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Kohya S
2023-08-20 18:30:49 +09:00
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docs/train_lllite_README.md
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@@ -37,10 +37,16 @@ conditioning_data_dir = "path/to/conditioning/image/dir"
At the moment, random_crop cannot be used.
As a training data, it seems to be better to use the images generated by the original model as training images and the images processed from them as conditioning images. If you use images with a different style from the original model as training images, the model will have to learn not only the control but also the style. ControlNet-LLLite is not suitable for style learning because of its small capacity.
If you use images other than the generated images as training images, please increase the dimension as described below.
### Training
Run `sdxl_train_control_net_lllite.py`. You can specify the dimension of the conditioning image embedding with `--cond_emb_dim`. You can specify the rank of the LoRA-like module with `--network_dim`. Other options are the same as `sdxl_train_network.py`, but `--network_module` is not required.
Since a large amount of memory is used during training, please enable memory-saving options such as cache and gradient checkpointing. It is also effective to use BFloat16 with the `--full_bf16` option (requires RTX 30 series or later GPU). It has been confirmed to work with 24GB VRAM.
For the sample Canny, the dimension of the conditioning image embedding is 32. The rank of the LoRA-like module is also 64. Adjust according to the features of the conditioning image you are targeting.
(The sample Canny is probably quite difficult. It may be better to reduce it to about half for depth, etc.)