import os import time import threading from typing import Union from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, FluxPipeline, StableDiffusion3Pipeline, ControlNetModel from modules.control.units import detect from modules.shared import log, opts, listdir from modules import errors, sd_models, devices, model_quant from modules.processing import StableDiffusionProcessingControl what = 'ControlNet' debug = os.environ.get('SD_CONTROL_DEBUG', None) is not None debug_log = log.trace if os.environ.get('SD_CONTROL_DEBUG', None) is not None else lambda *args, **kwargs: None 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/bin', 'Xinsir Union XL': 'xinsir/controlnet-union-sdxl-1.0', 'Xinsir ProMax XL': 'brad-twinkl/controlnet-union-sdxl-1.0-promax', 'Xinsir OpenPose XL': 'xinsir/controlnet-openpose-sdxl-1.0', 'Xinsir Canny XL': 'xinsir/controlnet-canny-sdxl-1.0', 'Xinsir Depth XL': 'xinsir/controlnet-depth-sdxl-1.0', 'Xinsir Scribble XL': 'xinsir/controlnet-scribble-sdxl-1.0', 'Xinsir Anime Painter XL': 'xinsir/anime-painter', 'Xinsir Tile XL': 'xinsir/controlnet-tile-sdxl-1.0', 'NoobAI Canny XL': 'Eugeoter/noob-sdxl-controlnet-canny', 'NoobAI Lineart Anime XL': 'Eugeoter/noob-sdxl-controlnet-lineart_anime', 'NoobAI Depth XL': 'Eugeoter/noob-sdxl-controlnet-depth', 'NoobAI Normal XL': 'Eugeoter/noob-sdxl-controlnet-normal', 'NoobAI SoftEdge XL': 'Eugeoter/noob-sdxl-controlnet-softedge_hed', 'NoobAI OpenPose XL': 'einar77/noob-openpose', 'TTPlanet Tile Realistic XL': 'Yakonrus/SDXL_Controlnet_Tile_Realistic_v2', # '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', } predefined_f1 = { "InstantX Union": 'InstantX/FLUX.1-dev-Controlnet-Union', "InstantX Canny": 'InstantX/FLUX.1-dev-Controlnet-Canny', "JasperAI Depth": 'jasperai/Flux.1-dev-Controlnet-Depth', "BlackForrestLabs Canny LoRA": '/huggingface.co/black-forest-labs/FLUX.1-Canny-dev-lora/flux1-canny-dev-lora.safetensors', "BlackForrestLabs Depth LoRA": '/huggingface.co/black-forest-labs/FLUX.1-Depth-dev-lora/flux1-depth-dev-lora.safetensors', "JasperAI Surface Normals": 'jasperai/Flux.1-dev-Controlnet-Surface-Normals', "JasperAI Upscaler": 'jasperai/Flux.1-dev-Controlnet-Upscaler', "Shakker-Labs Union": 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro', "Shakker-Labs Pose": 'Shakker-Labs/FLUX.1-dev-ControlNet-Pose', "Shakker-Labs Depth": 'Shakker-Labs/FLUX.1-dev-ControlNet-Depth', "XLabs-AI Canny": 'XLabs-AI/flux-controlnet-canny-diffusers', "XLabs-AI Depth": 'XLabs-AI/flux-controlnet-depth-diffusers', "XLabs-AI HED": 'XLabs-AI/flux-controlnet-hed-diffusers' } predefined_sd3 = { "StabilityAI Canny SD35": 'diffusers-internal-dev/sd35-controlnet-canny-8b', "StabilityAI Depth SD35": 'diffusers-internal-dev/sd35-controlnet-depth-8b', "StabilityAI Blur SD35": 'diffusers-internal-dev/sd35-controlnet-blur-8b', "InstantX Canny SD35": 'InstantX/SD3-Controlnet-Canny', "InstantX Pose SD35": 'InstantX/SD3-Controlnet-Pose', "InstantX Depth SD35": 'InstantX/SD3-Controlnet-Depth', "InstantX Tile SD35": 'InstantX/SD3-Controlnet-Tile', "Alimama Inpainting SD35": 'alimama-creative/SD3-Controlnet-Inpainting', "Alimama SoftEdge SD35": 'alimama-creative/SD3-Controlnet-Softedge', } variants = { 'NoobAI Canny XL': 'fp16', 'NoobAI Lineart Anime XL': 'fp16', 'NoobAI Depth XL': 'fp16', 'NoobAI Normal XL': 'fp16', 'NoobAI SoftEdge XL': 'fp16', 'TTPlanet Tile Realistic XL': 'fp16', } models = {} all_models = {} all_models.update(predefined_sd15) all_models.update(predefined_sdxl) all_models.update(predefined_f1) all_models.update(predefined_sd3) cache_dir = 'models/control/controlnet' load_lock = threading.Lock() def find_models(): path = os.path.join(opts.control_dir, 'controlnet') files = listdir(path) folders = [f for f in files if os.path.isdir(f) if os.path.exists(os.path.join(f, 'config.json'))] 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] = f for f in folders: basename = os.path.relpath(f, path) downloaded_models[basename] = f all_models.update(downloaded_models) return