This article examines the Baidu Search team’s “One Orange” IP case, revealing why they shifted from a traditional, labor‑intensive pipeline to an AI‑driven process, how multimodal models, LoRA and ControlNet enabled massive innovation and efficiency gains, and what this means for designers and the broader industry.
Ele.me created a domain-specific AIGC image model built from scratch on its own data using the DiT backbone, a three-stage training pipeline (transformer pre-training, prompt alignment, aesthetic fine-tuning), custom T5‑E‑CLIP text and visual encoders, ControlNet for layout control, and evaluated via FID, CLIP scores and a human rubric, enabling automated dish-image generation and UI asset creation for its vertical business.
DC-ControlNet introduces intra‑ and inter‑element controllers that decouple global conditions into separate content and layout signals, enabling finer‑grained, conflict‑aware control of multi‑condition image generation and achieving higher flexibility and accuracy than traditional ControlNet approaches.
Alibaba’s Mama Intelligent Creation team has open‑sourced a FLUX‑based ControlNet inpainting model that leverages a DiT‑backed Interleave design for superior repair quality, and an 8‑step LoRA‑Turbo model that cuts inference time three‑fold while preserving near‑original image fidelity, both now available on Hugging Face and ModelScope.
This article explains the core principles of Stable Diffusion, its training pipeline and limitations, then details how ControlNet adds controllable signals to diffusion models, outlines its architecture, ecosystem of model variants, and showcases diverse real‑world applications.
This tutorial shows how designers can streamline the creation of 3D frosted‑glass B‑end icons by leveraging Stable Diffusion and ControlNet, covering model setup, line‑art preparation, prompt engineering, generation parameters, and post‑processing to achieve high‑quality results efficiently.
This article introduces ControlNet for ComfyUI, explains its installation, required plugins, core nodes, parameters, and workflow examples, enabling users to harness AI image generation with precise structural and stylistic control while highlighting practical tips and common configurations.
This article introduces ControlNet for ComfyUI, explains its benefits for AI image generation, walks through local and cloud installation, plugin setup, node configuration, and provides practical examples and resources for creating high‑quality AI artwork.
This article details how Alibaba's Wanxiang Lab adapted the SDXL diffusion model for large‑scale e‑commerce image generation, introducing the EcomXL series, a weighted‑distillation fine‑tuning method, hierarchical model fusion, specialized ControlNet variants, and the SLAM inference accelerator to achieve high‑quality, controllable product images within three seconds while boosting business metrics.
EcomXL enhances SDXL for large‑scale e‑commerce image generation by leveraging tens of millions of curated images, a two‑stage fine‑tuning with denoising‑weighted distillation and layer‑wise fusion, specialized ControlNets for inpainting and soft‑edge consistency, and the SLAM inference accelerator to achieve sub‑second generation while boosting visual quality and adoption metrics.
Mask‑Guided Diffusion combines instance‑mask training, Masked Canny ControlNet, and Mask‑guided Attribute Binding to preserve product details, correctly bind attributes, fix hand distortion, and generate uniform colored backgrounds, enabling merchants to quickly create high‑quality, controllable product images with Stable Diffusion.
The AI Clothing Graffiti Project in Taobao Life 2 leverages Stable Diffusion, ControlNet, and LoRA to let users generate and stylize clothing designs via text‑image prompts, employing parallel processing, face repair, and content filtering, and has launched successfully, inviting algorithm engineers to join the team.
This guide explains how AI image generators such as Stable Diffusion and DALL·E 3 turn text prompts into pictures by using diffusion models, CLIP‑aligned embeddings, and optional controls like negative prompts, fine‑tuned LoRA checkpoints and ControlNet conditioning, highlighting their differences, workflow, and practical customization.
This article explores the IP-Adapter technique for Stable Diffusion, comparing it with conventional img2img, detailing its superior prompt integration, multi‑reference capabilities, workflow automation with ControlNet and ComfyUI, and how it enables instant LoRA creation for faster, more diverse AI‑generated images.
