Upload spaghetti_ai_script.py

spaghetti_ai_script.py

@@ -428,22 +428,20 @@ allow_other_model_versions = 1
 #
 
 enable_image_preview = 1
-image_preview_step_interval = 5 #10
+image_preview_step_interval = 10
 image_preview_seconds_interval = 30
 load_image_preview_frequency_in_seconds = 2
 delete_preview_images_immediately = 1
 
-default_create_preview_images = 1 # 1
-default_do_not_create_refining_preview_images = 0 # 0
-default_do_not_create_upscaling_preview_images = 0 # 1
+default_create_preview_images = 1
+default_do_not_create_refining_preview_images = 0
+default_do_not_create_upscaling_preview_images = 1
 
 ####################
 
 #
 #    Allow Longer Prompts for Stable Diffusion 1.5 Based Models
 #
-#        https://github.com/huggingface/diffusers/issues/2136#issuecomment-1514969011
-#
 
 enable_longer_prompts = 1
 
@@ -3637,7 +3635,7 @@ def create_preview_image (
 #####################
 
 def get_pipeline_embeds(
-    pipeline,
+    pipe,
     prompt,
     negative_prompt,
     device,
@@ -3645,28 +3643,55 @@ def get_pipeline_embeds(
     token_length_of_negative_prompt_text
 ):
 
-    max_length = pipeline.tokenizer.model_max_length
+    max_length = pipe.tokenizer.model_max_length
 
-    # create the tensor based on which prompt is longer
+    #    Create the tensor based on which prompt is longer
 
     if token_length_of_prompt_text >= token_length_of_negative_prompt_text:
 
-        input_ids = pipeline.tokenizer(prompt, return_tensors="pt", truncation=False).input_ids.to(device)
+        input_ids = pipe.tokenizer(
+            prompt,
+            return_tensors = "pt",
+            truncation = False
+        ).input_ids.to(device)
+
         shape_max_length = input_ids.shape[-1]
-        negative_ids = pipeline.tokenizer(negative_prompt, truncation=False, padding="max_length",
-                                          max_length=shape_max_length, return_tensors="pt").input_ids.to(device)
+
+        negative_ids = pipe.tokenizer(
+            negative_prompt,
+            truncation = False,
+            padding = "max_length",
+            max_length = shape_max_length,
+            return_tensors = "pt"
+        ).input_ids.to(device)
 
     else:
-        negative_ids = pipeline.tokenizer(negative_prompt, return_tensors="pt", truncation=False).input_ids.to(device)
+
+        negative_ids = pipe.tokenizer(
+            negative_prompt,
+            return_tensors = "pt",
+            truncation = False
+        ).input_ids.to(device)
+
         shape_max_length = negative_ids.shape[-1]
-        input_ids = pipeline.tokenizer(prompt, return_tensors="pt", truncation=False, padding="max_length",
-                                       max_length=shape_max_length).input_ids.to(device)
+
+        input_ids = pipe.tokenizer(
+            prompt,
+            return_tensors="pt",
+            truncation = False,
+            padding = "max_length",
+            max_length = shape_max_length
+        ).input_ids.to(device)
 
     concat_embeds = []
+
     neg_embeds = []
+
     for i in range(0, shape_max_length, max_length):
-        concat_embeds.append(pipeline.text_encoder(input_ids[:, i: i + max_length])[0])
-        neg_embeds.append(pipeline.text_encoder(negative_ids[:, i: i + max_length])[0])
+
+        concat_embeds.append(pipe.text_encoder(input_ids[:, i: i + max_length])[0])
+
+        neg_embeds.append(pipe.text_encoder(negative_ids[:, i: i + max_length])[0])
 
     return torch.cat(concat_embeds, dim=1), torch.cat(neg_embeds, dim=1)
 
@@ -4042,26 +4067,30 @@ def create_image_function (
 
 
 
+    tokenizer = pipe.tokenizer
 
+    max_token_length_of_model = pipe.tokenizer.model_max_length - number_of_reserved_tokens
 
+    token_length_of_prompt_text = len(tokenizer.tokenize(prompt_text))
+    token_length_of_negative_prompt_text = len(tokenizer.tokenize(negative_prompt_text))
 
+    both_prompts_under_length_limit = 0
 
+    if (
+        (token_length_of_prompt_text <= max_token_length_of_model) and
+        (token_length_of_negative_prompt_text <= max_token_length_of_model)
+    ):
 
+        #    No need to handle a long prompt
 
+        both_prompts_under_length_limit = 1
 
+        allow_longer_prompts_for_sd_1_5_based_models_field_value = 0
 
-    tokenizer = pipe.tokenizer
-
-    max_token_length_of_model = pipe.tokenizer.model_max_length - number_of_reserved_tokens
 
-    token_length_of_prompt_text = len(tokenizer.tokenize(prompt_text))
-    token_length_of_negative_prompt_text = len(tokenizer.tokenize(negative_prompt_text))
 
     if (
-        (
-            (token_length_of_prompt_text > max_token_length_of_model) or
-            (token_length_of_negative_prompt_text > max_token_length_of_model)
-        ) and
+        (both_prompts_under_length_limit == 0) and
         (allow_longer_prompts_for_sd_1_5_based_models_field_value == 1)
     ):
 
@@ -4083,7 +4112,10 @@ def create_image_function (
 
         #    Only 77 tokens are allowed in the prompt. 2 are reserved, meaning
         #    it is truncated to 75. This happens automatically, but we want to
-        #    tell people that
+        #    tell people that.
+        #
+        #    Also applies when both prompts are under the maximum and they do
+        #    allow longer prompts too.
 
         prompt_embeds = None
         negative_prompt_embeds = None
@@ -6825,7 +6857,7 @@ with gr.Blocks(
                         model_types_for_longer_prompts_html += " models"
 
                     allow_longer_prompts_for_sd_1_5_based_models_field = gr.Checkbox(
-                        label = "Allow longer prompts for " + model_types_for_longer_prompts_html + " models when not using the refiner or upscaler. (will sometimes fail)",
+                        label = "Allow longer prompts for " + model_types_for_longer_prompts_html + " models when not using the refiner or upscaler.",
                         value = default_allow_longer_prompts_for_sd_1_5_based_models_is_selected,
                         interactive = True,
                         container = True,