From 66820edb63858885561aa7d1d2d24a92f3e76615 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 12 Nov 2024 11:02:34 -0500 Subject: [PATCH] update Signed-off-by: Vladimir Mandic --- modules/face/instantid.py | 2 +- modules/face/photomaker.py | 2 +- modules/processing_args.py | 9 +++-- modules/processing_info.py | 17 +++++---- modules/prompt_parser.py | 16 ++++---- modules/prompt_parser_diffusers.py | 41 ++++++++++++++------- modules/prompt_parser_xhinker.py | 2 +- modules/shared.py | 59 ++++++++++++++++++++++++------ scripts/animatediff.py | 2 +- scripts/ctrlx.py | 2 +- scripts/ledits.py | 2 +- scripts/mixture_tiling.py | 2 +- scripts/mulan.py | 2 +- scripts/regional_prompting.py | 2 +- scripts/x_adapter.py | 2 +- scripts/xyz_grid_classes.py | 4 ++ wiki | 2 +- 17 files changed, 113 insertions(+), 55 deletions(-) diff --git a/modules/face/instantid.py b/modules/face/instantid.py index 9c7c16f61..662d17c7d 100644 --- a/modules/face/instantid.py +++ b/modules/face/instantid.py @@ -68,7 +68,7 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_images, stre processing.process_init(p) p.init(p.all_prompts, p.all_seeds, p.all_subseeds) orig_prompt_attention = shared.opts.prompt_attention - shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask + shared.opts.data['prompt_attention'] = 'fixed' # otherwise need to deal with class_tokens_mask p.task_args['image_embeds'] = face_embeds[0].shape # placeholder p.task_args['image'] = face_images[0] p.task_args['controlnet_conditioning_scale'] = float(conditioning) diff --git a/modules/face/photomaker.py b/modules/face/photomaker.py index c8f58b42a..b89f28a10 100644 --- a/modules/face/photomaker.py +++ b/modules/face/photomaker.py @@ -49,7 +49,7 @@ def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger, shared.sd_model.to(dtype=devices.dtype) orig_prompt_attention = shared.opts.prompt_attention - shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask + shared.opts.data['prompt_attention'] = 'fixed' # otherwise need to deal with class_tokens_mask p.task_args['input_id_images'] = input_images p.task_args['start_merge_step'] = int(start * p.steps) p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts is not None else p.prompt diff --git a/modules/processing_args.py b/modules/processing_args.py index 67066eedc..4cd12a04d 100644 --- a/modules/processing_args.py +++ b/modules/processing_args.py @@ -107,12 +107,11 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2 debug(f'Diffusers pipeline possible: {possible}') prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2) - parser = 'Fixed attention' steps = kwargs.get("num_inference_steps", None) or len(getattr(p, 'timesteps', ['1'])) clip_skip = kwargs.pop("clip_skip", 1) - # prompt_parser_diffusers.fix_position_ids(model) - if shared.opts.prompt_attention != 'Fixed attention' and 'Onnx' not in model.__class__.__name__ and ( + parser = 'fixed' + if shared.opts.prompt_attention != 'fixed' and 'Onnx' not in model.__class__.__name__ and ( 'StableDiffusion' in model.__class__.__name__ or 'StableCascade' in model.__class__.__name__ or 'Flux' in model.__class__.__name__ @@ -125,6 +124,8 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2 if os.environ.get('SD_PROMPT_DEBUG', None) is not None: errors.display(e, 'Prompt parser encode') timer.process.record('encode', reset=False) + else: + prompt_parser_diffusers.embedder = None if 'prompt' in possible: if 'OmniGen' in model.__class__.