mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-29 05:02:09 +03:00
312 lines
16 KiB
Python
312 lines
16 KiB
Python
import os
|
|
import math
|
|
import time
|
|
import typing
|
|
import torch
|
|
from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
|
|
from transformers import PreTrainedTokenizer
|
|
from modules import shared, prompt_parser, devices
|
|
|
|
|
|
debug_enabled = os.environ.get('SD_PROMPT_DEBUG', None)
|
|
debug = shared.log.trace if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
|
|
debug('Trace: PROMPT')
|
|
orig_encode_token_ids_to_embeddings = EmbeddingsProvider._encode_token_ids_to_embeddings # pylint: disable=protected-access
|
|
token_dict = None
|
|
token_type = None
|
|
cache = {}
|
|
cache_type = None
|
|
|
|
|
|
def compel_hijack(self, token_ids: torch.Tensor,
|
|
attention_mask: typing.Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
needs_hidden_states = self.returned_embeddings_type != 1
|
|
text_encoder_output = self.text_encoder(token_ids, attention_mask, output_hidden_states=needs_hidden_states, return_dict=True)
|
|
if not needs_hidden_states:
|
|
return text_encoder_output.last_hidden_state
|
|
try:
|
|
normalized = self.returned_embeddings_type > 0
|
|
clip_skip = math.floor(abs(self.returned_embeddings_type))
|
|
interpolation = abs(self.returned_embeddings_type) - clip_skip
|
|
except Exception:
|
|
normalized = False
|
|
clip_skip = 1
|
|
interpolation = False
|
|
if interpolation:
|
|
hidden_state = (1 - interpolation) * text_encoder_output.hidden_states[-clip_skip] + interpolation * text_encoder_output.hidden_states[-(clip_skip+1)]
|
|
else:
|
|
hidden_state = text_encoder_output.hidden_states[-clip_skip]
|
|
if normalized:
|
|
hidden_state = self.text_encoder.text_model.final_layer_norm(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
EmbeddingsProvider._encode_token_ids_to_embeddings = compel_hijack # pylint: disable=protected-access
|
|
|
|
|
|
# from https://github.com/damian0815/compel/blob/main/src/compel/diffusers_textual_inversion_manager.py
|
|
class DiffusersTextualInversionManager(BaseTextualInversionManager):
|
|
def __init__(self, pipe, tokenizer):
|
|
self.pipe = pipe
|
|
self.tokenizer = tokenizer
|
|
if hasattr(self.pipe, 'embedding_db'):
|
|
self.pipe.embedding_db.embeddings_used.clear()
|
|
|
|
# code from
|
|
# https://github.com/huggingface/diffusers/blob/705c592ea98ba4e288d837b9cba2767623c78603/src/diffusers/loaders.py
|
|
def maybe_convert_prompt(self, prompt: typing.Union[str, typing.List[str]], tokenizer: PreTrainedTokenizer):
|
|
prompts = [prompt] if not isinstance(prompt, typing.List) else prompt
|
|
prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
|
|
if not isinstance(prompt, typing.List):
|
|
return prompts[0]
|
|
return prompts
|
|
|
|
def _maybe_convert_prompt(self, prompt: str, tokenizer: PreTrainedTokenizer):
|
|
tokens = tokenizer.tokenize(prompt)
|
|
unique_tokens = set(tokens)
|
|
for token in unique_tokens:
|
|
if token in tokenizer.added_tokens_encoder:
|
|
if hasattr(self.pipe, 'embedding_db'):
|
|
self.pipe.embedding_db.embeddings_used.append(token)
|
|
replacement = token
|
|
i = 1
|
|
while f"{token}_{i}" in tokenizer.added_tokens_encoder:
|
|
replacement += f" {token}_{i}"
|
|
i += 1
|
|
prompt = prompt.replace(token, replacement)
|
|
if hasattr(self.pipe, 'embedding_db'):
|
|
self.pipe.embedding_db.embeddings_used = list(set(self.pipe.embedding_db.embeddings_used))
|
|
debug(f'Prompt: convert="{prompt}"')
|
|
return prompt
|
|
|
|
def expand_textual_inversion_token_ids_if_necessary(self, token_ids: typing.List[int]) -> typing.List[int]:
