Early 2000's Rebecca Gayheart.

Trained using 147, 512x512, 48 frames@24fps videos and 16, 1024px images on a 4090.

config.toml:

# This configuration should allow you to train Wan 14b t2v on 512x512x81 sized videos (or varying aspect ratios of the same size), with 24GB VRAM.

# change this

output_dir = '/mnt/d/Projects/video-training/gayheart/output'

# and this

dataset = '/mnt/d/Projects/video-training/gayheart/dataset.toml'

# training settings

epochs = 100

micro_batch_size_per_gpu = 1

pipeline_stages = 1

gradient_accumulation_steps = 1

gradient_clipping = 1

warmup_steps = 10

# eval settings

eval_every_n_epochs = 1

eval_before_first_step = true

eval_micro_batch_size_per_gpu = 1

eval_gradient_accumulation_steps = 1

# misc settings

save_every_n_epochs = 5

#checkpoint_every_n_minutes = 30

checkpoint_every_n_epochs = 5

activation_checkpointing = 'unsloth'

partition_method = 'parameters'

save_dtype = 'bfloat16'

caching_batch_size = 1

steps_per_print = 1

video_clip_mode = 'single_beginning'

blocks_to_swap = 24

[model]

type = 'wan'

ckpt_path = '/mnt/d/software_tools/diffusion-pipe/models/wan/Wan2.1-T2V-14B'

transformer_path = '/mnt/d/software_tools/diffusion-pipe/models/wan/Wan2_1-T2V-14B_fp8_e5m2.safetensors' #kijai

vae_path = '/mnt/d/software_tools/diffusion-pipe/models/wan/Wan_2_1_VAE_bf16.safetensors' #kijai

llm_path = '/mnt/d/software_tools/diffusion-pipe/models/wan/umt5-xxl-enc-bf16.safetensors'

dtype = 'bfloat16'

transformer_dtype = 'float8'

timestep_sample_method = 'logit_normal'

[adapter]

type = 'lora'

rank = 32

dtype = 'bfloat16'

[optimizer]

type = 'AdamW8bitKahan'

lr = 2e-5

betas = [0.9, 0.99]

weight_decay = 0.01

stabilize = false

dataset.toml

# Resolution settings.

# Can adjust this to 1024 for image training, especially on 24gb cards.

resolutions = [512]

#Aspect ratio bucketing settings

enable_ar_bucket = true

min_ar = 0.5

max_ar = 2.0

num_ar_buckets = 7

# Frame buckets (1 is for images)

frame_buckets = [1, 33]

[[directory]]

# Set this to where your dataset is

path = '/mnt/d/Projects/video-training/gayheart/512px-clips/'

# Reduce as necessary

num_repeats = 1

[[directory]]

path = '/mnt/d/Projects/video-training/gayheart/1024px-images/'

resolutions=[1024]

# Frame buckets (1 is for images)

frame_buckets = [1]

# Reduce as necessary

num_repeats = 5

Rebecca Gayheart - 1