Loading modules/generation_parameters_copypaste.py +3 −0 Original line number Original line Diff line number Diff line Loading @@ -280,6 +280,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "Hires sampler" not in res: if "Hires sampler" not in res: res["Hires sampler"] = "Use same sampler" res["Hires sampler"] = "Use same sampler" if "Hires checkpoint" not in res: res["Hires checkpoint"] = "Use same checkpoint" if "Hires prompt" not in res: if "Hires prompt" not in res: res["Hires prompt"] = "" res["Hires prompt"] = "" Loading modules/processing.py +66 −31 Original line number Original line Diff line number Diff line Loading @@ -539,8 +539,12 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x return x class DecodedSamples(list): already_decoded = True def decode_latent_batch(model, batch, target_device=None, check_for_nans=False): def decode_latent_batch(model, batch, target_device=None, check_for_nans=False): samples = [] samples = DecodedSamples() for i in range(batch.shape[0]): for i in range(batch.shape[0]): sample = decode_first_stage(model, batch[i:i + 1])[0] sample = decode_first_stage(model, batch[i:i + 1])[0] Loading Loading @@ -788,7 +792,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast(): with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast(): samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts) samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts) if getattr(samples_ddim, 'already_decoded', False): x_samples_ddim = samples_ddim else: x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = torch.stack(x_samples_ddim).float() x_samples_ddim = torch.stack(x_samples_ddim).float() x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) Loading Loading @@ -930,7 +938,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): cached_hr_uc = [None, None] cached_hr_uc = [None, None] cached_hr_c = [None, None] cached_hr_c = [None, None] def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs): def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs): super().__init__(**kwargs) super().__init__(**kwargs) self.enable_hr = enable_hr self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.denoising_strength = denoising_strength Loading @@ -941,11 +949,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_resize_y = hr_resize_y self.hr_resize_y = hr_resize_y self.hr_upscale_to_x = hr_resize_x self.hr_upscale_to_x = hr_resize_x self.hr_upscale_to_y = hr_resize_y self.hr_upscale_to_y = hr_resize_y self.hr_checkpoint_name = hr_checkpoint_name self.hr_checkpoint_info = None self.hr_sampler_name = hr_sampler_name self.hr_sampler_name = hr_sampler_name self.hr_prompt = hr_prompt self.hr_prompt = hr_prompt self.hr_negative_prompt = hr_negative_prompt self.hr_negative_prompt = hr_negative_prompt self.all_hr_prompts = None self.all_hr_prompts = None self.all_hr_negative_prompts = None self.all_hr_negative_prompts = None self.latent_scale_mode = None if firstphase_width != 0 or firstphase_height != 0: if firstphase_width != 0 or firstphase_height != 0: self.hr_upscale_to_x = self.width self.hr_upscale_to_x = self.width Loading @@ -968,6 +979,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def init(self, all_prompts, all_seeds, all_subseeds): def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: if self.enable_hr: if self.hr_checkpoint_name: self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name) if self.hr_checkpoint_info is None: raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}') self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name: if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name: self.extra_generation_params["Hires sampler"] = self.hr_sampler_name self.extra_generation_params["Hires sampler"] = self.hr_sampler_name Loading @@ -977,6 +996,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt): if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt): self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") if self.enable_hr and self.latent_scale_mode is None: if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers): raise Exception(f"could not find upscaler named {self.hr_upscaler}") if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): self.hr_resize_x = self.width self.hr_resize_x = self.width self.hr_resize_y = self.height self.hr_resize_y = self.height Loading Loading @@ -1015,14 +1039,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f # special case: the user has chosen to do nothing if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height: self.enable_hr = False self.denoising_strength = None self.extra_generation_params.pop("Hires upscale", None) self.extra_generation_params.pop("Hires resize", None) return if not state.processing_has_refined_job_count: if not state.processing_has_refined_job_count: if state.job_count == -1: if state.job_count == -1: state.job_count = self.n_iter state.job_count = self.n_iter Loading @@ -1040,17 +1056,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") if self.enable_hr and latent_scale_mode is None: if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers): raise Exception(f"could not find upscaler named {self.hr_upscaler}") x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) del x if not self.enable_hr: if not self.enable_hr: return samples return samples if self.latent_scale_mode is None: decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32) else: decoded_samples = None current = shared.sd_model.sd_checkpoint_info try: if self.hr_checkpoint_info is not None: self.sampler = None sd_models.reload_model_weights(info=self.hr_checkpoint_info) devices.torch_gc() return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts) finally: self.sampler = None sd_models.reload_model_weights(info=current) devices.torch_gc() def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts): self.is_hr_pass = True self.is_hr_pass = True target_width = self.hr_upscale_to_x target_width = self.hr_upscale_to_x Loading @@ -1068,11 +1099,18 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix") images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix") if latent_scale_mode is not None: img2img_sampler_name = self.hr_sampler_name or self.sampler_name if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM img2img_sampler_name = 'DDIM' self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model) if self.latent_scale_mode is not None: for i in range(samples.shape[0]): for i in range(samples.shape[0]): save_intermediate(samples, i) save_intermediate(samples, i) samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"]) # Avoid making the inpainting conditioning unless necessary as # Avoid making the inpainting conditioning unless necessary as # this does need some extra compute to decode / encode the image again. # this does need some extra compute to decode / encode the image again. Loading @@ -1081,7 +1119,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: else: image_conditioning = self.txt2img_image_conditioning(samples) image_conditioning = self.txt2img_image_conditioning(samples) else: else: decoded_samples = decode_first_stage(self.sd_model, samples) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) batch_images = [] batch_images = [] Loading @@ -1100,6 +1137,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = decoded_samples.to(shared.device) decoded_samples = decoded_samples.to(shared.device) decoded_samples = 2. * decoded_samples - 1. decoded_samples = 2. * decoded_samples - 1. decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae) samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) Loading @@ -1107,19 +1145,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob() shared.state.nextjob() img2img_sampler_name = self.hr_sampler_name or self.sampler_name if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM img2img_sampler_name = 'DDIM' self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2] samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2] noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) # GC now before running the next img2img to prevent running out of memory # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() devices.torch_gc() if not self.disable_extra_networks: if not self.disable_extra_networks: Loading @@ -1138,9 +1168,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True) self.is_hr_pass = False self.is_hr_pass = False return samples return decoded_samples def close(self): def close(self): super().close() super().close() Loading Loading @@ -1179,8 +1211,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.hr_c is not None: if self.hr_c is not None: return return self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data) hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y) self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data) hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True) self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data) self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data) def setup_conds(self): def setup_conds(self): super().setup_conds() super().setup_conds() Loading @@ -1188,7 +1223,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_uc = None self.hr_uc = None self.hr_c = None self.hr_c = None if self.enable_hr: if self.enable_hr and self.hr_checkpoint_info is None: if shared.opts.hires_fix_use_firstpass_conds: if shared.opts.hires_fix_use_firstpass_conds: self.calculate_hr_conds() self.calculate_hr_conds() Loading modules/sd_models.py +17 −10 Original line number Original line Diff line number Diff line Loading @@ -67,6 +67,7 @@ class CheckpointInfo: self.shorthash = self.sha256[0:10] if self.sha256 else None self.shorthash = self.sha256[0:10] if self.sha256 else None self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]' self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) Loading @@ -87,6 +88,7 @@ class CheckpointInfo: checkpoints_list.pop(self.title, None) checkpoints_list.pop(self.title, None) self.title = f'{self.name} [{self.shorthash}]' self.title = f'{self.name} [{self.shorthash}]' self.short_title = f'{self.name_for_extra} [{self.shorthash}]' self.register() self.register() return self.shorthash return self.shorthash Loading @@ -107,14 +109,8 @@ def setup_model(): enable_midas_autodownload() enable_midas_autodownload() def checkpoint_tiles(): def checkpoint_tiles(use_short=False): def convert(name): return [x.short_title if use_short else x.title for x in checkpoints_list.values()] return int(name) if name.isdigit() else name.lower() def alphanumeric_key(key): return [convert(c) for c in re.split('([0-9]+)', key)] return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key) def list_models(): def list_models(): Loading @@ -137,11 +133,14 @@ def list_models(): elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) for filename in sorted(model_list, key=str.lower): for filename in model_list: checkpoint_info = CheckpointInfo(filename) checkpoint_info = CheckpointInfo(filename) checkpoint_info.register() checkpoint_info.register() re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$") def get_closet_checkpoint_match(search_string): def get_closet_checkpoint_match(search_string): checkpoint_info = checkpoint_aliases.get(search_string, None) checkpoint_info = checkpoint_aliases.get(search_string, None) if checkpoint_info is not None: if checkpoint_info is not None: Loading @@ -151,6 +150,11 @@ def get_closet_checkpoint_match(search_string): if found: if found: return found[0] return found[0] search_string_without_checksum = re.sub(re_strip_checksum, '', search_string) found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title)) if found: return found[0] return None return None Loading Loading @@ -585,7 +589,10 @@ def reload_model_weights(sd_model=None, info=None): timer.record("find config") timer.record("find config") if sd_model is None or checkpoint_config != sd_model.used_config: if sd_model