|
|
if len(token_ids) == 0:
|
|
return token_ids
|
|
prompt = self.pipe.tokenizer.decode(token_ids)
|
|
prompt = self.maybe_convert_prompt(prompt, self.pipe.tokenizer)
|
|
debug(f'Prompt: expand="{prompt}"')
|
|
return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
|
|
|
|
|
|
def get_prompt_schedule(prompt, steps):
|
|
t0 = time.time()
|
|
temp = []
|
|
schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
|
|
if all(x == schedule[0] for x in schedule):
|
|
return [schedule[0][1]], False
|
|
for chunk in schedule:
|
|
for s in range(steps):
|
|
if len(temp) < s + 1 <= chunk[0]:
|
|
temp.append(chunk[1])
|
|
debug(f'Prompt: schedule={temp} time={(time.time() - t0):.3f}')
|
|
return temp, len(schedule) > 1
|
|
|
|
|
|
def get_tokens(msg, prompt):
|
|
global token_dict, token_type # pylint: disable=global-statement
|
|
if shared.backend != shared.Backend.DIFFUSERS:
|
|
return
|
|
if shared.sd_loaded and hasattr(shared.sd_model, 'tokenizer') and shared.sd_model.tokenizer is not None:
|
|
if token_dict is None or token_type != shared.sd_model_type:
|
|
token_type = shared.sd_model_type
|
|
fn = os.path.join(shared.sd_model.tokenizer.name_or_path, 'tokenizer', 'vocab.json')
|
|
token_dict = shared.readfile(fn, silent=True)
|
|
for k, v in shared.sd_model.tokenizer.added_tokens_decoder.items():
|
|
token_dict[str(v)] = k
|
|
shared.log.debug(f'Tokenizer: words={len(token_dict)} file="{fn}"')
|
|
has_bos_token = shared.sd_model.tokenizer.bos_token_id is not None
|
|
has_eos_token = shared.sd_model.tokenizer.eos_token_id is not None
|
|
ids = shared.sd_model.tokenizer(prompt)
|
|
ids = getattr(ids, 'input_ids', [])
|
|
tokens = []
|
|
for i in ids:
|
|
tokens.append(list(token_dict.keys())[list(token_dict.values()).index(i)])
|
|
token_count = len(ids) - int(has_bos_token) - int(has_eos_token)
|
|
shared.log.trace(f'Prompt tokenizer: type={msg} tokens={token_count} {tokens}')
|
|
|
|
|
|
def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, clip_skip: typing.Optional[int] = None):
|
|
if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion' not in pipe.__class__.__name__:
|
|
shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
|
|
return
|
|
elif prompts == cache.get('prompts', None) and negative_prompts == cache.get('negative_prompts', None) and cache.get('model_type', None) == shared.sd_model_type:
|
|
p.prompt_embeds = cache.get('prompt_embeds', None)
|
|
p.positive_pooleds = cache.get('positive_pooleds', None)
|
|
p.negative_embeds = cache.get('negative_embeds', None)
|
|
p.negative_pooleds = cache.get('negative_pooleds', None)
|
|
p.scheduled_prompt = cache.get('scheduled_prompt', None)
|
|
debug("Prompt encode: cached")
|
|
return
|
|
else:
|
|
t0 = time.time()
|
|
positive_schedule, scheduled = get_prompt_schedule(prompts[0], steps)
|
|
negative_schedule, neg_scheduled = get_prompt_schedule(negative_prompts[0], steps)
|
|
p.scheduled_prompt = scheduled or neg_scheduled
|
|
p.prompt_embeds = []
|
|
p.positive_pooleds = []
|
|
p.negative_embeds = []
|
|
p.negative_pooleds = []
|
|
|
|
for i in range(max(len(positive_schedule), len(negative_schedule))):
|
|
positive_prompt = positive_schedule[i % len(positive_schedule)]
|
|
negative_prompt = negative_schedule[i % len(negative_schedule)]
|
|
results = cache.get(positive_prompt + negative_prompt, None)
|
|
|
|
if results is None:
|
|
results = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
|
|
cache[positive_prompt + negative_prompt] = results
|
|
|
|
prompt_embed, positive_pooled, negative_embed, negative_pooled = results
|
|
if prompt_embed is not None:
|
|
