forked from lucidrains/DALLE2-pytorch
/
cli.py
51 lines (39 loc) · 1.73 KB
/
cli.py
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import click
import torch
import torchvision.transforms as T
from pathlib import Path
from dalle2_pytorch import DALLE2, Decoder, DiffusionPrior
def safeget(dictionary, keys, default = None):
return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split('.'), dictionary)
def simple_slugify(text, max_length = 255):
return text.replace("-", "_").replace(",", "").replace(" ", "_").replace("|", "--").strip('-_')[:max_length]
def get_pkg_version():
from pkg_resources import get_distribution
return get_distribution('dalle2_pytorch').version
def main():
pass
@click.command()
@click.option('--model', default = './dalle2.pt', help = 'path to trained DALL-E2 model')
@click.option('--cond_scale', default = 2, help = 'conditioning scale (classifier free guidance) in decoder')
@click.argument('text')
def dream(
model,
cond_scale,
text
):
model_path = Path(model)
full_model_path = str(model_path.resolve())
assert model_path.exists(), f'model not found at {full_model_path}'
loaded = torch.load(str(model_path))
version = safeget(loaded, 'version')
print(f'loading DALL-E2 from {full_model_path}, saved at version {version} - current package version is {get_pkg_version()}')
prior_init_params = safeget(loaded, 'init_params.prior')
decoder_init_params = safeget(loaded, 'init_params.decoder')
model_params = safeget(loaded, 'model_params')
prior = DiffusionPrior(**prior_init_params)
decoder = Decoder(**decoder_init_params)
dalle2 = DALLE2(prior, decoder)
dalle2.load_state_dict(model_params)
image = dalle2(text, cond_scale = cond_scale)
pil_image = T.ToPILImage()(image)
return pil_image.save(f'./{simple_slugify(text)}.png')