24 KiB
24 KiB
FlashHead-S2V Full Parameter Training Guide
This document provides a complete workflow for full parameter training of FlashHead-S2V (audio-driven talking head video generation model), including environment configuration, data preparation, distributed training, and inference testing.
Note
: FlashHead-S2V is an audio-driven talking head video generation model that requires both a reference image and an audio file to generate talking videos. The training data needs to include video, audio, and reference images.
Table of Contents
- 1. Environment Configuration
- 2. Data Preparation
- 3. Full Parameter Training
- 4. Inference Testing
- 5. Additional 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 GPU 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 audio-video training samples.
# Download official demo dataset
modelscope download --dataset PAI/X-Fun-Videos-Audios-Demo --local_dir ./datasets/X-Fun-Videos-Audios-Demo
2.2 Dataset Structure
📦 datasets/
├── 📂 my_dataset/
│ ├── 📂 train/
│ │ ├── 📄 video001.mp4
│ │ ├── 📄 video002.mp4
│ │ └── 📄 ...
│ ├── 📂 wav/
│ │ ├── 📄 audio001.wav
│ │ ├── 📄 audio002.wav
│ │ └── 📄 ...
│ └── 📄 metadata.json
2.3 metadata.json Format
⚠️ Important: FlashHead-S2V is an audio-driven talking head video generation model. Unlike regular video training, you must provide the
audio_pathfield in metadata.json.
Relative Path Format (example):
[
{
"file_path": "train/video001.mp4",
"audio_path": "wav/audio001.wav",
"text": "A person talking with natural expressions",
"type": "video",
"width": 512,
"height": 512
},
{
"file_path": "train/video002.mp4",
"audio_path": "wav/audio002.wav",
"text": "A speaker delivering a speech",
"type": "video",
"width": 512,
"height": 512
}
]
Absolute Path Format:
[
{
"file_path": "/mnt/data/videos/speaker.mp4",
"audio_path": "/mnt/data/wavs/speaker.wav",
"text": "A person talking with natural expressions",
"type": "video",
"width": 512,
"height": 512
}
]
Key Fields Description:
file_path: Video file path (relative or absolute)audio_path: Audio file path (FlashHead-S2V specific and required, main difference from regular video training)- Audio files are typically in
.wavformat - Path should correspond to
file_path, e.g.,train/video001.mp4corresponds towav/audio001.wav
- Audio files are typically in
text: Video description (English prompt, optional)type: Data type, fixed as"video"width/height: Video dimensions (recommended to provide for bucket training; if not provided, they will be automatically read during training, which may slow down training when data is stored on slow systems like OSS)- You can use
scripts/process_json_add_width_and_height.pyto add width and height fields to JSON files without these fields, supporting both images and videos - Usage:
python scripts/process_json_add_width_and_height.py --input_file datasets/X-Fun-Videos-Audios-Demo/metadata.json --output_file datasets/X-Fun-Videos-Audios-Demo/metadata_add_width_height.json
- You can use
Dataset Comparison: FlashHead-S2V vs Regular Video Training:
| Model Type | Required Fields | Audio Field |
|---|---|---|
| Regular Video (WAN, CogVideoX, etc.) | file_path, text, type |
❌ Not needed |
| FlashHead-S2V (Audio-Driven Generation) | file_path, audio_path, type |
✅ Required |
2.4 Relative vs Absolute Path Usage
Relative Paths:
If your data uses relative paths, configure the training script as follows:
export DATASET_NAME="datasets/X-Fun-Videos-Audios-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Audios-Demo/metadata_add_width_height.json"
Absolute Paths:
If your data uses absolute paths, configure the training script as follows:
export DATASET_NAME=""
export DATASET_META_NAME="/mnt/data/metadata_add_width_height.json"
💡 Recommendation: If the dataset is small and stored locally, use relative paths. If the dataset is stored on external storage (e.g., NAS, OSS) or shared across multiple machines, use absolute paths.
