Upload sd-webui-negpip using SD-Hub

sd-webui-negpip/.github/FUNDING.yml

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+# These are supported funding model platforms
+
+github:[hako-mikan] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

sd-webui-negpip/LICENSE

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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
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+
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+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

sd-webui-negpip/README.md

@@ -0,0 +1,74 @@
+# NegPiP - Negative Prompt in Prompt
+[<img src="https://img.shields.io/badge/lang-Egnlish-blue.svg?style=plastic" height="25" />](README.md)
+[<img src="https://img.shields.io/badge/言語-日本語-green.svg?style=plastic" height="25" />](README_jp.md)
+[<img src="https://img.shields.io/badge/语言-中文-red.svg?style=plastic" height="25" />](README_cn.md)
+[<img src="https://img.shields.io/badge/Support-%E2%99%A5-magenta.svg?logo=github&style=plastic" height="25" />](https://github.com/sponsors/hako-mikan)
+
+
+Extension for Stable Diffusion web-ui enables negative prompt in prompt
+
+## Update 2025.11.30.0100(JST)
+- support Z-Image in Forge NEO
+
+## Update 2023.10.29.2100(JST)
+- Option to hide this extention in t2i/i2i tab [Detail](#hide-this-extention-in-text2imgimg2img-tab),[詳細](README_jp.md#txt2imgimg2imgタブで拡張を表示しない),[解释](README_cn.md#在txt2imgimg2img标签中不显示扩展)
+
+### [For users of ADetailer](#for-users-of-adetailer)/[ADetailerとの併用について](README_jp.md#adetailerとの併用について)/[关于与ADetailer的同时使用](README_cn.md#关于与adetailer的同时使用)
+
+# Overview
+This extension enhances the stable diffusion web-ui prompts and cross-attention, allowing for the use of prompts with negative effects within regular prompts and prompts with positive effects within negative prompts. Typically, unwanted elements are placed in negative prompts, but negative prompts may not always have a significant impact in calculations. With this extension, it becomes possible to use negative prompts with effects comparable to regular prompts. This enables stronger effects even for words that might have collapsed when their values were increased too much in negative prompts before, by incorporating negative effects into the prompts.
+
+# Instructions
+By checking the "Active" box, it will become effective. In the prompt input screen, entering a negative value like `(word:-1)` will give it a negative effect. It also works with negative prompts, in which case it will have a positive effect.
+
+This was created with the prompt "gothic dress". Despite including `(black:1.8)` in the negative prompt, it's still black. It seems impossible to completely eliminate the blackness of word `gothic`.
+
+![image1](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample.jpg)
+
+Following image created using `(black:-1.8)` in the prompt with NegPiP. It's no longer black.
+
+![image2](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample2.jpg)
+
+By the way, this is what happens when you don't use either NegPiP or negative prompts.
+![image2](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample3.jpg)
+
+## Magical Dandy
+Magical Dandy is a magical dandy. Summoning a magical dandy is very difficult. That's because it requires coexistence of a magical girl and a dandy. But the dandy is weak. The girl is strong. Very strong. So the dandy ends up losing. Even if you put `(girl:1.8)` in the negative prompt, it won't come up. 
+![](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample4.jpg)
+
+Therefore, it may be necessary to input `(girl:-1.6)` in the prompt to remove the girl.
+![](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample5.jpg)
+
+## Hide this extention in text2img/img2img tab
+In the Web-UI, go to Settings > NegPiP.  
+Check the "Hide in Hide in Txt2Img/Img2Img tab" option.  
+If you check this, the "Active" in Settings will be effective.  
+
+## How to Use via API
+The following format is used when utilizing this extension via the API.
+
+```
+"alwayson_scripts": {
+	"NegPiP": {
+		"args": [True]
+}}
+```
+
+## For users of ADetailer
+In the Web-UI, go to Settings > ADetailer.  
+Add ",negpip" to the end of the text box labeled "Script names to apply to ADetailer (separated by comma)"  
+Click "Apply Settings.  
+
+### Update 2023.09.05.2000(JST)
+- Prompt Edittingに対応
+- Regional Prompterに対応(最新版のRegional Prompterが必要)
+- 負の値を入れていないときでも有効化したときに生成結果が変わる問題を修正
+
+- Supports Prompt Editing
+- Supports Regional Prompter (latest version of Regional Prompter required)
+- Fixed the issue where generated results change even when negative values are not entered
+
+- 支持Prompt Editting
+- 支持区Regional Prompter(需要最新版Regional Prompter)
+- 修复了即使没有输入负值时激活也会改变生成结果的问题
+

sd-webui-negpip/README_cn.md

@@ -0,0 +1,51 @@
+# NegPiP - Negative Prompt in Prompt
+[<img src="https://img.shields.io/badge/lang-Egnlish-blue.svg?style=plastic" height="25" />](README.md)
+[<img src="https://img.shields.io/badge/言語-日本語-green.svg?style=plastic" height="25" />](README_jp.md)
+[<img src="https://img.shields.io/badge/语言-中文-red.svg?style=plastic" height="25" />](README_cn.md)
+[<img src="https://img.shields.io/badge/Support-%E2%99%A5-magenta.svg?logo=github&style=plastic" height="25" />](https://github.com/sponsors/hako-mikan)
+
+
+在 SD WebUI 中允许使用反向咒语（提示词）
+
+# 摘要
+该扩展增强了 SD WebUI 的 提示 和 交叉注意力 功能，允许在常规咒语中使用反向咒语，在反向咒语中使用正向咒语。通常情况下，不需要的元素会被放置在反向咒语中，但反向咒语在计算中不一定会产生重大影响。有了这一扩展，就可以使用效果与常规咒语相当的反向咒语。通过在咒语中加入反向效果，即使是以前在反向咒语中数值增加过多而可能崩溃的咒语，也能获得更强的效果。
+
+# 使用
+选中“Active”复选框后，该插件将生效。在提示词输入框中，输入负值（如`(word:-1)`）时会产生反向作用。它也适用于反向咒语，在这种情况下，它将产生正向效应。
+
+这是根据“gothic dress”的提示创建的。尽管在否定提示中包含了`(black:1.8)`，但它仍然是黑色的。要完全消除`gothic`一词的黑色似乎是不可能的。
+
+![image1](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample.jpg)
+
+下图在 NegPiP 中使用`(black:-1.8)`创建，不再是黑色。
+
+![image2](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample2.jpg)
+
+顺带一提，这是不使用 NegPiP 或负面提示时的结果。
+![image2](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample3.jpg)
+
+## 魔法花公
+魔法花公就是带有花花公子属性的魔法少男，但是想要召唤他异常困难。这是因为它需要魔法少女（magical girl）和花花公子（dandy）共存。但dandy属性却很弱。girl属性很坚强。非常强。所以，dandy最终还是输了。即使在反向中输入`(girl:1.8)`，花花公子也还算是不会出现。
+![](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample4.jpg)
+
+因此，可能有必要在正向咒语中输入`(girl:-1.6)`来削弱 girl。
+![](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample5.jpg)
+
+## 在Txt2Img/Img2Img标签中不显示扩展
+在Web-UI中，转到Settings > NegPiP。  
+勾选"Hide in Hide in Txt2Img/Img2Img tab"选项。  
+如果您勾选此选项，Settings中的"Active"将生效。
+
+## 通过 API 使用的方法
+通过 API 使用此扩展时，使用以下格式。
+```
+"alwayson_scripts": {
+	"NegPiP": {
+		"args": [True]
+}}
+```
+
+## 关于与ADetailer的同时使用
+在Web-UI中，前往“Settings” > “ADetailer”。  
+在标有"Script names to apply to ADetailer (separated by comma)"”的文本框末尾添加“,negpip”。  
+点击“Apply Settings”。 

sd-webui-negpip/README_jp.md

@@ -0,0 +1,51 @@
+# NegPiP - Negative Prompt in Prompt
+[<img src="https://img.shields.io/badge/lang-Egnlish-blue.svg?style=plastic" height="25" />](README.md)
+[<img src="https://img.shields.io/badge/言語-日本語-green.svg?style=plastic" height="25" />](README_jp.md)
+[<img src="https://img.shields.io/badge/语言-中文-red.svg?style=plastic" height="25" />](README_cn.md)
+[<img src="https://img.shields.io/badge/Support-%E2%99%A5-magenta.svg?logo=github&style=plastic" height="25" />](https://github.com/sponsors/hako-mikan)
+
+
+負の効果を持つプロンプトを使えるようになります  
+
+# 概要
+この拡張は、stable diffusion web-uiのプロンプトおよびクロスアテンションを拡張して、負の効果を持つプロンプトをプロンプト内で、正の効果を持つプロンプトをネガティブプロンプト内で使用できるようにします。通常、描きたくないものはネガティブプロンプトに書かれますが、ネガティブプロンプトの計算上、あまり効果が現れないことがあります。この拡張では、プロンプトと同程度の効果を持つ負のプロンプトを使用できるようにします。これにより、以前はネガティブプロンプトに置いて値を大きくしすぎて崩壊していたような単語でも、プロンプトに負の効果を持たせることができ、より強い効果が期待できます。
+
+# 使い方
+Activeにチェックを入れることで有効になります。プロンプト入力画面において (word:-1)のようにマイナスの値を入れることで負の効果を持つようになります。ネガティブプロンプトでも有効で、この場合は正の効果を持ちます。値は1より大きな値を入力しないと効果が現れない場合があります。
