
# Latent Consistency Models

Official Repository of the paper: *[Latent Consistency Models](https://arxiv.org/abs/2310.04378)*.

Project Page: https://latent-consistency-models.github.io

## Model Descriptions:
Copied from [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) to experiment with quantization.
Originally distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable-Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). 

## Usage

To run the model yourself, you can leverage the 🧨 Diffusers library:
1. Install the library:
```
pip install --upgrade diffusers  # make sure to use at least diffusers >= 0.22
pip install transformers accelerate
```

2. Run the model:
```py
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("TobDeBer/lcm_dream7")

# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float32)

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

# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
num_inference_steps = 4 

images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images
```

For more information, please have a look at the official docs:
👉 https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#latent-consistency-models

{"language": ["en"], "license": "mit", "tags": ["text-to-image"], "pipeline_tag": "text-to-image"}
diffusers safetensors text-to-image en arxiv:2310.04378 license:mit diffusers:LatentConsistencyModelPipeline region:us