output_dir = "/workspace/ComfyUI/models/loras/out"
dataset = "/workspace/configs/dataset_wan.toml"
epochs = 1000
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1.0
warmup_steps = 40
activation_checkpointing = true
partition_method = "parameters"
save_dtype = "bfloat16"
caching_batch_size = 1
steps_per_print = 1
video_clip_mode = "single_beginning"
save_every_n_epochs = 10
checkpoint_every_n_minutes = 120
blocks_to_swap = 20

eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

[model]
type = "wan"
ckpt_path = "/workspace/Wan2.1"
transformer_path = '/workspace/ComfyUI/models/diffusion_models/wan2.1_i2v_480p_14B_bf16.safetensors'
llm_path = '/workspace/ComfyUI/models/text_encoders/umt5-xxl-enc-bf16.safetensors'
dtype = "bfloat16"
timestep_sample_method = "logit_normal"

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

[optimizer]
type = "adamw_optimi"
lr = 1e-5
betas = [ 0.9, 0.99,]
weight_decay = 0.01

[monitoring]
# Set to true and fill in these fields to enable wandb
enable_wandb = true
wandb_api_key = 'f46df1bb828b735bd22f94fff1be190ba5e046f9'
wandb_tracker_name = 'wan-lora'
wandb_run_name = 'wan-lora'