Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Fast-dLLM accelerates multi-modal diffusion VLM LLaDA-V 10 times! 🚀

xieenze_jr's profile picture

Enze Xie

1,618 subscribers

11,314 Aufrufe • vor 10 Monaten •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Nvidia presents Fast-dLLM v2 Efficient Block-Diffusion LLM
0:50

Nvidia presents Fast-dLLM v2 Efficient Block-Diffusion LLM

AK

18,753 Aufrufe • vor 9 Monaten

Just found the dLLM library to create Diffusion Language Models It's still early but it's insanely fun to experiment with diffusion (training, inference, eval) dLLM has the potential of becoming the main library for diffusion LLMs
0:26

Just found the dLLM library to create Diffusion Language Models It's still early but it's insanely fun to experiment with diffusion (training, inference, eval) dLLM has the potential of becoming the main library for diffusion LLMs

Maxime Labonne

75,421 Aufrufe • vor 7 Monaten

"An hour of planning can save you 10 hours of doing." ✨📝 Planned Diffusion 📝 ✨ makes a plan before parallel dLLM generation. Planned Diffusion runs 1.2-1.8× faster than autoregressive and an order of magnitude faster than diffusion, while staying within 0.9–5% AR quality.
0:21

"An hour of planning can save you 10 hours of doing." ✨📝 Planned Diffusion 📝 ✨ makes a plan before parallel dLLM generation. Planned Diffusion runs 1.2-1.8× faster than autoregressive and an order of magnitude faster than diffusion, while staying within 0.9–5% AR quality.

Daniel Israel

38,699 Aufrufe • vor 8 Monaten

🆕 Introducing JAT, the first open-source multi-modal, multi-task multi-domain agent! 🤖 A step toward open generalist agents! 🚀 📰 Blog:
0:22

🆕 Introducing JAT, the first open-source multi-modal, multi-task multi-domain agent! 🤖 A step toward open generalist agents! 🚀 📰 Blog:

Quentin Gallouédec

73,212 Aufrufe • vor 2 Jahren

Speed and quality can finally coexist in diffusion-based language generation. Introducing DiDi-Instruct, a Discrete Diffusion Divergence Instruct method that distills a pre-trained discrete diffusion language model (dLLM) into a few-step student for ultra-fast generation. Built on integral KL-divergence minimization, DiDi-Instruct achieves up to 64× faster decoding, surpasses both its teacher and GPT-2, and cuts training time by 20×. Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct Paper: Code: Project: Our report: 📬 #PapersAccepted by Jiqizhixin
0:40

Speed and quality can finally coexist in diffusion-based language generation. Introducing DiDi-Instruct, a Discrete Diffusion Divergence Instruct method that distills a pre-trained discrete diffusion language model (dLLM) into a few-step student for ultra-fast generation. Built on integral KL-divergence minimization, DiDi-Instruct achieves up to 64× faster decoding, surpasses both its teacher and GPT-2, and cuts training time by 20×. Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct Paper: Code: Project: Our report: 📬 #PapersAccepted by Jiqizhixin

机器之心 JIQIZHIXIN

18,126 Aufrufe • vor 8 Monaten

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 🧵
0:24

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 Aufrufe • vor 2 Jahren

🚀Meet Gemini Diffusion, our first diffusion-based and super fast language model, just announced at Google I/O!🚀 Very excited to be able to share what I've been working on for the past little while with our amazing small team Google DeepMind.
0:32

🚀Meet Gemini Diffusion, our first diffusion-based and super fast language model, just announced at Google I/O!🚀 Very excited to be able to share what I've been working on for the past little while with our amazing small team Google DeepMind.

Ivana Balazevic

43,472 Aufrufe • vor 1 Jahr

🚀High-Res 3D Text-to-Texture🚀 🎨#MVPaint delivers high-res 3D textures with minimal UV wrapping dependency and high versatility using synchronized multi-view diffusion 📑 Paper: 🏠 Project Page: 🧑‍💻 Code:
0:39

🚀High-Res 3D Text-to-Texture🚀 🎨#MVPaint delivers high-res 3D textures with minimal UV wrapping dependency and high versatility using synchronized multi-view diffusion 📑 Paper: 🏠 Project Page: 🧑‍💻 Code:

