Image-to-Image
Diffusers
Safetensors
diffusion
inpainting
360-panorama
nadir-removal
construction-inspection
lora
Instructions to use hiennthp/cubeinpaint360-nadir-memo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hiennthp/cubeinpaint360-nadir-memo 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("runwayml/stable-diffusion-inpainting", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hiennthp/cubeinpaint360-nadir-memo") 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
- Local Apps Settings
- Draw Things
CubeInpaint360: Nadir Tripod Removal for 360 Construction Inspection
Method
Cubemap-guided diffusion inpainting: EQR -> cubemap bottom face -> SD Inpainting + LoRA -> re-projection.
Metrics (Best Model: SD15)
- PSNR: 25.41
- SSIM: 0.7681
- LPIPS: 0.0983
Usage
from diffusers import StableDiffusionInpaintPipeline
from peft import PeftModel
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None)
pipe.unet = PeftModel.from_pretrained(pipe.unet, "hiennthp/cubeinpaint360-nadir-memo")
pipe = pipe.to("cuda")
- Downloads last month
- 11
Model tree for hiennthp/cubeinpaint360-nadir-memo
Base model
runwayml/stable-diffusion-inpainting