File size: 4,716 Bytes
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job: extension
config:
# this name will be the folder and filename name
name: "my_first_qwen_image_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
# Trigger words will not work when caching text embeddings
# trigger_word: "p3r5on"
network:
type: "lora"
linear: 16
linear_alpha: 16
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. 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"
- folder_path: "/path/to/images/folder"
caption_ext: "txt"
# default_caption: "a person" # if caching text embeddings, if you dont have captions, this will get cached
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 have a large dataset
# if you OOM, 1024 may be too much, but should work
resolution: [ 512, 768, 1024 ] # qwen image enjoys multiple resolutions
train:
batch_size: 1
# caching text embeddings is required for 24GB
cache_text_embeddings: true
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 qwen image
gradient_checkpointing: true # need the on unless you have a ton of vram
noise_scheduler: "flowmatch" # for training only
optimizer: "adamw8bit"
lr: 1e-4
# uncomment this to skip the pre training sample
# skip_first_sample: true
# uncomment to completely disable sampling
# disable_sampling: true
dtype: bf16
model:
# huggingface model name or path
name_or_path: "Qwen/Qwen-Image"
arch: "qwen_image"
quantize: true
# qtype_te: "qfloat8" Default float8 qquantization
# to use the ARA use the | pipe to point to hf path, or a local path if you have one.
# 3bit is required for 24GB
qtype: "uint3|ostris/accuracy_recovery_adapters/qwen_image_torchao_uint3.safetensors"
quantize_te: true
qtype_te: "qfloat8"
low_vram: true
sample:
sampler: "flowmatch" # must match train.noise_scheduler
sample_every: 250 # sample every this many steps
width: 1024
height: 1024
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
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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