Sync branch 'mashb1t_main' with develop_upstream

modules/config.py

@@ -7,6 +7,7 @@ import args_manager
 import tempfile
 import modules.flags
 import modules.sdxl_styles
+from modules.hash_cache import init_cache
 
 from modules.model_loader import load_file_from_url
 from modules.extra_utils import makedirs_with_log, get_files_from_folder, try_eval_env_var
@@ -98,7 +99,6 @@ def try_load_deprecated_user_path_config():
 
 try_load_deprecated_user_path_config()
 
-
 def get_presets():
     preset_folder = 'presets'
     presets = ['initial']
@@ -106,8 +106,11 @@ def get_presets():
         print('No presets found.')
         return presets
 
-    return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')]
+    return presets + [f[:f.index(".json")] for f in os.listdir(preset_folder) if f.endswith('.json')]
 
+def update_presets():
+    global available_presets
+    available_presets = get_presets()
 
 def try_get_preset_content(preset):
     if isinstance(preset, str):
@@ -198,6 +201,7 @@ path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vi
 path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
 path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/')
 path_safety_checker = get_dir_or_set_default('path_safety_checker', '../models/safety_checker/')
+path_sam = get_dir_or_set_default('path_sam', '../models/sam/')
 path_outputs = get_path_output()
 
 
@@ -397,7 +401,7 @@ default_prompt = get_config_item_or_set_default(
 default_performance = get_config_item_or_set_default(
     key='default_performance',
     default_value=Performance.SPEED.value,
-    validator=lambda x: x in Performance.list(),
+    validator=lambda x: x in Performance.values(),
     expected_type=str
 )
 default_advanced_checkbox = get_config_item_or_set_default(
@@ -442,6 +446,12 @@ embeddings_downloads = get_config_item_or_set_default(
     validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
     expected_type=dict
 )
+vae_downloads = get_config_item_or_set_default(
+    key='vae_downloads',
+    default_value={},
+    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
+    expected_type=dict
+)
 available_aspect_ratios = get_config_item_or_set_default(
     key='available_aspect_ratios',
     default_value=modules.flags.sdxl_aspect_ratios,
@@ -460,6 +470,12 @@ default_inpaint_engine_version = get_config_item_or_set_default(
     validator=lambda x: x in modules.flags.inpaint_engine_versions,
     expected_type=str
 )
+default_inpaint_method = get_config_item_or_set_default(
+    key='default_inpaint_method',
+    default_value=modules.flags.inpaint_option_default,
+    validator=lambda x: x in modules.flags.inpaint_options,
+    expected_type=str
+)
 default_cfg_tsnr = get_config_item_or_set_default(
     key='default_cfg_tsnr',
     default_value=7.0,
@@ -484,6 +500,11 @@ default_overwrite_switch = get_config_item_or_set_default(
     validator=lambda x: isinstance(x, int),
     expected_type=int
 )
+default_overwrite_upscale = get_config_item_or_set_default(
+    key='default_overwrite_upscale',
+    default_value=-1,
+    validator=lambda x: isinstance(x, numbers.Number)
+)
 example_inpaint_prompts = get_config_item_or_set_default(
     key='example_inpaint_prompts',
     default_value=[
@@ -492,6 +513,50 @@ example_inpaint_prompts = get_config_item_or_set_default(
     validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),
     expected_type=list
 )
+example_enhance_detection_prompts = get_config_item_or_set_default(
+    key='example_enhance_detection_prompts',
+    default_value=[
+        'face', 'eye', 'mouth', 'hair', 'hand', 'body'
+    ],
+    validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),
+    expected_type=list
+)
+default_enhance_tabs = get_config_item_or_set_default(
+    key='default_enhance_tabs',
+    default_value=3,
+    validator=lambda x: isinstance(x, int) and 1 <= x <= 5,
+    expected_type=int
+)
+default_enhance_checkbox = get_config_item_or_set_default(
+    key='default_enhance_checkbox',
+    default_value=False,
+    validator=lambda x: isinstance(x, bool),
+    expected_type=bool
+)
+default_enhance_uov_method = get_config_item_or_set_default(
+    key='default_enhance_uov_method',
+    default_value=modules.flags.disabled,
+    validator=lambda x: x in modules.flags.uov_list,
+    expected_type=int
+)
+default_enhance_uov_processing_order = get_config_item_or_set_default(
+    key='default_enhance_uov_processing_order',
+    default_value=modules.flags.enhancement_uov_before,
+    validator=lambda x: x in modules.flags.enhancement_uov_processing_order,
+    expected_type=int
+)
+default_enhance_uov_prompt_type = get_config_item_or_set_default(
+    key='default_enhance_uov_prompt_type',
+    default_value=modules.flags.enhancement_uov_prompt_type_original,
+    validator=lambda x: x in modules.flags.enhancement_uov_prompt_types,
+    expected_type=int
+)
+default_sam_max_detections = get_config_item_or_set_default(
+    key='default_sam_max_detections',
+    default_value=0,
+    validator=lambda x: isinstance(x, int) and 0 <= x <= 10,
+    expected_type=int
+)
 default_black_out_nsfw = get_config_item_or_set_default(
     key='default_black_out_nsfw',
     default_value=False,
@@ -518,10 +583,39 @@ metadata_created_by = get_config_item_or_set_default(
 )
 
