import argparse, os, sys, glob import cv2 import torch import numpy as np from omegaconf import OmegaConf from PIL import Image from tqdm import tqdm, trange from imwatermark import WatermarkEncoder from itertools import islice from einops import rearrange from torchvision.utils import make_grid import time from pytorch_lightning import seed_everything from torch import autocast from contextlib import contextmanager, nullcontext from ldm.util import instantiate_from_config from ldm.models.diffusion.psld import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler from ldm.models.diffusion.dpm_solver import DPMSolverSampler # from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from transformers import AutoFeatureExtractor import pdb # load safety model safety_model_id = "CompVis/stable-diffusion-safety-checker" safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id) # safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) def chunk(it, size): it = iter(it) return iter(lambda: tuple(islice(it, size)), ()) def numpy_to_pil(images): """ Convert a numpy image or a batch of images to a PIL image. """ if images.ndim == 3: images = images[None, ...] images = (images * 255).round().astype("uint8") pil_images = [Image.fromarray(image) for image in images] return pil_images 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 put_watermark(img, wm_encoder=None): if wm_encoder is not None: img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) img = wm_encoder.encode(img, 'dwtDct') img = Image.fromarray(img[:, :, ::-1]) return img def load_replacement(x): try: hwc = x.shape y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0])) y = (np.array(y)/255.0).astype(x.dtype) assert y.shape == x.shape return y except Exception: return x def check_safety(x_image): safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt") x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values) assert x_checked_image.shape[0] == len(has_nsfw_concept) for i in range(len(has_nsfw_concept)): if has_nsfw_concept[i]: x_checked_image[i] = load_replacement(x_checked_image[i]) return x_checked_image, has_nsfw_concept def main(): parser = argparse.ArgumentParser() parser.add_argument( "--prompt", type=str, nargs="?", default="", help="the prompt to render" ) parser.add_argument( "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples" ) parser.add_argument( "--skip_grid", action='store_false', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples", ) parser.add_argument( "--skip_save", action='store_true', help="do not save individual samples. For speed measurements.", ) parser.add_argument( "--ddim_steps", type=int, default=1000, help="number of ddim sampling steps", ) parser.add_argument( "--plms", action='store_true', help="use plms sampling", ) parser.add_argument( "--dpm_solver", action='store_true', help="use dpm_solver sampling", ) parser.add_argument( "--laion400m", action='store_true', help="uses the LAION400M model", ) parser.add_argument( "--fixed_code", action='store_true', help="if enabled, uses the same starting code across 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( "--H", type=int, default=512, help="image height, in pixel space", ) parser.add_argument( "--W", type=int, default=512, help="image width, in pixel space", ) parser.add_argument( "--C", type=int, default=4, help="latent channels", ) parser.add_argument( "--f", type=int, default=8, help="downsampling factor", ) parser.add_argument( "--n_samples", type=int, default=1, 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=7.5, help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", ) 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/v1-inference.yaml", help="path to config which constructs model", ) parser.add_argument( "--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", 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" ) ## parser.add_argument( "--dps_path", type=str, default='../diffusion-posterior-sampling/', help="DPS codebase path", ) parser.add_argument( "--task_config", type=str, default='configs/inpainting_config.yaml', help="task config yml file", ) parser.add_argument( "--diffusion_config", type=str, default='configs/diffusion_config.yaml', help="diffusion config yml file", ) parser.add_argument( "--model_config", type=str, default='configs/model_config.yaml', help="model config yml file", ) parser.add_argument( "--gamma", type=float, default=1e-1, help="inpainting error", ) parser.add_argument( "--omega", type=float, default=1, help="measurement error", ) parser.add_argument( "--inpainting", type=int, default=0, help="inpainting", ) parser.add_argument( "--general_inverse", type=int, default=1, help="general inverse", ) parser.add_argument( "--file_id", type=str, default='00014.png', help='input image', ) parser.add_argument( "--skip_low_res", action='store_true', help='downsample result to 256', ) parser.add_argument( "--ffhq256", action='store_true', help='load SD weights trained on FFHQ', ) ## opt = parser.parse_args() # pdb.set_trace() if opt.laion400m: print("Falling back to LAION 