False
+                reshaped_3d = False
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
@@ -1267,10 +1203,9 @@ 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 and quantize_input):
+                if (input.requires_grad and _use_quantized):
 
                     weight, bias, offload_stream = cast_bias_weight(
                         self,
@@ -1292,31 +1227,25 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     return output
 
                 # Inference path (unchanged)
-                if _use_quantized and quantize_input:
+                if _use_quantized:
 
-                    # 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
+                    # 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
 
                     # Fall back to non-quantized for non-2D tensors
                     if input_reshaped.ndim == 2:
-                        reshaped_nd = input.ndim >= 3
+                        reshaped_3d = 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)
 
-                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,
-                )
+                output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor))
 
-                # Reshape output back to original rank if input was >2D
-                if reshaped_nd:
-                    output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
+                # 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]))
 
                 return output
 
@@ -1328,7 +1257,8 @@ 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:
-                    weight = self.weight.requantize_from_float(weight, scale="recalculate", stochastic_rounding=seed, inplace_ops=True).to(self.weight.dtype)
+                    # 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)
                 else:
                     weight = weight.to(self.weight.dtype)
                 if return_weight:
@@ -1450,12 +1380,6 @@ 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

@@ -3,22 +3,6 @@ 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 (
@@ -26,8 +10,6 @@ try:
         QuantizedLayout,
         TensorCoreFP8Layout as _CKFp8Layout,
         TensorCoreNVFP4Layout as _CKNvfp4Layout,
-        TensorCoreConvRotW4A4Layout as _CKTensorCoreConvRotW4A4Layout,
-        TensorWiseINT8Layout as _CKTensorWiseINT8Layout,
         register_layout_op,
         register_layout_class,
         get_layout_class,
@@ -41,22 +23,10 @@ try:
             ck.registry.disable("cuda")
             logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
-    # 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()):
+    if args.enable_triton_backend:
         try:
             import triton
-            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")
+            logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
         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")
@@ -77,12 +47,6 @@ except ImportError as e:
     class _CKNvfp4Layout:
         pass
 
-    class _CKTensorWiseINT8Layout:
-        pass
-
-    class _CKTensorCoreConvRotW4A4Layout:
-        pass
-
     def register_layout_class(name, cls):
         pass
 
@@ -210,8 +174,6 @@ class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):
 
 # Backward compatibility alias - default to E4M3
 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout
-TensorWiseINT8Layout = _CKTensorWiseINT8Layout
-TensorCoreConvRotW4A4Layout = _CKTensorCoreConvRotW4A4Layout
 
 
 # ==============================================================================
@@ -222,8 +184,6 @@ 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)
 
@@ -254,20 +214,6 @@ 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
@@ -280,8 +226,6 @@ __all__ = [
     "TensorCoreFP8E4M3Layout",
     "TensorCoreFP8E5M2Layout",
     "TensorCoreNVFP4Layout",
-    "TensorCoreConvRotW4A4Layout",
-    "TensorWiseINT8Layout",
     "QUANT_ALGOS",
     "register_layout_op",
 ]

comfy/sd.py

@@ -16,7 +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.cube.vae
 import comfy.ldm.triposplat.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.cogvideo.vae
@@ -59,7 +59,6 @@ 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
@@ -69,14 +68,11 @@ 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
 import comfy.text_encoders.sa3
 import comfy.text_encoders.gpt_oss
-import comfy.text_encoders.joyimage
 
 import comfy.model_patcher
 import comfy.lora
@@ -470,13 +466,9 @@ 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):
-        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
+        if '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():
@@ -503,8 +495,6 @@ 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
 
@@ -551,22 +541,6 @@ 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}
@@ -804,6 +778,39 @@ class VAE:
                 self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE()
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
 
+            # Roblox Cube3D shape tokenizer (OneDAutoEncoder, decode-only)
+            elif "bottleneck.block.codebook.weight" in sd:
+                self.latent_dim = 1
+                # The VQ bottleneck (get_codebook/lookup_codebook) reads raw parameters
+                # outside any hooked forward, so the streaming-offload cast hooks can't
+                # relocate them; the model must be fully resident to decode. This is a
+                # correctness requirement, declared via the standard flag (like the audio
+                # VAEs) rather than managed manually in the node.
+                self.disable_offload = True
+                embed_dim = sd["bottleneck.block.codebook.weight"].shape[1]
+                num_codes = sd["bottleneck.block.codebook.weight"].shape[0]
+                width = sd["bottleneck.block.c_out.weight"].shape[0]
+                num_encoder_latents = sd["decoder.positional_encodings"].shape[0]
+                head_dim = sd["decoder.blocks.0.attn.q_norm.weight"].shape[0]
+                num_heads = width // head_dim
+                num_freqs = sd["embedder.weight"].shape[1]
+                num_decoder_layers = len({k.split(".")[2] for k in sd if k.startswith("decoder.blocks.")})
+                self.first_stage_model = comfy.ldm.cube.vae.CubeShapeVAE(
+                    num_encoder_latents=num_encoder_latents, embed_dim=embed_dim, width=width,
+                    num_heads=num_heads, num_freqs=num_freqs, num_decoder_layers=num_decoder_layers,
+                    num_codes=num_codes,
+                )
+                # Decode goes through the managed comfy.sd.VAE.decode path; the grid logits
+                # are float32 regardless of weight dtype, so keep process_output identity
+                # (the default clamps to [0, 1] in-place and would destroy the isosurface).
+                self.process_output = lambda image: image
+                self.process_input = lambda image: image
+                # shape is the token-ID latent (B, 1, num_tokens); size by num_tokens.
+                self.memory_used_decode = lambda shape, dtype: (1000 * shape[-1] * 768) * model_management.dtype_size(dtype)
+                # fp32-only (unlike most VAEs that allow fp16/bf16): the VQ codebook lookup
+                # and occupancy-grid query must run in fp32 to match upstream and keep the
+                # isosurface stable.
+                self.working_dtypes = [torch.float32]
 
             elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio
                 self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100)
@@ -1033,10 +1040,6 @@ 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)
@@ -1073,25 +1076,6 @@ 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
@@ -1139,19 +1123,11 @@ class VAE:
                 if dims == 1 or self.extra_1d_channel is not None:
                     pixel_samples = self.decode_tiled_1d(samples_in)
                 elif dims == 2:
-                    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)
+                    pixel_samples = self.decode_tiled_(samples_in)
                 elif dims == 3:
                     tile = 256 // self.spacial_compression_decode()
                     overlap = tile // 4
-                    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 = 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
@@ -1170,9 +1146,7 @@ class VAE:
             args["overlap"] = overlap
 
         with model_management.cuda_device_context(self.device):
-            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:
+            if dims == 1 or self.extra_1d_channel is not None:
                 args.pop("tile_y")
                 output = self.decode_tiled_1d(samples, **args)
             elif dims == 2:
@@ -1233,17 +1207,12 @@ class VAE:
                 if self.latent_dim == 3:
                     tile = 256
                     overlap = tile // 4
-                    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))
+                    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):
@@ -1251,7 +1220,7 @@ 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 and pixel_samples.ndim < 5:
+        if dims == 3:
             if not self.not_video:
                 pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
             else:
@@ -1275,27 +1244,21 @@ class VAE:
             elif dims == 2:
                 samples = self.encode_tiled_(pixel_samples, **args)
             elif dims == 3:
-                if self.handles_tiling:
-                    samples = self._encode_tiled_owned(pixel_samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
+                if tile_t is not None:
+                    tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
                 else:
-                    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)
+                    tile_t_latent = 9999
+                args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
 
-                    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))
+                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))
 
-                    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):
@@ -1319,11 +1282,6 @@ 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"):
@@ -1376,9 +1334,6 @@ class CLIPType(Enum):
     LENS = 28
     PIXELDIT = 29
     IDEOGRAM4 = 30
-    BOOGU = 31
-    KREA2 = 32
-    JOYIMAGE = 33
 
 
 
@@ -1432,9 +1387,6 @@ class TEModel(Enum):
     GEMMA_4_31B = 31
     T5_GEMMA = 32
     GPT_OSS_20B = 33
-    QWEN3VL_4B = 34
-    QWEN3VL_8B = 35
-    GEMMA_4_12B = 36
 
 
 def detect_te_model(sd):
@@ -1464,9 +1416,6 @@ def detect_te_model(sd):
     if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
         if 'model.layers.59.self_attn.q_norm.weight' in sd:
             return TEModel.GEMMA_4_31B
-        # Gemma4 12B Unified: 48 layers, encoder-free; global layers drop v_proj (attention_k_eq_v).
-        if 'model.layers.47.self_attn.q_norm.weight' in sd and 'model.layers.5.self_attn.v_proj.weight' not in sd:
-            return TEModel.GEMMA_4_12B
         if 'model.layers.41.self_attn.q_norm.weight' in sd and 'model.layers.47.self_attn.q_norm.weight' not in sd:
             return TEModel.GEMMA_4_E4B
         if 'model.layers.34.self_attn.q_norm.weight' in sd and 'model.layers.41.self_attn.q_norm.weight' not in sd:
@@ -1499,8 +1448,6 @@ 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:
@@ -1622,11 +1569,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.sa3.SAT5GemmaModel
             clip_target.tokenizer = comfy.text_encoders.sa3.SAT5GemmaTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
-        elif te_model in (TEModel.GEMMA_4_E4B, TEModel.GEMMA_4_E2B, TEModel.GEMMA_4_31B, TEModel.GEMMA_4_12B):
+        elif te_model in (TEModel.GEMMA_4_E4B, TEModel.GEMMA_4_E2B, TEModel.GEMMA_4_31B):
             variant = {TEModel.GEMMA_4_E4B: comfy.text_encoders.gemma4.Gemma4_E4B,
                        TEModel.GEMMA_4_E2B: comfy.text_encoders.gemma4.Gemma4_E2B,
-                       TEModel.GEMMA_4_31B: comfy.text_encoders.gemma4.Gemma4_31B,
-                       TEModel.GEMMA_4_12B: comfy.text_encoders.gemma4.Gemma4_12B}[te_model]
+                       TEModel.GEMMA_4_31B: comfy.text_encoders.gemma4.Gemma4_31B}[te_model]
             clip_target.clip = comfy.text_encoders.gemma4.gemma4_te(**llama_detect(clip_data), model_class=variant)
             clip_target.tokenizer = variant.tokenizer
             tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None)
@@ -1700,32 +1646,6 @@ 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 == CLIPType.JOYIMAGE and te_model == TEModel.QWEN3VL_8B:  # JoyImageEdit: full Qwen3-VL-8B, edit-conditioning template + drop_idx.
-                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.joyimage.te(**llama_detect(clip_data))
-                clip_target.tokenizer = comfy.text_encoders.joyimage.JoyImageTokenizer
-            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
@@ -1973,7 +1893,7 @@ 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, device=load_device)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
@@ -2114,7 +2034,7 @@ 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, device=load_device)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
     if custom_operations is not None:
         model_config.custom_operations = custom_operations

comfy/sd1_clip.py

@@ -543,24 +543,18 @@ 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, the cleaned embedding name, and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding 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, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, embedding_name, leftover)
+                return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -591,7 +585,7 @@ class SDTokenizer:
         tokens = []
         for weighted_segment, weight in parsed_weights:
             to_tokenize = unescape_important(weighted_segment)
-            split = re.split(r'(?<=\s){}'.format(re.escape(self.embedding_identifier)), to_tokenize)
+            split = re.split(' {0}|\n{0}'.format(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]))
@@ -601,7 +595,7 @@ 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, embedding_name, leftover = self._try_get_embedding(embedding_name)
+                    embed, 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

@@ -25,9 +25,6 @@ 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.joyimage
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
@@ -1553,6 +1550,32 @@ class Hunyuan3Dv2mini(Hunyuan3Dv2):
 
     latent_format = latent_formats.Hunyuan3Dv2mini
 
+
+class Cube3D(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "cube3d",
+    }
+
+    unet_extra_config = {}
+
+    sampling_settings = {}
+
+    latent_format = latent_formats.Cube3D
+
+    memory_usage_factor = 1.0
+
+    # Upstream keeps fp32 weights and uses bf16 autocast during the forward pass
+    # (see sample_cube). Prefer fp32 weights for parity; bf16 is the low-VRAM fallback.
+    supported_inference_dtypes = [torch.float32, torch.bfloat16]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.Cube3D(self, device=device)
+
+    def clip_target(self, state_dict={}):
+        # No bundled text encoder: the cube checkpoint is GPT-only. The graph wires a
+        # standard CLIPLoader(clip-l)/CLIPTextEncode, so there is no clip_target to build.
+        return None
+
 class TripoSplat(supported_models_base.BASE):
     # Image -> 3D gaussian splat flow denoiser
     unet_config = {
@@ -1686,40 +1709,6 @@ 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",
@@ -1795,27 +1784,6 @@ 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",
@@ -1854,35 +1822,6 @@ 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",
@@ -1912,38 +1851,6 @@ class QwenImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.qwen_image.QwenImageTokenizer, comfy.text_encoders.qwen_image.te(**hunyuan_detect))
 
