automatic/modules/processing_class.py

from __future__ import annotations

import os
import sys
import inspect
import hashlib
from collections import defaultdict
from typing import Any, TYPE_CHECKING
from dataclasses import dataclass, field
import numpy as np
from PIL import Image, ImageOps
from modules import shared, images, scripts_manager, masking, sd_models, sd_vae, processing_helpers
from modules.logger import log
from modules.paths import resolve_output_path
from modules.image.util import flatten

if TYPE_CHECKING:
    from modules.extra_networks import ExtraNetworkParams


debug = log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None


@dataclass(repr=False)
class StableDiffusionProcessing:
    def __init__(self,
                 sd_model_checkpoint: str | None = None, # # used only to set sd_model
                 sd_model=None, # pylint: disable=unused-argument # local instance of sd_model
                 # base params
                 prompt: str = "",
                 negative_prompt: str = "",
                 seed: int = -1,
                 subseed: int = -1,
                 subseed_strength: float = 0,
                 seed_resize_from_h: int = -1,
                 seed_resize_from_w: int = -1,
                 batch_size: int = 1,
                 n_iter: int = 1,
                 steps: int = 20,
                 clip_skip: int = 1,
                 width: int = 1024,
                 height: int = 1024,
                 # samplers
                 sampler_index: int | None = None, # pylint: disable=unused-argument # used only to set sampler_name
                 sampler_name: str | None = None,
                 hr_sampler_name: str | None = None,
                 eta: float | None = None,
                 # modular guidance
                 guidance_name: str = 'Default',
                 guidance_scale: float = 6.0,
                 guidance_rescale: float = 0.0,
                 guidance_start: float = 0.0,
                 guidance_stop: float = 1.0,
                 # legacy guidance
                 cfg_scale: float = 6.0,
                 cfg_end: float = 1,
                 cfg_rescale: float = 0.0,
                 cfg_true: float = 0.0,
                 cfg_adaptive: float = 0.5,
                 # styles
                 styles: list[str] | None = None,
                 # vae
                 tiling: bool = False,
                 vae_type: str = 'Full',
                 # other
                 hidiffusion: bool = False,
                 do_not_reload_embeddings: bool = False,
                 # detailer
                 detailer_enabled: bool = False,
                 detailer_prompt: str = '',
                 detailer_negative: str = '',
                 detailer_steps: int = 10,
                 detailer_strength: float = 0.3,
                 detailer_resolution: int = 1024,
                 detailer_segmentation: bool | None = None,
                 detailer_include_detections: bool | None = None,
                 detailer_merge: bool | None = None,
                 detailer_sort: bool | None = None,
                 detailer_classes: str | None = None,
                 detailer_conf: float | None = None,
                 detailer_iou: float | None = None,
                 detailer_max: int | None = None,
                 detailer_min_size: float | None = None,
                 detailer_max_size: float | None = None,
                 detailer_blur: int | None = None,
                 detailer_padding: int | None = None,
                 detailer_sigma_adjust: float | None = None,
                 detailer_sigma_adjust_max: float | None = None,
                 detailer_models: list | None = None,
                 detailer_augment: bool | None = None,
                 # img2img and mask
                 img2img_color_correction: bool | None = None,
                 color_correction_method: str | None = None,
                 img2img_background_color: str | None = None,
                 img2img_fix_steps: bool | None = None,
                 mask_apply_overlay: bool | None = None,
                 include_mask: bool | None = None,
                 inpainting_mask_weight: float | None = None,
                 # output and saving
                 samples_save: bool | None = None,
                 samples_format: str | None = None,
                 save_images_before_highres_fix: bool | None = None,
                 save_images_before_refiner: bool | None = None,
                 save_images_before_detailer: bool | None = None,
                 save_images_before_color_correction: bool | None = None,
                 grid_save: bool | None = None,
                 grid_format: str | None = None,
                 return_grid: bool | None = None,
                 keep_incomplete: bool | None = None,
                 image_metadata: bool | None = None,
                 jpeg_quality: int | None = None,
                 # lora behavior
                 lora_fuse_native: bool | None = None,
                 lora_fuse_diffusers: bool | None = None,
                 lora_force_reload: bool | None = None,
                 extra_networks_default_multiplier: float | None = None,
                 lora_apply_tags: int | None = None,
                 # hdr corrections
                 hdr_mode: int = 0,
                 hdr_brightness: float = 0,
                 hdr_color: float = 0,
                 hdr_sharpen: float = 0,
                 hdr_clamp: bool = False,
                 hdr_boundary: float = 4.0,
                 hdr_threshold: float = 0.95,
                 hdr_maximize: bool = False,
                 hdr_max_center: float = 0.6,
                 hdr_max_boundary: float = 1.0,
                 hdr_color_picker: str = "#000000",
                 hdr_tint_ratio: float = 0,
                 hdr_apply_hires: bool = True,
                 # color grading (pixel-space post-processing)
                 grading_brightness: float = 0.0,
                 grading_contrast: float = 0.0,
                 grading_saturation: float = 0.0,
                 grading_hue: float = 0.0,
                 grading_gamma: float = 1.0,
                 grading_sharpness: float = 0.0,
                 grading_color_temp: float = 6500,
                 grading_shadows: float = 0.0,
                 grading_midtones: float = 0.0,
                 grading_highlights: float = 0.0,
                 grading_clahe_clip: float = 0.0,
                 grading_clahe_grid: int = 8,
                 grading_shadows_tint: str = "#000000",
                 grading_highlights_tint: str = "#ffffff",
                 grading_split_tone_balance: float = 0.5,
                 grading_vignette: float = 0.0,
                 grading_grain: float = 0.0,
                 grading_lut_file: str = "",
                 grading_lut_strength: float = 1.0,
                 # img2img