is None or checkpoint_config != sd_model.used_config: del sd_model if sd_model is not None: sd_model.to(device="meta") devices.torch_gc() load_model(checkpoint_info, already_loaded_state_dict=state_dict) load_model(checkpoint_info, already_loaded_state_dict=state_dict) return model_data.sd_model return model_data.sd_model Loading modules/shared.py +13 −6 Original line number Original line Diff line number Diff line Loading @@ -220,6 +220,8 @@ class State: return return import modules.sd_samplers import modules.sd_samplers try: if opts.show_progress_grid: if opts.show_progress_grid: self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent)) self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent)) else: else: Loading @@ -227,6 +229,11 @@ class State: self.current_image_sampling_step = self.sampling_step self.current_image_sampling_step = self.sampling_step except Exception: # when switching models during genration, VAE would be on CPU, so creating an image will fail. # we silently ignore this error errors.record_exception() def assign_current_image(self, image): def assign_current_image(self, image): self.current_image = image self.current_image = image self.id_live_preview += 1 self.id_live_preview += 1 Loading Loading @@ -521,7 +528,7 @@ options_templates.update(options_section(('ui', "User interface"), { "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(), "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(), "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(), "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_restart(), "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(), "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(), "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(), "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(), })) })) Loading modules/txt2img.py +2 −1 Original line number Original line Diff line number Diff line Loading @@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html import gradio as gr import gradio as gr def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args): def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args): override_settings = create_override_settings_dict(override_settings_texts) override_settings = create_override_settings_dict(override_settings_texts) p = processing.StableDiffusionProcessingTxt2Img( p = processing.StableDiffusionProcessingTxt2Img( Loading Loading @@ -41,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step hr_second_pass_steps=hr_second_pass_steps, hr_second_pass_steps=hr_second_pass_steps, hr_resize_x=hr_resize_x, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y, hr_resize_y=hr_resize_y, hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name, hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None, hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None, hr_prompt=hr_prompt, hr_prompt=hr_prompt, hr_negative_prompt=hr_negative_prompt, hr_negative_prompt=hr_negative_prompt, Loading Loading
modules/generation_parameters_copypaste.py +3 −0 Original line number Original line Diff line number Diff line Loading @@ -280,6 +280,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "Hires sampler" not in res: if "Hires sampler" not in res: res["Hires sampler"] = "Use same sampler" res["Hires sampler"] = "Use same sampler" if "Hires checkpoint" not in res: res["Hires checkpoint"] = "Use same checkpoint" if "Hires prompt" not in res: if "Hires prompt" not in res: res["Hires prompt"] = "" res["Hires prompt"] = "" Loading
modules/processing.py +66 −31 Original line number Original line Diff line number Diff line Loading @@ -539,8 +539,12 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x return x class DecodedSamples(list): already_decoded = True def decode_latent_batch(model, batch, target_device=None, check_for_nans=False): def decode_latent_batch(model, batch, target_device=None, check_for_nans=False): samples = [] samples = DecodedSamples() for i in range(batch.shape[0]): for i in range(batch.shape[0]): sample = decode_first_stage(model, batch[i:i + 1])[0] sample = decode_first_stage(model, batch[i:i + 1])[0] Loading Loading @@ -788,7 +792,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast(): with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast(): samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts) samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts) if getattr(samples_ddim, 'already_decoded', False): x_samples_ddim = samples_ddim else: x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = torch.stack(x_samples_ddim).float() x_samples_ddim = torch.stack(x_samples_ddim).float() x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) Loading Loading @@ -930,7 +938,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): cached_hr_uc = [None, None] cached_hr_uc = [None, None] cached_hr_c = [None, None] cached_hr_c = [None, None] def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs): def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs): super().__init__(**kwargs) super().__init__(**kwargs) self.enable_hr = enable_hr self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.denoising_strength = denoising_strength Loading @@ -941,11 +949,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_resize_y = hr_resize_y self.hr_resize_y = hr_resize_y self.hr_upscale_to_x = hr_resize_x self.hr_upscale_to_x = hr_resize_x self.hr_upscale_to_y = hr_resize_y self.hr_upscale_to_y = hr_resize_y self.hr_checkpoint_name = hr_checkpoint_name self.hr_checkpoint_info = None self.hr_sampler_name = hr_sampler_name self.hr_sampler_name = hr_sampler_name self.hr_prompt = hr_prompt self.hr_prompt = hr_prompt self.hr_negative_prompt = hr_negative_prompt self.hr_negative_prompt = hr_negative_prompt self.all_hr_prompts = None self.all_hr_prompts = None self.all_hr_negative_prompts = None self.all_hr_negative_prompts = None self.latent_scale_mode = None if firstphase_width != 0 or firstphase_height != 0: if firstphase_width != 0 or firstphase_height != 0: self.hr_upscale_to_x = self.width self.hr_upscale_to_x = self.width Loading @@ -968,6 +979,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def init(self, all_prompts, all_seeds, all_subseeds): def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: if self.enable_hr: if self.hr_checkpoint_name: self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name) if self.hr_checkpoint_info is None: raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}') self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name: if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name: self.extra_generation_params["Hires sampler"] = self.hr_sampler_name self.extra_generation_params["Hires sampler"] = self.hr_sampler_name Loading @@ -977,6 +996,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt): if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt): self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") if self.enable_hr and self.latent_scale_mode is None: if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers): raise Exception(f"could not find upscaler named {self.hr_upscaler}") if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): self.hr_resize_x = self.width self.hr_resize_x = self.width self.hr_resize_y = self.height self.hr_resize_y = self.height Loading Loading @@ -1015,14 +1039,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f # special case: the user has chosen to do nothing if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height: self.enable_hr = False self.denoising_strength = None self.extra_generation_params.pop("Hires upscale", None) self.extra_generation_params.pop("Hires resize", None) return if not state.processing_has_refined_job_count: if not state.processing_has_refined_job_count: if state.job_count == -1: if state.job_count == -1: state.job_count = self.n_iter state.job_count = self.n_iter Loading @@ -1040,17 +1056,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") if self.enable_hr and latent_scale_mode is None: if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers): raise Exception(f"could not find upscaler named {self.hr_upscaler}") x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) del x if not self.enable_hr: if not self.enable_hr: return samples return samples if self.latent_scale_mode is None: decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32) else: decoded_samples = None current = shared.sd_model.sd_checkpoint_info try: if self.hr_checkpoint_info is not None: self.sampler = None sd_models.reload_model_weights(info=self.hr_checkpoint_info) devices.torch_gc() return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts) finally: self.sampler = None sd_models.reload_model_weights(info=current) devices.torch_gc() def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts): self.is_hr_pass = True self.is_hr_pass = True target_width = self.hr_upscale_to_x target_width = self.hr_upscale_to_x Loading @@ -1068,11 +1099,18 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix") images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix") if latent_scale_mode is not None: img2img_sampler_name = self.hr_sampler_name or self.sampler_name if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM img2img_sampler_name = 'DDIM' self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model) if self.latent_scale_mode is not None: for i in range(samples.shape[0]): for i in range(samples.shape[0]): save_intermediate(samples, i) save_intermediate(samples, i) samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"]) # Avoid making the inpainting conditioning unless necessary as # Avoid making the inpainting conditioning unless necessary as # this does need some extra compute to decode / encode the image again. # this does need some extra compute to decode / encode the image again. Loading @@ -1081,7 +1119,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: else: image_conditioning = self.txt2img_image_conditioning(samples) image_conditioning = self.txt2img_image_conditioning(samples) else: else: decoded_samples = decode_first_stage(self.sd_model, samples) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) batch_images = [] batch_images = [] Loading @@ -1100,6 +1137,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = decoded_samples.to(shared.device) decoded_samples = decoded_samples.to(shared.device) decoded_samples = 2. * decoded_samples - 1. decoded_samples = 2. * decoded_samples - 1. decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae) samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) Loading @@ -1107,19 +1145,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob() shared.state.nextjob() img2img_sampler_name = self.hr_sampler_name or self.sampler_name if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM img2img_sampler_name = 'DDIM' self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2] samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2] noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) # GC now before running the next img2img to prevent running out of memory # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() devices.torch_gc() if not self.disable_extra_networks: if not self.disable_extra_networks: Loading @@ -1138,9 +1168,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True) self.is_hr_pass = False self.is_hr_pass = False return samples return decoded_samples def close(self): def close(self): super().close() super().close() Loading Loading @@ -1179,8 +1211,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.hr_c is not None: if self.hr_c is not None: return return self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data) hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y) self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data) hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True) self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data) self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data) def setup_conds(self): def setup_conds(self): super().setup_conds() super().setup_conds() Loading @@ -1188,7 +1223,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_uc = None self.hr_uc = None self.hr_c = None self.hr_c = None if self.enable_hr: if self.enable_hr and self.hr_checkpoint_info is None: if shared.opts.hires_fix_use_firstpass_conds: if shared.opts.hires_fix_use_firstpass_conds: self.calculate_hr_conds() self.calculate_hr_conds() Loading
