# LDM3D-VR model

The LDM3D-VR model was proposed in ["LDM3D-VR: Latent Diffusion Model for 3D"](https://arxiv.org/pdf/2311.03226.pdf) by Gabriela Ben Melech Stan, Diana Wofk, Estelle Aflalo, Shao-Yen Tseng, Zhipeng Cai, Michael Paulitsch, Vasudev Lal.

LDM3D-VR got accepted to [NeurIPS Workshop'23 on Diffusion Models][https://neurips.cc/virtual/2023/workshop/66539].

This new checkpoint related to the upscaler called ldm3d-sr.

# Model description
The abstract from the paper is the following: Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano
and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods. 

## Examples

Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) in a simple and efficient manner.

```python
from PIL import Image
import os
import torch
from diffusers import StableDiffusionUpscaleLDM3DPipeline,  StableDiffusionLDM3DPipeline


#Generate a rgb/depth output from LDM3D
pipe_ldm3d = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d-4c")
pipe_ldm3d.to("cuda")

prompt =f"A picture of some lemons on a table"
output = pipe_ldm3d(prompt)
rgb_image, depth_image = output.rgb, output.depth
rgb_image[0].save(f"lemons_ldm3d_rgb.jpg")
depth_image[0].save(f"lemons_ldm3d_depth.png")


#Upscale the previous output to a resolution of (1024, 1024)
pipe_ldm3d_upscale = StableDiffusionUpscaleLDM3DPipeline.from_pretrained("Intel/ldm3d-sr")
pipe_ldm3d_upscale.to("cuda")

low_res_img = Image.open(f"lemons_ldm3d_rgb.jpg").convert("RGB")
low_res_depth = Image.open(f"lemons_ldm3d_depth.png").convert("L")
outputs = pipe_ldm3d_upscale(prompt="high quality high resolution uhd 4k image", rgb=low_res_img, depth=low_res_depth, num_inference_steps=50, target_res=[1024, 1024])

upscaled_rgb, upscaled_depth =outputs.rgb[0], outputs.depth[0]
upscaled_rgb.save(f"upscaled_lemons_rgb.png")
upscaled_depth.save(f"upscaled_lemons_depth.png")
```


## Results

Output of ldm3d-4c          |  Upscaled output
:-------------------------:|:-------------------------:
![ldm3d_rgb_results](lemons_ldm3d_rgb.jpg)  |  ![ldm3d_sr_rgb_results](upscaled_lemons_rgb.png)
![ldm3d_depth_results](lemons_ldm3d_depth.png)  |  ![ldm3d_sr_depth_results](upscaled_lemons_depth.png)



### BibTeX entry and citation info
@misc{stan2023ldm3dvr,
      title={LDM3D-VR: Latent Diffusion Model for 3D VR}, 
      author={Gabriela Ben Melech Stan and Diana Wofk and Estelle Aflalo and Shao-Yen Tseng and Zhipeng Cai and Michael Paulitsch and Vasudev Lal},
      year={2023},
      eprint={2311.03226},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}