ldm3d
Maintainer: Intel
48
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The ldm3d model, developed by Intel, is a Latent Diffusion Model for 3D that can generate both image and depth map data from a given text prompt. This allows users to create RGBD images from text prompts. The model was fine-tuned on a dataset of RGB images, depth maps, and captions, and validated through extensive experiments. Intel has also developed an application called DepthFusion, which uses the ldm3d model's img2img pipeline to create immersive and interactive 360-degree-view experiences.
The ldm3d model builds on research presented in the LDM3D paper, which was accepted to the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) in 2023. Intel has also released several new checkpoints for the ldm3d model, including ldm3d-4c with higher quality results, ldm3d-pano for panoramic images, and ldm3d-sr for upscaling.
Model inputs and outputs
Inputs
- Text prompt: The
ldm3dmodel takes a text prompt as input, which is used to generate the RGBD image.
Outputs
- RGBD image: The model outputs an RGBD (RGB + depth) image that corresponds to the given text prompt.
Capabilities
The ldm3d model is capable of generating high-quality, interactive 3D content from text prompts. This can be particularly useful for applications in the entertainment and gaming industries, as well as architecture and design. The model's ability to generate depth maps alongside the RGB images allows for the creation of immersive, 360-degree experiences using the DepthFusion application.
What can I use it for?
The ldm3d model can be used to create a wide range of 3D content, from static images to interactive experiences. Potential use cases include:
- Game and application development: Generate 3D assets and environments for games, virtual reality experiences, and other interactive applications.
- Architectural and design visualization: Create photorealistic 3D models of buildings, interiors, and landscapes based on textual descriptions.
- Entertainment and media production: Develop 3D assets and environments for films, TV shows, and other media productions.
- Educational and training applications: Generate 3D models and environments for educational purposes, such as virtual field trips or interactive learning experiences.
Things to try
One interesting aspect of the ldm3d model is its ability to generate depth information alongside the RGB image. This opens up possibilities for creating more immersive and interactive experiences, such as:
- Exploring the generated 3D scene from different perspectives using the depth information.
- Integrating the RGBD output into a virtual reality or augmented reality application for a truly immersive experience.
- Using the depth information to enable advanced rendering techniques, such as real-time lighting and shadows, for more realistic visuals.
Experimenting with different text prompts and exploring the range of 3D content the ldm3d model can generate can help uncover its full potential and inspire new and innovative applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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ldm3d-pano
43
The ldm3d-pano model is a new checkpoint released by Intel that extends their existing LDM3D-4c model to enable the generation of panoramic RGBD images from text prompts. This model is part of the LDM3D-VR suite of diffusion models introduced in the LDM3D-VR paper, which aims to enable virtual reality content creation from text. The ldm3d-pano model was fine-tuned on a dataset of panoramic RGB and depth images to add this new capability. Model inputs and outputs Inputs Text prompt**: A natural language description that the model uses to generate a corresponding panoramic RGBD image. Outputs RGB image**: A 1024x512 panoramic RGB image generated from the text prompt. Depth image**: A corresponding 1024x512 panoramic depth map generated from the text prompt. Capabilities The ldm3d-pano model can generate high-quality panoramic RGBD images based on textual descriptions. This allows users to create immersive 360-degree content for virtual reality applications such as gaming, architectural visualization, and digital entertainment. The model combines the text-to-image capabilities of Stable Diffusion with depth estimation to produce photorealistic and spatially-aware 3D environments. What can I use it for? The ldm3d-pano model enables the creation of immersive virtual environments from simple text prompts. This can be useful for a variety of applications, such as: Gaming and entertainment**: Generate custom 360-degree backgrounds, environments, and scenes for video games, virtual worlds, and other interactive experiences. Architectural visualization**: Create photorealistic 3D renderings of building interiors and exteriors for design, planning, and client presentations. Real estate and tourism**: Generate 360-degree panoramic views of properties, landmarks, and locations to showcase in virtual tours and online listings. Education and training**: Produce realistic 3D simulations and virtual environments for educational purposes, such as architectural walkthroughs or historical recreations. Things to try When using the ldm3d-pano model, consider experimenting with different levels of detail and complexity in your text prompts. Try adding specific elements like furniture, lighting, or weather conditions to see how they affect the generated output. You can also explore using the model in combination with other tools, such as inpainting or upscaling, to refine and enhance the final panoramic images.
