GIF-to-GIF transformations using animatediff workflow

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I'm using an animatediff workflow running on fal.ai to do gif2gif conversion in the style of a text prompt. It's fairly consistent in keeping text and structure while allowing for character/style swaps. I'm pretty happy with the quality - I've added a bunch of examples, some are good, some have clear AI-weirdness. They're reflective of the quality generally and not cherry-picked much. I really like the Mario and Pope variations of the gifs. The site is next.js with tailwindcss hosted on netlify, and the backend is a node express server running on a digital ocean droplet. Gif resizing and conversion is done with ffmpeg.wasm on the client side. It takes about 60s per gif, depending on dimensions and duration - this is long, but could be dropped quite a bit by reducing steps/fps. I think quality>speed in this case though. This was a solo project for a (paid) interview take-home. Would love any feedback and happy to answer questions.

Visit product View on Hacker NewsMar 12, 2024

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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