This repository contains a modified generate.py with some added features. Additional options can be viewed with generate.py -h

By default, a video and a <=8MB gif will be generated for the input prompt, in addition to the image itself.

The main additions include:

• Negative prompts (via -np): accuracy for negative prompts is optimised to be as low as possible
• A learning rate scheduler
• Allowing for a configurable amount of overtime to let the adaptive scheduler complete image generation
• Generating a gif, with automatic skip rate to stay within a size limit
• Video generation via ffmpeg (including adding the final image as a thumbnail)
• CUDA device selection
• Using the (now released) ViT-B/16 model by default (note: this will use more VRAM than ViT-B/32)

(A more complete documentation of changes may be added later)

The original readme is available below, with only the 'Advanced options' section being updated.

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:

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

Set up

Example set up using Anaconda to create a virtual Python environment with the prerequisites:

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

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

git clone https://github.com/openai/CLIP
git clone https://github.com/CompVis/taming-transformers.git

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

mkdir checkpoints

curl -L -o checkpoints/vqgan_imagenet_f16_16384.yaml -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.yaml' #ImageNet 16384
curl -L -o checkpoints/vqgan_imagenet_f16_16384.ckpt -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.ckpt' #ImageNet 16384

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 pre-trained models.

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.

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. For example:

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

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"

"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

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

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

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