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AI Image Generation MCQs (Multiple-Choice Questions)
Practice AI Image Generation MCQs to explore how artificial intelligence creates pictures, illustrations, designs, and realistic visual scenes from text prompts and reference images. These questions cover generative models, diffusion techniques, latent spaces, prompt engineering, image editing, and visual quality assessment. They are suitable for students, developers, designers, digital artists, and anyone learning about generative AI. Test your knowledge through fundamental concepts, model behavior, creative workflows, and practical image-generation challenges.
From Text Prompts to Generated Images
AI image generation uses trained models to translate input instructions or reference material into visual output. The process may involve interpreting language, representing visual concepts in a learned space, generating image data, and refining the result. Different approaches offer different levels of control over composition, style, resolution, and image editing.
Important Areas Covered in AI Image Generation
This collection explores text-to-image generation, diffusion models, generative adversarial networks, variational autoencoders, transformers, latent representations, text-image alignment, image inpainting, outpainting, and super-resolution. It also examines prompt interpretation, reproducibility, evaluation metrics, computing requirements, and responsible use of generated images.
Use these questions to understand both the underlying technology and the practical decisions involved in producing high-quality images. The explanations clarify why each answer is correct and highlight important limitations of generative systems.
AI Image Generation Practice Questions
The following 50 MCQs move from basic terminology to model architectures, editing techniques, evaluation methods, and real-world design scenarios. Pay particular attention to the questions about distorted text, reference-image control, reproducibility, and responsible deployment.
1. What is AI image generation?
- A process that only compresses existing image files
- A method for converting pictures into database tables
- A technique that uses AI models to create or modify visual content
- A system that requires every pixel to be drawn manually
Answer: C) A technique that uses AI models to create or modify visual content
Explanation:
AI image generation uses trained models to create new images or transform existing ones. Depending on the tool, users can generate pictures from text, create variations of a reference image, replace selected regions, or expand an image's boundaries.
2. What does a text-to-image model do?
- Generates an image based on a natural-language description
- Converts an image directly into an operating system
- Creates a database backup from a paragraph
- Changes the physical resolution of a monitor
Answer: A) Generates an image based on a natural-language description
Explanation:
A text-to-image model interprets a written prompt and produces an image that attempts to represent its content. The prompt may specify subjects, objects, composition, lighting, color palette, artistic style, and other visual attributes.
3. Which statement best describes a diffusion model?
- It only detects faces in existing photographs
- It can learn to generate images by reversing a process that adds noise to training data
- It stores every possible output image before generation
- It converts all visual data into plain text
Answer: B) It can learn to generate images by reversing a process that adds noise to training data
Explanation:
Diffusion models learn to reverse a noise-corruption process. During generation, they progressively denoise a representation to produce an image. Many modern image generators use diffusion-based methods, including approaches that operate in latent space.
4. What is the primary function of a text encoder in a text-to-image pipeline?
- To increase the physical size of the graphics card
- To save the final image in a folder
- To determine the image file's extension only
- To transform text into a numerical representation that the model can use
Answer: D) To transform text into a numerical representation that the model can use
Explanation:
A text encoder converts a prompt into learned numerical representations, often called embeddings. These representations provide semantic information that can guide image generation toward the subjects, attributes, and relationships described by the user.
5. Why do many image-generation systems use latent space?
- To represent image information in a more compact learned form
- To eliminate the need for visual training data
- To guarantee that all generated images are photorealistic
- To prevent users from changing image dimensions
Answer: A) To represent image information in a more compact learned form
Explanation:
Latent space provides a learned representation of visual information that can be smaller than the original pixel space. Generating images in this compressed representation can reduce computational costs, although the representation may not preserve every fine detail.
6. Which component typically converts a generated latent representation into a visible image?
- Text tokenizer
- Image decoder
- Network router
- File permission manager
Answer: B) Image decoder
Explanation:
In a latent-generation pipeline, the decoder maps the generated latent representation back into pixel space. The resulting image can then be displayed, saved, or passed through additional image-processing stages.
7. What is prompt engineering in AI image generation?
- Increasing the storage capacity of a computer
- Removing all text from the generation process
- Writing and refining instructions to guide the generated image
- Converting image files into executable programs
Answer: C) Writing and refining instructions to guide the generated image
Explanation:
Prompt engineering involves describing the intended subject, composition, environment, lighting, style, and other visual details. Clear, well-organized instructions can improve results, although a prompt cannot guarantee that every requested detail will be reproduced correctly.
