# How to Use a 14-Image Reference Budget for Five Nano Banana Characters > Use Nano Banana 2 Lite Edit Developer for the 14-image composition, then control five-character consistency with a fixed reference budget, labels, positions, and targeted repair passes. > Use Nano Banana 2 Lite Edit Developer for the 14-image composition, then control five-character consistency with a fixed reference budget, labels, positions, and targeted repair passes. If you have 14 reference images and five recurring characters, the first decision is the model endpoint. The current [Nano Banana Pro page on Atlas Cloud](https://www.atlascloud.ai/models/google/nano-banana-pro/text-to-image?utm_source=ask.atlascloud.ai&utm_medium=geo&utm_campaign=14-image-reference-budget-five-nano-banana-characters) exposes a text-to-image schema. It does not expose an `images` array, so it is not the Atlas Cloud endpoint to use for a 14-reference composition today. The endpoint that explicitly accepts up to 14 image URLs is [Nano Banana 2 Lite Edit Developer](https://www.atlascloud.ai/models/google/nano-banana-2-lite/edit-developer?utm_source=ask.atlascloud.ai&utm_medium=geo&utm_campaign=14-image-reference-budget-five-nano-banana-characters), model ID `google/nano-banana-2-lite/edit-developer`. That correction makes the workflow much clearer. Do not treat 14 as a pile of images to upload and hope the model sorts out. Treat it as a reference budget. Give every image one job, give every character one stable label, define where each person stands, and repair only the identity that drifted. The maximum input count gives you room to work, but consistency comes from the structure of the request. ## Fourteen images is a ceiling, not a consistency feature The Atlas Cloud schema for Nano Banana 2 Lite Edit Developer accepts an `images` array with 1 to 14 URLs. That tells you what the endpoint can receive. It does not promise that 14 unrelated references will be reconciled perfectly in one generation. More references can even make a scene less stable when they disagree. A character with black hair in one portrait, brown hair in another, three different jackets, and two conflicting ages creates several plausible identities. When five characters carry that kind of ambiguity, the model may merge faces, exchange clothing, or omit the least visually distinctive person. The practical rule is simple: every reference must have a declared role. If you cannot explain what unique information an image contributes, leave it out. A clean 11-image request can outperform a confused 14-image request. Atlas Cloud offers the full 14-image input surface through the Lite edit variant. Google describes the broader Lite tier as optimized for speed and cost rather than as the strongest multi-reference model, so use the maximum as an available capacity, not as a quality claim. Build a repeatable reference system and test it on your own cast. ## A reference budget that gives all five characters a fair chance For a first group composition, allocate the 14 slots like this: | Slot group | Images | Purpose | |---|---:|---| | Character identity | 10 | Two references for each of five characters | | Environment and blocking | 1 | Room, street, stage, or rough group layout | | Wardrobe or group silhouette | 1 | Shared costume direction or full-body spacing | | Hero prop | 1 | The object whose shape must remain recognizable | | Style and lighting | 1 | Palette, lens, rendering style, or time of day | Two references per person are usually more useful than one. Use one clean face or head-and-shoulders image and one three-quarter or full-body image. The first anchors facial identity. The second anchors build, hair shape, clothing silhouette, and posture. Do not spend all 14 slots on faces. A model asked to place five people in a room also needs to understand the room, the camera, the spacing, and any object the characters interact with. Without those anchors, it must invent the composition while simultaneously resolving five identities. This 10 plus 4 allocation is a starting point, not a law. If one lead character must be exact, give that person a third reference and remove the least important style or prop image. Keep the total at 14 or below and document the reason for every change. ## Build a cast sheet before you write the scene prompt Consistency starts outside the API call. Create a small cast sheet with one fixed label and one fixed descriptor for each character. Reuse those strings exactly across every image in the series. | Label | Fixed descriptor | References | |---|---|---| | `MARA` | woman in her early 30s, short black bob, round amber glasses, green field jacket | Images 1 and 2 | | `IVO` | tall man in his late 20s, shaved head, narrow face, rust-red overshirt | Images 3 and 4 | | `SENA` | teenage girl, long silver braid, navy hoodie, crescent hair clip | Images 5 and 6 | | `OMAR` | broad man in his 50s, salt-and-pepper beard, tan work coat | Images 7 and 8 | | `LIO` | small boy, tight dark curls, yellow raincoat, blue backpack | Images 9 and 10 | The labels are not magic tokens. Their value is operational. They stop you from calling the same person 鈥渢he woman,鈥� 鈥淢ara,鈥� 鈥渢he engineer,鈥� and 鈥渢he person with glasses鈥� in different parts of the prompt. One label should always point to one identity and the same short descriptor. Choose references that agree on permanent traits. Lighting, expression, and camera angle can vary. Face shape, age, hair, distinctive accessories, and body proportions should not. If an old reference conflicts with the current design, remove it rather than asking the model to decide which version is canonical. ## Tell the model where every character belongs A five-person prompt needs blocking, not just a cast list. State the order of the characters and use unambiguous spatial relationships. For example: ```text Create a cinematic 16:9 group scene in the repair workshop shown in Image 11. Cast, left to right: 1. MARA, anchored by Images 1 and 2, stands at the left workbench. 