From 433d0e217171cf8939de797a6db627fb55a52d13 Mon Sep 17 00:00:00 2001
From: AI Lab <129358391+1038lab@users.noreply.github.com>
Date: Thu, 1 Jan 2026 19:28:01 -0800
Subject: [PATCH] Delete AILab_InpaintTools.py

---
 AILab_InpaintTools.py | 146 ------------------------------------------
 1 file changed, 146 deletions(-)
 delete mode 100644 AILab_InpaintTools.py

diff --git a/AILab_InpaintTools.py b/AILab_InpaintTools.py
deleted file mode 100644
index f2764bb..0000000
--- a/AILab_InpaintTools.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# ComfyUI-RMBG
-#
-# AILab Inpaint Tools
-# A collection of specialized nodes for inpainting tasks in ComfyUI.
-# Features a set of utilities for mask processing, latent conditioning, and inpainting workflows.
-#
-# 1. Inpaint Nodes:
-#    - AILab_ReferenceLatentMask: A node for inpainting tasks with the Flux Kontext model, using a reference latent and mask for precise region conditioning
-#
-# License: GPL-3.0
-# These nodes are crafted to streamline common image and mask operations within ComfyUI workflows.
-
-import torch
-import node_helpers
-
-def expand_mask(mask, expand_amount):
-    if expand_amount == 0:
-        return mask
-        
-    import torch.nn.functional as F
-    
-    binary_mask = (mask > 0.5).float()
-    kernel_size = abs(expand_amount) * 2 + 1
-    kernel_size = max(3, kernel_size)
-    
-    kernel = torch.ones(1, 1, kernel_size, kernel_size, device=mask.device)
-    
-    if expand_amount > 0:
-        expanded = F.conv2d(
-            binary_mask.reshape(-1, 1, mask.shape[-2], mask.shape[-1]),
-            kernel,
-            padding=kernel_size // 2
-        )
-        result = (expanded > 0).float()
-    else:
-        eroded = F.conv2d(
-            binary_mask.reshape(-1, 1, mask.shape[-2], mask.shape[-1]),
-            kernel,
-            padding=kernel_size // 2
-        )
-        result = (eroded >= kernel_size * kernel_size).float()
-    
-    if len(mask.shape) == 3:
-        result = result.squeeze(1)
-        
-    return result
-
-
-def blur_mask(mask, blur_amount):
-    if blur_amount == 0:
-        return mask
-        
-    import torch.nn.functional as F
-    import math
-    
-    x = mask.reshape(-1, 1, mask.shape[-2], mask.shape[-1])
-    kernel_size = max(3, math.ceil(blur_amount * 3) * 2 + 1)
-    
-    sigma = blur_amount
-    half_kernel = kernel_size // 2
-    grid = torch.arange(-half_kernel, half_kernel + 1, device=mask.device).float()
-    
-    gaussian = torch.exp(-0.5 * (grid / sigma) ** 2)
-    gaussian = gaussian / gaussian.sum()
-    
-    gaussian_x = gaussian.view(1, 1, 1, kernel_size)
-    gaussian_y = gaussian.view(1, 1, kernel_size, 1)
-    
-    blurred = F.conv2d(x, gaussian_x, padding=(0, half_kernel))
-    blurred = F.conv2d(blurred, gaussian_y, padding=(half_kernel, 0))
-    
-    if len(mask.shape) == 3:
-        blurred = blurred.squeeze(1)
-        
-    return blurred
-
-class AILab_ReferenceLatentMask:
-    @classmethod
-    def INPUT_TYPES(cls):
-        tooltips = {
-            "conditioning": "Base conditioning input for inpainting task",
-            "latent": "Encoded latent from VAE",
-            "mask": "Area to inpaint (white regions)",
-            "expand": "Grow mask (+) or shrink mask (-)",
-            "blur": "Soften mask edges",
-            "mask_only": "Only generate content in masked area"
-        }
-        
-        return {
-            "required": {
-                "conditioning": ("CONDITIONING", {"tooltip": tooltips["conditioning"]}),
-                "latent": ("LATENT", {"tooltip": tooltips["latent"]}),
-                "mask": ("MASK", {"tooltip": tooltips["mask"]}),
-                "expand": ("INT", {"default": 5, "min": -64, "max": 64, "step": 1, "tooltip": tooltips["expand"]}),
-                "blur": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 64.0, "step": 0.1, "tooltip": tooltips["blur"]}),
-                "mask_only": ("BOOLEAN", {"default": True, "tooltip": tooltips["mask_only"]}),
-            }
-        }
-
-    RETURN_TYPES = ("CONDITIONING", "LATENT", "MASK")
-    RETURN_NAMES = ("CONDITIONING", "LATENT", "MASK")
-    FUNCTION = "prepare_inpaint_conditioning"
-    CATEGORY = "🧪AILab/🎭Inpaint"
-
-    def add_latent_to_conditioning(self, conditioning, latent=None):
-        if latent is not None:
-            return node_helpers.conditioning_set_values(
-                conditioning, 
-                {"reference_latents": [latent["samples"]]}, 
-                append=True
-            )
-        return conditioning
-
-    def prepare_inpaint_conditioning(self, conditioning, latent, mask, expand=5, blur=3.0, mask_only=True):
-        processed_mask = mask
-        
-        if expand != 0:
-            processed_mask = expand_mask(processed_mask, expand)
-            
-        if blur > 0:
-            processed_mask = blur_mask(processed_mask, blur)
-        
-        modified_cond = node_helpers.conditioning_set_values(
-            conditioning, 
-            {
-                "concat_latent_image": latent["samples"],
-                "concat_mask": processed_mask
-            }
-        )
-        
-        final_cond = self.add_latent_to_conditioning(modified_cond, latent)
-        
-        output_latent = {"samples": latent["samples"]}
-        if mask_only:
-            output_latent["noise_mask"] = processed_mask
-            
-        return (final_cond, output_latent, processed_mask)
-
-
-NODE_CLASS_MAPPINGS = {
-    "AILab_ReferenceLatentMask": AILab_ReferenceLatentMask,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = { 
-    "AILab_ReferenceLatentMask": "Reference Latent Mask (RMBG) 🖼️🎭",
-} 
\ No newline at end of file