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