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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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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

@@ -0,0 +1,53 @@
+## 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

@@ -0,0 +1,1030 @@
+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.guidance_end_step: int = 0
+        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
+        self.main_negative_is_empty: bool = False
+        self.is_negative_concept: bool = False
+
+
+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.",
+                )
+                guidance_end_step = gr.Slider(
+                    value=0,
+                    minimum=0,
+                    maximum=150,
+                    step=1,
+                    label="Guidance End Step",
+                    elem_id="sega_guidance_end_step",
+                    info="0 = keep guidance active until the final step. Use this to limit steering to a step range.",
+                )
+                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,
+            guidance_end_step,
+            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"),
+            (guidance_end_step, "SEGA Guidance End Step"),
+            (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_guidance_end_step",
+            "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,
+            guidance_end_step,
+            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,
+        guidance_end_step,
+        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)
+        guidance_end_step = getattr(p, "sega_guidance_end_step", guidance_end_step)
+        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 Guidance End Step": guidance_end_step,
+                "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])
+
+        main_negative_is_empty = self.is_effectively_empty_prompt(getattr(p, "all_negative_prompts", getattr(p, "negative_prompt", "")))
+
+        self.create_hook(
+            active,
+            concept_conds,
+            warmup,
+            guidance_end_step,
+            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,
+            main_negative_is_empty,
+        )
+
+    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,
+        guidance_end_step,
+        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,
+        main_negative_is_empty,
+    ):
+        if not concept_conds:
+            return
+
+        concepts_sega_params = []
+        for _, strength in concept_conds:
+            sega_params = SegaStateParams()
+            sega_params.warmup_period = warmup
+            sega_params.guidance_end_step = guidance_end_step
+            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
+            sega_params.main_negative_is_empty = bool(main_negative_is_empty)
+            sega_params.is_negative_concept = bool(strength < 0)
+            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 is_effectively_empty_prompt(self, prompt_value) -> bool:
+        if prompt_value is None:
+            return True
+        if isinstance(prompt_value, (list, tuple)):
+            return all(self.is_effectively_empty_prompt(x) for x in prompt_value)
+        return not str(prompt_value).strip()
+
+    def is_guidance_active(self, sampling_step: int, sega_param: SegaStateParams) -> bool:
+        start = max(0, int(getattr(sega_param, "warmup_period", 0)))
+        end = int(getattr(sega_param, "guidance_end_step", 0) or 0)
+        if sampling_step < start:
+            return False
+        if end > 0 and sampling_step > end:
+            return False
+        return True
+
+    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))
+        end = int(getattr(sega_param, "guidance_end_step", 0) or 0)
+        if sampling_step < warmup:
+            return 0.0
+        if end > 0 and sampling_step > end:
+            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
+            base = sega_param.vector_scale * self.get_vector_ramp_factor(sampling_step, sega_param)
+            if getattr(sega_param, "main_negative_is_empty", False):
+                base *= 0.5
+            return base
+        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.float(), percentile).to(dtype=scores.dtype, device=scores.device)
+            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.float(), percentile).to(dtype=max_sim.dtype, device=max_sim.device)
+        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
+        guidance_end_step = getattr(sega_params[0], "guidance_end_step", 0)
+        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
+        guidance_active = self.is_guidance_active(sampling_step, sega_params[0])
+
+        edit_dir_dict = {}
+
+        for key, concept_cond in batch_tensor.items():
+            per_concept_key_scales = []
+            base_references = []
+            for sp in sega_params:
+                key_scale = self.get_key_scale(key, sp, sampling_step)
+                if getattr(sp, "is_negative_concept", False):
+                    if key == "vector":
+                        key_scale *= 0.35
+                    elif key == "crossattn":
+                        key_scale *= 0.70
+                    else:
+                        key_scale *= 0.70
+                    base_ref = text_cond.get(key, text_uncond.get(key, concept_cond[0]))
+                elif getattr(sp, "main_negative_is_empty", False) and key in text_cond:
+                    base_ref = text_cond[key]
+                else:
+                    base_ref = text_uncond.get(key, text_cond.get(key, concept_cond[0]))
+                per_concept_key_scales.append(key_scale)
+                base_references.append(base_ref)
+
+            if all(float(scale) == 0.0 for scale in per_concept_key_scales):
+                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))
+            key_scale = torch.tensor(per_concept_key_scales, dtype=concept_cond.dtype, device=concept_cond.device)
+            key_scale = key_scale.view(self.make_tuple_dim(concept_cond))
+            base_reference = torch.stack(base_references, dim=0).to(dtype=concept_cond.dtype, device=concept_cond.device)
+
+            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 - base_reference
+            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 = 1.0 if guidance_active else 0.0
+            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 guidance_active:
+                    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 End Step", int, sega_apply_field("sega_guidance_end_step")),
+        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)