Ldm3d

🚀 LDM3D model

The LDM3D model is a latent diffusion model for 3D that can generate RGB images and depth maps from text prompts, with potential applications in various industries.

🚀 Quick Start

You can use this model to generate an RGB image and depth map given a text prompt. A short video summarizing the approach can be found at this url and a VR demo can be found here. A demo is also accessible on Spaces.

Here is how to use this model to get the features of a given text in PyTorch on both a CPU and GPU architecture:

from diffusers import StableDiffusionLDM3DPipeline

pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")

# On CPU
pipe.to("cpu")

# On GPU
pipe.to("cuda")

prompt = "A picture of some lemons on a table"
name = "lemons"

output = pipe(prompt)
rgb_image, depth_image = output.rgb, output.depth
rgb_image[0].save(name+"_ldm3d_rgb.jpg")
depth_image[0].save(name+"_ldm3d_depth.png")

This is the result:

✨ Features

• Text-to-3D Generation: Generate both RGB images and depth maps from text prompts.
• Multiple Checkpoints: New checkpoints like ldm3d-4c, ldm3d-pano, and ldm3d-sr are available for different use cases.
• Industry Potential: Has the potential to transform industries such as entertainment, gaming, architecture, and design.

📦 Installation

The provided README doesn't contain installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from diffusers import StableDiffusionLDM3DPipeline

pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")

# On CPU
pipe.to("cpu")

# On GPU
pipe.to("cuda")

prompt = "A picture of some lemons on a table"
name = "lemons"

output = pipe(prompt)
rgb_image, depth_image = output.rgb, output.depth
rgb_image[0].save(name+"_ldm3d_rgb.jpg")
depth_image[0].save(name+"_ldm3d_depth.png")

📚 Documentation

Model Details

The LDM3D model was proposed in the paper LDM3D: Latent Diffusion Model for 3D, authored by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. It was accepted to the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) in 2023.

The abstract from the paper is the following: This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the img2img pipeline to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences.

LDM3D overview taken from the LDM3D paper.

Training Data

The LDM3D model was finetuned on a dataset constructed from a subset of the LAION-400M dataset, a large-scale image-caption dataset that contains over 400 million image-caption pairs.

Finetuning

The fine-tuning process comprises two stages. In the first stage, we train an autoencoder to generate a lower-dimensional, perceptually equivalent data representation. Subsequently, we fine-tune the diffusion model using the frozen autoencoder

Evaluation Results

Quantitative Results

The table below shows the quantitative results of text-conditional image synthesis on the 512 x 512-sized MS-COCO dataset with 50 DDIM steps.

Our model is on par with the Stable Diffusion models with the same number of parameters (1.06B). IS and CLIP similarity scores are averaged over 30k captions from the MS-COCO dataset.

The following table shows the evaluation results of depth evaluation comparing LDM3D and DPT-Large with respect to ZoeDepth-N that serves as a reference model.

The results shown above can be referenced in Table 1 and Table 2 of the LDM3D paper.

Qualitative Results

The figure below shows some qualitative results comparing our method with Stable Diffusion v1.4 and with DPT-Large for the depth maps .

🔧 Technical Details

The fine-tuning process of the LDM3D model involves two stages. First, an autoencoder is trained to generate a lower-dimensional, perceptually equivalent data representation. Then, the diffusion model is fine-tuned using the frozen autoencoder.

📄 License

The license of this model is creativeml-openrail-m.

BibTeX entry and citation info

@misc{stan2023ldm3d,
    title={LDM3D: Latent Diffusion Model for 3D},
    author={Gabriela Ben Melech Stan and Diana Wofk and Scottie Fox and Alex Redden and Will Saxton and Jean Yu and Estelle Aflalo and Shao-Yen Tseng and Fabio Nonato and Matthias Muller and Vasudev Lal},
    year={2023},
    eprint={2305.10853},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

⚠️ Important Note

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

💡 Usage Tip

For better results, do not hesitate to use our new checkpoint, ldm3d-4c based on a slightly different architecture.

Here are a couple of useful links to learn more about Intel's AI software:

• Intel Extension for PyTorch
• Intel Neural Compressor