Spaces:
Runtime error
Runtime error
import datetime | |
import sys | |
import traceback | |
import pytz | |
import io | |
import math | |
import os | |
from collections import namedtuple | |
import re | |
import numpy as np | |
import piexif | |
import piexif.helper | |
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin | |
from fonts.ttf import Roboto | |
import string | |
import json | |
import hashlib | |
from modules import sd_samplers, shared, script_callbacks, errors | |
from modules.shared import opts, cmd_opts | |
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) | |
def image_grid(imgs, batch_size=1, rows=None): | |
if rows is None: | |
if opts.n_rows > 0: | |
rows = opts.n_rows | |
elif opts.n_rows == 0: | |
rows = batch_size | |
elif opts.grid_prevent_empty_spots: | |
rows = math.floor(math.sqrt(len(imgs))) | |
while len(imgs) % rows != 0: | |
rows -= 1 | |
else: | |
rows = math.sqrt(len(imgs)) | |
rows = round(rows) | |
if rows > len(imgs): | |
rows = len(imgs) | |
cols = math.ceil(len(imgs) / rows) | |
params = script_callbacks.ImageGridLoopParams(imgs, cols, rows) | |
script_callbacks.image_grid_callback(params) | |
w, h = imgs[0].size | |
grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black') | |
for i, img in enumerate(params.imgs): | |
grid.paste(img, box=(i % params.cols * w, i // params.cols * h)) | |
return grid | |
Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"]) | |
def split_grid(image, tile_w=512, tile_h=512, overlap=64): | |
w = image.width | |
h = image.height | |
non_overlap_width = tile_w - overlap | |
non_overlap_height = tile_h - overlap | |
cols = math.ceil((w - overlap) / non_overlap_width) | |
rows = math.ceil((h - overlap) / non_overlap_height) | |
dx = (w - tile_w) / (cols - 1) if cols > 1 else 0 | |
dy = (h - tile_h) / (rows - 1) if rows > 1 else 0 | |
grid = Grid([], tile_w, tile_h, w, h, overlap) | |
for row in range(rows): | |
row_images = [] | |
y = int(row * dy) | |
if y + tile_h >= h: | |
y = h - tile_h | |
for col in range(cols): | |
x = int(col * dx) | |
if x + tile_w >= w: | |
x = w - tile_w | |
tile = image.crop((x, y, x + tile_w, y + tile_h)) | |
row_images.append([x, tile_w, tile]) | |
grid.tiles.append([y, tile_h, row_images]) | |
return grid | |
def combine_grid(grid): | |
def make_mask_image(r): | |
r = r * 255 / grid.overlap | |
r = r.astype(np.uint8) | |
return Image.fromarray(r, 'L') | |
mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0)) | |
mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1)) | |
combined_image = Image.new("RGB", (grid.image_w, grid.image_h)) | |
for y, h, row in grid.tiles: | |
combined_row = Image.new("RGB", (grid.image_w, h)) | |
for x, w, tile in row: | |
if x == 0: | |
combined_row.paste(tile, (0, 0)) | |
continue | |
combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w) | |
combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0)) | |
if y == 0: | |
combined_image.paste(combined_row, (0, 0)) | |
continue | |
combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h) | |
combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap)) | |
return combined_image | |
class GridAnnotation: | |
def __init__(self, text='', is_active=True): | |
self.text = text | |
self.is_active = is_active | |
self.size = None | |
def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): | |
def wrap(drawing, text, font, line_length): | |
lines = [''] | |
for word in text.split(): | |
line = f'{lines[-1]} {word}'.strip() | |
if drawing.textlength(line, font=font) <= line_length: | |
lines[-1] = line | |
else: | |
lines.append(word) | |
return lines | |
def get_font(fontsize): | |
try: | |
return ImageFont.truetype(opts.font or Roboto, fontsize) | |
except Exception: | |
return ImageFont.truetype(Roboto, fontsize) | |
def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize): | |
for i, line in enumerate(lines): | |
fnt = initial_fnt | |
fontsize = initial_fontsize | |
while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0: | |
fontsize -= 1 | |
fnt = get_font(fontsize) | |
drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center") | |
if not line.is_active: | |
drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4) | |
draw_y += line.size[1] + line_spacing | |
fontsize = (width + height) // 25 | |
line_spacing = fontsize // 2 | |
fnt = get_font(fontsize) | |
color_active = (0, 0, 0) | |
color_inactive = (153, 153, 153) | |
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4 | |
cols = im.width // width | |
rows = im.height // height | |
assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}' | |
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}' | |
calc_img = Image.new("RGB", (1, 1), "white") | |
calc_d = ImageDraw.Draw(calc_img) | |