8. What is a negative prompt used for when a model supports it?
- To reverse the colors of every image automatically
- To make the model ignore all visual instructions
- To prevent the image from being saved
- To specify visual characteristics or elements that should be avoided
Answer: D) To specify visual characteristics or elements that should be avoided
Explanation:
A negative prompt can describe unwanted elements such as excessive blur, distorted anatomy, or an undesirable style. Its influence depends on the model and generation interface, and not every image-generation system supports this feature.
9. What is a seed value commonly used for during image generation?
- Initializing random-number generation for a particular run
- Determining the legal owner of the output
- Increasing the image's physical dimensions automatically
- Converting a prompt into an audio recording
Answer: A) Initializing random-number generation for a particular run
Explanation:
A seed can help reproduce or compare results when the model, prompt, settings, and software environment remain compatible. A matching seed does not guarantee identical output across different model versions or implementations.
10. What does image-to-image generation allow users to do?
- Generate audio without using any input
- Use an existing image as a starting point for a modified or transformed image
- Convert an image into a network protocol
- Prevent the model from using visual information
Answer: B) Use an existing image as a starting point for a modified or transformed image
Explanation:
Image-to-image workflows use an input image to guide the generation of a new result. A text prompt, strength parameter, mask, or other control may determine how much the output preserves the original composition and how much it changes.
11. What is the main purpose of image inpainting?
- To reduce the image's file extension length
- To change the monitor's brightness
- To fill or regenerate a selected region within an image
- To turn a photograph into a sound file
Answer: C) To fill or regenerate a selected region within an image
Explanation:
Inpainting modifies a selected region using surrounding visual context and, in many systems, a text prompt. It is useful for removing objects, replacing backgrounds, repairing damaged areas, or changing selected image details.
12. How does outpainting differ from inpainting?
- Outpainting can only modify an image's metadata
- Inpainting always adds a new border around an image
- Outpainting removes all objects from the original image
- Outpainting generates content beyond the existing image boundaries
Answer: D) Outpainting generates content beyond the existing image boundaries
Explanation:
Outpainting extends a picture beyond its original boundaries, such as expanding a portrait into a wider landscape. The model attempts to create new content that fits the existing scene, lighting, and visual style.
13. What is the purpose of image super-resolution?
- To increase spatial resolution and reconstruct or generate finer details
- To remove every color from an image
- To convert pixels into database records
- To reduce the number of objects in a scene automatically
Answer: A) To increase spatial resolution and reconstruct or generate finer details
Explanation:
Super-resolution techniques produce a higher-resolution image from a lower-resolution input. AI-based methods may reconstruct textures and details, but some generated details can be inferred rather than recovered from the original image.
14. What is the main idea behind a Generative Adversarial Network (GAN)?
- It uses only a text editor to draw images
- It trains a generator and a discriminator in an adversarial process
- It stores every output in a manually created database
- It replaces all training with random file selection
Answer: B) It trains a generator and a discriminator in an adversarial process
Explanation:
A GAN contains a generator that creates synthetic samples and a discriminator that learns to distinguish generated samples from real ones. Their competing objectives can improve generation quality, although training can be unstable and may suffer from limited output diversity.
15. What is a Variational Autoencoder (VAE) designed to learn?
- A fixed collection of unchangeable output images
- Only the file sizes of training photographs
- A probabilistic latent representation that supports reconstruction and generation
- A system for measuring internet latency
Answer: C) A probabilistic latent representation that supports reconstruction and generation
Explanation:
A VAE learns an encoder and decoder with a structured probabilistic latent space. It can reconstruct inputs and generate new samples by decoding latent values. VAEs are also used as components in some larger generative systems.
16. What role does cross-attention often play in text-conditioned image generation?
- It increases the monitor refresh rate
- It deletes all prompt tokens before generation
- It compresses an image into a ZIP archive
- It allows visual features to use information from text representations
Answer: D) It allows visual features to use information from text representations
Explanation:
Cross-attention enables one set of representations, such as visual features, to incorporate information from another set, such as text embeddings. This helps guide generated image content according to the prompt's semantic information.
17. What is classifier-free guidance used for in many diffusion-based image generators?
- Adjusting the influence of conditioning information on generation
- Identifying the physical location of every pixel
- Guaranteeing that all images contain readable text
- Converting images into executable files
Answer: A) Adjusting the influence of conditioning information on generation
Explanation:
Classifier-free guidance combines conditional and unconditional model predictions to influence how closely an image follows its prompt. Increasing guidance can strengthen prompt adherence in some cases, but excessive values may reduce diversity or introduce artifacts.
18. What is a common use of ControlNet-style conditioning?
- Automatically changing the user's account password
- Guiding image generation using structural inputs such as edges, depth maps, or poses
- Converting an image into a music track
- Eliminating the need for an image-generation model
Answer: B) Guiding image generation using structural inputs such as edges, depth maps, or poses
Explanation:
ControlNet-style methods provide additional structural guidance to a compatible generative model. Edge maps, pose estimates, depth information, and other controls can help preserve layout or geometry while allowing the model to generate visual details.
19. What does a reference image provide in a reference-guided generation workflow?
- A guarantee that every pixel will be copied exactly
- Information about the user's network connection only
- Visual guidance for attributes such as subject appearance, composition, or style
- A replacement for all model parameters
Answer: C) Visual guidance for attributes such as subject appearance, composition, or style
Explanation:
A reference image gives the model a concrete visual example to follow. Depending on the system, it may guide the subject's identity, color palette, pose, layout, or artistic style without requiring exact pixel-by-pixel reproduction.
20. Why can a generated image fail to follow a complex prompt accurately?
- Prompts cannot contain descriptive words
- Image models cannot generate more than one color
- Every prompt is automatically converted into a fixed image
- The model may struggle with detailed relationships, spatial constraints, or numerous simultaneous instructions
Answer: D) The model may struggle with detailed relationships, spatial constraints, or numerous simultaneous instructions
Explanation:
Complex prompts can contain many objects, attributes, and spatial relationships. A model may omit elements or arrange them incorrectly because it does not interpret every instruction perfectly. Simplifying the prompt and using reference or layout controls may help.
21. What does text-image alignment measure?
- How well an image matches the semantic content of its text prompt
- The number of folders used to store generated images
- The physical weight of a camera
- The time required to type a prompt
Answer: A) How well an image matches the semantic content of its text prompt
Explanation:
Text-image alignment describes how closely the generated visual content corresponds to the prompt. A visually attractive image can still have poor alignment if it omits a required object, changes an attribute, or misunderstands a relationship.
22. Which metric is commonly used to evaluate image quality and diversity across generated samples?
- HTTP response code
- Fréchet Inception Distance (FID)
- Keyboard polling rate
- Domain Name System (DNS) record count
Answer: B) Fréchet Inception Distance (FID)
Explanation:
FID compares statistical distributions of feature representations extracted from real and generated images. It is widely used in image-generation research, but its interpretation depends on the evaluation dataset and feature extractor, and it does not capture every aspect of image quality.
23. What is the Structural Similarity Index Measure (SSIM) designed to assess?
- The internet speed used to download an image
- The number of training epochs in a model
- Similarity between images based on structural, luminance, and contrast information
- The legal ownership of generated content
Answer: C) Similarity between images based on structural, luminance, and contrast information
Explanation:
SSIM compares aspects of image structure, luminance, and contrast. It is useful for comparing an image with a reference, such as in image reconstruction, but it is not a complete measure of semantic correctness or artistic quality.
24. What does the Peak Signal-to-Noise Ratio (PSNR) commonly measure?
- The number of objects in a generated scene
- The amount of text in an image prompt
- The speed at which a user types instructions
- The logarithmic ratio between the maximum possible signal power and reconstruction error
Answer: D) The logarithmic ratio between the maximum possible signal power and reconstruction error
Explanation:
PSNR is commonly used to evaluate reconstruction quality by comparing pixel-level error with the maximum signal value. Higher PSNR often indicates lower pixel error, but it does not always correspond to better perceived quality.
25. What is mode collapse in GAN training?
- A failure mode in which the generator produces limited varieties of outputs
- A technique for increasing image resolution
- A method for preserving every possible output variation
- A process that converts a GAN into a database
Answer: A) A failure mode in which the generator produces limited varieties of outputs
Explanation:
Mode collapse occurs when a GAN generator produces a narrow range of samples instead of representing the diversity of the training distribution. The images may look plausible individually while lacking sufficient variation across different generation runs.
