Skip to content
/ VQGAN-CLIP-Timeline Public
forked from nerdyrodent/VQGAN-CLIP

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

License

MIT license
2 stars 427 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

HenryTurbedsky/VQGAN-CLIP-Timeline

BranchesTags
 
 

Repository files navigation

VQGAN-CLIP Overview

A repo for running VQGAN+CLIP locally. This started out as a Katherine Crowson VQGAN+CLIP derived Google colab notebook.

Original notebook: Open In Colab

Some example images:

Environment:

  • Tested on Ubuntu 20.04
  • GPU: Nvidia RTX 3090
  • Typical VRAM requirements:
    • 24 GB for a 900x900 image
    • 10 GB for a 512x512 image
    • 8 GB for a 380x380 image

You may also be interested in CLIP Guided Diffusion

Set up

This example uses Anaconda to manage virtual Python environments.

Create a new virtual Python environment for VQGAN-CLIP:

conda create --name vqgan python=3.9
conda activate vqgan

Install Pytorch in the new enviroment:

Note: This installs the CUDA version of Pytorch, if you want to use an AMD graphics card, read the AMD section below.

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

Install other required Python packages:

pip install ftfy regex tqdm omegaconf pytorch-lightning IPython kornia imageio imageio-ffmpeg einops torch_optimizer

Or use the requirements.txt file, which includes version numbers.

Clone required repositories:

git clone 'https://github.com/nerdyrodent/VQGAN-CLIP'
cd VQGAN-CLIP
git clone 'https://github.com/openai/CLIP'
git clone 'https://github.com/CompVis/taming-transformers'

Note: In my development environment both CLIP and taming-transformers are present in the local directory, and so aren't present in the requirements.txt or vqgan.yml files.

As an alternative, you can also pip install taming-transformers and CLIP.

You will also need at least 1 VQGAN pretrained model. E.g.

mkdir checkpoints

curl -L -o checkpoints/vqgan_imagenet_f16_16384.yaml -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384
curl -L -o checkpoints/vqgan_imagenet_f16_16384.ckpt -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384

Note that users of curl on Microsoft Windows should use double quotes.

The download_models.sh script is an optional way to download a number of models. By default, it will download just 1 model.

See https://github.com/CompVis/taming-transformers#overview-of-pretrained-models for more information about VQGAN pre-trained models, including download links.

By default, the model .yaml and .ckpt files are expected in the checkpoints directory. See https://github.com/CompVis/taming-transformers for more information on datasets and models.

Video guides are also available:

Using an AMD graphics card

Note: This hasn't been tested yet.

ROCm can be used for AMD graphics cards instead of CUDA. You can check if your card is supported here: https://github.com/RadeonOpenCompute/ROCm#supported-gpus

Install ROCm accordng to the instructions and don't forget to add the user to the video group: https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html

The usage and set up instructions above are the same, except for the line where you install Pytorch. Instead of pip install torch==1.9.0+cu111 ..., use the one or two lines which are displayed here (select Pip -> Python-> ROCm): https://pytorch.org/get-started/locally/

Using the CPU

If no graphics card can be found, the CPU is automatically used and a warning displayed.

Regardless of an available graphics card, the CPU can also be used by adding this command line argument: -cd cpu

This works with the CUDA version of Pytorch, even without CUDA drivers installed, but doesn't seem to work with ROCm as of now.

Uninstalling

Remove the Python enviroment:

conda remove --name vqgan --all

and delete the VQGAN-CLIP directory.

Run

To generate images from text, specify your text prompt as shown in the example below:

python generate.py -p "A painting of an apple in a fruit bowl"

Multiple prompts

Text and image prompts can be split using the pipe symbol in order to allow multiple prompts. You can also use a colon followed by a number to set a weight for that prompt. For example:

python generate.py -p "A painting of an apple in a fruit bowl | psychedelic | surreal:0.5 | weird:0.25"

Image prompts can be split in the same way. For example:

python generate.py -p "A picture of a bedroom with a portrait of Van Gogh" -ip "samples/VanGogh.jpg | samples/Bedroom.png"

Story mode

Sets of text prompts can be created using the caret symbol, in order to generate a sort of story mode. For example:

python generate.py -p "A painting of a sunflower|photo:-1 ^ a painting of a rose ^ a painting of a tulip ^ a painting of a daisy flower ^ a photograph of daffodil" -cpe 1500 -zvid -i 6000 -zse 10 -vl 20 -zsc 1.005 -opt Adagrad -lr 0.15 -se 6000

"Style Transfer"

An input image with style text and a low number of iterations can be used create a sort of "style transfer" effect. For example:

python generate.py -p "A painting in the style of Picasso" -ii samples/VanGogh.jpg -i 80 -se 10 -opt AdamW -lr 0.25
Output Style
Picasso
Sketch
Psychedelic

A video style transfer effect can be achived by specifying a directory of video frames in video_style_dir. Output will be saved in the steps directory, using the original video frame filenames. You can also use this as a sort of "batch mode" if you have a directory of images you want to apply a style to. This can also be combined with Story Mode if you don't wish to apply the same style to every images, but instead roll through a list of styles.

