molbal / krea2-gguf

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 .