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config:
name: mugshot_prodigy
process:
- datasets:
- cache_latents_to_disk: true
caption_dropout_rate: 0.2
caption_ext: txt
folder_path: /root/lorahub/mugshot_prodigy/dataset
resolution:
- 1024
shuffle_tokens: true
token_dropout_rate: 0.01
device: cuda:0
model:
is_flux: true
name_or_path: black-forest-labs/FLUX.1-dev
quantize: true
text_encoder_bits: 8
network:
linear: 42
linear_alpha: 42
transformer_only: true
type: lora
performance_log_every: 500
sample:
height: 1024
neg: ''
prompts:
- A cartoon Jedi with green lightsaber [mug$hot]
- A lion roaring [mug$hot]
- AN ACTION SCENE [mug$hot]
- A woman holding a cartoon CAT [mug$hot]
- THE JOKER [mug$hot]
- BATMAN cartoon IN GOTHAM [mug$hot]
- a blue Teddy bear Kaiju vs Godzilla [mug$hot]
sample_every: 500
sample_steps: 25
sampler: flowmatch
seed: 777
walk_seed: true
width: 1024
save:
dtype: float16
max_step_saves_to_keep: 3
save_every: 500
save_format: diffusers
train:
batch_size: 1
dtype: bf16
ema_config:
ema_decay: 0.99
use_ema: true
gradient_accumulation_steps: 1
gradient_checkpointing: true
linear_timesteps: true
loss_type: mse
lr: 0.5
noise_scheduler: flowmatch
optimizer: prodigy
reg_weight: 0.5
steps: 3000
target_noise_multiplier: 1.0
train_text_encoder: false
train_unet: true
training_folder: /root/lorahub
trigger_word: mug$hot
type: sd_trainer
job: extension
meta:
description: trained on bad mugshots