Timeline

The Timeline tool can be used to create videos where the prompt and othere settings change through out the duration of the video.

python generate.py -tf "samples/timeline/sun.txt"

# [sun.txt] Example of a Timeline file. (This line is not needed for the actual file.)
prompt="Blue", frames=1, iterations=25,
prompt="Painting of a One Sun in the Sky", frames=30, iterations=20,
prompt="Painting of the Sun setting over a mountain", frames=40, iterations=10, zoom=1.03,
prompt="Painting of Stars in the sky at night", frames=15, iterations=4, zoom=0.95,
prompt="Painting of Stars in the sky at night", frames=30, iterations=20, zoom=0.97,
prompt="Painting of driving down a road at night", frames=40, iterations=20, zoom=1.05,

Timeline Keywords

Timeline Keywords:

  prompt=(String)
    Change the prompt.
    Default value: "Blue Duck"

  frames=(int)
    Number of frames for each line.
    Default value: 10

  iterations=(int)
    The number of iterations of training for each frame. (Lowering the number of iterations when changing prompts can add a more visible transition.)
    Default value: 10

  zoom=(float)
    How much to zoom per frame. =1 dont zoom, >1 zoom in, <1 zoom out.
    Default value: 1.0

  xshift=(int) / yshift=(int)
    How much to shift the image per frame in the X (left/right) or Y (up/down). Amount in pixels.
    Default value: 0

  # at the start of a line comments it out.

Timeline Tutorial/Rules

When a setting is changed that setting continues for each line (Including blank line) until it is either changed back to the prevouis or changed to a new value.

Example:

prompt="blue", zoom=1.1, frames=5
zoom=0.9, 
prompt="red", zoom=1.1, 
zoom=0.9, 

prompt="green", zoom=1, frames=10

  1. In the first line, the prompt, zoom and frames are given a value. Since Iterations and xshift/yshift are not set to any value, they are their default values. Since frames is set to 5 each line will creat 5 frames. After 5 frames the next line will run.

  2. The second line will also create 5 frames. The only change will be the zoom value. All the other settings will stay the same. The prompt is still blue and the number of frames is still 5.

  3. On the third line the prompt is changed to red and the zoom is adjusted again. Since the number of frames has not been changed it continues with creating 5 frames for this line.

  4. Similarly to line 2, the only change is the zoom. But unlike line 2, the prompt is red since red was the last value that prompt was set to.

  5. The fith line is blank. Since it changes no settings it runs exactly like line 4 did.

  6. The sixth line changes prompt, zoom and frames. The zoom effect stops since it was set to 1. The prompt is changed to green. And finally since the frames have been set to 10, unlike the previous lines, line 6 creates 10 frames.

  • Once all lines have been run the program then takes the images that have been created and saved to the steps file and turns them into a video.

Most all the arguments for generate.py work while using the Timeline tool. The only arguments that don't work are those that have an equivalent setting in the Timeline tool.

Feedback example

By feeding back the generated images and making slight changes, some interesting effects can be created.

The example zoom.sh shows this by applying a zoom and rotate to generated images, before feeding them back in again. To use zoom.sh, specifying a text prompt, output filename and number of frames. E.g.

./zoom.sh "A painting of a red telephone box spinning through a time vortex" Telephone.png 150

If you don't have ImageMagick installed, you can install it with sudo apt install imagemagick

There is also a simple zoom video creation option available. For example:

python generate.py -p "The inside of a sphere" -zvid -i 4500 -zse 20 -vl 10 -zsc 0.97 -opt Adagrad -lr 0.15 -se 4500

Random text example

Use random.sh to make a batch of images from random text. Edit the text and number of generated images to your taste!

./random.sh

Advanced options

To view the available options, use "-h".

