ComfyUI-TIDE

ComfyUI-TIDE is a ComfyUI custom node implementation of inference-time mechanisms from TIDE: Text-Informed Dynamic Extrapolation with Step-Aware Temperature Control for Diffusion Transformers.

The primary implementation targets Flux-style DiT attention in ComfyUI. The repository also includes a WAN 2.1/2.2 path for ComfyUI WAN video DiTs and an experimental SDXL/UNet adaptation that applies the usable attention-temperature part of the method to SDXL-style SpatialTransformer attention.

The nodes patch a cloned ComfyUI MODEL object. They do not add extra sampling steps, replace the sampler, replace the scheduler, or fork ComfyUI core.

Credits and attribution

The algorithmic method implemented here is based on the TIDE paper:

TIDE: Text-Informed Dynamic Extrapolation with Step-Aware Temperature Control for Diffusion Transformers
Yihua Liu, Fanjiang Ye, Bowen Lin, Rongyu Fang, Chengming Zhang
arXiv:2603.08928, 2026
https://arxiv.org/abs/2603.08928

Credit for the TIDE method, including Text Anchoring, Dynamic Temperature Control, and the paper's analysis of attention dilution in high-resolution Diffusion Transformer generation, belongs to the paper authors.

This repository is an independent ComfyUI custom-node implementation. It is not the official TIDE repository, not affiliated with the paper authors, and should not be cited as the original method. If this node is useful in your work, cite the TIDE paper.

@misc{liu2026tide,
  title         = {TIDE: Text-Informed Dynamic Extrapolation with Step-Aware Temperature Control for Diffusion Transformers},
  author        = {Yihua Liu and Fanjiang Ye and Bowen Lin and Rongyu Fang and Chengming Zhang},
  year          = {2026},
  eprint        = {2603.08928},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2603.08928}
}

Scope

This repository implements practical ComfyUI attention-side mechanisms inspired by TIDE.

For Flux-style joint text/image attention, it implements:

• text-token additive bias for Text Anchoring;
• step-aware RoPE temperature scaling for Dynamic Temperature Control;
• a lightweight ComfyUI model wrapper to pass the current denoising timestep into the attention patch;
• a small PyTorch SDPA fallback used only when an additive TIDE attention mask is active.

For WAN 2.1/2.2-style video DiT attention, it implements:

• step-aware RoPE temperature scaling for WAN self-attention;
• lazy wrapping of ComfyUI WAN rope_encode / forward_orig paths through a cloned-model diffusion wrapper;
• chaining through ComfyUI WrappersMP.DIFFUSION_MODEL without modifying ComfyUI core.

For SDXL-style UNet attention, it implements:

• step-aware attention-temperature scaling through ComfyUI's optimized_attention_override;
• optional application to SDXL cross-attention, self-attention, or both;
• model-local patching that chains with a pre-existing attention override when present.

It does not implement the full official Diffusers pipeline, benchmark harness, datasets, metric evaluation scripts, Qwen pipeline, or complete YaRN/DyPE/NTK positional interpolation stack.

Supported model paths

Flux / Flux.2-style DiT path

The main target path is FLUX-family DiT models in ComfyUI, including FLUX.2-style paths if they use the same practical structure as ComfyUI's Flux implementation:

• joint text/image attention;
• text tokens before image tokens;
• attn1_patch support;
• extra_options["img_slice"] available at the attention patch site;
• RoPE matrix passed as pe.

Other DiT models may require model-specific patch paths. They should not be assumed to work unless their ComfyUI implementation exposes the same attention-patch contract.

WAN 2.1 / 2.2 video DiT path

The WAN node targets ComfyUI WAN-family implementations based on comfy.ldm.wan.model.WanModel and close subclasses, including WAN 2.1 and WAN 2.2 paths that expose:

• rope_encode;
• forward_orig;
• rope_embedder.axes_dim;
• ComfyUI diffusion-model wrapper execution.

The WAN path applies Dynamic Temperature Control to the WAN self-attention RoPE matrices. It does not apply TIDE Text Anchoring because ComfyUI WAN uses separate self-attention over video tokens and cross-attention over text/context tokens. In that architecture, text keys and image/video keys are not competing inside one joint softmax, and adding the same positive bias to every text key in pure cross-attention would be cancelled by softmax shift invariance.

