Update readme and QwenImageTransformer2DModel (#321)

2025-09-16 15:15:03 +08:00
parent a40638f853
commit e98eb1385c
18 changed files with 377 additions and 59 deletions
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+## Training Code
+
+We can choose whether to use deepspeed or fsdp in qwen-image, which can save a lot of video memory. 
+
+Some parameters in the sh file can be confusing, and they are explained in this document:
+
+- `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images at the center, but instead, it trains the entire images after grouping them into buckets based on resolution.
+- `random_hw_adapt` is used to enable automatic height and width scaling for images. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `512` as the minimum. 
+  - For example, when `random_hw_adapt` is enabled, `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`
+- `resume_from_checkpoint` is used to set the training should be resumed from a previous checkpoint. Use a path or `"latest"` to automatically select the last available checkpoint.
+
+Without deepspeed:
+
+Training qwen-image without DeepSpeed may result in insufficient GPU memory.
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" scripts/qwenimage/train.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=2e-05 \
+  --lr_scheduler="constant_with_warmup" \
+  --lr_warmup_steps=100 \
+  --seed=42 \
+  --output_dir="output_dir" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --enable_bucket \
+  --uniform_sampling \
+  --trainable_modules "."
+```
+
+With deepspeed zero-2:
+
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/qwenimage/train.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --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 \
+  --enable_bucket \
+  --uniform_sampling \
+  --trainable_modules "."
+```
+
+Deepspeed zero-3:
+
+After training, you can use the following command to get the final model:
+```sh
+python scripts/zero_to_bf16.py output_dir/checkpoint-{our-num-steps} output_dir/checkpoint-{your-num-steps}-outputs --max_shard_size 80GB --safe_serialization
+```
+
+Training shell command is as follows:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --zero_stage 3 --zero3_save_16bit_model true --zero3_init_flag true --use_deepspeed --deepspeed_config_file config/zero_stage3_config.json --deepspeed_multinode_launcher standard scripts/qwenimage/train.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=2e-05 \
+  --lr_scheduler="constant_with_warmup" \
+  --lr_warmup_steps=100 \
+  --seed=42 \
+  --output_dir="output_dir" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --enable_bucket \
+  --uniform_sampling \
+  --trainable_modules "."
+```
+
+With FSDP:
+
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" --use_fsdp --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap=QwenImageTransformerBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False scripts/qwenimage/train.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --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 \
+  --enable_bucket \
+  --uniform_sampling \
+  --trainable_modules "."
+```
@@ -0,0 +1,153 @@
+## Lora Training Code
+
+We can choose whether to use deepspeed or fsdp in qwen-image, which can save a lot of video memory. 
+
+Some parameters in the sh file can be confusing, and they are explained in this document:
+
+- `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images at the center, but instead, it trains the entire images after grouping them into buckets based on resolution.
+- `random_hw_adapt` is used to enable automatic height and width scaling for images. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `512` as the minimum. 
+  - For example, when `random_hw_adapt` is enabled, `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`
+- `resume_from_checkpoint` is used to set the training should be resumed from a previous checkpoint. Use a path or `"latest"` to automatically select the last available checkpoint.
+
+Without deepspeed:
+
+Training qwen-image without DeepSpeed may result in insufficient GPU memory.
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" scripts/qwenimage/train_lora.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=1e-04 \
+  --seed=42 \
+  --output_dir="output_dir_lora" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --enable_bucket \
+  --uniform_sampling
+```
+
+With deepspeed zero-2:
+
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/qwenimage/train_lora.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=1e-04 \
+  --seed=42 \
+  --output_dir="output_dir_lora" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --enable_bucket \
+  --uniform_sampling
+```
+
+Deepspeed zero-3:
+
+After training, you can use the following command to get the final model:
+```sh
+python scripts/zero_to_bf16.py output_dir/checkpoint-{our-num-steps} output_dir/checkpoint-{your-num-steps}-outputs --max_shard_size 80GB --safe_serialization
+```
+
+Training shell command is as follows:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Qwen-Image"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --zero_stage 3 --zero3_save_16bit_model true --zero3_init_flag true --use_deepspeed --deepspeed_config_file config/zero_stage3_config.json --deepspeed_multinode_launcher standard scripts/qwenimage/train_lora.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=1e-04 \
+  --seed=42 \
+  --output_dir="output_dir_lora" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --enable_bucket \
+  --uniform_sampling
+```
+
+With FSDP:
+
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Wan2.2-Fun-A14B-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" --use_fsdp --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap=QwenImageTransformerBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False scripts/qwenimage/train_lora.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --train_batch_size=1 \
+  --image_sample_size=1024 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=1e-04 \
+  --seed=42 \
+  --output_dir="output_dir_lora" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --enable_bucket \
+  --uniform_sampling
+```