Easy Apply StyleAlign(styleAlignedBatchAlign)

Description

The Easy Apply StyleAlign node (styleAlignedBatchAlign) is a convenience adapter from the ComfyUI-Easy-Use package. It applies the StyleAligned batch-alignment technique to a diffusion MODEL, patching the model so that images in a batch share selected style information during sampling. This is useful for generating a set of images with a consistent style — for example, from multiple prompts or seeds — while keeping each prompt's content distinct.

The node is designed as an easy-to-use wrapper: it takes a model and returns a patched model, so it can be placed directly between the checkpoint loader and a sampler. It appears under the EasyUse/Adapter category in the node menu.

Inputs

model

• Type: MODEL
• Required: yes

The base diffusion model to be patched. Any standard ComfyUI model can be used.

share_norm

• Type: COMBO
• Required: yes

Controls how normalization statistics are shared across the batch. The selected mode determines which normalization layers contribute to the aligned style. The available modes are supplied by the upstream StyleAligned implementation.

share_attn

• Type: COMBO
• Required: yes

Controls which attention tensors are shared or aligned across batch entries. More attention sharing tends to produce stronger style consistency; less sharing preserves more per-item variation. The exact modes are provided by the upstream StyleAligned implementation.

scale

• Type: FLOAT
• Default: 1.0
• Min: 0.0
• Max: 1.0
• Step: 0.1
• Required: yes

Strength of the StyleAlign patch. 1.0 applies the full effect, 0.0 effectively disables the alignment, and values in between blend the patched behavior with the original model behavior.

Outputs

MODEL output

The patched diffusion model. Pass this to a sampler such as KSampler instead of the original model.

Usage Notes

• Apply this node after loading the base model and before sampling.
• The effect is batch-oriented: you need more than one image in the batch for the alignment to be meaningful.
• If you do not see a style-alignment effect, verify that your latents or conditioning are being batched as expected and that scale is not 0.
• For the strongest consistency, use scale = 1.0 and choose broad sharing options for both share_norm and share_attn.
• To preserve more individual variation between batch items, reduce scale or choose narrower attention sharing.