-class JoyImage(supported_models_base.BASE):
-    unet_config = {
-        "image_model": "joyimage",
-    }
-
-    sampling_settings = {
-        "multiplier": 1000,
-        "shift": 1.5,
-    }
-
-    memory_usage_factor = 1.8
-
-    unet_extra_config = {
-        "theta": 10000,
-        "rope_dim_list": [16, 56, 56],
-    }
-
-    latent_format = latent_formats.Wan21
-
-    supported_inference_dtypes = [torch.bfloat16, torch.float32]
-
-    vae_key_prefix = ["vae."]
-    text_encoder_key_prefix = ["text_encoders."]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        return model_base.JoyImage(self, device=device)
-
-    def clip_target(self, state_dict={}):
-        pref = self.text_encoder_key_prefix[0]
-        qwen3vl_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3vl.transformer.".format(pref))
-        return supported_models_base.ClipTarget(comfy.text_encoders.joyimage.JoyImageTokenizer, comfy.text_encoders.joyimage.te(**qwen3vl_detect))
-
 class HunyuanImage21(HunyuanVideo):
     unet_config = {
         "image_model": "hunyuan_video",
@@ -2411,20 +2318,17 @@ models = [
     Hunyuan3Dv2mini,
     Hunyuan3Dv2,
     Hunyuan3Dv2_1,
+    Cube3D,
     TripoSplat,
     HiDream,
     HiDreamO1,
     Chroma,
-    SeedVR2,
     ChromaRadiance,
     ACEStep,
     ACEStep15,
     Omnigen2,
-    Boogu,
     QwenImage,
-    JoyImage,
     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, unet_key_prefix=""):
+    def matches(s, unet_config, state_dict=None):
         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.format(unet_key_prefix) not in state_dict:
+                if k 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, device=None):
+    def set_inference_dtype(self, dtype, manual_cast_dtype):
         self.unet_config['dtype'] = dtype
         self.manual_cast_dtype = manual_cast_dtype
 

comfy/text_encoders/boogu.py

@@ -1,58 +0,0 @@
-"""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

@@ -1,15 +1,11 @@
 import torch
 import torch.nn as nn
-import torchaudio.functional as AF
-import torchvision.transforms.functional as TVF
 import numpy as np
-from tokenizers import Tokenizer
 from dataclasses import dataclass
 import math
 
 from comfy import sd1_clip
 import comfy.model_management
-import comfy.ops
 from comfy.ldm.modules.attention import optimized_attention_for_device
 from comfy.rmsnorm import rms_norm
 from comfy.text_encoders.llama import RMSNorm, MLP, BaseLlama, BaseGenerate, _make_scaled_embedding
@@ -25,10 +21,6 @@ GEMMA4_VISION_CONFIG = {"hidden_size": 768, "image_size": 896, "intermediate_siz
 GEMMA4_VISION_31B_CONFIG = {"hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 16, "head_dim": 72, "rms_norm_eps": 1e-6, "position_embedding_size": 10240, "pooling_kernel_size": 3}
 GEMMA4_AUDIO_CONFIG = {"hidden_size": 1024, "num_hidden_layers": 12, "num_attention_heads": 8, "intermediate_size": 4096, "conv_kernel_size": 5, "attention_chunk_size": 12, "attention_context_left": 13, "attention_context_right": 0, "attention_logit_cap": 50.0, "output_proj_dims": 1536, "rms_norm_eps": 1e-6, "residual_weight": 0.5}
 
-# Encoder-free (gemma4_unified) multimodal embedders: raw patches/waveform projected directly into LM space.
-GEMMA4_UNIFIED_VISION_CONFIG = {"model_patch_size": 48, "patch_size": 16, "pooling_kernel_size": 3, "mm_embed_dim": 3840, "mm_posemb_size": 1120, "output_proj_dims": 3840, "rms_norm_eps": 1e-6}
-GEMMA4_UNIFIED_AUDIO_CONFIG = {"audio_samples_per_token": 640, "output_proj_dims": 640, "rms_norm_eps": 1e-6}
-
 @dataclass
 class Gemma4Config:
     vocab_size: int = 262144
@@ -43,9 +35,6 @@ class Gemma4Config:
     transformer_type: str = "gemma4"
     head_dim = 256
     global_head_dim = 512
-    num_global_key_value_heads = None
-    attention_k_eq_v = False
-    vision_bidirectional = False
     rms_norm_add = False
     mlp_activation = "gelu_pytorch_tanh"
     qkv_bias = False
@@ -62,7 +51,6 @@ class Gemma4Config:
     num_kv_shared_layers: int = 18
     use_double_wide_mlp: bool = False
     stop_tokens = [1, 50, 106]
-    suppress_tokens = []
     vision_config = GEMMA4_VISION_CONFIG
     audio_config = GEMMA4_AUDIO_CONFIG
     mm_tokens_per_image = 280
@@ -84,30 +72,12 @@ class Gemma4_31B_Config(Gemma4Config):
     num_hidden_layers: int = 60
     num_attention_heads: int = 32
     num_key_value_heads: int = 16
-    vision_bidirectional = True
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     hidden_size_per_layer_input: int = 0
     num_kv_shared_layers: int = 0
     audio_config = None
     vision_config = GEMMA4_VISION_31B_CONFIG
 
-@dataclass
-class Gemma4_12B_Config(Gemma4Config):
-    hidden_size: int = 3840
-    intermediate_size: int = 15360
-    num_hidden_layers: int = 48
-    num_attention_heads: int = 16
-    num_key_value_heads: int = 8
-    num_global_key_value_heads = 1
-    attention_k_eq_v = True
-    vision_bidirectional = True
-    sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
-    hidden_size_per_layer_input: int = 0
-    num_kv_shared_layers: int = 0
-    audio_config = GEMMA4_UNIFIED_AUDIO_CONFIG
-    vision_config = GEMMA4_UNIFIED_VISION_CONFIG
-    suppress_tokens = [258883, 258882]
-
 
 # unfused RoPE as addcmul_ RoPE diverges from reference code
 def _apply_rotary_pos_emb(x, freqs_cis):
@@ -119,18 +89,17 @@ def _apply_rotary_pos_emb(x, freqs_cis):
     return out
 
 class Gemma4Attention(nn.Module):
-    def __init__(self, config, head_dim, num_kv_heads=None, k_eq_v=False, device=None, dtype=None, ops=None):
+    def __init__(self, config, head_dim, device=None, dtype=None, ops=None):
         super().__init__()
         self.num_heads = config.num_attention_heads
-        self.num_kv_heads = num_kv_heads if num_kv_heads is not None else config.num_key_value_heads
+        self.num_kv_heads = config.num_key_value_heads
         self.hidden_size = config.hidden_size
         self.head_dim = head_dim
         self.inner_size = self.num_heads * head_dim
 
         self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype)
         self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
-        # k_eq_v: V reuses the K projection (no separate v_proj weight)
-        self.v_proj = None if k_eq_v else ops.Linear(config.hidden_size, self.num_kv_heads * head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
         self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
         self.q_norm = None
@@ -164,10 +133,7 @@ class Gemma4Attention(nn.Module):
             shareable_kv = None
         else:
             xk = self.k_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim)
-            if self.v_proj is not None:
-                xv = self.v_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim)
-            else:
-                xv = xk  # k_eq_v: V is the raw K projection (before k_norm/RoPE)
+            xv = self.v_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim)
             if self.k_norm is not None:
                 xk = self.k_norm(xk)
             xv = rms_norm(xv)
@@ -220,10 +186,7 @@ class TransformerBlockGemma4(nn.Module):
 
         head_dim = config.head_dim if self.sliding_attention else config.global_head_dim
 
-        # k_eq_v only on global layers, which then use num_global_key_value_heads
-        k_eq_v = config.attention_k_eq_v and not self.sliding_attention
-        num_kv_heads = config.num_global_key_value_heads if k_eq_v else config.num_key_value_heads
-        self.self_attn = Gemma4Attention(config, head_dim=head_dim, num_kv_heads=num_kv_heads, k_eq_v=k_eq_v, device=device, dtype=dtype, ops=ops)
+        self.self_attn = Gemma4Attention(config, head_dim=head_dim, device=device, dtype=dtype, ops=ops)
 
         num_kv_shared = config.num_kv_shared_layers
         first_kv_shared = config.num_hidden_layers - num_kv_shared
@@ -240,9 +203,9 @@ class TransformerBlockGemma4(nn.Module):
             self.per_layer_input_gate = ops.Linear(config.hidden_size, self.hidden_size_per_layer_input, bias=False, device=device, dtype=dtype)
             self.per_layer_projection = ops.Linear(self.hidden_size_per_layer_input, config.hidden_size, bias=False, device=device, dtype=dtype)
             self.post_per_layer_input_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
-
-        # layer_scalar exists on every gemma4 variant, independent of per-layer input
-        self.register_buffer("layer_scalar", torch.empty(1, device=device, dtype=dtype))
+            self.register_buffer("layer_scalar", torch.ones(1, device=device, dtype=dtype))
+        else:
+            self.layer_scalar = None
 
     def forward(self, x, attention_mask=None, freqs_cis=None, past_key_value=None, per_layer_input=None, shared_kv=None):
         sliding_window = None
@@ -281,7 +244,8 @@ class TransformerBlockGemma4(nn.Module):
             x = self.post_per_layer_input_norm(x)
             x = residual + x
 
-        x = x * comfy.ops.cast_to_input(self.layer_scalar, x)
+        if self.layer_scalar is not None:
+            x = x * self.layer_scalar
 
         return x, present_key_value, shareable_kv
 
@@ -370,19 +334,6 @@ class Gemma4Transformer(nn.Module):
             causal_mask.masked_fill_(torch.ones_like(causal_mask, dtype=torch.bool).triu_(1), min_val)
             mask = mask + causal_mask if mask is not None else causal_mask
 
-            # Bidirectional attention within each image soft-token block (prefill only; text/audio stay causal).
-            if self.config.vision_bidirectional and past_len == 0 and embeds_info:
-                block_ids = torch.full((seq_len,), -1, dtype=torch.long, device=x.device)
-                group = 0
-                for info in embeds_info:
-                    if info.get("type") == "image":
-                        start = info["index"]
-                        block_ids[start:start + info["size"]] = group
-                        group += 1
-                if group > 0:
-                    same_block = (block_ids[:, None] == block_ids[None, :]) & (block_ids[:, None] >= 0)
-                    mask = mask.masked_fill(same_block, 0.0)
-
         # Per-layer inputs
         per_layer_inputs = None
         if self.hidden_size_per_layer_input:
@@ -403,24 +354,8 @@ class Gemma4Transformer(nn.Module):
         shared_global_kv = None   # KV from last non-shared global layer
 
         intermediate = None
-        all_intermediate = None
-        only_layers = None
-        if intermediate_output is not None:
-            if isinstance(intermediate_output, list):
-                all_intermediate = []
-                only_layers = {len(self.layers) + layer if layer < 0 else layer for layer in intermediate_output}
-            elif intermediate_output == "all":
-                all_intermediate = []
-                intermediate_output = None
-            elif intermediate_output < 0:
-                intermediate_output = len(self.layers) + intermediate_output
-
         next_key_values = []
         for i, layer in enumerate(self.layers):
-            if all_intermediate is not None:
-                if only_layers is None or (i in only_layers):
-                    all_intermediate.append(x.unsqueeze(1).clone())
-
             past_kv = past_key_values[i] if past_key_values is not None and len(past_key_values) > 0 else None
 
             layer_kwargs = {}
@@ -450,18 +385,7 @@ class Gemma4Transformer(nn.Module):
         if self.norm is not None:
             x = self.norm(x)
 
-        if all_intermediate is not None:
-            if only_layers is None or (len(self.layers) in only_layers):
-                all_intermediate.append(x.unsqueeze(1).clone())
-            if len(all_intermediate) > 0:
-                intermediate = torch.cat(all_intermediate, dim=1)
-
-        if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
-            intermediate = self.norm(intermediate)
-
-        # Only hand back the KV cache when caching was actually requested; SDClipModel reads
-        # outputs[2] as the pooled output.
-        if past_key_values is not None and len(next_key_values) > 0:
+        if len(next_key_values) > 0:
             return x, intermediate, next_key_values
         return x, intermediate
 
@@ -480,8 +404,6 @@ class Gemma4Base(BaseLlama, BaseGenerate, torch.nn.Module):
         cap = self.model.config.final_logit_softcapping
         if cap:
             logits = cap * torch.tanh(logits / cap)
-        if self.model.config.suppress_tokens:
-            logits[..., self.model.config.suppress_tokens] = torch.finfo(logits.dtype).min
         return logits
 
     def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
@@ -519,28 +441,6 @@ class Gemma4AudioMixin:
         return None, None
 
 
-class Gemma4UnifiedBase(Gemma4Base):
-    """Encoder-free multimodal Gemma4 (gemma4_unified, e.g. 12B): raw image patches and audio frames projected directly into LM space."""
-    def _init_model(self, config, dtype, device, operations):
-        self.num_layers = config.num_hidden_layers
-        self.model = Gemma4Transformer(config, device=device, dtype=dtype, ops=operations)
-        self.dtype = dtype
-        self.vision_model = Gemma4UnifiedVisionEmbedder(config.vision_config, device=device, dtype=dtype, ops=operations)
-        self.multi_modal_projector = Gemma4RMSNormProjector(config.vision_config["output_proj_dims"], config.hidden_size, dtype=dtype, device=device, ops=operations)
-        self.audio_projector = Gemma4RMSNormProjector(config.audio_config["output_proj_dims"], config.hidden_size, dtype=dtype, device=device, ops=operations)
-
-    def preprocess_embed(self, embed, device):
-        if embed["type"] == "image":
-            pixels = embed.pop("data").movedim(-1, 1).to(device, dtype=self.dtype)  # [B, H, W, C] -> [B, C, H, W], [0,1]
-            patches, positions = self.vision_model.patchify(pixels)
-            vision_out = self.vision_model(patches, positions)
-            return self.multi_modal_projector(vision_out), None
-        if embed["type"] == "audio":
-            audio = embed.pop("data").to(device, dtype=self.dtype)  # [1, T, audio_samples_per_token]
-            return self.audio_projector(audio), None
-        return None, None
-
-
 # Vision Encoder
 
 def _compute_vision_2d_rope(head_dim, pixel_position_ids, theta=100.0, device=None):
@@ -813,73 +713,6 @@ class Gemma4MultiModalProjector(Gemma4RMSNormProjector):
         super().__init__(config.vision_config["hidden_size"], config.hidden_size, dtype=dtype, device=device, ops=ops)
 
