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GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Use the model with ComfyUI and ComfyUI-GGUF. These GGUF files contain the Qwen-Image-2.1 image transformer; the original text encoder and VAE are also required for inference.

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> [!IMPORTANT] > The uncensored GGUFs are now available! > If you encounter any issues or would like to share your generated images, feel free to post them in the Community section. GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.

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GSQ-RCO upstream · layout preserved 144 tensors transplanted IQ3_S / IQ3_XXS / IQ2_XS / Q2_0 per-tensor blake2b verified 262K context APACHE-2.0 Qwen3.8-Flash-Next · GSQ-RCO-abliterated The refusal direction removed from an already-quantized GSQ-RCO model — 144 tensors across all 48 layers swapped for ready-made abliterated weights, with every value and scale GSQ learned left untouched and the upstream per-tensor type assignment restored exactly . The IQ3_S build is verified tensor by tensor: everything that changed was supposed to, nothing else moved. 38.0 GB · Q2_0 every tier costs only 0.25 – 0.44 GB over upstream 55.1 GB · IQ3_S 🧭 What this repository is An abliterated build of the official ISTA-DASLab GSQ-RCO quantizations . Not a single one of GSQ's learned quantized tensors was recomputed — 144 "write-to-residual-stream" projection tensors across all 48 layers were replaced with the corresponding tensors from a ready-made abliterated release. Base ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF — GSQ learned quantization + RCO budget-constrained type assignment Ablation source orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF — the 144 target tensors are taken straight from it Method Byte-level GGUF → GGUF tensor transplant (135 of 144 targets); the 9 remaining expert tensors, which upstream stores as Q2_0, are re-encoded from the abliterated values using GSQ's own learned block scales — no GSQ value is recomputed, only the codes Not done Any dequantize → quantize cycle; any re-quantization from BF16 / safetensors In one line: what you get is still the GSQ-RCO quantization you already know , with those 144 tensors abliterated. It is the only abliteration route that preserves GSQ's work.

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好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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