├── LICENSE ├── README.md ├── samples ├── comparison.png ├── concepts_1.jpg ├── enhance.jpg └── style_change.jpg └── scripts └── sega.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## sd-webui-semantic-guidance 2 | ### An unofficial implementation of *SEGA: Instructing Text-to-Image Models using Semantic Guidance* for Automatic1111 WebUI 3 | 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. 4 | 5 | ### Use Cases 6 | - Altering the style of an image without changing the composition 7 | ![image](samples/style_change.jpg) 8 | 9 | - Adding concepts to an image 10 | ![image](samples/concepts_1.jpg) 11 | 12 | Please see the bottom of the page for more examples. 13 | 14 | ### Issues / PR's are welcome! 15 | 16 | ### Settings 17 | 18 | * Positive / Negative Prompt: Concepts to emphasize / de-emphasize, separated by commas 19 | * Warmup Steps: How many steps to wait before applying semantic guidance 20 | * Edit Guidance Scale: Globally scale how much influence semantic guidance has on the image 21 | * Tail Percentage Threshold: The percentage of latents to use when calculating the semantic guidance 22 | * Momentum Scale: Scale the influence of the added momentum term 23 | * Momentum Beta: Higher values will make the influence of the momentum term more stable 24 | 25 | ### Feature / To-do List 26 | - [x] SD XL support 27 | - [x] Support A1111 prompt attention syntax and shortcuts for attention strength 28 | - [x] Support restoring parameter values from infotext (Send to Txt2Img, Send to Img2Img, etc.) 29 | - [x] XYZ plot support 30 | - [x] SD 1.5 support (untested) 31 | - [ ] Support semantic guidance prompt in regular Positive/Negative Prompt fields 32 | 33 | ### Credits 34 | - The authors of the original paper for their method (https://arxiv.org/abs/2301.12247): 35 | ``` 36 | @misc{brack2023sega, 37 | title={SEGA: Instructing Text-to-Image Models using Semantic Guidance}, 38 | author={Manuel Brack and Felix Friedrich and Dominik Hintersdorf and Lukas Struppek and Patrick Schramowski and Kristian Kersting}, 39 | year={2023}, 40 | eprint={2301.12247}, 41 | archivePrefix={arXiv}, 42 | primaryClass={cs.CV} 43 | } 44 | ``` 45 | - Official implementation of Semantic Guidance for Diffusion: (https://github.com/ml-research/semantic-image-editing) 46 | 47 | - @udon-universe's extension templates: (https://github.com/udon-universe/stable-diffusion-webui-extension-templates) 48 | 49 | ### More examples 50 | - Enhancing the details of an image 51 | ![image](samples/enhance.jpg) 52 | 53 | 54 | -------------------------------------------------------------------------------- /samples/comparison.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/v0xie/sd-webui-semantic-guidance/bdbd03c92ab5e2d3926cedd7364da61ed40e48d1/samples/comparison.png -------------------------------------------------------------------------------- /samples/concepts_1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/v0xie/sd-webui-semantic-guidance/bdbd03c92ab5e2d3926cedd7364da61ed40e48d1/samples/concepts_1.jpg -------------------------------------------------------------------------------- /samples/enhance.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/v0xie/sd-webui-semantic-guidance/bdbd03c92ab5e2d3926cedd7364da61ed40e48d1/samples/enhance.jpg -------------------------------------------------------------------------------- /samples/style_change.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/v0xie/sd-webui-semantic-guidance/bdbd03c92ab5e2d3926cedd7364da61ed40e48d1/samples/style_change.jpg -------------------------------------------------------------------------------- /scripts/sega.