save parameters for images when using the Save button.

modules/processing.py

@@ -100,7 +100,7 @@ class StableDiffusionProcessing:
 
 
 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
@@ -139,6 +139,7 @@ class Processed:
         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 = {
@@ -165,6 +166,7 @@ class Processed:
             "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)
@@ -322,6 +324,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     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)
@@ -404,6 +407,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 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()
@@ -416,6 +420,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             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
 
@@ -423,7 +428,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 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):

modules/shared.py

@@ -143,6 +143,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "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"), {
@@ -180,7 +181,6 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
     "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"), {

modules/ui.py

@@ -12,7 +12,7 @@ import traceback
 
 import numpy as np
 import torch
-from PIL import Image
+from PIL import Image, PngImagePlugin
 
 import gradio as gr
 import gradio.utils
@@ -97,10 +97,11 @@ def save_files(js_data, images, index):
     filenames = []
 
     data = json.loads(js_data)
-    
+    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
-    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
         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
@@ -116,8 +117,11 @@ def save_files(js_data, images, index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
 
-            with open(filepath, "wb") as imgfile:
+            pnginfo = PngImagePlugin.PngInfo()
-                imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
+            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)