 example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
+example_enhance_detection_prompts = [[x] for x in example_enhance_detection_prompts]
+
+default_inpaint_mask_model = get_config_item_or_set_default(
+    key='default_inpaint_mask_model',
+    default_value='isnet-general-use',
+    validator=lambda x: x in modules.flags.inpaint_mask_models,
+    expected_type=str
+)
+
+default_enhance_inpaint_mask_model = get_config_item_or_set_default(
+    key='default_enhance_inpaint_mask_model',
+    default_value='sam',
+    validator=lambda x: x in modules.flags.inpaint_mask_models,
+    expected_type=str
+)
+
+default_inpaint_mask_cloth_category = get_config_item_or_set_default(
+    key='default_inpaint_mask_cloth_category',
+    default_value='full',
+    validator=lambda x: x in modules.flags.inpaint_mask_cloth_category,
+    expected_type=str
+)
+
+default_inpaint_mask_sam_model = get_config_item_or_set_default(
+    key='default_inpaint_mask_sam_model',
+    default_value='vit_b',
+    validator=lambda x: x in [y[1] for y in modules.flags.inpaint_mask_sam_model if y[1] == x],
+    expected_type=str
+)
 
 config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
 
-# mapping config to meta parameter 
+# mapping config to meta parameter
 possible_preset_keys = {
     "default_model": "base_model",
     "default_refiner": "refiner_model",
@@ -537,6 +631,7 @@ possible_preset_keys = {
     "default_sampler": "sampler",
     "default_scheduler": "scheduler",
     "default_overwrite_step": "steps",
+    "default_overwrite_switch": "overwrite_switch",
     "default_performance": "performance",
     "default_image_number": "image_number",
     "default_prompt": "prompt",
@@ -547,7 +642,10 @@ possible_preset_keys = {
     "checkpoint_downloads": "checkpoint_downloads",
     "embeddings_downloads": "embeddings_downloads",
     "lora_downloads": "lora_downloads",
-    "default_vae": "vae"
+    "vae_downloads": "vae_downloads",
+    "default_vae": "vae",
+    # "default_inpaint_method": "inpaint_method", # disabled so inpaint mode doesn't refresh after every preset change
+    "default_inpaint_engine_version": "inpaint_engine_version",
 }
 
 REWRITE_PRESET = False
@@ -754,4 +852,44 @@ def downloading_safety_checker_model():
     return os.path.join(path_safety_checker, 'stable-diffusion-safety-checker.bin')
 