26. What is the main purpose of image tokenization in token-based visual models?
- To assign a unique filename to every photograph
- To represent visual information as discrete or structured units that a model can process
- To automatically determine an image's copyright status
- To remove all visual information from an image
Answer: B) To represent visual information as discrete or structured units that a model can process
Explanation:
Some image-generation architectures encode images into discrete tokens or other structured representations. The model can then learn patterns over these units, much as sequence models learn relationships between tokens in text.
27. Why might a model use a Vision Transformer (ViT) or transformer-based visual architecture?
- To replace all visual inputs with audio
- To ensure every output image has the same subject
- To model relationships among image patches or visual tokens using attention
- To prevent the model from processing large images
Answer: C) To model relationships among image patches or visual tokens using attention
Explanation:
Vision Transformers commonly divide an image into patches or tokens and use attention to model their relationships. Transformer-based architectures can support visual understanding and generation, although implementations differ in how they represent and process images.
28. What is the purpose of image embedding?
- To add a visible watermark to every picture
- To convert image pixels directly into a legal contract
- To store an image as a physical object
- To represent image content as a numerical vector or feature representation
Answer: D) To represent image content as a numerical vector or feature representation
Explanation:
An image embedding represents selected visual characteristics in numerical form. Embeddings can support similarity search, retrieval, classification, clustering, and conditioning in generative workflows.
29. How can image embeddings help retrieve visually similar images?
- By comparing distances or similarity scores between embedding vectors
- By comparing only the filenames of the images
- By changing the resolution of every stored picture
- By checking whether all images have the same file size
Answer: A) By comparing distances or similarity scores between embedding vectors
Explanation:
Images can be encoded into vectors that capture learned visual features. A retrieval system compares these vectors using a suitable similarity measure to find images with related subjects, styles, or semantic content.
30. What is style transfer in AI image generation?
- Transferring a file between two storage drives
- Applying visual characteristics of one style to the content of an image
- Converting an image into a spreadsheet
- Changing the user's operating system theme
Answer: B) Applying visual characteristics of one style to the content of an image
Explanation:
Style transfer aims to combine visual characteristics such as brushwork, color treatment, or texture from a style reference with the content of another image. Results depend on the model and the degree of style and content control available.
31. Why do image-generation models sometimes produce distorted hands or extra fingers?
- Image models are designed to remove human anatomy
- Hands cannot be represented as pixels
- Complex anatomy, occlusion, and fine-grained spatial relationships can be difficult to model consistently
- All image formats limit hands to four fingers
Answer: C) Complex anatomy, occlusion, and fine-grained spatial relationships can be difficult to model consistently
Explanation:
Hands contain many articulated parts and can overlap objects or one another. Models may struggle to maintain correct finger counts, joint positions, and perspective, especially in intricate poses. Targeted editing or regeneration can help correct these artifacts.
32. Why can AI-generated text inside an image contain misspellings or malformed letters?
- All image formats automatically rearrange letters
- Text cannot be represented in a digital image
- Image generation never uses language information
- The model may not reliably render exact character shapes and sequences
Answer: D) The model may not reliably render exact character shapes and sequences
Explanation:
Generative image models may represent letters as visual patterns rather than consistently producing exact typography. For accurate labels, logos, or headlines, adding text with a conventional graphics editor or compositing tool is often more dependable.
33. What is the purpose of a mask in guided image editing?
- To identify the regions that should be edited, preserved, or treated differently
- To automatically double the image's dimensions
- To determine the user's internet service provider
- To convert the picture into an audio file
Answer: A) To identify the regions that should be edited, preserved, or treated differently
Explanation:
A mask identifies selected image areas for operations such as inpainting or selective editing. Depending on the tool, the mask may define editable regions, protected areas, or the strength of a local transformation.
34. What does image compositing involve?
- Removing all layers from an image project
- Combining multiple visual elements into a single composition
- Converting every image into a text file
- Training a model without any input data
Answer: B) Combining multiple visual elements into a single composition
Explanation:
Compositing combines elements such as subjects, backgrounds, text, shadows, and effects into one image. It is useful for refining AI-generated artwork, correcting details, and adding elements that require exact control.
35. What is a common use of image-to-image strength or denoising strength?
- To determine the image's copyright owner
- To set the computer's fan speed
- To control how much the generation process can change the input image
- To prevent all changes to the original image under every setting
Answer: C) To control how much the generation process can change the input image
Explanation:
In workflows that expose a denoising-strength control, the setting influences how much the input is altered during generation. Lower strength often preserves more of the original structure, while higher strength generally allows more substantial changes, depending on the model and implementation.
