# stable-diffusion Published articles for stable-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. ## Beyond LoRA: Can you beat the most popular fine-tuning technique? DevFeed: [Beyond LoRA: Can you beat the most popular fine-tuning technique?]() Original publisher: [Read original article]() Author: Benjamin Bossan; Sayak Paul; Marian Tietz; Kashif Rasul Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [SDKs]() Tags: [artificial-intelligence](), [community](), [diffusers](), [fine-tuning](), [guide](), [lora](), [nlp](), [peft](), [quantization](), [stable-diffusion](), [training](), [transformers]() ### AI overview The article examines parameter-efficient fine-tuning (PEFT) for open models, focusing on whether LoRA is always the best technique. It explains how PEFT reduces memory requirements, can enable fine-tuning of quantized models, and offers a unified API for multiple techniques. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Replicate Handles Billing: A Complete Breakdown DevFeed: [How Replicate Handles Billing: A Complete Breakdown]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [AI Platforms/Deployment](), [AI Infrastructure](), [GPU](), [Multi-GPU](), [llama](), [stable-diffusion](), [Whisper]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [analysis](), [billing](), [compute](), [gpu](), [infrastructure](), [llama](), [models](), [multi-gpu](), [stable-diffusion](), [usage-based-billing](), [whisper]() ### AI overview The article analyzes Replicate's usage-based billing model, which charges for compute time by hardware type rather than by subscription, model, or token package. It explains hardware-tier pricing, multi-GPU committed-spend requirements, and model-agnostic billing, and discusses how to implement similar per-second billing for an AI platform. ### Source excerpt A detailed analysis of Replicate's pure usage-based billing model - per-second compute pricing across hardware tiers, cold start costs, and how to build the same pay-per-second infrastructure billing for your own AI platform. ## How to Sell AI Prompts Online: A Step-by-Step Guide for 2026 DevFeed: [How to Sell AI Prompts Online: A Step-by-Step Guide for 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-05T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Prompt Engineering](), [Artificial Intelligence](), [ChatGPT](), [Claude](), [stable-diffusion]() Tags: [2026](), [ai](), [chatgpt](), [claude](), [digital-products](), [guide](), [how-to](), [prompt](), [stable-diffusion]() ### AI overview A step-by-step guide to selling AI prompts online, covering prompt selection, packaging, pricing, delivery, marketplaces, and direct sales. It discusses text prompts for ChatGPT and Claude, as well as image-generation prompts for Midjourney and Stable Diffusion. ### Source excerpt Learn how to package, price, and sell AI prompts online - from ChatGPT and Midjourney prompts to full prompt libraries and subscription packs. ## 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 ## Image and audio models from fal now available on DigitalOcean DevFeed: [Image and audio models from fal now available on DigitalOcean]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2025-10-23T12:30:00Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Digital Ocean](), [multimodal-ai](), [API](), [stable-diffusion](), [flux](), [Serverless](), [voice ai]() Tags: [ai-platform](), [api](), [audio](), [digitalocean](), [flux](), [multimodal](), [multimodal-ai](), [product-updates](), [serverless](), [stable-diffusion]() ### AI overview DigitalOcean announces four multimodal AI models from fal in public preview on the Gradient AI Platform through Serverless Inference. The models support image generation, audio generation, and multilingual text-to-speech through an API. ### Source excerpt We're excited to announce the launch of four multimodal AI models from fal on the DigitalOcean Gradient™ AI Platform, now available in public preview through Serverless Inference. These models allow you to generate images and audio directly via API, without worrying about infrastructure, scaling, or vendor management. With this release, building AI-powered applications that include visual and audio content is easier than ever. Explore the new models The fal models, now in public preview, cover a variety of modalities, enabling you to experiment, prototype, and deploy multimodal AI features quickly: Image generation: Stable Diffusion XL fast (fal-ai/fast-sdxl) - High-resolution image generation FLUX.1 (schnell) (fal-ai/flux/schnell) - Fast image generation for quick prototyping Audio generation: Stable Audio (fal-ai/stable-audio-25/text-to-audio) - Convert text