This article explains the principle of LoRA (Low‑Rank Adaptation) for large language models, demonstrates how to decompose weight updates into low‑rank matrices, and provides a complete PyTorch implementation that fine‑tunes a small VGG‑19 network on a custom goldfish dataset.
This article walks through a complete methodology for creating brand symbol posters using AI, covering basic and advanced Stable Diffusion techniques such as ControlNet, depth‑map generation, semantic segmentation, LoRA integration, and post‑processing to achieve high‑quality, efficient visual assets.
This guide walks you through preparing the environment, downloading Qwen‑72B‑Chat, performing LoRA fine‑tuning on PAI‑DSW, merging weights, and deploying the model for offline inference, web UI, API, and PAI SDK services on Alibaba Cloud.
This article details how 58.com’s design team leveraged AI tools such as Stable Diffusion, LoRA models, and prompt engineering to create a visually rich September recruitment landing page, outlining the project background, design strategy, technical exploration, and measurable results.
Explore how AI tools like Stable Diffusion and Runway GEN‑2 can accelerate marketing design workflows, from converting 2D sketches to 3D assets, generating dynamic video posters, training custom LoRA models for travel backgrounds, and creating personalized avatar styles, with step‑by‑step case studies and practical tips.
SWIFT is an open‑source, PyTorch‑based framework that integrates multiple efficient fine‑tuning methods such as LoRA, QLoRA, Adapter, and the proprietary ResTuning, enabling developers to fine‑tune large language and multimodal models on consumer‑grade GPUs with significantly reduced memory and compute requirements.
This article introduces parameter‑efficient fine‑tuning (PEFT) techniques—including additive adapters, soft‑prompt methods, selection‑based BitFit, and re‑parameterization approaches like LoRA and AdaLoRA—explains their architectures, experimental results, and provides end‑to‑end code for fine‑tuning ChatGLM2‑6B on a Chinese medical QA dataset.
A leaked Google internal document titled “We have no moat, OpenAI also has none” reveals that both companies are losing the AI arms race to rapidly advancing open‑source models, which achieve comparable performance at a fraction of the cost, prompting a strategic rethink for Google.
This article introduces EasyPhoto, an open‑source SD‑WebUI plugin that lets users upload a handful of personal photos, quickly train a LoRA model, and generate high‑quality AI portraits using Stable Diffusion, ControlNet and face‑ID techniques, with step‑by‑step installation and usage guidance.
This study evaluates virtual clothing try‑on by fine‑tuning LoRA models on a handful of garment images and integrating Stable Diffusion with ControlNet, Inpainting, and Segment Anything, showing that the AI‑generated pipeline matches or exceeds traditional geometric‑matching VTON in visual fidelity, especially for upper‑body apparel, while running locally on an RTX 3060.
This guide compares Stable Diffusion fine‑tuning methods, shows why LoRA offers the best size‑and‑speed trade‑off, and provides a step‑by‑step workflow—from dataset collection and preprocessing to parameter tuning, 20‑minute training on a single GPU, and practical tips for successful custom model generation.
This article explains how designers can shorten 3D design cycles by using AIGC tools to train LoRA adapters for Stable Diffusion, covering the theory, required plugins, data preparation, labeling rules, training steps, result tweaking, and practical applications.
This article introduces the open‑source FaceChain AI portrait generation project, detailing background, demo results, environment setup on ModelScope notebooks, step‑by‑step usage instructions, and an in‑depth explanation of the Stable Diffusion‑based LoRA training and inference pipeline.
This practical guide walks users through installing Stable Diffusion WebUI, explains the differences between base, LoRA, VAE, and ControlNet models, shows how to derive prompts with CLIP or DeepBooru, and provides detailed text‑to‑image and image‑to‑image examples for effective prompt engineering.
This article details the step‑by‑step process of installing GPU drivers, setting up a Python environment, deploying the open‑source Alpaca‑LoRA large language model, fine‑tuning it with Chinese data on a multi‑GPU server, and running inference, while discussing practical challenges and performance observations.
This guide walks you through using Alibaba Cloud's PAI platform to quickly fine‑tune Llama 2 with LoRA or full‑parameter methods, deploy the models as online inference services, and launch an interactive WebUI, covering preparation, data formatting, training jobs, and deployment details.
The article explains how LoRA and its 4‑bit QLoRA extension dramatically reduce trainable parameters and GPU memory for fine‑tuning large language models, while GPTQ post‑training quantization compresses weights for cheap inference, and shows how KubeAI integrates these techniques into a one‑click workflow for 7 B, 13 B, and 33 B models from data upload to API deployment.
This article explains how to use PAI-Blade to accelerate Stable Diffusion inference by optimizing LoRA and ControlNet components, detailing configuration steps, code modifications, benchmark results on A100/A10 GPUs, and integration with both Diffusers and the popular Stable-Diffusion-webui, highlighting performance gains and memory savings.
This article introduces the upgraded PAI‑Diffusion Chinese models, highlighting major improvements in image quality and style diversity, detailing lightweight fine‑tuning methods such as LoRA and Textual Inversion, showcasing controllable editing, scenario‑specific customization, and providing step‑by‑step usage instructions on popular platforms.
An analysis of a leaked Google internal document reveals how open‑source large language models, low‑cost fine‑tuning techniques like LoRA, and rapid community innovation are reshaping the AI competition, challenging the dominance of both Google and OpenAI and prompting a strategic rethink.
This guide explains how AI creates photorealistic images, tracing the shift from VAEs and GANs to diffusion models, detailing latent diffusion, ControlNet conditioning, CLIP text‑image alignment, and lightweight fine‑tuning methods like DreamBooth and LoRA, plus practical tips for higher‑resolution results.
Tencent’s rapid‑deployment project creates a volunteer‑hosted, low‑cost atmospheric monitoring network using LoRa, NB‑IoT and Wi‑Fi, with edge‑agnostic data collection, cloud‑based device normalization, and business processing modules that manage devices, analyze PM2.5 sensor data, and visualize real‑time air quality across five community sites.
The article describes a complete LoRa‑based environmental monitoring and control system for data centers, built on TencentOS Tiny and featuring a LoRa gateway, sensor‑rich node, LCD display, relay and LED modules, cloud integration via Tencent Cloud IoT Explorer, and a WeChat mini‑program for remote management.
The article reviews the latest IoT and edge‑computing trends, outlines Tencent’s multi‑layer IoT platform, edge solutions, LoRa network deployment, and how AI‑driven data analysis will transform the fragmented IoT market into a smarter, more integrated ecosystem.
