Update README

README.md

@@ -11,23 +11,28 @@ inference: false
 
 # waifu-diffusion - Diffusion for Weebs
 
-waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning on high quality anime images.
+waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through [Textual Inversion](https://github.com/rinongal/textual_inversion).
+
+<img src=https://cdn.discordapp.com/attachments/872361510133981234/1016022078635388979/unknown.png?3867929 width=30% height=30%>
+<sub>Prompt: touhou 1girl komeiji_koishi portrait</sub>
 
 ## Model Description
 
-The model used for fine-tuning is [Stable Diffusion V1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent text-to-image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en).
+The model originally used for fine-tuning is [Stable Diffusion V1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en).
 
-The current model is fine-tuned from 56 thousand images from Danbooru selected with an aesthetic score greater than `6.0`.
+The current model is based from [Yasu Seno](https://twitter.com/naclbbr)'s [TrinArt Stable Diffusion](https://huggingface.co/naclbit/trinart_stable_diffusion) which has been fine-tuned on 30,000 high-resolution manga/anime-style images for 3.5 epochs.
 
 With [Textual Inversion](https://github.com/rinongal/textual_inversion), the embeddings for the text encoder has been trained to align more with anime-styled images, reducing excessive prompting.
 
 ## Training Data & Annotative Prompting
 
-The data used for fine-tuning has come from a random sample of 56k Danbooru images, which were filtered based on [CLIP Aesthetic Scoring](https://github.com/christophschuhmann/improved-aesthetic-predictor) where only images with an aesthetic score greater than `6.0` were used.
+The data used for Textual Inversion has come from a random sample of 25k Danbooru images, which were then filtered based on [CLIP Aesthetic Scoring](https://github.com/christophschuhmann/improved-aesthetic-predictor) where only images with an aesthetic score greater than `6.0` were used.
+
+Captions are Danbooru-style captions.
 
 ## Downstream Uses
 
-This model can be used for entertainment purposes and as a generative art assistant. The EMA model can be used for additional fine-tuning.
+This model can be used for entertainment purposes and as a generative art assistant.
 
 ## Example Code
 
@@ -52,7 +57,9 @@ image.save("reimu_hakurei.png")
 
 ## Team Members and Acknowledgements
 
-This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/).
+This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/) and the author of the original finetuned model that this work was based upon, [Yasu Seno](https://twitter.com/naclbbr)!
+
+Additionally, the methods presented in the [Textual Inversion](https://github.com/rinongal/textual_inversion) repo was an incredible help.
 
 - [Anthony Mercurio](https://github.com/harubaru)
 - [Salt](https://github.com/sALTaccount/)