downloaded_models find_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()) elif modules.shared.sd_model_type == 'f1': models = ['None'] + list(predefined_f1) + sorted(find_models()) elif modules.shared.sd_model_type == 'sd3': models = ['None'] + list(predefined_sd3) + sorted(find_models()) else: log.warning(f'Control {what} model list failed: unknown model type') models = ['None'] + sorted(predefined_sd15) + sorted(predefined_sdxl) + sorted(predefined_f1) + sorted(predefined_sd3) + sorted(find_models()) debug_log(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_log(f'Control {what} model unloaded') self.model = None self.model_id = None def get_class(self, model_id:str=''): from modules import shared if shared.sd_model_type == 'none': _load = shared.sd_model # trigger a load if shared.sd_model_type == 'sd': from diffusers import ControlNetModel as cls # pylint: disable=reimported config = 'lllyasviel/control_v11p_sd15_canny' elif shared.sd_model_type == 'sdxl': if 'union' in model_id.lower(): from diffusers import ControlNetUnionModel as cls config = 'xinsir/controlnet-union-sdxl-1.0' elif 'promax' in model_id.lower(): from diffusers import ControlNetUnionModel as cls config = 'brad-twinkl/controlnet-union-sdxl-1.0-promax' else: from diffusers import ControlNetModel as cls # pylint: disable=reimported # sdxl shares same model class config = 'Eugeoter/noob-sdxl-controlnet-canny' elif shared.sd_model_type == 'f1': from diffusers import FluxControlNetModel as cls config = 'InstantX/FLUX.1-dev-Controlnet-Union' elif shared.sd_model_type == 'sd3': from diffusers import SD3ControlNetModel as cls config = 'InstantX/SD3-Controlnet-Canny' else: log.error(f'Control {what}: type={shared.sd_model_type} unsupported model') return None, None return cls, config def load_safetensors(self, model_id, 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 cls, config = self.get_class(model_id) if cls is None: log.error(f'Control {what} model load: unknown base model') else: self.model = cls.from_single_file(model_path, config=config, **self.load_config) def load(self, model_id: str = None, force: bool = True) -> str: with load_lock: try: t0 = time.time() model_id = model_id or self.model_id if model_id is None or model_id == 'None': self.reset() return if model_id not in all_models: log.error(f'Control {what}: id="{model_id}" available={list(all_models)} unknown model') return model_path = all_models[model_id] if model_path == '': return if model_path is None: log.error(f'Control {what} model load: id="{model_id}" unknown model id') return if 'lora' in model_id.lower(): self.model = model_path return if model_id == self.model_id and not force: # log.debug(f'Control {what} model: id="{model_id}" path="{model_path}" already loaded') return log.debug(f'Control {what} model loading: id="{model_id}" path="{model_path}"') cls, _config = self.get_class(model_id) if model_path.endswith('.safetensors'): self.load_safetensors(model_id, model_path) else: kwargs = {} if '/bin' in model_path: model_path = model_path.replace('/bin', '') self.load_config['use_safetensors'] = False else: self.load_config['use_safetensors'] = True if cls is None: log.error(f'Control {what} model load: id="{model_id}" unknown base model') return if variants.get(model_id, None) is not None: kwargs['variant'] = variants[model_id] try: self.model = cls.from_pretrained(model_path, **self.load_config, **kwargs) except Exception as e: log.error(f'Control {what} model load: id="{model_id}" {e}') if debug: errors.display(e, 'Control') if self.model is None: return if self.dtype is not None: self.model.to(self.dtype) if "ControlNet" in opts.nncf_compress_weights: try: log.debug(f'Control {what} model NNCF Compress: id="{model_id}"') from modules.model_quant import nncf_compress_model self.model = nncf_compress_model(self.model) except Exception as e: log.error(f'Control {what} model NNCF Compression failed: id="{model_id}" {e}') elif "ControlNet" in opts.optimum_quanto_weights: try: log.debug(f'Control {what} model Optimum Quanto: id="{model_id}"') model_quant.load_quanto('Load model: type=ControlNet') from modules.model_quant import optimum_quanto_model self.model = optimum_quanto_model(self.model) except Exception as e: log.error(f'Control {what} model Optimum Quanto: id="{model_id}" {e}') if self.device is not None: self.model.to(self.device) t1 = time.time() self.model_id = model_id log.info(f'Control {what} model loaded: id="{model_id}" path="{model_path}" cls={cls.