New SDXL Version released!
I generated the showcase images with TensorArt you can see the details here https://tensor.art/models/679231485947122926
The generation data in the images was manually edited so it might not be perfect but the prompts and resources are correct.
Please checkout my other Game Assets: https://civarchive.com/collections/111873
Trigger: Isometric_Setting
There are infinite combinations of prompts because this can make any setting but here are a few I used. Please check my images for more ideas.
cave
cave, crystal, magic, glowing,
tropical beach and palm trees, tiny hut, ocean in background,
ancient ruins, forest in the background,
paradise, floating island, waterfall, clouds and sky in the background,
log cabin, snow, pine trees in background,
Description
[sdxl_arguments]
cache_text_encoder_outputs = true
no_half_vae = true
min_timestep = 0
max_timestep = 1000
shuffle_caption = false
lowram = true
[model_arguments]
pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
vae = "/content/vae/sdxl_vae.safetensors"
[dataset_arguments]
debug_dataset = false
in_json = "/content/LoRA/meta_lat.json"
train_data_dir = "/content/drive/MyDrive/lora_training/datasets/Stylized_Setting_SDXL"
dataset_repeats = 2
keep_tokens = 1
resolution = "1024,1024"
color_aug = false
token_warmup_min = 1
token_warmup_step = 0
[training_arguments]
output_dir = "/content/drive/MyDrive/kohya-trainer/output/Stylized_Setting_SDXL"
output_name = "Stylized_Setting_SDXL"
save_precision = "fp16"
save_every_n_epochs = 1
train_batch_size = 4
max_token_length = 225
mem_eff_attn = false
sdpa = true
xformers = false
max_train_epochs = 10
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
mixed_precision = "fp16"
[logging_arguments]
log_with = "tensorboard"
logging_dir = "/content/LoRA/logs"
log_prefix = "Stylized_Setting_SDXL"
[sample_prompt_arguments]
sample_sampler = "euler_a"
[saving_arguments]
save_model_as = "safetensors"
[optimizer_arguments]
optimizer_type = "AdaFactor"
learning_rate = 0.0001
max_grad_norm = 0
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100
[additional_network_arguments]
no_metadata = false
network_module = "networks.lora"
network_dim = 32
network_alpha = 16
network_args = []
network_train_unet_only = true