处理后端接口
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388
server.py
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388
server.py
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import argparse, os
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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 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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from pytorch_lightning import seed_everything
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from torch import autocast
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from contextlib import nullcontext
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from imwatermark import WatermarkEncoder
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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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torch.set_grad_enabled(False)
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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 load_model_from_config(config, ckpt, device=torch.device("cuda"), 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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if device == torch.device("cuda"):
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model.cuda()
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elif device == torch.device("cpu"):
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model.cpu()
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model.cond_stage_model.device = "cpu"
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else:
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raise ValueError(f"Incorrect device name. Received: {device}")
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model.eval()
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return model
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def parse_args():
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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 professional photograph of an astronaut riding a triceratops",
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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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"--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",
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action='store_true',
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help="use DPM (2) sampler",
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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 all 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=3,
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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, most often 8 or 16",
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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=9.0,
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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, separated by newlines",
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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/v2-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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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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parser.add_argument(
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"--repeat",
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type=int,
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default=1,
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help="repeat each prompt in file this often",
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)
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parser.add_argument(
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"--device",
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type=str,
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help="Device on which Stable Diffusion will be run",
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choices=["cpu", "cuda"],
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default="cpu"
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)
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parser.add_argument(
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"--torchscript",
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action='store_true',
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help="Use TorchScript",
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)
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parser.add_argument(
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"--ipex",
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action='store_true',
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help="Use Intel® Extension for PyTorch*",
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)
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parser.add_argument(
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"--bf16",
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action='store_true',
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help="Use bfloat16",
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)
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opt = parser.parse_args()
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return opt
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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 main(opt):
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seed_everything(opt.seed)
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config = OmegaConf.load(f"{opt.config}")
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device = torch.device("cuda") if opt.device == "cuda" else torch.device("cpu")
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model = load_model_from_config(config, f"{opt.ckpt}", device)
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if opt.plms:
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sampler = PLMSSampler(model, device=device)
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elif opt.dpm:
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sampler = DPMSolverSampler(model, device=device)
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else:
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sampler = DDIMSampler(model, device=device)
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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 = "SDV2"
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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 = [p for p in data for i in range(opt.repeat)]
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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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sample_count = 0
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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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if opt.torchscript or opt.ipex:
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transformer = model.cond_stage_model.model
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unet = model.model.diffusion_model
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decoder = model.first_stage_model.decoder
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additional_context = torch.cpu.amp.autocast() if opt.bf16 else nullcontext()
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shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
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if opt.bf16 and not opt.torchscript and not opt.ipex:
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raise ValueError('Bfloat16 is supported only for torchscript+ipex')
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if opt.bf16 and unet.dtype != torch.bfloat16:
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raise ValueError("Use configs/stable-diffusion/intel/ configs with bf16 enabled if " +
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"you'd like to use bfloat16 with CPU.")
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if unet.dtype == torch.float16 and device == torch.device("cpu"):
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raise ValueError("Use configs/stable-diffusion/intel/ configs for your model if you'd like to run it on CPU.")
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if opt.ipex:
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import intel_extension_for_pytorch as ipex
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bf16_dtype = torch.bfloat16 if opt.bf16 else None
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transformer = transformer.to(memory_format=torch.channels_last)
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transformer = ipex.optimize(transformer, level="O1", inplace=True)
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unet = unet.to(memory_format=torch.channels_last)
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unet = ipex.optimize(unet, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype)
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decoder = decoder.to(memory_format=torch.channels_last)
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decoder = ipex.optimize(decoder, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype)
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if opt.torchscript:
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with torch.no_grad(), additional_context:
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# get UNET scripted
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if unet.use_checkpoint:
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raise ValueError("Gradient checkpoint won't work with tracing. " +
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"Use configs/stable-diffusion/intel/ configs for your model or disable checkpoint in your config.")
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img_in = torch.ones(2, 4, 96, 96, dtype=torch.float32)
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t_in = torch.ones(2, dtype=torch.int64)
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context = torch.ones(2, 77, 1024, dtype=torch.float32)
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scripted_unet = torch.jit.trace(unet, (img_in, t_in, context))
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scripted_unet = torch.jit.optimize_for_inference(scripted_unet)
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print(type(scripted_unet))
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model.model.scripted_diffusion_model = scripted_unet
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# get Decoder for first stage model scripted
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samples_ddim = torch.ones(1, 4, 96, 96, dtype=torch.float32)
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scripted_decoder = torch.jit.trace(decoder, (samples_ddim))
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scripted_decoder = torch.jit.optimize_for_inference(scripted_decoder)
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print(type(scripted_decoder))
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model.first_stage_model.decoder = scripted_decoder
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prompts = data[0]
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print("Running a forward pass to initialize optimizations")
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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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with torch.no_grad(), additional_context:
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for _ in range(3):
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c = model.get_learned_conditioning(prompts)
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samples_ddim, _ = sampler.sample(S=5,
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conditioning=c,
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batch_size=batch_size,
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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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print("Running a forward pass for decoder")
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for _ in range(3):
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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precision_scope = autocast if opt.precision=="autocast" or opt.bf16 else nullcontext
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with torch.no_grad(), \
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precision_scope(opt.device), \
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model.ema_scope():
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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, _ = sampler.sample(S=opt.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 = model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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for x_sample in x_samples:
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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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sample_count += 1
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all_samples.append(x_samples)
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# additionally, save as 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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# to image
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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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grid = Image.fromarray(grid.astype(np.uint8))
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grid = put_watermark(grid, wm_encoder)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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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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opt = parse_args()
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main(opt)
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Reference in New Issue
Block a user