__name__} time={t1-t0:.2f}') return f'{what} loaded model: {model_id}' except Exception as e: log.error(f'Control {what} model load: id="{model_id}" {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, FluxPipeline, StableDiffusion3Pipeline], dtype = None, p: StableDiffusionProcessingControl = None, # pylint: disable=unused-argument ): t0 = time.time() self.orig_pipeline = pipeline self.pipeline = None controlnets = controlnet if isinstance(controlnet, list) else [controlnet] loras = [cn for cn in controlnets if isinstance(cn, str)] controlnets = [cn for cn in controlnets if not isinstance(cn, str)] if pipeline is None: log.error('Control model pipeline: model not loaded') return elif detect.is_sdxl(pipeline) and len(controlnets) > 0: from diffusers import StableDiffusionXLControlNetPipeline, StableDiffusionXLControlNetUnionPipeline classes = [c.__class__.__name__ for c in controlnets] if any(c == 'ControlNetUnionModel' for c in classes): if not all(c == 'ControlNetUnionModel' for c in classes): log.warning(f'Control {what}: units={classes} mixed type is not supported') return if isinstance(controlnets, list) and len(controlnets) == 1: controlnets = controlnets[0] cls = StableDiffusionXLControlNetUnionPipeline else: cls = StableDiffusionXLControlNetPipeline self.pipeline = cls( 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=controlnets, # can be a list ) elif detect.is_sd15(pipeline) and len(controlnets) > 0: from diffusers import StableDiffusionControlNetPipeline 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=controlnets, # can be a list ) sd_models.move_model(self.pipeline, pipeline.device) elif detect.is_f1(pipeline) and len(controlnets) > 0: from diffusers import FluxControlNetPipeline self.pipeline = FluxControlNetPipeline( vae=pipeline.vae.to(devices.device), text_encoder=pipeline.text_encoder, text_encoder_2=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer, tokenizer_2=pipeline.tokenizer_2, transformer=pipeline.transformer, scheduler=pipeline.scheduler, controlnet=controlnets, # can be a list ) elif detect.is_sd3(pipeline) and len(controlnets) > 0: from diffusers import StableDiffusion3ControlNetPipeline self.pipeline = StableDiffusion3ControlNetPipeline( vae=pipeline.vae, text_encoder=pipeline.text_encoder, text_encoder_2=pipeline.text_encoder_2, text_encoder_3=pipeline.text_encoder_3, tokenizer=pipeline.tokenizer, tokenizer_2=pipeline.tokenizer_2, tokenizer_3=pipeline.tokenizer_3, transformer=pipeline.transformer, scheduler=pipeline.scheduler, controlnet=controlnets, # can be a list ) elif len(loras) > 0: self.pipeline = pipeline for lora in loras: log.debug(f'Control {what} pipeline: lora="{lora}"') lora = lora.replace('/huggingface.co/', '') self.pipeline.load_lora_weights(lora) """ if p is not None: p.prompt += f'' """ else: log.error(f'Control {what} pipeline: class={pipeline.__class__.__name__} unsupported model type') return if self.pipeline is None: log.error(f'Control {what} pipeline: not initialized') return if dtype is not None: self.pipeline = self.pipeline.to(dtype) sd_models.copy_diffuser_options(self.pipeline, pipeline) if opts.diffusers_offload_mode == 'none': sd_models.move_model(self.pipeline, devices.device) from modules.sd_models import set_diffuser_offload set_diffuser_offload(self.pipeline, 'model') t1 = time.time() debug_log(f'Control {what} pipeline: class={self.pipeline.__class__.__name__} time={t1-t0:.2f}') def restore(self): self.pipeline.unload_lora_weights() self.pipeline = None return self.orig_pipeline