                 denoising_strength: float = 0.3,
                 init_images: list | None = None,
                 init_control: list | None = None,
                 cfg_image: float | None = None,
                 initial_noise_multiplier: float | None = None, # pylint: disable=unused-argument # a1111 compatibility
                 # resize
                 scale_by: float = 1,
                 selected_scale_tab: int = 0, # pylint: disable=unused-argument # a1111 compatibility
                 resize_mode: int = 0,
                 resize_name: str = 'None',
                 resize_context: str = 'None',
                 width_before:int = 0,
                 width_after:int = 0,
                 width_mask:int = 0,
                 height_before:int = 0,
                 height_after:int = 0,
                 height_mask:int = 0,
                 resize_name_before: str = 'None',
                 resize_name_after: str = 'None',
                 resize_name_mask: str = 'None',
                 resize_mode_before: int = 0,
                 resize_mode_after: int = 0,
                 resize_mode_mask: int = 0,
                 resize_context_before: str = 'None',
                 resize_context_after: str = 'None',
                 resize_context_mask: str = 'None',
                 selected_scale_tab_before: int = 0,
                 selected_scale_tab_after: int = 0,
                 selected_scale_tab_mask: int = 0,
                 scale_by_before: float = 1,
                 scale_by_after: float = 1,
                 scale_by_mask: float = 1,
                 # inpaint
                 mask: Any = None,
                 latent_mask: Any = None,
                 mask_for_overlay: Any = None,
                 mask_blur: int = 4,
                 paste_to: Any = None,
                 inpainting_fill: int = 1, # obsolete
                 inpaint_full_res: bool = False,
                 inpaint_full_res_padding: int = 0,
                 inpainting_mask_invert: int = 0,
                 overlay_images: Any = None,
                 # refiner
                 enable_hr: bool = False,
                 firstphase_width: int = 0,
                 firstphase_height: int = 0,
                 hr_scale: float = 2.0,
                 hr_force: bool = False,
                 hr_resize_mode: int = 0,
                 hr_resize_context: str = 'None',
                 hr_second_pass_steps: int = 0,
                 hr_resize_x: int = 0,
                 hr_resize_y: int = 0,
                 hr_denoising_strength: float = 0.0,
                 refiner_steps: int = 5,
                 hr_upscaler: str | None = None,
                 refiner_start: float = 0,
                 refiner_prompt: str = '',
                 refiner_negative: str = '',
                 hr_refiner_start: float = 0,
                 # prompt enhancer
                 enhance_prompt: bool = False,
                 # save options
                 outpath_samples=None,
                 outpath_grids=None,
                 do_not_save_samples: bool = False,
                 do_not_save_grid: bool = False,
                 # xyz flag
                 xyz: bool = False,
                 # scripts
                 script_args: list | None = None,
                 # scheduler/noise overrides
                 schedulers_prediction_type: str | None = None,
                 schedulers_beta_schedule: str | None = None,
                 schedulers_timesteps: str | None = None,
                 schedulers_sigma: str | None = None,
                 schedulers_use_thresholding: bool | None = None,
                 schedulers_use_loworder: bool | None = None,
                 schedulers_solver_order: int | None = None,
                 uni_pc_variant: str | None = None,
                 schedulers_beta_start: float | None = None,
                 schedulers_beta_end: float | None = None,
                 schedulers_shift: float | None = None,
                 schedulers_dynamic_shift: bool | None = None,
                 schedulers_base_shift: float | None = None,
                 schedulers_max_shift: float | None = None,
                 schedulers_rescale_betas: bool | None = None,
                 schedulers_timestep_spacing: str | None = None,
                 schedulers_timesteps_range: int | None = None,
                 schedulers_sigma_adjust: float | None = None,
                 schedulers_sigma_adjust_min: float | None = None,
                 schedulers_sigma_adjust_max: float | None = None,
                 scheduler_eta: float | None = None,
                 eta_noise_seed_delta: int | None = None,
                 enable_batch_seeds: bool | None = None,
                 diffusers_generator_device: str | None = None,
                 nan_skip: bool | None = None,
                 sequential_seed: bool | None = None,
                 # prompt/attention overrides
                 prompt_attention: str | None = None,
                 prompt_mean_norm: bool | None = None,
                 diffusers_zeros_prompt_pad: bool | None = None,
                 te_pooled_embeds: bool | None = None,
                 lora_apply_te: bool | None = None,
                 te_complex_human_instruction: str | None = None,
                 te_use_mask: bool | None = None,
                 # generation modifier overrides (hijack)
                 freeu_enabled: bool | None = None,
                 freeu_b1: float | None = None,
                 freeu_b2: float | None = None,
                 freeu_s1: float | None = None,
                 freeu_s2: float | None = None,
                 hypertile_unet_enabled: bool | None = None,
                 hypertile_hires_only: bool | None = None,
                 hypertile_unet_tile: int | None = None,
                 hypertile_unet_min_tile: int | None = None,
                 hypertile_unet_swap_size: int | None = None,
                 hypertile_unet_depth: int | None = None,
                 hypertile_vae_enabled: bool | None = None,
                 hypertile_vae_tile: int | None = None,
                 hypertile_vae_swap_size: int | None = None,
                 teacache_enabled: bool | None = None,
                 teacache_thresh: float | None = None,
                 token_merging_method: str | None = None,
                 tome_ratio: float | None = None,
                 todo_ratio: float | None = None,
                 # overrides
                 skip_processing: bool = False,
                 override_settings_restore_afterwards: bool = True,
                 override_settings: dict[str, Any] | None = None,
                 network_data: dict | None = None,
                 # metadata
                 # extra_generation_params: Dict[Any, Any] = {},
                 # task_args: Dict[str, Any] = {},
                 # ops: List[str] = [],
                 **kwargs,
                ):

