diff --git a/args_manager.py b/args_manager.py index 1675c31..c7c1b7a 100644 --- a/args_manager.py +++ b/args_manager.py @@ -20,7 +20,10 @@ args_parser.parser.add_argument("--disable-image-log", action='store_true', help="Prevent writing images and logs to hard drive.") args_parser.parser.add_argument("--disable-analytics", action='store_true', - help="Disables analytics for Gradio", default=False) + help="Disables analytics for Gradio.") + +args_parser.parser.add_argument("--disable-metadata", action='store_true', + help="Disables saving metadata to images.") args_parser.parser.add_argument("--disable-preset-download", action='store_true', help="Disables downloading models for presets", default=False) diff --git a/language/en.json b/language/en.json index a3e47c1..cb5603f 100644 --- a/language/en.json +++ b/language/en.json @@ -374,5 +374,12 @@ "* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)", "Fooocus Enhance": "Fooocus Enhance", "Fooocus Cinematic": "Fooocus Cinematic", - "Fooocus Sharp": "Fooocus Sharp" + "Fooocus Sharp": "Fooocus Sharp", + "Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here", + "Metadata": "Metadata", + "Apply Metadata": "Apply Metadata", + "Metadata Scheme": "Metadata Scheme", + "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.", + "fooocus (json)": "fooocus (json)", + "a1111 (plain text)": "a1111 (plain text)" } \ No newline at end of file diff --git a/modules/async_worker.py b/modules/async_worker.py index 2a31aae..677cf46 100644 --- a/modules/async_worker.py +++ b/modules/async_worker.py @@ -19,6 +19,7 @@ async_tasks = [] def worker(): global async_tasks + import os import traceback import math import numpy as np @@ -39,6 +40,7 @@ def worker(): import extras.ip_adapter as ip_adapter import extras.face_crop import fooocus_version + import args_manager from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays from modules.private_logger import log @@ -46,6 +48,8 @@ def worker(): from modules.util import remove_empty_str, HWC3, resize_image, \ get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix from modules.upscaler import perform_upscale + from modules.flags import Performance + from modules.meta_parser import get_metadata_parser, MetadataScheme pid = os.getpid() print(f'Started worker with PID {pid}') @@ -135,7 +139,7 @@ def worker(): prompt = args.pop() negative_prompt = args.pop() style_selections = args.pop() - performance_selection = args.pop() + performance_selection = Performance(args.pop()) aspect_ratios_selection = args.pop() image_number = args.pop() image_seed = args.pop() @@ -153,6 +157,7 @@ def worker(): inpaint_input_image = args.pop() inpaint_additional_prompt = args.pop() inpaint_mask_image_upload = args.pop() + disable_preview = args.pop() disable_intermediate_results = args.pop() disable_seed_increment = args.pop() @@ -190,8 +195,11 @@ def worker(): invert_mask_checkbox = args.pop() inpaint_erode_or_dilate = args.pop() + save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False + metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS + cn_tasks = {x: [] for x in flags.ip_list} - for _ in range(4): + for _ in range(flags.controlnet_image_count): cn_img = args.pop() cn_stop = args.pop() cn_weight = args.pop() @@ -216,17 +224,9 @@ def worker(): print(f'Refiner disabled because base model and refiner are same.') refiner_model_name = 'None' - assert performance_selection in ['Speed', 'Quality', 'Extreme Speed'] + steps = performance_selection.steps() - steps = 30 - - if performance_selection == 'Speed': - steps = 30 - - if performance_selection == 'Quality': - steps = 60 - - if performance_selection == 'Extreme Speed': + if performance_selection == Performance.EXTREME_SPEED: print('Enter LCM mode.') progressbar(async_task, 1, 'Downloading LCM components ...') loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)] @@ -244,7 +244,6 @@ def worker(): adm_scaler_positive = 1.0 adm_scaler_negative = 1.0 adm_scaler_end = 0.0 - steps = 8 print(f'[Parameters] Adaptive CFG = {adaptive_cfg}') print(f'[Parameters] Sharpness = {sharpness}') @@ -305,16 +304,7 @@ def worker(): if 'fast' in uov_method: skip_prompt_processing = True else: - steps = 18 - - if performance_selection == 'Speed': - steps = 18 - - if performance_selection == 'Quality': - steps = 36 - - if performance_selection == 'Extreme Speed': - steps = 8 + steps = performance_selection.steps_uov() progressbar(async_task, 1, 'Downloading upscale models ...') modules.config.downloading_upscale_model() @@ -830,31 +820,50 @@ def worker(): img_paths = [] for x in imgs: - d = [ - ('Prompt', task['log_positive_prompt']), - ('Negative Prompt', task['log_negative_prompt']), - ('Fooocus V2 Expansion', task['expansion']), - ('Styles', str(raw_style_selections)), - ('Performance', performance_selection), - ('Resolution', str((width, height))), - ('Sharpness', sharpness), - ('Guidance Scale', guidance_scale), - ('ADM Guidance', str(( - modules.patch.patch_settings[pid].positive_adm_scale, - modules.patch.patch_settings[pid].negative_adm_scale, - modules.patch.patch_settings[pid].adm_scaler_end))), - ('Base Model', base_model_name), - ('Refiner Model', refiner_model_name), - ('Refiner Switch', refiner_switch), - ('Sampler', sampler_name), - ('Scheduler', scheduler_name), - ('Seed', task['task_seed']), - ] + d = [('Prompt', 'prompt', task['log_positive_prompt']), + ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']), + ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']), + ('Styles', 'styles', str(raw_style_selections)), + ('Performance', 'performance', performance_selection.value), + ('Resolution', 'resolution', str((width, height))), + ('Guidance Scale', 'guidance_scale', guidance_scale), + ('Sharpness', 'sharpness', sharpness), + ('ADM Guidance', 'adm_guidance', str(( + modules.patch.patch_settings[pid].positive_adm_scale, + modules.patch.patch_settings[pid].negative_adm_scale, + modules.patch.patch_settings[pid].adm_scaler_end))), + ('Base Model', 'base_model', base_model_name), + ('Refiner Model', 'refiner_model', refiner_model_name), + ('Refiner Switch', 'refiner_switch', refiner_switch)] + + if refiner_model_name != 'None': + if overwrite_switch > 0: + d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch)) + if refiner_swap_method != flags.refiner_swap_method: + d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method)) + if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr: + d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg)) + + d.append(('Sampler', 'sampler', sampler_name)) + d.append(('Scheduler', 'scheduler', scheduler_name)) + d.append(('Seed', 'seed', task['task_seed'])) + + if freeu_enabled: + d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2)))) + + metadata_parser = None + if save_metadata_to_images: + metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) + metadata_parser.set_data(task['log_positive_prompt'], task['positive'], + task['log_negative_prompt'], task['negative'], + steps, base_model_name, refiner_model_name, loras) + for li, (n, w) in enumerate(loras): if n != 'None': - d.append((f'LoRA {li + 1}', f'{n} : {w}')) - d.append(('Version', 'v' + fooocus_version.version)) - img_paths.append(log(x, d)) + d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}')) + + d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version)) + img_paths.append(log(x, d, metadata_parser)) yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results) except ldm_patched.modules.model_management.InterruptProcessingException as e: diff --git a/modules/config.py b/modules/config.py index acf19b6..a393e24 100644 --- a/modules/config.py +++ b/modules/config.py @@ -8,7 +8,7 @@ import modules.sdxl_styles from modules.model_loader import load_file_from_url from modules.util import get_files_from_folder, makedirs_with_log - +from modules.flags import Performance, MetadataScheme config_path = os.path.abspath("./config.txt") config_example_path = os.path.abspath("config_modification_tutorial.txt") @@ -293,8 +293,8 @@ default_prompt = get_config_item_or_set_default( ) default_performance = get_config_item_or_set_default( key='default_performance', - default_value='Speed', - validator=lambda x: x in modules.flags.performance_selections + default_value=Performance.SPEED.value, + validator=lambda x: x in Performance.list() ) default_advanced_checkbox = get_config_item_or_set_default( key='default_advanced_checkbox', @@ -369,6 +369,21 @@ example_inpaint_prompts = get_config_item_or_set_default( ], validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x) ) +default_save_metadata_to_images = get_config_item_or_set_default( + key='default_save_metadata_to_images', + default_value=False, + validator=lambda x: isinstance(x, bool) +) +default_metadata_scheme = get_config_item_or_set_default( + key='default_metadata_scheme', + default_value=MetadataScheme.FOOOCUS.value, + validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x] +) +metadata_created_by = get_config_item_or_set_default( + key='metadata_created_by', + default_value='', + validator=lambda x: isinstance(x, str) +) example_inpaint_prompts = [[x] for x in example_inpaint_prompts] @@ -391,6 +406,7 @@ possible_preset_keys = [ "default_prompt_negative", "default_styles", "default_aspect_ratio", + "default_save_metadata_to_images", "checkpoint_downloads", "embeddings_downloads", "lora_downloads", diff --git a/modules/flags.py b/modules/flags.py index 27f2d71..206f512 100644 --- a/modules/flags.py +++ b/modules/flags.py @@ -1,3 +1,5 @@ +from enum import IntEnum, Enum + disabled = 'Disabled' enabled = 'Enabled' subtle_variation = 'Vary (Subtle)' @@ -10,16 +12,49 @@ uov_list = [ disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast ] -KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", - "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", - "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] +CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"] + +# fooocus: a1111 (Civitai) +KSAMPLER = { + "euler": "Euler", + "euler_ancestral": "Euler a", + "heun": "Heun", + "heunpp2": "", + "dpm_2": "DPM2", + "dpm_2_ancestral": "DPM2 a", + "lms": "LMS", + "dpm_fast": "DPM fast", + "dpm_adaptive": "DPM adaptive", + "dpmpp_2s_ancestral": "DPM++ 2S a", + "dpmpp_sde": "DPM++ SDE", + "dpmpp_sde_gpu": "DPM++ SDE", + "dpmpp_2m": "DPM++ 2M", + "dpmpp_2m_sde": "DPM++ 2M SDE", + "dpmpp_2m_sde_gpu": "DPM++ 2M SDE", + "dpmpp_3m_sde": "", + "dpmpp_3m_sde_gpu": "", + "ddpm": "", + "lcm": "LCM" +} + +SAMPLER_EXTRA = { + "ddim": "DDIM", + "uni_pc": "UniPC", + "uni_pc_bh2": "" +} + +SAMPLERS = KSAMPLER | SAMPLER_EXTRA + +KSAMPLER_NAMES = list(KSAMPLER.keys()) SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"] -SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] +SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys()) sampler_list = SAMPLER_NAMES scheduler_list = SCHEDULER_NAMES +refiner_swap_method = 'joint' + cn_ip = "ImagePrompt" cn_ip_face = "FaceSwap" cn_canny = "PyraCanny" @@ -33,8 +68,6 @@ default_parameters = { } # stop, weight inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6'] -performance_selections = ['Speed', 'Quality', 'Extreme Speed'] - inpaint_option_default = 'Inpaint or Outpaint (default)' inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)' inpaint_option_modify = 'Modify Content (add objects, change background, etc.)' @@ -42,3 +75,49 @@ inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option desc_type_photo = 'Photograph' desc_type_anime = 'Art/Anime' + + +class MetadataScheme(Enum): + FOOOCUS = 'fooocus' + A1111 = 'a1111' + + +metadata_scheme = [ + (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value), + (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value), +] + +lora_count = 5 + +controlnet_image_count = 4 + + +class Steps(IntEnum): + QUALITY = 60 + SPEED = 30 + EXTREME_SPEED = 8 + + +class StepsUOV(IntEnum): + QUALITY = 36 + SPEED = 18 + EXTREME_SPEED = 8 + + +class Performance(Enum): + QUALITY = 'Quality' + SPEED = 'Speed' + EXTREME_SPEED = 'Extreme Speed' + + @classmethod + def list(cls) -> list: + return list(map(lambda c: c.value, cls)) + + def steps(self) -> int | None: + return Steps[self.name].value if Steps[self.name] else None + + def steps_uov(self) -> int | None: + return StepsUOV[self.name].value if Steps[self.name] else None + + +performance_selections = Performance.list() diff --git a/modules/meta_parser.py b/modules/meta_parser.py index 061e1f8..e9f1d03 100644 --- a/modules/meta_parser.py +++ b/modules/meta_parser.py @@ -1,45 +1,113 @@ import json +import os +import re +from abc import ABC, abstractmethod +from pathlib import Path + import gradio as gr +from PIL import Image + import modules.config +import modules.sdxl_styles +from modules.flags import MetadataScheme, Performance, Steps +from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS +from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, calculate_sha256 + +re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)' +re_param = re.compile(re_param_code) +re_imagesize = re.compile(r"^(\d+)x(\d+)$") + +hash_cache = {} -def load_parameter_button_click(raw_prompt_txt, is_generating): - loaded_parameter_dict = json.loads(raw_prompt_txt) +def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool): + loaded_parameter_dict = raw_metadata + if isinstance(raw_metadata, str): + loaded_parameter_dict = json.loads(raw_metadata) assert isinstance(loaded_parameter_dict, dict) - results = [True, 1] + results = [len(loaded_parameter_dict) > 0, 1] + get_str('prompt', 'Prompt', loaded_parameter_dict, results) + get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results) + get_list('styles', 'Styles', loaded_parameter_dict, results) + get_str('performance', 'Performance', loaded_parameter_dict, results) + get_steps('steps', 'Steps', loaded_parameter_dict, results) + get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results) + get_resolution('resolution', 'Resolution', loaded_parameter_dict, results) + get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results) + get_float('sharpness', 'Sharpness', loaded_parameter_dict, results) + get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results) + get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results) + get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results) + get_str('base_model', 'Base Model', loaded_parameter_dict, results) + get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results) + get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results) + get_str('sampler', 'Sampler', loaded_parameter_dict, results) + get_str('scheduler', 'Scheduler', loaded_parameter_dict, results) + get_seed('seed', 'Seed', loaded_parameter_dict, results) + + if is_generating: + results.append(gr.update()) + else: + results.append(gr.update(visible=True)) + + results.append(gr.update(visible=False)) + + get_freeu('freeu', 'FreeU', loaded_parameter_dict, results) + + for i in range(modules.config.default_max_lora_number): + get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results) + + return results + + +def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('Prompt', None) + h = source_dict.get(key, source_dict.get(fallback, default)) assert isinstance(h, str) results.append(h) except: results.append(gr.update()) - try: - h = loaded_parameter_dict.get('Negative Prompt', None) - assert isinstance(h, str) - results.append(h) - except: - results.append(gr.update()) +def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('Styles', None) + h = source_dict.get(key, source_dict.get(fallback, default)) h = eval(h) assert isinstance(h, list) results.append(h) except: results.append(gr.update()) + +def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('Performance', None) - assert isinstance(h, str) + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = float(h) results.append(h) except: results.append(gr.update()) + +def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('Resolution', None) + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = int(h) + # if not in steps or in steps and performance is not the same + if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold(): + results.append(h) + return + results.append(-1) + except: + results.append(-1) + + +def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) width, height = eval(h) formatted = modules.config.add_ratio(f'{width}*{height}') if