save parameters for images when using the Save button.

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):

    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.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 = {

            "denoising_strength": self.denoising_strength,

            "extra_generation_params": self.extra_generation_params,

            "index_of_first_image": self.index_of_first_image,

            "infotexts": self.infotexts,

        }

        return json.dumps(obj)

    if os.path.exists(cmd_opts.embeddings_dir):

        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)

    infotexts = []

    output_images = []

    precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext

    ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)

                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)

                infotexts.append(infotext(n, i))

                output_images.append(image)

            state.nextjob()

            grid = images.image_grid(output_images, p.batch_size)

            if opts.return_grid:

                infotexts.insert(0, infotext())

                output_images.insert(0, grid)

                index_of_first_image = 1

                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(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)

    return Processed(p, output_images, all_seeds[0], infotext(), 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):

    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),

    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),

    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),

}))

options_templates.update(options_section(('saving-paths', "Paths for saving"), {

    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),

    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),

    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),

    "save_selected_only": OptionInfo(False, "When using 'Save' button, only save a single selected image"),

}))

options_templates.update(options_section(('system', "System"), {

import numpy as np

import torch

from PIL import Image

from PIL import Image, PngImagePlugin

import gradio as gr

import gradio.utils

    filenames = []

    data = json.loads(js_data)

    if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only

    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only

        images = [images[index]]

        data["seed"] += (index - 1 if opts.return_grid else index)

        infotexts = [data["infotexts"][index]]

    else:

        infotexts = data["infotexts"]

    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:

        at_start = file.tell() == 0

            if filedata.startswith("data:image/png;base64,"):

                filedata = filedata[len("data:image/png;base64,"):]

            with open(filepath, "wb") as imgfile:

                imgfile.write(base64.decodebytes(filedata.encode('utf-8')))

            pnginfo = PngImagePlugin.PngInfo()

            pnginfo.add_text('parameters', infotexts[i])

            image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))

            image.save(filepath, quality=opts.jpeg_quality, pnginfo=pnginfo)

            filenames.append(filename)