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-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-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 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
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

sd-webui-semantic-guidance-sdxl-hybrid/README.md

@@ -1,53 +0,0 @@
-## sd-webui-semantic-guidance
-### An unofficial implementation of *SEGA: Instructing Text-to-Image Models using Semantic Guidance* for Automatic1111 WebUI
-Semantic Guidance gives you more control over the fine details of an image by emphasizing or de-emphasizing concepts given in an additional positive / negative text prompt.
-
-### Use Cases
-- Altering the style of an image without changing the composition
-![image](samples/style_change.jpg)
-
-- Adding concepts to an image
-![image](samples/concepts_1.jpg)
-
-Please see the bottom of the page for more examples.
-
-### Issues / PR's are welcome!
-
-### Settings
-
-* Positive / Negative Prompt: Concepts to emphasize / de-emphasize, separated by commas
-* Warmup Steps: How many steps to wait before applying semantic guidance
-* Edit Guidance Scale: Globally scale how much influence semantic guidance has on the image
-* Tail Percentage Threshold: The percentage of latents to use when calculating the semantic guidance
-* Momentum Scale: Scale the influence of the added momentum term
-* Momentum Beta: Higher values will make the influence of the momentum term more stable
-
-### Feature / To-do List
-- [x] SD XL support  
-- [x] Support A1111 prompt attention syntax and shortcuts for attention strength
-- [x] Support restoring parameter values from infotext (Send to Txt2Img, Send to Img2Img, etc.)
-- [x] XYZ plot support
-- [x] SD 1.5 support (untested)
-- [ ] Support semantic guidance prompt in regular Positive/Negative Prompt fields
-
-### Credits
-- The authors of the original paper for their method (https://arxiv.org/abs/2301.12247):
-	```
-	@misc{brack2023sega,
-		title={SEGA: Instructing Text-to-Image Models using Semantic Guidance}, 
-		author={Manuel Brack and Felix Friedrich and Dominik Hintersdorf and Lukas Struppek and Patrick Schramowski and Kristian Kersting},
-		year={2023},
-		eprint={2301.12247},
-		archivePrefix={arXiv},
-		primaryClass={cs.CV}
-	}
-	```
-- Official implementation of Semantic Guidance for Diffusion: (https://github.com/ml-research/semantic-image-editing)
-
-- @udon-universe's extension templates: (https://github.com/udon-universe/stable-diffusion-webui-extension-templates)
-
-### More examples
-- Enhancing the details of an image
-![image](samples/enhance.jpg)
-
-

sd-webui-semantic-guidance-sdxl-hybrid/scripts/sega.py

@@ -1,955 +0,0 @@
-import logging
-import math
-from os import environ
-
-import gradio as gr
-import scipy.stats as stats
-import torch
-import torch.nn.functional as F
-
-import modules.scripts as scripts
-from modules import prompt_parser, script_callbacks, shared
-from modules.processing import StableDiffusionProcessing
-from modules.prompt_parser import reconstruct_multicond_batch
-from modules.script_callbacks import CFGDenoiserParams
-from modules.sd_samplers_cfg_denoiser import pad_cond
-
-logger = logging.getLogger(__name__)
-logger.setLevel(environ.get("SD_WEBUI_LOG_LEVEL", logging.INFO))
-
-"""
-An unofficial implementation of SEGA: Instructing Text-to-Image Models using Semantic Guidance for Automatic1111 WebUI.
-
-This patched variant focuses on robust SDXL support and safer hybrid steering improvements:
-- handles tensor and dict conditioning safely (SD 1.5 / SDXL)
-- lets you steer cross-attention and pooled vector conditioning separately on SDXL
-- applies SDXL vector edits with a safer warmup-aware schedule
-- optionally adds T2I-Zero-inspired similarity correction and CTNMS-inspired token suppression in embedding space
-- splits concept prompts on top-level commas only
-"""
-
-
-class SegaStateParams:
-    def __init__(self):
-        self.concept_name = ""
-        self.v = {}  # velocity
-        self.warmup_period: int = 10
-        self.edit_guidance_scale: float = 1.0
-        self.tail_percentage_threshold: float = 0.05
-        self.momentum_scale: float = 0.3
-        self.momentum_beta: float = 0.6
-        self.strength: float = 1.0
-        self.conditioning_mode: str = "Auto"
-        self.crossattn_scale: float = 1.0
-        self.vector_scale: float = 0.25
-        self.vector_schedule: str = "After warmup (soft)"
-        self.vector_ramp_steps: int = 8
-        self.similarity_correction_enabled: bool = False
-        self.similarity_topk_frac: float = 0.15
-        self.similarity_percentile: float = 0.75
-        self.similarity_window: int = 2
-        self.similarity_alpha: float = 0.20
-        self.embedding_suppression_enabled: bool = False
-        self.embedding_suppression_percentile: float = 0.80
-        self.embedding_suppression_alpha: float = 0.15
-
-
-class SegaExtensionScript(scripts.Script):
-    def __init__(self):
-        self.cached_c = [None, None]
-
-    def title(self):
-        return "Semantic Guidance"
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def ui(self, is_img2img):
-        with gr.Accordion("Semantic Guidance", open=False):
-            active = gr.Checkbox(value=False, default=False, label="Active", elem_id="sega_active")
-            with gr.Row():
-                prompt = gr.Textbox(lines=2, label="Prompt", elem_id="sega_prompt", elem_classes=["prompt"])
-            with gr.Row():
-                neg_prompt = gr.Textbox(lines=2, label="Negative Prompt", elem_id="sega_neg_prompt", elem_classes=["prompt"])
-            with gr.Row():
-                warmup = gr.Slider(
-                    value=10,
-                    minimum=0,
-                    maximum=30,
