--- job: extension config: # this name will be the folder and filename name name: "flux_dev-tok" process: - type: 'sd_trainer' # root folder to save training sessions/samples/weights training_folder: "../trained_LoRA" # 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_word: "p3r5on" trigger_word: "tok" 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: "../training_images-tok" 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: [ 512, 768, 1024 ] # flux enjoys multiple resolutions train: batch_size: 1 steps: 4000 # total number of steps to train 500 - 4000 is a good range gradient_accumulation_steps: 1 train_unet: true train_text_encoder: false # probably won't work with flux 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 # uncomment to use new vell curved weighting. Experimental but may produce better results # linear_timesteps: true # ema will smooth out learning, but could slow it down. Recommended to leave on. ema_config: use_ema: true ema_decay: 0.99 # will probably need this if gpu supports it for flux, other dtypes may not work correctly dtype: bf16 model: # huggingface model name or path name_or_path: "black-forest-labs/FLUX.1-dev" is_flux: true quantize: true # run 8bit mixed precision # low_vram: true # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower. sample: sampler: "flowmatch" # must match train.noise_scheduler sample_every: 250 # sample every this many steps width: 1024 height: 1024 prompts: - "a woman holding a coffee cup, in a beanie, sitting at a cafe" - "[trigger] holding a sign that says 'AI is fun'" - "a [trigger] man as a Jedi warrior, deep blue lightsaber, dark purple background with some multicolor neon light reflecting" - "a [trigger] man as a green lantern, cosmic background, creating a dragon from his ring, majestic, rule of third" - "a [trigger] man as Conan the barbarian, snow-covered fields, sword in hand, defiant, dynamic pose, golden ratio, asymmetric composition, photorealism, cinematic realism" - "a portrait of a [trigger] man as Superman flying in space, yellow sun in the background, majestic, comic style" - "a [trigger] man as a cyberpunk warrior, anime style" - "professional photograph portrait of a [trigger] man, black and white" - "a [trigger] man wearing sunglasses riding a high-speed motorbike, hyper-maximalist, octane render" - "a [trigger] man, lucid dream-like 3d model, game asset, blender, unreal engine, rule of thirds, wide angle shot, looking off in distance style, glowing background, vivid neon wonderland, particles, blue, green, orange" neg: "" # not used on flux seed: 42 walk_seed: true guidance_scale: 4 sample_steps: 20 # you can add any additional meta info here. [name] is replaced with config name at top meta: name: "[name]" version: '1.0'