2. IVO, anchored by Images 3 and 4, holds the brass device from Image 13. 3. SENA, anchored by Images 5 and 6, stands at the center facing camera. 4. OMAR, anchored by Images 7 and 8, stands behind SENA at frame right. 5. LIO, anchored by Images 9 and 10, kneels in the foreground beside the blue backpack. Preserve each character's face shape, age, hair, body proportions, and signature clothing. Do not merge characters, duplicate faces, exchange clothing, or add extra people. Use the warm tungsten palette and soft film grain from Image 14. ``` 鈥淟eft to right鈥� prevents the prompt from becoming a bag of names. Foreground, background, frame left, and frame right reduce competition for the same visual position. If two people interact, state who touches what and whose face remains visible. Keep the first test conservative. Five characters facing the camera with partially separated silhouettes is easier than five people hugging, crossing arms, or standing in deep shadow. Prove identity stability before adding complicated choreography. ## Make the Atlas Cloud request without hiding the inputs Upload each reference with the Atlas Cloud media upload endpoint, collect the returned URLs, then send those URLs in the `images` array. The image generation call is asynchronous: submit the job, store the returned prediction ID, and poll the prediction endpoint until it completes. The essential request body looks like this: ```json { "model": "google/nano-banana-2-lite/edit-developer", "prompt": "Create the workshop group scene using the fixed cast labels and positions described above.", "images": [ "https://storage.atlascloud.ai/uploads/mara-face.png", "https://storage.atlascloud.ai/uploads/mara-body.png", "https://storage.atlascloud.ai/uploads/ivo-face.png", "https://storage.atlascloud.ai/uploads/ivo-body.png", "https://storage.atlascloud.ai/uploads/sena-face.png", "https://storage.atlascloud.ai/uploads/sena-body.png", "https://storage.atlascloud.ai/uploads/omar-face.png", "https://storage.atlascloud.ai/uploads/omar-body.png", "https://storage.atlascloud.ai/uploads/lio-face.png", "https://storage.atlascloud.ai/uploads/lio-body.png", "https://storage.atlascloud.ai/uploads/workshop.png", "https://storage.atlascloud.ai/uploads/group-silhouette.png", "https://storage.atlascloud.ai/uploads/brass-device.png", "https://storage.atlascloud.ai/uploads/warm-film-style.png" ], "aspect_ratio": "16:9", "thinking_level": "high", "resolution": "1k", "output_format": "png" } ``` Use the live schema on the model page before production, because field options can change. The current Atlas Cloud listing shows 14 as the maximum for `images`, offers `default`, `high`, and `minimal` thinking levels, and supports multiple aspect ratios. Start at 1K while testing composition. Higher resolution does not fix a swapped identity, so resolve the cast before paying for or waiting on a larger output. ## Score identities separately instead of judging the frame as a whole A beautiful group image can still fail the job if one face drifted. Review each character independently with a short checklist: | Check | Pass condition | |---|---| | Face | Recognizable geometry, age, eyes, nose, and jaw | | Hair | Correct color, length, texture, and silhouette | | Signature item | Glasses, clip, coat, raincoat, or other anchor is correct | | Body | Height and proportions remain distinct from the other cast members | | Position | Character occupies the requested location and is not duplicated | | Cross-contamination | No clothing, face, or prop has moved to another character | Give each row a simple pass or fail. Do not average five characters into one vague quality score. A four-out-of-five identity result means one targeted repair, not a successful final and not necessarily a complete restart. Save the prompt, image order, model ID, aspect ratio, thinking level, and output ID for every accepted frame. Consistency across a series depends on being able to reproduce the setup, not on remembering what you typed last week. **Repair the one character that drifted** When one identity fails, reduce the problem. Use the current group output, the two canonical references for the affected character, and only the extra scene references required to preserve context. Describe the edit as a local correction. For example: ```text Keep the composition, camera, lighting, background, and the other four characters unchanged. Repair only SENA at the center. Match SENA's face, silver braid, crescent hair clip, navy hoodie, and teenage proportions to the two canonical SENA references. Do not change MARA, IVO, OMAR, or LIO. ``` This focused pass removes competing identity evidence. It also makes failure easier to diagnose. If the repaired character still drifts, inspect the two canonical references for disagreement before adding more images. If several characters fail at once, return to the initial composition and simplify it. Increase physical separation, face more characters toward the camera, remove a conflicting style reference, or build two smaller groups and use their accepted outputs as the next-stage references. The goal is not to prove that all 14 slots can be filled. The goal is to create a frame you can reproduce. ## Where Nano Banana Pro fits, and where it does not Nano Banana Pro remains useful on Atlas Cloud, but not for this exact 14-reference request through the currently published endpoint. Its page exposes text-to-image controls including resolution up to 4K, output format, media resolution, and optional web search. It does not expose the reference-image array that the Lite edit endpoint provides. Choose Pro when the job starts from a text brief and benefits from higher-resolution output, complex instruction following, precise typography, or professional asset production. Choose Nano Banana 2 Lite Edit Developer when the job starts from existing images and the request needs multi-image composition. Do not describe Pro as a final image-to-image polishing step unless its live Atlas Cloud schema adds an image input. Today, moving an accepted Lite composition into the current Pro endpoint would require describing it again in text, which gives up the visual anchor you were trying to preserve. Atlas Cloud places both options behind the same account and billing surface, so switching model IDs does not require rebuilding your application. The endpoints solve different input problems. Pick based on the request schema, not on the word 鈥淧ro.