Automatically add IPTC metadata (prompt and more) to JPG images generated by stable-diffusion

optimized_txt2img.py

# optimized_txt2img.py patched to include IPTC metadata to JPEG images
# Requires iptcinfo3: after "conda activate ldm", install latest version from GitHub with
# pip install git+https://github.com/james-see/iptcinfo3#egg=iptcinfo3
import argparse, os, re, sys
import torch
import numpy as np
from random import randint
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
from ldm.util import instantiate_from_config
from optimUtils import split_weighted_subprompts, logger
from transformers import logging
from iptcinfo3 import IPTCInfo
# from samplers import CompVisDenoiser
logging.set_verbosity_error()


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    return sd


config = "optimizedSD/v1-inference.yaml"
ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"

parser = argparse.ArgumentParser()

parser.add_argument(
    "--prompt", type=str, nargs="?", default="a painting of a virus monster playing guitar", help="the prompt to render"
)
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples")
parser.add_argument("--prefix", type=str, help="folder prefix", default="")
parser.add_argument(
    "--skip_grid",
    action="store_true",
    help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
    "--skip_save",
    action="store_true",
    help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
    "--ddim_steps",
    type=int,
    default=50,
    help="number of ddim sampling steps",
)

parser.add_argument(
    "--fixed_code",
    action="store_true",
    help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
    "--ddim_eta",
    type=float,
    default=0.0,
    help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
    "--n_iter",
    type=int,
    default=1,
    help="sample this often",
)
parser.add_argument(
    "--H",
    type=int,
    default=512,
    help="image height, in pixel space",
)
parser.add_argument(
    "--W",
    type=int,
    default=512,
    help="image width, in pixel space",
)
parser.add_argument(
    "--C",
    type=int,
    default=4,
    help="latent channels",
)
parser.add_argument(
    "--f",
    type=int,
    default=8,
    help="downsampling factor",
)
parser.add_argument(
    "--n_samples",
    type=int,
    default=5,
    help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
    "--n_rows",
    type=int,
    default=0,
    help="rows in the grid (default: n_samples)",
)
parser.add_argument(
    "--scale",
    type=float,
    default=7.5,
    help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
    "--device",
    type=str,
    default="cuda",
    help="specify GPU (cuda/cuda:0/cuda:1/...)",
)
parser.add_argument(
    "--from-file",
    type=str,
    help="if specified, load prompts from this file",
)
parser.add_argument(
    "--seed",
    type=int,
    default=None,
    help="the seed (for reproducible sampling)",
)
parser.add_argument(
    "--unet_bs",
    type=int,
    default=1,
    help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )",
)
parser.add_argument(
    "--turbo",
    action="store_true",
    help="Reduces inference time on the expense of 1GB VRAM",
)
parser.add_argument(
    "--precision", 
    type=str,
    help="evaluate at this precision",
    choices=["full", "autocast"],
    default="autocast"
)
parser.add_argument(
    "--format",
    type=str,
    help="output image format",
    choices=["jpg", "png"],
    default="png",
)
parser.add_argument(
    "--sampler",
    type=str,
    help="sampler",
    choices=["ddim", "plms"],
    default="plms",
)
parser.add_argument("--iptc", type=bool, nargs="?", default=True, help="save IPTC data to output image (only for JPG)")
opt = parser.parse_args()

tic = time.time()
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
grid_count = len(os.listdir(outpath)) - 1

if opt.seed == None:
    opt.seed = randint(0, 1000000)
seed_everything(opt.seed)

# Logging
logger(vars(opt), log_csv = "logs/txt2img_logs.csv")

sd = load_model_from_config(f"{ckpt}")
li, lo = [], []
for key, value in sd.items():
    sp = key.split(".")
    if (sp[0]) == "model":
        if "input_blocks" in sp:
            li.append(key)
        elif "middle_block" in sp:
            li.append(key)
        elif "time_embed" in sp:
            li.append(key)
        else:
            lo.append(key)
for key in li:
    sd["model1." + key[6:]] = sd.pop(key)
for key in lo:
    sd["model2." + key[6:]] = sd.pop(key)

config = OmegaConf.load(f"{config}")

