Qwen-Image-2.1-Turbo-GGUF

This is a GGUF quantized version of Qwen-Image-2.1-Turbo, the 8-step distilled checkpoint of Qwen-Image-2.1. unsloth/Qwen-Image-2.1-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision, per tensor, from a measured sensitivity scan. Every file uses the same per-tensor layout as the matching file in unsloth/Qwen-Image-2.1-GGUF, with an importance matrix collected on Turbo itself at its own 8-step schedule. - Run these with Unsloth Desktop, ComfyUI (ComfyUI-GGUF), stable-diffusion.cpp and more. A GGUF is the denoiser only, so it needs the VAE and the Qwen3-VL text encoder alongside it. Turbo uses the same text encoder and VAE as Qwen-Image-2.1. - VAE: unsloth/Qwen-Image-2.1-FP8 `vae/qwen_image_2.1_vae_bf16.safetensors`. Text encoder: unsloth/Qwen3-VL-8B-Instruct-GGUF `Qwen3-VL-8B-Instruct-UD-Q4_K_XL.gguf`, the Dynamic 2.0 4-bit rung rather than the uniform `Q4_K_M`. - Turbo is meant for **8 steps at CFG 1** with the sampling schedule it ships (`sample_sigmas` in model_index.json). Unsloth Desktop applies it automatically. LPIPS (AlexNet, lower is better) against the bf16 Turbo transformer, same text encoder, VAE, prompts and seeds, 1024x1024, 8 steps, CFG 1, rendered through Unsloth Desktop's diffusers route. Mean with 95% bootstrap CI over 8 prompts x 2 seeds; the held-out column uses 8 further prompts x 2 seeds that were not part of the importance-matrix calibration. Two bf16 runs of the same seeds give 0.000 (run-to-run floor).

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