ltx-2.3-22b-dev_transformer_only_mxfp8_block32.safetensors
模型信息
可以说是目前兼顾“极速出图”与“极致画质（低显存下）”的版本答案。
该mxfp8_block32模型它没有采用传统的全局缩放，而是将权重切分成每 32 个元素为一个区块 (block) 进行微缩放。
优势：这种分块量化极大地保留了模型的原始分布特征。它在拥有与普通 FP8 几乎相同文件大小和显存占用的同时，画质表现无限逼近满血的 BF16。它有效解决了普通 FP8 量化在视频生成中常见的闪烁和噪点问题。
transformer_only，这意味着它们仅仅包含了 LTX 2.3 的核心主干网络 (UNet/DiT)，让显存占用低的同时能搭配选择更高精度的text_projection_bf16
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This can be considered the current solution that balances "extremely fast image output" and "ultimate image quality (with low VRAM)".
This mxfp8_block32 model does not use traditional global scaling. Instead, it divides the weights into blocks of 32 elements each for micro-scaling.
Advantages: This block quantization greatly preserves the original distribution characteristics of the model. While having almost the same file size and VRAM usage as ordinary FP8, its image quality is nearly indistinguishable from full-fledged BF16. It effectively solves the flickering and noise problems commonly found in ordinary FP8 quantization during video generation.
"Transformer_only" means that they only include the core backbone network of LTX 2.3 (UNet/DiT), allowing for low VRAM usage while being compatible with higher-precision text_projection_bf16.
可以说是目前兼顾“极速出图”与“极致画质（低显存下）”的版本答案。
该mxfp8_block32模型它没有采用传统的全局缩放，而是将权重切分成每 32 个元素为一个区块 (block) 进行微缩放。
优势：这种分块量化极大地保留了模型的原始分布特征。它在拥有与普通 FP8 几乎相同文件大小和显存占用的同时，画质表现无限逼近满血的 BF16。它有效解决了普通 FP8 量化在视频生成中常见的闪烁和噪点问题。
transformer_only，这意味着它们仅仅包含了 LTX 2.3 的核心主干网络 (UNet/DiT)，让显存占用低的同时能搭配选择更高精度的text_projection_bf16
---
This can be considered the current solution that balances "extremely fast image output" and "ultimate image quality (with low VRAM)".
This mxfp8_block32 model does not use traditional global scaling. Instead, it divides the weights into blocks of 32 elements each for micro-scaling.
Advantages: This block quantization greatly preserves the original distribution characteristics of the model. While having almost the same file size and VRAM usage as ordinary FP8, its image quality is nearly indistinguishable from full-fledged BF16. It effectively solves the flickering and noise problems commonly found in ordinary FP8 quantization during video generation.
"Transformer_only" means that they only include the core backbone network of LTX 2.3 (UNet/DiT), allowing for low VRAM usage while being compatible with higher-precision text_projection_bf16.