metadata

base_model: bguisard/stable-diffusion-nano-2-1
library_name: diffusers
license: creativeml-openrail-m
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
inference: true

Text-to-image finetuning - coolcat21/kanjimaker128

This pipeline was finetuned from bguisard/stable-diffusion-nano-2-1 on the coolcat21/kanji dataset.

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("coolcat21/kanjimaker128", torch_dtype=torch.float16)
prompt = "="
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 224
Learning rate: 1e-05
Batch size: 32
Gradient accumulation steps: 1
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]