Instructions to use ovedrive/ERNIE-Image-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ovedrive/ERNIE-Image-nf4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ovedrive/ERNIE-Image-nf4", 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: 246 Bytes
c50702f | 1 2 3 4 5 6 | {
"quantization_method": "mixed_precision_nf4",
"model_type": "ernie",
"description": "First and last blocks plus boundary modules kept at bfloat16, middle layers quantized to NF4 (ernie architecture)",
"high_precision_layers_count": 20
} |