python generate.py -h
usage: generate.py [-h] [-p PROMPTS] [-ip IMAGE_PROMPTS] [-i MAX_ITERATIONS] [-se DISPLAY_FREQ]
[-s SIZE SIZE] [-ii INIT_IMAGE] [-in INIT_NOISE] [-iw INIT_WEIGHT] [-m CLIP_MODEL]
[-conf VQGAN_CONFIG] [-ckpt VQGAN_CHECKPOINT] [-nps [NOISE_PROMPT_SEEDS ...]]
[-npw [NOISE_PROMPT_WEIGHTS ...]] [-lr STEP_SIZE] [-cuts CUTN] [-cutp CUT_POW] [-sd SEED]
[-opt {Adam,AdamW,Adagrad,Adamax,DiffGrad,AdamP,RAdam,RMSprop}] [-o OUTPUT] [-vid] [-zvid]
[-zs ZOOM_START] [-zse ZOOM_FREQUENCY] [-zsc ZOOM_SCALE] [-cpe PROMPT_FREQUENCY]
[-vl VIDEO_LENGTH] [-ofps OUTPUT_VIDEO_FPS] [-ifps INPUT_VIDEO_FPS] [-d]
[-aug {Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} [{Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} ...]]
[-cd CUDA_DEVICE] [-tf TIMELINE_FILE]
optional arguments:
  -h, --help            show this help message and exit
  -p PROMPTS, --prompts PROMPTS
                        Text prompts
  -ip IMAGE_PROMPTS, --image_prompts IMAGE_PROMPTS
                        Image prompts / target image
  -i MAX_ITERATIONS, --iterations MAX_ITERATIONS
                        Number of iterations
  -se DISPLAY_FREQ, --save_every DISPLAY_FREQ
                        Save image iterations
  -s SIZE SIZE, --size SIZE SIZE
                        Image size (width height) (default: [512, 512])
  -ii INIT_IMAGE, --init_image INIT_IMAGE
                        Initial image
  -in INIT_NOISE, --init_noise INIT_NOISE
                        Initial noise image (pixels or gradient)
  -iw INIT_WEIGHT, --init_weight INIT_WEIGHT
                        Initial weight
  -m CLIP_MODEL, --clip_model CLIP_MODEL
                        CLIP model (e.g. ViT-B/32, ViT-B/16)
  -conf VQGAN_CONFIG, --vqgan_config VQGAN_CONFIG
                        VQGAN config
  -ckpt VQGAN_CHECKPOINT, --vqgan_checkpoint VQGAN_CHECKPOINT
                        VQGAN checkpoint
  -nps [NOISE_PROMPT_SEEDS ...], --noise_prompt_seeds [NOISE_PROMPT_SEEDS ...]
                        Noise prompt seeds
  -npw [NOISE_PROMPT_WEIGHTS ...], --noise_prompt_weights [NOISE_PROMPT_WEIGHTS ...]
                        Noise prompt weights
  -lr STEP_SIZE, --learning_rate STEP_SIZE
                        Learning rate
  -cuts CUTN, --num_cuts CUTN
                        Number of cuts
  -cutp CUT_POW, --cut_power CUT_POW
                        Cut power
  -sd SEED, --seed SEED
                        Seed
  -opt, --optimiser {Adam,AdamW,Adagrad,Adamax,DiffGrad,AdamP,RAdam,RMSprop}
                        Optimiser
  -o OUTPUT, --output OUTPUT
                        Output file
  -vid, --video         Create video frames?
  -zvid, --zoom_video   Create zoom video?
  -zs ZOOM_START, --zoom_start ZOOM_START
                        Zoom start iteration
  -zse ZOOM_FREQUENCY, --zoom_save_every ZOOM_FREQUENCY
                        Save zoom image iterations
  -zsc ZOOM_SCALE, --zoom_scale ZOOM_SCALE
                        Zoom scale
  -cpe PROMPT_FREQUENCY, --change_prompt_every PROMPT_FREQUENCY
                        Prompt change frequency
  -vl VIDEO_LENGTH, --video_length VIDEO_LENGTH
                        Video length in seconds
  -ofps OUTPUT_VIDEO_FPS, --output_video_fps OUTPUT_VIDEO_FPS
                        Create an interpolated video (Nvidia GPU only) with this fps (min 10. best set to 30 or 60)
  -ifps INPUT_VIDEO_FPS, --input_video_fps INPUT_VIDEO_FPS
                        When creating an interpolated video, use this as the input fps to interpolate from (>0 & <ofps)
  -d, --deterministic   Enable cudnn.deterministic?
  -aug, --augments {Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} [{Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} ...]
                        Enabled augments
  -cd CUDA_DEVICE, --cuda_device CUDA_DEVICE
                        Cuda device to use
  -tf TIMELINE_FILE, --timeline_file TIMELINE_FILE
                        File with timeline data

Troubleshooting

CUSOLVER_STATUS_INTERNAL_ERROR

For example:

RuntimeError: cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR, when calling cusolverDnCreate(handle)

Make sure you have specified the correct size for the image.

RuntimeError: CUDA out of memory

For example:

RuntimeError: CUDA out of memory. Tried to allocate 150.00 MiB (GPU 0; 23.70 GiB total capacity; 21.31 GiB already allocated; 78.56 MiB free; 21.70 GiB reserved in total by PyTorch)

Your request doesn't fit into your GPU's VRAM. Reduce the image size and/or number of cuts.

Citations

@misc{unpublished2021clip,
    title  = {CLIP: Connecting Text and Images},
    author = {Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal},
    year   = {2021}
}
@misc{esser2020taming,
      title={Taming Transformers for High-Resolution Image Synthesis}, 
      author={Patrick Esser and Robin Rombach and Björn Ommer},
      year={2020},
      eprint={2012.09841},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Katherine Crowson - https://github.com/crowsonkb

Public Domain images from Open Access Images at the Art Institute of Chicago - https://www.artic.edu/open-access/open-access-images

About

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Resources

Readme

License

MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

2 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Python 87.1%
  • Shell 12.9%