eCommerce AI Image Editing: Flux & Nano Banana Pro

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updated on Dec 11, 2025

AI image editing tools analyze and automatically adjust product photos, allowing eCommerce businesses to enhance quality, remove backgrounds, or modify details with minimal effort.

We tested the top 5 AI image editing tools on 20 images and 20 prompts across five dimensions, including prompt adaptability, realism, shadows, color rendering, and image quality.

Benchmark results

1. FLUX.2 Pro

FLUX.2 Pro demonstrated the most consistent performance across all evaluation criteria. It achieved strong results in realism, shadow consistency, and color stability and showed reliable adherence to detailed editing instructions.

The model performed effectively in tasks requiring accurate object removal, background reconstruction, and preservation of existing scene elements. Its limitations appeared in a small subset of prompts involving highly specific object isolation or complex scene reconfiguration, where prompt interpretation degraded and output quality decreased.

2. Nano Banana Pro

Nano Banana Pro delivered high-quality outputs with strong semantic understanding, particularly for tasks involving object-level adjustments, material changes, and preserving lighting conditions. In several cases, its realism and structural accuracy approached or matched those of FLUX.2 Pro.

However, the model underperformed in scenarios requiring comprehensive clutter removal or background simplification. In these cases, prompt adaptability, realism, and shadow accuracy declined, indicating reduced reliability for reconstruction-intensive tasks.

3. Qwen Image Edit

Qwen Image Edit produced satisfactory results for prompts involving targeted object modifications or straightforward substitutions, and it maintained acceptable color accuracy and overall image quality in simpler scenes. Its performance declined in tasks requiring precise geometric reconstruction, detailed background rebuilding, or high-fidelity shadow integration. Across multiple prompts, the model did not fully satisfy the instructions, leading to reduced prompt adaptability and inconsistent realism.

4. Seedream 4.0 Edit (ByteDance)

Seedream 4.0 demonstrated the capability to produce strong outputs in select scenarios, particularly when edits involved relatively simple structural changes or limited-area modifications. It also produced acceptable color rendering and shadow integration in some prompts.

Although it performed well in some areas, the model frequently struggled with complex background reconstruction, precise object removal, and accurate preservation of required scene elements. These limitations resulted in low prompt adaptability and reduced realism in several tasks.

5. Wan 2.5

Wan 2.5 failed to produce viable results for a substantial portion of the test set (unable to generate 12 of 20 images). The model frequently failed to interpret instructions, reconstruct missing details, or maintain visual coherence, resulting in scores of 0 across multiple prompts. This level of inconsistency significantly limits its suitability for structured or production-oriented image editing workflows.

AI image editing tools

FLUX.2 Pro (Image Editing)

FLUX.2 Pro is a production-grade image-editing model and supports multi-reference editing with up to nine images. It enables precise compositing, background replacement, and style alignment through natural-language prompts without requiring parameter tuning or masking.

The system provides reliable output quality across sequential edits and offers advanced control through JSON-structured prompts, HEX color specifications, and direct image referencing using the @ syntax. It is intended for automated workflows, eCommerce pipelines, and other high-volume editing environments.

Nano Banana Pro (Gemini 3 Pro Image)

Nano Banana Pro (also known as Nano Banana 2 and based on Google’s Gemini 3 Pro Image architecture) is an advanced image-generation and editing model. It interprets natural-language instructions without the need for masks or manual selections, supports multi-image composition with up to 14 references, and maintains character consistency across edits.

The model emphasizes semantic understanding of objects, lighting, and composition, enabling precise adjustments such as color changes, scene modifications, and text rendering. It prioritizes quality over speed, outputs up to 4K resolution, and includes SynthID watermarking.

Qwen Image Edit

Qwen Image Edit specializes in accurate text-based modifications, allowing users to transform visual elements through natural-language prompts. It supports commercial use, processes standard image formats, and applies changes such as object replacement or scene alteration with high fidelity.

The model is optimized for semantic understanding of image content and is suitable for prompt-driven editing workflows that require reliable interpretation of complex instructions.

Seedream 4.0 Edit (ByteDance)

Seedream 4.0 is ByteDance’s unified image-generation and image-editing model, designed to handle complex transformations that combine multiple reference images. It can modify clothing, add or remove objects, change backgrounds, and integrate compositional elements into a coherent scene.

The model offers flexible multi-image workflows suitable for advanced creative editing tasks that require consistent visual integration and high-quality output.

Wan 2.5 Image-to-Image

Wan 2.5 preview is designed to reinterpret existing visuals. It supports commercial use and applies stylistic, atmospheric, or structural transformations while preserving core elements of the source image.

Users can specify detailed scene changes, such as lighting conditions, weather effects, or thematic shifts, and the model produces a revised composition accordingly.