        if override_settings is None:
            override_settings = {}
        if script_args is None:
            script_args = []
        if init_control is None:
            init_control = []
        if init_images is None:
            init_images = []
        if styles is None:
            styles = []
        for k, v in kwargs.items():
            setattr(self, k, v)

        # extra args set by processing loop
        self.task_args = {}
        self.extra_generation_params = {}

        # state items
        self.state: str = ''
        self.ops = []
        self.skip = []
        self.color_corrections = None
        self.is_control = False
        self.is_hr_pass = False
        self.is_refiner_pass = False
        self.is_api = False
        self.scheduled_prompt = False
        self.enhance_prompt = enhance_prompt
        self.prompt_embeds = []
        self.positive_pooleds = []
        self.negative_embeds = []
        self.negative_pooleds = []
        self.prompt_attention_masks = []
        self.negative_prompt_attention_masks = []
        self.disable_extra_networks = False
        self.iteration = 0
        self.network_data: defaultdict[str, list[ExtraNetworkParams]] = defaultdict(list)
        if network_data is not None:
            self.network_data |= network_data

        # initializers
        self.prompt = prompt
        self.seed = int(seed)
        self.subseed = int(subseed)
        self.subseed_strength = subseed_strength
        self.seed_resize_from_h = seed_resize_from_h
        self.seed_resize_from_w = seed_resize_from_w
        self.batch_size = batch_size
        self.n_iter = n_iter
        self.steps = steps
        self.clip_skip = clip_skip
        self.width = width
        self.height = height
        self.negative_prompt = negative_prompt
        self.styles = styles
        self.tiling = tiling
        self.vae_type = vae_type
        self.hidiffusion = hidiffusion
        self.do_not_reload_embeddings = do_not_reload_embeddings
        self.detailer_enabled = detailer_enabled
        self.detailer_prompt = detailer_prompt
        self.detailer_negative = detailer_negative
        self.detailer_steps = detailer_steps
        self.detailer_strength = detailer_strength
        self.detailer_resolution = detailer_resolution
        self.detailer_segmentation = detailer_segmentation
        self.detailer_include_detections = detailer_include_detections
        self.detailer_merge = detailer_merge
        self.detailer_sort = detailer_sort
        self.detailer_classes = detailer_classes
        self.detailer_conf = detailer_conf
        self.detailer_iou = detailer_iou
        self.detailer_max = detailer_max
        self.detailer_min_size = detailer_min_size
        self.detailer_max_size = detailer_max_size
        self.detailer_blur = detailer_blur
        self.detailer_padding = detailer_padding
        self.detailer_sigma_adjust = detailer_sigma_adjust
        self.detailer_sigma_adjust_max = detailer_sigma_adjust_max
        self.detailer_models = detailer_models
        self.detailer_augment = detailer_augment
        self.img2img_color_correction = img2img_color_correction
        self.color_correction_method = color_correction_method
        self.img2img_background_color = img2img_background_color
        self.img2img_fix_steps = img2img_fix_steps
        self.mask_apply_overlay = mask_apply_overlay
        self.include_mask = include_mask
        self.inpainting_mask_weight = inpainting_mask_weight
        self.samples_save = samples_save
        self.samples_format = samples_format
        self.save_images_before_highres_fix = save_images_before_highres_fix
        self.save_images_before_refiner = save_images_before_refiner
        self.save_images_before_detailer = save_images_before_detailer
        self.save_images_before_color_correction = save_images_before_color_correction
        self.grid_save = grid_save
        self.grid_format = grid_format
        self.return_grid = return_grid
        self.keep_incomplete = keep_incomplete
        self.image_metadata = image_metadata
        self.jpeg_quality = jpeg_quality
        self.lora_fuse_native = lora_fuse_native
        self.lora_fuse_diffusers = lora_fuse_diffusers
        self.lora_force_reload = lora_force_reload
        self.extra_networks_default_multiplier = extra_networks_default_multiplier
        self.lora_apply_tags = lora_apply_tags
        self.init_images = init_images
        self.init_control = init_control
