diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/processing.py | 5 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 25 | ||||
| -rw-r--r-- | modules/shared.py | 3 |
3 files changed, 12 insertions, 21 deletions
diff --git a/modules/processing.py b/modules/processing.py index 3657fe69..d5162ddc 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -123,7 +123,6 @@ 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
@@ -171,7 +170,6 @@ class Processed: "infotexts": self.infotexts,
"styles": self.styles,
"job_timestamp": self.job_timestamp,
- "max_prompt_tokens": self.max_prompt_tokens,
}
return json.dumps(obj)
@@ -269,8 +267,6 @@ 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,
@@ -286,7 +282,6 @@ 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 340329c0..2c1332c9 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -36,6 +36,13 @@ def undo_optimizations(): ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+def get_target_prompt_token_count(token_count):
+ if token_count < 75:
+ return 75
+
+ return math.ceil(token_count / 10) * 10
+
+
class StableDiffusionModelHijack:
fixes = None
comments = []
@@ -84,7 +91,7 @@ class StableDiffusionModelHijack: def tokenize(self, text):
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
+ return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
@@ -114,7 +121,6 @@ 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 = opts.max_prompt_tokens
if opts.enable_emphasis:
parsed = prompt_parser.parse_prompt_attention(line)
@@ -146,19 +152,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): used_custom_terms.append((embedding.name, embedding.checksum()))
i += embedding_length_in_tokens
- if len(remade_tokens) > maxlen - 2:
- vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
- ovf = remade_tokens[maxlen - 2:]
- overflowing_words = [vocab.get(int(x), "") for x in ovf]
- overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
- hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
token_count = len(remade_tokens)
- remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
- remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+ prompt_target_length = get_target_prompt_token_count(token_count)
+ tokens_to_add = prompt_target_length - len(remade_tokens) + 1
- multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
- multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+ remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
+ multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
return remade_tokens, fixes, multipliers, token_count
diff --git a/modules/shared.py b/modules/shared.py index ca462628..475d7e52 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -123,8 +123,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = []
-vanilla_max_prompt_tokens = 77
-
def realesrgan_models_names():
import modules.realesrgan_model
@@ -225,7 +223,6 @@ 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()}),
}))
|