In this guide, we’ll walk through the full process of online training on TensorArt using Wan2.2. For this demo, we’ll be using image2video training so you can see direct results.

Step 1 – Open Online Training

Go to the Online Training page.
Here, you can choose between Text2Video or Image2Video.
👉 For this tutorial, we’ll select Image2Video.

Step 2 – Upload Training Data

Upload the materials you want to train on.

• You can upload them one by one.

• Or, if you’ve prepared everything locally, just zip the files and upload the package.
  
  

  

  Step 3 – Adjust Parameters

  Once the data is uploaded, you’ll see the parameter panel on the right.

  💡 Tip: If you’re training with video clips, keep them around 5 seconds for the best results.
  

  

  Step 4 – Set Prompts & Preview Frames

  • The prompt field defines what kind of results you’ll see during and after training.

  • As training progresses, you’ll see epoch previews. This helps you decide which version of the model looks best.

  • For image-to-video LoRA training, you can also set the first frame of the preview video.

    

    Step 5 – Start Training

    Click Start Training once your setup is ready.
    When training completes, each epoch will generate a preview video.

    You can then review these previews and publish the epoch that delivers the best result.

    

    Step 6 – Publish Your Model

    After publishing, wait a few minutes and your Wan2.2 LoRA model will be ready to use.

    

    Recommended Training Parameters (Balanced Quality)

    Network Module: LoRA
    Base Model: Wan2.2 – i2v-high-noise-a14b
    Trigger words: (use a unique short tag, e.g. your_project_tag*)*

    Image Processing Parameters

    • Repeat: 1

    • Epoch: 12

    • Save Every N Epochs: 1–2

    Video Processing Parameters

    • Frame Samples: 16

    • Target Frames: 20

    Training Parameters

    • Seed: –

    • Clip Skip: –

    • Text Encoder LR: 1e-5

    • UNet LR: 8e-5 (lower than 1e-4 for more stability)

    • LR Scheduler: cosine (warmup 100 steps if available)

    • Optimizer: AdamW8bit

    • Network Dim: 64

    • Network Alpha: 32

    • Gradient Accumulation Steps: 2 (use 1 if VRAM is limited)

    Label Parameters

    • Shuffle caption: –

    • Keep n tokens: –

    Advanced Parameters

    • Noise offset: 0.025–0.03 (recommended 0.03)

    • Multires noise discount: 0.1

    • Multires noise iterations: 10

    • conv_dim: –

    • conv_alpha: –

    • Batch Size: 1–2 (depending on VRAM)

    • Video Length: 2

    Sample Image Settings

    • Sampler: euler

    • Prompt (example):

    Tips

    • Keep training videos around ~5 seconds for best results.

    • Use a consistent dataset (lighting, framing, style) to avoid drift.

    • If previews show overfitting (blurry details, jitter), lower UNet LR to 6e-5 or reduce Epochs to 10.

    • For stronger style binding: increase Network Dim → 96 and Alpha → 64, while lowering UNet LR → 6e-5.