pretrained_model_name_or_path = "/workspace/models/flux1-dev.safetensors"
ae = "/workspace/models/ae.safetensors"
t5xxl = "/workspace/models/t5xxl_fp16.safetensors"
clip_l = "/workspace/models/clip_l.safetensors"
output_dir = "/workspace/kohya_models/rita-v5"
dataset_repeats = 16
resolution = "512,512"
train_batch_size = 2
network_dim = 16
network_alpha = 16
optimizer_type = "Adamw8bit"
unet_lr = 0.0005
epoch = 12
max_train_steps = 1500
apply_t5_attn_mask = false
cache_latents = true
cache_latents_to_disk = true
cache_text_encoder_outputs = true
cache_text_encoder_outputs_to_disk = true
clip_skip = 1
discrete_flow_shift = 3.1582
full_bf16 = true
mixed_precision = "bf16"
gradient_accumulation_steps = 1
gradient_checkpointing = true
guidance_scale = 1.0
highvram = true
huber_c = 0.1
huber_schedule = "snr"
loss_type = "l2"
lr_scheduler = "cosine_with_restarts"
lr_scheduler_args = []
lr_scheduler_num_cycles = 3
lr_scheduler_power = 1
max_data_loader_n_workers = 0
max_grad_norm = 1
max_timestep = 1000
min_snr_gamma = 5
model_prediction_type = "raw"
network_args = [ "train_double_block_indices=all", "train_single_block_indices=all",]
network_module = "networks.lora_flux"
network_train_unet_only = true
noise_offset = 0.1
noise_offset_type = "Original"
optimizer_args = []
prior_loss_weight = 1
sample_sampler = "euler"
sdpa = true
seed = 42
t5xxl_max_token_length = 512
text_encoder_lr = []
timestep_sampling = "sigmoid"
output_name = "lora"
save_state_to_huggingface = true
save_model_as = "safetensors"
save_every_n_epochs = 1
save_precision = "bf16"
caption_extension = ".txt"
xformers = "sdpa"
enable_bucket = false
dataset_config = "/workspace/kohya_models/rita-v5/dataset_config.json"