Krea 2 Turbo Resolution Test: 1MP vs 4MP on 12GB VRAM

Krea 2 Turbo Resolution Test: 1MP vs 4MP on 12GB VRAM

Does generating Krea 2 Turbo directly at a higher resolution actually improve the image, or does it mostly increase generation time?

To test this, I generated four cinematic character scenes across three fixed seeds and five square resolutions, from 1024x1024 to 2048x2048.

• 4 prompts

• 3 seeds

• 5 resolutions

• 60 validated images

• 0 failed generations

• 0 CUDA out-of-memory errors

The test was performed locally in ComfyUI on an NVIDIA GeForce RTX 3060 with 12GB of VRAM and 64GB of system RAM.

Workflow Settings

• Model: Krea 2 Turbo FP8

• Steps: 8

• CFG: 1.0

• Sampler: Euler

• Scheduler: beta57

• Batch size: 1

• LoRA: none

• Upscaling: none

• Post-processing: none

Tested Resolutions

1024x1024 — 1.05 MP
1216x1216 — 1.48 MP
1408x1408 — 1.98 MP
1600x1600 — 2.56 MP
2048x2048 — 4.19 MP

Performance Results

1024x1024
190.79s total
130.25s sampler
11,824 MB peak VRAM

1216x1216
202.42s total
155.97s sampler
11,798 MB peak VRAM

1408x1408
275.90s total
207.56s sampler
11,851 MB peak VRAM

1600x1600
284.63s total
211.82s sampler
11,772 MB peak VRAM

2048x2048
392.21s total
316.05s sampler
11,792 MB peak VRAM

Average generation time increased from approximately 3 minutes and 11 seconds at 1024x1024 to 6 minutes and 32 seconds at 2048x2048.

The pixel count increased by roughly four times, while total generation time increased by a little more than two times.

Peak VRAM remained close to 11.8GB at every resolution. The GPU was therefore already operating near its usable VRAM ceiling at the lower resolutions.

The combined on-disk model size exceeds the available VRAM, while the observed VRAM ceiling and non-sampler overhead are consistent with substantial model offloading and host-to-device transfers.

PCIe transfer volume was not measured directly, so this should be treated as an evidence-based interpretation rather than a direct measurement.

Did Higher Resolution Improve Image Quality?

The visual result was more subtle than I expected.

Across the 60 generated images and their native-resolution crops, I did not observe a consistent loss of overall image quality as resolution increased.

Different resolutions often produced different compositions, poses, camera distances, lighting choices and rendering styles. However, these variations were not systematically better or worse.

The seed frequently had a stronger visible influence on composition and scene interpretation than the starting resolution itself.

The clearest and most consistent benefit of 2048x2048 was its higher native pixel count and finer visible detail.

Higher starting resolutions can reveal finer native details, but they do not automatically improve composition or anatomy.

Hands and Extra Fingers

Some images contained familiar generative-model defects, including imperfect hands or an occasional extra finger.

These errors did not increase in a clear or consistent way with resolution. They appeared to depend more strongly on the pose, hand visibility, object interaction, framing and seed.

Changing the resolution sometimes changed the pose and placement of the hands, which makes it difficult to identify resolution as the direct cause of a finger error.

• Higher resolution did not eliminate anatomy problems.

• Lower resolution did not guarantee correct hands.

• 2048x2048 did not systematically produce more finger errors in this dataset.

A dedicated anatomy benchmark would require many more seeds and tightly controlled poses.

Native Detail at 100% Scale

The advantage of larger images became easier to see in native 1:1 crops than in downscaled full-image comparisons.

Higher-resolution outputs preserved more visible detail in areas such as:

• armor seams;

• fabric folds;

• hair strands;

• stone carvings;

• foliage;

• surface weathering;

• small metallic details.

Which Resolution Should You Use?

There was no universal visual sweet spot in this campaign.

The most practical choice depends on the final aspect ratio, the required native detail and how much generation time each image is worth.

• Around 1 megapixel: prompt exploration, seed testing and composition work.

• Around 2 megapixels: a practical balance for most serious portrait and landscape generations.

• Longest side near 2048 pixels: selected final images where native detail and cropping flexibility justify the additional time.

For non-square images, practical balanced starting points include:

• 4:5 portrait: 1280x1600

• 2:3 portrait: 1152x1728

• 9:16 vertical: 1056x1888

• 3:2 landscape: 1728x1152

• 16:9 landscape: 1888x1056

• 4:3 landscape: 1632x1216

These non-square dimensions were not included in the original 60-image campaign. They are practical equivalents based on similar pixel budgets, not separately benchmarked presets.

Final Verdict

Krea 2 Turbo remained visually convincing across the entire tested range.

Increasing the starting resolution from 1024x1024 to 2048x2048 did not consistently improve composition, and it did not consistently damage it either.

It mainly provided:

• more native pixels;

• finer visible detail;

• greater cropping flexibility;

• significantly longer generation times.

The best resolution is not a fixed number. It is the point where aspect ratio, native detail and generation time make sense for the image being created.

Limitations

This was a practical local benchmark using one GPU, one ComfyUI installation, one model variant, four prompts and three seeds per prompt.

The campaign used square images only and did not compare direct high-resolution generation with external upscaling.

The recognizable video-game characters used in this benchmark were technical test subjects only. They made it easier to observe anatomy, materials, clothing, silhouettes and prompt adherence.

This project is not affiliated with or endorsed by the owners of those characters.

Full benchmark article, additional charts and detailed resolution recommendations are available on MediaPixel Games.