min-dalle-test/replicate_predictor.py

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from min_dalle import MinDalle
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import tempfile
import torch, torch.backends.cudnn
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from typing import Iterator
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from cog import BasePredictor, Path, Input
torch.backends.cudnn.deterministic = False
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class ReplicatePredictor(BasePredictor):
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def setup(self):
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self.model = MinDalle(is_mega=True, is_reusable=True)
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def predict(
self,
text: str = Input(
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description='For long prompts, only the first 64 tokens will be used to generate the image.',
default='Dali painting of WALL·E'
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),
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intermediate_outputs: bool = Input(
description='Whether to show intermediate outputs while running. This adds less than a second to the run time.',
default=True
),
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grid_size: int = Input(
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description='Size of the image grid. 5x5 takes around 16 seconds, 8x8 takes around 36 seconds',
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ge=1,
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le=8,
default=4
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),
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log2_supercondition_factor: int = Input(
description='Higher values result in better agreement with the text but a narrower variety of generated images',
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ge=1,
le=6,
default=4
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),
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) -> Iterator[Path]:
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try:
seed = -1
log2_mid_count = 3 if intermediate_outputs else 0
image_stream = self.model.generate_image_stream(
text,
seed,
grid_size=grid_size,
log2_mid_count=log2_mid_count,
log2_supercondition_factor=log2_supercondition_factor,
is_verbose=True
)
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iter = 0
path = Path(tempfile.mkdtemp())
for image in image_stream:
iter += 1
image_path = path / 'min-dalle-iter-{}.jpg'.format(iter)
image.save(str(image_path))
yield image_path
except:
print("An error occured, deleting model")
del self.model
torch.cuda.empty_cache()
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self.setup()
raise Exception("There was an error, please try again")