Instructions to use WaveCut/sdxs-2b-sdnq-t4-tebf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/sdxs-2b-sdnq-t4-tebf16 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/sdxs-2b-sdnq-t4-tebf16", 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: 738 Bytes
1b8bd6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"variant": "t4-tebf16",
"description": "Cosmos transformer uint4, text encoder bf16, VAE bf16.",
"source_model": "AiArtLab/sdxs-2b",
"components": {
"transformer": "uint4"
},
"torch_dtype": "bfloat16",
"quantized_matmul_dtype": "int8",
"group_size": 0,
"use_quantized_matmul": true,
"use_svd": false,
"quant_conv": false,
"quant_embedding": false,
"dequantize_fp32": true,
"notes": [
"VAE remains bf16; it is small and decode quality-sensitive.",
"Embeddings are not quantized; SDNQ common/model skip keys leave fragile input/output projections in higher precision.",
"The first inference after load may include torch.compile/Triton warmup; compare steady-state second pass for speed."
]
}
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