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import argparse, os, sys, glob
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from imwatermark import WatermarkEncoder
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 ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
# load safety scripts
safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading scripts 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 put_watermark(img, wm_encoder=None):
if wm_encoder is not None:
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
img = wm_encoder.encode(img, 'dwtDct')
img = Image.fromarray(img[:, :, ::-1])
return img
def load_replacement(x):
try:
hwc = x.shape
y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
y = (np.array(y)/255.0).astype(x.dtype)
assert y.shape == x.shape
return y
except Exception:
return x
def check_safety(x_image):
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
assert x_checked_image.shape[0] == len(has_nsfw_concept)
for i in range(len(has_nsfw_concept)):
if has_nsfw_concept[i]:
x_checked_image[i] = load_replacement(x_checked_image[i])
return x_checked_image, has_nsfw_concept
class StableDiffusion:
def __init__(self):
seed = 42
config = "configs/stable-diffusion/v1-inference.yaml"
ckpt = "models/ldm/stable-diffusion-v4/model.ckpt"
seed_everything(seed)
config = OmegaConf.load(f"{config}")
model = load_model_from_config(config, f"{ckpt}")
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.model = model.to(self.device)
self.sampler = PLMSSampler(self.model)
print("Finishing Model Deployment")
def generatePics(self, opt):
if opt.laion400m:
print("Falling back to LAION 400M scripts...")
opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
opt.ckpt = "models/ldm/text2img-large/scripts.ckpt"
opt.outdir = "outputs/txt2img-samples-laion400m"
# if opt.plms:
# sampler = PLMSSampler(self.model)
# else:
# sampler = DDIMSampler(self.model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
wm = "StableDiffusionV1"
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
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
output_img_files = os.path.join(os.getcwd(), outpath, f'grid-{grid_count:04}.png')
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=self.device)
precision_scope = autocast if opt.precision == "autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
with self.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 = self.model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = self.model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = self.sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
x_T=start_code)
x_samples_ddim = self.model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim)
x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
# if not opt.skip_save:
# for x_sample in x_checked_image_torch:
# x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
# img = Image.fromarray(x_sample.astype(np.uint8))
# img = put_watermark(img, wm_encoder)
# img.save(os.path.join(sample_path, f"{base_count:05}.png"))
# base_count += 1
if not opt.skip_grid:
all_samples.append(x_checked_image_torch)
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()
img = Image.fromarray(grid.astype(np.uint8))
img = put_watermark(img, wm_encoder)
img.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
toc = time.time()
return output_img_files
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