        self.resize_mode = resize_mode
        self.resize_name = resize_name
        self.resize_context = resize_context
        self.denoising_strength = denoising_strength
        self.cfg_image = cfg_image
        self.scale_by = scale_by
        self.mask = mask
        self.image_mask = mask # TODO processing: remove duplicate mask params
        self.latent_mask = latent_mask
        self.mask_blur = mask_blur
        self.inpainting_fill = inpainting_fill
        self.inpaint_full_res_padding = inpaint_full_res_padding
        self.inpainting_mask_invert = inpainting_mask_invert
        self.overlay_images = overlay_images
        self.enable_hr = enable_hr
        self.firstphase_width = firstphase_width
        self.firstphase_height = firstphase_height
        # hires
        self.hr_scale = hr_scale
        self.hr_force = hr_force
        self.hr_resize_mode = hr_resize_mode
        self.hr_resize_context = hr_resize_context
        self.hr_upscaler = hr_upscaler
        self.hr_second_pass_steps = hr_second_pass_steps
        self.hr_resize_x = hr_resize_x
        self.hr_resize_y = hr_resize_y
        self.hr_upscale_to_x = hr_resize_x
        self.hr_upscale_to_y = hr_resize_y
        self.hr_denoising_strength = hr_denoising_strength
        # grading
        self.grading_brightness = grading_brightness
        self.grading_contrast = grading_contrast
        self.grading_saturation = grading_saturation
        self.grading_hue = grading_hue
        self.grading_gamma = grading_gamma
        self.grading_sharpness = grading_sharpness
        self.grading_color_temp = grading_color_temp
        self.grading_shadows = grading_shadows
        self.grading_midtones = grading_midtones
        self.grading_highlights = grading_highlights
        self.grading_clahe_clip = grading_clahe_clip
        self.grading_clahe_grid = grading_clahe_grid
        self.grading_shadows_tint = grading_shadows_tint
        self.grading_highlights_tint = grading_highlights_tint
        self.grading_split_tone_balance = grading_split_tone_balance
        self.grading_vignette = grading_vignette
        self.grading_grain = grading_grain
        self.grading_lut_file = grading_lut_file
        self.grading_lut_strength = grading_lut_strength
        # refiner
        self.refiner_steps = refiner_steps
        self.refiner_start = refiner_start
        self.refiner_prompt = refiner_prompt
        self.refiner_negative = refiner_negative
        self.hr_refiner_start = hr_refiner_start
        # other
        self.outpath_samples = outpath_samples
        self.outpath_grids = outpath_grids
        self.do_not_save_samples = do_not_save_samples
        self.do_not_save_grid = do_not_save_grid
        self.override_settings_restore_afterwards = override_settings_restore_afterwards
        self.eta = eta
        self.guidance_name = guidance_name
        self.guidance_scale = guidance_scale
        self.guidance_rescale = guidance_rescale
        self.guidance_start = guidance_start
        self.guidance_stop = guidance_stop
        self.cfg_scale = cfg_scale
        self.cfg_end = cfg_end
        self.cfg_rescale = cfg_rescale
        self.cfg_true = cfg_true
        self.cfg_adaptive = cfg_adaptive
        self.selected_scale_tab = selected_scale_tab
        self.mask_for_overlay = mask_for_overlay
        self.paste_to = paste_to
        self.init_latent = None
        self.width_before = width_before
        self.width_after = width_after
        self.width_mask = width_mask
        self.height_before = height_before
        self.height_after = height_after
        self.height_mask = height_mask
        self.resize_name_before = resize_name_before
        self.resize_name_after = resize_name_after
        self.resize_name_mask = resize_name_mask
        self.resize_mode_before = resize_mode_before
        self.resize_mode_after = resize_mode_after
        self.resize_mode_mask = resize_mode_mask
        self.resize_context_before = resize_context_before
        self.resize_context_after = resize_context_after
        self.resize_context_mask = resize_context_mask
        self.selected_scale_tab_before = selected_scale_tab_before
        self.selected_scale_tab_after = selected_scale_tab_after
        self.selected_scale_tab_mask = selected_scale_tab_mask
        self.scale_by_before = scale_by_before
        self.scale_by_after = scale_by_after
        self.scale_by_mask = scale_by_mask
        self.skip_processing = skip_processing