+
+これはgothic dressというプロンプトで作りました。ネガティブプロンプトに`(black:1.8)`と入れているにもかかわらず黒いですね。gothicの黒を消しきれないです。  
+![image1](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample.jpg)
+
+これはNegPiPでプロンプトに`(black:-1.8)`を入れました。黒くなくなりましたね。
+![image2](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample2.jpg)
+
+ちなみに、NegPiPもネガティブプロンプトも使わないとこうなります。
+![image3](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample3.jpg)
+
+## マジカルダンディ
+マジカルダンディはマジカルなダンディです。マジカルなダンディを呼び出すことはとても難しいです。それはmagical girlとdandyを共存させる必要があるからです。でもダンディは弱いです。girlは強いです。とても強いです。なのでダンディは負けてしまいます。ネガティブプロンプトに`(girl:1.8)`って入れても出てきません。
+![](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample4.jpg)
+
+なのでプロンプトの方に`(girl:-1.6)`と入れてgirlを消す必要があるんじゃんよ。
+![](https://github.com/hako-mikan/sd-webui-negpip/blob/imgs/sample5.jpg)
+
+## Txt2Img/Img2Imgタブで拡張を表示しない
+Web-UIで、Settings > NegPiPに移動します。  
+"Hide in Hide in Txt2Img/Img2Img tab"のオプションをチェックしてください。  
+これをチェックすると、Settingsの"Active"が有効になります。
+
+## APIで有効化する
+スクリプトの指定を以下のように記述してください。
+
+```
+"alwayson_scripts": {
+	"NegPiP": {
+		"args": [True]
+}}
+```
+
+## ADetailerとの併用について
+Web-UIで、Settings > ADetailerに移動してください。  
+「Script names to apply to ADetailer (separated by comma)」と書かれたテキストボックスの末尾に「,negpip」を追加し、Apply Settings  
+
+

sd-webui-negpip/javascript/inputAccordion_m.js

@@ -0,0 +1,56 @@
+function setupAccordion_m(accordion) {
+    // 既に処理済みの場合はスキップ
+    if (accordion.getAttribute('data-processed') === 'true') {
+        return;
+    }
+    accordion.setAttribute('data-processed', 'true'); // 処理済みフラグを追加
+
+    var labelWrap = accordion.querySelector('.label-wrap');
+    var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
+    var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
+    var span = labelWrap.querySelector('span');
+
+    // 初期状態をGradioの状態に基づいて設定
+    var visibleCheckbox = document.createElement('INPUT');
+    visibleCheckbox.type = 'checkbox';
+    visibleCheckbox.checked = gradioCheckbox.checked; // Gradioの初期状態を継承
+    visibleCheckbox.id = accordion.id + "-visible-checkbox";
+    visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox";
+
+    // 既にチェックボックスが存在していないか確認して追加
+    if (!span.querySelector(`#${visibleCheckbox.id}`)) {
+        span.insertBefore(visibleCheckbox, span.firstChild);
+    }
+
+    accordion.visibleCheckbox = visibleCheckbox;
+
+    if (extra) {
+        labelWrap.insertBefore(extra, labelWrap.lastElementChild);
+    }
+
+    // チェックボックスクリック時のイベント
+    visibleCheckbox.addEventListener('click', function(event) {
+        event.stopPropagation(); // クリックイベントの伝播を停止
+        console.log(`Checkbox in accordion ${accordion.id} is now: `, visibleCheckbox.checked);
+
+        // Gradioのチェックボックス状態を更新
+        gradioCheckbox.checked = visibleCheckbox.checked;
+        gradioCheckbox.dispatchEvent(new Event('input', { bubbles: true }));
+    });
+
+    // Gradioチェックボックスの変更を監視して表示用チェックボックスを更新
+    var observer = new MutationObserver(function(mutations) {
+        mutations.forEach(function(mutation) {
+            visibleCheckbox.checked = gradioCheckbox.checked;
+            console.log(`Visible checkbox in accordion ${accordion.id} updated to: `, visibleCheckbox.checked);
+        });
+    });
+
+    observer.observe(gradioCheckbox, { attributes: true, attributeFilter: ['checked'] });
+}
+
+onUiLoaded(function() {
+    for (var accordion of gradioApp().querySelectorAll('.input-accordion-m')) {
+        setupAccordion_m(accordion);
+    }
+});

sd-webui-negpip/scripts/negpip_v2_finals.py

@@ -0,0 +1,1418 @@
+import gradio as gr
+# ══════════════════════════════════════════════════════════════════════════════
+# NegPiP — Negative Prompt in Positive  (plan-based appended NegPiP v2)
+#
+# Architecture summary
+# ────────────────────
+# This is plan-based appended NegPiP v2.  It is NOT a v3 true token-level
+# / embedding-level rewrite.  What that means:
+#
+#   v2 (this file):
+#     - NegPiP targets are re-encoded as separate conditioning tensors
+#     - those tensors are APPENDed to the context before attention
+#     - NegpipSpan / NegpipRegionPlan describe exactly which appended tokens
+#       belong to which target, so per-target V inversion is precise
+#     - _current_negpip_plan is set in sub_forward for every call to
+#       main_forward so cond/uncond branches always use the right plan
+#     - NEGPIP_USE_V2 = False falls back to v1 rev2 behaviour globally
+#
+#   v3 (future, separate task):
+#     - no append-conditioning architecture
+#     - sign-aware manipulation inside the base context token encoding
+#     - exact in-base-context token spans
+#     - deeper integration by analogy with ComfyUI-ppm
+#
+# Extraction pipeline (from v1 rev2, preserved here):
+#   Phase A — _strip_negpip_from_original()  called ONCE per prompt, on the
+#              original (un-normalised) text, before the step loop.
+#   Phase B — _find_negpip_segments()        called per-step on schedule
+#              strings; adds targets to textweights WITHOUT removal gate so
+#              scheduled prompts never lose targets on repeated steps.
+#
+# Compatibility matrix (all paths preserved):
+#   classic SD1.5 / SDXL  →  _ClassicBackend  + hook_forwards
+#   Forge Flux             →  _FluxBackend     + hook_forwards_f
+#   Forge ZImage / Lumina  →  _ZImageBackend   + hook_forwards_z
+#   Regional Prompter      →  enable_rp_latent + region_index=self.latenti
+# ══════════════════════════════════════════════════════════════════════════════
+import torch
+import re
+import json
+from torch import nn, einsum
+from einops import rearrange, repeat
+from inspect import isfunction
+from modules.ui import versions_html
+from packaging import version
+from functools import wraps
+
+# ══════════════════════════════════════════════════════════════════════════════
+# v2 data model
+# ══════════════════════════════════════════════════════════════════════════════
+from dataclasses import dataclass, field
+from typing import Optional, List
+import warnings as _warnings
+
+# Feature flag — set to False to fall back to v1 behaviour globally.
+NEGPIP_USE_V2: bool = True
+
+@dataclass
+class NegpipSpan:
+    """
+    Describes one target's token range within the APPENDED NegPiP conditioning.
+
+    ``token_offset`` counts from the END of the appended block, in tokens.
+    ``token_count``  is the number of tokens occupied by this target.
+    ``sign``         is applied to V for this span (-1.0 for standard NegPiP).
+
+    Example: two targets appended as [T1-tokens | T2-tokens]:
+      T2: NegpipSpan(token_offset=0,             token_count=len_T2, sign=-1)
+      T1: NegpipSpan(token_offset=len_T2,        token_count=len_T1, sign=-1)
+    """
+    token_offset: int
+    token_count:  int
+    sign:         float = -1.0
+
+@dataclass
+class NegpipRegionPlan:
+    """
+    Fully describes the NegPiP conditioning appended for ONE region at ONE step.
+
+    Replaces the raw ``(cond_tensor, n_tokens)`` tuple returned by conddealer.
+    ``spans`` provides per-target metadata so attention can do precise,
+    per-span V inversion rather than a single blanket operation.
+    """
+    region_pad:   int                = 0
+    cond_tensor:  Optional[object]   = None  # torch.Tensor | None
+    spans:        List[NegpipSpan]   = field(default_factory=list)
+    total_tokens: int                = 0
+
+    def to_legacy(self):
+        """Return (cond_tensor, total_tokens) for v1-fallback paths."""
+        return self.cond_tensor, self.total_tokens
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+# Backend adapters
+# ══════════════════════════════════════════════════════════════════════════════
+
+class _NegpipBackendBase:
+    """
+    Thin interface isolating backend-specific conditioning logic.
