--- job: extension config: # this name will be the folder and filename name name: "my_first_flux_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_word: "p3r5on" network: type: "lora" linear: 16 linear_alpha: 16 save: dtype: float16 # precision to save save_every: 400 # save every this many steps max_step_saves_to_keep: 4 # how many intermittent saves to keep push_to_hub: false #change this to True to push your trained model to Hugging Face. # You can either set up a HF_TOKEN env variable or you'll be prompted to log-in # hf_repo_id: your-username/your-model-slug # hf_private: true #whether the repo is private or public 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: "/workspace/ai-toolkit/images" 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: 2000 # 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: 0.0004 # 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: 400 # sample every this many steps width: 1024 height: 1024 prompts: - Photo of p3r5on holding a sign that says 'I LOVE PROMPTS!' - Professional headshot of p3r5on in a business suit. - A happy pilot p3r5on of a Boeing 747. - A doctor p3r5on talking to a patient. - A chef p3r5on in the middle of a bustling kitchen, plating a beautifully arranged dish. - A young p3r5on with a big grin, holding a large ice cream cone in front of an old-fashioned ice cream parlor. - A person p3r5on in a tuxedo, looking directly into the camera with a confident smile, standing on a red carpet at a gala event. - Person p3r5on with a bitchin' 80's mullet hairstyle leaning out the window of a pontiac firebird neg: "" # not used on flux seed: 42 walk_seed: true guidance_scale: 4 sample_steps: 20 trigger_word: p3r5on # you can add any additional meta info here. [name] is replaced with config name at top meta: name: "[name]" version: '1.0'