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Stable Diffusion 2.1 unCLIP Release

doc/UNCLIP.MD

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+### Stable unCLIP
+
+[unCLIP](https://openai.com/dall-e-2/) is the approach behind OpenAI's [DALL·E 2](https://openai.com/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 from [https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/tree/main).
+To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder.  
+#### Image Variations
+![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png)
+
+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.
+
+![image-variations-noise](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)
+
+
+### Stable Diffusion Meets Karlo
+![panda](../assets/stable-samples/stable-unclip/panda.jpg) 
+
+Recently, [KakaoBrain](https://kakaobrain.com/) openly released [Karlo](https://github.com/kakaobrain/karlo), a pretrained, large-scale replication of [unCLIP](https://arxiv.org/abs/2204.06125).
+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
+```shell
+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](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt), 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 
+```shell
+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 ../../
+```