Archives de catégorie : Etats de l’art

Architecture, Artificial Intelligence, Etats de l'art, Méthodologie

AI – Flux Klein 2 – Inpainting

Laisser un commentaire

AI – Flux Klein 2 – Inpainting

presentation

Flux 2 provides superior precision for image creation, particularly when the process involves transformation, integration, or composition. Being free and open-source, it stands as a compelling alternative to Nano banana.

In architecture, certain functionalities are invaluable for rapidly transforming concepts into visual representations.

Detailed explanations on Flux2 can be found here : https://docs.comfy.org/tutorials/flux/flux-2-klein

Unlike a standard VAE, this approach utilizes novel nodes: an Inpaint crop acting as a ‘spying glass’ and the Flux Enhancer node, which is instrumental in preventing the model from generating hallucinations.

Continuer la lecture de AI – Flux Klein 2 – Inpainting →

Architecture, Artificial Intelligence, Conception, Etats de l'art, Méthodologie

COMFYUI – AI/Archi01 : Render from 3D

Laisser un commentaire

Task

ComfyUI Tutorial for Architecture: From Sketch to Realistic Render with ControlNet Canny

If you want to transform a simple sketch or plan into a detailed and realistic architectural render, ComfyUI, with its modular structure, is the perfect tool for this. In this tutorial, we will explore a simple yet powerful workflow using ControlNet Canny to turn a basic drawing into a high-quality image.

This tutorial is designed for beginners with ComfyUI. We will walk through each step, from importing models to achieving the final result.

Continuer la lecture de COMFYUI – AI/Archi01 : Render from 3D →

3D, Ambiance, Architecture, Artificial Intelligence, Conception, Creation, Etats de l'art, Immersion, Méthodologie, Simulations

IA-project workflow part10

Laisser un commentaire

Image to 3D

Trellis

A novel 3D generation method is introduced, enabling versatile and high-quality 3D asset creation. At its core is a unified Structured LATent (SLAT) representation, which supports decoding into multiple output formats, including Radiance Fields, 3D Gaussians, and meshes. This is achieved by combining a sparsely-populated 3D grid with dense multiview visual features extracted from a robust vision foundation model, effectively capturing both structural (geometry) and textural (appearance) information while preserving decoding flexibility.

Structured 3D Latents for Scalable and Versatile 3D Generation (https://trellis3d.github.io/) Continuer la lecture de IA-project workflow part10 →