Stylized Setting (Isometric) SDXL & SD1.5版本SDXL (ID: 255347)

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.

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Please checkout my other Game Assets: https://civitai.com/collections/111873

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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,

描述:

[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

训练词语: Isometric_Setting