Introduction
This workflow is designed to seamlessly combine multiple LoRA (Low-Rank Adaptation) models through the LoRA Stacker node. Each LoRA adapter is tailored for specific tasks or datasets, providing versatility across various creative applications such as image generation, text creation, and more. By effectively blending different LoRA models, users can achieve rich and diverse outputs that reflect multiple artistic influences.
LoRA Stacker
The LoRA Stacker node simplifies the management and combination of multiple LoRA (Low-Rank Adaptation) models for fine-tuning large language models. It supports both simple weight assignments and advanced configurations with separate weights for different components. This node streamlines the process, enabling easy application of multiple models to enhance AI art generation with nuanced and sophisticated outputs.
Workflow Overview：
How to use this workflow?
- Set the lora count and the lora you want to use.
lora_count: Specifies the number of LoRA models to stack. Set this to the number of models you want to process. The minimum is 1, with no fixed maximum, but it depends on how many models you have.
lora_name_X: Defines the names of the LoRA models to include in the stack (X ranges from 1 to lora_count). Each name must be a valid model name or path.
lora_wt_X: Assigns weights to each LoRA model in "simple" mode (X ranges from 1 to lora_count). These weights, typically between 0.0 and 1.0, control each model's influence in the final output.
2.Enter the prompt into the CLIPTextEncoder, which guides the image content.
3.Set image width and height
4.Output
