Make manual model download optional
This commit is contained in:
Augusto de la Torre
parent
7e20a74586
commit
e70c6cf743
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@ -51,10 +51,72 @@
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},
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{
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"cell_type": "markdown",
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"id": "3d9b0db8-c2b1-4f0a-b835-b6b2ef527019",
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"id": "f15fcd56-0418-4be1-a5c3-38aa679b1aaf",
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"metadata": {},
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"source": [
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"### HuggingFace Login\n",
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"# Start Training\n",
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"Naming your project will help you track what the heck you're doing when you're floating in checkpoint files later.\n",
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"\n",
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"You may wish to consider adding \"sd1\" or \"sd2v\" or similar to remember what the base was, as you'll also have to tell your inference app what you were using, as its difficult for programs to know what inference YAML to use automatically. For instance, Automatic1111 webui requires you to copy the v2 inference YAML and rename it to match your checkpoint name so it knows how to load the file, tough it assumes SD 1.x compatible. Something to keep in mind if you start training on SD2.1.\n",
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"\n",
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"`max_epochs`, `sample_steps`, and `save_every_n_epochs` should be tuned to your dataset. I like to generate one or two sets of samples per save, and aim for 5 (give or take 2) saved checkpoints.\n",
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"\n",
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"Next cell runs training. This will take a while depending on your number of images, repeats, and max_epochs.\n",
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"\n",
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"You can watch for test images in the logs folder.\n",
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"\n",
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"#### Weights and Balanaces\n",
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"I you pass the `--wandb` flag you will be prompted for your W&B `API Key`. W&B is a free online logging utility. If you don't have a W&B account, you can create one for free at https://wandb.ai/site. Your key is on this page: https://wandb.ai/settings under \"Danger Zone\" \"API Keys\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f73fb86-ebef-41e2-9382-4aa11be84be6",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"%run train.py --config train.json \\\n",
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"--resume_ckpt \"panopstor/EveryDream\" \\\n",
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"--project_name \"sd1_mymodel\" \\\n",
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"--data_root \"input\" \\\n",
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"--max_epochs 200 \\\n",
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"--sample_steps 150 \\\n",
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"--save_every_n_epochs 35 \\\n",
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"--lr 1.2e-6 \\\n",
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"--lr_scheduler constant \\\n",
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"--save_full_precision\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed464c6b-1a8d-48e4-9787-265e8acaac43",
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"metadata": {},
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"source": [
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"### Optionally you can chain trainings together using multiple configurations combined with `resume_ckpt: findlast`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "492350d4-9b2f-4d2a-9641-1f723125b296",
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"metadata": {},
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"outputs": [],
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"source": [
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"%run train.py --config chain0.json --project_name \"sd1_chain_a\" --data_root \"input\" --resume_ckpt \"panopstor/EveryDream\"\n",
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"%run train.py --config chain1.json --project_name \"sd1_chain_b\" --data_root \"input\" --resume_ckpt findlast\n",
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"%run train.py --config chain2.json --project_name \"sd1_chain_c\" --data_root \"input\" --resume_ckpt findlast"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c506e79-bf03-4e34-bf06-9371963d4d7d",
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"metadata": {},
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"source": [
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"# HuggingFace download (Optional)\n",
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"Run the cell below and paste your token into the prompt. You can get your token from your [huggingface account page](https://huggingface.co/settings/tokens).\n",
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"\n",
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"The token will not show on the screen, just press enter after you paste it."
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@ -74,10 +136,10 @@
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},
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{
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"cell_type": "markdown",
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"id": "7a96f2af-8c93-4460-aa9e-2ff795fb06ea",
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"id": "b252a308-49cf-443f-abbb-d08b471411fb",
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"metadata": {},
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"source": [
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"#### Then run the following cell to download the base checkpoint (may take a minute)."
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"Then run the following cell to download the base checkpoint (may take a minute)."
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]
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},
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{
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@ -111,68 +173,6 @@
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"print(\"DONE\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f15fcd56-0418-4be1-a5c3-38aa679b1aaf",
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"metadata": {},
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"source": [
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"# Start Training\n",
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"Naming your project will help you track what the heck you're doing when you're floating in checkpoint files later.\n",
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"\n",
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"You may wish to consider adding \"sd1\" or \"sd2v\" or similar to remember what the base was, as you'll also have to tell your inference app what you were using, as its difficult for programs to know what inference YAML to use automatically. For instance, Automatic1111 webui requires you to copy the v2 inference YAML and rename it to match your checkpoint name so it knows how to load the file, tough it assumes SD 1.x compatible. Something to keep in mind if you start training on SD2.1.\n",
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"\n",
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"`max_epochs`, `sample_steps`, and `save_every_n_epochs` should be tuned to your dataset. I like to generate one or two sets of samples per save, and aim for 5 (give or take 2) saved checkpoints.\n",
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"\n",
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"Next cell runs training. This will take a while depending on your number of images, repeats, and max_epochs.\n",
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"\n",
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"You can watch for test images in the logs folder.\n",
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"\n",
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"## Weights and Balanaces\n",
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"I you pass the `--wandb` flag you will be prompted for your W&B `API Key`. W&B is a free online logging utility. If you don't have a W&B account, you can create one for free at https://wandb.ai/site. Your key is on this page: https://wandb.ai/settings under \"Danger Zone\" \"API Keys\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f73fb86-ebef-41e2-9382-4aa11be84be6",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"%run train.py --config train.json \\\n",
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"--resume_ckpt \"sd_v1-5_vae\" \\\n",
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"--project_name \"sd1_mymodel\" \\\n",
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"--data_root \"input\" \\\n",
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"--max_epochs 200 \\\n",
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"--sample_steps 150 \\\n",
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"--save_every_n_epochs 35 \\\n",
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"--lr 1.2e-6 \\\n",
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"--lr_scheduler constant \\\n",
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"--save_full_precision\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed464c6b-1a8d-48e4-9787-265e8acaac43",
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"metadata": {},
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"source": [
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"### Optionally you can chain trainings together using multiple configurations combined with `resume_ckpt: findlast`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "492350d4-9b2f-4d2a-9641-1f723125b296",
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"metadata": {},
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"outputs": [],
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"source": [
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"%run train.py --config chain0.json --project_name \"sd1_chain_a\" --data_root \"input\" --resume_ckpt \"{ckpt_name}\"\n",
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"%run train.py --config chain1.json --project_name \"sd1_chain_b\" --data_root \"input\" --resume_ckpt findlast\n",
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"%run train.py --config chain2.json --project_name \"sd1_chain_c\" --data_root \"input\" --resume_ckpt findlast"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f24eee3d-f5df-45f3-9acc-ee0206cfe6b1",
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@ -351,7 +351,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -365,7 +365,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.10"
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"version": "3.10.6"
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},
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"vscode": {
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"interpreter": {
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