Wan2.1 LoRA Fine-tuning Training Guide

This document provides a complete workflow for Wan2.1 LoRA fine-tuning training, including environment configuration, data preparation, various distributed training strategies, and inference testing.

Note: Wan2.1 is a video generation model that supports text-to-video (T2V) and image-to-video (I2V). This guide covers the LoRA fine-tuning training workflow, suitable for fine-tuning scenarios with custom datasets.

1. Environment Configuration
2. Data Preparation
3. LoRA Training
4. Inference Testing
5. More Resources

1. Environment Configuration

Method 1: Using requirements.txt

pip install -r requirements.txt

Method 2: Manual Dependency Installation

pip install Pillow einops safetensors timm tomesd librosa "torch>=2.1.2" torchdiffeq torchsde decord datasets numpy scikit-image
pip install omegaconf SentencePiece imageio[ffmpeg] imageio[pyav] tensorboard beautifulsoup4 ftfy func_timeout onnxruntime
pip install "peft>=0.17.0" "accelerate>=0.25.0" "gradio>=3.41.2" "diffusers>=0.30.1" "transformers>=4.46.2"
pip install yunchang xfuser modelscope openpyxl
pip uninstall opencv-python opencv-contrib-python opencv-python-headless -y
pip install opencv-python-headless
pip install deepspeed==0.17.0 numpy==1.26.4

Method 3: Using Docker

When using Docker, please ensure that the graphics card driver and CUDA environment are correctly installed on your machine, then execute the following commands:

# pull image
docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun

# enter image
docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun

2. Data Preparation

2.1 Quick Test Dataset

We provide a test dataset containing several training samples.

# Download official example dataset
modelscope download --dataset PAI/X-Fun-Videos-Demo --local_dir ./datasets/X-Fun-Videos-Demo

2.2 Dataset Structure

📦 datasets/
├── 📂 my_dataset/
│   ├── 📂 train/
│   │   ├── 📄 video001.mp4
│   │   ├── 📄 video002.mp4
│   │   └── 📄 ...
│   └── 📄 metadata.json

2.3 metadata.json Format

Relative Path Format (example format):

[
  {
    "file_path": "train/video001.mp4",
    "text": "A beautiful sunset over the ocean, golden hour lighting",
    "type": "video",
    "width": 1024,
    "height": 1024
  },
  {
    "file_path": "train/video002.mp4",
    "text": "A person walking through a forest, cinematic view",
    "type": "video",
    "width": 1328,
    "height": 1328
  }
]

Absolute Path Format:

[
  {
    "file_path": "/mnt/data/videos/sunset.mp4",
    "text": "A beautiful sunset over the ocean",
    "type": "video",
    "width": 1024,
    "height": 1024
  }
]

Key Field Descriptions:

file_path: Video path (relative or absolute path)
text: Video description (English prompt)
type: Data type, fixed as "video"
width / height: Video width and height (highly recommended to provide, used for bucket training. If not provided, they will be automatically read during training, which may affect training speed when data is stored on slow systems like OSS).
- You can use scripts/process_json_add_width_and_height.py to extract width and height fields from JSON files without them, supporting both images and videos.
- Usage: python scripts/process_json_add_width_and_height.py --input_file datasets/X-Fun-Videos-Demo/metadata.json --output_file datasets/X-Fun-Videos-Demo/metadata_add_width_height.json.

2.4 Relative Path vs Absolute Path Usage

Relative Path:

If your data uses relative paths, set in the training script:

export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.json"

Absolute Path:

If your data uses absolute paths, set in the training script:

export DATASET_NAME=""
export DATASET_META_NAME="/mnt/data/metadata_add_width_height.json"

💡 Suggestion: If the dataset is small and stored locally, relative paths are recommended. If the dataset is stored on external storage (such as NAS, OSS) or shared across multiple machines, absolute paths are recommended.

3. LoRA Training

3.1 Download Pre-trained Model

# Create model directory
mkdir -p models/Diffusion_Transformer

# Download Wan2.1 official weights
# T2V model (text-to-video)
modelscope download --model Wan-AI/Wan2.1-T2V-1.3B --local_dir models/Diffusion_Transformer/Wan2.1-T2V-1.3B
# or I2V model (image-to-video)
# modelscope download --model Wan-AI/Wan2.1-I2V-14B-480P --local_dir models/Diffusion_Transformer/Wan2.1-I2V-14B-480P

3.2 Quick Start (DeepSpeed-Zero-2)

After following 2.1 Quick Test Dataset and 3.1 Download Pre-trained Model, you can directly copy the quick start command to launch training.

