How to control AI image generation
You generate an AI image that's almost perfect, but you want to create a variation with one small change. Or you create that incredible result you got yesterday but forgot to save the settings. Without understanding seeds, you're stuck regenerating randomly, hoping to stumble onto something similar.
Seeds are the control mechanism behind AI image generation. They transform random outputs into reproducible, refinable results. This guide explains what seeds are, why they matter and how to use them for precise, consistent image creation.
What is an AI image seed?
A seed is a numerical value that determines the starting point for AI image generation. It controls the initial noise pattern the AI uses to create your image. The seed parameter is one of the most powerful controls in AI image generation, yet it's often overlooked by beginners.
How seeds work
AI image generators like Runway don't instantly create finished images from text-to-image prompts. They start with random visual noise—think static on an old TV screen—and gradually refine it based on your prompt. The seed number controls that initial noise pattern. When you don't specify a seed, AI platforms use a random number generator to select one automatically—which is why you get different results each time you generate.
But you can also prompt for the same seed with the same prompt to produce nearly identical results every time. This is what gives you reproducibility. Use seed 12345 with the prompt "mountain landscape at sunset" and you'll get the same image each generation. Change the seed to 67890 with the identical prompt, and you'll get a completely different images—different peaks, different cloud formations, different lighting angles.
Seed numbers vs. seed images
Most platforms use numerical seeds: a random number like 3847562910 that determines your starting noise pattern. This is the standard approach across Runway, Stable Diffusion and other AI image tools.
Some advanced workflows support seed images, where you upload an existing image as the foundation instead of starting from noise. This approach is less common but useful for maintaining specific compositions across variations—particularly when you want to preserve exact spatial relationships or structural elements from a reference image.
Quick start: Using seeds in 3 steps
Let's walk through a practical example to illustrate how seeds work.
Step 1: Generate and capture
Create an image with your prompt: "A majestic lion standing on a savannah at sunset, photorealistic." After generation, find and copy the seed number from the image metadata. Every AI platform displays this differently, but it's typically in the image details panel or generation history.
Step 2: Test reproducibility
Regenerate using the same prompt and seed number. You'll get an almost identical image, proving the seed works. The composition, lighting, subject placement and overall aesthetic should match your original nearly perfectly. Minor variations might appear due to GPU processing differences, but the core image remains consistent.
Step 3: Create controlled variations
Keep the seed but try a different prompt: "A majestic lioness standing on a savannah at sunset, photorealistic." The result maintains the original composition and lighting but reflects your prompt changes. The savannah background stays similar, the sunset colors remain consistent, but now you have a lioness instead of a lion—exactly the targeted change you wanted.
This is the core workflow: lock the seed, adjust the prompt, iterate with precision. Once you understand this pattern, you can refine any aspect of your images without losing the elements that already work.
Why seeds matter
Seeds unlock control over your AI generation workflow in ways that random generation can't match. They transform AI image creation from a lottery into a precision tool.
Reproducibility
Generate an image you love? The seed lets you recreate it perfectly. This is essential for archiving successful outputs, sharing exact results with collaborators and maintaining version control across projects. Without seeds, that perfect image you generated yesterday is gone forever—you might spend hours trying to recreate it and never get close.
Controlled iteration
Fix a seed and adjust your prompt to explore variations systematically. This means you can tweak color schemes on the exact same image, try alternate lighting while keeping subject placement identical or refine specific elements without regenerating everything from scratch. Each variation builds on what works rather than starting over completely.
Random vs. fixed seeds: When to use each
Use random seeds when brainstorming and exploring diverse concepts, when you want unexpected creative results or when speed matters more than precision. Random seeds are perfect for the discovery phase of any project—casting a wide net to see what's possible.
Use fixed seeds when refining an existing image, creating variations of successful outputs, maintaining visual consistency across a series or working with clients who need targeted revisions. Fixed seeds give you surgical precision to adjust exactly what needs changing while preserving everything that already works.
Visual consistency
For brand work, character development or any project requiring cohesive aesthetics, seeds ensure your AI-generated assets maintain a consistent look. Seeds maintain consistency not just in composition but across artistic styles—whether you're working in photorealistic renders, oil painting aesthetics or minimalist illustrations. This is critical for brand marketing campaigns where every image needs to feel part of the same family, character design across multiple scenes where facial features must stay recognizable or social media content series where your audience expects visual continuity.
