# Note, Flex2 is a highly experimental WIP model. Finetuning a model with built in controls and inpainting has not # been done before, so you will be experimenting with me on how to do it. This is my recommended setup, but this is highly # subject to change as we learn more about how Flex2 works. --- job: extension config: # this name will be the folder and filename name name: "my_first_flex2_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: 32 linear_alpha: 32 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 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: "/path/to/images/folder" # Flex2 is trained with controls and inpainting. If you want the model to truely understand how the # controls function with your dataset, it is a good idea to keep doing controls during training. # this will automatically generate the controls for you before training. The current script is not # fully optimized so this could be rather slow for large datasets, but it caches them to disk so it # only needs to be done once. If you want to skip this step, you can set the controls to [] and it will controls: - "depth" - "line" - "pose" - "inpaint" # you can make custom inpainting images as well. These images must be webp or png format with an alpha. # just erase the part of the image you want to inpaint and save it as a webp or png. Again, erase your # train target. So the person if training a person. The automatic controls above with inpaint will # just run a background remover mask and erase the foreground, which works well for subjects. # inpaint_path: "/my/impaint/images" # you can also specify existing control image pairs. It can handle multiple groups and will randomly # select one for each step. # control_path: # - "/my/custom/control/images" # - "/my/custom/control/images2" caption_ext: "txt" caption_dropout_rate: 0.05 # will drop out the caption 5% of time resolution: [ 512, 768, 1024 ] # flex2 enjoys multiple resolutions train: batch_size: 1 # IMPORTANT! For Flex2, you must bypass the guidance embedder during training bypass_guidance_embedding: true steps: 3000 # 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 flex2 gradient_checkpointing: true # need the on unless you have a ton of vram noise_scheduler: "flowmatch" # for training only # shift works well for training fast and learning composition and style. # for just subject, you may want to change this to sigmoid timestep_type: 'shift' # 'linear', 'sigmoid', 'shift' optimizer: "adamw8bit" lr: 1e-4 optimizer_params: weight_decay: 1e-5 # 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. Defaults off ema_config: use_ema: false ema_decay: 0.99 # will probably need this if gpu supports it for flex, other dtypes may not work correctly dtype: bf16 model: # huggingface model name or path name_or_path: "ostris/Flex.2-preview" arch: "flex2" quantize: true # run 8bit mixed precision quantize_te: true # you can pass special training infor for controls to the model here # percentages are decimal based so 0.0 is 0% and 1.0 is 100% of the time. model_kwargs: # inverts the inpainting mask, good to learn outpainting as well, recommended 0.0 for characters invert_inpaint_mask_chance: 0.5 # this will do a normal t2i training step without inpaint when dropped out. REcommended if you want # your lora to be able to inference with and without inpainting. inpaint_dropout: 0.5 # randomly drops out the control image. Dropout recvommended if your want it to work without controls as well. control_dropout: 0.5 # does a random inpaint blob. Usually a good idea to keep. Without it, the model will learn to always 100% # fill the inpaint area with your subject. This is not always a good thing. inpaint_random_chance: 0.5 # generates random inpaint blobs if you did not provide an inpaint image for your dataset. Inpaint breaks down fast # if you are not training with it. Controls are a little more robust and can be left out, # but when in doubt, always leave this on do_random_inpainting: false # does random blurring of the inpaint mask. Helps prevent weird edge artifacts for real workd inpainting. Leave on. random_blur_mask: true # applies a small amount of random dialition and restriction to the inpaint mask. Helps with edge artifacts. # Leave on. random_dialate_mask: 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!'"\ # you can use a single inpaint or single control image on your samples. # for controls, the ctrl_idx is 1, the images can be any name and image format. # use either a pose/line/depth image or whatever you are training with. An example is # - "photo of [trigger] --ctrl_idx 1 --ctrl_img /path/to/control/image.jpg" # for an inpainting image, it must be png/webp. Erase the part of the image you want to inpaint # IMPORTANT! the inpaint images must be ctrl_idx 0 and have .inpaint.{ext} in the name for this to work right. # - "photo of [trigger] --ctrl_idx 0 --ctrl_img /path/to/inpaint/image.inpaint.png" - "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: "" # not used on flex2 seed: 42 walk_seed: true guidance_scale: 4 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'