DeepSpeed-Zero-2 and FSDP are recommended for training. Here we use DeepSpeed-Zero-2 as an example.

The difference between DeepSpeed-Zero-2 and FSDP in this repository lies in whether model weights are sharded. If you encounter insufficient GPU memory when using multiple GPUs with DeepSpeed-Zero-2, you can switch to FSDP for training.

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.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/wan2.1/train_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=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=1e-04 \
  --seed=42 \
  --output_dir="output_dir_wan2.1_lora" \
  --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 \
  --rank=64 \
  --network_alpha=32 \
  --target_name="q,k,v,ffn.0,ffn.2" \
  --use_peft_lora \
  --low_vram \
  --train_mode="normal"

3.3 LoRA-specific Parameter Explanation

Key LoRA Parameters:

Parameter	Description	Example Value
`--config_path`	Configuration file path	`config/wan2.1/wan_civitai.yaml`
`--pretrained_model_name_or_path`	Pre-trained model path	`models/Diffusion_Transformer/Wan2.1-T2V-1.3B`
`--train_data_dir`	Training data directory	`datasets/X-Fun-Videos-Demo/`
`--train_data_meta`	Training data metadata file	`datasets/X-Fun-Videos-Demo/metadata_add_width_height.json`
`--train_batch_size`	Number of samples per batch	1
`--image_sample_size`	Maximum training resolution for images	640
`--video_sample_size`	Maximum training resolution for videos	640
`--token_sample_size`	Token sampling size	640
`--video_sample_stride`	Video sampling stride	2
`--video_sample_n_frames`	Number of video frames to sample	81
`--gradient_accumulation_steps`	Gradient accumulation steps (effectively increases batch size)	1
`--dataloader_num_workers`	Number of DataLoader subprocesses	8
`--num_train_epochs`	Number of training epochs	100
`--checkpointing_steps`	Save checkpoint every N steps	50
`--learning_rate`	Initial learning rate (recommended for LoRA)	1e-04
`--lr_scheduler`	Learning rate scheduler	`constant_with_warmup`
`--lr_warmup_steps`	Learning rate warmup steps	100
`--seed`	Random seed (for reproducible training)	42
`--output_dir`	Output directory	`output_dir_wan2.1_lora`
`--gradient_checkpointing`	Activation recomputation	-
`--mixed_precision`	Mixed precision: `fp16/bf16`	`bf16`
`--adam_weight_decay`	AdamW weight decay	3e-2
`--adam_epsilon`	AdamW epsilon value	1e-10
`--vae_mini_batch`	Mini-batch size for VAE encoding	1
`--max_grad_norm`	Gradient clipping threshold	0.05
`--enable_bucket`	Enable bucket training, no cropping, group by resolution	-
`--random_hw_adapt`	Auto-scale images/videos to random sizes within `[min_size, max_size]`	-
`--training_with_video_token_length`	Train based on token length, supports arbitrary resolutions	-
`--uniform_sampling`	Uniform timestep sampling (recommended)	-
`--low_vram`	Low VRAM mode	-
`--train_mode`	Training mode: `normal` (text-to-video) or `i2v` (image-to-video)	`normal`
`--resume_from_checkpoint`	Resume training path, use `"latest"` to auto-select latest checkpoint	None
`--rank`	LoRA update matrix dimension (higher rank = stronger expression but more memory)	64
`--network_alpha`	LoRA update matrix scaling factor (usually set to half of rank or same)	32
`--target_name`	Components/modules to apply LoRA, comma-separated	`q,k,v,ffn.0,ffn.2`
`--use_peft_lora`	Use PEFT module to add LoRA (more memory-efficient)	-
`--validation_steps`	Run validation every N steps	100
`--validation_epochs`	Run validation every N epochs	100
`--validation_prompts`	Prompts for validating video generation	`"A brown dog shaking head..."`
`--validation_paths`	Reference image paths for I2V validation (i2v mode only)	`"asset/1.png"`

Sample Size Configuration Guide:

video_sample_size represents the resolution size of videos; when random_hw_adapt is True, it represents the minimum value between video and image resolutions.
image_sample_size represents the resolution size of images; when random_hw_adapt is True, it represents the maximum value between video and image resolutions.
token_sample_size represents the resolution corresponding to the maximum token length when training_with_video_token_length is True.
Due to potential confusion in configuration, if you don't require arbitrary resolution for finetuning, it is recommended to set video_sample_size, image_sample_size, and token_sample_size to the same fixed value, such as (320, 480, 512, 640, 960).
- All set to 320 represents 240P.
- All set to 480 represents 320P.
- All set to 640 represents 480P.
- All set to 960 represents 720P.

