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update replicate

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This commit is contained in:
Brett Kuprel 2022-07-12 11:20:37 -04:00
parent 107a86bd8a
commit 51e401ea9b
1 changed files with 26 additions and 27 deletions
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27
replicate_predictor.py
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@ -9,39 +9,38 @@ torch.backends.cudnn.deterministic = False
class ReplicatePredictor(BasePredictor): class ReplicatePredictor(BasePredictor):
def setup(self): def setup(self):
self.model = MinDalle(is_mega=True, is_reusable=True) self.model = MinDalle(
is_mega=True,
is_reusable=True,
dtype=torch.float32
)
def predict( def predict(
self, self,
text: str = Input(default='Dali painting of WALL·E'), text: str = Input(default='Dali painting of WALL·E'),
intermediate_outputs: bool = Input(default=True), intermediate_outputs: bool = Input(default=True),
grid_size: int = Input(ge=1, le=9, default=5), grid_size: int = Input(ge=1, le=9, default=5),
log2_temperature: float = Input(ge=-3, le=3, default=1), log2_temperature: float = Input(ge=-3, le=3, default=0.0),
log2_top_k: int = Input(ge=0, le=14, default=7), log2_top_k: int = Input(ge=0, le=14, default=7),
log2_supercondition_factor: int = Input(ge=2, le=6, default=4) log2_supercondition_factor: float = Input(ge=2, le=6, default=4)
) -> Iterator[Path]: ) -> Iterator[Path]:
try: log2_mid_count = 3 if intermediate_outputs else 0
image_stream = self.model.generate_image_stream( image_stream = self.model.generate_image_stream(
text = text, text = text,
seed = -1, seed = -1,
grid_size = grid_size, grid_size = grid_size,
log2_mid_count = 3 if intermediate_outputs else 0, log2_mid_count = log2_mid_count,
temperature = 2 ** log2_temperature, temperature = 2 ** log2_temperature,
supercondition_factor = 2 ** log2_supercondition_factor, supercondition_factor = 2 ** log2_supercondition_factor,
top_k = 2 ** log2_top_k, top_k = 2 ** log2_top_k,
is_verbose = True is_verbose = True
) )
iter = 0 i = 0
path = Path(tempfile.mkdtemp()) path = Path(tempfile.mkdtemp())
for image in image_stream: for image in image_stream:
iter += 1 i += 1
image_path = path / 'min-dalle-iter-{}.jpg'.format(iter) ext = 'png' if i == 2 ** log2_mid_count else 'jpg'
image_path = path / 'min-dalle-iter-{}.{}'.format(i, ext)
image.save(str(image_path)) image.save(str(image_path))
yield 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")