Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
Photorealistic
Food
Cars
Oragani
stable-diffusion
stable-diffusion-1.5
stable-diffusion-diffusers
Instructions to use Yntec/ReaDiff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yntec/ReaDiff with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/ReaDiff", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ea3f3d5720695556859c83c05034c820cf02905ef38e11d8af280e0dd330f269
- Size of remote file:
- 3.44 GB
- SHA256:
- c1dcdd73ff4896a65324bf9e4e87ab3f12a851255aff9dc43fd313c3a5d38e81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.