76 lines
3.3 KiB
Python
76 lines
3.3 KiB
Python
# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def find_phrase_positions_in_text(text, phrase):
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"""
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Return the position of the first character of the phrase in the text.
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"""
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position = -1
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positions = []
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while True:
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position = text.find(phrase, position + 1)
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if position == -1:
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break
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positions.append(position)
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return positions
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def classifier_free_guidance_image_prompt_cascade(
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pred_t_cond, pred_ti_cond, pred_uncond, guidance_weight_t=7.5, guidance_weight_i=7.5,
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guidance_stdev_rescale_factor=0.7, cfg_rescale_mode="none", super_cross_mask=None
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):
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if cfg_rescale_mode == "none":
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pred = pred_uncond + guidance_weight_t * (pred_t_cond - pred_uncond) + guidance_weight_i * (pred_ti_cond - pred_t_cond)
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elif cfg_rescale_mode == "none_direct":
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pred = pred_uncond + guidance_weight_i * (pred_ti_cond - pred_uncond)
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elif cfg_rescale_mode == "naive":
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assert super_cross_mask is not None
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pred_std_t_before = pred_t_cond.std([1, 2, 3], keepdim=True)
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pred_std_ti_before = pred_ti_cond.std([1, 2, 3], keepdim=True)
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pred = pred_uncond + guidance_weight_t * (pred_t_cond - pred_uncond) + guidance_weight_i * (pred_ti_cond - pred_t_cond)
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pred_std_after = pred.std([1, 2, 3], keepdim=True)
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pred_rescale_t_factor = guidance_stdev_rescale_factor * (pred_std_t_before / pred_std_after) + (1 - guidance_stdev_rescale_factor)
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pred_rescale_ti_factor = guidance_stdev_rescale_factor * (pred_std_ti_before / pred_std_after) + (1 - guidance_stdev_rescale_factor)
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pred_ti = pred * super_cross_mask
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pred_t = pred * (1 - super_cross_mask)
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pred = pred_ti * pred_rescale_ti_factor + pred_t * pred_rescale_t_factor
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elif cfg_rescale_mode == "naive_global":
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pred_std_ti_before = pred_ti_cond.std([1, 2, 3], keepdim=True)
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pred = pred_uncond + guidance_weight_t * (pred_t_cond - pred_uncond) + guidance_weight_i * (pred_ti_cond - pred_t_cond)
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pred_std_after = pred.std([1, 2, 3], keepdim=True)
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pred_rescale_ti_factor = guidance_stdev_rescale_factor * (pred_std_ti_before / pred_std_after) + (1 - guidance_stdev_rescale_factor)
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pred = pred * pred_rescale_ti_factor
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elif cfg_rescale_mode == "naive_global_direct":
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pred_std_ti_before = pred_ti_cond.std([1, 2, 3], keepdim=True)
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pred = pred_uncond + guidance_weight_i * (pred_ti_cond - pred_uncond)
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pred_std_after = pred.std([1, 2, 3], keepdim=True)
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pred_rescale_ti_factor = guidance_stdev_rescale_factor * (pred_std_ti_before / pred_std_after) + (1 - guidance_stdev_rescale_factor)
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pred = pred * pred_rescale_ti_factor
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else:
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raise NotImplementedError()
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return pred |