Examples from our benchmark

Figure 1: Image showing six different versions of a cushion and blanket scene.

Prompt: “Keep the cactus-pattern pillow in the center. Remove the green pillow on the left side and reconstruct the sofa texture behind it seamlessly. Leave the blanket on the right side untouched.”

This task requires strict adherence to the prompt: remove only one pillow, keep two elements untouched, and reconstruct the sofa texture seamlessly. Tools that strictly respected these constraints received higher adaptability scores, while those that altered unintended areas scored lower.

Nano Banana Pro and Wan 2.5 scored highly because they removed the correct pillow cleanly and preserved the remaining objects with higher realism and texture reconstruction.
Flux 2 Pro and Qwen showed partial prompt-following but sometimes modified or blurred areas that should remain intact, which lowered their adaptability and realism ratings despite acceptable overall image quality.
Seedream received a very low prompt adaptability score because it removed or altered elements that were supposed to remain untouched, failing the selective-edit requirement.

Figure 2: Image showing six different versions of a hand holding a gaming controller.

Prompt: “Keep the gaming controller and the hand exactly as they are. Remove the wooden floor background and replace it with a clean light-grey gradient studio backdrop. Ensure the edges of the hand remain natural, and lighting stays soft and realistic.”

This task requires preserving clean foregrounds (hand and controller) and replacing the background with a smooth and realistic one. Tools that maintained natural edges, consistent lighting, and untouched controller details scored highest.

Nano Banana Pro and Wan 2.5 excelled because they preserved the hand and controller perfectly and produced convincing studio-style gradients.
Seedream performed well on prompt adaptability for this task, preserving the controller and the hand accurately and following the instructions well.
Flux 2 Pro lost points for prompt adaptability in subtly altering foreground elements, such as the fingers and the controller’s color, while Qwen struggled the most, as its edits were caricaturistic, resulting in low realism and lower overall scores.

Figure 3: Image showing five different versions of a serum bottle.

Prompt: “Keep the serum bottle intact. Remove the hand holding the bottle and reconstruct the bottle’s missing edges realistically.”

The difficulty here lies in removing the hand while realistically reconstructing the bottle’s missing edges.

Nano Banana Pro and Qwen scored well because they followed instructions closely and produced believable bottle geometry and lighting.
Flux 2 Pro earned strong realism and lighting scores but failed prompt adaptability due to incomplete hand removal or incorrect reconstruction.
Seedream performed poorly here because it struggled to reconstruct the bottle realistically.
Wan 2.5 did not generate a valid output for this task. Its inability to complete the edit reflects a limitation in task compatibility rather than image quality.

Figure 4: Image showing five different versions of a wine glass.

Prompt: “Keep the wine glass unchanged. Replace the background with a clean black studio backdrop. Remove the blurred orange bottle in the background.”

This task involves strict object preservation and complex background replacement.

Qwen scored highly because it successfully kept the glass intact, removed the background bottle, and produced a clean black-studio aesthetic.
Flux 2 Pro struggled with this task because it introduced a lightning effect that was not specified in the prompt, resulting in a poor prompt-adaptability score.
Nano Banana Pro and Seedream changed the focus and location of the wine glass, resulting in lower scores for both prompt adaptability and image quality.
Wan 2.5 again failed to produce a usable output.

Key features of AI image editing tools

Object removal and cleanup

Many AI-powered editors help users remove distracting elements from a single image or from multiple images. These features allow you to clean up cables, background clutter, or accidental objects without resorting to complex software. This feature is helpful for content creators working with product photos, personal projects, or any situation where visual continuity matters.

Key points include:

Remove backgrounds or isolated objects with minimal manual editing.
Fill in gaps naturally so the final image looks consistent.
Produce professional-looking results even when starting from basic photos.

Background removal and replacement

A background remover isolates the subject of the photo and allows users to replace the background with solid colors, creative styles, or other images. This works well for product images, portraits, and social content.

Key aspects include:

Quick background removal without complex tools.
Ability to replace backgrounds while retaining the original subject’s edge detail.
Support for multiple formats, allowing you to start editing immediately after upload.

Generative editing

Some advanced AI tools provide generative functions that respond to a text prompt. These functions can extend a scene, add new elements, or reimagine part of the image. Unlike traditional software, this approach reduces the time needed for complex edits.

Applications include:

Using prompts to generate multiple variations of an idea.
Extending an image’s borders to fit design needs.
Adjusting creative styles without high-level design skills.

Automatic enhancement

Automatic enhancement features analyze the image and adjust lighting, color balance, exposure, shadows, and clarity. This helps users enhance photos without relying on complex programs or manual sliders.

These tools can help with:

Improving image quality in a single step.
Quick edits on mobile devices or through a simple online tool.
Enhancing portraits and other image types with minimal input.