3. Full Parameter Training
3.1 Download Pretrained Model
# Create model directory
mkdir -p models/Diffusion_Transformer
# Download FlashHead official weights
modelscope download --model Soul-AILab/SoulX-FlashHead-1_3B --local_dir models/Diffusion_Transformer/SoulX-FlashHead-1_3B
# Download audio encoder (wav2vec2)
modelscope download --model AI-ModelScope/wav2vec2-base-960h --local_dir models/Diffusion_Transformer/wav2vec2-base-960h
3.2 Quick Start (DeepSpeed-Zero-2)
If you have downloaded the data as per 2.1 Quick Test Dataset and the weights as per 3.1 Download Pretrained Model, you can directly copy and run the quick start command.
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 lies in whether the model weights are sharded. If VRAM is insufficient when using multiple GPUs with DeepSpeed-Zero-2, you can switch to FSDP.
export MODEL_NAME="models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
export AUDIO_MODEL_NAME="models/Diffusion_Transformer/wav2vec2-base-960h"
export DATASET_NAME="datasets/X-Fun-Videos-Audios-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Audios-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/flashhead/train_s2v.py \
--config_path="config/wan2.1/wan_civitai.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--audio_encoder_path=$AUDIO_MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--video_sample_size=512 \
--token_sample_size=512 \
--video_sample_stride=1 \
--video_sample_n_frames=33 \
--train_batch_size=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_flashhead" \
--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 \
--trainable_modules "."
3.3 Common Training Parameters
Key Parameter Descriptions:
| Parameter | Description | Example Value |
|---|---|---|
--config_path |
Model configuration file path | config/wan2.1/wan_civitai.yaml |
--pretrained_model_name_or_path |
Path to pretrained model | models/Diffusion_Transformer/SoulX-FlashHead-1_3B |
--audio_encoder_path |
Audio encoder path (FlashHead-S2V specific) | models/Diffusion_Transformer/wav2vec2-base-960h |
--train_data_dir |
Training data directory | datasets/X-Fun-Videos-Audios-Demo/ |
--train_data_meta |
Training data metadata file | datasets/X-Fun-Videos-Audios-Demo/metadata_add_width_height.json |
--train_batch_size |
Samples per batch | 1 |
--video_sample_size |
Maximum video resolution for training | 512 |
--token_sample_size |
Token length sampling size | 512 |
--video_sample_stride |
Frame sampling stride | 1 |
--video_sample_n_frames |
Number of frames to sample | 33 |
--gradient_accumulation_steps |
Gradient accumulation steps (equivalent to larger batch) | 1 |
--dataloader_num_workers |
DataLoader subprocesses | 8 |
--num_train_epochs |
Number of training epochs | 100 |
--checkpointing_steps |
Save checkpoint every N steps | 50 |
--learning_rate |
Initial learning rate | 2e-05 |
--lr_scheduler |
Learning rate scheduler | constant_with_warmup |
--lr_warmup_steps |
Learning rate warmup steps | 100 |
--seed |
Random seed | 42 |
--output_dir |
Output directory | output_dir_flashhead |
--gradient_checkpointing |
Enable activation checkpointing | - |
--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 |
--random_hw_adapt |
Auto-scale videos to random size in range [512, video_sample_size] |
- |
--training_with_video_token_length |
Train based on token length instead of fixed resolution | - |
--enable_bucket |
Enable bucket training: trains entire videos grouped by resolution without center cropping | - |
--uniform_sampling |
Uniform timestep sampling | - |
--low_vram |
Enable low VRAM optimizations | - |
--resume_from_checkpoint |
Resume training from checkpoint path, use "latest" to auto-select latest |
None |
--trainable_modules |
Trainable modules ("." means all modules, "audio" means audio modules only) |
"." |
--validation_steps |
Execute validation every N steps | 100 |
--validation_epochs |
Execute validation every N epochs | 500 |
--validation_image_paths |
Validation reference image paths, use multiple space-separated paths | "asset/8.png" |
--validation_audio_paths |
Validation audio paths, use multiple space-separated paths | "asset/talk.wav" |
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 |
Execute validation every N steps | 100 |
--validation_epochs |
Execute validation every N epochs | 500 |
--validation_image_paths |
Reference image paths for validation, use multiple space-separated paths | Multiple space-separated image paths |
--validation_audio_paths |
Audio paths for validation, use multiple space-separated paths | Multiple space-separated audio paths |
Example:
--validation_image_paths="asset/9.png" \
--validation_audio_paths="asset/talk.wav" \
--validation_steps=100 \
--validation_epochs=500
Notes:
- Validation videos will be saved to the
output_dir/sampledirectory - The number of image and audio paths must correspond one-to-one
- For multi-path validation, use:
--validation_image_paths "image1.png" "image2.png" --validation_audio_paths "audio1.wav" "audio2.wav"
3.5 Training with FSDP
If VRAM is insufficient when using multiple GPUs with DeepSpeed-Zero-2, you can switch to FSDP.