p.prompt_embeds.append(torch.cat([prompt_embed] * len(prompts), dim=0))
|
|
cache['prompt_embeds'] = p.prompt_embeds
|
|
if negative_embed is not None:
|
|
p.negative_embeds.append(torch.cat([negative_embed] * len(negative_prompts), dim=0))
|
|
cache['negative_embeds'] = p.negative_embeds
|
|
if positive_pooled is not None:
|
|
p.positive_pooleds.append(torch.cat([positive_pooled] * len(prompts), dim=0))
|
|
cache['positive_pooleds'] = p.positive_pooleds
|
|
if negative_pooled is not None:
|
|
p.negative_pooleds.append(torch.cat([negative_pooled] * len(negative_prompts), dim=0))
|
|
cache['negative_pooleds'] = p.negative_pooleds
|
|
|
|
cache['prompts'] = prompts
|
|
cache['negative_prompts'] = negative_prompts
|
|
cache['model_type'] = shared.sd_model_type
|
|
if debug_enabled:
|
|
get_tokens('positive', prompts[0])
|
|
get_tokens('negative', negative_prompts[0])
|
|
debug(f"Prompt encode: time={(time.time() - t0):.3f}")
|
|
return
|
|
|
|
|
|
def get_prompts_with_weights(prompt: str):
|
|
t0 = time.time()
|
|
manager = DiffusersTextualInversionManager(shared.sd_model,
|
|
shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
|
|
prompt = manager.maybe_convert_prompt(prompt, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
|
|
texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
|
|
texts, text_weights = zip(*texts_and_weights)
|
|
debug(f'Prompt: weights={texts_and_weights} time={(time.time() - t0):.3f}')
|
|
return texts, text_weights
|
|
|
|
|
|
def prepare_embedding_providers(pipe, clip_skip) -> list[EmbeddingsProvider]:
|
|
device = pipe.device if str(pipe.device) != 'meta' else devices.device
|
|
embeddings_providers = []
|
|
if 'XL' in pipe.__class__.__name__:
|
|
embedding_type = -(clip_skip + 1)
|
|
else:
|
|
embedding_type = clip_skip
|
|
if getattr(pipe, "tokenizer", None) is not None and getattr(pipe, "text_encoder", None) is not None:
|
|
provider = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type, device=device)
|
|
embeddings_providers.append(provider)
|
|
if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "text_encoder_2", None) is not None:
|
|
provider = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False, returned_embeddings_type=embedding_type, device=device)
|
|
embeddings_providers.append(provider)
|
|
return embeddings_providers
|
|
|
|
|
|
def pad_to_same_length(pipe, embeds):
|
|
if not hasattr(pipe, 'encode_prompt'):
|
|
return embeds
|
|
device = pipe.device if str(pipe.device) != 'meta' else devices.device
|
|
try: # SDXL
|
|
empty_embed = pipe.encode_prompt("")
|
|
except TypeError: # SD1.5
|
|
empty_embed = pipe.encode_prompt("", device, 1, False)
|
|
max_token_count = max([embed.shape[1] for embed in embeds])
|
|
repeats = max_token_count - min([embed.shape[1] for embed in embeds])
|
|
empty_batched = empty_embed[0].to(embeds[0].device).repeat(embeds[0].shape[0], repeats // empty_embed[0].shape[1], 1)
|
|
for i, embed in enumerate(embeds):
|
|
if embed.shape[1] < max_token_count:
|
|
embed = torch.cat([embed, empty_batched], dim=1)
|
|
embeds[i] = embed
|
|
return embeds
|
|
|
|
|
|
def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
|
|
device = pipe.device if str(pipe.device) != 'meta' else devices.device
|
|
prompt_split = prompt.split("TE2:")
|
|
prompt = prompt_split[0]
|
|
prompt_2 = prompt_split[-1]
|
|
neg_prompt_split = neg_prompt.split("TE2:")
|
|
neg_prompt_2 = neg_prompt_split[-1]
|
|
neg_prompt = neg_prompt_split[0]
|
|
|
|
if prompt != prompt_2:
|
|
ps = [get_prompts_with_weights(p) for p in [prompt, prompt_2]]
|
|
ns = [get_prompts_with_weights(p) for p in [neg_prompt, neg_prompt_2]]
|
|
