Krea 2 Turbo 2‑Bit Quantization on Low‑End GPU – Performance and Workarounds
Run the 2‑bit GGUF Krea 2 Turbo on low‑end GPUs by disabling TorchDynamo, ensuring the Qwen 3 4B VL text encoder and mmproj match, and using the provided workaround.
Set TORCHDYNAMO_DISABLE=1, verify mmproj matches Qwen 3 4B VL, and use the GGUF model for low‑end GPU inference.
Summary
A Reddit user reports running the Krea 2 Turbo model in a 2‑bit GGUF quantized version on a GTX 750 Ti with 4 GB VRAM, 16 GB RAM, and an i5‑4590 CPU. The quantized model uses the Qwen3 4B VL Instruct text encoder with a 3‑bit Q_K_M quantization and a Qwen VAE. Despite the low precision, the model retains strong text‑rendering ability, though generation is slow: about 209 seconds total or 36–39 seconds per step. The user shares a temporary solution for a GGUF error, involving disabling TorchDynamo (setting TORCHDYNAMO_DISABLE=1) and editing the ComfyUI launcher. They also note that the error arises when downloading Qwen 3 4B VL without the matching mmproj file. The solution is a GitHub issue comment that provides a workaround until a stable patch is released.
The post highlights the feasibility of running Krea 2 Turbo on modest hardware with careful configuration.
Key changes
- Krea 2 Turbo 2‑bit GGUF uses Qwen3 4B VL Instruct text encoder with 3‑bit Q_K_M quantization
- Qwen VAE is used for the VAE component
- Generation on GTX 750 Ti takes ~209 seconds total (~36–39 s per step)
- Temporary workaround: set TORCHDYNAMO_DISABLE=1 and use the GitHub issue comment solution
- Error arises if Qwen 3 4B VL is downloaded without matching mmproj file