diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/processing.py | 6 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 13 | ||||
| -rw-r--r-- | modules/shared.py | 5 |
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()}),
}))
|