Upload 2 files

dc.py

@@ -351,7 +351,6 @@ class GuiSD:
 
 ## BEGIN MOD
         loras_list = [s if s else "None" for s in loras_list]
-        prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
         global lora_model_list
         lora_model_list = get_lora_model_list()
 ## END MOD
@@ -706,7 +705,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
            model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
            lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
            sampler = "Euler", vae = None, translate=True, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
-           progress=gr.Progress(track_tqdm=True)):
+           recom_prompt = True, progress=gr.Progress(track_tqdm=True)):
     MAX_SEED = np.iinfo(np.int32).max
 
     image_previews = True
@@ -727,7 +726,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         prompt = translate_to_en(prompt)
         negative_prompt = translate_to_en(prompt)
 
-    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
+    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name, recom_prompt)
     progress(0.5, desc="Preparing...")
     lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
         set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
@@ -767,8 +766,8 @@ def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidanc
             model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
             lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
             sampler = "Euler", vae = None, translate = True, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
-            progress=gr.Progress(track_tqdm=True)):
-    return gr.update(visible=True)
+            recom_prompt = True, progress=gr.Progress(track_tqdm=True)):
+    return gr.update()
 
 
 infer.zerogpu = True
@@ -823,7 +822,6 @@ def load_model_prompt_dict():
 model_prompt_dict = load_model_prompt_dict()
 
 
-model_recom_prompt_enabled = True
 animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres")
 animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
 pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
@@ -832,7 +830,7 @@ other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, c
 other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly")
 default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres")
 default_nps = to_list("score_6, score_5, score_4, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
-def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"):
+def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None", model_recom_prompt_enabled = True):
     if not model_recom_prompt_enabled or not model_name: return prompt, neg_prompt
     prompts = to_list(prompt)
     neg_prompts = to_list(neg_prompt)
@@ -855,12 +853,6 @@ def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name
     return prompt, neg_prompt
 
 
-def enable_model_recom_prompt(is_enable: bool = True):
-    global model_recom_prompt_enabled
-    model_recom_prompt_enabled = is_enable
-    return is_enable
-
-
 private_lora_dict = {}
 try:
     with open('lora_dict.json', encoding='utf-8') as f: