diff options
Diffstat (limited to 'modules/sd_hijack.py')
| -rw-r--r-- | modules/sd_hijack.py | 30 |
1 files changed, 22 insertions, 8 deletions
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 7b2030d4..4d799ac0 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -180,6 +180,7 @@ class StableDiffusionModelHijack: dir_mtime = None
layers = None
circular_enabled = False
+ clip = None
def load_textual_inversion_embeddings(self, dirname, model):
mt = os.path.getmtime(dirname)
@@ -242,6 +243,7 @@ class StableDiffusionModelHijack: 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
if cmd_opts.opt_split_attention_v1:
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
@@ -268,6 +270,11 @@ class StableDiffusionModelHijack: for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
layer.padding_mode = 'circular' if enable else 'zeros'
+ def tokenize(self, text):
+ max_length = self.clip.max_length - 2
+ _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
+ return {"tokens": remade_batch_tokens[0], "token_count":token_count, "max_length":max_length}
+
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
def __init__(self, wrapped, hijack):
@@ -294,14 +301,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0:
self.token_mults[ident] = mult
- def forward(self, text):
- self.hijack.fixes = []
- self.hijack.comments = []
- remade_batch_tokens = []
+ def process_text(self, text):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
maxlen = self.wrapped.max_length
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"]
@@ -353,9 +362,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): 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))
-
- self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
+ 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)
@@ -364,8 +372,14 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
remade_batch_tokens.append(remade_tokens)
- self.hijack.fixes.append(fixes)
+ 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):
+ batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
+ self.hijack.fixes = hijack_fixes
+ self.hijack.comments = hijack_comments
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
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