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import argparse, os |
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import cv2 |
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import torch |
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import numpy as np |
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from omegaconf import OmegaConf |
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from PIL import Image |
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from tqdm import tqdm, trange |
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from itertools import islice |
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from einops import rearrange |
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from torchvision.utils import make_grid |
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from pytorch_lightning import seed_everything |
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from torch import autocast |
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from contextlib import nullcontext |
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from imwatermark import WatermarkEncoder |
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from ldm.util import instantiate_from_config |
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from ldm.models.diffusion.ddim import DDIMSampler |
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from ldm.models.diffusion.plms import PLMSSampler |
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from ldm.models.diffusion.dpm_solver import DPMSolverSampler |
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torch.set_grad_enabled(False) |
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def chunk(it, size): |
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it = iter(it) |
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return iter(lambda: tuple(islice(it, size)), ()) |
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def load_model_from_config(config, ckpt, verbose=False): |
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print(f"Loading model from {ckpt}") |
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pl_sd = torch.load(ckpt, map_location="cpu") |
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if "global_step" in pl_sd: |
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print(f"Global Step: {pl_sd['global_step']}") |
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sd = pl_sd["state_dict"] |
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model = instantiate_from_config(config.model) |
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m, u = model.load_state_dict(sd, strict=False) |
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if len(m) > 0 and verbose: |
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print("missing keys:") |
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print(m) |
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if len(u) > 0 and verbose: |
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print("unexpected keys:") |
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print(u) |
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model.cuda() |
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model.eval() |
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return model |
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def parse_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--prompt", |
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type=str, |
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nargs="?", |
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default="a professional photograph of an astronaut riding a triceratops", |
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help="the prompt to render" |
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) |
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parser.add_argument( |
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"--outdir", |
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type=str, |
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nargs="?", |
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help="dir to write results to", |
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default="outputs/txt2img-samples" |
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) |
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parser.add_argument( |
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"--steps", |
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type=int, |
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default=50, |
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help="number of ddim sampling steps", |
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) |
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parser.add_argument( |
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"--plms", |
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action='store_true', |
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help="use plms sampling", |
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) |
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parser.add_argument( |
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"--dpm", |
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action='store_true', |
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help="use DPM (2) sampler", |
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) |
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parser.add_argument( |
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"--fixed_code", |
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action='store_true', |
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help="if enabled, uses the same starting code across all samples ", |
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) |
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parser.add_argument( |
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"--ddim_eta", |
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type=float, |
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default=0.0, |
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help="ddim eta (eta=0.0 corresponds to deterministic sampling", |
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) |
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parser.add_argument( |
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"--n_iter", |
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type=int, |
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default=3, |
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help="sample this often", |
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) |
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parser.add_argument( |
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"--H", |
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type=int, |
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default=512, |
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help="image height, in pixel space", |
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) |
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parser.add_argument( |
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"--W", |
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type=int, |
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default=512, |
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help="image width, in pixel space", |
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) |
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parser.add_argument( |
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"--C", |
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type=int, |
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default=4, |
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help="latent channels", |
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) |
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parser.add_argument( |
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"--f", |
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type=int, |
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default=8, |
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help="downsampling factor, most often 8 or 16", |
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) |
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parser.add_argument( |
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"--n_samples", |
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type=int, |
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default=3, |
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help="how many samples to produce for each given prompt. A.k.a batch size", |
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) |
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parser.add_argument( |
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"--n_rows", |
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type=int, |
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default=0, |
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help="rows in the grid (default: n_samples)", |
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) |
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parser.add_argument( |
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"--scale", |
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type=float, |
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default=9.0, |
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help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", |
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) |
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parser.add_argument( |
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"--from-file", |
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type=str, |
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help="if specified, load prompts from this file, separated by newlines", |