SDXL / UNet path

The SDXL node targets ComfyUI's UNet SpatialTransformer attention path:

• SDXL-style cross-attention and self-attention;
• optimized_attention_override support;
• transformer_options["activations_shape"] present at the attention site.

The SDXL path is intentionally separate from the Flux path.

Important: SDXL support is not a full paper-faithful TIDE implementation. TIDE Text Anchoring is defined for joint text/image attention, where text keys and image keys compete inside one softmax. In SDXL UNet cross-attention, the keys/values are text-only. Adding the same positive bias to every text key would be cancelled by softmax shift invariance and would not change the output. SDXL self-attention has image tokens but no text keys. Therefore the SDXL node implements the usable part: step-aware attention-temperature control.

Nodes

TIDE High-Resolution Extrapolation

Use this node for FLUX-family DiT models.

Implemented mechanisms:

• Text Anchoring;
• Dynamic Temperature Control;
• optional PyTorch SDPA fallback when the additive text-anchor mask is active.

TIDE WAN High-Resolution Extrapolation

Use this node for WAN 2.1 / WAN 2.2-style video DiT models in ComfyUI.

Implemented mechanism:

• Dynamic Temperature Control on WAN self-attention RoPE.

Not implemented for WAN:

• Text Anchoring, because WAN text conditioning is separate cross-attention rather than Flux-style joint text/image attention.

TIDE SDXL High-Resolution Extrapolation

Use this node for SDXL-style UNet models.

Implemented mechanism:

• step-aware attention-temperature scaling.

Not implemented for SDXL:

• Text Anchoring;
• Flux-style RoPE temperature scaling;
• MM-DiT text/image token balancing.

What is implemented

1. Text Anchoring for Flux-style joint attention

TIDE identifies text-token influence decay as a core failure mode at high resolution: image token count grows with resolution while text token count stays fixed. Text Anchoring counteracts this by adding a positive bias to attention logits whose keys are text tokens.

This node computes the default adaptive bias as:

beta = log((target_width * target_height) / (base_width * base_height))

With the default base_width=1024 and base_height=1024, this is equivalent to:

log(width / 1024) + log(height / 1024)

The final applied value is:

applied_beta = text_anchor_strength * beta

By default, the node applies no Text Anchoring at native-or-smaller token counts unless apply_to_native_or_smaller=True.

2. Dynamic Temperature Control for Flux-style RoPE attention

TIDE uses a step-aware temperature curve so attention sharpening is stronger in the early/global part of denoising and relaxes toward the late/detail part of denoising. For Flux-style models, this repository applies that idea as a RoPE temperature multiplier, matching the reference implementation strategy rather than inserting a new attention kernel for every backend.

Default curve:

tau(t, f) = tau_max - (tau_max - tau_min) * t ** alpha(f)
alpha(f) = alpha_low + (alpha_high - alpha_low) * f

Defaults:

| Parameter | Default | | ---------------------- | -------------: | | tau_max | 1.0 | | alpha_low | 0.6 | | alpha_high | 0.2 | | temperature_strength | 1.0 | | frequency_mode | official_raw |

frequency_mode=official_raw uses raw RoPE frequencies, matching the released implementation behavior this port was written against. paper_normalized is exposed for comparison because the paper notation describes a normalized frequency variable.

3. Dynamic Temperature Control for WAN 2.1/2.2 RoPE attention

The WAN node applies the same step-aware RoPE temperature multiplier used by the Flux path, but at ComfyUI WAN's rope_encode / forward_orig boundary. WAN uses a three-axis RoPE layout (time, height, width), discovered from rope_embedder.axes_dim at runtime. Spatial scaling uses the node's width, height, base_width, and base_height; the temporal axis is left unscaled.

Because WAN does not expose a Flux-style joint text/image attention softmax, the WAN node sets text_anchor_strength=0.0 internally and only applies Dynamic Temperature Control.

