min-dalle-test/replicate_predictor.py

from min_dalle import MinDalle
import tempfile
import torch, torch.backends.cudnn
from typing import Iterator
from cog import BasePredictor, Path, Input

torch.backends.cudnn.deterministic = False


class ReplicatePredictor(BasePredictor):
    def setup(self):
        self.model = MinDalle(is_mega=True, is_reusable=True)

    def predict(
        self,
        text: str = Input(default='Dali painting of WALL·E'),
        intermediate_outputs: bool = Input(default=True),
        grid_size: int = Input(ge=1, le=9, default=5),
        log2_temperature: float = Input(ge=-3, le=3, default=1),
        log2_top_k: int = Input(ge=0, le=14, default=7),
        log2_supercondition_factor: int = Input(ge=2, le=6, default=4)
    ) -> Iterator[Path]:
        try:
            image_stream = self.model.generate_image_stream(
                text = text,
                seed = -1,
                grid_size = grid_size,
                log2_mid_count = 3 if intermediate_outputs else 0,
                temperature = 2 ** log2_temperature,
                supercondition_factor = 2 ** log2_supercondition_factor,
                top_k = 2 ** log2_top_k,
                is_verbose = True
            )

            iter = 0
            path = Path(tempfile.mkdtemp())
            for image in image_stream:
                iter += 1
                image_path = path / 'min-dalle-iter-{}.jpg'.format(iter)
                image.save(str(image_path))
                yield image_path
        except:
            print("An error occured, deleting model")
            del self.model
            torch.cuda.empty_cache()
            self.setup()
            raise Exception("There was an error, please try again")