agate-003

Agate Preview 003 renders at 512 × 512. It is the first Agate to score above Stable Diffusion 1.5 on Qwen-Image-Bench: 32.6 against SD 1.5's 29.1 and Preview 002's 28.9, on all 1,000 prompts, with 261M parameters. On GenEval (official scorer) it scores 0.554, level with Preview 001 (0.550) and below Preview 002 (0.577).

It is Agate's 256 px network made multi-resolution (additions that start as exact no-ops), trained at 256 and 512 px, then fine-tuned with perceptual losses. It runs in Python, in ComfyUI (0.4.0) and in the browser (WebGPU demo, the default version there).

Built by LogoLabs, which makes AI logo generation.

Why we built Agate: it is the result of an architecture study for glyph and symbol generation; see Why we built Agate below.

At a glance

What changed from 002

• 512 px. The thinker still plans on a 16 × 16 grid; it reads the 64 × 64 latent through its original read of the stride-2 subsample plus a new conv branch. The renderer runs at 64 × 64 with raw coordinates and an explicit resolution signal. The sampler uses the SD3 resolution shift 2 at 512 px.
• Prompt pipeline. Prompts are normalised (SHOUTED or Title Cased prompts lower-cased outside quotes, number words made canonical); "without X" / "no X" move to the negative prompt; text in double quotes is also spelled out letter by letter; a count code marks object counts. Training used the same pipeline, and AgatePipeline applies it.
• Perceptual fine-tune. The last run trained with a saliency-weighted flow loss plus 0.1 LPIPS and 0.01 P-DINO on the model's one-step prediction.

Put the loss where it matters

Plain flow loss averages the error over every latent cell, so a face, a word or the subject barely counts, and squared error rewards soft, averaged detail. 003's last run (mr512_perc1) kept the flow loss but weighted each cell by its UNISAL saliency, w = 1 + 0.1·t·(q − 1) (q: the cell's saliency relative to the image mean, capped at 5×; the maps are computed once and cached). It also added LPIPS (× 0.1, local texture) and DINOv2 patch features (P-DINO, × 0.01, structure) on the VAE-decoded one-step estimate, for t > 0.3 only, after the PixelGen recipe. What we know: the QIB gain over 002 is not attributed between 512 px and these losses, and a later fine-tune of 003 without them worsened LPIPS by 1.8% and P-DINO by 2.0% against the base, so fine-tunes of 003 should keep this objective.

Quick start

pip install torch transformers diffusers safetensors huggingface_hub pillow invisible-watermark

import sys
from huggingface_hub import snapshot_download

path = snapshot_download("Logolabs/agate-preview-003")
sys.path.insert(0, path)
import agate
from agate import AgatePipeline

pipe = AgatePipeline.from_pretrained(path, device="cuda")      # "cpu" works too: ~100 s per 512 px image
image = pipe('a shop sign that says "OPEN", three red apples on a table, no people', seed=0)[0]
agate.save(image, "open.png")            # keeps the AI-generated metadata in the PNG

pipe.prepare(prompt) shows the prompt as the text encoder sees it and the negative prompt it produced. Preview 003 has no autoguidance option.

Results

All scores were measured by us with the same seeds, sampler and scorer for every model. Preview 003 generates at 512 px, SD 1.5 at 512 px, Previews 001 and 002 at 256 px; every scorer saw 512 × 512.

Qwen-Image-Bench

| Model | Native | Total | Quality | Aesthetics | Alignment | Real-world Fidelity | Creative Generation | |---|---|---|---|---|---|---