classic video game graphics AI generation
The process revealed significant insights into the strengths and limitations of current text-to-image AI platforms. Generating faithful renditions demanded not only accurate subject descriptions but also precise stylistic keywords, often discovered through trial and error or by referencing curated prompt libraries like Lexica.
This approach helped navigate the arcane language required to coax models into producing images consistent with the vintage sci-fi aesthetic. The journey underscores how AI can serve as a creative collaborator, but also highlights the challenges in replicating complex compositions and specific visual elements from pixel art origins (Jalammar, 2024).
Prompt engineering challenges
A critical takeaway from this project is the importance of prompt engineering—the craft of writing text inputs that guide AI models toward desired outputs. Early prompts such as “fighter jets flying over a red planet in space” generated a variety of images but lacked fidelity to the original Nemesis 2 visuals.
To refine results, it became necessary to incorporate style cues and thematic references, like “realistic sci-fi spaceship,” “vintage retro,” and “dramatic lighting.” Searching galleries such as Lexica, which archives millions of prompt-image pairs, helped identify effective prompt structures that could be adapted to the subject matter. However, even with refined prompts, reproducing exact poses or iconic imagery proved difficult. For example, attempts to recreate Dr.
Venom’s unique appearance—green skin, bald head, red eyes, and his restrained posture behind prison bars—required several iterations and creative prompt tweaks. Midjourney excelled in producing aesthetically pleasing images with minimal prompt adjustment but struggled with precise narrative details like character restraint or specific multi-eyed features.
This indicates that while AI can rapidly generate high-quality visuals, it still falls short in replicating nuanced, context-specific elements without manual intervention or post-processing (Jalammar, 2024).
AI Image Platforms Comparison
The three major AI platforms used—Stable Diffusion (via Dream Studio), Midjourney, and DALL-E—each displayed distinct capabilities aligned with different use cases. Stable Diffusion offers open-source flexibility and a robust API, making it ideal for users who want programmatic access and customization.
Dream Studio’s interface balances powerful options like in-painting and candidate selection without overwhelming users, positioning it as a versatile tool for both hobbyists and professionals. However, keeping track of image generation history remains a weak point in Dream Studio, and newer model versions are still undergoing community-driven optimization. Midjourney stands out for delivering the highest image quality with less need for extensive prompt engineering.
Its user interface automatically archives all generated images, facilitating iterative workflows. The platform’s community features also provide rich inspiration and prompt sharing, accelerating creative exploration.
Yet, Midjourney occasionally sacrifices fidelity to the source material’s narrative details in favor of aesthetic appeal, which may limit its use for projects requiring strict visual consistency. DALL-E’s unique outpainting feature enables expanding existing images by generating additional surrounding content, a capability that proved valuable when extending the canvas of a close-up pilot portrait. However, DALL-E struggles with textual accuracy inside images, making it less suitable where legible text or specific iconography is essential.
None of the platforms fully support precise text rendering or exact element placement within images, requiring external editing tools for these tasks (Jalammar, 2024).
Nemesis 2 cinematic panel workflow insights
Recreating each cinematic panel entailed a multi-step workflow combining prompt refinement, multiple generations, and selective editing. For instance, the first panel depicting fighter jets over a planet initially produced generic sci-fi scenes.
Incorporating style modifiers and referencing Lexica’s prompts led to more faithful images. Subsequent panels, such as the prison cell scene with Dr. Venom, demanded careful prompt adjustments to reflect mood and pose, including shifting from chains to bars to better capture the character’s restraint.
The AI’s inability to accurately replicate text or logos meant the starmap panel required manual addition of lines and labels post-generation using Photoshop. Similarly, reproducing Dr.
Venom’s three-eyed visage proved elusive, even with in-painting techniques that target specific image areas, revealing current limitations in controlling fine-grained details. Outpainting with DALL-E showcased how AI can extend existing images to create immersive scenes, but this process necessitates iterative prompt changes tailored to each new portion of the expanded canvas. Overall, the project demonstrated that while AI image generators can produce striking, high-resolution reinterpretations of retro game art, human oversight and traditional editing remain essential for maintaining narrative coherence and visual accuracy (Jalammar, 2024).
AI graphic editing strategies
For professionals aiming to remaster or reimagine visual media, combining AI generation with traditional graphic editing workflows will remain the pragmatic approach. This hybrid method leverages AI’s ability to create detailed, stylistically rich imagery while relying on human expertise to ensure narrative fidelity and design precision.
As AI models evolve, we can expect these tools to become more intuitive and capable, reducing the manual burden and expanding creative possibilities in visual storytelling (Jalammar, 2024). What challenges have you encountered using AI for image generation in your projects
How do you see AI reshaping the restoration or reinterpretation of legacy digital art?
