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LegoGPT, an LLM-based system that generates physically stable LEGO structures from text prompts, backed by a new 47,000+ sample dataset and physics-aware filtering during inference. → LegoGPT is trained on a custom dataset, StableText2Lego, which includes 47,000+ 3D LEGO models mapped to text, spanning 28,000+ unique objects. → The... model predicts LEGO bricks sequentially like tokens, using next-token prediction in a transformer setup. → To ensure physical stability, LegoGPT integrates physics-aware rollback and validity filtering, pruning out structurally invalid brick placements. → The generated designs are aesthetically aligned with prompts, physically buildable, and tested both with human manual assembly and robotic arms. → The team also introduced a text-driven LEGO coloring/texturing pipeline, enabling more expressive and customized outputs. → The dataset, code, and models are all publicly released under an open-access license.show more

Rohan Paul

149,147 subscribers

75,248 次观看 • 1 年前 •via X (Twitter)

艺术科学技术教育

Anya Rossi• Live Now

Private livecam show

10 条评论

RTTS 的头像

RTTS1 年前

Testing Salesforce presents unique challenges due to its complexity, scalability and customizability. RTTS can plan, design & automate a successful testing process for you.

Justin Obney 的头像

Justin Obney1 年前

This is dope. Check out this exploration I did with my kids.

Rohan Paul 的头像

Rohan Paul1 年前

cool.. 👍

Sanskar Pandey 的头像

Sanskar Pandey1 年前

the logical conclusion to NLP is its intersection with robotics @ruhzi57

Jacek (Jomsborg.eth) 的头像

Jacek (Jomsborg.eth)1 年前

L(L)M. We should care more about what cannot be expressed through language. All rest is like LEGO.

✨ 的头像

✨1 年前

@_mcbench irl

Jack Lau 的头像

Jack Lau1 年前

Can't wait to see what others come up with using it.

Varun K | AI Insights 的头像

Varun K | AI Insights1 年前

LegoGPT actually sounds like the future of playtime! 47k+ models, physics checks, AND it’s tested with real humans and robots building the stuff?? according to Tom's Hardware, it nails physical stability 98% of the time. gonna try this out for my next LEGO binge lol

Vinayak 的头像

Vinayak1 年前

Damn it's sooo cool, I wanna work on this amazing stuff one day!

cryptobiot 的头像

cryptobiot1 年前

don't tell me chatgpt is now taking my childhood lego master builder dream job, too

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We've released the code for LegoGPT. This autoregressive model generates physically stable and buildable designs from text prompts, by integrating physics laws and assembly constraints into LLM training and inference. This work is led by PhD students Ava Pun, Kangle Deng, Ruixuan Liu, and in collaboration with CMU faculty Changliu Liu and Deva Ramanan. LegoGPT is a small first step towards the ultimate goal of generative manufacturing of physical objects. Our implementation is limited to 20x20x20 dimensions, 21 object categories, and simple brick types, but we are working on scaling it up! Code: Website: Demo:

We've released the code for LegoGPT. This autoregressive model generates physically stable and buildable designs from text prompts, by integrating physics laws and assembly constraints into LLM training and inference. This work is led by PhD students Ava Pun, Kangle Deng, Ruixuan Liu, and in collaboration with CMU faculty Changliu Liu and Deva Ramanan. LegoGPT is a small first step towards the ultimate goal of generative manufacturing of physical objects. Our implementation is limited to 20x20x20 dimensions, 21 object categories, and simple brick types, but we are working on scaling it up! Code: Website: Demo:

Jun-Yan Zhu

38,582 次观看 • 1 年前

Boom! Open source LegoGPT is a building AI, and sure it can be used for Legos but it can also be used for Lego-like building of homes. LegoGPT converts meshes to Lego in one step using 1×1, 1×2, 1×4, 1×6, 1×8, 2×2, 2×4, and 2×6 bricks. Then they evaluate the stability of the design. Finally, they render an image and ask GPT-4o to produce captions to go with the image. This sent to robots and they complete the real-time build. This can absolutely scale and as with Legos, you can use smaller pieces for higher resolution. Testing it detail now with robot assembly. Link:

Boom! Open source LegoGPT is a building AI, and sure it can be used for Legos but it can also be used for Lego-like building of homes. LegoGPT converts meshes to Lego in one step using 1×1, 1×2, 1×4, 1×6, 1×8, 2×2, 2×4, and 2×6 bricks. Then they evaluate the stability of the design. Finally, they render an image and ask GPT-4o to produce captions to go with the image. This sent to robots and they complete the real-time build. This can absolutely scale and as with Legos, you can use smaller pieces for higher resolution. Testing it detail now with robot assembly. Link:

Brian Roemmele

22,363 次观看 • 1 年前

AI Text-to-Music has arrived. MusicLM is a model by Google Research that generates high-fidelity music from text descriptions. Basically just enter some text and it will create the music. 🤯 Attached video shows sample text prompts and AI generated music.

