Image-to-Image
Diffusers
StableDiffusionImageVariationPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use lambda/sd-image-variations-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lambda/sd-image-variations-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lambda/sd-image-variations-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36db03e0d9fed0f8f8223d62a0fe04076689b2dae2d73c35a37ec6cae6f15521
- Size of remote file:
- 1.22 GB
- SHA256:
- 89d2aa29b5fdf64f3ad4f45fb4227ea98bc45156bbae673b85be1af7783dbabb
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