Instructions to use CCMat/ddpm-bored-apes-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CCMat/ddpm-bored-apes-128 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CCMat/ddpm-bored-apes-128", torch_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
- Xet hash:
- aec18e96ad542f668bd7f3747d05471a2fa940ea9b3a59e3457511d9b9b8e0cf
- Size of remote file:
- 455 MB
- SHA256:
- 50fa80c6bb6001da89b29a2fbeddbd22b14a381052f58adfe6209e1eb69d1e70
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