Subjective functions
Multi-scale neural texture synthesis
By using a Gaussian pyramid and extracting Gatys-style "style" descriptors
using the Gramian of intermediate VGG layers for each spatial scale in the
pyramid, we can create much higher-resolution images, with structure at
multiple image scales, not necessarily aligned with the receptive field of the
layers of Gatys.
More information at the research page.
This code will hopefully, one day, improve. I've rushed it online pre-publication for a workshop at InterAccess.
Quickstart
Install the pre-requisites
pip3 install -r requirements.txt
Run locally (this will likely be very slow as it runs on your GPU)
KERAS_BACKEND=tensorflow python3 synthesize.py -s bark.jpg
Look for output files inside the "outputs" directory that will be created by this command.
Run in FloydHub
To quickly run this on a GPU if you have an account with FloydHub you can run;
floyd init subjective-functions
then try:
floyd run --gpu "python3 synthesize.py -s bark.jpg --data-dir /vgg_weights --output-dir /output" --data wxswxs/vgg-weights/1:vgg_weights
Configuration
Most immediately useful flags:
-s - Path to source texture image
--output-width, --output-height, output dimensions in pixels
To see all the other flags run
python3 synthesize.py -h
which, as of this writing, outputs:
usage: synthesize.py [-h] [--output-width OUTPUT_WIDTH]
[--output-height OUTPUT_HEIGHT] [--octaves OCTAVES]
[--layers LAYERS [LAYERS ...]] [--max-iter MAX_ITER]
[--output-prefix OUTPUT_PREFIX] [--save-every SAVE_EVERY]
[--source-scale SOURCE_SCALE]
[--source-width SOURCE_WIDTH]
[--padding-mode {valid,same}] [--join-mode {average,max}]
[--count COUNT] [--mul MUL] [--if-weight IF_WEIGHT]
[--if-shift IF_SHIFT] [--if-order IF_ORDER]
[--seed {random,symmetric}] [--data-dir DATA_DIR]
[--output-dir OUTPUT_DIR] [--tol TOL] --source SOURCE
[SOURCE ...]
Synthesize image from texture
optional arguments:
-h, --help show this help message and exit
--output-width OUTPUT_WIDTH, -ow OUTPUT_WIDTH
Pixel width of generated image (default: 512)
--output-height OUTPUT_HEIGHT, -oh OUTPUT_HEIGHT
Pixel height of generated image. If not specified,
equal to output-width. (default: None)
--octaves OCTAVES, -o OCTAVES
Number of octaves (where 1 means "Consider only 1
scale") (default: 4)
--layers LAYERS [LAYERS ...], -l LAYERS [LAYERS ...]
Which layers to match gram matrices on (default: [2,
7])
--max-iter MAX_ITER, -m MAX_ITER
Maximum iterations for the L-BFGS-B optimizer
(default: 500)
--output-prefix OUTPUT_PREFIX, -op OUTPUT_PREFIX
Prefix to append to output directory (default: out)
--save-every SAVE_EVERY, -n SAVE_EVERY
Save an in-progress optimization image every
SAVE_EVERY iterations (default: 10)
--source-scale SOURCE_SCALE, -ss SOURCE_SCALE
How much to scale the source image by (default: None)
--source-width SOURCE_WIDTH, -sw SOURCE_WIDTH
Scale source to this width. Mutually exclusive with
source-scale (default: None)
--padding-mode {valid,same}, -p {valid,same}
What boundary condition to use for convolutions
(default: valid)
--join-mode {average,max}, -j {average,max}
How to combine gram matrices when multiple sources
given (default: average)
--count COUNT, -c COUNT
How many images to generate simultaneously (default:
1)
--mul MUL Multiply target grams by this amount (default: 1.0)
--if-weight IF_WEIGHT
Inter-frame loss weight (default: 1.0)
--if-shift IF_SHIFT How many pixel-shift should inter-frame loss
approximate? (default: 5.0)
--if-order IF_ORDER How many frames should we 'tie' together? (default:
2.0)
--seed {random,symmetric}
How to seed the optimization (default: random)
--data-dir DATA_DIR, -d DATA_DIR
Where to find the VGG weight files (default:
model_data)
--output-dir OUTPUT_DIR
Where to save the generated outputs (default: outputs)
--tol TOL Gradient scale at which to terminate optimization
(default: 1e-09)
--source SOURCE [SOURCE ...], -s SOURCE [SOURCE ...]
List of file to use as source textures (default: None)