mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-23 17:38:10 +00:00
new wan trainer
This commit is contained in:
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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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--dataset_metadata_path data/example_video_dataset/metadata_motion_bucket_id.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth,DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1:model.safetensors" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-1.3b-speedcontrol-v1_lora" \
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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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--input_contains_motion_bucket_id
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@@ -0,0 +1,16 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "Wan-AI/Wan2.1-FLF2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-FLF2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-FLF2V-14B-720P_lora" \
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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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--input_contains_input_image \
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--input_contains_end_image
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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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--dataset_metadata_path data/example_video_dataset/metadata_control.csv \
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--data_file_keys "video,control_video" \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-1.3B-Control_lora" \
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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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--input_contains_control_video
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16
examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh
Normal file
16
examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-1.3B-InP_lora" \
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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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--input_contains_input_image \
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--input_contains_end_image
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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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--dataset_metadata_path data/example_video_dataset/metadata_control.csv \
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--data_file_keys "video,control_video" \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-14B-Control_lora" \
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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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--input_contains_control_video
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16
examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh
Normal file
16
examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh
Normal file
@@ -0,0 +1,16 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-14B-InP_lora" \
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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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--input_contains_input_image \
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--input_contains_end_image
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@@ -0,0 +1,17 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \
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--data_file_keys "video,control_video,reference_image" \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control_lora" \
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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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--input_contains_control_video \
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--input_contains_reference_image
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@@ -0,0 +1,17 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \
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--data_file_keys "video,control_video,reference_image" \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control_lora" \
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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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--input_contains_control_video \
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--input_contains_reference_image
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15
examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh
Normal file
15
examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh
Normal file
@@ -0,0 +1,15 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-I2V-14B-480P_lora" \
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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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--input_contains_input_image
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15
examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh
Normal file
15
examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh
Normal file
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-I2V-14B-720P_lora" \
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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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--input_contains_input_image
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14
examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh
Normal file
14
examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh
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@@ -0,0 +1,14 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-T2V-1.3B_lora" \
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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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14
examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh
Normal file
14
examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh
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@@ -0,0 +1,14 @@
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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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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-T2V-14B_lora" \
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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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25
examples/wanvideo/model_training/lora/run_test.py
Normal file
25
examples/wanvideo/model_training/lora/run_test.py
Normal file
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import multiprocessing, os
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def run_task(scripts, thread_id, thread_num):
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for script_id, script in enumerate(scripts):
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if script_id % thread_num == thread_id:
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log_file_name = script.replace("/", "_") + ".txt"
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cmd = f"CUDA_VISIBLE_DEVICES={thread_id} bash {script} > data/log/{log_file_name} 2>&1"
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os.makedirs("data/log", exist_ok=True)
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print(cmd, flush=True)
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os.system(cmd)
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if __name__ == "__main__":
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scripts = []
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for file_name in os.listdir("examples/wanvideo/model_training/lora"):
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if file_name != "run_test.py":
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scripts.append(os.path.join("examples/wanvideo/model_training/lora", file_name))
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processes = [multiprocessing.Process(target=run_task, args=(scripts, i, 8)) for i in range(8)]
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for p in processes:
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p.start()
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for p in processes:
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p.join()
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print("Done!")
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Reference in New Issue
Block a user