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---
job: extension
config:
name: example_name
process:
- type: 'image_reference_slider_trainer'
training_folder: "/mnt/Train/out/LoRA"
device: cuda:0
# for tensorboard logging
log_dir: "/home/jaret/Dev/.tensorboard"
network:
type: "lora"
linear: 8
linear_alpha: 8
train:
noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
steps: 5000
lr: 1e-4
train_unet: true
gradient_checkpointing: true
train_text_encoder: true
optimizer: "adamw"
optimizer_params:
weight_decay: 1e-2
lr_scheduler: "constant"
max_denoising_steps: 1000
batch_size: 1
dtype: bf16
xformers: true
skip_first_sample: true
noise_offset: 0.0
model:
name_or_path: "/path/to/model.safetensors"
is_v2: false # for v2 models
is_xl: false # for SDXL models
is_v_pred: false # for v-prediction models (most v2 models)
save:
dtype: float16 # precision to save
save_every: 1000 # save every this many steps
max_step_saves_to_keep: 2 # only affects step counts
sample:
sampler: "ddpm" # must match train.noise_scheduler
sample_every: 100 # sample every this many steps
width: 512
height: 512
prompts:
- "photo of a woman with red hair taking a selfie --m -3"
- "photo of a woman with red hair taking a selfie --m -1"
- "photo of a woman with red hair taking a selfie --m 1"
- "photo of a woman with red hair taking a selfie --m 3"
- "close up photo of a man smiling at the camera, in a tank top --m -3"
- "close up photo of a man smiling at the camera, in a tank top--m -1"
- "close up photo of a man smiling at the camera, in a tank top --m 1"
- "close up photo of a man smiling at the camera, in a tank top --m 3"
- "photo of a blonde woman smiling, barista --m -3"
- "photo of a blonde woman smiling, barista --m -1"
- "photo of a blonde woman smiling, barista --m 1"
- "photo of a blonde woman smiling, barista --m 3"
- "photo of a Christina Hendricks --m -1"
- "photo of a Christina Hendricks --m -1"
- "photo of a Christina Hendricks --m 1"
- "photo of a Christina Hendricks --m 3"
- "photo of a Christina Ricci --m -3"
- "photo of a Christina Ricci --m -1"
- "photo of a Christina Ricci --m 1"
- "photo of a Christina Ricci --m 3"
neg: "cartoon, fake, drawing, illustration, cgi, animated, anime"
seed: 42
walk_seed: false
guidance_scale: 7
sample_steps: 20
network_multiplier: 1.0
logging:
log_every: 10 # log every this many steps
use_wandb: false # not supported yet
verbose: false
slider:
datasets:
- pair_folder: "/path/to/folder/side/by/side/images"
network_weight: 2.0
target_class: "" # only used as default if caption txt are not present
size: 512
- pair_folder: "/path/to/folder/side/by/side/images"
network_weight: 4.0
target_class: "" # only used as default if caption txt are not present
size: 512
# you can put any information you want here, and it will be saved in the model
# the below is an example. I recommend doing trigger words at a minimum
# in the metadata. The software will include this plus some other information
meta:
name: "[name]" # [name] gets replaced with the name above
description: A short description of your model
trigger_words:
- put
- trigger
- words
- here
version: '0.1'
creator:
name: Your Name
email: your@email.com
website: https://yourwebsite.com
any: All meta data above is arbitrary, it can be whatever you want. |