__name__: @@ -156,7 +157,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2 else: args['negative_prompt'] = negative_prompts - if 'clip_skip' in possible and parser == 'Fixed attention': + if 'clip_skip' in possible and parser == 'fixed': if clip_skip == 1: pass # clip_skip = None else: diff --git a/modules/processing_info.py b/modules/processing_info.py index e798211b1..f721b3717 100644 --- a/modules/processing_info.py +++ b/modules/processing_info.py @@ -4,6 +4,7 @@ from modules import shared, sd_samplers_common, sd_vae, generation_parameters_co from modules.processing_class import StableDiffusionProcessing +debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None if not shared.native: from modules import sd_hijack else: @@ -39,27 +40,27 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No ops.reverse() args = { # basic + "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None, + "Sampler": p.sampler_name if p.sampler_name != 'Default' else None, "Steps": p.steps, "Seed": all_seeds[index], - "Sampler": p.sampler_name if p.sampler_name != 'Default' else None, + "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}", "CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else None, "CFG end": p.cfg_end if p.cfg_end < 1.0 else None, - "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None, + "Clip skip": p.clip_skip if p.clip_skip > 1 else None, "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None, - "Parser": shared.opts.prompt_attention.split()[0], "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''), "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash), "VAE": (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD', - "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}", - "Clip skip": p.clip_skip if p.clip_skip > 1 else None, "Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None, "Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None, "Styles": "; ".join(p.styles) if p.styles is not None and len(p.styles) > 0 else None, - "Tiling": p.tiling if p.tiling else None, # sdnext - "Backend": 'Diffusers' if shared.native else 'Original', "App": 'SD.Next', "Version": git_commit, + "Backend": 'Diffusers' if shared.native else 'Original', + "Pipeline": 'LDM', + "Parser": shared.opts.prompt_attention.split()[0], "Comment": comment, "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none', } @@ -165,7 +166,9 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No if isinstance(v, str): if len(v) == 0 or v == '0x0': del args[k] + debug(f'Infotext: args={args}') params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in args.items()]) negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else "" infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip() + debug(f'Infotext: "{infotext}"') return infotext diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 2a71d5053..3a1288097 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -308,11 +308,11 @@ def parse_prompt_attention(text): res = [] round_brackets = [] square_brackets = [] - if opts.prompt_attention == 'Fixed attention': + if opts.prompt_attention == 'fixed': res = [[text, 1.0]] debug(f'Prompt: parser="{opts.prompt_attention}" {res}') return res - elif opts.prompt_attention == 'Compel parser': + elif opts.prompt_attention == 'compel': conjunction = Compel.parse_prompt_string(text) if conjunction is None or conjunction.prompts is None or conjunction.prompts is None or len(conjunction.prompts[0].children) == 0: return [["", 1.0]] @@ -321,7 +321,7 @@ def parse_prompt_attention(text): res.append([frag.text, frag.weight]) debug(f'Prompt: parser="{opts.prompt_attention}" {res}') return res - elif opts.prompt_attention == 'A1111 parser': + elif opts.prompt_attention == 'a1111': re_attention = re_attention_v1 whitespace = '' else: @@ -360,7 +360,7 @@ def parse_prompt_attention(text): for i, part in enumerate(parts): if i > 0: res.append(["BREAK", -1]) - if opts.prompt_attention == 'Full parser': + if opts.prompt_attention == 'native': part = re_clean.sub("", part) part = re_whitespace.sub(" ", part).strip() if len(part) == 0: @@ -392,15 +392,15 @@ if __name__ == "__main__": log.info(f'Schedules: {all_schedules}') for schedule in all_schedules: log.info(f'Schedule: {schedule[0]}') - opts.data['prompt_attention'] = 'Fixed attention' + opts.data['prompt_attention'] = 'fixed' output_list = parse_prompt_attention(schedule[1]) log.info(f' Fixed: {output_list}') - opts.data['prompt_attention'] = 'Compel parser' + opts.data['prompt_attention'] = 'compel' output_list = parse_prompt_attention(schedule[1]) log.info(f' Compel: {output_list}') - opts.data['prompt_attention'] = 'A1111 parser' + opts.data['prompt_attention'] = 'a1111' output_list = parse_prompt_attention(schedule[1]) log.info(f' A1111: {output_list}') - opts.data['prompt_attention'] = 'Full parser' + opts.data['prompt_attention'] = 'native' log.info = parse_prompt_attention(schedule[1]) log.info(f' Full: {output_list}') diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py index 0e10e5fe1..e53af7957 100644 --- a/modules/prompt_parser_diffusers.py +++ b/modules/prompt_parser_diffusers.py @@ -47,6 +47,7 @@ class PromptEmbedder: self.prompts = prompts self.negative_prompts = negative_prompts self.batchsize = len(self.prompts) + self.attention = None self.allsame = self.compare_prompts() # collapses batched prompts to single prompt if possible self.steps = steps self.clip_skip = clip_skip @@ -75,6 +76,10 @@ class PromptEmbedder: def checkcache(self, p): if shared.opts.sd_textencoder_cache_size == 0: return False + if self.attention != shared.opts.prompt_attention: + debug(f"Prompt change: parser={shared.opts.prompt_attention}") + cache.clear() + return False def flatten(xss): return [x for xs in xss for x in xs] @@ -97,23 +102,22 @@ class PromptEmbedder: 'positive_pooleds': self.positive_pooleds, 'negative_pooleds': self.negative_pooleds, } - debug(f"Prompt cache: Adding {key}") + debug(f"Prompt cache: add={key}") while len(cache) > int(shared.opts.sd_textencoder_cache_size): cache.popitem(last=False) if item: self.__dict__.update(cache[key]) cache.move_to_end(key) - if self.allsame and len(self.prompt_embeds) < self.batchsize: # If current batch larger than cached + if self.allsame and len(self.prompt_embeds) < self.batchsize: self.prompt_embeds = [self.prompt_embeds[0]] * self.batchsize self.positive_pooleds = [self.positive_pooleds[0]] * self.batchsize self.negative_prompt_embeds = [self.negative_prompt_embeds[0]] * self.batchsize self.negative_pooleds = [self.negative_pooleds[0]] * self.batchsize - debug(f"Prompt cache: Retrieving {key}") + debug(f"Prompt cache: get={key}") return True def compare_prompts(self): - same = (self.prompts == [self.prompts[0]] * len(self.prompts) and - self.negative_prompts == [self.negative_prompts[0]] * len(self.negative_prompts)) + same = (self.prompts == [self.prompts[0]] * len(self.prompts) and self.negative_prompts == [self.negative_prompts[0]] * len(self.negative_prompts)) if same: self.prompts = [self.prompts[0]] self.negative_prompts = [self.negative_prompts[0]] @@ -123,6 +127,7 @@ class PromptEmbedder: self.positive_schedule, scheduled = get_prompt_schedule(prompt, self.steps) self.negative_schedule, neg_scheduled = get_prompt_schedule(negative_prompt, self.steps) self.scheduled_prompt = scheduled or neg_scheduled + debug(f"Prompt schedule: positive={self.positive_schedule} negative={self.negative_schedule} scheduled={scheduled}") def scheduled_encode(self, pipe, batchidx): prompt_dict = {} # index cache @@ -138,20 +143,21 @@ class PromptEmbedder: prompt_dict[positive_prompt+negative_prompt] = i def extend_embeds(self, batchidx, idx): # Extends scheduled prompt via index - self.prompt_embeds[batchidx].append(self.prompt_embeds[batchidx][idx]) - self.negative_prompt_embeds[batchidx].append(self.negative_prompt_embeds[batchidx][idx]) + if len(self.prompt_embeds[batchidx]) > 0: + self.prompt_embeds[batchidx].append(self.prompt_embeds[batchidx][idx]) + if len(self.negative_prompt_embeds[batchidx]) > 0: + self.negative_prompt_embeds[batchidx].append(self.negative_prompt_embeds[batchidx][idx]) if len(self.positive_pooleds[batchidx]) > 0: self.positive_pooleds[batchidx].append(self.positive_pooleds[batchidx][idx]) if len(self.negative_pooleds[batchidx]) > 0: self.negative_pooleds[batchidx].append(self.negative_pooleds[batchidx][idx]) def encode(self, pipe, positive_prompt, negative_prompt, batchidx): - if shared.opts.prompt_attention == "xhinker parser" or 'Flux' in pipe.