Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Related tags

Deep Learningglide-finetune
Overview

glide-finetune

Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset.

Installation

git clone https://github.com/afiaka87/glide-finetune.git
cd glide-finetune/
python3 -m venv .venv # create a virtual environment to keep global install clean.
source .venv/bin/activate
(.venv) # optionally install pytorch manually for your own specific env first...
(.venv) python -m pip install -r requirements.txt

Usage

(.venv) python glide-finetune.py 
    --data_dir=./data \
    --batch_size=1 \
    --grad_acc=1 \
    --guidance_scale=4.0 \
    --learning_rate=2e-5 \
    --dropout=0.1 \
    --timestep_respacing=1000 \
    --side_x=64 \
    --side_y=64 \
    --resume_ckpt='' \
    --checkpoints_dir='./glide_checkpoints/' \
    --use_fp16 \
    --device='' \
    --freeze_transformer \
    --freeze_diffusion \
    --weight_decay=0.0 \
    --project_name='glide-finetune'

Known issues:

  • batching isn't handled in the dataloader
  • NaN/Inf errors
  • Resizing doesn't handle non-square aspect ratios properly
  • some of the code is messy, needs refactoring.
Comments
  • Fixed a couple of minor issues

    Fixed a couple of minor issues

    • Pinned webdataset version to work with python 3.7 which is the version being used in Colab, Kaggle. A new version of this module is releaed few days back which only works with 3.8/9
    • Fixed an issue with data_dir arg not getting picked up.
    opened by vanga 1
  • Fix NameError when using --data_dir

    Fix NameError when using --data_dir

    Hello and thank you for your great work.

    Right now using a local data folder with --data_dir results in

    Traceback (most recent call last):
  File "/content/glide-finetune/train_glide.py", line 292, in <module>
    data_dir=data_dir,
NameError: name 'data_dir' is not defined

    This PR fixes that.

    opened by tillfalko 0
  • mention mpi4py dependency

    mention mpi4py dependency

    mpi4py installation will fail unless the user has this package installed. Since MPI is not a ubiquitous dependency it should probably be mentioned. Edit: Since torch==1.10.1 is a requirement, and torch versions come with their own cuda versions (torch 1.10.1 uses cuda 10.2), I don't see a reason not to just include bitsandbytes-cuda102 in requirements.txt.

    $ py -m venv .venv
$ source .venv/bin/activate
$ pip install torch==1.10.1
Collecting torch==1.10.1
  Downloading torch-1.10.1-cp39-cp39-manylinux1_x86_64.whl (881.9 MB)
     |████████████████████████████████| 881.9 MB 15 kB/s
Collecting typing-extensions
  Downloading typing_extensions-4.0.1-py3-none-any.whl (22 kB)
Installing collected packages: typing-extensions, torch
Successfully installed torch-1.10.1 typing-extensions-4.0.1
$ py -c "import torch; print(torch.__version__)"
1.10.1+cu102
    opened by tillfalko 0
  • Fixed half precision optimizer bug

    Fixed half precision optimizer bug

    Problem

    In half precision, after the first iteration nan values start appearing regardless of input data or gradients since the adam optimizer breaks in float16. The discussion for that can be viewed here.

    Solution

    This can be fixed by setting the eps variable to 1e-4 instead of the default 1e-8. This is the only thing this pr does

    opened by isamu-isozaki 0
  • Training on half precision leads to nan values

    Training on half precision leads to nan values

    I was training my model and I noticed that after just the first iteration I was running into nan values. As it turns out my gradients and input values/images were all normal but the adam optimizer by pytorch does has some weird behavior on float16 precision where it produces nans probably because of a divide by 0 error. A discussion can be found below

    https://discuss.pytorch.org/t/adam-half-precision-nans/1765/4

    I hear changing the epison parameter for the adam weights parameter when on half precisions works but I haven't tested it yet. Will make one once I tested.

    And also let me say thanks for this repo. I wanted to fine tune the glide model and this made it so much easier.

    opened by isamu-isozaki 1
  • Where is the resume_ckpt

    Where is the resume_ckpt

    Hi, thanks for your job.

    I noticed to finetune the glide, we should have a base_model, namely "resume_ckpt". --resume_ckpt 'ckpt_to_resume_from.pt'
    Where can we get this model? Because I find Glide also didn't provide any checkpoint. Thanks for your help.

    opened by zhaobingbingbing 0
Releases(v0.0.1)

  • v0.0.1(Feb 20, 2022)

    Having some experience with finetuning GLIDE on laion/alamy, etc. I think this code works great now and hope as many people can use it as possible. Please file bugs - I know there may be a few.

