Krea 2 GGUF

Quantized GGUF diffusion transformer weights for Krea 2, converted from the original BF16 releases for use with ComfyUI GGUF loader nodes.

This repository provides GGUF files for two checkpoints of the Krea 2 model family:

• krea2_raw_bf16-*.gguf — converted from krea/Krea-2-Raw, the base release checkpoint.
• krea2_turbo_bf16-*.gguf — converted from krea/Krea-2-Turbo, the post-trained checkpoint with additional fine-tuning and distillation.

Krea 2 is a 12-billion parameter Diffusion Transformer with a novel architecture featuring layerwise and refiner text-fusion blocks. It is not based on Flux or any prior open-weight architecture.

These files are not a complete standalone Krea 2 package. Your workflow still needs the text encoder and VAE components.

ComfyUI Support

Use these models with the ComfyUI nodes from molbal/ComfyUI-GGUF. Install that custom node repository into your ComfyUI custom_nodes folder, then restart ComfyUI.

Important: This repository requires molbal/ComfyUI-GGUF, which is a fork of city96/ComfyUI-GGUF with added support for the Krea 2 architecture. The original city96 plugin does not support these files. (as of 2026-06-24)

Place the downloaded .gguf files in one of ComfyUI's diffusion model folders:

ComfyUI/models/diffusion_models/
ComfyUI/models/unet/

Load the file with Unet Loader (GGUF) in a Krea 2 workflow. Krea 2 uses a single transformer (unlike Ideogram 4, there is no separate unconditional transformer component).

Files

Choose either the Raw or Turbo variant depending on your workflow; they are not paired with each other.

Which Checkpoint to Use

The Turbo checkpoint has been post-trained with distillation and runs well at 8 steps with CFG=1. The Raw checkpoint behaves like a standard flow-matching DiT and benefits from more steps and positive CFG.

When GGUFs Are Worth the Tradeoff

The BF16 source weights for Krea 2 are 26.6 GB each — far beyond what most consumer GPUs can hold entirely in VRAM. GGUFs make sense when:

• Limited VRAM: Q4_0 at 7.74 GB fits entirely in an 8 GB GPU; Q5_1 at 9.93 GB targets 10–12 GB cards. Running BF16 on these GPUs would require heavy CPU offloading and become impractically slow.
• CPU offload workflows: If you are already offloading model layers to RAM, GGUF reduces the RAM footprint proportionally alongside VRAM, which is often the actual bottleneck.
• Acceptable quality loss at Q5+: At Q5_0 and above the visual output of Krea 2 is very close to BF16. Q4 levels show mild softening on fine detail but remain usable for most creative tasks.

GGUFs are generally not worth it if you have a 24 GB+ GPU and want maximum fidelity — load the FP8 or BF16 source directly in that case.

Download

Download the file you want from the Files tab, or use the Hugging Face CLI. For example:

huggingface-cli download molbal/krea2-gguf krea2_turbo_bf16-Q5_1.gguf --local-dir ComfyUI/models/diffusion_models

Compatibility Notes

These are non-K GGUF quantizations intended for PyTorch dequantization in ComfyUI. K-quants are not included because this ComfyUI loading path does not use fused quantized linear kernels.

Krea 2 GGUF support requires ComfyUI to have the krea2 architecture registered in its model detection system. If your ComfyUI installation does not recognise the checkpoint, update ComfyUI core and molbal/ComfyUI-GGUF to their latest versions.

License

These files are derived from krea/Krea-2-Raw and krea/Krea-2-Turbo and follow the Krea 2 Community License.