CLIPTextEncodeFlux - ComfyUI Built-in Node Documentation

Complete documentation for the CLIPTextEncodeFlux node in ComfyUI. Learn its inputs, outputs, parameters and usage.

CLIPTextEncodeFlux is an advanced text encoding node in ComfyUI, specifically designed for the Flux architecture. It uses a dual-encoder mechanism (CLIP-L and T5XXL) to process both structured keywords and detailed natural language descriptions, providing the Flux model with more accurate and comprehensive text understanding for improved text-to-image generation quality.

This node is based on a dual-encoder collaboration mechanism:

1. The clip_l input is processed by the CLIP-L encoder, extracting style, theme, and other keyword features—ideal for concise descriptions.
2. The t5xxl input is processed by the T5XXL encoder, which excels at understanding complex and detailed natural language scene descriptions.
3. The outputs from both encoders are fused, and combined with the guidance parameter to generate unified conditioning embeddings (CONDITIONING) for downstream Flux sampler nodes, controlling how closely the generated content matches the text description.

Inputs

Parameter	Data Type	Input Method	Default	Range	Description
clip	CLIP	Node input	None	-	Must be a CLIP model supporting the Flux architecture, including both CLIP-L and T5XXL encoders
clip_l	STRING	Text box	None	Up to 77 tokens	Suitable for concise keyword descriptions, such as style or theme
t5xxl	STRING	Text box	None	Nearly unlimited	Suitable for detailed natural language descriptions, expressing complex scenes and details
guidance	FLOAT	Slider	3.5	0.0 - 100.0	Controls the influence of text conditions on the generation process; higher values mean stricter adherence to the text

Outputs

Output Name	Data Type	Description
CONDITIONING	CONDITIONING	Contains the fused embeddings from both encoders and the guidance parameter, used for conditional image generation

Usage Examples

Prompt Examples

• clip_l input (keyword style):

  • Use structured, concise keyword combinations
  • Example: masterpiece, best quality, portrait, oil painting, dramatic lighting
  • Focus on style, quality, and main subject

• t5xxl input (natural language description):

  • Use complete, fluent scene descriptions
  • Example: A highly detailed portrait in oil painting style, featuring dramatic chiaroscuro lighting that creates deep shadows and bright highlights, emphasizing the subject's features with renaissance-inspired composition.
  • Focus on scene details, spatial relationships, and lighting effects

Notes

1. Make sure to use a CLIP model compatible with the Flux architecture
2. It is recommended to fill in both clip_l and t5xxl to leverage the dual-encoder advantage
3. Note the 77-token limit for clip_l
4. Adjust the guidance parameter based on the generated results