Update README.md

README.md

@@ -7,22 +7,19 @@ pipeline_tag: text-to-image
 LoRA weights of Stable Diffusion XL for fast text-to-image generation.
 
 
-Important Usage Guidance
-Use DDIM or Euler instead of LCM for sampling! When using DDIM, set timestep_spacing="trailing".
+## Important Usage Guidance
 
-The name of each LoRA weights indicates how many inference steps they should be applied.
+1. Use DDIM or Euler instead of LCM for sampling! When using DDIM, set timestep_spacing="trailing".
 
-The name of each LoRA weights indicates whether they are able to use normal CFGs or small CFGs 
+2. The name of each LoRA weights indicates how many inference steps they should be applied.
 
-NormalCFG means that model equipped with the LoRA can use CFG value 2-9 for generation. Yet you should adjust the CFG values given the steps you applied.
+3. The name of each LoRA weights indicates whether they are able to use normal CFGs or small CFGs
+   - NormalCFG means that model equipped with the LoRA can use CFG value 2-9 for generation. Yet you should adjust the CFG values given the steps you applied.
 When using fewer steps, you should use smaller CFGs. For example, use CFG 2.5 - 3.5 with 4 four steps and use CFG 3 - 6 with 8 steps. This is because that fewer-step means the model has fewer chance to fix the issues caused by the CFG.
+   - SmallCFG means that the model equipped with the LoRA can use CFG value 1-2 for generation.
 
-SmallCFG means that the model equipped with the LoRA can use CFG value 1-2 for generation.
 
-About the performance of normal CFG LoRAs.
 
-
-
-Note: Just find the normalCFG with 4-step is not working well. Trying to solve the issue.
+Note: Just find the normalCFG with 4-step is not working very well. Trying to solve the issue.
 
 [[paper](https://huggingface.co/papers/2405.18407)] [[arXiv](https://arxiv.org/abs/2405.18407)]  [[code](https://github.com/G-U-N/Phased-Consistency-Model)] [[project page](https://g-u-n.github.io/projects/pcm)]