latent-consistency-models

Latent Consistency Models - Text-to-Image

Input

Text to render

• Example

"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, Tokyo, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography"

Output

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 latent-consistency-models.py

if you are facing error 128, use following command to run your model with cpu:

$ python3 latent-consistency-models.py -e 1

If you want to specify the input text, put the text after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 latent-consistency-models.py --input TEXT --savepath SAVE_IMAGE_PATH

Quality, sampling speed and diversity are best controlled via the --guidance_scale and --num_inference_steps options. Higher values of scale produce better samples at the cost of a reduced output diversity.

Reference

• Latent Consistency Model

Framework

Pytorch

Model Format

ONNX opset=14

Netron

text_encoder.onnx.prototxt
unet.onnx.prototxt
vae_encoder.onnx.prototxt