# text-to-image Published articles for text-to-image. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Introducing ChatGPT Images 2.5 DevFeed: [Introducing ChatGPT Images 2.5]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-08T22:46:33Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [ChatGPT](), [OpenAI](), [Generative AI](), [text-to-image](), [Command-line interface]() Tags: [ai](), [ai-2-236](), [chatgpt](), [generative-ai](), [generative-ai-1-982](), [openai](), [openai-463](), [text-to-image](), [text-to-image-47](), [tools](), [tools-78](), [uv](), [uv-100]() ### AI overview OpenAI's ChatGPT Images 2.5 release adds improved multi-turn instruction following, faster responses, and better preservation of subjects in reference photos. The API introduces the gpt-image-2.5-sunburst and gpt-image-2.5-flare model IDs, with Sunburst positioned for precise editing and Flare for faster everyday generation. ### Source excerpt Introducing ChatGPT Images 2.5 OpenAI's image generation models are apparently used "more than 3 billion images across ChatGPT Images and the GPT-Image models in the API". This latest release improves their instruction-following ability across multiple turns, responds faster, and "is better at preserving the subjects in your reference photos". There are two new model IDs in the API: gpt-image-2.5-sunburst and gpt-image-2.5-flare. Based on this I think Sunburst is the stronger option: Choose Sunburst for workflows where editing precision matters most, and Flare for fast, high-quality everyday image generation. I upgraded my openai_image.py CLI tool to support passing in one or more reference images, so now this works: uv run https://tools.simonwillison.net/python/openai_image.py \ 'add a raccoon scientist studying the chart thoughtfully' \ -i https://static.simonwillison.net/static/2026/openai-agent-usage.webp \ -m gpt-image-2.5-sunburst This is the original image, and here's what I got back from that prompt to "add a raccoon scientist studying the chart thoughtfully": Tags: tools, ai, openai, generative-ai, uv, text-to-image ## One-shotting a Raccoon Heist game using Claude Fable 5 DevFeed: [One-shotting a Raccoon Heist game using Claude Fable 5]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-05T19:42:38Z Content type: tutorial Language: en Sources: [Simon Willison]() Topics: [Claude](), [Claude Code](), [GitHub Pages](), [GitHub](), [Code]() Tags: [ai](), [ai-2-235](), [anthropic](), [anthropic-336](), [browser](), [claude](), [claude-310](), [claude-code](), [claude-mythos-fable](), [claude-mythos-fable-41](), [code](), [coding-agents](), [coding-agents-248](), [fable](), [game-design](), [game-design-22](), [generative-ai](), [generative-ai-1-981](), [github](), [github-pages](), [iphone](), [llms](), [llms-1-947](), [prompt-engineering](), [prompt-engineering-197](), [text-to-image](), [text-to-image-47](), [vibe-coding](), [vibe-coding-97]() ### AI overview The article describes an experiment in which Claude Fable 5, running through Claude Code for web, built a Raccoon Heist game from an older GPT-3 game concept and screenshots. It also explains using GitHub Pages to preview work from Claude Code for web and notes that published content from a private repository may be viewable by anyone who guesses the repository name. ### Source excerpt Back in 2022 I tweeted screenshots of a game concept generated by GPT-3 and some concept "art" created using DALL-E. Today, on the fourth anniversary of that tweet, I decided to see if Claude Fable 5 (running in Claude Code for web) could build the entire game from the content of that tweet. It did a pretty good job of it! You can play the game here. Here's the GitHub repo, and a short video demo: Your browser does not support HTML5 video. How I built this This is the August 5th, 2022 tweet: My GPT-3 prompt back then was: Write a detailed product description of a computer game where a team of raccoons go on heists. This was in the text completion days, where you asked GPT-3 to complete the rest of the text. It completed: In "Raccoon Heist", you and your team of thieving raccoons are tasked with pulling off a series of daring heists. From robbing banks to stealing priceless art, no job is too big or too small for your furry crew. You'll need to use your wits and your skills to avoid the police and make a clean getaway with the loot. With exciting gameplay and a charming cast of characters, "Raccoon Heist" is the perfect game for anyone looking for a light-hearted caper. My prompt to DALL-E was short: Screenshot from a video game where a team of raccoons go on a heist Today's experiment: can I dump those screenshots into Fable 5 with a prompt telling it to write a game, then leave it to its own devices and get a working game at the end? Setting Claude Code for web up to use GitHub Pages A frustrating thing about Claude Code for web is that it can be hard to test what it's working on while it's still working. I've been using GitHub Pages to work around that limitation, and found it to work really well. Here's my process: Create a new repository for the project at https://github.com/new - this can be public or private, the trick works