Diffusion Models: Unlocking the “4 secrets” of High-quality Image Generation

Diffusion Models: Unlocking the “4 secrets” of High-quality Image Generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diffusion Models: Unlocking the “4 secrets” of High-quality Image Generation Tao Zhou, zhe zhang, Mingzhe Zhang, Wenwen Chai, Yong Xia, fuyuan Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5455299/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diffusion Model (DM) is a hot topic in deep generative models, and it is widely applied in the image generation fields. In diffusion models, there are 4 main “secrets” that affect the generation high-quality image generation: constructing diffusion model, improving the sampling speed, designing diffusion process, and guiding diffusion models. However, how to construct the diffusion model? How to improve the sampling speed? How to design the diffusion process? How to guide diffusion models? These are critical to enhancing the performance of diffusion models. However, so far, most of the review papers are summarized from the application aspect, and the 4 key technologies of diffusion model are few. In response to the above issues, this paper summarizes 4 key technologies and 6 applications. The main innovative works are as following: Firstly, how to construct diffusion models? The basic principle of the diffusion model are summarized from 3 aspects: denoising diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Secondly, how to improve the sampling speed? Some key techniques for improving the sampling speed are summarized from 3 aspects: non-Markovian sampling, knowledge distillation, and discrete optimization. Thirdly, how to design the diffusion process? This paper summarizes how to design diffusion process from 3 aspects: Latent Space, diffusion process based on transformer and non-Euclidean space. Fourthly, how to guide diffusion models? This paper summarizes how to guide the diffusion model from 3 aspects: classifier guidance, classifier-free guidance, and multimodal guidance. Fifthly, the applications of diffusion models in various fields are discussed from 6 aspects: image fusion, medical image segmentation, image restoration, text-to-image generation, image super-resolution and text-to-video generation. Finally, this paper discusses the challenges faced by diffusion model in image generation, compares the diffusion model with other generation models, and looks forward to the future development direction of diffusion model. This paper systematically points out the "4 secrets" of diffusion models in the image generation fields, providing significant reference value for their research in this field. Diffusion Model Image Generation Denoising Diffusion Model Noisy Conditional Scoring Network Score-based Models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5455299","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380311617,"identity":"cf614918-5663-438f-b41c-d7025c2ca1fb","order_by":0,"name":"Tao Zhou","email":"","orcid":"","institution":"North Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhou","suffix":""},{"id":380311618,"identity":"aa77888f-fbf6-43c3-b11f-c3b0383a0505","order_by":1,"name":"zhe zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCTB5gIEfScyAOC2SDcykajE4QKwW+dnNzx5++XNHzvj8+WMSjDu2JTawN2+TYKi5g1ML45xj5sYyPM+MzW4ks0kwnrmd2MBzrEyC4dgznFqYJRLMpCUkDiduu8EM1NIG1CKRYybB2HAYpxY2ifRv0hIGh+s39x+GapF/g18LD9BMyQ8JhxMMGJJhtvDg1yIhkVMmzXDgsOGMG8nGFoltt43beNKKLRKO4dYiPyN9m+SPP4fl+fsPPrzxse22bD/74Y03PtTg1gIOAh4YKwHkOxgDH2D8QUDBKBgFo2AUjHAAABwuUpYXd7vmAAAAAElFTkSuQmCC","orcid":"","institution":"North Minzu University","correspondingAuthor":true,"prefix":"","firstName":"zhe","middleName":"","lastName":"zhang","suffix":""},{"id":380311619,"identity":"fe91b256-0769-4dc4-91ff-27d61f164757","order_by":2,"name":"Mingzhe Zhang","email":"","orcid":"","institution":"North Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Mingzhe","middleName":"","lastName":"Zhang","suffix":""},{"id":380311620,"identity":"27c84109-bbf3-40f8-8d6f-721f1a615775","order_by":3,"name":"Wenwen Chai","email":"","orcid":"","institution":"North Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Chai","suffix":""},{"id":380311621,"identity":"1da22c7c-149f-4d09-8640-7e027d185535","order_by":4,"name":"Yong Xia","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Xia","suffix":""},{"id":380311622,"identity":"112c388e-7c33-4a13-b0fc-fc02c48693c1","order_by":5,"name":"fuyuan Hu","email":"","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"fuyuan","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-11-14 16:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5455299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5455299/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72125635,"identity":"9b79014b-d16f-4792-8cbd-dfc6168c2800","added_by":"auto","created_at":"2024-12-23 02:53:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864943,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5455299/v1_covered_725c7bb2-8aed-4651-b45e-242ed660d984.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diffusion Models: Unlocking the “4 secrets” of High-quality Image Generation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diffusion Model, Image Generation, Denoising Diffusion Model, Noisy Conditional Scoring Network, Score-based Models","lastPublishedDoi":"10.21203/rs.3.rs-5455299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5455299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Diffusion Model (DM) is a hot topic in deep generative models, and it is widely applied in the image generation fields. In diffusion models, there are 4 main “secrets” that affect the generation high-quality image generation: constructing diffusion model, improving the sampling speed, designing diffusion process, and guiding diffusion models. However, how to construct the diffusion model? How to improve the sampling speed? How to design the diffusion process? How to guide diffusion models? These are critical to enhancing the performance of diffusion models. However, so far, most of the review papers are summarized from the application aspect, and the 4 key technologies of diffusion model are few. In response to the above issues, this paper summarizes 4 key technologies and 6 applications. The main innovative works are as following: Firstly, how to construct diffusion models? The basic principle of the diffusion model are summarized from 3 aspects: denoising diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Secondly, how to improve the sampling speed? Some key techniques for improving the sampling speed are summarized from 3 aspects: non-Markovian sampling, knowledge distillation, and discrete optimization. Thirdly, how to design the diffusion process? This paper summarizes how to design diffusion process from 3 aspects: Latent Space, diffusion process based on transformer and non-Euclidean space. Fourthly, how to guide diffusion models? This paper summarizes how to guide the diffusion model from 3 aspects: classifier guidance, classifier-free guidance, and multimodal guidance. Fifthly, the applications of diffusion models in various fields are discussed from 6 aspects: image fusion, medical image segmentation, image restoration, text-to-image generation, image super-resolution and text-to-video generation. Finally, this paper discusses the challenges faced by diffusion model in image generation, compares the diffusion model with other generation models, and looks forward to the future development direction of diffusion model. 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