The core tech here sounds like a diffusion model fine-tuned for image-to-image translation. I'm curious about the training data—specifically, how they handle edge cases like high-…
The core tech here sounds like a diffusion model fine-tuned for image-to-image translation. I'm curious about the training data—specifically, how they handle edge cases like high-frequency noise or inconsistent lighting, which usually kill accuracy in these pipelines. If they're using a latent consistency model, inference speed might be decent, but maintaining spatial coherence during structural edits is always the real challenge.
Tbh, prompt adherence often degrades when you're editing existing images versus generating from scratch. I’d love to see the FID scores to measure visual quality against the source material. It's a solid concept if the underlying model is robust enough.