LoRA AIMatch your model to the creative goal
Characters, styles and products need different source material. Use Krea 2 for cloud training and each model’s compatible adapters for MOSS or Anima generation.
Your character. Your style. Your next idea.
Train LoRA models. Generate AI images and videos.
Build datasets for characters, styles and products. Train custom LoRAs with Krea 2, FLUX, Qwen Image and Z-Image, explore Wan 2.2 and LTX video training, and generate AI images with MOSS, Anima and compatible LoRAs.
Try:
Quick answer: Train supported image and video LoRAs with configured services, sufficient credits and a compatible dataset. Each LoRA must use its paired generator; catalog listings do not establish compatibility.
Made with LoRA AI
Images and videos generated with LoRA AI Image and LoRA AI Video. Click one to use its prompt.
What you can train
Explore examplesThese original images were AI-generated as concept references. They are not LoRA AI training outputs, before/after comparisons or evidence of model performance.
From dataset preparation to LoRA training and generation
Krea 2 connects cloud training to image generation. MOSS and Anima offer separate generation workflows, alongside video model exploration and draft tools. Training and generation require sign-in and sufficient credits.
Train a custom Krea 2 LoRAInspect images, edit captions and submit training. Follow task progress in My LoRAs, then use the trained weights to generate images.Explore video models and workflowsCompare Wan and LTX families and plan motion datasets. The video workspace organizes references and start/end-frame drafts; cloud video training and generation are not available.
Each model needs compatible LoRA weights; adapters are not interchangeable. Resolution options and credit costs appear in the generation workspace.
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 |
|---|---|---|---|---|
| Krea 2 | Integrated; configuration required | 1–30 JPEG, PNG or WebP images with captions and a trigger phrase. Up to 15 MiB per image; 25 MiB total. | Training: 100 credits / task. Generation: 10 credits / image. Fixed training steps: 100. | LoRA weights after success; use the paired Krea 2 generator. Compatibility with other base models is not implied. |
| FLUX.1 Dev | Integrated; configuration required | 4–30 JPEG, PNG or WebP images with captions and a trigger phrase. Up to 15 MiB per image; 25 MiB total. | Training: 167 credits / task. Generation: 10 credits / image. Fixed training steps: 500. | LoRA weights after success; use the paired FLUX.1 Dev generator. Compatibility with other base models is not implied. |
| FLUX.2 Dev | Integrated; configuration required | 10–30 JPEG, PNG or WebP images with captions and a trigger phrase. Up to 15 MiB per image; 25 MiB total. | Training: 2134 credits / task. Generation: 14 credits / image. Fixed training steps: 1000. | LoRA weights after success; use the paired FLUX.2 Dev generator. Compatibility with other base models is not implied. |
| Qwen Image 2512 | Integrated; configuration required | 4–30 JPEG, PNG or WebP images with captions and a trigger phrase. Up to 15 MiB per image; 25 MiB total. | Training: 167 credits / task. Generation: 10 credits / image. Fixed training steps: 500. | LoRA weights after success; use the paired Qwen Image 2512 generator. Compatibility with other base models is not implied. |
| Z-Image Turbo | Integrated; configuration required | 4–30 JPEG, PNG or WebP images with captions and a trigger phrase. Up to 15 MiB per image; 25 MiB total. | Training: 209 credits / task. Generation: 10 credits / image. Fixed training steps: 500. | LoRA weights after success; use the paired Z-Image Turbo generator. Compatibility with other base models is not implied. |
| 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. |
| LTX-2 19B | Integrated; configuration required | 3–10 MP4 clips with a trigger phrase. Up to 8 MiB per clip; 24 MiB total. | Training: 584 credits / task. Generation: 34 credits / 5-second video. Fixed training steps: 500. | LoRA weights after success; use the paired LTX-2 19B 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
Prepare your dataset, train a LoRA, test new images
Organize training images around a character, visual style or product. Align your dataset, captions, base model and test prompts. Review the material, submit Krea 2 training, then test different scenes and compositions in Generate.
Inspect the image dataset
Review dimensions and repeated images. Keep useful changes in angle, lighting and background, and check upload limits before submitting cloud training.
Review and edit each caption
Describe changing outfits, poses and backgrounds while keeping the intended character or product concept clear. Check the trigger word before training.
Generate and compare after training
Check results in My LoRAs and generate images with completed weights. Keep prompts and settings for comparing new scenes, alongside an exported dataset draft.
Ideas worth teaching
Character, style and product concepts for LoRA datasets
Explore three ways to define a training concept. Each reference links to a dedicated workflow, with dataset advice and evaluation prompts.
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 LoRA AI training outputs, before/after comparisons or evidence of model performance.
From intent to evaluation
How to start a LoRA training run
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
Prepare a useful dataset for your custom LoRA
Choose the base model before preparing a dataset. Krea 2 training weights belong in a compatible Krea 2 generation workflow. MOSS and Anima require their own adapters; similar model names do not establish compatibility.
Read the dataset preparation note- Choose one concept for the first run.
- Use examples you have the rights to train on.
- Remove corrupted files and unnecessary repeats.
- Keep untouched images separate from exported JSON drafts.
Prompt notebook
Test characters, styles and products with new-scene prompts
Save these ideas for evaluating a compatible trained adapter later. They are prompt examples, not generated results or a promise of consistent output.
Architecture
A hillside library in ohwx_style, warm flat colors, textured paper and delicate ink contours.
An everyday object
A vintage desk lamp in ohwx_style, simple composition, warm ochre and indigo palette.
A natural scene
A quiet forest path after rain in ohwx_style, layered shapes, visible gouache texture.
A different subject
A small sailboat in ohwx_style, bold simplified forms, soft paper grain, no lettering.
A character study
An original fictional gardener in ohwx_style, full-body pose, simple pale background.
A new palette test
A night market in ohwx_style, restrained blue and amber palette, hand-painted details.
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
LoRA training and AI image generation FAQ
Model-specific guidance, with the current availability stated clearly.
What can I train with LoRA AI, and how do I start?
Krea 2 image LoRA training is connected. Prepare images around a character, visual style or product concept, review captions and check the trigger word in Train. Sign in with sufficient credits to submit, then follow progress and access completed weights in My LoRAs. Other training families and cloud video training are not available.
How do I generate after training? Can MOSS or Anima reuse those weights?
After Krea 2 training succeeds, open its generation workflow from My LoRAs and test new prompts. MOSS and Anima also generate images independently but require compatible adapters; Krea 2 weights cannot be reused directly. Generation shows the required credits before submission. Local dataset preparation and draft export are free.
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
Explore more LoRA model families and use cases
Compare related model families before choosing a training workflow. Adapter formats and base-model versions must remain compatible.
Start your next LoRA project with your own images
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.