AI Generative Art Specialist Interview Questions

Explain that text-to-image generates from pure text prompts, while image-to-image transforms an existing input image guided by text.

To exclude unwanted elements, improve coherence, or avoid common artifacts like distorted hands.

Using upscaling models (e.g., Real-ESRGAN) or post-processing in software like Photoshop with AI super-resolution.

It adjusts how strongly the model follows the prompt-higher values increase prompt adherence but can reduce diversity.

A seed initializes the random number generator, allowing reproducible results when all other parameters are identical.

It allows conditioning on edges, depth maps, or poses, giving spatial control that text alone cannot achieve.

LoRA is parameter-efficient, faster, and merges with base models; full fine-tuning may yield higher fidelity but requires more data and compute.

Mention techniques like using a fixed seed, textual inversion, or training a character-specific LoRA.

Use color-specific prompts, post-processing color grading, or train a style LoRA on branded assets.

It reverse-engineers a text prompt from an existing image, useful for understanding style or recreating similar content.

They start from random noise and iteratively predict and remove noise conditioned on the prompt, using a U-Net architecture and noise schedule.

IP-Adapter allows direct image input alongside text, while CLIP image embeddings can be used for similarity but are less direct for style transfer.

Local offers control and customization but requires hardware; cloud is scalable and lower maintenance but may limit customization and incur costs.

Mention metrics like FID, CLIP score, or specialized models trained on aesthetic prediction datasets.

Use ControlNet with depth/normal maps from 3D renders, or generate a base image and use inpainting/outpainting for variations.

Inpaint only the hands, use a specialized hand model or LoRA, or generate at higher resolution and crop.

Use textual inversion embeddings or a lightweight LoRA for the character, combined with ControlNet for pose consistency.

Use Load Image nodes, connect to a fine-tuned model with style embeddings, and output via Save Image nodes in a loop or queue system.

Load the pipeline with `from_pretrained`, pass prompts, and generate with `pipe(prompt).images[0]`; mention environment setup and GPU management.

GANs can be faster for specific tasks like super-resolution or style transfer where training data is limited, but diffusion models offer more diversity and control.

Focus on communication, iterating with feedback, and adjusting the technical approach to better align with the creative vision.

Mention communities (e.g., Reddit, Discord), academic papers, GitHub repositories, and experimentation with new tools.

Explain prioritizing prompt refinement and using efficient workflows to meet deadlines while delivering quality.

Discuss using filtered datasets, watermarking, being transparent about AI use, and respecting opt-in/opt-out preferences.