Image-to-Image
Diffusers
Safetensors
diffusion
inpainting
360-panorama
nadir-removal
memo-insertion
lora
Instructions to use hiennthp/cubeinpaint360-nadir-memo-sdxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hiennthp/cubeinpaint360-nadir-memo-sdxl 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("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hiennthp/cubeinpaint360-nadir-memo-sdxl") 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: Multi-Task 360 Editing (Nadir + Memo)
Cubemap-guided diffusion: EQR -> cubemap face -> SD Inpainting + LoRA -> re-projection.
| Metric | Value |
|---|---|
| PSNR | N/A |
| SSIM | N/A |
| LPIPS | N/A |
from diffusers import StableDiffusionInpaintPipeline
from peft import PeftModel
pipe = StableDiffusionInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, safety_checker=None)
pipe.unet = PeftModel.from_pretrained(pipe.unet, "hiennthp/cubeinpaint360-nadir-memo-sdxl")
pipe = pipe.to("cuda")
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Model tree for hiennthp/cubeinpaint360-nadir-memo-sdxl
Base model
stabilityai/stable-diffusion-xl-base-1.0