mirror of https://github.com/hpcaitech/ColossalAI
345 lines
11 KiB
Python
345 lines
11 KiB
Python
import argparse, os, sys, glob
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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 imwatermark import WatermarkEncoder
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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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import time
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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 contextmanager, nullcontext
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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 diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from transformers import AutoFeatureExtractor
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# load safety model
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safety_model_id = "CompVis/stable-diffusion-safety-checker"
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safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
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safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
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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 numpy_to_pil(images):
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"""
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Convert a numpy image or a batch of images to a PIL image.
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"""
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if images.ndim == 3:
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images = images[None, ...]
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images = (images * 255).round().astype("uint8")
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pil_images = [Image.fromarray(image) for image in images]
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return pil_images
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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 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 load_replacement(x):
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try:
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hwc = x.shape
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y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
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y = (np.array(y)/255.0).astype(x.dtype)
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assert y.shape == x.shape
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return y
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except Exception:
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return x
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def check_safety(x_image):
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safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
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x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
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assert x_checked_image.shape[0] == len(has_nsfw_concept)
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for i in range(len(has_nsfw_concept)):
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if has_nsfw_concept[i]:
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x_checked_image[i] = load_replacement(x_checked_image[i])
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return x_checked_image, has_nsfw_concept
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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(
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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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"--skip_grid",
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action='store_true',
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help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
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)
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parser.add_argument(
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"--skip_save",
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action='store_true',
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help="do not save individual samples. For speed measurements.",
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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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"--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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"--laion400m",
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action='store_true',
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help="uses the LAION400M model",
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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 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=2,
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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",
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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=7.5,
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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",
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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/v1-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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default="models/ldm/stable-diffusion-v1/model.ckpt",
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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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opt = parser.parse_args()
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if opt.laion400m:
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print("Falling back to LAION 400M model...")
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opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
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opt.ckpt = "models/ldm/text2img-large/model.ckpt"
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opt.outdir = "outputs/txt2img-samples-laion400m"
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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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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 = "StableDiffusionV1"
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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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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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with precision_scope("cuda"):
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with model.ema_scope():
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tic = time.time()
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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_ddim, _ = sampler.sample(S=opt.ddim_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_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
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x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim)
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x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
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if not opt.skip_save:
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for x_sample in x_checked_image_torch:
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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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if not opt.skip_grid:
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all_samples.append(x_checked_image_torch)
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if not opt.skip_grid:
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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. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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img = Image.fromarray(grid.astype(np.uint8))
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img = put_watermark(img, wm_encoder)
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img.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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toc = time.time()
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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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main()
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