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Define what “on brand” looks like before generating
Translate the existing brand guide into visible instructions a generator can act on. Avoid relying on broad adjectives such as “premium” or “friendly” by themselves; pair them with concrete details, such as a warm cream background, soft side lighting and restrained contrast.
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- Palette: name the main colors and how they should be used.
- Lighting and contrast: specify qualities such as soft side lighting, even studio light or low contrast.
- Medium and texture: describe whether the images should look photographic, illustrated, grainy, glossy or matte.
- Composition: set preferences for framing, negative space, camera angle and background treatment.
- Recurring details: list product, character or subject features that should remain recognizable.
- Exclusions: identify visual elements, colors or treatments that should not appear.
Keep this brief compact. It is the stable visual system for the series; individual prompts can then describe each image’s subject and scene.
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Choose references that actually represent the brand
Select a small set of approved images that clearly show the intended look. Use assets your organization owns or has permission to use, and avoid combining examples from incompatible art directions. A style reference is guidance about appearance, not necessarily a way to preserve the exact subject or layout.
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Midjourney says its Style Reference guides qualities such as colors, medium, textures and lighting, but “It doesn’t copy objects or people, just the overall style—like colors, medium, textures, or lighting, helping you achieve a consistent visual theme.” Adobe similarly describes style references as guiding the look and feel of image variations. If an exact product, character or composition must recur, check whether the platform offers a separate feature for that need.
Reuse a stable prompt and generation setup
Keep the reference images, prompt scaffold, model and version, generation settings, and approval status together in a team-accessible source of truth. Reusing the same inputs makes it easier to identify what changed when a new image drifts away from the intended look.
A practical prompt scaffold is:
Subject + action or context + composition or framing + lighting + required brand details + exclusions
For example: “A ceramic travel mug on a desk beside an open notebook, medium close-up with open space on the left, soft side lighting, warm cream background and muted forest-green accents, matte editorial illustration, no extra lettering.” Keep the brand details stable and change only the scene-specific content from one asset to the next.
Midjourney recommends simple prompts, selectively using style words that match the reference and describing the desired content rather than telling the model how to modify the reference. Too many competing style instructions can work against the reference.
Apply references and tune their influence
In Midjourney, attach or select an image as a Style Reference; its documentation also describes pinning references for reuse. In Discord, use --sref followed by a valid image URL. The --sw parameter adjusts reference influence from 0 to 1000, with a documented default of 100. Midjourney lists Style References as compatible with versions 6 and later; confirm current version support and controls in its Style Reference documentation.
Adobe Firefly’s web experience supports uploaded or curated style-reference images. For Adobe’s API, the style-reference strength accepts values from 1 to 100 and defaults to 50 when omitted, according to its Style Image Reference documentation. These Adobe and Midjourney values are different platform controls, not equivalent scales.
Generate batches and review them consistently
Generate several candidates using the same brand inputs, then compare them with a fixed checklist. If the images drift, simplify conflicting prompt language or adjust reference influence before changing unrelated settings. Treat that as an iteration method, not a guarantee: vendor documentation does not establish a setting that ensures visual consistency.
- Do the intended colors and contrast appear?
- Are lighting, medium and texture consistent with the approved direction?
- Do recurring subjects retain the required features?
- Does the crop or composition leave room for the asset’s intended use?
- Are embedded words, labels and headlines accurate and placed correctly?
- Are the image, reference materials and any likenesses cleared for the intended use?
- Does the image meet the requirements of its publishing channel?
For exact logos or precise typography, add those elements in an appropriate design workflow rather than relying on generated reproduction. OpenAI Academy recommends making image-text instructions specific: quote or capitalize the wording, state type style, size, color and placement, keep copy short, and spell uncommon brand words letter by letter. For dense layouts or substantial in-image text, it advises considering polish in design tools. See OpenAI Academy’s image-generation guidance.
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Choose a workflow that fits the consistency problem
| Approach | Setup and documented use | Important limitation |
|---|---|---|
| Reusable reference and prompt | Low setup: choose a reference, use a stable prompt and tune the platform’s influence control. Intended to guide style across variations. | A style reference may not preserve specific objects, people or layouts; feature support depends on the platform. |
| Custom model | More preparation: curate training images and captions or tags. Adobe describes Custom Models for reflecting a style or subject in generated variations. | Adobe’s overview labels the feature beta and says supported plans and use cases vary. Training images need to be consistent, high quality and cleared for use. |
| Shared style kit or template | Save and share approved prompts and generation settings so collaborators can reuse them. | Adobe limits creation and saving of Style Kits to select enterprise plans; access to a shared kit may depend on invitations and account entitlements. |
When a custom model or shared kit is worth considering
Custom models for recurring styles or subjects
When the same character, product, illustration system or visual style must recur across many outputs, Adobe Firefly Custom Models may be relevant. Adobe’s overview, updated September 21, 2026, describes training on a user’s own images and labels the feature beta; availability depends on plan and use case. Check the current Custom Models overview before building a workflow around it.
Adobe’s training guide, updated September 24, 2026, recommends 10–30 representative JPG or PNG images, consistent high-quality examples, and reviewed captions and tags. It estimates training can take 30 minutes to several hours depending on dataset complexity. These are Adobe’s training guidance and estimate, not general benchmarks for other image generators. The guide also asks users to confirm they have rights to submitted images. Details are in Adobe’s custom-model training guide.
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Adobe Style Kits can preserve and share generation settings, including prompt, content, structure and style. Creating and saving kits requires entitlement to select enterprise plans, so confirm availability in the account before making a shared-kit workflow part of team operations. See Adobe’s Style Kits documentation.
Check rights before training or publishing
Use source images, characters, product photos, logos and likeness references only when permitted for the intended training and generation use. Adobe’s training flow asks users to confirm they have the necessary rights to uploaded images. OpenAI Academy advises getting permission for real-person likeness references and suggests generic or ownable designs instead of imitating a particular brand, product or artwork. These are workflow checks, not a complete legal determination for every jurisdiction.
If using another organization’s logo or marks, consult that organization’s current guidelines. For example, OpenAI’s brand guidelines say use of its marks must comply with its rules, that permission is non-exclusive and non-transferable, that its marks must not be more prominent than the user’s own, and that OpenAI may update or terminate permission. Those OpenAI-specific terms should not be assumed to apply to other organizations.
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