EMG / command.sh
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nautilus sftp://edessale@nexuscfar00.umiacs.umd.edu/fs/vulcan-projects/Force_Learning
/fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun/models/Wan2.1-Fun-V1.1-1.3B-Control-Camera
https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control
https://huggingface.co/alibaba-pai/Wan2.1-Fun-V1.1-1.3B-Control-Camera
export DATASET_NAME=""
export DATASET_META_NAME="/fs/vulcan-projects/Force_Learning/EMG/videoxfun_metadata.json"
export MODEL_NAME="/fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun/models/Wan2.1-Fun-V1.1-1.3B-Control-Camera"
export MODEL_NAME="/fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun/models/Wan2.2-Fun-5B-Control"
export DATASET_NAME=""
export DATASET_META_NAME="/fs/vulcan-projects/Force_Learning/EMG/videoxfun_metadata.json"
accelerate launch --mixed_precision="bf16" scripts/wan2.2_fun/train_control.py \
--config_path="config/wan2.2/wan_civitai_5b.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--image_sample_size=640 \
--video_sample_size=640 \
--token_sample_size=640 \
--video_sample_stride=1 \
--video_sample_n_frames=49 \
--train_batch_size=1 \
--video_repeat=1 \
--gradient_accumulation_steps=1 \
--dataloader_num_workers=2 \
--num_train_epochs=100 \
--checkpointing_steps=200 \
--learning_rate=2e-05 \
--lr_scheduler="constant_with_warmup" \
--lr_warmup_steps=100 \
--seed=42 \
--output_dir="output_dir" \
--gradient_checkpointing \
--mixed_precision="bf16" \
--adam_weight_decay=3e-2 \
--adam_epsilon=1e-10 \
--vae_mini_batch=1 \
--max_grad_norm=0.05 \
--random_hw_adapt \
--control_signal_mode emg \
--training_with_video_token_length \
--uniform_sampling \
--boundary_type="full" \
--train_mode="control_ref" \
--control_ref_image="first_frame" \
--add_inpaint_info \
--add_full_ref_image_in_self_attention \
--trainable_modules "."
accelerate launch --mixed_precision="bf16" scripts/wan2.1_fun/train_control.py \
--config_path="config/wan2.1/wan_civitai.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--image_sample_size=640 \
--video_sample_size=640 \
--token_sample_size=640 \
--video_sample_stride=1 \
--video_sample_n_frames=49 \
--train_batch_size=1 \
--video_repeat=1 \
--gradient_accumulation_steps=1 \
--dataloader_num_workers=2 \
--num_train_epochs=100 \
--checkpointing_steps=200 \
--learning_rate=2e-05 \
--lr_scheduler="constant_with_warmup" \
--lr_warmup_steps=100 \
--seed=42 \
--output_dir="output_dir" \
--gradient_checkpointing \
--mixed_precision="bf16" \
--adam_weight_decay=3e-2 \
--adam_epsilon=1e-10 \
--vae_mini_batch=1 \
--max_grad_norm=0.05 \
--random_hw_adapt \
--training_with_video_token_length \
--uniform_sampling \
--train_mode="control_ref" \
--control_ref_image="random" \
--add_full_ref_image_in_self_attention \
--trainable_modules "." \
--profile_timings_every=100 \
--validation_num_samples 200 \
--validation_steps 3 \
--resume_from_checkpoint "/fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun/output_dir/checkpoint-2800"
cd /fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun
python3 scripts/wan2.1_fun/generate_emg_dataset_wan.py \
--config_path config/wan2.1/wan_civitai.yaml \
--pretrained_model_name_or_path "$MODEL_NAME" \
--transformer_path /fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun/output_dir/checkpoint-2800 \
--train_data_dir "$DATASET_NAME" \
--train_data_meta "$DATASET_META_NAME" \
--image_sample_size 640 \
--video_sample_size 640 \
--video_sample_stride 1 \
--video_sample_n_frames 49 \
--train_mode "control_ref" \
--add_full_ref_image_in_self_attention \
--n_samples 8 \
--output_dir output_dir/emg_generations
export DATASET_NAME=""
export DATASET_META_NAME="/fs/vulcan-projects/Force_Learning/EMG/videoxfun_metadata.json"
export MODEL_NAME="/fs/vulcan-projects/Force_Learning/EMG_Learning/VideoX-Fun/models/Wan2.1-Fun-V1.1-1.3B-Control-Camera"
accelerate launch --mixed_precision="bf16" scripts/wan2.1_fun/train_control_lora.py \
--config_path="config/wan2.1/wan_civitai.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--image_sample_size=640 \
--video_sample_size=640 \
--token_sample_size=640 \
--video_sample_stride=1 \
--video_sample_n_frames=49 \
--train_batch_size=4 \
--video_repeat=1 \
--gradient_accumulation_steps=1 \
--dataloader_num_workers=2 \
--num_train_epochs=100 \
--checkpointing_steps=200 \
--learning_rate=1e-04 \
--seed=42 \
--output_dir="output_dir" \
--gradient_checkpointing \
--mixed_precision="bf16" \
--adam_weight_decay=3e-2 \
--adam_epsilon=1e-10 \
--vae_mini_batch=1 \
--max_grad_norm=0.05 \
--random_hw_adapt \
--training_with_video_token_length \
--uniform_sampling \
--train_mode="control_ref" \
--control_ref_image="random" \
--add_full_ref_image_in_self_attention \
--rank=64 \
--network_alpha=32 \
--target_name="q,k,v,ffn.0,ffn.2" \
--use_peft_lora \
--profile_timings_every=100