    New additions:

    • dataloader for LAION400M
    • dataloader for alamy
    • train the upsample model instead of just the base model
    • (early) code for training the released noisy CLIP. still a WIP.
    Source code(tar.gz)
    Source code(zip)
Owner
Clay Mullis
Software engineer working with multi-modal deep learning.
Clay Mullis
GitHub Repository
Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

Xinyi Wang 21 May 18, 2022
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (EMNLP Founding 2021)

Introduction K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce. Installation PyTor

Xu Song 21 Nov 16, 2022
This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework

neon_course This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework. For more information, see

Nervana 92 Jan 03, 2023
Mixed Neural Likelihood Estimation for models of decision-making

Mixed neural likelihood estimation for models of decision-making Mixed neural likelihood estimation (MNLE) enables Bayesian parameter inference for mo

mackelab 9 Dec 22, 2022
Implementation of paper "Graph Condensation for Graph Neural Networks"

GCond A PyTorch implementation of paper "Graph Condensation for Graph Neural Networks" Code will be released soon. Stay tuned :) Abstract We propose a

Wei Jin 66 Dec 04, 2022
Re-implementation of 'Grokking: Generalization beyond overfitting on small algorithmic datasets'

Re-implementation of the paper 'Grokking: Generalization beyond overfitting on small algorithmic datasets' Paper Original paper can be found here Data

Tom Lieberum 38 Aug 09, 2022
Best practices for segmentation of the corporate network of any company

Best-practice-for-network-segmentation What is this? This project was created to publish the best practices for segmentation of the corporate network

2k Jan 07, 2023
Implementation of Google Brain's WaveGrad high-fidelity vocoder

WaveGrad Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder (paper). First implementation on GitHub with high-quality generatio

Ivan Vovk 363 Dec 27, 2022
A certifiable defense against adversarial examples by training neural networks to be provably robust

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the

SRI Lab, ETH Zurich 202 Dec 13, 2022
This repo holds codes of the ICCV21 paper: Visual Alignment Constraint for Continuous Sign Language Recognition.

VAC_CSLR This repo holds codes of the paper: Visual Alignment Constraint for Continuous Sign Language Recognition.(ICCV 2021) [paper] Prerequisites Th

Yuecong Min 64 Dec 19, 2022
CKD - Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding

Collaborative Knowledge Distillation for Heterogeneous Information Network Embed

zhousheng 9 Dec 05, 2022
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection Acknowledgement We implement our model, BtcDet, based on [OpenPcdet 0.3.0]. Insta

Qiangeng Xu 163 Dec 19, 2022
EdMIPS: Rethinking Differentiable Search for Mixed-Precision Neural Networks

EdMIPS is an efficient algorithm to search the optimal mixed-precision neural network directly without proxy task on ImageNet given computation budgets. It can be applied to many popular network arch

Zhaowei Cai 47 Dec 30, 2022
Cervix ROI Segmentation Using U-NET

Cervix ROI Segmentation Using U-NET Overview This code illustrate how to segment the ROI in cervical images using U-NET. The ROI here meant to include

Scotty Kwok 35 Sep 14, 2022
Object detection on multiple datasets with an automatically learned unified label space.

Simple multi-dataset detection An object detector trained on multiple large-scale datasets with a unified label space; Winning solution of E

Xingyi Zhou 407 Dec 30, 2022
Good Classification Measures and How to Find Them

Good Classification Measures and How to Find Them This repository contains supplementary materials for the paper "Good Classification Measures and How

Yandex Research 7 Nov 13, 2022
PECOS - Prediction for Enormous and Correlated Spaces

PECOS - Predictions for Enormous and Correlated Output Spaces PECOS is a versatile and modular machine learning (ML) framework for fast learning and i

Amazon 387 Jan 04, 2023
VACA: Designing Variational Graph Autoencoders for Interventional and Counterfactual Queries

VACA Code repository for the paper "VACA: Designing Variational Graph Autoencoders for Interventional and Counterfactual Queries (arXiv)". The impleme

Pablo Sánchez-Martín 16 Oct 10, 2022
Official repository for "Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring".

RNN-MBP Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring (AAAI-2022) by Chao Zhu, Hang Dong, Jinshan Pan

SIV-LAB 22 Aug 31, 2022
CondNet: Conditional Classifier for Scene Segmentation

CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis

ycszen 31 Jul 22, 2022