 
-# Encoder-free vision (gemma4_unified): raw merged pixel patches projected directly into LM space.
-
-def _patches_merge(patches, positions_xy, length):
-    patch_size = math.isqrt(patches.shape[-1] // 3)
-    k = math.isqrt(patches.shape[-2] // length)
-    batch = patches.shape[:-2]
-
-    max_x = positions_xy[..., 0].max(dim=-1, keepdim=True)[0] + 1
-    kidx = torch.div(positions_xy, k, rounding_mode="floor")
-    rem = torch.remainder(positions_xy, k)
-    order = rem[..., 0] + rem[..., 1] * k + k * k * kidx[..., 0] + k * max_x * kidx[..., 1]
-    perm = order.long().argsort(dim=-1)
-
-    merged = patches.gather(-2, perm.unsqueeze(-1).expand_as(patches))
-    merged = merged.reshape(*batch, length, k, k, patch_size, patch_size, 3)
-    merged = merged.permute(*range(len(batch)), -6, -5, -3, -4, -2, -1).reshape(*batch, length, (k * patch_size) ** 2 * 3)
-
-    pos = positions_xy.gather(-2, perm.unsqueeze(-1).expand_as(positions_xy))
-    pad = (positions_xy == -1).all(dim=-1, keepdim=True)
-    pos = torch.where(pad, positions_xy, pos).reshape(*batch, length, k * k, 2)
-    pos = torch.div(pos, k, rounding_mode="floor").min(dim=-2)[0]
-    return merged, pos
-
-
-class Gemma4UnifiedVisionEmbedder(nn.Module):
-    """Encoder-free patch embedder (LN -> Dense -> LN -> +2D posemb -> LN); projection to text space is the separate multi_modal_projector."""
-    def __init__(self, config, device=None, dtype=None, ops=None):
-        super().__init__()
-        self.patch_size = config["patch_size"]
-        self.pooling_kernel_size = config["pooling_kernel_size"]
-        patch_dim = config["model_patch_size"] ** 2 * 3
-        mm_embed_dim = config["mm_embed_dim"]
-        self.patch_ln1 = ops.LayerNorm(patch_dim, device=device, dtype=dtype)
-        self.patch_dense = ops.Linear(patch_dim, mm_embed_dim, device=device, dtype=dtype)
-        self.patch_ln2 = ops.LayerNorm(mm_embed_dim, device=device, dtype=dtype)
-        self.pos_embedding = nn.Parameter(torch.empty(config["mm_posemb_size"], 2, mm_embed_dim, device=device, dtype=dtype))
-        self.pos_norm = ops.LayerNorm(mm_embed_dim, device=device, dtype=dtype)
-
-    def patchify(self, pixels):
-        """pixels: [B, C, H, W] in [0,1] -> merged patches [B, N, 6912], positions [B, N, 2]."""
-        ps, k = self.patch_size, self.pooling_kernel_size
-        out_patches, out_positions = [], []
-        for img in pixels:
-            ph, pw = img.shape[-2] // ps, img.shape[-1] // ps
-            teacher = img.reshape(img.shape[0], ph, ps, pw, ps).permute(1, 3, 2, 4, 0).reshape(ph * pw, -1)
-            grid = torch.meshgrid(torch.arange(pw, device=img.device), torch.arange(ph, device=img.device), indexing="xy")
-            tpos = torch.stack(grid, dim=-1).reshape(teacher.shape[0], 2)
-            n_model = teacher.shape[0] // (k * k)
-            mp, mpos = _patches_merge(teacher.unsqueeze(0), tpos.unsqueeze(0), n_model)
-            out_patches.append(mp.squeeze(0))
-            out_positions.append(mpos.squeeze(0))
-        return torch.stack(out_patches), torch.stack(out_positions)
-
-    def forward(self, pixel_values, image_position_ids):
-        x = self.patch_ln1(pixel_values)
-        x = self.patch_dense(x)
-        x = self.patch_ln2(x)
-
-        clamped = image_position_ids.clamp(min=0).long()
-        valid = (image_position_ids != -1).to(x.dtype).unsqueeze(-1)
-        axes = torch.arange(2, device=image_position_ids.device)
-        pos = comfy.model_management.cast_to_device(self.pos_embedding, x.device, x.dtype)
-        pos_embs = (pos[clamped, axes] * valid).sum(-2)
-        x = x + pos_embs
-        return self.pos_norm(x)
-
-
 # Audio Encoder
 
 class Gemma4AudioConvSubsampler(nn.Module):
@@ -1157,30 +990,6 @@ class Gemma4AudioProjector(Gemma4RMSNormProjector):
 
 # Tokenizer and Wrappers
 
-def _get_aspect_ratio_preserving_size(height, width, patch_size, max_patches, pooling_kernel_size):
-    target_px = max_patches * patch_size ** 2
-    factor = math.sqrt(target_px / (height * width))
-    side_mult = pooling_kernel_size * patch_size
-    target_height = math.floor(factor * height / side_mult) * side_mult
-    target_width = math.floor(factor * width / side_mult) * side_mult
-
-    if target_height == 0 and target_width == 0:
-        raise ValueError(f"Attempting to resize to a 0 x 0 image. Resized height should be divisible by {side_mult}.")
-
-    max_side_length = (max_patches // pooling_kernel_size ** 2) * side_mult
-    if target_height == 0:
-        target_height = side_mult
-        target_width = min(math.floor(width / height) * side_mult, max_side_length)
-    elif target_width == 0:
-        target_width = side_mult
-        target_height = min(math.floor(height / width) * side_mult, max_side_length)
-
-    if target_height * target_width > target_px:
-        raise ValueError(f"Resizing [{height}x{width}] to [{target_height}x{target_width}] exceeds the patch budget.")
-
-    return target_height, target_width
-
-
 class Gemma4_Tokenizer():
     tokenizer_json_data = None
 
@@ -1189,35 +998,25 @@ class Gemma4_Tokenizer():
             return {"tokenizer_json": self.tokenizer_json_data}
         return {}
 
-    def _audio_token_count(self, num_samples):
-        # Default (E2B/E4B): mel frames after two stride-2 conv subsamples.
-        _fl = 320  # int(round(16000 * 20.0 / 1000.0))
-        _hl = 160  # int(round(16000 * 10.0 / 1000.0))
-        _nmel = (num_samples + _fl // 2 - (_fl + 1)) // _hl + 1
-        _t = _nmel
-        for _ in range(2):
-            _t = (_t + 2 - 3) // 2 + 1
-        return min(_t, 750)
-
-    @staticmethod
-    def _resample_16k(waveform, sample_rate):
-        """Mix to mono and resample to 16kHz. Kaiser params reproduce the reference (transformers
-        load_audio -> librosa/soxr_hq) to ~1e-12 MSE using only torchaudio."""
+    def _extract_mel_spectrogram(self, waveform, sample_rate):
+        """Extract 128-bin log mel spectrogram.
+        Uses numpy for FFT/matmul/log to produce bit-identical results with reference code.
+        """
+        # Mix to mono first, then resample to 16kHz
         if waveform.dim() > 1 and waveform.shape[0] > 1:
             waveform = waveform.mean(dim=0, keepdim=True)
         if waveform.dim() == 1:
             waveform = waveform.unsqueeze(0)
-        audio = waveform.float()
+        audio = waveform.squeeze(0).float().numpy()
         if sample_rate != 16000:
-            audio = AF.resample(audio, sample_rate, 16000, resampling_method="sinc_interp_kaiser",
-                                lowpass_filter_width=121, rolloff=0.9568384289091556, beta=21.01531462440614)
-        return audio.squeeze(0).contiguous()
-
-    def _extract_audio_features(self, waveform, sample_rate):
-        """Default (E2B/E4B): 128-bin log mel spectrogram for the conformer audio encoder.
-        Uses numpy for FFT/matmul/log to produce bit-identical results with reference code.
-        """
-        audio = self._resample_16k(waveform, sample_rate).numpy()
+            # Use scipy's resample_poly with a high-quality FIR filter to get as close as possible to librosa's resampling (while still not full match)
+            from scipy.signal import resample_poly, firwin
+            from math import gcd
+            g = gcd(sample_rate, 16000)
+            up, down = 16000 // g, sample_rate // g
+            L = max(up, down)
+            h = firwin(160 * L + 1, 0.96 / L, window=('kaiser', 6.5))
+            audio = resample_poly(audio, up, down, window=h).astype(np.float32)
         n = len(audio)
 
         # Pad to multiple of 128, build sample-level mask
@@ -1265,8 +1064,8 @@ class Gemma4_Tokenizer():
         if audio is not None:
             waveform = audio["waveform"].squeeze(0) if hasattr(audio, "__getitem__") else audio
             sample_rate = audio.get("sample_rate", 16000) if hasattr(audio, "get") else 16000
-            feat, feat_mask = self._extract_audio_features(waveform, sample_rate)
-            audio_features = [(feat.unsqueeze(0), feat_mask.unsqueeze(0))]  # ([1, T, D], [1, T])
+            mel, mel_mask = self._extract_mel_spectrogram(waveform, sample_rate)
+            audio_features = [(mel.unsqueeze(0), mel_mask.unsqueeze(0))]  # ([1, T, 128], [1, T])
 
         # Process image/video frames
         is_video = video is not None
@@ -1289,10 +1088,15 @@ class Gemma4_Tokenizer():
             h, w = samples.shape[2], samples.shape[3]
             patch_size = 16
             pooling_k = 3
-            max_soft_tokens = kwargs.get("max_soft_tokens", 70 if is_video else 280)
+            max_soft_tokens = 70 if is_video else 280  # video uses smaller token budget per frame
             max_patches = max_soft_tokens * pooling_k * pooling_k
-            target_h, target_w = _get_aspect_ratio_preserving_size(h, w, patch_size, max_patches, pooling_k)
+            target_px = max_patches * patch_size * patch_size
+            factor = (target_px / (h * w)) ** 0.5
+            side_mult = pooling_k * patch_size
+            target_h = max(int(factor * h // side_mult) * side_mult, side_mult)
+            target_w = max(int(factor * w // side_mult) * side_mult, side_mult)
 
+            import torchvision.transforms.functional as TVF
             for i in range(num_frames):
                 # rescaling to match reference code
                 s = (samples[i].clamp(0, 1) * 255).to(torch.uint8)  # [C, H, W] uint8
@@ -1311,7 +1115,7 @@ class Gemma4_Tokenizer():
                 llama_text = llama_template.format(text)
             else:
                 # Build template from modalities present
-                system = "<|turn>system\n<|think|>\n<turn|>\n" if thinking else ""
+                system = "<|turn>system\n<|think|><turn|>\n" if thinking else ""
                 media = ""
                 if len(images) > 0:
                     if is_video:
@@ -1331,11 +1135,15 @@ class Gemma4_Tokenizer():
                 if len(audio_features) > 0:
                     # Compute audio token count (always at 16kHz)
                     num_samples = int(waveform.shape[-1] * 16000 / sample_rate) if sample_rate != 16000 else waveform.shape[-1]
-                    n_audio_tokens = self._audio_token_count(num_samples)
+                    _fl = 320  # int(round(16000 * 20.0 / 1000.0))
+                    _hl = 160  # int(round(16000 * 10.0 / 1000.0))
+                    _nmel = (num_samples + _fl // 2 - (_fl + 1)) // _hl + 1
+                    _t = _nmel
+                    for _ in range(2):
+                        _t = (_t + 2 - 3) // 2 + 1
+                    n_audio_tokens = min(_t, 750)
                     media += "<|audio>" + "<|audio|>" * n_audio_tokens + "<audio|>"
-                # Non-thinking mode primes an empty thought channel so the model answers directly.
-                model_open = "" if thinking else "<|channel>thought\n<channel|>"
-                llama_text = f"{system}<|turn>user\n{text}{media}<turn|>\n<|turn>model\n{model_open}"
+                llama_text = f"{system}<|turn>user\n{media}{text}<turn|>\n<|turn>model\n"
 
         text_tokens = super().tokenize_with_weights(llama_text, return_word_ids)
 
@@ -1370,6 +1178,7 @@ class Gemma4_Tokenizer():
 class _Gemma4Tokenizer:
     """Tokenizer using the tokenizers (Gemma4 doesn't come with sentencepiece model)"""
     def __init__(self, tokenizer_json_bytes=None, **kwargs):
+        from tokenizers import Tokenizer
         if isinstance(tokenizer_json_bytes, torch.Tensor):
             tokenizer_json_bytes = bytes(tokenizer_json_bytes.tolist())
         self.tokenizer = Tokenizer.from_str(tokenizer_json_bytes.decode("utf-8"))
@@ -1415,30 +1224,6 @@ class Gemma4Tokenizer(sd1_clip.SD1Tokenizer):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma4", tokenizer=self.tokenizer_class)
 