+
+    Subclasses implement encode_target() for their backend.
+    The returned (cond_data_tensor, raw_tokenlen) is consumed by
+    _encode_negpip_plan() which then builds NegpipRegionPlan.
+    """
+    def encode_target(self, text: str, abs_weight: float, sd_model, p):
+        """
+        Encode ONE NegPiP target.
+
+        Returns (cond_data, tokenlen) where:
+          cond_data  — raw conditioning tensor (before slicing / padding)
+          tokenlen   — token count for 'text' alone (no BOS/EOS)
+        """
+        raise NotImplementedError
+
+    def slice_cond(self, cond_data, tokenlen: int, isxl: bool, cond_key: str):
+        """
+        Slice cond_data to the relevant token range for this backend.
+        Default: SD/SDXL behaviour (1:tokenlen+2 rows, skipping BOS/EOS).
+        """
+        if isxl:
+            return cond_data[cond_key][1:tokenlen + 2, :]
+        return cond_data[1:tokenlen + 2, :]
+
+
+class _ClassicBackend(_NegpipBackendBase):
+    """A1111 classic / Forge classic."""
+    def __init__(self, tokenizer, isxl: bool, cond_key: str, batch: int):
+        self.tokenizer = tokenizer
+        self.isxl      = isxl
+        self.cond_key  = cond_key
+        self.batch     = batch
+
+    def encode_target(self, text, abs_weight, sd_model, p):
+        from modules import devices, prompt_parser
+        inp = SdConditioning([f"({text}:{abs_weight})"], width=p.width, height=p.height)
+        with devices.autocast():
+            cond = prompt_parser.get_learned_conditioning(sd_model, inp, p.steps)
+        cond_data = cond[0][0].cond
+        _, tokenlen = self.tokenizer(text)
+        return cond_data, tokenlen
+
+    def slice_cond(self, cond_data, tokenlen, isxl, cond_key):
+        if isxl:
+            return cond_data[cond_key][1:tokenlen + 2, :]
+        return cond_data[1:tokenlen + 2, :]
+
+
+class _FluxBackend(_NegpipBackendBase):
+    """Forge Flux."""
+    def __init__(self, tokenizer, cond_key: str, batch: int):
+        self.tokenizer = tokenizer
+        self.cond_key  = cond_key
+        self.batch     = batch
+
+    def encode_target(self, text, abs_weight, sd_model, p):
+        from modules import devices, prompt_parser
+        inp = SdConditioning([f"({text}:{abs_weight})"], width=p.width, height=p.height)
+        with devices.autocast():
+            cond = prompt_parser.get_learned_conditioning(sd_model, inp, p.steps)
+        cond_data = cond[0][0].cond
+        _, tokenlen = self.tokenizer(text)
+        return cond_data, tokenlen
+
+    def slice_cond(self, cond_data, tokenlen, isxl, cond_key):
+        return cond_data[cond_key][0:tokenlen + 1, :]
+
+
+class _ZImageBackend(_NegpipBackendBase):
+    """Forge ZImage / Lumina."""
+    def __init__(self, tokenizer, batch: int):
+        self.tokenizer = tokenizer
+        self.batch     = batch
+
+    def encode_target(self, text, abs_weight, sd_model, p):
+        from modules import devices, prompt_parser
+        inp = SdConditioning([f"({text}:{abs_weight})"], width=p.width, height=p.height)
+        with devices.autocast():
+            cond = prompt_parser.get_learned_conditioning(sd_model, inp, p.steps)
+        cond_data = cond[0][0].cond
+        _, tokenlen = self.tokenizer(text)
+        return cond_data, tokenlen
+
+    def slice_cond(self, cond_data, tokenlen, isxl, cond_key):
+        # ZImage: skip first 3 and last 5 special tokens
+        return cond_data[3:-5, :]
+
+
+def _apply_negpip_spans(v_tensor, plan: NegpipRegionPlan) -> None:
+    """
+    Apply per-span V inversion in-place using a NegpipRegionPlan.
+
+    v_tensor shape: (batch*heads, seq_len, head_dim)   [main_forward2]
+                or  (batch, seq_len, dim)               [main_forward]
+
+    The NegPiP conditioning block is APPENDED to the context, so the
+    last ``plan.total_tokens`` positions in seq_len belong to NegPiP.
+    Within that block, each NegpipSpan tracks offset-from-end and count.
+    """
+    if not plan.spans or plan.total_tokens == 0:
+        return
+    total = plan.total_tokens
+    for span in plan.spans:
+        if span.token_count <= 0:
+            continue
+        # Absolute positions from the end of v_tensor (seq dimension = dim 1):
+        #   end_pos   = -(span.token_offset)          or None if offset == 0
+        #   start_pos = -(span.token_offset + span.token_count)
+        end_pos   = -span.token_offset if span.token_offset > 0 else None
+        start_pos = -(span.token_offset + span.token_count)
+        if end_pos is not None:
+            v_tensor[:, start_pos:end_pos, :] *= span.sign
+        else:
+            v_tensor[:, start_pos:, :] *= span.sign
+
+
+classic = forge = reforge = False
+try:
+    import ldm.modules.attention as atm
+    forge = False
+except:
+    #forge
+    try:
+        from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+        from backend.nn.flux import attention, fp16_fix
+        forge = True
+    except:
+        classic = True
+
+reforge = "reForge" in versions_html()
+
+import torch.nn.functional as F
+import modules.ui
+import modules
+from modules import prompt_parser, devices
+from modules import shared
+from modules.script_callbacks import CFGDenoiserParams, on_cfg_denoiser, on_ui_settings
+
+debug = False
+debug_p = False
+
+OPT_ACT = "negpip_active"
+OPT_HIDE = "negpip_hide"
+
+NEGPIP_T = "customscript/negpip.py/txt2img/Active/value"
+NEGPIP_I = "customscript/negpip.py/img2img/Active/value"
+CONFIG = shared.cmd_opts.ui_config_file
+
+try:
+    with open(CONFIG, 'r', encoding="utf-8") as json_file:
+        ui_config = json.load(json_file)
+except Exception:
+    ui_config = {}
+
+startup_t = ui_config[NEGPIP_T] if NEGPIP_T in ui_config else None
+startup_i = ui_config[NEGPIP_I] if NEGPIP_I in ui_config else None
+active_t = "Active" if startup_t else "Not Active"
+active_i = "Active" if startup_i else "Not Active"
+
+opt_active = getattr(shared.opts,OPT_ACT, True)
+opt_hideui = getattr(shared.opts,OPT_HIDE, False)
+
+# ──────────────────────────────────────────────────────────────────────────────
+# NegPiP extraction: structural bracket-aware scanner
+#
+# Why not regex?
+# The old minusgetter regex r'\(([^(:)]*):...\)' breaks on:
+#   - scientific notation:  (cat:-1e-3)   — [^(:)]* stops at 'e'
+#   - nested emphasis:      (red (glowing eyes):-1.4)  — [^(:]* refuses '('
+#
+# The new approach scans matching parentheses structurally,
+# then checks if the content ends with :−NUMBER.
+# This is O(n) and handles arbitrary nesting depth.
+#
+# LEGACY fallback regex (kept for reference; no longer used as primary path):
+_LEGACY_MINUSGETTER = r'\(([^(:)]*):\s*-[\d]+(\.[/\d]+)?(?:\s*)\)'
+
+def _find_negpip_segments(prompt: str) -> list:
+    """
+    Structural NegPiP extractor.
+
+    Scans ``prompt`` for all top-level ``(inner : -NUMBER)`` constructs,
+    where inner may itself contain nested parentheses (e.g. emphasis).
+
+    Returns a list of tuples:
+        (start: int, end: int, inner: str, weight: float)
+
+    where [start:end] is the full span including outer parens,
+    ``inner`` is the raw text before the colon, and ``weight`` is the
+    (negative) float.
+
+    Scientific notation (1e-3, 1E+2, …) and leading-dot numbers (-.5)
+    are all supported.
+
+    Edge cases:
+    - weight == 0 or -0.0  → not treated as NegPiP, skipped
+    - BREAK token           → no parens, never matched
+    - Positive weights      → weight > 0, skipped by caller
+    """
+    results = []
+    i = 0
+    n = len(prompt)
+    # Regex for the LAST colon + negative number inside bracket content.
+    # re.DOTALL so '.' matches newlines in pathological inputs.
+    _tail = re.compile(
+        r'^(.*?):\s*'
+        r'(-(?:\d+(?:\.\d*)?(?:[eE][+-]?\d+)?|\.\d+(?:[eE][+-]?\d+)?))'