400M model...") opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml" opt.ckpt = "models/ldm/text2img-large/model.ckpt" ## if opt.ffhq256: print("Using FFHQ 256 finetuned model...") opt.config = "models/ldm/ffhq256/config.yaml" opt.ckpt = "models/ldm/ffhq256/model.ckpt" ## 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) if opt.dpm_solver: sampler = DPMSolverSampler(model) elif opt.plms: sampler = PLMSSampler(model) else: # pdb.set_trace() 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 = "StableDiffusionV1" 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 ######################################################### ## DPS configs ######################################################### sys.path.append(opt.dps_path) import yaml from guided_diffusion.measurements import get_noise, get_operator from util.img_utils import clear_color, mask_generator import torch.nn.functional as f import matplotlib.pyplot as plt def load_yaml(file_path: str) -> dict: with open(file_path) as f: config = yaml.load(f, Loader=yaml.FullLoader) return config model_config=opt.dps_path+opt.model_config diffusion_config=opt.dps_path+opt.diffusion_config task_config=opt.dps_path+opt.task_config # pdb.set_trace() # Load configurations model_config = load_yaml(model_config) diffusion_config = load_yaml(diffusion_config) task_config = load_yaml(task_config) task_config['data']['root'] = opt.dps_path + 'data/samples/' img = plt.imread(task_config['data']['root']+opt.file_id) # img = next(iter(loader)) img = img - img.min() img = img / img.max() img = torch.FloatTensor(img) img = torch.unsqueeze(img, dim=0).permute(0,3,1,2) img = img[:,:3,:,:].cuda() # Prepare Operator and noise measure_config = task_config['measurement'] operator = get_operator(device=device, **measure_config['operator']) noiser = get_noise(**measure_config['noise']) # Exception) In case of inpainting, we need to generate a mask if measure_config['operator']['name'] == 'inpainting': mask_gen = mask_generator( **measure_config['mask_opt'] ) img = f.interpolate(img, opt.H) x_checked_image_torch = img[:,:3,:,:].cuda() org_image = torch.clone(x_checked_image_torch[0].detach()) org_image = (org_image - 0.5)/0.5 org_image = org_image[None,:,:,:].cuda() # Exception) In case of inpainging, if measure_config['operator'] ['name'] == 'inpainting': mask = mask_gen(org_image) # dps mask # mask = torch.ones_like(org_image) # no mask mask = mask[:, 0, :, :].unsqueeze(dim=0) # Forward measurement model (Ax + n) y = operator.forward(org_image, mask=mask) y_n = noiser(y) else: # Forward measurement model (Ax + n) y = operator.forward(org_image) y_n = noiser(y) mask = None ######################################################### start_code = None if opt.fixed_code: start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) precision_scope = autocast if opt.precision=="autocast" else nullcontext with precision_scope("cuda"): with model.ema_scope(): tic = time.time() all_samples = list() for n in trange(opt.n_iter, desc="Sampling"): for prompts in tqdm(data, desc="data"): uc = None if opt.ffhq256: shape = [opt.C, opt.H // opt.f, opt.W // opt.f] samples_ddim, _ = sampler.sample(S=opt.ddim_steps, batch_size=opt.n_samples, shape=shape, verbose=False, eta=opt.ddim_eta, x_T=start_code, ip_mask = mask, measurements = y_n, operator = operator, gamma = opt.gamma, inpainting = opt.inpainting, omega = opt.omega, general_inverse=opt.general_inverse, noiser=noiser, ffhq256=opt.ffhq256) else: # pdb.set_trace() 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) shape = [opt.C, opt.H // opt.f, opt.W // opt.f] samples_ddim, _ = sampler.sample(S=opt.ddim_steps, conditioning=c, batch_size=opt.n_samples, shape=shape, verbose=False, unconditional_guidance_scale=opt.scale, unconditional_conditioning=uc, eta=opt.ddim_eta, x_T=start_code, ip_mask = mask, measurements = y_n, operator = operator, gamma = opt.gamma, inpainting = opt.inpainting, omega = opt.omega, general_inverse=opt.general_inverse, noiser=noiser) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy() # x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim) # x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2) x_checked_image_torch = torch.from_numpy(x_samples_ddim).permute(0, 3, 1, 2) if not opt.skip_save: for x_sample in x_checked_image_torch: 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 if not opt.skip_grid: all_samples.append(x_checked_image_torch) # pdb.set_trace() if not opt.skip_low_res: if not opt.skip_save: inpainted_image_low_res = f.interpolate(x_checked_image_torch.type(torch.float32), size=(opt.H//2, opt.W//2)) for x_sample in inpainted_image_low_res: 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}_low_res.png")) base_count += 1 if not opt.skip_grid: # 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() img = Image.fromarray(grid.astype(np.uint8)) # img = put_watermark(img, wm_encoder) img.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) grid_count += 1 toc = time.time() print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.") if __name__ == "__main__": main()