# Copyright 2024 Adobe. All rights reserved. import argparse, os, sys, glob # sys.path.append('.') 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 import torchvision from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from transformers import AutoFeatureExtractor import clip from torchvision.transforms import Resize import json wm = "Paint-by-Example" wm_encoder = WatermarkEncoder() wm_encoder.set_watermark('bytes', wm.encode('utf-8')) 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 get_tensor_clip(normalize=True, toTensor=True): transform_list = [] if toTensor: transform_list += [torchvision.transforms.ToTensor()] if normalize: transform_list += [torchvision.transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))] return torchvision.transforms.Compose(transform_list) 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) # print('NOTE: NO CHECKPOINT IS LOADED') 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 get_tensor(normalize=True, toTensor=True): transform_list = [] if toTensor: transform_list += [torchvision.transforms.ToTensor()] if normalize: transform_list += [torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] transform_list += [ torchvision.transforms.Resize(512), torchvision.transforms.CenterCrop(512) ] return torchvision.transforms.Compose(transform_list) def get_tensor_clip(normalize=True, toTensor=True): transform_list = [] if toTensor: transform_list += [torchvision.transforms.ToTensor()] if normalize: transform_list += [torchvision.transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))] return torchvision.transforms.Compose(transform_list) def main(): parser = argparse.ArgumentParser() parser.add_argument( "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples" ) parser.add_argument( "--skip_grid", action='store_true', 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=50, help="number of ddim sampling steps", ) parser.add_argument( "--plms", action='store_true', help="use plms sampling", ) 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=2, 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( "--n_imgs", type=int, default=100, 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 reference image. 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=1, help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", ) parser.add_argument( "--config", type=str, default="", help="path to config which constructs model", ) parser.add_argument( "--ckpt", type=str, default="", 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( "--image_path", type=str, help="evaluate at this precision", default="" ) parser.add_argument( "--mask_path", type=str, help="evaluate at this precision", default="" ) parser.add_argument( "--reference_path", type=str, help="evaluate at this precision", default="" ) 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) if opt.plms: sampler = PLMSSampler(model) else: sampler = DDIMSampler(model) os.makedirs(opt.outdir, exist_ok=True) outpath = opt.outdir batch_size = opt.n_samples n_rows = opt.n_rows if opt.n_rows > 0 else batch_size sample_path = os.path.join(outpath, "source") result_path = os.path.join(outpath, "results") grid_path=os.path.join(outpath, "grid") os.makedirs(sample_path, exist_ok=True) os.makedirs(result_path, exist_ok=True) os.makedirs(grid_path, exist_ok=True) 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 # split_path = '' # with open(split_path) as f: # sample_paths = json.load(f) # np.random.seed(opt.seed) # np.random.shuffle(sample_paths) # print(sample_paths[0]) # raise ValueError with torch.no_grad(): with precision_scope("cuda"): for i in range(1): with model.ema_scope(): filename=os.path.basename(opt.image_path) img_p = Image.open(opt.image_path).convert("RGB") image_tensor = get_tensor()(img_p) image_tensor = image_tensor.unsqueeze(0) ref_p = Image.open(opt.reference_path).convert("RGB") width, height = ref_p.size # Get dimensions new_width = min(width, height) new_height = new_width left = (width - new_width)/2 top = (height - new_height)/2 right = (width + new_width)/2 bottom = (height + new_height)/2 # Crop the center of the image ref_p = ref_p.crop((left, top, right, bottom)) ref_p = ref_p.resize((224,224)) ref_tensor=get_tensor_clip()(ref_p) ref_tensor = ref_tensor.unsqueeze(0) mask=Image.open(opt.mask_path).convert("L") mask = mask.crop((left, top, right, bottom)) mask = np.array(mask)[None,None] mask = mask.astype(np.float32)/255.0 mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask_tensor = torch.from_numpy(mask) inpaint_image = image_tensor#*mask_tensor # mask_tensor = torch.ones_like(inpaint_image) # mask_tensor = mask_tensor[:, :1] # TODO PLEASE COMMENT OUT SOON print('inpaint image size', inpaint_image.shape) test_model_kwargs={} test_model_kwargs['inpaint_mask']=mask_tensor.to(device) test_model_kwargs['inpaint_image']=inpaint_image.to(device) ref_tensor=ref_tensor.to(device) uc = None if opt.scale != 1.0: uc = model.learnable_vector c = model.get_learned_conditioning(ref_tensor.to(torch.float16)) c = model.proj_out(c) inpaint_mask=test_model_kwargs['inpaint_mask'] z_inpaint = model.encode_first_stage(test_model_kwargs['inpaint_image']) z_inpaint = model.get_first_stage_encoding(z_inpaint).detach() test_model_kwargs['inpaint_image']=z_inpaint test_model_kwargs['inpaint_mask']=Resize([z_inpaint.shape[-2],z_inpaint.shape[-1]])(test_model_kwargs['inpaint_mask']) shape = [opt.C, opt.H // opt.f, opt.W // opt.f] # compute context here # ref_latent = model.encode_first_stage(img maybe) # contexts = context_unet.compute_context() 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, test_model_kwargs=test_model_kwargs) 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=x_samples_ddim x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2) def un_norm(x): return (x+1.0)/2.0 def un_norm_clip(x): x[0,:,:] = x[0,:,:] * 0.26862954 + 0.48145466 x[1,:,:] = x[1,:,:] * 0.26130258 + 0.4578275 x[2,:,:] = x[2,:,:] * 0.27577711 + 0.40821073 return x if not opt.skip_save: for i,x_sample in enumerate(x_checked_image_torch): all_img=[] all_img.append(un_norm(image_tensor[i]).cpu()) all_img.append(un_norm(inpaint_image[i]).cpu()) ref_img=ref_tensor ref_img=Resize([opt.H, opt.W])(ref_img) all_img.append(un_norm_clip(ref_img[i]).cpu()) all_img.append(x_sample) grid = torch.stack(all_img, 0) grid = make_grid(grid) 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(grid_path, 'grid-'+filename[:-4]+'_'+str(opt.seed)+f'_{i}.png')) 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(result_path, filename[:-4]+'_'+str(opt.seed)+f"_{i}.png")) mask_save=255.*rearrange(un_norm(inpaint_mask[i]).cpu(), 'c h w -> h w c').numpy() mask_save= cv2.cvtColor(mask_save,cv2.COLOR_GRAY2RGB) mask_save = Image.fromarray(mask_save.astype(np.uint8)) mask_save.save(os.path.join(sample_path, filename[:-4]+'_'+str(opt.seed)+f"_mask_{i}.png")) GT_img=255.*rearrange(all_img[0], 'c h w -> h w c').numpy() GT_img = Image.fromarray(GT_img.astype(np.uint8)) GT_img.save(os.path.join(sample_path, filename[:-4]+'_'+str(opt.seed)+f"_GT_{i}.png")) inpaint_img=255.*rearrange(all_img[1], 'c h w -> h w c').numpy() inpaint_img = Image.fromarray(inpaint_img.astype(np.uint8)) inpaint_img.save(os.path.join(sample_path, filename[:-4]+'_'+str(opt.seed)+f"_inpaint_{i}.png")) ref_img=255.*rearrange(all_img[2], 'c h w -> h w c').numpy() ref_img = Image.fromarray(ref_img.astype(np.uint8)) ref_img.save(os.path.join(sample_path, filename[:-4]+'_'+str(opt.seed)+f"_ref_{i}.png")) print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.") if __name__ == "__main__": main()