Sakura-Iris-3B-GGUF

Community quantization of speridlabs/iris-3b (3 B parameter pixel-space diffusion transformer, no VAE) for stable-diffusion.cpp, used with the official Qwen3-VL-4B-Instruct text encoder. Independent work, not made or endorsed by speridlabs or Alibaba/Qwen. Apache-2.0.

Sample generations

Image model Q8_0 (Sakura-Iris-3B-Q8_0-4.15GiB.gguf) and the official Qwen3-VL-4B-Instruct Q8_0 text encoder, stable-diffusion.cpp (Vulkan, Radeon 8060S), 1024x1024, 30 steps, CFG 3, dpm++2m. One seed per prompt, all nine prompts are shown, no re-rolls and no selection. About 213 to 738 s per image including model loading (the spread comes from other jobs running on the same GPU; the fastest image is the undisturbed one); the whole-GPU memory counter peaked at 29.7 GiB while generating (other jobs were running on the same GPU, so this is an upper bound). The images were made with stable-diffusion.cpp.

The same nine prompts and seeds are used in the sibling repository Sakura-Iris-3B-Uncensored-GGUF with our abliterated encoder, so the two sets can be compared. Seeds 1202, 1302, 1305, 1306, 1105, 1103, 1503, 1506, 1406 in the order shown; the prompts are the captions of the images above.

What you need to run it

Iris-3B has no VAE: do not pass --vae.

An abliterated text encoder for the same image models is in Sakura-Iris-3B-Uncensored-GGUF.

Files

Why the image files are not smaller: stable-diffusion.cpp keeps 3.05 GiB of the model in FP32 at every quantization level: the FFN w2 weights (row length 6826 is not divisible by the quantization block size, 2.21 GiB) and the text adapter, timestep embedder and small tensors (0.83 GiB, excluded by its own rule). In our files the w2 weights are stored as F16 (saves 1.04 GiB; with the sd.cpp default rule they would stay FP32 and the files would be 4.19 / 4.44 / 4.71 / 5.20 GiB). The F16 variant was within ±0.6 dB PSNR of the default rule in every pair we compared (one image each, see below).

Run with stable-diffusion.cpp

sd-cli \
  --diffusion-model Sakura-Iris-3B-Q5_K_M-3.40GiB.gguf \
  --llm Qwen3VL-4B-Instruct-Q8_0.gguf \
  --steps 30 --cfg-scale 3 --sampling-method dpm++2m --diffusion-fa \
  -W 1024 -H 1024 -p "your prompt" -o out.png

Width and height must be multiples of 16, prompts are limited to 300 tokens. The original model card uses 100 steps and CFG 3 with a DPM-Solver++ order-2 sampler. We measured and made the images with 30 steps and the dpm++2m sampler of stable-diffusion.cpp.

Steps (Q8_0 image model made with the sd.cpp default rule, 5.20 GiB, not shipped; same prompt and seed, 1024x1024, one image each):

All times include model loading and were measured on a Radeon 8060S (Vulkan); the 30- and 50-step times were taken while other jobs were running on the machine. Iris is not a distilled model: 8 steps is a preview setting, 20 to 30 steps is a good compromise.

Image-model quality (what we measured)

Reference: the official FP32 model.safetensors run by the same program with the same encoder (official Qwen3-VL-4B-Instruct Q8_0 GGUF), 1024x1024, 30 steps, CFG 3, dpm++2m, seed 42, same machine, prompt "A small orange cat sitting on a blue chair in a sunny room, natural colors, realistic photograph." One image per file, so differences of about one dB or less are noise, and the order of Q5_K_M and Q4_K_M is not resolved by PSNR. Higher is closer to the FP32 reference; a diffusion model changes details even with tiny weight changes, so these are deviation numbers, not a quality score.

For the same prompt with the sd.cpp default rule (w2 FP32, not shipped): Q8_0 26.84 dB / 0.875, Q6_K 25.23 dB / 0.863, Q5_K_M 20.01 dB / 0.777, Q4_K_M 20.19 dB / 0.703. A second prompt (a travel poster with the word "LISBOA") was measured for the default-rule Q8_0 (19.80 dB / 0.813) and Q4_K_M (15.41 dB / 0.604) and for Q4_K_M with w2 as F16 (15.49 dB / 0.600); the text is rendered correctly in all three. Peak whole-GPU memory with nothing else running: 13.5 GiB for the default-rule Q8_0 (5.20 GiB, not shipped) at 100 steps (2.2 GiB were in use before), 19.0 GiB for the FP32 original at 30 steps. We did not measure the memory of the other quantizations with nothing else running.

What we changed (Apache License 2.0, section 4)

• Sakura-Iris-3B-*.gguf: made with sd-cli -M convert directly from the official FP32 model.safetensors of speridlabs/iris-3b (sha256 a90e1f4c197bac8491f82078def68884b6a3527ea7fc97a693e3e367372aa80f, checked against the Hub), with --type q8_0|q6_K|q5_K|q4_K and --tensor-type-rules "mlp(_x|_y)?\.w2\.weight$=f16". Modified files.
• LICENSE is the Apache License 2.0 text of the original repository; NOTICE carries the original notice and this list of changes.

License and use

Apache License 2.0 (LICENSE), same as Iris-3B. Iris-3B is by speridlabs; this is an independent community release. Part of the Sakura Image collection.

Credits

• Original model: speridlabs/iris-3b (speridlabs); text encoder Qwen/Qwen3-VL-4B-Instruct (Qwen / Alibaba).
• GGUF conversion and inference: leejet/stable-diffusion.cpp (MIT).

sd-cli \
  --diffusion-model Sakura-Iris-3B-Q5_K_M-3.40GiB.gguf \
  --llm Qwen3VL-4B-Instruct-Q8_0.gguf \
  --steps 30 --cfg-scale 3 --sampling-method dpm++2m --diffusion-fa \
  -W 1024 -H 1024 -p "your prompt" -o out.png