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
remove text_unet
#9 opened over 1 year ago
by
nirajan111
Adding `safetensors` variant of this model
#6 opened over 1 year ago
by
SFconvertbot
Issue with missing text_unet/versatile_diffusion.py file in model_index.json
5
#5 opened over 1 year ago
by
PromiseZ5Q2SQ
Add `scale_factor` to vae config.
1
#4 opened over 3 years ago
by
valhalla
[Tutorial] How to Run and Convert Stable Diffusion Diffusers (.bin Weights) & Dreambooth Models to CKPT File
#3 opened over 3 years ago
by
MonsterMMORPG
missing text2image pipeline
#2 opened over 3 years ago
by
nazarenodefrancesco