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I am a Japanese university student studying Text-to-Image. I have read #307 and created a custom dataset, but I don't know what to do next. Can you give me some hints?
This command should work well on a TPU v3-8. You'd need to adapt it slightly if you're on a GPU.
If you use a trained model, replace config_name with model_name_or_path and add a reference to a local path, huggingface hub path, or wandb artifact (you can use the small model from the inference notebook for example).
I should probably add it in the README.md somewhere…
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borisdayma commentedon Jan 25, 2023
Hi,
To train the model you need to use the script
tools/train/train.py.You can use a command such as:
python train.py \ --assert_TPU_available \ --do_train --do_eval \ --config_name config/mini_glu --dtype bfloat16 \ --tokenizer_name boris/dalle-mini-tokenizer \ --dataset_repo_or_path PATH_TO_DATASET \ --blank_caption_prob 0.1 \ --output_dir output --overwrite_output_dir \ --per_device_train_batch_size 34 --gradient_accumulation_steps 3 \ --optim distributed_shampoo --block_size 1024 --beta1 0.9 --beta2 0.99 \ --learning_rate 0.0001 --warmup_steps 2000 \ --mp_devices 1 \ --logging_steps 100 --eval_steps 400 --save_steps 4000 --log_modelThis command should work well on a TPU v3-8. You'd need to adapt it slightly if you're on a GPU.
If you use a trained model, replace
config_namewithmodel_name_or_pathand add a reference to a local path, huggingface hub path, or wandb artifact (you can use the small model from the inference notebook for example).I should probably add it in the README.md somewhere…
kartikpodugu commentedon May 10, 2023
which framework is used for training ? I didn't find any torch or tf import in train.py ?