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.
The AIGC Poster Generation Project employs Stable Diffusion enhanced with VAE, ControlNet, LoRA and other extensions to create product posters in four visual styles, exploring outpainting, inpainting, reference‑based diffusion and DreamBooth prototypes, and optimizes detail preservation, super‑resolution text, and masking to achieve over 90% detail fidelity, 95% success rate, and 3–5 second inference per image.
This guide shows how to extract a product's dominant colors, create a gradient map, and use ControlNet with specific weight and step parameters to seamlessly blend AI‑generated items into backgrounds, delivering natural, high‑quality visuals without tedious manual editing.
This guide explains how to install and use the Auto‑Photoshop‑StableDiffusion plugin, enabling seamless Stable Diffusion AI image generation inside Photoshop, covering installation steps, core features like txt2img and img2img, and a real‑world design workflow case study.
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.
Learn a low‑cost, high‑security method to generate multiple personalized AI digital avatars from a single portrait by using Stable Diffusion WebUI, img2img inpainting, and the ControlNet openpose plugin, covering prompt design, image masking, and step‑by‑step workflow without uploading data to external servers.
This guide walks you through the fundamentals of Stable Diffusion AI painting, covering diffusion theory, prompt crafting, parameter tuning, model selection, high‑resolution upscaling, inpainting, and the powerful ControlNet extension for precise image control.
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 guide walks you through registering an Alibaba Cloud account, enabling Function Compute and NAS, deploying a Stable Diffusion instance, adding ControlNet, and handling common cost questions, enabling you to create light‑shadow text images in just a few minutes.
The article demonstrates how to create visually appealing, scannable QR‑code artworks using ControlNet and Stable Diffusion, explaining QR‑code structure, contrast preservation, and several pipelines—including tile‑based, OpenPose‑combined, and community QR‑code models—while detailing WebUI settings, prompt examples, weight tuning, and a custom ControlNet that reduces grid artifacts.
This guide compares Midjourney and Stable Diffusion, showing how SD’s designer‑friendly interface and ControlNet plugins enable exact composition, style control, and high‑quality batch image generation for various industry scenarios.
The article introduces Stable Diffusion, explains key terms and parameters, guides model checkpoint merging and fine‑tuning with embeddings, LoRA, and hypernetworks, details ControlNet pose control, sampling choices, prompt engineering techniques—including weighting and negative prompts—and explores advanced uses such as inpainting, Pix2Pix, custom training, highlighting personal and commercial applications and the technology’s growing impact across industries.
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 comprehensive guide walks you through the hardware requirements, environment deployment, key parameters, prompt techniques, ControlNet integration, model download and installation, as well as style and character training for Stable Diffusion, providing practical code snippets and visual examples for each step.
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.
This article surveys the key challenges of controllable text‑to‑image generation and explains the architectures, components, and training details of major diffusion‑based models such as DALL‑E‑2, Google Imagen, Stability AI's Latent Stable Diffusion, and the ControlNet extension.
This article reviews the key components and design choices of recent text‑controlled image generation systems—including DALL·E‑2, Google Imagen, Stability AI's Latent Stable Diffusion, and the ControlNet extension—highlighting how diffusion models, text encoders, prior modules, super‑resolution, and conditioning mechanisms enable high‑quality, controllable visual synthesis.
This article walks through a practical AIGC workflow—defining a three‑step human‑machine blueprint, crafting effective prompts and negative prompts, leveraging ControlNet and DreamBooth for custom models, and refining outputs with Inpaint—showing how to boost creative efficiency with open‑source AI tools.
This guide explores how to craft effective prompts for Stable Diffusion image creation, ChatGPT article writing, and GitHub Copilot code generation, covering prompt evolution, negative prompts, ControlNet enhancements, model selection, and practical tips for iterative refinement and context building.
