Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers — mentioned by godelski

Fri, 27 Jun 2025 23:48:00 GMT

As far as I'm aware, this is the largest Normalizing Flow that exists, and I think they undermined their work by not mentioning this... Their ImageNet model (4_1024_8_8_0.05[0]) is ~820M while AFHQ is ~472M. Prior to that there is DenseFlow[1] and MaCow[2], which are both <200M parameters. For more comparison, that makes DenseFlow and MaCow smaller than iDDPM[3] (270M params) and ADM[4] (553M for 256 unconditional). And now, it isn't uncommon for modern diffusion models to have several billion parameters![5] (from this we get some numbers on ImageNet-256, which allows a direct comparison…

Paper and discussion history. A mention is not an endorsement.

Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers — HN submission

Tue, 23 Jan 2024 11:59:41 GMT

Scalable High-Resolution Pixel-Space Image Synthesis

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers · HackerPapers discussions

Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers — mentioned by godelski

Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers — HN submission