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.
"a portrait of an old monk, highly detailed."
"Photo of a business woman, silver hair"
"A teddy bear on a skateboard, children drawing style."
"Goryeo celadon in the shape of bird"
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.
- [2022-12-01] Karlo-v1.0.alpha is released!
- [2022-12-19] Karlo-v1.0.alpha was integrated into the 🧨 diffusers library
- [2022-12-20] Karlo-v1.0.alpha was integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo:
Karlo is a text-conditional diffusion model based on unCLIP, composed of prior, decoder, and super-resolution modules. In this repository, we include the improved version of the standard super-resolution module for upscaling 64px to 256px only in 7 reverse steps, as illustrated in the figure below:
In specific, the standard SR module trained by DDPM objective upscales 64px to 256px in the first 6 denoising steps based on the respacing technique. Then, the additional fine-tuned SR module trained by VQ-GAN-style loss performs the final reverse step to recover high-frequency details. We observe that this approach is very effective to upscale the low-resolution in a small number of reverse steps.
We train all components from scratch on 115M image-text pairs including COYO-100M, CC3M, and CC12M. In the case of Prior and Decoder, we use ViT-L/14 provided by OpenAI’s CLIP repository. Unlike the original implementation of unCLIP, we replace the trainable transformer in the decoder into the text encoder in ViT-L/14 for efficiency. In the case of the SR module, we first train the model using the DDPM objective in 1M steps, followed by additional 234K steps to fine-tune the additional component. The table below summarizes the important statistics of our components:
| Prior | Decoder | SR | |
|---|---|---|---|
| CLIP | ViT-L/14 | ViT-L/14 | - |
| #param | 1B | 900M | 700M + 700M |
| #optimization steps | 1M | 1M | 1M + 0.2M |
| #sampling steps | 25 | 50 (default), 25 (fast) | 7 |
| Checkpoint links | ViT-L-14, ViT-L-14 stats, model | model | model |
In the checkpoint links, ViT-L-14 is equivalent to the original version, but we include it for convenience. We also remark that ViT-L-14-stats is required to normalize the outputs of the prior module.
We quantitatively measure the performance of Karlo-v1.0.alpha in the validation split of CC3M and MS-COCO. The table below presents CLIP-score and FID. To measure FID, we resize the image of the shorter side to 256px, followed by cropping it at the center. We set classifier-free guidance scales for prior and decoder to 4 and 8 in all cases. We observe that our model achieves reasonable performance even with 25 sampling steps of decoder.
CC3M
| Sampling step | CLIP-s (ViT-B/16) | FID (13k from val) |
|---|---|---|
| Prior (25) + Decoder (25) + SR (7) | 0.3081 | 14.37 |
| Prior (25) + Decoder (50) + SR (7) | 0.3086 | 13.95 |
MS-COCO
| Sampling step | CLIP-s (ViT-B/16) | FID (30k from val) |
|---|---|---|
| Prior (25) + Decoder (25) + SR (7) | 0.3192 | 15.24 |
| Prior (25) + Decoder (50) + SR (7) | 0.3192 | 14.43 |
For more information, please refer to the upcoming technical report.
Our unCLIP implemenetation is officially integrated in the 🧨 diffusers library
#Requisits to run Karlo unCLIP on diffusers
pip install diffusers transformers accelerate safetensors
from diffusers import UnCLIPPipeline
import torch
pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", 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")Check out the diffusers docs for the full usage of the unCLIPPipeline
We use a single V100 of 32GB VRAM for sampling under PyTorch >= 1.10 and CUDA >= 11. The following commands install additional python packages and get pretrained model checkpoints. Or, you can simply install the package and download the weights via setup.sh
- Additional python packages
pip install -r requirements.txt
- Model checkpoints
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt -P $KARLO_ROOT_DIR # same with the official ViT-L/14 from OpenAI
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th -P $KARLO_ROOT_DIR
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt -P $KARLO_ROOT_DIR
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt -P $KARLO_ROOT_DIR
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt -P $KARLO_ROOT_DIR
The following command launches gradio demo for text-to-image generation and image variation. We notice that the second run in the gradio is unexpectedly slower than the usual case in PyTorch>=1.12. We guess that this happens because launching the cuda kernels takes some time, usually up to 2 minutes.
python demo/product_demo.py --host 0.0.0.0 --port $PORT --root-dir $KARLO_ROOT_DIR
Samples below are non-cherry picked T2I and image variation examples of random seed 0. In each case, the first row shows T2I samples and the second shows the image variation samples of the leftmost image in the first row.
[T2I + Image variation] "A man with a face of avocado, in the drawing style of Rene Magritte."
[T2I + Image variation] "a black porcelain in the shape of pikachu"
Here, we include the command line example of T2I. For image variation, you can refer to karlo/sampler/i2i.py on how to replace the prior into the clip image feature.
python example.py --root-dir=$KARLO_ROOT_DIR \
--prompt="A man with a face of avocado, in the drawing style of Rene Magritte" \
--output-dir=$OUTPUT_DIR \
--max-bsz=2 \
--sampling-type=fastThis project including the weights are distributed under CreativeML Open RAIL-M license, equivalent version of Stable Diffusion v1. You may use this model in commercial applications, but it is highly recommended to adopt a powerful safe checker as a post-processing. We also remark that we are not responsible for any kinds of use of the generated images.
If you find this repository useful in your research, please cite:
@misc{kakaobrain2022karlo-v1-alpha,
title = {Karlo-v1.0.alpha on COYO-100M and CC15M},
author = {Donghoon Lee, Jiseob Kim, Jisu Choi, Jongmin Kim, Minwoo Byeon, Woonhyuk Baek and Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/karlo}},
}
- We deeply appreciate all the contributors to OpenAI’s Guided-Diffusion project.
- We also greatly appreciate Apolinário Passos and Will Berman from Huggingface for integrating this model to diffusers.
If you would like to collaborate with us or share a feedback, please e-mail to us, contact@kakaobrain.com