File size: 13,707 Bytes
2d0e22d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
import argparse, os, sys, glob
import clip
import torch
import torch.nn as nn
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
import scann
import time
from multiprocessing import cpu_count

from ldm.util import instantiate_from_config, parallel_data_prefetch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder

DATABASES = [
    "openimages",
    "artbench-art_nouveau",
    "artbench-baroque",
    "artbench-expressionism",
    "artbench-impressionism",
    "artbench-post_impressionism",
    "artbench-realism",
    "artbench-romanticism",
    "artbench-renaissance",
    "artbench-surrealism",
    "artbench-ukiyo_e",
]


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model.cuda()
    model.eval()
    return model


class Searcher(object):
    def __init__(self, database, retriever_version='ViT-L/14'):
        assert database in DATABASES
        # self.database = self.load_database(database)
        self.database_name = database
        self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
        self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
        self.retriever = self.load_retriever(version=retriever_version)
        self.database = {'embedding': [],
                         'img_id': [],
                         'patch_coords': []}
        self.load_database()
        self.load_searcher()

    def train_searcher(self, k,
                       metric='dot_product',
                       searcher_savedir=None):

        print('Start training searcher')
        searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
                                                  np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
                                                  k, metric)
        self.searcher = searcher.score_brute_force().build()
        print('Finish training searcher')

        if searcher_savedir is not None:
            print(f'Save trained searcher under "{searcher_savedir}"')
            os.makedirs(searcher_savedir, exist_ok=True)
            self.searcher.serialize(searcher_savedir)

    def load_single_file(self, saved_embeddings):
        compressed = np.load(saved_embeddings)
        self.database = {key: compressed[key] for key in compressed.files}
        print('Finished loading of clip embeddings.')

    def load_multi_files(self, data_archive):
        out_data = {key: [] for key in self.database}
        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
            for key in d.files:
                out_data[key].append(d[key])

        return out_data

    def load_database(self):

        print(f'Load saved patch embedding from "{self.database_path}"')
        file_content = glob.glob(os.path.join(self.database_path, '*.npz'))

        if len(file_content) == 1:
            self.load_single_file(file_content[0])
        elif len(file_content) > 1:
            data = [np.load(f) for f in file_content]
            prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
                                                     n_proc=min(len(data), cpu_count()), target_data_type='dict')

            self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
                             self.database}
        else:
            raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')

        print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')

    def load_retriever(self, version='ViT-L/14', ):
        model = FrozenClipImageEmbedder(model=version)
        if torch.cuda.is_available():
            model.cuda()
        model.eval()
        return model

    def load_searcher(self):
        print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
        self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
        print('Finished loading searcher.')

    def search(self, x, k):
        if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
            self.train_searcher(k)   # quickly fit searcher on the fly for small databases
        assert self.searcher is not None, 'Cannot search with uninitialized searcher'
        if isinstance(x, torch.Tensor):
            x = x.detach().cpu().numpy()
        if len(x.shape) == 3:
            x = x[:, 0]
        query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]

        start = time.time()
        nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
        end = time.time()

        out_embeddings = self.database['embedding'][nns]
        out_img_ids = self.database['img_id'][nns]
        out_pc = self.database['patch_coords'][nns]

        out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
               'img_ids': out_img_ids,
               'patch_coords': out_pc,
               'queries': x,
               'exec_time': end - start,
               'nns': nns,
               'q_embeddings': query_embeddings}

        return out

    def __call__(self, x, n):
        return self.search(x, n)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
    # TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
    parser.add_argument(
        "--prompt",
        type=str,
        nargs="?",
        default="a painting of a virus monster playing guitar",
        help="the prompt to render"
    )

    parser.add_argument(
        "--outdir",
        type=str,
        nargs="?",
        help="dir to write results to",
        default="outputs/txt2img-samples"
    )

    parser.add_argument(
        "--skip_grid",
        action='store_true',
        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
    )

    parser.add_argument(
        "--ddim_steps",
        type=int,
        default=50,
        help="number of ddim sampling steps",
    )

    parser.add_argument(
        "--n_repeat",
        type=int,
        default=1,
        help="number of repeats in CLIP latent space",
    )

