This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the takanori39/kanji-v1 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Elon Musk', 'Internet', 'Fish', 'Ice cream', 'Car', 'Language model']:

Pipeline usage

You can use the pipeline like so:

python

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("takanori39/kanji-v5", torch_dtype=torch.float16)
prompt = "Elon Musk"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 10
Learning rate: 0.0001
Batch size: 4
Gradient accumulation steps: 4
Image resolution: 128
Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]

takanori39/hoge-v5

Text-to-image finetuning - takanori39/kanji-v5