Instructions to use imagepipeline/InteriorDesign-Checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use imagepipeline/InteriorDesign-Checkpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("imagepipeline/InteriorDesign-Checkpoint", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download vae/diffusion_pytorch_model.fp32.safetensors from imagepipeline/InteriorDesign-Checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/imagepipeline/InteriorDesign-Checkpoint/resolve/main/vae/diffusion_pytorch_model.fp32.safetensors
- Command line
-
hf download hf://imagepipeline/InteriorDesign-Checkpoint/vae/diffusion_pytorch_model.fp32.safetensors
-
curl -L -o diffusion_pytorch_model.fp32.safetensors https://huggingface.co/imagepipeline/InteriorDesign-Checkpoint/resolve/main/vae/diffusion_pytorch_model.fp32.safetensors
167 MB
- Xet hash:
- cba85bcab387fa25ef74d943841f2a68bc007b8512400aea0d927994b7bc07ae
- Size of remote file:
- 167 MB
- SHA256:
- a6dd4d904784d32b55bb718a13fdad4c05965dd9fda29d8c6a311b9a86678945
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.