import json
import math
import os
import sys

import torch
import numpy as np
from PIL import Image, ImageFilter, ImageOps
import random
import cv2
from skimage import exposure

import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.face_restoration
import modules.images as images
import modules.styles
import logging


# 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, image):
    logging.info("Applying color correction.")
    image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
        cv2.cvtColor(
            np.asarray(image),
            cv2.COLOR_RGB2LAB
        ),
        correction,
        channel_axis=2
    ), cv2.COLOR_LAB2RGB).astype("uint8"))

    return image


class StableDiffusionProcessing:
    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
        self.sd_model = sd_model
        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_index: int = sampler_index
        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.paste_to = None
        self.color_corrections = None
        self.denoising_strength: float = 0
        self.sampler_noise_scheduler_override = None
        self.ddim_discretize = opts.ddim_discretize
        self.s_churn = opts.s_churn
        self.s_tmin = opts.s_tmin
        self.s_tmax = float('inf')  # not representable as a standard ui option
        self.s_noise = opts.s_noise

        if not seed_enable_extras:
            self.subseed = -1
            self.subseed_strength = 0
            self.seed_resize_from_h = 0
            self.seed_resize_from_w = 0

    def init(self, all_prompts, all_seeds, all_subseeds):
        pass

    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
        raise NotImplementedError()


class Processed:
    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
        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.width = p.width
        self.height = p.height
        self.sampler_index = p.sampler_index
        self.sampler = sd_samplers.samplers[p.sampler_index].name
        self.cfg_scale = p.cfg_scale
        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_hash = shared.sd_model.sd_model_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_ignore_last_layers

        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.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
        self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
        self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1

        self.all_prompts = all_prompts or [self.prompt]
        self.all_seeds = all_seeds or [self.seed]
        self.all_subseeds = all_subseeds or [self.subseed]
        self.infotexts = infotexts or [info]

    def js(self):
        obj = {
            "prompt": self.prompt,
            "all_prompts": self.all_prompts,
            "negative_prompt": self.negative_prompt,
            "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_index": self.sampler_index,
            "sampler": self.sampler,
            "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_hash": self.sd_model_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,
        }

        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)


# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
    low_norm = low/torch.norm(low, dim=1, keepdim=True)
    high_norm = high/torch.norm(high, dim=1, keepdim=True)
    dot = (low_norm*high_norm).sum(1)

    if dot.mean() > 0.9995:
        return low * val + high * (1 - val)

    omega = torch.acos(dot)
    so = torch.sin(omega)
    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
    return res


def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
    xs = []

    # if we have multiple seeds, this means we are working with batch size>1; this then
    # enables the generation of additional tensors with noise that the sampler will use during its processing.
    # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
    # produce the same images as with two batches [100], [101].
    if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
        sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
    else:
        sampler_noises = None

    for i, seed in enumerate(seeds):
        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)

        subnoise = None
        if subseeds is not None:
            subseed = 0 if i >= len(subseeds) else subseeds[i]

            subnoise = devices.randn(subseed, noise_shape)

        # randn results depend on device; gpu and cpu get different results for same seed;
        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
        # but the original script had it like this, so I do not dare change it for now because
        # it will break everyone's seeds.
        noise = devices.randn(seed, noise_shape)

        if subnoise is not None:
            noise = slerp(subseed_strength, noise, subnoise)

        if noise_shape != shape:
            x = devices.randn(seed, shape)
            dx = (shape[2] - noise_shape[2]) // 2
            dy = (shape[1] - noise_shape[1]) // 2
            w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
            h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
            tx = 0 if dx < 0 else dx
            ty = 0 if dy < 0 else dy
            dx = max(-dx, 0)
            dy = max(-dy, 0)

            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
            noise = x

        if sampler_noises is not None:
            cnt = p.sampler.number_of_needed_noises(p)

            for j in range(cnt):
                sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))

        xs.append(noise)

    if sampler_noises is not None:
        p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]

    x = torch.stack(xs).to(shared.device)
    return x


def get_fixed_seed(seed):
    if seed is None or seed == '' or 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 create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
    index = position_in_batch + iteration * p.batch_size

    clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers)

    generation_params = {
        "Steps": p.steps,
        "Sampler": sd_samplers.samplers[p.sampler_index].name,
        "CFG scale": p.cfg_scale,
        "Seed": all_seeds[index],
        "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
        "Size": f"{p.width}x{p.height}",
        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
        "Batch size": (None if p.batch_size < 2 else p.batch_size),
        "Batch pos": (None if p.batch_size < 2 else position_in_batch),
        "Variation seed": (None if p.subseed_strength == 0 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),
        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
        "Clip skip": None if clip_skip==0 else clip_skip,
    }

    generation_params.update(p.extra_generation_params)

    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])

    negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""

