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 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 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.

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 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 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.

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
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

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 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
└── tests/
    ├── test_attention_patch.py
    └── test_math.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.

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

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.

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.
• 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.