Merge branch 'main' into main
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68616d0ebe
@ -22,7 +22,7 @@ python3 main.py --device=cuda --port=8080 --model=lama
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- `--crop-trigger-size`: If image size large then crop-trigger-size, crop each area from original image to do inference.
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- `--crop-trigger-size`: If image size large then crop-trigger-size, crop each area from original image to do inference.
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Mainly for performance and memory reasons on **very** large image.Default is 2042,2042
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Mainly for performance and memory reasons on **very** large image.Default is 2042,2042
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- `--crop-size`: Crop size for `--crop-trigger-size`. Default is 512,512.
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- `--crop-margin`: Margin around bounding box of painted stroke when crop mode triggered. Default is 256.
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### Start server with LDM model
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### Start server with LDM model
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@ -14,16 +14,16 @@ LAMA_MODEL_URL = os.environ.get(
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class LaMa:
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class LaMa:
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def __init__(self, crop_trigger_size: List[int], crop_size: List[int], device):
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def __init__(self, crop_trigger_size: List[int], crop_margin: int, device):
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"""
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"""
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Args:
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Args:
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crop_trigger_size: h, w
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crop_trigger_size: h, w
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crop_size: h, w
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crop_margin:
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device:
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device:
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"""
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"""
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self.crop_trigger_size = crop_trigger_size
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self.crop_trigger_size = crop_trigger_size
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self.crop_size = crop_size
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self.crop_margin = crop_margin
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self.device = device
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self.device = device
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if os.environ.get("LAMA_MODEL"):
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if os.environ.get("LAMA_MODEL"):
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@ -79,12 +79,10 @@ class LaMa:
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box_w = box[2] - box[0]
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box_w = box[2] - box[0]
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cx = (box[0] + box[2]) // 2
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cx = (box[0] + box[2]) // 2
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cy = (box[1] + box[3]) // 2
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cy = (box[1] + box[3]) // 2
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crop_h, crop_w = self.crop_size
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img_h, img_w = image.shape[1:]
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img_h, img_w = image.shape[1:]
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# TODO: when box_w > crop_w, add some margin around?
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w = box_w + self.crop_margin * 2
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w = max(crop_w, box_w)
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h = box_h + self.crop_margin * 2
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h = max(crop_h, box_h)
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l = max(cx - w // 2, 0)
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l = max(cx - w // 2, 0)
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t = max(cy - h // 2, 0)
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t = max(cy - h // 2, 0)
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@ -94,7 +92,7 @@ class LaMa:
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crop_img = image[:, t:b, l:r]
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crop_img = image[:, t:b, l:r]
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crop_mask = mask[:, t:b, l:r]
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crop_mask = mask[:, t:b, l:r]
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print(f"Apply zoom in size width x height: {crop_img.shape}")
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print(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
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return self._run(crop_img, crop_mask), [l, t, r, b]
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return self._run(crop_img, crop_mask), [l, t, r, b]
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@ -318,8 +318,10 @@ class LDM:
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cx = box_x + box_w // 2
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cx = box_x + box_w // 2
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cy = box_y + box_h // 2
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cy = box_y + box_h // 2
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w = max(512, box_w)
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# w = max(512, box_w)
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h = max(512, box_h)
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# h = max(512, box_h)
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w = box_w + 512
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h = box_h + 512
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left = max(cx - w // 2, 0)
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left = max(cx - w // 2, 0)
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top = max(cy - h // 2, 0)
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top = max(cy - h // 2, 0)
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7
main.py
7
main.py
@ -107,7 +107,8 @@ def get_args_parser():
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"crop each area from original image to do inference."
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"crop each area from original image to do inference."
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"Mainly for performance and memory reasons"
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"Mainly for performance and memory reasons"
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"Only for lama")
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"Only for lama")
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parser.add_argument("--crop-size", default="512,512")
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parser.add_argument("--crop-margin", type=int, default=256,
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help="Margin around bounding box of painted stroke when crop mode triggered")
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parser.add_argument(
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parser.add_argument(
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"--ldm-steps",
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"--ldm-steps",
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default=50,
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default=50,
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@ -131,11 +132,9 @@ def main():
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device = torch.device(args.device)
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device = torch.device(args.device)
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crop_trigger_size = [int(it) for it in args.crop_trigger_size.split(",")]
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crop_trigger_size = [int(it) for it in args.crop_trigger_size.split(",")]
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crop_size = [int(it) for it in args.crop_size.split(",")]
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if args.model == "lama":
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if args.model == "lama":
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model = LaMa(crop_trigger_size=crop_trigger_size,
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model = LaMa(crop_trigger_size=crop_trigger_size, crop_margin=args.crop_margin, device=device)
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crop_size=crop_size, device=device)
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elif args.model == "ldm":
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elif args.model == "ldm":
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model = LDM(device, steps=args.ldm_steps)
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model = LDM(device, steps=args.ldm_steps)
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else:
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else:
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