formatted in modules.config.available_aspect_ratios: @@ -55,24 +123,22 @@ def load_parameter_button_click(raw_prompt_txt, is_generating): results.append(gr.update()) results.append(gr.update()) + +def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('Sharpness', None) + h = source_dict.get(key, source_dict.get(fallback, default)) assert h is not None - h = float(h) + h = int(h) + results.append(False) results.append(h) except: results.append(gr.update()) - - try: - h = loaded_parameter_dict.get('Guidance Scale', None) - assert h is not None - h = float(h) - results.append(h) - except: results.append(gr.update()) + +def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('ADM Guidance', None) + h = source_dict.get(key, source_dict.get(fallback, default)) p, n, e = eval(h) results.append(float(p)) results.append(float(n)) @@ -82,69 +148,368 @@ def load_parameter_button_click(raw_prompt_txt, is_generating): results.append(gr.update()) results.append(gr.update()) - try: - h = loaded_parameter_dict.get('Base Model', None) - assert isinstance(h, str) - results.append(h) - except: - results.append(gr.update()) +def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: - h = loaded_parameter_dict.get('Refiner Model', None) - assert isinstance(h, str) - results.append(h) + h = source_dict.get(key, source_dict.get(fallback, default)) + b1, b2, s1, s2 = eval(h) + results.append(True) + results.append(float(b1)) + results.append(float(b2)) + results.append(float(s1)) + results.append(float(s2)) except: - results.append(gr.update()) - - try: - h = loaded_parameter_dict.get('Refiner Switch', None) - assert h is not None - h = float(h) - results.append(h) - except: - results.append(gr.update()) - - try: - h = loaded_parameter_dict.get('Sampler', None) - assert isinstance(h, str) - results.append(h) - except: - results.append(gr.update()) - - try: - h = loaded_parameter_dict.get('Scheduler', None) - assert isinstance(h, str) - results.append(h) - except: - results.append(gr.update()) - - try: - h = loaded_parameter_dict.get('Seed', None) - assert h is not None - h = int(h) results.append(False) - results.append(h) + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_lora(key: str, fallback: str | None, source_dict: dict, results: list): + try: + n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ') + w = float(w) + results.append(True) + results.append(n) + results.append(w) except: - results.append(gr.update()) - results.append(gr.update()) + results.append(True) + results.append('None') + results.append(1) - if is_generating: - results.append(gr.update()) - else: - results.append(gr.update(visible=True)) - - results.append(gr.update(visible=False)) - for i in range(1, modules.config.default_max_lora_number + 1): - try: - n, w = loaded_parameter_dict.get(f'LoRA {i}', ' : ').split(' : ') - w = float(w) - results.append(True) - results.append(n) - results.append(w) - except: - results.append(True) - results.append('None') - results.append(1.0) +def get_sha256(filepath): + global hash_cache - return results + if filepath not in hash_cache: + hash_cache[filepath] = calculate_sha256(filepath) + + return hash_cache[filepath] + + +class MetadataParser(ABC): + def __init__(self): + self.raw_prompt: str = '' + self.full_prompt: str = '' + self.raw_negative_prompt: str = '' + self.full_negative_prompt: str = '' + self.steps: int = 30 + self.base_model_name: str = '' + self.base_model_hash: str = '' + self.refiner_model_name: str = '' + self.refiner_model_hash: str = '' + self.loras: list = [] + + @abstractmethod + def get_scheme(self) -> MetadataScheme: + raise NotImplementedError + + @abstractmethod + def parse_json(self, metadata: dict | str) -> dict: + raise NotImplementedError + + @abstractmethod + def parse_string(self, metadata: dict) -> str: + raise NotImplementedError + + def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name, refiner_model_name, loras): + self.raw_prompt = raw_prompt + self.full_prompt = full_prompt + self.raw_negative_prompt = raw_negative_prompt + self.full_negative_prompt = full_negative_prompt + self.steps = steps + self.base_model_name = Path(base_model_name).stem + + base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints) + self.base_model_hash = get_sha256(base_model_path) + + if refiner_model_name not in ['', 'None']: + self.refiner_model_name = Path(refiner_model_name).stem + refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints) + self.refiner_model_hash = get_sha256(refiner_model_path) + + self.loras = [] + for (lora_name, lora_weight) in loras: + if lora_name != 'None': + lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras) + lora_hash = get_sha256(lora_path) + self.loras.append((Path(lora_name).stem, lora_weight, lora_hash)) + + +class A1111MetadataParser(MetadataParser): + def get_scheme(self) -> MetadataScheme: + return MetadataScheme.A1111 + + fooocus_to_a1111 = { + 'raw_prompt': 'Raw prompt', + 'raw_negative_prompt': 'Raw negative prompt', + 'negative_prompt': 'Negative prompt', + 'styles': 'Styles', + 'performance': 'Performance', + 'steps': 'Steps', + 'sampler': 'Sampler', + 'scheduler': 'Scheduler', + 'guidance_scale': 'CFG scale', + 'seed': 'Seed', + 'resolution': 'Size', + 'sharpness': 'Sharpness', + 'adm_guidance': 'ADM Guidance', + 'refiner_swap_method': 'Refiner Swap Method', + 'adaptive_cfg': 'Adaptive CFG', + 'overwrite_switch': 'Overwrite Switch', + 'freeu': 'FreeU', + 