3.5 KiB
Stable unCLIP
++++++ NOTE: preliminary checkpoints for internal testing ++++++
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.
We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available
[TODO: +++prelim private upload on HF+++] from https://huggingface.co/stabilityai/stable-unclip-preview.
To use them, download from Hugging Face, and put and the weights into the checkpoints folder.
Image Variations
++TODO: Input images from the DIV2K dataset. check license++
Run
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.
noise_level = 0
noise_level = 500
noise_level = 800
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 [TODO: +++prelim private upload on HF+++] from https://huggingface.co/stabilityai/stable-unclip-preview, 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 ../../