else:
|
|
ps = 2 * [get_prompts_with_weights(prompt)]
|
|
ns = 2 * [get_prompts_with_weights(neg_prompt)]
|
|
|
|
positives, positive_weights = zip(*ps)
|
|
negatives, negative_weights = zip(*ns)
|
|
if hasattr(pipe, "tokenizer_2") and not hasattr(pipe, "tokenizer"):
|
|
positives.pop(0)
|
|
positive_weights.pop(0)
|
|
negatives.pop(0)
|
|
negative_weights.pop(0)
|
|
|
|
embedding_providers = prepare_embedding_providers(pipe, clip_skip)
|
|
prompt_embeds = []
|
|
negative_prompt_embeds = []
|
|
pooled_prompt_embeds = None
|
|
negative_pooled_prompt_embeds = None
|
|
for i in range(len(embedding_providers)):
|
|
t0 = time.time()
|
|
text = list(positives[i])
|
|
weights = list(positive_weights[i])
|
|
text.append('BREAK')
|
|
weights.append(-1)
|
|
provider_embed = []
|
|
ptokens = 0
|
|
while 'BREAK' in text:
|
|
pos = text.index('BREAK')
|
|
debug(f'Prompt: section="{text[:pos]}" len={len(text[:pos])} weights={weights[:pos]}')
|
|
if len(text[:pos]) > 0:
|
|
embed, ptokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(
|
|
text_batch=[text[:pos]], fragment_weights_batch=[weights[:pos]], device=device,
|
|
should_return_tokens=True)
|
|
provider_embed.append(embed)
|
|
text = text[pos + 1:]
|
|
weights = weights[pos + 1:]
|
|
prompt_embeds.append(torch.cat(provider_embed, dim=1))
|
|
# negative prompt has no keywords
|
|
embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]], fragment_weights_batch=[negative_weights[i]], device=device, should_return_tokens=True)
|
|
negative_prompt_embeds.append(embed)
|
|
debug(f'Prompt: unpadded shape={prompt_embeds[0].shape} TE{i+1} ptokens={torch.count_nonzero(ptokens)} ntokens={torch.count_nonzero(ntokens)} time={(time.time() - t0):.3f}')
|
|
|
|
if prompt_embeds[-1].shape[-1] > 768:
|
|
t0 = time.time()
|
|
if shared.opts.diffusers_pooled == "weighted":
|
|
pooled_prompt_embeds = prompt_embeds[-1][
|
|
torch.arange(prompt_embeds[-1].shape[0], device=device),
|
|
(ptokens.to(dtype=torch.int, device=device) == 49407)
|
|
.int()
|
|
.argmax(dim=-1),
|
|
]
|
|
negative_pooled_prompt_embeds = negative_prompt_embeds[-1][
|
|
torch.arange(negative_prompt_embeds[-1].shape[0], device=device),
|
|
(ntokens.to(dtype=torch.int, device=device) == 49407)
|
|
.int()
|
|
.argmax(dim=-1),
|
|
]
|
|
else:
|
|
try:
|
|
pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[prompt_2], device=device) if prompt_embeds[-1].shape[-1] > 768 else None
|
|
negative_pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[neg_prompt_2], device=device) if negative_prompt_embeds[-1].shape[-1] > 768 else None
|
|
except Exception:
|
|
pooled_prompt_embeds = None
|
|
negative_pooled_prompt_embeds = None
|
|
debug(f'Prompt: pooled shape={pooled_prompt_embeds[0].shape} time={(time.time() - t0):.3f}')
|
|
|
|
prompt_embeds = torch.cat(prompt_embeds, dim=-1) if len(prompt_embeds) > 1 else prompt_embeds[0]
|
|
negative_prompt_embeds = torch.cat(negative_prompt_embeds, dim=-1) if len(negative_prompt_embeds) > 1 else \
|
|
negative_prompt_embeds[0]
|
|
debug(f'Prompt: positive={prompt_embeds.shape if prompt_embeds is not None else None} pooled={pooled_prompt_embeds.shape if pooled_prompt_embeds is not None else None} negative={negative_prompt_embeds.shape if negative_prompt_embeds is not None else None} pooled={negative_pooled_prompt_embeds.shape if negative_pooled_prompt_embeds is not None else None}')
|
|
if prompt_embeds.shape[1] != negative_prompt_embeds.shape[1]:
|
|
[prompt_embeds, negative_prompt_embeds] = pad_to_same_length(pipe, [prompt_embeds, negative_prompt_embeds])
|
|
return prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds
|