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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This is a GGUF quantized version of Qwen-Image-2512. unsloth/Qwen-Image-2512-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - To use the model, read our guides for ComfyUI or stable-diffusion.cpp. - Uses tooling from ComfyUI-GGUF by city96. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog   
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> [!NOTE] > This is a GGUF quantized version of Qwen-Image-Edit-2509. > unsloth/Qwen-Image-Edit-2509-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github   
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This is a GGUF quantized version of Qwen-Image-Edit-2511. unsloth/Qwen-Image-Edit-2511-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. - For more information on how to use and run the model in ComfyUI, read our guide. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog   
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> [!NOTE] > This is a GGUF quantized version of Qwen-Image-Edit. > unsloth/Qwen-Image-Edit-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github   
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This is an FP8 / INT8 quantized version of Qwen-Image. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.057 INT8; FP8 needs activation_value_lb handling - use INT8 for stability. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
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> [!NOTE] > This is a GGUF quantized version of Qwen-Image. > unsloth/Qwen-Image-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   
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> [!NOTE] > This is a GGUF quantized version of Qwen-Image-Layered. > unsloth/Qwen-Image-Layered-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision.   🤗 HuggingFace    |   🤖 ModelScope    |    📑 Research Paper    |    📑 Blog    |    🤗 Demo    We are excited to introduce **Qwen-Image-Layered**, a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks **inherent editability**: each layer can be independently manipulated without affecting other content. Meanwhile, such a layered representation naturally supports **high-fidelity elementary operations**-such as resizing, reposition, and recoloring. By physically isolating semantic or structural components into distinct layers, our approach enables high-fidelity and consistent editing.
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We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
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We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
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