## TT SD3.5 Text Encoder *TT_Sd35TextEncoderNode* *This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.* Encodes text prompts for SD3.5 using all three text encoders (CLIP-L, CLIP-G, T5-XXL) and returns a configured Guider object ready for sampling. ### Inputs | Parameter | Type | Required | Description | |-------------------|--------|----------|-----------------------------------------------------------| | `model` | MODEL | Yes | SD3.5 diffusion model | | `clip` | CLIP | Yes | SD3.5 text encoders (CLIP-L, CLIP-G, T5-XXL) | | `cfg` | FLOAT | No | Classifier-free guidance scale. Default: `5.0` | | `clip_l_positive` | STRING | No | Positive prompt for CLIP-L encoder | | `clip_g_positive` | STRING | No | Positive prompt for CLIP-G encoder | | `t5xxl_positive` | STRING | No | Positive prompt for T5-XXL encoder | | `clip_l_negative` | STRING | No | Negative prompt for CLIP-L encoder | | `clip_g_negative` | STRING | No | Negative prompt for CLIP-G encoder | | `t5xxl_negative` | STRING | No | Negative prompt for T5-XXL encoder | | `lora_triggers` | STRING | No | LoRA trigger words appended to positive prompts | | `width` | INT | No | Output width for resolution conditioning. Default: `512` | | `height` | INT | No | Output height for resolution conditioning. Default: `512` | | `target_width` | INT | No | Target width for resolution conditioning. Default: `512` | | `target_height` | INT | No | Target height for resolution conditioning. Default: `512` | ### Outputs | Parameter | Type | Description | |-----------|--------|---------------------------------------------------------| | `GUIDER` | GUIDER | Configured guider with encoded conditioning for sampler | ### Usage Connect `MODEL` and `CLIP` from a SD3.5 models loader node. Connect `GUIDER` to a sampler node. Use different prompts per encoder for fine-grained control — T5-XXL handles long descriptive prompts best, CLIP-L and CLIP-G work better with short style tags. --- [View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)