        # special handled items
        if firstphase_width != 0 or firstphase_height != 0:
            self.hr_upscale_to_x = self.width
            self.hr_upscale_to_y = self.height
            self.width = firstphase_width
            self.height = firstphase_height
        self.sampler_name = sampler_name or processing_helpers.get_sampler_name(sampler_index, img=True)
        self.hr_sampler_name: str = hr_sampler_name if hr_sampler_name != 'Same as primary' else self.sampler_name
        self.inpaint_full_res = inpaint_full_res if isinstance(inpaint_full_res, bool) else self.inpaint_full_res
        self.inpaint_full_res = inpaint_full_res != 0 if isinstance(inpaint_full_res, int) else self.inpaint_full_res
        try:
            self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
        except Exception as e:
            log.error(f'Override: {override_settings} {e}')
            self.override_settings = {}

        # scheduler/noise overrides
        self.schedulers_prediction_type = schedulers_prediction_type
        self.schedulers_beta_schedule = schedulers_beta_schedule
        self.schedulers_timesteps = schedulers_timesteps
        self.schedulers_sigma = schedulers_sigma
        self.schedulers_use_thresholding = schedulers_use_thresholding
        self.schedulers_use_loworder = schedulers_use_loworder
        self.schedulers_solver_order = schedulers_solver_order
        self.uni_pc_variant = uni_pc_variant
        self.schedulers_beta_start = schedulers_beta_start
        self.schedulers_beta_end = schedulers_beta_end
        self.schedulers_shift = schedulers_shift
        self.schedulers_dynamic_shift = schedulers_dynamic_shift
        self.schedulers_base_shift = schedulers_base_shift
        self.schedulers_max_shift = schedulers_max_shift
        self.schedulers_rescale_betas = schedulers_rescale_betas
        self.schedulers_timestep_spacing = schedulers_timestep_spacing
        self.schedulers_timesteps_range = schedulers_timesteps_range
        self.schedulers_sigma_adjust = schedulers_sigma_adjust
        self.schedulers_sigma_adjust_min = schedulers_sigma_adjust_min
        self.schedulers_sigma_adjust_max = schedulers_sigma_adjust_max
        self.scheduler_eta = scheduler_eta
        self.eta_noise_seed_delta = eta_noise_seed_delta
        self.enable_batch_seeds = enable_batch_seeds
        self.diffusers_generator_device = diffusers_generator_device
        self.nan_skip = nan_skip
        self.sequential_seed = sequential_seed
        # prompt/attention overrides
        self.prompt_attention = prompt_attention
        self.prompt_mean_norm = prompt_mean_norm
        self.diffusers_zeros_prompt_pad = diffusers_zeros_prompt_pad
        self.te_pooled_embeds = te_pooled_embeds
        self.lora_apply_te = lora_apply_te
        self.te_complex_human_instruction = te_complex_human_instruction
        self.te_use_mask = te_use_mask
        # generation modifier overrides (hijack)
        self.freeu_enabled = freeu_enabled
        self.freeu_b1 = freeu_b1
        self.freeu_b2 = freeu_b2
        self.freeu_s1 = freeu_s1
        self.freeu_s2 = freeu_s2
        self.hypertile_unet_enabled = hypertile_unet_enabled
        self.hypertile_hires_only = hypertile_hires_only
        self.hypertile_unet_tile = hypertile_unet_tile
        self.hypertile_unet_min_tile = hypertile_unet_min_tile
        self.hypertile_unet_swap_size = hypertile_unet_swap_size
        self.hypertile_unet_depth = hypertile_unet_depth
        self.hypertile_vae_enabled = hypertile_vae_enabled
        self.hypertile_vae_tile = hypertile_vae_tile
        self.hypertile_vae_swap_size = hypertile_vae_swap_size
        self.teacache_enabled = teacache_enabled
        self.teacache_thresh = teacache_thresh
        self.token_merging_method = token_merging_method
        self.tome_ratio = tome_ratio
        self.todo_ratio = todo_ratio

        self.prompts = []
        self.negative_prompts = []
        self.all_prompts = []
        self.all_negative_prompts = []
        self.all_templates = []
        self.all_negative_templates = []
        self.seeds = []
        self.subseeds = []
        self.all_seeds = []
        self.all_subseeds = []