+        r'\s*$',
+        re.DOTALL,
+    )
+    while i < n:
+        if prompt[i] != '(':
+            i += 1
+            continue
+        # Walk forward tracking bracket depth
+        depth = 1
+        j = i + 1
+        while j < n and depth > 0:
+            c = prompt[j]
+            if c == '(':
+                depth += 1
+            elif c == ')':
+                depth -= 1
+            j += 1
+        # prompt[i:j] is the full "(…)" span; content is without outer parens
+        content = prompt[i + 1: j - 1]
+        m = _tail.match(content)
+        if m:
+            weight = float(m.group(2))
+            if weight < 0:                          # strict negative only
+                results.append((i, j, m.group(1), weight))
+        i = j
+    return results
+
+def _strip_negpip_from_original(prompt: str) -> str:
+    """
+    Remove all NegPiP segments from the ORIGINAL (pre-parser) prompt string.
+
+    Must be called ONCE per prompt, before the per-step schedule loop.
+    Uses _find_negpip_segments on the original text so that un-normalised
+    forms like (red (glowing eyes):-1.4) are found and removed correctly —
+    not the v21-normalised schedule form which may differ in number format
+    or nested-emphasis structure.
+
+    Spans are removed in reverse order so earlier indices remain valid.
+    """
+    segs = _find_negpip_segments(prompt)
+    if not segs:
+        return prompt
+    chars = list(prompt)
+    for start, end, _inner, _w in reversed(segs):
+        del chars[start:end]
+    return ''.join(chars)
+
+
+COND_KEY_C = "crossattn"
+COND_KEY_V = "vector"
+
+class Script(modules.scripts.Script):   
+    def __init__(self):
+        self.active = False
+        self.conds = None
+        self.unconds = None
+        self.conlen = []   
+        self.unlen = []
+        self.contokens = []   
+        self.untokens = []
+        self.hr = False
+        self.x = None
+
+        self.ipa = None
+
+        self.enable_rp_latent = False
+        
+    def title(self):
+        return "NegPiP"
+
+    def show(self, is_img2img):
+        return modules.scripts.AlwaysVisible
+
+    infotext_fields = None
+    paste_field_names = []
+
+    def ui(self, is_img2img):    
+        with InputAccordion(startup_i if is_img2img else startup_t, label=self.title(), visible = not opt_hideui) as active:
+            toggle = gr.Button(elem_id="switch_default", value=f"Toggle startup with Active(Now:{startup_i if is_img2img else startup_t}), Needs Restart to Apply", variant="primary")
+
+        def f_toggle(is_img2img):
+            key = NEGPIP_I if is_img2img else NEGPIP_T
+
+            # Safe read: if config file is missing, start with empty dict
+            try:
+                with open(CONFIG, 'r', encoding="utf-8") as json_file:
+                    data = json.load(json_file)
+            except Exception:
+                data = {}
+
+            data[key] = not data.get(key, False)
+
+            # Safe write: create parent dir if needed
+            try:
+                import os
+                os.makedirs(os.path.dirname(CONFIG), exist_ok=True)
+                with open(CONFIG, 'w', encoding="utf-8") as json_file:
+                    json.dump(data, json_file, indent=4)
+            except Exception as e:
+                print(f"[NegPiP] WARNING: could not write config: {e}")
+
+            return gr.update(value = f"Toggle startup Active(Now:{data[key]})")
+
+        toggle.click(fn=f_toggle,inputs=[gr.Checkbox(value = is_img2img, visible = False)],outputs=[toggle])
+
+        self.infotext_fields = [
+                (active, "NegPiP Active"),
+        ]
+
+        for _,name in self.infotext_fields:
+            self.paste_field_names.append(name)
+
+        return [active]
+
+    def process_batch(self, p, active,**kwargs):
+        self.__init__()
+        flag = False
+
+        if getattr(shared.opts,OPT_HIDE, False) and not getattr(shared.opts,OPT_ACT, False): return
+        elif not active: return
+
+        self.rpscript = None
+        #get infomation of regponal prompter
+        from modules.scripts import scripts_txt2img
+        for script in scripts_txt2img.alwayson_scripts:
+            if "rp.py" in script.filename:
+                self.rpscript = script
+
+        self.hrp, self.hrn = hr_dealer(p)
+
+        self.active = active
+        self.batch = p.batch_size
+        # old call, hasattr(shared.sd_model,"conditioner"), no longer works on forge backend, but forge backend provides its own way to do this.
+        self.isxl = p.sd_model.is_sdxl
+        
+        # if you want to change other things to be more mnemonic to the current backend, here's the pprint calls i used to figure all this out in my initial port.
+        # lllyasviel should really document this stuff. it's a nice backend! but he hasn't told any of us how to use it.
+        #pprint(dir(p))
+        #pprint(dir(p.sd_model))
+        #pprint(dir(p.sd_model.forge_objects.unet))
+        #pprint(dir(p.sd_model.forge_objects.clip))
+        #pprint(dir(p.sd_model.forge_objects.clip.tokenizer))
+        #pprint(p.sd_model.is_sdxl)
+        
+        self.rev = p.sampler_name in ["DDIM", "PLMS", "UniPC"]
+        if forge or reforge or classic: self.rev =  not self.rev 
+        self.modeltype = modeltype = "SD"
+         
+        if forge:
+            if type(p.sd_model).__name__ == "ZImage":
+                tokenizer = p.sd_model.text_processing_engine_gemma.tokenize_line
+                self.modeltype = modeltype = "ZImage"
+                input = SdConditioning([""], width=p.width, height=p.height)
+                p.sd_model.text_processing_engine_gemma(input)
+            elif hasattr(p.sd_model, "text_processing_engine_l"):
+                tokenizer = p.sd_model.text_processing_engine_l.tokenize_line
+            else:
+                tokenizer = p.sd_model.text_processing_engine.tokenize_line
+            if "flux" in str(type(p.sd_model.forge_objects.unet.model.diffusion_model)):
+                self.modeltype = modeltype = "flux"  
+                
+        else:
+            tokenizer = shared.sd_model.conditioner.embedders[0].tokenize_line if self.isxl else shared.sd_model.cond_stage_model.tokenize_line
+
+        def _normalize_sep(text: str, seps) -> str:
+            """
+            Normalise Regional Prompter separator.
+
+            prompt_parser_fixed_v21 converts 'AND' -> '&' internally, so
+            schedule strings may arrive with '&' even when the RP script
+            reports seps='AND'.  Normalise so split() always works.
+            """
+            if seps == "AND":
+                return text.replace(" & ", " AND ")
+            return text
+
+        def getshedulednegs(scheduled, prompts):
+            """
+            Extract NegPiP targets from prompt schedules.
+
+            v1-patch (rev2): architectural fix — strip and extract are now
+            SEPARATE operations with separate responsibilities.
+
+            Phase A — strip (called once per prompt, on the ORIGINAL):
+              _strip_negpip_from_original() scans the original (un-normalised)
+              prompt and removes all negpip spans.  This is done before the
+              step loop so that:
+              - the original un-normalised form is found correctly, not a
+                v21-normalised schedule string
+              - subsequent steps do not attempt to re-strip an already-clean prompt
+
+            Phase B — extract (per step, from schedule string, no removal gate):
+              _find_negpip_segments() scans the schedule string for each step.
+              Targets are added to textweights REGARDLESS of whether Phase A
+              succeeded in removing them from the original.  This fixes the
+              regression where scheduled targets were lost on repeated steps.
+
+            Fixes vs v1 rev1:
+              - nested emphasis (red (glowing eyes):-1.4) now ACTUALLY applied,
+                not silently skipped when v21 normalises the inner text
+              - [cat:(dog:-1):0.5] no longer loses target after the first step
+              - AND/& separator handled correctly for Regional Prompter
+            """
+            output = []
+            nonlocal flag
+
+            # ── Phase A: strip negpip spans from original prompts ONCE ────────
+            # We scan each original prompt (not a schedule string) so that
+            # the un-normalised form is used for removal.  This runs before
+            # the step loop so repeated steps never try to re-strip.
+            for i in range(len(prompts)):
+                prompts[i] = _strip_negpip_from_original(prompts[i])
+
+            # ── Phase B: extract targets per step (no removal) ─────────────────
+            for i, batch_shedule in enumerate(scheduled):
+                stepout = []
+                seps = None
+                if self.rpscript:
+                    if hasattr(self.rpscript, "seps"):
+                        seps = self.rpscript.seps
+                    self.enable_rp_latent = seps == "AND"
+
+                for step, prompt in batch_shedule:
+                    # Normalise AND/& before splitting for Regional Prompter
+                    prompt_norm = _normalize_sep(prompt, seps)
+                    sep_prompts = prompt_norm.split(seps) if seps else [prompt_norm]
+
+                    padd = 0
+                    padtextweight = []
+                    for sep_prompt in sep_prompts:
+                        textweights = []
+
+                        # Structural extraction from schedule string.
+                        # textweights.append is NOT gated on removal —
+                        # removal already happened in Phase A.
