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