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60 lines
3.1 KiB
Markdown
60 lines
3.1 KiB
Markdown
# About ConrtolNet-LLLite
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## Overview
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ConrtolNet-LLLite is a lightweight version of [ConrtolNet](https://github.com/lllyasviel/ControlNet). It is a "LoRA Like Lite" that is inspired by LoRA and has a lightweight structure. Currently, only SDXL is supported.
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## Sample weight file and inference
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Sample weight file is available here: https://huggingface.co/kohya-ss/controlnet-lllite
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A custom node for ComfyUI is available: https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI
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Sample images are at the end of this page.
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## Model structure
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A single LLLite module consists of a conditioning image embedding that maps a conditioning image to a latent space and a small network with a structure similar to LoRA. The LLLite module is added to U-Net's Linear and Conv in the same way as LoRA. Please refer to the source code for details.
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Due to the limitations of the inference environment, only CrossAttention (attn1 q/k/v, attn2 q) is currently added.
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## Model training
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### Preparing the dataset
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In addition to the normal dataset, please store the conditioning image in the directory specified by `conditioning_data_dir`. The conditioning image must have the same basename as the training image. The conditioning image will be automatically resized to the same size as the training image.
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```toml
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[[datasets.subsets]]
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image_dir = "path/to/image/dir"
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caption_extension = ".txt"
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conditioning_data_dir = "path/to/conditioning/image/dir"
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```
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At the moment, random_crop cannot be used.
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### Training
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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.
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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.
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(The sample Canny is probably quite difficult. It may be better to reduce it to about half for depth, etc.)
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### Inference
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If you want to generate images with a script, run `sdxl_gen_img.py`. You can specify the LLLite model file with `--control_net_lllite_models`. The dimension is automatically obtained from the model file.
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Specify the conditioning image to be used for inference with `--guide_image_path`. Since preprocess is not performed, if it is Canny, specify an image processed with Canny (white line on black background). `--control_net_preps`, `--control_net_weights`, and `--control_net_ratios` are not supported.
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## Sample
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Canny
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