Text-to-image finetuning - coolcat21/kanjimaker128
This pipeline was finetuned from bguisard/stable-diffusion-nano-2-1 on the coolcat21/kanji dataset.
Results for "fire nation" (left) and "Lebron" (right)
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]
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Model tree for coolcat21/kanjimaker128
Base model
stabilityai/stable-diffusion-2-1-base
Finetuned
bguisard/stable-diffusion-nano-2-1