Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui

README.md

@@ -146,3 +146,12 @@ to get otherwise.
 Example: (cherrypicked result; original picture by anon)
 
 ![](images/loopback.jpg)
+
+### Png info
+Adds information about generation parameters to PNG as a text chunk. You
+can view this information later using any software that supports viewing
+PNG chunk info, for example: https://www.nayuki.io/page/png-file-chunk-inspector
+
+This can be disabled using the `--disable-pnginfo` command line option.
+
+![](images/pnginfo.png)

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
@@ -12,6 +12,8 @@ from contextlib import contextmanager, nullcontext
 import mimetypes
 import random
 import math
+import html
+import time
 
 import k_diffusion as K
 from ldm.util import instantiate_from_config
@@ -49,8 +51,13 @@ 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("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--grid-format",  type=str, default='png', help="file format for saved grids; can be png or jpg")
+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("--disable-pnginfo", action='store_true', help="disable saving text information about generation parameters as chunks to png files")
+
 parser.add_argument("--inversion", action='store_true', help="switch to stable inversion version; allows for uploading embeddings; this option should be used only with textual inversion repo")
 opt = parser.parse_args()
 
@@ -130,6 +137,37 @@ def create_random_tensors(shape, seeds):
     return x
 
 
+def torch_gc():
+    torch.cuda.empty_cache()
+    torch.cuda.ipc_collect()
+
+
+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, info=None, short_filename=False):
+    prompt = sanitize_filename_part(prompt)
+
+    if short_filename:
+        filename = f"{basename}.{extension}"
+    else:
+        filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
+
+    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 plaintext_to_html(text):
+    text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
+    return text
+
+
 def load_GFPGAN():
     model_name = 'GFPGANv1.3'
     model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
@@ -301,11 +339,25 @@ def check_prompt_length(prompt, comments):
     comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
 
 
+def wrap_gradio_call(func):
+    def f(*p1, **p2):
+        t = time.perf_counter()
+        res = list(func(*p1, **p2))
+        elapsed = time.perf_counter() - t
+
+        # last item is always HTML
+        res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
+
+        return tuple(res)
+
+    return f
+
+
 def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     assert prompt is not None
-    torch.cuda.empty_cache()
+    torch_gc()
 
     if seed == -1:
         seed = random.randrange(4294967294)
@@ -351,6 +403,11 @@ 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():
@@ -385,9 +442,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
                         x_sample = restored_img
 
                     image = Image.fromarray(x_sample)
-                    filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
+                    save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opt.save_format, info=info)
-
-                    image.save(os.path.join(sample_path, filename))
 
                     output_images.append(image)
                     base_count += 1
@@ -406,17 +461,10 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
 
                 output_images.insert(0, grid)
 
-            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}'))
+            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"""
+    torch_gc()
-{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
-
     return output_images, seed, info
 
 
@@ -465,7 +513,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
 
     del sampler
 
-    return output_images, seed, info
+    return output_images, seed, plaintext_to_html(info)
 
 
 class Flagging(gr.FlaggingCallback):
@@ -510,7 +558,7 @@ class Flagging(gr.FlaggingCallback):
 
 
 txt2img_interface = gr.Interface(
-    txt2img,
+    wrap_gradio_call(txt2img),
     inputs=[
         gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
         gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
@@ -529,7 +577,7 @@ txt2img_interface = gr.Interface(
     outputs=[
         gr.Gallery(label="Images"),
         gr.Number(label='Seed'),
-        gr.Textbox(label="Copy-paste generation parameters"),
+        gr.HTML(),
     ],
     title="Stable Diffusion Text-to-Image K",
     description="Generate images from text with Stable Diffusion (using K-LMS)",
@@ -608,7 +656,8 @@ 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)
-        grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}'))
+
+        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
@@ -633,14 +682,14 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
 
     del sampler
 
-    return output_images, seed, info
+    return output_images, seed, plaintext_to_html(info)
 
 
 sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
 sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
 
 img2img_interface = gr.Interface(
-    img2img,
+    wrap_gradio_call(img2img),
     inputs=[
         gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
         gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
@@ -661,7 +710,7 @@ img2img_interface = gr.Interface(
     outputs=[
         gr.Gallery(),
         gr.Number(label='Seed'),
-        gr.Textbox(label="Copy-paste generation parameters"),
+        gr.HTML(),
     ],
     title="Stable Diffusion Image-to-Image",
     description="Generate images from images with Stable Diffusion",
@@ -682,7 +731,7 @@ def run_GFPGAN(image, strength):
     if strength < 1.0:
         res = Image.blend(image, res, strength)
 
-    return res
+    return res, 0, ''
 
 
 if GFPGAN is not None:
@@ -694,6 +743,8 @@ if GFPGAN is not None:
         ],
         outputs=[
             gr.Image(label="Result"),
+            gr.Number(label='Seed', visible=False),
+            gr.HTML(),
         ],
         title="GFPGAN",
         description="Fix faces on images",
@@ -704,7 +755,10 @@ if GFPGAN is not None:
 demo = gr.TabbedInterface(
     interface_list=[x[0] for x in interfaces],
     tab_names=[x[1] for x in interfaces],
-    css=("" if opt.no_progressbar_hiding else css_hide_progressbar)
+    css=("" if opt.no_progressbar_hiding else css_hide_progressbar) + """
+.output-html p {margin: 0 0.5em;}
+.performance { font-size: 0.85em; color: #444; }
+"""
 )
 
 demo.launch()