Text-to-Image
Diffusers
Safetensors
ErnieImagePipeline
ernie-image
sdnq
quantized
uint4
static
quantized-matmul
Instructions to use WaveCut/ERNIE-Image-Turbo-SDNQ-uint4-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/ERNIE-Image-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/ERNIE-Image-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: 577 Bytes
f2b7557 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"name": "quantize_and_save",
"seconds": 71.19859568402171,
"gpu_start_mib": 270,
"gpu_end_mib": 312,
"gpu_peak_mib": 1276,
"torch_peak_allocated_mib": 870,
"torch_peak_reserved_mib": 964,
"base_model": "baidu/ERNIE-Image-Turbo",
"output": "artifacts/ERNIE-Image-Turbo-SDNQ-uint4-static",
"output_size_bytes": 10048077414,
"recipe": {
"weights_dtype": "uint4",
"group_size": 0,
"use_svd": false,
"svd_rank": 32,
"svd_steps": 8,
"dynamic_loss_threshold": null,
"use_quantized_matmul": false,
"dequantize_fp32": false
}
}
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