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Log/YujinIVE20230821105554/network_train/events.out.tfevents.1692615423.ae63f807da5a.12587.0

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+size 604020

Model/Yujin_V1/YujinIVE-30.safetensors

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+size 37869086

SS_config.json

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+{
    "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5",
    "v2": false,
    "v_parameterization": false,
    "logging_dir": "/workspace/KaraDetroit_loar/Log",
    "train_data_dir": "/workspace/KaraDetroit_loar/Image",
    "reg_data_dir": "",
    "output_dir": "/workspace/KaraDetroit_loar/Model",
    "max_resolution": "768,768",
    "learning_rate": "5e-5",
    "lr_scheduler": "constant",
    "lr_warmup": "0",
    "train_batch_size": 2,
    "epoch": 10,
    "save_every_n_epochs": 1,
    "mixed_precision": "bf16",
    "save_precision": "fp16",
    "seed": "",
    "num_cpu_threads_per_process": 2,
    "cache_latents": true,
    "caption_extension": ".txt",
    "enable_bucket": true,
    "gradient_checkpointing": true,
    "full_fp16": false,
    "no_token_padding": false,
    "stop_text_encoder_training": 0,
    "xformers": true,
    "save_model_as": "safetensors",
    "shuffle_caption": true,
    "save_state": false,
    "resume": "",
    "prior_loss_weight": 1.0,
    "text_encoder_lr": "5e-5",
    "unet_lr": "0.0001",
    "network_dim": 200,
    "lora_network_weights": "",
    "color_aug": false,
    "flip_aug": false,
    "clip_skip": "1",
    "gradient_accumulation_steps": 1.0,
    "mem_eff_attn": false,
    "output_name": "my_model",
    "model_list": "runwayml/stable-diffusion-v1-5",
    "max_token_length": "225",
    "max_train_epochs": "",
    "max_data_loader_n_workers": "0",
    "network_alpha": 200,
    "training_comment": "",
    "keep_tokens": "0",
    "lr_scheduler_num_cycles": "",
    "lr_scheduler_power": "",
    "persistent_data_loader_workers": false,
    "bucket_no_upscale": true,
    "random_crop": false,
    "bucket_reso_steps": 64.0,
    "caption_dropout_every_n_epochs": 0.0,
    "caption_dropout_rate": 0,
    "optimizer": "AdamW8bit",
    "optimizer_args": "",
    "noise_offset": "",
    "LoRA_type": "Standard",
    "conv_dim": 1,
    "conv_alpha": 1,
    "sample_every_n_steps": 0,
    "sample_every_n_epochs": 0,
    "sample_sampler": "euler_a",
    "sample_prompts": "",
    "additional_parameters": "",
    "vae_batch_size": 0,
    "min_snr_gamma": 0,
    "down_lr_weight": "",
    "mid_lr_weight": "",
    "up_lr_weight": "",
    "block_lr_zero_threshold": "",
    "block_dims": "",
    "block_alphas": "",
    "conv_dims": "",
    "conv_alphas": ""

