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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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