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from os import environ 3 | import modules.scripts as scripts 4 | import gradio as gr 5 | import scipy.stats as stats 6 | 7 | from modules import script_callbacks, prompt_parser 8 | from modules.script_callbacks import CFGDenoiserParams 9 | from modules.prompt_parser import reconstruct_multicond_batch 10 | from modules.processing import StableDiffusionProcessing 11 | #from modules.shared import sd_model, opts 12 | from modules.sd_samplers_cfg_denoiser import pad_cond 13 | from modules import shared 14 | 15 | import torch 16 | 17 | logger = logging.getLogger(__name__) 18 | logger.setLevel(environ.get("SD_WEBUI_LOG_LEVEL", logging.INFO)) 19 | 20 | """ 21 | 22 | An unofficial implementation of SEGA: Instructing Text-to-Image Models using Semantic Guidance for Automatic1111 WebUI 23 | 24 | @misc{brack2023sega, 25 | title={SEGA: Instructing Text-to-Image Models using Semantic Guidance}, 26 | author={Manuel Brack and Felix Friedrich and Dominik Hintersdorf and Lukas Struppek and Patrick Schramowski and Kristian Kersting}, 27 | year={2023}, 28 | eprint={2301.12247}, 29 | archivePrefix={arXiv}, 30 | primaryClass={cs.CV} 31 | } 32 | 33 | Author: v0xie 34 | GitHub URL: https://github.com/v0xie/sd-webui-semantic-guidance 35 | 36 | """ 37 | 38 | class SegaStateParams: 39 | def __init__(self): 40 | self.concept_name = '' 41 | self.v = {} # velocity 42 | self.warmup_period: int = 10 # [0, 20] 43 | self.edit_guidance_scale: float = 1 # [0., 1.] 44 | self.tail_percentage_threshold: float = 0.05 # [0., 1.] if abs value of difference between uncodition and concept-conditioned is less than this, then zero out the concept-conditioned values less than this 45 | self.momentum_scale: float = 0.3 # [0., 1.] 46 | self.momentum_beta: float = 0.6 # [0., 1.) # larger bm is less volatile changes in momentum 47 | self.strength = 1.0 48 | 49 | class SegaExtensionScript(scripts.Script): 50 | def __init__(self): 51 | self.cached_c = [None, None] 52 | 53 | # Extension title in menu UI 54 | def title(self): 55 | return "Semantic Guidance" 56 | 57 | # Decide to show menu in txt2img or img2img 58 | def show(self, is_img2img): 59 | return scripts.AlwaysVisible 60 | 61 | # Setup menu ui detail 62 | def ui(self, is_img2img): 63 | with gr.Accordion('Semantic Guidance', open=False): 64 | active = gr.Checkbox(value=False, default=False, label="Active", elem_id='sega_active') 65 | with gr.Row(): 66 | prompt = gr.Textbox(lines=2, label="Prompt", elem_id = 'sega_prompt', elem_classes=["prompt"]) 67 | with gr.Row(): 68 | neg_prompt = gr.Textbox(lines=2, label="Negative Prompt", elem_id = 'sega_neg_prompt', elem_classes=["prompt"]) 69 | with gr.Row(): 70 | 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, default 10") 71 | 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="Scale of edit guidance, default 1.0") 72 | 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="The percentage of latents to modify, default 0.05") 73 | 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="Scale of momentum, default 0.3") 74 | 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="Beta for momentum, default 0.6") 75 | active.do_not_save_to_config = True 76 | prompt.do_not_save_to_config = True 77 | neg_prompt.do_not_save_to_config = True 78 | warmup.do_not_save_to_config = True 79 | edit_guidance_scale.do_not_save_to_config = True 80 | tail_percentage_threshold.do_not_save_to_config = True 81 | momentum_scale.do_not_save_to_config = True 82 | momentum_beta.do_not_save_to_config = True 83 | self.infotext_fields = [ 84 | (active, lambda d: gr.Checkbox.update(value='SEGA Active' in d)), 85 | (prompt, 'SEGA Prompt'), 86 | (neg_prompt, 'SEGA Negative Prompt'), 87 | (warmup, 'SEGA Warmup Period'), 88 | (edit_guidance_scale, 'SEGA Edit Guidance Scale'), 89 | (tail_percentage_threshold, 'SEGA Tail Percentage Threshold'), 90 | (momentum_scale, 'SEGA Momentum Scale'), 91 | (momentum_beta, 'SEGA Momentum Beta'), 92 | ] 93 | self.paste_field_names = [ 94 | 'sega_active', 95 | 'sega_prompt', 96 | 'sega_neg_prompt', 97 | 'sega_warmup', 98 | 'sega_edit_guidance_scale', 99 | 'sega_tail_percentage_threshold', 100 | 'sega_momentum_scale', 101 | 'sega_momentum_beta' 102 | ] 103 | return [active, prompt, neg_prompt, warmup, edit_guidance_scale, tail_percentage_threshold, momentum_scale, momentum_beta] 104 | 105 | def process_batch(self, p: StableDiffusionProcessing, active, prompt, neg_prompt, warmup, edit_guidance_scale, tail_percentage_threshold, momentum_scale, momentum_beta, *args, **kwargs): 106 | active = getattr(p, "sega_active", active) 107 | if active is False: 108 | return 109 | prompt = getattr(p, "sega_prompt", prompt) 110 | neg_prompt = getattr(p, "sega_neg_prompt", neg_prompt) 111 | warmup = getattr(p, "sega_warmup", warmup) 112 | edit_guidance_scale = getattr(p, "sega_edit_guidance_scale", edit_guidance_scale) 113 | tail_percentage_threshold = getattr(p, "sega_tail_percentage_threshold", tail_percentage_threshold) 114 | momentum_scale = getattr(p, "sega_momentum_scale", momentum_scale) 115 | momentum_beta = getattr(p, "sega_momentum_beta", momentum_beta) 116 | # FIXME: must have some prompt 117 | #if prompt is None: 118 | # return 119 | #if len(prompt) == 0: 120 | # return 121 | p.extra_generation_params.update({ 122 | "SEGA Active": active, 123 | "SEGA Prompt": prompt, 124 | "SEGA Negative Prompt": neg_prompt, 125 | "SEGA Warmup Period": warmup, 126 | "SEGA Edit Guidance Scale": edit_guidance_scale, 127 | "SEGA Tail Percentage Threshold": tail_percentage_threshold, 128 | "SEGA Momentum Scale": momentum_scale, 129 | "SEGA Momentum Beta": momentum_beta, 130 | }) 131 | 132 | # separate concepts by comma 133 | concept_prompts = self.parse_concept_prompt(prompt) 134 | concept_prompts_neg = self.parse_concept_prompt(neg_prompt) 135 | # [[concept_1, strength_1], ...] 136 | concept_prompts = [prompt_parser.parse_prompt_attention(concept)[0] for concept in concept_prompts] 137 | concept_prompts_neg = [prompt_parser.parse_prompt_attention(neg_concept)[0] for neg_concept in concept_prompts_neg] 138 | concept_prompts_neg = [[concept, -strength] for concept, strength in concept_prompts_neg] 139 | concept_prompts.extend(concept_prompts_neg) 140 | 141 | concept_conds = [] 142 | for concept, strength in concept_prompts: 143 | prompt_list = [concept] * p.batch_size 144 | prompts = prompt_parser.SdConditioning(prompt_list, width=p.width, height=p.height) 145 | c = p.