'base_model': 'Model', + 'base_model_hash': 'Model hash', + 'refiner_model': 'Refiner', + 'refiner_model_hash': 'Refiner hash', + 'lora_hashes': 'Lora hashes', + 'lora_weights': 'Lora weights', + 'created_by': 'User', + 'version': 'Version' + } + + def parse_json(self, metadata: str) -> dict: + metadata_prompt = '' + metadata_negative_prompt = '' + + done_with_prompt = False + + *lines, lastline = metadata.strip().split("\n") + if len(re_param.findall(lastline)) < 3: + lines.append(lastline) + lastline = '' + + for line in lines: + line = line.strip() + if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"): + done_with_prompt = True + line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip() + if done_with_prompt: + metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line + else: + metadata_prompt += ('' if metadata_prompt == '' else "\n") + line + + found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt) + + data = { + 'prompt': prompt, + 'negative_prompt': negative_prompt + } + + for k, v in re_param.findall(lastline): + try: + if v != '' and v[0] == '"' and v[-1] == '"': + v = unquote(v) + + m = re_imagesize.match(v) + if m is not None: + data['resolution'] = str((m.group(1), m.group(2))) + else: + data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v + except Exception: + print(f"Error parsing \"{k}: {v}\"") + + # workaround for multiline prompts + if 'raw_prompt' in data: + data['prompt'] = data['raw_prompt'] + raw_prompt = data['raw_prompt'].replace("\n", ', ') + if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles: + found_styles.append(modules.sdxl_styles.fooocus_expansion) + + if 'raw_negative_prompt' in data: + data['negative_prompt'] = data['raw_negative_prompt'] + + data['styles'] = str(found_styles) + + # try to load performance based on steps, fallback for direct A1111 imports + if 'steps' in data and 'performance' not in data: + try: + data['performance'] = Performance[Steps(int(data['steps'])).name].value + except ValueError | KeyError: + pass + + if 'sampler' in data: + data['sampler'] = data['sampler'].replace(' Karras', '') + # get key + for k, v in SAMPLERS.items(): + if v == data['sampler']: + data['sampler'] = k + break + + for key in ['base_model', 'refiner_model']: + if key in data: + for filename in modules.config.model_filenames: + path = Path(filename) + if data[key] == path.stem: + data[key] = filename + break + + if 'lora_hashes' in data: + lora_filenames = modules.config.lora_filenames.copy() + lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora()) + for li, lora in enumerate(data['lora_hashes'].split(', ')): + lora_name, lora_hash, lora_weight = lora.split(': ') + for filename in lora_filenames: + path = Path(filename) + if lora_name == path.stem: + data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}' + break + + return data + + def parse_string(self, metadata: dict) -> str: + data = {k: v for _, k, v in metadata} + + width, height = eval(data['resolution']) + + sampler = data['sampler'] + scheduler = data['scheduler'] + if sampler in SAMPLERS and SAMPLERS[sampler] != '': + sampler = SAMPLERS[sampler] + if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras': + sampler += f' Karras' + + generation_params = { + self.fooocus_to_a1111['steps']: self.steps, + self.fooocus_to_a1111['sampler']: sampler, + self.fooocus_to_a1111['seed']: data['seed'], + self.fooocus_to_a1111['resolution']: f'{width}x{height}', + self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'], + self.fooocus_to_a1111['sharpness']: data['sharpness'], + self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'], + self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem, + self.fooocus_to_a1111['base_model_hash']: self.base_model_hash, + + self.fooocus_to_a1111['performance']: data['performance'], + self.fooocus_to_a1111['scheduler']: scheduler, + # workaround for multiline prompts + self.fooocus_to_a1111['raw_prompt']: self.raw_prompt, + self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt, + } + + if self.refiner_model_name not in ['', 'None']: + generation_params |= { + self.fooocus_to_a1111['refiner_model']: self.refiner_model_name, + self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash + } + + for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']: + if key in data: + generation_params[self.fooocus_to_a1111[key]] = data[key] + + lora_hashes = [] + for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras): + # workaround for Fooocus not knowing LoRA name in LoRA metadata + lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}') + lora_hashes_string = ', '.join(lora_hashes) + + generation_params |= { + self.fooocus_to_a1111['lora_hashes']: lora_hashes_string, + self.fooocus_to_a1111['version']: data['version'] + } + + if modules.config.metadata_created_by != '': + generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by + + generation_params_text = ", ".join( + [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if + v is not None]) + positive_prompt_resolved = ', '.join(self.full_prompt) + negative_prompt_resolved = ', '.join(self.full_negative_prompt) + negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else "" + return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip() + + +class FooocusMetadataParser(MetadataParser): + def get_scheme(self) -> MetadataScheme: + return MetadataScheme.FOOOCUS + + def parse_json(self, metadata: dict) -> dict: + model_filenames = modules.config.model_filenames.copy() + lora_filenames = modules.config.lora_filenames.copy() + lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora()) + + for key, value in metadata.items(): + if value in ['', 'None']: + continue + if key in ['base_model', 'refiner_model']: + metadata[key] = self.replace_value_with_filename(key, value, model_filenames) + elif key.startswith('lora_combined_'): + metadata[key] = self.replace_value_with_filename(key, value, lora_filenames) + else: + continue + + return metadata + + def parse_string(self, metadata: list) -> str: + for li, (label, key, value) in enumerate(metadata): + # remove model folder paths from metadata + if key.startswith('lora_combined_'): + name, weight = value.split(' : ') + name = Path(name).stem + value = f'{name} : {weight}' + metadata[li] = (label, key, value) + + res = {k: v for _, k, v in metadata} + + res['full_prompt'] = self.full_prompt + res['full_negative_prompt'] = self.full_negative_prompt + res['steps'] = self.steps + res['base_model'] = self.base_model_name + res['base_model_hash'] = self.base_model_hash + + if self.refiner_model_name not in ['', 'None']: + res['refiner_model'] = self.refiner_model_name + res['refiner_model_hash'] = self.refiner_model_hash + + res['loras'] = self.loras + + if modules.config.metadata_created_by != '': + res['created_by'] = modules.config.metadata_created_by + + return json.dumps(dict(sorted(res.items()))) + + @staticmethod + def replace_value_with_filename(key, value, filenames): + for filename in filenames: + path = Path(filename) + if key.startswith('lora_combined_'): + name, weight = value.split(' : ') + if name == path.stem: + return f'{filename} : {weight}' + elif value == path.stem: + return filename + + +def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser: + match metadata_scheme: + case MetadataScheme.FOOOCUS: + return FooocusMetadataParser() + case MetadataScheme.A1111: + return A1111MetadataParser() + case _: + raise NotImplementedError + + +def read_info_from_image(filepath) -> tuple[str | None, dict, MetadataScheme | None]: + with Image.open(filepath) as image: + items = (image.info or {}).copy() + + parameters = items.pop('parameters', None) + if parameters is not None and is_json(parameters): + parameters = json.loads(parameters) + + try: + metadata_scheme = MetadataScheme(items.pop('fooocus_scheme', None)) + except ValueError: + metadata_scheme = None + + # broad fallback + if isinstance(parameters, dict): + metadata_scheme = MetadataScheme.FOOOCUS + + if isinstance(parameters, str): + metadata_scheme = MetadataScheme.A1111 + + return parameters, items, metadata_scheme diff --git a/modules/private_logger.py b/modules/private_logger.py index 506b105..2213cbb 100644 --- a/modules/private_logger.py +++ b/modules/private_logger.py @@ -5,7 +5,9 @@ import json import urllib.parse from PIL import Image +from PIL.PngImagePlugin import PngInfo from modules.util import generate_temp_filename +from modules.meta_parser import MetadataParser from tempfile import gettempdir log_cache = {} @@ -18,11 +20,21 @@ def get_current_html_path(): return html_name -def log(img, dic) -> str: +def log(img, metadata, metadata_parser: MetadataParser | None = None) -> str: path_outputs = args_manager.args.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension='png') os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True) - Image.fromarray(img).save(local_temp_filename) + + parsed_parameters = metadata_parser.parse_string(metadata) if metadata_parser is not None else '' + image = Image.fromarray(img) + + if parsed_parameters != '': + pnginfo = PngInfo() + pnginfo.add_text('parameters', parsed_parameters) + pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value) + else: + pnginfo = None + image.save(local_temp_filename, pnginfo=pnginfo) if args_manager.args.disable_image_log: return local_temp_filename @@ -34,7 +46,7 @@ def log(img, dic) -> str: "body { background-color: #121212; color: #E0E0E0; } " "a { color: #BB86FC; } " ".metadata { border-collapse: collapse; width: 100%; } " - ".metadata .key { width: 15%; } " + ".metadata .label { width: 15%; } " ".metadata .value { width: 85%; font-weight: bold; } " ".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } " ".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } " @@ -87,13 +99,13 @@ def log(img, dic) -> str: item = f"
\n" item += f"" item += "" item += "
{only_name}
" - for key, value in dic: - value_txt = str(value).replace('\n', '
') - item += f"\n" + for label, key, value in metadata: + value_txt = str(value).replace('\n', '
') + item += f"\n" item += "
{key}{value_txt}
{label}{value_txt}
" - js_txt = urllib.parse.quote(json.dumps({k: v for k, v in dic}, indent=0), safe='') - item += f"
" + js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v in metadata}, indent=0), safe='') + item += f"
" item += "
