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config:
name: noise_2
process:
- datasets:
- cache_latents_to_disk: true
caption_dropout_rate: 0.2
caption_ext: txt
folder_path: /root/lorahub/noise_2/dataset
resolution:
- 512
- 768
- 1024
shuffle_tokens: false
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:
- man yelling[trigger]
- glitch art [trigger]
- tree[trigger]
- computer screen
- skull
sample_every: 500
sample_steps: 25
sampler: flowmatch
seed: 42
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: 0.0002
noise_scheduler: flowmatch
optimizer: adamw8bit
reg_weight: 1.0
steps: 3000
target_noise_multiplier: 1.0
train_text_encoder: false
train_unet: true
training_folder: /root/lorahub
trigger_word: in a white noise style
type: sd_trainer
job: extension
meta:
description: is trained on a dataset filled with white noise and glitch art, designed
to explore what visuals can emerge from the chaos. By pushing through the layers
of distortion, it seeks to reveal hidden patterns and unexpected beauty within
the noise.
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