__class__.__name__: - prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings( - pipe, positive_prompt, negative_prompt, self.clip_skip) + self.attention = shared.opts.prompt_attention + if self.attention == "xhinker" or 'Flux' in pipe.__class__.__name__: + prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(pipe, positive_prompt, negative_prompt, self.clip_skip) else: - prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings( - pipe, positive_prompt, negative_prompt, self.clip_skip) + prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, self.clip_skip) if prompt_embed is not None: self.prompt_embeds[batchidx].append(prompt_embed) if negative_embed is not None: @@ -311,6 +317,7 @@ def get_tokens(msg, prompt): tokens.append(f'UNK_{i}') token_count = len(ids) - int(has_bos_token) - int(has_eos_token) debug(f'Prompt tokenizer: type={msg} tokens={token_count} {tokens}') + return token_count def normalize_prompt(pairs: list): @@ -338,6 +345,12 @@ def get_prompts_with_weights(prompt: str): if shared.opts.prompt_mean_norm: texts_and_weights = normalize_prompt(texts_and_weights) texts, text_weights = zip(*texts_and_weights) + if debug_enabled: + all_tokens = 0 + for text in texts: + tokens = get_tokens('section', text) + all_tokens += tokens + debug(f'Prompt tokenizer: parser={shared.opts.prompt_attention} tokens={all_tokens}') debug(f'Prompt: weights={texts_and_weights} time={(time.time() - t0):.3f}') return texts, text_weights @@ -479,7 +492,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c # negative prompt has no keywords embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]], fragment_weights_batch=[negative_weights[i]], device=device, should_return_tokens=True) negative_prompt_embeds.append(embed) - debug(f'Prompt: unpadded shape={prompt_embeds[0].shape} TE{i+1} ptokens={torch.count_nonzero(ptokens)} ntokens={torch.count_nonzero(ntokens)} time={(time.time() - t0):.3f}') + debug(f'Prompt: unpadded={prompt_embeds[0].shape} TE{i+1} ptokens={torch.count_nonzero(ptokens)} ntokens={torch.count_nonzero(ntokens)} time={(time.time() - t0):.3f}') if SD3: t0 = time.time() pooled_prompt_embeds.append(embedding_providers[0].get_pooled_embeddings(texts=positives[0] if len(positives[0]) == 1 else [" ".join(positives[0])], device=device)) @@ -488,7 +501,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c negative_pooled_prompt_embeds.append(embedding_providers[1].get_pooled_embeddings(texts=negatives[-1] if len(negatives[-1]) == 1 else [" ".join(negatives[-1])], device=device)) pooled_prompt_embeds = torch.cat(pooled_prompt_embeds, dim=-1) negative_pooled_prompt_embeds = torch.cat(negative_pooled_prompt_embeds, dim=-1) - debug(f'Prompt: pooled shape={pooled_prompt_embeds[0].shape} time={(time.time() - t0):.3f}') + debug(f'Prompt: pooled={pooled_prompt_embeds[0].shape} time={(time.time() - t0):.3f}') elif prompt_embeds[-1].shape[-1] > 768: t0 = time.time() if shared.opts.diffusers_pooled == "weighted": diff --git a/modules/prompt_parser_xhinker.py b/modules/prompt_parser_xhinker.py index 6a8acf8c6..c0ddc9bc7 100644 --- a/modules/prompt_parser_xhinker.py +++ b/modules/prompt_parser_xhinker.py @@ -1305,7 +1305,7 @@ def get_weighted_text_embeddings_sd3( # ---------------------- get neg t5 embeddings ------------------------- neg_prompt_tokens_3 = torch.tensor([neg_prompt_tokens_3], dtype=torch.long) - t5_neg_prompt_embeds = pipe.text_encoder_3(neg_prompt_tokens_3.to(pipe.pipe.text_encoder_3.device))[0].squeeze(0) + t5_neg_prompt_embeds = pipe.text_encoder_3(neg_prompt_tokens_3.to(pipe.text_encoder_3.device))[0].squeeze(0) t5_neg_prompt_embeds = t5_neg_prompt_embeds.to(device=pipe.text_encoder_3.device) # add weight to neg t5 embeddings diff --git a/modules/shared.py b/modules/shared.py index 867651e7a..171a777e9 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -273,6 +273,40 @@ class OptionInfo: self.comment_after += " (requires restart)" return self + def validate(self, opt, value): + args = self.component_args if self.component_args is not None else {} + if callable(args): + try: + args = args() + except Exception: + args = {} + choices = args.get("choices", []) + if callable(choices): + try: + choices = choices() + except Exception: + choices = [] + if len(choices) > 0: + if not isinstance(value, list): + value = [value] + for v in value: + if v not in choices: + log.warning(f'Setting validation: "{opt}"="{v}" default="{self.default}" choices={choices}') + return False + minimum = args.get("minimum", None) + maximum = args.get("maximum", None) + if (minimum is not None and value < minimum) or (maximum is not None and value > maximum): + log.error(f'Setting validation: "{opt}"={value} default={self.default} minimum={minimum} maximum={maximum}') + return False + return True + + def __str__(self) -> str: + args = self.component_args if self.component_args is not None else {} + if callable(args): + args = args() + choices = args.get("choices", []) + return f'OptionInfo: label="{self.label}" section="{self.section}" component="{self.component}" default="{self.default}" refresh="{self.refresh is not None}" change="{self.onchange is not None}" args={args} choices={choices}' + def options_section(section_identifier, options_dict): for v in options_dict.values(): @@ -442,7 +476,7 @@ options_templates.update(options_section(('sd', "Execution & Models"), { "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}), "prompt_mean_norm": OptionInfo(False, "Prompt attention normalization", gr.Checkbox), "comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": not native }), - "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "xhinker parser", "A1111 parser", "Fixed attention"] }), + "prompt_attention": OptionInfo("native", "Prompt attention parser", gr.Radio, {"choices": ["native", "compel", "xhinker", "a1111", "fixed"] }), "latent_history": OptionInfo(16, "Latent history size", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": not native }), "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}), @@ -994,7 +1028,7 @@ class Options: if filename is None: filename = self.filename if cmd_opts.freeze: - log.warning(f'Settings saving is disabled: {filename}') + log.warning(f'Setting: fn="{filename}" save disabled') return try: # output = json.dumps(self.data, indent=2) @@ -1002,12 +1036,12 @@ class Options: unused_settings = [] if os.environ.get('SD_CONFIG_DEBUG', None) is not None: - log.debug('Config: user settings') + log.debug('Settings: user') for k, v in self.data.items(): log.trace(f' Config: item={k} value={v} default={self.data_labels[k].default if k in self.data_labels else None}') - log.debug('Config: default settings') + log.debug('Settings: defaults') for k in self.data_labels.keys(): - log.trace(f' Config: item={k} default={self.data_labels[k].default}') + log.trace(f' Setting: item={k} default={self.data_labels[k].default}') for k, v in self.data.items(): if k in self.data_labels: @@ -1022,9 +1056,9 @@ class Options: unused_settings.append(k) writefile(diff, filename, silent=silent) if len(unused_settings) > 0: - log.debug(f"Unused settings: {unused_settings}") + log.debug(f"Settings: unused={unused_settings}") except Exception as err: - log.error(f'Save settings failed: {filename} {err}') + log.error(f'Settings: fn="{filename}" {err}') def