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I finally released my new video on YouTube about Diffusion Models / Score-Based Generative Models. Literally planned this for a year and put so much work in. I think this approach to diffusion models is so intuitive and highly recommend giving that a go! Video is 38min long, so...

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dome | Outlier

3,060 subscribers

54,560 görüntüleme • 1 yıl önce •via X (Twitter)

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1LittleCoder💻 profil fotoğrafı
1LittleCoder💻1 yıl önce

That's a crazy timeline. Respect!

dome | Outlier profil fotoğrafı
dome | Outlier1 yıl önce

Thank you haha

Robin Rombach profil fotoğrafı
Robin Rombach1 yıl önce

Nice, richtig gut 😍

dome | Outlier profil fotoğrafı
dome | Outlier1 yıl önce

Nächstes video dann über Flow Matching. Ich werd auf dich zurückkommen mit paar Fragen :c

Ryan Tabrizi profil fotoğrafı
Ryan Tabrizi1 yıl önce

Learned a lot following your derivations. Thanks!

dome | Outlier profil fotoğrafı
dome | Outlier1 yıl önce

So awesome to hear. One small addition, the solution to this is not just Sliced Score Matching. It‘s what Pascal Vincent came up with when he combined Score Matching & Denoising Autoencoders -> Denoising Score Matching So basically two solutions to this problem, and mostly DSM is used :) Super happy the video was helpful

Ariel N. Lee profil fotoğrafı
Ariel N. Lee1 yıl önce

This is awesome

Ashutosh Narang profil fotoğrafı
Ashutosh Narang1 yıl önce

@jeremyphoward Watching it today, thank you

The Variational Book profil fotoğrafı
The Variational Book1 yıl önce

@dome_271 looks great, what did you use to make the video? Seems like would be right up your alley

dome | Outlier profil fotoğrafı
dome | Outlier1 yıl önce

Manim and Premiere Pro :c

Benzer Videolar

Diffusion clicked for me when I read about score-based models, a line of work pioneered by Stefano Ermon (et al.) at Stanford. So it was a full-circle moment to collab with him and Inception on a video about training & sampling techniques for making diffusion LLMs faster.
1:01

Diffusion clicked for me when I read about score-based models, a line of work pioneered by Stefano Ermon (et al.) at Stanford. So it was a full-circle moment to collab with him and Inception on a video about training & sampling techniques for making diffusion LLMs faster.

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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

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Announcing Diffusion Forcing Transformer (DFoT), our new video diffusion algorithm that generates ultra-long videos of 800+ frames. DFoT enables History Guidance, a simple add-on to any existing video diffusion models for a quality boost. Website: (1/7)

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This is the first time in a long while that I’ve let myself think about this again and rewatch this video since the first time. It hurt so much I could barely handle it…. Thank you so much Seungkwan and 17, i love you guys so much
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This is the first time in a long while that I’ve let myself think about this again and rewatch this video since the first time. It hurt so much I could barely handle it…. Thank you so much Seungkwan and 17, i love you guys so much

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Nvidia presents Align Your Steps Optimizing Sampling Schedules in Diffusion Models Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed,
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Nvidia presents Align Your Steps Optimizing Sampling Schedules in Diffusion Models Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed,

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32,888 görüntüleme • 2 yıl önce

working towards action-conditioned video diffusion models for now this is just cs2 gameplay, and i parse the keypresses from the .dem game file next steps will be to work on a scalable data loader then coding the model
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working towards action-conditioned video diffusion models for now this is just cs2 gameplay, and i parse the keypresses from the .dem game file next steps will be to work on a scalable data loader then coding the model

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Excited to share our interview with Sander Dieleman! This is Part 3 in our History of Diffusion series. We talk about diffusion as spectral autoregression, diffusion language models, flow matching, and much more. Enjoy!
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Excited to share our interview with Sander Dieleman! This is Part 3 in our History of Diffusion series. We talk about diffusion as spectral autoregression, diffusion language models, flow matching, and much more. Enjoy!

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Synthesizing worlds with video diffusion models is often inconsistent — moving the camera back and forth leads to different scenes. We propose 🌐𝗪𝗼𝗿𝗹𝗱𝗠𝗲𝗺, a memory-based approach that ensures consistent world simulation without relying on explicit 3D reconstruction.
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Synthesizing worlds with video diffusion models is often inconsistent — moving the camera back and forth leads to different scenes. We propose 🌐𝗪𝗼𝗿𝗹𝗱𝗠𝗲𝗺, a memory-based approach that ensures consistent world simulation without relying on explicit 3D reconstruction.

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Ill Try to make a model pack of the npcs soon, since the models uses layered masks I will need to make some custom textures that I will add with the material info so people can use the models better, so for those who want models I will ask patience n.n
1:18

Ill Try to make a model pack of the npcs soon, since the models uses layered masks I will need to make some custom textures that I will add with the material info so people can use the models better, so for those who want models I will ask patience n.n

ChicoEevee

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LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.
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LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

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1:00

Glad that our work “Inference-Time Enhancement of Generative Robot Policies via Predictive World Modeling”, led by Han Qi, has been accepted to IEEE Robotics and Automation Letters! 🎉 We propose Generative Predictive Control (GPC): sample action proposals from a pretrained diffusion policy (“look back”), roll them out with a diffusion-based action-conditioned video world model (“look forward”), then rank or optimize the actions using either a learned reward model or VLM preferences. Conceptually, this is trajectory optimization / MPC with hybrid sampling + gradient optimization, interpreted through modern diffusion priors and video world models. Interestingly, we first posted the paper on arXiv in Feb 2025, when action-conditioned video world models for planning were still rare—now this direction is rapidly gaining traction. Still many open questions, e.g., • how to avoid local minima in planning • what representations work best for world models • how to balance physics priors vs. data-driven learning Paper:

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Procedural models are so fun to work with! You can have so much control over your own models and creations. 💜 Let me know if I should turn this into a plugin... 👀 #RobloxDev #Roblox

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History-Guided Video Diffusion TL;DR: Diffuse long videos by performing guidance over different histories, enabled by Diffusion Forcing Transformer, a simple finetunable add-on to any existing sequence diffusion models.

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Took some time off but I’m excited to be back and find out that we reached 3k!!! I’m so happy for that thank you so so much for this 🥰🥰🥰 (a video that I took on my little vacation😜)
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Took some time off but I’m excited to be back and find out that we reached 3k!!! I’m so happy for that thank you so so much for this 🥰🥰🥰 (a video that I took on my little vacation😜)

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My craft is so different, even my biggest inspirations and role models don’t even know where to put me as I respectfully say that. It’s a gift and a curse.
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My craft is so different, even my biggest inspirations and role models don’t even know where to put me as I respectfully say that. It’s a gift and a curse.

🦇 beastboi.🦇

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I highly recommend Tesla investors watch this video. First time I go directly to video in my research since I had to cover too much context.
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I highly recommend Tesla investors watch this video. First time I go directly to video in my research since I had to cover too much context.

AJ Investment Research

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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 🧵
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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 🧵

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I’m so honored to run this scheme, Ryan meant so much to my development racing late models out west and with this year being a “throwback to your roots” theme I knew exactly who I had to throwback to
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I’m so honored to run this scheme, Ryan meant so much to my development racing late models out west and with this year being a “throwback to your roots” theme I knew exactly who I had to throwback to

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