# diffusion Published articles for diffusion. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models DevFeed: [DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [grpo](), [Reinforcement learning](), [large-language-models](), [LLM Techniques](), [Code]() Tags: [code-generation](), [denoising](), [diffusion](), [grpo](), [json-schema](), [optimization](), [reinforcement-learning]() ### AI overview The article introduces DACA-GRPO, a denoising-aware enhancement for GRPO-style reinforcement-learning trainers used with diffusion language models. It uses per-token denoising progress scores and stratified masking likelihood to improve credit assignment and reduce bias in likelihood estimates. Across seven benchmarks, the method reports gains in mathematical reasoning, code generation, constraint satisfaction, and JSON Schema adherence. ### Source excerpt Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps as equally important and rely on biased, high-variance likelihood estimates. We identify two fundamental weaknesses: the absence of temporal credit assignment across the denoising trajectory, and the systematic bias of mean-field likelihood estimates used for policy optimization. To address these, we propose Denoising-Aware Credit Assignment for GRPO (DACA-GRPO), a lightweight, plug-and-play enhancement for any GRPO-style trainer. DACA-GRPO... ## Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train DevFeed: [Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train]() Original publisher: [Read original article]() Published: 2026-09-15T20:00:35Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI search](), [Reinforcement learning](), [Artificial Intelligence](), [Google](), [LLMs](), [Algorithms & Theory]() Tags: [ai](), [ai-search](), [algorithms-theory](), [data-mining-modeling](), [diffusion](), [generative-ai](), [google](), [icml](), [icml-2026](), [reinforcement-learning](), [research](), [retrieval](), [rl](), [search]() ### AI overview Google Research presents Retrieve-for-Train, a framework that uses offline reinforcement learning to compile reward-aligned query fan-outs into training data for a lightweight diffusion retriever. The approach is intended to produce diverse, complementary, and coherent search-result sets in a single inference pass, reducing reliance on expensive inference-time reasoning. ### Source excerpt Algorithms & Theory ## Rebuilding AUTOMATIC1111 with Gradio Workflow DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Graphs](), [stable-diffusion](), [Inference](), [Large Language Model](), [hugging face](), [vlm]() Tags: [automatic1111](), [comfyui](), [diffusion](), [flux](), [gradio](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [inference-providers](), [llm](), [models](), [python](), [space](), [spaces](), [text-to-image](), [vlm](), [workflow](), [workflows]() ### AI overview A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Temporally stable generative illumination with a one-step diffusion model DevFeed: [Temporally stable generative illumination with a one-step diffusion model]() Original publisher: [Read original article]() Author: SungYe Kim; Harish Anand; Alexandr Kuznetsov; Wojciech Uss; Wojciech Kaliński; Rama Harihara Published: 2026-09-09T13:00:00Z Content type: article Language: en Sources: [AMD GPUOpen]() Topics: [real-time rendering](), [VAE]() Tags: [arr-group](), [article-release](), [diffusion](), [generation](), [gi](), [inference](), [lighting](), [ml](), [model](), [optimization](), [quality](), [ray-tracing](), [raytracing](), [real-time](), [real-time-rendering](), [research](), [white-paper]() ### AI overview The article presents a single-step latent diffusion method for real-time global illumination. It conditions image generation on scene signals and lighting hints, and uses a Temporal VAE decoder with motion-vector reprojection to improve temporal stability and reduce flicker. ### Source excerpt A generative method for real-time global illumination using a single-step latent diffusion model, delivering stable, high-quality lighting without costly iterative processing. ## NeoMME: an efficient Multimodal-native and Multilingual Encoder DevFeed: [NeoMME: an efficient Multimodal-native and Multilingual Encoder]() Original publisher: [Read original article]() Author: Tony Wu; Aurélien Lac Published: 2026-09-03T13:13:48Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [Fine-tuning](), [Large Language Model](), [GPU]() Tags: [apache](), [architecture](), [diffusion](), [embeddings](), [fine-tuning](), [gpu](), [hugging-face](), [multimodal](), [quantization](), [retrieval](), [training](), [transformers](), [vector](), [vision](), [vlm]() ### AI overview NeoMME is a family of multilingual multimodal encoders trained from scratch with a masked discrete-diffusion objective. It uses one bidirectional Transformer for text tokens and image patches, and is fine-tuned for visual document retrieval with dense and late-interaction embeddings. ### Source excerpt We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model. A single bidirectional Transformer processes both text tokens and raw image patches, and we train the entire model from scratch with a masked discrete-diffusion objective. We fine-tuned NeoMME for visual document retrieval using ColPali's page-image