Efficiency and collaboration
Seeds eliminate the "generate until lucky" approach that wastes time and credits. When working in teams, share a seed number with your prompt so everyone starts from the same visual foundation—streamlining feedback loops and reducing iteration time. Your art director can reference "seed 12345 but with warmer tones" instead of trying to describe what they want changed.
How to find your image seed in Runway
Locating your seed number after generation is straightforward:
- Click on the generated image in your project or history
- Open the details panel (look for "Info," "Details," "Metadata" or an "i" icon)
- Copy the seed number listed alongside your prompt, model version and aspect ratio
- Save this number with your prompt and settings for future use
Practical applications
Brand marketing
Create campaign variations while maintaining brand guidelines. Generate different backgrounds or compositions for a product shot without changing its core appearance. If you nail the perfect lighting and angle for your hero product image, lock that seed and explore different seasonal backgrounds, lifestyle contexts or color palettes—all while keeping the product presentation identical.
Product photography
Show multiple angles or settings for e-commerce without losing the product's visual identity. Use the same seed to generate your product in studio lighting, outdoor settings and lifestyle contexts. The product maintains consistent color, texture and form while the environment changes—perfect for building comprehensive product galleries.
Character design
Iterate on poses, expressions or costumes while keeping fundamental character features consistent throughout your story or series. Lock a seed that generates your protagonist's facial structure, then explore different emotional expressions, outfits or action poses. Your character remains recognizable while the storytelling elements evolve.
Architectural visualization
Test different lighting conditions, materials or landscaping around a building design while maintaining the core structure. Show clients their building concept in morning light, sunset glow and dramatic nighttime illumination—all from the same architectural foundation. Change exterior materials from brick to glass without altering the building's proportions or design.
Social media content
Develop cohesive post series by adjusting minor elements like text overlays or background details while retaining your signature style. Your audience recognizes your content instantly because the underlying aesthetic remains consistent, even as individual posts vary in subject matter or messaging.
Client revisions
Address feedback with targeted changes instead of regenerating entirely new images. When a client says "love everything except make the sky more dramatic," you can keep the seed and adjust only the sky description in your prompt. This saves time and ensures revisions align with the original vision rather than creating something entirely different.
A/B testing
Create two similar designs with small differences—button placement, color scheme, composition—to test which performs better. Use the same seed with minimal prompt variations to isolate exactly what you're testing, eliminating confounding variables that make results harder to interpret.
Common mistakes to avoid
Cross-platform incompatibility
Seeds don't transfer between different AI models. That’s A Runway seed won't recreate the same image in Stable Diffusion or Midjourney because each model uses different underlying algorithms and noise generation processes. Different versions of the same model may produce different results from identical seeds—model updates change how the AI interprets noise patterns. Always document which exact model version you used alongside your seed number.
Missing metadata
Don't just save the seed values. Document your exact prompt text, model name and version, negative prompts if used and generation parameters like aspect ratio and quality settings. Without complete metadata, the seed alone won't reproduce your output. You'll have the starting noise pattern but none of the instructions that shaped it into your final image.
Over-reliance on seeds
Seeds are powerful but shouldn't limit exploration. Sometimes you need a completely new random seed to break out of creative ruts or discover genuinely novel directions. If you keep using the same seed family, you're constraining the AI to variations of the same underlying structure. Use seeds strategically for refinement, not exclusively for all generation.
Expecting pixel-perfect reproduction
Seeds offer high reproducibility, but minor factors—platform updates, GPU processing variations, environmental differences—can occasionally create tiny differences between generations. Aim for highly similar results, not absolutely identical ones. The composition, lighting and subject matter will match, but you might see subtle variations in texture details or edge rendering.
Using seeds to fix bad prompts
Seeds control initial noise patterns. They won't compensate for poorly written prompts or overcome AI model limitations. If your prompt produces mediocre results, trying different seeds just gives you different mediocre results. Start with clear, effective prompts, then use seeds for refinement once you've found something worth iterating on.
Master AI image control
Seeds transform generation from chance into craft. Control initial noise, get reproducibility, enable systematic iteration and unlock consistency across projects.
Start experimenting: generate an image, note the seed number, then try variations with the same seed but modified prompts. Watch controlled iteration turn random outputs into deliberate creative decisions.
