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Upload training config

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  1. training_config.yaml +88 -0
training_config.yaml ADDED
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+ ---
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+ job: extension
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+ config:
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+ # this name will be the folder and filename name
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+ name: "maccinft"
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+ process:
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+ - type: 'sd_trainer'
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+ # root folder to save training sessions/samples/weights
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+ training_folder: "/opt/stationthis/jobs/training-1770726292083/output"
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+ # uncomment to see performance stats in the terminal every N steps
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+ # performance_log_every: 1000
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+ device: cuda:0
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+ # if a trigger word is specified, it will be added to captions of training data if it does not already exist
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+ # alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
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+ trigger_word: "maccinft"
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+ network:
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+ type: "lora"
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+ linear: 32
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+ linear_alpha: 32
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+ save:
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+ dtype: float16 # precision to save
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+ save_every: 250 # save every this many steps (min of 250 or total steps)
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+ max_step_saves_to_keep: 2 # how many intermittent saves to keep
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+ push_to_hub: false #change this to True to push your trained model to Hugging Face.
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+ # You can either set up a HF_TOKEN env variable or you'll be prompted to log-in
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+ # hf_repo_id: your-username/your-model-slug
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+ # hf_private: true #whether the repo is private or public
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+ datasets:
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+ # datasets are a folder of images. captions need to be txt files with the same name as the image
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+ # for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
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+ # images will automatically be resized and bucketed into the resolution specified
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+ # on windows, escape back slashes with another backslash so
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+ # "C:\\path\\to\\images\\folder"
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+ - folder_path: "/opt/stationthis/jobs/training-1770726292083/dataset"
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+ caption_ext: "txt"
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+ caption_dropout_rate: 0.05 # will drop out the caption 5% of time
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+ shuffle_tokens: false # shuffle caption order, split by commas
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+ cache_latents_to_disk: true # leave this true unless you know what you're doing
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+ resolution: [ 512, 768, 1024 ] # flux enjoys multiple resolutions
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+ train:
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+ batch_size: 1
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+ steps: 4000 # total number of steps to train 500 - 4000 is a good range
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+ gradient_accumulation_steps: 1
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+ train_unet: true
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+ train_text_encoder: false # probably won't work with flux
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+ gradient_checkpointing: true # need the on unless you have a ton of vram
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+ noise_scheduler: "flowmatch" # for training only
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+ optimizer: "adamw8bit"
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+ lr: 1e-4
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+ # Skip baseline samples, only sample during/after training
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+ skip_first_sample: true
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+ # set to true to completely disable sampling
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+ # disable_sampling: true
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+ # uncomment to use new vell curved weighting. Experimental but may produce better results
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+ # linear_timesteps: true
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+
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+ # ema will smooth out learning, but could slow it down. Recommended to leave on.
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+ ema_config:
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+ use_ema: true
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+ ema_decay: 0.99
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+
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+ # will probably need this if gpu supports it for flux, other dtypes may not work correctly
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+ dtype: bf16
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+ model:
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+ # huggingface model name or path
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+ name_or_path: "black-forest-labs/FLUX.1-dev"
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+ is_flux: true
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+ quantize: true # run 8bit mixed precision
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+ # low_vram: true # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower.
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+ sample:
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+ sampler: "flowmatch" # must match train.noise_scheduler
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+ sample_every: 3999 # sample at final step (TRAIN_STEPS - 1 for 0-indexed)
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+ width: 1024
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+ height: 1024
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+ prompts:
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+ # Dynamic prompts from dataset captions (injected by launch-training.js)
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+ # These will be replaced with actual captions for HuggingFace samples
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+ - "maccinft, portrait, soft lighting, detailed"
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+ - "maccinft, artistic composition, high quality"
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+ neg: "" # not used on flux
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+ seed: 42
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+ walk_seed: true
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+ guidance_scale: 4
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+ sample_steps: 20
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+ # you can add any additional meta info here. [name] is replaced with config name at top
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+ meta:
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+ name: "maccinft"
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+ version: '1.0'