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69 lines
3.0 KiB
Python
69 lines
3.0 KiB
Python
import torch
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import modules.shared as shared
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import modules.prompt_parser as prompt_parser
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from compel import Compel, ReturnedEmbeddingsType
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import diffusers
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import typing
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def convert_to_compel(prompt: str):
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if prompt is None:
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return None
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all_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt, 100)[0] #100 should be steps, but doesn't actually matter because we can't schedule yet
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output_list = prompt_parser.parse_prompt_attention(all_schedules[0][1])
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converted_prompt = []
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for subprompt, weight in output_list:
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if subprompt != " ":
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if weight == 1:
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converted_prompt.append(subprompt)
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else:
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converted_prompt.append(f"({subprompt}){weight}")
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converted_prompt = " ".join(converted_prompt)
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return converted_prompt
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def compel_encode_prompt(pipeline: typing.Any, *args, **kwargs):
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compel_encode_fn = COMPEL_ENCODE_FN_DICT.get(type(pipeline), None)
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if compel_encode_fn is None:
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raise TypeError(f"Compel encoding not yet supported for {type(pipeline).__name__}.")
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return compel_encode_fn(pipeline, *args, **kwargs)
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def compel_encode_prompt_sdxl(pipeline: diffusers.StableDiffusionXLPipeline, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str]=None, negative_prompt_2: typing.Optional[str]=None, is_refiner: bool = None):
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if shared.opts.data['prompt_attention'] != 'Compel parser':
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prompt = convert_to_compel(prompt)
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negative_prompt = convert_to_compel(negative_prompt)
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prompt_2 = convert_to_compel(prompt_2)
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negative_prompt_2 = convert_to_compel(negative_prompt_2)
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compel_te1 = Compel(
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tokenizer=pipeline.tokenizer,
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text_encoder=pipeline.text_encoder,
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
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requires_pooled=False,
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)
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compel_te2 = Compel(
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tokenizer=pipeline.tokenizer_2,
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text_encoder=pipeline.text_encoder_2,
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
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requires_pooled=True,
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)
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if not is_refiner:
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positive_te1 = compel_te1(prompt)
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positive_te2, positive_pooled = compel_te2(prompt_2)
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positive = torch.cat((positive_te1, positive_te2), dim=-1)
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negative_te1 = compel_te1(negative_prompt)
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negative_te2, negative_pooled = compel_te2(negative_prompt_2)
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negative = torch.cat((negative_te1, negative_te2), dim=-1)
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else:
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positive, positive_pooled = compel_te2(prompt)
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negative, negative_pooled = compel_te2(negative_prompt)
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shared.log.debug(compel_te1.parse_prompt_string(prompt))
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[prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length([positive, negative])
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return prompt_embed, positive_pooled, negative_embed, negative_pooled
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COMPEL_ENCODE_FN_DICT = {
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diffusers.StableDiffusionXLPipeline: compel_encode_prompt_sdxl,
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diffusers.StableDiffusionImg2ImgPipeline: compel_encode_prompt_sdxl,
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}
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