Text-to-Image
Diffusers
Safetensors
English
Russian
LensPipeline
LensPipeline
sdnq
quantized
uint4
static-quantization
ablation
Instructions to use WaveCut/Lens-Turbo-SDNQ-uint4-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Lens-Turbo-SDNQ-uint4-static 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/Lens-Turbo-SDNQ-uint4-static", 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
- Draw Things
- DiffusionBee
File size: 845 Bytes
09f4a4a c5fedc7 09f4a4a c5fedc7 09f4a4a | 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 | {
"source_model": "microsoft/Lens-Turbo",
"method": "SDNQ uint4 static",
"scope": "transformer only, excluding modulation linears",
"ablation_fix": "Transformer block img_mod and txt_mod linears are left in bfloat16 because UINT4 quantization caused periodic grid artifacts and severe text degradation.",
"config": {
"weights_dtype": "uint4",
"quantized_matmul_dtype": "int8",
"group_size": 0,
"use_static_quantization": true,
"use_dynamic_quantization": false,
"use_quantized_matmul": true,
"use_svd": false,
"use_hadamard": false,
"quant_conv": false,
"quant_embedding": false,
"dequantize_fp32": false,
"modules_to_not_convert": [
"*.img_mod.*",
"*.txt_mod.*"
],
"modules_to_not_use_matmul": [],
"quantization_device": "cuda",
"return_device": "cuda"
}
} |