High-resolution image generation (4-16 megapixels) can be achieved efficiently by generating a base image, upsampling it, then refining latent space patches with guided semantic noise — running 35x faster than full-resolution approaches.
PixelRush achieves high-resolution (4-16MP) image generation by cascading base generation with upsampling and latent-space patchification, achieving 35x speedup while maintaining quality through guided semantic noise injection. ✦ AI generated
Fatih Porikli · The TWIML AI Podcast · 2026-08-12 · original ↗
starts at this moment · 33:49
“Talk a little bit about what these two papers are doing with PixelRush and InverField.”
Now we are saying that can we even push it to the next level because before we have been talking about let's say 1K resolution and that's the current sort of even the cloud models are kind of limited to that resolution. But the question is now can we do like 4 megapixel image generation, 16 megapixel image generation. The challenge is not only how fast you can run the model... but there's a memory challenge also because when the image resolution gets larger we need to retain this diffusion process the latent features in the memory somewhere on the device... when I say much faster, it's not like two times faster. It's maybe 35 times faster. From let's say 10 minutes to around 20 seconds type of acceleration.
verbatim transcript · starts at 33:49
33:49a different objective. Now we ask how to generate images much more efficiently than even what we did before because Qualcomm proposed and you know presented many papers at the previous CDPRs how to run such models much more efficiently on mobile phones you know but now we are saying that can we even push it to the next level because before we have been talking about let's say 1K resolution
34:15and that's the current sort of even the cloud models are kind of limited to that resolution. But the question is now can we do like uh 4 megapixel image generation, 16 megapixel image generation. The challenge is not only you know kind of like how fast you can run the model. By the way such models existing solutions may take anywhere from you know kind of uh seconds like 50 seconds
34:47to minutes. they are not that fast. Um um but there's a memory challenge also because when the image resolution gets larger we need to retain this diffusion process uh the latent features in the memory somewhere on the device. So that uh requires big footprints. So how can we run a model such that we generate a extremely large image high resolution image and it of course the quality has
35:22to be also still you know real high resolution not just you know kind of like high resolution of sampled image but real details a lot of details are there and it will run at a reasonable time you don't need to wait like 10 minutes which is the current you know kind of uh I mean XFR solution uh the time such model states requires and it would run on let's say a memory