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This is the only way it worked for me. Thanks!
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Same here. The only way I managed to run TI on macOS with M2 CPU |
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Running on
Setup
accelerate config
This machine/NO/NO/NO
RUN and Var
accelerate launch --num_cpu_threads_per_process=10 "./train_network.py" --enable_bucket --pretrained_model_name_or_path="~/_ML/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned -emaonly.safetensors" --train_data_dir="~/_ML/_TrainingX/modelName/img" --resolution="512,512"--output_dir="~/_ML/_TrainingX/modelName/out" --logging_dir="~/_ML/_TrainingX/modelName/log"--network_alpha="2" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-05 --unet_lr=0.0001 --network_dim=40 --output_name="viwa_x" --lr_scheduler_num_cycles="10" --no_half_vae --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="440" --train_batch_size="1"--max_train_steps="4400" --save_every_n_epochs="1" --mixed_precision="no" --save_precision="float"--seed="1234" --cache_latents --cache_latents_to_disk --optimizer_type="AdamW"--max_data_loader_n_workers="0" --bucket_reso_steps=64 --bucket_no_upscale
key setup
Time
Mac M2 32G using full GPU start training w/11 photos on 4400 steps around 1hr10min.
For anyone using Mac reference.
thx ありがとう Kohya ( If one day come to our country, please allow us to take you to enjoy our delicious Taiwanese street food.)
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