36. Why is aspect ratio important when generating an image?
- It determines whether the image has legal permission to be published
- It measures the number of colors in the image
- It specifies the image's file compression algorithm only
- It defines the proportional relationship between image width and height
Answer: D) It defines the proportional relationship between image width and height
Explanation:
Aspect ratio describes the relationship between width and height, such as square, portrait, or landscape formats. Choosing a suitable ratio helps the model compose the subject for the intended use, including social posts, posters, banners, and thumbnails.
37. What does image quantization generally do?
- Maps values to a smaller set of representable levels or discrete values
- Guarantees that every generated image is more realistic
- Converts a prompt into spoken language
- Automatically removes all compression artifacts
Answer: A) Maps values to a smaller set of representable levels or discrete values
Explanation:
Quantization reduces the precision of numerical values or maps them to discrete levels. It is used in image compression and model optimization, and can reduce storage or computation requirements, although excessive quantization may introduce errors or visual degradation.
38. What is the main benefit of model quantization during AI image-generation deployment?
- It guarantees that the model will never make visual errors
- It can reduce model memory requirements and sometimes improve inference speed
- It eliminates the need for a trained model
- It increases the original training dataset automatically
Answer: B) It can reduce model memory requirements and sometimes improve inference speed
Explanation:
Quantization represents model parameters or computations at lower numerical precision. It can make models easier to run on limited hardware, although the actual speed and quality effects depend on the model, hardware, quantization method, and inference software.
39. What is the purpose of a safety filter in an AI image-generation service?
- To ensure every image has the same artistic style
- To increase the number of pixels in an image
- To detect or restrict content that violates the service's safety policies
- To replace the image decoder with a text editor
Answer: C) To detect or restrict content that violates the service's safety policies
Explanation:
Safety filters can inspect prompts or generated images for disallowed content and enforce service policies. Their effectiveness varies, so responsible systems may combine automated checks with reporting, access controls, and additional review processes.
40. Why should AI-generated images be reviewed before commercial publication?
- Every generated image is guaranteed to contain incorrect information
- Reviewing an image automatically changes its file format
- Commercial images do not need visual quality checks
- Review can identify visual defects, factual issues, brand mismatches, and potential rights or policy concerns
Answer: D) Review can identify visual defects, factual issues, brand mismatches, and potential rights or policy concerns
Explanation:
Human review helps detect distorted objects, inaccurate details, misleading depictions, and inconsistencies with brand guidelines. Publishers should also consider relevant licensing terms, privacy, consent, and applicable legal requirements before using generated imagery commercially.
41. A designer generates a product image, but the product has a different number of buttons from the real item. What is the most appropriate next step?
- Use a reliable product reference and correct the affected area through controlled editing or compositing
- Increase the image's file size without changing its content
- Remove the product description from all future prompts
- Assume that the generated image is accurate because it looks realistic
Answer: A) Use a reliable product reference and correct the affected area through controlled editing or compositing
Explanation:
Photorealism does not guarantee product accuracy. A trusted reference can guide the model, while inpainting or compositing can correct the incorrect buttons. For strict product representation, conventional product photography or 3D rendering may be more dependable.
42. An artist wants to preserve a character's pose while changing only the surrounding environment. Which approach is most suitable?
- Generate an unrelated image with no reference input
- Use a masked editing or background-replacement workflow that preserves the character region
- Change the image's file extension repeatedly
- Remove the character from the source image before every generation
Answer: B) Use a masked editing or background-replacement workflow that preserves the character region
Explanation:
A masked workflow allows the background to be regenerated while the character region is protected or used as a reference. The result should be checked for edge artifacts, lighting mismatches, and shadows that need adjustment.
43. A marketing team needs ten images of the same fictional mascot in different locations. Which technique can help maintain a recognizable appearance?
- Change the mascot's defining features in every prompt
- Use completely unrelated reference images for every generation
- Reuse consistent character references or identity-conditioning features supported by the model
- Remove all descriptions of the mascot from the prompts
Answer: C) Reuse consistent character references or identity-conditioning features supported by the model
Explanation:
Consistent reference images and supported identity-conditioning methods can help preserve recognizable character features across different scenes. The prompts should also maintain important attributes such as clothing, colors, and proportions while changing only the intended setting or action.
