metadata

license: mit
language:
  - en
pipeline_tag: text-to-image
tags:
  - text-to-image

Latent Consistency Models

Official Repository of the paper: Latent Consistency Models.

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

Model Descriptions:

Copied from SimianLuo/LCM_Dreamshaper_v7 to experiment with quantization. Originally distilled from Dreamshaper v7 fine-tune of 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:

Install the library:

pip install --upgrade diffusers  # make sure to use at least diffusers >= 0.22
pip install transformers accelerate

Run the model:

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