Token Length Training Explanation:

When training_with_video_token_length is enabled, the model trains based on token length.
For example: A video with 512x512 resolution and 49 frames has a token length of 13,312, requiring 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 frame counts allow the model to generate videos of different sizes.

3.4 Training Validation

You can configure validation parameters to periodically generate test videos during training, allowing you to monitor training progress and model quality.

Validation Parameters:

Parameter	Description	Recommended Value
`--validation_steps`	Run validation every N steps	100
`--validation_epochs`	Run validation every N epochs	100
`--validation_prompts`	Prompts for video generation validation	English prompts
`--validation_paths`	Reference image paths for I2V validation (i2v/inpaint mode only)	`"asset/1.png"`

Normal Mode Example (T2V validation):

  --validation_steps=100 \
  --validation_epochs=100 \
  --validation_prompts="A brown dog shaking its head, sitting on a light-colored sofa in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. The soft, warm lighting in the room creates a comfortable atmosphere."

I2V/Inpaint Mode Example (I2V validation):

  --validation_paths "asset/1.png" \
  --validation_steps=100 \
  --validation_epochs=100 \
  --validation_prompts="A brown dog shaking its head, sitting on a light-colored sofa in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. The soft, warm lighting in the room creates a comfortable atmosphere."

Notes:

Validation videos are saved to the output_dir directory
Multi-prompt validation format: --validation_prompts "prompt1" "prompt2" "prompt3"
i2v or inpaint mode must provide the --validation_paths parameter

3.5 Training with FSDP

If you encounter insufficient GPU memory when using multiple GPUs with DeepSpeed-Zero-2, you can switch to FSDP for training.

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.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=WanAttentionBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False scripts/wan2.1/train_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=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=1e-04 \
  --seed=42 \
  --output_dir="output_dir_wan2.1_lora" \
  --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 \
  --rank=64 \
  --network_alpha=32 \
  --target_name="q,k,v,ffn.0,ffn.2" \
  --use_peft_lora \
  --low_vram \
  --train_mode="normal"

3.6 Other Backends

3.6.1 Training with DeepSpeed-Zero-3

DeepSpeed Zero-3 is not highly recommended at the moment. In this repository, using FSDP has fewer errors and is more stable.

DeepSpeed Zero-3 is suitable for high-resolution 14B Wan. After training, you can use the following command to obtain the final model:

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:

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.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/wan2.1/train_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=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=1e-04 \
  --seed=42 \
  --output_dir="output_dir_wan2.1_lora" \
  --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 \
  --rank=64 \
  --network_alpha=32 \
  --target_name="q,k,v,ffn.0,ffn.2" \
  --use_peft_lora \
  --low_vram \
  --train_mode="normal"

3.6.2 Training without DeepSpeed and FSDP

This approach is not recommended, as without memory-saving backends, it easily causes out-of-memory errors. Only provided here for reference.

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.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/wan2.1/train_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=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=1e-04 \
  --seed=42 \
  --output_dir="output_dir_wan2.1_lora" \
  --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 \
  --rank=64 \
  --network_alpha=32 \
  --target_name="q,k,v,ffn.0,ffn.2" \
  --use_peft_lora \
  --low_vram \
  --train_mode="normal"

3.7 Multi-machine Distributed Training

Suitable for: Ultra-large-scale datasets, faster training speed

3.7.1 Environment Configuration

Assuming 2 machines, each with 8 GPUs:

Machine 0 (Master):

