89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
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import argparse, os, sys, glob
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sys.path.append(os.path.join(sys.path[0], '..'))
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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 einops import rearrange
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from torchvision.utils import make_grid
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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.data.personalized import PersonalizedBase
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from evaluation.clip_eval import LDMCLIPEvaluator
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def load_model_from_config(config, ckpt, 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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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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model.cuda()
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model.eval()
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return model
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if __name__ == "__main__":
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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 painting of a virus monster playing guitar",
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help="the prompt to render"
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)
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parser.add_argument(
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"--ckpt_path",
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type=str,
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default="/data/pretrained_models/ldm/text2img-large/model.ckpt",
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help="Path to pretrained ldm text2img model")
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parser.add_argument(
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"--embedding_path",
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type=str,
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help="Path to a pre-trained embedding manager checkpoint")
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parser.add_argument(
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"--data_dir",
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type=str,
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help="Path to directory with images used to train the embedding vectors"
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)
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opt = parser.parse_args()
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config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-eval_with_tokens.yaml") # TODO: Optionally download from same location as ckpt and chnage this logic
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model = load_model_from_config(config, opt.ckpt_path) # TODO: check path
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model.embedding_manager.load(opt.embedding_path)
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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model = model.to(device)
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evaluator = LDMCLIPEvaluator(device)
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prompt = opt.prompt
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data_loader = PersonalizedBase(opt.data_dir, size=256, flip_p=0.0)
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images = [torch.from_numpy(data_loader[i]["image"]).permute(2, 0, 1) for i in range(data_loader.num_images)]
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images = torch.stack(images, axis=0)
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sim_img, sim_text = evaluator.evaluate(model, images, opt.prompt)
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output_dir = os.path.join(opt.out_dir, prompt.replace(" ", "-"))
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print("Image similarity: ", sim_img)
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print("Text similarity: ", sim_text)
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