-                    step=1,
-                    label="Warmup Period",
-                    elem_id="sega_warmup",
-                    info="How many steps to wait before applying semantic guidance.",
-                )
-                edit_guidance_scale = gr.Slider(
-                    value=1.0,
-                    minimum=0.0,
-                    maximum=20.0,
-                    step=0.01,
-                    label="Edit Guidance Scale",
-                    elem_id="sega_edit_guidance_scale",
-                    info="Global scale of semantic guidance.",
-                )
-                tail_percentage_threshold = gr.Slider(
-                    value=0.05,
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.01,
-                    label="Tail Percentage Threshold",
-                    elem_id="sega_tail_percentage_threshold",
-                    info="Only edit values in the strongest tail of the semantic direction.",
-                )
-                momentum_scale = gr.Slider(
-                    value=0.3,
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.01,
-                    label="Momentum Scale",
-                    elem_id="sega_momentum_scale",
-                    info="Adds momentum to the semantic direction.",
-                )
-                momentum_beta = gr.Slider(
-                    value=0.6,
-                    minimum=0.0,
-                    maximum=0.999,
-                    step=0.01,
-                    label="Momentum Beta",
-                    elem_id="sega_momentum_beta",
-                    info="Higher values make momentum more stable.",
-                )
-            with gr.Row():
-                conditioning_mode = gr.Dropdown(
-                    choices=["Auto", "CrossAttn only", "CrossAttn + Vector"],
-                    value="Auto",
-                    label="SDXL Conditioning Mode",
-                    elem_id="sega_conditioning_mode",
-                    info="Auto = SD 1.5 uses cross-attention only; SDXL edits cross-attention and optionally pooled vector.",
-                )
-                crossattn_scale = gr.Slider(
-                    value=1.0,
-                    minimum=0.0,
-                    maximum=2.0,
-                    step=0.01,
-                    label="CrossAttn Scale",
-                    elem_id="sega_crossattn_scale",
-                    info="Relative scale for token/cross-attention conditioning.",
-                )
-                vector_scale = gr.Slider(
-                    value=0.25,
-                    minimum=0.0,
-                    maximum=2.0,
-                    step=0.01,
-                    label="Vector Scale",
-                    elem_id="sega_vector_scale",
-                    info="Relative scale for SDXL pooled vector conditioning. Lower is usually safer.",
-                )
-            with gr.Row():
-                vector_schedule = gr.Dropdown(
-                    choices=["After warmup (soft)", "After warmup (step)", "Always on"],
-                    value="After warmup (soft)",
-                    label="Vector Schedule",
-                    elem_id="sega_vector_schedule",
-                    info="Safer SDXL vector steering: keep pooled vector off until warmup ends, then enable it softly or in one step.",
-                )
-                vector_ramp_steps = gr.Slider(
-                    value=8,
-                    minimum=0,
-                    maximum=30,
-                    step=1,
-                    label="Vector Ramp Steps",
-                    elem_id="sega_vector_ramp_steps",
-                    info="Used by the soft vector schedule. 0 behaves like a step switch.",
-                )
-            with gr.Accordion("Hybrid Embedding Edits (experimental)", open=False):
-                with gr.Row():
-                    similarity_correction_enabled = gr.Checkbox(
-                        value=False,
-                        label="Enable Similarity Correction",
-                        elem_id="sega_similarity_correction_enabled",
-                        info="T2I-Zero-inspired correction applied only to cross-attention embeddings before SEGA.",
-                    )
-                    embedding_suppression_enabled = gr.Checkbox(
-                        value=False,
-                        label="Enable Token Competition Suppression",
-                        elem_id="sega_embedding_suppression_enabled",
-                        info="CTNMS-inspired embedding-space suppression. Safer than attention hooks, but still experimental.",
-                    )
-                with gr.Row():
-                    similarity_topk_frac = gr.Slider(
-                        value=0.15,
-                        minimum=0.01,
-                        maximum=0.50,
-                        step=0.01,
-                        label="Selected Token Fraction",
-                        elem_id="sega_similarity_topk_frac",
-                        info="Fraction of the most concept-salient tokens to target for hybrid edits.",
-                    )
-                    similarity_percentile = gr.Slider(
-                        value=0.75,
-                        minimum=0.10,
-                        maximum=0.99,
-                        step=0.01,
-                        label="Similarity Percentile",
-                        elem_id="sega_similarity_percentile",
-                        info="Higher values make similarity correction more selective.",
-                    )
-                    similarity_window = gr.Slider(
-                        value=2,
-                        minimum=0,
-                        maximum=12,
-                        step=1,
