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Shawn 2023-05-20 04:05:56 -05:00 committed by Victor Hall
parent 6a4e32f7cf
commit 927944a5ca
1 changed files with 14 additions and 12 deletions
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Train_Colab.ipynb
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@ -251,35 +251,33 @@
"\n",
"#@markdown * The learning rate affects how much \"training\" is done on the model per training step. It is a very careful balance to select a value that will learn your data and not wreck the model. \n",
"#@markdown Leave this default unless you are very comfortable with training and know what you are doing.\n",
"\n",
"Learning_Rate = 1e-6 #@param{type: 'number'}\n",
"#@markdown * chosing this will allow you to ignore any settings specific to the text encode and will match it with the Unets settings, recommended for beginers.\n",
"Match_text_to_Unet = False #@param{type:\"boolean\"}\n",
"Text_lr = 0.5e-6 #@param {type:\"number\"}\n",
"#@markdown * A learning rate scheduler can change your learning rate as training progresses.\n",
"#@markdown * I recommend sticking with constant until you are comfortable with general training. \n",
"\n",
"data['base']['lr'] = Learning_Rate\n",
"Schedule = \"constant\" #@param [\"constant\", \"polynomial\", \"linear\", \"cosine\"] {allow-input: true}\n",
"data['base']['lr_scheduler'] = Schedule\n",
"data['text_encoder_overrides']['lr'] = Text_lr \n",
"Text_lr_scheduler = \"constant\" #@param [\"constant\", \"polynomial\", \"linear\", \"cosine\"] {allow-input: true}\n",
"data['text_encoder_overrides']['lr_scheduler'] = Text_lr_scheduler\n",
"#@markdown * warm up steps are useful for validation and cosine lrs\n",
"lr_warmup_steps = 0 #@param{type:\"integer\"}\n",
"data['base']['lr_warmup_steps'] = lr_warmup_steps\n",
"lr_decay_steps = 0 #@param {type:\"number\"} \n",
"data['base']['lr_decay_steps'] = lr_decay_steps\n",
"Text_lr_warmup_steps = 0 #@param {type:\"number\"}\n",
"data['text_encoder_overrides']['lr_warmup_steps'] = Text_lr_warmup_steps\n",
"Text_lr_decay_steps = 0 #@param {type:\"number\"} \n",
"data['text_encoder_overrides']['lr_decay_steps'] = Text_lr_decay_steps\n",
"\n",
"if Match_text_to_Unet:\n",
" Text_lr = Learning_Rate\n",
" Text_lr_scheduler = Schedule\n",
" Text_lr_warmup_steps = lr_warmup_steps\n",
"\n",
"data['base']['lr'] = Learning_Rate\n",
"data['text_encoder_overrides']['lr'] = Text_lr \n",
"data['base']['lr_scheduler'] = Schedule\n",
"data['text_encoder_overrides']['lr_scheduler'] = Text_lr_scheduler\n",
"data['base']['lr_warmup_steps'] = lr_warmup_steps\n",
"data['base']['lr_decay_steps'] = lr_decay_steps\n",
"data['text_encoder_overrides']['lr_warmup_steps'] = Text_lr_warmup_steps\n",
"data['text_encoder_overrides']['lr_decay_steps'] = Text_lr_decay_steps\n",
"\n",
"# Save the updated JSON data back to the file\n",
"with open('optimizer.json', 'w') as file:\n",
@ -450,12 +448,16 @@
"# Finish the training process\n",
"clear_output()\n",
"time.sleep(2)\n",
"print(\"Training is complete.\")\n",
"print(\"Training is complete, select a model to start training again\")\n",
"time.sleep(2)\n",
"\n",
"if Disconnect_after_training:\n",
" print(\"Training is complete, in 30 seconds the instance will reset, you will need to chose a model and you can resume training again\")\n",
" time.sleep(40)\n",
" runtime.unassign()\n"
" runtime.unassign()\n",
"\n",
"os.kill(os.getpid(), 9)\n",
"\n"
]
},
{