Easy Apply LoraStack(applyLoraStack)

The Easy Apply LoraStack node applies a prepared LoRA stack to a base model and, optionally, a CLIP model. It is designed to make applying one or more LoRAs in sequence as a single step, with the LoRA configuration kept in a dedicated LORA_STACK value. The node lives in the EasyUse/Adapter category and has the class name applyLoraStack.

Inputs

LORA_STACK (lora_stack)

The LoRA stack to apply. This is a structured list of LoRA entries, typically created by another Easy-Use node that builds a LORA_STACK. Each entry defines a LoRA to apply and its model/clip strengths. The node applies the entries in the order they appear in the stack.

MODEL (model)

The base model to which the LoRA stack is applied. This should be the model you intend to use for sampling after LoRA modifications.

CLIP (optional_clip)

An optional CLIP model. If provided, the same LoRA stack is also applied to the CLIP model, allowing LoRAs with clip strengths to affect text encoding. This input is optional, but supplying it is necessary to get a meaningful CLIP output.

Outputs

MODEL

The modified model with the LoRA stack applied. Use this in place of the original model in later sampling nodes.

CLIP

The modified CLIP model with the LoRA stack applied. This output is only useful when optional_clip was provided. Pass it to CLIP text encoding nodes when generating conditioning.

Usage Notes

• Build a LORA_STACK beforehand using a compatible stack-building node. This keeps the workflow compact when applying multiple LoRAs.
• Because the LoRA stack is applied as a whole, the order of entries in the stack can matter. Check the output if you are combining LoRAs that affect similar layers.
• If you only need to modify the model and not the text encoder, you can leave optional_clip disconnected. The CLIP output will not carry a LoRA-modified CLIP in that case.
• When using models with multiple text encoders, such as SDXL, providing optional_clip ensures LoRAs with CLIP strength are applied consistently to the CLIP models used for conditioning.