Unconditional Image Generation
Diffusers
TensorBoard
Safetensors
PyTorch
DDPMPipeline
diffusion-models-class
Instructions to use afshr/forCAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use afshr/forCAM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("afshr/forCAM", 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:
- f3c152cf217b7ab40f48fe0edb75d1d6d6615441490723a16f70538d13a55bf6
- Size of remote file:
- 143 MB
- SHA256:
- 28c930c03ddaee7d3317f0707a946678c6fa3401cecacfc033c28917690ba35e
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