\n\n" diff --git a/modules/util.py b/modules/util.py index 1b16511..29d4869 100644 --- a/modules/util.py +++ b/modules/util.py @@ -1,15 +1,20 @@ +import typing + import numpy as np import datetime import random import math import os import cv2 +import json from PIL import Image +from hashlib import sha256 +import modules.sdxl_styles LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) - +HASH_SHA256_LENGTH = 10 def erode_or_dilate(x, k): k = int(k) @@ -170,13 +175,173 @@ def get_files_from_folder(folder_path, exensions=None, name_filter=None): relative_path = "" for filename in sorted(files, key=lambda s: s.casefold()): _, file_extension = os.path.splitext(filename) - if (exensions == None or file_extension.lower() in exensions) and (name_filter == None or name_filter in _): + if (exensions is None or file_extension.lower() in exensions) and (name_filter is None or name_filter in _): path = os.path.join(relative_path, filename) filenames.append(path) return filenames +def calculate_sha256(filename, length=HASH_SHA256_LENGTH) -> str: + hash_sha256 = sha256() + blksize = 1024 * 1024 + + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(blksize), b""): + hash_sha256.update(chunk) + + res = hash_sha256.hexdigest() + return res[:length] if length else res + + +def quote(text): + if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text): + return text + + return json.dumps(text, ensure_ascii=False) + + +def unquote(text): + if len(text) == 0 or text[0] != '"' or text[-1] != '"': + return text + + try: + return json.loads(text) + except Exception: + return text + + +def unwrap_style_text_from_prompt(style_text, prompt): + """ + Checks the prompt to see if the style text is wrapped around it. If so, + returns True plus the prompt text without the style text. Otherwise, returns + False with the original prompt. + + Note that the "cleaned" version of the style text is only used for matching + purposes here. It isn't returned; the original style text is not modified. + """ + stripped_prompt = prompt + stripped_style_text = style_text + if "{prompt}" in stripped_style_text: + # Work out whether the prompt is wrapped in the style text. If so, we + # return True and the "inner" prompt text that isn't part of the style. + try: + left, right = stripped_style_text.split("{prompt}", 2) + except ValueError as e: + # If the style text has multple "{prompt}"s, we can't split it into + # two parts. This is an error, but we can't do anything about it. + print(f"Unable to compare style text to prompt:\n{style_text}") + print(f"Error: {e}") + return False, prompt, '' + + left_pos = stripped_prompt.find(left) + right_pos = stripped_prompt.find(right) + if 0 <= left_pos < right_pos: + real_prompt = stripped_prompt[left_pos + len(left):right_pos] + prompt = stripped_prompt.replace(left + real_prompt + right, '', 1) + if prompt.startswith(", "): + prompt = prompt[2:] + if prompt.endswith(", "): + prompt = prompt[:-2] + return True, prompt, real_prompt + else: + # Work out whether the given prompt starts with the style text. If so, we + # return True and the prompt text up to where the style text starts. + if stripped_prompt.endswith(stripped_style_text): + prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)] + if prompt.endswith(", "): + prompt = prompt[:-2] + return True, prompt, prompt + + return False, prompt, '' + + +def extract_original_prompts(style, prompt, negative_prompt): + """ + Takes a style and compares it to the prompt and negative prompt. If the style + matches, returns True plus the prompt and negative prompt with the style text + removed. Otherwise, returns False with the original prompt and negative prompt. + """ + if not style.prompt and not style.negative_prompt: + return False, prompt, negative_prompt + + match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt( + style.prompt, prompt + ) + if not match_positive: + return False, prompt, negative_prompt, '' + + match_negative, extracted_negative, _ = unwrap_style_text_from_prompt( + style.negative_prompt, negative_prompt + ) + if not match_negative: + return False, prompt, negative_prompt, '' + + return True, extracted_positive, extracted_negative, real_prompt + + +def extract_styles_from_prompt(prompt, negative_prompt): + extracted = [] + applicable_styles = [] + + for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items(): + applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt)) + + real_prompt = '' + + while True: + found_style = None + + for style in applicable_styles: + is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts( + style, prompt, negative_prompt + ) + if is_match: + found_style = style + prompt = new_prompt + negative_prompt = new_neg_prompt + if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt: + real_prompt = new_real_prompt + break + + if not found_style: + break + + applicable_styles.remove(found_style) + extracted.append(found_style.name) + + # add prompt expansion if not all styles could be resolved + if prompt != '': + if real_prompt != '': + extracted.append(modules.sdxl_styles.fooocus_expansion) + else: + # find real_prompt when only prompt expansion is selected + first_word = prompt.split(', ')[0] + first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)] + if len(first_word_positions) > 1: + real_prompt = prompt[:first_word_positions[-1]] + extracted.append(modules.sdxl_styles.fooocus_expansion) + if real_prompt.endswith(', '): + real_prompt = real_prompt[:-2] + + return list(reversed(extracted)), real_prompt, negative_prompt + + +class PromptStyle(typing.NamedTuple): + name: str + prompt: str + negative_prompt: str + + +def is_json(data: str) -> bool: + try: + loaded_json = json.loads(data) + assert isinstance(loaded_json, dict) + except (ValueError, AssertionError): + return False + return True + + def get_file_from_folder_list(name, folders): for folder in folders: filename = os.path.abspath(os.path.realpath(os.path.join(folder, name))) diff --git a/webui.py b/webui.py index a3663df..7020438 100644 --- a/webui.py +++ b/webui.py @@ -20,6 +20,7 @@ from