# diffusion-transformers Transformer-based diffusion-model architecture that replaces a U-Net backbone with a transformer operating on latent image patches. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Bringing Nunchaku 4-bit Diffusion Inference to Diffusers DevFeed: [Bringing Nunchaku 4-bit Diffusion Inference to Diffusers]() Original publisher: [Read original article]() Author: Pham Hong Vinh; Sayak Paul Published: 2026-07-23T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [diffusers](), [Inference](), [quantization](), [diffusion-transformers](), [NVFP4](), [GPU](), [NCCL]() Tags: [diffusers](), [diffusion](), [diffusion-transformers](), [gpu](), [guide](), [hardware](), [inference](), [kernels](), [memory](), [nvfp4](), [nvidia](), [quantization](), [speed]() ### AI overview This article explains how Nunchaku brings 4-bit diffusion inference to Hugging Face Diffusers. It describes SVDQuant's W4A4 approach, integration of pre-quantized checkpoints without local CUDA compilation, and hardware requirements for NVFP4 and INT4 variants. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Mistral's Open TTS, Anthropic's Activation Translator, and Matt Pocock's Skills Repo: Tokenizer #28 DevFeed: [Mistral's Open TTS, Anthropic's Activation Translator, and Matt Pocock's Skills Repo: Tokenizer #28]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-05-17T16:49:38Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Large Language Model](), [anthropic](), [multimodal](), [MCP](), [diffusion-transformers](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [anthropic](), [articles](), [claude](), [github](), [language-models](), [mcp](), [multimodal](), [rag](), [safety]() ### AI overview A weekly AI and machine learning resource roundup covering Anthropic safety evaluation work, large-model training on a single GPU, unified multimodal video generation, agent systems, semantic code search, and related papers, videos, articles, tools, and learning resources. ### Source excerpt This week's most valuable resources ## Netflix's VOID: Fixing the Physics Problem in Video Editing DevFeed: [Netflix's VOID: Fixing the Physics Problem in Video Editing]() Original publisher: [Read original article]() Author: RisingStack Engineering Published: 2026-04-06T22:41:15Z Content type: article Language: en Sources: [RisingStack]() Topics: [AI Models](), [Artificial Intelligence](), [diffusion-transformers](), [Netflix](), [Simulation]() Tags: [ai](), [ai-models](), [diffusion](), [netflix](), [simulation](), [training](), [video]() ### AI overview The article explains Netflix's VOID model for removing objects from videos while also recomputing their downstream physical effects. It describes VOID as a counterfactual video-generation system using video diffusion and training data produced with physics simulation and human-object interaction data. ### Source excerpt There's a familiar trick in modern video editing with AI - taking an object out, slapping some new background in, - and calling it good. It does the trick for simple things, but it all falls apart the moment the object actually starts to move or interact with anything. Take a domino chain for example, [...] The post Netflix's VOID: Fixing the Physics Problem in Video Editing appeared first on RisingStack Engineering. ## LLaDA: A Diffusion-Based Language Model for Revisable Text Generation DevFeed: [LLaDA: A Diffusion-Based Language Model for Revisable Text Generation]() Original publisher: [Read original article]() Published: 2025-02-17T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [diffusion-transformers](), [Large Language Model](), [AI Architecture]() Tags: [ai-architecture](), [consistency](), [diffusion](), [hallucinations](), [language](), [llms](), [model]() ### AI overview The article introduces LLaDA, a language model that uses diffusion rather than autoregressive next-token prediction. It explains how this approach can generate and revise text globally, potentially helping with hallucinations, reasoning loops, and coherence in structured writing such as contracts. ### Source excerpt A new language model uses diffusion instead of next-token prediction. That means the text it can back out of a hallucination before it commits. This is a big win for areas like law & contracts, where global consistency is valued ## Memory-efficient Diffusion Transformers with Quanto and Diffusers DevFeed: [Memory-efficient Diffusion Transformers with Quanto and Diffusers]() Original publisher: [Read original article]() Author: Sayak Paul; David Corvoysier Published: 