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Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing

AK

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19,381 görüntüleme • 1 yıl önce •via X (Twitter)

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In this free Substack post I share code for several machine learning models and engage in hyperparameter tuning that yields a model that delivers superior returns in the Gold market.

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Our research team just released Flex-Forcing: a video generation method that lets a single model switch between generation methods at inference time. Right now there are two main approaches to video generation. Bidirectional diffusion models attend to every frame at once, holding structure well at the cost of speed. Autoregressive models generate frame by frame, so they stream fast and scale to long clips, but accumulate error and drift over time. Flex-Forcing trains a single model to do both, letting you choose from the range at inference based on your compute budget.

Our research team just released Flex-Forcing: a video generation method that lets a single model switch between generation methods at inference time. Right now there are two main approaches to video generation. Bidirectional diffusion models attend to every frame at once, holding structure well at the cost of speed. Autoregressive models generate frame by frame, so they stream fast and scale to long clips, but accumulate error and drift over time. Flex-Forcing trains a single model to do both, letting you choose from the range at inference based on your compute budget.

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(1/n) Time to unify your favorite visual generative models, VLMs, and simulators for controllable visual generation—Introducing a Product of Experts (PoE) framework for inference-time knowledge composition from heterogeneous models.

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📢📢 "Align Your Flow: Scaling Continuous-Time Flow Map Distillation" New flow map framework for state-of-the-art few-step generation, w/ the amazing Amirmojtaba Sabour and Sanja Fidler. 🔥 Project page: 📜 Paper: 🧵Thread below... (1/n)

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Struggling with slow inference of diffusion and flow models? Check out the video below—I’ve been using our new FastGen library to achieve 7-28x acceleration for text-2-image and {text,image,video}-2-video generation without sacrificing visual fidelity!

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⭐️New Paper⭐️ just in time for NeurIPS. Presenting: Test-time scaling of diffusions with flow maps. project: arXiv: Want to properly adapt your diffusion at inference time with a reward? You should do everything at the level of the flow map. With the amazing Amirmojtaba Sabour, Carles Domingo-Enrich, Nicholas Boffi, Sanja Fidler , Karsten Kreis, and Eric Vanden-Eijnden.

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Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote at #CES2026. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote at #CES2026. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

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Introducing Modality Forcing, a recipe for post-training T2I models for SOTA RGB-Depth generation! Text-to-image (T2I) models learn rich representations of the spatial world. How do we build on this prior for high-quality depth generation? 🧵 [1/6]

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$META is betting on $AMD for AI inference. As AI models become more commoditized, total cost of ownership for token generation matters more than ever. $AMD GPUs are far more cost-efficient than $NVDA for inference. With 3.5B daily users ready to use its AI models, $META is going all-in on $AMD

$META is betting on $AMD for AI inference. As AI models become more commoditized, total cost of ownership for token generation matters more than ever. $AMD GPUs are far more cost-efficient than $NVDA for inference. With 3.5B daily users ready to use its AI models, $META is going all-in on $AMD

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HandX Scaling Bimanual Motion and Interaction Generation paper:

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FLUX.2 [klein] 4B + 9B are the newest BFL models in the Flux family—combining image generation + image editing in one compact architecture. Built for interactive workflows and quick iteration, with extremely fast inference on distilled variants. Two models. Two modes. ⚡️

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Long video generation is a systems problem. Introducing LongLive-2.0 from NVIDIA Research: an end-to-end NVFP4 training and inference system for long video generation. Low-precision deployment often relies on post-training quantization, creating a gap between how models are trained and how they run. LongLive-2.0 aligns NVFP4-aware training, distillation, and W4A4 inference, maintaining strong benchmark quality while improving speed and memory efficiency.

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world models are here! fal is the best platform for real-time audio and video inference. RT for access 😇 incredible work by Eloi Alonso to open source this model

world models are here! fal is the best platform for real-time audio and video inference. RT for access 😇 incredible work by Eloi Alonso to open source this model

fal

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Crusoe Managed Inference is now available. Run model inference faster, easier, and more cost-effectively. Large-context AI solutions demand fast outputs, massive throughput, and resilient scaling. Crusoe Managed Inference delivers breakthrough speed & reduced input-token spend.

Crusoe Managed Inference is now available. Run model inference faster, easier, and more cost-effectively. Large-context AI solutions demand fast outputs, massive throughput, and resilient scaling. Crusoe Managed Inference delivers breakthrough speed & reduced input-token spend.

Crusoe

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Free, instant access to 1M inference tok/day is now waitlist-free! Building the new onboarding flow and removing our waitlist were so fun to ship & all API signups automatically get > Free 1,000,000 toks/day > SOTA models on the world's fastest inference provider

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Sarah Chieng

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Compute is the backbone of the AI-driven future. OptimAI Compute Engine would enable scalable, high-performance workloads across image and video models, supporting: • Text-to-image generation • Image editing and inpainting • 2K / 4K / 8K super-resolution • Brand-aligned visual synthesis • OCR and image intelligence • Video generation and motion synthesis • Frame interpolation and enhancement • Multimodal model inference Distributed compute built for faster inference, shorter training cycles, and production-grade AI execution.

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