diff --git a/README.md b/README.md
index faf63d4..6dfc1e8 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,10 @@
 # ComfyUI-TIDE
 
-ComfyUI-TIDE is a ComfyUI custom node for applying the core inference-time mechanisms from **TIDE: Text-Informed Dynamic Extrapolation with Step-Aware Temperature Control for Diffusion Transformers** to Flux-style DiT attention in ComfyUI.
+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 node patches a cloned ComfyUI `MODEL` object. It does **not** add extra sampling steps, replace the sampler, replace the scheduler, or fork ComfyUI core.
+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
 
@@ -27,34 +29,82 @@ This repository is an independent ComfyUI custom-node implementation. It is not
   primaryClass  = {cs.CV},
   url           = {https://arxiv.org/abs/2603.08928}
 }
-```
+````
 
 ## Scope
 
-This repository implements the practical ComfyUI attention-side parts of TIDE for Flux-style joint text/image attention:
+This repository implements practical ComfyUI attention-side mechanisms inspired by TIDE.
 
-- 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 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 path
+## Supported model paths
 
-The 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:
+### Flux / Flux.2-style DiT path
 
-- 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`.
+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
+### 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.
 
@@ -78,9 +128,9 @@ 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
+### 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. 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.
+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:
 
@@ -91,18 +141,63 @@ 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` |
+| 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:
+
+```text
+attention_logits = (Q * inv_tau) K^T / sqrt(d)
+```
+
+This is equivalent to applying the temperature factor to the attention logits:
+
+```text
+attention_logits = Q K^T / (tau * sqrt(d))
+```
+
+The SDXL curve uses a single exponent:
+
+```text
+tau(t) = tau_max - (tau_max - tau_min) * t ** alpha
+```
+
+The minimum temperature is derived from the YaRN-style extrapolation scale:
+
+```text
+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:
+
+```text
+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"]`.
@@ -111,18 +206,32 @@ Defaults:
 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` |
-| 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 |
+| 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
 
@@ -139,6 +248,8 @@ No extra runtime dependency is required beyond the PyTorch/ComfyUI environment.
 
 ## 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.
@@ -147,51 +258,94 @@ No extra runtime dependency is required beyond the PyTorch/ComfyUI environment.
 
 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` |
+| 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` |
+| 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` |
+| 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
 
-### Required
+### TIDE High-Resolution Extrapolation
 
-| 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. |
+#### 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. |
+| `temperature_strength` | Multiplier on Dynamic Temperature Control. `1.0` follows the reference curve. `0.0` disables DTC.       |
 
-### Optional
+#### 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. |
+| 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
 
@@ -199,6 +353,7 @@ Ablation settings:
 ComfyUI-TIDE/
 ├── __init__.py
 ├── nodes.py
+├── nodes_sdxl.py
 ├── requirements.txt
 ├── README.md
 ├── examples/
@@ -224,16 +379,28 @@ 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.
+* 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:
+
+```bash
+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.
@@ -256,22 +423,35 @@ The method is architecture-relevant to DiTs, but this implementation is tied to
 
 ## Assumptions
 
-- 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.
+### 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.
-- Visual quality still requires live workflow testing.
-- Very large resolutions still require sufficient VRAM for the selected model, sampler, attention path, latent size, and VAE path.
+* 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