Upscaling and noise reduction

If a photo is low-resolution or captured in challenging lighting, an AI image editor can upscale and restore it. These functions improve clarity and reduce noise, making older or low-quality photos more usable.

Capabilities typically include:

Increasing resolution while protecting fine detail.
Improving clarity in photos originally captured on mobile devices.
Preparing images for prints, presentations, or online use.

Batch processing for multiple images

Some photo editing software allows users to edit multiple images at once. This helps maintain visual continuity across product photos, social content, or any project that includes multiple images.

Benefits include:

Faster workflows for eCommerce or content teams.
Consistent adjustments are applied across an entire collection.
Time savings when preparing product photos in multiple formats.

Limitations and what AI editing does not automatically guarantee

AI still requires human judgment

Although an AI image editor can perform advanced corrections, the user still guides the creative process. Artificial intelligence may misinterpret lighting, perspective, or artistic intent, especially in complex edits. A trained eye often improves the outcome. Situations where this matters include:

Subtle color grading choices.
Scenes with layered reflections or unusual lighting.
Projects requiring complete control over small details.

Possibility of an unnatural appearance

Overusing portrait tools or enhancement features may result in heavily modified-looking results. When enhancing portraits, balance is essential to maintain a natural look. Examples include:

Excessive smoothing that removes texture.
Strong contrast edits that distort the original mood.

Inconsistent generative results

When relying on a text prompt to transform images or generate multiple variations, the output may contain unintended elements or visual inconsistencies. This can occur in scenes with many objects, complex backgrounds, or intricate patterns.

Quality depends on the original photo

While AI can enhance the quality of an image or upscale it, severely damaged or extremely low-resolution photos may not produce high-quality results. The initial file limits how far enhancement can go. Factors include:

Motion blur or deep pixelation.
Photos captured in extremely low light.

Ethics and authenticity considerations

AI tools can replace backgrounds, remove people, or add elements that were not initially present. This raises ethical concerns in fields such as journalism, documentation, and certain personal photos. Users should apply these features responsibly. Considerations include:

Maintaining authenticity in professional contexts.
Avoiding misleading edits in sensitive situations.
Being transparent when images are significantly altered.

Methodology

Tools evaluated

We benchmarked the following models with the endpoints on fal.ai¹:

flux-2-pro/edit
nano-banana-pro/edit
qwen-image-edit/image-to-image
bytedance/seedream/v4/edit
wan-25-preview/image-to-image

All tools were evaluated in January 2025. The images are gathered from Pexels.²

Dataset and editing objectives

The benchmark utilized a dataset of 20 images representing eCommerce products and lifestyle scenarios. Each image was assigned a unique prompt containing context-dependent editing instructions. These instructions required precise object removal, background reconstruction, and preservation of photorealistic attributes.

Examples of prompt categories include the following:

Mini figures: Remove the second hiker in the blue outfit and leave only the hiker in the hat and red backpack. Rebuild the rocky terrain and background naturally so the scene looks complete.
Candles: Keep the two front candles exactly as they are. Remove the green candle in the back completely and fill in the wooden table naturally. Adjust lighting and shadows to stay consistent.
Room fragrance: Keep the glass fragrance bottle with diffuser sticks exactly as is. Replace the background with a blue and grey gradient and remove the decorative object on the right side. Maintain realistic shadows beneath the bottle.

We aim to ensure a controlled and repeatable testing environment with fine-grained editing capabilities across all tools.

Evaluation criteria

Each generated image was assessed using five criteria. Every criterion was scored on a scale of 1 to 5, with higher values indicating better performance.

1. Prompt adaptability

This criterion measured how accurately each tool followed the specific instructions contained in the prompt. The assessment focused on the correct removal of objects, the preservation of required elements, and the proper execution of environmental modifications.

2. Realism

This criterion evaluated the naturalness of the edited regions relative to the original image. The assessment considered texture continuity, artifact avoidance, and the visual coherence of reconstructed areas.

3. Shadows

This criterion examined the accuracy and consistency of shadows following the applied edits. Elements reviewed included the direction, softness, and integration of shadows within the scene lighting.

4. Color rendering

This criterion assessed whether the resulting image demonstrated accurate and stable color reproduction. The evaluation included vibrancy, consistency with the prompt, and the absence of unnatural shifts.

5. Image quality

This criterion measured the overall technical quality of the output. Areas of focus included resolution, clarity, sharpness preservation, and avoidance of unintended resizing or distortion.

Scoring approach

The total score for each image was calculated by summing the five criteria, resulting in a maximum possible score of 25 points. All tools received identical prompts, enabling consistent comparison across varied editing objectives.

Reference Links

https://fal.ai/dashboard

Free Stock Photos, Royalty Free Stock Images & Copyright Free Pictures · Pexels

Industry Analyst

Sıla Ermut

Industry Analyst

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Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

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