LLaDA-Image Turbo for ComfyUI

UPDATE! made optimizations to speed up gen time.

Custom ComfyUI nodes for LLaDA-Image-Turbo, with support for text-to-image generation and native image editing.

This project is built around the official LLaDA-Image-Turbo model from inclusionAI and adds a ComfyUI pipeline designed to run the model with RebelAI's optimized weights.

Links

• Model weights / Hugging Face: https://huggingface.co/realrebelai/LLaDa-Image-Turbo_ComfyUI
• This ComfyUI node repository: https://github.com/RealRebelAI/LLaDa-Image_ComfyUI
• Official LLaDA-Image-Turbo: https://huggingface.co/inclusionAI/LLaDA-Image-Turbo
• Official LLaDA-Image source: https://github.com/inclusionAI/LLaDA-Image

Features

• LLaDA-Image-Turbo text-to-image generation
• Native LLaDA image editing
• Native INT8 transformer support
• Q4_K_M GGUF LLaDA2-MoE text encoder support
• BF16/full transformer support
• ComfyUI model dropdowns
• 4-step Turbo generation
• Seed, resolution, CFG and negative-prompt controls
• Shared pipeline loader for generation and editing

Native Image Editing

Editing is not conventional diffusion img2img and does not emulate editing with a denoise-strength slider.

The LLaDA Image Edit node invokes LLaDA-Image's native editing path with:

generation_mode="editing"

The source image is passed through the model's image-conditioning/SigVQ path and combined with the edit instruction.

Basic workflow:

LLaDA Image Loader
        |
        +--------------------> LLaDA Image Text to Image
        |
        +--------------------> LLaDA Image Edit <---- Load Image
                                      |
                                      v
                                  Save Image

For Turbo, start with 4 steps and CFG 1.0. Editing dimensions must be divisible by 32.

Example edit prompt:

Turn the fox into a white arctic fox while preserving the forest composition and realistic photography.

Model Files

The optimized model files are hosted separately on Hugging Face:

https://huggingface.co/realrebelai/LLaDa-Image-Turbo_ComfyUI

Supported weights include:

The INT8 file is a native Safetensors transformer, not a transformer GGUF. GGUF is used for the quantized LLaDA2-MoE text encoder.

The remaining LLaDA components are based on the official LLaDA-Image-Turbo release.

ComfyUI Model Placement

Place transformer weights in:

ComfyUI/models/diffusion_models/

Place the GGUF text encoder in:

ComfyUI/models/text_encoders/

Place the LLaDA VAE in your normal ComfyUI VAE directory:

ComfyUI/models/vae/

The loader scans ComfyUI's model directories rather than relying on machine-specific absolute paths.

Nodes

LLaDA Image Loader

Loads the selected transformer, LLaDA2 text encoder, VAE, and supporting LLaDA pipeline components.

LLaDA Image Text to Image

Runs native LLaDA-Image-Turbo text-to-image generation.

Recommended Turbo starting point:

• Steps: 4
• CFG: 1.0

LLaDA Image Edit

Runs native LLaDA editing using a source image and an edit instruction.

Inputs include the pipeline, source image, prompt, width, height, steps, CFG, seed, and optional negative prompt.

LLaDA Image Unload

Releases the loaded LLaDA pipeline when you are finished with it.

Quantization

This project supports a mixed optimized configuration:

• Transformer: native INT8 Safetensors
• Text encoder: Q4_K_M GGUF
• Alternative transformer: full BF16 Safetensors

The quantized weights are derivatives of:

inclusionAI/LLaDA-Image-Turbo

See the Hugging Face model repository for the weight files and model card:

https://huggingface.co/realrebelai/LLaDa-Image-Turbo_ComfyUI

Upstream Project

LLaDA-Image and LLaDA-Image-Turbo are developed by inclusionAI.

Official model:

https://huggingface.co/inclusionAI/LLaDA-Image-Turbo

Official source:

https://github.com/inclusionAI/LLaDA-Image

This repository provides an independent ComfyUI integration/optimized runtime and is not the upstream LLaDA-Image repository.

License

Use the upstream LLaDA-Image/LLaDA-Image-Turbo license and terms applicable to the original model, along with any license included in this repository for the integration code.