---
library_name: diffusers
base_model: segmind/Segmind-Vega
tags:
- lora
- text-to-image
license: apache-2.0
inference: false
---
# Segmind-VegaRT - Latent Consistency Model (LCM) LoRA of Segmind-Vega 

Try real-time inference here **[VegaRT demo⚡](https://www.segmind.com/segmind-vega-rt)**

API for **[Segmind-VegaRT](https://www.segmind.com/models/segmind-vega-rt-v1/api)**

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/WacXd5DqP5hx8iEGTPt16.mp4"></video>

Segmind-VegaRT a distilled consistency adapter for [Segmind-Vega](https://huggingface.co/segmind/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](https://arxiv.org/abs/2311.05556) 
by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.*

# Image comparison (Segmind-VegaRT vs SDXL-Turbo)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/AvzWnh6udMuFG5pfxydxT.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/BMbs5oUWIO9fFQQgah_OR.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/9DlECXpJNrR3rEFWYbNZK.png)

# Speed comparison (Segmind-VegaRT vs SDXL-Turbo) on A100 80GB

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/j884CHWAuaDMyhdzIWTCx.png)

| Model                                                                      | Params / M | 
|----------------------------------------------------------------------------|------------|
| [lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5)   | 67.5       |
| [**Segmind-VegaRT**](https://huggingface.co/segmind/Segmind-VegaRT)   | **119**        |
| [lcm-lora-sdxl](https://huggingface.co/latent-consistency/lcm-lora-sdxl) | 197  |

## Usage

LCM-LoRA is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first 
install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`.
audio dataset from the Hugging Face Hub:

```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate peft
```

### Text-to-Image

Let's load the base model `segmind/Segmind-Vega` first. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0.

```python
import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image

model_id = "segmind/Segmind-Vega"
adapter_id = "segmind/Segmind-VegaRT"

pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")

# load and fuse lcm lora
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()


prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# disable guidance_scale by passing 0
image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]
```