-                        label="Similarity Window",
-                        elem_id="sega_similarity_window",
-                        info="Local token window around each selected token for similarity correction.",
-                    )
-                    similarity_alpha = gr.Slider(
-                        value=0.20,
-                        minimum=0.0,
-                        maximum=1.0,
-                        step=0.01,
-                        label="Similarity Alpha",
-                        elem_id="sega_similarity_alpha",
-                        info="Blend strength for similarity correction.",
-                    )
-                with gr.Row():
-                    embedding_suppression_percentile = gr.Slider(
-                        value=0.80,
-                        minimum=0.10,
-                        maximum=0.99,
-                        step=0.01,
-                        label="Suppression Percentile",
-                        elem_id="sega_embedding_suppression_percentile",
-                        info="Only suppress strongly competing non-selected tokens above this percentile.",
-                    )
-                    embedding_suppression_alpha = gr.Slider(
-                        value=0.15,
-                        minimum=0.0,
-                        maximum=1.0,
-                        step=0.01,
-                        label="Suppression Alpha",
-                        elem_id="sega_embedding_suppression_alpha",
-                        info="Strength of embedding-space token competition suppression.",
-                    )
-
-        for comp in [
-            active,
-            prompt,
-            neg_prompt,
-            warmup,
-            edit_guidance_scale,
-            tail_percentage_threshold,
-            momentum_scale,
-            momentum_beta,
-            conditioning_mode,
-            crossattn_scale,
-            vector_scale,
-            vector_schedule,
-            vector_ramp_steps,
-            similarity_correction_enabled,
-            similarity_topk_frac,
-            similarity_percentile,
-            similarity_window,
-            similarity_alpha,
-            embedding_suppression_enabled,
-            embedding_suppression_percentile,
-            embedding_suppression_alpha,
-        ]:
-            comp.do_not_save_to_config = True
-
-        self.infotext_fields = [
-            (active, lambda d: gr.Checkbox.update(value="SEGA Active" in d)),
-            (prompt, "SEGA Prompt"),
-            (neg_prompt, "SEGA Negative Prompt"),
-            (warmup, "SEGA Warmup Period"),
-            (edit_guidance_scale, "SEGA Edit Guidance Scale"),
-            (tail_percentage_threshold, "SEGA Tail Percentage Threshold"),
-            (momentum_scale, "SEGA Momentum Scale"),
-            (momentum_beta, "SEGA Momentum Beta"),
-            (conditioning_mode, "SEGA SDXL Conditioning Mode"),
-            (crossattn_scale, "SEGA CrossAttn Scale"),
-            (vector_scale, "SEGA Vector Scale"),
-            (vector_schedule, "SEGA Vector Schedule"),
-            (vector_ramp_steps, "SEGA Vector Ramp Steps"),
-            (similarity_correction_enabled, lambda d: gr.Checkbox.update(value="SEGA Similarity Correction" in d)),
-            (similarity_topk_frac, "SEGA Selected Token Fraction"),
-            (similarity_percentile, "SEGA Similarity Percentile"),
-            (similarity_window, "SEGA Similarity Window"),
-            (similarity_alpha, "SEGA Similarity Alpha"),
-            (embedding_suppression_enabled, lambda d: gr.Checkbox.update(value="SEGA Token Competition Suppression" in d)),
-            (embedding_suppression_percentile, "SEGA Suppression Percentile"),
-            (embedding_suppression_alpha, "SEGA Suppression Alpha"),
-        ]
-        self.paste_field_names = [
-            "sega_active",
-            "sega_prompt",
-            "sega_neg_prompt",
-            "sega_warmup",
-            "sega_edit_guidance_scale",
-            "sega_tail_percentage_threshold",
-            "sega_momentum_scale",
-            "sega_momentum_beta",
-            "sega_conditioning_mode",
-            "sega_crossattn_scale",
-            "sega_vector_scale",
-            "sega_vector_schedule",
-            "sega_vector_ramp_steps",
-            "sega_similarity_correction_enabled",
-            "sega_similarity_topk_frac",
-            "sega_similarity_percentile",
-            "sega_similarity_window",
-            "sega_similarity_alpha",
-            "sega_embedding_suppression_enabled",
-            "sega_embedding_suppression_percentile",
-            "sega_embedding_suppression_alpha",
-        ]
-        return [
-            active,
-            prompt,
-            neg_prompt,
-            warmup,
-            edit_guidance_scale,
-            tail_percentage_threshold,
-            momentum_scale,
-            momentum_beta,
-            conditioning_mode,
-            crossattn_scale,
-            vector_scale,
-            vector_schedule,
-            vector_ramp_steps,
-            similarity_correction_enabled,
-            similarity_topk_frac,
-            similarity_percentile,
-            similarity_window,
-            similarity_alpha,
-            embedding_suppression_enabled,
-            embedding_suppression_percentile,
-            embedding_suppression_alpha,
-        ]
-
-    def process_batch(
-        self,
-        p: StableDiffusionProcessing,
-        active,
-        prompt,
-        neg_prompt,
-        warmup,
-        edit_guidance_scale,
-        tail_percentage_threshold,
-        momentum_scale,
-        momentum_beta,
-        conditioning_mode,
-        crossattn_scale,
-        vector_scale,
-        vector_schedule,
-        vector_ramp_steps,
