Krea 2 GGUF Sample Generations
KREA-2 GGUF Weights
Quantized GGUF weights for the Krea-2 Base model, optimized for low-VRAM and consumer hardware local workflows via ComfyUI.
🚀 Overview
Krea-2 is a Diffusion Transformer (DiT). Standard GGUF custom nodes will throw an Unexpected architecture type error natively. To run these weights, you must use our patched custom node fork.
📥 Downloading and Installation
1. Custom Node Requirements (Mandatory)
Standard loaders do not yet recognize krea2 architecture tags in GGUF metadata. Clone the following custom node into your ComfyUI/custom_nodes/ directory:
2. Required Support Models
Place these base model files in your standard ComfyUI models/ directory:
- Text Encoder (CLIP): Qwen3-VL-4B-FP8-Scaled.safetensors (Load via
CLIPLoader, ensuring Type is set to krea2)
- VAE: qwen_image_vae.safetensors (Load via
VAELoader)
3. GGUF Weight Installation
- Download your preferred quantization file (e.g.,
Krea-2-Base-Q4_K_S.gguf) from the Files and versions tab of this repository.
- Place the downloaded
.gguf file into your ComfyUI/models/unet/ (or equivalent GGUF directory) folder.
- Load the model in ComfyUI using the
UnetLoaderGGUF node.
⚙️ Workflow Notes
Ensure your conditioning setup is mapped appropriately to handle the stacked hidden state requirements of multimodal DiTs to avoid latent sequence mismatch errors.
