A merged of Krea 2 with the Krea 2 Identity Edit lora v1.2 adaptation by conradlocke and SNOFS v1.4 LOKR by Ashen [Huggingface] Both baked directly into the model so the separate loras are not required.

Requires the ComfyUI-Krea2Edit node pack, the edit lora is trained with dual conditioning (in-context VAE tokens + image-grounded Qwen3-VL encoding) that stock nodes don't provide. Example Workflow is attached on the Optional File (Change the GGUF loader node to Load Diffusion Model node if you are using Int8 or Int4 model).

All example images were created using the Q4_0 GGUF variant at 0.7 MP resolution with a 3:4 aspect ratio. The Int8_convrot variant hasn’t been tested extensively, so please share your results here.

Precision variants

Several precision variants are available to let you choose the best balance between quality, VRAM usage, and performance:

• INT8 ConvRot —> Higher precision with lower quantization loss. Requires more VRAM than the INT4 variant.

• Q5_1 GGUF —> High-quality 5-bit quantization with a good balance between quality and VRAM usage.

• Q4_0 GGUF —> More aggressive 4-bit quantization with lower VRAM usage while retaining good image quality (The best balance for low vram user).

• INT4 ConvRot W4A4 —> The most aggressive option, designed for lower VRAM usage and faster inference on supported hardware. Quality is slightly below the Q4 GGUF variants (The fastest).

In general, expect the precision trade off to be approximately:

INT8 ConvRot > Q5_1 > Q4_0 > INT4 ConvRot W4A4

Higher precision generally provides better quality, while lower precision reduces VRAM requirements.

Credits

• Krea 2 -> Original base model by Krea AI

• Krea 2 Identity Edit -> Adaptation by conradlocke

• SNOFS -> by Ashen [Huggingface]

This is an unofficial community modification and is not affiliated with or endorsed by Krea.ai.