| 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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|
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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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|
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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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|
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|
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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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|
|
|
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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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|
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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 scripts 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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|
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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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|
|
|
|
| class StableDiffusion:
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| def __init__(self):
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| seed = 42
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| config = "configs/stable-diffusion/v1-inference.yaml"
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| ckpt = "models/ldm/stable-diffusion-v4/model.ckpt"
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|
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| seed_everything(seed)
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| config = OmegaConf.load(f"{config}")
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| model = load_model_from_config(config, f"{ckpt}")
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|
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| self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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| self.model = model.to(self.device)
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| self.sampler = PLMSSampler(self.model)
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| print("Finishing Model Deployment")
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|
|
|
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| def generatePics(self, opt):
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| if opt.laion400m:
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| print("Falling back to LAION 400M scripts...")
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| opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
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| opt.ckpt = "models/ldm/text2img-large/scripts.ckpt"
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| opt.outdir = "outputs/txt2img-samples-laion400m"
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|
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|
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| os.makedirs(opt.outdir, exist_ok=True)
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| outpath = opt.outdir
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|
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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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|
|
|
|
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|
|
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| grid_count = len(os.listdir(outpath)) - 1
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| output_img_files = os.path.join(os.getcwd(), outpath, f'grid-{grid_count:04}.png')
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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=self.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 self.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 = self.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 = self.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, _ = self.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 = self.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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|
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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_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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|
|
| return output_img_files
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|
|
|
|
