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csai9203/noise/config.yaml ADDED
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+ config:
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+ name: noise
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+ process:
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+ - datasets:
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+ - cache_latents_to_disk: true
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+ caption_dropout_rate: 0.2
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+ caption_ext: txt
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+ folder_path: /root/lorahub/noise/dataset
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+ resolution:
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+ - 512
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+ - 768
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+ - 1024
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+ shuffle_tokens: false
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+ token_dropout_rate: 0.01
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+ device: cuda:0
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+ model:
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+ is_flux: true
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+ name_or_path: black-forest-labs/FLUX.1-dev
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+ quantize: true
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+ text_encoder_bits: 8
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+ network:
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+ linear: 42
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+ linear_alpha: 42
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+ transformer_only: true
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+ type: lora
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+ performance_log_every: 500
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+ sample:
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+ height: 1024
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+ neg: ''
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+ prompts:
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+ - white noise, glitch art, [trigger]
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+ - distortions, surreal, ghostly face [trigger]
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+ - distorted faces, static, [trigger]
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+ sample_every: 500
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+ sample_steps: 25
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+ sampler: flowmatch
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+ seed: 593146
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+ walk_seed: true
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+ width: 1024
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+ save:
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+ dtype: float16
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+ max_step_saves_to_keep: 3
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+ save_every: 500
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+ save_format: diffusers
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+ train:
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+ batch_size: 1
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+ dtype: bf16
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+ ema_config:
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+ ema_decay: 0.99
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+ use_ema: true
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+ gradient_accumulation_steps: 1
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+ gradient_checkpointing: true
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+ linear_timesteps: true
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+ loss_type: mse
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+ lr: 0.0002
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+ noise_scheduler: flowmatch
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+ optimizer: adamw8bit
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+ reg_weight: 1
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+ steps: 1500
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+ target_noise_multiplier: 1
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+ train_text_encoder: false
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+ train_unet: true
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+ training_folder: /root/lorahub
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+ trigger_word: in the style of white noise, glitchy
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+ type: sd_trainer
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+ job: extension
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+ meta:
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+ description: is trained on a dataset filled with white noise and glitch art, designed
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+ to explore what visuals can emerge from the chaos. By pushing through the layers
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+ of distortion, it seeks to reveal hidden patterns and unexpected beauty within
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+ the noise.
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