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)): | |
items = [] + texts | |
texts.clear() | |
for line in items: | |
wrapped = wrap(calc_d, line.text, fnt, allowed_width) | |
texts += [GridAnnotation(x, line.is_active) for x in wrapped] | |
for line in texts: | |
bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt) | |
line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1]) | |
line.allowed_width = allowed_width | |
hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts] | |
ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts] | |
pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2 | |
result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white") | |
for row in range(rows): | |
for col in range(cols): | |
cell = im.crop((width * col, height * row, width * (col+1), height * (row+1))) | |
result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row)) | |
d = ImageDraw.Draw(result) | |
for col in range(cols): | |
x = pad_left + (width + margin) * col + width / 2 | |
y = pad_top / 2 - hor_text_heights[col] / 2 | |
draw_texts(d, x, y, hor_texts[col], fnt, fontsize) | |
for row in range(rows): | |
x = pad_left / 2 | |
y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2 | |
draw_texts(d, x, y, ver_texts[row], fnt, fontsize) | |
return result | |
def draw_prompt_matrix(im, width, height, all_prompts, margin=0): | |
prompts = all_prompts[1:] | |
boundary = math.ceil(len(prompts) / 2) | |
prompts_horiz = prompts[:boundary] | |
prompts_vert = prompts[boundary:] | |
hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))] | |
ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))] | |
return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin) | |
def resize_image(resize_mode, im, width, height, upscaler_name=None): | |
""" | |
Resizes an image with the specified resize_mode, width, and height. | |
Args: | |
resize_mode: The mode to use when resizing the image. | |
0: Resize the image to the specified width and height. | |
1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. | |
2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. | |
im: The image to resize. | |
width: The width to resize the image to. | |
height: The height to resize the image to. | |
upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img. | |
""" | |
upscaler_name = upscaler_name or opts.upscaler_for_img2img | |
def resize(im, w, h): | |
if upscaler_name is None or upscaler_name == "None" or im.mode == 'L': | |
return im.resize((w, h), resample=LANCZOS) | |
scale = max(w / im.width, h / im.height) | |
if scale > 1.0: | |
upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name] | |
assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}" | |
upscaler = upscalers[0] | |
im = upscaler.scaler.upscale(im, scale, upscaler.data_path) | |
if im.width != w or im.height != h: | |
im = im.resize((w, h), resample=LANCZOS) | |
return im | |
if resize_mode == 0: | |
res = resize(im, width, height) | |
elif resize_mode == 1: | |
ratio = width / height | |
src_ratio = im.width / im.height | |
src_w = width if ratio > src_ratio else im.width * height // im.height | |
src_h = height if ratio <= src_ratio else im.height * width // im.width | |
resized = resize(im, src_w, src_h) | |
res = Image.new("RGB", (width, height)) | |
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) | |
else: | |
ratio = width / height | |
src_ratio = im.width / im.height | |
src_w = width if ratio < src_ratio else im.width * height // im.height | |
src_h = height if ratio >= src_ratio else im.height * width // im.width | |
resized = resize(im, src_w, src_h) | |
res = Image.new("RGB", (width, height)) | |
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) | |
if ratio < src_ratio: | |
fill_height = height // 2 - src_h // 2 | |
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) | |
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) | |
elif ratio > src_ratio: | |
fill_width = width // 2 - src_w // 2 | |
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) | |
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) | |
return res | |
invalid_filename_chars = '<>:"/\\|?*\n' | |
invalid_filename_prefix = ' ' | |
invalid_filename_postfix = ' .' | |
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+') | |
re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)") | |
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$") | |
max_filename_part_length = 128 | |
def sanitize_filename_part(text, replace_spaces=True): | |
if text is None: | |
return None | |
if replace_spaces: | |
text = text.replace(' ', '_') | |
text = text.translate({ord(x): '_' for x in invalid_filename_chars}) | |
text = text.lstrip(invalid_filename_prefix)[:max_filename_part_length] | |
text = text.rstrip(invalid_filename_postfix) | |