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, [self.cached_c], p.extra_network_data) 146 | concept_conds.append([c, strength]) 147 | 148 | self.create_hook(p, active, concept_conds, None, warmup, edit_guidance_scale, tail_percentage_threshold, momentum_scale, momentum_beta) 149 | 150 | def parse_concept_prompt(self, prompt:str) -> list[str]: 151 | """ 152 | Separate prompt by comma into a list of concepts 153 | TODO: parse prompt into a list of concepts using A1111 functions 154 | >>> g = lambda prompt: self.parse_concept_prompt(prompt) 155 | >>> g("") 156 | [] 157 | >>> g("apples") 158 | ['apples'] 159 | >>> g("apple, banana, carrot") 160 | ['apple', 'banana', 'carrot'] 161 | """ 162 | if len(prompt) == 0: 163 | return [] 164 | return [x.strip() for x in prompt.split(",")] 165 | 166 | def create_hook(self, p, active, concept_conds, concept_conds_neg, warmup, edit_guidance_scale, tail_percentage_threshold, momentum_scale, momentum_beta, *args, **kwargs): 167 | # Create a list of parameters for each concept 168 | concepts_sega_params = [] 169 | for _, strength in concept_conds: 170 | sega_params = SegaStateParams() 171 | sega_params.warmup_period = warmup 172 | sega_params.edit_guidance_scale = edit_guidance_scale 173 | sega_params.tail_percentage_threshold = tail_percentage_threshold 174 | sega_params.momentum_scale = momentum_scale 175 | sega_params.momentum_beta = momentum_beta 176 | sega_params.strength = strength 177 | concepts_sega_params.append(sega_params) 178 | 179 | # Use lambda to call the callback function with the parameters to avoid global variables 180 | y = lambda params: self.on_cfg_denoiser_callback(params, concept_conds, concepts_sega_params) 181 | 182 | logger.debug('Hooked callbacks') 183 | script_callbacks.on_cfg_denoiser(y) 184 | script_callbacks.on_script_unloaded(self.unhook_callbacks) 185 | 186 | def postprocess_batch(self, p, active, neg_text, *args, **kwargs): 187 | active = getattr(p, "sega_active", active) 188 | if active is False: 189 | return 190 | self.unhook_callbacks() 191 | 192 | def unhook_callbacks(self): 193 | logger.debug('Unhooked callbacks') 194 | script_callbacks.remove_current_script_callbacks() 195 | 196 | def on_cfg_denoiser_callback(self, params: CFGDenoiserParams, concept_conds, sega_params: list[SegaStateParams]): 197 | # TODO: add option to opt out of batching for performance 198 | sampling_step = params.sampling_step 199 | text_cond = params.text_cond 200 | text_uncond = params.text_uncond 201 | 202 | # pad text_cond or text_uncond to match the length of the longest prompt 203 | # i would prefer to let sd_samplers_cfg_denoiser.py handle the padding, but 204 | # there isn't a callback that returns the padded conds 205 | if text_cond.shape[1] != text_uncond.shape[1]: 206 | empty = shared.sd_model.cond_stage_model_empty_prompt 207 | num_repeats = (text_cond.shape[1] - text_uncond.shape[1]) // empty.shape[1] 208 | 209 | if num_repeats < 0: 210 | text_cond = pad_cond(text_cond, -num_repeats, empty) 211 | elif num_repeats > 0: 212 | text_uncond = pad_cond(text_uncond, num_repeats, empty) 213 | 214 | batch_conds_list = [] 215 | batch_tensor = {} 216 | 217 | # sd 1.5 support 218 | if isinstance(text_cond, torch.Tensor): 219 | text_cond = {'crossattn': text_cond} 220 | if isinstance(text_uncond, torch.Tensor): 221 | text_uncond = {'crossattn': text_uncond} 222 | 223 | for i, _ in enumerate(sega_params): 224 | concept_cond, _ = concept_conds[i] 225 | conds_list, tensor_dict = reconstruct_multicond_batch(concept_cond, sampling_step) 226 | 227 | # sd 1.5 support 228 | if isinstance(tensor_dict, torch.Tensor): 229 | tensor_dict = {'crossattn': tensor_dict} 230 | 231 | # initialize here because we don't know the shape/dtype of the tensor until we reconstruct it 232 | for key, tensor in tensor_dict.items(): 233 | if tensor.shape[1] != text_uncond[key].shape[1]: 234 | empty = shared.sd_model.cond_stage_model_empty_prompt 235 | # sd 1.5 236 | if key == "crossattn": 237 | num_repeats = (tensor.shape[1] - text_uncond[key].shape[1]) // empty.shape[1] 238 | # sdxl 239 | else: 240 | num_repeats = (tensor.shape[1] - text_uncond.shape[1]) // empty.shape[1] 241 | if num_repeats < 0: 242 | tensor = pad_cond(tensor, -num_repeats, empty) 243 | tensor = tensor.unsqueeze(0) 244 | if key not in batch_tensor.keys(): 245 | batch_tensor[key] = tensor 246 | else: 247 | batch_tensor[key] = torch.cat((batch_tensor[key], tensor), dim=0) 248 | batch_conds_list.append(conds_list) 249 | self.sega_routine_batch(params, batch_conds_list, batch_tensor, sega_params, text_cond, text_uncond) 250 | 251 | def make_tuple_dim(self, dim): 252 | # sd 1.5 support 253 | if isinstance(dim, torch.Tensor): 254 | dim = dim.dim() 255 | return (-1,) + (1,) * (dim - 1) 256 | 257 | def sega_routine_batch(self, params: CFGDenoiserParams, batch_conds_list, batch_tensor, sega_params: list[SegaStateParams], text_cond, text_uncond): 258 | # FIXME: these parameters should be specific to each concept 259 | warmup_period = sega_params[0].warmup_period 260 | edit_guidance_scale = sega_params[0].edit_guidance_scale 261 | tail_percentage_threshold = sega_params[0].tail_percentage_threshold 262 | momentum_scale = sega_params[0].momentum_scale 263 | momentum_beta = sega_params[0].momentum_beta 264 | 265 | sampling_step = params.sampling_step 266 | 267 | # Semantic Guidance 268 | edit_dir_dict = {} 269 | 270 | # batch_tensor: [num_concepts, batch_size, tokens(77, 154, etc.), 2048] 271 | # Calculate edit direction 272 | for key, concept_cond in batch_tensor.items(): 273 | new_shape = self.make_tuple_dim(concept_cond) 274 | strength = torch.Tensor([params.strength for params in sega_params]).to(dtype=concept_cond.dtype, device=concept_cond.device) 275 | strength = strength.view(new_shape) 276 | 277 | if key not in edit_dir_dict.keys(): 278 | edit_dir_dict[key] = torch.zeros_like(concept_cond, dtype=concept_cond.dtype, device=concept_cond.device) 279 | 280 | # filter out values in-between tails 281 | # FIXME: does this take into account image batch size?, i.e. dim 1 282 | inside_dim = tuple(range(-concept_cond.dim() + 1, 0)) # for tensor of dim 4, returns (-3, -2, -1), for tensor of dim 3, returns (-2, -1) 283 | cond_mean, cond_std = torch.mean(concept_cond, dim=inside_dim), torch.std(concept_cond, dim=inside_dim) 284 | 285 | # broadcast element-wise subtraction 286 | edit_dir = concept_cond - text_uncond[key] 287 | 288 | # multiply by strength for positive / negative direction 289 | edit_dir = torch.mul(strength, edit_dir) 290 | 291 | # z-scores for tails 292 | upper_z = stats.norm.ppf(1.0 - tail_percentage_threshold) 293 | 294 | # numerical thresholds 295 | # FIXME: does this take into account image batch size?, i.e. dim 1 296 | upper_threshold = cond_mean + (upper_z * cond_std) 297 | 298 | # reshape to be able to broadcast / use torch.where to filter out values for each concept 299 | #new_shape = (-1,) + (1,) * (concept_cond.dim() - 1) 300 | new_shape = self.make_tuple_dim(concept_cond) 301 | upper_threshold_reshaped = upper_threshold.view(new_shape) 302 | 303 | # zero out values in-between tails 304 | # elementwise multiplication between scale tensor and edit direction 305 | zero_tensor = torch.zeros_like(concept_cond, dtype=concept_cond.dtype, device=concept_cond.device) 306 | scale_tensor = torch.ones_like(concept_cond, dtype=concept_cond.dtype, device=concept_cond.device) * edit_guidance_scale 307 | edit_dir_abs = edit_dir.abs() 308 | scale_tensor = torch.where((edit_dir_abs > upper_threshold_reshaped), scale_tensor, zero_tensor) 