Instructions to use ShinnosukeU/kanji_vae_decoder_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShinnosukeU/kanji_vae_decoder_only with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShinnosukeU/kanji_vae_decoder_only", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: CompVis/stable-diffusion-v1-4 | |
| datasets: | |
| - ShinnosukeU/kanji_diffusion_dataset | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| inference: true | |
| # Text-to-image finetuning - ShinnosukeU/kanji_vae_decoder_only | |
| This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **ShinnosukeU/kanji_diffusion_dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: Nothing: | |
| ## Training info | |
| These are the key hyperparameters used during training: | |
| * Epochs: 100 | |
| * Learning rate: 1.2e-06 | |
| * Batch size: 2 | |
| * Gradient accumulation steps: 4 | |
| * Image resolution: 128 | |
| * Mixed-precision: None | |
| More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/shinnosukeu/vae-fine-tune/runs/9bt51ib7). | |