Agate
huggingface.co- Category
- AI Tools
- Rank
- No. 4083Tools index
Previous survey · No. 3876 ·
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Design & Media
- Date
About
Agate is a 260-million-parameter text-to-image model trained from scratch by LogoLabs in about 145 GPU-hours, splitting generation between a small transformer "thinker" that plans a coarse 16x16 region layout and a convolutional U-Net "renderer" that paints the image from that plan. It scores 0.550 on the GenEval benchmark, matching SDXL and beating SD 1.5, SD 2.1, and PixArt-alpha despite being much smaller, and generates an image in under two seconds on a consumer GPU or directly in the browser via WebGPU.
At a glance
Agate is a small preview model that turns English prompts into 256 × 256 images. It plans the composition on a coarse grid before rendering it. It is worth comparing for fast layout drafts when you need to place and color a few objects on modest hardware.
Good fit
Developers evaluating small image generators for on-device previews or experiments with composition.
Consider something else when
Skip it if your workflow needs finished high-resolution images or reliable placement of many objects.
Start here
Start by comparing the model card’s published examples. Look for whether the colors attach to the right objects and whether the objects appear in the requested positions. Use those same checks on prompts from your own workflow if you evaluate the model later.
Cost and requirements
The publisher lists MIT terms for the code and weights. The documented Python setup supports CUDA and CPU, with slower CPU generation. The base output is 256 × 256.
Limits to know
The publisher’s GenEval breakdown shows better color results than position or counting results. Its preview is a general-purpose checkpoint. Judge it on your actual prompts before committing to it for icon generation.
Sources checked 2026-11-05. Source-based assessment.
Why it made the leaderboard
A from-scratch 260M-parameter text-to-image model trained in under 13 hours that matches SDXL's GenEval score, showing a small, cheaply-trained model can reach competitive quality for narrow use cases like icon and logo generation.
Tags
Media
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