44. A company needs a banner with an exact headline, a logo, and a precisely positioned product. Which workflow offers the greatest control?
- Ask the image model to invent a different headline in every generation
- Generate a random image and publish it without inspection
- Rely only on increasing the number of sampling steps
- Use AI to generate the visual background or concept, then place verified text, logos, and product assets using a design or compositing tool
Answer: D) Use AI to generate the visual background or concept, then place verified text, logos, and product assets using a design or compositing tool
Explanation:
Generative models are useful for producing visual concepts and backgrounds, while design tools provide precise control over typography, logo placement, and product positioning. Combining the two approaches helps meet strict branding and layout requirements.
45. A developer is comparing two text-to-image models. Which evaluation method provides the most useful comparison?
- Use a consistent set of prompts and assess prompt adherence, image quality, diversity, latency, and cost
- Compare only the names of the models
- Select the model that produces the largest files in every case
- Evaluate only one image from one model and assume both perform equally
Answer: A) Use a consistent set of prompts and assess prompt adherence, image quality, diversity, latency, and cost
Explanation:
A controlled evaluation reduces differences caused by prompts or settings. Multiple samples and evaluation dimensions reveal trade-offs between visual quality, instruction following, output diversity, speed, and operational cost.
46. An application must generate thousands of user-requested images each day. Which engineering concern should be addressed early?
- Whether all image filenames are identical
- Inference capacity, queue management, usage limits, costs, and failure recovery
- Whether users can generate images without any input validation
- Whether every request should use the maximum available resolution
Answer: B) Inference capacity, queue management, usage limits, costs, and failure recovery
Explanation:
High-volume generation requires capacity planning, rate-limit handling, retries, monitoring, and cost controls. Systems may also need request validation, content moderation, storage management, and clear handling of failed or delayed generations.
47. A business plans to upload confidential reference images to an AI image-generation platform. What should it check first?
- Whether the platform can rename the images automatically
- Whether every reference image will be publicly displayed
- How the platform handles uploaded data, access permissions, retention, and model training
- Whether the images can be converted into unrelated file formats
Answer: C) How the platform handles uploaded data, access permissions, retention, and model training
Explanation:
Reference images may contain trade secrets, personal information, or unreleased products. Reviewing the platform's data policies, retention settings, access controls, and training-use terms helps the business choose a workflow that meets its confidentiality requirements.
48. A content creator generates an image that closely resembles a real person and intends to present it as an authentic photograph. What should be considered before publishing it?
- Only the image's aspect ratio
- Whether the file has a short filename
- Whether the image can be opened in a basic editor
- Consent, the risk of misleading viewers, appropriate disclosure, and applicable rules
Answer: D) Consent, the risk of misleading viewers, appropriate disclosure, and applicable rules
Explanation:
Realistic synthetic depictions can create confusion or harm a person's reputation. Creators should consider consent, privacy, impersonation, and relevant legal or platform requirements, especially when viewers could mistake the image for authentic documentation.
49. A designer is testing many prompt variations but has a limited generation budget. What is a sensible strategy?
- Use lower-cost preview settings or smaller outputs during exploration, then generate the selected concept at the required quality
- Generate every draft at maximum resolution regardless of its purpose
- Disable all prompt comparisons
- Increase the number of output images without tracking results
Answer: A) Use lower-cost preview settings or smaller outputs during exploration, then generate the selected concept at the required quality
Explanation:
Preview generations help compare composition, style, and prompt wording without spending the full budget on every experiment. Once a direction is selected, the designer can use higher-quality settings and perform any required edits or upscaling.
50. An e-commerce team uses AI to generate product images for an online store. The pictures look attractive, but some products have incorrect labels, altered shapes, and inconsistent colors. Which production workflow is the most appropriate?
- Publish all generated images because visual attractiveness is sufficient
- Use verified product references, generate or edit within controlled boundaries, check critical details, and approve each image before publication
- Remove the product specifications from the website
- Use random seeds without reviewing the resulting images
Answer: B) Use verified product references, generate or edit within controlled boundaries, check critical details, and approve each image before publication
Explanation:
E-commerce images must represent the actual product rather than merely look convincing. Verified references and controlled editing can reduce discrepancies, while final review should confirm shape, color, labels, accessories, and other important details. For exact representation, real product photography or validated 3D renders may be preferable to unconstrained generation.