Instructions to use bhugxer/ddpm-rocole-256-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bhugxer/ddpm-rocole-256-v3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bhugxer/ddpm-rocole-256-v3", 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
This is a Diffusion U-Net Model trained on a few images from the RoCoLe dataset to see how it can be used in conjunction with RePaint. Results weren't very good but, I'll keep it around for the time being
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