Stable unCLIP

unCLIP is the approach behind OpenAI's DALL·E 2, trained to invert CLIP image embeddings. We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings. This means that the model can be used to produce image variations, but can also be combined with a text-to-image embedding prior to yield a full text-to-image model at 768x768 resolution.

If you would like to try a demo of this model on the web, please visit https://clipdrop.co/stable-diffusion-reimagine

We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available from https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip. To use them, download from Hugging Face, and put and the weights into the checkpoints folder.

Image Variations

Diffusers integration Stable UnCLIP Image Variations is integrated with the 🧨 diffusers library

#pip install git+https://github.com/huggingface/diffusers.git transformers accelerate
import requests
import torch
from PIL import Image
from io import BytesIO

from diffusers import StableUnCLIPImg2ImgPipeline

#Start the StableUnCLIP Image variations pipeline
pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
pipe = pipe.to("cuda")

#Get image from URL
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")

#Pipe to make the variation
images = pipe(init_image).images
images[0].save("tarsila_variation.png")

Check out the Stable UnCLIP pipeline docs here

Streamlit UI demo

streamlit run scripts/streamlit/stableunclip.py

to launch a streamlit script than can be used to make image variations with both models (CLIP-L and OpenCLIP-H). These models can process a noise_level, which specifies an amount of Gaussian noise added to the CLIP embeddings. This can be used to increase output variance as in the following examples.

Stable Diffusion Meets Karlo

Recently, KakaoBrain openly released Karlo, a pretrained, large-scale replication of unCLIP. We introduce Stable Karlo, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768.

To run the model, first download the KARLO checkpoints

mkdir -p checkpoints/karlo_models
cd checkpoints/karlo_models
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt
cd ../../

and the finetuned SD2.1 unCLIP-L checkpoint from here, and put the ckpt into the checkpoints folder

Then, run

streamlit run scripts/streamlit/stableunclip.py

and pick the use_karlo option in the GUI. The script optionally supports sampling from the full Karlo model. To use it, download the 64x64 decoder and 64->256 upscaler via

cd checkpoints/karlo_models
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt
cd ../../

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