Instructions to use johnowhitaker/rainbowdiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnowhitaker/rainbowdiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("johnowhitaker/rainbowdiffusion", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| inference: true | |
| To use the mode for inference, just load it like a normal stable diffusion pipeline: | |
| ```python | |
| from diffusers import StableDiffusionPipeline | |
| model_path = "johnowhitaker/rainbowdiffusion" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) | |
| pipe.to("cuda") | |
| image = pipe(prompt="A cat").images[0] | |
| image | |
| ``` |