LakonLab
Research index for Hansheng Chen's lab: flow matching, diffusion, and 3D generation papers with arXiv, GitHub, and demo links.
A high-signal index for anyone tracking flow-based and diffusion generative models research. The track record is real: ICML, ICLR, ECCV, SIGGRAPH Asia, CVPR, and ICCV papers, including a CVPR Best Student Paper for EPro-PnP, and 2026 entries like pi-Flow at ICLR and AsymFlow's 1.57 FID on ImageNet are genuine contributions rather than incremental tweaks. What you get is a bibliography with links to arXiv, GitHub, and demos such as pi-Qwen, pi-FLUX, and pi-FLUX.2. If you need a hosted tool or a supported API you can point at a task without reading a paper first, look at commercial image or 3D generation products instead.
Verified 5d ago · liveness 62/100 · cite: rightaichoice.com/tools/lakonlab
- AI researchers studying flow matching and diffusion generative models
- Computer vision researchers working on 3D generation and reconstruction
- Students and academics seeking reproducible state-of-the-art baselines
- Advanced practitioners comparing few-step sampling methods
- Non-technical users who need a ready-to-use product or GUI
- Teams requiring commercial support, SLAs, or vendor accountability
- Beginners unfamiliar with diffusion and flow matching literature
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Skip LakonLab if you need a hosted tool or documented API you can call today, since every entry here is a paper with a GitHub repository and demo link that you clone, configure, and run on your own hardware.
LakonLab is a freely accessible academic index page, so the relevant comparison is not price but access: hosted image and 3D generation products with published commercial tiers give you an interface and support, while this page gives you papers, code, and demo links and expects you to supply the GPUs. Budget for compute and reproduction time rather than a subscription.
In short
LakonLab — Research index for Hansheng Chen's lab: flow matching, diffusion, and 3D generation papers with arXiv, GitHub, and demo links. Best for AI researchers studying flow matching and diffusion generative models, Computer vision researchers working on 3D generation and reconstruction, Students and academics seeking reproducible state-of-the-art baselines. Free to use.
What's new in LakonLab
Checked 5 days agoAcross the latest 2 updates: 2 feature updates.
pi-Flow published at ICLR 2026
pi-Flow introduces policy-based few-step generation via imitation distillation, distilling a pre-trained flow model into a policy-based model for state-of-the-art 4-step text-to-image generation, with pi-Qwen, pi-FLUX, and pi-FLUX.2 demos.
GMFlow published at ICML 2025
GMFlow generalizes diffusion and flow matching by predicting Gaussian mixture denoising distributions, adding novel GM-SDE/ODE solvers and probabilistic guidance for few-step sampling.
What people actually say about LakonLab — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
10 mentions across 2 sources (GitHub, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Innovative flow distillation methods with strong benchmark results (1.57 FID).
- +Open-source code with GitHub repositories and online demos.
- +Covers multiple domains: image, video, and 3D generation.
- +Peer-reviewed publications at top venues like ICML and ICLR.
- +Few-step sampling methods like pi-Flow achieve teacher-aligned quality.
- −Reproducibility issues reported on fine-tuned checkpoints.
- −No official support for custom LoRA loading.
- −Integration with non-default models like Qwen-Image is unclear.
- −Documentation is sparse and assumes expert-level knowledge.
- −No clear roadmap or community support infrastructure.
