Instructions to use eurecom-ds/mnist_conditional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use eurecom-ds/mnist_conditional with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("eurecom-ds/mnist_conditional", 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
File size: 227 Bytes
39a379c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"_class_name": [
"conditional_pipeline",
"ScoreSdeVePipelineConditioned"
],
"scheduler": [
"sde_ve_scheduler",
"ScoreSdeVeScheduler"
],
"unet": [
"conditional_unet_model",
"UNet2DModel"
]
} |