The art of storyboarding stands as the cornerstone of modern content creation, weaving its essential role through filmmaking, animation, advertising, and UX design. Though traditionally, creators have relied on hand-drawn sequential illustrations to map their narratives, today’s AI foundation models (FMs) are transforming this landscape. FMs like Amazon Nova Canvas and Amazon Nova Reel offer capabilities in transforming text and image inputs into professional-grade visuals and short clips that promise to revolutionize preproduction workflows.
This technological leap forward, however, presents its own set of challenges. Although these models excel at generating diverse concepts rapidly—a boon for creative exploration—maintaining consistent character designs and stylistic coherence across scenes remains a significant hurdle. Even subtle modifications to prompts or model configurations can yield dramatically different visual outputs, potentially disrupting narrative continuity and creating additional work for content creators.
To address these challenges, we’ve developed this two-part series exploring practical solutions for achieving visual consistency. In Part 1, we deep dive into prompt engineering and character development pipelines, sharing tested prompt patterns that deliver reliable, consistent results with Amazon Nova Canvas and Amazon Nova Reel. Part 2 explores techniques like fine-tuning Amazon Nova Canvas to achieve exceptional visual consistency and precise character control.
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Consistent character design with Amazon Nova Canvas
The foundation of effective storyboarding begins with establishing well-defined character designs. Amazon Nova Canvas offers several powerful techniques to create and maintain character consistency throughout your visual narrative. To help you implement these techniques in your own projects, we’ve provided comprehensive code examples and resources in our GitHub repository. We encourage you to follow along as we walk through each step in detail. If you’re new to Amazon Nova Canvas, we recommend first reviewing Generating images with Amazon Nova to familiarize yourself with the basic concepts.
Basic text prompting
Amazon Nova Canvas transforms text descriptions into visual representations. Unlike large language models (LLMs), image generation models don’t interpret commands or engage in reasoning—they respond best to descriptive captions. Including specific details in your prompts, such as physical attributes, clothing, and styling elements, directly influences the generated output.
For example, “A 7-year-old Peruvian girl with dark hair in two low braids wearing a school uniform” provides clear visual elements for the model to generate an initial character concept, as shown in the following example image.
Visual style implementation
Consistency in storyboarding requires both character features and unified visual style. Our approach separates style information into two key components in the prompt:
- Style description – An opening phrase that defines the visual medium (for example, “A graphic novel style illustration of”) Style details – A closing phrase that specifies artistic elements (for example, “Bold linework, dramatic shadows, flat color palettes”)
This structured technique enables exploration of various artistic styles, including graphic novels, sketches, and 3D illustrations, while maintaining character consistency throughout the storyboard. The following is an example prompt template and some style information you can experiment with:
Character variation through seed values
The seed parameter serves as a tool for generating character variations while adhering to the same prompt. By keeping the text description constant and varying only the seed value, creators can explore multiple interpretations of their character design without starting from scratch, as illustrated in the following example images.
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Seed = 1 | Seed = 20 | Seed = 57 | Seed = 139 | Seed = 12222 |
Prompt adherence control with cfgScale
The cfgScale parameter is another tool for maintaining character consistency, controlling how strictly Amazon Nova Canvas follows your prompt. Operating on a scale from 1.1–10, lower values give the model more creative freedom and higher values enforce strict prompt adherence. The default value of 6.5 typically provides an optimal balance, but as demonstrated in the following images, finding the right setting is crucial. Too low a value can result in inconsistent character representations, whereas too high a value might overemphasize prompt elements at the cost of natural composition.
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| Seed = 57, cfgScale = 1.1 | Seed = 57, cfgScale = 3.5 | Seed = 57, cfgScale = 6.5 | Seed = 57, cfgScale = 8.0 | Seed = 57, cfgScale = 10 |
Scene integration with consistent parameters
Now we can put these techniques together to test for character consistency across different narrative contexts, as shown in the following example images. We maintain consistent input for style, seed, and cfgScale, varying only the scene description to make sure character remains recognizable throughout the scene sequences.