into natural-sounding audio ElevenLabs TTS Multilingual v2 9 (fal-ai/elevenlabs/tts/multilingual-v2) - Multilingual text-to-speech These models are available via Serverless Inference, letting you generate images and audio through the same simple API-driven workflow you already use on Gradient AI Platform. Try it out You can start using these models through the Serverless Inference API (https://inference.do-ai.run) after opting in to the public preview in the DigitalOcean console. Here's a quick look at how to interact with them: First, opt in to the public preview to access the fal models on the Gradient AI Platform. Once opting in, it should take about 10 to 15 minutes for your access to be granted. Example: Generate an Image export MODEL_ACCESS_KEY="YOUR_KEY" curl -sS -X POST 'https://inference.do-ai.run/v1/async-invoke' \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model_id": "fal-ai/flux/schnell", "input": { "prompt": "A high-quality photo of a futuristic city at sunset" } }' Example: Generate an Image with Customized Parameters export MODEL_ACCESS_KEY="YOUR_ ## From Idea to Full Stack App in One Conversation with Create DevFeed: [From Idea to Full Stack App in One Conversation with Create]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-02-12T01:21:10Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [App](), [Artificial Intelligence](), [Database](), [Serverless](), [Large Language Model](), [ChatGPT](), [Provisioning](), [ui](), [OpenAI](), [Image]() Tags: [agent](), [ai](), [anthropic](), [app](), [backend](), [case-studies](), [chatgpt](), [database](), [openai](), [provisioning](), [serverless](), [stable-diffusion](), [stripe](), [ui]() ### AI overview The article presents Create as a tool that turns natural-language instructions into production-ready full-stack applications. It describes an agent that generates and updates pages, components, backend functions, database setup, integrations, AI features, image generation, and payments, with real-time visual feedback and deployment on developer-grade infrastructure. It also highlights Neon's serverless Postgres database for rapid provisioning and scale-to-zero deployment. ### Source excerpt These are wild times. It's now possible to describe a fancy app in a single text prompt, e.g., "Make an AI recipe generator with ChatGPT that lets me enter a food, generate recipes and save my favorites to a gallery for later" And watch a complete app materialize in seconds: If y... ## 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. ## Diffusers welcomes Stable Diffusion 3.5 Large DevFeed: [Diffusers welcomes Stable Diffusion 3.5 Large]() Original publisher: [Read original article]() Author: YiYi Xu; Aryan V S; Dhruv Nair; Sayak Paul; Linoy Tsaban; Apolinário from multimodal AI art; Alvaro Somoza; Aritra Roy Gosthipaty Published: 2024-10-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [diffusers](), [stable-diffusion](), [quantization](), [Transformer architecture](), [Optimization](), [Artificial Intelligence](), [Open Source]() Tags: [artificial-intelligence](), [diffusers](), [diffusion](), [guide](), [inference](), [memory-optimization](), [open-source](), [quantization](), [sd3-5](), [stable-diffusion](), [transformer-architecture]() ### AI overview Hugging Face introduces Stable Diffusion 3.5 Large, providing an 8B base model and a timestep-distilled variant for few-step image generation. The article explains how to use the models with Diffusers for inference and training, including gated access, memory optimization, and quantized execution for lower-memory systems. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## We're Cutting L40S Prices In Half DevFeed: [We're Cutting L40S Prices In Half]() Original publisher: [Read original article]() Published: 2024-08-15T00:00:00Z Content type: news Language: en Sources: [The Fly Blog]() Topics: [fly.io](), [GPU](), [fly](), [Inference](), [Cloud](), [Artificial Intelligence](), [Generative AI](), [NeMo](), [Nvidia](), [stable-diffusion](), [NVLink]() Tags: [ai](), [cdn](), [close-to-users](), [cloud](), [deploy-app-servers](), [docker](), [elixir](), [fly](), [fly-io](), [generative-ai](), [gpu](), [hardware](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [inference](), [nemo](), [networking](), [nvidia](), [nvlink](), [postgresql-clusters](), [servers](), [stable-diffusion]() ### AI overview Fly.io announces that it has cut the price of NVIDIA L40S GPUs to $1.25 per hour. The article explains that customers most often choose the less powerful A10 for inference and mid-sized generative AI workloads such as Mistral Nemo and Stable Diffusion, while Fly.io's earlier GPU strategy focused