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update examples for release

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Robin Rombach 2023-02-23 11:33:20 +01:00
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10
README.md
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@ -8,11 +8,13 @@ new checkpoints. The following list provides an overview of all currently availa
## News
**February X, 2023**
**February 27, 2023**
*Stable UnCLIP 2.1*
- New stable diffusion finetune (_Stable unCLIP 2.1_, [HuggingFace](https://huggingface.co/stabilityai/stable-unclip-2-1)) at 768x768 resolution,
based on SD2.1-768. This model allows for image variations and mixing operations as described in TODO, and, thanks to its modularity, can be combined with other models
such as [KARLO](https://github.com/kakaobrain/karlo). Documentation [here](doc/UNCLIP.MD).
- New stable diffusion finetune (_Stable unCLIP 2.1_, [HuggingFace](https://huggingface.co/stabilityai/)) at 768x768 resolution,
based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models
such as [KARLO](https://github.com/kakaobrain/karlo). Documentation [here](doc/UNCLIP.MD). Comes in two variants: [*Stable unCLIP-L*](TODO) and [*Stable unCLIP-H*](TODO), which are conditioned on CLIP
ViT-L and ViT-H image embeddings, respectively.
**December 7, 2022**

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doc/UNCLIP.MD
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@ -1,19 +1,14 @@
### Stable unCLIP
_++++++ NOTE: preliminary checkpoints for internal testing ++++++_
[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
_[TODO: +++prelim private upload on HF+++]_ from [https://huggingface.co/stabilityai/stable-unclip-preview](https://huggingface.co/stabilityai/stable-unclip-preview).
We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available from [https://huggingface.co/stabilityai/](TODO).
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/houses_out.jpeg)
![image-variations-l-2](../assets/stable-samples/stable-unclip/plates_out.jpeg)
_++TODO: Input images from the DIV2K dataset. check license++_
![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png)
Run
@ -24,16 +19,7 @@ to launch a streamlit script than can be used to make image variations with both
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**
![image-variations-l-3](../assets/stable-samples/stable-unclip/oldcar000.jpeg)
**noise_level = 500**
![image-variations-l-4](../assets/stable-samples/stable-unclip/oldcar500.jpeg)
**noise_level = 800**
![image-variations-l-6](../assets/stable-samples/stable-unclip/oldcar800.jpeg)
![image-variations-noise](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)
### Stable Diffusion Meets Karlo
@ -51,7 +37,7 @@ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b623
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](https://huggingface.co/stabilityai/stable-unclip-preview), and put the ckpt into the `checkpoints folder`
and the finetuned SD2.1 unCLIP-L checkpoint from [https://huggingface.co/stabilityai/](https://huggingface.co/stabilityai/TODO), and put the ckpt into the `checkpoints folder`
Then, run

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scripts/streamlit/stableunclip.py
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@ -276,7 +276,6 @@ if __name__ == "__main__":
version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
use_karlo = version in ["Stable unCLIP-L"] and st.checkbox("Use KARLO prior", False)
state = init(version=version, load_karlo_prior=use_karlo)
st.info(state["msg"])
prompt = st.text_input("Prompt", "a professional photograph")
negative_prompt = st.text_input("Negative Prompt", "")
scale = st.number_input("cfg-scale", value=10., min_value=-100., max_value=100.)