 
-class Gemma4UnifiedSDTokenizer(Gemma4SDTokenizer):
-    """Encoder-free (gemma4_unified) audio: raw 16kHz waveform frames instead of mel spectrogram."""
-    embedding_size = 3840
-
-    def _extract_audio_features(self, waveform, sample_rate):
-        audio = self._resample_16k(waveform, sample_rate)
-        spt = 640  # audio_samples_per_token (40ms at 16kHz)
-        pad = (-audio.shape[0]) % spt
-        if pad:
-            audio = torch.nn.functional.pad(audio, (0, pad))
-        num_tokens = audio.shape[0] // spt
-        feats = audio[:num_tokens * spt].reshape(num_tokens, spt)
-        feats = feats[:750]  # audio_seq_length cap (matches reference truncation, ~30s)
-        mask = torch.ones(feats.shape[0], dtype=torch.bool)
-        return feats, mask
-
-    def _audio_token_count(self, num_samples):
-        return min((num_samples + 639) // 640, 750)
-
-
-class Gemma4UnifiedTokenizer(Gemma4Tokenizer):
-    tokenizer_class = Gemma4UnifiedSDTokenizer
-
-
 # Model wrappers
 class Gemma4Model(sd1_clip.SDClipModel):
     model_class = None
@@ -1471,7 +1256,7 @@ class Gemma4Model(sd1_clip.SDClipModel):
                 expanded_idx += 1
         initial_token_ids = [ids]
         input_ids = torch.tensor(initial_token_ids, device=self.execution_device)
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, initial_tokens=initial_token_ids[0], presence_penalty=presence_penalty, initial_input_ids=input_ids, embeds_info=embeds_info)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, initial_tokens=initial_token_ids[0], presence_penalty=presence_penalty, initial_input_ids=input_ids)
 
 
 def gemma4_te(dtype_llama=None, llama_quantization_metadata=None, model_class=None):
@@ -1511,11 +1296,3 @@ def _make_variant(config_cls):
 Gemma4_E4B = _make_variant(Gemma4Config)
 Gemma4_E2B = _make_variant(Gemma4_E2B_Config)
 Gemma4_31B = _make_variant(Gemma4_31B_Config)
-
-
-# Gemma4 12B Unified: encoder-free multimodal, distinct base/tokenizer (not via _make_variant).
-class Gemma4_12B(Gemma4UnifiedBase):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        self._init_model(Gemma4_12B_Config(**config_dict), dtype, device, operations)
-Gemma4_12B.tokenizer = Gemma4UnifiedTokenizer

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 optimized_attention_for_device
+from comfy.ldm.modules.attention import TORCH_HAS_GQA, optimized_attention_for_device
 from comfy.text_encoders.llama import RMSNorm, apply_rope
 
 
@@ -110,6 +110,10 @@ 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

@@ -9,7 +9,6 @@ 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.
@@ -78,43 +77,3 @@ 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/joyimage.py

@@ -1,97 +0,0 @@
-import torch
-
-from comfy import sd1_clip
-import comfy.text_encoders.qwen_vl
-from comfy.text_encoders.qwen3vl import Qwen3VL, Qwen3VLTokenizer
-
-JOYIMAGE_VISION_BLOCK = "<|vision_start|><|image_pad|><|vision_end|>"
-JOYIMAGE_TEMPLATE_TEXT = (
-    "<|im_start|>system\n \\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"
-)
-JOYIMAGE_TEMPLATE_IMAGE = (
-    "<|im_start|>system\n \\nDescribe the image by detailing the color, shape, size, texture, "
-    "quantity, text, spatial relationships of the objects and background:<|im_end|>\n"
-    f"<|im_start|>user\n{JOYIMAGE_VISION_BLOCK}{{}}<|im_end|>\n<|im_start|>assistant\n"
-)
-# The DiT was trained without the leading system-prompt tokens.
-JOYIMAGE_DROP_IDX = 34
-PAD_TOKEN = 151643
-
-
-class Qwen3VL8B_JoyImage(Qwen3VL):
-    model_type = "qwen3vl_8b"
-
-    def preprocess_embed(self, embed, device):
-        if embed["type"] == "image":
-            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(
-                embed["data"], min_pixels=65536, max_pixels=16777216, patch_size=16,
-                image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5],
-                interpolation="bicubic",
-            )
-            merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid)
-            return merged, {"grid": grid, "deepstack": deepstack}
-        return None, None
-
-
-class JoyImageTokenizer(Qwen3VLTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        super().__init__(
-            embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
-            model_type="qwen3vl_8b",
-        )
-        self.llama_template = JOYIMAGE_TEMPLATE_TEXT
-        self.llama_template_images = JOYIMAGE_TEMPLATE_IMAGE
-
-    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=None, **kwargs):
-        kwargs.pop("thinking", None)
-        return super().tokenize_with_weights(
-            text, return_word_ids=return_word_ids, llama_template=llama_template,
-            images=images or [], thinking=True, **kwargs,
-        )
-
-
-class _JoyImageClipModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None,
-                 attention_mask=True, model_options={}):
-        super().__init__(
-            device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
-            # JoyImage conditions on the pre-final-norm output of the last decoder layer.
-            dtype=dtype, special_tokens={"pad": PAD_TOKEN}, layer_norm_hidden_state=False,
-            model_class=Qwen3VL8B_JoyImage, enable_attention_masks=attention_mask,
-            return_attention_masks=attention_mask, model_options=model_options,
-        )
-
-
-class JoyImageTEModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(
-            device=device, dtype=dtype, name="qwen3vl_8b",
-            clip_model=_JoyImageClipModel, model_options=model_options,
-        )
-
-    def encode_token_weights(self, token_weight_pairs):
-        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
-        if out.shape[1] <= JOYIMAGE_DROP_IDX:
-            raise ValueError(
-                f"JoyImageTEModel: encoded sequence length {out.shape[1]} is shorter "
-                f"than drop_idx={JOYIMAGE_DROP_IDX}; the prompt did not include the "
-                f"template prefix."
-            )
-        out = out[:, JOYIMAGE_DROP_IDX:]
-        if "attention_mask" in extra:
-            extra["attention_mask"] = extra["attention_mask"][:, JOYIMAGE_DROP_IDX:]
-        return out, pooled, extra
-
-
-def te(dtype_llama=None, llama_quantization_metadata=None):
-    class JoyImageTEModel_(JoyImageTEModel):
-        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 JoyImageTEModel_

comfy/text_encoders/krea2.py

@@ -1,84 +0,0 @@
-"""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

@@ -251,19 +251,6 @@ 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
@@ -550,8 +537,10 @@ class Attention(nn.Module):
                 xv = xv[:, :, -sliding_window:]
                 attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None
 
-        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)
+        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)
         return self.o_proj(output), present_key_value
 
 class MLP(nn.Module):
@@ -714,8 +703,7 @@ 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,deepstack_embeds=None, visual_pos_masks=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):
         if embeds is not None:
             x = embeds
         else:
@@ -779,10 +767,6 @@ 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()
 
@@ -876,7 +860,7 @@ 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, position_ids=None, deepstack_embeds=None, visual_pos_masks=None, embeds_info=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):
         device = embeds.device
 
         if stop_tokens is None:
@@ -900,18 +884,10 @@ 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"):
-            # 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, embeds_info=(embeds_info if step == 0 else None))
+            x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids)
             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()
@@ -919,9 +895,6 @@ 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:
@@ -935,41 +908,22 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        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 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 temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            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)
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy/text_encoders/qwen35.py

@@ -3,6 +3,7 @@ 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
@@ -366,8 +367,12 @@ class GatedAttention(nn.Module):
                     xv = torch.cat((past_value[:, :, :index], xv), dim=2)
                 present_key_value = (xk, xv, index + num_tokens)
 
-        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)
+        # 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)
         output = output * gate.sigmoid()
 
         return self.o_proj(output), present_key_value
@@ -558,8 +563,6 @@ 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
@@ -661,14 +664,9 @@ 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)
-        deepstack_features = []
-        for layer_num, blk in enumerate(self.blocks):
+        for blk in 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
@@ -692,7 +690,30 @@ 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):
-        position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, embeds.shape[1], embeds.device)
+        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
+
         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

@@ -1,214 +0,0 @@
-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

@@ -15,7 +15,6 @@ def process_qwen2vl_images(
     merge_size: int = 2,
     image_mean: list = None,
     image_std: list = None,
-    interpolation: str = "bilinear",
 ):
     if image_mean is None:
         image_mean = [0.48145466, 0.4578275, 0.40821073]
@@ -48,9 +47,10 @@ def process_qwen2vl_images(
     img_resized = F.interpolate(
         img.unsqueeze(0),
         size=(h_bar, w_bar),
-        mode=interpolation,
+        mode='bilinear',
         align_corners=False
     ).squeeze(0)
+
     normalized = img_resized.clone()
     for c in range(3):
         normalized[c] = (img_resized[c] - image_mean[c]) / image_std[c]
@@ -88,32 +88,6 @@ 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

@@ -818,44 +818,6 @@ 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

@@ -25,11 +25,6 @@ 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",
-    },
 }
 
 
@@ -100,7 +95,6 @@ def _parse_cli_feature_flags() -> dict[str, Any]:
 # Default server capabilities
 _CORE_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": 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_impl/video_types.py

@@ -1,6 +1,5 @@
 from av.container import InputContainer
 from av.subtitles.stream import SubtitleStream
-from av.video.reformatter import ColorRange
 from fractions import Fraction
 from typing import Optional
 from .._input import AudioInput, VideoInput
@@ -10,7 +9,6 @@ import itertools
 import json
 import numpy as np
 import math
-import os
 import torch
 from .._util import VideoContainer, VideoCodec, VideoComponents
 import logging
@@ -60,57 +58,6 @@ def video_stream_bit_depth(stream) -> int:
     return max(component.bits for component in stream.format.components)
 
 
-def last_decodable_audio_stream(container: InputContainer):
-    """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)."""
-    stream = next(
-        (s for s in reversed(container.streams.audio) if s.codec_context is not None),
-        None,
-    )
-    if stream is None and len(container.streams.audio):
-        logging.warning("No decodable audio stream found in video; ignoring audio.")
-    return stream
-
-
-def probe_audio_params(container: InputContainer, audio_stream, max_packets: int = 200):
-    """Containers probed only up to a window (mpegts) leave audio codec parameters unset when
-    audio starts beyond it; learn them by decoding ahead. The caller must seek back afterwards.
-    Returns (sample_rate, channels), zeros when the stream never yields a decodable frame."""
-    for i, packet in enumerate(container.demux(audio_stream)):
-        try:
-            frames = packet.decode()
-        except av.error.FFmpegError:
-            frames = ()
-        if frames:
-            return frames[0].sample_rate, frames[0].layout.nb_channels
-        if i >= max_packets:
-            break
-    return 0, 0
-
-
-def write_output_metadata(container: InputContainer, output, metadata: dict | None):
-    """Copy the source container's metadata, then overlay the caller's tags."""
-    for key, value in container.metadata.items():
-        if metadata is None or key not in metadata:
-            output.metadata[key] = value
-    if metadata is not None:
-        for key, value in metadata.items():
-            output.metadata[key] = value if isinstance(value, str) else json.dumps(value)
-
-
-def mp4_output_open_kwargs(path: str | io.BytesIO, format: VideoContainer, codec: VideoCodec) -> dict:
-    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")
-    open_kwargs = {"mode": "w", "options": {"movflags": "use_metadata_tags"}}
-    if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
-        open_kwargs["format"] = format.value
-    elif isinstance(path, io.BytesIO):
-        open_kwargs["format"] = "mp4"  # no file extension to infer the format from
-    return open_kwargs
-
-
 class VideoFromFile(VideoInput):
     """
     Class representing video input from a file.
@@ -245,10 +192,13 @@ class VideoFromFile(VideoInput):
                     return estimated_frames
 
             # 3. Last resort: decode frames and count them (streaming)
-            start_time, duration = self.get_active_trim_window()
+            if self.__start_time < 0:
+                start_time = max(self._get_raw_duration() + self.__start_time, 0)
+            else:
+                start_time = self.__start_time
             frame_count = 1
             start_pts = int(start_time / video_stream.time_base)
-            end_pts = int((start_time + duration) / video_stream.time_base)
+            end_pts = int((start_time + self.__duration) / video_stream.time_base)
             container.seek(start_pts, stream=video_stream)
             frame_iterator = (
                 container.decode(video_stream)
@@ -303,14 +253,17 @@ class VideoFromFile(VideoInput):
 
     def get_components_internal(self, container: InputContainer) -> VideoComponents:
         video_stream = self._get_first_video_stream(container)
-        start_time, duration = self.get_active_trim_window()
+        if self.__start_time < 0:
+            start_time = max(self._get_raw_duration() + self.__start_time, 0)
+        else:
+            start_time = self.__start_time
 
         # Get video frames
         frames = []
         audio_frames = []
         alphas = None
         start_pts = int(start_time / video_stream.time_base)
-        end_pts = int((start_time + duration) / video_stream.time_base)
+        end_pts = int((start_time + self.__duration) / video_stream.time_base)
 
         if start_pts != 0:
             container.seek(start_pts, stream=video_stream)
@@ -328,8 +281,8 @@ class VideoFromFile(VideoInput):
         video_done = False
         audio_done = True
 
-        audio_stream = last_decodable_audio_stream(container)
-        if audio_stream is not None:
+        if len(container.streams.audio):
+            audio_stream = container.streams.audio[-1]
             streams += [audio_stream]
             resampler = av.audio.resampler.AudioResampler(format='fltp')
             audio_done = False
@@ -345,7 +298,7 @@ class VideoFromFile(VideoInput):
                     for frame in packet.decode():
                         if frame.pts < start_pts:
                             continue
-                        if duration and frame.pts >= end_pts:
+                        if self.__duration and frame.pts >= end_pts:
                             video_done = True
                             break
 
@@ -372,25 +325,21 @@ class VideoFromFile(VideoInput):
                             checked_alpha = True
 
                         # 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
+                        # For non-yuvj pixel formats (all H.264/H.265 video)
                         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_pad = g.add('pad', f'{pad_w}:{frame.height}:0:0')
                                 g_sink = g.add('buffersink')
                                 g_src.link_to(g_pad)
-                                g_pad.link_to(g_fill)
-                                g_fill.link_to(g_sink)
+                                g_pad.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])
+                            img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:, :frame.width])
                         else:
                             img = frame.to_ndarray(format=image_format)
                         if frame.rotation != 0:
@@ -412,7 +361,7 @@ class VideoFromFile(VideoInput):
                     map(resampler.resample, packet.decode())
                 )
                 for frame in aframes:
-                    if duration and frame.time > start_time + duration:
+                    if self.__duration and frame.time > start_time + self.__duration:
                         audio_done = True
                         break
 
@@ -434,8 +383,8 @@ class VideoFromFile(VideoInput):
 
         if len(audio_frames) > 0:
             audio_data = np.concatenate(audio_frames, axis=1)  # shape: (channels, total_samples)
-            if duration:
-                audio_data = audio_data[..., :int(duration * audio_stream.sample_rate)]
+            if self.__duration:
+                audio_data = audio_data[..., :int(self.__duration * audio_stream.sample_rate)]
 
             audio_tensor = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, channels, total_samples)
             audio = AudioInput({
@@ -481,22 +430,33 @@ class VideoFromFile(VideoInput):
             if not reuse_streams:
                 if bit_depth is None:
                     bit_depth = source_bit_depth
-                return self._save_transcoded(container, path, format=format, codec=codec, metadata=metadata, bit_depth=bit_depth)
+                components = self.get_components_internal(container)
+                video = VideoFromComponents(components)
+                return video.save_to(
+                    path, format=format, codec=codec, metadata=metadata, bit_depth=bit_depth,
+                )
 
             streams = container.streams
 
             open_kwargs = get_open_write_kwargs(path, container_format, format)
             with av.open(path, **open_kwargs) as output_container:
-                # Add metadata before writing any streams
-                write_output_metadata(container, output_container, metadata)
+                # Copy over the original metadata
+                for key, value in container.metadata.items():
+                    if metadata is None or key not in metadata:
+                        output_container.metadata[key] = value
 
-                # Add streams to the new container. Streams with no codec context cannot be used as an output template.
+                # Add our new metadata
+                if metadata is not None:
+                    for key, value in metadata.items():
+                        if isinstance(value, str):
+                            output_container.metadata[key] = value
+                        else:
+                            output_container.metadata[key] = json.dumps(value)
+
+                # Add streams to the new container
                 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
 
@@ -506,282 +466,6 @@ class VideoFromFile(VideoInput):
                         packet.stream = stream_map[packet.stream]
                         output_container.mux(packet)
 
-    def _save_transcoded(
-        self,
-        container: InputContainer,
-        path: str | io.BytesIO,
-        format: VideoContainer,
-        codec: VideoCodec,
-        metadata: dict | None,
-        bit_depth: int,
-    ):
-        """Re-encode to H.264/AAC one frame at a time; peak memory does not scale with video length."""
-        open_kwargs = mp4_output_open_kwargs(path, format, codec)
-        video_stream = self._get_first_video_stream(container)
-        start_time, duration = self.get_active_trim_window()
-        start_pts = int(start_time / video_stream.time_base)
-        end_pts = int((start_time + duration) / video_stream.time_base) if duration else None
-        stream_end_pts = None
-        if video_stream.duration is not None:
-            stream_end_pts = (video_stream.start_time or 0) + video_stream.duration
-        output_end_pts = end_pts
-        if stream_end_pts is not None and (output_end_pts is None or stream_end_pts < output_end_pts):
-            output_end_pts = stream_end_pts
-        if start_pts != 0:
-            container.seek(start_pts, stream=video_stream)
-
-        audio_stream = last_decodable_audio_stream(container)
-        pix_fmt = "yuv420p10le" if bit_depth >= 10 else "yuv420p"
-        rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
-
-        resampler = None
-        sample_rate = 0
-        audio_time_base = None
-        duration_cap = None
-        if audio_stream is not None:
-            sample_rate = audio_stream.codec_context.sample_rate
-            channels = audio_stream.codec_context.channels
-            if not sample_rate:
-                sample_rate, channels = probe_audio_params(container, audio_stream)
-                container.seek(start_pts, stream=video_stream)
-                if sample_rate:
-                    audio_stream.codec_context.flush_buffers()
-                else:
-                    logging.warning("Audio stream parameters could not be determined; ignoring audio.")
-                    audio_stream = None
-        if audio_stream is not None:
-            audio_time_base = Fraction(1, sample_rate)
-            layout = {1: "mono", 2: "stereo", 6: "5.1"}.get(channels, "stereo")
-            resampler = av.audio.resampler.AudioResampler(format="fltp", layout=layout, rate=sample_rate)
-            if duration:
-                duration_cap = math.ceil(duration * sample_rate)
-
-        streams = [video_stream] if audio_stream is None else [video_stream, audio_stream]
-        pts_step = max(1, int(round((1 / rate) / video_stream.time_base)))
-        video_done = False
-        audio_done = audio_stream is None
-        video_pts_offset = None
-        last_video_pts = None
-        last_video_end = None
-        # rebased pts -> true display duration: the mp4 muxer pads the last sample with 1/rate otherwise
-        video_frame_durations = {}
-        source_size = None
-        rotation_k = 0
-        rotation_filter = None
-        audio_started = False
-        samples_written = 0
-        pending_audio = []
-        # The output opens lazily on the first kept frame: it decides the geometry (90/270 rotation swaps dims),
-        # and never seeking back keeps webm/mkv leading audio intact.
-        output = None
-        out_video = None
-        out_audio = None
-
-        def audio_frame_from_ndarray(nd_planar):
-            frame = av.AudioFrame.from_ndarray(np.ascontiguousarray(nd_planar), format="fltp", layout=layout)
-            frame.sample_rate = sample_rate
-            return frame
-
-        def drain_audio(final=False):
-            # Audio may cover the pts span of the video written so far, capped by the requested duration
-            nonlocal samples_written, audio_done
-            if last_video_end is None:
-                cap = 0
-            else:
-                cap = math.ceil(last_video_end * video_stream.time_base * sample_rate)
-            if duration_cap is not None:
-                cap = min(cap, duration_cap)
-            while pending_audio and not audio_done:
-                frame = pending_audio[0]
-                if samples_written + frame.samples <= cap:
-                    frame.pts = samples_written
-                    frame.time_base = audio_time_base
-                    output.mux(out_audio.encode(frame))
-                    samples_written += frame.samples
-                    pending_audio.pop(0)
-                    continue
-                if final:
-                    keep = frame.to_ndarray()[..., :cap - samples_written]
-                    if keep.shape[-1] > 0:
-                        tail = audio_frame_from_ndarray(keep)
-                        tail.pts = samples_written
-                        tail.time_base = audio_time_base
-                        output.mux(out_audio.encode(tail))
-                        samples_written += keep.shape[-1]
-                    pending_audio.clear()
-                break
-            if duration_cap is not None and samples_written >= duration_cap:
-                audio_done = True
-            return cap
-
-        try:
-            for packet in container.demux(*streams):
-                if video_done and audio_done:
-                    break
-
-                if packet.stream == video_stream and not video_done:
-                    try:
-                        frames = packet.decode()
-                    except av.error.InvalidDataError:
-                        logging.info("pyav decode error")
-                        continue
-                    for frame in frames:
-                        if frame.pts is not None and frame.pts < start_pts:
-                            continue
-                        if end_pts is not None and frame.pts is not None and frame.pts >= end_pts:
-                            video_done = True
-                            if last_video_pts is not None:
-                                # the source continues past the window: hold the last kept frame to the window end
-                                end_offset = video_pts_offset if video_pts_offset is not None else start_pts
-                                last_video_end = max(last_video_end, end_pts - end_offset)
-                            break
-                        # the source's true display duration of this frame; average_rate is not a
-                        # frame duration (sparse/VFR sources), so it is only the fallback
-                        frame_duration = frame.duration if frame.duration else pts_step
-                        if end_pts is not None and frame.pts is not None:
-                            frame_duration = min(frame_duration, end_pts - frame.pts)
-                        if output is None:
-                            rotation_k = int(round(frame.rotation // 90)) % 4 if frame.rotation else 0
-                            if rotation_k % 2:
-                                out_width, out_height = frame.height, frame.width
-                            else:
-                                out_width, out_height = frame.width, frame.height
-                            if out_width % 2 or out_height % 2:
-                                raise ValueError(f"H.264 output requires even dimensions, got {out_width}x{out_height}")
-                            source_size = (frame.width, frame.height)
-                            output = av.open(path, **open_kwargs)
-                            # Add metadata before writing any streams
-                            write_output_metadata(container, output, metadata)
-                            out_video = output.add_stream("h264", rate=rate)
-                            # no B-frames: reordering makes mp4 sample durations follow decode order,
-                            # so irregular-VFR spans and trim windows land wrong
-                            out_video.codec_context.max_b_frames = 0
-                            out_video.width = out_width
-                            out_video.height = out_height
-                            out_video.pix_fmt = pix_fmt
-                            # source pts pass through (rebased to 0), so variable frame rate survives
-                            out_video.codec_context.time_base = video_stream.time_base
-                            if audio_stream is not None:
-                                out_audio = output.add_stream("aac", rate=sample_rate, layout=layout)
-                        if (frame.width, frame.height) != source_size:
-                            # encoding would silently rescale the new geometry into the old one
-                            raise ValueError(
-                                f"Video resolution changes mid-stream "
-                                f"({source_size[0]}x{source_size[1]} -> {frame.width}x{frame.height}); cannot transcode"
-                            )
-                        if rotation_k:
-                            if rotation_filter is None:
-                                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)
-                                tail = g_src
-                                for filter_name, filter_args in {1: [("transpose", "cclock")],
-                                                                 2: [("hflip", None), ("vflip", None)],
-                                                                 3: [("transpose", "clock")]}[rotation_k]:
-                                    step = g.add(filter_name, filter_args)
-                                    tail.link_to(step)
-                                    tail = step
-                                g_sink = g.add("buffersink")
-                                tail.link_to(g_sink)
-                                g.configure()
-                                rotation_filter = (g_src, g_sink)
-                            rotation_filter[0].push(frame)
-                            frame = rotation_filter[1].pull()
-                        if frame.color_range == ColorRange.JPEG:
-                            # compress full-range sources (yuvj/MJPEG) to limited range
-                            frame = frame.reformat(format=pix_fmt, src_color_range="JPEG", dst_color_range="MPEG")
-                        else:
-                            frame = frame.reformat(format=pix_fmt)
-                        frame_output_end = None
-                        if frame.pts is not None:
-                            if video_pts_offset is None:
-                                video_pts_offset = frame.pts
-                            frame.pts -= video_pts_offset
-                            if output_end_pts is not None:
-                                frame_output_end = output_end_pts - video_pts_offset
-                                if frame.pts + frame_duration > frame_output_end:
-                                    clamped_pts = frame_output_end - frame_duration
-                                    if clamped_pts >= 0 and (last_video_pts is None or clamped_pts > last_video_pts):
-                                        frame.pts = min(frame.pts, clamped_pts)
-                                    elif frame.pts < frame_output_end:
-                                        frame_duration = frame_output_end - frame.pts
-                                    else:
-                                        continue
-                        if frame.pts is None or (last_video_pts is not None and frame.pts <= last_video_pts):
-                            # broken sources emit missing/backward timestamps mid-stream, which the
-                            # muxer rejects; nudge them forward by one nominal frame interval
-                            frame.pts = 0 if last_video_pts is None else last_video_pts + pts_step
-                            if frame_output_end is not None and frame.pts + frame_duration > frame_output_end:
-                                if frame.pts >= frame_output_end:
-                                    continue
-                                frame_duration = frame_output_end - frame.pts
-                        last_video_pts = frame.pts
-                        last_video_end = frame.pts + frame_duration
-                        video_frame_durations[frame.pts] = frame_duration
-                        # the decoded pict_type would force x264's frame types (intra-only
-                        # sources like MJPEG/ProRes would come out all-keyframe)
-                        frame.pict_type = 0
-                        for out_packet in out_video.encode(frame):
-                            out_packet.duration = video_frame_durations.pop(out_packet.pts, 0)
-                            output.mux(out_packet)
-                        drain_audio()
-
-                elif packet.stream == audio_stream and not audio_done:
-                    for resampled in itertools.chain.from_iterable(map(resampler.resample, packet.decode())):
-                        frame_start = None
-                        if resampled.pts is not None:
-                            # passthrough frames keep the source stream's time base
-                            tb = resampled.time_base if resampled.time_base else audio_time_base
-                            frame_start = float(resampled.pts * tb)
-                            if duration and not audio_started and frame_start >= start_time + duration:
-                                audio_done = True
-                                break
-                        if not audio_started:
-                            if frame_start is None:
-                                frame_start = 0.0
-                            to_skip = max(0, int((start_time - frame_start) * sample_rate))
-                            if to_skip >= resampled.samples:
-                                continue
-                            audio_started = True
-                            if duration and frame_start > start_time:
-                                duration_cap = min(duration_cap, math.ceil((start_time + duration - frame_start) * sample_rate))
-                            if to_skip:
-                                pending_audio.append(audio_frame_from_ndarray(resampled.to_ndarray()[..., to_skip:]))
-                                continue
-                        pending_audio.append(resampled)
-                        if video_done:
-                            # the video window is complete so the cap is final, but containers
-                            # that interleave audio behind video (fragmented mp4) still owe most
-                            # of it: stop only once the demuxed audio covers the cap
-                            cap = drain_audio()
-                            if pending_audio or samples_written >= cap:
-                                drain_audio(final=True)
-                                audio_done = True
-                                break
-
-            if output is None:
-                raise ValueError(f"No decodable video frames found in file '{self.__file}'")
-            if out_audio is not None and not audio_done:
-                drain_audio(final=True)
-            window_fill = last_video_end - last_video_pts if video_done and last_video_pts is not None else 0
-            for out_packet in out_video.encode(None):
-                duration = video_frame_durations.pop(out_packet.pts, 0)
-                if out_packet.pts == last_video_pts:
-                    duration = max(duration, window_fill)
-                out_packet.duration = duration
-                output.mux(out_packet)
-            if out_audio is not None:
-                output.mux(out_audio.encode(None))
-        except BaseException:
-            if output is not None:
-                output.close()
-                if isinstance(path, (str, os.PathLike)) and os.path.exists(path):
-                    os.remove(path)
-            raise
-        else:
-            if output is not None:
-                output.close()
-
     def _get_first_video_stream(self, container: InputContainer):
         if len(container.streams.video):
             return container.streams.video[0]
@@ -829,12 +513,22 @@ class VideoFromComponents(VideoInput):
         bit_depth: int | None = None,
     ):
         """Save the video to a file path or BytesIO buffer."""
-        open_kwargs = mp4_output_open_kwargs(path, format, codec)
+        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
-        with av.open(path, **open_kwargs) as output:
+        extra_kwargs = {}
+        if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
+            extra_kwargs["format"] = format.value
+        elif isinstance(path, io.BytesIO):
+            # BytesIO has no file extension, so av.open can't infer the format.
+            # Default to mp4 since that's the only supported format anyway.
+            extra_kwargs["format"] = "mp4"
+        with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}, **extra_kwargs) as output:
             # Add metadata before writing any streams
             if metadata is not None:
                 for key, value in metadata.items():

