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modules/processing.py

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+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
+from PIL import Image, ImageOps
+import random
+import cv2
+from skimage import exposure
+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
+from modules.rng import slerp # noqa: F401
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
+from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
+import modules.paths as paths
+import modules.face_restoration
+import modules.images as images
+import modules.styles
+import modules.sd_models as sd_models
+import modules.sd_vae as sd_vae
+from ldm.data.util import AddMiDaS
+from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
+
+from einops import repeat, rearrange
+from blendmodes.blend import blendLayers, BlendType
+
+
+# some of those options should not be changed at all because they would break the model, so I removed them from options.
+opt_C = 4
+opt_f = 8
+
+
+def setup_color_correction(image):
+    logging.info("Calibrating color correction.")
+    correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
+    return correction_target
+
+
+def apply_color_correction(correction, original_image):
+    logging.info("Applying color correction.")
+    image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
+        cv2.cvtColor(
+            np.asarray(original_image),
+            cv2.COLOR_RGB2LAB
+        ),
+        correction,
+        channel_axis=2
+    ), cv2.COLOR_LAB2RGB).astype("uint8"))
+
+    image = blendLayers(image, original_image, BlendType.LUMINOSITY)
+
+    return image.convert('RGB')
+
+
+def apply_overlay(image, paste_loc, index, overlays):
+    if overlays is None or index >= len(overlays):
+        return image
+
+    overlay = overlays[index]
+
+    if paste_loc is not None:
+        x, y, w, h = paste_loc
+        base_image = Image.new('RGBA', (overlay.width, overlay.height))
+        image = images.resize_image(1, image, w, h)
+        base_image.paste(image, (x, y))
+        image = base_image
+
+    image = image.convert('RGBA')
+    image.alpha_composite(overlay)
+    image = image.convert('RGB')
+
+    return image
+
+def create_binary_mask(image):
+    if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
+        image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+    else:
+        image = image.convert('L')
+    return image
+
+def txt2img_image_conditioning(sd_model, x, width, height):
+    if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+
+        # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
+        image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
+        image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)
+
+        return image_conditioning
+
+    elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
+
+        return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+    else:
+        sd = sd_model.model.state_dict()
+        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+        if diffusion_model_input is not None:
+            if diffusion_model_input.shape[1] == 9:
+                # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
+                image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
+                image_conditioning = images_tensor_to_samples(image_conditioning,
+                                                              approximation_indexes.get(opts.sd_vae_encode_method))
+
+                # Add the fake full 1s mask to the first dimension.
+                image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+                image_conditioning = image_conditioning.to(x.dtype)
+
+                return image_conditioning
+
+        # Dummy zero conditioning if we're not using inpainting or unclip models.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+
+@dataclass(repr=False)
+class 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] = 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[str, Any] = None
+    overlay_images: list = None
+    eta: float = None
+    do_not_reload_embeddings: bool = False
+    denoising_strength: float = None
+    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
+
+    scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+    script_args_value: list = field(default=None, init=False)
+    scripts_setup_complete: bool = field(default=False, init=False)
+
+    cached_uc = [None, None]
+    cached_c = [None, None]
+
+    comments: dict = 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)
+
+    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)
+
+    is_api: bool = field(default=False, 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.comments = {}
+
+        if self.styles is None:
+            self.styles = []
+
+        self.sampler_noise_scheduler_override = None
+        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 self.seed_enable_extras:
+            self.subseed = -1
+            self.subseed_strength = 0
+            self.seed_resize_from_h = 0
+            self.seed_resize_from_w = 0
+
+        self.cached_uc = StableDiffusionProcessing.cached_uc
+        self.cached_c = StableDiffusionProcessing.cached_c
+
+    @property
+    def sd_model(self):
+        return shared.sd_model
+
+    @sd_model.setter
+    def sd_model(self, value):
+        pass
+
+    @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_scrips(self, is_ui=not self.is_api)
+
+    def comment(self, text):
+        self.comments[text] = 1
+
+    def txt2img_image_conditioning(self, x, width=None, height=None):
+        self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
+
+        return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
+
+    def depth2img_image_conditioning(self, source_image):
+        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
+        transformer = AddMiDaS(model_type="dpt_hybrid")
+        transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+        midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+        midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
+
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+        conditioning = torch.nn.functional.interpolate(
+            self.sd_model.depth_model(midas_in),
+            size=conditioning_image.shape[2:],
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        (depth_min, depth_max) = torch.aminmax(conditioning)
+        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+        return conditioning
+
+    def edit_image_conditioning(self, source_image):
+        conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
+
+        return conditioning_image
+
+    def unclip_image_conditioning(self, source_image):
+        c_adm = self.sd_model.embedder(source_image)
+        if self.sd_model.noise_augmentor is not None:
+            noise_level = 0 # TODO: Allow other noise levels?
+            c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+            c_adm = torch.cat((c_adm, noise_level_emb), 1)
+        return c_adm
+
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
+        self.is_using_inpainting_conditioning = True
+
+        # Handle the different mask inputs
+        if image_mask is not None:
+            if torch.is_tensor(image_mask):
+                conditioning_mask = image_mask
+            else:
+                conditioning_mask = np.array(image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
+        conditioning_image = torch.lerp(
+            source_image,
+            source_image * (1.0 - conditioning_mask),
+            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+        )
+
+        # Encode the new masked image using first stage of network.
