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46 lines
1.5 KiB
46 lines
1.5 KiB
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 |