comfy_api/latest/_io.py

@@ -891,14 +891,6 @@ 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
@@ -1287,19 +1279,6 @@ 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):
@@ -1347,20 +1326,6 @@ 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."""
@@ -2411,8 +2376,6 @@ __all__ = [
     "AnyType",
     "MultiType",
     "Tracks",
-    "Dict",
-    "Array",
     "Color",
     # Dynamic Types
     "MatchType",
@@ -2431,8 +2394,6 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
-    "BoundingBoxes",
-    "Colors",
     "Curve",
     "Histogram",
     "Range",

comfy_api_nodes/apis/bytedance.py

@@ -1,4 +1,4 @@
-from typing import Any, Literal
+from typing import Literal
 
 from pydantic import BaseModel, Field
 
@@ -17,10 +17,6 @@ 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(...)
@@ -28,11 +24,10 @@ 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 | None = Field("disabled")
-    sequential_image_generation_options: Seedream4Options | None = Field(Seedream4Options(max_images=15))
+    sequential_image_generation: str = Field("disabled")
+    sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
     watermark: bool = Field(False)
     output_format: str | None = None
-    optimize_prompt_options: Seedream5OptimizePromptOptions | None = None
 
 
 class ImageTaskCreationResponse(BaseModel):
@@ -168,31 +163,15 @@ 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, resolution).
+# Dollars per 1K tokens, keyed by (model_id, has_video_input).
 SEEDANCE2_PRICE_PER_1K_TOKENS = {
-    ("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,
+    ("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,
 }
 
 
-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),
@@ -266,19 +245,6 @@ _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
 )
@@ -300,10 +266,6 @@ 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.
@@ -334,36 +296,3 @@ 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

@@ -1,6 +1,6 @@
 from datetime import date
 from enum import Enum
-from typing import Any, Literal
+from typing import Any
 
 from pydantic import BaseModel, Field
 
@@ -121,7 +121,6 @@ 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):
@@ -242,60 +241,3 @@ class GeminiGenerateContentResponse(BaseModel):
     promptFeedback: GeminiPromptFeedback | None = Field(None)
     usageMetadata: GeminiUsageMetadata | None = Field(None)
     modelVersion: str | None = Field(None)
-
-
-class GeminiInteractionTextPart(BaseModel):
-    type: Literal["text"] = "text"
-    text: str = Field(...)
-
-
-class GeminiInteractionMediaPart(BaseModel):
-    type: str = Field(..., description="One of: image, video, audio, document.")
-    data: str | None = Field(None, description="Base64-encoded media bytes.")
-    uri: str | None = Field(None, description="URI of the media, as an alternative to inline data.")
-    mime_type: str | None = Field(None)
-
-
-class GeminiInteractionGenerationConfig(BaseModel):
-    temperature: float | None = Field(None, ge=0.0, le=2.0)
-    top_p: float | None = Field(None, ge=0.0, le=1.0)
-
-
-class GeminiInteractionRequest(BaseModel):
-    model: str = Field(...)
-    input: list[GeminiInteractionTextPart | GeminiInteractionMediaPart] = Field(...)
-    generation_config: GeminiInteractionGenerationConfig | None = Field(None)
-
-
-class GeminiInteractionModalityTokens(BaseModel):
-    modality: str | None = Field(None, description="One of: text, image, audio, video, document.")
-    tokens: int | None = Field(None)
-
-
-class GeminiInteractionUsage(BaseModel):
-    input_tokens_by_modality: list[GeminiInteractionModalityTokens] | None = Field(None)
-    output_tokens_by_modality: list[GeminiInteractionModalityTokens] | None = Field(None)
-    total_thought_tokens: int | None = Field(None)
-
-
-class GeminiInteractionContent(BaseModel):
-    type: str | None = Field(None)
-    text: str | None = Field(None)
-    data: str | None = Field(None)
-    uri: str | None = Field(None)
-    mime_type: str | None = Field(None)
-
-
-class GeminiInteractionStep(BaseModel):
-    type: str | None = Field(None)
-    content: list[GeminiInteractionContent] | None = Field(None)
-
-
-class GeminiInteraction(BaseModel):
-    id: str | None = Field(None)
-    status: str | None = Field(
-        None,
-        description="One of: in_progress, requires_action, completed, failed, cancelled, incomplete.",
-    )
-    steps: list[GeminiInteractionStep] | None = Field(None)
-    usage: GeminiInteractionUsage | None = Field(None)

