Hey, Dora is here. "Everyone says Flux 1.1 nails typography now." I've heard that a dozen times. So I set up a clean, repeatable Flux 1.1 benchmark focused on what creators actually need: photorealistic portraits, convincing product shots, and, most painful of all, accurate text. Counter-intuitively, I found that some of Flux's strengths show up when you turn a few "default" instincts on their head. Here's where the logic shifts...
Flux 1.1 Benchmark Setup: Test Methodology & Environment
I kept this benchmark simple and reproducible so independent creators can mirror it.
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Hardware: RTX 4090 (24 GB VRAM), 64 GB RAM, AMD 7950X. Local runs with consistent thermals.
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Resolution: 1024×1024 base: upscaling where noted using a standard Latent upscaler.
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Sampler & Steps: 28–35 steps (DPM++ 2M Karras equivalent). CFG/intensity held steady per test family to avoid cherry-picking.
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Seeds: Fixed seeds per prompt family for A/B parity. Three seeds per scene to validate variance.
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Negative prompts: Mild, only to remove artifacts (e.g., "watermark, misspelled text, extra fingers").
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No LoRAs or fine-tunes: Out-of-the-box Flux 1.1 behavior.
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Baselines: Midjourney V6 and SD3 (vanilla) with equivalent prompts and aspect ratios.
Prompts (representative):
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Portrait: "natural light headshot of a 30-year-old creative director, shallow depth of field, subtle skin texture, pores visible, neutral backdrop, 35mm lens, f/2.0, photorealistic."
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Product: "studio photo of a matte-black wireless earbud on white acrylic surface, softbox lighting, crisp reflections, specular highlights, 1:1, e-commerce ready."
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Typography: "poster design reading ‘Friday Night Market' in bold condensed sans-serif, letter spacing tight, centered layout, high contrast, clean edges."
Evidence standard: I assessed outputs across three axes, fidelity (detail, lighting physics), control (prompt adherence, consistency), and reliability (fails/misspell rates across seeds). I can't pull live API telemetry here: if you want to replicate, fix your seeds, lock steps, and export grid comparisons. See also: Official Flux implementation, Flow matching research paper.
Flux 1.1 Portrait Results
Expectation vs. reality: the internet positions Flux 1.1 as a portrait specialist. My runs largely agree.
What I saw:
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Skin texture: Flux retained pores and tiny specular micro-highlights without the "plastic wrap" sheen that creeps into many models at high denoise. Using the portrait prompt above, jawline microcontrast was clear and hair flyaways looked natural.
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Lighting: With "window light" phrasing, it produced believable falloff and catchlights. Rim lighting was slightly softer than Midjourney's stylized pop but more photographically plausible.
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Anatomy & symmetry: Eye alignment and ear geometry were stable across seeds. Hands, when included, were competent, not flawless.
Why it happens: Flux 1.1 seems tuned for photographic priors: lower CFG-style intensity preserved tonal gradients better. This is the detail that changes the outcome: a modest guidance strength kept the skin from over-sharpening while retaining pores.
Where it falters: Heavy accessories (elaborate earrings, patterned hats) sometimes merged into hair at 1024px. Upscaling to 2K reduced the mush but didn't fully resolve complex filigree. If you shoot for stylized glamour lighting, Midjourney still adds punchier drama out of the box.
Product Photo Results
For e-commerce style packshots, Flux 1.1 did solid work with reflections and edges.
What I saw:
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Materials: Metals and matte plastics behaved correctly. The earbud prompt produced clean bevels, believable micro-scratches, and accurate softbox reflections.
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Shadows & contact: Contact shadows didn't "float", a common sin. On glossy surfaces, Flux kept a consistent Fresnel roll-off.
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Background cleanup: Minimal background noise. White backgrounds were closer to RGB(245–250) than pure 255: quick Levels correction fixes it.
Rationale: By keeping the step count at 28–32 and avoiding aggressive negative prompts, I preserved photometric consistency. When I pushed steps to 50+, the image sharpened slightly but introduced edge ringing, so I'd stay under 35 for product.
Where it falters: Text or logos embossed on products can wobble. If you need vector-perfect logos, this isn't the right tool, hand off to Illustrator or overlay real vectors in post. Also, extreme macro (2:1) introduced aliasing along curved edges: use a 2x upscale before cropping.
