Loading modules/processing.py +172 −146 Original line number Diff line number Diff line from __future__ import annotations import json import logging import math import os import sys import hashlib from dataclasses import dataclass, field import torch import numpy as np Loading @@ -11,7 +13,7 @@ from PIL import Image, ImageOps import random import cv2 from skimage import exposure from typing import Any, Dict, List from typing import Any import modules.sd_hijack from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng Loading Loading @@ -104,106 +106,126 @@ def txt2img_image_conditioning(sd_model, x, width, height): return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) @dataclass(repr=False) class StableDiffusionProcessing: """ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing """ sd_model: object = None outpath_samples: str = None outpath_grids: str = None prompt: str = "" prompt_for_display: str = None negative_prompt: str = "" styles: list[str] = field(default_factory=list) seed: int = -1 subseed: int = -1 subseed_strength: float = 0 seed_resize_from_h: int = -1 seed_resize_from_w: int = -1 seed_enable_extras: bool = True sampler_name: str = None batch_size: int = 1 n_iter: int = 1 steps: int = 50 cfg_scale: float = 7.0 width: int = 512 height: int = 512 restore_faces: bool = None tiling: bool = None do_not_save_samples: bool = False do_not_save_grid: bool = False extra_generation_params: dict[str, Any] = None overlay_images: list = None eta: float = None do_not_reload_embeddings: bool = False denoising_strength: float = 0 ddim_discretize: str = None s_min_uncond: float = None s_churn: float = None s_tmax: float = None s_tmin: float = None s_noise: float = None override_settings: dict[str, Any] = None override_settings_restore_afterwards: bool = True sampler_index: int = None refiner_checkpoint: str = None refiner_switch_at: float = None token_merging_ratio = 0 token_merging_ratio_hr = 0 disable_extra_networks: bool = False script_args: list = None cached_uc = [None, None] cached_c = [None, None] def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, refiner_checkpoint: str = None, refiner_switch_at: float = None, script_args: list = None): if sampler_index is not None: sampler: sd_samplers_common.Sampler | None = field(default=None, init=False) is_using_inpainting_conditioning: bool = field(default=False, init=False) paste_to: tuple | None = field(default=None, init=False) is_hr_pass: bool = field(default=False, init=False) c: tuple = field(default=None, init=False) uc: tuple = field(default=None, init=False) rng: rng.ImageRNG | None = field(default=None, init=False) step_multiplier: int = field(default=1, init=False) color_corrections: list = field(default=None, init=False) scripts: list = field(default=None, init=False) all_prompts: list = field(default=None, init=False) all_negative_prompts: list = field(default=None, init=False) all_seeds: list = field(default=None, init=False) all_subseeds: list = field(default=None, init=False) iteration: int = field(default=0, init=False) main_prompt: str = field(default=None, init=False) main_negative_prompt: str = field(default=None, init=False) prompts: list = field(default=None, init=False) negative_prompts: list = field(default=None, init=False) seeds: list = field(default=None, init=False) subseeds: list = field(default=None, init=False) extra_network_data: dict = field(default=None, init=False) user: str = field(default=None, init=False) sd_model_name: str = field(default=None, init=False) sd_model_hash: str = field(default=None, init=False) sd_vae_name: str = field(default=None, init=False) sd_vae_hash: str = field(default=None, init=False) def __post_init__(self): if self.sampler_index is not None: print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr) self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids self.prompt: str = prompt self.prompt_for_display: str = None self.negative_prompt: str = (negative_prompt or "") self.styles: list = styles or [] self.seed: int = seed self.subseed: int = subseed self.subseed_strength: float = subseed_strength self.seed_resize_from_h: int = seed_resize_from_h self.seed_resize_from_w: int = seed_resize_from_w self.sampler_name: str = sampler_name self.batch_size: int = batch_size self.n_iter: int = n_iter self.steps: int = steps self.cfg_scale: float = cfg_scale self.width: int = width self.height: int = height self.restore_faces: bool = restore_faces self.tiling: bool = tiling self.do_not_save_samples: bool = do_not_save_samples self.do_not_save_grid: bool = do_not_save_grid self.extra_generation_params: dict = extra_generation_params or {} self.overlay_images = overlay_images self.eta = eta self.do_not_reload_embeddings = do_not_reload_embeddings self.paste_to = None self.color_corrections = None self.denoising_strength: float = denoising_strength self.sampler_noise_scheduler_override = None self.ddim_discretize = ddim_discretize or opts.ddim_discretize self.s_min_uncond = s_min_uncond or opts.s_min_uncond self.s_churn = s_churn or opts.s_churn self.s_tmin = s_tmin or opts.s_tmin self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf') self.s_noise = s_noise if s_noise is not None else opts.s_noise self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts} self.override_settings_restore_afterwards = override_settings_restore_afterwards self.refiner_checkpoint = refiner_checkpoint self.refiner_switch_at = refiner_switch_at self.is_using_inpainting_conditioning = False self.disable_extra_networks = False self.token_merging_ratio = 0 self.token_merging_ratio_hr = 0 self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf') self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise self.extra_generation_params = self.extra_generation_params or {} self.override_settings = self.override_settings or {} self.script_args = self.script_args or {} self.refiner_checkpoint_info = None if not seed_enable_extras: if not self.seed_enable_extras: self.subseed = -1 self.subseed_strength = 0 self.seed_resize_from_h = 0 self.seed_resize_from_w = 0 self.scripts = None self.script_args = script_args self.all_prompts = None self.all_negative_prompts = None self.all_seeds = None self.all_subseeds = None self.iteration = 0 self.is_hr_pass = False self.sampler = None self.main_prompt = None self.main_negative_prompt = None self.prompts = None self.negative_prompts = None self.extra_network_data = None self.seeds = None self.subseeds = None self.step_multiplier = 1 self.cached_uc = StableDiffusionProcessing.cached_uc self.cached_c = StableDiffusionProcessing.cached_c self.uc = None self.c = None self.rng: rng.ImageRNG = None self.user = None self.sd_model_name = None self.sd_model_hash = None self.sd_vae_name = None self.sd_vae_hash = None @property def sd_model(self): return shared.sd_model @sd_model.setter def sd_model(self, value): pass def txt2img_image_conditioning(self, x, width=None, height=None): self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'} Loading Loading @@ -932,49 +954,51 @@ def old_hires_fix_first_pass_dimensions(width, height): return width, height @dataclass(repr=False) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None enable_hr: bool = False denoising_strength: float = 0.75 firstphase_width: int = 0 firstphase_height: int = 0 hr_scale: float = 2.0 hr_upscaler: str = None hr_second_pass_steps: int = 0 hr_resize_x: int = 0 hr_resize_y: int = 0 hr_checkpoint_name: str = None hr_sampler_name: str = None hr_prompt: str = '' hr_negative_prompt: str = '' cached_hr_uc = [None, None] cached_hr_c = [None, None] def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.hr_scale = hr_scale 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_checkpoint_name = hr_checkpoint_name self.hr_checkpoint_info = None self.hr_sampler_name = hr_sampler_name self.hr_prompt = hr_prompt self.hr_negative_prompt = hr_negative_prompt self.all_hr_prompts = None self.all_hr_negative_prompts = None self.latent_scale_mode = None if firstphase_width != 0 or firstphase_height != 0: hr_checkpoint_info: dict = field(default=None, init=False) hr_upscale_to_x: int = field(default=0, init=False) hr_upscale_to_y: int = field(default=0, init=False) truncate_x: int = field(default=0, init=False) truncate_y: int = field(default=0, init=False) applied_old_hires_behavior_to: tuple = field(default=None, init=False) latent_scale_mode: dict = field(default=None, init=False) hr_c: tuple | None = field(default=None, init=False) hr_uc: tuple | None = field(default=None, init=False) all_hr_prompts: list = field(default=None, init=False) all_hr_negative_prompts: list = field(default=None, init=False) hr_prompts: list = field(default=None, init=False) hr_negative_prompts: list = field(default=None, init=False) hr_extra_network_data: list = field(default=None, init=False) def __post_init__(self): super().__post_init__() if self.firstphase_width != 0 or self.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.truncate_x = 0 self.truncate_y = 0 self.applied_old_hires_behavior_to = None self.hr_prompts = None self.hr_negative_prompts = None self.hr_extra_network_data = None self.width = self.firstphase_width self.height = self.firstphase_height self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c self.hr_c = None self.hr_uc = None def calculate_target_resolution(self): if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): Loading Loading @@ -1252,7 +1276,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return super().get_conds() def parse_extra_network_prompts(self): res = super().parse_extra_network_prompts() Loading @@ -1265,32 +1288,37 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return res @dataclass(repr=False) class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images