How do you generate from multiple reference images? How do you edit part of an image? NadouPro image generation and editing workflow: character consistency and series image practice

For content such as character design, brand visuals, and serialized comics, the most frustrating part is not being unable to create an image, but that "each image looks right on its own, yet they do not look right together." The solution in AI image generation is to make use of reference images: let the model generate new scenes based on fixed baseline images, then use local editing to refine the details. This article explains the workflow for multi-reference generation, local editing, and series images.

Local editing: change details without changing the whole image

Local editing means using natural-language instructions to modify a specific part of an existing image: adding something, removing something, or changing details while keeping the rest unchanged.

There are many practical uses: adjusting a detail on product packaging, replacing an object in the background, changing a character's expression, or refining a visual element. In NadouPro, image editing capabilities include built-in operations such as removal, cutout, and repainting, and these kinds of changes can be completed with instructions.

There are two key points for writing good local editing instructions:

State clearly where to change and what to change it into. Directly specify the target area and the expected change, such as "replace the glass on the desk with a ceramic mug."

State clearly what must not be changed. Make clear which surrounding details should remain consistent, such as "keep the original lighting, color tone, and background furnishings."

Multi-reference generation: the core method for character consistency

For content that demands high consistency, reference images are the most important means of control. The model can extract features from multiple reference images and lock in the subject outline, core elements, and color mood. Reference images can guide the result on three levels:

Subject features: preserve recognizable features—face, clothing, product shape, and object details;

Style and color tone: bring the colors, lighting atmosphere, and material texture of the reference images into the new image;

Composition: use reference materials to guide angle, framing, pose, or scene relationships.

Take a typical scenario as an example: you have a front-facing baseline image of a character, a scene mood image, and a lighting reference image. When generating, you can organize the instruction like this—

Keep the character appearance from the first image unchanged, change the scene to the rainy night street in the second image, reference the warm backlight atmosphere of the third image, and maintain an overall cinematic color tone.

The goal of multi-reference fusion is a new image that feels fully integrated, not a collage. The key is to clearly state the role of each reference image in the instruction: which one provides the subject, which one provides the environment, and which one provides the style.

NadouPro's image node provides image-fusion mode and supports generation by fusing multiple reference images; fusing multiple reference images is precisely a strength of the Nano Banana Pro model, with precise instruction understanding that suits generation tasks with clearly defined role assignments.

Series images: let a set of images tell the same story

Series images are a step beyond single-image needs: multiple images share the same subject, the same style, and the same atmosphere, with narrative or logical continuity between them. Storyboards, brand campaigns, character design sheets, and serialized visual content all fall into this category.

NadouPro has several capabilities that correspond directly to this:

Multi-panel grids: official templates include full-body three-view character sheets, three-view character face sheets, nine-panel character expression grids, character profile images, four-panel scene mood grids, nine-panel scene setting grids, nine-panel multi-camera grids, and four-panel plot progression grids. Taking character design as an example, one generation can produce consistent images of the same character from multiple angles and with multiple expressions, forming the foundation for series content.

Four-panel plot progression grid: given a starting image, it progresses into four consecutive plot images, suitable for storyboard-style narrative sequences.

Nine-panel multi-camera grid: outputs different camera angles of the same scene at once, suitable for previewing shot staging.

By combining these capabilities, a complete visual sequence "from character to story" can be completed in one environment, without repeatedly moving images between multiple tools.

High resolution: the final mile for professional delivery

Series images often need to be enlarged for final use—posters, brand key visuals, product texture images, scene design drafts, and film or television previs images. At this point, resolution becomes a hard requirement: detail textures must withstand enlargement, color transitions must be smooth, and large-size images must not become blurry.

NadouPro's Ultra HD feature can increase resolution after generation, while the Midjourney 8 model natively supports high-definition output and, together with advanced aesthetic expression, is suitable for commercial materials that require a high-quality finished look.

A complete workflow example

Putting the capabilities above together, a typical project process looks like this:

1. Set the baseline: use text-to-image to create the character's standard appearance, then use a full-body three-view character sheet and a nine-panel character expression grid to lock in consistency;

2. Build the scenes: use a nine-panel scene setting grid or a four-panel scene mood grid to generate a series of scenes and keep the worldbuilding unified;

3. Organize the narrative: use a four-panel plot progression grid to advance key story moments, or use multi-reference generation to place the character into a specified scene;

4. Fix the details: correct unsatisfactory areas image by image with image editing capabilities such as repainting and removal;

5. Improve image quality: after finalizing, use the Ultra HD feature to increase resolution and deliver large-format materials.

The output of each step becomes reference material for the next. The cleaner the baseline image, the easier the subsequent generation will be.

Frequently Asked Questions

Q: What is the difference between local editing and regenerating the entire image?
A: Regeneration creates a brand-new image from prompts or references; local editing changes only the selected part of an existing image while keeping the rest unchanged. When you need to preserve the overall image and only refine details, local editing is more efficient and gives more controllable results.

Q: How can multi-reference generation keep a character consistent across scenes?
A: Use the character's baseline image as a fixed reference, include it every time you generate, and clearly state in the instruction to "keep the character appearance from the reference image." The more angles covered by the reference images, the more stable the model's understanding of the character will be; using character three-view templates as a foundation works even better.

Q: How should local editing instructions be described?
A: Directly state the target area and the change: what to add, what to delete, what to replace, and what color or style to change it to, then specify which surrounding details should remain unchanged. The more specific the description, the more precise the change.

Q: What kinds of projects are series images suitable for?
A: Storyboards, brand visual systems, character design sheets, wallpaper sets, and serialized social media content—any project that needs multiple images to share the same subject, style, and atmosphere is suitable for a series-based workflow.

Q: How can the final image meet commercial delivery standards?
A: Choose a strong base model during generation, then use Ultra HD after finalizing to increase resolution. Color consistency can be constrained with color-card capabilities. Check detail textures and text elements at enlarged size, and confirm there is no deformation before delivery.

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