Text-to-image finetuning - takanori39/kanji-v2
This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the takanori39/kanji-v2 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:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("takanori39/kanji-v2", 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: 8
- 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]
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Model tree for takanori39/hoge-v2
Base model
CompVis/stable-diffusion-v1-4