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# IMPORTANT: The Wan2.1 14B model is huge. This config should work on 24GB GPUs. It cannot
# support keeping the text encoder on GPU while training with 24GB, so it is only good
# for training on a single prompt, for example a person with a trigger word.
# to train on captions, you need more vran for now.
---
job: extension
config:
# this name will be the folder and filename name
name: "my_first_wan21_14b_lora_v1"
process:
- type: 'sd_trainer'
# root folder to save training sessions/samples/weights
training_folder: "output"
# uncomment to see performance stats in the terminal every N steps
# performance_log_every: 1000
device: cuda:0
# if a trigger word is specified, it will be added to captions of training data if it does not already exist
# alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
# this is probably needed for 24GB cards when offloading TE to CPU
trigger_word: "p3r5on"
network:
type: "lora"
linear: 32
linear_alpha: 32
save:
dtype: float16 # precision to save
save_every: 250 # save every this many steps
max_step_saves_to_keep: 4 # how many intermittent saves to keep
push_to_hub: false #change this to True to push your trained model to Hugging Face.
# You can either set up a HF_TOKEN env variable or you'll be prompted to log-in
# hf_repo_id: your-username/your-model-slug
# hf_private: true #whether the repo is private or public
datasets:
# datasets are a folder of images. captions need to be txt files with the same name as the image
# for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
# images will automatically be resized and bucketed into the resolution specified
# on windows, escape back slashes with another backslash so
# "C:\\path\\to\\images\\folder"
# AI-Toolkit does not currently support video datasets, we will train on 1 frame at a time
# it works well for characters, but not as well for "actions"
- folder_path: "/path/to/images/folder"
caption_ext: "txt"
caption_dropout_rate: 0.05 # will drop out the caption 5% of time
shuffle_tokens: false # shuffle caption order, split by commas
cache_latents_to_disk: true # leave this true unless you know what you're doing
resolution: [ 632 ] # will be around 480p
train:
batch_size: 1
steps: 2000 # total number of steps to train 500 - 4000 is a good range
gradient_accumulation: 1
train_unet: true
train_text_encoder: false # probably won't work with wan
gradient_checkpointing: true # need the on unless you have a ton of vram
noise_scheduler: "flowmatch" # for training only
timestep_type: 'sigmoid'
optimizer: "adamw8bit"
lr: 1e-4
optimizer_params:
weight_decay: 1e-4
# uncomment this to skip the pre training sample
# skip_first_sample: true
# uncomment to completely disable sampling
# disable_sampling: true
# ema will smooth out learning, but could slow it down. Recommended to leave on.
ema_config:
use_ema: true
ema_decay: 0.99
dtype: bf16
# required for 24GB cards
# this will encode your trigger word and use those embeddings for every image in the dataset
unload_text_encoder: true
model:
# huggingface model name or path
name_or_path: "Wan-AI/Wan2.1-T2V-14B-Diffusers"
arch: 'wan21'
# these settings will save as much vram as possible
quantize: true
quantize_te: true
low_vram: true
sample:
sampler: "flowmatch"
sample_every: 250 # sample every this many steps
width: 832
height: 480
num_frames: 40
fps: 15
# samples take a long time. so use them sparingly
# samples will be animated webp files, if you don't see them animated, open in a browser.
prompts:
# you can add [trigger] to the prompts here and it will be replaced with the trigger word
# - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\
- "woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
neg: ""
seed: 42
walk_seed: true
guidance_scale: 5
sample_steps: 30
# you can add any additional meta info here. [name] is replaced with config name at top
meta:
name: "[name]"
version: '1.0'
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