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ChaoGong
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Inference-time scaling meets Flux.1-Dev (and others) ๐ฅ Presenting a simple re-implementation of "Inference-time scaling diffusion models beyond denoising steps" by Ma et al. I did the simplest random search strategy, but results can potentially be improved with better-guided search methods. Supports Gemini 2 Flash & Qwen2.5 as verifiers for "LLMGrading" ๐ค The steps are simple: For each round: 1> Starting by sampling 2 starting noises with different seeds. 2> Score the generations w.r.t a metric. 3> Obtain the best generation from the current round. If you have more compute budget, go to the next search round. Scale the noise pool (`2 ** search_round`) and repeat 1 - 3. This constitutes the random search method as done in the paper by Google DeepMind. Code, more results, and a bunch of other stuff are in the repository. Check it out here: https://github.com/sayakpaul/tt-scale-flux/ ๐ค
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