Flux 1.1 Typography Tests
This is the land of broken promises for most models. Flux 1.1 is better, but not magic.
Prompt: "poster design reading ‘Friday Night Market' …"
Results across three seeds:
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Seed A: "Friday Night Market" rendered correctly, tight kerning, minor waviness on the vertical stems.
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Seed B: "Friday Night Markel" (one letter substitution) with a subtle baseline drift.
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Seed C: All text correct but a slight ghosted outline on "Night."
Why it happens: Text rendering in diffusion is a tug-of-war between layout priors and image realism. Flux 1.1's priors are stronger than older models, but the raster-first nature still means letterforms can bend under denoise pressure.
Workarounds that actually help:
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Specify type attributes: "bold condensed sans-serif, geometric, monoline" reduced letter-shape improvisation.
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Keep text under ~20 characters per line: wrap manually in the prompt ("line 1: …, line 2: …").
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Add "flat color background, high contrast, no texture." Text stays cleaner without background noise.
Bottom line: Flux 1.1 can output clean, readable titles 60–75% of the time at 1024px. For production, I'd comp real text in Figma/Photoshop over a Flux background.
Style Diversity
Flux 1.1 isn't trapped in a single aesthetic. I stress-tested:
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Documentary/cinematic: Porous skin, muted palettes, realistic grain, excellent.
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Clean studio: Minimalist product scenes and beauty headshots, stable and consistent.
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Illustration/graphic: Competent, but SD3 leaned more flexible with painterly looks.
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Fantasy/high drama: Midjourney still carries that "art director" bias that many clients love.
Notably, switching from "natural light" to "hard key light, 1-stop over" produced measurable differences in shadow density and edge acutance. Adjusting the "camera" phrasing (35mm vs. 85mm) felt like changing lenses on-set, depth compression reacted predictably.
Flux vs Midjourney vs SD3: Side-by-Side Benchmark Comparison Table
Here's a quick read on the three for this benchmark set. These are qualitative ratings from my fixed-seed tests: run your own to confirm.
| Use Case | Flux 1.1 | Midjourney V6 | SD3 (vanilla) |
|---|---|---|---|
| Portrait realism | Strong skin detail, natural light | Stylized pop, dramatic contrast | Good, sometimes softer microtexture |
| Product edges / reflections | Clean geometry, accurate speculars | Highly polished, sometimes exaggerated | Solid, may need more prompt nudging |
| Typography accuracy | Best of the three, still imperfect | Often deformed under stylization | Inconsistent without control nets |
| Consistency across seeds | High | High but more style variance | Medium |
| Ease of prompt control | High, responds predictably | Medium, great style bias | Medium–high with more tweaking |
Final Verdict
Flux 1.1 is a confident daily driver for creators who need photorealistic portraits, believable product images, and better-than-average text. It's not a silver bullet for typography, but it's the most reliable of the three I tested for short titles and signage.
Who it's NOT for:
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Vector-perfect logos or long paragraphs of copy in-frame, use layout tools and composite.
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Heavy stylization-first workflows, Midjourney still wins on dramatic art direction out of the gate.
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Extreme macro fidelity without post, expect to upscale and retouch.
If you're overwhelmed and on deadline, Flux 1.1 gets you 80% of the way with less prompt wrangling. The real power of this feature lies in pairing Flux's photographic priors with light post: add true vector text and make quick tonal passes. If you run your own tests, fix your seed, keep steps under 35, and compare grids A/B.
Ethical Considerations
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Transparency: If AI assists your visuals, label it. Clients and audiences increasingly expect clarity about synthetic elements. Add a note in credits or alt text.
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Bias mitigation: Diversify your prompt descriptors (age, skin tone, body type) and review outputs for stereotyping. Where you find skew, say, defaulting to certain genders for specific jobs, counterbalance in your wording or use curated reference boards.
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Copyright/Ownership in 2025: Check your model's license and your client contracts. When using brand assets (logos, product shapes), ensure you have permission and keep original vectors separate. For stock-like uses, confirm that Flux 1.1's license terms allow commercial deployment in your jurisdiction. When in doubt, composite real brand elements and keep a clear audit trail of sources.
Be honest: how many times has Flux 1.1 turned your beautiful slogan into abstract art this week? Let’s see those glorious fails; I’ll bring the popcorn.