export MODEL_NAME="models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
export AUDIO_MODEL_NAME="models/Diffusion_Transformer/wav2vec2-base-960h"
export DATASET_NAME="datasets/X-Fun-Videos-Audios-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Audios-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 FlashHeadAttentionBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False scripts/flashhead/train_s2v.py \
--config_path="config/wan2.1/wan_civitai.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--audio_encoder_path=$AUDIO_MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--video_sample_size=512 \
--token_sample_size=512 \
--video_sample_stride=1 \
--video_sample_n_frames=33 \
--train_batch_size=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_flashhead" \
--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 \
--trainable_modules "."
3.6 Training Without DeepSpeed or FSDP
This approach is not recommended as it lacks VRAM-saving backends and may easily cause out-of-memory errors. This is provided for reference only.
export MODEL_NAME="models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
export AUDIO_MODEL_NAME="models/Diffusion_Transformer/wav2vec2-base-960h"
export DATASET_NAME="datasets/X-Fun-Videos-Audios-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Audios-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/flashhead/train_s2v.py \
--config_path="config/wan2.1/wan_civitai.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--audio_encoder_path=$AUDIO_MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--video_sample_size=512 \
--token_sample_size=512 \
--video_sample_stride=1 \
--video_sample_n_frames=33 \
--train_batch_size=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_flashhead" \
--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 \
--trainable_modules "."
3.7 Multi-Machine Distributed Training
Suitable for: Ultra-large-scale datasets, faster training speed
3.7.1 Environment Configuration
Assuming 2 machines with 8 GPUs each:
Machine 0 (Master):
export MODEL_NAME="models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
export AUDIO_MODEL_NAME="models/Diffusion_Transformer/wav2vec2-base-960h"
export DATASET_NAME="datasets/X-Fun-Videos-Audios-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Audios-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=$RANK --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/flashhead/train_s2v.py \
--config_path="config/wan2.1/wan_civitai.yaml" \
--pretrained_model_name_or_path=$MODEL_NAME \
--audio_encoder_path=$AUDIO_MODEL_NAME \
--train_data_dir=$DATASET_NAME \
--train_data_meta=$DATASET_META_NAME \
--video_sample_size=512 \
--token_sample_size=512 \
--video_sample_stride=1 \
--video_sample_n_frames=33 \
--train_batch_size=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_flashhead" \
--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 \
--trainable_modules "."