AI Text-to-Music has arrived. MusicLM is a model by Google Research that generates high-fidelity music from text descriptions. Basically just enter some text and it will create the music. 🤯 Attached video shows sample text prompts and AI generated music.

Dave Lee

72,546 次观看 • 3 年前

LETS GOO! Parler TTS 🔥 A fully open-source, Apache 2.0 licensed Text-to-speech model focused on providing maximum controllability. Through voice prompts, you can control the pitch, speed, gender, noise levels, emotion characteristics and more! > Trained on 10K hours of permissive data. > Offers control over the generations. > Training + Inference code released. > The processed dataset and tagging scripts were released for further research. > English only for now. Next, we're scaling the training to 50K hours and even better dataset processing! Want to help us out? DMs open! 🤗

LETS GOO! Parler TTS 🔥 A fully open-source, Apache 2.0 licensed Text-to-speech model focused on providing maximum controllability. Through voice prompts, you can control the pitch, speed, gender, noise levels, emotion characteristics and more! > Trained on 10K hours of permissive data. > Offers control over the generations. > Training + Inference code released. > The processed dataset and tagging scripts were released for further research. > English only for now. Next, we're scaling the training to 50K hours and even better dataset processing! Want to help us out? DMs open! 🤗

Vaibhav (VB) Srivastav

156,386 次观看 • 2 年前

Last week we released Meta Chameleon: a new mixed-modal research model from Meta FAIR. Get the models ➡️ The 7B & 34B safety tuned models we’ve released can take any combination of text and images as input and produce text outputs using a new early fusion approach. While some LLMs have separate image and text encoders or decoders, Chameleon is one of the first publicly released approaches using a single unified architecture. We’re releasing Chameleon models under a research license to help democratize access to foundational mixed-modal models & further research on early fusion. Approach & training details in the paper ➡️

Last week we released Meta Chameleon: a new mixed-modal research model from Meta FAIR. Get the models ➡️ The 7B & 34B safety tuned models we’ve released can take any combination of text and images as input and produce text outputs using a new early fusion approach. While some LLMs have separate image and text encoders or decoders, Chameleon is one of the first publicly released approaches using a single unified architecture. We’re releasing Chameleon models under a research license to help democratize access to foundational mixed-modal models & further research on early fusion. Approach & training details in the paper ➡️

AI at Meta

54,409 次观看 • 1 年前

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,657 次观看 • 2 年前

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

SkalskiP

111,792 次观看 • 2 年前

OpenAI just released a new model to distinguish between AI/human written text to protect against ChatGPT. The classifier was trained on a pair of AI/human written dataset. However.. I was easily able to trick it by using GPT3 to rewrite the text. Demo:

OpenAI just released a new model to distinguish between AI/human written text to protect against ChatGPT. The classifier was trained on a pair of AI/human written dataset. However.. I was easily able to trick it by using GPT3 to rewrite the text. Demo:

Lior Alexander

195,419 次观看 • 3 年前

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

Tencent Hy

412,523 次观看 • 8 个月前

A new real-time world model is here 👀 I tested out Dynamics Lab's Mirage 2, which was just publicly released. You can upload an image and step inside it - with game controls that guide movement + the ability to change the scene with text prompts.

A new real-time world model is here 👀 I tested out Dynamics Lab's Mirage 2, which was just publicly released. You can upload an image and step inside it - with game controls that guide movement + the ability to change the scene with text prompts.

Justine Moore

24,289 次观看 • 9 个月前

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,257 次观看 • 9 个月前

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 次观看 • 9 个月前

LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM LEGO-SLAM running at 15 FPS on a ScanNet scene with language-based loop closing for drift correction. LEGO-SLAM is a 3DGS-based SLAM framework that supports open-vocabulary semantic querying and rendering. It tracks via G-ICP and efficiently builds a map by embedding Gaussians with scene-adaptive 16D language features. Map management is achieved through Language Pruning and Language-Based Loop Detection. The generated map enables open-vocabulary 3D Object Localization.

LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM LEGO-SLAM running at 15 FPS on a ScanNet scene with language-based loop closing for drift correction. LEGO-SLAM is a 3DGS-based SLAM framework that supports open-vocabulary semantic querying and rendering. It tracks via G-ICP and efficiently builds a map by embedding Gaussians with scene-adaptive 16D language features. Map management is achieved through Language Pruning and Language-Based Loop Detection. The generated map enables open-vocabulary 3D Object Localization.

Ryohei Sasaki@engineer

14,935 次观看 • 2 个月前

LEGO announced a new “Smart Brick” at CES: ▫️built a tiny computer into 2x4 bricks ▫️bricks “come alive” by lighting up or making sounds (triggered by NFC smart tags inside smart LEGO tiles and minifigs) ▫️LEGO built its own ASIC chip (fits into brick stud and smartphone app provides firmware updates) ▫️chip takes sound as input only (for privacy, it doesn’t record and no camera and no AI features) ▫️multiple Smart Bricks can make a Bluetooth mesh network (by telling position and direction of bricks, it can trigger crash sounds if brick is hit and track “winning” vehicles in a race) LEGO says it is the “the most significant evolution in the Lego System-in-Play since the introduction of the Lego Minifigure in 1978.” First Smart Bricks set come out in March and are Star Wars themes (X-Wing, Tie Fighter). *** More via The Verge:

LEGO announced a new “Smart Brick” at CES: ▫️built a tiny computer into 2x4 bricks ▫️bricks “come alive” by lighting up or making sounds (triggered by NFC smart tags inside smart LEGO tiles and minifigs) ▫️LEGO built its own ASIC chip (fits into brick stud and smartphone app provides firmware updates) ▫️chip takes sound as input only (for privacy, it doesn’t record and no camera and no AI features) ▫️multiple Smart Bricks can make a Bluetooth mesh network (by telling position and direction of bricks, it can trigger crash sounds if brick is hit and track “winning” vehicles in a race) LEGO says it is the “the most significant evolution in the Lego System-in-Play since the introduction of the Lego Minifigure in 1978.” First Smart Bricks set come out in March and are Star Wars themes (X-Wing, Tie Fighter). *** More via The Verge:

Trung Phan

1,601,426 次观看 • 5 个月前

Using the new WordPress Command Palette to call an assistant that adds LLM generated text to a 3D world using natural language commands! "add text: write a short poem about the metaverse" This extends to image, audio and 3D objects in the future. WebXR holodeck style editing!

Using the new WordPress Command Palette to call an assistant that adds LLM generated text to a 3D world using natural language commands! "add text: write a short poem about the metaverse" This extends to image, audio and 3D objects in the future. WebXR holodeck style editing!

XR Publisher

17,891 次观看 • 2 年前

🌍 Journey to a LEGO future! Explore a world made of LEGO bricks, where nature reigns & machines roam! Join Aloy and friends to battle an ancient demon and uncover secrets of the past. 🦖🌄 LEGO Horizon Adventures is out now on #NintendoSwitch:

🌍 Journey to a LEGO future! Explore a world made of LEGO bricks, where nature reigns & machines roam! Join Aloy and friends to battle an ancient demon and uncover secrets of the past. 🦖🌄 LEGO Horizon Adventures is out now on #NintendoSwitch:

Nintendo of America

212,792 次观看 • 1 年前

AI models are trained on roughly 20 trillion tokens, or every public text on the internet. and it's still not enough. The next frontier isn't more text, It's the physical world, and 375ai is already capturing it.

AI models are trained on roughly 20 trillion tokens, or every public text on the internet. and it's still not enough. The next frontier isn't more text, It's the physical world, and 375ai is already capturing it.

375ai

10,725 次观看 • 2 个月前

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️

AI at Meta

380,649 次观看 • 2 年前

Lego announced its Lego Smart Play platform on Monday, which introduces new smart bricks, tags and special minifigs for your collection. The new bricks contain sensors that enable them to sense light and distance, and to provide an array of responses.

Lego announced its Lego Smart Play platform on Monday, which introduces new smart bricks, tags and special minifigs for your collection. The new bricks contain sensors that enable them to sense light and distance, and to provide an array of responses.

The Associated Press

108,216 次观看 • 5 个月前

To generate 3D models in Codex with text-to-cad: Start a new project, write your prompt for a 3D model, and include a link to clone the text-to-cad repo. Codex handles the rest. The repo is open source and all scripts / ui run locally. Link below.

To generate 3D models in Codex with text-to-cad: Start a new project, write your prompt for a 3D model, and include a link to clone the text-to-cad repo. Codex handles the rest. The repo is open source and all scripts / ui run locally. Link below.

Jake Fitzgerald

22,113 次观看 • 1 个月前