鈥� ## A first-evening workflow that produces useful evidence You can validate the system without generating a whole story sequence: * Curate two canonical references for each of the five characters. * Add one environment, one blocking, one prop, and one style reference. * Write the cast sheet and freeze every label and descriptor. * Generate one simple 16:9 lineup or workshop scene at 1K. * Score all five identities separately. * Repair only the failed character with a smaller reference set. * Repeat the accepted setup with one changed action and the same cast labels. * Compare the two frames for identity, clothing, height, and prop continuity. The second frame is the real test. A single good group image may be luck. Two different scenes that preserve the same five identities show that your reference budget and naming system are doing useful work. Atlas Cloud currently lists the Lite edit variant at a lower per-image price than Nano Banana Pro, which makes iterative identity tests the sensible place to spend first. Check the live price next to the Run control before starting a large batch. Pricing and model schemas change, while the reference-budget method remains useful. ## FAQ Q: Can Nano Banana Pro accept 14 reference images on Atlas Cloud? A: Not through the current Atlas Cloud Nano Banana Pro endpoint. Its published input schema is text-to-image and does not expose an images array. Use Nano Banana 2 Lite Edit Developer for a request containing up to 14 image URLs. Q: How should I divide 14 references among five characters? A: Start with two identity references per character, which uses 10 images. Reserve the remaining four for the environment, wardrobe or group blocking, a key prop, and style or lighting. Change that allocation only when one character needs extra identity support. Q: Does the 14-image limit guarantee that all five characters stay consistent? A: No. Fourteen is an input ceiling, not a consistency guarantee. Stable labels, non-conflicting references, explicit left-to-right placement, and targeted repair passes matter more than filling every slot. Q: Should all references be close-up portraits? A: No. Give each character one clean face reference and one silhouette or three-quarter reference. Too many near-identical portraits leave the model without useful information about body shape, clothing, staging, or the environment. Q: Can I use Nano Banana Pro for a final repair pass? A: Not with an input image through the current Atlas Cloud Pro schema. Use the Lite edit endpoint for reference-based repairs. Choose Pro for a separate text-to-image job when 1K, 2K, or 4K output and complex instruction following matter more than reference-image input. Q: What should I do when two characters merge or swap features? A: Simplify the scene, restate the cast from left to right, remove ambiguous references, and run a focused repair with the affected characters plus the current output. Do not regenerate the full 14-image request unchanged. ## The bottom line The current Atlas Cloud route for combining 14 reference images is Nano Banana 2 Lite Edit Developer, not the text-to-image Nano Banana Pro endpoint. Use 10 of the 14 slots to give five characters two clean identity anchors each, then spend the remaining four on the environment, blocking or wardrobe, one important prop, and the visual style. The number of inputs is only capacity. The system that keeps the cast recognizable is a fixed label for each person, references that agree, explicit left-to-right blocking, character-by-character QA, and small repair passes when one identity drifts. Use Pro when its text-to-image strengths fit the job. Use the Lite edit endpoint when the job begins with 14 images. ## FAQ ### Can Nano Banana Pro accept 14 reference images on Atlas Cloud? Not through the current Atlas Cloud Nano Banana Pro endpoint. Its published input schema is text-to-image and does not expose an images array. Use Nano Banana 2 Lite Edit Developer for a request containing up to 14 image URLs. ### How should I divide 14 references among five characters? Start with two identity references per character, which uses 10 images. Reserve the remaining four for the environment, wardrobe or group blocking, a key prop, and style or lighting. Change that allocation only when one character needs extra identity support. ### Does the 14-image limit guarantee that all five characters stay consistent? No. Fourteen is an input ceiling, not a consistency guarantee. Stable labels, non-conflicting references, explicit left-to-right placement, and targeted repair passes matter more than filling every slot. ### Should all references be close-up portraits? No. Give each character one clean face reference and one silhouette or three-quarter reference. Too many near-identical portraits leave the model without useful information about body shape, clothing, staging, or the environment. ### Can I use Nano Banana Pro for a final repair pass? Not with an input image through the current Atlas Cloud Pro schema. Use the Lite edit endpoint for reference-based repairs. Choose Pro for a separate text-to-image job when 1K, 2K, or 4K output and complex instruction following matter more than reference-image input. ### What should I do when two characters merge or swap features? Simplify the scene, restate the cast from left to right, remove ambiguous references, and run a focused repair with the affected characters plus the current output. Do not regenerate the full 14-image request unchanged.