comfy_api_nodes/apis/heygen.py

@@ -1,452 +0,0 @@
-# (label, avatar_id, avatar_type, supported engines)
-HEYGEN_AVATAR_LOOKS: list[tuple[str, str, str, tuple[str, ...]]] = [
-    (
-        "Annie Lounge Standing Side",
-        "Annie_Lounge_Standing_Side_public",
-        "studio_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Yara Modern Lecture Hall",
-        "fd6814ecc5e143cd899e615a80eaa2dc",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Brandon Business Sitting Front",
-        "Brandon_Business_Sitting_Front_public",
-        "studio_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Caroline Business Sitting Side",
-        "Caroline_Business_Sitting_Side_public",
-        "studio_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Ursula Lawyer Angle 4",
-        "f7173d2bb8584c00bfec6905c5e9a492",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Sofia Corporate Presenter 01 Angle 3",
-        "fe563971fd2d438e957372dac9e2be8c",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Seoyeon Health Nutrition Coach Angle 3",
-        "fe3c5d5028d941398d064b8fc64a2dea",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Sanne Fitness Coach Angle 4",
-        "d967f935a8bf4a0c8f0bccfd66c501d2",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    ("Sander", "f5cd7b94056f495ca0610602d64a9aa3", "photo_avatar", ("avatar_v", "avatar_iv", "avatar_iii")),
-    (
-        "Rupert Personal Development Coach Angle 4",
-        "f57b3e626adb4bc997b38f64884adce4",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Olivier Professor Angle 2",
-        "f6659bbb094b459c87c967edbb9ee481",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Obi Health Nutrition Coach Angle 5",
-        "f3dc2c38201d414382f506d2d8e8d029",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Matilda Modern Office Setting",
-        "fda889ac354a440da8dbecc410981273",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Mateo Traditional Law Office",
-        "ff172d6c499c4e47ba6fcc5de631e9fc",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Marlon Inviting Armchair Setting",
-        "f5a57db099ab462daa3e7c604a05dacc",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Margaret Professor Angle 1",
-        "fb472bc29ab04bcca576e3703978fecb",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Marek Therapy Coach Angle 3",
-        "e197768703f1463a93dc25ada1f421fb",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Maeve Warm, Professional Setting",
-        "faf66681d8cc48dc82c4283200b3e782",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    (
-        "Lorenzo Professor Angle 5",
-        "fc268dc244bb40d7a554663ce723dcf0",
-        "photo_avatar",
-        ("avatar_v", "avatar_iv", "avatar_iii"),
-    ),
-    ("Luca", "Luca_public", "studio_avatar", ("avatar_iii",)),
-    ("Bruce", "Bruce_public", "studio_avatar", ("avatar_iii",)),
-    ("Nico", "Nico_public", "studio_avatar", ("avatar_iii",)),
-    ("Lisa", "Lisa_public", "studio_avatar", ("avatar_iii",)),
-    ("Sophie", "Sophie_public", "studio_avatar", ("avatar_iii",)),
-    ("Aiko", "Aiko_public", "studio_avatar", ("avatar_iii",)),
-    ("Rebecca (portrait)", "Rebecca_public", "studio_avatar", ("avatar_iii",)),
-    ("Daphne in Grey blazer (portrait)", "Daphne_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Bryce in Black t-shirt", "Bryce_public_5", "studio_avatar", ("avatar_iii",)),
-    ("Diora in White shirt", "Diora_public_3", "studio_avatar", ("avatar_iii",)),
-    ("Freja in White blazer", "Freja_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Albert in Blue blazer", "Albert_public_2", "studio_avatar", ("avatar_iii",)),
-    ("Emery in Red blazer", "Emery_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Minho in Blue shirt", "Minho_public_6", "studio_avatar", ("avatar_iii",)),
-    ("Aditya in Brown blazer", "Aditya_public_4", "studio_avatar", ("avatar_iii",)),
-    ("Nadim in Blue blazer", "Nadim_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Iker in Black blazer", "Iker_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Nour in Black blazer", "Nour_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Saskia in Blue blazer", "Saskia_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Lucien in Blue blazer", "Lucien_public_1", "studio_avatar", ("avatar_iii",)),
-    ("Esmond in Blue suit", "Esmond_public_3", "studio_avatar", ("avatar_iii",)),
-    ("Jinwoo in Blue suit", "Jinwoo_public_5", "studio_avatar", ("avatar_iii",)),
-    ("Annelore in Red sweater (portrait)", "Annelore_public_3", "studio_avatar", ("avatar_iii",)),
-    ("Bastien in Blue shirt", "Bastien_public_4", "studio_avatar", ("avatar_iii",)),
-    ("Zosia in Khaki blazer", "Zosia_public_3", "studio_avatar", ("avatar_iii",)),
-    ("Tahlia in Dark blue suit", "Tahlia_public_4", "studio_avatar", ("avatar_iii",)),
-]
-HEYGEN_AVATAR_OPTIONS = [x[0] for x in HEYGEN_AVATAR_LOOKS]
-HEYGEN_AVATAR_MAP = {x[0]: (x[1], x[2], x[3]) for x in HEYGEN_AVATAR_LOOKS}
-
-# (label, voice_id) — Starfish-compatible voices for the TTS endpoint
-HEYGEN_VOICE_TTS: list[tuple[str, str]] = [
-    ("Chill Brian (English, male)", "d2f4f24783d04e22ab49ee8fdc3715e0"),
-    ("Zain (English, female)", "0047732240584155b1588455313e78ec"),
-    ("Narrator Mateo - Excited 🤩 (Spanish, male)", "0077225a877e457db4572ccaf245910b"),
-    ("Aria (English, female)", "007e1378fc454a9f976db570ba6164a7"),
-    ("Caryns (English, female)", "0082e70326864107823605db0d77f5e0"),
-    ("Klara (English, female)", "01209fdcd1c24a109c86dc24ee0f71c0"),
-    ("Bold Kasia - Excited 🤩 (Polish, female)", "015482a78b9a46ebae74bd0beb17765b"),
-    ("Shaun (English, male)", "01c42cddcfdc4665a57b8d89cba8ffc1"),
-    ("Senthil (English, male)", "01d674cfd32b4728a3fddd21b7e7d543"),
-    ("Cody (English, male)", "01f98ed43e6140349f47dbd37a416827"),
-    ("Saffron (English, female)", "0258bbc2cd8648cfa357adfb833f6d7b"),
-    ("Blanka - Lifelike (English, female)", "02880d1c6fd94b7799d91135581ed810"),
-    ("Rami (English, male)", "02d5366a90af4c7a87157808ff352e33"),
-    ("Rhodes (English, female)", "02dce0a169b3460084b6c914d18fb2c8"),
-    ("Michelle - Voice 1 (English, female)", "02X8sHnuxFpsq1caYWN0"),
-    ("Autumn - UGC 3 (English, female)", "03dca9ebfca441dba55fb14afa0791b7"),
-    ("Reassuring Rupert (English, male)", "03fcf8ecb0a94b6b94e9007edb7c35f8"),
-    ("Rose - UGC -2  (English, female)", "0495e14c2bd74eb3aeeef03583e0bce5"),
-    ("Derya - Lifelike - Broadcaster 🎙️ (English, female)", "04d0ae1d0af2489ca7d3bb402a39a890"),
-    ("Dynamic Derek (English, male)", "0516c2d857eb425c94e90b068241914e"),
-    ("Lotte (English, female)", "052fcfb83d1a4c2f8d0368c226fea4b9"),
-    ("Thanos - Broadcaster 🎙️ (English, male)", "054af44a167344d0af2722fdfef08d17"),
-    ("Marcia (English, female)", "05f19352e8f74b0392a8f411eba40de1"),
-    ("Camden (English, male)", "06468055edd4458aa131a1dfd813c1e9"),
-    ("Rumi (English, female)", "06672207805f41a9ad0af6797f8aa14b"),
-    ("Pippa (English, female)", "06b68c4dbb544935b9af984e80efa4fb"),
-    ("William Prescott - Broadcaster 🎙️ (English, male)", "06c816b952f14fa9b3a6c42aa151f731"),
-    ("Sammy (English, female)", "06e6facd99654b9dbb9308f67bf3a31c"),
-    ("Breezy Bagus (Indonesian, male)", "06e81a5d7c8b41818d3f0b38f7cf15a1"),
-    ("Ben (English, male)", "07ca39b243184dbcb82e7e0f0e524b21"),
-    ("Smooth Dev (English, male)", "07d2ba65847541feb97abc9b60181555"),
-    ("Daran inside booth (English, male)", "080d9383c0314056aef392892e009806"),
-    ("Peppy Stella (English, female)", "084760b4922a44599575c770070ec2d7"),
-    ("Silas (English, male)", "08f561403ec846dbbd8c691cc448f45a"),
-    ("Aditya (English, male)", "09c3d65e44e247dd8b78a97a903feb58"),
-    ("Christy (English, female)", "09d88c036bf449fa905900c08b235a37"),
-    ("Elio (English, male)", "0a0b38624ac64ec6afcd5842a977ca10"),
-    ("Luminous Laksh (Hindi, male)", "0adc547b76a5401c856274c379904eb7"),
-    ("Jeff (English, male)", "0add542e349f4ccaba6ecb3b7ced6034"),
-    ("Tahlia Brooks - Excited 🤩 (English, female)", "0b440d1ac2454d69a73302fc806522b1"),
-    ("Riya Mehta (Hindi, female)", "0b464b2f4e2249a4b5a05e60eaf41e7e"),
-    ("Ben Hart (English, male)", "0b47b5a637e944f9bfd49913999b344b"),
-    ("Skylar (English, female)", "0bbfbda5aa924a68a9d1da7b8496052a"),
-    ("Relaxed Reece (English, male)", "0c2151d538844c70a8b096de533f2828"),
-    ("Daniel (English, male)", "0c23804af39a4946ac6fda42bfff2738"),
-    ("Melani (English, female)", "0c54c6399ad64551a304e1a346677723"),
-    ("Clover (English, female)", "0ccb0bea067d4449ad367baeed7ea2e9"),
-    ("Pedro Lima - Serious 😐 (Portuguese, male)", "0d0e23e8170446e38b18a7380b2d30a8"),
-    ("Ana Carvalho (Portuguese, female)", "0d23c5b2f6004e909802a2e8bfcd52c2"),
-    ("Confident Connor - Excited 🤩 (English, male)", "0dd34c3eb79247238219eea35aeb58cd"),
-    ("Vibrant Victor (Spanish, male)", "1062976ea8bf42f4adc27c7e868b8fde"),
-    ("Young Olivier (French, male)", "1c5dc9a8f8cf4de0932f91d75f43a15d"),
-    ("Émile Noir (French, male)", "25a6a67280574d3da78e97b1935ebfc7"),
-    ("Steadfast Stefan (German, male)", "0eb85e6e8710473b82f7e88609ba3053"),
-    ("Deep Dieter (German, male)", "118949676b0a46629d1ad52981c3ef84"),
-    ("Serene Marco (Italian, male)", "72e922488a614041b5ab5f6ee07e3deb"),
-    ("Murmuring Matteo (Italian, male)", "755902b751654f30a6ef49e8bbcacfec"),
-    ("Gail in car (Multilingual, female)", "0214ac51f93e420f8711d568dcfbc50e"),
-    ("Daran outside walking (Multilingual, male)", "0ac81e725f4948dfa9638ceca216bcfa"),
-    ("BOB - Voice 1 (Chinese, unknown)", "dMkR1XwIkarpNqWUJLnX"),
-    ("Hakeem Hassan (Arabic, male)", "61a4359785664d01a59664ceb87ce6d4"),
-    ("Rami Idris (Arabic, male)", "a0bd2e5d41a74643be47ac75ca9171a2"),
-    ("Bold Kasia - Friendly 😊 (Polish, female)", "331624aec8b24a6c9287b8e16bdf54e8"),
-    ("Tranquil Tulin (Turkish, female)", "61646c861eb64e2d9036d8db51385356"),
-    ("Dynamic Derya (Turkish, female)", "664b73058b784aa89ddb2924c141d441"),
-    ("Quiet Dewa (Indonesian, male)", "1fa1193cf1d74f27ba58531c07ef9862"),
-    ("Cuong (Vietnamese, male)", "8af68d7ea38f4e7ca05cf46c3f7a590b"),
-]
-HEYGEN_VOICE_TTS_OPTIONS = [x[0] for x in HEYGEN_VOICE_TTS]
-HEYGEN_VOICE_TTS_MAP = dict(HEYGEN_VOICE_TTS)
-
-# (label, voice_id) — top-ranked voices for video narration (any engine)
-HEYGEN_VOICE_GENERAL: list[tuple[str, str]] = [
-    ("Cassidy (English, female)", "16a09e4706f74997ba4ed05ea11470f6"),
-    ("Hope (English, female)", "42d00d4aac5441279d8536cd6b52c53c"),
-    ("Archer (English, male)", "453c20e1525a429080e2ad9e4b26f2cd"),
-    ("Brittney (English, female)", "4754e1ec667544b0bd18cdf4bec7d6a7"),
-    ("Mark (English, male)", "5d8c378ba8c3434586081a52ac368738"),
-    ("Andrew (English, male)", "6be73833ef9a4eb0aeee399b8fe9d62b"),
-    ("Spuds Oxley (English, male)", "76940a9adcd0490a9ce2cfe9a64a2664"),
-    ("Patrick (English, male)", "7e157ec62c9c45f1adca12faae72c86f"),
-    ("David Castlemore (English, male)", "828b59f834fd4c7188da322b6d9b6c75"),
-    ("Michael C (English, male)", "8661cd40d6c44c709e2d0031c0186ada"),
-    ("Adam Stone (English, male)", "88bb9ee1c81b466eb2a08fdde86d3619"),
-    ("Alex (English, male)", "897d6a9b2c844f56aa077238768fe10a"),
-    ("Monika Sogam (English, female)", "97dd67ab8ce242b6a9e7689cb00c6414"),
-    ("Jessica Anne Bogart (English, female)", "b966c31caf124c2a99f19ff1479c964f"),
-    ("John Doe (English, male)", "c4a8ceb7a2954500bc047fb092bcff3f"),
-    ("Ivy (English, female)", "cef3bc4e0a84424cafcde6f2cf466c97"),
-    ("Chill Brian (English, male)", "d2f4f24783d04e22ab49ee8fdc3715e0"),
-    ("Allison (English, female)", "f8c69e517f424cafaecde32dde57096b"),
-    ("Mia Starset (Norwegian, female)", "000466f8ac6d47a49f5743d50b3778de"),
-    ("William Shanks (Spanish, male)", "001248bb63f847888d37b766ee8b3a47"),
-    ("Zain (English, female)", "0047732240584155b1588455313e78ec"),
-    ("Jora Slobod (Romanian, male)", "00631519159a402ab5d8f719e51532bb"),
-    ("Narrator Mateo - Excited 🤩 (Spanish, male)", "0077225a877e457db4572ccaf245910b"),
-    ("Aria (English, female)", "007e1378fc454a9f976db570ba6164a7"),
-    ("Caryns (English, female)", "0082e70326864107823605db0d77f5e0"),
-    ("Klara (English, female)", "01209fdcd1c24a109c86dc24ee0f71c0"),
-    ("Son Tran (Vietnamese, male)", "0132f85950a94d11ba180f885101bf84"),
-    ("Bold Kasia - Excited 🤩 (Polish, female)", "015482a78b9a46ebae74bd0beb17765b"),
-    ("Marc Aurèle (French, male)", "018a94cf15574491a0bab7f6799ac15b"),
-    ("Shaun (English, male)", "01c42cddcfdc4665a57b8d89cba8ffc1"),
-    ("Senthil (English, male)", "01d674cfd32b4728a3fddd21b7e7d543"),
-    ("Cody (English, male)", "01f98ed43e6140349f47dbd37a416827"),
-    ("Saffron (English, female)", "0258bbc2cd8648cfa357adfb833f6d7b"),
-    ("Blanka - Lifelike (English, female)", "02880d1c6fd94b7799d91135581ed810"),
-    ("Rami (English, male)", "02d5366a90af4c7a87157808ff352e33"),
-    ("Rhodes (English, female)", "02dce0a169b3460084b6c914d18fb2c8"),
-    ("Michelle - Voice 1 (English, female)", "02X8sHnuxFpsq1caYWN0"),
-    ("Tuba (, female)", "034ca0c32b6542028748d6d365d90d6a"),
-    ("Autumn - UGC 3 (English, female)", "03dca9ebfca441dba55fb14afa0791b7"),
-    ("Reassuring Rupert (English, male)", "03fcf8ecb0a94b6b94e9007edb7c35f8"),
-]
-HEYGEN_VOICE_GENERAL_OPTIONS = [x[0] for x in HEYGEN_VOICE_GENERAL]
-HEYGEN_VOICE_GENERAL_MAP = dict(HEYGEN_VOICE_GENERAL)
-
-HEYGEN_TRANSLATE_LANGUAGES = [
-    "English",
-    "Spanish",
-    "Spanish (Spain)",
-    "Spanish (Mexico)",
-    "French",
-    "French (France)",
-    "German",
-    "German (Germany)",
-    "Portuguese",
-    "Portuguese (Brazil)",
-    "Italian",
-    "Italian (Italy)",
-    "Japanese",
-    "Japanese (Japan)",
-    "Korean",
-    "Chinese (Mandarin, Simplified)",
-    "Arabic",
-    "Hindi",
-    "Hindi (India)",
-    "Russian",
-    "Russian (Russia)",
-    "Dutch",
-    "Polish",
-    "Turkish",
-    "Indonesian",
-    "Vietnamese",
-    "Ukrainian",
-    "Afrikaans (South Africa)",
-    "Albanian (Albania)",
-    "Amharic (Ethiopia)",
-    "Arabic (Algeria)",
-    "Arabic (Bahrain)",
-    "Arabic (Egypt)",
-    "Arabic (Iraq)",
-    "Arabic (Jordan)",
-    "Arabic (Kuwait)",
-    "Arabic (Lebanon)",
-    "Arabic (Libya)",
-    "Arabic (Morocco)",
-    "Arabic (Oman)",
-    "Arabic (Qatar)",
-    "Arabic (Saudi Arabia)",
-    "Arabic (Syria)",
-    "Arabic (Tunisia)",
-    "Arabic (United Arab Emirates)",
-    "Arabic (World)",
-    "Arabic (Yemen)",
-    "Armenian (Armenia)",
-    "Azerbaijani (Latin, Azerbaijan)",
-    "Bangla (Bangladesh)",
-    "Basque",
-    "Belarusian (Belarus)",
-    "Bengali (India)",
-    "Bosnian (Bosnia and Herzegovina)",
-    "Bulgarian",
-    "Bulgarian (Bulgaria)",
-    "Burmese (Myanmar)",
-    "Catalan",
-    "Chinese (Cantonese, Traditional)",
-    "Chinese (Jilu Mandarin, Simplified)",
-    "Chinese (Northeastern Mandarin, Simplified)",
-    "Chinese (Southwestern Mandarin, Simplified)",
-    "Chinese (Taiwanese Mandarin, Traditional)",
-    "Chinese (Wu, Simplified)",
-    "Chinese (Zhongyuan Mandarin Henan, Simplified)",
-    "Chinese (Zhongyuan Mandarin Shaanxi, Simplified)",
-    "Croatian",
-    "Croatian (Croatia)",
-    "Czech",
-    "Czech (Czechia)",
-    "Danish",
-    "Danish (Denmark)",
-    "Dutch (Belgium)",
-    "Dutch (Netherlands)",
-    "English (Australia)",
-    "English (Canada)",
-    "English (Hong Kong SAR)",
-    "English (India)",
-    "English (Ireland)",
-    "English (Kenya)",
-    "English (New Zealand)",
-    "English (Nigeria)",
-    "English (Philippines)",
-    "English (Singapore)",
-    "English (South Africa)",
-    "English (Tanzania)",
-    "English (UK)",
-    "English (United States)",
-    "Estonian (Estonia)",
-    "Filipino",
-    "Filipino (Cebuano)",
-    "Filipino (Philippines)",
-    "Finnish",
-    "Finnish (Finland)",
-    "French (Belgium)",
-    "French (Canada)",
-    "French (Switzerland)",
-    "Galician",
-    "Georgian (Georgia)",
-    "German (Austria)",
-    "German (Switzerland)",
-    "Greek",
-    "Greek (Greece)",
-    "Gujarati (India)",
-    "Haitian Creole (Haiti)",
-    "Hebrew (Israel)",
-    "Hungarian (Hungary)",
-    "Icelandic (Iceland)",
-    "Indonesian (Indonesia)",
-    "Irish (Ireland)",
-    "Javanese (Latin, Indonesia)",
-    "Kannada (India)",
-    "Kazakh (Kazakhstan)",
-    "Khmer (Cambodia)",
-    "Konkani (India)",
-    "Korean (Korea)",
-    "Lao (Laos)",
-    "Latin (Vatican City)",
-    "Latvian (Latvia)",
-    "Lithuanian (Lithuania)",
-    "Luxembourgish (Luxembourg)",
-    "Macedonian (North Macedonia)",
-    "Maithili (India)",
-    "Malagasy (Madagascar)",
-    "Malay",
-    "Malay (Malaysia)",
-    "Malayalam (India)",
-    "Maltese (Malta)",
-    "Mandarin",
-    "Marathi (India)",
-    "Mongolian (Mongolia)",
-    "Nepali (Nepal)",
-    "Norwegian Bokmål (Norway)",
-    "Norwegian Nynorsk (Norway)",
-    "Odia (India)",
-    "Pashto (Afghanistan)",
-    "Persian (Iran)",
-    "Polish (Poland)",
-    "Portuguese (Portugal)",
-    "Punjabi (India)",
-    "Romanian",
-    "Romanian (Romania)",
-    "Serbian (Latin, Serbia)",
-    "Sindhi (India)",
-    "Sinhala (Sri Lanka)",
-    "Slovak",
-    "Slovak (Slovakia)",
-    "Slovenian (Slovenia)",
-    "Somali (Somalia)",
-    "Spanish (Argentina)",
-    "Spanish (Bolivia)",
-    "Spanish (Chile)",
-    "Spanish (Colombia)",
-    "Spanish (Costa Rica)",
-    "Spanish (Cuba)",
-    "Spanish (Dominican Republic)",
-    "Spanish (Ecuador)",
-    "Spanish (El Salvador)",
-    "Spanish (Equatorial Guinea)",
-    "Spanish (Guatemala)",
-    "Spanish (Honduras)",
-    "Spanish (Latin America)",
-    "Spanish (Nicaragua)",
-    "Spanish (Panama)",
-    "Spanish (Paraguay)",
-    "Spanish (Peru)",
-    "Spanish (Puerto Rico)",
-    "Spanish (United States)",
-    "Spanish (Uruguay)",
-    "Spanish (Venezuela)",
-    "Sundanese (Indonesia)",
-    "Swahili (Kenya)",
-    "Swahili (Tanzania)",
-    "Swedish",
-    "Swedish (Sweden)",
-    "Tamil",
-    "Tamil (India)",
-    "Tamil (Malaysia)",
-    "Tamil (Singapore)",
-    "Tamil (Sri Lanka)",
-    "Telugu (India)",
-    "Thai (Thailand)",
-    "Turkish (Türkiye)",
-    "Ukrainian (Ukraine)",
-    "Urdu (India)",
-    "Urdu (Pakistan)",
-    "Uzbek (Latin, Uzbekistan)",
-    "Vietnamese (Vietnam)",
-    "Welsh (United Kingdom)",
-    "Zulu (South Africa)",
-]

