Update control training code (#130)

2025-03-27 11:52:15 +08:00
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+## Training Code
+
+The default training commands for the different versions are as follows:
+
+We can choose whether to use deep speed in Wan-Fun, which can save a lot of video memory. 
+
+The metadata_control.json is a little different from normal json in Wan-Fun, you need to add a control_file_path, and [DWPose](https://github.com/IDEA-Research/DWPose) is suggested as tool to generate control file.
+
+```json
+[
+    {
+      "file_path": "train/00000001.mp4",
+      "control_file_path": "control/00000001.mp4",
+      "text": "A group of young men in suits and sunglasses are walking down a city street.",
+      "type": "video"
+    },
+    {
+      "file_path": "train/00000002.jpg",
+      "control_file_path": "control/00000002.jpg",
+      "text": "A group of young men in suits and sunglasses are walking down a city street.",
+      "type": "image"
+    },
+    .....
+]
+```
+
+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 and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution.
+- `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts.
+- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum.
+  - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the resolution of video inputs for training is `512x512x49` to `1024x1024x49`.
+  - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49`.
+- `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum.
+  - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=1024`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x49`.
+  - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x9`.
+  - The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the `token_sample_size = 512`.
+    - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512).
+    - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768).
+    - At 1024x1024 resolution, the number of video frames is 9 (~= 512 * 512 * 49 / 1024 / 1024).
+    - These resolutions combined with their corresponding lengths allow the model to generate videos of different sizes.
+- `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.
+
+Wan-Fun without deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-Fun-14B-Control"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+export NCCL_IB_DISABLE=1
+export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
+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=$DATASET_META_NAME \
+  --image_sample_size=1024 \
+  --video_sample_size=256 \
+  --token_sample_size=512 \
+  --video_sample_stride=2 \
+  --video_sample_n_frames=81 \
+  --train_batch_size=1 \
+  --video_repeat=1 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=2e-05 \
+  --lr_scheduler="constant_with_warmup" \
+  --lr_warmup_steps=100 \
+  --seed=42 \
+  --output_dir="output_dir" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --random_hw_adapt \
+  --training_with_video_token_length \
+  --enable_bucket \
+  --uniform_sampling \
+  --low_vram \
+  --train_mode="control_object" \
+  --control_ref_image="first_frame" \
+  --trainable_modules "."
+```
+
+Wan-Fun with deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-Fun-14B-Control"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+export NCCL_IB_DISABLE=1
+export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
+accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/wan2.1_fun/train_control.py \
+  --config_path="config/wan2.1/wan_civitai.yaml" \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --image_sample_size=1024 \
+  --video_sample_size=256 \
+  --token_sample_size=512 \
+  --video_sample_stride=2 \
+  --video_sample_n_frames=81 \
+  --train_batch_size=1 \
+  --video_repeat=1 \
+  --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 \
+  --random_hw_adapt \
+  --training_with_video_token_length \
+  --enable_bucket \
+  --uniform_sampling \
+  --low_vram \
+  --use_deepspeed \
+  --train_mode="control_object" \
+  --control_ref_image="first_frame" \
+  --trainable_modules "."
+```
+
+Wan T2V with deepspeed zero-3:
+
+Wan with DeepSpeed Zero-3 is suitable for 14B Wan at high resolutions. 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/Wan2.1-Fun-14B-Control"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+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_stage2.1_config.json --deepspeed_multinode_launcherr standard scripts/wan2.1_fun/train_control.py \
+  --config_path="config/wan2.1_fun/wan_civitai.yaml" \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --image_sample_size=1024 \
+  --video_sample_size=256 \
+  --token_sample_size=512 \
+  --video_sample_stride=3 \
+  --video_sample_n_frames=81 \
+  --train_batch_size=1 \
+  --video_repeat=1 \
+  --gradient_accumulation_steps=1 \
+  --dataloader_num_workers=8 \
+  --num_train_epochs=100 \
+  --checkpointing_steps=50 \
+  --learning_rate=1e-04 \
+  --seed=42 \
+  --output_dir="output_dir" \
+  --gradient_checkpointing \
+  --mixed_precision="bf16" \
+  --adam_weight_decay=3e-2 \
+  --adam_epsilon=1e-10 \
+  --vae_mini_batch=1 \
+  --max_grad_norm=0.05 \
+  --random_hw_adapt \
+  --training_with_video_token_length \
+  --enable_bucket \
+  --uniform_sampling \
+  --use_deepspeed \
+  --train_mode="inpaint" \
+  --low_vram
+```
@@ -0,0 +1,43 @@
+export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-Fun-14B-Control"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+export NCCL_IB_DISABLE=1
+export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
+accelerate launch --mixed_precision="bf16" scripts/wan2.1_fun/train_control.py \
+  --config_path="config/wan2.1/wan_civitai.yaml" \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATASET_NAME \
+  --train_data_meta=$DATASET_META_NAME \
+  --image_sample_size=1024 \
+  --video_sample_size=256 \
+  --token_sample_size=512 \
+  --video_sample_stride=2 \
+  --video_sample_n_frames=81 \
+  --train_batch_size=1 \
+  --video_repeat=1 \
+  --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 \
+  --random_hw_adapt \
+  --training_with_video_token_length \
+  --enable_bucket \
+  --uniform_sampling \
+  --low_vram \
+  --train_mode="control_object" \
+  --control_ref_image="first_frame" \
+  --trainable_modules "."