Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

AnyFlow Any-Step Video Diffusion Model with On-Policy Flow Map Distillation

AK

535,472 subscribers

14,152 просмотров • 6 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 1

Фото профиля AK

AK6 месяцев назад

paper:

Похожие видео

📢📢 "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)

📢📢 "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)

Karsten Kreis

21,766 просмотров • 1 год назад

Diffusion models generate high-quality images but require hundreds of forward passes. MIT CSAIL and Adobe Research introduce Distribution Matching Distillation (DMD), a distillation approach that converts costly multi-step diffusion models into fast one-step generators. A thread 🧵

Diffusion models generate high-quality images but require hundreds of forward passes. MIT CSAIL and Adobe Research introduce Distribution Matching Distillation (DMD), a distillation approach that converts costly multi-step diffusion models into fast one-step generators. A thread 🧵

MIT CSAIL

34,383 просмотров • 2 лет назад

Meta presents Video Editing via Factorized Diffusion Distillation We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing

Meta presents Video Editing via Factorized Diffusion Distillation We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing

AK

115,612 просмотров • 2 лет назад

We release Cosmos Policy 💫: a state-of-the-art robot policy built on a video diffusion model backbone. - policy + world model + value function — in 1 model - no architectural changes to the base video model - SOTA in LIBERO (98.5%), RoboCasa (67.1%), & ALOHA tasks (93.6%) 🧵👇

We release Cosmos Policy 💫: a state-of-the-art robot policy built on a video diffusion model backbone. - policy + world model + value function — in 1 model - no architectural changes to the base video model - SOTA in LIBERO (98.5%), RoboCasa (67.1%), & ALOHA tasks (93.6%) 🧵👇

Moo Jin Kim

152,847 просмотров • 9 месяцев назад

Introducing Streaming Diffusion Policy (SDP)! SDP allows you to generate actions using a single denoising step, with no distillation! SDP keeps an action buffer of partially denoised future actions, which is refined over time. Led by Sigmund Hennum Høeg

Introducing Streaming Diffusion Policy (SDP)! SDP allows you to generate actions using a single denoising step, with no distillation! SDP keeps an action buffer of partially denoised future actions, which is refined over time. Led by Sigmund Hennum Høeg

Yilun Du

33,838 просмотров • 2 лет назад

LIFT Layout-In-Future Video Generation under Large Viewpoint Change via On-Policy Self-Distillation paper:

LIFT Layout-In-Future Video Generation under Large Viewpoint Change via On-Policy Self-Distillation paper:

AK

17,977 просмотров • 1 месяц назад

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,960 просмотров • 1 год назад

FasterCache Training-Free Video Diffusion Model Acceleration with High Quality

FasterCache Training-Free Video Diffusion Model Acceleration with High Quality

AK

91,309 просмотров • 2 лет назад

Presto! Distilling Steps and Layers for Accelerating Music Generation Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge.

Presto! Distilling Steps and Layers for Accelerating Music Generation Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge.

AK

30,450 просмотров • 2 лет назад

Any-to-Any Generation via Composable Diffusion present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities paper page:

Any-to-Any Generation via Composable Diffusion present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities paper page:

AK

280,316 просмотров • 3 лет назад

Cosmos Policy turns a pretrained video diffusion model into a robot controller. Instead of redesigning the architecture, it injects robot state, actions, and values directly as latent frames inside the video model

Cosmos Policy turns a pretrained video diffusion model into a robot controller. Instead of redesigning the architecture, it injects robot state, actions, and values directly as latent frames inside the video model

Robots Digest 🤖

22,933 просмотров • 9 месяцев назад

Tired of waiting for your diffusion models? 🚢 We just shipped a major FastGen update: - new models: H3, LTX-2.5, LingBot-World, Cosmos 3, FLUX.2 - new methods: PDD, AYF, AnyFlow, Causal Forcing - new features: multimodal distillation, image editing, camera conditioning, KV cache management, ComfyUI 🔗 Try it out here:

Tired of waiting for your diffusion models? 🚢 We just shipped a major FastGen update: - new models: H3, LTX-2.5, LingBot-World, Cosmos 3, FLUX.2 - new methods: PDD, AYF, AnyFlow, Causal Forcing - new features: multimodal distillation, image editing, camera conditioning, KV cache management, ComfyUI 🔗 Try it out here:

Julius Berner

35,034 просмотров • 16 дней назад

Netflix just dropped Go-with-the-Flow Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise

Netflix just dropped Go-with-the-Flow Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise

AK

95,564 просмотров • 1 год назад

🔊New NVIDIA paper: Audio-SDS🔊 We repurpose Score Distillation Sampling (SDS) for audio, turning any pretrained audio diffusion model into a tool for diverse tasks, including source separation, impact synthesis & more. 🎧 Demos, audio examples, paper:

🔊New NVIDIA paper: Audio-SDS🔊 We repurpose Score Distillation Sampling (SDS) for audio, turning any pretrained audio diffusion model into a tool for diverse tasks, including source separation, impact synthesis & more. 🎧 Demos, audio examples, paper:

Jonathan Lorraine

39,443 просмотров • 1 год назад

If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to a remarkably efficient RL method! 🧵👇

If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to a remarkably efficient RL method! 🧵👇

Sergey Levine

153,323 просмотров • 1 год назад

Whole-body Large Behavior Model diffusion policy from Boston Dynamics and TRI. Most advanced mobile-manipulation demo with error recovery I've seen to date.

Whole-body Large Behavior Model diffusion policy from Boston Dynamics and TRI. Most advanced mobile-manipulation demo with error recovery I've seen to date.

Simon Kalouche

41,706 просмотров • 1 год назад