Karlo v1 alpha
Karlo is a text-conditional image generation model based on OpenAI's unCLIP architecture with the improvement over the standard super-resolution model from 64px to 256px, recovering high-frequency details only in the small number of denoising steps.
Karlo is available in diffusers!
from diffusers import UnCLIPPipeline
import torch
pipe = UnCLIPPipeline.from_pretrained("fusing/karlo_unclip", torch_dtype=torch.float16)
pipe = pipe.to('cuda')
prompt = "a high-resolution photograph of a big red frog on a green leaf."
image = pipe([prompt]).images[0]
image.save("./frog.png")
This alpha version of Karlo is trained on 115M image-text pairs, including COYO-100M high-quality subset, CC3M, and CC12M. For those who are interested in a better version of Karlo trained on more large-scale high-quality datasets, please visit the landing page of our application B^DISCOVER.