approach. ## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-04T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [foundation-models](), [Cosmos](), [model-deployment](), [Reinforcement learning](), [Inference](), [GitHub](), [hugging face](), [Open Source Models & Datasets]() Tags: [accelerate](), [automotive-transportation](), [autonomous](), [benchmarks](), [cosmos](), [customization](), [data](), [developer-tools-techniques](), [developers](), [development](), [diffusion](), [drive](), [driving](), [evaluation](), [featured](), [fine-tuning](), [generate](), [generation](), [github](), [hugging-face](), [inference](), [learning](), [models](), [nvidia](), [reasoning](), [robot-navigation](), [robotics](), [simulation-modeling-design](), [top-stories]() ### AI overview NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows. ### Source excerpt Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data... ## 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. ## Towards demystifying the creativity of diffusion models DevFeed: [Towards demystifying the creativity of diffusion models]() Original publisher: [Read original article]() Published: 2026-07-15T18:06:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [Mathematics](), [Neural Network](), [Algorithms & Theory](), [Training AI Models](), [datasets](), [Google]() Tags: [algorithms-theory](), [data](), [datasets](), [diffusion](), [generation](), [generative-ai](), [google](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [model](), [models](), [research](), [retrieval](), [training]() ### AI overview Google Research explains that diffusion models can generate novel data rather than merely memorize training examples. It attributes this creativity to neural networks learning a smoothed score function, which causes denoising to interpolate between training data points along a hidden data manifold. ### Source excerpt Algorithms & Theory ## DiScoFormer: One transformer for density and score, across distributions DevFeed: [DiScoFormer: One transformer for density and score, across distributions]() Original publisher: [Read original article]() Author: Kyle Wiggers Published: 2026-06-29T18:02:48Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [data](), [diffusion](), [kernel](), [machine-learning]() ### AI overview DiScoFormer is a transformer model for estimating a distribution's density and score from a set of data points in one forward pass, without retraining for each distribution. It uses cross-attention and coupled density and score output heads, with their mathematical relationship providing a label-free consistency loss. ### Source excerpt Many problems in machine learning and the sciences come down to the same task: you have a collection of data points and want to recover the distribution they came from--which values are common, and which are rare. Pinning down that distribution means estimating two quantities: the distribution's density and, more useful as dimensionality grows, its score. The density is the smooth version of a histogram--high where points cluster and low where they're scarce. ## DiffusionGemma: 4x faster text generation DevFeed: [DiffusionGemma: 4x faster text generation]() Original publisher: [Read original article]() Author: Brendan O'Donoghue Published: 2026-06-10T16:24:11Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Language models](), [Local AI]() Tags: [diffusion](), [gemma](), [inference](), [latency](), [local](), [mixture-of-experts-moe](), [none](), [text-generation]() ### AI overview DiffusionGemma is an experimental Apache 2.0-licensed 26B MoE text-diffusion model that generates text blocks in parallel for up to 4x faster GPU generation. It targets speed-critical local interactive workflows, while standard Gemma 4 remains recommended for maximum output quality. ### Source excerpt An overview of DiffusionGemma, an exceptionally fast text generation model with up to 4x faster speeds. ## DeepSeek V4, LeCun's Bet Against LLMs, and Lovable's Self-Improving Agent - The Tokenizer Edition #30 DevFeed: [DeepSeek V4, LeCun's Bet Against LLMs, and Lovable's Self-Improving Agent - The Tokenizer Edition #30]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-06-04T13:31:46Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Large Language Model](), [deepseek](), [Machine learning](), [World models](), [Benchmark]() Tags: [ai](), [ai-ml](), [benchmarks](), [deepseek](), [diffusion](), [llms](), [research](), [training]() ### AI overview This English-language weekly roundup surveys AI and machine-learning resources, including papers on text-to-image generation, reinforcement learning for research agents, spatial reasoning, and benchmarks; videos about DeepSeek V4, JEPA world models, and agent improvement; and reads on FP8 KV-cache quantization, diffusion generation, and delegated-work fidelity. ### Source excerpt This week's most valuable AI resources ## Diffusion LLMs, Explained Simply DevFeed: [Diffusion LLMs, Explained Simply]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-04-24T14:23:01Z Content type: tutorial Language: en Sources: [Dr. Ashish Bamania]() Topics: [Large Language Model](), [text-generation](), [Transformer]() Tags: [artificial-intelligence](), [data-science](), [diffusion](), [introduction](), [llms](), [machine-learning](), [programming](), [technology](), [text-generation](), [tokens]() ### AI overview An introductory