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import os.path
import stat
from collections import OrderedDict
from modules import shared, scripts, sd_models
from modules.paths import models_path
from scripts.enums import StableDiffusionVersion
from scripts.supported_preprocessor import Preprocessor
from typing import Dict, Tuple, List
CN_MODEL_EXTS = [".pt", ".pth", ".ckpt", ".safetensors", ".bin"]
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)"
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 get_sd_version() -> StableDiffusionVersion:
if hasattr(shared.sd_model, 'is_sdxl'):
if shared.sd_model.is_sdxl:
return StableDiffusionVersion.SDXL
elif shared.sd_model.is_sd2:
return StableDiffusionVersion.SD2x
elif shared.sd_model.is_sd1:
return StableDiffusionVersion.SD1x
else:
return StableDiffusionVersion.UNKNOWN
# backward compability for webui < 1.5.0
else:
if hasattr(shared.sd_model, 'conditioner'):
return StableDiffusionVersion.SDXL
elif hasattr(shared.sd_model.cond_stage_model, 'model'):
return StableDiffusionVersion.SD2x
else:
return StableDiffusionVersion.SD1x
def select_control_type(
control_type: str,
sd_version: StableDiffusionVersion = StableDiffusionVersion.UNKNOWN,
cn_models: Dict = cn_models, # Override or testing
) -> Tuple[List[str], List[str], str, str]:
pattern = control_type.lower()
all_models = list(cn_models.keys())
if pattern == "all":
return [
[p.label for p in Preprocessor.get_sorted_preprocessors()],
all_models,
'none', #default option
"None" #default model
]
filtered_model_list = [
model for model in all_models
if model.lower() == "none" or
((
pattern in model.lower() or
any(a in model.lower() for a in Preprocessor.tag_to_filters(control_type))
) and (
sd_version.is_compatible_with(StableDiffusionVersion.detect_from_model_name(model))
))
]
assert len(filtered_model_list) > 0, "'None' model should always be available."
if len(filtered_model_list) == 1:
default_model = "None"
else:
default_model = filtered_model_list[1]
for x in filtered_model_list:
if "11" in x.split("[")[0]:
default_model = x
break
return (
[p.label for p in Preprocessor.get_filtered_preprocessors(control_type)],
filtered_model_list,
Preprocessor.get_default_preprocessor(control_type).label,
default_model
)
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