Merge branch 'a1111' into vae-fix-none

from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image

from fastapi import APIRouter, Depends, FastAPI, HTTPException

import modules.shared as shared

from modules import sd_samplers

from modules.api.models import *

from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images

from modules.sd_samplers import all_samplers

from modules.extras import run_extras, run_pnginfo

from PIL import PngImagePlugin

from modules.sd_models import checkpoints_list

        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")

sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)

def validate_sampler_name(name):

    config = sd_samplers.all_samplers_map.get(name, None)

    if config is None:

        raise HTTPException(status_code=404, detail="Sampler not found")

    return name

def setUpscalers(req: dict):

    reqDict = vars(req)

        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])

    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):

        sampler_index = sampler_to_index(txt2imgreq.sampler_index)

        if sampler_index is None:

            raise HTTPException(status_code=404, detail="Sampler not found")

        populate = txt2imgreq.copy(update={ # Override __init__ params

            "sd_model": shared.sd_model,

            "sampler_index": sampler_index[0],

            "sampler_name": validate_sampler_name(txt2imgreq.sampler_index),

            "do_not_save_samples": True,

            "do_not_save_grid": True

            }

        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())

    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):

        sampler_index = sampler_to_index(img2imgreq.sampler_index)

        if sampler_index is None:

            raise HTTPException(status_code=404, detail="Sampler not found")

        init_images = img2imgreq.init_images

        if init_images is None:

            raise HTTPException(status_code=404, detail="Init image not found")

        if mask:

            mask = decode_base64_to_image(mask)

        populate = img2imgreq.copy(update={ # Override __init__ params

            "sd_model": shared.sd_model,

            "sampler_index": sampler_index[0],

            "sampler_name": validate_sampler_name(img2imgreq.sampler_index),

            "do_not_save_samples": True,

            "do_not_save_grid": True,

            "mask": mask

        return vars(shared.cmd_opts)

    def get_samplers(self):

        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]

        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]

    def get_upscalers(self):

        upscalers = []

        self.can_update = False

        self.status = "latest"

    def pull(self):

    def fetch_and_reset_hard(self):

        repo = git.Repo(self.path)

        repo.remotes.origin.pull()

        # Fix: `error: Your local changes to the following files would be overwritten by merge`,

        # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.

        repo.git.fetch('--all')

        repo.git.reset('--hard', 'origin')

def list_extensions():

import tqdm

from einops import rearrange, repeat

from ldm.util import default

from modules import devices, processing, sd_models, shared

from modules import devices, processing, sd_models, shared, sd_samplers

from modules.textual_inversion import textual_inversion

from modules.textual_inversion.learn_schedule import LearnRateScheduler

from torch import einsum

                p.prompt = preview_prompt

                p.negative_prompt = preview_negative_prompt

                p.steps = preview_steps

                p.sampler_index = preview_sampler_index

                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name

                p.cfg_scale = preview_cfg_scale

                p.seed = preview_seed

                p.width = preview_width

        'width': lambda self: self.image.width,

        'height': lambda self: self.image.height,

        'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),

        'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),

        'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),

        'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),

        'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),

        'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]

import numpy as np

from PIL import Image, ImageOps, ImageChops

from modules import devices

from modules import devices, sd_samplers

from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images

from modules.shared import opts, state

import modules.shared as shared

        seed_resize_from_h=seed_resize_from_h,

        seed_resize_from_w=seed_resize_from_w,

        seed_enable_extras=seed_enable_extras,

        sampler_index=sampler_index,

        sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,

        batch_size=batch_size,

        n_iter=n_iter,

        steps=steps,