+                        for _s, _e, inner, weight in _find_negpip_segments(sep_prompt):
+                            text = inner.strip()
+                            if text == "BREAK":
+                                continue
+                            textweights.append([text, weight])
+                            flag = True
+
+                        padtextweight.append([padd, textweights])
+                        tokens, tokensnum = tokenizer(sep_prompt)
+                        padd = tokensnum // 75 + 1 + padd
+
+                    stepout.append([step, padtextweight])
+                output.append(stepout)
+            return output
+      
+        
+        scheduled_p = prompt_parser.get_learned_conditioning_prompt_schedules(p.prompts,p.steps)
+        scheduled_np = prompt_parser.get_learned_conditioning_prompt_schedules(p.negative_prompts,p.steps)
+
+        if self.hrp: scheduled_hr_p = prompt_parser.get_learned_conditioning_prompt_schedules(p.hr_prompts,p.hr_second_pass_steps if p.hr_second_pass_steps > 0 else p.steps)
+        if self.hrn: scheduled_hr_np = prompt_parser.get_learned_conditioning_prompt_schedules(p.hr_negative_prompts,p.hr_second_pass_steps if p.hr_second_pass_steps > 0 else p.steps)
+
+        nip = getshedulednegs(scheduled_p,p.prompts)
+        pin = getshedulednegs(scheduled_np,p.negative_prompts)
+
+        if self.hrp: hr_nip = getshedulednegs(scheduled_hr_p,p.hr_prompts)
+        if self.hrn: hr_pin = getshedulednegs(scheduled_hr_np,p.hr_negative_prompts)
+
+        cond_key = COND_KEY_C
+
+        # ── Build backend adapter ─────────────────────────────────────────
+        if NEGPIP_USE_V2:
+            if modeltype == "flux":
+                _backend = _FluxBackend(tokenizer, cond_key, self.batch)
+            elif modeltype == "ZImage":
+                _backend = _ZImageBackend(tokenizer, self.batch)
+            else:
+                _backend = _ClassicBackend(tokenizer, self.isxl, cond_key, self.batch)
+
+        def _encode_negpip_plan(region_pad: int, targets) -> NegpipRegionPlan:
+            """
+            v2 replacement for conddealer.
+
+            Encodes each target individually, tracks its token count as a
+            NegpipSpan, and assembles a NegpipRegionPlan.
+
+            Spans are ordered so that the FIRST target appended has the
+            highest offset-from-end (it's furthest from the V tail).
+
+            Falls back to legacy behaviour (NegpipRegionPlan wrapping the
+            old return values) if NEGPIP_USE_V2 is False.
+            """
+            if not NEGPIP_USE_V2:
+                # v1 fallback path — original conddealer logic
+                cond_t, n_tok = _conddealer_v1(targets)
+                plan = NegpipRegionPlan(region_pad=region_pad,
+                                        cond_tensor=cond_t,
+                                        total_tokens=n_tok if n_tok else 0)
+                if n_tok:
+                    plan.spans = [NegpipSpan(token_offset=0, token_count=n_tok, sign=-1.0)]
+                return plan
+
+            slices = []
+            spans  = []
+            running_offset = 0          # total tokens accumulated so far
+            strength_list  = []         # for flux/ZImage strength array
+
+            for text, weight in targets:
+                abs_w = abs(weight)
+                try:
+                    cond_data, tokenlen = _backend.encode_target(text, abs_w, shared.sd_model, p)
+                except Exception as exc:
+                    print(f"[NegPiP v2] WARNING: failed to encode {text!r}: {exc}. Skipping.")
+                    continue
+
+                slc = _backend.slice_cond(cond_data, tokenlen, self.isxl, cond_key)
+                actual_len = slc.shape[0]
+
+                slices.append(slc)
+                running_offset += actual_len
+
+                # Strength bookkeeping (flux/ZImage only)
+                if modeltype in ("flux", "ZImage"):
+                    strength_list.extend([weight] * actual_len)
+
+            if not slices:
+                return NegpipRegionPlan(region_pad=region_pad)
+
+            # Build spans AFTER we know all lengths (offsets count from end)
+            # Rebuild running_offset from slice shapes in reverse:
+            total = sum(s.shape[0] for s in slices)
+            acc   = 0
+            for slc in reversed(slices):
+                cnt = slc.shape[0]
+                spans.append(NegpipSpan(token_offset=acc, token_count=cnt, sign=-1.0))
+                acc += cnt
+            spans.reverse()   # restore order (first target = highest offset)
+
+            # Assemble conditioning tensor
+            conds = torch.cat(slices, 0).unsqueeze(0).repeat(self.batch, 1, 1)
+
+            if modeltype in ("flux", "ZImage"):
+                self.strength = strength_list
+
+            return NegpipRegionPlan(
+                region_pad   = region_pad,
+                cond_tensor  = conds,
+                spans        = spans,
+                total_tokens = total,
+            )
+
+        def _conddealer_v1(targets):
+            """Original conddealer logic, kept verbatim as v1 fallback."""
+            conds = []
+            start = None
+            end   = None
+            if modeltype == "flux":
+                strength = []
+                for target in targets:
+                    input = SdConditioning([f"({target[0]}:{-target[1]})"], width=p.width, height=p.height)
+                    with devices.autocast():
+                        cond = prompt_parser.get_learned_conditioning(shared.sd_model,input,p.steps)
+                    cond_data = cond[0][0].cond
+                    token, tokenlen = tokenizer(target[0])
+                    conds.append(cond_data[cond_key][0:tokenlen + 1, :])
+                    strength.extend([target[1]]*(cond_data.shape[0]))
+                conds = torch.cat(conds,0).unsqueeze(0)
+                conds = conds.repeat(self.batch,1,1)
+                self.strength = strength
+                return conds, conds.shape[1]
+            if modeltype == "ZImage":
+                strength = []
+                for target in targets:
+                    input = SdConditioning([f"({target[0]}:{-target[1]})"], width=p.width, height=p.height)
+                    with devices.autocast():
+                        cond = prompt_parser.get_learned_conditioning(shared.sd_model,input,p.steps)
+                    cond_data = cond[0][0].cond
+                    conds.append(cond_data[3:-5, :])
+                    strength.extend([target[1]]*(cond_data.shape[0]-8))
+                conds = torch.cat(conds,0).unsqueeze(0)
+                conds = conds.repeat(self.batch,1,1)
+                self.strength = strength
+                return conds, conds.shape[1]
+            for target in targets:
+                input = SdConditioning([f"({target[0]}:{-target[1]})"], width=p.width, height=p.height)
+                with devices.autocast():
+                    cond = prompt_parser.get_learned_conditioning(shared.sd_model,input,p.steps)
+                cond_data = cond[0][0].cond
+                if start is None: start = cond_data[0:1, :] if not self.isxl else cond_data[cond_key][0:1, :]
+                if end is None: end = cond_data[-1:, :] if not self.isxl else cond_data[cond_key][-1:, :]
+                token, tokenlen = tokenizer(target[0])
+                conds.append(cond_data[1:tokenlen+2,:] if not self.isxl else cond_data[cond_key][1:tokenlen+2, :])
+            conds = torch.cat(conds, 0).unsqueeze(0)
+            return conds.repeat(self.batch,1,1), conds.shape[1]
+
+        def calcconds(targetlist):
+            """
+            v2: returns list of NegpipRegionPlan objects instead of
+            raw (tensor, n_tokens) tuples.  Legacy .to_legacy() is
+            available on each plan for any code that needs the old format.
+            """
+            outconds = []
+            for batch in targetlist:
+                stepconds = []
+                for step, regions in batch:
+                    regionplans = []
+                    for region_pad, targets in regions:
+                        if targets:
+                            plan = _encode_negpip_plan(region_pad, targets)
+                        else:
+                            plan = NegpipRegionPlan(region_pad=region_pad)
+                        regionplans.append(plan)
+                    stepconds.append([step, regionplans])
+                outconds.append(stepconds)
+            return outconds
+            
+        self.conds_all = calcconds(nip)
+        self.unconds_all = calcconds(pin)
+
+        if self.hrp: self.hr_conds_all = calcconds(hr_nip)
+        if self.hrn: self.hr_unconds_all = calcconds(hr_pin)
+
+        #print(self.conds_all)
+        #print(self.unconds_all)
+
+        resetpcache(p)
+        
+        def calcsets(A, B):
+            return A // B if A % B == 0 else A // B + 1
+
+        # NOTE: conlen/unlen are computed from prompts[0] only.
+        # Heterogeneous batches (different prompt lengths per item) are a
+        # pre-existing limitation inherited from orig; not introduced by v2.
+        self.conlen = calcsets(tokenizer(p.prompts[0])[1],75)
+        self.unlen  = calcsets(tokenizer(p.negative_prompts[0])[1],75)
+
+        if not flag:
+            self.active = False
+            unload(self,p)
+            return   
+
+        if not hasattr(self,"negpip_dr_callbacks"):
+            self.negpip_dr_callbacks = on_cfg_denoiser(self.denoiser_callback)
+
+        #disable hookforward if hookfoward in regional prompter is eanble. 