on more demanding ideas involving A100 partitioning, NVLink clusters, and training. ### Source excerpt We're Fly.io, a new public cloud with simple, developer-friendly ergonomics. And as of today, cheaper GPUs. Try it out; you'll be deployed in just minutes. We just lowered the prices on NVIDIA L40s GPUs to $1.25 per hour. Why? Because our feet are cold and we burn processor cycles for heat. But also other reasons. Let's back up. We offer 4 different NVIDIA GPU models; in increasing order of performance, they're the A10, the L40S, the 40G PCI A100, and the 80G SXM A100. Guess which one is most popular. We guessed wrong, and spent a lot of time working out how to maximize the amount of GPU power we could deliver to a single Fly Machine. Users surprised us. By a wide margin, the most popular GPU in our inventory is the A10. The A10 is an older generation of NVIDIA GPU with fewer, slower cores and less memory. It's the least capable GPU we offer. But that doesn't matter, because it's capable enough. It's solid for random inference tasks, and handles mid-sized generative AI stuff like Mistral Nemo or Stable Diffusion. For those workloads, there's not that much benefit in getting a beefier GPU. As a result, we can't get new A10s in fast enough for our users. If there's one thing we've learned by talking to our customers over the last 4 years, it's that y'all love a peek behind the curtain. So we're going to let you in on a little secret about how a hardware provider like Fly.io formulates GPU strategy: none of us know what the hell we're doing. If you had asked us in 2023 what the biggest GPU problem we could solve was, we'd have said "selling fractional A100 slices". We burned a whole quarter trying to get MIG, or at least vGPUs, working through IOMMU PCI passthrough on Fly Machines, in a project so cursed that Thomas has forsworn ever programming again. Then we went to market selling whole A100s, and for several more months it looked like the biggest problem we needed to solve was finding a secure way to expose NVLink-ganged A100 clusters to VMs so users could run train ## 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. ## Hack Day: Food Glorious Food DevFeed: [Hack Day: Food Glorious Food]() Original publisher: [Read original article]() Author: Rasha Ardati Published: 2023-05-31T08:45:02Z Content type: article Language: en Sources: [Guardian]() Topics: [Browser Extension](), [App](), [Artificial Intelligence](), [web browser](), [Image](), [stable-diffusion]() Tags: [ai](), [browser](), [extension](), [hack](), [images](), [stable-diffusion]() ### AI overview The Guardian's first hack day of 2023 focused on food-related ideas and produced more than 22 hacks over two days. Projects included browser extensions, recipe interfaces, a restaurant review mapper, microwave-friendly recipes, and an AI tool that generates recipe-step images using stable diffusion or DALL-E. ### Source excerpt This month, colleagues across the product and engineering department, as well as other parts of the organisation, came together for another exciting hack day For our first hack day of 2023 we kicked off with a delicious theme around 'Food', with a side of the usual 'free-for-all' of course. Hackers were invited to think about all things edible, whether as an app, a new search method for recipes, a meal planner or perhaps a choose your own adventure game where your date is coming for dinner in an hour and a half, and you have decided, perhaps too ambitiously, to cook them an elaborate Ottolenghi recipe from the Guardian archives. We produced and presented over 22 amazing hacks over the two-day event. These included: Migrating our tone/recipes page to our latest rendering tiers, A web browser extension to help users understand cooking language and become better cooks, making Guardian recipes microwave-friendly, a restaurant review mapper, implementing a Guardian app section for food content and a web browser extension to appeal to our tofu-eating readers. Continue reading... ## Watermarks DevFeed: [Watermarks]() Original publisher: [Read original article]() Author: Adam Published: 2023-02-03T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Artificial Intelligence](), [stable-diffusion](), [Image]() Tags: [ai](), [ai-images](), [copyright](), [diffusion](), [stable-diffusion]() ### AI overview The article examines watermarks produced by diffusion image generators, arguing that these artifacts reveal how training data influences generated images. It also discusses copyright disputes involving Stable Diffusion and the ethical and economic implications of AI-generated images. ### Source excerpt Watermarks show us wierd edges of AI work