    parser.add_argument(
        "--plms",
        action='store_true',
        help="use plms sampling",
    )

    parser.add_argument(
        "--ddim_eta",
        type=float,
        default=0.0,
        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
    )
    parser.add_argument(
        "--n_iter",
        type=int,
        default=1,
        help="sample this often",
    )

    parser.add_argument(
        "--H",
        type=int,
        default=768,
        help="image height, in pixel space",
    )

    parser.add_argument(
        "--W",
        type=int,
        default=768,
        help="image width, in pixel space",
    )

    parser.add_argument(
        "--n_samples",
        type=int,
        default=3,
        help="how many samples to produce for each given prompt. A.k.a batch size",
    )

    parser.add_argument(
        "--n_rows",
        type=int,
        default=0,
        help="rows in the grid (default: n_samples)",
    )

    parser.add_argument(
        "--scale",
        type=float,
        default=5.0,
        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
    )

    parser.add_argument(
        "--from-file",
        type=str,
        help="if specified, load prompts from this file",
    )

    parser.add_argument(
        "--config",
        type=str,
        default="configs/retrieval-augmented-diffusion/768x768.yaml",
        help="path to config which constructs model",
    )

    parser.add_argument(
        "--ckpt",
        type=str,
        default="models/rdm/rdm768x768/model.ckpt",
        help="path to checkpoint of model",
    )

    parser.add_argument(
        "--clip_type",
        type=str,
        default="ViT-L/14",
        help="which CLIP model to use for retrieval and NN encoding",
    )
    parser.add_argument(
        "--database",
        type=str,
        default='artbench-surrealism',
        choices=DATABASES,
        help="The database used for the search, only applied when --use_neighbors=True",
    )
    parser.add_argument(
        "--use_neighbors",
        default=False,
        action='store_true',
        help="Include neighbors in addition to text prompt for conditioning",
    )
    parser.add_argument(
        "--knn",
        default=10,
        type=int,
        help="The number of included neighbors, only applied when --use_neighbors=True",
    )

    opt = parser.parse_args()

    config = OmegaConf.load(f"{opt.config}")
    model = load_model_from_config(config, f"{opt.ckpt}")

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)

    clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)

    if opt.plms:
        sampler = PLMSSampler(model)
    else:
        sampler = DDIMSampler(model)

    os.makedirs(opt.outdir, exist_ok=True)
    outpath = opt.outdir

    batch_size = opt.n_samples
    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
    if not opt.from_file:
        prompt = opt.prompt
        assert prompt is not None
        data = [batch_size * [prompt]]

    else:
        print(f"reading prompts from {opt.from_file}")
        with open(opt.from_file, "r") as f:
            data = f.read().splitlines()
            data = list(chunk(data, batch_size))

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    grid_count = len(os.listdir(outpath)) - 1

    print(f"sampling scale for cfg is {opt.scale:.2f}")

    searcher = None
    if opt.use_neighbors:
        searcher = Searcher(opt.database)

    with torch.no_grad():
        with model.ema_scope():
            for n in trange(opt.n_iter, desc="Sampling"):
                all_samples = list()
                for prompts in tqdm(data, desc="data"):
                    print("sampling prompts:", prompts)
                    if isinstance(prompts, tuple):
                        prompts = list(prompts)
                    c = clip_text_encoder.encode(prompts)
                    uc = None
                    if searcher is not None:
                        nn_dict = searcher(c, opt.knn)
                        c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
                    if opt.scale != 1.0:
                        uc = torch.zeros_like(c)
                    if isinstance(prompts, tuple):
                        prompts = list(prompts)
                    shape = [16, opt.H // 16, opt.W // 16]  # note: currently hardcoded for f16 model
                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                     conditioning=c,
                                                     batch_size=c.shape[0],
                                                     shape=shape,
                                                     verbose=False,
                                                     unconditional_guidance_scale=opt.scale,
                                                     unconditional_conditioning=uc,
                                                     eta=opt.ddim_eta,
                                                     )

                    x_samples_ddim = model.decode_first_stage(samples_ddim)
                    x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

                    for x_sample in x_samples_ddim:
                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                        Image.fromarray(x_sample.astype(np.uint8)).save(
                            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.")