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EveryDream2trainer
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nawnie 2023-02-18 23:54:27 -06:00
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Train_Colab.ipynb
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@ -68,7 +68,7 @@
"outputs": [],
"source": [
"#@title Optional connect Gdrive\n",
"#@markdown # but strongly recommended\n",
"#@markdown # But strongly recommended\n",
"#@markdown This will let you put all your training data and checkpoints directly on your drive. Much faster/easier to continue later, less setup time.\n",
"\n",
"#@markdown Creates /content/drive/MyDrive/everydreamlogs/ckpt\n",
@ -82,8 +82,8 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "hAuBbtSvGpau"
"id": "hAuBbtSvGpau",
"cellView": "form"
},
"outputs": [],
"source": [
@ -94,7 +94,7 @@
"s = getoutput('nvidia-smi')\n",
"!pip install -q torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url \"https://download.pytorch.org/whl/cu117\"\n",
"!pip install -q transformers==4.25.1\n",
"!pip install -q diffusers[torch]==0.10.2\n",
"!pip install -q diffusers[torch]==0.13.0\n",
"!pip install -q pynvml==11.4.1\n",
"!pip install -q bitsandbytes==0.35.0\n",
"!pip install -q ftfy==6.1.1\n",
@ -290,7 +290,7 @@
"\n",
"#@markdown * Batch size impacts VRAM use. 8 should work on SD1.x models and 5 for SD2.x models at 512 resolution. Lower this if you get CUDA out of memory errors. You can check resources on your instance and watch the GPU RAM.\n",
"\n",
"Batch_Size = 6 #@param{type: 'number'}\n",
"Batch_Size = 8 #@param{type: 'number'}\n",
"\n",
"#@markdown * Gradient accumulation is sort of like a virtual batch size increase use this to increase batch size with out increasing vram usage\n",
"#@markdown Increasing from 1 to 2 will have a minor impact on vram use, but more beyond that will not.\n",
@ -306,7 +306,7 @@
"\n",
"#@markdown * Max Epochs to train for, this defines how many total times all your training data is used. Default of 100 is a good start if you are training ~30-40 images of one subject. If you have 100 images, you can reduce this to 40-50 and so forth.\n",
"\n",
"Max_Epochs = 100 #@param {type:\"slider\", min:0, max:200, step:5}\n",
"Max_Epochs = 200 #@param {type:\"slider\", min:0, max:200, step:5}\n",
"\n",
"#@markdown * How often to save checkpoints.\n",
"Save_every_N_epoch = 20 #@param{type:\"integer\"}\n",
@ -329,7 +329,12 @@
"#@markdown * Using the same seed each time you train allows for more accurate a/b comparison of models, leave at -1 for random\n",
"#@markdown * The seed also effects your training samples, if you want the same seed each sample you will need to change it from -1\n",
"Training_Seed = -1 #@param{type:\"integer\"}\n",
"\n",
"#@markdown * use this option to configure a sample_prompts.json\n",
"#@markdown * check out /content/EveryDream2trainer/doc/logging.md. for more details\n",
"Advance_Samples = False #@param{type:\"boolean\"}\n",
"Sample_File = \"sample_prompts.txt\"\n",
"if Advance_Samples:\n",
" Sample_File = \"sample_prompts.json\"\n",
"#@markdown * Checkpointing Saves Vram to allow larger batch sizes minor slow down on a single batch size but will can allow room for a higher traning resolution (suggested on Colab Free tier, turn off for A100)\n",
"Gradient_checkpointing = True #@param{type:\"boolean\"}\n",
"Disable_Xformers = False #@param{type:\"boolean\"}\n",
@ -405,7 +410,7 @@
" --max_epochs $Max_Epochs \\\n",
" --project_name \"$Project_Name\" \\\n",
" --resolution $Resolution \\\n",
" --sample_prompts \"sample_prompts.txt\" \\\n",
" --sample_prompts \"$Sample_File\" \\\n",
" --sample_steps $Steps_between_samples \\\n",
" --save_every_n_epoch $Save_every_N_epoch \\\n",
" --seed $Training_Seed \\\n",
@ -501,4 +506,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}