4. Dynamic attention temperature for SDXL/UNet attention

The SDXL node applies the attention-temperature part of the method by scaling the attention query tensor before ComfyUI's optimized attention function:

attention_logits = (Q * inv_tau) K^T / sqrt(d)

This is equivalent to applying the temperature factor to the attention logits:

attention_logits = Q K^T / (tau * sqrt(d))

The SDXL curve uses a single exponent:

tau(t) = tau_max - (tau_max - tau_min) * t ** alpha

The minimum temperature is derived from the YaRN-style extrapolation scale:

scale = sqrt((width * height) / (base_width * base_height))
sqrt(1 / tau_min) = 0.1 * log(scale) + 1

The node blends from no-op to full temperature control with:

applied_inv_tau = 1 + (inv_tau - 1) * temperature_strength

SDXL defaults:

| Parameter | Default | | ---------------------------- | --------------: | | base_width / base_height | 1024 / 1024 | | temperature_strength | 1.0 | | alpha | 0.6 | | tau_max | 1.0 | | apply_to | both |

Implementation notes

Flux path

1. The node clones and patches the incoming ComfyUI MODEL.
2. TIDE state is stored on the cloned model through ComfyUI model options.
3. TIDEModelWrapper injects current timestep metadata into transformer_options["tide"].
4. TIDEAttentionPatch applies Text Anchoring and Dynamic Temperature Control through ComfyUI's attn1_patch hook.
5. TIDEAttentionOverride forces a compact PyTorch SDPA path only when an additive TIDE mask is active. This avoids attention backends that reject additive masks or try to materialize a dense high-resolution mask.
6. No global monkey-patching is used.
7. No sampler or scheduler rewrite is performed.

WAN path

1. The WAN node clones and patches the incoming ComfyUI MODEL.
2. It installs a ComfyUI WrappersMP.DIFFUSION_MODEL wrapper on the cloned model.
3. The wrapper injects current timestep metadata into transformer_options["tide"].
4. The wrapper lazily wraps the live WAN model's rope_encode and forward_orig methods.
5. rope_encode output or externally supplied freqs are multiplied by the TIDE per-frequency temperature scale once per forward path.
6. The implementation preserves ComfyUI's native WAN block loop, sampler, scheduler, attention backend, and dynamic-VRAM lifetime.

SDXL path

1. The SDXL node clones and patches the incoming ComfyUI MODEL.

2. It installs an optimized_attention_override on the cloned model only.

3. The override detects SDXL/UNet SpatialTransformer attention by checking for transformer_options["activations_shape"].

4. It avoids Flux-style paths by skipping attention calls that expose Flux-specific block_type or img_slice metadata.

5. It classifies attention as:

   • self when query-token count equals key-token count;
   • cross when query-token count differs from key-token count.

6. It applies query scaling only to the selected attention kind: cross, self, or both.

7. If another attention override already exists, the SDXL node delegates to it after applying its query scaling.

Paper / reference-code / local-module mapping

| Paper component | Reference implementation behavior | This repository | | --------------------------------------------------- | ------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | | Text-token influence decay | Additive text-token attention mask | tide_core.math.adaptive_text_bias, tide_core.patches.TIDEAttentionPatch | | Text Anchoring, beta = log(lambda) | log(width / 1024) + log(height / 1024) for FLUX-sized text prefix | Adaptive beta from node width, height, base_width, base_height; text-token count inferred from ComfyUI img_slice | | YaRN temperature baseline | get_mscale, default temperature from extrapolation scale | tide_core.math.get_mscale, get_default_temperature | | Dynamic Temperature Control | dyheating() / temperature-aware RoPE scaling | tide_core.math.rope_temperature_scale, applied to ComfyUI pe | | Denoising-step-aware behavior | Update position embedding state from current timestep | TIDEModelWrapper injects normalized timestep into transformer_options | | FLUX attention integration | Modified Diffusers FLUX transformer/processor | ComfyUI attn1_patch plus optional optimized_attention_override | | WAN attention integration | Not part of the paper's main Flux/MM-DiT implementation | tide_core.wan, ComfyUI diffusion-model wrapper, RoPE scaling for WAN self-attention | | SDXL attention integration | Not part of the paper's main Flux/MM-DiT implementation | nodes_sdxl.py, ComfyUI optimized_attention_override, query scaling for UNet attention | | Logarithmic FLUX scheduler shift | Scheduler/pipeline-level change | Not implemented by this node | | DyPE / NTK-by-parts / YaRN positional interpolation | Custom positional interpolation stack | Not fully implemented; this node implements the TIDE attention-side mechanisms only |

Installation

Clone this repository into ComfyUI's custom node directory:

cd ComfyUI/custom_nodes
git clone <this-repo-url> ComfyUI-TIDE

Restart ComfyUI.