Machine 1 (Worker):
export MODEL_NAME="models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
export AUDIO_MODEL_NAME="models/Diffusion_Transformer/wav2vec2-base-960h"
export DATASET_NAME="datasets/X-Fun-Videos-Audios-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Videos-Audios-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 paths (NFS/shared storage)
4. Inference Testing
4.1 Inference Parameters
Key Parameter Descriptions:
| Parameter | Description | Example Value |
|---|---|---|
GPU_memory_mode |
GPU memory mode, see table below for options | model_full_load |
ulysses_degree |
Head dimension parallelization degree, 1 for single GPU | 1 |
ring_degree |
Sequence dimension parallelization degree, 1 for single GPU | 1 |
fsdp_dit |
Use FSDP for Transformer in multi-GPU inference to save VRAM | False |
compile_dit |
Compile Transformer to accelerate inference (effective at fixed resolution) | False |
config_path |
Model configuration file path | config/wan2.1/wan_civitai.yaml |
model_name |
Model path | models/Diffusion_Transformer/SoulX-FlashHead-1_3B |
model_name_audio |
Audio encoder path | models/Diffusion_Transformer/wav2vec2-base-960h |
sampler_name |
Sampler type: Flow, Flow_Unipc, Flow_DPM++ |
Flow |
shift |
Sampler shift parameter | 5.0 |
transformer_path |
Path to trained Transformer weights | None |
vae_path |
Path to trained VAE weights | None |
lora_path |
LoRA weights path | None |
sample_size |
Generated video resolution [height, width] |
[512, 512] |
segment_frame_length |
Number of frames to generate | 33 |
fps |
Frames per second | 25 |
weight_dtype |
Model weight precision, use torch.float16 for GPUs without bf16 support |
torch.bfloat16 |
ref_image |
Reference image path | "asset/9.png" |
audio_path |
Input audio path | "asset/talk.wav" |
audio_guide_scale |
Audio guidance strength | 1.0 |
seed |
Random seed for reproducibility | 42 |
num_inference_steps |
Inference steps | 4 |
lora_weight |
LoRA weight strength | 0.55 |
save_path |
Generated video save path | samples/flashhead-videos |
max_frames_num |
Maximum frames to generate | 500 |
color_correction_strength |
Color correction strength | 1.0 |
use_apg |
Whether to use APG (Anti-Phase Guidance) | False |
apg_momentum |
APG momentum parameter | 0.5 |
apg_norm_threshold |
APG norm threshold | 1.0 |
audio_encode_mode |
Audio encoding mode: once or chunked |
once |
GPU Memory Mode Descriptions:
| Mode | Description | VRAM Usage |
|---|---|---|
model_full_load |
Load entire model 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 group offload between CPU/CUDA | Low |
sequential_cpu_offload |
Offload each layer individually (slowest) | Lowest |
4.2 Single GPU Inference
Run single GPU inference with:
python examples/flashhead/predict_s2v.py
Edit examples/flashhead/predict_s2v.py according to your needs. For first-time inference, focus on these parameters. For other parameters, see the Inference Parameters section above.
# Choose based on your GPU VRAM
GPU_memory_mode = "model_full_load"
# Model configuration file path
config_path = "config/wan2.1/wan_civitai.yaml"
# Your actual model path
model_name = "models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
# Audio encoder path
model_name_audio = "models/Diffusion_Transformer/wav2vec2-base-960h"
# Trained weights path, e.g. "output_dir_flashhead/checkpoint-xxx/diffusion_pytorch_model.safetensors"
transformer_path = None
# Reference image path
ref_image = "asset/9.png"
# Input audio path
audio_path = "asset/talk.wav"
# ...
4.3 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/flashhead/predict_s2v.py:
# Ensure ulysses_degree × ring_degree = number of GPUs
# For example, using 2 GPUs:
ulysses_degree = 2 # Head dimension parallelization
ring_degree = 1 # Sequence dimension parallelization
Configuration Principles:
ulysses_degreemust evenly divide the model's number of headsring_degreesplits on sequence dimension, affecting communication overhead; avoid using it when heads can be divided
Example Configurations:
| GPU Count | ulysses_degree | ring_degree | Description |
|---|---|---|---|
| 1 | 1 | 1 | Single GPU |
| 4 | 4 | 1 | Head parallelization |
| 8 | 2 | 4 | Hybrid parallelization |
| 8 | 8 | 1 | Head parallelization |
Run Multi-GPU Inference
torchrun --nproc-per-node=2 examples/flashhead/predict_s2v.py
5. Additional Resources
- Official GitHub: https://github.com/aigc-apps/VideoX-Fun