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
| { | |
| "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." | |
| ] | |
| } | |