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import os
import time
from typing import Union
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, ControlNetModel, StableDiffusionControlNetPipeline, StableDiffusionXLControlNetPipeline
from modules.control.units import detect
from modules.shared import log, opts, listdir
from modules import errors, sd_models
what = 'ControlNet'
debug = log.trace if os.environ.get('SD_CONTROL_DEBUG', None) is not None else lambda *args, **kwargs: None
debug('Trace: CONTROL')
predefined_sd15 = {
'Canny': "lllyasviel/control_v11p_sd15_canny",
'Depth': "lllyasviel/control_v11f1p_sd15_depth",
'HED': "lllyasviel/sd-controlnet-hed",
'IP2P': "lllyasviel/control_v11e_sd15_ip2p",
'LineArt': "lllyasviel/control_v11p_sd15_lineart",
'LineArt Anime': "lllyasviel/control_v11p_sd15s2_lineart_anime",
'MLDS': "lllyasviel/control_v11p_sd15_mlsd",
'NormalBae': "lllyasviel/control_v11p_sd15_normalbae",
'OpenPose': "lllyasviel/control_v11p_sd15_openpose",
'Scribble': "lllyasviel/control_v11p_sd15_scribble",
'Segment': "lllyasviel/control_v11p_sd15_seg",
'Shuffle': "lllyasviel/control_v11e_sd15_shuffle",
'SoftEdge': "lllyasviel/control_v11p_sd15_softedge",
'Tile': "lllyasviel/control_v11f1e_sd15_tile",
'Depth Anything': 'vladmandic/depth-anything',
'Canny FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_canny.safetensors',
'Inpaint FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_inpaint.safetensors',
'LineArt Anime FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_animeline.safetensors',
'LineArt FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_lineart.safetensors',
'MLSD FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_mlsd.safetensors',
'NormalBae FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_normal.safetensors',
'OpenPose FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_openpose.safetensors',
'Pix2Pix FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_pix2pix.safetensors',
'Scribble FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_scribble.safetensors',
'Segment FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_seg.safetensors',
'Shuffle FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_shuffle.safetensors',
'SoftEdge FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_softedge.safetensors',
'Tile FP16': 'Aptronym/SDNext/ControlNet11/controlnet11Models_tileE.safetensors',
'CiaraRowles TemporalNet': "CiaraRowles/TemporalNet",
'Ciaochaos Recolor': 'ioclab/control_v1p_sd15_brightness',
'Ciaochaos Illumination': 'ioclab/control_v1u_sd15_illumination/illumination20000.safetensors',
}
predefined_sdxl = {
'Canny Small XL': 'diffusers/controlnet-canny-sdxl-1.0-small',
'Canny Mid XL': 'diffusers/controlnet-canny-sdxl-1.0-mid',
'Canny XL': 'diffusers/controlnet-canny-sdxl-1.0',
'Depth Zoe XL': 'diffusers/controlnet-zoe-depth-sdxl-1.0',
'Depth Mid XL': 'diffusers/controlnet-depth-sdxl-1.0-mid',
'OpenPose XL': 'thibaud/controlnet-openpose-sdxl-1.0',
# 'StabilityAI Canny R128': 'stabilityai/control-lora/control-LoRAs-rank128/control-lora-canny-rank128.safetensors',
# 'StabilityAI Depth R128': 'stabilityai/control-lora/control-LoRAs-rank128/control-lora-depth-rank128.safetensors',
# 'StabilityAI Recolor R128': 'stabilityai/control-lora/control-LoRAs-rank128/control-lora-recolor-rank128.safetensors',
# 'StabilityAI Sketch R128': 'stabilityai/control-lora/control-LoRAs-rank128/control-lora-sketch-rank128-metadata.safetensors',
# 'StabilityAI Canny R256': 'stabilityai/control-lora/control-LoRAs-rank256/control-lora-canny-rank256.safetensors',
# 'StabilityAI Depth R256': 'stabilityai/control-lora/control-LoRAs-rank256/control-lora-depth-rank256.safetensors',
# 'StabilityAI Recolor R256': 'stabilityai/control-lora/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors',
# 'StabilityAI Sketch R256': 'stabilityai/control-lora/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors',
}
models = {}
all_models = {}
all_models.update(predefined_sd15)
all_models.update(predefined_sdxl)
cache_dir = 'models/control/controlnet'
def find_models():
path = os.path.join(opts.control_dir, 'controlnet')
files = listdir(path)
files = [f for f in files if f.endswith('.safetensors')]
downloaded_models = {}
for f in files:
basename = os.path.splitext(os.path.relpath(f, path))[0]
downloaded_models[basename] = os.path.join(path, f)
all_models.update(downloaded_models)
return downloaded_models
def list_models(refresh=False):
import modules.shared
global models # pylint: disable=global-statement
if not refresh and len(models) > 0:
return models
models = {}
if modules.shared.sd_model_type == 'none':
models = ['None']
elif modules.shared.sd_model_type == 'sdxl':
