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---
tags:
- discodiffusion
- guideddiffusion
thumbnail: https://de.gravatar.com/userimage/52045156/8ab369c1d246e65bda88813ce7c4cb81.jpeg
datasets:
- wikiart
---
# Ukiyo-e Diffusion
If you make something using these models, you're welcome to mention me [@thegenerativegeneration](https://www.instagram.com/thegenerativegeneration/)
Named by dataset used. Current and best version is [models/ukiyoe-all/v1/ema_0.9999_056000.pt](models/ukiyoe-all/v1/ema_0.9999_056000.pt)
# Current Plans
* clean dataset
* remove borders
* remove some of the samples with text in them
# Models
## Ukiyo-e-all
### v1
[models/ukiyoe-all/v1/ema_0.9999_056000.pt](models/ukiyoe-all/v1/ema_0.9999_056000.pt)
Model configuration is:
```python
model_config = {
'attention_resolutions': '32, 16, 8',
'class_cond': False,
'image_size': 256,
'learn_sigma': True,
'rescale_timesteps': True,
'noise_schedule': 'linear',
'num_channels': 128,
'num_heads': 4,
'num_res_blocks': 2,
'resblock_updown': True,
'use_checkpoint': True,
'use_fp16': True,
'use_scale_shift_norm': True,
}
```
#### Tips
- Results closest to original training data are achieved by turning off the secondary model in Disco Diffusion.
- Turning secondary model on can lead to very creative results
- It is not necessary to specify Ukiyo-e as artstyle to get ukiyo-e-like images.
#### Examples
If you make something nice using these models, I would like to link your image.
##### Secondary Off
##### Secondary On
#### About
Trained from scratch on a ~170000 images corpus of [ukiyo-e.org](https://ukiyo-e.org) filtered by [colorfulness](https://pyimagesearch.com/2017/06/05/computing-image-colorfulness-with-opencv-and-python/
) >= 5.
## (Deprecated) Ukiyo-e-few
[models/ukiyoe-few/v1/ukiyoe_diffusion_256_022000.pt](models/ukiyoe-few/v1/ukiyoe_diffusion_256_022000.pt)
Finetuned on 5224 images from Wikiart (1168) and ? ().
Model configuration is
```python
model_config = {
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_sigma': True,
'noise_schedule': 'linear',
'num_channels': 128,
'num_heads': 1,
'num_res_blocks': 2,
'use_checkpoint': True,
'use_scale_shift_norm': False
}
```
Trained using a fork of [guided-diffusion-sxela](https://github.com/thegenerativegeneration/guided-diffusion-sxela). Added random crop which did not lead to good results.
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