Generating high-resolution images (4-16 megapixels) on memory-constrained devices requires a cascade approach: base generation, upsampling, then latent-space patch refinement with semantic noise injection.
Pixel Rush enables 35x faster high-resolution generation by producing a base 1K image, upsampling it, patchifying into latent space, refining with semantic noise, and blending patches with boundary-aware noise injection—all without modifying the base model. ✦ AI generated
Fati Periqi · The TWIML AI Podcast · 2026-08-12 · original ↗
starts at this moment · 36:47
“talk a little bit about the generation process like what what's different about the way you've approached this”
it again starts with a prompt and there is this base generation like any model it could be let's say flat model and then it generates let's say a base image what base image what I mean by that let's say 1k image. Then we have this cascade upsampling stage. That is the part that is no about this paper. This cascade upsample what it takes it takes this image which is like no RGB pixels not latent space and then it case for instance any using any OP image super resolution solution. It could be by cubic up sampling or you know kind of it could be something smarter it generates let's say higher resolution image. So when we do that okay we have now let's say 16 megapixel in the image not one megapix we have like a lot of pixels and then we take that image sam and then we apply an encoder a va then we go into a know kind of latent space in that latent space of course I mentioned that we want to we are concerned about the memory we now divide that latent space into manageable chunks we pify them.
verbatim transcript · starts at 36:47
36:47different about the way you've approached this >> yeah absolutely so it again starts with a prompt and there is this base generation like any model it could be let's say flat model and then it generates let's say a base image what base image what I mean by that let's say 1k image and Then we have this cascade upsampling stage. That is the part that uh is no about this paper. That's why I
37:14say CPR paper. This cascade upsample um what it takes it takes this image which is like no RGB pixels not latent space and then it uh case for instance any using any OP image super resolution solution. It could be by cubic up sampling or you know kind of it could be something smarter it generates let's say higher resolution image. So when we do that okay we have now let's say 16
37:46megapixel in the image not one megapix we have like a lot of pixels and then we take that image sam and then we apply an encoder a va then we go into a know kind of latent space in that latent space of course I mentioned that we want to we are concerned about the memory we now divide that uh latent space into manageable chunks we pify them. So then
38:17we do improve those latent space uh features but then uh this this we are still in the latent space we go to the um through a VA decoder in this case to the pixel space something we need to be very careful here yeah there are solutions also you know kind of using pification like I'm going to take and create a you know kind of like patch then another patch another patch then
38:46When you do that you create artifacts actually you know like scenes visible >> meaning when you do that in your origin space you create patches what's different here is that you're doing it in the latent space >> absolutely there are many reasons one is latent space is much smaller special uh dimensionalty than the original pixel space the other one is in lat space we can induce nodes