self.resize_mode: int = resize_mode self.denoising_strength: float = denoising_strength self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None self.init_latent = None self.image_mask = mask self.latent_mask = None self.mask_for_overlay = None self.mask_blur_x = mask_blur_x self.mask_blur_y = mask_blur_y if mask_blur is not None: self.mask_blur = mask_blur self.inpainting_fill = inpainting_fill self.inpaint_full_res = inpaint_full_res self.inpaint_full_res_padding = inpaint_full_res_padding self.inpainting_mask_invert = inpainting_mask_invert self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier init_images: list = None resize_mode: int = 0 denoising_strength: float = 0.75 image_cfg_scale: float = None mask: Any = None mask_blur_x: int = 4 mask_blur_y: int = 4 mask_blur: int = None inpainting_fill: int = 0 inpaint_full_res: bool = True inpaint_full_res_padding: int = 0 inpainting_mask_invert: int = 0 initial_noise_multiplier: float = None latent_mask: Image = None image_mask: Any = field(default=None, init=False) nmask: torch.Tensor = field(default=None, init=False) image_conditioning: torch.Tensor = field(default=None, init=False) init_img_hash: str = field(default=None, init=False) mask_for_overlay: Image = field(default=None, init=False) init_latent: torch.Tensor = field(default=None, init=False) def __post_init__(self): super().__post_init__() self.image_mask = self.mask self.mask = None self.nmask = None self.image_conditioning = None self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier @property def mask_blur(self): Loading @@ -1300,15 +1328,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): @mask_blur.setter def mask_blur(self, value): if isinstance(value, int): self.mask_blur_x = value self.mask_blur_y = value @mask_blur.deleter def mask_blur(self): del self.mask_blur_x del self.mask_blur_y def init(self, all_prompts, all_seeds, all_subseeds): self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) crop_region = None Loading modules/sd_samplers_common.py +4 −1 Original line number Diff line number Diff line Loading @@ -305,5 +305,8 @@ class Sampler: current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size] return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds) def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): raise NotImplementedError() def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): raise NotImplementedError() Loading
modules/processing.py +172 −146 Original line number Diff line number Diff line from __future__ import annotations import json import logging import math import os import sys import hashlib from dataclasses import dataclass, field import torch import numpy as np Loading @@ -11,7 +13,7 @@ from PIL import Image, ImageOps import random import cv2 from skimage import exposure from typing import Any, Dict, List from typing import Any import modules.sd_hijack from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng Loading Loading @@ -104,106 +106,126 @@ def txt2img_image_conditioning(sd_model, x, width, height): return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) @dataclass(repr=False) class StableDiffusionProcessing: """ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing """ sd_model: object = None outpath_samples: str = None outpath_grids: str = None prompt: str = "" prompt_for_display: str = None negative_prompt: str = "" styles: list[str] = field(default_factory=list) seed: int = -1 subseed: int = -1 subseed_strength: float = 0 seed_resize_from_h: int = -1 seed_resize_from_w: int = -1 seed_enable_extras: bool = True sampler_name: str = None batch_size: int = 1 n_iter: int = 1 steps: int = 50 cfg_scale: float = 7.0 width: int = 512 height: int = 512 restore_faces: bool = None tiling: bool = None do_not_save_samples: bool = False do_not_save_grid: bool = False extra_generation_params: dict[str, Any] = None overlay_images: list = None eta: float = None do_not_reload_embeddings: bool = False denoising_strength: float = 0 ddim_discretize: str = None s_min_uncond: float = None s_churn: float = None s_tmax: float = None s_tmin: float = None s_noise: float = None override_settings: dict[str, Any] = None override_settings_restore_afterwards: bool = True sampler_index: int = None refiner_checkpoint: str = None refiner_switch_at: float = None token_merging_ratio = 0 token_merging_ratio_hr = 0 disable_extra_networks: bool = False script_args: list = None cached_uc = [None, None] cached_c = [None, None] def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, refiner_checkpoint: str = None, refiner_switch_at: float = None, script_args: list = None): if sampler_index is not None: sampler: sd_samplers_common.Sampler | None = field(default=None, init=False) is_using_inpainting_conditioning: bool = field(default=False, init=False) paste_to: tuple | None = field(default=None, init=False) is_hr_pass: bool = field(default=False, init=False) c: tuple = field(default=None, init=False) uc: tuple = field(default=None, init=False) rng: rng.ImageRNG | None = field(default=None, init=False) step_multiplier: int = field(default=1, init=False) color_corrections: list = field(default=None, init=False) scripts: list = field(default=None, init=False) all_prompts: list = field(default=None, init=False) all_negative_prompts: list = field(default=None, init=False) all_seeds: list = field(default=None, init=False) all_subseeds: list = field(default=None, init=False) iteration: int = field(default=0, init=False) main_prompt: str = field(default=None, init=False) main_negative_prompt: str = field(default=None, init=False) prompts: list = field(default=None, init=False) negative_prompts: list = field(default=None, init=False) seeds: list = field(default=None, init=False) subseeds: list = field(default=None, init=False) extra_network_data: dict = field(default=None, init=False) user: str = field(default=None, init=False) sd_model_name: str = field(default=None, init=False) sd_model_hash: str = field(default=None, init=False) sd_vae_name: str = field(default=None, init=False) sd_vae_hash: str = field(default=None, init=False) def __post_init__(self): if self.sampler_index is not None: print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr) self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids self.prompt: str = prompt self.prompt_for_display: str = None self.negative_prompt: str = (negative_prompt or "") self.styles: list = styles or [] self.seed: int = seed self.subseed: int = subseed self.subseed_strength: float = subseed_strength self.seed_resize_from_h: int = seed_resize_from_h self.seed_resize_from_w: int = seed_resize_from_w self.sampler_name: str = sampler_name self.batch_size: int = batch_size self.n_iter: int = n_iter self.steps: int = steps self.cfg_scale: float = cfg_scale self.width: int = width self.height: int = height self.restore_faces: bool = restore_faces self.tiling: bool = tiling self.do_not_save_samples: bool = do_not_save_samples self.do_not_save_grid: bool = do_not_save_grid self.extra_generation_params: dict = extra_generation_params or {} self.overlay_images = overlay_images self.eta = eta self.do_not_reload_embeddings = do_not_reload_embeddings self.paste_to = None self.color_corrections = None self.denoising_strength: float = denoising_strength self.sampler_noise_scheduler_override = None self.ddim_discretize = ddim_discretize or opts.ddim_discretize self.s_min_uncond = s_min_uncond or opts.s_min_uncond self.s_churn = s_churn or opts.s_churn self.s_tmin = s_tmin or opts.s_tmin self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf') self.s_noise = s_noise if s_noise is not None else opts.s_noise self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts} self.override_settings_restore_afterwards = override_settings_restore_afterwards self.refiner_checkpoint = refiner_checkpoint self.refiner_switch_at = refiner_switch_at self.is_using_inpainting_conditioning = False self.disable_extra_networks = False self.token_merging_ratio = 0 self.token_merging_ratio_hr = 0 self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf') self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise self.extra_generation_params = self.extra_generation_params or {} self.override_settings = self.override_settings or {} self.script_args = self.script_args or {} self.refiner_checkpoint_info = None if not seed_enable_extras: if not self.seed_enable_extras: self.subseed = -1 self.subseed_strength = 0 self.seed_resize_from_h = 0 self.seed_resize_from_w = 0 self.scripts = None self.script_args = script_args self.all_prompts = None self.all_negative_prompts = None self.all_seeds = None self.all_subseeds = None self.iteration = 0 self.is_hr_pass = False self.sampler = None self.main_prompt = None self.main_negative_prompt = None self.prompts = None self.negative_prompts = None self.extra_network_data = None self.seeds = None self.subseeds = None self.step_multiplier = 1 self.cached_uc = StableDiffusionProcessing.cached_uc self.cached_c = StableDiffusionProcessing.cached_c self.uc = None self.c = None self.rng: rng.ImageRNG = None self.user = None self.sd_model_name = None self.sd_model_hash = None self.sd_vae_name = None self.sd_vae_hash = None @property def sd_model(self): return shared.sd_model @sd_model.setter def sd_model(self, value): pass def txt2img_image_conditioning(self, x, width=None, height=None): self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'} Loading Loading @@ -932,49 +954,51 @@ def old_hires_fix_first_pass_dimensions(width, height): return width, height @dataclass(repr=False) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None enable_hr: bool = False denoising_strength: float = 0.75 firstphase_width: int = 0 firstphase_height: int = 0 hr_scale: float = 2.0 hr_upscaler: str = None hr_second_pass_steps: int = 0 hr_resize_x: int = 0 hr_resize_y: int = 0 hr_checkpoint_name: str = None hr_sampler_name: str = None hr_prompt: str = '' hr_negative_prompt: str = '' cached_hr_uc = [None, None] cached_hr_c = [None, None] def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.hr_scale = hr_scale 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_checkpoint_name = hr_checkpoint_name self.hr_checkpoint_info = None self.hr_sampler_name = hr_sampler_name self.hr_prompt = hr_prompt self.hr_negative_prompt = hr_negative_prompt self.all_hr_prompts = None self.all_hr_negative_prompts = None self.latent_scale_mode = None if firstphase_width != 0 or firstphase_height != 0: hr_checkpoint_info: dict = field(default=None, init=False) hr_upscale_to_x: int = field(default=0, init=False) hr_upscale_to_y: int = field(default=0, init=False) truncate_x: int = field(default=0, init=False) truncate_y: int = field(default=0, init=False) applied_old_hires_behavior_to: tuple = field(default=None, init=False) latent_scale_mode: dict = field(default=None, init=False) hr_c: tuple | None = field(default=None, init=False) hr_uc: tuple | None = field(default=None, init=False) all_hr_prompts: list = field(default=None, init=False) all_hr_negative_prompts: list = field(default=None, init=False) hr_prompts: list = field(default=None, init=False) hr_negative_prompts: list = field(default=None, init=False) hr_extra_network_data: list = field(default=None, init=False) def __post_init__(self): super().__post_init__() if self.firstphase_width != 0 or self.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.truncate_x = 0 self.truncate_y = 0 self.applied_old_hires_behavior_to = None self.hr_prompts = None self.hr_negative_prompts = None self.hr_extra_network_data = None self.width = self.firstphase_width self.height = self.firstphase_height self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c self.hr_c = None self.hr_uc = None def calculate_target_resolution(self): if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): Loading Loading @@ -1252,7 +1276,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return super().get_conds() def parse_extra_network_prompts(self): res = super().parse_extra_network_prompts() Loading @@ -1265,32 +1288,37 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return res @dataclass(repr=False) class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images self.resize_mode: int = resize_mode self.denoising_strength: float = denoising_strength self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None self.init_latent = None self.image_mask = mask self.latent_mask = None self.mask_for_overlay = None self.mask_blur_x = mask_blur_x self.mask_blur_y = mask_blur_y if mask_blur is not None: self.mask_blur = mask_blur self.inpainting_fill = inpainting_fill self.inpaint_full_res = inpaint_full_res self.inpaint_full_res_padding = inpaint_full_res_padding self.inpainting_mask_invert = inpainting_mask_invert self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier init_images: list = None resize_mode: int = 0 denoising_strength: float = 0.75 image_cfg_scale: float = None mask: Any = None mask_blur_x: int = 4 mask_blur_y: int = 4 mask_blur: int = None inpainting_fill: int = 0 inpaint_full_res: bool = True inpaint_full_res_padding: int = 0 inpainting_mask_invert: int = 0 initial_noise_multiplier: float = None latent_mask: Image = None image_mask: Any = field(default=None, init=False) nmask: torch.Tensor = field(default=None, init=False) image_conditioning: torch.Tensor = field(default=None, init=False) init_img_hash: str = field(default=None, init=False) mask_for_overlay: Image = field(default=None, init=False) init_latent: torch.Tensor = field(default=None, init=False) def __post_init__(self): super().__post_init__() self.image_mask = self.mask self.mask = None self.nmask = None self.image_conditioning = None self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier @property def mask_blur(self): Loading @@ -1300,15 +1328,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): @mask_blur.setter def mask_blur(self, value): if isinstance(value, int): self.mask_blur_x = value self.mask_blur_y = value @mask_blur.deleter def mask_blur(self): del self.mask_blur_x del self.mask_blur_y def init(self, all_prompts, all_seeds, all_subseeds): self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) crop_region = None Loading
modules/sd_samplers_common.py +4 −1 Original line number Diff line number Diff line Loading @@ -305,5 +305,8 @@ class Sampler: current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size] return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds) def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): raise NotImplementedError() def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): raise NotImplementedError()
