https://llmobile.kavents.com/tags/image-generation/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.kavents.com/ticker/off-grid-offline-ai-app/https://llmobile.kavents.com/ticker/off-grid-offline-ai-app/Sat, 14 Feb 2026 23:39:00 +0100Ali Chherawalla has posted Off Grid to Show HN, an Android and iOS app that runs text generation, image generation, vision and speech recognition entirely on the device. The submission reached 124 points and 66 comments. The repository is licensed under MIT. The app bundles llama.cpp for text, Stable Diffusion for images and whisper.cpp for speech to text. It accepts any GGUF file and lists Qwen 3, Llama 3.2, Gemma 3 and Phi-4 among supported models; vision runs on SmolVLM, Qwen3-VL or Gemma 3n. It also connects to OpenAI-compatible servers on a local network, and builds a project knowledge base from PDFs chunked and embedded on-device with a bundled MiniLM model into SQLite. The README reports 15 to 30 tokens per second on flagship CPUs and 20 to 40 tokens per second through Adreno GPUs via OpenCL on a Snapdragon 8 Gen 2 or newer. Image generation is given as 5 to 10 seconds on the Hexagon NPU and vision inference at about 7 seconds. The NPU path is marked experimental: it accelerates only Q4_0 and Q8_0 quantisation, and K-quants fall back to the CPU. Comments disputed whether models of that size are usable. One reader wrote: Ive tried a lot of self hosted models at a lot of sizes; those small models are not good enough, and do not have a context long enough to be useful for most everyday operations. Chherawalla replied that “you don’t need claude level performance for a lot of day to day tasks” and that Qwen3 8B, or 4B quantised, is “actually quite good”. Other threads compared the app with PocketPal AI, SmolChat and Google’s AI Edge Gallery, asked for an F-Droid build, and reported that image generation required network access on GrapheneOS. A reader on a Samsung device reported the interface did not move for the on-screen keyboard; the developer shipped a fix the same day. Source: https://news.ycombinator.com/item?id=47019133 Read the article: https://llmobile.kavents.com/ticker/off-grid-offline-ai-app/AndroidiOSOpen sourcellama.cppImage generationhttps://llmobile.kavents.com/ticker/qualcomm-stable-diffusion-android/https://llmobile.kavents.com/ticker/qualcomm-stable-diffusion-android/Thu, 23 Feb 2023 17:00:00 +0100Qualcomm AI Research has demonstrated Stable Diffusion v1.5, a model with more than 1 billion parameters, running entirely on an Android phone powered by a Snapdragon 8 Gen 2. The company reports image generation in under 15 seconds for 20 inference steps at 512x512 pixels, a workload that until then ran in the cloud. Images: Qualcomm. Sample outputs from the demo, generated on the phone. To fit the model on the phone, Qualcomm quantised it from FP32 to INT8 using its AI Model Efficiency Toolkit and a post-training method called AdaRound, which adapts how individual weights are rounded. According to the company, the model needed no retraining after the conversion. Inference runs through the Qualcomm AI Engine direct framework on the Hexagon processor, with the model prepared in Qualcomm AI Studio. The company sums up the result in its post. This is the fastest inference on a smartphone and comparable to cloud latency. Source: https://www.qualcomm.com/news/onq/2023/02/worlds-first-on-device-demonstration-of-stable-diffusion-on-android Read the article: https://llmobile.kavents.com/ticker/qualcomm-stable-diffusion-android/QualcommImage generationQuantisationAndroidNPU