Unconditional Image Generation
Diffusers
TensorBoard
Safetensors
English
DDPMPipeline
text-generation-inference
Instructions to use hecool108/ct-p2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hecool108/ct-p2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hecool108/ct-p2", 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
Download pipeline_stable_diffusion.py from hecool108/ct-p2: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/hecool108/ct-p2/resolve/main/pipeline_stable_diffusion.py
- Command line
-
hf download hf://hecool108/ct-p2/pipeline_stable_diffusion.py
-
curl -L -o pipeline_stable_diffusion.py https://huggingface.co/hecool108/ct-p2/resolve/main/pipeline_stable_diffusion.py
125 Bytes
| from diffusers import ( | |
| StableDiffusionPipeline, | |
| ) | |
| text2img = StableDiffusionPipeline.from_pretrained("hecool108/ct-p2") |