Define one movement
Separate subject motion, camera behavior and visual treatment.
LoRA training / Wan 2.2
Plan a model-specific Wan 2.2 video LoRA.
Understand how a Wan 2.2 video dataset differs from an image dataset. Define one motion, organize clip metadata and preserve the complete compatible adapter output.
Quick answer: Wan 2.2 video LoRA training is integrated at 1667 credits per task. Prepare MP4 clips, configure WaveSpeed and R2, then use the paired generator.
Cloud training is integrated for Krea 2, FLUX.1, FLUX.2 Dev, Qwen Image, Z-Image, Wan 2.2 I2V and LTX-2 19B. Configure the required services, sign in and check the quoted credit cost before submitting.
Train a video LoRA
Upload 3–10 MP4 clips, up to 8 MB each and 24 MB total. Use short clips with consistent subjects and include enough motion. Captions use your trigger word.
Training capabilities and requirements
Costs and limits below follow the application configuration. Integration describes the code, not verified provider uptime or a successful live training run.
| Model | Integration | Cloud input limits | Credits and settings | Output and compatibility |
|---|---|---|---|---|
| Wan 2.2 | Integrated; configuration required | 3–10 MP4 clips with a trigger phrase. Up to 8 MiB per clip; 24 MiB total. | Training: 1667 credits / task. Generation: 67 credits / 5-second video. Fixed training steps: 100. | LoRA weights after success; use the paired Wan 2.2 generator. Compatibility with other base models is not implied. |
Cloud tasks require sign-in and sufficient credits. Krea 2 needs Fal; WaveSpeed image and video trainers need WaveSpeed access and R2 storage.
Cloud training sends your selected media and captions to the provider. Image and video dataset limits differ. Confirm account readiness before submitting.
A focused workflow
A focused workflow for Wan 2.2.
Motion needs temporal evidence. A folder of still images is not a substitute for a compatible clip workflow, and a single weight file may not describe every video adapter.
Plan clip requirements
Check the chosen trainer’s duration, frame and input requirements when available.
Preserve the exact version
A different LTX or Wan release may require a different adapter workflow.
Evaluate across time
Plan tests for motion identity, temporal artifacts and prompt changes.
Ideas worth teaching
Concept ideas for Wan 2.2.
These still images illustrate visual concepts only. Use them to plan identity and style, then prepare motion clips separately for the selected video trainer.
A style that travels.
Use this coastal illustration as a visual-language reference. A style dataset should vary the subject while keeping its materials, colors and mark-making coherent.
Caption exampleA coastal village in ohwx_style, gouache texture, warm flat colors and fine ink contours.
Explore style LoRA training
One character. New settings.
An original ceramic explorer gives a dataset a clear identity. Vary the angle, pose and surroundings instead of repeating one composition.
Caption exampleohwx_robot, a ceramic explorer with blue glass eyes, standing beside an alpine lake in daylight.
Explore character LoRA training
Keep the product recognizable.
A blank amber bottle makes shape and materials easy to discuss. Include alternate views of its glass body and cap rather than teaching a single background.
Caption exampleohwx_bottle, an amber glass bottle with a round walnut cap, on a travertine surface in window light.
Explore product LoRA trainingThese original images were AI-generated as concept references. They are not Loras AI training outputs, before/after comparisons or evidence of model performance.
From intent to evaluation
How to prepare your Wan 2.2 concept.
Prepare the image or video dataset required by the selected model, review its trigger and captions, then submit training. Use the completed weights with their matching generator.
- 1
Choose one concept
Decide whether you want a recognizable character, a transferable style, a distinctive product or a specific motion.
Read the note - 2
Collect useful examples
Keep the intended concept consistent while varying details you do not want the model to memorize.
Read the note - 3
Review captions & export
Inspect file dimensions, edit each caption and export the local configuration before leaving the workspace.
Read the note - 4
Plan your test prompts
Test completed, compatible LoRA weights with new prompts. Record the model, settings, successes and failures, and compare with the base model to evaluate the concept.
Read the note
Dataset & compatibility
Build a better Wan 2.2 dataset.
Wan image datasets and Wan video clips should not share an indistinguishable upload flow. A real video integration must preserve all required adapter outputs and loading metadata.
Read the dataset preparation note- Start with one motion or one camera behavior.
- Keep action descriptions separate from appearance descriptions.
- Wait for the selected trainer’s clip rules before packaging files.
- Preserve every required adapter file and its metadata.
Prompt notebook
New-prompt ideas for Wan 2.2.
Save these ideas for evaluating a compatible trained adapter later. They are prompt examples, not generated results or a promise of consistent output.
Subject motion
ohwx_motion, a ceramic explorer slowly opens a field notebook, fixed camera, calm natural daylight.
Camera orbit
ohwx_motion, a slow controlled camera orbit around an amber glass bottle, neutral studio setting.
A new setting
ohwx_motion, the same deliberate movement in a quiet greenhouse, stable lighting and simple composition.
Controlled framing
ohwx_motion, a gentle forward camera movement along a coastal stairway, restrained perspective change.
Material behavior
ohwx_motion, a linen cloth slowly moving in a light breeze beside a still product, fixed framing.
A minimal baseline
ohwx_motion, one clear action on a plain background, fixed camera, even lighting, no cuts.
Related notes
A little preparation goes a long way.
Practical reading on image selection, captions, training settings and evaluating what a model actually learned.
How many images do you need?Choose useful variation rather than relying on one universal image count.A practical LoRA captioning guideSeparate the concept from changing details, with character, style and product examples.Training steps without the guessworkUnderstand steps, learning rate and overfitting before changing advanced settings.How to evaluate a trained LoRATest new prompts, compare a baseline and document useful failures.
FAQ
Wan 2.2: questions before you train.
Model-specific guidance, with the current availability stated clearly.
What should I prepare for Wan 2.2?
Motion needs temporal evidence. A folder of still images is not a substitute for a compatible clip workflow, and a single weight file may not describe every video adapter. Wan image datasets and Wan video clips should not share an indistinguishable upload flow. A real video integration must preserve all required adapter outputs and loading metadata.
How should I evaluate a Wan 2.2 concept?
After training succeeds, select the owned compatible LoRA on Generate. Test new scenes and prompts; compare timing, subject identity and temporal artifacts. Wan I2V also requires a starting image.
Can I submit a paid training job on this page?
Krea 2, FLUX.1, FLUX.2 Dev, Qwen Image, Z-Image, Wan 2.2 I2V and LTX-2 19B have training and paired generation integrations. Paid tasks require configured services, sign-in and sufficient credits. Other catalog models remain previews.
What happens to the images I add?
Local draft preparation processes images in the current browser tab; exported JSON excludes source files. Refreshing or leaving clears local images. Submitting cloud training sends the selected dataset and captions to the provider; WaveSpeed training also uses configured R2 storage for datasets and archived artifacts. Storage privacy depends on bucket access settings. Generation sends prompts and selected LoRA references to the relevant service. Only submit material you have permission to use.
Can I reuse one LoRA with every image or video model?
Adapters are not interchangeable. Match the model architecture, base version and loading workflow. Krea 2, MOSS and Anima need their own compatible weights; similar names do not imply compatibility. Video workflows may also require multiple adapter files.
Keep exploring
Find a compatible next step.
Compare related model families before choosing a training workflow. Adapter formats and base-model versions must remain compatible.
Your Wan 2.2 concept starts here.
Choose a compatible model, prepare images and captions, then submit an available training or generation workflow. You can also export a dataset configuration draft for free.