Key points
- It uses pixel-space diffusion instead of latent diffusion, reportedly converging 11 times faster.
- The authors view this as a proof-of-concept step toward cheaper generative video models.
The release includes Apache 2.0 licensed weights and aims to demonstrate lower-cost inference methods for future video generation tools.reported
Evidence: reported
Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)aifleet-hn-front
The claim
The developers state that their primary goal for the upcoming Linum v3 video model was to significantly reduce inference costs.reported They argue that current video models are too slow and expensive for iterative creative workflows. By releasing this smaller image model, they share an architecture that learns a latent space optimized specifically for its task, rather than relying on a separate Variational Autoencoder.
Evidence: reported
Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)aifleet-hn-front
The response
The post frames the release as a surprising result given the model's small size. The authors note they were pleased with the visual quality and decided to share the work publicly. They position this image model as an intermediate step in their research, intended to validate the efficiency gains of their new architectural approach before applying it to more complex video generation tasks.
The bottom line for teams
The material presents this as an experimental release rather than a production-ready tool. The focus is on the architectural shift from two-stage latent diffusion to single-stage pixel-space diffusion. Teams interested in cost-efficient model training may find the reported convergence speed relevant, but the authors explicitly label this a proof-of-concept for their broader video model ambitions.reported
Evidence: reported
Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)aifleet-hn-front
What the paper shows
The research tests whether removing the Variational Autoencoder (VAE) improves training efficiency. The reported result is an 11x faster convergence rate compared to their previous latent diffusion model.reported The stated limitation is that this is an image model serving as a proof-of-concept for future video work. The authors claim this approach simplifies architecture by eliminating two-stage training and allows the diffusion model to learn a purpose-built latent space, potentially achieving higher compression than traditional VAEs which they suggest have an empirical ceiling.
Evidence: reported
Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)aifleet-hn-front
Why it matters
- Model evaluators can benchmark the claimed 11x convergence speed against standard latent diffusion baselines.
- The Apache 2.0 licensing allows independent verification of the architectural claims without proprietary restrictions.
- The sub-$21k training cost provides a concrete data point for budgeting experimental generative model projects.
- The shift to pixel-space diffusion challenges the industry standard of using separate compression models.
Topics: generative AI, diffusion models, open source, video generation, machine learning
Updated
Then / now / next
Generative video models typically relied on separate VAEs for compression, creating complex two-stage training processes.
This experimental release demonstrates a pixel-space diffusion approach that learns its own latent space without a VAE.
The lab plans to apply these efficiency gains to their Linum v3 video model.
Story timeline
- Then Generative video models typically relied on separate VAEs for compression, creating complex two-stage training processes.
- 04 Nov 2026 Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)
- Now This experimental release demonstrates a pixel-space diffusion approach that learns its own latent space without a VAE.
- Next The lab plans to apply these efficiency gains to their Linum v3 video model.
The strongest doubt
Counterpoint from the sources
The authors acknowledge that current video models lack sufficient controls and are effectively useless for storytelling, limiting the immediate applicability of these efficiency gains to narrative creation.
Ask the article
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Your choice stays on this device. No public tally.Terms in this story
VAE
Variational Autoencoder, a model used to compress input data into a smaller latent space.
Latent diffusion
A method where a diffusion model generates images within a compressed subspace created by a VAE.
Pixel-space diffusion
A method where the diffusion model learns compression and generation simultaneously without a separate VAE.
Questions readers ask
What license are the weights released under?
The weights are released under the Apache 2.0 license.
Does this model generate video?
No, it is a text-to-image model intended as a proof-of-concept for future video models.
Connected stories
Reading receipt
- It uses pixel-space diffusion instead of latent diffusion, reportedly converging 11 times faster.
- The authors view this as a proof-of-concept step toward cheaper generative video models.
Still open: How does the image quality compare to state-of-the-art latent diffusion models at similar compute costs?
Still open: Will the pixel-space approach scale effectively to the higher dimensionality of video data?