-        similarity_correction_enabled,
-        similarity_topk_frac,
-        similarity_percentile,
-        similarity_window,
-        similarity_alpha,
-        embedding_suppression_enabled,
-        embedding_suppression_percentile,
-        embedding_suppression_alpha,
-        *args,
-        **kwargs,
-    ):
-        active = getattr(p, "sega_active", active)
-        if active is False:
-            return
-
-        prompt = getattr(p, "sega_prompt", prompt)
-        neg_prompt = getattr(p, "sega_neg_prompt", neg_prompt)
-        warmup = getattr(p, "sega_warmup", warmup)
-        edit_guidance_scale = getattr(p, "sega_edit_guidance_scale", edit_guidance_scale)
-        tail_percentage_threshold = getattr(p, "sega_tail_percentage_threshold", tail_percentage_threshold)
-        momentum_scale = getattr(p, "sega_momentum_scale", momentum_scale)
-        momentum_beta = getattr(p, "sega_momentum_beta", momentum_beta)
-        conditioning_mode = getattr(p, "sega_conditioning_mode", conditioning_mode)
-        crossattn_scale = getattr(p, "sega_crossattn_scale", crossattn_scale)
-        vector_scale = getattr(p, "sega_vector_scale", vector_scale)
-        vector_schedule = getattr(p, "sega_vector_schedule", vector_schedule)
-        vector_ramp_steps = getattr(p, "sega_vector_ramp_steps", vector_ramp_steps)
-        similarity_correction_enabled = getattr(p, "sega_similarity_correction_enabled", similarity_correction_enabled)
-        similarity_topk_frac = getattr(p, "sega_similarity_topk_frac", similarity_topk_frac)
-        similarity_percentile = getattr(p, "sega_similarity_percentile", similarity_percentile)
-        similarity_window = getattr(p, "sega_similarity_window", similarity_window)
-        similarity_alpha = getattr(p, "sega_similarity_alpha", similarity_alpha)
-        embedding_suppression_enabled = getattr(p, "sega_embedding_suppression_enabled", embedding_suppression_enabled)
-        embedding_suppression_percentile = getattr(p, "sega_embedding_suppression_percentile", embedding_suppression_percentile)
-        embedding_suppression_alpha = getattr(p, "sega_embedding_suppression_alpha", embedding_suppression_alpha)
-
-        p.extra_generation_params.update(
-            {
-                "SEGA Active": active,
-                "SEGA Prompt": prompt,
-                "SEGA Negative Prompt": neg_prompt,
-                "SEGA Warmup Period": warmup,
-                "SEGA Edit Guidance Scale": edit_guidance_scale,
-                "SEGA Tail Percentage Threshold": tail_percentage_threshold,
-                "SEGA Momentum Scale": momentum_scale,
-                "SEGA Momentum Beta": momentum_beta,
-                "SEGA SDXL Conditioning Mode": conditioning_mode,
-                "SEGA CrossAttn Scale": crossattn_scale,
-                "SEGA Vector Scale": vector_scale,
-                "SEGA Vector Schedule": vector_schedule,
-                "SEGA Vector Ramp Steps": vector_ramp_steps,
-                **({"SEGA Similarity Correction": True} if similarity_correction_enabled else {}),
-                "SEGA Selected Token Fraction": similarity_topk_frac,
-                "SEGA Similarity Percentile": similarity_percentile,
-                "SEGA Similarity Window": similarity_window,
-                "SEGA Similarity Alpha": similarity_alpha,
-                **({"SEGA Token Competition Suppression": True} if embedding_suppression_enabled else {}),
-                "SEGA Suppression Percentile": embedding_suppression_percentile,
-                "SEGA Suppression Alpha": embedding_suppression_alpha,
-            }
-        )
-
-        concept_prompts = self.parse_concept_prompt(prompt)
-        concept_prompts_neg = self.parse_concept_prompt(neg_prompt)
-        concept_prompts = [prompt_parser.parse_prompt_attention(concept)[0] for concept in concept_prompts]
-        concept_prompts_neg = [prompt_parser.parse_prompt_attention(neg_concept)[0] for neg_concept in concept_prompts_neg]
-        concept_prompts_neg = [[concept, -strength] for concept, strength in concept_prompts_neg]
-        concept_prompts.extend(concept_prompts_neg)
-
-        if not concept_prompts:
-            logger.warning("SEGA: active but no concept prompts were provided; skipping hook creation.")
-            return
-
-        concept_conds = []
-        for concept, strength in concept_prompts:
-            prompt_list = [concept] * p.batch_size
-            prompts = prompt_parser.SdConditioning(prompt_list, width=p.width, height=p.height)
-            c = p.get_conds_with_caching(
-                prompt_parser.get_multicond_learned_conditioning,
-                prompts,
-                p.steps,
-                [self.cached_c],
-                p.extra_network_data,
-            )
-            concept_conds.append([c, strength])
-
-        self.create_hook(
-            active,
-            concept_conds,
-            warmup,
-            edit_guidance_scale,
-            tail_percentage_threshold,
-            momentum_scale,
-            momentum_beta,
-            conditioning_mode,
-            crossattn_scale,
-            vector_scale,
-            vector_schedule,
-            vector_ramp_steps,
-            similarity_correction_enabled,
-            similarity_topk_frac,
-            similarity_percentile,
-            similarity_window,
-            similarity_alpha,
-            embedding_suppression_enabled,
-            embedding_suppression_percentile,
-            embedding_suppression_alpha,
-        )
-
-    def parse_concept_prompt(self, prompt: str) -> list[str]:
-        """Split on top-level commas only.
-
-        This keeps prompts like `(red, blue:1.2)` or `[a, b]` intact.
-        Backslash-escaped commas are also preserved.
