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README.md

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 # ACertainModel
 
+*Try full functions with Google Colab free T4* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/joseph-cheung/From-MLP-to-CNN/blob/main/%E4%BB%8EMLP%E5%88%B0CNN_%E4%BB%A5%E8%AF%86%E5%88%AB%E6%89%8B%E5%86%99%E6%95%B0%E5%AD%97%E4%B8%BA%E4%BE%8B.ipynb)
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 Welcome to ACertainModel - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style pictures with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags, including artists, to generate images.
 
 Since I noticed that the laion-aesthetics introduced in the Stable-Diffusion-v-1-4 checkpoint hindered finetuning anime style illustration generation model, Dreambooth was used to finetune some tags separately to make it closer to what it was in SD1.2. To avoid overfitting and possible language drift, I added a huge amount of auto-generated pictures from a single word prompt to the training set, using models that are popular in the community such as Anything-3.0, together with partially manual selected full-danbooru images within a year, for further native training. I am also aware of a method of [LoRA](https://arxiv.org/abs/2106.09685), with a similar idea, finetuning attention layer solely, to have better performance on eyes, hands, and other details.