comfy_api_nodes/apis/hunyuan3d.py

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

comfy_api_nodes/apis/ideogram.py

@@ -33,6 +33,53 @@ 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.'
@@ -66,6 +113,20 @@ 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

@@ -149,59 +149,3 @@ 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

@@ -10,7 +10,6 @@ from pydantic import BaseModel, Field, confloat
 class LumaIO:
     LUMA_REF = "LUMA_REF"
     LUMA_CONCEPTS = "LUMA_CONCEPTS"
-    LUMA_RAY32_KEYFRAME = "LUMA_RAY32_KEYFRAME"
 
 
 class LumaReference:
@@ -21,14 +20,13 @@ 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):
@@ -126,7 +124,7 @@ def get_luma_concepts(include_none=False):
         "pull_out",
         "aerial",
         "crane_up",
-        "eye_level",
+        "eye_level"
     ]
 
 
@@ -164,8 +162,8 @@ class LumaVideoModelOutputDuration(str, Enum):
 
 
 class LumaGenerationType(str, Enum):
-    video = "video"
-    image = "image"
+    video = 'video'
+    image = 'image'
 
 
 class LumaState(str, Enum):
@@ -176,109 +174,86 @@ 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)
-    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, 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')
 
 
 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(...)
+    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")
 
 
 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):
@@ -291,7 +266,6 @@ 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):
@@ -303,31 +277,3 @@ 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/openai.py

@@ -128,7 +128,7 @@ class OpenAIResponse(ModelResponseProperties, ResponseProperties):
     parallel_tool_calls: bool | None = Field(True)
     status: str | None = Field(
         None,
-        description="One of `completed`, `failed`, `in_progress`, `incomplete`, `queued`, or `cancelled`.",
+        description="One of `completed`, `failed`, `in_progress`, or `incomplete`.",
     )
     usage: ResponseUsage | None = Field(None)
 

comfy_api_nodes/apis/stability.py

@@ -0,0 +1,147 @@
+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/sync_so.py

@@ -1,49 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class SyncInputItem(BaseModel):
-    type: str = Field(..., description="Input kind: 'video', 'image' or 'audio'.")
-    url: str = Field(...)
-
-
-class SyncActiveSpeakerDetection(BaseModel):
-    auto_detect: bool | None = Field(
-        None, description="Detect the active speaker automatically. Video input only; rejected for images."
-    )
-    frame_number: int | None = Field(
-        None, description="Frame used for manual speaker selection. Must be 0 for image inputs."
-    )
-    coordinates: list[int] | None = Field(
-        None, description="Pixel [x, y] of the speaker's face in the frame selected by frame_number."
-    )
-
-
-class SyncGenerationOptions(BaseModel):
-    sync_mode: str | None = Field(
-        None,
-        description="How to resolve an audio/video duration mismatch: "
-        "cut_off, bounce, loop, silence or remap. Ignored for image inputs.",
-    )
-    i2v_prompt: str | None = Field(
-        None, description="Motion prompt for image-to-video generation. Image input only."
-    )
-    active_speaker_detection: SyncActiveSpeakerDetection | None = Field(None)
-
-
-class SyncGenerationRequest(BaseModel):
-    model: str = Field(..., description="Generation model, e.g. 'sync-3'.")
-    input: list[SyncInputItem] = Field(
-        ..., description="Exactly one visual input (video or image) plus one audio input."
-    )
-    options: SyncGenerationOptions | None = Field(None)
-
-
-class SyncGeneration(BaseModel):
-    """Subset of the Generation object returned by POST /v2/generate and GET /v2/generate/{id}."""
-
-    id: str = Field(...)
-    status: str = Field(..., description="PENDING | PROCESSING | COMPLETED | FAILED | REJECTED")
-    outputUrl: str | None = Field(None)
-    outputDuration: float | None = Field(None)
-    error: str | None = Field(None, description="Human-readable failure message.")
-    errorCode: str | None = Field(None, description="Stable machine-readable code from the GET /v2/errors catalog.")

comfy_api_nodes/nodes_bytedance.py

@@ -1,4 +1,3 @@
-import base64
 import hashlib
 import logging
 import math
@@ -16,16 +15,12 @@ from comfy_api_nodes.apis.bytedance import (
     RECOMMENDED_PRESETS_SEEDREAM_4_0,
     RECOMMENDED_PRESETS_SEEDREAM_4_5,
     RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
-    RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+    SEEDANCE2_PRICE_PER_1K_TOKENS,
     SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
     VIDEO_TASKS_EXECUTION_TIME,
     GetAssetResponse,
     Image2VideoTaskCreationRequest,
     ImageTaskCreationResponse,
-    SeedAudioConfig,
-    SeedAudioReference,
-    SeedAudioRequest,
-    SeedAudioResponse,
     Seedance2TaskCreationRequest,
     SeedanceCreateAssetRequest,
     SeedanceCreateAssetResponse,
@@ -34,7 +29,6 @@ from comfy_api_nodes.apis.bytedance import (
     SeedanceVirtualLibraryCreateAssetRequest,
     Seedream4Options,
     Seedream4TaskCreationRequest,
-    Seedream5OptimizePromptOptions,
     TaskAudioContent,
     TaskAudioContentUrl,
     TaskCreationResponse,
@@ -46,12 +40,9 @@ 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,
@@ -60,14 +51,11 @@ 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,
@@ -82,14 +70,12 @@ _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,
@@ -103,7 +89,6 @@ 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"}
@@ -156,7 +141,7 @@ SEEDANCE2_RATIO_WH = {
     "9:16": (9, 16),
     "21:9": (21, 9),
 }
-SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080, "4k": 2160}
+SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080}
 
 
 def _seedance2_target_dims(resolution: str, ratio: str, image: torch.Tensor) -> tuple[int, int]:
@@ -392,9 +377,9 @@ 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, resolution: str):
+def _seedance2_price_extractor(model_id: str, has_video_input: bool):
     """Returns a price_extractor closure for Seedance 2.0 poll_op."""
-    rate = seedance2_price_per_1k_tokens(model_id, has_video_input, resolution)
+    rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input))
     if rate is None:
         return None
 
@@ -747,15 +732,8 @@ 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,
-    max_width: int = 6240,
-    max_height: int = 4992,
-    supports_batch: bool = True,
-):
-    inputs = [
+def _seedream_model_inputs(*, max_ref_images: int, presets: list):
+    return [
         IO.Combo.Input(
             "size_preset",
             options=[label for label, _, _ in presets],
@@ -765,7 +743,7 @@ def _seedream_model_inputs(
             "width",
             default=2048,
             min=1024,
-            max=max_width,
+            max=6240,
             step=2,
             tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
         ),
@@ -773,27 +751,22 @@ def _seedream_model_inputs(
             "height",
             default=2048,
             min=1024,
-            max=max_height,
+            max=4992,
             step=2,
             tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
         ),
-    ]
-    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.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.",
+        ),
         IO.Autogrow.Input(
             "images",
             template=IO.Autogrow.TemplateNames(
@@ -803,18 +776,14 @@ def _seedream_model_inputs(
             ),
             tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. "
             f"Up to {max_ref_images} images.",
-        )
-    )
-    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
+        ),
+        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,
+        ),
+    ]
 
 
 class ByteDanceSeedreamNodeV2(IO.ComfyNode):
@@ -836,16 +805,6 @@ 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),
@@ -876,17 +835,6 @@ 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(),
@@ -898,27 +846,15 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["model", "model.size_preset", "model.width", "model.height"]
-                ),
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
                 expr="""
                 (
-                  $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;
+                  $price := $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": $isPro ? "/Image" : " x images/Run", "approximate": true }
+                    "format": { "suffix":" x images/Run", "approximate": true }
                   }
                 )
                 """,
@@ -932,12 +868,10 @@ 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)
@@ -957,29 +891,19 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
 
         out_num_pixels = w * h
         mp_provided = out_num_pixels / 1_000_000.0
-        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."
-                )
+        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."
+            )
 
         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)
@@ -992,10 +916,6 @@ 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:
@@ -1009,9 +929,6 @@ 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"),
@@ -1022,10 +939,9 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 image=reference_images_urls,
                 size=f"{w}x{h}",
                 seed=seed,
-                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),
+                sequential_image_generation=sequential_image_generation,
+                sequential_image_generation_options=Seedream4Options(max_images=max_images),
                 watermark=watermark,
-                optimize_prompt_options=optimize_prompt_options,
             ),
         )
         if len(response.data) == 1:
@@ -1705,12 +1621,10 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
-                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p", "4k"])),
+                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p"])),
                         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; Fast for speed optimization; "
-                    "Mini for the fastest, lowest-cost generation.",
+                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -1746,16 +1660,11 @@ 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");
-                  $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 :
+                  $rate := $res = "1080p" ? $rate1080 :
                            $res = "720p"  ? $rate720 :
                                             $rate480;
                   $cost := $dur * $rate * $pricePer1K / 1000;
@@ -1794,7 +1703,7 @@ 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, resolution=model["resolution"]),
+            price_extractor=_seedance2_price_extractor(model_id, has_video_input=False),
             poll_interval=9,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
@@ -1815,19 +1724,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Seedance 2.0",
-                            _seedance2_text_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
+                            _seedance2_text_inputs(["480p", "720p", "1080p"], 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; Fast for speed optimization; "
-                    "Mini for the fastest, lowest-cost generation.",
+                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
                 IO.Image.Input(
                     "first_frame",
@@ -1887,16 +1791,11 @@ 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");
-                  $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 :
+                  $rate := $res = "1080p" ? $rate1080 :
                            $res = "720p"  ? $rate720 :
                                             $rate480;
                   $cost := $dur * $rate * $pricePer1K / 1000;
@@ -2014,7 +1913,7 @@ 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, resolution=model["resolution"]),
+            price_extractor=_seedance2_price_extractor(model_id, has_video_input=False),
             poll_interval=9,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
@@ -2111,19 +2010,14 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Seedance 2.0",
-                            _seedance2_reference_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
+                            _seedance2_reference_inputs(["480p", "720p", "1080p"], 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; Fast for speed optimization; "
-                    "Mini for the fastest, lowest-cost generation.",
+                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -2162,21 +2056,13 @@ 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");
-                  $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 :
+                  $rate := $res = "1080p" ? $rate1080 :
                            $res = "720p"  ? $rate720 :
                                             $rate480;
                   $noVideoCost := $dur * $rate * $noVideoPricePer1K / 1000;
@@ -2372,9 +2258,7 @@ 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, resolution=model["resolution"]
-            ),
+            price_extractor=_seedance2_price_extractor(model_id, has_video_input=has_video_input),
             poll_interval=9,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
@@ -2557,311 +2441,6 @@ 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]]:
@@ -2878,7 +2457,6 @@ class ByteDanceExtension(ComfyExtension):
             ByteDance2ReferenceNode,
             ByteDanceCreateImageAsset,
             ByteDanceCreateVideoAsset,
-            ByteDanceSeedAudioNode,
         ]