File size: 5,245 Bytes
41d48f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | # this example focuses mainly for training Wan2.2 14b on images. It will work for video as well by increasing
# the number of frames in the dataset and samples. Training on and generating video is very VRAM intensive.
---
job: extension
config:
# this name will be the folder and filename name
name: "my_first_wan22_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
# Use a trigger word if train.unload_text_encoder is true, however, if caching text embeddings, do not use a trigger word
# 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
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.
# "C:\\path\\to\\images\\folder"
- folder_path: "/path/to/images/or/video/folder"
caption_ext: "txt"
caption_dropout_rate: 0.05 # will drop out the caption 5% of time
# number of frames to extract from your video. It will automatically extract them evenly spaced
# set to 1 frame for images
num_frames: 1
resolution: [ 512, 768, 1024]
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: 'linear'
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
dtype: bf16
# IMPORTANT: this is for Wan 2.2 MOE. It will switch training one stage or the other every this many steps
switch_boundary_every: 10
# required for 24GB cards. You must do either unload_text_encoder or cache_text_embeddings but not both
# this will encode your trigger word and use those embeddings for every image in the dataset, captions will be ignored
# unload_text_encoder: true
# this will cache all captions in your dataset.
cache_text_embeddings: true
model:
# huggingface model name or path, this one if bf16, vs the float32 of the official repo
name_or_path: "ai-toolkit/Wan2.2-T2V-A14B-Diffusers-bf16"
arch: 'wan22_14b'
quantize: true
# This will pull and use a custom Accuracy Recovery Adapter to train at 4bit
qtype: "uint4|ostris/accuracy_recovery_adapters/wan22_14b_t2i_torchao_uint4.safetensors"
quantize_te: true
qtype_te: "qfloat8"
low_vram: true
model_kwargs:
# you can train high noise, low noise, or both. With low vram it will automatically unload the one not being trained.
train_high_noise: true
train_low_noise: true
sample:
sampler: "flowmatch"
sample_every: 250 # sample every this many steps
width: 1024
height: 1024
# set to 1 for images
num_frames: 1
fps: 16
# 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 with red hair, playing chess at the park, bomb going off in the background"
- "a woman holding a coffee cup, in a beanie, sitting at a cafe"
- "a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini"
- "a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background"
- "a bear building a log cabin in the snow covered mountains"
- "woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
- "hipster man with a beard, building a chair, in a wood shop"
- "photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop"
- "a man holding a sign that says, 'this is a sign'"
- "a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle"
neg: ""
seed: 42
walk_seed: true
guidance_scale: 3.5
sample_steps: 25
# 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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