modules/sd_hijack.py (change is at line 351 to 357)

  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
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
import math
import os
import sys
import traceback
import torch
import numpy as np
from torch import einsum
from torch.nn.functional import silu

import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
from modules.shared import opts, device, cmd_opts
from modules.sd_hijack_optimizations import invokeAI_mps_available

import ldm.modules.attention
import ldm.modules.diffusionmodules.model

attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward


def apply_optimizations():
    undo_optimizations()

    ldm.modules.diffusionmodules.model.nonlinearity = silu

    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
        print("Applying xformers cross attention optimization.")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
    elif cmd_opts.opt_split_attention_v1:
        print("Applying v1 cross attention optimization.")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
        if not invokeAI_mps_available and shared.device.type == 'mps':
            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
            print("Applying v1 cross attention optimization.")
            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
        else:
            print("Applying cross attention optimization (InvokeAI).")
            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
        print("Applying cross attention optimization (Doggettx).")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward


def undo_optimizations():
    from modules.hypernetworks import hypernetwork

    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward


def get_target_prompt_token_count(token_count):
    return math.ceil(max(token_count, 1) / 75) * 75


class StableDiffusionModelHijack:
    fixes = None
    comments = []
    layers = None
    circular_enabled = False
    clip = None

    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)

    def hijack(self, m):
        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings

        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
        m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)

        self.clip = m.cond_stage_model

        apply_optimizations()

        def flatten(el):
            flattened = [flatten(children) for children in el.children()]
            res = [el]
            for c in flattened:
                res += c
            return res

        self.layers = flatten(m)

    def undo_hijack(self, m):
        if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
            m.cond_stage_model = m.cond_stage_model.wrapped

        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
        if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
            model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped

        self.layers = None
        self.circular_enabled = False
        self.clip = None

    def apply_circular(self, enable):
        if self.circular_enabled == enable:
            return

        self.circular_enabled = enable

        for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
            layer.padding_mode = 'circular' if enable else 'zeros'

    def clear_comments(self):
        self.comments = []

    def tokenize(self, text):
        _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
        return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)


class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
    def __init__(self, wrapped, hijack):
        super().__init__()
        self.wrapped = wrapped
        self.hijack: StableDiffusionModelHijack = hijack
        self.tokenizer = wrapped.tokenizer
        self.token_mults = {}

        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]

        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
        for text, ident in tokens_with_parens:
            mult = 1.0
            for c in text:
                if c == '[':
                    mult /= 1.1
                if c == ']':
                    mult *= 1.1
                if c == '(':
                    mult *= 1.1
                if c == ')':
                    mult /= 1.1

            if mult != 1.0:
                self.token_mults[ident] = mult

    def tokenize_line(self, line, used_custom_terms, hijack_comments):
        id_end = self.wrapped.tokenizer.eos_token_id

        if opts.enable_emphasis:
            parsed = prompt_parser.parse_prompt_attention(line)
        else:
            parsed = [[line, 1.0]]

        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]

        fixes = []
        remade_tokens = []
        multipliers = []
        last_comma = -1

        for tokens, (text, weight) in zip(tokenized, parsed):
            i = 0
            while i < len(tokens):
                token = tokens[i]

                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

                if token == self.comma_token:
                    last_comma = len(remade_tokens)
                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
                    last_comma += 1
                    reloc_tokens = remade_tokens[last_comma:]
                    reloc_mults = multipliers[last_comma:]

                    remade_tokens = remade_tokens[:last_comma]
                    length = len(remade_tokens)

                    rem = int(math.ceil(length / 75)) * 75 - length
                    remade_tokens += [id_end] * rem + reloc_tokens
                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults

                if embedding is None:
                    remade_tokens.append(token)
                    multipliers.append(weight)
                    i += 1
                else:
                    emb_len = int(embedding.vec.shape[0])
                    iteration = len(remade_tokens) // 75
                    if (len(remade_tokens) + emb_len) // 75 != iteration:
                        rem = (75 * (iteration + 1) - len(remade_tokens))
                        remade_tokens += [id_end] * rem
                        multipliers += [1.0] * rem
                        iteration += 1
                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
                    remade_tokens += [0] * emb_len
                    multipliers += [weight] * emb_len
                    used_custom_terms.append((embedding.name, embedding.checksum()))
                    i += embedding_length_in_tokens

        token_count = len(remade_tokens)
        prompt_target_length = get_target_prompt_token_count(token_count)
        tokens_to_add = prompt_target_length - len(remade_tokens)

        remade_tokens = remade_tokens + [id_end] * tokens_to_add
        multipliers = multipliers + [1.0] * tokens_to_add

        return remade_tokens, fixes, multipliers, token_count

    def process_text(self, texts):
        used_custom_terms = []
        remade_batch_tokens = []
        hijack_comments = []
        hijack_fixes = []
        token_count = 0

        cache = {}
        batch_multipliers = []
        for line in texts:
            if line in cache:
                remade_tokens, fixes, multipliers = cache[line]
            else:
                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
                token_count = max(current_token_count, token_count)

                cache[line] = (remade_tokens, fixes, multipliers)

            remade_batch_tokens.append(remade_tokens)
            hijack_fixes.append(fixes)
            batch_multipliers.append(multipliers)

        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

    def process_text_old(self, text):
        id_start = self.wrapped.tokenizer.bos_token_id
        id_end = self.wrapped.tokenizer.eos_token_id
        maxlen = self.wrapped.max_length  # you get to stay at 77
        used_custom_terms = []
        remade_batch_tokens = []
        overflowing_words = []
        hijack_comments = []
        hijack_fixes = []
        token_count = 0

        cache = {}
        batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
        batch_multipliers = []
        for tokens in batch_tokens:
            tuple_tokens = tuple(tokens)

            if tuple_tokens in cache:
                remade_tokens, fixes, multipliers = cache[tuple_tokens]
            else:
                fixes = []
                remade_tokens = []
                multipliers = []
                mult = 1.0

                i = 0
                while i < len(tokens):
                    token = tokens[i]

                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                    if mult_change is not None:
                        mult *= mult_change
                        i += 1
                    elif embedding is None:
                        remade_tokens.append(token)
                        multipliers.append(mult)
                        i += 1
                    else:
                        emb_len = int(embedding.vec.shape[0])
                        fixes.append((len(remade_tokens), embedding))
                        remade_tokens += [0] * emb_len
                        multipliers += [mult] * emb_len
                        used_custom_terms.append((embedding.name, embedding.checksum()))
                        i += embedding_length_in_tokens

                if len(remade_tokens) > maxlen - 2:
                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
                    ovf = remade_tokens[maxlen - 2:]
                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")

                token_count = len(remade_tokens)
                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)

            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]

            remade_batch_tokens.append(remade_tokens)
            hijack_fixes.append(fixes)
            batch_multipliers.append(multipliers)
        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

    def forward(self, text):
        use_old = opts.use_old_emphasis_implementation
        if use_old:
            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
        else:
            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)

        self.hijack.comments += hijack_comments

        if len(used_custom_terms) > 0:
            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))

        if use_old:
            self.hijack.fixes = hijack_fixes
            return self.process_tokens(remade_batch_tokens, batch_multipliers)

        z = None
        i = 0
        while max(map(len, remade_batch_tokens)) != 0:
            rem_tokens = [x[75:] for x in remade_batch_tokens]
            rem_multipliers = [x[75:] for x in batch_multipliers]

            self.hijack.fixes = []
            for unfiltered in hijack_fixes:
                fixes = []
                for fix in unfiltered:
                    if fix[0] == i:
                        fixes.append(fix[1])
                self.hijack.fixes.append(fixes)

            tokens = []
            multipliers = []
            for j in range(len(remade_batch_tokens)):
                if len(remade_batch_tokens[j]) > 0:
                    tokens.append(remade_batch_tokens[j][:75])
                    multipliers.append(batch_multipliers[j][:75])
                else:
                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
                    multipliers.append([1.0] * 75)

            z1 = self.process_tokens(tokens, multipliers)
            z = z1 if z is None else torch.cat((z, z1), axis=-2)

            remade_batch_tokens = rem_tokens
            batch_multipliers = rem_multipliers
            i += 1

        return z

    def process_tokens(self, remade_batch_tokens, batch_multipliers):
        if not opts.use_old_emphasis_implementation:
            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]

            k = math.sqrt(2)
            for i in range(len(remade_batch_tokens)):
                for j in range(75):
                    x = j % 75 / 74
                    x = x * (k - 1) + 1
                    batch_multipliers[i][j + 1] *= x

        tokens = torch.asarray(remade_batch_tokens).to(device)
        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)

        if opts.CLIP_stop_at_last_layers > 1:
            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
            z = self.wrapped.transformer.text_model.final_layer_norm(z)
        else:
            z = outputs.last_hidden_state

        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
        original_mean = z.mean()
        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
        new_mean = z.mean()
        z *= original_mean / new_mean

        return z


class EmbeddingsWithFixes(torch.nn.Module):
    def __init__(self, wrapped, embeddings):
        super().__init__()
        self.wrapped = wrapped
        self.embeddings = embeddings

    def forward(self, input_ids):
        batch_fixes = self.embeddings.fixes
        self.embeddings.fixes = None

        inputs_embeds = self.wrapped(input_ids)

        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
            return inputs_embeds

        vecs = []
        for fixes, tensor in zip(batch_fixes, inputs_embeds):
            for offset, embedding in fixes:
                emb = embedding.vec
                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])

            vecs.append(tensor)

        return torch.stack(vecs)


def add_circular_option_to_conv_2d():
    conv2d_constructor = torch.nn.Conv2d.__init__

    def conv2d_constructor_circular(self, *args, **kwargs):
        return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)

    torch.nn.Conv2d.__init__ = conv2d_constructor_circular


model_hijack = StableDiffusionModelHijack()
Edit
Pub: 05 Nov 2022 22:02 UTC
Views: 2526