Core techniques in AI visual generation
Artificial intelligence has ushered in a wave of new tools and workflows that empower individuals and organizations to create stunning visuals, from images to videos, with remarkable ease. In this section, we’ll explore the core techniques that define AI visual generation today. We’ll look at how these techniques work for image generation as well as how they extend into the realm of video. As you read through each technique, consider how they build upon one another to unlock creative possibilities that were once reserved for professional studios.
Generating images from text prompts
One of the most accessible and widely adopted techniques is text-to-image generation. This approach allows you to describe an image in natural language and receive a visual output that matches your description. Tools such as Midjourney, Stable Diffusion, Adobe Firefly, and others rely on this process as the foundation of their workflows.
At the core of text-to-image generation is a type of machine learning model trained on vast collections of image-text pairs. These models learn associations between visual features and descriptive language, enabling them to translate your prompt into a coherent composition. For example, if you enter a prompt like “a serene landscape with rolling hills at sunset,” the model interprets the concepts—landscape, hills, sunset—and composes a corresponding image.
In practical use, you can refine results by adjusting parameters:
- Guidance scale or classifier-free guidance controls how closely the output follows your prompt.
- Sampling steps affect the clarity and detail of the result.
- Seed numbers can be used to reproduce a particular image or introduce controlled randomness.
These techniques make it possible for creators to iterate rapidly, generating multiple variations from the same prompt to find the perfect match.
Image-to-image generation and reference conditioning
Beyond text prompts, many platforms support using an existing image as a reference. This technique is commonly called image-to-image generation or conditioning.
In this workflow, you upload or link to an image, which the AI uses as a visual guide for creating new content. For example, you might start with a rough sketch, a photo of a product, or a piece of concept art, then specify how you’d like the output to change. You can prompt the model to:
- Improve quality and detail.
- Modify colors or styles.
- Add new elements while preserving the main structure.
This technique is especially helpful when you need to maintain consistency across a series of visuals or want to transform assets for different contexts.
In tools like Stable Diffusion, this process often involves setting a denoising strength, which determines how much the final image departs from the original reference. Lower values keep the composition similar, while higher values allow more creative freedom.
Image inpainting and outpainting
Another essential capability is the ability to fill in, replace, or extend parts of an image. Inpainting involves removing a selected area—such as an object or background—and having the AI generate new content to seamlessly fill the gap. This is useful for tasks like cleaning up artifacts, removing unwanted elements, or experimenting with different visual variations.
Outpainting takes this further by extending an image beyond its original borders. For example, you might start with a square portrait and outpaint additional background to create a landscape-oriented version. This is often used to adapt images to different formats or imagine what exists outside the frame.
Inpainting and outpainting workflows are typically guided by a combination of:
- Selection tools or masks to define the area.
- Prompts describing what you want the AI to generate in place of the removed or expanded content.
- Reference images to maintain stylistic consistency.
Most modern AI platforms include these techniques, making them indispensable for polishing and customizing generated visuals.
Text-to-video generation
While text-to-image models are now commonplace, text-to-video generation represents the next frontier of AI creativity. This technique enables you to produce short video clips directly from descriptive prompts. The model not only renders a single frame but also synthesizes motion, timing, and transitions over several seconds.
For example, you might prompt: “A futuristic city skyline at night with flying cars moving between skyscrapers.” The AI will generate a video sequence depicting the dynamic motion you described.
This process involves complex modeling, combining knowledge of:
- Object representation and appearance.
- Temporal consistency to ensure elements remain coherent across frames.
- Motion patterns and transitions.
As of this writing, text-to-video tools are more computationally intensive and often limited to shorter clips (typically 3–5 seconds). However, they offer a glimpse into workflows that will soon become more refined and accessible.
Image-to-video transformation
Another emerging technique is image-to-video transformation. In this approach, you start with an existing still image—like a photograph or illustration—and ask the AI to animate it. This might involve:
- Simulating camera movement (such as zooming in or panning across the scene).
- Creating subtle motion (like flowing water, moving clouds, or flickering lights).
-
Morphing the subject into another
form.
This technique is often used to breathe life into static content or create visually engaging social media clips. Some platforms blend image-to-video capabilities with generative motion models to craft looping animations or dynamic backdrops.
While tools for image-to-video generation are still evolving, they share many foundational concepts with text-to-video systems, including the importance of maintaining temporal coherence and visual quality over time.
Combining techniques for richer outputs
Many creators find that combining multiple techniques leads to the most compelling results. For example, you might:
- Use text-to-image generation to create an initial concept.
- Apply image-to-image conditioning to refine the style.
- Inpaint specific details.
- Transform the final image into a short animated clip.
This layered workflow gives you the flexibility to iterate and polish at every stage, whether you are creating marketing visuals, product mockups, or art pieces.
As the capabilities of AI visual generation continue to expand, these techniques will converge into more integrated and intuitive platforms. Over time, we can expect workflows that move seamlessly between images and videos, bridging still and moving media in ways that feel effortless.
By learning these core techniques, you’ll be well equipped to explore the growing ecosystem of AI tools and to create visuals that are not only technically impressive but also genuinely impactful. In the next section, we’ll take a closer look at some of the most popular platforms and how they put these techniques into practice.