
# Precomputed SDXL dataset

This dataset contains approximately 137,800 Midjourney 5.1 images from Vodka's publicly released training dataset.

Each image has had its caption computed into a SDXL-compatible text embed file, saved as a Pytorch tensor file in the `split_text_embeds` directory.

Each image in PNG format is available in the `split_train` directory.
```
41G     split_text_embeds - text embeds from OpenCLIP G/CLIP L

162G    split_train       - training data / images

20G     split_vae_cache   - VAE encoded training data, via 0.9 VAE (no watermarks)
```
# How to use this dataset

1. Download [SimpleTuner](https://github.com/bghira/SimpleTuner) as this dataset is compatible with that trainer.
2. SimpleTuner/cache should contain the contents of `split_text_embeds` - for example:
```
mv split_text_embeds/**/* cache/
```
3. The contents of `split_train` should be combined similarly into a single folder:
```
mv split_train/**/* /path/to/training_data
```
4. The contents of `split_vae_cache` need to be similarly relocated into the `SimpleTuner/vae_cache` folder.
5. Configure `sdxl-env.sh.example` in SimpleTuner and save it as `sdxl-env.sh`
6. Execute `train_sdxl.sh`
license:creativeml-openrail-m region:us
{"license": "creativeml-openrail-m"}
TAGS
#license-creativeml-openrail-m #region-us 


# Precomputed SDXL dataset

This dataset contains approximately 137,800 Midjourney 5.1 images from Vodka's publicly released training dataset.

Each image has had its caption computed into a SDXL-compatible text embed file, saved as a Pytorch tensor file in the 'split_text_embeds' directory.

Each image in PNG format is available in the 'split_train' directory.

# How to use this dataset

1. Download SimpleTuner as this dataset is compatible with that trainer.
2. SimpleTuner/cache should contain the contents of 'split_text_embeds' - for example:

3. The contents of 'split_train' should be combined similarly into a single folder:

4. The contents of 'split_vae_cache' need to be similarly relocated into the 'SimpleTuner/vae_cache' folder.
5. Configure 'URL.example' in SimpleTuner and save it as 'URL'
6. Execute 'train_sdxl.sh'
# Precomputed SDXL dataset

This dataset contains approximately 137,800 Midjourney 5.1 images from Vodka's publicly released training dataset.

Each image has had its caption computed into a SDXL-compatible text embed file, saved as a Pytorch tensor file in the 'split_text_embeds' directory.

Each image in PNG format is available in the 'split_train' directory. # How to use this dataset

1. Download SimpleTuner as this dataset is compatible with that trainer.
2. SimpleTuner/cache should contain the contents of 'split_text_embeds' - for example:

3. The contents of 'split_train' should be combined similarly into a single folder:

4. The contents of 'split_vae_cache' need to be similarly relocated into the 'SimpleTuner/vae_cache' folder.
5. Configure 'URL.example' in SimpleTuner and save it as 'URL'
6. Execute 'train_sdxl.sh'
TAGS
#license-creativeml-openrail-m #region-us 
 # Precomputed SDXL dataset

This dataset contains approximately 137,800 Midjourney 5.1 images from Vodka's publicly released training dataset.

Each image has had its caption computed into a SDXL-compatible text embed file, saved as a Pytorch tensor file in the 'split_text_embeds' directory.

Each image in PNG format is available in the 'split_train' directory. # How to use this dataset

1. Download SimpleTuner as this dataset is compatible with that trainer.
2. SimpleTuner/cache should contain the contents of 'split_text_embeds' - for example:

3. The contents of 'split_train' should be combined similarly into a single folder:

4. The contents of 'split_vae_cache' need to be similarly relocated into the 'SimpleTuner/vae_cache' folder.
5. Configure 'URL.example' in SimpleTuner and save it as 'URL'
6. Execute 'train_sdxl.sh'
passage: TAGS
#license-creativeml-openrail-m #region-us 
# Precomputed SDXL dataset

This dataset contains approximately 137,800 Midjourney 5.1 images from Vodka's publicly released training dataset.

Each image has had its caption computed into a SDXL-compatible text embed file, saved as a Pytorch tensor file in the 'split_text_embeds' directory.

Each image in PNG format is available in the 'split_train' directory.# How to use this dataset

1. Download SimpleTuner as this dataset is compatible with that trainer.
2. SimpleTuner/cache should contain the contents of 'split_text_embeds' - for example:

3. The contents of 'split_train' should be combined similarly into a single folder:

4. The contents of 'split_vae_cache' need to be similarly relocated into the 'SimpleTuner/vae_cache' folder.
5. Configure 'URL.example' in SimpleTuner and save it as 'URL'
6. Execute 'train_sdxl.sh'