Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

AccVideo just dropped on Hugging Face Accelerating Video Diffusion Model with Synthetic Dataset present a efficient distillation method to accelerate video diffusion models with synthetic dataset method is 8.5x faster than HunyuanVideo

AK

400,944 subscribers

20,633 görüntüleme • 1 yıl önce •via X (Twitter)

Eğitim Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

5 Yorum

AK profil fotoğrafı

AK1 yıl önce

discuss with author:

AK profil fotoğrafı

AK1 yıl önce

model:

Lucid Scientific, Inc. profil fotoğrafı

Lucid Scientific, Inc.1 yıl önce

Expand the possibilities of your metabolic research. Resipher tracks real-time cellular oxygen consumption in standard 96-well plates, delivering continuous real-time data directly from your incubator. Request a free virtual demo or quote today >>

Hussain Asghar profil fotoğrafı

Hussain Asghar1 yıl önce

Fantastic Share!

Anda profil fotoğrafı

Anda1 yıl önce

*Panda ears perk up at the mention of diffusion models* I wonder if these accelerated video seeds could help us grow blockchain-native cinema orchards? 🎋🎥

Benzer Videolar

Ditto Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset

Ditto Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset

AK

18,042 görüntüleme • 7 ay önce

Dreamix: Video Diffusion Models are General Video Editors abs: project page: present diffusion-based method that is able to perform text-based motion and appearance editing of general videos

Dreamix: Video Diffusion Models are General Video Editors abs: project page: present diffusion-based method that is able to perform text-based motion and appearance editing of general videos

AK

398,132 görüntüleme • 3 yıl önce

SkyReels-A2 just dropped on Hugging Face Compose Anything in Video Diffusion Transformers

SkyReels-A2 just dropped on Hugging Face Compose Anything in Video Diffusion Transformers

AK

73,605 görüntüleme • 1 yıl önce

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

30,430 görüntüleme • 1 yıl önce

EasyControl just dropped on Hugging Face Adding Efficient and Flexible Control for Diffusion Transformer Free chatgpt style ghibli image generation with easy control

EasyControl just dropped on Hugging Face Adding Efficient and Flexible Control for Diffusion Transformer Free chatgpt style ghibli image generation with easy control

AK

222,119 görüntüleme • 1 yıl önce

PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Gradio demo is out on Hugging Face Spaces demo:

PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Gradio demo is out on Hugging Face Spaces demo:

AK

87,679 görüntüleme • 3 yıl önce

Tencent just dropped GeometryCrafter on Hugging Face Consistent Geometry Estimation for Open-world Videos with Diffusion Priors

Tencent just dropped GeometryCrafter on Hugging Face Consistent Geometry Estimation for Open-world Videos with Diffusion Priors

AK

55,079 görüntüleme • 1 yıl önce

VideoScene announced on Hugging Face Distilling Video Diffusion Model to Generate 3D Scenes in One Step

VideoScene announced on Hugging Face Distilling Video Diffusion Model to Generate 3D Scenes in One Step

AK

21,301 görüntüleme • 1 yıl önce

Nvidia presents Articulated Kinematics Distillation from Video Diffusion Models

Nvidia presents Articulated Kinematics Distillation from Video Diffusion Models

AK

39,189 görüntüleme • 1 yıl önce

DiffPortrait360 just dropped on Hugging Face Consistent Portrait Diffusion for 360 View Synthesis

DiffPortrait360 just dropped on Hugging Face Consistent Portrait Diffusion for 360 View Synthesis

AK

57,593 görüntüleme • 1 yıl önce

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

152,785 görüntüleme • 11 ay önce

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048 abs: project page:

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048 abs: project page:

AK

718,682 görüntüleme • 3 yıl önce

try out the Gradio Demo for AudioLDM: Text-to-Audio Generation with Latent Diffusion Models on Hugging Face demo:

try out the Gradio Demo for AudioLDM: Text-to-Audio Generation with Latent Diffusion Models on Hugging Face demo:

AK

82,137 görüntüleme • 3 yıl önce

Stability AI just dropped Stable Virtual Camera on Hugging Face a generalist diffusion model designed to address the exciting challenge of Novel View Synthesis (NVS). With just one or a few images, it allows you to create a smooth trajectory video from any viewpoint you desire.

Stability AI just dropped Stable Virtual Camera on Hugging Face a generalist diffusion model designed to address the exciting challenge of Novel View Synthesis (NVS). With just one or a few images, it allows you to create a smooth trajectory video from any viewpoint you desire.

AK

133,583 görüntüleme • 1 yıl önce

LiveCC just dropped on Hugging Face Learning Video LLM with Streaming Speech Transcription at Scale video LLM capable of real-time commentary, trained with a novel video-ASR streaming method, SOTA on both streaming and offline benchmarks.

LiveCC just dropped on Hugging Face Learning Video LLM with Streaming Speech Transcription at Scale video LLM capable of real-time commentary, trained with a novel video-ASR streaming method, SOTA on both streaming and offline benchmarks.

AK

45,758 görüntüleme • 1 yıl önce

NVIDIA just released AnyFlow on Hugging Face The first any-step video diffusion model that generates high-quality text-to-video with any inference budget - 4 steps or 50, quality scales smoothly without degradation.

NVIDIA just released AnyFlow on Hugging Face The first any-step video diffusion model that generates high-quality text-to-video with any inference budget - 4 steps or 50, quality scales smoothly without degradation.

DailyPapers

41,469 görüntüleme • 28 gün önce

Progressive Rendering Distillation released on Hugging Face Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data

Progressive Rendering Distillation released on Hugging Face Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data

AK

12,586 görüntüleme • 1 yıl önce

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

95,542 görüntüleme • 1 yıl önce

GameFactory Creating New Games with Generative Interactive Videos present GameFactory, a generalizable world model that learns from a small-scale dataset of Minecraft game videos. By leveraging the prior knowledge of a pretrained video diffusion model, it can create new games in an open domain.

GameFactory Creating New Games with Generative Interactive Videos present GameFactory, a generalizable world model that learns from a small-scale dataset of Minecraft game videos. By leveraging the prior knowledge of a pretrained video diffusion model, it can create new games in an open domain.

AK

70,029 görüntüleme • 1 yıl önce

We present TeSMo ( ), a text-controlled scene-aware motion generation method based on denoising diffusion models. It’s an exciting collaboration with Justus Thies, Michael Black, Jason Peng, Davis Rempe. (1/7)

We present TeSMo ( ), a text-controlled scene-aware motion generation method based on denoising diffusion models. It’s an exciting collaboration with Justus Thies, Michael Black, Jason Peng, Davis Rempe. (1/7)

Hongwei Yi

45,724 görüntüleme • 2 yıl önce