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+        return image_conditioning
+
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        source_image = devices.cond_cast_float(source_image)
+
+        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+        # identify itself with a field common to all models. The conditioning_key is also hybrid.
+        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+            return self.depth2img_image_conditioning(source_image)
+
+        if self.sd_model.cond_stage_key == "edit":
+            return self.edit_image_conditioning(source_image)
+
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+        if self.sampler.conditioning_key == "crossattn-adm":
+            return self.unclip_image_conditioning(source_image)
+
+        sd = self.sampler.model_wrap.inner_model.model.state_dict()
+        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+        if diffusion_model_input is not None:
+            if diffusion_model_input.shape[1] == 9:
+                return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+        # Dummy zero conditioning if we're not using inpainting or depth model.
+        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
+    def init(self, all_prompts, all_seeds, all_subseeds):
+        pass
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+        raise NotImplementedError()
+
+    def close(self):
+        self.sampler = None
+        self.c = None
+        self.uc = None
+        if not opts.persistent_cond_cache:
+            StableDiffusionProcessing.cached_c = [None, None]
+            StableDiffusionProcessing.cached_uc = [None, None]
+
+    def get_token_merging_ratio(self, for_hr=False):
+        if for_hr:
+            return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio
+
+        return self.token_merging_ratio or opts.token_merging_ratio
+
+    def setup_prompts(self):
+        if isinstance(self.prompt,list):
+            self.all_prompts = self.prompt
+        elif isinstance(self.negative_prompt, list):
+            self.all_prompts = [self.prompt] * len(self.negative_prompt)
+        else:
+            self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
+
+        if isinstance(self.negative_prompt, list):
+            self.all_negative_prompts = self.negative_prompt
+        else:
+            self.all_negative_prompts = [self.negative_prompt] * len(self.all_prompts)
+
+        if len(self.all_prompts) != len(self.all_negative_prompts):
+            raise RuntimeError(f"Received a different number of prompts ({len(self.all_prompts)}) and negative prompts ({len(self.all_negative_prompts)})")
+
+        self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
+        self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
+
+        self.main_prompt = self.all_prompts[0]
+        self.main_negative_prompt = self.all_negative_prompts[0]
+
+    def cached_params(self, required_prompts, steps, extra_network_data, hires_steps=None, use_old_scheduling=False):
+        """Returns parameters that invalidate the cond cache if changed"""
+
+        return (
+            required_prompts,
+            steps,
+            hires_steps,
+            use_old_scheduling,
+            opts.CLIP_stop_at_last_layers,
+            shared.sd_model.sd_checkpoint_info,
+            extra_network_data,
+            opts.sdxl_crop_left,
+            opts.sdxl_crop_top,
+            self.width,
+            self.height,
+        )
+
+    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
+        """
+        Returns the result of calling function(shared.sd_model, required_prompts, steps)
+        using a cache to store the result if the same arguments have been used before.
+
+        cache is an array containing two elements. The first element is a tuple
+        representing the previously used arguments, or None if no arguments
+        have been used before. The second element is where the previously
+        computed result is stored.
+
+        caches is a list with items described above.
+        """
+
+        if shared.opts.use_old_scheduling:
+            old_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, False)
+            new_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, True)
+            if old_schedules != new_schedules:
+                self.extra_generation_params["Old prompt editing timelines"] = True
+
+        cached_params = self.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)
+
+        for cache in caches:
+            if cache[0] is not None and cached_params == cache[0]:
+                return cache[1]
+
+        cache = caches[0]
+
+        with devices.autocast():
+            cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
+
+        cache[0] = cached_params
+        return cache[1]
+
+    def setup_conds(self):
+        prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
+        negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
+
+        sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
+        total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps
+        self.step_multiplier = total_steps // self.steps
+        self.firstpass_steps = total_steps
+
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
+
+    def get_conds(self):
+        return self.c, self.uc
+
+    def parse_extra_network_prompts(self):
+        self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
+
+    def save_samples(self) -> bool:
+        """Returns whether generated images need to be written to disk"""
+        return opts.samples_save and not self.do_not_save_samples and (opts.save_incomplete_images or not state.interrupted and not state.skipped)
+
+
+class Processed:
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
+        self.images = images_list
+        self.prompt = p.prompt
+        self.negative_prompt = p.negative_prompt
+        self.seed = seed
+        self.subseed = subseed
+        self.subseed_strength = p.subseed_strength
+        self.info = info
+        self.comments = "".join(f"{comment}\n" for comment in p.comments)
+        self.width = p.width
+        self.height = p.height
+        self.sampler_name = p.sampler_name
+        self.cfg_scale = p.cfg_scale
+        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.steps = p.steps
+        self.batch_size = p.batch_size
+        self.restore_faces = p.restore_faces
+        self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
+        self.sd_model_name = p.sd_model_name
+        self.sd_model_hash = p.sd_model_hash
+        self.sd_vae_name = p.sd_vae_name
+        self.sd_vae_hash = p.sd_vae_hash
+        self.seed_resize_from_w = p.seed_resize_from_w
+        self.seed_resize_from_h = p.seed_resize_from_h
+        self.denoising_strength = getattr(p, 'denoising_strength', None)
+        self.extra_generation_params = p.extra_generation_params
+        self.index_of_first_image = index_of_first_image
+        self.styles = p.styles
+        self.job_timestamp = state.job_timestamp
+        self.clip_skip = opts.CLIP_stop_at_last_layers
+        self.token_merging_ratio = p.token_merging_ratio
+        self.token_merging_ratio_hr = p.token_merging_ratio_hr
+
+        self.eta = p.eta
+        self.ddim_discretize = p.ddim_discretize
+        self.s_churn = p.s_churn
+        self.s_tmin = p.s_tmin
+        self.s_tmax = p.s_tmax
+        self.s_noise = p.s_noise
+        self.s_min_uncond = p.s_min_uncond
