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---
job: extension
config:
# this name will be the folder and filename name
name: "my_first_flex_redux_finetune_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
adapter:
type: "redux"
# you can finetune an existing adapter or start from scratch. Set to null to start from scratch
name_or_path: '/local/path/to/redux_adapter_to_finetune.safetensors'
# name_or_path: null
# image_encoder_path: 'google/siglip-so400m-patch14-384' # Flux.1 redux adapter
image_encoder_path: 'google/siglip2-so400m-patch16-512' # Flex.1 512 redux adapter
# image_encoder_arch: 'siglip' # for Flux.1
image_encoder_arch: 'siglip2'
# You need a control input for each sample. Best to do squares for both images
test_img_path:
- "/path/to/x_01.jpg"
- "/path/to/x_02.jpg"
- "/path/to/x_03.jpg"
- "/path/to/x_04.jpg"
- "/path/to/x_05.jpg"
- "/path/to/x_06.jpg"
- "/path/to/x_07.jpg"
- "/path/to/x_08.jpg"
- "/path/to/x_09.jpg"
- "/path/to/x_10.jpg"
clip_layer: 'last_hidden_state'
train: true
save:
dtype: bf16 # precision to save
save_every: 250 # save every this many steps
max_step_saves_to_keep: 4
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"
# clip_image_path is directory containting your control images. They must have filename as their train image. (extension does not matter)
# for normal redux, we are just recreating the same image, so you can use the same folder path above
clip_image_path: "/path/to/control/images/folder"
caption_ext: "txt"
caption_dropout_rate: 0.05 # will drop out the caption 5% of time
resolution: [ 512, 768, 1024 ] # flex enjoys multiple resolutions
train:
# this is what I used for the 24GB card, but feel free to adjust
# total batch size is 6 here
batch_size: 3
gradient_accumulation: 2
# captions are not needed for this training, we cache a blank proompt and rely on the vision encoder
unload_text_encoder: true
loss_type: "mse"
train_unet: true
train_text_encoder: false
steps: 4000000 # I set this very high and stop when I like the results
content_or_style: balanced # content, style, balanced
gradient_checkpointing: true
noise_scheduler: "flowmatch" # or "ddpm", "lms", "euler_a"
timestep_type: "flux_shift"
optimizer: "adamw8bit"
lr: 1e-4
# this is for Flex.1, comment this out for FLUX.1-dev
bypass_guidance_embedding: true
dtype: bf16
ema_config:
use_ema: true
ema_decay: 0.99
model:
name_or_path: "ostris/Flex.1-alpha"
is_flux: true
quantize: true
text_encoder_bits: 8
sample:
sampler: "flowmatch" # must match train.noise_scheduler
sample_every: 250 # sample every this many steps
width: 1024
height: 1024
# I leave half blank to test prompt and unprompted
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"
- ""
- ""
- ""
- ""
- ""
neg: ""
seed: 42
walk_seed: true
guidance_scale: 4
sample_steps: 25
network_multiplier: 1.0
# 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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