explanation of diffusion large language models, contrasting them with autoregressive Transformer-based chatbots that generate responses sequentially, one token at a time. ### Source excerpt A gentle but comprehensive introduction to Diffusion LLMs. Continue reading on Level Up Coding " ## Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs DevFeed: [Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs]() Original publisher: [Read original article]() Author: Andrew Lapp; Louis Castricato; Scott Fox; Shahbuland Matiana; David Rossi Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [World models](), [Hardware](), [real-time](), [data](), [AI, ML & Data Engineering](), [systems](), [Artificial Intelligence]() Tags: [announcement](), [artificial-intelligence](), [compute](), [data](), [diffusion](), [game-dev](), [hardware](), [local](), [models](), [performance](), [real-time](), [training](), [world-model]() ### AI overview Waypoint-1.5 is Overworld's real-time video world model, designed to generate interactive environments locally on consumer hardware. The release adds 720p and 360p model tiers, supports up to 60 FPS on higher-end desktop GPUs, broadens hardware accessibility, uses nearly 100 times more training data than Waypoint-1, and applies more efficient video modeling to improve coherence and responsiveness. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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. ## Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines DevFeed: [Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines]() Original publisher: [Read original article]() Author: YiYi Xu; Alvaro Somoza; Dhruv Nair; Sayak Paul Published: 2026-03-05T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [modular-diffusers](), [diffusers](), [Open Source](), [Artificial Intelligence]() Tags: [api](), [artificial-intelligence](), [building](), [data](), [diffusers](), [diffusion](), [documentation](), [generative](), [guide](), [how-to](), [hub](), [images](), [inference](), [model](), [models](), [modular-diffusers](), [open-source](), [python](), [qwen](), [text-to-image](), [text-to-video](), [work](), [workflow]() ### AI overview Modular Diffusers presents diffusion pipelines as composable blocks for text encoding, image encoding, denoising, and decoding. The article explains how to run inference, build and publish custom Python blocks, compose workflows, integrate with the node-based Mellon interface, and manage model loading and memory. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## PRX Part 3 -- Training a Text-to-Image Model in 24h! DevFeed: [PRX Part 3 -- Training a Text-to-Image Model in 24h!]() Original publisher: [Read original article]() Author: David Bertoin; Roman Frigg; Jon Almazán Published: 2026-03-03T16:50:49Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [text-to-image](), [GPU](), [Code](), [Hardware]() Tags: [code](), [compute](), [computer-vision](), [cost](), [diffusion](), [engineering](), [experiment](), [gpu](), [hardware](), [model](), [open](), [performance](), [text-to-image](), [training]() ### AI overview The article describes a 24-hour speedrun for training a text-to-image diffusion model by combining architectural and training techniques from earlier posts. It targets 512px training followed by 1024px fine-tuning on 32 H200 GPUs within an approximately $1,500 compute budget, and says the training code and experimental framework will be open-sourced. ### Source excerpt Welcome back 👋 In the last two posts (Part 1 and Part 2), we explored a wide range of architectural and training tricks for diffusion models. We tried to evaluate each idea in isolation, measuring throughput, convergence speed, and final image quality, and tried to understand what actually moves the needle. In this post, we want to answer a much more practical question: What happens when we combine all the tricks that worked? ## Introducing Waypoint-1: Real-time interactive video diffusion from Overworld DevFeed: [Introducing Waypoint-1: Real-time interactive video diffusion from Overworld]() Original publisher: [Read original article]() Author: Andrew Lapp; Louis Castricato; Scott Fox; Shahbuland Matiana; David Rossi Published: 2026-01-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [World models](), [real-time](), [Artificial Intelligence](), [Inference](), [Latency](), [Streaming](), [Transformer](), [Tooling](), [AI Development](), [Open Source]() Tags: [announcement](), [artificial-intelligence](), [diffusion](), [inference](), [latency](), [model](), [models](), [open-source](), [performance](), [procedural](), [real-time](), [streaming](), [tooling](), [train](), [world-model]() ### AI overview Overworld introduces Waypoint-1, a real-time interactive video diffusion model that generates explorable worlds from frames and responds to text, mouse, and keyboard controls. The article describes its frame-causal rectified flow transformer, training on diverse video game footage, diffusion forcing, self-forcing, and the WorldEngine inference library. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Diffusers welcomes FLUX-2 DevFeed: [Diffusers welcomes FLUX-2]() Original publisher: [Read original article]() Author: YiYi Xu; Daniel Gu; Sayak Paul; Alvaro Somoza; Dhruv Nair; Aritra Roy Gosthipaty; Linoy Tsaban; Apolinário from multimodal AI art Published: 2025-11-25T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering]() Tags: [architecture](), [artificial-intelligence](), [black-forest-labs](), [diffusers](), [diffusion](), [embedding](), [flux](), [generation](), [images](), [lora](), [mlp](), [model](), [open-source](), [quantization](), [streams]() ### AI overview The article introduces FLUX.2, an image-generation and editing model that supports text-guided and image-guided generation with multiple reference images. It outlines changes to its text encoder and diffusion-transformer architecture relative to FLUX.1. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Replicate Integration with Encore.ts DevFeed: [Replicate Integration with Encore.ts]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2025-11-17T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [AI Development](), [text-to-image](), [API](), [flux](), [stable-diffusion](), [Node.js](), [Secrets Management]() Tags: [ai](), [api](), [diffusion](), [flux](), [integration](), [node-js](), [secrets-management](), [stable-diffusion](), [tutorial]() ### AI overview This tutorial explains how to build a type-safe backend with Encore.ts and Replicate to run AI model predictions and generate images. It covers asynchronous results, image storage, API authentication, secret management, and models such as FLUX and Stable Diffusion. ### Source excerpt Running AI models with Replicate and Encore ## Implementing MCP Servers in Python: An AI Shopping Assistant with Gradio DevFeed: [Implementing MCP Servers in Python: An AI Shopping Assistant with Gradio]() Original publisher: [Read original article]() Author: Freddy Boulton Published: 2025-07-31T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Chat](), [web applications]() Tags: [artificial-intelligence](), [community](), [diffusion](), [gradio](), [llm](), [mcp](), [mcp-server](), [open-source](), [python](), [vs-code]() ### AI overview A tutorial for building a Python Gradio MCP server that lets an LLM-powered shopping assistant invoke the IDM-VTON virtual try-on model through VS Code AI Chat. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Fast LoRA inference for Flux with Diffusers and PEFT DevFeed: [Fast LoRA inference for Flux with Diffusers and PEFT]() Original publisher: [Read original article]() Author: Sayak Paul; Benjamin Bossan Published: 2025-07-23T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [flux](), [lora](), [Inference Performance](), [Hacktoberfest](), [diffusers](), [Optimization](), [peft](), [quantization](), [model architecture](), [text-to-image](), [GPU]() Tags: [amd](), [diffusers](), [diffusion](), [flux](), [gpu](), [guide](), [inference](), [latency](), [lora](), [model-architecture](), [optimization](), [peft](), [quantization](), [text-to-image]() ### AI overview This article presents an optimization recipe for faster LoRA inference with the Flux.1-Dev text-to-image model using Diffusers and PEFT. The approach addresses LoRA hotswapping and recompilation issues with Flash Attention 3, FP8 quantization from TorchAO, and hotswapping-ready compilation, achieving about a 2.3x speedup while balancing inference speed and memory use. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Exploring Quantization Backends in Diffusers DevFeed: [Exploring Quantization Backends in Diffusers]() Original publisher: [Read original article]() Author: Derek Liu; Marc Sun; Sayak Paul Published: 2025-05-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering]() Tags: [artificial-intelligence](), [backends](), [benchmarks](), [black-forest-labs](), [diffusers](), [diffusion](), [diffusion-transformers](), [embeddings](), [flux](), [gpu](), [guide](), [hugging-face](), [images](), [memory](), [quantization]() ### AI overview The article examines quantization backends in Hugging Face Diffusers using the FLUX.1-dev image-generation pipeline. It compares high-precision and 4-bit or 8-bit variants, explains pipeline components and memory use, and discusses backends including bitsandbytes, GGUF, torchao, Quanto, and FP8. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Remote VAEs for decoding with Inference Endpoints 🤗 DevFeed: [Remote VAEs for decoding with Inference Endpoints 🤗]() Original publisher: [Read original article]() Author: hlky; Sayak Paul Published: 2025-02-24T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [VAE](), [inference-endpoints](), [diffusers](), [hugging face](), [Inference Performance](), [Open Source](), [Deadlock]() Tags: [artificial-intelligence](), [concurrency](), [cpu](), [data](), [diffusers](), [diffusion](), [generate](), [generation](), [getting-started](), [gpu](), [hdd](), [inference](), [inference-endpoints](), [nvme](), [open-source](), [queue](), [remote](), [tensors](), [time](), [vae]() ### AI overview This article presents an experimental approach for decoding latent-space diffusion outputs with remote VAEs hosted on Inference Endpoints. It explains how remote decoding can reduce consumer GPU memory pressure, avoid the quality loss associated with tiled decoding, and improve concurrency by queueing generation requests. The article includes setup and usage examples for random tensors and pipelines involving SD v1.5, Flux, and HunyuanVideo. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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 [Next page]()