LLaDa-Image_ComfyUI / README.md
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---
base_model: inclusionAI/LLaDA-Image
base_model_relation: quantized
library_name: diffusers
pipeline_tag: text-to-image
tags:
- llada-image
- image-generation
- image-editing
- comfyui
- int8
- gguf
- quantized
- rebelai
---
# LLaDA-Image Base β€” ComfyUI INT8 + GGUF
Quantized **LLaDA-Image Base** weights for the **RealRebelAI LLaDA-Image ComfyUI** custom nodes.
Based on **inclusionAI/LLaDA-Image**, this release is intended for lower-memory ComfyUI inference while retaining the Base model workflow.
## ComfyUI Custom Nodes
https://github.com/RealRebelAI/LLaDa-Image_ComfyUI
Install into:
```text
ComfyUI/custom_nodes/ComfyUI-LLaDA-Image/
```
Restart ComfyUI after installation.
## Upstream Model
Official Base model:
https://huggingface.co/inclusionAI/LLaDA-Image
Official project:
https://github.com/inclusionAI/LLaDA-Image
This is an unofficial quantized derivative and is not affiliated with inclusionAI.
## Included Weights
### Base INT8 Transformer
```text
LLaDA-Image-Base-transformer-INT8.safetensors
```
Place in:
```text
ComfyUI/models/diffusion_models/
```
This is a native INT8 **Safetensors transformer**, not a transformer GGUF.
### Base Q4_K_M Text Encoder
```text
LLaDA-Image-Base-text_encoder-Q4_K_M.gguf
```
Place in:
```text
ComfyUI/models/text_encoders/
```
The custom runtime uses City96 ComfyUI-GGUF support for the quantized LLaDA2 MoE text encoder.
## Requirement
Install City96 ComfyUI-GGUF:
https://github.com/city96/ComfyUI-GGUF
## VAE
Place the compatible LLaDA VAE in:
```text
ComfyUI/models/vae/
```
Then select it in **LLaDA Image Loader**.
The current custom nodes expose native VAE tiled decoding:
```text
vae_tiling:
On
Auto
Off
```
For low-VRAM GPUs, **On** is a good starting point.
## Base Recommended Settings
```text
Steps: 50
CFG: 5.0
```
LLaDA-Image Base is the full model rather than the distilled few-step Turbo variant.
## Text-to-Image
```text
LLaDA Image Loader
|
v
LLaDA Image Text to Image
|
v
Save Image
```
Loader example:
```text
diffusion_model: LLaDA-Image-Base-transformer-INT8.safetensors
text_encoder: LLaDA-Image-Base-text_encoder-Q4_K_M.gguf
vae: your LLaDA VAE
dtype: bfloat16
vae_tiling: On
```
Start with **50 steps / CFG 5.0**.
## Native Image Editing
The custom nodes support LLaDA-Image's **native image-editing mode**.
This is not conventional img2img or denoise-strength emulation. The source image is passed through LLaDA-Image's native:
```text
generation_mode="editing"
```
using its image-conditioning/SigVQ path.
```text
LLaDA Image Loader -----------+
|
Load Image -------------------+--> LLaDA Image Edit --> Save Image
```
Example:
```text
Turn the fox into a white arctic fox while preserving the forest composition and realistic photography.
```
For Base editing, start with:
```text
Steps: 50
CFG: 5.0
```
Editing width and height must be divisible by **32**.
The same Base transformer and text encoder are used for generation and editing. No separate editing checkpoint is required.
## Base vs Turbo
```text
Base:
Steps: ~50
CFG: ~5.0
Turbo:
Steps: ~4
CFG: ~1.0
```
## Low-VRAM Notes
- Use the INT8 transformer.
- Use the Q4_K_M GGUF text encoder.
- Enable VAE tiling.
- Use CPU offload when necessary.
- Do not load the original full text encoder alongside the quantized encoder.
## Model Placement
```text
ComfyUI/
└── models/
β”œβ”€β”€ diffusion_models/
β”‚ └── LLaDA-Image-Base-transformer-INT8.safetensors
β”œβ”€β”€ text_encoders/
β”‚ └── LLaDA-Image-Base-text_encoder-Q4_K_M.gguf
└── vae/
└── <LLaDA VAE>.safetensors
```
## Important
Do **not** load the INT8 transformer through a GGUF diffusion loader. It is native INT8 Safetensors.
The `.gguf` file in this release is the **text encoder**, not the diffusion transformer.
## Credits
- **inclusionAI** β€” LLaDA-Image architecture and official model
- **Hugging Face Diffusers** β€” pipeline/component infrastructure
- **City96** β€” ComfyUI-GGUF / GGML support
- **ComfyUI** β€” node and inference ecosystem
- **RealRebelAI** β€” custom ComfyUI integration and quantized release
## License
The upstream LLaDA-Image model and components remain subject to their original licenses and terms. Review the upstream license before redistribution or commercial use.