model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
model.unet_bs = opt.unet_bs
model.cdevice = opt.device
model.turbo = opt.turbo

modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelCS.cond_stage_model.device = opt.device

modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd

if opt.device != "cpu" and opt.precision == "autocast":
    model.half()
    modelCS.half()

start_code = None
if opt.fixed_code:
    start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=opt.device)


batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
    prompt = opt.prompt
    assert prompt is not None
    data = [batch_size * [prompt]]

else:
    print(f"reading prompts from {opt.from_file}")
    with open(opt.from_file, "r") as f:
        data = f.read().splitlines()
        data = batch_size * list(data)
        data = list(chunk(sorted(data), batch_size))


if opt.precision == "autocast" and opt.device != "cpu":
    precision_scope = autocast
else:
    precision_scope = nullcontext

seeds = ""
with torch.no_grad():

    all_samples = list()
    for n in trange(opt.n_iter, desc="Sampling"):
        for prompts in tqdm(data, desc="data"):

            sample_path = os.path.join(outpath, opt.prefix+"_".join(re.split(":| ", prompts[0])))[:150]
            os.makedirs(sample_path, exist_ok=True)
            base_count = len(os.listdir(sample_path))

            with precision_scope("cuda"):
                modelCS.to(opt.device)
                uc = None
                if opt.scale != 1.0:
                    uc = modelCS.get_learned_conditioning(batch_size * [""])
                if isinstance(prompts, tuple):
                    prompts = list(prompts)

                subprompts, weights = split_weighted_subprompts(prompts[0])
                if len(subprompts) > 1:
                    c = torch.zeros_like(uc)
                    totalWeight = sum(weights)
                    # normalize each "sub prompt" and add it
                    for i in range(len(subprompts)):
                        weight = weights[i]
                        # if not skip_normalize:
                        weight = weight / totalWeight
                        c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
                else:
                    c = modelCS.get_learned_conditioning(prompts)

                shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]

                if opt.device != "cpu":
                    mem = torch.cuda.memory_allocated() / 1e6
                    modelCS.to("cpu")
                    while torch.cuda.memory_allocated() / 1e6 >= mem:
                        time.sleep(1)

                samples_ddim = model.sample(
                    S=opt.ddim_steps,
                    conditioning=c,
                    seed=opt.seed,
                    shape=shape,
                    verbose=False,
                    unconditional_guidance_scale=opt.scale,
                    unconditional_conditioning=uc,
                    eta=opt.ddim_eta,
                    x_T=start_code,
                    sampler = opt.sampler,
                )

                modelFS.to(opt.device)

                print(samples_ddim.shape)
                print("saving images")
                for i in range(batch_size):

                    x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))
                    x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
                    x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
                    fn = os.path.join(sample_path, "seed_" + str(opt.seed) + "_" + f"{base_count:05}.{opt.format}")
                    Image.fromarray(x_sample.astype(np.uint8)).save(
                        fp = fn,
                        quality = 95,
                    )
                    if opt.iptc and opt.format == "jpg":
                        info = IPTCInfo(fn, force=True)
                        cmd  = " ".join([f'"{x}"' if " " in x else x for x in sys.argv]) # How can I get the raw cmd line?
                        info["caption/abstract"] = f"{cmd}{f' --seed {opt.seed}' if '--seed' not in cmd else ''}"[:2000]
                        info["supplemental category"] = ["stable diffusion"]
                        info.save(options="overwrite")
                    seeds += str(opt.seed) + ","
                    opt.seed += 1
                    base_count += 1

                if opt.device != "cpu":
                    mem = torch.cuda.memory_allocated() / 1e6
                    modelFS.to("cpu")
                    while torch.cuda.memory_allocated() / 1e6 >= mem:
                        time.sleep(1)
                del samples_ddim
                print("memory_final = ", torch.cuda.memory_allocated() / 1e6)

toc = time.time()

time_taken = (toc - tic) / 60.0

print(
    (
        "Samples finished in {0:.2f} minutes and exported to "
        + sample_path
        + "\n Seeds used = "
        + seeds[:-1]
    ).format(time_taken)
)