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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 ldm.models.diffusion.dpm_solver import DPMSolverSampler |
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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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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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"--dpm_solver", |
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action='store_true', |
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help="use dpm_solver 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.dpm_solver: |
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sampler = DPMSolverSampler(model) |
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elif 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 = 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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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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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() |
