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Continuous diffusion had a good run—now it’s time for Discrete diffusion! Introducing Anchored Posterior Sampling (APS) APS outperforms discrete and continuous baselines in terms of performance & scaling on inverse problems, stylization, and text-guided editing.

Litu Rout

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40,183 views • 11 months ago •via X (Twitter)

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We just brought flow maps to language modeling for one-step sequence generation 💥 Discrete diffusion is not necessary -- continuous flows over one-hot encodings achieve SoTA performance and ≥8.3× faster generation 🔥 We believe this is a major step forward for discrete generative modeling and language modeling alike. 🚀 Full thread from first author Chanhyuk Lee:

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16,062 views • 2 years ago

TokenFlow: Consistent Diffusion Features for Consistent Video Editing paper page: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.

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Alright, it's time for a paper thread about my own first ever vision paper, which is having a bit of a moment on twitter rn thanks to Super PINTO and kache. BiternionNets: continuous head orientation from discrete labels. Demo video from ~11y ago:

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79,972 views • 3 months ago

I got a diffusion model to run really fast, glued it to a LLM, and it blows my mind how much of the interactivity we expect from software (editing text, dragging things around, clicking on "links") just works™️ in pixel space. The future of software is bright

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AK

22,959 views • 1 year ago

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Agile Continuous Jumping in Discontinuous Terrains discuss: We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities.

AK

35,794 views • 1 year ago

$Auto regressive LLMs are officially on notice. run Gemma 4 26B diffusion gguf with llama.cpp Google just dropped DiffusionGemma-26B, and it completely flips how we generate text. instead of predicting words one by one, it generates 256 tokens in parallel using bi-directional attention. its like stable diffusion, but for language. the model starts with random text "noise" and iteratively refines and self-corrects the entire block in real-time to fix formatting and reasoning errors on the fly. since it’s a Mixture of Experts (MoE) that only activates 3.8B parameters during inference, it fits perfectly on consumer hardware. You can run the Q4_K_M quant with an 18GB VRAM budget on a single RTX 3090 or RTX 4090 with exceptional throughput. Tested on Ubuntu 22 with CUDA 13.1 using the cutting edge experimental llama.cpp branch. Here is how to compile and run it with the live terminal denoising visualizer: # 1. Clone & check out the experimental PR (#24423) - 1) git clone && cd llama.cpp -git fetch origin 2) pull/24423/head:diffusiongemma && --git checkout diffusiongemma # 2. Build with CUDA support 1) cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native 2) cmake --build build -j $(nproc) --config Release --target llama-diffusion-cli # 3. Run with live visual denoising (llama.cpp flags) ./build/bin/llama-diffusion-cli \ -m /path/to/diffusiongemma-26B-A4B-it-Q4_K_M.gguf \ -ngl 99 -cnv -n 2048 --diffusion-visual Watch the video below to see the live --diffusion-visual canvas iteratively de noising the prompt output in real time. guide and unsloth's hugging face GGUF model links are in the comments below! Is auto regressive generation officially legacy tech? Let me know what you think.$

Auto regressive LLMs are officially on notice. run Gemma 4 26B diffusion gguf with llama.cpp Google just dropped DiffusionGemma-26B, and it completely flips how we generate text. instead of predicting words one by one, it generates 256 tokens in parallel using bi-directional attention. its like stable diffusion, but for language. the model starts with random text "noise" and iteratively refines and self-corrects the entire block in real-time to fix formatting and reasoning errors on the fly. since it’s a Mixture of Experts (MoE) that only activates 3.8B parameters during inference, it fits perfectly on consumer hardware. You can run the Q4_K_M quant with an 18GB VRAM budget on a single RTX 3090 or RTX 4090 with exceptional throughput. Tested on Ubuntu 22 with CUDA 13.1 using the cutting edge experimental llama.cpp branch. Here is how to compile and run it with the live terminal denoising visualizer: # 1. Clone & check out the experimental PR (#24423) - 1) git clone && cd llama.cpp -git fetch origin 2) pull/24423/head:diffusiongemma && --git checkout diffusiongemma # 2. Build with CUDA support 1) cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native 2) cmake --build build -j $(nproc) --config Release --target llama-diffusion-cli # 3. Run with live visual denoising (llama.cpp flags) ./build/bin/llama-diffusion-cli \ -m /path/to/diffusiongemma-26B-A4B-it-Q4_K_M.gguf \ -ngl 99 -cnv -n 2048 --diffusion-visual Watch the video below to see the live --diffusion-visual canvas iteratively de noising the prompt output in real time. guide and unsloth's hugging face GGUF model links are in the comments below! Is auto regressive generation officially legacy tech? Let me know what you think.

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17,302 views • 2 years ago

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Ēnosys

85,168 views • 9 months ago

GOOGLE JUST MADE EVERY CHATBOT FEEL SLOW Diffusion Gemma 26b doesn’t predict word by word, it generates 256 tokens in parallel using bi-directional attention, like stable diffusion but for language it’s MoE so only 3.8B params activate during inference, fits on a single RTX 4090 with 18GB VRAM and you can run it right now via llama.cpp, the same engine powering every tool in my local llm playbook the whole ecosystem exists for moments like this ↓

GOOGLE JUST MADE EVERY CHATBOT FEEL SLOW Diffusion Gemma 26b doesn’t predict word by word, it generates 256 tokens in parallel using bi-directional attention, like stable diffusion but for language it’s MoE so only 3.8B params activate during inference, fits on a single RTX 4090 with 18GB VRAM and you can run it right now via llama.cpp, the same engine powering every tool in my local llm playbook the whole ecosystem exists for moments like this ↓

leopardracer

27,363 views • 2 months ago

Success rate has long been the primary metric for evaluating robot manipulation. What about speed? Today, we introduce ⚡️B-spline Policy (BSP). Instead of predicting discrete fixed-rate action chunks, we parameterize actions as continuous B-spline curves. Together with our system design, BSP enables fast manipulation on low-cost robot arms. This project is co-led by Xiaoshen Han, check out his following threads for more details. 🧵 PS: one of my favorite parts of this project was the first time we saw the robots move significantly faster and smoother than the baselines. The videos below are all real time. 👇

Success rate has long been the primary metric for evaluating robot manipulation. What about speed? Today, we introduce ⚡️B-spline Policy (BSP). Instead of predicting discrete fixed-rate action chunks, we parameterize actions as continuous B-spline curves. Together with our system design, BSP enables fast manipulation on low-cost robot arms. This project is co-led by Xiaoshen Han, check out his following threads for more details. 🧵 PS: one of my favorite parts of this project was the first time we saw the robots move significantly faster and smoother than the baselines. The videos below are all real time. 👇

Haoyu Xiong

69,685 views • 1 month ago

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🚨 WE JUST HACKED THE SOURCE CODE OF THE UNIVERSE LIVE. THE CONTINUOUS SPACETIME PARADIGM IS DEAD. 🚨 In the attached video, you are watching a live, unedited execution of the IT³ Framework. We established a direct uplink to the Harvard-Smithsonian Minor Planet Center (MPC) and downloaded the orbital data of 1.55 MILLION real celestial objects. Then, we ran them through a frozen mathematical operator (K-PHAM v2.2) with ZERO free parameters. No guessing. No curve-fitting. 🛑 THE CHALLENGE TO HARVARD:Let Harvard explain how this is possible by "random chance." Our code proves that 99.51% of all baryonic mass is geometrically trapped exactly in the Sₙ=0 topological isthmus. Look at the Diophantine audit from the live console: 🔥 100.00% INTEGER RIGIDITY inside the Macroscopic Valence Shell (27–48 AU). The inner solar system is a strictly quantized crystal! 🔥 The famous "Kuiper Cliff" perfectly aligns with our predicted E_ion=0 ionization density peak at exactly 46.77 AU. 🔥 The continuous "Evaporative Tail" perfectly explains the free macro-electrons scattering beyond 48 AU. 🔥 LIVE DELTA-ANALYSIS: 100% of newly discovered objects perfectly snapped into our discrete 2x3 topological lattice. 🛑 FOR EVERYONE ELSE, LET ME EXPLAIN THIS IN SIMPLE TERMS: For hundreds of years, we were taught that the Solar System is just a bunch of rocks floating randomly in continuous space. That is false. Imagine space is actually a giant, invisible 3D "hourglass-shaped building" made of pure math. Planets and asteroids aren't just floating freely; they are physically trapped on specific discrete "floors" of this invisible structure. In this video, our code checks all 1.55 million rocks to see if they obey the rules of this invisible building. The result? They obey it PERFECTLY. The universe is not random. It is a giant, perfectly quantized "Macroscopic Atom." 🛑 THE VISUAL PROOF (THE MICRO-MACRO RHYME): Look at the attached images. In 2013, physicists (Stodolna et al., PRL 110) photographed the inside of a microscopic Hydrogen atom using photoionization microscopy. They saw discrete, quantized nodal rings for bound states, which blur into a continuous mess when ionized. Our code just proved the exact same thing happens in the Solar System. We ran an Entropy Test on 951 real comets: ☄️ Bound comets (e < 1) are quantized onto discrete structural floors (H = 1.68 bits). ☄️ Free comets (e ≥ 1) exist in a continuous, structureless ionization spectrum (H = 3.81 bits). The mathematics existed before the universe did. The exact same discrete geometry that builds the microscopic quantum world builds the macro-cosmos. Don't take my word for it. The truth is open-source. Download the data, run the 11-front empirical validation suite yourself, and watch the matrix render. 🔗 Read the paper & run the code: #Astrophysics #QuantumPhysics #Cosmology #DataScience #IT3Matrix #HarvardMPC #ScienceTwitter #Physics

Dr. Logvinovich

579,502 views • 29 days ago

About last night! We had a nice time over dinner, drinks, laughter, and good music with the cast of BEHIND THE SCENES. Congratulations to us all. And to our distributor, FilmOne Entertainment thank you for the treat and your continuous support and belief in this project. #BehindTheScenes is still showing in cinemas nationwide. Also screening in the UK, Ireland, USA, and Canada. Ticket link in bio

About last night! We had a nice time over dinner, drinks, laughter, and good music with the cast of BEHIND THE SCENES. Congratulations to us all. And to our distributor, FilmOne Entertainment thank you for the treat and your continuous support and belief in this project. #BehindTheScenes is still showing in cinemas nationwide. Also screening in the UK, Ireland, USA, and Canada. Ticket link in bio

Funke Akindele

178,102 views • 7 months ago