text stringlengths 1 93.6k |
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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max_length = text_input.input_ids.shape[-1]
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uncond_input = self.tokenizer(
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[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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latents = init_latents
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t_start = max(num_inference_steps - init_timestep + offset, 0)
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for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
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# scale and decode the image latents with vae
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents)
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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# run safety checker
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safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
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#image_, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
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has_nsfw_concept = []
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for hni in range(0, len(image)):
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has_nsfw_concept.append(0)
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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return {"sample": image, "nsfw_content_detected": has_nsfw_concept}
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# <FILESEP>
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#!/usr/local/bin/python
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# -*- coding:utf-8 -*-
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# @Time : 2019/4/10 10:10 PM
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# @Author : Jerry
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# @Desc :
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# @File : SensitivesHunter.py
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import json
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import os
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import sys
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import subprocess
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from lib.config import log
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from lib.common.basic import getCurrentPath, makeDir, getDomain
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from Downloader import DownLoader
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from SensitiveFileParser import SensitiveFileParser
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import config
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class SensitivesHunter():
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def __init__(self, url, project_name):
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self.start_url = url
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self.project_name = project_name
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self.crawled_file_links_dict = {}
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self.result_dict = {}
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self.skip_flag = False # 跳过爬取的标志
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def startHunt(self):
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self.prepare()
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self.tryToSkipCrawled()
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if not self.skip_flag:
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self.crawlLinks() # 爬取链接
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self.parseFileLinks() # 解析爬取到的文件url
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for file_type, url_file_list in self.crawled_file_links_dict.items():
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downloaded_file_path_dict = self.downloadFile(url_file_list, file_type)
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self.detectSensitiveFile(downloaded_file_path_dict, file_type)
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self.saveResultFile()
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def tryToSkipCrawled(self):
|
'''
|
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