save image generation params into text chunks for png images

webui.py

@@ -4,7 +4,7 @@ import torch.nn as nn
 import numpy as np
 import gradio as gr
 from omegaconf import OmegaConf
-from PIL import Image, ImageFont, ImageDraw
+from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from itertools import islice
 from einops import rearrange, repeat
 from torch import autocast
@@ -49,11 +49,13 @@ parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=(
 parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long")
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)")
-parser.add_argument("--max-batch-count",  type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--save-format",  type=str, default='png', help="file format for saved indiviual samples; can be png or jpg")
-parser.add_argument("--grid-format",  type=str, default='png', help="file format for saved grids; can be png or jpg")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--save-format", type=str, default='png', help="file format for saved indiviual samples; can be png or jpg")
+parser.add_argument("--grid-format", type=str, default='png', help="file format for saved grids; can be png or jpg")
 parser.add_argument("--grid-extended-filename",  action='store_true', help="save grid images to filenames with extended info: seed, prompt")
-parser.add_argument("--jpeg-quality",  type=int, default=80, help="quality for saved jpeg images")
+parser.add_argument("--jpeg-quality", type=int, default=80, help="quality for saved jpeg images")
+parser.add_argument("--disable-pnginfo", action='store_true', help="disable saving text information about generation parameters as chunks to png files")
+
 opt = parser.parse_args()
 
 GFPGAN_dir = opt.gfpgan_dir
@@ -141,7 +143,7 @@ def sanitize_filename_part(text):
     return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
 
 
-def save_image(image, path, basename, seed, prompt, extension, short_filename=False):
+def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False):
     prompt = sanitize_filename_part(prompt)
 
     if short_filename:
@@ -149,7 +151,13 @@ def save_image(image, path, basename, seed, prompt, extension, short_filename=Fa
     else:
         filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
 
-    image.save(os.path.join(path, filename), quality=opt.jpeg_quality)
+    if extension == 'png' and not opt.disable_pnginfo:
+        pnginfo = PngImagePlugin.PngInfo()
+        pnginfo.add_text("parameters", info)
+    else:
+        pnginfo = None
+
+    image.save(os.path.join(path, filename), quality=opt.jpeg_quality, pnginfo=pnginfo)
 
 
 def load_GFPGAN():
@@ -373,6 +381,11 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
         all_prompts = batch_size * n_iter * [prompt]
         all_seeds = [seed + x for x in range(len(all_prompts))]
 
+    info = f"""
+    {prompt}
+    Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
+    """.strip() + "".join(["\n\n" + x for x in comments])
+
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
     output_images = []
     with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
@@ -407,7 +420,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
                         x_sample = restored_img
 
                     image = Image.fromarray(x_sample)
-                    save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opt.save_format)
+                    save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opt.save_format, info=info)
 
                     output_images.append(image)
                     base_count += 1
@@ -426,16 +439,9 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
 
                 output_images.insert(0, grid)
 
-            save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opt.grid_format, short_filename=not opt.grid_extended_filename)
+            save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opt.grid_format, info=info, short_filename=not opt.grid_extended_filename)
             grid_count += 1
 
-    info = f"""
-{prompt}
-Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
-        """.strip()
-
-    for comment in comments:
-        info += "\n\n" + comment
     torch_gc()
     return output_images, seed, info
 
@@ -619,7 +625,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
         grid_count = len(os.listdir(outpath)) - 1
         grid = image_grid(history, batch_size, force_n_rows=1)
 
-        save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opt.grid_format, short_filename=not opt.grid_extended_filename)
+        save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opt.grid_format, info=info, short_filename=not opt.grid_extended_filename)
 
         output_images = history
         seed = initial_seed