+        #negpip operation is treated in regional prompter
+
+        already_hooked = False
+        if self.rpscript is not None and hasattr(self.rpscript,"hooked"):already_hooked = self.rpscript.hooked
+
+        if not already_hooked:
+            if forge:
+                if modeltype == "flux":
+                    self.handle = hook_forwards_f(self, p.sd_model.forge_objects.unet.model) 
+                elif modeltype == "ZImage":
+                    self.handle = hook_forwards_z(self, p.sd_model.forge_objects.unet.model) 
+                else:
+                    self.handle = hook_forwards(self, p.sd_model.forge_objects.unet.model) 
+            else:
+                self.handle = hook_forwards(self, p.sd_model.model.diffusion_model)
+
+        def _plan_tokens(all_conds):
+            try:
+                plan = all_conds[0][0][1][0]   # batch[0], step[0], regions[0]
+                return plan.total_tokens if isinstance(plan, NegpipRegionPlan) else plan[2]
+            except Exception:
+                return "?"
+        print(f"NegPiP enable, Positive:{_plan_tokens(self.conds_all)}, Negative:{_plan_tokens(self.unconds_all)}")
+
+        p.extra_generation_params.update({
+            "NegPiP Active":active,
+        })
+
+    def postprocess(self, p, processed, *args):
+        unload(self,p)
+        self.conds_all = None
+        self.unconds_all = None
+       
+    def denoiser_callback(self, params: CFGDenoiserParams):
+        if debug: print("denoiser_callback",params.sampling_step, params.text_cond.shape)
+        if self.active:
+            if self.x is None: self.x = params.x.shape
+            if self.x != params.x.shape: self.hr = True
+
+            self.latenti = 0
+            # Cleanup: reset plan lists each callback so no stale state leaks
+            # between steps on exotic paths.  They are repopulated below.
+            self._cond_plans   = []
+            self._uncond_plans = []
+            # Keep Flux/ZImage plan aligned with the actual appended cond tensor.
+            self._flux_cond_plan   = None
+            self._zimage_cond_plan = None
+
+            condslist = []
+            tokenslist = []
+            conds = self.hr_conds_all if self.hrp and self.hr else  self.conds_all
+            if conds is not None:
+                for step, regions in conds[0]:
+                    #print(" ", step,params.sampling_step)
+                    if step >= params.sampling_step + 2:
+                        for plan in regions:
+                            # plan is NegpipRegionPlan; .to_legacy() for v1 compat
+                            condslist.append(plan.cond_tensor)
+                            tokenslist.append(plan.total_tokens)
+                            if debug: print(f"current:{params.sampling_step + 2},selected:{step}")
+                        # v2 fix: select plans from the SAME step as cond tensors.
+                        # Previously used a ">= step" comprehension which could collect
+                        # plans from multiple future steps, making plan/tensor mismatched.
+                        self._cond_plans = list(regions)   # same 'regions' from this break
+                        break
+                self.conds     = condslist
+                self.contokens = tokenslist
+
+            uncondslist = []
+            untokenslist = []
+            unconds = self.hr_unconds_all if self.hrn and self.hr else  self.unconds_all
+            if unconds is not None:
+                for step, regions in unconds[0]:
+                    if step >= params.sampling_step + 2:
+                        for plan in regions:
+                            uncondslist.append(plan.cond_tensor)
+                            untokenslist.append(plan.total_tokens)
+                        # v2 fix: same-step synchronisation as cond above.
+                        self._uncond_plans = list(regions)
+                        break
+                self.unconds   = uncondslist
+                self.untokens  = untokenslist
+                
+            global pn, count
+            #pn = False if forge or reforge or classic else True
+            pn = True
+            count = 0
+        
+        if self.modeltype == "flux" and self.conds:
+            # v2 fix:
+            # choose the FIRST non-None cond tensor AND remember the MATCHING plan.
+            # This keeps appended conditioning and per-span inversion in sync even if
+            # region[0] is empty and a later region is the first real NegPiP target.
+            for _cond, _plan in zip(self.conds, getattr(self, '_cond_plans', [])):
+                if _cond is not None:
+                   self._flux_cond_plan = _plan
+                   self.orig_tokens = params.text_cond[COND_KEY_C].shape[1]
+                   params.text_cond[COND_KEY_C] = torch.cat([params.text_cond[COND_KEY_C], _cond], 1)
+                   break
+
+        if self.modeltype == "ZImage" and self.conds:
+            # v2 fix:
+            # keep the selected appended cond tensor and its plan aligned.
+            for _cond, _plan in zip(self.conds, getattr(self, '_cond_plans', [])):
+                if _cond is not None:
+                    self._zimage_cond_plan = _plan
+                    self.orig_tokens = params.text_cond.shape[1] - 5
+                    params.text_cond = torch.cat(
+                        [params.text_cond[:, :-5, :], _cond, params.text_cond[:, -5:, :]],
+                        1
+                    )
+                    break
+            
+def _get_negpip_plan(self, is_cond: bool, region_index: int = 0) -> 'NegpipRegionPlan':
+    """
+    Return the NegpipRegionPlan for the specified cond/uncond branch and region.
+
+    is_cond=True  → cond (positive) branch  → self._cond_plans
+    is_cond=False → uncond (negative) branch → self._uncond_plans
+
+    region_index is used for Regional Prompter multi-region routing.
+    Falls back to a null plan (no NegPiP effect) rather than crashing if
+    the requested index is out of range.
+    """
+    plans = self._cond_plans if is_cond else self._uncond_plans
+    if not plans:
+        return NegpipRegionPlan()
+    idx = min(region_index, len(plans) - 1)
+    return plans[idx]
+
+
+from pprint import pprint
+
+def unload(self,p):
+    if hasattr(self,"handle"):
+        if forge:
+            if self.modeltype == "flux":
+                hook_forwards_f(self, p.sd_model.forge_objects.unet.model, remove=True)   
+            elif self.modeltype == "ZImage":
+                hook_forwards_z(self, p.sd_model.forge_objects.unet.model, remove=True)
+            else:
+                hook_forwards(self, p.sd_model.forge_objects.unet.model, remove=True)
+        else:
+            hook_forwards(self, p.sd_model.model.diffusion_model, remove=True)
+        del self.handle
+
+# helper functions from LDM
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+def hook_forward(self, module):
+    def forward(x, context=None, mask=None, value=None, additional_tokens=None, *args, **kwargs):
+        if debug: print(" x.shape:",x.shape,"context.shape:",context.shape,"self.contokens",self.contokens,"self.untokens",self.untokens)
+        
+        def sub_forward(x, context, mask, additional_tokens, conds,contokens,unconds,untokens, latent = None):
+            if debug: print(" x.shape[0]:",x.shape[0],"batch:",self.batch *2)
+            
+            if x.shape[0] == self.batch *2:
+                if debug: print(" x.shape[0] == self.batch *2")
+            
+                if self.rev:
+                    contn,contp = context.chunk(2)
+                    ixn,ixp = x.chunk(2)
+                else:
+                    contp,contn =  context.chunk(2)
+                    ixp,ixn = x.chunk(2)  #x[0:self.batch,:,:],x[self.batch:,:,:]
+                
+                if conds is not None:
+                    if contp.shape[0] != conds.shape[0]:
+                        conds = conds.expand(contp.shape[0],-1,-1)
+                    contp = torch.cat((contp,conds),1)
+                if unconds is not None:
+                    if contn.shape[0] != unconds.shape[0]:
+                        unconds = unconds.expand(contn.shape[0],-1,-1)
+                    contn =  torch.cat((contn,unconds),1)
+                
+                # v2: set current plan so main_forward picks the right one
+                self._current_negpip_plan = _get_negpip_plan(self, is_cond=True,  region_index=0)
+                xp = main_forward(self, module, ixp,contp,value,mask,additional_tokens,contokens,args,kwargs)
+                self._current_negpip_plan = _get_negpip_plan(self, is_cond=False, region_index=0)
+                xn = main_forward(self, module, ixn,contn,value,mask,additional_tokens,untokens,args,kwargs)
+                self._current_negpip_plan = None
+            
+                out = torch.cat([xn,xp]) if self.rev else torch.cat([xp,xn])
+                return out
+
+            elif latent is not None:
+                if debug:print(" latent is not None")
+                if latent:
+                    conds = conds if conds is not None else None
+                else:
+                    conds = unconds if unconds is not None else None
+                if conds is not None:
+                    if context.shape[0] != conds.shape[0]:
+                        conds = conds.expand(context.shape[0],-1,-1)
+                    context = torch.cat([context,conds],1)
+                
+                tokens = contokens if contokens is not None else untokens
+
+                out = main_forward(self, module, x,context,value,mask,additional_tokens,tokens,args,kwargs)
+                return out
+
+            else:
+                if debug:
+                    print(" Else")
+                    print(context.shape[1] , self.conlen,self.unlen)
+
+                tokens = []
+                # v2 fix: track branch EXPLICITLY from the context-length test.
+                # Do NOT infer cond/uncond by comparing token-list contents —
+                # if contokens and untokens happen to be equal, that would misroute.