return text | |
class FilenameGenerator: | |
replacements = { | |
'seed': lambda self: self.seed if self.seed is not None else '', | |
'steps': lambda self: self.p and self.p.steps, | |
'cfg': lambda self: self.p and self.p.cfg_scale, | |
'width': lambda self: self.image.width, | |
'height': lambda self: self.image.height, | |
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False), | |
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False), | |
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash), | |
'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False), | |
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'), | |
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>] | |
'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp), | |
'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8], | |
'prompt': lambda self: sanitize_filename_part(self.prompt), | |
'prompt_no_styles': lambda self: self.prompt_no_style(), | |
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False), | |
'prompt_words': lambda self: self.prompt_words(), | |
} | |
default_time_format = '%Y%m%d%H%M%S' | |
def __init__(self, p, seed, prompt, image): | |
self.p = p | |
self.seed = seed | |
self.prompt = prompt | |
self.image = image | |
def prompt_no_style(self): | |
if self.p is None or self.prompt is None: | |
return None | |
prompt_no_style = self.prompt | |
for style in shared.prompt_styles.get_style_prompts(self.p.styles): | |
if len(style) > 0: | |
for part in style.split("{prompt}"): | |
prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',') | |
prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip() | |
return sanitize_filename_part(prompt_no_style, replace_spaces=False) | |
def prompt_words(self): | |
words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0] | |
if len(words) == 0: | |
words = ["empty"] | |
return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False) | |
def datetime(self, *args): | |
time_datetime = datetime.datetime.now() | |
time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format | |
try: | |
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None | |
except pytz.exceptions.UnknownTimeZoneError as _: | |
time_zone = None | |
time_zone_time = time_datetime.astimezone(time_zone) | |
try: | |
formatted_time = time_zone_time.strftime(time_format) | |
except (ValueError, TypeError) as _: | |
formatted_time = time_zone_time.strftime(self.default_time_format) | |
return sanitize_filename_part(formatted_time, replace_spaces=False) | |
def apply(self, x): | |
res = '' | |
for m in re_pattern.finditer(x): | |
text, pattern = m.groups() | |
res += text | |
if pattern is None: | |
continue | |
pattern_args = [] | |
while True: | |
m = re_pattern_arg.match(pattern) | |
if m is None: | |
break | |
pattern, arg = m.groups() | |
pattern_args.insert(0, arg) | |
fun = self.replacements.get(pattern.lower()) | |
if fun is not None: | |
try: | |
replacement = fun(self, *pattern_args) | |
except Exception: | |
replacement = None | |
print(f"Error adding [{pattern}] to filename", file=sys.stderr) | |
print(traceback.format_exc(), file=sys.stderr) | |
if replacement is not None: | |
res += str(replacement) | |
continue | |
res += f'[{pattern}]' | |
return res | |
def get_next_sequence_number(path, basename): | |
""" | |
Determines and returns the next sequence number to use when saving an image in the specified directory. | |
The sequence starts at 0. | |
""" | |
result = -1 | |
if basename != '': | |
basename = basename + "-" | |
prefix_length = len(basename) | |
for p in os.listdir(path): | |
if p.startswith(basename): | |
l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element) | |
try: | |
result = max(int(l[0]), result) | |
except ValueError: | |
pass | |
return result + 1 | |
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None): | |
"""Save an image. | |
Args: | |
image (`PIL.Image`): | |
The image to be saved. | |
path (`str`): | |
The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory. | |
basename (`str`): | |
The base filename which will be applied to `filename pattern`. | |
seed, prompt, short_filename, | |
extension (`str`): | |
Image file extension, default is `png`. | |
pngsectionname (`str`): | |
Specify the name of the section which `info` will be saved in. | |
info (`str` or `PngImagePlugin.iTXt`): | |
PNG info chunks. | |
existing_info (`dict`): | |
Additional PNG info. `existing_info == {pngsectionname: info, ...}` | |
no_prompt: | |
TODO I don't know its meaning. | |
p (`StableDiffusionProcessing`) | |
forced_filename (`str`): | |
If specified, `basename` and filename pattern will be ignored. | |
save_to_dirs (bool): | |
If true, the image will be saved into a subdirectory of `path`. | |
Returns: (fullfn, txt_fullfn) | |
