UNet diffusion model in pure CUDA

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Hi HN! I was inspired by Andrej Karpathy's llm.c ( https://github.com/karpathy/llm.c ), and wrote a full diffusion model training loop in CUDA. I learnt a lot about CUDA from Simon Boehm's Matmul blog ( https://siboehm.com/articles/22/CUDA-MMM ). Currently there is still a lot of room for optimization: the model is running at 45% speed of PyTorch with torch.compile. I'm curious about any thoughts or CUDA tips for convolutions.

Visit product View on Hacker NewsJun 28, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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