mirror of
https://github.com/vladmandic/automatic
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AI-Casanova
170 lines
8.4 KiB
Python
170 lines
8.4 KiB
Python
import os
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import typing
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import torch
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from compel import ReturnedEmbeddingsType
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from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
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from modules import shared, prompt_parser
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debug_output = os.environ.get('SD_PROMPT_DEBUG', None)
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debug = shared.log.info if debug_output is not None else lambda *args, **kwargs: None
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CLIP_SKIP_MAPPING = {
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None: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
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1: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
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2: ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NORMALIZED,
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}
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# from https://github.com/damian0815/compel/blob/main/src/compel/diffusers_textual_inversion_manager.py
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class DiffusersTextualInversionManager(BaseTextualInversionManager):
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def __init__(self, pipe, tokenizer):
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self.pipe = pipe
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self.tokenizer = tokenizer
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if hasattr(self.pipe, 'embedding_db'):
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self.pipe.embedding_db.embeddings_used.clear()
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# from https://github.com/huggingface/diffusers/blob/705c592ea98ba4e288d837b9cba2767623c78603/src/diffusers/loaders.py#L599
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def maybe_convert_prompt(self, prompt: typing.Union[str, typing.List[str]], tokenizer="PreTrainedTokenizer"):
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prompts = [prompt] if not isinstance(prompt, typing.List) else prompt
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prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
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if not isinstance(prompt, typing.List):
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return prompts[0]
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return prompts
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def _maybe_convert_prompt(self, prompt: str, tokenizer="PreTrainedTokenizer"):
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tokens = tokenizer.tokenize(prompt)
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unique_tokens = set(tokens)
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for token in unique_tokens:
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if token in tokenizer.added_tokens_encoder:
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if hasattr(self.pipe, 'embedding_db'):
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self.pipe.embedding_db.embeddings_used.append(token)
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replacement = token
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i = 1
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while f"{token}_{i}" in tokenizer.added_tokens_encoder:
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replacement += f" {token}_{i}"
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i += 1
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prompt = prompt.replace(token, replacement)
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if hasattr(self.pipe, 'embedding_db'):
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self.pipe.embedding_db.embeddings_used = list(set(self.pipe.embedding_db.embeddings_used))
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return prompt
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def expand_textual_inversion_token_ids_if_necessary(self, token_ids: typing.List[int]) -> typing.List[int]:
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if len(token_ids) == 0:
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return token_ids
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prompt = self.pipe.tokenizer.decode(token_ids)
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prompt = self.maybe_convert_prompt(prompt, self.pipe.tokenizer)
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print(prompt)
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return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
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def encode_prompts(
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pipeline,
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prompts: list,
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negative_prompts: list,
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clip_skip: typing.Optional[int] = None,
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):
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if 'StableDiffusion' not in pipeline.__class__.__name__:
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shared.log.warning(f"Prompt parser not supported: {pipeline.__class__.__name__}")
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return None, None, None, None
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else:
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prompt_embeds = []
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positive_pooleds = []
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negative_embeds = []
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negative_pooleds = []
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for i in range(len(prompts)):
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prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings_sdxl(pipeline,prompts[i], negative_prompts[i], clip_skip)
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prompt_embeds.append(prompt_embed)
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positive_pooleds.append(positive_pooled)
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negative_embeds.append(negative_embed)
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negative_pooleds.append(negative_pooled)
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if prompt_embeds is not None:
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prompt_embeds = torch.cat(prompt_embeds, dim=0)
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if negative_embeds is not None:
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negative_embeds = torch.cat(negative_embeds, dim=0)
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if positive_pooleds is not None and shared.sd_model_type == "sdxl":
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positive_pooleds = torch.cat(positive_pooleds, dim=0)
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if negative_pooleds is not None and shared.sd_model_type == "sdxl":
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negative_pooleds = torch.cat(negative_pooleds, dim=0)
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return prompt_embeds, positive_pooleds, negative_embeds, negative_pooleds
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def get_prompts_with_weights(prompt: str):
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prompt = DiffusersTextualInversionManager(shared.sd_model,
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shared.sd_model.tokenizer or shared.sd_model.tokenizer_2).maybe_convert_prompt(
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prompt, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
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texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
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texts = [t for t, w in texts_and_weights]
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text_weights = [w for t, w in texts_and_weights]
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return texts, text_weights
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def prepare_embedding_providers(pipe, clip_skip):
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embeddings_providers = []
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if 'XL' in pipe.__class__.__name__:
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embedding_type = ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED
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else:
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if clip_skip > 2:
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shared.log.warning(f"Prompt parser unsupported: clip_skip={clip_skip}")
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clip_skip = 2
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embedding_type = CLIP_SKIP_MAPPING[clip_skip]
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if hasattr(pipe, "tokenizer") and hasattr(pipe, "text_encoder"):
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embeddings_providers.append(
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EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder,
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truncate=False,
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returned_embeddings_type=embedding_type))
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if hasattr(pipe, "tokenizer_2") and hasattr(pipe, "text_encoder_2"):
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embeddings_providers.append(
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EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2,
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truncate=False,
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returned_embeddings_type=embedding_type))
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return embeddings_providers
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def get_weighted_text_embeddings_sdxl(
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pipe,
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prompt: str = "",
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neg_prompt: str = "",
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clip_skip: int = None
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):
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prompt_2 = prompt.split("TE2:")[-1]
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neg_prompt_2 = neg_prompt.split("TE2:")[-1]
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prompt = prompt.split("TE2:")[0]
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neg_prompt = neg_prompt.split("TE2:")[0]
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ps = [get_prompts_with_weights(p) for p in [prompt, prompt_2]]
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positives = [t for t, w in ps]
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positive_weights = [w for t, w in ps]
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ns = [get_prompts_with_weights(p) for p in [neg_prompt, neg_prompt_2]]
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negatives = [t for t, w in ns]
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negative_weights = [w for t, w in ns]
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if hasattr(pipe, "tokenizer_2") and not hasattr(pipe, "tokenizer"):
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positives.pop(0)
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positive_weights.pop(0)
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negatives.pop(0)
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negative_weights.pop(0)
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embedding_providers = prepare_embedding_providers(pipe, clip_skip)
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prompt_embeds = []
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negative_prompt_embeds = []
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for i in range(len(embedding_providers)):
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prompt_embeds.append(
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embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[positives[i]],
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fragment_weights_batch=[
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positive_weights[i]],
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device=pipe.device))
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negative_prompt_embeds.append(
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embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]],
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fragment_weights_batch=[
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negative_weights[i]],
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device=pipe.device))
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prompt_embeds = torch.cat(prompt_embeds, dim=-1) if len(prompt_embeds) > 1 else prompt_embeds[0]
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negative_prompt_embeds = torch.cat(negative_prompt_embeds, dim=-1) if len(negative_prompt_embeds) > 1 else negative_prompt_embeds[0]
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pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[prompt_2], device=pipe.device) if prompt_embeds.shape[-1] > 768 else None
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negative_pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[neg_prompt_2],
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device=pipe.device) if negative_prompt_embeds.shape[-1] > 768 else None
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return prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds
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