123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279 |
- """make variations of input image"""
- import argparse, os
- import PIL
- 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
- from torch import autocast
- from contextlib import nullcontext
- from pytorch_lightning import seed_everything
- from imwatermark import WatermarkEncoder
- from scripts.txt2img import put_watermark
- from ldm.util import instantiate_from_config
- from ldm.models.diffusion.ddim import DDIMSampler
- 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 = Image.open(path).convert("RGB")
- w, h = image.size
- print(f"loaded input image of size ({w}, {h}) from {path}")
- w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
- image = image.resize((w, h), resample=PIL.Image.LANCZOS)
- image = np.array(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(
- "--ddim_steps",
- type=int,
- default=50,
- help="number of ddim sampling steps",
- )
- 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(
- "--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=2,
- 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=9.0,
- help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
- )
- parser.add_argument(
- "--strength",
- type=float,
- default=0.8,
- 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="configs/stable-diffusion/v2-inference.yaml",
- help="path to config which constructs model",
- )
- parser.add_argument(
- "--ckpt",
- type=str,
- 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()
- 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)
- 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 = "SDV2"
- 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
- 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")
- precision_scope = autocast if opt.precision == "autocast" else nullcontext
- with torch.no_grad():
- with precision_scope("cuda"):
- with model.ema_scope():
- 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)
- for x_sample in x_samples:
- 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
- all_samples.append(x_samples)
- # 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()
- grid = Image.fromarray(grid.astype(np.uint8))
- grid = put_watermark(grid, wm_encoder)
- grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
- grid_count += 1
- print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
- if __name__ == "__main__":
- main()
|