Quwe Video LoRA Training Complete Tutorial

Discover the cutting-edge Quwe video generation architecture featuring the revolutionary Temporal-Anchored Transformer (TAT) that prevents background warping during complex character movements. Train LoRAs for identity consistency in long-form cinematic shots and artistic physics that standard models struggle with.

Product Type: Digital Tutorial (HTML + Markdown)

Why Quwe for Video Training?
- Temporal-Anchored Transformer (TAT): Prevents background warping
- Identity consistency: Maintain character appearance in long shots
- Artistic physics: Learn specific motion styles standard models can't handle
- 7B & 21B variants: Scalable model options
- Motion-Stable architecture: Designed for complex character movements

Tutorial Structure (6 Chapters):

【Chapter 1: Hardware & Environment】
- GPU requirements: RTX 4090 (24GB) minimum, RTX 5090/H200 optimal
- System RAM: 64-128GB for 21B model loading
- Storage: 100GB+ NVMe Gen 5 SSD for rapid checkpointing
- Software: quwe-toolkit, PyTorch 2.6+, xformers 0.0.28+

【Chapter 2: Dataset Preparation】
- FPS requirements: Exactly 24fps or 30fps (mixing causes temporal jitter)
- Resolution: 1024x1024 (1:1) or 1280x720 (16:9)
- Duration: 4-8 second clips (longer temporal windows than Wan/Flux)
- Anchored captioning system: Subject + Anchor Point descriptions
- Caption format: [Trigger Word], [Subject Action], anchored by [Static Background]

【Chapter 3: Configuration (YAML)】
- Quwe-specific job configuration
- Network settings: Rank 64 (higher for motion stability), Alpha 32
- Prodigy optimizer for auto-tuning learning rates
- BF16 precision mandatory
- low_vram flag for 24GB cards
- 65-frame temporal window standard

【Chapter 4: Training Process】
- Python train.py launch command
- Temporal Loss monitoring (Motion Loss metric)
- Motion Loss threshold: Should drop below 0.5 after 1000 steps
- Speed estimates: 3.2s/it on RTX 4090
- Total time: ~4 hours for 4000 steps

【Chapter 5: Conversion & Pruning】
- Proprietary .quwe format conversion
- convert_to_safetensors.py script usage
- Expected file size: ~180MB for Rank 64

【Chapter 6: Testing (ComfyUI Workflow)】
- QuweSamplerCustom node setup
- LoRA strength: 0.75 recommended (1.0+ causes "statue syndrome")
- Motion Bucket settings: 128 for high-intensity, 64 for slow cinematic

Bonus: FAQ & Troubleshooting
- "Statue Syndrome" fixes (overfitting/rank adjustment)
- Background melting remedies (anchored caption improvement)
- OOM solutions (T5 CPU offloading)
- Color shifting fixes (BF16 vs FP16 precision)

Technical Requirements:
- NVIDIA GPU with 24GB+ VRAM (32GB+ recommended)
- 64-128GB System RAM
- NVMe Gen 5 SSD storage
- Windows/Linux with quwe-toolkit
- 150GB+ free storage space

Package Contents:
- Quwe Video LoRA Training Complete Tutorial.html (formatted tutorial)
- Quwe Video LoRA Training Complete Tutorial.md (markdown source)

Who This Is For:
- Advanced video creators needing background stability
- Cinematic artists requiring long-form identity consistency
- Studios working with complex character movements
- Professionals seeking cutting-edge video AI technology

The purchased content will be sent to your email address in zip format, including course materials, workflows, and HTML course materials. Please contact our customer service if you have any questions.

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)