meshgpt pytorch تحميل - meshgpt pytorch تحميل كود المصدر

تنفيذ إنشاء شبكة MeshGPT وSOTA باستخدام Attention في Pytorch

سيتم أيضًا إضافة تكييف النص، لتحويل النص إلى أصل ثلاثي الأبعاد في نهاية المطاف

يرجى الانضمام إذا كنت مهتمًا بالتعاون مع الآخرين لتكرار هذا العمل

تحديث: قام ماركوس بتدريب نموذج عمل وتحميله إلى؟ معانقة الوجه!

$ pip install meshgpt-pytorch

 import torch

from meshgpt_pytorch import (
    MeshAutoencoder ,
    MeshTransformer
)

# autoencoder

autoencoder = MeshAutoencoder (
    num_discrete_coors = 128
)

# mock inputs

vertices = torch . randn (( 2 , 121 , 3 ))            # (batch, num vertices, coor (3))
faces = torch . randint ( 0 , 121 , ( 2 , 64 , 3 ))      # (batch, num faces, vertices (3))

# make sure faces are padded with `-1` for variable lengthed meshes

# forward in the faces

loss = autoencoder (
    vertices = vertices ,
    faces = faces
)

loss . backward ()

# after much training...
# you can pass in the raw face data above to train a transformer to model this sequence of face vertices

transformer = MeshTransformer (
    autoencoder ,
    dim = 512 ,
    max_seq_len = 768
)

loss = transformer (
    vertices = vertices ,
    faces = faces
)

loss . backward ()

# after much training of transformer, you can now sample novel 3d assets

faces_coordinates , face_mask = transformer . generate ()

# (batch, num faces, vertices (3), coordinates (3)), (batch, num faces)
# now post process for the generated 3d asset

بالنسبة لتركيب الأشكال ثلاثية الأبعاد المشروطة بالنص، ما عليك سوى تعيين condition_on_text = True على MeshTransformer ، ثم قم بتمرير قائمة الأوصاف الخاصة بك كوسيطة الكلمات الرئيسية texts

 transformer = MeshTransformer (
    autoencoder ,
    dim = 512 ,
    max_seq_len = 768 ,
    condition_on_text = True
)


loss = transformer (
    vertices = vertices ,
    faces = faces ,
    texts = [ 'a high chair' , 'a small teapot' ],
)

loss . backward ()

# after much training of transformer, you can now sample novel 3d assets conditioned on text

faces_coordinates , face_mask = transformer . generate (
    texts = [ 'a long table' ],
    cond_scale = 8. ,  # a cond_scale > 1. will enable classifier free guidance - can be placed anywhere from 3. - 10.
    remove_parallel_component = True # from https://arxiv.org/abs/2410.02416
)

إذا كنت ترغب في ترميز الشبكات، لاستخدامها في المحول متعدد الوسائط، فما عليك سوى استدعاء .tokenize على جهاز التشفير التلقائي الخاص بك (أو نفس الطريقة في مثيل مدرب جهاز التشفير التلقائي للنموذج السلس بشكل كبير)

 mesh_token_ids = autoencoder . tokenize (
    vertices = vertices ,
    faces = faces
)

# (batch, num face vertices, residual quantized layer)

$ cp .env.sample .env

 @inproceedings { Siddiqui2023MeshGPTGT ,
    title   = { MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers } ,
    author  = { Yawar Siddiqui and Antonio Alliegro and Alexey Artemov and Tatiana Tommasi and Daniele Sirigatti and Vladislav Rosov and Angela Dai and Matthias Nie{ss}ner } ,
    year    = { 2023 } ,
    url     = { https://api.semanticscholar.org/CorpusID:265457242 }
}

 @inproceedings { dao2022flashattention ,
    title   = { Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness } ,
    author  = { Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{'e}, Christopher } ,
    booktitle = { Advances in Neural Information Processing Systems } ,
    year    = { 2022 }
}

 @inproceedings { Leviathan2022FastIF ,
    title   = { Fast Inference from Transformers via Speculative Decoding } ,
    author  = { Yaniv Leviathan and Matan Kalman and Y. Matias } ,
    booktitle = { International Conference on Machine Learning } ,
    year    = { 2022 } ,
    url     = { https://api.semanticscholar.org/CorpusID:254096365 }
}

 @misc { yu2023language ,
    title   = { Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation } , 
    author  = { Lijun Yu and José Lezama and Nitesh B. Gundavarapu and Luca Versari and Kihyuk Sohn and David Minnen and Yong Cheng and Agrim Gupta and Xiuye Gu and Alexander G. Hauptmann and Boqing Gong and Ming-Hsuan Yang and Irfan Essa and David A. Ross and Lu Jiang } ,
    year    = { 2023 } ,
    eprint  = { 2310.05737 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}

 @article { Lee2022AutoregressiveIG ,
    title   = { Autoregressive Image Generation using Residual Quantization } ,
    author  = { Doyup Lee and Chiheon Kim and Saehoon Kim and Minsu Cho and Wook-Shin Han } ,
    journal = { 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) } ,
    year    = { 2022 } ,
    pages   = { 11513-11522 } ,
    url     = { https://api.semanticscholar.org/CorpusID:247244535 }
}

 @inproceedings { Katsch2023GateLoopFD ,
    title   = { GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling } ,
    author  = { Tobias Katsch } ,
    year    = { 2023 } ,
    url     = { https://api.semanticscholar.org/CorpusID:265018962 }
}