iris

Generative priors are a promising foundation for downstream vision tasks. In this project we explore pixel-space generative models as an alternative to vision foundation models such as DINOv2.

Most image generators work in a compressed "latent" space and rely on a separate decoder to turn that into an image. Iris-3B skips that step: the network itself outputs every pixel, so nothing is lost to a lossy, texture-biased latent. You type a prompt, and you get a 1024-pixel image.

Iris-3B is not only an image generator. The same pixel-space prior, fine-tuned with no architectural change, is a general vision learner for dense tasks where detail matters: monocular depth estimation and image restoration and upscaling ship in this repo (see A general vision learner below).

Iris-3B Turbo, in the turbo/ folder, is a 4-step distilled version of Iris-3B: a 1024×1024 image in about 0.7 s on one H100.

Examples

Generated at native aspect ratios of about one megapixel with the default settings (CFG 3, 100 steps).

Get started

1. Install the code

git clone https://github.com/speridlabs/iris-3b.git
cd iris-3b
pip install -e .          # Python 3.11+, PyTorch 2.7.1+

2. Download the weights (about 12 GB for text-to-image; the depth/ and upscaler/ folders add about 12 GB each, turbo/ about 6 GB)

hf download speridlabs/iris-3b --local-dir iris-3b --exclude "depth/*" "upscaler/*" "turbo/*"

3. Generate an image

python scripts/sample.py --checkpoint iris-3b \
    --prompt "a red fox sleeping in fresh snow, golden hour"

The text encoder (Qwen3-VL-4B-Instruct) downloads automatically on the first run. You need an NVIDIA GPU with CUDA.

Tips

Default output is 1024×1024. Write prompts as plain descriptive English sentences.

Iris-3B Turbo

Iris-3B Turbo, in the turbo/ folder of this repo, is a 4-step distilled version of Iris-3B. Classifier-free guidance is baked into the weights, so each step is a single forward pass; a 1024×1024 image takes about 0.7 s on one H100 (batch 4). It is a full fine-tune of all 3B parameters, not a LoRA. Try it in the Turbo demo.

With the code installed (step 1 above):

hf download speridlabs/iris-3b --include "turbo/*" --local-dir iris-3b    # about 6 GB
python scripts/sample.py --checkpoint iris-3b/turbo \
    --prompt "a red fox sleeping in fresh snow, golden hour"

The script reads the 4-step schedule from turbo/distill.yaml (σ = 1, 0.938, 0.833, 0.625, deterministic flow-Euler steps) and samples without CFG. The schedule is part of the model: --steps, --cfg-scale and --negative-prompt do not apply, while the prompt, --height, --width and --seed do. Turbo was distilled at about one megapixel in 7 aspect buckets: 1024×1024, 752×1344, 896×1344, 896×1200 and their transposes. The image above was generated on an H100 with --seed 6 and the prompt Colorful animated cartoon poster: a smiling shiba inu in aviator sunglasses behind the wheel of a low futuristic supercar, dynamic angle, neon sunset sky, with big bold vector lettering "IRIS TURBO" in the sky.

Benchmarks

All models at 1024×1024 with the official scorers and the same prompts and seeds; Iris-3B with its default sampling settings. The community adapter was run with its recommended settings through its own code. Turbo is slightly ahead of the base model on prompt following (GenEval, DPG-Bench) and behind it on long rendered text and on OneIG-Bench, where the gap is mostly text and diversity.

Training recipe

Turbo is distilled from Iris-3B with Trajectory Distribution Matching (TDM), data-free, as a full fine-tune of all 3B parameters in two stages. The trainer and both configs are in the code repository (scripts/distill.py).

• Networks: frozen Iris-3B teacher, the student and a "fake" score network trained online on the student's outputs; deterministic flow-Euler step transitions.
• Guidance: CFG 3.5 (empty negative prompt) baked into the teacher target; the student samples without CFG.
• Optimizer: AdamW, betas (0, 0.999), weight decay 0, gradient clipping 1.0, constant LR, one fake update per student update.
• Precision: FP32 weights under BF16 autocast.
• Resolution: about 1 MP in 7 aspect buckets, one per global batch: 1024×1024, 752×1344, 896×1344, 896×1200 and their transp