z_image_toml.toml

# Output path for training runs. Each training run makes a new directory in here.
output_dir = '/diffusion_pipe_working_folder/output_folder/z_image_lora'

save_every_n_epochs = 10
epochs = 20
pipeline_stages = 1
micro_batch_size_per_gpu = 1  
gradient_accumulation_steps = 1
activation_checkpointing = true
dataset = 'examples/dataset.toml'

[model]
type = 'z_image'
diffusion_model = '/diffusion_pipe_working_folder/models/z_image/z_image_turbo_bf16.safetensors'
vae = '/diffusion_pipe_working_folder/models/z_image/ae.safetensors'
text_encoders = [
    {path = '/diffusion_pipe_working_folder/models/z_image/qwen_3_4b.safetensors', type = 'lumina2'}
]
# Use if training Z-Image-Turbo
merge_adapters = ['/diffusion_pipe_working_folder/models/z_image/zimage_turbo_training_adapter_v2.safetensors']
dtype = 'bfloat16'


[adapter]
type = "lora"  
rank = 32 
dtype = "bfloat16"   



[optimizer]
type = 'adamw_optimi'
lr = 2e-4
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8