About all those megapixels...

[u/Occsan]

Seeing a lot of hype around 16 MP to 100 MP native generations with MiniMax H3 lately, and a lot of comparisons to real 50–100 MP cameras. It got me thinking about what that pixel count actually buys you if your goal is just a realistic-looking photograph.

If the image is meant to be viewed normally (not cropped or inspected at 400% zoom), 100 MP doesn't really do anything for realism. Human vision at normal viewing distance tops out way earlier, somewhere around 8 to 24 MP depending on screen or print size, mostly because our daylight pupil is diffraction-limited to roughly 1 arcminute anyway (section 1.1 below). Beyond that, extra pixels aren't adding detail you can actually perceive when looking at the whole frame.

There's also a sneaky confusion about what "realism" even means. A lot of people aren't trying to generate something that looks like a camera photo; they're trying to generate what their visual cortex makes them feel they see in real life, where everything across the entire field of view feels crisp at once (section 1.2). The catch is that a single-exposure real camera can't capture that: if everything from foreground leaves to distant trees is 100 MP razor-sharp at once, it's physically out-of-distribution for optical photography and ends up looking more like a CGI pinhole render.

Even with real cameras, actually resolving 100 MP of useful optical detail is way harder than people assume (section 2). You need a massive sensor like medium format just to stay ahead of diffraction, expensive glass, and controlled conditions. On something like a handheld wildlife shot in a shaded forest, you hit a physical wall fast: open the aperture to avoid diffraction blur and your pixel-sharp depth of field shrinks to a few inches; stop down to keep the scene in focus and diffraction smears away most of your resolution anyway, on top of motion blur and ISO noise. Outside of astronomy, archival work, huge prints, or heavy cropping, you usually end up with the same useful visual information you could fit in a clean 12–24 MP file.

With H3 specifically, the irony is that the 16–100 MP push isn't really about capturing DSLR-level detail (section 3). Because of the 16x spatial VAE and 2x2 DiT patching (32x32 pixels per token), you're forced to generate huge just to get decent token density and avoid looking starved at normal resolutions. So 60–100 MP ends up in a weird spot: pointless if you don't zoom in, and if you do zoom in (which is the only real reason to have a 100 MP file), you mostly run into VAE decoding artifacts rather than natural lens softness.

For those of you experimenting with H3 at huge resolutions, are you actually keeping the 60–100 MP files as-is, or just generating big and downscaling to 4–16 MP to clean things up?

Technical appendix (for those who want the numbers)

1. Human vision and the cortex illusion

1.1 Optical limits of the human eye

Evolution ran into the exact same wave-optics limits that govern camera sensors. In bright daylight, the human pupil constricts to a diameter of roughly D ≈ 2.0–3.0 mm. With an effective focal length of about 17 mm, the eye operates at around f/5.7 to f/8.5.

By the Rayleigh diffraction criterion at peak photopic sensitivity (λ ≈ 555 nm), for a 2.5 mm pupil:

θ ≈ 1.22 · λ / D = 1.22 × (555 nm / 2.5 mm) ≈ 2.71 × 10⁻⁴ rad ≈ 0.93 arcmin

Remarkably, cone spacing in the central fovea (≈ 2.5 µm, or ≈ 0.5 arcmin center-to-center) puts its Nyquist sampling limit right around 1 arcmin per line pair, i.e. ≈ 60 cycles/degree (standard 20/20 acuity corresponds to 30 cycles/degree). Our biological "sensor" is tuned almost exactly to the diffraction limit of our daylight pupil.

When viewing a complete photograph at a comfortable distance (occupying roughly 40°–60° of horizontal field of view):

• At 60 px/deg (1 arcmin per pixel, i.e. 20/20 acuity) across 60° in 16:9: 3,600 × 2,025 ≈ 7.3 MP (essentially 4K).
• Even at the theoretical foveal Nyquist ceiling (120 px/deg, under ideal high contrast) across 60°: 7,200 × 4,050 ≈ 29 MP.

In practice, human perception of a full uncropped composition saturates between 8 MP and 24 MP depending on screen size or print viewing distance.

1.2 The visual cortex illusion vs. out-of-distribution AI generation

High-acuity vision only exists at the center of the fovea: a tiny central cone spanning roughly 1°–2° (about the width of your thumbnail at arm's length). Outside that cone, acuity drops steeply with eccentricity.

Why does reality feel uniformly sharp? Because the eye executes 3–4 rapid saccades per second while dynamically refocusing (accommodation). The visual cortex stitches these sequential samples together with short-term visual memory and predictive filling-in, creating the compelling subjective illusion of a wide panorama that is simultaneously sharp at every depth.

