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events.out.tfevents.1740782093.panopstor-nv6k-001.2603219.0

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+oid sha256:a1dd25b1b34ab98b6b372044d64861f824b07e0ef6da7b07d4d969a36b48cfa3
+size 537131

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

input.toml

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+# Resolutions to train on, given as the side length of a square image. You can have multiple sizes here.
+# !!!WARNING!!!: this might work differently to how you think it does. Images are first grouped to aspect ratio
+# buckets, then each image is resized to ALL of the areas specified by the resolutions list. This is a way to do
+# multi-resolution training, i.e. training on multiple total pixel areas at once. Your dataset is effectively duplicated
+# as many times as the length of this list.
+# If you just want to use predetermined (width, height, frames) size buckets, see the example cosmos_dataset.toml
+# file for how you can do that.
+resolutions = [512]
+
+# You can give resolutions as (width, height) pairs also. This doesn't do anything different, it's just
+# another way of specifying the area(s) (i.e. total number of pixels) you want to train on.
+# resolutions = [[1280, 720]]
+
+# Enable aspect ratio bucketing. For the different AR buckets, the final size will be such that
+# the areas match the resolutions you configured above.
+enable_ar_bucket = true
+
+# The aspect ratio and frame bucket settings may be specified for each [[directory]] entry as well.
+# Directory-level settings will override top-level settings.
+
+# Min and max aspect ratios, given as width/height ratio.
+min_ar = 0.5
+max_ar = 2.0
+# Total number of aspect ratio buckets, evenly spaced (in log space) between min_ar and max_ar.
+num_ar_buckets = 7
+
+# Can manually specify ar_buckets instead of using the range-style config above.
+# Each entry can be width/height ratio, or (width, height) pair. But you can't mix them, because of TOML.
+# ar_buckets = [[512, 512], [448, 576]]
+# ar_buckets = [1.0, 1.5]
+
+# For video training, you need to configure frame buckets (similar to aspect ratio buckets). There will always
+# be a frame bucket of 1 for images. Videos will be assigned to the first frame bucket that the video is greater than or equal to in length.
+# But videos are never assigned to the image frame bucket (1); if the video is very short it would just be dropped.
+frame_buckets = [1, 33]
+# If you have >24GB VRAM, or multiple GPUs and use pipeline parallelism, or lower the spatial resolution, you could maybe train with longer frame buckets
+# frame_buckets = [1, 33, 65, 97]
+
+
+[[directory]]
+# Path to directory of images/videos, and corresponding caption files. The caption files should match the media file name, but with a .txt extension.
+# A missing caption file will log a warning, but then just train using an empty caption.
+path = 'input'
+# How many repeats for 1 epoch. The dataset will act like it is duplicated this many times.
+# The semantics of this are the same as sd-scripts: num_repeats=1 means one epoch is a single pass over all examples (no duplication).
+num_repeats = 10
+# Example of overriding some settings, and using ar_buckets to directly specify ARs.
+# ar_buckets = [[448, 576]]
+# resolutions = [[448, 576]]
+# frame_buckets = [1]
+
+
+# You can list multiple directories.
+
+# [[directory]]
+# path = '/home/anon/data/images/something_else'
+# num_repeats = 5