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Z-Image-Turbo

unsloth

> Mirror of Tongyi-MAI/Z-Image-Turbo, republished by Unsloth. The weights > are unmodified. See NOTICE and the licence file in this repo for the terms, which are > the upstream terms and are unchanged. ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer Z-Image is a powerful and highly efficient image generation model family with **6B** parameters. Currently there are four variants:

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Z-Image-Turbo-ComfyUI

unsloth

The two single-file Z-Image assets that stable-diffusion.cpp needs, mirrored from Comfy-Org/z_image_turbo. Unsloth Studio fetches these on the no-GPU (native sd.cpp) path: both for Z-Image, and the Qwen3-4B conditioner again for FLUX.2 klein 4B, which shares it. They used to come straight from the community repack; a rename or a takedown there turns every CPU Z-Image and klein-4B load into a 404, so they are mirrored here. **The weights are unmodified:** byte for byte the files of the same name in the source repo. Only these two files are mirrored. The source repo also ships the Z-Image diffusion models, a distill LoRA and quantised conditioners; Studio does not use them from there, so they are not here.

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Z-Image-Turbo-FP8

unsloth

This is an FP8 / INT8 quantized version of Z-Image-Turbo. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.181 INT8, 0.130 FP8. ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer Z-Image is a powerful and highly efficient image generation model family with **6B** parameters. Currently there are four variants:

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Z-Image-Turbo-GGUF

unsloth

> [!NOTE] > This is a GGUF quantized version of Z-Image-Turbo. > unsloth/Z-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. --- ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer Z-Image is a powerful and highly efficient image generation model with **6B** parameters. Currently there are three variants:

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