equally well for both. Start a Claude Code for web session, in the Claude iPhone or Desktop apps or in the browser at https://claude.ai/c ## Alice AI ART 2.0: путь к unified-модели, которая одинаково хорошо умеет генерировать и редактировать картинки DevFeed: [Alice AI ART 2.0: путь к unified-модели, которая одинаково хорошо умеет генерировать и редактировать картинки]() Original publisher: [Read original article]() Author: snk4tr (Яндекс) Published: 2026-07-14T08:40:58Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Artificial Intelligence](), [text-to-image]() Tags: [ai](), [ai-art](), [alice-ai](), [image-editing](), [image-to-image](), [tag-5db67948c7d9](), [tag-d346fb5ae499](), [tag-e493b9551702](), [text-to-image](), [unified-models]() ### AI overview A team developing Alice AI ART describes its effort to unify text-to-image generation and instruction-based image editing in one model. The article covers the previous split architecture, the motivation for unification, and the evaluation datasets used to measure quality. ### Source excerpt Привет, Хабр! На связи команда генеративных моделей в компьютерном зрении. Вместе с другими командами мы делаем мультимодального ассистента Алиса AI. Внутри него мы развиваем несколько вариантов визуальной генерации с помощью отдельной модели Alice AI ART. Два базовых сценария её работы -- генерация по тексту (Text-to-Image, T2I) и редактирование по картинке с инструкцией (Image-to-Image, I2I). Именно о них пойдёт речь. Всё это время эти сценарии жили как два разных стека: свои базовые модели, свои данные, свои метрики и, честно говоря, своя отдельная боль в разработке и поддержке. В этом году мы поставили себе цель, которая звучала просто, а на практике оказалась полугодовым приключением: не только подтянуть качество, а сделать одну модель, которая одинаково хорошо умеет и в T2I, и в I2I. Внутри мы называем такой режим unified или просто uni. Вас ждёт рассказ об отдельных экспериментах и наблюдениях, которые помогли нам сделать первый шаг в этом направлении и привели нас к Alice AI ART 2.0, -- включая те, которые красиво не сработали (спойлер: их хватало). Читать далее ## DeepInfra on Hugging Face Inference Providers 🔥 DevFeed: [DeepInfra on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Aray Sultanbekova; Shang-Pin; Utemuratov; Yessen K; Oguz Vuruskaner; Célina Hanouti; Simon Brandeis; Lucain Pouget Published: 2026-04-29T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [deepinfra](), [hugging face](), [inference-providers](), [AI Inference](), [SDKs](), [Large Language Model](), [API keys](), [text-generation](), [text-to-image]() Tags: [ai-inference](), [api](), [api-keys](), [applications](), [artificial-intelligence](), [code](), [deepinfra](), [enterprise](), [hub](), [hugging-face](), [inference](), [inference-providers](), [javascript](), [llms](), [open](), [open-source](), [partnerships](), [pricing](), [python](), [sdks](), [text-generation](), [text-to-image]() ### AI overview DeepInfra is now a supported Inference Provider on the Hugging Face Hub, with serverless access to more than 100 models and integration with Hugging Face's JavaScript and Python SDKs. The article describes provider selection, API-key and routed-by-Hugging-Face modes, supported model tasks, and initial access to conversational and text-generation models. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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? ## Training Design for Text-to-Image Models: Lessons from Ablations DevFeed: [Training Design for Text-to-Image Models: Lessons from Ablations]() Original publisher: [Read original article]() Author: David Bertoin; Roman Frigg; Jon Almazán Published: 2026-02-03T11:25:53Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [text-to-image](), [foundation-models](), [Optimization]() Tags: [efficiency](), [experiments](), [models](), [open](), [optimization](), [scale](), [series](), [techniques](), [text-to-image](), [training]() ### AI overview This second article in a series documents experiments for training efficient text-to-image foundation models from scratch. Using PRX as the case study, it examines techniques that improve training speed, convergence reliability, and learned representations, comparing them through controlled experiments and combinations against a clean Flow Matching baseline. ### Source excerpt Welcome back! This is the second part of our series on training efficient text-to-image models from scratch. In the first post of this series, we introduced our goal: training a competitive text-to-image foundation model entirely from scratch, in the open, and at scale. We focused primarily on architectural choices and motivated the core design decisions behind our model PRX. ## Architectural Choices in China's Open-Source AI Ecosystem: Building Beyond DeepSeek DevFeed: [Architectural Choices in China's Open-Source AI Ecosystem: Building Beyond DeepSeek]() Original publisher: [Read original article]() Author: Adina Yakefu; Irene Solaiman Published: 2026-01-27T15:01:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Open Source](), [Artificial Intelligence](), [Mixture of Experts (MoE)](), [Hardware](), [multimodal](), [Open Source Models & Datasets](), [Inference](), [model-deployment](), [datasets](), [toolchains](), [stable-diffusion]() Tags: [ai](), [china](), [compute](), [datasets](), [deployment](), [evaluation](), [mixture-of-experts-moe](), [multimodal](), [open-source](), [performance](), [text-to-image](), [toolchains]() ### AI overview This second article in a three-part series examines architectural and hardware choices in China's open-source AI ecosystem after the DeepSeek Moment. It explains why Mixture-of-Experts architectures became widespread, emphasizing flexible compute allocation, sustainable cost-performance balance, deployment across varied hardware, and expansion into multimodal models, agents, datasets, evaluation, and toolchains. ### Source excerpt This is the second blog in a three-part series on China's open source community's historical advancements since January 2025's "DeepSeek Moment." The first blog is available here, and the third blog is available here. In this second piece we turn our focus from models to the architectural and hardware choices Chinese companies have made as openness becomes the norm. ## DigitalOcean Gradient AI Platform adds image generation, knowledge-base auto-indexing, VPC security, and developer tools DevFeed: [DigitalOcean Gradient AI Platform adds image generation, knowledge-base auto-indexing, VPC security, and developer tools]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2025-10-02T07:12:50Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Artificial Intelligence](), [Digital Ocean](), [text-to-image](), [API](), [Serverless](), [stable-diffusion](), [multimodal](), [Security](), [Image](), [OpenAI]() Tags: [2025](), [accelerate](), [ai](), [ai-platform](), [ai-tools](), [api](), [api-integration](), [authentication](), [code](), [data](), [development](), [generation](), [github](), [image](), [openai](), [platform](), [product-updates](), [range](), [text-to-image](), [vpc](), [workflow]() ### AI overview DigitalOcean announces new Gradient AI Platform capabilities, including programmatic image generation through the Serverless Inference API, Knowledge Base Auto-Indexing, security controls, and additional developer tools for building AI applications. ### Source excerpt At Deploy London 2025, we shared the next chapter of the Gradient AI Platform. We're making it easier for developers and businesses to build production-ready AI applications, whether you're experimenting with your first agent or scaling an enterprise workload. Today, in that spirit, we're introducing a new wave of features that expand what you can build with the Gradient AI Platform, give you greater security and control, and accelerate your development workflow. Expanding capabilities for Gradient AI Platform AI applications are becoming more multimodal and data-driven, able to work with text, images, audio, and other formats. With Image Model Support and Knowledge Base Auto-Indexing, these new Gradient AI Platform features make it easier than ever to give your agents a wide range of inputs and knowledge sources. Image Model Support You can now generate images programmatically using text prompts through Gradient AI Platform's Serverless Inference API, powered by OpenAI's gpt-image-1 model. This is the platform's first non-text modality (with more coming soon), expanding our capabilities from text-only to include text-to-image generation. Text-to-image generation - Generate images directly via the Serverless Inference API. API integration - OpenAI compatible using the image generation endpoint. For code examples and setup instructions, check out the DigitalOcean Gradient AI starter kit on GitHub. Unified billing - Image generation charges appear on your DigitalOcean account alongside other services. Developer-first workflow - Direct API access without requiring UI-based tools or self-hosting. Enterprise authentication - Use Model Access Keys for secure access. With this capability, you can create images using natural language prompts for projects such as content generation, marketing assets, product imagery, and more. Get started with Image Model Support -> Knowledge Base Auto-Indexing Keep your agents up to date without the manual work. With auto-indexing, new and u ## Visible Watermarking with Gradio DevFeed: [Visible Watermarking with Gradio]() Original publisher: [Read original article]() Author: Margaret Mitchell Published: 2025-09-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [watermarking](), [gradio](), [hugging face](), [Artificial Intelligence](), [Generative AI]() Tags: [ai-content](), [ai-transparency](), [building](), [ethics](), [gradio](), [guide](), [hugging-face](), [open-source](), [text-generation](), [text-to-image](), [text-to-video](), [watermarking]() ### AI overview Hugging Face explains how Gradio can add visible watermarks to AI-generated images, video, and text in Spaces. The article highlights filename-based, open-image, NumPy-array, QR, custom text, and chatbot watermarking options to support attribution, transparency, and disclosure. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Make your ZeroGPU Spaces go brrr with ahead-of-time compilation DevFeed: [Make your ZeroGPU Spaces go brrr with ahead-of-time compilation]() Original publisher: [Read original article]() Author: Charles Bensimon; Sayak Paul; Linoy Tsaban; Apolinário from multimodal AI art Published: 2025-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [RAPIDS](), [GPU](), [fly](), [PyTorch](), [quantization](), [Open Source]() Tags: [compilation](), [gpu](), [guide](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [latency](), [models](), [optimization](), [python](), [pytorch](), [quantization](), [spaces](), [text-to-image](), [zerogpu]() ### AI overview This tutorial explains how to use PyTorch ahead-of-time compilation in ZeroGPU Spaces. It covers faster model startup and inference, FP8 quantization, dynamic shapes, and the process-based GPU allocation model used by ZeroGPU, with reported speedups of 1.3x-1.8x on Flux, Wan, and LTX models. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Generate Images with Claude and Hugging Face DevFeed: [Generate Images with Claude and Hugging Face]() Original publisher: [Read original article]() Author: shaun smith Published: 2025-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Claude](), [hugging face](), [Model Context Protocol](), [MCP Server](), [Artificial Intelligence](), [gradio](), [flux](), [Image](), [qwen](), [spaces]() Tags: [anthropic](), [applications](), [article](), [artificial-intelligence](), [blog](), [claude](), [flux](), [generation](), [gradio](), [how-to](), [hugging-face](), [image](), [images](), [llm](), [mcp](), [mcp-server](), [models](), [qwen](), [spaces](), [text-to-image](), [tools]() ### AI overview This tutorial explains how to connect Claude to Hugging Face Spaces through the Hugging Face MCP Server to generate detailed images with AI models. It highlights prompt assistance, visual iteration, model selection, and examples including FLUX.1 Krea [dev] and Qwen-Image. ### 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. ## Microsoft and Hugging Face expand collaboration DevFeed: [Microsoft and Hugging Face expand collaboration]() Original publisher: [Read original article]() Author: Jeff Boudier; Simon Pagezy; Alvaro Bartolome Published: 2025-05-19T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [vulnerability]() Tags: [announcement](), [artificial-intelligence](), [audio](), [azure](), [classification](), [collaboration](), [deployment](), [hugging-face](), [images](), [microsoft](), [model](), [models](), [open](), [open-source](), [partnership](), [security](), [text-generation](), [text-to-image](), [vulnerability]() ### AI overview Microsoft and Hugging Face expanded their collaboration to make more than 10,000 Hugging Face models available through Azure AI Foundry. The collection supports deployment of models for text, audio, and image tasks, with listed security screening requirements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The AI tools for Art Newsletter - Issue 1 DevFeed: [The AI tools for Art Newsletter - Issue 1]() Original publisher: [Read original article]() Author: Linoy Tsaban; Apolinário from multimodal AI art Published: 2025-01-31T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [releases]() Tags: [ai](), [ai-art](), [aiart](), [community](), [diffusers](), [diffusion](), [open-source](), [releases](), [text-to-image](), [tools]() ### AI overview A newsletter issue reviewing 2024 developments in open-source creative AI, especially image and video generation. It discusses diffusion and flow-matching approaches, image-model releases including Stable Diffusion 3, HunyuanDiT, AuraFlow, Flux.1, and Stable Diffusion 3.5, plus personalization techniques for text-to-image models. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Open Preference Dataset for Text-to-Image Generation by the 🤗 Community DevFeed: [Open Preference Dataset for Text-to-Image Generation by the 🤗 Community]() Original publisher: [Read original article]() Author: David Berenstein; ben burtenshaw; Daniel Vila; Daniel van Strien; Sayak Paul; Ame Vi; Linoy Tsaban Published: 2024-12-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Open Source Models & Datasets](), [datasets](), [Synthetic Data Generation](), [flux](), [stable-diffusion](), [argilla](), [distilabel](), [data observability](), [GitHub](), [hugging face]() Tags: [argilla](), [artificial-intelligence](), [data](), [data-is-better-together](), [data-quality](), [datasets](), [distilabel](), [flux](), [generation](), [github](), [hugging-face](), [image](), [open](), [open-source](), [stable-diffusion](), [synthetic-data](), [synthetic-data-generation](), [text-to-image]() ### AI overview The article describes an open community effort to create an image-preference dataset for text-to-image generation. It covers prompt preparation with distilabel, synthetic data generation, image generation with Flux and Stable Diffusion, and filtering with text- and image-based classifiers plus manual review. The resulting dataset and related code are available through the Hugging Face Hub and GitHub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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. ## Launching