content_tf = test_transform(0, False)
style_tf = test_transform(0, False)
return {
'vgg': vgg,
'decoder': decoder,
'content_tf': content_tf,
'style_tf': style_tf,
}
@runway.command(name='generate',
inputs={
'content_image': image(description='Content Image'),
'style_image': image(description='Style Image'),
'preserve_color': boolean(description='Preserve content image color'),
'alpha': number(description='Controls the degree of stylization',
min=0, max=1, step=0.01, default=1)
},
outputs={ 'image': image(description='Output image') })
def generate(model, args):
content_image = args['content_image'].convert('RGB')
style_image = args['style_image'].convert('RGB')
preserve_color = args['preserve_color']
alpha = args['alpha']
print('[GENERATE] Ran with preserve_color "{}". alpha "{}"'.format(preserve_color, alpha))
vgg = model['vgg']
decoder = model['decoder']
content_tf = model['content_tf']
style_tf = model['style_tf']
# =========================================================================
# Import the Runway SDK. Please install it first with
# `pip install runway-python`.
import runway
from runway.data_types import number, text, image, boolean
from deblur2runway import DeblurHelper
import time
# Setup the model, initialize weights, set the configs of the model, etc.
# Every model will have a different set of configurations and requirements.
# Check https://docs.runwayapp.ai/#/python-sdk to see a complete list of
# supported configs. The setup function should return the model ready to be
# used.
setup_options = {
'use_single_gpu': boolean(default=False),
}
@runway.setup(options=setup_options)
def setup(opts):
model = DeblurHelper(opts)
return model
# Every model needs to have at least one command. Every command allows to send
# inputs and process outputs. To see a complete list of supported inputs and
# outputs data types: https://sdk.runwayml.com/en/latest/data_types.html
COUNT = 0
category(choices=[
"conv2d0 (max:64)", "maxpool0 (max:64)", "conv2d1 (max:64)",
"conv2d2 (max:192)", "maxpool1 (max:192)", "mixed3a (max:256)",
"mixed3b (max:480)", "maxpool4 (max:480)", "mixed4a (max:508)",
"mixed4b (max:512)", "mixed4c (max:512)", "mixed4d (max:528)",
"mixed4e (max:832)", "maxpool10 (max:832)", "mixed5a (max:832)",
"mixed5b (max:1024)"
],
default="mixed5b (max:1024)",
description='choose layer of network to visualize'),
'neuron':
number(default=0, min=0, max=1023, step=1, description='Neuron ID'),
'size':
number(default=128, min=128, max=1024, step=128, description='Image Size'),
'transforms':
boolean(default=False, description='Vary size of visualization'),
'transform_min':
number(default=0.3,
min=0.0,
max=1.0,
step=.1,
description='Minimum scaling amount'),
'transform_max':
number(default=0.5,
min=0.0,
max=1.0,
step=.1,
description='Maximum scaling amount')
}
config = {
"image_path": None,
"layers": [model.inception4b],
"custom_func": [None]
}
input_dict = {
"image": image(),
"octave_scale": number(step=0.05, min=1.0, max=1.7, default=1.2),
"num_octaves": number(step=1, min=1, max=25, default=5),
"iterations": number(step=1, min=1, max=100, default=14),
"lr": number(step=1e-4, min=1e-9, max=1e-1, default=0.05),
"max_rotation": number(step=0.1, min=0.0, max=1.5, default=0.9),
"layer_index": number(step=1, min=0, max=len(layers), default=0),
"channel_index": number(step=1, min=-1, max=511, default=0),
"invert_mask": boolean(default=False)
}
@runway.setup
def setup():
dreamy_boi = dreamer(model)
return dreamy_boi
@runway.command(name="generate", inputs=input_dict, outputs={"image": image()})
def generate(dreamy_boi, input):
image_np = preprocess_numpy_img(
np.array(input["image"]).astype(np.float32) / 255.0)
"""
#setup_options = {
# 'seed': number(min=0, max=10000, step=1, default=101, description='Seed for the random number generator.'),
#}
#@runway.setup(options=setup_options)
def setup():
model = NeuralStyle()
return model
input_list = {
'content_image':
image,
'style_image_1':
image,
'original_colors':
boolean(default=False),
'style_only':
boolean(default=False),
'max_iterations':
number(min=50, max=1500, step=50, default=500, description='Iterations'),
'content_layer':
category(choices=[
'conv1_1', 'conv1_2', 'conv2_1', 'conv2_2', 'conv3_1', 'conv3_2',
'conv4_1', 'conv4_2', 'conv4_3', 'conv4_4', 'conv5_1', 'conv5_2'
],
default='conv4_2',
description='what VGG19 layer to use for content'),
'style_scale':
number(min=0.1,
max=2.0,
step=.05,
print('[SETUP] loading model...')
model = UniSentenceEncXling()
print('[SETUP] done.')
return model
desc = """\
Infers embeddings for input. Returns an array with the lines of
text and an array with the corresponding embeddings for each line.
"""
@runway.command(name='embed',
inputs={
'text': text(),
'tokenize_sentences': boolean(default=True)
},
outputs={
'sentences': array(item_type=text),
'embeddings': array(item_type=vector(length=512))
},
description=desc)
def embed(model, args):
if args['tokenize_sentences']:
sentences = sent_tokenize(args['text'])
else:
sentences = args['text'].split("\n")
print('[EMBED] Embedding {} sentences'.format(len(sentences)))
results = model.embed(sentences)
return {'sentences': sentences, 'embeddings': results}
number(default=24, min=1, max=120, step=1),
'length_sec':
number(default=2,
min=1,
max=10,
step=0.2,
description='Output video length in seconds.'),
'effect_type':
category(choices=effect_types,
default=effect_types[1],
description='Video effect.'),
'effect_size':
number(default=0.5, min=0, max=1, step=0.1),
'reuse':
boolean(
default=False,
description='Reuse depth map and continue from previous iteration.'
),
},
outputs={'image': image()},
description='Cartoonize.')
def paint(model, args):
x_shift = 0
y_shift = 0
z_shift = 0
traj_type = 'double-straight-line'
effect_type = args['effect_type']
effect_size = args['effect_size'] * 2
if effect_type == 'dolly-zoom-in':
x_shift = 0.00
y_shift = 0.00
"image_path": None,
"layers": [model.inception4b],
"custom_func": [None]
}
input_dict = {
"image": image(),
"octave_scale": number(step=0.05, min=1.0, max=1.7, default=1.2),
"num_octaves": number(step=1, min=1, max=25, default=5),
"iterations": number(step=1, min=1, max=100, default=14),
"lr": number(step=1e-4, min=1e-9, max=1e-1, default=0.05),
"max_rotation": number(step=0.1, min=0.0, max=1.5, default=0.9)
}
for key in list(layers.keys()):
input_dict[key] = boolean(default=False)
@runway.setup
def setup():
dreamy_boi = dreamer(model)
return dreamy_boi
@runway.command(name="generate", inputs=input_dict, outputs={"image": image()})
def generate(dreamy_boi, input):
image_np = preprocess_numpy_img(
np.array(input["image"]).astype(np.float32) / 255.0)
"""
generate mask