lm/server_lm/scripts/model.py

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.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler

from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor


# load safety scripts
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 scripts 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


class StableDiffusion:
    def __init__(self):
        seed = 42
        config = "configs/stable-diffusion/v1-inference.yaml"
        ckpt = "models/ldm/stable-diffusion-v4/model.ckpt"

        seed_everything(seed)
        config = OmegaConf.load(f"{config}")
        model = load_model_from_config(config, f"{ckpt}")

        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
        self.model = model.to(self.device)
        self.sampler = PLMSSampler(self.model)
        print("Finishing Model Deployment")


    def generatePics(self, opt):
        if opt.laion400m:
            print("Falling back to LAION 400M scripts...")
            opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
            opt.ckpt = "models/ldm/text2img-large/scripts.ckpt"
            opt.outdir = "outputs/txt2img-samples-laion400m"

        # if opt.plms:
        #     sampler = PLMSSampler(self.model)
        # else:
        #     sampler = DDIMSampler(self.model)

        os.makedirs(opt.outdir, exist_ok=True)
        outpath = opt.outdir

        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
        output_img_files = os.path.join(os.getcwd(), outpath, f'grid-{grid_count:04}.png')

        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=self.device)

        precision_scope = autocast if opt.precision == "autocast" else nullcontext
        with torch.no_grad():
            with precision_scope("cuda"):
                with self.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.scale != 1.0:
                                uc = self.model.get_learned_conditioning(batch_size * [""])
                            if isinstance(prompts, tuple):
                                prompts = list(prompts)
                            c = self.model.get_learned_conditioning(prompts)
                            shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
                            samples_ddim, _ = self.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)

                            x_samples_ddim = self.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)

                            # 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)

                    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()

        return output_img_files