+        self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
+        self.prompt = self.prompt if not isinstance(self.prompt, list) else self.prompt[0]
+        self.negative_prompt = self.negative_prompt if not isinstance(self.negative_prompt, list) else self.negative_prompt[0]
+        self.seed = int(self.seed if not isinstance(self.seed, list) else self.seed[0]) if self.seed is not None else -1
+        self.subseed = int(self.subseed if not isinstance(self.subseed, list) else self.subseed[0]) if self.subseed is not None else -1
+        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
+
+        self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
+        self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
+        self.all_seeds = all_seeds or p.all_seeds or [self.seed]
+        self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
+        self.infotexts = infotexts or [info]
+        self.version = program_version()
+
+    def js(self):
+        obj = {
+            "prompt": self.all_prompts[0],
+            "all_prompts": self.all_prompts,
+            "negative_prompt": self.all_negative_prompts[0],
+            "all_negative_prompts": self.all_negative_prompts,
+            "seed": self.seed,
+            "all_seeds": self.all_seeds,
+            "subseed": self.subseed,
+            "all_subseeds": self.all_subseeds,
+            "subseed_strength": self.subseed_strength,
+            "width": self.width,
+            "height": self.height,
+            "sampler_name": self.sampler_name,
+            "cfg_scale": self.cfg_scale,
+            "steps": self.steps,
+            "batch_size": self.batch_size,
+            "restore_faces": self.restore_faces,
+            "face_restoration_model": self.face_restoration_model,
+            "sd_model_name": self.sd_model_name,
+            "sd_model_hash": self.sd_model_hash,
+            "sd_vae_name": self.sd_vae_name,
+            "sd_vae_hash": self.sd_vae_hash,
+            "seed_resize_from_w": self.seed_resize_from_w,
+            "seed_resize_from_h": self.seed_resize_from_h,
+            "denoising_strength": self.denoising_strength,
+            "extra_generation_params": self.extra_generation_params,
+            "index_of_first_image": self.index_of_first_image,
+            "infotexts": self.infotexts,
+            "styles": self.styles,
+            "job_timestamp": self.job_timestamp,
+            "clip_skip": self.clip_skip,
+            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
+            "version": self.version,
+        }
+
+        return json.dumps(obj)
+
+    def infotext(self, p: StableDiffusionProcessing, index):
+        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+
+    def get_token_merging_ratio(self, for_hr=False):
+        return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
+
+
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+    g = rng.ImageRNG(shape, seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w)
+    return g.next()
+
+
+class DecodedSamples(list):
+    already_decoded = True
+
+
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+    samples = DecodedSamples()
+
+    for i in range(batch.shape[0]):
+        sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if check_for_nans:
+            try:
+                devices.test_for_nans(sample, "vae")
+            except devices.NansException as e:
+                if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+                    raise e
+
+                errors.print_error_explanation(
+                    "A tensor with all NaNs was produced in VAE.\n"
+                    "Web UI will now convert VAE into 32-bit float and retry.\n"
+                    "To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
+                    "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+                )
+
+                devices.dtype_vae = torch.float32
+                model.first_stage_model.to(devices.dtype_vae)
+                batch = batch.to(devices.dtype_vae)
+
+                sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if target_device is not None:
+            sample = sample.to(target_device)
+
+        samples.append(sample)
+
+    return samples
+
+
+def get_fixed_seed(seed):
+    if seed == '' or seed is None:
+        seed = -1
+    elif isinstance(seed, str):
+        try:
+            seed = int(seed)
+        except Exception:
+            seed = -1
+
+    if seed == -1:
+        return int(random.randrange(4294967294))
+
+    return seed
+
+
+def fix_seed(p):
+    p.seed = get_fixed_seed(p.seed)
+    p.subseed = get_fixed_seed(p.subseed)
+
+
+def program_version():
+    import launch
+
+    res = launch.git_tag()
+    if res == "<none>":
+        res = None
+
+    return res
+
+
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    if index is None:
+        index = position_in_batch + iteration * p.batch_size
+
+    if all_negative_prompts is None:
+        all_negative_prompts = p.all_negative_prompts
+
+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+    enable_hr = getattr(p, 'enable_hr', False)
+    token_merging_ratio = p.get_token_merging_ratio()
+    token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
+
+    uses_ensd = opts.eta_noise_seed_delta != 0
+    if uses_ensd:
+        uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
+
+    generation_params = {
+        "Steps": p.steps,
+        "Sampler": p.sampler_name,
+        "CFG scale": p.cfg_scale,
+        "Image CFG scale": getattr(p, 'image_cfg_scale', None),
+        "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
+        "Face restoration": opts.face_restoration_model if p.restore_faces else None,
+        "Size": f"{p.width}x{p.height}",
+        "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
+        "Model": p.sd_model_name if opts.add_model_name_to_info else None,
+        "VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
+        "VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
+        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
+        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
+        "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
+        "Denoising strength": getattr(p, 'denoising_strength', None),
+        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
+        "Clip skip": None if clip_skip <= 1 else clip_skip,
+        "ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
+        "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
+        "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
+        "Init image hash": getattr(p, 'init_img_hash', None),
+        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+        "Tiling": "True" if p.tiling else None,
+        **p.extra_generation_params,
+        "Version": program_version() if opts.add_version_to_infotext else None,
+        "User": p.user if opts.add_user_name_to_info else None,
+    }
+
+    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
+
+    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
+    negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
+
+    return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
+
+
+def process_images(p: StableDiffusionProcessing) -> Processed:
+    if p.scripts is not None:
+        p.scripts.before_process(p)
+
+    stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
+
+    try:
+        # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
+        # and if after running refiner, the refiner model is not unloaded - webui swaps back to main model here, if model over is present it will be reloaded afterwards
+        if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
+            p.override_settings.pop('sd_model_checkpoint', None)
+            sd_models.reload_model_weights()
+
+        for k, v in p.override_settings.items():
+            opts.set(k, v, is_api=True, run_callbacks=False)
+
+            if k == 'sd_model_checkpoint':
+                sd_models.reload_model_weights()
+
+            if k == 'sd_vae':
+                sd_vae.reload_vae_weights()
+
+        sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
+
+        res = process_images_inner(p)
+
+    finally:
+        sd_models.apply_token_merging(p.sd_model, 0)
+
+        # restore opts to original state
+        if p.override_settings_restore_afterwards:
+            for k, v in stored_opts.items():
+                setattr(opts, k, v)
+
+                if k == 'sd_vae':
+                    sd_vae.reload_vae_weights()
+
+    return res
+
+
+def process_images_inner(p: StableDiffusionProcessing) -> Processed:
+    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
+
+    if isinstance(p.prompt, list):
+        assert(len(p.prompt) > 0)
+    else:
+        assert p.prompt is not None
+
+    devices.torch_gc()
+
+    seed = get_fixed_seed(p.seed)
+    subseed = get_fixed_seed(p.subseed)
+
+    if p.restore_faces is None:
+        p.restore_faces = opts.face_restoration
+
+    if p.tiling is None:
+        p.tiling = opts.tiling
+
+    if p.refiner_checkpoint not in (None, "", "None", "none"):
+        p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
+        if p.refiner_checkpoint_info is None:
+            raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
+
+    p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
+    p.sd_model_hash = shared.sd_model.sd_model_hash
+    p.sd_vae_name = sd_vae.get_loaded_vae_name()
+    p.sd_vae_hash = sd_vae.get_loaded_vae_hash()
+
+    modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+    modules.sd_hijack.model_hijack.clear_comments()
+
+    p.setup_prompts()
+
+    if isinstance(seed, list):
+        p.all_seeds = seed
+    else:
+        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
+
+    if isinstance(subseed, list):
+        p.all_subseeds = subseed
+    else:
+        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
+
+    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
+        model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    if p.scripts is not None:
+        p.scripts.process(p)
+
+    infotexts = []
+    output_images = []
+    with torch.no_grad(), p.sd_model.ema_scope():
+        with devices.autocast():
+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
+
+            # for OSX, loading the model during sampling changes the generated picture, so it is loaded here
+            if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
+                sd_vae_approx.model()
+
+            sd_unet.apply_unet()
+
+        if state.job_count == -1:
+            state.job_count = p.n_iter
+
+        for n in range(p.n_iter):
+            p.iteration = n
+
+            if state.skipped:
+                state.skipped = False
+
+            if state.interrupted:
+                break
+
+            sd_models.reload_model_weights()  # model can be changed for example by refiner
+
+            p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+
+            p.rng = rng.ImageRNG((opt_C, p.height // opt_f, p.width // opt_f), p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
+
+            if p.scripts is not None:
+                p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+
+            if len(p.prompts) == 0:
+                break
+
+            p.parse_extra_network_prompts()
+
+            if not p.disable_extra_networks:
+                with devices.autocast():
+                    extra_networks.activate(p, p.extra_network_data)
+
+            if p.scripts is not None:
+                p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+
+            # params.txt should be saved after scripts.process_batch, since the
+            # infotext could be modified by that callback
+            # Example: a wildcard processed by process_batch sets an extra model
+            # strength, which is saved as "Model Strength: 1.0" in the infotext
+            if n == 0:
+                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+                    processed = Processed(p, [])
+                    file.write(processed.infotext(p, 0))
+
+            p.setup_conds()
+
+            for comment in model_hijack.comments:
+                p.comment(comment)
+
+            p.extra_generation_params.update(model_hijack.extra_generation_params)
+
+            if p.n_iter > 1:
+                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+
+            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
+                samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
+
+            if getattr(samples_ddim, 'already_decoded', False):
+                x_samples_ddim = samples_ddim
+            else:
+                if opts.sd_vae_decode_method != 'Full':
+                    p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
+                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+
+            x_samples_ddim = torch.stack(x_samples_ddim).float()
+            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+
+            del samples_ddim
+
+            if lowvram.is_enabled(shared.sd_model):
+                lowvram.send_everything_to_cpu()
+
+            devices.torch_gc()
+
+            state.nextjob()
+
+            if p.scripts is not None:
+                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
+
+                p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
+                batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+                x_samples_ddim = batch_params.images
+
+            def infotext(index=0, use_main_prompt=False):
+                return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
+
+            save_samples = p.save_samples()
+
+            for i, x_sample in enumerate(x_samples_ddim):
+                p.batch_index = i
+
+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+                x_sample = x_sample.astype(np.uint8)
+
+                if p.restore_faces:
+                    if save_samples and opts.save_images_before_face_restoration:
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
+
+                    devices.torch_gc()
+
+                    x_sample = modules.face_restoration.restore_faces(x_sample)
+                    devices.torch_gc()
+
+                image = Image.fromarray(x_sample)
+
+                if p.scripts is not None:
+                    pp = scripts.PostprocessImageArgs(image)
+                    p.scripts.postprocess_image(p, pp)
+                    image = pp.image
+                if p.color_corrections is not None and i < len(p.color_corrections):
+                    if save_samples and opts.save_images_before_color_correction:
+                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
+                    image = apply_color_correction(p.color_corrections[i], image)
+
+                image = apply_overlay(image, p.paste_to, i, p.overlay_images)
+
+                if save_samples:
+                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
+
+                text = infotext(i)
+                infotexts.append(text)
+                if opts.enable_pnginfo:
+                    image.info["parameters"] = text
+                output_images.append(image)
+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+                    if opts.return_mask or opts.save_mask:
+                        image_mask = p.mask_for_overlay.convert('RGB')
+                        if save_samples and opts.save_mask:
+                            images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
+                        if opts.return_mask:
+                            output_images.append(image_mask)
+
+                    if opts.return_mask_composite or opts.save_mask_composite:
+                        image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
+                        if save_samples and opts.save_mask_composite:
+                            images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
+                        if opts.return_mask_composite:
+                            output_images.append(image_mask_composite)
+
+            del x_samples_ddim
+
+            devices.torch_gc()
+
+        if not infotexts:
+            infotexts.append(Processed(p, []).infotext(p, 0))
+
+        p.color_corrections = None
+
+        index_of_first_image = 0
+        unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
+        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+            grid = images.image_grid(output_images, p.batch_size)
+
+            if opts.return_grid:
+                text = infotext(use_main_prompt=True)
+                infotexts.insert(0, text)
+                if opts.enable_pnginfo:
+                    grid.info["parameters"] = text
+                output_images.insert(0, grid)
+                index_of_first_image = 1
+            if opts.grid_save:
+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+
+    if not p.disable_extra_networks and p.extra_network_data:
+        extra_networks.deactivate(p, p.extra_network_data)
+
+    devices.torch_gc()
+
+    res = Processed(
+        p,
+        images_list=output_images,
+        seed=p.all_seeds[0],
+        info=infotexts[0],
+        subseed=p.all_subseeds[0],
+        index_of_first_image=index_of_first_image,
+        infotexts=infotexts,
+    )
+
+    if p.scripts is not None:
+        p.scripts.postprocess(p, res)
+
+    return res
+
+
+def old_hires_fix_first_pass_dimensions(width, height):
+    """old algorithm for auto-calculating first pass size"""
+
+    desired_pixel_count = 512 * 512
+    actual_pixel_count = width * height
+    scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+    width = math.ceil(scale * width / 64) * 64
+    height = math.ceil(scale * height / 64) * 64
+
+    return width, height
+
+
+@dataclass(repr=False)
+class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
+    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]
+
+    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 = self.firstphase_width
+            self.height = self.firstphase_height
+
+        self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
+        self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
+
+    def calculate_target_resolution(self):
+        if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
+            self.hr_resize_x = self.width
+            self.hr_resize_y = self.height
+            self.hr_upscale_to_x = self.width
+            self.hr_upscale_to_y = self.height
+
+            self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
+            self.applied_old_hires_behavior_to = (self.width, self.height)
+
+        if self.hr_resize_x == 0 and self.hr_resize_y == 0:
+            self.extra_generation_params["Hires upscale"] = self.hr_scale
+            self.hr_upscale_to_x = int(self.width * self.hr_scale)
+            self.hr_upscale_to_y = int(self.height * self.hr_scale)
+        else:
+            self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
+
+            if self.hr_resize_y == 0:
+                self.hr_upscale_to_x = self.hr_resize_x
+                self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+            elif self.hr_resize_x == 0:
+                self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+                self.hr_upscale_to_y = self.hr_resize_y
+            else:
+                target_w = self.hr_resize_x
+                target_h = self.hr_resize_y
+                src_ratio = self.width / self.height
+                dst_ratio = self.hr_resize_x / self.hr_resize_y
+
+                if src_ratio < dst_ratio:
+                    self.hr_upscale_to_x = self.hr_resize_x
+                    self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+                else:
+                    self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+                    self.hr_upscale_to_y = self.hr_resize_y
+
+                self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
+                self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+
+    def init(self, all_prompts, all_seeds, all_subseeds):
+        if self.enable_hr:
+            if self.hr_checkpoint_name:
+                self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
+
+                if self.hr_checkpoint_info is None:
+                    raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
+
+                self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+
+            if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
+                self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
+
+            if tuple(self.hr_prompt) != tuple(self.prompt):
+                self.extra_generation_params["Hires prompt"] = self.hr_prompt
+
+            if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
+                self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
+
+            self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+            if self.enable_hr and self.latent_scale_mode is None:
+                if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+                    raise Exception(f"could not find upscaler named {self.hr_upscaler}")
+
+            self.calculate_target_resolution()
+
+            if not state.processing_has_refined_job_count:
+                if state.job_count == -1:
+                    state.job_count = self.n_iter
+
+                shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
+                state.job_count = state.job_count * 2
+                state.processing_has_refined_job_count = True
+
+            if self.hr_second_pass_steps:
+                self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps
+
+            if self.hr_upscaler is not None:
+                self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+
+        x = self.rng.next()
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+        del x
+
+        if not self.enable_hr:
+            return samples
+        devices.torch_gc()
+
+        if self.latent_scale_mode is None:
+            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
+        else:
+            decoded_samples = None
+
+        with sd_models.SkipWritingToConfig():
+            sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+
+        return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
+
+    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
+        if shared.state.interrupted:
+            return samples
+
+        self.is_hr_pass = True
+        target_width = self.hr_upscale_to_x
+        target_height = self.hr_upscale_to_y
+
+        def save_intermediate(image, index):
+            """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
+
+            if not self.save_samples() or not opts.save_images_before_highres_fix:
+                return
+
+            if not isinstance(image, Image.Image):
+                image = sd_samplers.sample_to_image(image, index, approximation=0)
+
+            info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
+
+        img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
+        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
+
+        if self.latent_scale_mode is not None:
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
+
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"])
+
+            # Avoid making the inpainting conditioning unless necessary as
+            # this does need some extra compute to decode / encode the image again.
+            if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+                image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
+            else:
+                image_conditioning = self.txt2img_image_conditioning(samples)
+        else:
+            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+            batch_images = []
+            for i, x_sample in enumerate(lowres_samples):
+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+                x_sample = x_sample.astype(np.uint8)
+                image = Image.fromarray(x_sample)
+
+                save_intermediate(image, i)
+
+                image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = np.moveaxis(image, 2, 0)
+                batch_images.append(image)
+
+            decoded_samples = torch.from_numpy(np.array(batch_images))
+            decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
+
+            if opts.sd_vae_encode_method != 'Full':
+                self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+            samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
+
+            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
+
+        shared.state.nextjob()
+
+        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
+
+        self.rng = rng.ImageRNG(samples.shape[1:], self.seeds, subseeds=self.subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w)
+        noise = self.rng.next()
+
+        # GC now before running the next img2img to prevent running out of memory
+        devices.torch_gc()
+
+        if not self.disable_extra_networks:
+            with devices.autocast():
+                extra_networks.activate(self, self.hr_extra_network_data)
+
+        with devices.autocast():
+            self.calculate_hr_conds()
+
+        sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
+
+        if self.scripts is not None:
+            self.scripts.before_hr(self)
+
+        samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+
+        sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
+
+        self.sampler = None
+        devices.torch_gc()
+
+        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
+        self.is_hr_pass = False
+        return decoded_samples
+
+    def close(self):
+        super().close()
+        self.hr_c = None
+        self.hr_uc = None
+        if not opts.persistent_cond_cache:
+            StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
+            StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
+
+    def setup_prompts(self):
+        super().setup_prompts()
+
+        if not self.enable_hr:
+            return
+
+        if self.hr_prompt == '':
+            self.hr_prompt = self.prompt
+
+        if self.hr_negative_prompt == '':
+            self.hr_negative_prompt = self.negative_prompt
+
+        if isinstance(self.hr_prompt, list):
+            self.all_hr_prompts = self.hr_prompt
+        else:
+            self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
+
+        if isinstance(self.hr_negative_prompt, list):
+            self.all_hr_negative_prompts = self.hr_negative_prompt
+        else:
+            self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
+
+        self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
+        self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
+
+    def calculate_hr_conds(self):
+        if self.hr_c is not None:
+            return
+
+        hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
+        hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
+
+        sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)
+        steps = self.hr_second_pass_steps or self.steps
+        total_steps = sampler_config.total_steps(steps) if sampler_config else steps
+
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)
+
+    def setup_conds(self):
+        if self.is_hr_pass:
+            # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
+            self.hr_c = None
+            self.calculate_hr_conds()
+            return
+
+        super().setup_conds()
+
+        self.hr_uc = None
+        self.hr_c = None
+
+        if self.enable_hr and self.hr_checkpoint_info is None:
+            if shared.opts.hires_fix_use_firstpass_conds:
+                self.calculate_hr_conds()
+
+            elif lowvram.is_enabled(shared.sd_model) and shared.sd_model.sd_checkpoint_info == sd_models.select_checkpoint():  # if in lowvram mode, we need to calculate conds right away, before the cond NN is unloaded
+                with devices.autocast():
+                    extra_networks.activate(self, self.hr_extra_network_data)
+
+                self.calculate_hr_conds()
+
+                with devices.autocast():
+                    extra_networks.activate(self, self.extra_network_data)
+
+    def get_conds(self):
+        if self.is_hr_pass:
+            return self.hr_c, self.hr_uc
+
+        return super().get_conds()
+
+    def parse_extra_network_prompts(self):
+        res = super().parse_extra_network_prompts()
+
+        if self.enable_hr:
+            self.hr_prompts = self.all_hr_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+            self.hr_negative_prompts = self.all_hr_negative_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+
+            self.hr_prompts, self.hr_extra_network_data = extra_networks.parse_prompts(self.hr_prompts)
+
+        return res
+
+
+@dataclass(repr=False)
+class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
+    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.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier
+
+    @property
+    def mask_blur(self):
+        if self.mask_blur_x == self.mask_blur_y:
+            return self.mask_blur_x
+        return None
+
+    @mask_blur.setter
+    def mask_blur(self, value):
+        if isinstance(value, int):
+            self.mask_blur_x = value
+            self.mask_blur_y = value
+
+    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
+
+        image_mask = self.image_mask
+
+        if image_mask is not None:
+            # image_mask is passed in as RGBA by Gradio to support alpha masks,
+            # but we still want to support binary masks.
+            image_mask = create_binary_mask(image_mask)
+
+            if self.inpainting_mask_invert:
+                image_mask = ImageOps.invert(image_mask)
+
+            if self.mask_blur_x > 0:
+                np_mask = np.array(image_mask)
+                kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
+                np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
+                image_mask = Image.fromarray(np_mask)
+
+            if self.mask_blur_y > 0:
+                np_mask = np.array(image_mask)
+                kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
+                np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
+                image_mask = Image.fromarray(np_mask)
+
+            if self.inpaint_full_res:
+                self.mask_for_overlay = image_mask
+                mask = 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
+
+                mask = mask.crop(crop_region)
+                image_mask = images.resize_image(2, mask, self.width, self.height)
+                self.paste_to = (x1, y1, x2-x1, y2-y1)
+            else:
+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
+                np_mask = np.array(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 = []
+
+        latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
+
+        add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
+        if add_color_corrections:
+            self.color_corrections = []
+        imgs = []
+        for img in self.init_images:
+
+            # Save init image
+            if opts.save_init_img:
+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
+            image = images.flatten(img, opts.img2img_background_color)
+
+            if crop_region is None and self.resize_mode != 3:
+                image = images.resize_image(self.resize_mode, image, self.width, self.height)
+
+            if image_mask is not None:
+                image_masked = Image.new('RGBa', (image.width, image.height))
+                image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
+
+                self.overlay_images.append(image_masked.convert('RGBA'))
+
+            # crop_region is not None if we are doing inpaint full res
+            if crop_region is not None:
+                image = image.crop(crop_region)
+                image = images.resize_image(2, image, self.width, self.height)
+
+            if image_mask is not None:
+                if self.inpainting_fill != 1:
+                    image = masking.fill(image, latent_mask)
+
+            if add_color_corrections:
+                self.color_corrections.append(setup_color_correction(image))
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = np.moveaxis(image, 2, 0)
+
+            imgs.append(image)
+
+        if len(imgs) == 1:
+            batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
+            if self.overlay_images is not None:
+                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
+
+        elif len(imgs) <= self.batch_size:
+            self.batch_size = len(imgs)
+            batch_images = np.array(imgs)
+        else:
+            raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
+
+        image = torch.from_numpy(batch_images)
+        image = image.to(shared.device, dtype=devices.dtype_vae)
+
+        if opts.sd_vae_encode_method != 'Full':
+            self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+
+        self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
+        devices.torch_gc()
+
+        if self.resize_mode == 3:
+            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+
+        if image_mask is not None:
+            init_mask = latent_mask
+            latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
+            latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
+            latmask = latmask[0]
+            latmask = np.around(latmask)
+            latmask = np.tile(latmask[None], (4, 1, 1))
+
+            self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
+            self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
+
+            # this needs to be fixed to be done in sample() using actual seeds for batches
+            if self.inpainting_fill == 2:
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+            elif self.inpainting_fill == 3:
+                self.init_latent = self.init_latent * self.mask
+
+        self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+        x = self.rng.next()
+
+        if self.initial_noise_multiplier != 1.0:
+            self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
+            x *= self.initial_noise_multiplier
+
+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
+
+        if self.mask is not None:
+            samples = samples * self.nmask + self.init_latent * self.mask
+
+        del x
+        devices.torch_gc()
+
+        return samples
+
+    def get_token_merging_ratio(self, for_hr=False):
+        return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and opts.token_merging_ratio) or opts.token_merging_ratio_img2img or opts.token_merging_ratio

modules/sd_models_config.py

@@ -0,0 +1,131 @@
+import os
+
+import torch
+
+from modules import shared, paths, sd_disable_initialization, devices
+
+sd_configs_path = shared.sd_configs_path
+sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
+
+
+config_default = shared.sd_default_config
+config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
+config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
+config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
+config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
+config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
+config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
+config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
+config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
+
+def is_using_v_parameterization_for_sd2(state_dict):
+    """
+    Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
+    """
+
+    import ldm.modules.diffusionmodules.openaimodel
+
+    device = devices.cpu
+
+    with sd_disable_initialization.DisableInitialization():
+        unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
+            use_checkpoint=True,
+            use_fp16=False,
+            image_size=32,
+            in_channels=4,
+            out_channels=4,
+            model_channels=320,
+            attention_resolutions=[4, 2, 1],
+            num_res_blocks=2,
+            channel_mult=[1, 2, 4, 4],
+            num_head_channels=64,
+            use_spatial_transformer=True,
+            use_linear_in_transformer=True,
+            transformer_depth=1,
+            context_dim=1024,
+            legacy=False
+        )
+        unet.eval()
+
+    with torch.no_grad():
+        unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
+        unet.load_state_dict(unet_sd, strict=True)
+        unet.to(device=device, dtype=torch.float)
+
+        test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
+        x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
+
+        out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
+
+    return out < -1
+
+
+def guess_model_config_from_state_dict(sd, filename):
+    sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
+    diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+    sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
+
+    if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
+        if diffusion_model_input.shape[1] == 9:
+            return config_sdxl_inpainting
+        else:
+            return config_sdxl
+    if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
+        return config_sdxl_refiner
+    elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+        return config_depth_model
+    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+        return config_unclip
+    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+        return config_unopenclip
+
+    if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
+        if diffusion_model_input.shape[1] == 9:
+            return config_sd2_inpainting
+        elif is_using_v_parameterization_for_sd2(sd):
+            return config_sd2v
+        else:
+            return config_sd2
+
+    if diffusion_model_input is not None:
+        if diffusion_model_input.shape[1] == 9:
+            return config_inpainting
+        if diffusion_model_input.shape[1] == 8:
+            return config_instruct_pix2pix
+
+
+    if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
+        if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
+            return config_alt_diffusion_m18
+        return config_alt_diffusion
+
+    return config_default
+
+
+def find_checkpoint_config(state_dict, info):
+    if info is None:
+        return guess_model_config_from_state_dict(state_dict, "")
+
+    config = find_checkpoint_config_near_filename(info)
+    if config is not None:
+        return config
+
+    return guess_model_config_from_state_dict(state_dict, info.filename)
+
+
+def find_checkpoint_config_near_filename(info):
+    if info is None:
+        return None
+
+    config = f"{os.path.splitext(info.filename)[0]}.yaml"
+    if os.path.exists(config):
+        return config
+
+    return None
+

modules/sd_models_xl.py

@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+import torch
+
+import sgm.models.diffusion
+import sgm.modules.diffusionmodules.denoiser_scaling
+import sgm.modules.diffusionmodules.discretizer
+from modules import devices, shared, prompt_parser
+
+
+def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
+    for embedder in self.conditioner.embedders:
+        embedder.ucg_rate = 0.0
+
+    width = getattr(batch, 'width', 1024)
+    height = getattr(batch, 'height', 1024)
+    is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
+    aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
+
+    devices_args = dict(device=devices.device, dtype=devices.dtype)
+
+    sdxl_conds = {
+        "txt": batch,
+        "original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+        "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
+        "target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+        "aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
+    }
+
+    force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
+    c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
+
+    return c
+
+
+def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
+    sd = self.model.state_dict()
+    diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+    if diffusion_model_input is not None:
+        if diffusion_model_input.shape[1] == 9:
+            x = torch.cat([x] + cond['c_concat'], dim=1)
+
+    return self.model(x, t, cond)
+
+
+def get_first_stage_encoding(self, x):  # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
+    return x
+
+
+sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
+sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
+sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
+
+
+def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
+    res = []
+
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
+        encoded = embedder.encode_embedding_init_text(init_text, nvpt)
+        res.append(encoded)
+
+    return torch.cat(res, dim=1)
+
+
+def tokenize(self: sgm.modules.GeneralConditioner, texts):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
+        return embedder.tokenize(texts)
+
+    raise AssertionError('no tokenizer available')
+
+
+
+def process_texts(self, texts):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
+        return embedder.process_texts(texts)
+
+
+def get_target_prompt_token_count(self, token_count):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
+        return embedder.get_target_prompt_token_count(token_count)
+
+
+# those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
+sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
+sgm.modules.GeneralConditioner.tokenize = tokenize
+sgm.modules.GeneralConditioner.process_texts = process_texts
+sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
+
+
+def extend_sdxl(model):
+    """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
+
+    dtype = next(model.model.diffusion_model.parameters()).dtype
+    model.model.diffusion_model.dtype = dtype
+    model.model.conditioning_key = 'crossattn'
+    model.cond_stage_key = 'txt'
+    # model.cond_stage_model will be set in sd_hijack
+
+    model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
+
+    discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
+    model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+
+    model.conditioner.wrapped = torch.nn.Module()
+
+
+sgm.modules.attention.print = shared.ldm_print
+sgm.modules.diffusionmodules.model.print = shared.ldm_print
+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
+sgm.modules.encoders.modules.print = shared.ldm_print
+
+# this gets the code to load the vanilla attention that we override
+sgm.modules.attention.SDP_IS_AVAILABLE = True
+sgm.modules.attention.XFORMERS_IS_AVAILABLE = False