TheLastBen
commited on
Commit
•
866e444
1
Parent(s):
8434af7
Update Notebooks/PPS-Dreambooth-v2.ipynb
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Notebooks/PPS-Dreambooth-v2.ipynb
CHANGED
@@ -233,21 +233,21 @@
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"# If you're not satisfied with the result, Set to True, run again the cell and it will continue training the current model.\n",
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"\n",
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"\n",
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"UNet_Training_Steps=
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"\n",
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"UNet_Learning_Rate
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"\n",
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"# If you use 10 images, use 650 steps, if you're not satisfied with the result, resume training for another 200 steps with a lower learning rate (8e-6), and so on ...\n",
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"\n",
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"\n",
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"Text_Encoder_Training_Steps=
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"\n",
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"Text_Encoder_Learning_Rate= \"1e-6\"\n",
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"\n",
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"# 350-600 steps is enough for a small dataset, keep this number small to avoid overfitting, set to 0 to disable, set it to 0 before resuming training if it is already trained.\n",
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"\n",
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"\n",
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"Text_Encoder_Concept_Training_Steps=0\n",
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"\n",
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"# Suitable for training a style/concept as it acts as regularization, with a minimum of 300 steps, 1 step/image is enough to train the concept(s), set to 0 to disable, set both the settings above to 0 to fintune only the text_encoder on the concept, set it to 0 before resuming training if it is already trained.\n",
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"\n",
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@@ -257,9 +257,9 @@
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"# Get the captions from a text file for each instance image.\n",
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"
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"#
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"Resolution = 768\n",
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@@ -269,20 +269,20 @@
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"\n",
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"#---------------------------------------------------------------\n",
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"\n",
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"Save_Checkpoint_Every_n_Steps
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"Save_Checkpoint_Every=500\n",
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"\n",
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"# Minimum 200 steps between each save.\n",
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"\n",
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"\n",
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"Start_saving_from_the_step=500\n",
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"\n",
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"# Start saving intermediary checkpoints from this step.\n",
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"\n",
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"\n",
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"#-----------------\n",
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"resumev2=dbtrainv2(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate,
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]
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},
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{
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"# If you're not satisfied with the result, Set to True, run again the cell and it will continue training the current model.\n",
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"\n",
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"\n",
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"UNet_Training_Steps= 1500\n",
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"\n",
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"UNet_Learning_Rate= \"2e-6\"\n",
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"\n",
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"# If you use 10 images, use 650 steps, if you're not satisfied with the result, resume training for another 200 steps with a lower learning rate (8e-6), and so on ...\n",
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"\n",
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"\n",
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"Text_Encoder_Training_Steps= 350\n",
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"\n",
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"Text_Encoder_Learning_Rate= \"1e-6\"\n",
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"\n",
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"# 350-600 steps is enough for a small dataset, keep this number small to avoid overfitting, set to 0 to disable, set it to 0 before resuming training if it is already trained.\n",
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"\n",
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"\n",
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"Text_Encoder_Concept_Training_Steps= 0\n",
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"\n",
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"# Suitable for training a style/concept as it acts as regularization, with a minimum of 300 steps, 1 step/image is enough to train the concept(s), set to 0 to disable, set both the settings above to 0 to fintune only the text_encoder on the concept, set it to 0 before resuming training if it is already trained.\n",
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"\n",
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"# Get the captions from a text file for each instance image.\n",
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"\n",
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"\n",
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"Offset_Noise= False\n",
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"\n",
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"# Always use it for style training.\n",
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"\n",
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"\n",
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"Resolution = 768\n",
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"\n",
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"#---------------------------------------------------------------\n",
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"\n",
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"Save_Checkpoint_Every_n_Steps= False\n",
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"\n",
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"Save_Checkpoint_Every= 500\n",
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"\n",
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"# Minimum 200 steps between each save.\n",
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"\n",
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"\n",
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"Start_saving_from_the_step= 500\n",
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"\n",
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"# Start saving intermediary checkpoints from this step.\n",
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"\n",
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"\n",
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"#-----------------\n",
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"resumev2=dbtrainv2(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate, Offset_Noise, Resolution, MODEL_NAMEv2, SESSION_DIR, INSTANCE_DIR, CONCEPT_DIR, CAPTIONS_DIR, External_Captions, INSTANCE_NAME, Session_Name, OUTPUT_DIR, PT, resumev2, Save_Checkpoint_Every_n_Steps, Start_saving_from_the_step, Save_Checkpoint_Every)"
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]
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},
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{
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