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fix training boundary for wan2.2 A14B
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@@ -14,8 +14,9 @@ accelerate launch examples/wanvideo/model_training/train.py \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32 \
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--extra_inputs "input_image" \
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--max_timestep_boundary 1 \
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--min_timestep_boundary 0.875
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--max_timestep_boundary 0.358 \
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--min_timestep_boundary 0
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# boundary corresponds to timesteps [900, 1000]
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accelerate launch examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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@@ -33,5 +34,6 @@ accelerate launch examples/wanvideo/model_training/train.py \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32 \
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--extra_inputs "input_image" \
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--max_timestep_boundary 0.875 \
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--min_timestep_boundary 0
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--max_timestep_boundary 1 \
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--min_timestep_boundary 0.358
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# boundary corresponds to timesteps [0, 900)
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@@ -13,8 +13,9 @@ accelerate launch examples/wanvideo/model_training/train.py \
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--lora_base_model "dit" \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32 \
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--max_timestep_boundary 1 \
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--min_timestep_boundary 0.875
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--max_timestep_boundary 0.417 \
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--min_timestep_boundary 0
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# boundary corresponds to timesteps [875, 1000]
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accelerate launch examples/wanvideo/model_training/train.py \
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@@ -32,5 +33,6 @@ accelerate launch examples/wanvideo/model_training/train.py \
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--lora_base_model "dit" \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32 \
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--max_timestep_boundary 0.875 \
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--min_timestep_boundary 0
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--max_timestep_boundary 1 \
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--min_timestep_boundary 0.417
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# boundary corresponds to timesteps [0, 875)
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