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
| { | |
| "load": { | |
| "base": { | |
| "load_time_s": 19.272, | |
| "peak_allocated_gb": 20.807, | |
| "peak_reserved_gb": 20.928, | |
| "end_allocated_gb": 20.807, | |
| "end_reserved_gb": 20.928 | |
| }, | |
| "quant": { | |
| "load_time_s": 13.461, | |
| "peak_allocated_gb": 17.179, | |
| "peak_reserved_gb": 17.244, | |
| "end_allocated_gb": 17.179, | |
| "end_reserved_gb": 17.244 | |
| } | |
| }, | |
| "quantization_time_s": 0.178 | |
| } |