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removing space after backslash. can lead to problems
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‎docs/source/en/training/controlnet.md‎

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@@ -282,7 +282,7 @@ export OUTPUT_DIR="path/to/save/model"
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
@@ -315,8 +315,8 @@ If you run into version conflicts with the plugin, try uninstalling and reinstal
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python3 train_controlnet_flax.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \

‎docs/source/en/training/t2i_adapters.md‎

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@@ -179,7 +179,7 @@ export OUTPUT_DIR="path to save model"
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accelerate launch train_t2i_adapter_sdxl.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--mixed_precision="fp16" \
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--resolution=1024 \

‎docs/source/ko/training/controlnet.md‎

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@@ -95,7 +95,7 @@ export OUTPUT_DIR="path to save model"
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \

‎examples/controlnet/README.md‎

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@@ -65,7 +65,7 @@ export OUTPUT_DIR="path to save model"
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \
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accelerate launch train_controlnet.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
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python3 train_controlnet_flax.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--resolution=512 \
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--learning_rate=1e-5 \

‎examples/controlnet/README_flux.md‎

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@@ -58,7 +58,7 @@ When running `accelerate config`, if we specify torch compile mode to True there
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## Custom Datasets
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We support dataset formats:
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The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script. To use our example, add `--dataset_name=fusing/fill50k \
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The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script. To use our example, add `--dataset_name=fusing/fill50k \
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--trust_remote_code \` to the script and remove line `--jsonl_for_train` mentioned below.
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@@ -85,7 +85,7 @@ we can define the num_layers, num_single_layers, which determines the size of th
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```bash
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accelerate launch train_controlnet_flux.py \
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--pretrained_model_name_or_path="black-forest-labs/FLUX.1-dev" \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--conditioning_image_column=conditioning_image \
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--image_column=image \

‎examples/controlnet/README_sdxl.md‎

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@@ -67,7 +67,7 @@ export OUTPUT_DIR="path to save model"
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accelerate launch train_controlnet_sdxl.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--mixed_precision="fp16" \
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--resolution=1024 \

‎examples/t2i_adapter/README_sdxl.md‎

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@@ -67,7 +67,7 @@ export OUTPUT_DIR="path to save model"
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accelerate launch train_t2i_adapter_sdxl.py \
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--pretrained_model_name_or_path=$MODEL_DIR \
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--output_dir=$OUTPUT_DIR \
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--dataset_name=fusing/fill50k \
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--dataset_name=fusing/fill50k \
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--trust_remote_code \
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--mixed_precision="fp16" \
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--resolution=1024 \

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