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) |
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parser.add_argument( |
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"--config", |
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type=str, |
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default="configs/stable-diffusion/v2-inference.yaml", |
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help="path to config which constructs model", |
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) |
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parser.add_argument( |
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"--ckpt", |
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type=str, |
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help="path to checkpoint of model", |
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) |
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parser.add_argument( |
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"--seed", |
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type=int, |
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default=42, |
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help="the seed (for reproducible sampling)", |
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) |
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parser.add_argument( |
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"--precision", |
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type=str, |
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help="evaluate at this precision", |
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choices=["full", "autocast"], |
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default="autocast" |
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) |
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parser.add_argument( |
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"--repeat", |
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type=int, |
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default=1, |
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help="repeat each prompt in file this often", |
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) |
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opt = parser.parse_args() |
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return opt |
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def put_watermark(img, wm_encoder=None): |
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if wm_encoder is not None: |
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img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) |
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img = wm_encoder.encode(img, 'dwtDct') |
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img = Image.fromarray(img[:, :, ::-1]) |
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return img |
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def main(opt): |
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seed_everything(opt.seed) |
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config = OmegaConf.load(f"{opt.config}") |
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model = load_model_from_config(config, f"{opt.ckpt}") |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model = model.to(device) |
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if opt.plms: |
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sampler = PLMSSampler(model) |
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elif opt.dpm: |
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sampler = DPMSolverSampler(model) |
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else: |
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sampler = DDIMSampler(model) |
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os.makedirs(opt.outdir, exist_ok=True) |
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outpath = opt.outdir |
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print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") |
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wm = "SDV2" |
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wm_encoder = WatermarkEncoder() |
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wm_encoder.set_watermark('bytes', wm.encode('utf-8')) |
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batch_size = opt.n_samples |
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n_rows = opt.n_rows if opt.n_rows > 0 else batch_size |
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if not opt.from_file: |
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prompt = opt.prompt |
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assert prompt is not None |
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data = [batch_size * [prompt]] |
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else: |
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print(f"reading prompts from {opt.from_file}") |
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with open(opt.from_file, "r") as f: |
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data = f.read().splitlines() |
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data = [p for p in data for i in range(opt.repeat)] |
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data = list(chunk(data, batch_size)) |
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sample_path = os.path.join(outpath, "samples") |
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os.makedirs(sample_path, exist_ok=True) |
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sample_count = 0 |
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base_count = len(os.listdir(sample_path)) |
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grid_count = len(os.listdir(outpath)) - 1 |
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start_code = None |
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if opt.fixed_code: |
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start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) |
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precision_scope = autocast if opt.precision == "autocast" else nullcontext |
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with torch.no_grad(), \ |
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precision_scope("cuda"), \ |
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model.ema_scope(): |
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all_samples = list() |
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for n in trange(opt.n_iter, desc="Sampling"): |
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for prompts in tqdm(data, desc="data"): |
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uc = None |
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if opt.scale != 1.0: |
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uc = model.get_learned_conditioning(batch_size * [""]) |
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if isinstance(prompts, tuple): |
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prompts = list(prompts) |
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c = model.get_learned_conditioning(prompts) |
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shape = [opt.C, opt.H // opt.f, opt.W // opt.f] |
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samples, _ = sampler.sample(S=opt.steps, |
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conditioning=c, |
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batch_size=opt.n_samples, |
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shape=shape, |
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verbose=False, |
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unconditional_guidance_scale=opt.scale, |
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unconditional_conditioning=uc, |
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eta=opt.ddim_eta, |
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x_T=start_code) |
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x_samples = model.decode_first_stage(samples) |
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) |
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for x_sample in x_samples: |
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') |
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img = Image.fromarray(x_sample.astype(np.uint8)) |
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img = put_watermark(img, wm_encoder) |
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img.save(os.path.join(sample_path, f"{base_count:05}.png")) |
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base_count += 1 |
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sample_count += 1 |
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all_samples.append(x_samples) |
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grid = torch.stack(all_samples, 0) |
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grid = rearrange(grid, 'n b c h w -> (n b) c h w') |
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grid = make_grid(grid, nrow=n_rows) |
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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() |
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grid = Image.fromarray(grid.astype(np.uint8)) |
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grid = put_watermark(grid, wm_encoder) |
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) |
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grid_count += 1 |
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n" |
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f" \nEnjoy.") |
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if __name__ == "__main__": |
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opt = parse_args() |
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main(opt) |
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