309 | 310 | # update edit direction with the edit dir for this concept 311 | guidance_strength = 0.0 if sampling_step < warmup_period else 1.0 # FIXME: Use appropriate guidance strength 312 | edit_dir = torch.mul(scale_tensor, edit_dir) 313 | edit_dir_dict[key] = edit_dir_dict[key] + guidance_strength * edit_dir 314 | 315 | # TODO: batch this 316 | for i, sega_param in enumerate(sega_params): 317 | for key, dir in edit_dir_dict.items(): 318 | # calculate momentum scale and velocity 319 | if key not in sega_param.v.keys(): 320 | slice_idx = 1 - dir.dim() # should be negative, for dim=4, slice_idx = -3 321 | sega_param.v[key] = torch.zeros(dir.shape[slice_idx:], dtype=dir.dtype, device=dir.device) 322 | 323 | # add to text condition 324 | v_t = sega_param.v[key] 325 | dir[i] = dir[i] + torch.mul(momentum_scale, v_t) 326 | 327 | # calculate v_t+1 and update state 328 | v_t_1 = momentum_beta * ((1 - momentum_beta) * v_t) * dir[i] 329 | 330 | # add to cond after warmup elapsed 331 | # for sd 1.5, we must add to the original params.text_cond because we reassigned text_cond 332 | if sampling_step >= warmup_period: 333 | if isinstance(params.text_cond, dict): 334 | params.text_cond[key] = params.text_cond[key] + dir[i] 335 | else: 336 | params.text_cond = params.text_cond + dir[i] 337 | 338 | # update velocity 339 | sega_param.v[key] = v_t_1 340 | 341 | # XYZ Plot 342 | # Based on @mcmonkey4eva's XYZ Plot implementation here: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding/blob/master/scripts/dynamic_thresholding.py 343 | def sega_apply_override(field, boolean: bool = False): 344 | def fun(p, x, xs): 345 | if boolean: 346 | x = True if x.lower() == "true" else False 347 | setattr(p, field, x) 348 | return fun 349 | 350 | def sega_apply_field(field): 351 | def fun(p, x, xs): 352 | if not hasattr(p, "sega_active"): 353 | setattr(p, "sega_active", True) 354 | setattr(p, field, x) 355 | 356 | return fun 357 | 358 | def make_axis_options(): 359 | xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ in ("xyz_grid.py", "scripts.xyz_grid")][0].module 360 | extra_axis_options = { 361 | xyz_grid.AxisOption("[Semantic Guidance] Active", str, sega_apply_override('sega_active', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), 362 | xyz_grid.AxisOption("[Semantic Guidance] Prompt", str, sega_apply_field("sega_prompt")), 363 | xyz_grid.AxisOption("[Semantic Guidance] Negative Prompt", str, sega_apply_field("sega_neg_prompt")), 364 | xyz_grid.AxisOption("[Semantic Guidance] Warmup Steps", int, sega_apply_field("sega_warmup")), 365 | xyz_grid.AxisOption("[Semantic Guidance] Guidance Scale", float, sega_apply_field("sega_edit_guidance_scale")), 366 | xyz_grid.AxisOption("[Semantic Guidance] Tail Percentage Threshold", float, sega_apply_field("sega_tail_percentage_threshold")), 367 | xyz_grid.AxisOption("[Semantic Guidance] Momentum Scale", float, sega_apply_field("sega_momentum_scale")), 368 | xyz_grid.AxisOption("[Semantic Guidance] Momentum Beta", float, sega_apply_field("sega_momentum_beta")), 369 | } 370 | if not any("[Semantic Guidance]" in x.label for x in xyz_grid.axis_options): 371 | xyz_grid.axis_options.extend(extra_axis_options) 372 | 373 | def callback_before_ui(): 374 | try: 375 | make_axis_options() 376 | except: 377 | logger.exception("Semantic Guidance: Error while making axis options") 378 | 379 | script_callbacks.on_before_ui(callback_before_ui) 380 | --------------------------------------------------------------------------------