update examples for release

README.md

@@ -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, 
+- 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 TODO, and, thanks to its modularity, can be combined with other models
+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).
+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**

doc/UNCLIP.MD

@@ -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
+We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available from [https://huggingface.co/stabilityai/](TODO).
-_[TODO: +++prelim private upload on HF+++]_ from [https://huggingface.co/stabilityai/stable-unclip-preview](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
-![image-variations-l-1](../assets/stable-samples/stable-unclip/houses_out.jpeg)
+![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png)
-![image-variations-l-2](../assets/stable-samples/stable-unclip/plates_out.jpeg)
-
-_++TODO: Input images from the DIV2K dataset. check license++_
 
 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-noise](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)
-![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)
-
-
 
 
 ### 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
 

scripts/streamlit/stableunclip.py

@@ -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.)