#MicroLad
This is always people's favourite bit. Microlad
July 18, 2026 at 11:43 PM
Legion of Super-Heroes vol 3 #1 by #PaulLevitz, #KeithGiffen, Larry Mahlstedt, and Carl Gafford.

In Ventura: DreamGirl, StarBoy, and Violet vs MicroLad.

In Takron-Galtos: Mon-El, UltraBoy, TimberWolf, Chamaleon, and ShadowLass vs Ol-Vir.

In Winath: Light Lass vs Radiation Roy.

#LongLiveTheLegion
September 1, 2025 at 10:21 AM
4 panels reveal #ShrinkingViolet transformation in the #LegionOfSuperHeroes
First laying into #DuplicateBoy for remaining silent, and then beating the snot out of #MicroLad
The images of Salu in the senstank were disturbing to say the least. Keith Giffen art.
May 19, 2026 at 1:07 AM
Choose 25 Legion of Super-Heroes Villains. One per day for 25 days, in no particular order.
No explanations, no reviews.
Day 12.
#LegionofSuperHeroes #LongLivetheLegion
#MicroLad
June 21, 2025 at 5:10 PM
MIT's MicroLad framework converts 2D microstructural data into stable 3D models for targeted design of material properties. This method expands the analysis space and integrates property-specific objectives, offering advances in predictive materials design. https://arxiv.org/abs/2508.20138
MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
ArXiv link for MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
arxiv.org
August 29, 2025 at 5:10 PM
MIT has developed MicroLad, a method converting 2D microstructure data into 3D models, enhancing material property exploration and incorporating property objectives, thus advancing design in solid oxide fuel cells. https://arxiv.org/abs/2508.20138
MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
ArXiv link for MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
arxiv.org
November 7, 2025 at 6:41 PM
MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
https://arxiv.org/pdf/2508.20138
Kang-Hyun Lee, Faez Ahmed.
https://arxiv.org/abs/2508.20138
arXiv abstract link
arxiv.org
August 29, 2025 at 4:34 AM
MIT researchers launched MicroLad, which transforms 2D microstructural data into 3D models using latent diffusion and score distillation. This generates realistic microstructures while embedding property goals, improving material engineering and predictive design. https://arxiv.org/abs/2508.20138
MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
ArXiv link for MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
arxiv.org
November 6, 2025 at 11:51 AM
MIT's MicroLad framework turns 2D images into detailed 3D models, expanding the design space for materials engineering. It uses latent diffusion and score distillation to generate diverse microstructures linked to properties, enhancing predictive design. https://arxiv.org/abs/2508.20138
MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
ArXiv link for MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
arxiv.org
November 5, 2025 at 10:51 AM
Kang-Hyun Lee, Faez Ahmed: MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation https://arxiv.org/abs/2508.20138 https://arxiv.org/pdf/2508.20138 https://arxiv.org/html/2508.20138
August 29, 2025 at 6:43 AM