399 lines
13 KiB
Python
399 lines
13 KiB
Python
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import argparse, os, sys, glob
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import clip
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import torch
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import torch.nn as nn
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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, repeat
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from torchvision.utils import make_grid
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import scann
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import time
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from multiprocessing import cpu_count
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from ldm.util import instantiate_from_config, parallel_data_prefetch
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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.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
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DATABASES = [
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"openimages",
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"artbench-art_nouveau",
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"artbench-baroque",
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"artbench-expressionism",
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"artbench-impressionism",
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"artbench-post_impressionism",
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"artbench-realism",
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"artbench-romanticism",
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"artbench-renaissance",
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"artbench-surrealism",
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"artbench-ukiyo_e",
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]
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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, 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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model.cuda()
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model.eval()
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return model
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class Searcher(object):
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def __init__(self, database, retriever_version='ViT-L/14'):
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assert database in DATABASES
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# self.database = self.load_database(database)
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self.database_name = database
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self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
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self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
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self.retriever = self.load_retriever(version=retriever_version)
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self.database = {'embedding': [],
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'img_id': [],
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'patch_coords': []}
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self.load_database()
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self.load_searcher()
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def train_searcher(self, k,
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metric='dot_product',
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searcher_savedir=None):
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print('Start training searcher')
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searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
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np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
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k, metric)
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self.searcher = searcher.score_brute_force().build()
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print('Finish training searcher')
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if searcher_savedir is not None:
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print(f'Save trained searcher under "{searcher_savedir}"')
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os.makedirs(searcher_savedir, exist_ok=True)
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self.searcher.serialize(searcher_savedir)
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def load_single_file(self, saved_embeddings):
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compressed = np.load(saved_embeddings)
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self.database = {key: compressed[key] for key in compressed.files}
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print('Finished loading of clip embeddings.')
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def load_multi_files(self, data_archive):
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out_data = {key: [] for key in self.database}
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for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
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for key in d.files:
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out_data[key].append(d[key])
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return out_data
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def load_database(self):
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print(f'Load saved patch embedding from "{self.database_path}"')
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file_content = glob.glob(os.path.join(self.database_path, '*.npz'))
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if len(file_content) == 1:
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self.load_single_file(file_content[0])
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elif len(file_content) > 1:
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data = [np.load(f) for f in file_content]
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prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
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n_proc=min(len(data), cpu_count()), target_data_type='dict')
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self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
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self.database}
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else:
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raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
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print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
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def load_retriever(self, version='ViT-L/14', ):
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model = FrozenClipImageEmbedder(model=version)
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if torch.cuda.is_available():
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model.cuda()
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model.eval()
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return model
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def load_searcher(self):
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print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
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self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
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print('Finished loading searcher.')
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def search(self, x, k):
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if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
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self.train_searcher(k) # quickly fit searcher on the fly for small databases
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assert self.searcher is not None, 'Cannot search with uninitialized searcher'
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if isinstance(x, torch.Tensor):
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x = x.detach().cpu().numpy()
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if len(x.shape) == 3:
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x = x[:, 0]
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query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]
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start = time.time()
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nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
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end = time.time()
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out_embeddings = self.database['embedding'][nns]
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out_img_ids = self.database['img_id'][nns]
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out_pc = self.database['patch_coords'][nns]
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out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
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'img_ids': out_img_ids,
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'patch_coords': out_pc,
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'queries': x,
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'exec_time': end - start,
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'nns': nns,
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'q_embeddings': query_embeddings}
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return out
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def __call__(self, x, n):
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return self.search(x, n)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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# TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
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# TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
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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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"--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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"--skip_grid",
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action='store_true',
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help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
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)
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parser.add_argument(
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"--ddim_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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"--n_repeat",
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type=int,
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default=1,
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help="number of repeats in CLIP latent space",
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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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"--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=1,
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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=768,
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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=768,
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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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"--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=5.0,
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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",
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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/retrieval-augmented-diffusion/768x768.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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default="models/rdm/rdm768x768/model.ckpt",
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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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"--clip_type",
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type=str,
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default="ViT-L/14",
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help="which CLIP model to use for retrieval and NN encoding",
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)
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parser.add_argument(
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"--database",
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type=str,
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default='artbench-surrealism',
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choices=DATABASES,
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help="The database used for the search, only applied when --use_neighbors=True",
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)
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parser.add_argument(
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"--use_neighbors",
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default=False,
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action='store_true',
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help="Include neighbors in addition to text prompt for conditioning",
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)
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parser.add_argument(
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"--knn",
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default=10,
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type=int,
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help="The number of included neighbors, only applied when --use_neighbors=True",
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)
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opt = parser.parse_args()
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config = OmegaConf.load(f"{opt.config}")
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model = load_model_from_config(config, f"{opt.ckpt}")
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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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clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)
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if opt.plms:
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sampler = PLMSSampler(model)
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else:
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sampler = DDIMSampler(model)
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os.makedirs(opt.outdir, exist_ok=True)
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outpath = opt.outdir
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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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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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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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print(f"sampling scale for cfg is {opt.scale:.2f}")
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searcher = None
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if opt.use_neighbors:
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searcher = Searcher(opt.database)
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with torch.no_grad():
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with model.ema_scope():
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for n in trange(opt.n_iter, desc="Sampling"):
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all_samples = list()
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for prompts in tqdm(data, desc="data"):
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print("sampling prompts:", prompts)
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = clip_text_encoder.encode(prompts)
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uc = None
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if searcher is not None:
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nn_dict = searcher(c, opt.knn)
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c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
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if opt.scale != 1.0:
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uc = torch.zeros_like(c)
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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shape = [16, opt.H // 16, opt.W // 16] # note: currently hardcoded for f16 model
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samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
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conditioning=c,
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batch_size=c.shape[0],
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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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||
|
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unconditional_conditioning=uc,
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||
|
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eta=opt.ddim_eta,
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||
|
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)
|
||
|
|
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||
|
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x_samples_ddim = 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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for x_sample in x_samples_ddim:
|
||
|
|
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||
|
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Image.fromarray(x_sample.astype(np.uint8)).save(
|
||
|
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os.path.join(sample_path, f"{base_count:05}.png"))
|
||
|
|
base_count += 1
|
||
|
|
all_samples.append(x_samples_ddim)
|
||
|
|
|
||
|
|
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()
|
||
|
|
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
|
||
|
|
grid_count += 1
|
||
|
|
|
||
|
|
print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
|