        # a1111 compatibility items
        self.seed_enable_extras: bool = True
        self.is_using_inpainting_conditioning = False # a111 compatibility
        self.batch_index = 0
        self.refiner_switch_at = 0
        self.hr_prompt = ''
        self.all_hr_prompts = []
        self.hr_negative_prompt = ''
        self.all_hr_negative_prompts = []
        self.comments = {}
        self.sampler = None
        self.nmask = None
        self.initial_noise_multiplier = initial_noise_multiplier if initial_noise_multiplier is not None else shared.opts.initial_noise_multiplier
        self.image_conditioning = None
        self.prompt_for_display: str = None

        # scripts
        self.scripts_value: scripts_manager.ScriptRunner = field(default=None, init=False)
        self.script_args_value: list = field(default=None, init=False)
        self.scripts_setup_complete: bool = field(default=False, init=False)
        self.script_args = script_args
        self.per_script_args = {}

        # ip adapter
        self.ip_adapter_names = []
        self.ip_adapter_scales = [0.0]
        self.ip_adapter_images = []
        self.ip_adapter_starts = [0.0]
        self.ip_adapter_ends = [1.0]
        self.ip_adapter_crops = []

        # hdr
        self.hdr_mode=hdr_mode
        self.hdr_brightness=hdr_brightness
        self.hdr_color=hdr_color
        self.hdr_sharpen=hdr_sharpen
        self.hdr_clamp=hdr_clamp
        self.hdr_boundary=hdr_boundary
        self.hdr_threshold=hdr_threshold
        self.hdr_maximize=hdr_maximize
        self.hdr_max_center=hdr_max_center
        self.hdr_max_boundary=hdr_max_boundary
        self.hdr_color_picker=hdr_color_picker
        self.hdr_tint_ratio=hdr_tint_ratio
        self.hdr_apply_hires=hdr_apply_hires

        # globals
        self.embedder = None
        self.override = None
        self.scheduled_prompt: bool = False
        self.prompt_embeds = []
        self.positive_pooleds = []
        self.negative_embeds = []
        self.negative_pooleds = []
        self.prompt_attention_masks = []
        self.negative_prompt_attention_masks = []
        self.xyz = xyz
        self.abort = False

        # set model
        if sd_model_checkpoint is not None and len(sd_model_checkpoint) > 0:
            from modules import sd_checkpoint
            if sd_checkpoint.select_checkpoint(op='model', sd_model_checkpoint=sd_model_checkpoint) is None:
                log.error(f'Processing: model="{sd_model_checkpoint}" not found')
                self.abort = True
            else:
                shared.opts.sd_model_checkpoint = sd_model_checkpoint
                sd_models.reload_model_weights()

    def __repr__(self):
        return f'{self.__class__.__name__}({", ".join([f"{k}={v}" for k, v in self.__dict__.items() if k not in ["scripts_value", "script_args_value"]])})'

    @property
    def sd_model(self):
        return shared.sd_model

    @property
    def scripts(self):
        return self.scripts_value

    @scripts.setter
    def scripts(self, value):
        self.scripts_value = value
        if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
            self.setup_scripts()

    @property
    def script_args(self):
        return self.script_args_value

    @script_args.setter
    def script_args(self, value):
        self.script_args_value = value
        if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
            self.setup_scripts()

    def setup_scripts(self):
        self.scripts_setup_complete = True
        self.scripts.setup_scripts()

    def comment(self, text):
        self.comments[text] = 1

    def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
        pass

    def close(self):
        self.sampler = None
        self.scripts = None


class StableDiffusionProcessingVideo(StableDiffusionProcessing):
    def __init__(self, **kwargs):
        self.prompt_template: str = None
        self.frames: int = kwargs.pop('frames', 1)
        self.vae_tile_frames: int = kwargs.pop('vae_tile_frames', 0)
        self.video_engine: str = kwargs.pop('video_engine', None)
        self.video_model: str = kwargs.pop('video_model', None)
        self.video_interpolate: int = kwargs.pop('video_interpolate', 0)
        self.video_interpolate_scale: float = kwargs.pop('video_interpolate_scale', 1.0)
        self.video_interpolated: bool = False
        self.scheduler_shift: float = 0.0
        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
        super().__init__(**kwargs)