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| Seed = 57, Cfg_scale: 6.5 | Seed = 57, Cfg_scale: 6.5 | Seed = 57, Cfg_scale: 6.5 |
| A graphic novel style illustration of a 7 year old Peruvian girl with dark hair in two low braids wearing a school uniform riding a bike on a mountain pass Bold linework, dramatic shadows, and flat color palettes. Use high contrast lighting and cinematic composition typical of comic book panels. Include expressive line work to convey emotion and movement. | A graphic novel style illustation of a 7 year old Peruvian girl with dark hair in two low braids wearing a school uniform walking on a path through tall grass in the Andes Bold linework, dramatic shadows, and flat color palettes. Use high contrast lighting and cinematic composition typical of comic book panels. Include expressive line work to convey emotion and movement. | A graphic novel style illustration of a 7 year old Peruvian girl with dark hair in two low braids wearing a school uniform eating ice cream at the beach Bold linework, dramatic shadows, and flat color palettes. Use high contrast lighting and cinematic composition typical of comic book panels. Include expressive line work to convey emotion and movement. |
Storyboard development pipeline
Building upon the character consistency techniques we’ve discussed, we can now implement an end-to-end storyboard development pipeline that transforms written scene and character descriptions into visually coherent storyboards. This systematic approach uses our established parameters for style descriptions, seed values, and cfgScale values to provide character consistency while adapting to different narrative contexts. The following are some example scene and character descriptions:
Our pipeline uses Amazon Nova Lite to first craft optimized image prompts incorporating our established best practices, which are then passed to Amazon Nova Canvas for image generation. By setting numberOfImages higher (typically three variations), while maintaining consistent seed and cfgScale values, we give creators multiple options that preserve character consistency. We used the following prompt for Amazon Nova Lite to generate optimized image prompts:
Our pipeline generated the following storyboard panels.
 | Mayu stands at the edge of a mountainous path, clutching a book. Her mother, Maya, kneels beside her, offering words of encouragement and handing her the book. Mayu looks nervous but determined as she prepares to start her journey. |
 | Mayu encounters a ‘danger’ sign with a drawing of a snake. She looks scared, but then remembers her mother’s words. She takes a deep breath, looks at her book for reassurance, and then searches for a stick on the ground. |
 | Mayu bravely makes her way through tall grass, swinging her stick and making noise to scare off potential snakes. Her face shows a mix of fear and courage as she pushes forward on her journey. |
Although these techniques noticeably improve character consistency, they aren’t perfect. Upon closer inspection, you will notice that even images within the same scene show variations in character consistency. Using consistent seed values helps control these variations, and the techniques outlined in this post significantly improve consistency compared to basic prompt engineering. However, if your use case requires near-perfect character consistency, we recommend proceeding to Part 2, where we explore advanced fine-tuning techniques.
Video generation for animated storyboards
If you want to go beyond static scene images to transform your storyboard into short, animated video clips, you can use Amazon Nova Reel. We use Amazon Nova Lite to convert image prompts into video prompts, adding subtle motion and camera movements optimized for the Amazon Nova Reel model. These prompts, along with the original images, serve as creative constraints for Amazon Nova Reel to generate the final animated sequences. The following is the example prompt and its resulting animated scene in GIF format:
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| Input Image | Output Video |
Conclusion
In this first part of our series, we explored fundamental techniques for achieving character and style consistency using Amazon Nova Canvas, from structured prompt engineering to building an end-to-end storyboarding pipeline. We demonstrated how combining style descriptions, seed values, and careful cfgScale parameter control can significantly improve character consistency across different scenes. We also showed how integrating Amazon Nova Lite with Amazon Nova Reel can enhance the storyboarding workflow, enabling both optimized prompt generation and animated sequences.
Although these techniques provide a solid foundation for consistent storyboard generation, they aren’t perfect—subtle variations might still occur. We invite you to continue to Part 2, where we explore advanced model fine-tuning techniques that can help achieve near-perfect character consistency and visual fidelity.
About the authors
Alex Burkleaux is a Senior AI/ML Specialist Solution Architect at AWS. She helps customers use AI Services to build media solutions using Generative AI. Her industry experience includes over-the-top video, database management systems, and reliability engineering.
James Wu is a Senior AI/ML Specialist Solution Architect at AWS, helping customers design and build AI/ML solutions. James’s work covers a wide range of ML use cases, with a primary interest in computer vision, deep learning, and scaling ML across the enterprise. Prior to joining AWS, James was an architect, developer, and technology leader for over 10 years, including 6 years in engineering and 4 years in marketing & advertising industries.
Vladimir Budilov is a Principal Solutions Architect at AWS focusing on agentic & generative AI, and software architecture. He leads large-scale GenAI implementations, bridging cutting-edge AI capabilities with production-ready business solutions, while optimizing for cost and solution resilience.
Nora Shannon Johnson is a Solutions Architect at Amazon Music focused on discovery and growth through AI/ML. In the past, she supported AWS through the development of generative AI prototypes and tools for developers in financial services, health care, retail, and more. She has been an engineer and consultant in various industries including DevOps, fintech, industrial AI/ML, and edtech in the United States, Europe, and Latin America.
Ehsan Shokrgozar is a Senior Solutions Architect specializing in Media and Entertainment at AWS. He is passionate about helping M&E customers build more efficient workflows. He combines his previous experience as Technical Director and Pipeline Engineer at various Animation/VFX studios with his knowledge of building M&E workflows in the cloud to help customers achieve their business goals.
