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61 lines
2.0 KiB
61 lines
2.0 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(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=3, |
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default=1 |
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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") |