Standard AI enhancement often produces an unnatural "plastic" look, trading off original textures for smoothness. I’ve released GoHard AI Upscaler to solve both.
- High-fidelity AI Upscaling: Preserves natural grain and structural details without aggressive over-smoothing.
- Clean and Intuitive UI: A streamlined interface designed for a seamless, distraction-free workflow.
- Privacy-first: Your data stays with you through local processing or a secure Google Colab environment.
🔗 Links
- Google Colab: Google Colab
- The Critical Reason (Video): youtube
Note: If this open-source tool is useful, show some developer conscience and leave a Star! ⭐
Top comments (1)
Really valuable framing here — texture fidelity and privacy are usually siloed conversations in the upscaling world, so covering them together is useful.
A couple of things worth adding:
On texture: There's an important distinction between models that preserve existing texture detail versus those that regenerate it. Both can look sharp, but regeneration subtly alters original content — which matters in product photography, print, or medical imaging. The cleaner design choice is to expose these as separate, explicit operations rather than bundling them into one unpredictable model. (Disclosure: I'm the CTO of Bria — we handle this as two distinct endpoints, platform.bria.ai/image-editing/inc... and platform.bria.ai/image-editing/enh..., but the principle holds regardless of which tool you use.)
On privacy: "Local model = private" is a reasonable first approximation, but the full pipeline matters. Preprocessing steps, telemetry, or logging layers upstream of the model can quietly send data elsewhere. For strict data residency requirements, auditing the entire inference pipeline — not just the model itself — is the only complete answer. On-prem or VPC deployment closes that gap more reliably than local-only model weights.