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---
library_name: diffusers
pipeline_tag: text-to-image
---
# Phased Consistency Model
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".
The name of each LoRA weights indicates how many inference steps they should be applied.
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.
About the performance of normal CFG LoRAs.
Note: Just find the normalCFG with 4-step is not working 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)] |