class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
    def __init__(self, **kwargs):
        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
        super().__init__(**kwargs)

    def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
        shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
        self.width = self.width or 1024
        self.height = self.height or 1024
        if all_prompts is not None:
            self.all_prompts = all_prompts
        if all_seeds is not None:
            self.all_seeds = all_seeds
        if all_subseeds is not None:
            self.all_subseeds = all_subseeds

    def init_hr(self, scale = None, upscaler = None, force = False): # pylint: disable=unused-argument
        scale = scale or self.hr_scale
        upscaler = upscaler or self.hr_upscaler
        if self.hr_resize_x == 0 and self.hr_resize_y == 0:
            self.hr_upscale_to_x = int(self.width * scale)
            self.hr_upscale_to_y = int(self.height * scale)
        else:
            if self.hr_resize_y == 0:
                self.hr_upscale_to_x = int(self.hr_resize_x)
                self.hr_upscale_to_y = int(self.hr_resize_x * self.height // self.width)
            elif self.hr_resize_x == 0:
                self.hr_upscale_to_x = int(self.hr_resize_y * self.width // self.height)
                self.hr_upscale_to_y = int(self.hr_resize_y)
            elif self.hr_resize_x > 0 and self.hr_resize_y > 0:
                self.hr_upscale_to_x = int(self.hr_resize_x)
                self.hr_upscale_to_y = int(self.hr_resize_y)
        log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.hr_sampler_name}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')


class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
    def __init__(self, **kwargs):
        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
        super().__init__(**kwargs)

    def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
        if self.init_images is not None and len(self.init_images) > 0:
            vae_scale_factor = sd_vae.get_vae_scale_factor()
            if self.width is None or self.width == 0:
                self.width = int(vae_scale_factor * (self.init_images[0].width * self.scale_by // vae_scale_factor))
            if self.height is None or self.height == 0:
                self.height = int(vae_scale_factor * (self.init_images[0].height * self.scale_by // vae_scale_factor))
        if (getattr(self, 'image_mask', None) is not None) and ((len(self.image_mask) > 0) if isinstance(self.image_mask, list) else True):
            shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
        elif (getattr(self, 'init_images', None) is not None) and ((len(self.init_images) > 0) if isinstance(self.init_images, list) else True):
            shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)

        if all_prompts is not None:
            self.all_prompts = all_prompts
        if all_seeds is not None:
            self.all_seeds = all_seeds
        if all_subseeds is not None:
            self.all_subseeds = all_subseeds
        if self.image_mask is not None:
            self.ops.append('inpaint')
        elif self.init_images is not None and len(self.init_images) > 0:
            self.ops.append('img2img')
        crop_region = None

        if type(self.image_mask) == list:
            self.image_mask = self.image_mask[0]
        if 'Control' in self.__class__.__name__:
            self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, invert=self.inpainting_mask_invert==1) # blur/padding are handled in masking module
        elif self.image_mask is not None:
            self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, invert=self.inpainting_mask_invert==1, mask_blur=self.mask_blur, mask_padding=self.inpaint_full_res_padding) # old img2img
        if self.inpaint_full_res and self.image_mask is not None: # mask only inpaint
            self.mask_for_overlay = self.image_mask
            mask = self.image_mask.convert('L')
            crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
            crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
            x1, y1, x2, y2 = crop_region
            crop_mask = mask.crop(crop_region)
            self.image_mask = images.resize_image(resize_mode=2, im=crop_mask, width=self.width, height=self.height)
            self.paste_to = (x1, y1, x2-x1, y2-y1)
        elif self.image_mask is not None: # full image inpaint
            self.image_mask = images.resize_image(resize_mode=self.resize_mode, im=self.image_mask, width=self.width, height=self.height)
            np_mask = np.array(self.image_mask)
            np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
            self.mask_for_overlay = Image.fromarray(np_mask)
        self.overlay_images = []

