61 lines
2.0 KiB
Python
61 lines
2.0 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(is_mega=True, is_reusable=True)
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def predict(
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self,
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text: str = Input(
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description='For long prompts, only the first 64 tokens will be used to generate the image.',
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default='Dali painting of WALL·E'
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),
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intermediate_outputs: bool = Input(
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description='Whether to show intermediate outputs while running. This adds less than a second to the run time.',
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default=True
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),
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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,
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default=4
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),
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temperature: float = Input(
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description='A higher temperature results in more variety.',
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ge=0.01,
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le=10,
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default=2
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),
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) -> Iterator[Path]:
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try:
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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 = 3 if intermediate_outputs else 0,
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temperature = temperature,
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supercondition_factor = 2 ** 4,
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top_k = 2 ** 8,
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is_verbose = True
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)
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iter = 0
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path = Path(tempfile.mkdtemp())
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for image in image_stream:
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iter += 1
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image_path = path / 'min-dalle-iter-{}.jpg'.format(iter)
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image.save(str(image_path))
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yield image_path
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except:
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print("An error occured, deleting model")
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del self.model
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torch.cuda.empty_cache()
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self.setup()
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raise Exception("There was an error, please try again") |