 
+def download_sam_model(sam_model: str) -> str:
+    match sam_model:
+        case 'vit_b':
+            return downloading_sam_vit_b()
+        case 'vit_l':
+            return downloading_sam_vit_l()
+        case 'vit_h':
+            return downloading_sam_vit_h()
+        case _:
+            raise ValueError(f"sam model {sam_model} does not exist.")
+
+
+def downloading_sam_vit_b():
+    load_file_from_url(
+        url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_b_01ec64.pth',
+        model_dir=path_sam,
+        file_name='sam_vit_b_01ec64.pth'
+    )
+    return os.path.join(path_sam, 'sam_vit_b_01ec64.pth')
+
+
+def downloading_sam_vit_l():
+    load_file_from_url(
+        url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_l_0b3195.pth',
+        model_dir=path_sam,
+        file_name='sam_vit_l_0b3195.pth'
+    )
+    return os.path.join(path_sam, 'sam_vit_l_0b3195.pth')
+
+
+def downloading_sam_vit_h():
+    load_file_from_url(
+        url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_h_4b8939.pth',
+        model_dir=path_sam,
+        file_name='sam_vit_h_4b8939.pth'
+    )
+    return os.path.join(path_sam, 'sam_vit_h_4b8939.pth')
+
+
 update_files()
+init_cache(model_filenames, paths_checkpoints, lora_filenames, paths_loras)

modules/flags.py

@@ -8,9 +8,15 @@ upscale_15 = 'Upscale (1.5x)'
 upscale_2 = 'Upscale (2x)'
 upscale_fast = 'Upscale (Fast 2x)'
 
-uov_list = [
-    disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast
-]
+uov_list = [disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast]
+
+enhancement_uov_before = "Before First Enhancement"
+enhancement_uov_after = "After Last Enhancement"
+enhancement_uov_processing_order = [enhancement_uov_before, enhancement_uov_after]
+
+enhancement_uov_prompt_type_original = 'Original Prompts'
+enhancement_uov_prompt_type_last_filled = 'Last Filled Enhancement Prompts'
+enhancement_uov_prompt_types = [enhancement_uov_prompt_type_original, enhancement_uov_prompt_type_last_filled]
 
 CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"]
 
@@ -75,6 +81,10 @@ default_parameters = {
 
 output_formats = ['png', 'jpeg', 'webp']
 
+inpaint_mask_models = ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam']
+inpaint_mask_cloth_category = ['full', 'upper', 'lower']
+inpaint_mask_sam_model = ['vit_b', 'vit_l', 'vit_h']
+
 inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
 inpaint_option_default = 'Inpaint or Outpaint (default)'
 inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)'
@@ -104,7 +114,6 @@ metadata_scheme = [
 ]
 
 controlnet_image_count = 4
-preparation_step_count = 13
 
 
 class OutputFormat(Enum):
@@ -154,7 +163,7 @@ class Performance(Enum):
 
     @classmethod
     def list(cls) -> list:
-        return list(map(lambda c: c.value, cls))
+        return list(map(lambda c: (c.name, c.value), cls))
 
     @classmethod
     def values(cls) -> list:
@@ -178,3 +187,12 @@ class Performance(Enum):
 
     def lora_filename(self) -> str | None:
         return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None
+
+
+performance_selections = []
+
+for name, value in Performance.list():
+    restricted_text = ''
+    if Performance.has_restricted_features(value):
+        restricted_text = '*'
+    performance_selections.append((f'{value} <span style="color: grey;"> \U00002223  {Steps[name].value} steps {restricted_text}</span>', Performance[name].value))

