InvokeAI/ldm/dream/pngwriter.py

"""
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)