{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/simpler-diffusion-sid2-1-5-fid-on-imagenet512","title":"Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion","arxiv_id":"2410.19324","date":"2024-10-25","proceeding":null,"authors":["Emiel Hoogeboom","Thomas Mensink","Jonathan Heek","Kay Lamerigts","Ruiqi Gao","Tim Salimans"],"abstract":"Latent diffusion models have become the popular choice for scaling up diffusion models for high resolution image synthesis. Compared to pixel-space models that are trained end-to-end, latent models are perceived to be more efficient and to produce higher image quality at high resolution. Here we challenge these notions, and show that pixel-space models can be very competitive to latent models both in quality and efficiency, achieving 1.5 FID on ImageNet512 and new SOTA results on ImageNet128, ImageNet256 and Kinetics600. We present a simple recipe for scaling end-to-end pixel-space diffusion models to high resolutions. 1: Use the sigmoid loss-weighting (Kingma & Gao, 2023) with our prescribed hyper-parameters. 2: Use our simplified memory-efficient architecture with fewer skip-connections. 3: Scale the model to favor processing the image at a high resolution with fewer parameters, rather than using more parameters at a lower resolution. Combining these with guidance intervals, we obtain a family of pixel-space diffusion models we call Simpler Diffusion (SiD2).","url_abs":"https://arxiv.org/abs/2410.19324v2","url_pdf":"https://arxiv.org/pdf/2410.19324v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"video-prediction","task_name":"Video Prediction"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-generation-on-imagenet-128x128","task":"Image Generation","dataset":"ImageNet 128x128","model":"SiD2","rank_in_archive_order":1,"of":23,"metrics":{"FID":"1.26"},"uses_additional_data":false},{"leaderboard":"/sota/image-generation-on-imagenet-256x256","task":"Image Generation","dataset":"ImageNet 256x256","model":"SiD2","rank_in_archive_order":15,"of":94,"metrics":{"FID":"1.38"},"uses_additional_data":false},{"leaderboard":"/sota/image-generation-on-imagenet-512x512","task":"Image Generation","dataset":"ImageNet 512x512","model":"SiD2","rank_in_archive_order":11,"of":52,"metrics":{"FID":"1.48"},"uses_additional_data":false},{"leaderboard":"/sota/video-prediction-on-kinetics-600-12-frames","task":"Video Prediction","dataset":"Kinetics-600 12 frames, 64x64","model":"SiD2","rank_in_archive_order":1,"of":16,"metrics":{"Cond":"5","FVD":"2.3","Pred":"11"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.19324","atlas_url":"https://app.syntology.ai/?focus=2410.19324","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}