stable-diffusion-finetune/scripts/img2img.py

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Python
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2022-07-25 00:40:16 +02:00
"""make variations of input image"""
import argparse, os, sys, glob
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(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"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def load_img(path):
image = np.array(Image.open(path).convert("RGB"))
image = image.astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1.
def main():
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(
"--init-img",
type=str,
nargs="?",
help="path to the input image"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/img2img-samples"
)
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 indiviual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--fixed_code",
action='store_true',
help="if enabled, uses the same starting code across all 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=256,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=256,
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, most often 8 or 16",
)
parser.add_argument(
"--n_samples",
type=int,
default=8,
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=5.0,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--strength",
type=float,
default=0.3,
help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="logs/f8-kl-clip-encoder-256x256-run1/configs/2022-06-01T22-11-40-project.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="logs/f8-kl-clip-encoder-256x256-run1/checkpoints/last.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
opt = parser.parse_args()
seed_everything(opt.seed)
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
if opt.plms:
raise NotImplementedError("check for plms")
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
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 = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
assert os.path.isfile(opt.init_img)
init_image = load_img(opt.init_img).to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(opt.strength * opt.ddim_steps)
print(f"target t_enc is {t_enc} steps")
with torch.no_grad():
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,)
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
if not opt.skip_save:
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
all_samples.append(x_samples)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=n_rows)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
toc = time.time()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f"Sampling took {toc - tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
f" \nEnjoy.")
if __name__ == "__main__":
main()