Delete hunyuan_video.toml

hunyuan_video.toml

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-# 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
-# Batch size of a single forward/backward pass for one GPU.
-micro_batch_size_per_gpu = 1
-# 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 = 4
-# Grad norm clipping.
-gradient_clipping = 1.0
-# Learning rate warmup.
-warmup_steps = 100
-
-# eval settings
-
-eval_every_n_epochs = 1
-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
-eval_gradient_accumulation_steps = 1
-
-# 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
-# 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.
-activation_checkpointing = true
-# Controls how Deepspeed decides how to divide layers across GPUs. Probably don't change this.
-partition_method = 'parameters'
-# 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 = 2
-# 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)
-# multiple_overlapping: extract the minimum number of clips to cover the full range of the video. They might overlap some.
-# default is single_middle
-video_clip_mode = 'single_middle'
-
-[model]
-type = 'hunyuan-video'
-# Can load Hunyuan Video 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'
-
-[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