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Refactor SimpleLamaInpainter: streamline inpainting process by removing unnecessary device checks and enhance postprocessing with NaN/Inf safety handling.

This commit is contained in:
vast 2025-10-16 17:19:34 +09:00
parent 907d28c8bf
commit 249422c227
1 changed files with 5 additions and 7 deletions
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12
app/models/simple_lama.py
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@ -156,11 +156,7 @@ class SimpleLamaInpainter:
# 성능 최적화: AMP + cuDNN benchmark
torch.backends.cudnn.benchmark = True
with torch.no_grad():
if self._device.type == 'cuda':
with torch.cuda.amp.autocast(enabled=True):
inpainted_batch = self._model.model(image_batch, mask_batch)
else:
inpainted_batch = self._model.model(image_batch, mask_batch)
inpainted_batch = self._model.model(image_batch, mask_batch)
# 후처리
result_images = []
@ -190,8 +186,10 @@ class SimpleLamaInpainter:
def _postprocess(self, tensor: torch.Tensor, original_size: Tuple[int, int], original_image: Image.Image, original_mask: Image.Image) -> Image.Image:
"""모델 출력 텐서를 PIL 이미지로 후처리하고 원본에 합성합니다."""
# 텐서를 PIL 이미지로 변환
result_np = tensor.permute(1, 2, 0).cpu().numpy()
result_np = np.clip(result_np * 255, 0, 255).astype(np.uint8)
result_np = tensor.permute(1, 2, 0).detach().float().cpu().numpy()
# NaN/Inf 안전 처리 후 범위 클램프
result_np = np.nan_to_num(result_np, nan=0.0, posinf=1.0, neginf=0.0)
result_np = (np.clip(result_np, 0.0, 1.0) * 255.0).astype(np.uint8)
inpainted_image_512 = Image.fromarray(result_np)
# 원본 크기로 리사이즈