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.json"
export MASTER_ADDR="192.168.1.100"  # Master machine IP
export MASTER_PORT=10086
export WORLD_SIZE=2                  # Total number of machines
export NUM_PROCESS=16                # Total processes = machines × 8
export RANK=0                        # Current machine rank (0 or 1)
# 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" --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --num_machines=$WORLD_SIZE --num_processes=$NUM_PROCESS --machine_rank=$RANK --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/wan2.1/train_lora.py \
  --config_path="config/wan2.1/wan_civitai.yaml" \
  --pretrained_model_name_or_path=$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=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=1e-04 \
  --seed=42 \
  --output_dir="output_dir_wan2.1_lora" \
  --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 \
  --rank=64 \
  --network_alpha=32 \
  --target_name="q,k,v,ffn.0,ffn.2" \
  --use_peft_lora \
  --low_vram \
  --train_mode="normal"

Machine 1 (Worker):

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
export DATASET_NAME="datasets/X-Fun-Videos-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Demo/metadata_add_width_height.json"
export MASTER_ADDR="192.168.1.100"  # Same as Master
export MASTER_PORT=10086
export WORLD_SIZE=2
export NUM_PROCESS=16
export RANK=1  # Note: this is 1
# 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

# Use the same accelerate launch command as Machine 0

3.7.2 Multi-machine Training Notes

Network Requirements:
- RDMA/InfiniBand recommended (high performance)
- Without RDMA, add environment variables:
```
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
```
Data Synchronization: All machines must be able to access the same data path (NFS/shared storage)

4. Inference Testing

4.1 Inference Parameter Explanation

Key Parameters:

Parameter	Description	Example Value
`GPU_memory_mode`	Memory management mode, see table below for options	`model_group_offload`
`ulysses_degree`	Head dimension parallelism degree, 1 for single GPU	1
`ring_degree`	Sequence dimension parallelism degree, 1 for single GPU	1
`fsdp_dit`	Use FSDP for Transformer during multi-GPU inference to save memory	`False`
`fsdp_text_encoder`	Use FSDP for text encoder during multi-GPU inference	`True`
`compile_dit`	Compile Transformer for faster inference (effective for fixed resolution)	`False`
`model_name`	Model path	`models/Diffusion_Transformer/Wan2.1-T2V-1.3B`
`sampler_name`	Sampler type: `Flow`, `Flow_Unipc`, `Flow_DPM++`	`Flow_Unipc`
`transformer_path`	Path to load trained Transformer weights	`None`
`vae_path`	Path to load trained VAE weights	`None`
`lora_path`	LoRA weights path	`None`
`sample_size`	Generated video resolution `[height, width]`	`[480, 832]` or `[832, 480]`
`video_length`	Number of video frames	`81`
`fps`	Frames per second	`16`
`weight_dtype`	Model weight precision, use `torch.float16` for GPUs without bf16 support	`torch.bfloat16`
`validation_image_start`	Reference image path for image-to-video (I2V mode)	`"asset/1.png"`
`prompt`	Positive prompt describing generated content	`"A brown dog shaking head..."`
`negative_prompt`	Negative prompt to avoid certain content	`"low resolution, low quality..."`
`guidance_scale`	Guidance strength	6.0
`seed`	Random seed for reproducibility	43
`num_inference_steps`	Number of inference steps	50
`lora_weight`	LoRA weight strength	0.55
`save_path`	Path to save generated videos	`samples/wan-videos-i2v` or `samples/wan-videos-t2v`

Memory Management Modes:

Mode	Description	Memory Usage
`model_full_load`	Entire model loaded to GPU	Highest
`model_full_load_and_qfloat8`	Full load + FP8 quantization	High
`model_cpu_offload`	Offload model to CPU after use	Medium
`model_cpu_offload_and_qfloat8`	CPU offload + FP8 quantization	Medium-Low
`model_group_offload`	Layer groups switch between CPU/CUDA	Low
`sequential_cpu_offload`	Layer-by-layer offload (slowest)	Lowest

4.2 Text-to-Video (T2V) Inference

Run the following command for single-GPU inference:

python examples/wan2.1/predict_t2v.py

Modify examples/wan2.1/predict_t2v.py according to your needs. For first-time inference, focus on the parameters below. If you're interested in other parameters, refer to the inference parameter explanation above.

# Choose based on GPU memory
GPU_memory_mode = "sequential_cpu_offload"
# Based on actual model path
model_name = "models/Diffusion_Transformer/Wan2.1-T2V-1.3B"  
# Path to trained weights, e.g., "output_dir_wan2.1_lora/checkpoint-xxx/lora_weights.safetensors"
lora_path = None
# LoRA weight strength
lora_weight = 0.55
# Write based on generated content
prompt = "A brown dog shaking its head, sitting on a light-colored sofa in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. The soft, warm lighting in the room creates a comfortable atmosphere."  
# ...