No extra runtime dependency is required beyond the PyTorch/ComfyUI environment. requirements.txt lists torch for standalone tests.

Usage

Flux / Flux.2 usage

1. Load a FLUX-family model as usual.
2. Add TIDE High-Resolution Extrapolation after the model loader.
3. Connect the patched model output to your sampler.
4. Set width and height to the final generated image dimensions used by your latent node.
5. Keep base_width=1024 and base_height=1024 for FLUX-family models unless you know the model's native training target differs.

Recommended starting values:

| Setting | Value | | ----------------------------------- | ---------------------: | | width / height | final image dimensions | | base_width / base_height | 1024 / 1024 | | text_anchor_strength | 1.0 | | temperature_strength | 1.0 | | alpha_low | 0.6 | | alpha_high | 0.2 | | tau_max | 1.0 | | frequency_mode | official_raw | | force_pytorch_attention_with_mask | True |

Ablation settings:

| Test | Settings | | ------------------------ | ------------------------------------------------------ | | Text Anchoring only | text_anchor_strength=1.0, temperature_strength=0.0 | | Dynamic Temperature only | text_anchor_strength=0.0, temperature_strength=1.0 | | Disabled | text_anchor_strength=0.0, temperature_strength=0.0 |

WAN 2.1 / 2.2 usage

1. Load a WAN 2.1 or WAN 2.2 model as usual.
2. Add TIDE WAN High-Resolution Extrapolation after the model loader.
3. Connect the patched model output to your sampler.
4. Set width and height to the final video frame dimensions in pixels.
5. Set base_width and base_height to the resolution you want to treat as the model's native/reference resolution for this workflow. The node defaults to 640x640 because ComfyUI's WAN 2.2 text-to-video blueprint currently uses that size, but WAN checkpoints and workflows vary.

Recommended starting values:

| Setting | Value | | ---------------------------- | ---------------------------: | | width / height | final video frame dimensions | | base_width / base_height | workflow/model reference | | temperature_strength | 1.0 | | alpha_low | 0.6 | | alpha_high | 0.2 | | tau_max | 1.0 | | frequency_mode | official_raw |

WAN ablation settings:

| Test | Settings | | ----------------------- | -------------------------------- | | Dynamic Temperature | temperature_strength=1.0 | | Disabled | temperature_strength=0.0 | | Force native-size patch | apply_to_native_or_smaller=True |

SDXL usage

1. Load an SDXL checkpoint as usual.
2. Add TIDE SDXL High-Resolution Extrapolation after the model loader.
3. Connect the patched model output to your sampler.
4. Set width and height to the final generated image dimensions used by your latent node.
5. Keep base_width=1024 and base_height=1024 for SDXL unless you intentionally want a different native-resolution reference.
6. Start with apply_to=both. If results are unstable, test cross and self separately.

Recommended starting values:

| Setting | Value | | ---------------------------- | ---------------------: | | width / height | final image dimensions | | base_width / base_height | 1024 / 1024 | | temperature_strength | 1.0 | | alpha | 0.6 | | tau_max | 1.0 | | apply_to | both |

SDXL ablation settings:

| Test | Settings | | -------------------- | -------------------------------------------- | | Cross-attention only | apply_to=cross, temperature_strength=1.0 | | Self-attention only | apply_to=self, temperature_strength=1.0 | | Both | apply_to=both, temperature_strength=1.0 | | Disabled | temperature_strength=0.0 |

Node inputs

TIDE High-Resolution Extrapolation

Required

| Input | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------- | | model | ComfyUI MODEL object to patch. | | width, height | Final target generation dimensions in pixels. Must match the latent/image size used by the workflow. | | text_anchor_strength | Multiplier on adaptive beta. 1.0 follows the paper/reference behavior. 0.0 disables Text Anchoring. | | temperature_strength | Multiplier on Dynamic Temperature Control. 1.0 follows the reference curve. 0.0 disables DTC. |

