Flux 2 vs GPT-Image 1.5
Top-down arranged compositions and aesthetic product flat lays — see how these models compare with real AI-generated outputs.
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Flat lay content lives or dies by composition: clean top-down perspective, believable object scale, intentional negative space, and consistent styling across a series. In Influencer Studio, both Flux 2 and GPT-Image 1.5 can produce polished flat lays, but they shine in different parts of the workflow.
This comparison focuses on how each model handles top-down arranged scenes (products, props, textures, and typography-like elements), plus practical factors like editing flexibility, consistency for campaigns, and credits-per-image pricing.
Flat Lay — Side-by-Side Results
Prompt
"Top-down flat lay shot on a sunlit kitchen counter: a 20s woman with shoulder-length wavy brown hair and a cozy oatmeal hoodie leans into the frame from the top edge, looking up toward the phone camera with a relaxed half-smile while her hands hover near an iced coffee and a croissant. Aesthetic overhead arrangement includes a planner open to a to-do list, lip balm, wireless earbuds, phone with Instagram Story draft on-screen, and a small vase of daisies on a clean white surface. Soft natural window light, casual candid “morning reset” vibe like a real TikTok thumbnail/UGC ad."
Feature Comparison
| Feature | Flux 2 | GPT-Image 1.5 |
|---|---|---|
| Provider | Black Forest Labs | OpenAI |
| Subcategories | text-to-image, image-to-image | text-to-image |
| 1080p / 2k Mode | Yes | Yes |
| 4k Mode | Yes | No |
| NSFW Rating | Low | Strict |
| Aspect Ratio | 1:1, 16:9, 9:16, 3:4, 4:3 | 1:1, 16:9, 9:16, 3:4, 4:3 |
| Model Variant | Standard, Klein 9B | — |
| Starting Price | 22 credits | 8 credits |
Flux 2 Strengths
- Strong for iterative flat lay building via image-to-image editing (refine spacing, swap props, adjust background materials without restarting)
- LoRA support helps lock in a repeatable flat lay “brand set” (lighting mood, surface texture, prop style) across multiple outputs
- Up to 4MP output is useful for crisp product edges, fabric texture, and print-ready crops in flat lay formats
- Versatile style transfer enables quick exploration of flat lay aesthetics (minimal, editorial, colorful, seasonal) while keeping composition
- Face-swap support can help when flat lay includes hands/partial lifestyle elements and you need consistent identity cues
GPT-Image 1.5 Strengths
- Strong prompt adherence is helpful for precise flat lay layouts (object lists, placement instructions, color palettes, negative space)
- High-fidelity rendering suits premium flat lays where materials and small details (labels, reflections, packaging) matter
- Handles detailed scenes well when flat lays include many items (kits, routines, bundles) and still need visual clarity
- Flexible quality tiers (low/medium/high) make it easy to balance iteration speed vs final-polish renders for flat lay campaigns
Verdict
Choose Flux 2 if your flat lay workflow depends on controlled iteration and consistency—especially when you want to start from a reference image, keep a layout stable, and make targeted edits (swap a prop, change a surface, adjust styling) while maintaining a cohesive brand look via LoRA.
Choose GPT-Image 1.5 if you prioritize prompt-accurate, high-fidelity flat lays from scratch—particularly when you need the model to follow detailed arrangement instructions and deliver a clean, premium result at your chosen quality/credit level. Many teams use GPT-Image 1.5 for initial composition drafts, then Flux 2 for structured editing and series consistency.
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