modules/hash_cache.py

@@ -0,0 +1,84 @@
+import json
+import os
+from concurrent.futures import ThreadPoolExecutor
+from multiprocessing import cpu_count
+
+import args_manager
+from modules.util import get_file_from_folder_list
+from modules.util import sha256, HASH_SHA256_LENGTH
+
+hash_cache_filename = 'hash_cache.txt'
+hash_cache = {}
+
+
+def sha256_from_cache(filepath):
+    global hash_cache
+    if filepath not in hash_cache:
+        print(f"[Cache] Calculating sha256 for {filepath}")
+        hash_value = sha256(filepath)
+        print(f"[Cache] sha256 for {filepath}: {hash_value}")
+        hash_cache[filepath] = hash_value
+        save_cache_to_file(filepath, hash_value)
+
+    return hash_cache[filepath]
+
+
+def load_cache_from_file():
+    global hash_cache
+
+    try:
+        if os.path.exists(hash_cache_filename):
+            with open(hash_cache_filename, 'rt', encoding='utf-8') as fp:
+                for line in fp:
+                    entry = json.loads(line)
+                    for filepath, hash_value in entry.items():
+                        if not os.path.exists(filepath) or not isinstance(hash_value, str) and len(hash_value) != HASH_SHA256_LENGTH:
+                            print(f'[Cache] Skipping invalid cache entry: {filepath}')
+                            continue
+                        hash_cache[filepath] = hash_value
+    except Exception as e:
+        print(f'[Cache] Loading failed: {e}')
+
+
+def save_cache_to_file(filename=None, hash_value=None):
+    global hash_cache
+
+    if filename is not None and hash_value is not None:
+        items = [(filename, hash_value)]
+        mode = 'at'
+    else:
+        items = sorted(hash_cache.items())
+        mode = 'wt'
+
+    try:
+        with open(hash_cache_filename, mode, encoding='utf-8') as fp:
+            for filepath, hash_value in items:
+                json.dump({filepath: hash_value}, fp)
+                fp.write('\n')
+    except Exception as e:
+        print(f'[Cache] Saving failed: {e}')
+
+
+def init_cache(model_filenames, paths_checkpoints, lora_filenames, paths_loras):
+    load_cache_from_file()
+
+    if args_manager.args.rebuild_hash_cache:
+        max_workers = args_manager.args.rebuild_hash_cache if args_manager.args.rebuild_hash_cache > 0 else cpu_count()
+        rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers)
+
+    # write cache to file again for sorting and cleanup of invalid cache entries
+    save_cache_to_file()
+
+
+def rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers=cpu_count()):
+    def thread(filename, paths):
+        filepath = get_file_from_folder_list(filename, paths)
+        sha256_from_cache(filepath)
+
+    print('[Cache] Rebuilding hash cache')
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        for model_filename in model_filenames:
+            executor.submit(thread, model_filename, paths_checkpoints)
+        for lora_filename in lora_filenames:
+            executor.submit(thread, lora_filename, paths_loras)
+    print('[Cache] Done')

modules/meta_parser.py

@@ -9,18 +9,18 @@ from PIL import Image
 import fooocus_version
 import modules.config
 import modules.sdxl_styles
+from modules import hash_cache
 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, sha256
+from modules.hash_cache import sha256_from_cache
+from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list
 
 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_metadata: dict | str, is_generating: bool):
+def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool, inpaint_mode: str):
     loaded_parameter_dict = raw_metadata
     if isinstance(raw_metadata, str):
         loaded_parameter_dict = json.loads(raw_metadata)
@@ -49,6 +49,8 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
     get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
     get_str('vae', 'VAE', loaded_parameter_dict, results)
     get_seed('seed', 'Seed', loaded_parameter_dict, results)
+    get_inpaint_engine_version('inpaint_engine_version', 'Inpaint Engine Version', loaded_parameter_dict, results, inpaint_mode)
+    get_inpaint_method('inpaint_method', 'Inpaint Mode', loaded_parameter_dict, results)
 
     if is_generating:
         results.append(gr.update())
@@ -160,6 +162,36 @@ def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, d
         results.append(gr.update())
 
 
+def get_inpaint_engine_version(key: str, fallback: str | None, source_dict: dict, results: list, inpaint_mode: str, default=None) -> str | None:
+    try:
+        h = source_dict.get(key, source_dict.get(fallback, default))
+        assert isinstance(h, str) and h in modules.flags.inpaint_engine_versions
+        if inpaint_mode != modules.flags.inpaint_option_detail:
+            results.append(h)
+        else:
+            results.append(gr.update())
+        results.append(h)
+        return h
+    except:
+        results.append(gr.update())
+        results.append('empty')
+        return None
+
+
+def get_inpaint_method(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:
+    try:
+        h = source_dict.get(key, source_dict.get(fallback, default))
+        assert isinstance(h, str) and h in modules.flags.inpaint_options
+        results.append(h)
+        for i in range(modules.config.default_enhance_tabs):
+            results.append(h)
+        return h
+    except:
+        results.append(gr.update())
+        for i in range(modules.config.default_enhance_tabs):
+            results.append(gr.update())
+
+
 def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
         h = source_dict.get(key, source_dict.get(fallback, default))
@@ -215,14 +247,6 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list, p
         results.append(1)
 
