|
|
|
@ -6,7 +6,6 @@ from cog import BasePredictor, Path, Input |
|
|
|
|
|
|
|
|
|
torch.backends.cudnn.deterministic = False |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ReplicatePredictor(BasePredictor): |
|
|
|
|
def setup(self): |
|
|
|
|
self.model = MinDalle( |
|
|
|
@ -18,22 +17,37 @@ class ReplicatePredictor(BasePredictor): |
|
|
|
|
def predict( |
|
|
|
|
self, |
|
|
|
|
text: str = Input(default='Dali painting of WALL·E'), |
|
|
|
|
output_png: bool = Input(default=False), |
|
|
|
|
intermediate_outputs: bool = Input(default=True), |
|
|
|
|
save_as_png: bool = Input(default=False), |
|
|
|
|
progressive_outputs: bool = Input(default=True), |
|
|
|
|
grid_size: int = Input(ge=1, le=9, default=5), |
|
|
|
|
log2_temperature: float = Input(ge=-3, le=3, default=2), |
|
|
|
|
log2_top_k: int = Input(ge=0, le=14, default=4), |
|
|
|
|
log2_supercondition_factor: float = Input(ge=2, le=6, default=4) |
|
|
|
|
temperature: str = Input( |
|
|
|
|
choices=( |
|
|
|
|
['1/{}'.format(2 ** i) for i in range(4, 0, -1)] + |
|
|
|
|
[str(2 ** i) for i in range(5)] |
|
|
|
|
), |
|
|
|
|
default='4', |
|
|
|
|
description='Advanced Setting, see Readme below if interested.' |
|
|
|
|
), |
|
|
|
|
top_k: int = Input( |
|
|
|
|
choices=[2 ** i for i in range(15)], |
|
|
|
|
default=64, |
|
|
|
|
description='Advanced Setting, see Readme below if interested.' |
|
|
|
|
), |
|
|
|
|
supercondition_factor: int = Input( |
|
|
|
|
choices=[2 ** i for i in range(2, 7)], |
|
|
|
|
default=16, |
|
|
|
|
description='Advanced Setting, see Readme below if interested.' |
|
|
|
|
) |
|
|
|
|
) -> Iterator[Path]: |
|
|
|
|
log2_mid_count = 3 if intermediate_outputs else 0 |
|
|
|
|
log2_mid_count = 3 if progressive_outputs else 0 |
|
|
|
|
image_stream = self.model.generate_image_stream( |
|
|
|
|
text = text, |
|
|
|
|
seed = -1, |
|
|
|
|
grid_size = grid_size, |
|
|
|
|
log2_mid_count = log2_mid_count, |
|
|
|
|
temperature = 2 ** log2_temperature, |
|
|
|
|
supercondition_factor = 2 ** log2_supercondition_factor, |
|
|
|
|
top_k = 2 ** log2_top_k, |
|
|
|
|
temperature = eval(temperature), |
|
|
|
|
supercondition_factor = float(supercondition_factor), |
|
|
|
|
top_k = top_k, |
|
|
|
|
is_verbose = True |
|
|
|
|
) |
|
|
|
|
|
|
|
|
@ -41,7 +55,7 @@ class ReplicatePredictor(BasePredictor): |
|
|
|
|
path = Path(tempfile.mkdtemp()) |
|
|
|
|
for image in image_stream: |
|
|
|
|
i += 1 |
|
|
|
|
ext = 'png' if i == 2 ** log2_mid_count and output_png else 'jpg' |
|
|
|
|
ext = 'png' if i == 2 ** log2_mid_count and save_as_png else 'jpg' |
|
|
|
|
image_path = path / 'min-dalle-iter-{}.{}'.format(i, ext) |
|
|
|
|
image.save(str(image_path)) |
|
|
|
|
yield image_path |