Wan 2.1 (Wanxian) Video LoRA Training Tutorial

Enter the world of AI video model training with Wan 2.1 (Alibaba Wanxiang) — a high-performance video generation model known for strong semantic understanding and physical law adherence. Learn to train custom camera movements, object dynamics, and character consistency for video generation.

Product Type: Digital Tutorial (HTML + Markdown)

Why Wan 2.1 for Video Training?
- Strong semantic understanding: Accurate interpretation of complex prompts
- Physical law adherence: Realistic motion and object interactions
- Dual model options: 1.3B (accessible) and 14B (high quality)
- ComfyUI integration: Best-in-class workflow support
- Alibaba backing: Continuous development and community support

Tutorial Structure (5 Chapters):

【Chapter 1: Hardware & Environment】
- GPU requirements: RTX 3090/4090 (24GB) minimum, A100/H100 (80GB) optimal
- System RAM: 64GB+ recommended
- Software stack: Wan-Factory, Diffusers-Wan, PyTorch 2.4+, FFmpeg
- DeepSpeed Zero-2 / CPU Offload for consumer GPUs

【Chapter 2: Dataset Preparation (Video Dataset)】
- Video clip processing: 2-4 second clips, 32-64 frames
- Resolution: 832x480 (480p) for 24GB VRAM, 1280x720 (720p) for higher
- Video captioning with PLLaVA/Video-LLaVA
- Action descriptions: Camera movements, subject actions
- Latent caching preprocessing for VRAM optimization

【Chapter 3: Configuration (YAML)】
- Video-specific parameters: sample_n_frames, frame_stride
- Network settings: Rank 32-64, target_modules for Transformer
- Learning rate: 5e-5 (lower than image models)
- BF16 precision mandatory
- Batch size: Strictly 1 for 24GB VRAM

【Chapter 4: Training Process】
- Accelerate launch command
- Loss monitoring (video models fluctuate more)
- VRAM management: Reduce frames if OOM
- Time estimates: 2-3 hours for 2000 steps on RTX 4090

【Chapter 5: Testing & Inference (ComfyUI)】
- WanVideo Wrapper node setup
- LoRA strength: Start at 0.6 (video models are sensitive)
- Frame generation: 81 frames (~5 seconds) recommended
- Trigger word usage in video prompts

Bonus: FAQ & Troubleshooting
- OOM solutions (frame reduction, gradient checkpointing)
- Static/no motion fixes (dataset filtering)
- Flickering/artifact remedies (LR and weight adjustment)
- Distorted character fixes (aspect ratio bucketing)

Technical Requirements:
- NVIDIA GPU with 24GB+ VRAM (80GB optimal)
- 64GB+ System RAM
- Windows/Linux with Wan-Factory or Diffusers-Wan
- FFmpeg for video processing
- 100GB+ free storage space

Package Contents:
- Wan 2.1 (Wanxian) Video LoRA Training Tutorial.html (formatted tutorial)
- Wan 2.1 (Wanxian) Video LoRA Training Tutorial.md (markdown source)

Who This Is For:
- Video creators wanting custom AI video generation
- Artists seeking character consistency in video outputs
- Professionals needing specific camera movement training
- Users with high-end GPUs ready for video model training

Version differences；
Personal Basic Edition: 1 license for course content   Standard customer service consultation support (response within 24 hours on working days)
Personal advanced version: 1 set of course content Authorized priority customer service consultation support (response within 12 hours on working days)
Small Team Edition :1 copy of course content authorization Exclusive customer service for the team (response within 8 hours on working days)