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69 lines
No EOL
3.5 KiB
Markdown
69 lines
No EOL
3.5 KiB
Markdown
### Stable unCLIP
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_++++++ NOTE: preliminary checkpoints for internal testing ++++++_
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[unCLIP](https://openai.com/dall-e-2/) is the approach behind OpenAI's [DALL·E 2](https://openai.com/dall-e-2/),
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trained to invert CLIP image embeddings.
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We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings.
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This means that the model can be used to produce image variations, but can also be combined with a text-to-image
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embedding prior to yield a full text-to-image model at 768x768 resolution.
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We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available
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_[TODO: +++prelim private upload on HF+++]_ from [https://huggingface.co/stabilityai/stable-unclip-preview](https://huggingface.co/stabilityai/stable-unclip-preview).
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To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder.
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#### Image Variations
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_++TODO: Input images from the DIV2K dataset. check license++_
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Run
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```
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streamlit run scripts/streamlit/stableunclip.py
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```
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to launch a streamlit script than can be used to make image variations with both models (CLIP-L and OpenCLIP-H).
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These models can process a `noise_level`, which specifies an amount of Gaussian noise added to the CLIP embeddings.
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This can be used to increase output variance as in the following examples.
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**noise_level = 0**
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**noise_level = 500**
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**noise_level = 800**
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### Stable Diffusion Meets Karlo
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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).
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We introduce _Stable Karlo_, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768.
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To run the model, first download the KARLO checkpoints
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```shell
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mkdir -p checkpoints/karlo_models
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cd checkpoints/karlo_models
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wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt
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wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th
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wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt
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cd ../../
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```
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and the finetuned SD2.1 unCLIP-L checkpoint _[TODO: +++prelim private upload on HF+++]_ from [https://huggingface.co/stabilityai/stable-unclip-preview](https://huggingface.co/stabilityai/stable-unclip-preview), and put the ckpt into the `checkpoints folder`
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Then, run
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```
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streamlit run scripts/streamlit/stableunclip.py
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```
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and pick the `use_karlo` option in the GUI.
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The script optionally supports sampling from the full Karlo model. To use it, download the 64x64 decoder and 64->256 upscaler
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via
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```shell
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cd checkpoints/karlo_models
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wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt
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wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt
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cd ../../
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``` |