modules/sd_models.py +17 −10 Original line number Original line Diff line number Diff line Loading @@ -67,6 +67,7 @@ class CheckpointInfo: self.shorthash = self.sha256[0:10] if self.sha256 else None self.shorthash = self.sha256[0:10] if self.sha256 else None self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]' self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) Loading @@ -87,6 +88,7 @@ class CheckpointInfo: checkpoints_list.pop(self.title, None) checkpoints_list.pop(self.title, None) self.title = f'{self.name} [{self.shorthash}]' self.title = f'{self.name} [{self.shorthash}]' self.short_title = f'{self.name_for_extra} [{self.shorthash}]' self.register() self.register() return self.shorthash return self.shorthash Loading @@ -107,14 +109,8 @@ def setup_model(): enable_midas_autodownload() enable_midas_autodownload() def checkpoint_tiles(): def checkpoint_tiles(use_short=False): def convert(name): return [x.short_title if use_short else x.title for x in checkpoints_list.values()] return int(name) if name.isdigit() else name.lower() def alphanumeric_key(key): return [convert(c) for c in re.split('([0-9]+)', key)] return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key) def list_models(): def list_models(): Loading @@ -137,11 +133,14 @@ def list_models(): elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) for filename in sorted(model_list, key=str.lower): for filename in model_list: checkpoint_info = CheckpointInfo(filename) checkpoint_info = CheckpointInfo(filename) checkpoint_info.register() checkpoint_info.register() re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$") def get_closet_checkpoint_match(search_string): def get_closet_checkpoint_match(search_string): checkpoint_info = checkpoint_aliases.get(search_string, None) checkpoint_info = checkpoint_aliases.get(search_string, None) if checkpoint_info is not None: if checkpoint_info is not None: Loading @@ -151,6 +150,11 @@ def get_closet_checkpoint_match(search_string): if found: if found: return found[0] return found[0] search_string_without_checksum = re.sub(re_strip_checksum, '', search_string) found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title)) if found: return found[0] return None return None Loading Loading @@ -585,7 +589,10 @@ def reload_model_weights(sd_model=None, info=None): timer.record("find config") timer.record("find config") if sd_model is None or checkpoint_config != sd_model.used_config: if sd_model is None or checkpoint_config != sd_model.used_config: del sd_model if sd_model is not None: sd_model.to(device="meta") devices.torch_gc() load_model(checkpoint_info, already_loaded_state_dict=state_dict) load_model(checkpoint_info, already_loaded_state_dict=state_dict) return model_data.sd_model return model_data.sd_model Loading
modules/shared.py +13 −6 Original line number Original line Diff line number Diff line Loading @@ -220,6 +220,8 @@ class State: return return import modules.sd_samplers import modules.sd_samplers try: if opts.show_progress_grid: if opts.show_progress_grid: self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent)) self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent)) else: else: Loading @@ -227,6 +229,11 @@ class State: self.current_image_sampling_step = self.sampling_step self.current_image_sampling_step = self.sampling_step except Exception: # when switching models during genration, VAE would be on CPU, so creating an image will fail. # we silently ignore this error errors.record_exception() def assign_current_image(self, image): def assign_current_image(self, image): self.current_image = image self.current_image = image self.id_live_preview += 1 self.id_live_preview += 1 Loading Loading @@ -521,7 +528,7 @@ options_templates.update(options_section(('ui', "User interface"), { "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(), "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(), "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(), "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_restart(), "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(), "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(), "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(), "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(), })) })) Loading
modules/txt2img.py +2 −1 Original line number Original line Diff line number Diff line Loading @@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html import gradio as gr import gradio as gr def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args): def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args): override_settings = create_override_settings_dict(override_settings_texts) override_settings = create_override_settings_dict(override_settings_texts) p = processing.StableDiffusionProcessingTxt2Img( p = processing.StableDiffusionProcessingTxt2Img( Loading Loading @@ -41,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step hr_second_pass_steps=hr_second_pass_steps, hr_second_pass_steps=hr_second_pass_steps, hr_resize_x=hr_resize_x, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y, hr_resize_y=hr_resize_y, hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name, hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None, hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None, hr_prompt=hr_prompt, hr_prompt=hr_prompt, hr_negative_prompt=hr_negative_prompt, hr_negative_prompt=hr_negative_prompt, Loading