train.ps1

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+$pretrained_model="/path/to/directorychilloutmix_NiPrunedFp32Fix.safetensors"
+$train_data_dir="/path/to/directory"
+$reg_data_dir = ""
+
+$resolution = "768,768"
+$batch_size = 2
+$max_train_epoches = 10
+$save_every_n_epochs = 1
+$network_dim = 64
+$network_alpha = 32
+$clip_skip = 2
+$train_unet_only = 0 
+$train_text_encoder_only = 0
+
+$lr = "5e-5"
+$unet_lr = "5e-5"
+$text_encoder_lr = "6e-6"
+$lr_scheduler = "cosine_with_restarts"
+$lr_warmup_steps = 50
+$lr_restart_cycles = 1
+
+$output_name = "yujinive_v2"
+$save_model_as = "safetensors"
+
+$network_weights = ""
+# $network_weights = "D:\workspace\stable-diffusion-webui\models\Lora\koreanDollLikeness_v10.safetensors" # pretrained weights for LoRA network 
+$min_bucket_reso = 256
+$max_bucket_reso = 1024
+$persistent_data_loader_workers = 0
+
+$use_8bit_adam = 0
+$use_lion = 1
+
+.\venv\Scripts\activate
+
+$Env:HF_HOME = "huggingface"
+$ext_args = [System.Collections.ArrayList]::new()
+
+if ($train_unet_only) {
+  [void]$ext_args.Add("--network_train_unet_only")
+}
+
+if ($train_text_encoder_only) {
+  [void]$ext_args.Add("--network_train_text_encoder_only")
+}
+
+if ($network_weights) {
+  [void]$ext_args.Add("--network_weights=" + $network_weights)
+}
+
+if ($reg_data_dir) {
+  [void]$ext_args.Add("--reg_data_dir=" + $reg_data_dir)
+}
+
+if ($use_8bit_adam) {
+  [void]$ext_args.Add("--use_8bit_adam")
+}
+
+if ($use_lion) {
+  [void]$ext_args.Add("--use_lion_optimizer")
+}
+
+if ($persistent_data_loader_workers) {
+  [void]$ext_args.Add("--persistent_data_loader_workers")
+}
+
+# run train
+accelerate launch --num_cpu_threads_per_process=8 "./sd-scripts/train_network.py" `
+  --enable_bucket `
+  --pretrained_model_name_or_path=$pretrained_model `
+  --train_data_dir=$train_data_dir `
+  --output_dir="./output" `
+  --logging_dir="./logs" `
+  --resolution=$resolution `
+  --network_module=networks.lora `
+  --max_train_epochs=$max_train_epoches `
+  --learning_rate=$lr `
+  --unet_lr=$unet_lr `
+  --text_encoder_lr=$text_encoder_lr `
+  --lr_scheduler=$lr_scheduler `
+  --lr_warmup_steps=$lr_warmup_steps `
+  --lr_scheduler_num_cycles=$lr_restart_cycles `
+  --network_dim=$network_dim `
+  --network_alpha=$network_alpha `
+  --output_name=$output_name `
+  --train_batch_size=$batch_size `
+  --save_every_n_epochs=$save_every_n_epochs `
+  --mixed_precision="fp16" `
+  --save_precision="fp16" `
+  --seed="1337" `
+  --cache_latents `
+  --clip_skip=$clip_skip `
+  --prior_loss_weight=1 `
+  --max_token_length=225 `
+  --caption_extension=".txt" `
+  --save_model_as=$save_model_as `
+  --min_bucket_reso=$min_bucket_reso `
+  --max_bucket_reso=$max_bucket_reso `
+  --xformers --shuffle_caption $ext_args
+
+Write-Output "Train finished"
+Read-Host | Out-Null ;
+

train.sh

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+#!/bin/bash
+
+source venv/bin/activate
+
+pretrained_model="/path/to/directory/chilloutmix_NiPrunedFp32Fix.safetensors" # Base model path
+train_data_dir="/path/to/directory"
+output_dir="./output"
+logging_dir="./logs"
+
+resolution="768,768"
+batch_size=2
+max_train_epochs=10
+save_every_n_epochs=1
+network_dim=64
+network_alpha=32
+clip_skip=2
+train_unet_only=0
+train_text_encoder_only=0
+
+lr="5e-5"
+unet_lr="5e-5"
+text_encoder_lr="6e-6"
+lr_scheduler="cosine_with_restarts"
+lr_warmup_steps=50
+lr_restart_cycles=1
+
+output_name="yujinive_v2"
+save_model_as="safetensors"
+
+min_bucket_reso=64
+max_bucket_reso=768
+
+python "./sd-scripts/train_network.py" \
+--enable_bucket \
+--pretrained_model_name_or_path="$pretrained_model" \
+--train_data_dir="$train_data_dir" \
+--output_dir="$output_dir" \
+--resolution="$resolution" \
+--network_module=networks.lora \
+--max_train_epochs="$max_train_epochs" \
+--learning_rate="$lr" \
+--unet_lr="$unet_lr" \
+--text_encoder_lr="$text_encoder_lr" \
+--lr_scheduler="$lr_scheduler" \
+--lr_warmup_steps="$lr_warmup_steps" \
+--lr_scheduler_num_cycles="$lr_restart_cycles" \
+--network_dim="$network_dim" \
+--network_alpha="$network_alpha" \
+--output_name="$output_name" \
+--train_batch_size="$batch_size" \
+--save_every_n_epochs="$save_every_n_epochs" \
+--mixed_precision="fp16" \
+--save_precision="fp16" \
+--seed="1337" \
+--cache_latents \
+--clip_skip="$clip_skip" \
+--prior_loss_weight=1 \
+--max_token_length=225 \
+--caption_extension=".txt" \
+--save_model_as="$save_model_as" \
+--min_bucket_reso="$min_bucket_reso" \
+--max_bucket_reso="$max_bucket_reso" \
+--xformers \
+--shuffle_caption
+
+echo "Training completed"
+read -n 1 -s -r -p "Press any key to continue..."

