Diffusers
TensorBoard
Safetensors
PyTorch
DDPMPipeline
conditional-image-generation
diffusion-models-class
Instructions to use 1aurent/ddpm-mnist-conditional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 1aurent/ddpm-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("1aurent/ddpm-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
Commit History
-
Delete events.out.tfevents.1697126101.79c810d71619.1725.0
1f64e01 -
Upload README.md with huggingface_hub
b4422aa -
Upload model_index.json with huggingface_hub
ae1fc27 -
Upload folder using huggingface_hub
c029bd9 -
Upload folder using huggingface_hub
b954797 -
Upload folder using huggingface_hub
ab0bf2b -
initial commit
f20a019