-        """
-        if not prompt:
-            return []
-
-        parts = []
-        cur = []
-        depth_round = 0
-        depth_square = 0
-        depth_curly = 0
-        escape = False
-
-        for ch in prompt:
-            if escape:
-                cur.append(ch)
-                escape = False
-                continue
-            if ch == "\\":
-                cur.append(ch)
-                escape = True
-                continue
-            if ch == "(":
-                depth_round += 1
-            elif ch == ")" and depth_round > 0:
-                depth_round -= 1
-            elif ch == "[":
-                depth_square += 1
-            elif ch == "]" and depth_square > 0:
-                depth_square -= 1
-            elif ch == "{":
-                depth_curly += 1
-            elif ch == "}" and depth_curly > 0:
-                depth_curly -= 1
-
-            if ch == "," and depth_round == 0 and depth_square == 0 and depth_curly == 0:
-                item = "".join(cur).strip()
-                if item:
-                    parts.append(item)
-                cur = []
-                continue
-
-            cur.append(ch)
-
-        item = "".join(cur).strip()
-        if item:
-            parts.append(item)
-        return parts
-
-    def create_hook(
-        self,
-        active,
-        concept_conds,
-        warmup,
-        edit_guidance_scale,
-        tail_percentage_threshold,
-        momentum_scale,
-        momentum_beta,
-        conditioning_mode,
-        crossattn_scale,
-        vector_scale,
-        vector_schedule,
-        vector_ramp_steps,
-        similarity_correction_enabled,
-        similarity_topk_frac,
-        similarity_percentile,
-        similarity_window,
-        similarity_alpha,
-        embedding_suppression_enabled,
-        embedding_suppression_percentile,
-        embedding_suppression_alpha,
-    ):
-        if not concept_conds:
-            return
-
-        concepts_sega_params = []
-        for _, strength in concept_conds:
-            sega_params = SegaStateParams()
-            sega_params.warmup_period = warmup
-            sega_params.edit_guidance_scale = edit_guidance_scale
-            sega_params.tail_percentage_threshold = tail_percentage_threshold
-            sega_params.momentum_scale = momentum_scale
-            sega_params.momentum_beta = momentum_beta
-            sega_params.strength = strength
-            sega_params.conditioning_mode = conditioning_mode
-            sega_params.crossattn_scale = crossattn_scale
-            sega_params.vector_scale = vector_scale
-            sega_params.vector_schedule = vector_schedule
-            sega_params.vector_ramp_steps = vector_ramp_steps
-            sega_params.similarity_correction_enabled = similarity_correction_enabled
-            sega_params.similarity_topk_frac = similarity_topk_frac
-            sega_params.similarity_percentile = similarity_percentile
-            sega_params.similarity_window = similarity_window
-            sega_params.similarity_alpha = similarity_alpha
-            sega_params.embedding_suppression_enabled = embedding_suppression_enabled
-            sega_params.embedding_suppression_percentile = embedding_suppression_percentile
-            sega_params.embedding_suppression_alpha = embedding_suppression_alpha
-            concepts_sega_params.append(sega_params)
-
-        callback = lambda params: self.on_cfg_denoiser_callback(params, concept_conds, concepts_sega_params)
-        logger.debug("Hooked callbacks")
-        script_callbacks.on_cfg_denoiser(callback)
-        script_callbacks.on_script_unloaded(self.unhook_callbacks)
-
-    def postprocess_batch(self, p, active, neg_text, *args, **kwargs):
-        active = getattr(p, "sega_active", active)
-        if active is False:
-            return
-        self.unhook_callbacks()
-
-    def unhook_callbacks(self):
-        logger.debug("Unhooked callbacks")
-        script_callbacks.remove_current_script_callbacks()
-
-    def normalize_conditioning(self, cond):
-        if isinstance(cond, dict):
-            return cond
-        return {"crossattn": cond}
-
-    def get_empty_crossattn(self):
-        return getattr(shared.sd_model, "cond_stage_model_empty_prompt", None)
-
-    def pad_crossattn_to_match(self, tensor: torch.Tensor, reference: torch.Tensor):
-        if tensor is None or reference is None:
-            return tensor
-        if tensor.dim() < 3 or reference.dim() < 3:
-            return tensor
-        if tensor.shape[1] == reference.shape[1]:
-            return tensor
-
-        empty = self.get_empty_crossattn()
-        if empty is None or empty.ndim < 2 or empty.shape[1] == 0:
-            logger.warning("SEGA: could not pad cross-attention conditioning safely; empty prompt tensor unavailable.")
-            return tensor
-
-        diff = reference.shape[1] - tensor.shape[1]
-        if diff <= 0:
-            return tensor
-        if diff % empty.shape[1] != 0:
-            logger.warning(
-                "SEGA: cross-attention length mismatch (%s vs %s) is not divisible by empty prompt width %s; leaving tensor unchanged.",
-                tensor.shape[1],
-                reference.shape[1],
-                empty.shape[1],
-            )
-            return tensor
-        return pad_cond(tensor, diff // empty.shape[1], empty)
-
-    def pad_conditioning_pair(self, cond_a: dict, cond_b: dict):
-        if "crossattn" not in cond_a or "crossattn" not in cond_b:
-            return cond_a, cond_b
-
-        a = cond_a["crossattn"]
-        b = cond_b["crossattn"]
-        if a.shape[1] == b.shape[1]:
