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--- |
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license: apache-2.0 |
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base_model: |
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- google/flan-t5-base |
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- openai/clip-vit-large-patch14 |
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datasets: |
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- AbstractPhil/human-templated-captions-1b |
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--- |
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## Simple Summary |
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This project provides an advanced text control system for any AI generator that uses VIT-L-14 as a basis. Also known as CLIP_L. |
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It lets you “steer” how AI interprets your written prompts by adding a smart adapter between the text input and the image model. |
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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. |
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## More technical summary |
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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. |
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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. |
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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. |
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### Code |
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The model code is present in model.py. Inference code will be available in the long-winded article. |
