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291 lines
9.0 KiB
291 lines
9.0 KiB
"""make variations of input image"""
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import argparse
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import os
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from contextlib import nullcontext
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from itertools import islice
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import numpy as np
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import PIL
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import torch
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from einops import rearrange, repeat
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from omegaconf import OmegaConf
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from PIL import Image
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from torch import autocast
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from torchvision.utils import make_grid
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from tqdm import tqdm, trange
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try:
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from lightning.pytorch import seed_everything
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except:
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from pytorch_lightning import seed_everything
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from imwatermark import WatermarkEncoder
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.util import instantiate_from_config
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from scripts.txt2img import put_watermark
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from utils import replace_module
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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.eval()
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return model
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def load_img(path):
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image = Image.open(path).convert("RGB")
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w, h = image.size
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print(f"loaded input image of size ({w}, {h}) from {path}")
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w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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def main():
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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 painting of a virus monster playing guitar",
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help="the prompt to render",
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)
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parser.add_argument("--init-img", type=str, nargs="?", help="path to the input image")
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parser.add_argument(
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"--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/img2img-samples"
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)
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parser.add_argument(
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"--ddim_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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"--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=1,
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help="sample this often",
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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=2,
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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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"--strength",
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type=float,
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default=0.8,
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help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
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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",
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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", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
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)
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parser.add_argument(
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"--use_int8",
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type=bool,
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default=False,
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help="use int8 for inference",
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)
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opt = parser.parse_args()
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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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# quantize model
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if opt.use_int8:
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model = replace_module(model)
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# # to compute the model size
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# getModelSize(model)
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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 = 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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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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assert os.path.isfile(opt.init_img)
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init_image = load_img(opt.init_img).to(device)
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init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
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sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
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assert 0.0 <= opt.strength <= 1.0, "can only work with strength in [0.0, 1.0]"
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t_enc = int(opt.strength * opt.ddim_steps)
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print(f"target t_enc is {t_enc} steps")
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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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with precision_scope("cuda"):
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with 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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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
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# decode it
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samples = sampler.decode(
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z_enc,
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c,
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t_enc,
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,
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)
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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 = 255.0 * 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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all_samples.append(x_samples)
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# additionally, save as grid
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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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# to image
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grid = 255.0 * 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} \nEnjoy.")
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if __name__ == "__main__":
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main()
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# # to compute the mem allocated
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# print(torch.cuda.max_memory_allocated() / 1024 / 1024)
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