language:
- en
tags:
- stable-diffusion
- text-to-image
license: bigscience-bloom-rail-1.0
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 Textual Inversion.
Model Description
The model originally used for fine-tuning is Stable Diffusion V1-4, which is a latent image diffusion model trained on LAION2B-en.
The current model is based from Yasu Seno's TrinArt Stable Diffusion which has been fine-tuned on 30,000 high-resolution manga/anime-style images for 3.5 epochs.
With 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 Textual Inversion has come from a random sample of 25k Danbooru images, which were then filtered based on CLIP Aesthetic Scoring where only images with an aesthetic score greater than 6.0 were used.
Then, the embeddings were further tuned on a smaller subset of 2k higher quality aesthetic images which had an aesthetic score greater than 6.0 and featured diverse subjects, backgrounds, and compositions.
Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
Example Code
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
model_id = "hakurei/waifu-diffusion"
device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)
pipe = pipe.to(device)
prompt = "a photo of reimu hakurei. anime style"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5)["sample"][0]
image.save("reimu_hakurei.png")
Team Members and Acknowledgements
This project would not have been possible without the incredible work by the CompVis Researchers and the author of the original finetuned model that this work was based upon, Yasu Seno!
Additionally, the methods presented in the Textual Inversion repo was an incredible help.