| config: | |
| name: noise | |
| process: | |
| - datasets: | |
| - cache_latents_to_disk: true | |
| caption_dropout_rate: 0.2 | |
| caption_ext: txt | |
| folder_path: /root/lorahub/noise/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: | |
| - white noise, glitch art, [trigger] | |
| - distortions, surreal, ghostly face [trigger] | |
| - distorted faces, static, [trigger] | |
| sample_every: 500 | |
| sample_steps: 25 | |
| sampler: flowmatch | |
| seed: 593146 | |
| 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 | |
| steps: 1500 | |
| target_noise_multiplier: 1 | |
| train_text_encoder: false | |
| train_unet: true | |
| training_folder: /root/lorahub | |
| trigger_word: in the style of white noise, glitchy | |
| 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. | |