models = ['None'] + list(predefined_sdxl) + sorted(find_models())
elif modules.shared.sd_model_type == 'sd':
models = ['None'] + list(predefined_sd15) + sorted(find_models())
else:
log.warning(f'Control {what} model list failed: unknown model type')
models = ['None'] + sorted(predefined_sd15) + sorted(predefined_sdxl) + sorted(find_models())
debug(f'Control list {what}: path={cache_dir} models={models}')
return models
class ControlNet():
def __init__(self, model_id: str = None, device = None, dtype = None, load_config = None):
self.model: ControlNetModel = None
self.model_id: str = model_id
self.device = device
self.dtype = dtype
self.load_config = { 'cache_dir': cache_dir }
if load_config is not None:
self.load_config.update(load_config)
if model_id is not None:
self.load()
def reset(self):
if self.model is not None:
debug(f'Control {what} model unloaded')
self.model = None
self.model_id = None
def load_safetensors(self, model_path):
name = os.path.splitext(model_path)[0]
config_path = None
if not os.path.exists(model_path):
import huggingface_hub as hf
parts = model_path.split('/')
repo_id = f'{parts[0]}/{parts[1]}'
filename = os.path.splitext('/'.join(parts[2:]))[0]
model_path = hf.hf_hub_download(repo_id=repo_id, filename=f'{filename}.safetensors', cache_dir=cache_dir)
if config_path is None:
try:
config_path = hf.hf_hub_download(repo_id=repo_id, filename=f'{filename}.yaml', cache_dir=cache_dir)
except Exception:
pass # no yaml file
if config_path is None:
try:
config_path = hf.hf_hub_download(repo_id=repo_id, filename=f'{filename}.json', cache_dir=cache_dir)
except Exception:
pass # no yaml file
elif os.path.exists(name + '.yaml'):
config_path = f'{name}.yaml'
elif os.path.exists(name + '.json'):
config_path = f'{name}.json'
if config_path is not None:
self.load_config['original_config_file '] = config_path
self.model = ControlNetModel.from_single_file(model_path, **self.load_config)
def load(self, model_id: str = None) -> str:
try:
t0 = time.time()
model_id = model_id or self.model_id
if model_id is None or model_id == 'None':
self.reset()
return
model_path = all_models[model_id]
if model_path == '':
return
if model_path is None:
log.error(f'Control {what} model load failed: id="{model_id}" error=unknown model id')
return
log.debug(f'Control {what} model loading: id="{model_id}" path="{model_path}"')
if model_path.endswith('.safetensors'):
self.load_safetensors(model_path)
else:
self.model = ControlNetModel.from_pretrained(model_path, **self.load_config)
if self.device is not None:
self.model.to(self.device)
if self.dtype is not None:
self.model.to(self.dtype)
t1 = time.time()
self.model_id = model_id
log.debug(f'Control {what} model loaded: id="{model_id}" path="{model_path}" time={t1-t0:.2f}')
return f'{what} loaded model: {model_id}'
except Exception as e:
log.error(f'Control {what} model load failed: id="{model_id}" error={e}')
errors.display(e, f'Control {what} load')
return f'{what} failed to load model: {model_id}'
class ControlNetPipeline():
def __init__(self, controlnet: Union[ControlNetModel, list[ControlNetModel]], pipeline: Union[StableDiffusionXLPipeline, StableDiffusionPipeline], dtype = None):
t0 = time.time()
self.orig_pipeline = pipeline
self.pipeline = None
if pipeline is None:
log.error('Control model pipeline: model not loaded')
return
elif detect.is_sdxl(pipeline):
self.pipeline = StableDiffusionXLControlNetPipeline(
vae=pipeline.vae,
text_encoder=pipeline.text_encoder,
text_encoder_2=pipeline.text_encoder_2,
tokenizer=pipeline.tokenizer,
tokenizer_2=pipeline.tokenizer_2,
unet=pipeline.unet,
scheduler=pipeline.scheduler,
feature_extractor=getattr(pipeline, 'feature_extractor', None),
controlnet=controlnet, # can be a list
)
sd_models.move_model(self.pipeline, pipeline.device)
elif detect.is_sd15(pipeline):
self.pipeline = StableDiffusionControlNetPipeline(
vae=pipeline.vae,
text_encoder=pipeline.text_encoder,
tokenizer=pipeline.tokenizer,
unet=pipeline.unet,
scheduler=pipeline.scheduler,
feature_extractor=getattr(pipeline, 'feature_extractor', None),
requires_safety_checker=False,
safety_checker=None,
controlnet=controlnet, # can be a list
)
sd_models.move_model(self.pipeline, pipeline.device)
else:
log.error(f'Control {what} pipeline: class={pipeline.__class__.__name__} unsupported model type')
return
if dtype is not None and self.pipeline is not None:
self.pipeline = self.pipeline.to(dtype)
t1 = time.time()
if self.pipeline is not None:
log.debug(f'Control {what} pipeline: class={self.pipeline.__class__.__name__} time={t1-t0:.2f}')
else:
log.error(f'Control {what} pipeline: not initialized')
def restore(self):
self.pipeline = None
return self.orig_pipeline
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