- • Compute costs for running large models (e.g., video generation) not included
- • No official support or consulting
Viability Score
How well maintained and how widely used is LakonLab? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- AsymFlow: rank-asymmetric flow parameterization for pixel-space generation, reporting 1.57 FID on ImageNet
- AsymFlow ranks #1 on the HPSv3 text-to-image benchmark
- pi-Flow: policy-based few-step generation via imitation distillation (ICLR 2026)
- pi-Flow achieves 4-step text-to-image generation with teacher-aligned quality
- pi-Flow demo pages for pi-Qwen, pi-FLUX, and pi-FLUX.2
- GMFlow: Gaussian mixture denoising distributions with GM-SDE/ODE solvers and probabilistic guidance (ICML 2025)
- Mode Seeking meets Mean Seeking: fast long-video generation via mode-seeking teacher distillation plus mean-seeking long-video supervision (ICML 2026)
- Zero-shot inversion-free editing that adapts off-the-shelf flow-matching I2V models without significant computational overhead
- Img2CAD: VLM-assisted conditional factorization for editable CAD from single-view images (SIGGRAPH Asia 2025)
- 3D-Adapter: geometry-consistent multi-view diffusion with a 3D feedback module on a base image diffusion model
- MVEdit: training-free 3D diffusion adapter using off-the-shelf 2D Stable Diffusion, later merged into 3D-Adapter
- GRM: sparse-view feed-forward 3D Gaussian splatting reconstruction at high efficiency (~0.1s)
- One-2-3-45++: single RGB image to textured 3D mesh in under one minute
- Zero123++: single image to consistent multi-view diffusion base model for image-to-3D
- SSDNeRF: single-stage diffusion NeRF unifying 3D generation and reconstruction (ICCV 2023)
About LakonLab
LakonLab is the research index page of Hansheng Chen, cataloging peer-reviewed generative AI work spanning image, video, and 3D generation. Each entry links to an arXiv paper, a GitHub repository, and where available a live demo. The 2026 work includes pi-Flow (ICLR 2026), which distills a pre-trained flow model into a policy-based flow model via imitation learning to reach 4-step text-to-image generation with pi-Qwen, pi-FLUX, and pi-FLUX.2 demos, and AsymFlow, a rank-asymmetric flow parameterization for scalable generation in pixel space that reports 1.57 FID on ImageNet and ranks #1 on the HPSv3 text-to-image benchmark. GMFlow (ICML 2025) predicts Gaussian mixture denoising distributions and adds GM-SDE/ODE solvers plus probabilistic guidance for few-step sampling. Video work includes Mode Seeking meets Mean Seeking (ICML 2026) for fast long-video generation and a zero-shot inversion-free method that adapts off-the-shelf flow-matching I2V models for video editing. The 3D track covers Img2CAD (SIGGRAPH Asia 2025) for VLM-assisted reverse engineering of editable CAD from single-view images, GRM for feed-forward Gaussian splatting reconstruction at roughly 0.1s, 3D-Adapter for geometry-consistent multi-view diffusion, One-2-3-45++ for single-image to textured mesh in under a minute, Zero123++, and SSDNeRF. This is an academic portfolio, not a product: you follow the links out to GitHub and demo spaces and run the code yourself, which suits researchers and advanced practitioners already comfortable with diffusion and flow matching literature.
Behind the Verdict
LakonLab functions as a curated reading list plus code index rather than a software product, and judged on that basis it is unusually well organized. Every entry states the venue and year (ICLR 2026 for pi-Flow, ICML 2026 for Mode Seeking meets Mean Seeking, ICML 2025 for GMFlow, SIGGRAPH Asia 2025 for Img2CAD, ECCV 2024 for GRM, CVPR 2024 for One-2-3-45++, ICCV 2023 for SSDNeRF, CVPR 2022 Best Student Paper for EPro-PnP) and links out to the paper, the repository, and a demo where one exists. Strengths: the coverage is narrow and deep, the through-line is legible (flow matching and Gaussian mixture denoising underpin both the 2D and 3D work), and several projects ship runnable artifacts, from pi-Qwen, pi-FLUX and pi-FLUX.2 demo pages to the MVEdit note that an updated version has been merged into the 3D-Adapter project. Weaknesses: there is no hosted interface, no single unified codebase across projects, and no customer support, so every experiment starts with cloning a separate repository and assembling its Python stack. Results like 1.57 FID on ImageNet or GRM's ~0.1s reconstruction are reported as research claims tied to papers, not as benchmarks you can reproduce on a laptop. Where it fits: literature reviews, baseline selection, and reproduction work for generative modelling and 3D reconstruction researchers who already read papers. Where it does not: product teams wanting an endpoint to call, designers wanting a UI, or anyone unwilling to invest in GPU time and environment debugging before seeing output.