-            return cond_a, cond_b
-
-        if a.shape[1] < b.shape[1]:
-            cond_a["crossattn"] = self.pad_crossattn_to_match(a, b)
-        else:
-            cond_b["crossattn"] = self.pad_crossattn_to_match(b, a)
-        return cond_a, cond_b
-
-    def get_vector_ramp_factor(self, sampling_step: int, sega_param: SegaStateParams) -> float:
-        if sega_param.conditioning_mode == "CrossAttn only":
-            return 0.0
-        schedule = getattr(sega_param, "vector_schedule", "After warmup (soft)")
-        if schedule == "Always on":
-            return 1.0
-
-        warmup = max(0, int(sega_param.warmup_period))
-        if sampling_step < warmup:
-            return 0.0
-
-        ramp_steps = max(0, int(getattr(sega_param, "vector_ramp_steps", 8)))
-        if schedule == "After warmup (step)" or ramp_steps == 0:
-            return 1.0
-
-        progress = min(1.0, max(0.0, (sampling_step - warmup + 1) / max(1, ramp_steps)))
-        return progress * progress * (3.0 - 2.0 * progress)
-
-    def get_key_scale(self, key: str, sega_param: SegaStateParams, sampling_step: int) -> float:
-        if key == "crossattn":
-            return sega_param.crossattn_scale
-        if key == "vector":
-            if sega_param.conditioning_mode == "CrossAttn only":
-                return 0.0
-            return sega_param.vector_scale * self.get_vector_ramp_factor(sampling_step, sega_param)
-        return 1.0 if sega_param.conditioning_mode == "CrossAttn + Vector" else 0.0
-
-    def get_empty_reference_for(self, tensor: torch.Tensor):
-        if tensor is None or tensor.dim() < 3:
-            return None
-
-        empty = self.get_empty_crossattn()
-        if empty is None:
-            return None
-        if empty.dim() == 2:
-            empty = empty.unsqueeze(0)
-
-        empty = empty.to(dtype=tensor.dtype, device=tensor.device)
-        if empty.shape[0] != tensor.shape[0]:
-            if empty.shape[0] == 1:
-                empty = empty.repeat(tensor.shape[0], 1, 1)
-            else:
-                empty = empty[:1].repeat(tensor.shape[0], 1, 1)
-
-        if empty.shape[1] != tensor.shape[1]:
-            empty = self.pad_crossattn_to_match(empty, tensor)
-            if empty is None or empty.shape[1] != tensor.shape[1]:
-                return None
-        return empty
-
-    def select_salient_token_indices(self, f: torch.Tensor, reference: torch.Tensor | None, topk_frac: float, max_tokens: int = 8) -> list[int]:
-        if f.ndim != 2 or f.shape[0] == 0:
-            return []
-
-        if reference is not None and reference.shape == f.shape:
-            scores = torch.linalg.norm((f - reference).float(), dim=-1)
-        else:
-            scores = torch.linalg.norm(f.float(), dim=-1)
-
-        k = max(1, int(math.ceil(float(f.shape[0]) * max(0.01, topk_frac))))
-        k = min(int(f.shape[0]), max_tokens, k)
-        vals, idx = torch.topk(scores, k=k)
-        idx = idx[torch.isfinite(vals)]
-        if idx.numel() == 0:
-            return []
-        return idx.sort().values.tolist()
-
-    def correction_by_similarities(self, f: torch.Tensor, selected_tokens: list[int], percentile: float, window: int, alpha: float) -> torch.Tensor:
-        if alpha <= 0.0 or not selected_tokens or f.ndim != 2:
-            return f
-
-        corrected = f.detach().clone()
-        normed = F.normalize(f.float(), dim=-1, eps=1e-8)
-        n = f.shape[0]
-        percentile = min(0.999, max(0.001, float(percentile)))
-        window = max(0, int(window))
-
-        for token_idx in selected_tokens:
-            if token_idx < 0 or token_idx >= n:
-                continue
-            scores = torch.mv(normed, normed[token_idx])
-            positive = scores[scores > 0]
-            if positive.numel() == 0:
-                continue
-            tau = torch.quantile(positive, percentile)
-            weights = torch.where(scores > tau, scores, torch.zeros_like(scores))
-            if window > 0:
-                mask = torch.zeros_like(weights)
-                start = max(0, token_idx - window)
-                end = min(n, token_idx + window + 1)
-                mask[start:end] = 1
-                weights = weights * mask
-            total = weights.sum()
-            if total <= 0:
-                continue
-            weights = (weights / total).to(dtype=f.dtype, device=f.device)
-            mixed = torch.sum(weights.unsqueeze(1) * f, dim=0)
-            corrected[token_idx] = ((1.0 - alpha) * f[token_idx]) + (alpha * mixed)
-        return corrected
-
-    def suppress_competing_tokens(self, f: torch.Tensor, selected_tokens: list[int], percentile: float, alpha: float) -> torch.Tensor:
-        if alpha <= 0.0 or not selected_tokens or f.ndim != 2:
-            return f
-
-        corrected = f.detach().clone()
-        normed = F.normalize(corrected.float(), dim=-1, eps=1e-8)
-        selected = normed[selected_tokens]
-        similarities = torch.matmul(normed, selected.transpose(0, 1))
-        max_sim, _ = similarities.max(dim=1)
-
-        suppression_scores = max_sim.clone()
-        suppression_scores[selected_tokens] = -1.0
-        positive = suppression_scores[suppression_scores > 0]
-        if positive.numel() == 0:
-            return corrected
-
-        percentile = min(0.999, max(0.001, float(percentile)))
-        tau = torch.quantile(positive, percentile)
-        mask = max_sim > tau
-        if selected_tokens:
-            mask[torch.tensor(selected_tokens, device=mask.device)] = False
-        if not torch.any(mask):
-            return corrected
-
-        denom = max(1e-6, float(1.0 - tau))
