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import os.path
import stat
import functools
from collections import OrderedDict
from modules import shared, scripts, sd_models
from modules.paths import models_path
from scripts.processor import *
from scripts.utils import ndarray_lru_cache
from scripts.logging import logger
from typing import Dict, Callable, Optional, Tuple, List
CN_MODEL_EXTS = [".pt", ".pth", ".ckpt", ".safetensors"]
cn_models_dir = os.path.join(models_path, "ControlNet")
cn_models_dir_old = os.path.join(scripts.basedir(), "models")
cn_models = OrderedDict() # "My_Lora(abcd1234)" -> C:/path/to/model.safetensors
cn_models_names = {} # "my_lora" -> "My_Lora(abcd1234)"
def cache_preprocessors(preprocessor_modules: Dict[str, Callable]) -> Dict[str, Callable]:
""" We want to share the preprocessor results in a single big cache, instead of a small
cache for each preprocessor function. """
CACHE_SIZE = getattr(shared.cmd_opts, "controlnet_preprocessor_cache_size", 0)
# Set CACHE_SIZE = 0 will completely remove the caching layer. This can be
# helpful when debugging preprocessor code.
if CACHE_SIZE == 0:
return preprocessor_modules
logger.debug(f'Create LRU cache (max_size={CACHE_SIZE}) for preprocessor results.')
@ndarray_lru_cache(max_size=CACHE_SIZE)
def unified_preprocessor(preprocessor_name: str, *args, **kwargs):
logger.debug(f'Calling preprocessor {preprocessor_name} outside of cache.')
return preprocessor_modules[preprocessor_name](*args, **kwargs)
# TODO: Introduce a seed parameter for shuffle preprocessor?
uncacheable_preprocessors = ['shuffle']
return {
k: (
v if k in uncacheable_preprocessors
else functools.partial(unified_preprocessor, k)
)
for k, v
in preprocessor_modules.items()
}
cn_preprocessor_modules = {
"none": lambda x, *args, **kwargs: (x, True),
"canny": canny,
"depth": midas,
"depth_leres": functools.partial(leres, boost=False),
"depth_leres++": functools.partial(leres, boost=True),
"hed": hed,
"hed_safe": hed_safe,
"mediapipe_face": mediapipe_face,
"mlsd": mlsd,
"normal_map": midas_normal,
"openpose": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=False, include_face=False),
"openpose_hand": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=True, include_face=False),
"openpose_face": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=False, include_face=True),
"openpose_faceonly": functools.partial(g_openpose_model.run_model, include_body=False, include_hand=False, include_face=True),
"openpose_full": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=True, include_face=True),
"dw_openpose_full": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=True, include_face=True, use_dw_pose=True),
"clip_vision": functools.partial(clip, config='clip_vitl'),
"revision_clipvision": functools.partial(clip, config='clip_g'),
"revision_ignore_prompt": functools.partial(clip, config='clip_g'),
"ip-adapter_clip_sd15": functools.partial(clip, config='clip_h'),
"ip-adapter_clip_sdxl": functools.partial(clip, config='clip_g'),
"color": color,
"pidinet": pidinet,
"pidinet_safe": pidinet_safe,
"pidinet_sketch": pidinet_ts,
"pidinet_scribble": scribble_pidinet,
"scribble_xdog": scribble_xdog,
"scribble_hed": scribble_hed,
"segmentation": uniformer,
"threshold": threshold,
"depth_zoe": zoe_depth,
"normal_bae": normal_bae,
"oneformer_coco": oneformer_coco,
"oneformer_ade20k": oneformer_ade20k,
"lineart": lineart,
"lineart_coarse": lineart_coarse,
"lineart_anime": lineart_anime,
"lineart_standard": lineart_standard,
"shuffle": shuffle,
"tile_resample": tile_resample,
"invert": invert,
"lineart_anime_denoise": lineart_anime_denoise,
"reference_only": identity,
"reference_adain": identity,
"reference_adain+attn": identity,
"inpaint": identity,
"inpaint_only": identity,
"inpaint_only+lama": lama_inpaint,
"tile_colorfix": identity,
"tile_colorfix+sharp": identity,
"recolor_luminance": recolor_luminance,
"recolor_intensity": recolor_intensity,
"blur_gaussian": blur_gaussian,
}
cn_preprocessor_unloadable = {
"hed": unload_hed,
"fake_scribble": unload_hed,
"mlsd": unload_mlsd,
"clip_vision": functools.partial(unload_clip, config='clip_vitl'),
"revision_clipvision": functools.partial(unload_clip, config='clip_g'),
"revision_ignore_prompt": functools.partial(unload_clip, config='clip_g'),
"ip-adapter_clip_sd15": functools.partial(unload_clip, config='clip_h'),
"ip-adapter_clip_sdxl": functools.partial(unload_clip, config='clip_g'),
"depth": unload_midas,
"depth_leres": unload_leres,
"normal_map": unload_midas,
"pidinet": unload_pidinet,
"openpose": g_openpose_model.unload,
"openpose_hand": g_openpose_model.unload,
"openpose_face": g_openpose_model.unload,
"openpose_full": g_openpose_model.unload,
"dw_openpose_full": g_openpose_model.unload,
"segmentation": unload_uniformer,
"depth_zoe": unload_zoe_depth,
"normal_bae": unload_normal_bae,
"oneformer_coco": unload_oneformer_coco,
