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62 lines
2.0 KiB
62 lines
2.0 KiB
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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 |