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

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" 🤗

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/ 🤗
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