| ## 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! | |
| ```python | |
| 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") | |
| ``` | |
|  | |
| [Original codebase](https://github.com/kakaobrain/karlo) | |
| This alpha version of Karlo is trained on 115M image-text pairs, including [COYO](https://github.com/kakaobrain/coyo-dataset)-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](https://bdiscover.kakaobrain.com/). | |