from diffusers.pipelines.stablediffusion.safetychecker import StableDiffusionSafetyChecker
September 7, 2022 ยท View on GitHub
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 import re
sys.path.append(os.path.join(os.path.dirname(file), ".."))
from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler
def get_device(): if(torch.cuda.is_available()): return 'cuda' elif(torch.backends.mps.is_available()): return 'mps' else: return 'cpu'
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
load safety model
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 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.to(get_device())
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 return x_image, False
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(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-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 individual 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(
"--laion400m",
action='store_true',
help="uses the LAION400M model",
)
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=1,
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(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
if opt.laion400m:
print("Falling back to LAION 400M model...")
opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
opt.ckpt = "models/ldm/text2img-large/model.ckpt"
opt.outdir = "outputs/txt2img-samples-laion400m"
seed_everything(opt.seed)
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device(get_device())
model = model.to(device)
if opt.plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
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
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="cpu"
).to(torch.device(device))
precision_scope = autocast if opt.precision=="autocast" else nullcontext
if device.type == 'mps':
precision_scope = nullcontext # have to use f32 on mps
with torch.no_grad():
with precision_scope(device.type):
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)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = 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 = 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)
# ================ save params to txt file ================
# @see https://github.com/CompVis/stable-diffusion/issues/121
outFileName = opt.prompt
outFileName = re.sub(', ' , '-', outFileName)
outFileName = re.sub(',' , '-', outFileName)
outFileName = re.sub(' ' , ' ', outFileName)
outFileName = re.sub(' ' , '_', outFileName)
outFileName = re.sub('__' , '_', outFileName)
outFileName = re.sub('\'' , '', outFileName)
outFileName = re.sub('\/' , '', outFileName)
outFileName = outFileName[0:240]
outFileName += "-" + f"{base_count:05}.png"
# img.save(os.path.join(sample_path, f"{base_count:05}.png"))
img.save(os.path.join(sample_path, outFileName))
with open(sample_path + "/" + outFileName + ".txt", 'w') as outFileParams:
outFileParams.write("Prompt: " + str(opt.prompt) + "\n")
outFileParams.write("Output Directory: " + str(opt.outdir) + "\n")
outFileParams.write("Number of Sampling Steps: " + str(opt.ddim_steps) + "\n")
outFileParams.write("Deterministic ETA Sampling: " + str(opt.ddim_eta) + "\n")
outFileParams.write("Iterations: " + str(opt.n_iter) + "\n")
outFileParams.write("Channels: " + str(opt.C) + "\n")
outFileParams.write("Factor: " + str(opt.f) + "\n")
outFileParams.write("Number of Samples: " + str(opt.n_samples) + "\n")
outFileParams.write("Number of Rows: " + str(opt.n_rows) + "\n")
outFileParams.write("Scale: " + str(opt.scale) + "\n")
outFileParams.write("Config: " + str(opt.config) + "\n")
outFileParams.write("Model: " + str(opt.ckpt) + "\n")
outFileParams.write("Seed: " + str(opt.seed) + "\n")
outFileParams.write("Height: " + str(opt.H) + "\n")
outFileParams.write("Width: " + str(opt.W) + "\n")
if opt.laion400m:
outFileParams.write("laion400m: " + "True" + "\n")
else:
outFileParams.write("laion400m: " + "False" + "\n")
if opt.plms:
outFileParams.write("PLMS: " + "True" + "\n")
else:
outFileParams.write("PLMS: " + "False" + "\n")
if opt.skip_grid:
outFileParams.write("Skip Grid: " + "--skip_grid Enabled\n")
else:
outFileParams.write("Skip Grid: " + "--skip_grid Disabled\n")
if opt.skip_save:
outFileParams.write("Skip Save: " + "--skip_save Enabled\n")
else:
outFileParams.write("Skip Save: " + "--skip_save Disabled\n")
if opt.from_file:
outFileParams.write("Prompt File: " + str(opt.from_file) + "\n")
if opt.fixed_code:
outFileParams.write("Fixed Input: " + str(opt.fixed_code) + "\n")
if opt.precision:
outFileParams.write("Precision Scope: " + str(opt.precision) + "\n")
# ================ ENDsave params to txt file ================
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()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
if name == "main": main()