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Real-world workflow fit
Concrete scenarios for the personas LakonLab actually fits — and what changes day-one when you adopt it.
You are choosing a few-step sampling baseline and start from the pi-Flow ICLR 2026 entry, then clone the GitHub repository and run the pi-FLUX demo to compare its 4-step output against your current sampler.
Outcome: You get a concrete, citable baseline plus a runnable demo, and can position your method against teacher-aligned 4-step generation rather than an abstract claim.
You need to turn single-view images into 3D assets and evaluate two routes: GRM for feed-forward Gaussian splatting reconstruction and One-2-3-45++ for a textured mesh in under a minute, using the linked repositories and demos.
Outcome: You pick the representation that fits downstream editing, with real code and demo outputs to verify quality before committing engineering time.
You work through the page's 2D, video, and 3D sections in order, collecting the venue and year for each entry (ICLR 2026, ICML 2026, SIGGRAPH Asia 2025, ECCV 2024) and following arXiv links.
Outcome: You assemble a citation-ready map of the flow matching and Gaussian mixture denoising line of work in a single pass instead of hunting across venues.
Use Cases
- Study AsymFlow's rank-asymmetric flow parameterization and its reported 1.57 FID result on ImageNet
- Use pi-Flow to understand 4-step text-to-image generation via imitation distillation from a pre-trained flow model
- Apply GMFlow's Gaussian mixture denoising distributions and GM-SDE/ODE solvers for few-step sampling and probabilistic guidance
- Reconstruct 3D objects from sparse views with GRM's feed-forward Gaussian splatting model
- Generate or edit 3D content with MVEdit's training-free adapter layered on off-the-shelf Stable Diffusion
- Edit videos by adapting flow-matching I2V models with a zero-shot inversion-free method
- Reverse-engineer editable CAD models from single-view images with Img2CAD
- Reproduce Zero123++ or One-2-3-45++ as image-to-3D baselines for your own pipeline
Models Under the Hood
as of 2026-10-15
Limitations
- LakonLab is a research index page, not a commercial product: there is no hosted interface, no customer support, and the page itself is a directory of links.
- Every project is delivered as open-source code you clone and run yourself, which requires deep familiarity with diffusion models, flow matching, and the associated Python stacks.
- Actual usage depends on external platforms such as GitHub, arXiv, and demo spaces, and results such as 1.57 FID on ImageNet or GRM's ~0.1s reconstruction are research claims tied to papers rather than benchmarks you can reproduce without GPU resources.
- There is no single unified codebase across the listed projects, so comparing methods means setting up several separate repositories.
- If you need a tool you can point at a task without reading a paper first, this is not it.
as of 2026-10-13
Verification history
We have re-verified LakonLab 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where LakonLab's pricing actually pencils out — and where peers do it cheaper.
LakonLab is a freely accessible academic index page, so the relevant comparison is not price but access: hosted image and 3D generation products with published commercial tiers give you an interface and support, while this page gives you papers, code, and demo links and expects you to supply the GPUs. Budget for compute and reproduction time rather than a subscription.
Setup time & first value
How long it actually takes to get something useful out of LakonLab — broken out by persona, not the marketing-page minute.
For researchers: minutes to read an abstract and pick a method, but plan on a few hours per repository to clone the code, install a compatible Python and CUDA stack, and download weights before a first generation. GRM and One-2-3-45++ demos are the fastest path to seeing output without a local GPU; pi-Flow, AsymFlow, and GMFlow reproduction realistically takes a day plus GPU access.
Resources & Guides
Tutorials & Learning
YouTube returned 4 videos for “LakonLab”, and we withheld 4: 4 could not be judged, because “LakonLab” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about LakonLab.
Official links
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