the Artificial Analysis Text to Image Leaderboard & Arena DevFeed: [Launching the Artificial Analysis Text to Image Leaderboard & Arena]() Original publisher: [Read original article]() Author: Micah Hill-Smith; George Cameron Published: 2024-06-06T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [text-to-image](), [Human-AI evaluation](), [Artificial Intelligence](), [Open Source](), [stable-diffusion]() Tags: [artificial-intelligence](), [collaboration](), [community](), [diffusion](), [evaluation](), [generation](), [leaderboard](), [open-source](), [openai](), [ranking](), [research](), [text-to-image]() ### AI overview The Artificial Analysis Text to Image Leaderboard ranks open-source and proprietary image-generation models using ELO scores derived from more than 45,000 human image preferences. Its Image Arena uses crowdsourced comparisons across diverse prompts and use cases. The article highlights the rapid evolution of image models, the strong performance of proprietary systems, and the growing competitiveness of open-source models such as Playground AI v2.5 and Stable Diffusion 3. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## MobileDiffusion: Rapid text-to-image generation on-device DevFeed: [MobileDiffusion: Rapid text-to-image generation on-device]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-01-31T21:59:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Machine learning](), [Mobile](), [text-to-image](), [Inference](), [model-deployment](), [Optimization](), [Android](), [iOS](), [MediaPipe]() Tags: [android](), [computer-vision](), [deployment](), [diffusion](), [generation](), [image](), [inference](), [ios](), [machine-learning](), [mediapipe](), [mobile](), [on-device](), [on-device-learning](), [optimization](), [text-to-image]() ### AI overview Google researchers introduce MobileDiffusion, a latent diffusion model designed for on-device text-to-image generation. Using DiffusionGAN for one-step sampling, it was tested on premium iOS and Android devices and generated 512x512 images in about half a second with a 520-million-parameter model. ### Source excerpt Posted by Yang Zhao, Senior Software Engineer, and Tingbo Hou, Senior Staff Software Engineer, Core ML Text-to-image diffusion models have shown exceptional capabilities in generating high-quality images from text prompts. However, leading models feature billions of parameters and are consequently expensive to run, requiring powerful desktops or servers (e.g., Stable Diffusion, DALL-E, and Imagen). While recent advancements in inference solutions on Android via MediaPipe and iOS via Core ML have been made in the past year, rapid (sub-second) text-to-image generation on mobile devices has remained out of reach. To that end, in "MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices", we introduce a novel approach with the potential for rapid text-to-image generation on-device. MobileDiffusion is an efficient latent diffusion model specifically designed for mobile devices. We also adopt DiffusionGAN to achieve one-step sampling during inference, which fine-tunes a pre-trained diffusion model while leveraging a GAN to model the denoising step. We have tested MobileDiffusion on iOS and Android premium devices, and it can run in half a second to generate a 512x512 high-quality image. Its comparably small model size of just 520M parameters makes it uniquely suited for mobile deployment. Rapid text-to-image generation on-device. Background The relative inefficiency of text-to-image diffusion models arises from two primary challenges. First, the inherent design of diffusion models requires iterative denoising to generate images, necessitating multiple evaluations of the model. Second, the complexity of the network architecture in text-to-image diffusion models involves a substantial number of parameters, regularly reaching into the billions and resulting in computationally expensive evaluations. As a result, despite the potential benefits of deploying generative models on mobile devices, such as enhancing user experience and addressing emerging privacy concer ## Introducing Similarity Search at Flickr DevFeed: [Introducing Similarity Search at Flickr]() Original publisher: [Read original article]() Author: Clayton Mellina Published: 2017-03-07T18:04:36Z Content type: release Language: en Sources: [Flickr]() Topics: [Image]() Tags: [discovery](), [images](), [machine-learning](), [machine-tags](), [neural-network](), [photos](), [search](), [similarity-search](), [text-to-image](), [uncategorized](), [visual-similarity]() ### AI overview Flickr introduces similarity search, allowing users to search for photos similar to a selected photo from a search result. The article explains how photo discovery differs from traditional text-based web search and describes the feature as a new way to explore visual concepts and Flickr's image collection. ### Source excerpt At Flickr, we understand that the value in our image corpus is only unlocked when our members can find photos and photographers that inspire them, so we strive to enable the discovery and appreciation of new photos. To further that ... Continue reading ->