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
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