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Added support for finetuning OmniGen2.
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config/examples/train_lora_omnigen2_24gb.yaml
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94
config/examples/train_lora_omnigen2_24gb.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_omnigen2_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: 250 # 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: "/path/to/images/folder"
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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 ] # omnigen2 should work with multiple resolutions
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train:
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batch_size: 1
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steps: 3000 # total number of steps to train 500 - 4000 is a good range
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gradient_accumulation: 1
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train_unet: true
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train_text_encoder: false # probably won't work with omnigen2
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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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timestep_type: 'sigmoid' # sigmoid, linear, shift
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# uncomment this to skip the pre training sample
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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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# ema will smooth out learning, but could slow it down.
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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 omnigen2, other dtypes may not work correctly
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dtype: bf16
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model:
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name_or_path: "OmniGen2/OmniGen2
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arch: "omnigen2"
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quantize_te: true # quantize_only te
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# quantize: true # quantize transformer
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sample:
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sampler: "flowmatch" # must match train.noise_scheduler
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sample_every: 250 # 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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# you can add [trigger] to the prompts here and it will be replaced with the trigger word
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# - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\
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- "woman with red hair, playing chess at the park, bomb going off in the background"
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- "a woman holding a coffee cup, in a beanie, sitting at a cafe"
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- "a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini"
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- "a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background"
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- "a bear building a log cabin in the snow covered mountains"
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- "woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
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- "hipster man with a beard, building a chair, in a wood shop"
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- "photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop"
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- "a man holding a sign that says, 'this is a sign'"
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- "a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle"
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neg: "" # negative prompt, optional
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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: 25
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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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