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https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
synced 2026-03-16 14:27:05 +08:00
Some fixes/adjustments
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629a2bf423
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@ -2476,7 +2476,7 @@ class ImageResizeKJv2:
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"optional" : {
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"mask": ("MASK",),
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"device": (["cpu", "gpu"],),
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"per_batch": ("INT", { "default": 16, "min": 0, "max": 4096, "step": 1, "tooltip": "Process images in sub-batches to reduce memory usage. 0 disables sub-batching."}),
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"per_batch": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, "tooltip": "Process images in sub-batches to reduce memory usage. 0 disables sub-batching."}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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@ -2495,7 +2495,7 @@ Keep proportions keeps the aspect ratio of the image, by
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highest dimension.
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"""
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def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position, unique_id, device="cpu", mask=None, per_batch=16):
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def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position, unique_id, device="cpu", mask=None, per_batch=0):
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B, H, W, C = image.shape
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if device == "gpu":
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@ -2511,6 +2511,10 @@ highest dimension.
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height = H
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pillarbox_blur = keep_proportion == "pillarbox_blur"
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# Initialize padding variables
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pad_left = pad_right = pad_top = pad_bottom = 0
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if keep_proportion == "resize" or keep_proportion.startswith("pad") or pillarbox_blur:
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# If one of the dimensions is zero, calculate it to maintain the aspect ratio
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if width == 0 and height != 0:
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@ -2529,7 +2533,6 @@ highest dimension.
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new_width = width
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new_height = height
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pad_left = pad_right = pad_top = pad_bottom = 0
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if keep_proportion.startswith("pad") or pillarbox_blur:
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# Calculate padding based on position
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if crop_position == "center":
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@ -2566,7 +2569,7 @@ highest dimension.
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height = height - (height % divisible_by)
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# Preflight estimate (log-only when batching is active)
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if per_batch and B > per_batch:
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if per_batch != 0 and B > per_batch:
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try:
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bytes_per_elem = image.element_size() # typically 4 for float32
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est_total_bytes = B * height * width * C * bytes_per_elem
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@ -2582,7 +2585,7 @@ highest dimension.
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except:
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pass
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def _process_subbatch(in_image, in_mask):
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def _process_subbatch(in_image, in_mask, pad_left, pad_right, pad_top, pad_bottom):
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# Avoid unnecessary clones; only move if needed
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out_image = in_image if in_image.device == device else in_image.to(device)
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out_mask = None if in_mask is None else (in_mask if in_mask.device == device else in_mask.to(device))
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@ -2650,8 +2653,8 @@ highest dimension.
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return out_image, out_mask
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# If batching disabled (per_batch==0) or batch fits, process whole batch
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if per_batch is None or per_batch == 0 or B <= per_batch:
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out_image, out_mask = _process_subbatch(image, mask)
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if per_batch == 0 or B <= per_batch:
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out_image, out_mask = _process_subbatch(image, mask, pad_left, pad_right, pad_top, pad_bottom)
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else:
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chunks = []
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mask_chunks = [] if mask is not None else None
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@ -2662,7 +2665,7 @@ highest dimension.
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end_idx = min(start_idx + per_batch, B)
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sub_img = image[start_idx:end_idx]
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sub_mask = mask[start_idx:end_idx] if mask is not None else None
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sub_out_img, sub_out_mask = _process_subbatch(sub_img, sub_mask)
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sub_out_img, sub_out_mask = _process_subbatch(sub_img, sub_mask, pad_left, pad_right, pad_top, pad_bottom)
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chunks.append(sub_out_img.cpu())
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if mask is not None:
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mask_chunks.append(sub_out_mask.cpu() if sub_out_mask is not None else None)
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