InvokeAI/ldm/dream/pngwriter.py

86 lines
3.4 KiB
Python

"""
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
into the PNG.
PromptFormatter -- Utility for converting a Namespace of prompt parameters
back into a formatted prompt string with command-line switches.
"""
import os
import re
from PIL import PngImagePlugin
# -------------------image generation utils-----
class PngWriter:
def __init__(self, outdir):
self.outdir = outdir
os.makedirs(outdir, exist_ok=True)
# 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}'
# 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, prompt, name):
path = os.path.join(self.outdir, name)
info = PngImagePlugin.PngInfo()
info.add_text('Dream', prompt)
image.save(path, 'PNG', pnginfo=info)
return path
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 hasattr(opt, 'embiggen') and opt.embiggen:
switches.append(f'-embiggen {" ".join([str(u) for u in opt.embiggen])}')
if hasattr(opt, 'embiggen_tiles') and opt.embiggen_tiles:
switches.append(f'-embiggen_tiles {" ".join([str(u) for u in opt.embiggen_tiles])}')
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)