Flow-Matching vs Diffusion Models explained side by side

We explain diffusion models and flow-matching models side by side to highlight the key differences between them. Flow-Matching Models are the new generation of AI image generators that are quickly replacing diffusion models — they take everything diffusion did well, but make it faster, smoother, and deterministic. AI Coffee Break Merch! 🛍️ ai-coffee-break-with-letitia.myspreadshop.de/ Text to image diffusion models: youtu.be/J87hffSMB60 Useful deeper reading: • 🌊 Lipman et al., “Flow Matching for Generative Modeling” (2023) — arxiv.org/abs/2210.02747 • 🧮 Kingma and Gao, "Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation" (2022) — arxiv.org/abs/2210.02747 • ⚡ Esser et al, "Scaling Rectified Flow Transformers for High-Resolution Image Synthesis" (2024) — arxiv.org/abs/2403.03206 Thanks to our Patrons who support us in Tier 2, 3, 4: 🙏 Vignesh Valliappan, Ivan Janov, Sunny Dhiana, Andy Ma Outline: 00:00 Difference between Flow-matching and Diffusion 01:07 Training Diffusion Models 05:45 Inference for Diffusion Models 09:03 Training Flow-Matching 11:55 Inference with Flow-Matching 14:02 Side-by-Side Comparison ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔥 Optionally, pay us a coffee to help with our Coffee Bean production! ☕ Patreon: patreon.com/AICoffeeBreak Ko-fi: ko-fi.com/aicoffeebreak Join this channel as a Bean Member to get access to perks: youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQA/join ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔗 Links: AICoffeeBreakQuiz: youtube.com/c/AICoffeeBreak/community Twitter / X: twitter.com/AICoffeeBreak LinkedIn: linkedin.com/in/letitia-parcalabescu/ Threads: threads.net/@ai.coffee.break Bluesky: bsky.app/profile/aicoffeebreak.bsky.social Reddit: reddit.com/r/AICoffeeBreak/ YouTube: youtube.com/AICoffeeBreak Substack: aicoffeebreakwl.substack.com/ Web: explanationmark.de/letitia aicoffeebreak.com #AICoffeeBreak #MsCoffeeBean #MachineLearning #AI #research​ Video editing: Nils Trost