Text-to-Image
Diffusers
Safetensors
English
Chinese
Russian
QwenImage21Pipeline
image-editing
qwen-image
orbitquant
w4a4
4-bit precision
quantized
turbo
few-step
8-bit precision
Instructions to use WaveCut/Turbo-Image-2.1-OrbitQuant-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Turbo-Image-2.1-OrbitQuant-W4A4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/Turbo-Image-2.1-OrbitQuant-W4A4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,074 Bytes
926d100 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"label": "orbitquant_staged",
"mode": "staged",
"quantized": true,
"gpu": "NVIDIA GeForce RTX 5090",
"torch": "2.8.0+cu128",
"cuda": "12.8",
"python": "3.12.3",
"baseline_nvml_gib": 0.4864501953125,
"jobs": [
"00-studio-product",
"01-human-portrait",
"02-mucha-poster",
"03-hokusai-wave",
"04-bauhaus-risograph",
"05-technical-cutaway",
"06-long-latin-text",
"07-long-cyrillic-text",
"08-mixed-diagram",
"09-wide-city-scene"
],
"load_cpu_s": 1.4314896063879132,
"te_to_gpu_s": 1.1430589817464352,
"te_peak_nvml_gib": 8.95538330078125,
"te_peak_torch_gib": 7.857428550720215,
"dit_vae_to_gpu_s": 0.7595742871053517,
"dit_peak_nvml_gib": 13.40069580078125,
"dit_peak_torch_gib": 11.470617771148682,
"hot_medians": {
"1024": {
"total_s": 2.121799952350557,
"encode_s": 0.07719557080417871,
"denoise_s": 1.815999576356262,
"decode_s": 0.22877847496420145,
"dit_vae_s": 2.0447780513204634,
"cold_first_total_s": 3.018771235831082,
"n_hot": 20
}
}
}
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