Wan v2.6 is a production-oriented AI image generation and editing model designed for prompt-accurate visual synthesis and structure-preserving image transformation. It supports both text-to-image creation and image-to-image editing workflows, with a focus on reliable composition, strong prompt adherence, and controlled use of reference imagery. Wan v2.6 is well-suited for iterative creative pipelines where visual consistency and predictable edits are required without extensive manual tuning.
Key Features
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Text-to-Image Generation
- Wan v2.6 generates high-quality images directly from natural language prompts, with strong alignment to subject descriptions, composition, lighting, and overall visual intent.
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Image Editing & Context-Aware Transformations
- Image-to-image workflows in Wan v2.6 are optimized for controlled edits that retain the original image’s layout, pose, and spatial relationships.
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Multi-Reference Image Handling
- Supports the use of up to 3 reference images in image-to-image workflows. When multiple references are used, they can be explicitly described and prioritized within the prompt.
Technical Capabilities
- Modalities: Text to Video, Image to Video
- Native Outputs: 1K
- Flexible Ratios: 1:1, 16:9, 9:16, 4:3, 3:4
- Max input image: 2
- Max output image: 2
Best Use Cases
Creative Asset Generation: Generate original visuals for campaigns, editorial content, concept art, and digital experiences using detailed text prompts with consistent visual interpretation.
Brand & Style Iteration: Use image-to-image editing to explore style variations, color palettes, or aesthetic directions while maintaining a consistent composition or subject identity.
Image Refinement & Visual Adjustments: Apply natural language guided edits to existing images, such as background changes, material swaps, lighting adjustments, or stylistic enhancements.
Strengths and Limitations
Strengths
- Structure Stability: Image-to-image edits prioritize preserving geometry, pose, and layout, making the model reliable for iterative design workflows.
- Reference-Aware Editing: Supports controlled use of reference images to guide style or identity without overpowering the base image.
Limitations
- Reference Count & Control: Reference handling is more limited and less granular than models designed for heavy multi-reference compositing.
Tips for Better Prompts
- Be Explicit with Details: Clearly outline subjects, surrounding context, lighting, mood, and composition.
- Use Negative Prompts When Available: Exclude unwanted traits or artifacts explicitly to improve clarity and consistency in results.
- Guide References Intentionally: When using reference images, clearly explain what each reference should influence.
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