from typing import List, Any, Optional, Union, Tuple, Dict from modules import scripts, processing, shared from scripts.controlnet import ResizeMode, update_cn_models, cn_models_names, PARAM_COUNT import numpy as np """ Resize modes for ControlNet input images. """ ResizeMode = ResizeMode class ControlNetUnit: """ Represents an entire ControlNet processing unit. """ def __init__( self, enabled: bool=True, module: Optional[str]=None, model: Optional[str]=None, weight: float=1.0, image: Optional[Union[Dict[str, np.ndarray], Tuple[np.ndarray, np.ndarray], np.ndarray]]=None, invert_image: bool=False, resize_mode: Union[ResizeMode, int, str]=ResizeMode.INNER_FIT, rgbbgr_mode: bool=False, low_vram: bool=False, processor_res: int=64, threshold_a: float=64, threshold_b: float=64, guidance_start: float=0.0, guidance_end: float=1.0, guess_mode: bool=True, ): if image is not None: if isinstance(image, tuple): image = {'image': image[0], 'mask': image[1]} elif isinstance(image, np.ndarray): image = {'image': image, 'mask': np.zeros_like(image, dtype=np.uint8)} while len(image['mask'].shape) < 3: image['mask'] = image['mask'][..., np.newaxis] self.enabled = enabled self.module = module self.model = model self.weight = weight self.image = image self.invert_image = invert_image self.resize_mode = resize_mode self.rgbbgr_mode = rgbbgr_mode self.low_vram = low_vram self.processor_res = processor_res self.threshold_a = threshold_a self.threshold_b = threshold_b self.guidance_start = guidance_start self.guidance_end = guidance_end self.guess_mode = guess_mode def get_all_units_in_processing(p: processing.StableDiffusionProcessing) -> List[ControlNetUnit]: """ Fetch ControlNet processing units from a StableDiffusionProcessing. """ return get_all_units(p.scripts, p.script_args) def get_all_units(script_runner: scripts.ScriptRunner, script_args: List[Any]) -> List[ControlNetUnit]: """ Fetch ControlNet processing units from an existing script runner. Use this function to fetch units from the list of all scripts arguments. """ cn_script = find_cn_script(script_runner) if cn_script: return get_all_units_from(script_args[cn_script.args_from:cn_script.args_to]) return [] def get_all_units_from(script_args: List[Any], strip_positional_args=True) -> List[ControlNetUnit]: """ Fetch ControlNet processing units from ControlNet script arguments. Use `external_code.get_all_units` to fetch units from the list of all scripts arguments. Keyword arguments: strip_positional_args -- Whether positional arguments are present in `script_args`. (default True) """ if strip_positional_args: script_args = script_args[2:] res = [] for i in range(len(script_args) // PARAM_COUNT): res.append(get_single_unit_from(script_args, i)) return res def get_single_unit_from(script_args: List[Any], index: int=0) -> ControlNetUnit: """ Fetch a single ControlNet processing unit from ControlNet script arguments. The list must not contain script positional arguments. It must only consist of flattened processing unit parameters. """ index_from = index * PARAM_COUNT index_to = index_from + PARAM_COUNT return ControlNetUnit(*script_args[index_from:index_to]) def update_cn_script_in_processing( p: processing.StableDiffusionProcessing, cn_units: List[ControlNetUnit], is_img2img: Optional[bool] = None, is_ui: Optional[bool] = None ): """ Update the arguments of the ControlNet script in `p.script_args` in place, reading from `cn_units`. `cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want. Does not update `p.script_args` if any of the folling is true: - ControlNet is not present in `p.scripts` - `p.script_args` is not filled with script arguments for scripts that are processed before ControlNet Keyword arguments: is_img2img -- whether to run the script as img2img. In general, this should be set to the appropriate value depending on the `StableDiffusionProcessing` subclass used for generating. If set to None, do not change existing value. (default None) is_ui -- whether to run the script as if from the gradio interface. If set to None, do not change existing value. (default None) """ cn_units_type = type(cn_units) if type(cn_units) in (list, tuple) else list script_args = list(p.script_args) update_cn_script_in_place(p.scripts, script_args, cn_units, is_img2img, is_ui) p.script_args = cn_units_type(script_args) def update_cn_script_in_place( script_runner: scripts.ScriptRunner, script_args: List[Any], cn_units: List[ControlNetUnit], is_img2img: Optional[bool] = None, is_ui: Optional[bool] = None, ): """ Update the arguments of the ControlNet script in `script_args` in place, reading from `cn_units`. `cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want. Does not update `script_args` if any of the folling is true: - ControlNet is not present in `script_runner` - `script_args` is not filled with script arguments for scripts that are processed before ControlNet Keyword arguments: is_img2img -- whether to run the script as img2img. In general, this should be set to the appropriate value depending on the `StableDiffusionProcessing` subclass used for generating. If set to None, do not change existing value. (default None) is_ui -- whether to run the script as if from the gradio interface. If set to None, do not change existing value. (default None) """ cn_script = find_cn_script(script_runner) if cn_script is None or len(script_args) < cn_script.args_from: return cn_script_has_args = len(script_args[cn_script.args_from:cn_script.args_to]) > 0 if is_img2img is None: is_img2img = script_args[cn_script.args_from] if cn_script_has_args else False if is_ui is None: is_ui = script_args[cn_script.args_from + 1] if cn_script_has_args else False # fill in remaining parameters to satisfy max models, just in case script needs it. max_models = shared.opts.data.get("control_net_max_models_num", 1) cn_units = cn_units + [ControlNetUnit(enabled=False)] * max(max_models - len(cn_units), 0) flattened_cn_args: List[Any] = [is_img2img, is_ui] for unit in cn_units: flattened_cn_args.extend(( unit.enabled, unit.module if unit.module is not None else "none", unit.model if unit.model is not None else "None", unit.weight, unit.image, unit.invert_image, unit.resize_mode, unit.rgbbgr_mode, unit.low_vram, unit.processor_res, unit.threshold_a, unit.threshold_b, unit.guidance_start, unit.guidance_end, unit.guess_mode)) cn_script_args_diff = 0 for script in script_runner.alwayson_scripts: if script is cn_script: cn_script_args_diff = len(flattened_cn_args) - (cn_script.args_to - cn_script.args_from) script_args[script.args_from:script.args_to] = flattened_cn_args script.args_to = script.args_from + len(flattened_cn_args) else: script.args_from += cn_script_args_diff script.args_to += cn_script_args_diff def get_models(update: bool=False) -> List[str]: """ Fetch the list of available models. Each value is a valid candidate of `ControlNetUnit.model`. Keyword arguments: update -- Whether to refresh the list from disk. (default False) """ if update: update_cn_models() return list(cn_models_names.values()) def find_cn_script(script_runner: scripts.ScriptRunner) -> Optional[scripts.Script]: """ Find the ControlNet script in `script_runner`. Returns `None` if `script_runner` does not contain a ControlNet script. """ for script in script_runner.alwayson_scripts: if is_cn_script(script): return script def is_cn_script(script: scripts.Script) -> bool: """ Determine whether `script` is a ControlNet script. """ return script.title().lower() == 'controlnet'