2024-07-30T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [diffusers](), [diffusion-transformers](), [quantization](), [optimum](), [PyTorch](), [stable-diffusion](), [text-to-image](), [benchmarking]() Tags: [benchmarking](), [diffusers](), [diffusion-transformers](), [efficiency](), [gpu](), [guide](), [optimum](), [pytorch](), [quality](), [quantization](), [sd3](), [text-to-image]() ### AI overview This tutorial explains how to reduce the memory requirements of Transformer-based diffusion pipelines using Quanto quantization utilities from the Diffusers library. It benchmarks FP8 quantization on PixArt-Sigma, Stable Diffusion 3, and Aura Flow, reporting memory savings with slightly higher latency and little quality degradation. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Diffusers welcomes Stable Diffusion 3 DevFeed: [Diffusers welcomes Stable Diffusion 3]() Original publisher: [Read original article]() Author: Dhruv Nair; YiYi Xu; Sayak Paul; Alvaro Somoza; Kashif Rasul; Apolinário from multimodal AI art Published: 2024-06-12T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [stable-diffusion](), [diffusers](), [diffusion-transformers](), [Artificial Intelligence](), [text-to-image](), [multimodal](), [lora](), [Inference](), [Open Source]() Tags: [artificial-intelligence](), [diffusers](), [diffusion](), [guide](), [inference](), [lora](), [multimodal](), [open-source](), [sd3](), [stable-diffusion](), [text-to-image]() ### AI overview The article announces Stable Diffusion 3 Medium, a 2B-parameter latent diffusion model. It describes the MMDiT architecture, multimodal text and image processing, rectified flow matching, inference support through a new scheduler, and accompanying Diffusers, DreamBooth, and LoRA resources. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Random Things I Liked, Mar 2023 DevFeed: [Random Things I Liked, Mar 2023]() Original publisher: [Read original article]() Author: Andrew Khmylov Published: 2023-03-27T00:00:00Z Content type: opinion Language: en Sources: [Despite the odds]() Topics: [LLMs](), [AsyncAPI Specification](), [diffusion-transformers](), [Concurrent Programming](), [Prompt Engineering](), [OCaml](), [OpenAPI Specification]() Tags: [diffusion](), [language-models](), [ocaml](), [openapi](), [programming](), [reasoning]() ### AI overview A personal roundup of developer topics and papers the author liked in March 2023, covering Automerge 2.0, AsyncAPI for event-based architecture, using external tools with large language models, diffusion-based image reconstruction from brain activity, effect handlers in OCaml, and prompt-based perspectives on LLM behavior. ### Source excerpt Automerge 2.0 https://automerge.org/blog/automerge-2/ The complete rewrite of Automerge dropped right after discussing CRDTs optimizations the last time, addressing performance concerns. AsyncAPI https://www.asyncapi.com/docs It's like Swagger/OpenAPI, but for event-based architecture. I've been working with events and messages for quite a long time now, and tried some approaches to spec description, like Protocol Buffers, but it just didn't feel right. Too much hassle and not so much benefits. I'm eager to try this one though. LLMs + external tools = 🔥 As of today, large language models are trained on "static" data sets, they don't have a memory, and don't know about the world around them. However, if the only thing they do and do so extremely well is predicting next token, it's only natural to wonder "why can't we teach the model to predict some API call when it needs external data?". There have been a couple of papers on that topic: Toolformer: Language Models Can Teach Themselves to Use Tools ReAct: Synergizing Reasoning and Acting in Language Models Reading your mind with diffusion models I've already noticed multiple times that casual news headlines today sound exactly like futuristic imaginations from the original Deus Ex game, but this paper, "High-resolution image reconstruction with latent diffusion models from human brain activity", takes it to the next level. The research behind this has been going on for more than a decade, and the recent advances in computational capabilities and diffusion models accelerated the progress. Concurrent Programming with Effect Handlers https://github.com/ocaml-multicore/ocaml-effects-tutorial Every developer is familiar with exceptions. Algebraic effects take them to the next level of abstraction, and expose the manipulation of control flow as a first-class citizen. This is a nice write-up and a collection of examples of various effects implementations in OCaml. The Waluigi Effect https://www.lesswrong.com/posts/D7PumeYTDP