46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
from min_dalle import MinDalle
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import tempfile
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import torch, torch.backends.cudnn
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from typing import Iterator
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from cog import BasePredictor, Path, Input
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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(
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is_mega=True,
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is_reusable=True,
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dtype=torch.float32
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)
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def predict(
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self,
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text: str = Input(default='Dali painting of WALL·E'),
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intermediate_outputs: bool = Input(default=True),
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grid_size: int = Input(ge=1, le=9, default=5),
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log2_temperature: float = Input(ge=-3, le=3, default=0.0),
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log2_top_k: int = Input(ge=0, le=14, default=7),
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log2_supercondition_factor: float = Input(ge=2, le=6, default=4)
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) -> Iterator[Path]:
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log2_mid_count = 3 if intermediate_outputs else 0
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image_stream = self.model.generate_image_stream(
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text = text,
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seed = -1,
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grid_size = grid_size,
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log2_mid_count = log2_mid_count,
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temperature = 2 ** log2_temperature,
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supercondition_factor = 2 ** log2_supercondition_factor,
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top_k = 2 ** log2_top_k,
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is_verbose = True
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)
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i = 0
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path = Path(tempfile.mkdtemp())
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for image in image_stream:
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i += 1
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ext = 'png' if i == 2 ** log2_mid_count else 'jpg'
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image_path = path / 'min-dalle-iter-{}.{}'.format(i, ext)
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image.save(str(image_path))
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yield image_path |