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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_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) |
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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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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] |
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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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unconditional_conditioning=uc, |
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eta=opt.ddim_eta, |
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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: |
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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")) |
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base_count += 1 |
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all_samples.append(x_samples_ddim) |
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if not opt.skip_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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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() |
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Image.fromarray(grid.astype(np.uint8)).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} \nEnjoy.") |
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