4.3 Image-to-Video (I2V) Inference

Run the following command for single-GPU inference:

python examples/wan2.1/predict_i2v.py

Modify examples/wan2.1/predict_i2v.py according to your needs. For first-time inference, focus on the parameters below. If you're interested in other parameters, refer to the inference parameter explanation above.

# Choose based on GPU memory
GPU_memory_mode = "sequential_cpu_offload"
# Based on actual model path
model_name = "models/Diffusion_Transformer/Wan2.1-I2V-14B-480P"  
# LoRA weights path, e.g., "output_dir_wan2.1_lora/checkpoint-xxx/lora_weights.safetensors"
lora_path = None
# LoRA weight strength
lora_weight = 0.55
# Starting image for image-to-video
validation_image_start = "asset/1.png"
# Write based on generated content
prompt = "A brown dog shaking its head, sitting on a light-colored sofa in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. The soft, warm lighting in the room creates a comfortable atmosphere."  
# ...

4.4 Multi-GPU Parallel Inference

Suitable for: High-resolution generation, accelerated inference

Install Parallel Inference Dependencies

pip install xfuser==0.4.2 yunchang==0.6.2

Configure Parallel Strategy

Edit examples/wan2.1/predict_t2v.py or examples/wan2.1/predict_i2v.py:

# Ensure ulysses_degree × ring_degree = number of GPUs used
# For example, using 2 GPUs:
ulysses_degree = 2  # Head dimension parallelism
ring_degree = 1     # Sequence dimension parallelism

Configuration Principles:

ulysses_degree must evenly divide the model's head count
ring_degree splits along the sequence dimension, which affects communication overhead. Avoid using it when heads can be evenly divided.

Configuration Examples:

GPU Count	ulysses_degree	ring_degree	Description
1	1	1	Single GPU
4	4	1	Head parallelism
8	8	1	Head parallelism
8	4	2	Hybrid parallelism

Run Multi-GPU Inference

torchrun --nproc-per-node=2 examples/wan2.1/predict_t2v.py

5. More Resources

Official GitHub: https://github.com/aigc-apps/VideoX-Fun

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Wan2.1 LoRA Fine-tuning Training Guide

Table of Contents

1. Environment Configuration

2. Data Preparation

2.1 Quick Test Dataset

2.2 Dataset Structure

2.3 metadata.json Format

2.4 Relative Path vs Absolute Path Usage

3. LoRA Training

3.1 Download Pre-trained Model

3.2 Quick Start (DeepSpeed-Zero-2)

3.3 LoRA-specific Parameter Explanation

3.4 Training Validation

3.5 Training with FSDP

3.6 Other Backends

3.6.1 Training with DeepSpeed-Zero-3

3.6.2 Training without DeepSpeed and FSDP

3.7 Multi-machine Distributed Training

3.7.1 Environment Configuration

3.7.2 Multi-machine Training Notes

4. Inference Testing

4.1 Inference Parameter Explanation

4.2 Text-to-Video (T2V) Inference

4.3 Image-to-Video (I2V) Inference

4.4 Multi-GPU Parallel Inference

Install Parallel Inference Dependencies

Configure Parallel Strategy

Run Multi-GPU Inference

5. More Resources

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Wan2.1 LoRA Fine-tuning Training Guide

Table of Contents

1. Environment Configuration

2. Data Preparation

2.1 Quick Test Dataset

2.2 Dataset Structure

2.3 metadata.json Format

2.4 Relative Path vs Absolute Path Usage

3. LoRA Training

3.1 Download Pre-trained Model

3.2 Quick Start (DeepSpeed-Zero-2)

3.3 LoRA-specific Parameter Explanation

3.4 Training Validation

3.5 Training with FSDP

3.6 Other Backends

3.6.1 Training with DeepSpeed-Zero-3

3.6.2 Training without DeepSpeed and FSDP

3.7 Multi-machine Distributed Training

3.7.1 Environment Configuration

3.7.2 Multi-machine Training Notes

4. Inference Testing

4.1 Inference Parameter Explanation

4.2 Text-to-Video (T2V) Inference

4.3 Image-to-Video (I2V) Inference

4.4 Multi-GPU Parallel Inference

Install Parallel Inference Dependencies

Configure Parallel Strategy

Run Multi-GPU Inference

5. More Resources