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

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
An end-to-end framework for mixed-integer optimization with data-driven learned constraints.

OptiCL OptiCL is an end-to-end framework for mixed-integer optimization (MIO) with data-driven learned constraints. We address a problem setting in wh

Holly Wiberg 57 Dec 26, 2022
Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting

StochFuzz: A New Solution for Binary-only Fuzzing StochFuzz is a (probabilistically) sound and cost-effective fuzzing technique for stripped binaries.

Zhuo Zhang 164 Dec 05, 2022
AITUS - An atomatic notr maker for CYTUS

AITUS an automatic note maker for CYTUS. 利用AI根据指定乐曲生成CYTUS游戏谱面。 效果展示:https://www

GradiusTwinbee 6 Feb 24, 2022
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urba

Yu Tian 115 Dec 29, 2022
TDN: Temporal Difference Networks for Efficient Action Recognition

TDN: Temporal Difference Networks for Efficient Action Recognition Overview We release the PyTorch code of the TDN(Temporal Difference Networks).

Multimedia Computing Group, Nanjing University 326 Dec 13, 2022
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang SenseTime, Tsinghua Unive

33 Oct 14, 2022
Server files for UltimateLabeling

UltimateLabeling server files Server files for UltimateLabeling. git clone https://github.com/alexandre01/UltimateLabeling_server.git cd UltimateLabel

Alexandre Carlier 4 Oct 10, 2022
PyTorch implementation of the ACL, 2021 paper Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks This repo contains the PyTorch implementation of the ACL, 2021 pa

Rabeeh Karimi Mahabadi 98 Dec 28, 2022
TransPrompt - Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

TransPrompt This code is implement for our EMNLP 2021's paper 《TransPrompt:Towards an Automatic Transferable Prompting Framework for Few-shot Text Cla

WangJianing 23 Dec 21, 2022
GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

If you are using this code in your own project, please cite our paper: @inproceedings{awiszus2020toadgan, title={TOAD-GAN: Coherent Style Level Gene

Maren A. 13 Dec 14, 2022
Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges for the practitioner

Sparse network learning with snlpy Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges

Andrew Stolman 1 Apr 30, 2021
[SIGGRAPH 2021 Asia] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

DeepVecFont This is the official Pytorch implementation of the paper: Yizhi Wang and Zhouhui Lian. DeepVecFont: Synthesizing High-quality Vector Fonts

Yizhi Wang 146 Dec 18, 2022
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

3k Jan 08, 2023
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

An Empirical Investigation of 3D Anomaly Detection and Segmentation Project | Paper Official PyTorch Implementation for the "An Empirical Investigatio

Eliahu Horwitz 55 Dec 14, 2022
BRNet - code for Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function

BRNet code for "Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss func

Yong Pi 2 Mar 09, 2022
Learning Versatile Neural Architectures by Propagating Network Codes

Learning Versatile Neural Architectures by Propagating Network Codes Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang,

Mingyu Ding 36 Dec 06, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

1.3k Dec 25, 2022
PuppetGAN - Cross-Domain Feature Disentanglement and Manipulation just got way better! 🚀

Better Cross-Domain Feature Disentanglement and Manipulation with Improved PuppetGAN Quite cool... Right? Introduction This repo contains a TensorFlow

Giorgos Karantonis 5 Aug 25, 2022