Clybius-ComfyUI-Chroma-RoPE/README.md

ComfyUI-Chroma-RoPE

Advanced Rotary Position Embedding (RoPE) modifications for Chroma/FLUX models
. Enable ultra-high-resolution image generation with YaRN-like modifications.

About The Project

ComfyUI-Chroma-RoPE is a ComfyUI custom node that implements advanced RoPE (Rotary Position Embedding) modifications for Chroma and FLUX-based diffusion models. It enables generating images at resolutions far beyond the model's native training scale without additional training.

Key Features

• YaRN (Yet another RoPE extensioN): Extrapolate position encodings to handle longer sequences/resolutions
• p-RoPE (Proportional RoPE): Selectively truncate low-frequency RoPE dimensions for semantic coherence (Kinda not done right)
• DyPE: Timestep-dependent frequency modulation that adapts to the diffusion process
• Multiple Ramp Functions: Linear, sigmoid, power-2, and square root blending options
• Timestep Modulation: Dynamic theta scaling based on diffusion noise levels
• Zero Training Required: Works with pre-trained models out of the box

This implementation is based on concepts from the original ComfyUI-DyPE repository and extends it with additional RoPE modification methods.

Installation

Via ComfyUI Manager (Recommended)

1. Open ComfyUI Manager in your ComfyUI interface
2. Click "Install Custom Nodes"
3. Search for ComfyUI-Chroma-RoPE
4. Click Install

Manual Installation

1. Navigate to your ComfyUI/custom_nodes/ directory:

cd ComfyUI/custom_nodes/

2. Clone this repository:

git clone https://github.com/Clybius/ComfyUI-Chroma-RoPE.git

3. Restart ComfyUI

Usage

1. Load your Chroma/FLUX model using a standard model loader
2. Add the Chroma RoPE Patch node (found under model_patches/unet)
3. Connect the model output from your loader to the model input
4. Configure parameters based on your target resolution
5. Connect to KSampler and generate

Node Parameters

Parameter	Type	Default	Description
model	Model	Required	The Chroma/FLUX model to patch
method	Combo	yarn_freq_stretch	Position encoding method: yarn, yarn_freq_stretch, yarn+dynamic_ntk, dynamic_ntk, ntk, base
rope_percentage	Float	1.0	p-RoPE proportion (0.0-1.0). 1.0=full RoPE, 0.0=NoPE (semantic only)
dype	Boolean	True	Enable DyPE (Dynamic Position Extrapolation) with timestep modulation
max_pe_length	Int	64	Original trained position encoding length
yarn_ramp_type	Combo	sqrt	Frequency blending function: linear, sigmoid, pow2, sqrt
yarn_ratio	Float	1.0	YaRN scaling ratio multiplier
yarn_beta_fast	Int	32	High-frequency rotation cutoff
yarn_beta_slow	Int	2	Low-frequency rotation cutoff
timestep_modulation	Boolean	False	Enable timestep-dependent theta scaling
timestep_period_min	Float	1000.0	Theta period at max noise (t=1.0)
timestep_period_max	Float	10000.0	Theta period at min noise (t=0.0)
attn_ratio	Float	1.0	Attention scaling factor for RoPE embeddings

Position Encoding Methods

YaRN (yarn)

The default and recommended method. Combines interpolation and extrapolation with frequency-aware blending for smooth high-resolution generation.

YaRN Frequency Stretch (yarn_freq_stretch)

Novel method that non-linearly stretches the frequency space, preserving high-frequency relationships while extrapolating low frequencies.

YaRN + Dynamic NTK (yarn+dynamic_ntk)

Combines YaRN with Dynamic NTK scaling for aggressive extrapolation scenarios.

Dynamic NTK (dynamic_ntk)

Dynamic scaling based on sequence length ratio. More stable for moderate extrapolation.

NTK (ntk)

Standard NTK-aware scaling with fixed extrapolation factor.

Base (base)

No extrapolation. Uses original model position encodings.

Ramp Functions

The ramp function controls how interpolated and extrapolated frequencies blend:

• Linear: Smooth linear interpolation between regions
• Sigmoid: S-curve transition for sharper boundaries
• Pow2: Aggressive blending
• Sqrt: Conservative blending (gentler transitions)

Compatibility

• Models: Chroma, FLUX-based architectures
• ComfyUI: Compatible with standard ComfyUI workflows
• Other Nodes: Works alongside quantization, attention optimization, and other model patches

Not compatible with: SD 1.5, SDXL, or non-FLUX architectures.

Known Limitations

• FLUX/Chroma architectures only
• Parameter tuning required for optimal results at different resolutions
• Higher resolutions may require more sampling steps for best quality

Credits

• Original ComfyUI-DyPE repository by wildminder
• YaRN paper and implementation concepts
• The ComfyUI team for the extensible platform

License

This project is released under the Apache 2.0 License. See LICENSE file for details.

Acknowledgments

This project builds upon the work from the DyPE research and the original ComfyUI-DyPE implementation. Special thanks to the diffusion model research community for advancing position encoding techniques.

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