Upgraded from 3090 to 5090… local video generation is again a thing now!

by AI Generated Robotic ContentImagePosted on Comments are Disabled

Wan2.1 720p fp8_e5m2, fast_fp16_accumulation, sage attention, torch compile, TeaCache, no block swap. Made using Kijai WanVideoWrapper, 9 min per video (81 frames), impressed by the quality! https://github.com/kijai/ComfyUI-WanVideoWrapper/ https://reddit.com/link/1jkkpw6/video/k4fnrevw73re1/player https://reddit.com/link/1jkkpw6/video/m8zgyaxx73re1/player https://reddit.com/link/1jkkpw6/video/v600jtpy73re1/player https://reddit.com/link/1jkkpw6/video/mzbh4f5z73re1/player submitted by /u/3Dave_ [link] [comments]

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Amazon SageMaker JumpStart adds fine-tuning support for models in a private model hub

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Amazon SageMaker JumpStart is a machine learning (ML) hub that provides pre-trained models, solution templates, and algorithms to help developers quickly get started with machine learning. Within SageMaker JumpStart, the private model hub feature allows organizations to create their own internal repository of ML models, enabling teams to share and manage models securely within their …

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highway wave

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

by AI Generated Robotic ContentAI/ML ResearchPosted on Comments are Disabled

Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient …

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Exploring Empty Spaces: Human-in-the-Loop Data Augmentation

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential harms. Creating high-quality augmentations that cover these “unknown unknowns” is a time- and creativity-intensive task. In this work, …

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