fullfn (`str`): | |
The full path of the saved imaged. | |
txt_fullfn (`str` or None): | |
If a text file is saved for this image, this will be its full path. Otherwise None. | |
""" | |
namegen = FilenameGenerator(p, seed, prompt, image) | |
if save_to_dirs is None: | |
save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) | |
if save_to_dirs: | |
dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /') | |
path = os.path.join(path, dirname) | |
os.makedirs(path, exist_ok=True) | |
if forced_filename is None: | |
if short_filename or seed is None: | |
file_decoration = "" | |
elif opts.save_to_dirs: | |
file_decoration = opts.samples_filename_pattern or "[seed]" | |
else: | |
file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]" | |
add_number = opts.save_images_add_number or file_decoration == '' | |
if file_decoration != "" and add_number: | |
file_decoration = "-" + file_decoration | |
file_decoration = namegen.apply(file_decoration) + suffix | |
if add_number: | |
basecount = get_next_sequence_number(path, basename) | |
fullfn = None | |
for i in range(500): | |
fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}" | |
fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}") | |
if not os.path.exists(fullfn): | |
break | |
else: | |
fullfn = os.path.join(path, f"{file_decoration}.{extension}") | |
else: | |
fullfn = os.path.join(path, f"{forced_filename}.{extension}") | |
pnginfo = existing_info or {} | |
if info is not None: | |
pnginfo[pnginfo_section_name] = info | |
params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo) | |
script_callbacks.before_image_saved_callback(params) | |
image = params.image | |
fullfn = params.filename | |
info = params.pnginfo.get(pnginfo_section_name, None) | |
def _atomically_save_image(image_to_save, filename_without_extension, extension): | |
# save image with .tmp extension to avoid race condition when another process detects new image in the directory | |
temp_file_path = filename_without_extension + ".tmp" | |
image_format = Image.registered_extensions()[extension] | |
if extension.lower() == '.png': | |
pnginfo_data = PngImagePlugin.PngInfo() | |
if opts.enable_pnginfo: | |
for k, v in params.pnginfo.items(): | |
pnginfo_data.add_text(k, str(v)) | |
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data) | |
elif extension.lower() in (".jpg", ".jpeg", ".webp"): | |
if image_to_save.mode == 'RGBA': | |
image_to_save = image_to_save.convert("RGB") | |
elif image_to_save.mode == 'I;16': | |
image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L") | |
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality) | |
if opts.enable_pnginfo and info is not None: | |
exif_bytes = piexif.dump({ | |
"Exif": { | |
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode") | |
}, | |
}) | |
piexif.insert(exif_bytes, temp_file_path) | |
else: | |
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality) | |
# atomically rename the file with correct extension | |
os.replace(temp_file_path, filename_without_extension + extension) | |
fullfn_without_extension, extension = os.path.splitext(params.filename) | |
_atomically_save_image(image, fullfn_without_extension, extension) | |
image.already_saved_as = fullfn | |
oversize = image.width > opts.target_side_length or image.height > opts.target_side_length | |
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024): | |
ratio = image.width / image.height | |
if oversize and ratio > 1: | |
image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS) | |
elif oversize: | |
image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS) | |
try: | |
_atomically_save_image(image, fullfn_without_extension, ".jpg") | |
except Exception as e: | |
errors.display(e, "saving image as downscaled JPG") | |
if opts.save_txt and info is not None: | |
txt_fullfn = f"{fullfn_without_extension}.txt" | |
with open(txt_fullfn, "w", encoding="utf8") as file: | |
file.write(info + "\n") | |
else: | |
txt_fullfn = None | |
script_callbacks.image_saved_callback(params) | |
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") | |
if exif_comment: | |
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("Error parsing NovelAI image 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, _ = read_info_from_image(image) | |
return textinfo, None | |
except Exception: | |
pass | |
try: | |
text = data.decode('utf8') | |
assert len(text) < 10000 | |
return text, None | |
except Exception: | |
pass | |
return '', None | |
def flatten(img, bgcolor): | |
"""replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency""" | |
if img.mode == "RGBA": | |
background = Image.new('RGBA', img.size, bgcolor) | |
background.paste(img, mask=img) | |
img = background | |
return img.convert('RGB') | |