InvokeAI/ldm/invoke/pngwriter.py

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"""
Two helper classes for dealing with PNG images and their path names.
PngWriter -- Converts Images generated by T2I into PNGs, finds
appropriate names for them, and writes prompt metadata
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into the PNG.
Exports function retrieve_metadata(path)
"""
import os
import re
import json
from PIL import PngImagePlugin, Image
# -------------------image generation utils-----
class PngWriter:
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def __init__(self, outdir):
self.outdir = outdir
os.makedirs(outdir, exist_ok=True)
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# gives the next unique prefix in outdir
def unique_prefix(self):
# sort reverse alphabetically until we find max+1
dirlist = sorted(os.listdir(self.outdir), reverse=True)
# find the first filename that matches our pattern or return 000000.0.png
existing_name = next(
(f for f in dirlist if re.match('^(\d+)\..*\.png', f)),
'0000000.0.png',
)
basecount = int(existing_name.split('.', 1)[0]) + 1
return f'{basecount:06}'
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# saves image named _image_ to outdir/name, writing metadata from prompt
# returns full path of output
def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None, compress_level=6):
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path = os.path.join(self.outdir, name)
info = PngImagePlugin.PngInfo()
info.add_text('Dream', dream_prompt)
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if metadata:
info.add_text('sd-metadata', json.dumps(metadata))
image.save(path, 'PNG', pnginfo=info, compress_level=compress_level)
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return path
def retrieve_metadata(self,img_basename):
'''
Given a PNG filename stored in outdir, returns the "sd-metadata"
metadata stored there, as a dict
'''
path = os.path.join(self.outdir,img_basename)
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all_metadata = retrieve_metadata(path)
return all_metadata['sd-metadata']
def retrieve_metadata(img_path):
'''
Given a path to a PNG image, returns the "sd-metadata"
metadata stored there, as a dict
'''
im = Image.open(img_path)
if hasattr(im, 'text'):
md = im.text.get('sd-metadata', '{}')
dream_prompt = im.text.get('Dream', '')
else:
# When trying to retrieve metadata from images without a 'text' payload, such as JPG images.
md = '{}'
dream_prompt = ''
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return {'sd-metadata': json.loads(md), 'Dream': dream_prompt}
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def write_metadata(img_path:str, meta:dict):
im = Image.open(img_path)
info = PngImagePlugin.PngInfo()
info.add_text('sd-metadata', json.dumps(meta))
im.save(img_path,'PNG',pnginfo=info)
class PromptFormatter:
def __init__(self, t2i, opt):
self.t2i = t2i
self.opt = opt
# note: the t2i object should provide all these values.
# there should be no need to or against opt values
def normalize_prompt(self):
"""Normalize the prompt and switches"""
t2i = self.t2i
opt = self.opt
switches = list()
switches.append(f'"{opt.prompt}"')
switches.append(f'-s{opt.steps or t2i.steps}')
switches.append(f'-W{opt.width or t2i.width}')
switches.append(f'-H{opt.height or t2i.height}')
switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
# to do: put model name into the t2i object
# switches.append(f'--model{t2i.model_name}')
if opt.seamless or t2i.seamless:
switches.append(f'--seamless')
if opt.init_img:
switches.append(f'-I{opt.init_img}')
if opt.fit:
switches.append(f'--fit')
if opt.strength and opt.init_img is not None:
switches.append(f'-f{opt.strength or t2i.strength}')
if opt.gfpgan_strength:
switches.append(f'-G{opt.gfpgan_strength}')
if opt.upscale:
switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
if opt.variation_amount > 0:
switches.append(f'-v{opt.variation_amount}')
if opt.with_variations:
formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
switches.append(f'-V{formatted_variations}')
return ' '.join(switches)