+                _branch_is_cond = False          # will be set below when branch is known
+                concon = counter(self.isxl)
+                if debug: print(concon)
+                if context.shape[1] == self.conlen * 77 and concon:
+                    if conds is not None:
+                        if context.shape[0] != conds.shape[0]:
+                            conds = conds.expand(context.shape[0],-1,-1)
+                        context = torch.cat([context,conds],1)
+                        tokens = contokens
+                    _branch_is_cond = True        # context length matched cond → positive branch
+                elif context.shape[1] == self.unlen * 77 and concon:
+                    if unconds is not None:
+                        if context.shape[0] != unconds.shape[0]:
+                            unconds = unconds.expand(context.shape[0],-1,-1)
+                        context = torch.cat([context,unconds],1)
+                        tokens = untokens
+                    _branch_is_cond = False       # context length matched uncond → negative branch
+                # v2: set plan using explicitly-determined branch flag, not list comparison
+                self._current_negpip_plan = _get_negpip_plan(self, is_cond=_branch_is_cond, region_index=0)
+                out = main_forward(self, module, x,context,value,mask,additional_tokens,tokens,args,kwargs)
+                self._current_negpip_plan = None
+                return out
+
+        if self.enable_rp_latent:
+            if len(self.conds) - 1 >= self.latenti:
+                # v2: cond branch uses region-specific plan
+                self._current_negpip_plan = _get_negpip_plan(self, is_cond=True, region_index=self.latenti)
+                out = sub_forward(x, context, mask, additional_tokens, self.conds[self.latenti],self.contokens[self.latenti],None,None ,latent = True)
+                self.latenti += 1
+            else:
+                # v2: uncond branch uses uncond plan[0]
+                self._current_negpip_plan = _get_negpip_plan(self, is_cond=False, region_index=0)
+                _unc  = self.unconds[0]  if (self.unconds  and len(self.unconds)  > 0) else None
+                _utok = self.untokens[0] if (self.untokens and len(self.untokens) > 0) else None
+                out = sub_forward(x, context, mask, additional_tokens, None, None, _unc, _utok, latent=False)
+                self.latenti = 0
+            self._current_negpip_plan = None
+            return out
+        else:
+            if self.conds is not None and self.unconds is not None and len(self.conds) > 0 and len(self.unconds) > 0:
+                return sub_forward(x, context, mask, additional_tokens, self.conds[0],self.contokens[0],self.unconds[0],self.untokens[0])
+            else:
+                return sub_forward(x, context, mask, additional_tokens, None,None,None,None)
+    
+    return forward
+
+count = 0
+pn = True
+
+def counter(isxl):
+    global count, pn
+    count += 1
+
+    limit = 70 if isxl else 16
+    outpn = pn
+
+    if count == limit:
+        pn = not pn
+        count = 0
+    return outpn
+
+def main_forward2(self, module, x, context, mask, additional_tokens, tokens, args, kwargs):
+    h = module.heads
+    context = context.to(x.dtype)
+    q = module.to_q(x)
+
+    context = default(context, x)
+    k = module.to_k(context)
+    v = module.to_v(context)
+    if debug: print(h,context.shape,q.shape,k.shape,v.shape)
+
+    _, _, dim_head = q.shape
+    dim_head //= h
+    scale = dim_head ** -0.5
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    sim = einsum('b i d, b j d -> b i j', q, k) * scale
+
+    if self.active:
+        if tokens:
+            for token in tokens:
+                start = (v.shape[1]//77 - len(tokens)) * 77
+                #print("v.shape:",v.shape,"start:",start+1,"stop:",start+token)
+                v[:,start+1:start+token,:] = -v[:,start+1:start+token,:] 
+
+    if exists(mask):
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    attn = sim.softmax(dim=-1)
+    #print(h,context.shape,q.shape,k.shape,v.shape,attn.shape)
+    out = einsum('b i j, b j d -> b i d', attn, v)
+
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+
+    return module.to_out(out)
+
+def main_forward(self, attn, x, context, value = None ,mask = None, temb = None, tokens = [], args = None, kwargs = None):
+        q = attn.to_q(x)
+        context = context.to(x.dtype)
+        context = default(context, x)
+        k = attn.to_k(context)
+        if value is not None:
+            v = attn.to_v(value)
+            del value
+        else:
+            v = attn.to_v(context)
+
+        if self.active:
+            if tokens:
+                if NEGPIP_USE_V2 and hasattr(self, '_current_negpip_plan')                         and self._current_negpip_plan is not None:
+                    # v2: use the plan that sub_forward set for THIS branch/region
+                    _apply_negpip_spans(v, self._current_negpip_plan)
+                else:
+                    # v1 fallback: uniform inversion of last N tokens
+                    v[:, -tokens:, :] = -v[:, -tokens:, :]
+
+        out = attention_function(q, k, v, attn.heads, mask)
+        return attn.to_out(out)
+    
+
+def attention_function(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    out = (
+        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+    )
+    return out
+
+def hook_forwards(self, root_module: torch.nn.Module, remove=False):
+    for name, module in root_module.named_modules():
+        if "attn2" in name and module.__class__.__name__ == "CrossAttention":
+            module.forward = hook_forward(self, module)
+            if remove:
+                del module.forward
+
+def hook_forwards_f(self, root_module: torch.nn.Module, remove=False):
+    for name, module in root_module.named_modules():
+        if "double_blocks" in name and module.__class__.__name__ == "DoubleStreamBlock":
+            module.forward = hook_forward_f_d(self, module)
+            if remove:
+                del module.forward
+
+        if "single_blocks" in name and module.__class__.__name__ == "SingleStreamBlock":
+                    module.forward = hook_forward_f_s(self, module)
+                    if remove:
+                        del module.forward
+
+def hook_forwards_z(self, root_module: torch.nn.Module, remove=False):
+    for name, module in root_module.named_modules():
+        if "layers" in name and module.__class__.__name__ == "JointAttention":
+            module.forward = hook_forward_f_z(self, module)
+            if remove:
+                del module.forward
+
+def resetpcache(p):
+    p.cached_c = [None,None]
+    p.cached_uc = [None,None]
+    p.cached_hr_c = [None, None]
+    p.cached_hr_uc = [None, None]
+
+
+class SdConditioning(list):
+    def __init__(self, prompts, is_negative_prompt=False, width=None, height=None, copy_from=None):
+        super().__init__()
+        self.extend(prompts)
+
+        if copy_from is None:
+            copy_from = prompts
+
+        self.is_negative_prompt = is_negative_prompt or getattr(copy_from, 'is_negative_prompt', False)
+        self.width = width or getattr(copy_from, 'width', None)
+        self.height = height or getattr(copy_from, 'height', None)
+
+def ext_on_ui_settings():
+    # [setting_name], [default], [label], [component(blank is checkbox)], [component_args]debug_level_choices = []
+    negpip_options = [
+        (OPT_HIDE, False, "Hide in Txt2Img/Img2Img tab(Reload UI required)"),
+        (OPT_ACT, True, "Active(Effective when Hide is Checked)",),
+    ]
+    section = ('negpip', "NegPiP")
+
+    for cur_setting_name, *option_info in negpip_options:
+        shared.opts.add_option(cur_setting_name, shared.OptionInfo(*option_info, section=section))
+
+on_ui_settings(ext_on_ui_settings)
+
+def hr_dealer(p):
+    if not hasattr(p, "hr_prompts"):
+        p.hr_prompts = None
+    if not hasattr(p, "hr_negative_prompts"):
+        p.hr_negative_prompts = None
+
+    return bool(p.hr_prompts), bool(p.hr_negative_prompts )
+
+def hook_forward_f_d(self, module):
+    def double_s_forward(img, txt, vec, pe):
+        img_mod1_shift, img_mod1_scale, img_mod1_gate, img_mod2_shift, img_mod2_scale, img_mod2_gate = module.img_mod(vec)
+
+        img_modulated = module.img_norm1(img)
+        img_modulated = (1 + img_mod1_scale) * img_modulated + img_mod1_shift
+        del img_mod1_shift, img_mod1_scale
+        img_qkv = module.img_attn.qkv(img_modulated)
+        del img_modulated
+
+        # img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        B, L, _ = img_qkv.shape
+        H = module.num_heads
+        D = img_qkv.shape[-1] // (3 * H)
+        img_q, img_k, img_v = img_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
+        del img_qkv
+
+        img_q, img_k = module.img_attn.norm(img_q, img_k, img_v)
+
+        txt_mod1_shift, txt_mod1_scale, txt_mod1_gate, txt_mod2_shift, txt_mod2_scale, txt_mod2_gate = module.txt_mod(vec)
+        del vec
+
+        txt_modulated = module.txt_norm1(txt)
+
+        txt_modulated = (1 + txt_mod1_scale) * txt_modulated + txt_mod1_shift
+        
+        del txt_mod1_shift, txt_mod1_scale
+        txt_qkv = module.txt_attn.qkv(txt_modulated)
+        del txt_modulated
+
+        B, L, _ = txt_qkv.shape
+        txt_q, txt_k, txt_v = txt_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
+        del txt_qkv
+
+        if NEGPIP_USE_V2:
+            plan = getattr(self, '_flux_cond_plan', None)
+            if plan is not None and plan.total_tokens > 0:
+                # Flux double block processes only the cond stream here.
