---
license: apache-2.0
base_model:
- google/flan-t5-base
- laion/CLIP-ViT-bigG-14-laion2B-39B-b160k
datasets:
- AbstractPhil/human-templated-captions-1b
---

## Simple Summary
This project provides an advanced text control system for any AI generator that uses CLIP-ViT-bigG-14-laion2B-39B-b160k as a basis. Also known as CLIP_G.


It lets you “steer” how AI interprets your written prompts by adding a smart adapter between the text input and the image model.
By fine-tuning how the prompt is understood, you get more accurate, creative, or controllable AI-generated images—especially in complex or multi-style models like Stable Diffusion XL.


## More technical summary
This repository contains code, configuration, and weights for the Dual Shunt Adapter: a modular cross-attention prompt embedding controller designed for SDXL and multi-CLIP diffusion systems.
The adapter bridges T5 (or other transformer) text encoders with CLIP-based pooled embedding spaces, providing delta, gate, log_sigma, anchor, and guidance outputs for per-token, per-field semantic modulation.
Compatible with custom and parallel CLIP streams (e.g., SDXL’s CLIP-L/CLIP-G), the system enables targeted latent field steering, dynamic classifier-free guidance, and localized prompt injection for advanced generative workflows—including direct integration with ComfyUI and HuggingFace Diffusers.


### Code
The model code is present in model.py. Inference code will be available in the long-winded article.