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
| { | |
| "_class_name": "VersatileDiffusionPipeline", | |
| "_diffusers_version": "0.8.0.dev0", | |
| "image_encoder": [ | |
| "transformers", | |
| "CLIPVisionModelWithProjection" | |
| ], | |
| "image_feature_extractor": [ | |
| "transformers", | |
| "CLIPImageProcessor" | |
| ], | |
| "image_unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "text_unet": [ | |
| "versatile_diffusion", | |
| "UNetFlatConditionModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |