main_example.toml

# Output path for training runs. Each training run makes a new directory in here.
output_dir = '/data/diffusion_pipe_training_runs/hunyuan_video_test'

# Dataset config file.
dataset = 'examples/dataset.toml'
# You can have separate eval datasets. Give them a name for Tensorboard metrics.
# eval_datasets = [
#     {name = 'something', config = 'path/to/eval_dataset.toml'},
# ]

# training settings

# I usually set this to a really high value because I don't know how long I want to train.
epochs = 1000
# Maximum number of steps to train.
#max_steps = 5000
# Batch size of a single forward/backward pass for one GPU.
# Can also do per-resolution batch sizes, like this: micro_batch_size_per_gpu = [[512, 4], [1024, 1]]
micro_batch_size_per_gpu = 1
# For mixed video / image training, you can have a different batch size for images.
#image_micro_batch_size_per_gpu = 4
# Pipeline parallelism degree. A single instance of the model is divided across this many GPUs.
pipeline_stages = 1
# Number of micro-batches sent through the pipeline for each training step.
# If pipeline_stages > 1, a higher GAS means better GPU utilization due to smaller pipeline bubbles (where GPUs aren't overlapping computation).
gradient_accumulation_steps = 1
# Grad norm clipping.
gradient_clipping = 1.0
# Learning rate warmup.
warmup_steps = 100
# Force the learning rate to be this value, regardless of what the optimizer or anything else says.
# Can be used to change learning rate even when resuming from checkpoint.
#force_constant_lr = 1e-5
# Can be 'constant' or 'linear'. If unset, will default to 'constant', i.e. no LR scheduler.
#lr_scheduler = 'linear'

# Block swapping is supported for Wan, HunyuanVideo, Flux, and Chroma. This value controls the number
# of blocks kept offloaded to RAM. Increasing it lowers VRAM use, but has a performance penalty. The
# exactly performance penalty depends on the model and the type of training you are doing (e.g. images vs video).
# Block swapping only works for LoRA training, and requires pipeline_stages=1.
#blocks_to_swap = 20

# Use pseudo Huber loss with constant c. Only works on models that use the default loss function.
#pseudo_huber_c = 0.5

# eval settings

eval_every_n_epochs = 1
# You can also specify eval frequency using either of these.
#eval_every_n_steps = 100
#eval_every_n_examples = 1000
eval_before_first_step = true
# Might want to set these lower for eval so that less images get dropped (eval dataset size is usually much smaller than training set).
# Each size bucket of images/videos is rounded down to the nearest multiple of the global batch size, so higher global batch size means
# more dropped images. Usually doesn't matter for training but the eval set is much smaller so it can matter.
eval_micro_batch_size_per_gpu = 1
# Batch size for images when doing mixed image / video training. Will be micro_batch_size_per_gpu if not set.
#image_eval_micro_batch_size_per_gpu = 4
eval_gradient_accumulation_steps = 1
# If using block swap, you can disable it for eval. Eval uses less memory, so depending on block swapping amount you can maybe get away with
# doing this, and then eval is much faster.
#disable_block_swap_for_eval = true

# misc settings

# Probably want to set this a bit higher if you have a smaller dataset so you don't end up with a million saved models.
save_every_n_epochs = 2
# You can also specify save frequency using either of these.
#save_every_n_steps = 100
#save_every_n_examples = 1000
# Can checkpoint the training state every n number of epochs or minutes. Set only one of these. You can resume from checkpoints using the --resume_from_checkpoint flag.
#checkpoint_every_n_epochs = 1
checkpoint_every_n_minutes = 120
# Always set to true unless you have a huge amount of VRAM.
# This can also be 'unsloth' to reduce VRAM even more, with a slight performance hit.
activation_checkpointing = true
# Use reentrant activation checkpointing method (set this in addition to `activation_checkpointing`). Might be required for some models
# when using pipeline parallelism (pipeline_stages>1). Otherwise recommended to not use it.
#reentrant_activation_checkpointing = true