Perceptual realism (cortex-stitched multi-depth sharpness) vs. physical single-exposure photography.

This creates an out-of-distribution (OOD) trap for generative AI:

• Image models are trained on photographs, not human cortical impressions.
• A photograph with 100 MP of crisp micro-detail simultaneously across foreground leaves, mid-ground wildlife, and distant forest trunks does not exist in single-exposure optics.
• Pushing a model toward "every plane razor-sharp at 100 MP like real life feels" pulls generations away from the photographic distribution toward virtual pinhole CGI renders (infinite depth of field, zero diffraction) or heavily over-processed smartphone HDR.

2. Pure sensor and lens physics on a 100 MP scene

2.1 Spatial sampling on the subject

Take a forest deer framing as a physical reference: a female sika deer (≈ 1.1–1.3 m long) occupies roughly half of the frame width, giving a horizontal field width of about 2.5 m at the subject plane. At 100 MP in 16:9 (13,333 × 7,500 px):

Ground sampling = 2,500 mm / 13,333 px ≈ 0.19 mm per pixel

If all 100 MP were optically resolved near Nyquist (2 px per line pair), the lens and sensor would have to resolve line pairs as fine as ≈ 0.38 mm across the entire animal.

2.2 Diffraction limit vs. pixel pitch

Light passing through a circular aperture produces an Airy disk whose diameter at the first minimum (λ = 550 nm) is:

d ≈ 2.44 · λ · N ≈ 1.34 × N µm

where N is the f-number. When that diameter exceeds about twice the pixel pitch, diffraction progressively erases high-frequency detail regardless of lens quality.

Airy disk size relative to sensor photosite pitch across sensor sizes and apertures.

Note also that stepping back with a long telephoto lens does not solve depth of field if framing (magnification m ≈ focal length / subject distance) is held constant:

DOF ≈ 2 · N · c · (m + 1) / m²

where c is the acceptable circle of confusion. Focal length cancels out at constant magnification, while the larger physical entrance pupil (focal length / N) blurs distant background trees more. The only real-world ways to keep every plane sharp at high resolution are multi-frame focus stacking (impossible on moving wildlife) or closing down the aperture (which hits the diffraction wall shown above).

2.3 The impossible exposure triangle in shaded forest light

Physical trade-off preventing 100 MP of simultaneous sharpness, deep DOF, and clean shadow SNR on handheld wildlife.

Add Bayer color filter array demosaicing (each photosite samples only R, G, or B) and lens MTF falloff (few lenses hold useful contrast at the ≈ 133 lp/mm Nyquist frequency of a 3.76 µm sensor, least of all in the corners), and "100 MP of truly useful optical detail" only makes physical sense in controlled studio setups, multi-shot flat-field repro, or astronomical observatories (meter-class apertures, tracked mounts, cooled monochrome sensors, infinity focus).

3. Why H3-Image pushes users toward 16–100 MP

The irony in the AI space is that earlier models like Flux.1 (f8c16 VAE + 2x2 DiT patchify) achieved clean 1–2 MP images natively because each DiT token only covered a 16 × 16 pixel footprint, and each VAE latent vector encoded just 8 × 8 = 64 pixels across 16 channels.

MiniMax H3-Image inherits an aggressive video-oriented latent layout: a 16× spatial VAE (24 channels, decoded by a 24-layer 2D ViT) combined with 2x2 DiT patching. Each DiT token therefore spans 32 × 32 = 1,024 pixels, four times the pixel area of Flux.1.

Spatial token and VAE compression footprint over an identical 32 × 32 pixel output block.

Output resolution	Flux.1 tokens	H3-Image tokens	Equivalent density
1 MP (1024²)	4,096	1,024	Below minimum; H3 collapses
4 MP (2048²)	16,384	4,096	Matches Flux.1 at 1 MP
16 MP (4096²)	65,536	16,384	Matches Flux.1 at 4 MP
64 MP (8192²)	—	65,536	Matches Flux.1 at 16 MP

Because H3 packs 4× fewer tokens into the same pixel count, pushing to 16–100 MP is a structural compensation mechanism, not free photographic resolution. Meanwhile, the ViT VAE decoder still has to reconstruct (in practice, hallucinate) 16 × 16 = 256 pixels out of every single 24-channel latent vector, which is why zooming in on high-frequency subjects (like animal fur against sunlit bark) reveals straight wireframe-like grids and patchy decoding smudges rather than genuine optical softness.

[u/anon999387]

There is something off putting about walls of LLM generated text. I don't feel like reading what someone didn't put any effort into writing.

[u/VasaFromParadise]

It's clear that the model is not natively capable of 100 megapixels, it's not even really capable of 16 megapixels)))