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: 421 Bytes
09f4a4a c5fedc7 09f4a4a c5fedc7 09f4a4a c5fedc7 09f4a4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"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
} |