feat: add sample training images, captions, and LoRA configuration for zibo robot model
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---
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job: extension
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config:
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# This name becomes the output folder and filename.
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name: "my_first_flux_lora_v1"
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process:
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- type: 'sd_trainer'
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# run_beam.py enforces this persistent Beam Volume path at runtime.
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training_folder: "/volumes/ai-toolkit-output"
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device: cuda:0
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trigger_word: "zb0rbt"
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network:
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type: "lora"
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linear: 16
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linear_alpha: 16
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save:
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dtype: float16
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save_every: 100
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max_step_saves_to_keep: 4
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datasets:
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# Beam synchronizes the repository to /mnt/code. Keep your dataset
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# inside this repository and update this path to match its folder.
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- folder_path: "/mnt/code/sample"
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caption_ext: "txt"
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caption_dropout_rate: 0.05
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shuffle_tokens: false
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# Add a mirrored training item for every source image (4 -> 8).
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flip_x: true
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cache_latents_to_disk: true
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resolution: [512, 768, 1024]
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train:
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batch_size: 1
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# Short smoke-test run for the tiny sample dataset.
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steps: 300
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gradient_accumulation_steps: 1
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train_unet: true
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train_text_encoder: false
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gradient_checkpointing: true
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noise_scheduler: "flowmatch"
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optimizer: "adamw8bit"
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lr: 1e-4
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dtype: bf16
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model:
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name_or_path: "black-forest-labs/FLUX.1-dev"
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is_flux: true
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quantize: true
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sample:
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sampler: "flowmatch"
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sample_every: 100
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sample_start_step: 0
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width: 1024
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height: 1024
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prompts:
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- "zb0rbt robot standing on a kitchen counter, natural morning light"
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- "zb0rbt robot walking through a rainy city street at night"
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- "zb0rbt robot sitting on a mossy rock in a forest"
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neg: ""
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seed: 42
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walk_seed: true
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guidance_scale: 4
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sample_steps: 20
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meta:
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name: "[name]"
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version: '1.0'
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[trigger] robot standing on a wooden desk beside a ceramic mug, three-quarter front view, soft morning window light
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[trigger] robot standing on a bright windowsill, three-quarter side view, soft daylight
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[trigger] robot walking across a workshop bench while holding a small wrench, warm indoor light
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[trigger] robot examining a yellow flower on a garden path, outdoor daylight
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