 
-def get_sha256(filepath):
-    global hash_cache
-    if filepath not in hash_cache:
-        hash_cache[filepath] = sha256(filepath)
-
-    return hash_cache[filepath]
-
-
 def parse_meta_from_preset(preset_content):
     assert isinstance(preset_content, dict)
     preset_prepared = {}
@@ -245,8 +269,7 @@ def parse_meta_from_preset(preset_content):
                 height = height[:height.index(" ")]
             preset_prepared[meta_key] = (width, height)
         else:
-            preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[
-                settings_key] is not None else getattr(modules.config, settings_key)
+            preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[settings_key] is not None else getattr(modules.config, settings_key)
 
         if settings_key == "default_styles" or settings_key == "default_aspect_ratio":
             preset_prepared[meta_key] = str(preset_prepared[meta_key])
@@ -290,18 +313,18 @@ class MetadataParser(ABC):
         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)
+        self.base_model_hash = sha256_from_cache(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.refiner_model_hash = sha256_from_cache(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)
+                lora_hash = sha256_from_cache(lora_path)
                 self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
         self.vae_name = Path(vae_name).stem
 

modules/style_sorter.py

@@ -37,6 +37,7 @@ def sort_styles(selected):
     global all_styles
     unselected = [y for y in all_styles if y not in selected]
     sorted_styles = selected + unselected
+    """
     try:
         with open('sorted_styles.json', 'wt', encoding='utf-8') as fp:
             json.dump(sorted_styles, fp, indent=4)
@@ -44,6 +45,7 @@ def sort_styles(selected):
         print('Write style sorting failed.')
         print(e)
     all_styles = sorted_styles
+    """
     return gr.CheckboxGroup.update(choices=sorted_styles)
 
 

modules/translator.py

@@ -0,0 +1,15 @@
+import translators
+from functools import lru_cache
+
+@lru_cache(maxsize=32, typed=False)
+def translate2en(text, element):
+    if not text:
+        return text
+
+    try:
+        result = translators.translate_text(text,to_language='en')
+        print(f'[Parameters] Translated {element}: {result}')
+        return result
+    except Exception as e:
+        print(f'[Parameters] Error during translation of {element}: {e}')
+        return text

modules/upscaler.py

@@ -1,13 +1,11 @@
-import os
-import torch
-import modules.core as core
-
-from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN
-from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel
 from collections import OrderedDict
-from modules.config import path_upscale_models
 
-model_filename = os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')
+import modules.core as core
+import torch
+from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel
+from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN
+from modules.config import downloading_upscale_model
+
 opImageUpscaleWithModel = ImageUpscaleWithModel()
 model = None
 
@@ -18,6 +16,7 @@ def perform_upscale(img):
     print(f'Upscaling image with shape {str(img.shape)} ...')
 
     if model is None:
+        model_filename = downloading_upscale_model()
         sd = torch.load(model_filename)
         sdo = OrderedDict()
         for k, v in sd.items():

modules/util.py

@@ -176,13 +176,11 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
 
 
 def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
-    print(f"Calculating sha256 for {filename}: ", end='')
     if use_addnet_hash:
         with open(filename, "rb") as file:
             sha256_value = addnet_hash_safetensors(file)
     else:
         sha256_value = calculate_sha256(filename)
-    print(f"{sha256_value}")
 
     return sha256_value[:length] if length is not None else sha256_value
 
@@ -383,13 +381,6 @@ def get_file_from_folder_list(name, folders):
     return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
 
 
-def makedirs_with_log(path):
-    try:
-        os.makedirs(path, exist_ok=True)
-    except OSError as error:
-        print(f'Directory {path} could not be created, reason: {error}')
-
-
 def get_enabled_loras(loras: list, remove_none=True) -> list:
     return [(lora[1], lora[2]) for lora in loras if lora[0] and (lora[1] != 'None' if remove_none else True)]
 
@@ -397,6 +388,9 @@ def get_enabled_loras(loras: list, remove_none=True) -> list:
 def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
                                       skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,
                                       lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:
+    # prevent unintended side effects when returning without detection
+    loras = loras.copy()
+
     if lora_filenames is None:
         lora_filenames = []