trainmacos.py

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+import subprocess
+
+pretrained_model = "/path/to/directorychilloutmix_NiPrunedFp32Fix.safetensors"
+train_data_dir = "/path/to/directory"
+reg_data_dir = "/path/to/directory"
+
+resolution = "768,768"
+batch_size = 2
+max_train_epochs = 10
+save_every_n_epochs = 1
+network_dim = 64
+network_alpha = 32
+clip_skip = 2
+train_unet_only = 0
+train_text_encoder_only = 0
+
+lr = "5e-5"
+unet_lr = "5e-5"
+text_encoder_lr = "6e-6"
+lr_scheduler = "cosine_with_restarts"
+lr_warmup_steps = 50
+lr_restart_cycles = 1
+
+output_name = "yujinive_v2"
+save_model_as = "safetensors"
+
+network_weights = ""
+min_bucket_reso = 256
+max_bucket_reso = 1024
+persistent_data_loader_workers = 0
+
+subprocess.run(["source", "venv/bin/activate"], shell=True)
+
+subprocess.run(["export", "HF_HOME=huggingface"], shell=True)
+
+subprocess.run([
+    "python", "-m", "accelerate", "launch", "--num_processes=1", "--num_workers=8", "--use_env",
+    "./sd-scripts/train_network.py",
+    "--enable_bucket",
+    f"--pretrained_model_name_or_path={pretrained_model}",
+    f"--train_data_dir={train_data_dir}",
+    "--output_dir=./output",
+    "--logging_dir=./logs",
+    f"--resolution={resolution}",
+    "--network_module=networks.lora",
+    f"--max_train_epochs={max_train_epochs}",
+    f"--learning_rate={lr}",
+    f"--unet_lr={unet_lr}",
+    f"--text_encoder_lr={text_encoder_lr}",
+    f"--lr_scheduler={lr_scheduler}",
+    f"--lr_warmup_steps={lr_warmup_steps}",
+    f"--lr_scheduler_num_cycles={lr_restart_cycles}",
+    f"--network_dim={network_dim}",
+    f"--network_alpha={network_alpha}",
+    f"--output_name={output_name}",
+    f"--train_batch_size={batch_size}",
+    f"--save_every_n_epochs={save_every_n_epochs}",
+    "--mixed_precision=fp16",
+    "--save_precision=fp16",
+    "--seed=1337",
+    "--cache_latents",
+    f"--clip_skip={clip_skip}",
+    "--prior_loss_weight=1",
+    "--max_token_length=225",
+    "--caption_extension=.txt",
+    f"--save_model_as={save_model_as}",
+    f"--min_bucket_reso={min_bucket_reso}",
+    f"--max_bucket_reso={max_bucket_reso}",
+    "--xformers",
+    "--shuffle_caption"
+])
+
+print("Train finished")
+input("Press any key to continue...")

yujinIVElocal.py

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+import os
+import torch
+import torchvision
+from torch.utils.data import DataLoader
+from torchvision.transforms import transforms
+import toml
+
+batch_size = 8
+num_epochs = 10
+learning_rate = 0.001
+
+class LoRAModel(torch.nn.Module):
+    def __init__(self):
+        super(LoRAModel, self).__init__()
+
+    def forward(self, x):
+        pass
+
+custom_dataset = """
+[[datasets]]
+
+[[datasets.subsets]]
+image_dir = "/path/to/directory"
+num_repeats = 10
+
+[[datasets.subsets]]
+image_dir = "/path/to/directory"
+is_reg = true
+num_repeats = 1
+"""
+
+dataset_config = toml.loads(custom_dataset)
+datasets = dataset_config.get("datasets", [])
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+])
+
+train_datasets = []
+for dataset in datasets:
+    subsets = dataset.get("subsets", [])
+    for subset in subsets:
+        image_dir = subset.get("image_dir")
+        num_repeats = subset.get("num_repeats", 1)
+        is_reg = subset.get("is_reg", False)
+
+        dataset = torchvision.datasets.ImageFolder(root=image_dir, transform=transform)
+        train_datasets.extend([dataset] * num_repeats)
+
+train_dataset = torch.utils.data.ConcatDataset(train_datasets)
+
+dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+model = LoRAModel()
+criterion = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+total_step = len(dataloader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(dataloader):
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if (i + 1) % 100 == 0:
+            print(f"Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{total_step}], Loss: {loss.item()}")
+
+save_path = "/path/to/directory/model.pth"
+torch.save(model.state_dict(), save_path)