Image-to-Image
Diffusers
StableDiffusionImageVariationEmbedsPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use matttrent/sd-image-variations-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use matttrent/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("matttrent/sd-image-variations-diffusers", torch_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:
- 1cc42f6dca295521183e48a523369c7b44aa54a8386600b7b9b5b0bb3fd99ccc
- Size of remote file:
- 1.71 GB
- SHA256:
- f1a17cdbe0f36fec524f5cafb1c261ea3bbbc13e346e0f74fc9eb0460dedd0d3
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