62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
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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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device='cuda'
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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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save_as_png: bool = Input(default=False),
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progressive_outputs: bool = Input(default=True),
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seamless: bool = Input(default=False),
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grid_size: int = Input(ge=1, le=9, default=5),
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temperature: float = Input(
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ge=0.01,
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le=16,
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default=4
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),
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top_k: int = Input(
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choices=[2 ** i for i in range(15)],
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default=64,
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description='Advanced Setting, see Readme below if interested.'
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),
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supercondition_factor: int = Input(
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choices=[2 ** i for i in range(2, 7)],
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default=16,
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description='Advanced Setting, see Readme below if interested.'
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)
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) -> Iterator[Path]:
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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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progressive_outputs = progressive_outputs,
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is_seamless = seamless,
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temperature = temperature,
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supercondition_factor = float(supercondition_factor),
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top_k = 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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is_final = i == 8 if progressive_outputs else True
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ext = 'png' if is_final and save_as_png 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 |