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neural-chat-7b-v3-2
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The neural-chat-7b-v3-2 model is a fine-tuned 7B parameter Large Language Model (LLM) developed by the Intel team. It was trained on the meta-math/MetaMathQA dataset using the Direct Performance Optimization (DPO) method. This model was originally fine-tuned from the Intel/neural-chat-7b-v3-1 model, which was in turn fine-tuned from the mistralai/Mistral-7B-v-0.1 model. According to the Medium blog, the neural-chat-7b-v3-2 model demonstrates significantly improved performance compared to the earlier versions. Model inputs and outputs Inputs Prompts**: The model takes in text prompts as input, which can be in the form of a conversational exchange between a user and an assistant. Outputs Text generation**: The model outputs generated text that continues or responds to the provided prompt. The output is an attempt to provide a relevant and coherent continuation of the input text. Capabilities The neural-chat-7b-v3-2 model can be used for a variety of language-related tasks, such as open-ended dialogue, question answering, and text summarization. The model's fine-tuning on the MetaMathQA dataset suggests it may have particular strengths in understanding and generating text around mathematical concepts and reasoning. What can I use it for? This model can be used for a wide range of language tasks, from chatbots and virtual assistants to content generation and augmentation. Developers can fine-tune the model further on domain-specific data to adapt it for their particular use cases. The LLM Leaderboard provides a good overview of the model's performance on various benchmarks, which can help inform how it might be applied. Things to try One interesting aspect of the neural-chat-7b-v3-2 model is its potential for mathematical reasoning and problem-solving, given its fine-tuning on the MetaMathQA dataset. Developers could explore using the model to generate step-by-step explanations for math problems, or to assist users in understanding complex mathematical concepts. The model's broader language understanding capabilities also make it well-suited for tasks like open-ended dialogue, creative writing, and content summarization.
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neural-chat-7b-v3-3
71
The neural-chat-7b-v3-3 model is a fine-tuned 7B parameter large language model (LLM) from Intel. It was trained on the meta-math/MetaMathQA dataset and aligned using the Direct Performance Optimization (DPO) method with the Intel/orca_dpo_pairs dataset. The model was originally fine-tuned from the mistralai/Mistral-7B-v0.1 model. This model achieves state-of-the-art performance compared to similar 7B parameter models on various language tasks. Model inputs and outputs The neural-chat-7b-v3-3 model is a text-to-text transformer model that takes natural language text as input and generates natural language text as output. It can be used for a variety of language-related tasks such as question answering, dialogue, and summarization. Inputs Natural language text prompts Outputs Generated natural language text Capabilities The neural-chat-7b-v3-3 model demonstrates impressive performance on a wide range of language tasks, including question answering, dialogue, and summarization. It outperforms many similar-sized models on benchmarks such as the Open LLM Leaderboard, showcasing its strong capabilities in natural language understanding and generation. What can I use it for? The neural-chat-7b-v3-3 model can be used for a variety of language-related applications, such as building conversational AI assistants, generating helpful responses to user queries, summarizing long-form text, and more. Due to its strong performance on benchmarks, it could be a good starting point for developers looking to build high-quality language models for their projects. Things to try One interesting aspect of the neural-chat-7b-v3-3 model is its ability to handle long-form inputs and outputs, thanks to its 8192 token context length. This makes it well-suited for tasks that require reasoning over longer sequences, such as question answering or dialogue. You could try using the model to engage in extended conversations and see how it performs on tasks that require maintaining context over multiple turns. Additionally, the model's strong performance on mathematical reasoning tasks, as demonstrated by its results on the MetaMathQA dataset, suggests that it could be a useful tool for building applications that involve solving complex math problems. You could experiment with prompting the model to solve math-related tasks and see how it performs.
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neural-chat-7b-v3
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The neural-chat-7b-v3 is a 7B parameter large language model (LLM) fine-tuned by Intel on the open source Open-Orca/SlimOrca dataset. The model was further aligned using the Direct Performance Optimization (DPO) method with the Intel/orca_dpo_pairs dataset. This fine-tuned model builds upon the base mistralai/Mistral-7B-v0.1 model. Intel has also released similar fine-tuned models like neural-chat-7b-v3-1 and neural-chat-7b-v3-3, which build on top of this base model with further fine-tuning and optimization. Model Inputs and Outputs Inputs Text prompts of up to 8192 tokens, which is the same context length as the base mistralai/Mistral-7B-v0.1 model. Outputs Continuation of the input text, generating coherent and contextually relevant responses. Capabilities The neural-chat-7b-v3 model can be used for a variety of language-related tasks such as question answering, language generation, and text summarization. The model's fine-tuning on the Open-Orca/SlimOrca dataset and alignment using DPO is intended to improve its performance on conversational and open-ended tasks. What Can I Use It For? You can use the neural-chat-7b-v3 model for different language-related projects and applications. Some potential use cases include: Building chatbots and virtual assistants Generating coherent text for creative writing or storytelling Answering questions and providing information on a wide range of topics Summarizing long-form text into concise summaries To see how the model is performing on various benchmarks, you can check the LLM Leaderboard. Things to Try One interesting aspect of the neural-chat-7b-v3 model is its ability to adapt to different prompting styles and templates. You can experiment with providing the model with system prompts or using chat-based templates like the one provided in the how-to-use section to see how it responds in a conversational setting. Additionally, you can try fine-tuning or further optimizing the model for your specific use case, as the model was designed to be adaptable to a variety of language-related tasks.
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