202 lines
7.4 KiB
Python
202 lines
7.4 KiB
Python
# ComfyUI-RMBG
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# Interactive Image Compare Node (ComfyUI Standard)
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# License: GPL-3.0
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import os
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import random
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image
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import folder_paths
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class AILab_ImageCompareView:
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"""Real-time Interactive Image Compare node.
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Renders two images side-by-side or with various comparison modes directly
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on the node canvas. All UI controls (mode dropdown, match_size checkbox)
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are native ComfyUI widgets — the frontend JS handles canvas rendering and
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mouse interaction only.
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Outputs ``IMAGE`` (the composed result for the current mode) and
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``DIFF_MASK`` (the absolute-difference mask between the two inputs).
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"""
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DESCRIPTION = (
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"Real-time Interactive Image Compare (RMBG) - Compare two images on-node. "
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"Supports wipe (L/R & T/B), overlay opacity blend, pixel difference, "
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"side-by-side, and highlight-diff (red on grey). Uses native ComfyUI "
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"widgets for mode selection; the frontend handles interactive canvas "
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"rendering."
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)
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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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"image1": ("IMAGE", {"tooltip": "First image to compare (e.g. before / original)."}),
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"image2": ("IMAGE", {"tooltip": "Second image to compare (e.g. after / processed)."}),
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"mode": (
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[
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"left_right",
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"up_down",
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"overlay",
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"difference",
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"side_by_side",
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"highlight_diff",
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],
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{"default": "left_right", "tooltip": "Comparison mode."},
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),
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"match_size": (
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"BOOLEAN",
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{
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"default": True,
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"tooltip": "If True, resize image2 to match image1 dimensions when they differ.",
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},
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),
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},
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"hidden": {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO",
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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RETURN_NAMES = ("IMAGE", "DIFF_MASK")
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FUNCTION = "compare_images"
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OUTPUT_NODE = True
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CATEGORY = "🧪AILab/🖼️IMAGE"
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def __init__(self):
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prefix_append = "_cmp_" + "".join(
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random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5)
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)
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self.compress_level = 4
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# ── Core entry point ───────────────────────────────────────
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def compare_images(self, image1, image2, mode="left_right",
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match_size=True, prompt=None, extra_pnginfo=None):
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# ── Both images provided (required inputs) ──────────────
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# Ensure RGB (strip alpha if present)
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t1 = image1[..., :3] if image1.shape[-1] == 4 else image1
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t2 = image2[..., :3] if image2.shape[-1] == 4 else image2
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# Align batch size
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b1, b2 = t1.shape[0], t2.shape[0]
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if b1 != b2:
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if b1 == 1:
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t1 = t1.repeat(b2, 1, 1, 1)
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elif b2 == 1:
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t2 = t2.repeat(b1, 1, 1, 1)
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else:
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min_b = min(b1, b2)
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t1 = t1[:min_b]
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t2 = t2[:min_b]
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# Match spatial size if requested (resize image2 → image1)
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if match_size and t1.shape[1:3] != t2.shape[1:3]:
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nchw2 = t2.permute(0, 3, 1, 2)
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resized2 = F.interpolate(
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nchw2, size=(t1.shape[1], t1.shape[2]),
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mode="bicubic", align_corners=False,
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)
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t2 = torch.clamp(resized2.permute(0, 2, 3, 1), 0.0, 1.0)
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# Safety: if sizes still differ (match_size=False), crop both to the
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# common minimum so _compose and diff_mask never crash on shape mismatch.
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if t1.shape[1:3] != t2.shape[1:3]:
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min_h = min(t1.shape[1], t2.shape[1])
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min_w = min(t1.shape[2], t2.shape[2])
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t1 = t1[:, :min_h, :min_w, :]
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t2 = t2[:, :min_h, :min_w, :]
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# Save both images to temp directory for frontend interaction
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results = self._save_images([(1, t1), (2, t2)])
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# Difference mask (used by all modes)
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diff_mask = torch.clamp(torch.abs(t1 - t2).mean(dim=-1), 0.0, 1.0)
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# Compose output based on mode
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out_img = self._compose(mode, t1, t2)
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return {"ui": {"images": results}, "result": (out_img, diff_mask)}
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# ── Mode composition ──────────────────────────────────────
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@staticmethod
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def _compose(mode, t1, t2):
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"""Return the composed output tensor for *mode*."""
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if mode == "left_right":
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mid = t1.shape[2] // 2
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out = t2.clone()
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out[:, :, :mid, :] = t1[:, :, :mid, :]
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elif mode == "up_down":
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mid = t1.shape[1] // 2
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out = t2.clone()
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out[:, :mid, :, :] = t1[:, :mid, :, :]
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elif mode == "overlay":
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out = torch.clamp(0.5 * t1 + 0.5 * t2, 0.0, 1.0)
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elif mode == "difference":
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out = torch.clamp(torch.abs(t1 - t2), 0.0, 1.0)
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elif mode == "side_by_side":
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out = torch.cat([t1, t2], dim=2)
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elif mode == "highlight_diff":
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# grey base (luminance of image1), red where pixels differ
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grey = t1.mean(dim=-1, keepdim=True).repeat(1, 1, 1, 3)
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diff = torch.abs(t1 - t2).mean(dim=-1, keepdim=True)
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red = torch.zeros_like(t1)
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red[..., 0] = 1.0 # pure red channel
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threshold = 0.15
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mask = (diff > threshold).expand_as(t1).float()
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out = grey * (1 - mask) + red * mask
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else:
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out = t1
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return out
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# ── Image saving helper ────────────────────────────────────
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def _save_images(self, slot_tensors):
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"""Save tensors to temp dir, tagged with *slot* (1 or 2).
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The frontend uses ``slot`` to distinguish image1 from image2.
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"""
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results = []
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os.makedirs(self.output_dir, exist_ok=True)
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first_tensor = slot_tensors[0][1]
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prefix = "ailab_cmp" + self.prefix_append
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w = first_tensor.shape[2]
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h = first_tensor.shape[1]
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full_output_folder, filename, counter, subfolder, _ = (
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folder_paths.get_save_image_path(prefix, self.output_dir, w, h)
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)
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for slot, tensor in slot_tensors:
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arr = np.clip(255.0 * tensor[0].cpu().numpy(), 0, 255).astype(np.uint8)
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img = Image.fromarray(arr)
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file = f"{filename}_{counter:05}_{slot}.png"
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img.save(
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os.path.join(full_output_folder, file),
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compress_level=self.compress_level,
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)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": self.type,
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"slot": slot,
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})
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counter += 1
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return results
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NODE_CLASS_MAPPINGS = {
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"AILab_ImageCompareView": AILab_ImageCompareView,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AILab_ImageCompareView": "Image Compare (RMBG) 🔀🖼️",
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}
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