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iris-3b

Iris-3B: pixel-space generation & general vision learner. A 3B diffusion transformer that generates every pixel directly (no VAE); the same prior, fine-tuned, does monocular depth estimation and 4x image restoration. Paper, weights, training code and demo.

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This is 1 of 140 launches in open-weight LLM inference and deployment — see how it stacks up on momentum and crowding →

41 other launches read as similar to this one →

Details

External ID
1405931849
Source
GITHUB
Company
—
Product
iris-3b
Website domain
speridlabs.com
Launched
Oct. 5, 2026
Cohort
—
Upvotes
74
Upvotes percentile
0.78
Tags
depth-estimation, diffusion, flow-matching, generative-prior, image-restoration, pixel-space, pytorch, super-resolution, text-to-image, vision-foundation-model

Enrichment

Niche
open-weight LLM inference and deployment
Vertical
Media & entertainment
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
pixel-space diffusion transformer model for generation and restoration
Manually corrected
False

Could you build this?

No Iris-3B is a 3-billion-parameter foundation model operating directly in pixel space without a VAE, requiring novel machine learning research, massive compute clusters, and proprietary training datasets.

What it would actually take: Training a 3B diffusion transformer in pixel space requires millions of dollars in GPU compute (e.g., hundreds of H100s), customized distributed training frameworks (PyTorch, Megatron-LM/DeepSpeed), and novel spatial architectures that handle raw pixel tokens without blowing up attention memory costs. Deep ML research expertise in diffusion architectures and vision representation learning is required.

Competitors

Other products that read as similar to this one — 41 launches clear the similarity bar, closest 8 shown.

Attention rank: #9 of 42 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 236 days after the earliest competitor.

Other launches for this product