rework #5012 to also work for pictures dragged into the prompt and also add...

from __future__ import annotations

import math

import os

import sys

import traceback

import numpy as np

from PIL import Image

from functools import partial

from dataclasses import dataclass

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

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

from modules.shared import opts

import modules.gfpgan_model

from modules.ui import plaintext_to_html

import gradio as gr

import safetensors.torch

class LruCache(OrderedDict):

    @dataclass(frozen=True)

    class Key:

    if image is None:

        return '', '', ''

    items = image.info

    geninfo = ''

    if "exif" in image.info:

        exif = piexif.load(image.info["exif"])

        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')

        try:

            exif_comment = piexif.helper.UserComment.load(exif_comment)

        except ValueError:

            exif_comment = exif_comment.decode('utf8', errors="ignore")

        items['exif comment'] = exif_comment

        geninfo = exif_comment

        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',

                      'loop', 'background', 'timestamp', 'duration']:

            items.pop(field, None)

    geninfo = items.get('parameters', geninfo)

    # nai prompt

    if "Software" in items.keys() and items["Software"] == "NovelAI":

        import json

        json_info = json.loads(items["Comment"])

        geninfo = f'{items["Description"]}\r\nNegative prompt: {json_info["uc"]}\r\n'

        sampler = "Euler a"

        if json_info["sampler"] == "k_euler_ancestral":

            sampler = "Euler a"

        elif json_info["sampler"] == "k_euler":

            sampler = "Euler"

        model_hash = '925997e9'  # assuming this is the correct model hash

        # not sure with noise and strength parameter

        geninfo += f'Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Model hash: {model_hash}'  # , Denoising strength: {json_info["noise"]}'

    geninfo, items = images.read_info_from_image(image)

    items = {**{'parameters': geninfo}, **items}

    info = ''

    for key, text in items.items():

        'CLIP_stop_at_last_layers': 'Clip skip',

        'inpainting_mask_weight': 'Conditional mask weight',

        'sd_model_checkpoint': 'Model hash',

        'eta_noise_seed_delta': 'ENSD',

    }

    settings_paste_fields = [

        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))

from PIL import Image, ImageFont, ImageDraw, PngImagePlugin

from fonts.ttf import Roboto

import string

import json

from modules import sd_samplers, shared, script_callbacks

from modules.shared import opts, cmd_opts

    return fullfn, txt_fullfn

def read_info_from_image(image):

    items = image.info or {}

    geninfo = items.pop('parameters', None)

    if "exif" in items:

        exif = piexif.load(items["exif"])

        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')

        try:

            exif_comment = piexif.helper.UserComment.load(exif_comment)

        except ValueError:

            exif_comment = exif_comment.decode('utf8', errors="ignore")

        items['exif comment'] = exif_comment

        geninfo = exif_comment

        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',

                      'loop', 'background', 'timestamp', 'duration']:

            items.pop(field, None)

    if items.get("Software", None) == "NovelAI":

        try:

            json_info = json.loads(items["Comment"])

            sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")

            geninfo = f"""{items["Description"]}

Negative prompt: {json_info["uc"]}

Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""

        except Exception:

            print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)

            print(traceback.format_exc(), file=sys.stderr)

    return geninfo, items

def image_data(data):

    try:

        image = Image.open(io.BytesIO(data))

        textinfo = image.text["parameters"]

        textinfo, _ = read_info_from_image(image)

        return textinfo, None

    except Exception:

        pass

SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])

samplers_k_diffusion = [

    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),

    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),

    ('Euler', 'sample_euler', ['k_euler'], {}),

    ('LMS', 'sample_lms', ['k_lms'], {}),

    ('Heun', 'sample_heun', ['k_heun'], {}),