# Controls how Deepspeed decides how to divide layers across GPUs. Probably don't change this.
partition_method = 'parameters'
# Alternatively you can use 'manual' in combination with partition_split, which specifies the split points for dividing
# layers between GPUs. For example, with two GPUs, partition_split=[10] puts layers 0-9 on GPU 0, and the rest on GPU 1.
# With three GPUs, partition_split=[10, 20] puts layers 0-9 on GPU 0, layers 10-19 on GPU 1, and the rest on GPU 2.
# Length of partition_split must be pipeline_stages-1.
#partition_split = [N]

# dtype for saving the LoRA or model, if different from training dtype
save_dtype = 'bfloat16'
# Batch size for caching latents and text embeddings. Increasing can lead to higher GPU utilization during caching phase but uses more memory.
caching_batch_size = 1

# Number of parallel processes to use in map() calls when caching the dataset. Defaults to min(8, num_cpu_cores) if unset.
# If you have a lot of cores and multiple GPUs, raising this can increase throughput of caching, but it may use more memory,
# especially for video data.
#map_num_proc = 32

# Use torch.compile on the model. Can speed up training throughput by a decent amount. Not tested on all models.
#compile = true

# How often deepspeed logs to console.
steps_per_print = 1
# How to extract video clips for training from a single input video file.
# The video file is first assigned to one of the configured frame buckets, but then we must extract one or more clips of exactly the right
# number of frames for that bucket.
# single_beginning: one clip starting at the beginning of the video
# single_middle: one clip from the middle of the video (cutting off the start and end equally)
# default is single_beginning
video_clip_mode = 'single_beginning'

# By default, the loss graphs in Tensorboard / WandB have step as the x-axis. You can change it to number of examples seen instead.
#x_axis_examples = true

# This is how you configure HunyuanVideo. Other models will be different. See docs/supported_models.md for
# details on the configuration and options for each model.
[model]
type = 'hunyuan-video'
# Can load HunyuanVideo entirely from the ckpt path set up for the official inference scripts.
#ckpt_path = '/home/anon/HunyuanVideo/ckpts'
# Or you can load it by pointing to all the ComfyUI files.
transformer_path = '/data2/imagegen_models/hunyuan_video_comfyui/hunyuan_video_720_cfgdistill_fp8_e4m3fn.safetensors'
vae_path = '/data2/imagegen_models/hunyuan_video_comfyui/hunyuan_video_vae_bf16.safetensors'
llm_path = '/data2/imagegen_models/hunyuan_video_comfyui/llava-llama-3-8b-text-encoder-tokenizer'
clip_path = '/data2/imagegen_models/hunyuan_video_comfyui/clip-vit-large-patch14'
# Base dtype used for all models.
dtype = 'bfloat16'
# Hunyuan Video supports fp8 for the transformer when training LoRA.
transformer_dtype = 'float8'
# How to sample timesteps to train on. Can be logit_normal or uniform.
timestep_sample_method = 'logit_normal'

# For models that support full fine tuning, simply delete or comment out the [adapter] table to FFT.
[adapter]
type = 'lora'
rank = 32
# Dtype for the LoRA weights you are training.
dtype = 'bfloat16'
# You can initialize the lora weights from a previously trained lora.
#init_from_existing = '/data/diffusion_pipe_training_runs/something/epoch50'
# Experimental. Can fuse LoRAs into the base weights before training. Right now only for Flux.
#fuse_adapters = [
#    {path = '/data2/imagegen_models/loras/some_lora.safetensors', weight = 1.0}
#]

[optimizer]
# AdamW from the optimi library is a good default since it automatically uses Kahan summation when training bfloat16 weights.
# Look at train.py for other options. You could also easily edit the file and add your own.
type = 'adamw_optimi'
lr = 2e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8

# Can use this optimizer for a bit less memory usage.
# [optimizer]
# type = 'AdamW8bitKahan'
# lr = 2e-5
# betas = [0.9, 0.99]
# weight_decay = 0.01
# stabilize = false

# Automagic optimizer from AI-Toolkit.
# In my experience, this gives slightly worse results than AdamW with a properly tuned LR, but you can try it.

# [optimizer]
# type = 'automagic'
# weight_decay = 0.01

# Any optimizer not explicitly supported will be dynamically loaded from the pytorch-optimizer library.
# [optimizer]
# type = 'Prodigy'
# lr = 1
# betas = [0.9, 0.99]
# weight_decay = 0.01

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