def setup(opts):
checkpoint_path = opts['checkpoint']
model = load_model_from_checkpoint(checkpoint_path)
msg = '[SETUP] Ran with options: seed = {}, truncation = {}'
print(msg.format(opts['seed'], opts['truncation']))
model = ExampleModel(opts)
return model
def setup(opts):
msg = '[SETUP] Ran with options: seed = {}, truncation = {}'
print(msg.format(opts['seed'], opts['truncation']))
model = ExampleModel(opts)
return model
from os import environ
from datetime import datetime
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from example_model import Base, ExampleModel
# Create engine
# db_uri = environ.get('SQLALCHEMY_DATABASE_URI')
db_uri = 'sqlite:///data.db'
engine = create_engine(db_uri, echo=True)
# Create All Tables
Base.metadata.create_all(engine)
# create session
Session = sessionmaker(bind=engine)
session = Session()
# Adding a Record
newModel = ExampleModel(name='todd',
description='im testing this',
vip=True,
join_date=datetime.now())
session.add(newModel)
session.commit()
print(newModel)
# 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://sdk.runwayml.com/en/latest/runway_module.html to see a complete
# list of supported configs. The setup function should return the model ready to
# be used.
setup_options = {
'truncation': number(min=5, max=100, step=1, default=10),
'seed': number(min=0, max=1000000)
}
@runway.setup(options=setup_options)
def setup(opts):
msg = '[SETUP] Run with options: seed = {}, truncation = {}'
print(msg.format(opts['seed'], opts['truncation']))
model = ExampleModel(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
@runway.command(name='generate',
inputs={ 'caption': text() },
outputs={ 'image': image(width=512, height=512) })
def generate(model, args):
print('[GENERATE] Ran with caption value "{}"'.format(args['caption']))
# Generate a PIL or Numpy image based on the input caption, and return it
output_image = model.run_on_input(args['caption'])
return {
'image': output_image
}