        _cc = self.img2img_color_correction if self.img2img_color_correction is not None else shared.opts.img2img_color_correction
        add_color_corrections = _cc and self.color_corrections is None
        if add_color_corrections:
            self.color_corrections = []
        processed_images = []
        if self.init_images is None:
            return
        if not isinstance(self.init_images, list):
            self.init_images = [self.init_images]
        for img in self.init_images:
            if img is None:
                continue
            self.init_img_hash = getattr(self, 'init_img_hash', hashlib.sha256(img.tobytes()).hexdigest()[0:8]) # pylint: disable=attribute-defined-outside-init
            self.init_img_width = getattr(self, 'init_img_width', img.width) # pylint: disable=attribute-defined-outside-init
            self.init_img_height = getattr(self, 'init_img_height', img.height) # pylint: disable=attribute-defined-outside-init
            if shared.opts.save_init_img:
                images.save_image(img, path=resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_init_images), basename=None, forced_filename=self.init_img_hash, suffix="-init-image")
            image = flatten(img, self.img2img_background_color if self.img2img_background_color is not None else shared.opts.img2img_background_color)
            if crop_region is None and self.resize_mode > 0:
                image = images.resize_image(self.resize_mode, image, self.width, self.height, upscaler_name=self.resize_name, context=self.resize_context)
                self.width = image.width
                self.height = image.height
            _overlay = self.mask_apply_overlay if self.mask_apply_overlay is not None else shared.opts.mask_apply_overlay
            if self.image_mask is not None and _overlay:
                image_masked = Image.new('RGBa', (image.width, image.height))
                image_to_paste = image.convert("RGBA").convert("RGBa")
                image_to_mask = ImageOps.invert(self.mask_for_overlay.convert('L')) if self.mask_for_overlay is not None else None
                image_to_mask = image_to_mask.resize((image.width, image.height), Image.Resampling.BILINEAR) if image_to_mask is not None else None
                image_masked.paste(image_to_paste, mask=image_to_mask)
                image_masked = image_masked.convert('RGBA')
                self.overlay_images.append(image_masked)
            if crop_region is not None: # crop_region is not None if we are doing inpaint full res
                image = image.crop(crop_region)
                if image.width != self.width or image.height != self.height:
                    image = images.resize_image(3, image, self.width, self.height, self.resize_name)
            # if self.image_mask is not None and self.inpainting_fill != 1:
            #     image = masking.fill(image, latent_mask)
            if add_color_corrections:
                self.color_corrections.append(processing_helpers.setup_color_correction(image))
            processed_images.append(image)
        self.init_images = processed_images
        # self.batch_size = len(self.init_images)
        if self.overlay_images is not None and len(self.overlay_images) > 0:
            self.overlay_images = self.overlay_images * self.batch_size
        if self.color_corrections is not None and len(self.color_corrections) == 1:
            self.color_corrections = self.color_corrections * self.batch_size


class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
    def __init__(self, **kwargs):
        debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
        super().__init__(**kwargs)

    def init_hr(self, scale: float | None = None, upscaler: str | None = None, force = False):
        scale = scale or self.scale_by or self.scale_by_before
        upscaler = upscaler or self.hr_upscaler or self.resize_name or self.resize_name_before
        if upscaler is None:
            upscaler = 'None'
        # self.hr_upscaler = upscaler or 'None'
        use_scale = self.hr_resize_x == 0 or self.hr_resize_y == 0
        if upscaler == 'None' or (use_scale and scale == 1.0):
            return
        self.is_hr_pass = True
        self.hr_force = force
        if use_scale:
            vae_scale_factor = sd_vae.get_vae_scale_factor()
            self.hr_upscale_to_x, self.hr_upscale_to_y = int(vae_scale_factor * int(self.width * scale / vae_scale_factor)), int(vae_scale_factor * int(self.height * scale / vae_scale_factor))
        else:
            self.hr_upscale_to_x, self.hr_upscale_to_y = int(self.hr_resize_x), int(self.hr_resize_y)


def switch_class(p: StableDiffusionProcessing, new_class: type, dct: dict | None = None):
    kwargs = {}
    signature = inspect.signature(StableDiffusionProcessing.__init__, follow_wrapped=True) # base class
    possible = list(signature.parameters)
    for k, v in p.__dict__.copy().items():
        if k in possible:
            kwargs[k] = v
    signature = inspect.signature(type(new_class).__init__, follow_wrapped=True) # target class
    possible = list(signature.parameters)
    for k, v in p.__dict__.copy().items():
        if k in possible:
            kwargs[k] = v
    if dct is not None: # overrides
        for k, v in dct.items():
            if k in possible:
                kwargs[k] = v
    if new_class == StableDiffusionProcessingTxt2Img:
        sd_models.clean_diffuser_pipe(shared.sd_model)
    fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
    debug(f"Switching class: {p.__class__.__name__} -> {new_class.__name__} fn={fn}") # pylint: disable=protected-access
    p.__class__ = new_class
    p.__init__(**kwargs)
    for k, v in p.__dict__.items():
        if hasattr(p, k):
            setattr(p, k, v)
    if dct is not None: # post init set additional values
        for k, v in dct.items():
            if hasattr(p, k):
                valtype = type(getattr(p, k, None))
                if valtype in [int, float, str]:
                    setattr(p, k, valtype(v))
                else:
                    setattr(p, k, v)
    return p