-        damp = 1.0 - (alpha * ((max_sim - tau).clamp(min=0.0) / denom).clamp(max=1.0))
-        corrected[mask] = corrected[mask] * damp[mask].unsqueeze(1).to(dtype=corrected.dtype)
-        return corrected
-
-    def apply_hybrid_crossattn_edits(self, tensor: torch.Tensor, sega_param: SegaStateParams) -> torch.Tensor:
-        if tensor is None or tensor.dim() != 3:
-            return tensor
-        if not getattr(sega_param, "similarity_correction_enabled", False) and not getattr(sega_param, "embedding_suppression_enabled", False):
-            return tensor
-
-        reference = self.get_empty_reference_for(tensor)
-        edited = tensor.detach().clone()
-        for batch_idx in range(tensor.shape[0]):
-            ref = None if reference is None else reference[batch_idx]
-            selected_tokens = self.select_salient_token_indices(
-                edited[batch_idx],
-                ref,
-                getattr(sega_param, "similarity_topk_frac", 0.15),
-            )
-            if getattr(sega_param, "similarity_correction_enabled", False):
-                edited[batch_idx] = self.correction_by_similarities(
-                    edited[batch_idx],
-                    selected_tokens,
-                    getattr(sega_param, "similarity_percentile", 0.75),
-                    getattr(sega_param, "similarity_window", 2),
-                    getattr(sega_param, "similarity_alpha", 0.20),
-                )
-            if getattr(sega_param, "embedding_suppression_enabled", False):
-                edited[batch_idx] = self.suppress_competing_tokens(
-                    edited[batch_idx],
-                    selected_tokens,
-                    getattr(sega_param, "embedding_suppression_percentile", 0.80),
-                    getattr(sega_param, "embedding_suppression_alpha", 0.15),
-                )
-        return edited
-
-    def on_cfg_denoiser_callback(self, params: CFGDenoiserParams, concept_conds, sega_params: list[SegaStateParams]):
-        if not concept_conds or not sega_params:
-            return
-
-        sampling_step = params.sampling_step
-        text_cond_is_dict = isinstance(params.text_cond, dict)
-        text_uncond_is_dict = isinstance(params.text_uncond, dict)
-        text_cond = self.normalize_conditioning(params.text_cond)
-        text_uncond = self.normalize_conditioning(params.text_uncond)
-        text_cond, text_uncond = self.pad_conditioning_pair(text_cond, text_uncond)
-        if not text_cond_is_dict:
-            params.text_cond = text_cond["crossattn"]
-        if not text_uncond_is_dict:
-            params.text_uncond = text_uncond["crossattn"]
-
-        batch_conds_list = []
-        batch_tensor = {}
-
-        for idx, (concept_cond, _) in enumerate(concept_conds):
-            conds_list, tensor_dict = reconstruct_multicond_batch(concept_cond, sampling_step)
-            tensor_dict = self.normalize_conditioning(tensor_dict)
-            tensor_dict, _ = self.pad_conditioning_pair(tensor_dict, text_uncond)
-            if "crossattn" in tensor_dict:
-                tensor_dict["crossattn"] = self.apply_hybrid_crossattn_edits(tensor_dict["crossattn"], sega_params[idx])
-
-            for key, tensor in tensor_dict.items():
-                tensor = tensor.unsqueeze(0)
-                if key not in batch_tensor:
-                    batch_tensor[key] = tensor
-                else:
-                    batch_tensor[key] = torch.cat((batch_tensor[key], tensor), dim=0)
-            batch_conds_list.append(conds_list)
-
-        self.sega_routine_batch(params, batch_conds_list, batch_tensor, sega_params, text_cond, text_uncond)
-
-    def make_tuple_dim(self, value):
-        if isinstance(value, torch.Tensor):
-            value = value.dim()
-        return (-1,) + (1,) * (value - 1)
-
-    def sega_routine_batch(
-        self,
-        params: CFGDenoiserParams,
-        batch_conds_list,
-        batch_tensor,
-        sega_params: list[SegaStateParams],
-        text_cond,
-        text_uncond,
-    ):
-        warmup_period = sega_params[0].warmup_period
-        edit_guidance_scale = sega_params[0].edit_guidance_scale
-        tail_percentage_threshold = sega_params[0].tail_percentage_threshold
-        momentum_scale = sega_params[0].momentum_scale
-        momentum_beta = sega_params[0].momentum_beta
-        sampling_step = params.sampling_step
-
-        edit_dir_dict = {}
-
-        for key, concept_cond in batch_tensor.items():
-            key_scale = self.get_key_scale(key, sega_params[0], sampling_step)
-            if key_scale == 0.0:
-                continue
-
-            strength = torch.tensor([sp.strength for sp in sega_params], dtype=concept_cond.dtype, device=concept_cond.device)
-            strength = strength.view(self.make_tuple_dim(concept_cond))
-
-            if key not in edit_dir_dict:
-                edit_dir_dict[key] = torch.zeros_like(concept_cond, dtype=concept_cond.dtype, device=concept_cond.device)
-
-            inside_dim = tuple(range(-concept_cond.dim() + 1, 0))
-            cond_mean = torch.mean(concept_cond, dim=inside_dim)
-            cond_std = torch.std(concept_cond, dim=inside_dim)
-
-            edit_dir = concept_cond - text_uncond[key]
-            edit_dir = torch.mul(strength, edit_dir)
-
-            tail_q = min(max(1e-6, 1.0 - float(tail_percentage_threshold)), 1.0 - 1e-6)
-            upper_z = stats.norm.ppf(tail_q)
-            upper_threshold = cond_mean + (upper_z * cond_std)
-            upper_threshold_reshaped = upper_threshold.view(self.make_tuple_dim(concept_cond))
-
