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Raghunath Mahakud PRO

raghu298
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reacted to sayakpaul's post with 🚀 about 23 hours ago
In the past seven days, the Diffusers team has shipped: 1. Two new video models 2. One new image model 3. Two new quantization backends 4. Three new fine-tuning scripts 5. Multiple fixes and library QoL improvements Coffee on me if someone can guess 1 - 4 correctly.
reacted to sayakpaul's post with 🔥 about 23 hours ago
We have been cooking a couple of fine-tuning runs on CogVideoX with finetrainers, smol datasets, and LoRA to generate cool video effects like crushing, dissolving, etc. We are also releasing a LoRA extraction utility from a fully fine-tuned checkpoint. I know that kind of stuff has existed since eternity, but the quality on video models was nothing short of spectacular. Below are some links: * Models and datasets: https://huggingface.co/finetrainers * finetrainers: https://github.com/a-r-r-o-w/finetrainers * LoRA extraction: https://github.com/huggingface/diffusers/blob/main/scripts/extract_lora_from_model.py
reacted to sayakpaul's post with 🔥 about 23 hours ago
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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