Ziwei Liu

28,122 Aufrufe • vor 1 Jahr

We figured out how to train VLAs with diffusion outputs much faster (7.5x faster), inheriting better language following from the VLM, and leading to better results. The key: protect the VLM backbone during training with knowledge insulation. Let’s talk about what we learned👇
0:31

We figured out how to train VLAs with diffusion outputs much faster (7.5x faster), inheriting better language following from the VLM, and leading to better results. The key: protect the VLM backbone during training with knowledge insulation. Let’s talk about what we learned👇

Physical Intelligence

100,142 Aufrufe • vor 1 Jahr

Google presents CAT4D Create Anything in 4D with Multi-View Video Diffusion Models
2:12

Google presents CAT4D Create Anything in 4D with Multi-View Video Diffusion Models

AK

61,949 Aufrufe • vor 1 Jahr

NOW you can BUILD REAL Multi-Modal Agents with our API! 🚀 Excited to announce our API is now publicly available Memories.ai 😃 Memories.ai is building the visual memory layer for AI, it enables AI to see and remember like humans do. 2025 is the year of agents, 2026 is the year of multi-modal agents! 🌟
1:03

NOW you can BUILD REAL Multi-Modal Agents with our API! 🚀 Excited to announce our API is now publicly available Memories.ai 😃 Memories.ai is building the visual memory layer for AI, it enables AI to see and remember like humans do. 2025 is the year of agents, 2026 is the year of multi-modal agents! 🌟

Shawn Shen

530,527 Aufrufe • vor 11 Monaten

MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.
0:44

MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.

AK

294,442 Aufrufe • vor 2 Jahren

GLM 5.2 runs pretty fast on Modal.
0:24

GLM 5.2 runs pretty fast on Modal.

Charles 🎉 Frye

20,342 Aufrufe • vor 15 Tagen

Flow reversal steering allows "steering" diffusion-based VLAs with high-level actions, for example from VLM reasoning. This also lets us run RL in the diffusion noise space with exploration guided by high-level reasoning: think through a task, then practice it! 👇
0:55

Flow reversal steering allows "steering" diffusion-based VLAs with high-level actions, for example from VLM reasoning. This also lets us run RL in the diffusion noise space with exploration guided by high-level reasoning: think through a task, then practice it! 👇

Sergey Levine

73,360 Aufrufe • vor 26 Tagen

Very, very fast voice bots. Llama 3.1 running on Groq Inc. 🚀 500ms voice-to-voice response times
1:52

Very, very fast voice bots. Llama 3.1 running on Groq Inc. 🚀 500ms voice-to-voice response times

kwindla

386,505 Aufrufe • vor 1 Jahr

Excited to share what my team has been working on lately - Gemini diffusion! We bring diffusion to language modeling, yielding more power and blazing speeds! 🚀🚀🚀 Gemini diffusion is especially strong at coding. In this example the model generates at 2000 tokens/sec, including overheads like tokenization, prefill, safety filters etc.
0:24

Excited to share what my team has been working on lately - Gemini diffusion! We bring diffusion to language modeling, yielding more power and blazing speeds! 🚀🚀🚀 Gemini diffusion is especially strong at coding. In this example the model generates at 2000 tokens/sec, including overheads like tokenization, prefill, safety filters etc.

Brendan O'Donoghue

577,625 Aufrufe • vor 1 Jahr

🚀 Can blockchain oracles be fast, flexible and secure? Chainsight says yes - with zkTLS and multi-source feeds. Founder Shumpei Koike (Shumpei) explains how: 🎥
6:40

🚀 Can blockchain oracles be fast, flexible and secure? Chainsight says yes - with zkTLS and multi-source feeds. Founder Shumpei Koike (Shumpei) explains how: 🎥

DFINITY Foundation

20,109 Aufrufe • vor 1 Jahr

🚨In our NeurIPS paper, we bring encoder-decoders back.. for diffusion language models! ⚡️Encoder-decoders make diffusion sampling fast: a small (fast) decoder denoises tokens progressively and a large (slower) encoder represents clean context.
0:31

🚨In our NeurIPS paper, we bring encoder-decoders back.. for diffusion language models! ⚡️Encoder-decoders make diffusion sampling fast: a small (fast) decoder denoises tokens progressively and a large (slower) encoder represents clean context.

Marianne Arriola

32,003 Aufrufe • vor 8 Monaten