xhinker parser implementation

modules/prompt_parser_diffusers.py

@@ -6,8 +6,10 @@
 from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
 from transformers import PreTrainedTokenizer
 from modules import shared, prompt_parser, devices, sd_models
+from modules.prompt_parser_xhinker import get_weighted_text_embeddings_sd15, get_weighted_text_embeddings_sdxl_2p, \
+    get_weighted_text_embeddings_sd3, get_weighted_text_embeddings_flux1
 
-
+print("testing")
 debug_enabled = os.environ.get('SD_PROMPT_DEBUG', None)
 debug = shared.log.trace if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
 debug('Trace: PROMPT')
@@ -183,7 +185,10 @@ def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, c
         for i in range(max(len(positive_schedule), len(negative_schedule))):
             positive_prompt = positive_schedule[i % len(positive_schedule)]
             negative_prompt = negative_schedule[i % len(negative_schedule)]
-            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
+            if shared.opts.prompt_attention == "xhinker parser":
+                prompt_embed, positive_pooled, negative_embed, negative_pooled = get_xhinker_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
+            else:
+                prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
             if prompt_embed is not None:
                 p.prompt_embeds.append(torch.cat([prompt_embed] * len(prompts), dim=0))
             if negative_embed is not None:
@@ -442,3 +447,28 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
         ).to(device)
         negative_prompt_embeds = torch.cat([negative_prompt_embeds, t5_negative_prompt_embed], dim=-2)
     return prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds
+
+
+def get_xhinker_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
+    print("using xhinker parser")
+    device = devices.device
+    SD3 = hasattr(pipe, 'text_encoder_3')
+    prompt, prompt_2, prompt_3 = split_prompts(prompt, SD3)
+    neg_prompt, neg_prompt_2, neg_prompt_3 = split_prompts(neg_prompt, SD3)
+    try:
+        prompt = pipe.maybe_convert_prompt(prompt, pipe.tokenizer)
+        neg_prompt = pipe.maybe_convert_prompt(neg_prompt, pipe.tokenizer)
+        prompt_2 = pipe.maybe_convert_prompt(prompt_2, pipe.tokenizer_2)
+        neg_prompt_2 = pipe.maybe_convert_prompt(neg_prompt_2, pipe.tokenizer_2)
+    except:
+        pass
+    prompt_embed = positive_pooled = negative_embed = negative_pooled = None
+    if SD3:
+        prompt_embed, negative_embed, positive_pooled, negative_pooled = get_weighted_text_embeddings_sd3(pipe=pipe, prompt=prompt, neg_prompt=neg_prompt, use_t5_encoder=bool(pipe.text_encoder_3))
+    elif 'Flux' in pipe.__class__.__name__:
+        prompt_embed, positive_pooled = get_weighted_text_embeddings_flux1(pipe=pipe, prompt=prompt, prompt_2=prompt_2)
+    elif 'XL' in pipe.__class__.__name__:
+        prompt_embed, negative_embed, positive_pooled, negative_pooled = get_weighted_text_embeddings_sdxl_2p(pipe=pipe, prompt=prompt, prompt_2=prompt_2, neg_prompt=neg_prompt, neg_prompt_2=neg_prompt_2)
+    else:
+        prompt_embed, negative_embed = get_weighted_text_embeddings_sd15(pipe=pipe, prompt=prompt, neg_prompt=neg_prompt, clip_skip=clip_skip)
+    return prompt_embed, positive_pooled, negative_embed, negative_pooled