black-formatter pass
This commit is contained in:
Sebastian Monroy
+121
-20
@@ -1442,6 +1442,7 @@ class NilorToSparseIndexMethod:
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class NilorImageResizeV2:
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upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
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@classmethod
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def INPUT_TYPES(s):
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return {
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@@ -1450,15 +1451,41 @@ class NilorImageResizeV2:
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"width": ("INT", {"default": 512, "min": 0, "max": BIGMAX, "step": 1}),
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"height": ("INT", {"default": 512, "min": 0, "max": BIGMAX, "step": 1}),
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"upscale_method": (s.upscale_methods,),
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"keep_proportion": (["stretch", "resize", "pad", "pad_edge", "pad_edge_pixel", "crop", "pillarbox_blur"], {"default": False}),
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"keep_proportion": (
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[
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"stretch",
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"resize",
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"pad",
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"pad_edge",
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"pad_edge_pixel",
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"crop",
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"pillarbox_blur",
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],
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{"default": False},
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),
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"pad_color": ("STRING", {"default": "0, 0, 0"}),
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"crop_position": (["center", "top", "bottom", "left", "right"], {"default": "center"}),
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"divisible_by": ("INT", {"default": 2, "min": 0, "max": 512, "step": 1}),
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"crop_position": (
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["center", "top", "bottom", "left", "right"],
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{"default": "center"},
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),
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"divisible_by": (
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"INT",
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{"default": 2, "min": 0, "max": 512, "step": 1},
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),
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},
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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. 0 disables."}),
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"per_batch": (
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"INT",
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{
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"default": 16,
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"min": 0,
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"max": 4096,
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"step": 1,
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"tooltip": "Process images in sub-batches. 0 disables.",
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},
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),
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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@@ -1471,7 +1498,21 @@ class NilorImageResizeV2:
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Resizes images with optional aspect preservation, padding/cropping, and sub-batching to lower peak memory.
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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(
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self,
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image,
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width,
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height,
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keep_proportion,
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upscale_method,
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divisible_by,
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pad_color,
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crop_position,
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unique_id,
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device="cpu",
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mask=None,
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per_batch=16,
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):
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B, H, W, C = image.shape
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if device == "gpu":
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@@ -1487,7 +1528,11 @@ Resizes images with optional aspect preservation, padding/cropping, and sub-batc
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height = H
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pillarbox_blur = keep_proportion == "pillarbox_blur"
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if keep_proportion == "resize" or keep_proportion.startswith("pad") or pillarbox_blur:
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if (
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keep_proportion == "resize"
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or keep_proportion.startswith("pad")
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or pillarbox_blur
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):
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if width == 0 and height != 0:
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ratio = height / H
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new_width = round(W * ratio)
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@@ -1544,13 +1589,19 @@ Resizes images with optional aspect preservation, padding/cropping, and sub-batc
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bytes_per_elem = image.element_size()
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est_total_bytes = B * height * width * C * bytes_per_elem
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est_mb = est_total_bytes / (1024 * 1024)
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print(f"[NilorImageResizeV2] estimated output ~{est_mb:.2f} MB; batching {per_batch}/{B}")
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print(
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f"[NilorImageResizeV2] estimated output ~{est_mb:.2f} MB; batching {per_batch}/{B}"
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)
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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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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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out_mask = (
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None
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if in_mask is None
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else (in_mask if in_mask.device == device else in_mask.to(device))
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)
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if keep_proportion == "crop":
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old_height = out_image.shape[-3]
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@@ -1582,14 +1633,30 @@ Resizes images with optional aspect preservation, padding/cropping, and sub-batc
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if out_mask is not None:
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out_mask = out_mask.narrow(-1, x, crop_w).narrow(-2, y, crop_h)
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out_image = common_upscale(out_image.movedim(-1, 1), width, height, upscale_method, crop="disabled").movedim(1, -1)
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out_image = common_upscale(
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out_image.movedim(-1, 1), width, height, upscale_method, crop="disabled"
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).movedim(1, -1)
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if out_mask is not None:
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if upscale_method == "lanczos":
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out_mask = common_upscale(out_mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop="disabled").movedim(1, -1)[:, :, :, 0]
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out_mask = common_upscale(
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out_mask.unsqueeze(1).repeat(1, 3, 1, 1),
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width,
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height,
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upscale_method,
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crop="disabled",
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).movedim(1, -1)[:, :, :, 0]
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else:
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out_mask = common_upscale(out_mask.unsqueeze(1), width, height, upscale_method, crop="disabled").squeeze(1)
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out_mask = common_upscale(
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out_mask.unsqueeze(1),
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width,
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height,
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upscale_method,
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crop="disabled",
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).squeeze(1)
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if (keep_proportion.startswith("pad") or pillarbox_blur) and (pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0):
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if (keep_proportion.startswith("pad") or pillarbox_blur) and (
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pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0
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):
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padded_width = width + pad_left + pad_right
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padded_height = height + pad_top + pad_bottom
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if divisible_by > 1:
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@@ -1603,12 +1670,30 @@ Resizes images with optional aspect preservation, padding/cropping, and sub-batc
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pad_bottom += extra_height
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pad_mode = (
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"pillarbox_blur" if pillarbox_blur else
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"edge" if keep_proportion == "pad_edge" else
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"edge_pixel" if keep_proportion == "pad_edge_pixel" else
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"color"
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"pillarbox_blur"
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if pillarbox_blur
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else (
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"edge"
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if keep_proportion == "pad_edge"
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else (
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"edge_pixel"
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if keep_proportion == "pad_edge_pixel"
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else "color"
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)
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)
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)
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out_image, out_mask = ImagePadKJ.pad(
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self,
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out_image,
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pad_left,
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pad_right,
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pad_top,
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pad_bottom,
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0,
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pad_color,
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pad_mode,
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mask=out_mask,
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)
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out_image, out_mask = ImagePadKJ.pad(self, out_image, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, pad_mode, mask=out_mask)
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return out_image, out_mask
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@@ -1627,9 +1712,13 @@ Resizes images with optional aspect preservation, padding/cropping, and sub-batc
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sub_out_img, sub_out_mask = _process_subbatch(sub_img, sub_mask)
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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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mask_chunks.append(
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sub_out_mask.cpu() if sub_out_mask is not None else None
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)
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try:
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print(f"[NilorImageResizeV2] batch {current_batch}/{total_batches} · images {end_idx}/{B}")
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print(
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f"[NilorImageResizeV2] batch {current_batch}/{total_batches} · images {end_idx}/{B}"
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)
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except:
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pass
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out_image = torch.cat(chunks, dim=0)
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@@ -1638,7 +1727,19 @@ Resizes images with optional aspect preservation, padding/cropping, and sub-batc
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else:
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out_mask = None
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return (out_image.cpu(), out_image.shape[2], out_image.shape[1], out_mask.cpu() if out_mask is not None else torch.zeros(64, 64, device=torch.device("cpu"), dtype=torch.float32))
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return (
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out_image.cpu(),
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out_image.shape[2],
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out_image.shape[1],
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(
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out_mask.cpu()
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if out_mask is not None
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else torch.zeros(
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64, 64, device=torch.device("cpu"), dtype=torch.float32
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)
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),
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)
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# Mapping class names to objects for potential export
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NODE_CLASS_MAPPINGS = {
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