Match the base model
Compare families, versions and availability before choosing a workflow.
LoRA training / Product LoRA
Keep the details that make your product yours.
Prepare a product dataset with clear views of its shape, materials and distinctive details. Plan new environments without teaching one fixed campaign composition.
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.
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.
Training dataset
Add images of the style, character or object you want to teach.
Images0 / 30
Avg. shortest edge—
Image captions0 / 0
Before submission, files stay in this tab. Submitting Krea 2 training uploads images and captions to fal. Reloading clears local files; cloud jobs remain in My LoRAs.
A focused dataset works better
Include clear views of the same product. Capture its shape, materials and distinctive details.
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
A focused workflow for Product LoRA.
The product is the concept; the studio setup is context. Capture enough visual evidence to distinguish the object from the surface, props and lighting around it.
Inspect the source files
Read real image dimensions and check the local file limits before export.
Review every caption
Describe changing details instead of silently teaching them as part of the concept.
Keep a reusable draft
Export settings and captions together. Source image files stay separate.
Ideas worth teaching
Concept ideas for Product LoRA.
Explore three ways to define a training concept. Each reference links to a dedicated workflow, with dataset advice and evaluation prompts.
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 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
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 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 Product LoRA 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 Product LoRA dataset.
Product training is an objective, not a guarantee of exact design reproduction. Evaluate proportions, materials and key details on new prompts with a compatible generator.
Read the dataset preparation note- Show the same object from multiple useful viewpoints.
- Capture material, silhouette and distinctive details clearly.
- Vary the surface, background and lighting around the product.
- Avoid teaching packaging labels you do not want reproduced.
Prompt notebook
New-prompt ideas for Product LoRA.
Save these ideas for evaluating a compatible trained adapter later. They are prompt examples, not generated results or a promise of consistent output.
A clean packshot
ohwx_bottle on a plain pale gray background, front three-quarter view, soft studio light.
A new material context
ohwx_bottle on dark stone, gentle side light, visible amber glass and round walnut cap.
A campaign setting
ohwx_bottle beside folded cream linen, soft window light, restrained editorial composition.
A detail study
A close-up of the round walnut cap and upper glass edge of ohwx_bottle, natural texture.
Changing the viewpoint
ohwx_bottle viewed from slightly above on a neutral surface, precise proportions, even lighting.
A wider composition
ohwx_bottle on a travertine plinth with generous empty space, warm afternoon shadows.
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
Product LoRA: questions before you train.
Model-specific guidance, with the current availability stated clearly.
What should I prepare for Product LoRA?
The product is the concept; the studio setup is context. Capture enough visual evidence to distinguish the object from the surface, props and lighting around it. Product training is an objective, not a guarantee of exact design reproduction. Evaluate proportions, materials and key details on new prompts with a compatible generator.
How should I evaluate a Product LoRA concept?
Compare silhouette, proportions, cap or part details and material appearance in new settings. Keep the same prompt and comparable settings for the base-model comparison.
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 Product LoRA 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.