Update app.py

app.py

@@ -82,6 +82,9 @@ for i in range(len(embeddings_dict["clip_l"])):
     pipe.text_encoder.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_l"][i]
     pipe.text_encoder_2.get_input_embeddings().weight.data[token_id] = embeddings_dict["clip_g"][i]
 
+
+compel = Compel(tokenizer=[pipeline.tokenizer, pipeline.tokenizer_2] , text_encoder=[pipeline.text_encoder, pipeline.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True])
+
 def error_str(error, title="Error"):
     return f"""#### {title}
             {error}"""  if error else ""
@@ -124,7 +127,7 @@ def auto_prompt_correction(prompt_ui,neg_prompt_ui,disable_auto_prompt_correctio
 
     if(prompt=="" and neg_prompt==""):
         prompt="1girl, sunflowers, brown bob hair, brown eyes, sky"
-        neg_prompt=f"{unaestheticXLv31}, {unaestheticXLv31}, {unaestheticXLv13}, {unaestheticXLv13}, {unaestheticXLv1}"
+        neg_prompt=f"({unaestheticXLv31})+++, ({unaestheticXLv13})+++, ({unaestheticXLv1})+"
         return prompt, neg_prompt
 
     splited_prompt=prompt.replace(","," ").replace("_"," ").split(" ")
@@ -153,14 +156,18 @@ def auto_prompt_correction(prompt_ui,neg_prompt_ui,disable_auto_prompt_correctio
     return prompt,neg_prompt
     
 def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
+    conditioning, pooled = compel([prompt, negative_prompt])
+    
     result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
+        prompt_embeds=conditioning[0:1],
+        pooled_prompt_embeds=pooled[0:1], 
+        negative_prompt_embeds=conditioning[1:2], 
+        negative_pooled_prompt_embeds=pooled[1:2],
+        num_inference_steps = int(steps),
+        guidance_scale = guidance,
+        width = width,
+        height = height,
+        generator = generator)
     
     return result.images[0]