Text-to-Image
Diffusers
VersatileDiffusionPipeline
image-to-text
image-to-image
text-to-text
image-editing
image-variation
generation
vision
Instructions to use shi-labs/versatile-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shi-labs/versatile-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "A high tech solarpunk utopia in the Amazon rainforest" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 69de2fb833a09f0ebc7f35b1d4e5ff05d6c7a6891446323ccdef8483293613fc
- Size of remote file:
- 1.02 GB
- SHA256:
- 5f82e8759f7ecf1e20397f0c70b6b0b5d84149cdc1875be1016142c0de053d69
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