Update README.md

README.md

@@ -46,34 +46,18 @@ For better results, please use it in conjunction with the dedicated VAE, **[Z-Im
 
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-## Model Format and Quality
-
-Generally, model size and image quality are proportional.
-
-- **FP32 > FP16 ≒ BF16 > MXFP8 > FP8_scaled > NVFP4 > FP8**
-
-Generation time varies greatly depending on **whether the model can be loaded into VRAM**, and the supported formats are determined by the **GPU generation**.
-
-| | FP32 | FP16 | BF16 | FP8 | FP8_scaled | MXFP8 | FP4 |
-|--|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| **NVIDIA** | | | | | | | |
-| RTX 5000 (Blackwell) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| RTX 4000 (Ada Lovelace) | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-| RTX 3000 (Ampere) | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-| RTX 2000 (Turing) | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
-| GTX 1000 (Pascal) | ✅ | ⚠️ | ❌ | ❌ | ❌ | ❌ | ❌ |
-| **AMD** | | | | | | | |
-| RX 9000 (RDNA4) | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-| RX 7000 (RDNA3) | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-| RX 6000 (RDNA2) | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
-| RX 5000 (RDNA1) | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
-| **Intel** | | | | | | | |
-| Arc B (Battlemage) | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-| Arc A (Alchemist) | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-
-**MXFP8** and **NVFP4** are the latest standards designed for the **RTX 5000 series**. On unsupported GPUs, however, fallback mechanisms will actually **slow down** processing, so their use is not highly recommended.
-
-For a comparison of image quality and generation time by format, please see [this page](https://www.ai-image-journey.com/2026/06/floating-point-precision-speed.html).
+## Model Formats and Image Quality
+
+Generally, the image quality ranks in the following order:
+
+**FP32 > FP16 ≒ BF16 > INT8_ConvRot_HQ > MXFP8 > FP8_svd_scaled > FP8**
+
+- **FP32** is mainly recommended for training purposes.
+- **FP16 / BF16** can generate high-quality illustrations.
+- **INT8_ConvRot_HQ** offers an excellent balance of speed and quality while using roughly half the VRAM of FP16 / BF16.
+- **MXFP8, FP8_svd_scaled, and FP8** are currently **not recommended**, as they are inferior to INT8_ConvRot_HQ in both speed and image quality.
+
+For a detailed comparison of each format, please refer to [this page](https://www.ai-image-journey.com/2026/06/floating-point-precision-speed.html). 
 
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