stable-diffusion-webui/modules/processing.py

import json
import math
import os
import sys
import warnings

import torch
import numpy as np
from PIL import Image, ImageFilter, ImageOps
import random
import cv2
from skimage import exposure
from typing import Any, Dict, List, Optional

import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks
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 modules.sd_models as sd_models
import modules.sd_vae as sd_vae
import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion

from einops import repeat, rearrange

# 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


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


class StableDiffusionProcessing():
    """
    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
    """
    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
        if sampler_index is not None:
            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)

        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_name: str = sampler_name
        self.batch_size: int = batch_size
        self.n_iter: int = n_iter
        self.steps: int = steps
        self.cfg_scale: float = cfg_scale
        self.width: int = width
        self.height: int = height
        self.restore_faces: bool = restore_faces
        self.tiling: bool = tiling
        self.do_not_save_samples: bool = do_not_save_samples
        self.do_not_save_grid: bool = do_not_save_grid
        self.extra_generation_params: dict = extra_generation_params or {}
        self.overlay_images = overlay_images
        self.eta = eta
        self.do_not_reload_embeddings = do_not_reload_embeddings
        self.paste_to = None
        self.color_corrections = None
        self.denoising_strength: float = denoising_strength
        self.sampler_noise_scheduler_override = None
        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
        self.s_churn = s_churn or opts.s_churn
        self.s_tmin = s_tmin or opts.s_tmin
        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
        self.s_noise = s_noise or opts.s_noise
        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
        self.is_using_inpainting_conditioning = False

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

        self.scripts = None
        self.script_args = None
        self.all_prompts = None
        self.all_negative_prompts = None
        self.all_seeds = None
        self.all_subseeds = None

    def txt2img_image_conditioning(self, x, width=None, height=None):
        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
            # Dummy zero conditioning if we're not using inpainting model.
            # 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)

        self.is_using_inpainting_conditioning = True

        height = height or self.height
        width = width or self.width

        # The "masked-image" in this case will just be all zeros since the entire image is masked.
        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 

        # 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

    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 = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
        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 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(source_image.device).to(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):
        # 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.sampler.conditioning_key in {'hybrid', 'concat'}:
            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.sd_model = None
        self.sampler = None


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):
        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_name = p.sampler_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_stop_at_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]) if self.seed is not None else -1
        self.subseed = int(self.subseed if type(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]

    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_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,
            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
        }

        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 or opts.eta_noise_seed_delta > 0):
        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)

            if opts.eta_noise_seed_delta > 0:
                torch.manual_seed(seed + opts.eta_noise_seed_delta)

            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 decode_first_stage(model, x):
    with devices.autocast(disable=x.dtype == devices.dtype_vae):
        x = model.decode_first_stage(x)

    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_stop_at_last_layers)

    generation_params = {
        "Steps": p.steps,
        "Sampler": p.sampler_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),
        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
        "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
        "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),
        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else 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 <= 1 else clip_skip,
        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
    }

    generation_params.update(p.extra_generation_params)

    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])

    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if  p.all_negative_prompts[index] else ""

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


def process_images(p: StableDiffusionProcessing) -> Processed:
    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}

    try:
        for k, v in p.override_settings.items():
            setattr(opts, k, v)
            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet
            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()  # make onchange call for changing SD model
            if k == 'sd_vae': sd_vae.reload_vae_weights()  # make onchange call for changing VAE

        res = process_images_inner(p)

    finally:  # restore opts to original state
        for k, v in stored_opts.items():
            setattr(opts, k, v)
            if k == 'sd_hypernetwork': shared.reload_hypernetworks()
            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
            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 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)

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

    comments = {}

    if type(p.prompt) == list:
        p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
    else:
        p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]

    if type(p.negative_prompt) == list:
        p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
    else:
        p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]

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

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

    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
        processed = Processed(p, [], p.seed, "")
        file.write(processed.infotext(p, 0))

    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)

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

            if len(prompts) == 0:
                break

            if p.scripts is not None:
                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)

            with devices.autocast():
                uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, 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, prompts=prompts)

            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
            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 shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
                lowvram.send_everything_to_cpu()

            devices.torch_gc()

            if p.scripts is not None:
                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)

            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:
                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                        images.save_image(image_without_cc, 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)

                image = apply_overlay(image, p.paste_to, i, p.overlay_images)

                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)
                if opts.enable_pnginfo:
                    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)
                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(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

    devices.torch_gc()

    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), 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


class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
    sampler = None

    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
        super().__init__(**kwargs)
        self.enable_hr = enable_hr
        self.denoising_strength = denoising_strength
        self.firstphase_width = firstphase_width
        self.firstphase_height = firstphase_height
        self.truncate_x = 0
        self.truncate_y = 0

    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

            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"

            if self.firstphase_width == 0 or self.firstphase_height == 0:
                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
                firstphase_width_truncated = int(scale * self.width)
                firstphase_height_truncated = int(scale * self.height)

            else:

                width_ratio = self.width / self.firstphase_width
                height_ratio = self.height / self.firstphase_height

                if width_ratio > height_ratio:
                    firstphase_width_truncated = self.firstphase_width
                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
                else:
                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
                    firstphase_height_truncated = self.firstphase_height

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

    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
        self.sampler = sd_samplers.create_sampler(self.sampler_name, 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, image_conditioning=self.txt2img_image_conditioning(x))
            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, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))

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

        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
        def save_intermediate(image, index):
            if not opts.save or self.do_not_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)

            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")

        if opts.use_scale_latent_for_hires_fix:
            for i in range(samples.shape[0]):
                save_intermediate(samples, i)

            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

            # 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:
            decoded_samples = decode_first_stage(self.sd_model, samples)
            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, 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))

            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)

        shared.state.nextjob()

        self.sampler = sd_samplers.create_sampler(self.sampler_name, 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, image_conditioning=image_conditioning)

        return samples


class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
    sampler = None

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

        self.init_images = init_images
        self.resize_mode: int = resize_mode
        self.denoising_strength: float = denoising_strength
        self.init_latent = None
        self.image_mask = mask
        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.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
        self.mask = None
        self.nmask = None
        self.image_conditioning = None

    def init(self, all_prompts, all_seeds, all_subseeds):
        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 = image_mask.convert('L')

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

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

            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:
            image = img.convert("RGB")

            if crop_region is None:
                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'))

            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 = 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 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, self.init_latent, image_mask)

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

        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