modules.sdxl_styles import legal_style_names from modules.private_logger import get_current_html_path from modules.ui_gradio_extensions import reload_javascript from modules.auth import auth_enabled, check_auth +from modules.util import is_json def get_task(*args): args = list(args) @@ -158,7 +159,7 @@ with shared.gradio_root: ip_weights = [] ip_ctrls = [] ip_ad_cols = [] - for _ in range(4): + for _ in range(flags.controlnet_image_count): with gr.Column(): ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300) ip_images.append(ip_image) @@ -216,6 +217,30 @@ with shared.gradio_root: value=flags.desc_type_photo) desc_btn = gr.Button(value='Describe this Image into Prompt') gr.HTML('\U0001F4D4 Document') + with gr.TabItem(label='Metadata') as load_tab: + with gr.Column(): + metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath') + metadata_json = gr.JSON(label='Metadata') + metadata_import_button = gr.Button(value='Apply Metadata') + + def trigger_metadata_preview(filepath): + parameters, items, metadata_scheme = modules.meta_parser.read_info_from_image(filepath) + + results = {} + if parameters is not None: + results['parameters'] = parameters + + if items: + results['items'] = items + + if isinstance(metadata_scheme, flags.MetadataScheme): + results['metadata_scheme'] = metadata_scheme.value + + return results + + metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image, + outputs=metadata_json, queue=False, show_progress=True) + switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}" down_js = "() => {viewer_to_bottom();}" @@ -359,7 +384,7 @@ with shared.gradio_root: step=0.001, value=0.3, info='When to end the guidance from positive/negative ADM. ') - refiner_swap_method = gr.Dropdown(label='Refiner swap method', value='joint', + refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method, choices=['joint', 'separate', 'vae']) adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01, @@ -407,6 +432,16 @@ with shared.gradio_root: info='Disable automatic seed increment when image number is > 1.', value=False) + if not args_manager.args.disable_metadata: + save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images, + info='Adds parameters to generated images allowing manual regeneration.') + metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme, + info='Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.', + visible=modules.config.default_save_metadata_to_images) + + save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme], + queue=False, show_progress=False) + with gr.Tab(label='Control'): debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False, info='See the results from preprocessors.') @@ -484,7 +519,6 @@ with shared.gradio_root: results += [gr.update(choices=['None'] + modules.config.model_filenames)] for i in range(modules.config.default_max_lora_number): results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()] - return results model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls, queue=False, show_progress=False) @@ -555,20 +589,18 @@ with shared.gradio_root: ctrls += [refiner_swap_method, controlnet_softness] ctrls += freeu_ctrls ctrls += inpaint_ctrls + + if not args_manager.args.disable_metadata: + ctrls += [save_metadata_to_images, metadata_scheme] + ctrls += ip_ctrls state_is_generating = gr.State(False) def parse_meta(raw_prompt_txt, is_generating): loaded_json = None - try: - if '{' in raw_prompt_txt: - if '}' in raw_prompt_txt: - if ':' in raw_prompt_txt: - loaded_json = json.loads(raw_prompt_txt) - assert isinstance(loaded_json, dict) - except: - loaded_json = None + if is_json(raw_prompt_txt): + loaded_json = json.loads(raw_prompt_txt) if loaded_json is None: if is_generating: @@ -580,31 +612,29 @@ with shared.gradio_root: prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False) - load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=[ - advanced_checkbox, - image_number, - prompt, - negative_prompt, - style_selections, - performance_selection, - aspect_ratios_selection, - overwrite_width, - overwrite_height, - sharpness, - guidance_scale, - adm_scaler_positive, - adm_scaler_negative, - adm_scaler_end, - base_model, - refiner_model, - refiner_switch, - sampler_name, - scheduler_name, - seed_random, - image_seed, - generate_button, - load_parameter_button - ] + lora_ctrls, queue=False, show_progress=False) + load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections, + performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection, + overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive, + adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model, + refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed, + generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls + + load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False) + + def trigger_metadata_import(filepath, state_is_generating): + parameters, items, metadata_scheme = modules.meta_parser.read_info_from_image(filepath) + if parameters is None: + print('Could not find metadata in the image!') + parsed_parameters = {} + else: + metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) + parsed_parameters = metadata_parser.parse_json(parameters) + + return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating) + + + metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \ + .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True), outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \