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English(EN) 4-bit isn't always 4-bit. I was experimenting with SDXL on my 4 GB RX 570 when I noticed something weird: black artifacts in my renders (image 3). The original

4位AI模型量化对低显存硬件上的图像质量有影响

作者 PulseAugur 编辑部 · [1 个来源] ·

在4GB RX 570显卡上对SDXL Anime v3.1模型进行的实验表明，不同的4位量化方法会产生不同的结果。用户观察到Q4_0量化会出现黑色伪影和褪色外观，而Q4_K方法则产生更清晰的图像，并保留了精细细节。使用不同采样器进行的进一步测试表明，DPM2/Karras比Euler/Simple保留了更多细节，尽管两者在使用Q4_K量化时都产生了无伪影的图像。 AI

影响 展示了量化技术如何在消费级硬件上影响模型性能和输出质量。

排序理由 用户对现有模型和硬件的实验。

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4位AI模型量化对低显存硬件上的图像质量有影响

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报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    4位不总是4位。我在4GB RX 570上试验SDXL时注意到一些奇怪的现象：渲染图像出现黑色瑕疵（图3）。原始

    4-bit isn't always 4-bit. I was experimenting with SDXL on my 4 GB RX 570 when I noticed something weird: black artifacts in my renders (image 3). The original SDXL Anime v3.1 model was FP16, and I had quantized it myself to Q4_0. So I started investigating whether the quantizati…

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