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csai9203/noise_2/config.yaml

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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.

csai9203/noise_2/noise_2.safetensors

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csai9203/noise_2/noise_2_000001500.safetensors

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