ComfyUI LLM SDXL Adapter

A comprehensive set of ComfyUI nodes for using Large Language Models (LLM) as text encoders for SDXL image generation through a trained adapter.

<img width="1643" alt="image" src="https://github.com/user-attachments/assets/f1517b7a-2497-4eea-9034-802b3469f172" />

Image with workflow

🚀 Key Features

• Multi-LLM Support: Gemma and other compatible models (extensible architecture)
• Flexible Architecture: Modular nodes for customizable workflows
• Transformer Adapter: Convert LLM embeddings to SDXL format
• Chat Templates: Support for system prompts and conversation formatting
• Memory Optimization: Model loading management and caching
• Configurable Parameters: Flexible adapter architecture configuration

📦 Installation

Requirements

• Python 3.8+
• ComfyUI
• CUDA (recommended)
• Latest transformers library (tested on 4.53.1)

Install Dependencies

pip install transformers>=4.53.1 safetensors einops torch

Install Nodes

1. Clone the repository to ComfyUI/custom_nodes/:

cd ComfyUI/custom_nodes/
git clone https://github.com/NeuroSenko/ComfyUI_LLM_SDXL_Adapter.git

2. Restart ComfyUI

🗂️ Project Structure

comfyui_llm_sdxl_adapter/
├── __init__.py                  # Entry point and node registration
├── llm_model_loader.py          # LLM model loader
├── llm_text_encoder.py          # LLM text encoder
├── lll_adapter_loader.py        # Adapter loader
├── llm_to_sdxl_adapter.py       # Adapter architecture
├── apply_llm_to_sdxl_adapter.py # Adapter application
├── utils.py                     # Utility functions
└── README.md                    # Documentation

🔧 ComfyUI Nodes

LLM Model Loader

Loads language model and tokenizer.

Inputs:

• model_path: Path to LLM model
• device: Device (auto/cuda:0/cuda:1/cpu)
• force_reload: Force reload model

Outputs:

• model: LLM model instance
• tokenizer: Tokenizer
• info: Model information

LLM Text Encoder

Encodes text using loaded LLM.

Inputs:

• model: LLM model
• tokenizer: Tokenizer
• text: Text to encode
• system_prompt: System prompt (optional)
• skip_first: Number of tokens to skip

Outputs:

• hidden_states: LLM hidden states
• info: Encoding information

LLM Adapter Loader

Loads trained LLM -> SDXL adapter.

Inputs:

• adapter_path: Path to adapter file (.safetensors)
• llm_dim: LLM dimension (default 1152)
• sdxl_seq_dim: SDXL sequence dimension (2048)
• sdxl_pooled_dim: SDXL pooled output dimension (1280)
• target_seq_len: Target sequence length (308)
• n_wide_blocks: Number of wide blocks (2)
• n_narrow_blocks: Number of narrow blocks (3)

Outputs:

• adapter: Adapter instance
• info: Adapter information

Apply LLM To SDXL Adapter

Applies adapter to LLM hidden states.

Inputs:

• llm_hidden_states: LLM hidden states
• adapter: Loaded adapter

Outputs:

• conditioning: SDXL conditioning
• info: Application information

🎯 Basic Workflow

LLM Model Loader → LLM Text Encoder → LLM Adapter Loader → Apply LLM To SDXL Adapter → KSampler

Usage Example:

1. Load Model: Use LLM Model Loader to load an LLM (e.g., Gemma-3-1b-it)
2. Encode Text: Connect model to LLM Text Encoder and input your prompt
3. Load Adapter: Use LLM Adapter Loader to load trained adapter
4. Apply Adapter: Connect hidden states and adapter through Apply LLM To SDXL Adapter
5. Generate: Connect the resulting conditioning to standard SDXL pipeline

📁 File Structure

LLM Models

Place LLM models in the folder:

ComfyUI/models/LLM/
└── gemma-3-1b-it/

Adapters

Place trained adapters in the folder:

ComfyUI/models/llm_adapters/
└── gemma_to_sdxl_adapter.safetensors

⚙️ Adapter Architecture

The LLMToSDXLAdapter uses a transformer architecture with two stages:

1. Wide Processing: Process full sequence (512 tokens)
2. Compression: Compress to target length (308 tokens) via cross-attention
3. Narrow Processing: Final processing of compressed sequence
4. Pooling: Create pooled output for SDXL

Key Features:

• Positional embeddings for input and output sequences
• Learnable compression queries for controlled compression
• Attention masking for proper padding handling
• Dropout and LayerNorm for training stability

🔍 Debugging

To enable detailed logging:

import logging
logging.basicConfig(level=logging.INFO)

Information about loaded nodes is displayed during ComfyUI initialization.