Tiny 100M-parameter image model (417 MB) that can run on a phone; image quality is low but it is about 60 times smaller than Z Image.
Supra2 Img
by SupraLabs
Tiny 100M-parameter open-weights text-to-image diffusion transformer from SupraLabs, generating 256×256 images.
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Supra2 Img at a glance
- What is Supra2 Img?
- Supra2 Img is a model from SupraLabs. Tiny 100M-parameter open-weights text-to-image diffusion transformer from SupraLabs, generating 256×256 images.
- Who makes Supra2 Img?
- Supra2 Img is made by SupraLabs.
- Does Supra2 Img work in the EU?
- Available in the EU. Weights are downloadable from Hugging Face under Apache-2.0 with no regional restrictions, so they can be run locally in the EU/EEA. Last verified 14 Nov 2026.
- How much does Supra2 Img cost?
- Open source. Free to download and run locally; no hosted service listed. Licence: Apache-2.0.
- Where was Supra2 Img shown?
- In 1 creator video by AI Search. Most recently by AI Search on 27 Sept 2026, at 36:53 in “Waifus incoming, GPT 6 Sol, Grok 4.7, Opus 5.5, Mimo 2.6, Step 5, OpenMuse: AI NEWS”.
Sources: the vendor's own pages (linked above) and creator videos at the linked timestamps. EU status is information, not legal advice; see how we check.
Supra2-IMG is a small text-to-image model from SupraLabs, built as a diffusion transformer (SupraDiT) with about 104M parameters. It was trained from scratch on the FLUX-Reason-6M dataset (5.6M images, 10 epochs) on a single H100 GPU in roughly 9 hours. It uses a frozen Flan-T5-Base text encoder (128-token context) and the SD-VAE-FT-MSE autoencoder, and outputs 256×256 images from 32×32 latents.
The model card reports FID 27.9 and CLIP score 29.4. A YouTube reviewer (AI Search) describes the checkpoint as about 417 MB, small enough to run on a phone and roughly 60 times smaller than Z Image, but notes image quality is low compared with larger models.
It is distributed as downloadable weights on Hugging Face under Apache-2.0 and run locally with a provided inference.py script (recommended: Euler flow sampling, 50 steps, CFG 3.0). It targets hobbyists and researchers interested in very small, low-resource image generators. Limits include the low resolution, English-oriented prompts and the custom code requirement; the card's "SOTA quality" claim refers to its size class only.
- ~104M-parameter diffusion transformer (SupraDiT)
- 256×256 output with Euler flow sampling
- Frozen Flan-T5-Base text encoder and SD-VAE-FT-MSE
- Trained from scratch on FLUX-Reason-6M (5.6M images)
- Apache-2.0 weights; local inference script
- Small checkpoint (~417 MB) suited to low-resource devices
As seen on YouTube
Watch the moment: the exact second top creators show it. Videos load only when you press play, via youtube-nocookie.com.
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