save(self, filename=None, silent=False): threading.Thread(target=self.save_atomic, args=(filename, silent)).start() @@ -1040,7 +1074,7 @@ class Options: if filename is None: filename = self.filename if not os.path.isfile(filename): - log.debug(f'Created default config: {filename}') + log.debug(f'Settings: fn="{filename}" created') self.save(filename) return self.data = readfile(filename, lock=True) @@ -1048,13 +1082,16 @@ class Options: self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')] unknown_settings = [] for k, v in self.data.items(): - info = self.data_labels.get(k, None) + info: OptionInfo = self.data_labels.get(k, None) + if not info.validate(k, v): + self.data[k] = info.default if info is not None and not self.same_type(info.default, v): - log.error(f"Error: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})") + log.warning(f"Setting validation: {k}={v} ({type(v).__name__} expected={type(info.default).__name__})") + self.data[k] = info.default if info is None and k not in compatibility_opts and not k.startswith('uiux_'): unknown_settings.append(k) if len(unknown_settings) > 0: - log.debug(f"Unknown settings: {unknown_settings}") + log.warning(f"Setting validation: unknown={unknown_settings}") def onchange(self, key, func, call=True): item = self.data_labels.get(key) diff --git a/scripts/animatediff.py b/scripts/animatediff.py index 09f9e33a9..fca09424b 100644 --- a/scripts/animatediff.py +++ b/scripts/animatediff.py @@ -258,7 +258,7 @@ class Script(scripts.Script): shared.log.debug(f'AnimateDiff args: {p.task_args}') set_prompt(p) orig_prompt_attention = shared.opts.prompt_attention - shared.opts.data['prompt_attention'] = 'Fixed attention' + shared.opts.data['prompt_attention'] = 'fixed' processed: processing.Processed = processing.process_images(p) # runs processing using main loop shared.opts.data['prompt_attention'] = orig_prompt_attention devices.torch_gc() diff --git a/scripts/ctrlx.py b/scripts/ctrlx.py index acfdd5d6e..69d5994df 100644 --- a/scripts/ctrlx.py +++ b/scripts/ctrlx.py @@ -49,7 +49,7 @@ class Script(scripts.Script): from modules.ctrlx.utils import get_self_recurrence_schedule orig_prompt_attention = shared.opts.prompt_attention - shared.opts.data['prompt_attention'] = 'Fixed attention' + shared.opts.data['prompt_attention'] = 'fixed' shared.sd_model = sd_models.switch_pipe(CtrlXStableDiffusionXLPipeline, shared.sd_model) shared.sd_model.restore_pipeline = self.restore diff --git a/scripts/ledits.py b/scripts/ledits.py index 1a0e929f0..ba9d49f89 100644 --- a/scripts/ledits.py +++ b/scripts/ledits.py @@ -44,7 +44,7 @@ class Script(scripts.Script): orig_offload = shared.opts.diffusers_model_cpu_offload orig_prompt_attention = shared.opts.prompt_attention shared.opts.data['diffusers_model_cpu_offload'] = False - shared.opts.data['prompt_attention'] = 'Fixed attention' + shared.opts.data['prompt_attention'] = 'fixed' # shared.sd_model.maybe_free_model_hooks() # ledits is not compatible with offloading # shared.sd_model.has_accelerate = False sd_models.move_model(shared.sd_model, devices.device, force=True) diff --git a/scripts/mixture_tiling.py b/scripts/mixture_tiling.py index 5dcaf0156..13e48ce11 100644 --- a/scripts/mixture_tiling.py +++ b/scripts/mixture_tiling.py @@ -66,7 +66,7 @@ class Script(scripts.Script): shared.sd_model = orig_pipeline return sd_models.set_diffuser_options(shared.sd_model) - shared.opts.data['prompt_attention'] = 'Fixed attention' # this pipeline is not compatible with embeds + shared.opts.data['prompt_attention'] = 'fixed' # this pipeline is not compatible with embeds shared.sd_model.to(torch.float32) # this pipeline unet is not compatible with fp16 processing.fix_seed(p) # set pipeline specific params, note that standard params are applied when applicable diff --git a/scripts/mulan.py b/scripts/mulan.py index 4b80a7c87..c2ad10d2e 