Upload ai-toolkit_config.yaml with huggingface_hub
Browse files- ai-toolkit_config.yaml +94 -0
ai-toolkit_config.yaml
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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: "my_first_flux_lora_v1"
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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: "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: "p3r5on"
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network:
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type: "lora"
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linear: 16
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linear_alpha: 16
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save:
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dtype: float16 # precision to save
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save_every: 400 # save every this many steps
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max_step_saves_to_keep: 4 # 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: "/workspace/ai-toolkit/images"
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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: 2000 # 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: 0.0004
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# skip_first_sample: true
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# uncomment 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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# 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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# 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: 400 # sample every this many steps
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width: 1024
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height: 1024
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prompts:
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- Photo of p3r5on holding a sign that says 'I LOVE PROMPTS!'
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- Professional headshot of p3r5on in a business suit.
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- A happy pilot p3r5on of a Boeing 747.
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- A doctor p3r5on talking to a patient.
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- A chef p3r5on in the middle of a bustling kitchen, plating a beautifully arranged dish.
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- A young p3r5on with a big grin, holding a large ice cream cone in front of an old-fashioned ice cream parlor.
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- A person p3r5on in a tuxedo, looking directly into the camera with a confident smile, standing on a red carpet at a gala event.
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- Person p3r5on with a bitchin' 80's mullet hairstyle leaning out the window of a pontiac firebird
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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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trigger_word: p3r5on
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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: "[name]"
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version: '1.0'
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