Optional

| Input | Default | Description | | ----------------------------------- | -------------: | ---------------------------------------------------------------------------- | | base_width, base_height | 1024, 1024 | Native/training resolution used for adaptive scaling. | | alpha_low, alpha_high | 0.6, 0.2 | DTC exponents for low/high RoPE frequency behavior. | | tau_max | 1.0 | Maximum temperature reached near the end of denoising. | | frequency_mode | official_raw | official_raw or paper_normalized. | | apply_to_double_blocks | True | Apply patch to FLUX double-stream blocks. | | apply_to_single_blocks | True | Apply patch to FLUX single-stream blocks. | | apply_to_native_or_smaller | False | Allow patching even when target token count is not above base token count. | | force_pytorch_attention_with_mask | True | Use internal PyTorch SDPA only when TIDE's additive mask is active. | | preserve_existing_wrapper | True | Delegate to an existing ComfyUI model wrapper after injecting TIDE metadata. | | debug | False | Log skipped DTC shape mismatches and exceptions. |

TIDE WAN High-Resolution Extrapolation

| Input | Default | Description | | ---------------------------- | -------------: | ---------------------------------------------------------------------------------------------------- | | model | required | ComfyUI MODEL object to patch. | | width, height | 1280, 720 | Final target video frame dimensions in pixels. Must match the latent/video size used by the workflow. | | temperature_strength | 1.0 | Strength of WAN RoPE Dynamic Temperature Control. 0.0 disables the WAN patch. | | base_width, base_height | 640, 640 | Reference/native resolution used for adaptive scaling. Adjust for the checkpoint/workflow. | | alpha_low, alpha_high | 0.6, 0.2 | DTC exponents for low/high RoPE frequency behavior. | | tau_max | 1.0 | Maximum temperature reached near the end of denoising. | | frequency_mode | official_raw | official_raw or paper_normalized. | | apply_to_native_or_smaller | False | Allow patching even when target token count is not above base token count. | | preserve_existing_wrapper | True | Delegate to an existing ComfyUI model wrapper after injecting TIDE metadata. | | debug | False | Log skipped WAN wrapping/shape cases. |

TIDE SDXL High-Resolution Extrapolation

| Input | Default | Description | | --------------------------- | -------------: | ---------------------------------------------------------------------------------------------------- | | model | required | ComfyUI MODEL object to patch. | | width, height | 1536, 1536 | Final target generation dimensions in pixels. Must match the latent/image size used by the workflow. | | temperature_strength | 1.0 | Strength of SDXL attention-temperature scaling. 0.0 disables the patch. | | base_width, base_height | 1024, 1024 | Native/training resolution used for adaptive scaling. | | alpha | 0.6 | Single exponent for the SDXL step-aware temperature curve. | | tau_max | 1.0 | Maximum temperature reached near the end of denoising. | | apply_to | both | cross, self, or both. Controls which SDXL attention calls are patched. |

Repository structure

ComfyUI-TIDE/
├── __init__.py
├── nodes.py
├── nodes_sdxl.py
├── requirements.txt
├── README.md
├── examples/
│   └── README.md
├── tide_core/
│   ├── __init__.py
│   ├── config.py
│   ├── math.py
│   ├── patches.py
│   └── wan.py
└── tests/
    ├── test_attention_patch.py
    ├── test_math.py
    └── test_wan.py

Tests

Run the standalone tests from the repository root:

python -m pip install pytest torch
python -m pytest -q

The tests cover:

• adaptive Text Anchoring beta computation;
• YaRN/default temperature formula;
• RoPE temperature scale shape and timestep progression;
• additive attention-mask creation;
• masked SDPA override behavior;
• WAN RoPE temperature scaling helper behavior.

The tests do not validate visual quality or live ComfyUI execution.

For the SDXL module, a basic syntax check can be run with:

python -m py_compile nodes_sdxl.py

Paper vs implementation differences

WAN support is an adaptation, not full Flux/MM-DiT TIDE

The full Text Anchoring mechanism is defined for MM-DiT joint attention where text keys and image keys compete inside one softmax. ComfyUI WAN uses self-attention for video/image tokens and separate cross-attention for text/context tokens. Therefore this repository applies the TIDE Dynamic Temperature Control mechanism to WAN self-attention RoPE, but does not apply Text Anchoring to WAN cross-attention.

