Merge pull request #1169 from Feng0w0/sample_add

Docs:Supplement NPU training script samples and documentation instruction

examples/flux/model_training/special/npu_training/FLUX.1-Kontext-dev-NPU.sh

@@ -0,0 +1,17 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/flux/model_training/full/accelerate_config_zero2offload.yaml examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_kontext.csv \
+  --data_file_keys "image,kontext_images" \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/*.safetensors,black-forest-labs/FLUX.1-dev:ae.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 1 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.1-Kontext-dev_full" \
+  --trainable_models "dit" \
+  --extra_inputs "kontext_images" \
+  --use_gradient_checkpointing

examples/flux/model_training/special/npu_training/FLUX.1-dev-NPU.sh

@@ -0,0 +1,15 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/flux/model_training/full/accelerate_config_zero2offload.yaml examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/*.safetensors,black-forest-labs/FLUX.1-dev:ae.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 1 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.1-dev_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing

examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-LoRA-NPU.sh

@@ -0,0 +1,38 @@
+# Due to memory limitations, split training is required to train the model on NPU
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 1 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:text_encoder/model*.safetensors,Qwen/Qwen-Image-Edit-2509:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Edit-2509-LoRA-splited-cache" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --task "sft:data_process"
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path "./models/train/Qwen-Image-Edit-2509-LoRA-splited-cache" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Edit-2509-LoRA-splited" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --task "sft:train"

examples/qwen_image/model_training/special/npu_training/Qwen-Image-LoRA-NPU.sh

@@ -0,0 +1,38 @@
+# Due to memory limitations, split training is required to train the model on NPU
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 1 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-LoRA-splited-cache" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --task "sft:data_process"
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path "./models/train/Qwen-Image-LoRA-splited-cache" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-LoRA-splited" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --task "sft:train"

examples/wanvideo/model_training/special/npu_training/Wan2.1-T2V-14B-NPU.sh

@@ -0,0 +1,16 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --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" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.1-T2V-14B_full" \
+  --trainable_models "dit" \
+  --initialize_model_on_cpu

examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh

@@ -0,0 +1,38 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --num_frames 49 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_full" \
+  --trainable_models "dit" \
+  --max_timestep_boundary 0.417 \
+  --min_timestep_boundary 0 \
+  --initialize_model_on_cpu
+# boundary corresponds to timesteps [875, 1000]
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --num_frames 49 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_full" \
+  --trainable_models "dit" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.417 \
+  --initialize_model_on_cpu
+# boundary corresponds to timesteps [0, 875)

examples/wanvideo/model_training/special/npu_training/Wan2.2-VACE-Fun-A14B-NPU.sh

@@ -0,0 +1,45 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \
+  --data_file_keys "video,vace_video,vace_reference_image" \
+  --height 480 \
+  --width 832 \
+  --num_frames 17 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.vace." \
+  --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_full" \
+  --trainable_models "vace" \
+  --extra_inputs "vace_video,vace_reference_image" \
+  --use_gradient_checkpointing_offload \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0 \
+  --initialize_model_on_cpu
+# boundary corresponds to timesteps [900, 1000]
+
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \
+  --data_file_keys "video,vace_video,vace_reference_image" \
+  --height 480 \
+  --width 832 \
+  --num_frames 17 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.vace." \
+  --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_full" \
+  --trainable_models "vace" \
+  --extra_inputs "vace_video,vace_reference_image" \
+  --use_gradient_checkpointing_offload \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358 \
+  --initialize_model_on_cpu
+# boundary corresponds to timesteps [0, 900]

examples/z_image/model_training/special/npu_training/Z-Image-Turbo-NPU.sh

@@ -0,0 +1,16 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/z_image/model_training/full/accelerate_config.yaml examples/z_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --model_id_with_origin_paths "Tongyi-MAI/Z-Image-Turbo:transformer/*.safetensors,Tongyi-MAI/Z-Image-Turbo:text_encoder/*.safetensors,Tongyi-MAI/Z-Image-Turbo:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Z-Image-Turbo_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8