357 lines
12 KiB
Python
357 lines
12 KiB
Python
import time
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import requests
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import argparse
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import 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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# 对任务状态的修改
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def update_task_status(task: dict, status: str, progress: int, data: list = []):
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task["status"] = status
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task["progress"] = progress
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task["data"] = data
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requests.put(f"http://localhost:3000/api/drawing/{task['id']}", json=task)
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# 从局域网中获取一组任务(如果列表为空，等待2s后重新获取)
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def get_tasks(tasks:list=[]):
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while len(tasks) == 0:
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try:
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tasks = requests.get("http://localhost:3000/api/drawing?status=waiting").json()
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if len(tasks) == 0: time.sleep(2)
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except:
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# 打印当前时间
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print("get tasks error", time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
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time.sleep(2)
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return tasks
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def main_dev(opt):
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model_name = '' # 默认模型
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model = None # 默认模型
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config = None # 默认配置
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device = None # 默认设备
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while True:
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for task in get_tasks(): # 遍历 tasks 返回 dict
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print('task:', task) # 打印任务
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# 如果模型不同，重新加载模型(注意释放内存)
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if task['ckpt'] != model_name:
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# 修改状态为加载模型
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update_task_status(task, "init", 0)
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# 获取环境配置
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model_name = task['ckpt']
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opt.config = f'/data/{model_name}.yaml'
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opt.ckpt = f'/data/{model_name}.ckpt'
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opt.device = 'cuda'
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print(f"config: {opt.config}", f"ckpt: {opt.ckpt}", f"device: {opt.device}")
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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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# 加载模型(到显存)
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print(f"加载模型到显存: {model_name}..")
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model = load_model_from_config(config, f"{opt.ckpt}", device)
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print(f"加载到显存完成: {model_name}")
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# 更新任务状态为运行中
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update_task_status(task, "running", 0)
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# 使用指定的模型和配置文件进行推理一组参数
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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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# 检查输出目录是否存在
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os.makedirs(opt.outdir, exist_ok=True)
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outpath = opt.outdir
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# 创建水印编码器
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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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# x
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batch_size = task['number']
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if not opt.from_file:
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prompt = task['prompt']
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assert prompt is not None
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data = [batch_size * [prompt]]
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print("data:", data)
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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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print("data:", data)
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# x
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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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# x
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start_code = None
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if opt.fixed_code:
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start_code = torch.randn([task['number'], opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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# 更新进度
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update_task_status(task, "running", 0.1)
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# 生成图片
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precision_scope = autocast if opt.precision == "autocast" or opt.bf16 else nullcontext
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with torch.no_grad(), precision_scope(opt.device), model.ema_scope():
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images = []
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# 执行指定的任务批次 (row)(task['number'])
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for n in trange(1, 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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update_task_status(task=task, status='diffusing', progress=0.5) # 修改任务状态
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samples, _ = sampler.sample(S=opt.steps,
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conditioning=c,
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batch_size=task['number'],
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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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update_task_status(task=task, status='build', progress=0.8) # 修改任务状态
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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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imge_path = os.path.join(sample_path, f"{base_count:05}.png")
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imge_path = os.path.abspath(imge_path) # 转换为绝对路径
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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(imge_path)
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base_count += 1
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sample_count += 1
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images.append(imge_path)
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update_task_status(task=task, status='done', progress=1, data=images) # 修改任务状态为完成
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print("批次任务结束..")
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#break
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if __name__ == "__main__":
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opt = parse_args()
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main_dev(opt)
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