SDXL support is an adaptation, not full TIDE

The full TIDE method is designed around DiT/MM-DiT attention where text tokens and image tokens are present in the same attention sequence. That makes Text Anchoring meaningful because a positive bias on text-key logits changes the balance between text keys and image keys.

SDXL uses a UNet architecture with separate attention patterns. In cross-attention, the keys and values are text-only, so adding the same bias to all text logits would not change the softmax result. In self-attention, the keys are image tokens and there are no text keys to anchor. For that reason, the SDXL node only applies Dynamic Temperature Control as attention-logit sharpening.

Scheduler time shifting

The paper appendix describes a logarithmic FLUX time-shift schedule for high resolutions. This node receives an already-built ComfyUI sampler schedule and does not silently rewrite it. Use a scheduler setup that does not over-shift high-resolution FLUX timesteps.

Positional interpolation

The paper evaluates TIDE in combination with positional extrapolation/interpolation methods such as YaRN/DyPE-style handling. This repository does not port the full positional interpolation stack. It applies the TIDE attention-side mechanisms through ComfyUI's patch system.

Frequency variable

The paper notation describes a normalized frequency variable for alpha(f). The reference implementation behavior used by this port applies the curve using raw RoPE frequencies. This mismatch is exposed as frequency_mode so the default can follow the reference behavior while still allowing controlled comparison.

Text-token count

The paper writes the text-token length abstractly as L_T. Some FLUX implementations use a fixed text-token prefix length. This repository infers the text prefix from ComfyUI's img_slice rather than hard-coding a token count.

General DiT support

The method is architecture-relevant to DiTs, but this implementation is tied to ComfyUI's Flux-style patch interface. Non-Flux DiTs need compatible attention hooks and token-layout metadata.

Assumptions

Flux path

• The model uses Flux-style joint attention with text tokens before image tokens.
• extra_options["img_slice"] identifies the split between text and image tokens.
• The ComfyUI attention patch receives a RoPE matrix through pe.
• The node width and height match the actual generated dimensions.
• The timestep passed through the model wrapper is normalized or sigma-like in [0, 1]; values outside the interval are clamped.
• FLUX-family image token granularity is 16 pixels per transformer token.

WAN path

• The model uses a ComfyUI WAN implementation with rope_encode and forward_orig.
• The node width and height match the actual generated video frame dimensions.
• base_width and base_height are chosen as the intended native/reference dimensions for the specific WAN checkpoint/workflow.
• WAN RoPE axes are ordered as (time, height, width), matching ComfyUI's WanModel.rope_encode.
• The temporal RoPE axis is not scaled by this node.
• The timestep passed through the model wrapper is normalized or sigma-like in [0, 1]; values outside the interval are clamped.

SDXL path

• The model uses ComfyUI's UNet SpatialTransformer attention path.
• optimized_attention_override is honored by the active ComfyUI attention backend.
• transformer_options["activations_shape"] is present for the attention calls that should be patched.
• The node width and height match the actual generated dimensions.
• SDXL's native-resolution reference is treated as 1024x1024 unless overridden.
• Sigma metadata is available through transformer_options; if not, the SDXL patch falls back to the noisy/start side of the curve.

Limitations

• Not an official TIDE release.
• Not a full reproduction of the paper's complete experimental pipeline.
• Does not include paper benchmark scripts, datasets, generated result images, or metric evaluation.
• Does not modify the sampler's high-resolution time-shift schedule.
• Does not fully implement NTK-by-parts, YaRN positional interpolation, or DyPE positional interpolation.
• Flux support is tied to ComfyUI's Flux-style attention patch contract.
• WAN support applies Dynamic Temperature Control only; WAN Text Anchoring is intentionally not implemented.
• WAN visual quality needs live workflow testing across WAN 2.1/2.2 variants, frame counts, resolutions, samplers, and attention backends.
• SDXL support is an experimental attention-temperature adaptation, not full Text Anchoring.
• SDXL visual quality needs live workflow testing across checkpoints, resolutions, samplers, and attention backends.
• Very large resolutions still require sufficient VRAM for the selected model, sampler, attention path, latent size, and VAE path.

License

This repository is released under the MIT License; see LICENSE.

ComfyUI is GPL-3.0 licensed. This custom node is distributed as a separate plugin, but it imports and runs inside ComfyUI. Review license compatibility before redistributing this node as part of a larger bundled package.