+                # Use the exact plan that matched the actually appended cond tensor.
+                total = plan.total_tokens
+                for span in plan.spans:
+                    cnt, off = span.token_count, span.token_offset
+                    end_i = self.orig_tokens + total - off
+                    sta_i = end_i - cnt
+                    txt_v[:, :, sta_i:end_i, :] = txt_v[:, :, sta_i:end_i, :] * span.sign
+        else:
+            if self.contokens:
+                txt_v[:, :, self.orig_tokens:self.orig_tokens + self.contokens[0], :] = -txt_v[:, :, self.orig_tokens:self.orig_tokens + self.contokens[0], :]
+
+        txt_q, txt_k = module.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        q = torch.cat((txt_q, img_q), dim=2)
+        del txt_q, img_q
+        k = torch.cat((txt_k, img_k), dim=2)
+        del txt_k, img_k
+        v = torch.cat((txt_v, img_v), dim=2)
+        del txt_v, img_v
+
+        attn = attention(q, k, v, pe=pe)
+        del pe, q, k, v
+        txt_attn, img_attn = attn[:, :txt.shape[1]], attn[:, txt.shape[1]:]
+
+        del attn
+
+        img = img + img_mod1_gate * module.img_attn.proj(img_attn)
+        del img_attn, img_mod1_gate
+        img = img + img_mod2_gate * module.img_mlp((1 + img_mod2_scale) * module.img_norm2(img) + img_mod2_shift)
+        del img_mod2_gate, img_mod2_scale, img_mod2_shift
+
+        txt = txt + txt_mod1_gate * module.txt_attn.proj(txt_attn)
+        del txt_attn, txt_mod1_gate
+        txt = txt + txt_mod2_gate * module.txt_mlp((1 + txt_mod2_scale) * module.txt_norm2(txt) + txt_mod2_shift)
+        del txt_mod2_gate, txt_mod2_scale, txt_mod2_shift
+
+        txt = fp16_fix(txt)
+
+        return img, txt
+    
+    return double_s_forward
+
+
+def hook_forward_f_s(self, module):
+    def single_s_forward(x, vec, pe):
+            mod_shift, mod_scale, mod_gate = module.modulation(vec)
+            del vec
+            x_mod = (1 + mod_scale) * module.pre_norm(x) + mod_shift
+            del mod_shift, mod_scale
+            qkv, mlp = torch.split(module.linear1(x_mod), [3 * module.hidden_size, module.mlp_hidden_dim], dim=-1)
+            del x_mod
+
+            # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+            qkv = qkv.view(qkv.size(0), qkv.size(1), 3, module.num_heads, module.hidden_size // module.num_heads)
+            q, k, v = qkv.permute(2, 0, 3, 1, 4)
+            del qkv
+            if NEGPIP_USE_V2:
+                plan = getattr(self, '_flux_cond_plan', None)
+                if plan is not None and plan.total_tokens > 0:
+                    # Flux single block: use the same exact matched plan as append-time.
+                    total = plan.total_tokens
+                    for span in plan.spans:
+                        cnt, off = span.token_count, span.token_offset
+                        end_i = self.orig_tokens + total - off
+                        sta_i = end_i - cnt
+                        v[:, :, sta_i:end_i, :] = v[:, :, sta_i:end_i, :] * span.sign
+            else:
+                if self.contokens:
+                    v[:, :, self.orig_tokens:self.orig_tokens + self.contokens[0], :] = -v[:, :, self.orig_tokens:self.orig_tokens + self.contokens[0], :]
+            q, k = module.norm(q, k, v)
+
+            attn = attention(q, k, v, pe=pe)
+            del q, k, v, pe
+            output = module.linear2(torch.cat((attn, module.mlp_act(mlp)), dim=2))
+            del attn, mlp
+
+            x = x + mod_gate * output
+            del mod_gate, output
+
+            x = fp16_fix(x)
+
+            return x
+    
+    return single_s_forward
+
+def hook_forward_f_z(self, module):
+    from backend.nn.lumina import JointAttention
+    from backend.memory_management import xformers_enabled
+    if xformers_enabled():
+        from backend.attention import attention_xformers as attention_function_z
+    else:
+        from backend.attention import attention_pytorch as attention_function_z
+
+    hook_self = self
+
+    def joint_atten_forward(x: torch.Tensor, x_mask: torch.Tensor, freqs_cis: torch.Tensor, transformer_options={}) -> torch.Tensor:
+        bsz, seqlen, _ = x.shape
+
+        xq, xk, xv = torch.split(
+            module.qkv(x),
+            [
+                module.n_local_heads * module.head_dim,
+                module.n_local_kv_heads * module.head_dim,
+                module.n_local_kv_heads * module.head_dim,
+            ],
+            dim=-1,
+        )
+
+        xq = xq.view(bsz, seqlen, module.n_local_heads, module.head_dim)
+        xk = xk.view(bsz, seqlen, module.n_local_kv_heads, module.head_dim)
+        xv = xv.view(bsz, seqlen, module.n_local_kv_heads, module.head_dim)
+
+        if NEGPIP_USE_V2:
+            plan = getattr(hook_self, '_zimage_cond_plan', None)
+            if plan is not None and plan.total_tokens > 0:
+                # v2: ZImage per-span sign application (replaces strength_tensor path)
+                # Use the exact plan that matched the appended cond tensor.
+                # xv shape: (bsz, seqlen, n_heads, head_dim) — span on dim 1
+                total = plan.total_tokens
+                orig = hook_self.orig_tokens
+                for span in plan.spans:
+                    cnt, off = span.token_count, span.token_offset
+                    end_i = orig + total - off
+                    sta_i = end_i - cnt
+                    xv[:, sta_i:end_i, :, :] = xv[:, sta_i:end_i, :, :] * span.sign
+        else:
+            if hook_self.contokens and hook_self.conds and hook_self.conds[0] is not None:
+                # v1 fallback: strength_tensor (preserves original ZImage behaviour)
+                start = hook_self.orig_tokens
+                end = start + hook_self.conds[0].shape[1]
+                strength_tensor = torch.tensor(hook_self.strength, device=xv.device, dtype=xv.dtype)
+                strength_tensor = strength_tensor.view(1, -1, 1, 1)
+                xv[:, start:end, :, :] = xv[:, start:end, :, :] * strength_tensor
+
+        xq = module.q_norm(xq)
+        xk = module.k_norm(xk)
+
+        xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
+        xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
+
+        n_rep = module.n_local_heads // module.n_local_kv_heads
+        if n_rep >= 1:
+            xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+            xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+        
+        output = attention_function_z(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), module.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)
+
+        return module.out(output)
+    
+    return joint_atten_forward
+
+class InputAccordionImpl(gr.Checkbox):
+    webui_do_not_create_gradio_pyi_thank_you = True
+    global_index = 2244096 + 2 #NegPiP
+
+    @wraps(gr.Checkbox.__init__)
+    def __init__(self, value=None, setup=False, **kwargs):
+        if not setup:
+            super().__init__(value=value, **kwargs)
+            return
+
+        self.accordion_id = kwargs.get('elem_id')
+        if self.accordion_id is None:
+            self.accordion_id = f"input-accordion-m-{InputAccordionImpl.global_index}"
+            InputAccordionImpl.global_index += 1
+
+        kwargs_checkbox = {
+            **kwargs,
+            "elem_id": f"{self.accordion_id}-checkbox",
+            "visible": False,
+        }
+        super().__init__(value=value, **kwargs_checkbox)
+        self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
+
+        kwargs_accordion = {
+            **kwargs,
+            "elem_id": self.accordion_id,
+            "label": kwargs.get('label', 'Accordion'),
+            "elem_classes": ['input-accordion-m'],
+            "open": False,
+        }
+
+        self.accordion = gr.Accordion(**kwargs_accordion)
+
+    def extra(self):
+        return gr.Column(elem_id=self.accordion_id + '-extra', elem_classes='input-accordion-extra', min_width=0)
+
+    def __enter__(self):
+        self.accordion.__enter__()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.accordion.__exit__(exc_type, exc_val, exc_tb)
+
+    def get_block_name(self):
+        return "checkbox"
+
+def InputAccordion(value=None, **kwargs):
+    return InputAccordionImpl(value=value, setup=True, **kwargs)
+
+# Check for Gradio version 4; see Forge architecture rework
+IS_GRADIO_4 = version.parse(gr.__version__) >= version.parse("4.0.0")
+# check if Forge or auto1111 pure; extremely hacky
+
+# Forge patches
+
+# See discussion at, class versus instance __module__
+# https://github.com/LEv145/--sd-webui-ar-plus/issues/24
+# Hack for Forge with Gradio 4.0; see `get_component_class_id` in `venv/lib/site-packages/gradio/components/base.py`
+if IS_GRADIO_4:
+    InputAccordionImpl.__module__ = "modules.ui_components"