ComfyUI DreamLight Node

Installation

1. Clone this repository into your ComfyUI's custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/APZmedia/ComfyUI-Dreamlight.git

2. Install dependencies:

cd ComfyUI-Dreamlight
pip install -r requirements.txt

3. Set up HuggingFace authentication (optional but recommended):

# Copy the example environment file
cp .env.example .env

# Edit .env and add your HuggingFace token
# Get your token from: https://huggingface.co/settings/tokens
echo "HF_TOKEN=your_huggingface_token_here" > .env

4. Download DreamLight models:

# Create model directories
mkdir -p ckpt/FLUX/transformer
mkdir -p ckpt/CLIP

# Download FLUX transformer weights
curl -L "https://huggingface.co/LYAWWH/DreamLight/resolve/main/FLUX/transformer/model.pth" -o ckpt/FLUX/transformer/model.pth

# Download CLIP model
curl -L "https://huggingface.co/LYAWWH/DreamLight/resolve/main/CLIP/models/config.json" -o ckpt/CLIP/config.json
curl -L "https://huggingface.co/LYAWWH/DreamLight/resolve/main/CLIP/models/pytorch_model.bin" -o ckpt/CLIP/pytorch_model.bin

Node Usage

The node appears under "image/postprocessing" as "DreamLightNode"

Inputs:

• foreground_image: Subject to be relit (IMAGE)
• background_image: New background (IMAGE)
• mask: Foreground subject mask (MASK)
• prompt: Lighting guidance text
• seed: Random seed for reproducibility
• resolution: Output resolution (256-2048)
• environment_map: Optional environment map (IMAGE)

Output:

• relit_image: Final image with consistent lighting

Example Workflow

1. Load test_dreamlight_workflow.json in ComfyUI
2. Provide these files in the project root:
   • example_foreground.png
   • example_background.png
   • example_mask.png

Model Management

• Smart Model Detection: DreamLight automatically searches for existing FLUX.1-dev models in ComfyUI's standard directories before downloading
• HuggingFace Authentication: Set HF_TOKEN in .env file for authenticated downloads
• Fallback Download: If no local model is found, downloads from HuggingFace (~23GB)

Notes

• First run will take longer as it downloads additional dependencies
• Requires GPU with at least 8GB VRAM
• For optimal results, use 1024x1024 resolution
• Environment maps should be 360° equirectangular images

Troubleshooting

If you encounter issues:

1. Verify all model files are in the correct directories
2. Ensure you have the required dependencies
3. Check console for specific error messages
4. Try reducing resolution if out of memory