100644 --- a/scripts/mulan.py +++ b/scripts/mulan.py @@ -87,7 +87,7 @@ class Script(scripts.Script): # mulan only works with single image, single prompt and in fixed attention p.batch_size = 1 p.n_iter = 1 - shared.opts.prompt_attention = 'Fixed attention' + shared.opts.prompt_attention = 'fixed' if isinstance(p.prompt, list): p.prompt = p.prompt[0] p.task_args['prompt'] = p.prompt diff --git a/scripts/regional_prompting.py b/scripts/regional_prompting.py index cecef747d..08b84dd94 100644 --- a/scripts/regional_prompting.py +++ b/scripts/regional_prompting.py @@ -64,7 +64,7 @@ class Script(scripts.Script): shared.sd_model = orig_pipeline return sd_models.set_diffuser_options(shared.sd_model) - shared.opts.data['prompt_attention'] = 'Fixed attention' # this pipeline is not compatible with embeds + shared.opts.data['prompt_attention'] = 'fixed' # this pipeline is not compatible with embeds processing.fix_seed(p) # set pipeline specific params, note that standard params are applied when applicable rp_args = { diff --git a/scripts/x_adapter.py b/scripts/x_adapter.py index 553a20d30..c67eca18b 100644 --- a/scripts/x_adapter.py +++ b/scripts/x_adapter.py @@ -107,7 +107,7 @@ class Script(scripts.Script): pipe.to(device=devices.device, dtype=devices.dtype) except Exception: pass - shared.opts.data['prompt_attention'] = 'Fixed attention' + shared.opts.data['prompt_attention'] = 'fixed' prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles) negative = shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles) p.task_args['prompt'] = prompt diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py index 4898c6b73..08ea279f4 100644 --- a/scripts/xyz_grid_classes.py +++ b/scripts/xyz_grid_classes.py @@ -37,6 +37,7 @@ class SharedSettingsStackHelper(object): sd_text_encoder = None extra_networks_default_multiplier = None disable_weights_auto_swap = None + prompt_attention = None def __enter__(self): #Save overridden settings so they can be restored later. @@ -52,6 +53,7 @@ class SharedSettingsStackHelper(object): self.sd_text_encoder = shared.opts.sd_text_encoder self.extra_networks_default_multiplier = shared.opts.extra_networks_default_multiplier self.disable_weights_auto_swap = shared.opts.disable_weights_auto_swap + self.prompt_attention = shared.opts.prompt_attention shared.opts.data["disable_weights_auto_swap"] = False def __exit__(self, exc_type, exc_value, tb): @@ -62,6 +64,7 @@ class SharedSettingsStackHelper(object): shared.opts.data["tome_ratio"] = self.tome_ratio shared.opts.data["todo_ratio"] = self.todo_ratio shared.opts.data["extra_networks_default_multiplier"] = self.extra_networks_default_multiplier + shared.opts.data["prompt_attention"] = self.prompt_attention if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint: shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint sd_models.reload_model_weights(op='model') @@ -92,6 +95,7 @@ axis_options = [ AxisOption("[Model] Dictionary", str, apply_dict, fmt=format_value_add_label, cost=0.9, choices=lambda: ['None'] + list(sd_models.checkpoints_list)), AxisOption("[Prompt] Search & replace", str, apply_prompt, fmt=format_value_add_label), AxisOption("[Prompt] Prompt order", str_permutations, apply_order, fmt=format_value_join_list), + AxisOption("[Prompt] Prompt parser", str, apply_setting("prompt_attention"), choices=lambda: ["native", "compel", "xhinker", "a1111", "fixed"]), AxisOption("[Network] LoRA", str, apply_lora, cost=0.5, choices=list_lora), AxisOption("[Network] LoRA strength", float, apply_setting('extra_networks_default_multiplier')), AxisOption("[Network] Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]), diff --git a/wiki b/wiki index 47ea50e91..352fc655b 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 47ea50e9152a13325dd1daf92bc50b700783182f +Subproject commit 352fc655b0dc9edb22aac093186da087ba18b474