let user choose his own prompt token count limit

3 files changed, 16 insertions, 8 deletions

diff --git a/modules/processing.py b/modules/processing.py
index f773a30e..d814d5ac 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,6 +123,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image

         self.styles = p.styles

         self.job_timestamp = state.job_timestamp

+        self.max_prompt_tokens = opts.max_prompt_tokens

 

         self.eta = p.eta

         self.ddim_discretize = p.ddim_discretize

@@ -141,6 +142,7 @@ class Processed:
         self.all_subseeds = all_subseeds or [self.subseed]

         self.infotexts = infotexts or [info]

 

+

     def js(self):

         obj = {

             "prompt": self.prompt,

@@ -169,6 +171,7 @@ class Processed:
             "infotexts": self.infotexts,

             "styles": self.styles,

             "job_timestamp": self.job_timestamp,

+            "max_prompt_tokens": self.max_prompt_tokens,

         }

 

         return json.dumps(obj)

@@ -266,6 +269,8 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):

     index = position_in_batch + iteration * p.batch_size

 

+    max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens)

+

     generation_params = {

         "Steps": p.steps,

         "Sampler": sd_samplers.samplers[p.sampler_index].name,

@@ -281,6 +286,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

         "Denoising strength": getattr(p, 'denoising_strength', None),

         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

+        "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens)

     }

 

     generation_params.update(p.extra_generation_params)

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

diff --git a/modules/shared.py b/modules/shared.py
index 879d8424..864e772c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -118,8 +118,8 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
 interrogator = modules.interrogate.InterrogateModels("interrogate")

 

 face_restorers = []

-# This was moved to webui.py with the other model "setup" calls.

-# modules.sd_models.list_models()

+

+vanilla_max_prompt_tokens = 77

 

 

 def realesrgan_models_names():

@@ -221,6 +221,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),

     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),

     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),

+    "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}),

     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),

 }))