Ethanfel
726cd7bf17
Menu category on all nodes now reads 'Dataset Gates', matching the repo name. Internal identifiers (routes, socket events, extension ids) left unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
"""BucketResize node: cover-crop an image (and optional mask) onto a Klein
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training bucket. Pure compute (torch + PIL); no comfy imports in run()."""
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import numpy as np
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import torch
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from PIL import Image
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from . import buckets
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NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
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def _resize_crop_pil(pil, new_w, new_h, left, top, W, H):
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pil = pil.resize((new_w, new_h), Image.LANCZOS)
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return pil.crop((left, top, left + W, top + H))
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def fit_image(image, W, H):
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"""image [B,H,W,3] -> [B,H,W,3] at (W,H) using the first image's geometry."""
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b, ih, iw = image.shape[0], image.shape[1], image.shape[2]
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new_w, new_h, left, top, scale = buckets.cover_crop_params(iw, ih, W, H)
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out = []
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for i in range(b):
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arr = (image[i].cpu().numpy() * 255.0).clip(0, 255).astype("uint8")
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pil = _resize_crop_pil(Image.fromarray(arr), new_w, new_h, left, top, W, H)
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out.append(torch.from_numpy(np.array(pil, dtype=np.float32) / 255.0))
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return torch.stack(out, 0), scale
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def fit_mask(mask, W, H):
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b, ih, iw = mask.shape[0], mask.shape[1], mask.shape[2]
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new_w, new_h, left, top, _ = buckets.cover_crop_params(iw, ih, W, H)
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out = []
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for i in range(b):
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arr = (mask[i].cpu().numpy() * 255.0).clip(0, 255).astype("uint8")
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pil = _resize_crop_pil(Image.fromarray(arr), new_w, new_h, left, top, W, H)
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out.append(torch.from_numpy(np.array(pil, dtype=np.float32) / 255.0))
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return torch.stack(out, 0)
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class BucketResize:
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CATEGORY = "Dataset Gates"
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FUNCTION = "run"
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RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING")
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RETURN_NAMES = ("image", "mask", "width", "height", "label")
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"resolution": ("INT", {"default": 1280, "min": 64, "max": 8192}),
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"divisible": ("INT", {"default": 64, "min": 8, "max": 256}),
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"max_upscale": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 8.0, "step": 0.1}),
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},
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"optional": {"mask": ("MASK",)},
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}
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def run(self, image, resolution=1280, divisible=64, max_upscale=1.5, mask=None):
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ih, iw = int(image.shape[1]), int(image.shape[2])
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W, H = buckets.pick_bucket(iw, ih, resolution, divisible)
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out_img, scale = fit_image(image, W, H)
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if scale > max_upscale:
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print(f"[BucketResize] cover scale {scale:.2f}x exceeds max_upscale "
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f"{max_upscale} for {iw}x{ih} -> {W}x{H}")
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out_mask = fit_mask(mask, W, H) if mask is not None \
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else torch.zeros((out_img.shape[0], H, W), dtype=torch.float32)
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return (out_img, out_mask, W, H, f"{W}x{H}")
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NODE_CLASS_MAPPINGS = {"BucketResize": BucketResize}
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NODE_DISPLAY_NAME_MAPPINGS = {"BucketResize": "Bucket Resize (Klein 9B)"}
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