From 706d5944a075a6523ea7f00165d630efc085ca22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 13:38:57 +0300
Subject: let user choose his own prompt token count limit

---
 modules/sd_hijack.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

(limited to 'modules/sd_hijack.py')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d68f89cc..340329c0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -18,7 +18,6 @@ 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()
 
@@ -83,7 +82,7 @@ class StableDiffusionModelHijack:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
     def tokenize(self, text):
-        max_length = self.clip.max_length - 2
+        max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, max_length
 
@@ -94,7 +93,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.wrapped = wrapped
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
-        self.max_length = wrapped.max_length
         self.token_mults = {}
 
         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]
@@ -116,7 +114,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
+        maxlen = opts.max_prompt_tokens
 
         if opts.enable_emphasis:
             parsed = prompt_parser.parse_prompt_attention(line)
@@ -191,7 +189,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     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
+        maxlen = self.wrapped.max_length  # you get to stay at 77
         used_custom_terms = []
         remade_batch_tokens = []
         overflowing_words = []
@@ -268,8 +266,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
 
+        position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76]
+        position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
+
         tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens)
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
         z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-- 
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