"oneformer_ade20k": unload_oneformer_ade20k,
"lineart": unload_lineart,
"lineart_coarse": unload_lineart_coarse,
"lineart_anime": unload_lineart_anime,
"lineart_anime_denoise": unload_lineart_anime_denoise,
"inpaint_only+lama": unload_lama_inpaint
}
preprocessor_aliases = {
"invert": "invert (from white bg & black line)",
"lineart_standard": "lineart_standard (from white bg & black line)",
"lineart": "lineart_realistic",
"color": "t2ia_color_grid",
"clip_vision": "t2ia_style_clipvision",
"pidinet_sketch": "t2ia_sketch_pidi",
"depth": "depth_midas",
"normal_map": "normal_midas",
"hed": "softedge_hed",
"hed_safe": "softedge_hedsafe",
"pidinet": "softedge_pidinet",
"pidinet_safe": "softedge_pidisafe",
"segmentation": "seg_ufade20k",
"oneformer_coco": "seg_ofcoco",
"oneformer_ade20k": "seg_ofade20k",
"pidinet_scribble": "scribble_pidinet",
"inpaint": "inpaint_global_harmonious",
}
ui_preprocessor_keys = ['none', preprocessor_aliases['invert']]
ui_preprocessor_keys += sorted([preprocessor_aliases.get(k, k)
for k in cn_preprocessor_modules.keys()
if preprocessor_aliases.get(k, k) not in ui_preprocessor_keys])
reverse_preprocessor_aliases = {preprocessor_aliases[k]: k for k in preprocessor_aliases.keys()}
def get_module_basename(module: Optional[str]) -> str:
if module is None:
module = 'none'
return reverse_preprocessor_aliases.get(module, module)
default_detectedmap_dir = os.path.join("detected_maps")
script_dir = scripts.basedir()
os.makedirs(cn_models_dir, exist_ok=True)
def traverse_all_files(curr_path, model_list):
f_list = [
(os.path.join(curr_path, entry.name), entry.stat())
for entry in os.scandir(curr_path)
if os.path.isdir(curr_path)
]
for f_info in f_list:
fname, fstat = f_info
if os.path.splitext(fname)[1] in CN_MODEL_EXTS:
model_list.append(f_info)
elif stat.S_ISDIR(fstat.st_mode):
model_list = traverse_all_files(fname, model_list)
return model_list
def get_all_models(sort_by, filter_by, path):
res = OrderedDict()
fileinfos = traverse_all_files(path, [])
filter_by = filter_by.strip(" ")
if len(filter_by) != 0:
fileinfos = [x for x in fileinfos if filter_by.lower()
in os.path.basename(x[0]).lower()]
if sort_by == "name":
fileinfos = sorted(fileinfos, key=lambda x: os.path.basename(x[0]))
elif sort_by == "date":
fileinfos = sorted(fileinfos, key=lambda x: -x[1].st_mtime)
elif sort_by == "path name":
fileinfos = sorted(fileinfos)
for finfo in fileinfos:
filename = finfo[0]
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
res[name + f" [{sd_models.model_hash(filename)}]"] = filename
return res
def update_cn_models():
cn_models.clear()
ext_dirs = (shared.opts.data.get("control_net_models_path", None), getattr(shared.cmd_opts, 'controlnet_dir', None))
extra_lora_paths = (extra_lora_path for extra_lora_path in ext_dirs
if extra_lora_path is not None and os.path.exists(extra_lora_path))
paths = [cn_models_dir, cn_models_dir_old, *extra_lora_paths]
for path in paths:
sort_by = shared.opts.data.get(
"control_net_models_sort_models_by", "name")
filter_by = shared.opts.data.get("control_net_models_name_filter", "")
found = get_all_models(sort_by, filter_by, path)
cn_models.update({**found, **cn_models})
# insert "None" at the beginning of `cn_models` in-place
cn_models_copy = OrderedDict(cn_models)
cn_models.clear()
cn_models.update({**{"None": None}, **cn_models_copy})
cn_models_names.clear()
for name_and_hash, filename in cn_models.items():
if filename is None:
continue
name = os.path.splitext(os.path.basename(filename))[0].lower()
cn_models_names[name] = name_and_hash
def select_control_type(control_type: str) -> Tuple[List[str], List[str], str, str]:
default_option = preprocessor_filters[control_type]
pattern = control_type.lower()
preprocessor_list = ui_preprocessor_keys
model_list = list(cn_models.keys())
if pattern == "all":
return [
preprocessor_list,
model_list,
'none', #default option
"None" #default model
]
filtered_preprocessor_list = [
x
for x in preprocessor_list
if pattern in x.lower() or any(a in x.lower() for a in preprocessor_filters_aliases.get(pattern, [])) or x.lower() == "none"
]
if pattern in ["canny", "lineart", "scribble/sketch", "mlsd"]:
filtered_preprocessor_list += [
x for x in preprocessor_list if "invert" in x.lower()
]
filtered_model_list = [
x for x in model_list if pattern in x.lower() or any(a in x.lower() for a in preprocessor_filters_aliases.get(pattern, [])) or x.lower() == "none"
]
if default_option not in filtered_preprocessor_list:
default_option = filtered_preprocessor_list[0]
if len(filtered_model_list) == 1:
default_model = "None"
filtered_model_list = model_list
else:
default_model = filtered_model_list[1]
for x in filtered_model_list:
if "11" in x.split("[")[0]:
default_model = x
break
return (
filtered_preprocessor_list,
filtered_model_list,
default_option,
default_model
)
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