Image-to-Image
Diffusers
Safetensors
English
diffusion
image-to-3d
3d-reconstruction
gaussian-splatting
pose-free
sparse-view
rgbd
Instructions to use mvp18/gscenes-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mvp18/gscenes-checkpoints 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("mvp18/gscenes-checkpoints", 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:
- 687c5208db967a8f19ed79a6c7a96baf6001a2dc63a6d390911900fb517fc242
- Size of remote file:
- 669 MB
- SHA256:
- 014c1c42aa3ba39f35ca38613565e492073a32422aba11f715cc166132b7247a
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