LakonLab
Research hub for flow-based generative models and 3D generation
Essential bookmark for researchers tracking flow-based and diffusion models. The breadth of high-impact publications is impressive, but don't expect a usable product—it's a project listing with links to code and demos. Go straight to the GitHub repos for hands-on work.
- AI researchers studying flow-based and diffusion generative models
- Deep learning practitioners experimenting with few-step generation
- Computer vision researchers working on 3D generation and reconstruction
- Students and academics seeking reproducible state-of-the-art baselines
- Non-technical users needing a ready-to-use product or API
- Those seeking commercial support or SLAs
- Beginners lacking familiarity with diffusion/flow matching literature
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In short
LakonLab — Research hub for flow-based generative models and 3D generation. Best for AI researchers studying flow-based and diffusion generative models, Deep learning practitioners experimenting with few-step generation, Computer vision researchers working on 3D generation and reconstruction. Free to use.
Viability Score
How likely is LakonLab to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- AsymFlow: rank-asymmetric flow parameterization for pixel-space generation
- pi-Flow: policy-based few-step imitation distillation for 4-step generation
- GMFlow: Gaussian mixture denoising distributions with GM-SDE/ODE solvers
- 3D-Adapter: geometry-consistent multi-view diffusion for 3D generation
- MVEdit: training-free 3D diffusion adapter for Stable Diffusion
- GRM: large Gaussian reconstruction model for feed-forward 3D (~0.1s)
- Img2CAD: VLM-assisted reverse engineering of CAD from single images
- Video generation: mode-seeking meets mean-seeking for long videos
- Flow-based I2V inversion for video editing without retraining
- Open-source code with GitHub repos and online demos
- Published at top venues: ICML, ICLR, ECCV, SIGGRAPH Asia, CVPR
- Supports image, video, and 3D generation modalities
About LakonLab
LakonLab is the personal research page of Hansheng Chen, showcasing state-of-the-art generative AI models focused on scalable high-dimensional generation in pixel space, few-step flow distillation, and Gaussian mixture flow matching. Targeting AI researchers and advanced practitioners, the page provides open-source implementations of key methods: AsymFlow achieves 1.57 FID on ImageNet and ranks #1 on the HPSv3 text-to-image benchmark using rank-asymmetric flow parameterization. pi-Flow distills pre-trained flow models into policy-based models for 4-step generation with teacher-aligned quality, featured in ICLR 2026. GMFlow generalizes diffusion/flow matching with Gaussian mixture denoising distributions, novel GM-SDE/ODE solvers, and probabilistic guidance, published at ICML 2025. The page also includes substantial 3D generation works: 3D-Adapter for geometry-consistent multi-view diffusion, MVEdit for training-free 3D editing via off-the-shelf Stable Diffusion, GRM for large Gaussian reconstruction in ~0.1s, and Img2CAD for reverse engineering CAD models from single images using VLM assistance. Video generation research covers mode-seeking teacher distillation for long videos and inversion-free flow-based I2V editing. All core projects link to arXiv papers, GitHub repositories, and online demos, allowing full reproducibility. What sets LakonLab apart is the track record of peer-reviewed publications at top venues (ICML, ICLR, ECCV, SIGGRAPH Asia, CVPR) and the balance of theoretical novelty with practical scalability. Unlike product-oriented platforms, LakonLab is an academic portfolio meant for exploration, not a tool with a polished interface or support. It is best suited for researchers aiming to reproduce and build upon cutting-edge generative models.
Behind the Verdict
LakonLab is a goldmine for anyone serious about generative modeling research. The page aggregates Hansheng Chen's high-impact publications spanning pixel-space generation (AsymFlow, sub-2 FID), few-step distillation (pi-Flow, 4-step), and Gaussian mixture flows (GMFlow). For 3D, GRM and 3D-Adapter offer strong baselines. The code is open-source, and demos are available, making reproduction straightforward. When to pick this: You are a researcher or advanced practitioner who wants to experiment with state-of-the-art flow matching, few-step sampling, or 3D generation. The implementations are clearly documented via papers and repos, and the projects are active (as recent as 2026 publications). When to pass: You need a ready-to-use API or a user-friendly interface. LakonLab is not a product; it's a research portfolio. There's no customer support, no hosted service, and no integration with commercial tools. Comparison to alternatives: Compared to Sora or Stable Diffusion, LakonLab's models are more experimental and less polished for production. However, they often push boundaries (e.g., AsymFlow's pixel-space generation outperforms latent models in FID). For 3D, it competes with OpenLRM and Instant3D but offers more diversity (GRM, 3D-Adapter). Real-world caveats: Some repos may require significant compute (e.g., large-scale diffusion models). Demos are limited to smaller samples. Documentation is paper-level—be prepared to read the papers. The page lists many projects; not all are actively maintained simultaneously.
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Use Cases
- Explore AsymFlow for scalable pixel-space generation with state-of-the-art FID scores on ImageNet
- Use pi-Flow to generate high-quality images in just 4 steps with teacher-aligned fidelity
- Apply GMFlow's probabilistic guidance for controllable generation with Gaussian mixture denoising
- Generate 3D models from single images using GRM or 3D-Adapter with consistent multi-view outputs
- Edit videos using flow-based I2V inversion with zero-shot, training-free adaptation
- Reverse engineer editable CAD models from single-view images with Img2CAD
Models Under the Hood
Limitations
- LakonLab is a research project page, not a product.
- There are no hosted APIs, customer support, or user-friendly interfaces.
- All models require technical expertise to use and are provided as open-source code.
- The page primarily serves as a directory; actual usage depends on third-party platforms (GitHub, Hugging Face demos).
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