-            zero_tensor = torch.zeros_like(concept_cond, dtype=concept_cond.dtype, device=concept_cond.device)
-            effective_scale = edit_guidance_scale * key_scale
-            scale_tensor = torch.ones_like(concept_cond, dtype=concept_cond.dtype, device=concept_cond.device) * effective_scale
-            edit_dir_abs = edit_dir.abs()
-            scale_tensor = torch.where(edit_dir_abs > upper_threshold_reshaped, scale_tensor, zero_tensor)
-
-            guidance_strength = 0.0 if sampling_step < warmup_period else 1.0
-            edit_dir = torch.mul(scale_tensor, edit_dir)
-            edit_dir_dict[key] = edit_dir_dict[key] + guidance_strength * edit_dir
-
-        for i, sega_param in enumerate(sega_params):
-            for key, direction in edit_dir_dict.items():
-                if key not in sega_param.v:
-                    slice_idx = 1 - direction.dim()
-                    sega_param.v[key] = torch.zeros(direction.shape[slice_idx:], dtype=direction.dtype, device=direction.device)
-
-                v_t = sega_param.v[key]
-                direction[i] = direction[i] + torch.mul(momentum_scale, v_t)
-                v_t_1 = momentum_beta * ((1 - momentum_beta) * v_t) * direction[i]
-
-                if sampling_step >= warmup_period:
-                    if isinstance(params.text_cond, dict):
-                        if key in params.text_cond:
-                            params.text_cond[key] = params.text_cond[key] + direction[i]
-                    else:
-                        if key == "crossattn":
-                            params.text_cond = params.text_cond + direction[i]
-
-                sega_param.v[key] = v_t_1
-
-
-# XYZ Plot
-# Based on @mcmonkey4eva's XYZ Plot implementation here:
-# https://github.com/mcmonkeyprojects/sd-dynamic-thresholding/blob/master/scripts/dynamic_thresholding.py
-
-def sega_apply_override(field, boolean: bool = False, activate: bool = False):
-    def fun(p, x, xs):
-        if boolean:
-            x = True if x.lower() == "true" else False
-        if activate and not hasattr(p, "sega_active"):
-            setattr(p, "sega_active", True)
-        setattr(p, field, x)
-    return fun
-
-
-
-def sega_apply_field(field):
-    def fun(p, x, xs):
-        if not hasattr(p, "sega_active"):
-            setattr(p, "sega_active", True)
-        setattr(p, field, x)
-
-    return fun
-
-
-
-def make_axis_options():
-    xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ in ("xyz_grid.py", "scripts.xyz_grid")][0].module
-    extra_axis_options = {
-        xyz_grid.AxisOption("[Semantic Guidance] Active", str, sega_apply_override("sega_active", boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
-        xyz_grid.AxisOption("[Semantic Guidance] Prompt", str, sega_apply_field("sega_prompt")),
-        xyz_grid.AxisOption("[Semantic Guidance] Negative Prompt", str, sega_apply_field("sega_neg_prompt")),
-        xyz_grid.AxisOption("[Semantic Guidance] Warmup Steps", int, sega_apply_field("sega_warmup")),
-        xyz_grid.AxisOption("[Semantic Guidance] Guidance Scale", float, sega_apply_field("sega_edit_guidance_scale")),
-        xyz_grid.AxisOption("[Semantic Guidance] Tail Percentage Threshold", float, sega_apply_field("sega_tail_percentage_threshold")),
-        xyz_grid.AxisOption("[Semantic Guidance] Momentum Scale", float, sega_apply_field("sega_momentum_scale")),
-        xyz_grid.AxisOption("[Semantic Guidance] Momentum Beta", float, sega_apply_field("sega_momentum_beta")),
-        xyz_grid.AxisOption("[Semantic Guidance] SDXL Conditioning Mode", str, sega_apply_field("sega_conditioning_mode")),
-        xyz_grid.AxisOption("[Semantic Guidance] CrossAttn Scale", float, sega_apply_field("sega_crossattn_scale")),
-        xyz_grid.AxisOption("[Semantic Guidance] Vector Scale", float, sega_apply_field("sega_vector_scale")),
-        xyz_grid.AxisOption("[Semantic Guidance] Vector Schedule", str, sega_apply_field("sega_vector_schedule")),
-        xyz_grid.AxisOption("[Semantic Guidance] Vector Ramp Steps", int, sega_apply_field("sega_vector_ramp_steps")),
-        xyz_grid.AxisOption("[Semantic Guidance] Similarity Correction", str, sega_apply_override("sega_similarity_correction_enabled", boolean=True, activate=True), choices=xyz_grid.boolean_choice(reverse=True)),
-        xyz_grid.AxisOption("[Semantic Guidance] Selected Token Fraction", float, sega_apply_field("sega_similarity_topk_frac")),
-        xyz_grid.AxisOption("[Semantic Guidance] Similarity Percentile", float, sega_apply_field("sega_similarity_percentile")),
-        xyz_grid.AxisOption("[Semantic Guidance] Similarity Window", int, sega_apply_field("sega_similarity_window")),
-        xyz_grid.AxisOption("[Semantic Guidance] Similarity Alpha", float, sega_apply_field("sega_similarity_alpha")),
-        xyz_grid.AxisOption("[Semantic Guidance] Token Competition Suppression", str, sega_apply_override("sega_embedding_suppression_enabled", boolean=True, activate=True), choices=xyz_grid.boolean_choice(reverse=True)),
-        xyz_grid.AxisOption("[Semantic Guidance] Suppression Percentile", float, sega_apply_field("sega_embedding_suppression_percentile")),
-        xyz_grid.AxisOption("[Semantic Guidance] Suppression Alpha", float, sega_apply_field("sega_embedding_suppression_alpha")),
-    }
-    if not any("[Semantic Guidance]" in x.label for x in xyz_grid.axis_options):
-        xyz_grid.axis_options.extend(extra_axis_options)
-
-
-
-def callback_before_ui():
-    try:
-        make_axis_options()
-    except Exception:
-        logger.exception("Semantic Guidance: Error while making axis options")
-
-
-script_callbacks.on_before_ui(callback_before_ui)