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
Update scheduler/scheduler_config.json
#1
by patrickvonplaten - opened
Hey,
We have introduced a steps_offset variable to scheduler configs to have a cleaner API and now want to incentivize SD schedulers to add this to the config as otherwise it'll eventually lead to silent errors.
See: https://github.com/huggingface/diffusers/blob/7258dc4943e73b8799003771e86a44424afb996d/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L60
Best,
Diffusers Team!
justinpinkney changed pull request status to merged