    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()


def process_images(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 type(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.outpath_samples is not None:
        os.makedirs(p.outpath_samples, exist_ok=True)

    if p.outpath_grids is not None:
        os.makedirs(p.outpath_grids, exist_ok=True)

    modules.sd_hijack.model_hijack.apply_circular(p.tiling)
    modules.sd_hijack.model_hijack.clear_comments()

    comments = {}

    shared.prompt_styles.apply_styles(p)

    if type(p.prompt) == list:
        all_prompts = p.prompt
    else:
        all_prompts = p.batch_size * p.n_iter * [p.prompt]

    if type(seed) == list:
        all_seeds = seed
    else:
        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]

    if type(subseed) == list:
        all_subseeds = subseed
    else:
        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]

    def infotext(iteration=0, position_in_batch=0):
        return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)

    if os.path.exists(cmd_opts.embeddings_dir):
        model_hijack.embedding_db.load_textual_inversion_embeddings()

    infotexts = []
    output_images = []

    with torch.no_grad(), p.sd_model.ema_scope():
        with devices.autocast():
            p.init(all_prompts, all_seeds, all_subseeds)

        if state.job_count == -1:
            state.job_count = p.n_iter

        for n in range(p.n_iter):
            if state.skipped:
                state.skipped = False
            
            if state.interrupted:
                break

            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

            if (len(prompts) == 0):
                break

            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
            #c = p.sd_model.get_learned_conditioning(prompts)
            with devices.autocast():
                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)

            if len(model_hijack.comments) > 0:
                for comment in model_hijack.comments:
                    comments[comment] = 1

            if p.n_iter > 1:
                shared.state.job = f"Batch {n+1} out of {p.n_iter}"

            with devices.autocast():
                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)

            if state.interrupted or state.skipped:

                # if we are interrupted, sample returns just noise
                # use the image collected previously in sampler loop
                samples_ddim = shared.state.current_latent

            samples_ddim = samples_ddim.to(devices.dtype)

            x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

            del samples_ddim

            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
                lowvram.send_everything_to_cpu()

            devices.torch_gc()

            if opts.filter_nsfw:
                import modules.safety as safety
                x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)

            for i, x_sample in enumerate(x_samples_ddim):
                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                x_sample = x_sample.astype(np.uint8)

                if p.restore_faces:
                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, 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.color_corrections is not None and i < len(p.color_corrections):
                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                    image = apply_color_correction(p.color_corrections[i], image)

                if p.overlay_images is not None and i < len(p.overlay_images):
                    overlay = p.overlay_images[i]

                    if p.paste_to is not None:
                        x, y, w, h = p.paste_to
                        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')

                if opts.samples_save and not p.do_not_save_samples:
                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)

                text = infotext(n, i)
                infotexts.append(text)
                image.info["parameters"] = text
                output_images.append(image)

            del x_samples_ddim 

            devices.torch_gc()

            state.nextjob()

        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()
                infotexts.insert(0, text)
                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", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

    devices.torch_gc()
    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)


class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
    sampler = None
    firstphase_width = 0
    firstphase_height = 0
    firstphase_width_truncated = 0
    firstphase_height_truncated = 0

    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
        super().__init__(**kwargs)
        self.enable_hr = enable_hr
        self.scale_latent = scale_latent
        self.denoising_strength = denoising_strength

    def init(self, all_prompts, all_seeds, all_subseeds):
        if self.enable_hr:
            if state.job_count == -1:
                state.job_count = self.n_iter * 2
            else:
                state.job_count = state.job_count * 2

            desired_pixel_count = 512 * 512
            actual_pixel_count = self.width * self.height
            scale = math.sqrt(desired_pixel_count / actual_pixel_count)

            self.firstphase_width = math.ceil(scale * self.width / 64) * 64
            self.firstphase_height = math.ceil(scale * self.height / 64) * 64
            self.firstphase_width_truncated = int(scale * self.width)
            self.firstphase_height_truncated = int(scale * self.height)

    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

        if not self.enable_hr:
            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
            return samples

        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

        truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
        truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f

        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]

        if self.scale_latent:
            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
        else:
            decoded_samples = self.sd_model.decode_first_stage(samples)

            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
            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)
                    image = images.resize_image(0, image, self.width, self.height)
                    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)
                decoded_samples = 2. * decoded_samples - 1.

            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

        shared.state.nextjob()

        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

        # GC now before running the next img2img to prevent running out of memory
        x = None
        devices.torch_gc()

        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)

        return samples


class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
    sampler = None

    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
        super().__init__(**kwargs)

        self.init_images = init_images
        self.resize_mode: int = resize_mode
        self.denoising_strength: float = denoising_strength
        self.init_latent = None
        self.image_mask = mask
        #self.image_unblurred_mask = None
        self.latent_mask = None
        self.mask_for_overlay = 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.mask = None
        self.nmask = None

    def init(self, all_prompts, all_seeds, all_subseeds):
        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
        crop_region = None

        if self.image_mask is not None:
            self.image_mask = self.image_mask.convert('L')

            if self.inpainting_mask_invert:
                self.image_mask = ImageOps.invert(self.image_mask)

            #self.image_unblurred_mask = self.image_mask

            if self.mask_blur > 0:
                self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))

            if self.inpaint_full_res:
                self.mask_for_overlay = self.image_mask
                mask = self.image_mask.convert('L')
                crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
                crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
                x1, y1, x2, y2 = crop_region

                mask = mask.crop(crop_region)
                self.image_mask = images.resize_image(2, mask, self.width, self.height)
                self.paste_to = (x1, y1, x2-x1, y2-y1)
            else:
                self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
                np_mask = np.array(self.image_mask)
                np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
                self.mask_for_overlay = Image.fromarray(np_mask)

            self.overlay_images = []

        latent_mask = self.latent_mask if self.latent_mask is not None else self.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:
            image = img.convert("RGB")

            if crop_region is None:
                image = images.resize_image(self.resize_mode, image, self.width, self.height)

            if self.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'))

            if crop_region is not None:
                image = image.crop(crop_region)
                image = images.resize_image(2, image, self.width, self.height)

            if self.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
        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 = 2. * image - 1.
        image = image.to(shared.device)

        self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))

        if self.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

    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)

        if self.mask is not None:
            samples = samples * self.nmask + self.init_latent * self.mask

        del x
        devices.torch_gc()

        return samples