Segmind-VegaRT

Segmind-VegaRT - Latent Consistency Model (LCM) LoRA of Segmind-Vega

Segmind-VegaRT a distilled consistency adapter for Segmind-Vega that allows to reduce the number of inference steps to only between 2 - 8 steps.

Latent Consistency Model (LCM) LoRA was proposed in LCM-LoRA: A universal Stable-Diffusion Acceleration Module by Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.

This model is the first base model showing real-time capabilities at higher image resolutions, but has its own limitations;

1. The model is good at close up portrait images of humans but tends to do poorly on full body images.

2. Full body images may show deformed limbs and faces.

3. This model is an LCM-LoRA model, so negative prompt and guidance scale parameters would not be applicable.

4. Since it is a small model, the variability is low and hence may be best used for specific use cases when fine-tuned.

We will be releasing more fine tuned versions of this model so improve upon these specified limitations.