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+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
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+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
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+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
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+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
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+ END OF TERMS AND CONDITIONS
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+ Copyright 2025 Hang Yang, Shuwei Shi, Changhao Nan, Jingyi Yu, Lan Xu
+
+ Licensed under the Apache License, Version 2.0 (the "License");
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+ You may obtain a copy of the License at
+
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diff --git a/README.md b/README.md
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+
+
+
+
+
+
+A ComfyUI node implementing **SMC-CFG** (Sliding Mode Control CFG) from the paper:
+
+> **CFG-Ctrl: A Control-Theoretic Perspective on Classifier-Free Guidance**
+> Hang Yang, Shuwei Shi, Changhao Nan, Jingyi Yu, Lan Xu — *CVPR 2026*
+> [[Paper]](https://github.com/hanyang-21/CFG-Ctrl) [[Original Code]](https://github.com/hanyang-21/CFG-Ctrl)
+
+Standard CFG uses a fixed linear gain that becomes unstable at high guidance scales — causing oversaturation, artifacts, and semantic drift. This node replaces that linear controller with a **nonlinear sliding mode controller** that keeps guidance stable at any CFG scale.
+
+---
+
+## How It Works
+
+The paper reframes CFG as a control problem. Standard CFG is P-control (proportional control with fixed gain). SMC-CFG adds a bounded nonlinear correction that enforces convergence along an exponential sliding surface.
+
+
+
+
+
+
+
+### The math in brief
+
+At each denoising step *t*:
+
+```
+e_t = noise_cond - noise_uncond # guidance error signal
+s_t = (e_t - e_{t-1}) + λ · e_{t-1} # sliding surface
+u_sw = -K · sign(s_t) # switching control (bounded)
+e_corrected = e_t + u_sw # corrected guidance
+v_out = v_uncond + w · e_corrected # final guided prediction
+```
+
+The correction `u_sw` is bounded to `[-K, +K]` per element — this is what prevents the unbounded growth that plagues standard CFG. The sliding surface combines the error derivative with a decayed previous error, creating a feedback loop with Lyapunov-stable convergence.
+
+---
+
+## Installation
+
+### Manual
+
+Clone into your ComfyUI `custom_nodes` directory:
+
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/ethanfel/ComfyUI-CFG-CTRL.git
+```
+
+Restart ComfyUI. No additional dependencies required — the node only uses PyTorch (already in ComfyUI).
+
+---
+
+## Usage
+
+
+
+
+
+
+
+1. Add the **SMC-CFG Ctrl** node (found in `sampling/custom_sampling`)
+2. Connect your model loader's `MODEL` output to the node's `model` input
+3. Connect the node's `MODEL` output to your KSampler's `model` input
+4. Set your KSampler's CFG scale — you can push it **higher than usual** thanks to the SMC stabilization
+
+That's it. The node patches the model's internal CFG function. It works with **KSampler**, **KSampler Advanced**, and any sampler that respects the model's CFG configuration.
+
+---
+
+## Parameters
+
+
+
+
+
+
+
+### `smc_cfg_lambda` — Sliding Surface Coefficient
+**Default: `5.0`** | Range: `0.0 – 50.0`
+
+Controls the sliding surface equation `s_t = (e_t - e_{t-1}) + λ · e_{t-1}`. Higher values weight the previous error magnitude more heavily, making the controller more aggressive about correcting accumulated guidance drift.
+
+| Value | Behavior |
+|-------|----------|
+| `0.0` | Surface uses only error derivative — very gentle |
+| `1.0 – 5.0` | Balanced correction (recommended range) |
+| `> 10.0` | Aggressive — may over-constrain the generation |
+
+### `smc_cfg_K` — Switching Gain
+**Default: `0.2`** | Range: `0.0 – 5.0`
+
+Bounds the correction to `[-K, +K]` per tensor element. This is the "safety clamp" that prevents instability. Think of it as how hard the controller can push back.
+
+| Value | Behavior |
+|-------|----------|
+| `0.0` | No correction — equivalent to standard CFG |
+| `0.1 – 0.3` | Subtle stabilization (recommended) |
+| `> 0.5` | Strong correction — may introduce chattering artifacts |
+
+### `warmup_steps` — No-Guidance Warmup
+**Default: `0`** | Range: `0 – 100`
+
+Number of initial denoising steps where guidance is completely disabled (pure conditional prediction). The model establishes rough composition and structure before the SMC controller begins steering.
+
+| Value | Behavior |
+|-------|----------|
+| `0` | SMC-CFG active from the first step |
+| `2 – 5` | Lets structure form first — can improve diversity |
+| `> 10` | Long warmup — useful for experimentation |
+
+### Quick Start Settings
+
+For most models, start with the defaults and only adjust if needed:
+
+```
+smc_cfg_lambda = 5.0
+smc_cfg_K = 0.2
+warmup_steps = 0
+```
+
+These match the paper's recommended values and work well across SD 1.5, SDXL, SD3, FLUX, and Wan models.
+
+---
+
+## Compatibility
+
+- Works with any model that uses CFG (SD 1.5, SDXL, SD3, FLUX, Wan, etc.)
+- Compatible with **KSampler**, **KSampler Advanced**, and custom sampling workflows
+- Stacks with `sampler_post_cfg_function` nodes (e.g., RescaleCFG can be used alongside)
+- **Note:** Replaces the model's CFG function — only one `sampler_cfg_function` node can be active at a time. If another node also sets a custom CFG function, the last one in the chain wins.
+
+---
+
+## Technical Details
+
+### How it integrates with ComfyUI
+
+The node uses `model.clone()` + `set_model_sampler_cfg_function()` — the standard model patching pattern. It intercepts the CFG combination step without modifying model weights, conditioning, or the sampling loop itself.
+
+### State management
+
+The SMC algorithm requires memory of the previous step's guidance error (`e_{t-1}`). This state is held in a closure and automatically resets when a new generation starts (detected via sigma schedule discontinuity).
+
+### Performance
+
+The correction adds only lightweight tensor operations (`sign`, multiply, add) per denoising step. The overhead is negligible compared to the model forward pass. However, `disable_cfg1_optimization=True` is set to ensure both conditional and unconditional predictions are always computed.
+
+---
+
+## Citation
+
+If you use this in your work, please cite the original paper:
+
+```bibtex
+@article{yang2025cfg,
+ title={CFG-Ctrl: A Control-Theoretic Perspective on Classifier-Free Guidance},
+ author={Yang, Hang and Shi, Shuwei and Nan, Changhao and Yu, Jingyi and Xu, Lan},
+ journal={arXiv preprint arXiv:2505.18727},
+ year={2025}
+}
+```
+
+---
+
+## License
+
+This ComfyUI node is released under the [Apache 2.0 License](LICENSE), matching the original CFG-Ctrl repository.
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+