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temp-9384289
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Parent(s):
test
Browse files- .gitattributes +35 -0
- README.md +13 -0
- app.py +400 -0
- flagged/log.csv +2 -0
- flagged/output/a7d3e3ed399e14f7629e/image.webp +0 -0
- requirements.txt +16 -0
- tester/generation/1714450388.794121/generated_image.png +0 -0
- tester/generation/1714450388.794121/keeper.png +0 -0
- tester/generation/1714450388.794121/real_deal.png +0 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: ModelProblems
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emoji: 🧠
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colorFrom: pink
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colorTo: indigo
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sdk: gradio
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sdk_version: 4.28.3
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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# notes https://huggingface.co/spaces/Joeythemonster/Text-To-image-AllModels/blob/main/app.py
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from diffusers import StableDiffusionPipeline
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from diffusers import DiffusionPipeline
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4 |
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import torch
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import time
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6 |
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import matplotlib.pyplot as plt
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import tensorflow as tf
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import os
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import sys
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import requests
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from image_similarity_measures.evaluate import evaluation
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from PIL import Image
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from huggingface_hub import from_pretrained_keras
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from math import sqrt, ceil
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import numpy as np
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modelieo=[
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'nathanReitinger/MNIST-diffusion',
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'nathanReitinger/MNIST-diffusion-oneImage',
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'nathanReitinger/MNIST-GAN',
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'nathanReitinger/MNIST-GAN-noDropout'
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]
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def get_sims(gen_filepath, gen_label, file_path, hunting_time_limit):
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(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
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train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
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27 |
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train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]
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28 |
+
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29 |
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print("how long to hunt", hunting_time_limit)
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30 |
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if hunting_time_limit == None:
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31 |
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hunting_time_limit = 2
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32 |
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lowest_score = 10000
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lowest_image = None
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35 |
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lowest_image_path = ''
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36 |
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37 |
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start = time.time()
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38 |
+
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39 |
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for i in range(len(train_labels)):
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40 |
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# print(i)
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41 |
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if train_labels[i] == gen_label:
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42 |
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43 |
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###
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44 |
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# get a real image (of correct number)
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45 |
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###
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46 |
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47 |
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# print(i)
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48 |
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to_check = train_images[i]
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49 |
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fig = plt.figure(figsize=(1, 1))
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50 |
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plt.subplot(1, 1, 0+1)
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51 |
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plt.imshow(to_check, cmap='gray')
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52 |
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plt.axis('off')
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53 |
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plt.savefig(file_path + 'real_deal.png')
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plt.close()
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55 |
+
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56 |
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# baseline = evaluation(org_img_path='results/real_deal.png', pred_img_path='results/real_deal.png', metrics=["rmse", "psnr"])
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57 |
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# print("---")
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58 |
+
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59 |
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###
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60 |
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# check how close that real training data is to generated number
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61 |
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###
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results = evaluation(org_img_path=file_path + 'real_deal.png', pred_img_path=file_path+'generated_image.png', metrics=["rmse", "psnr"])
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63 |
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if results['rmse'] < lowest_score:
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lowest_score = results['rmse']
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lowest_image = to_check
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66 |
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to_save = train_images[i]
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fig = plt.figure(figsize=(1, 1))
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plt.subplot(1, 1, 0+1)
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plt.imshow(to_save, cmap='gray')
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plt.axis('off')
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plt.savefig(file_path + 'keeper.png')
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plt.close()
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lowest_image_path = file_path + 'keeper.png'
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print(lowest_score, str(round( ((i/len(train_labels)) * 100),2 )) + '%')
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now = time.time()
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if now-start > hunting_time_limit:
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print(str(now-start) + "s")
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return lowest_image_path
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return lowest_image_path
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84 |
+
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def digit_recognition(filename):
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API_URL = "https://api-inference.huggingface.co/models/farleyknight/mnist-digit-classification-2022-09-04"
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special_string = '-h-f-_-RT-U-J-E-M-Pb-GC-c-i-v-sji-bMsQmxuh-x-h-C-W-B-F-W-z-Gv-'
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is_escaped = special_string.replace("-", '')
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bear = "Bearer " + is_escaped
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91 |
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headers = {"Authorization": bear}
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92 |
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# get a prediction on what number this is
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93 |
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def query(filename):
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with open(filename, "rb") as f:
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data = f.read()
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response = requests.post(API_URL, headers=headers, data=data)
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return response.json()
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98 |
+
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99 |
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# use latest model to generate a new image, return path
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ret = False
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101 |
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output = None
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102 |
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while ret == False:
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103 |
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output = query(filename + 'generated_image.png')
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104 |
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if 'error' in output:
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105 |
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time.sleep(10)
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106 |
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ret = False
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107 |
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else:
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108 |
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ret = True
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109 |
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print(output)
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110 |
+
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111 |
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low_score_log = ''
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112 |
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this_label_for_this_image = int(output[0]['label'])
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113 |
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return {'full': output, 'number': this_label_for_this_image}
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114 |
+
|
115 |
+
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116 |
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def get_other(original_image, hunting_time_limit):
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117 |
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RANDO = str(time.time())
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118 |
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file_path = 'tester/' + 'generation' + "/" + RANDO + '/'
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119 |
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os.makedirs(file_path)
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120 |
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fig = plt.figure(figsize=(1, 1))
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121 |
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plt.subplot(1, 1, 0+1)
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122 |
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plt.imshow(original_image, cmap='gray')
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123 |
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plt.axis('off')
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124 |
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plt.savefig(file_path + 'generated_image.png')
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125 |
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plt.close()
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126 |
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print('[+] done saving generation')
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127 |
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print("[-] what digit is this")
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128 |
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ret = digit_recognition(file_path)
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129 |
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print(ret['full'])
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130 |
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print(ret['number'])
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131 |
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print("[+]", ret['number'])
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132 |
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print("[-] show some most similar numbers")
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133 |
+
if ret["full"][0]['score'] <= 0.90:
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134 |
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print("[!] error in image digit recognition, likely to not find a similar score")
|
135 |
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sys.exit()
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136 |
+
gen_filepath = file_path + 'generated_image.png'
|
137 |
+
gen_label = ret['number']
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138 |
+
ret_sims = get_sims(gen_filepath, gen_label, file_path, hunting_time_limit)
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139 |
+
print("[+] done sims")
|
140 |
+
# get the file-Path
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141 |
+
return (file_path + 'generated_image.png', ret_sims)
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142 |
+
|
143 |
+
def generate_and_save_images(model):
|
144 |
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noise_dim = 100
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145 |
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num_examples_to_generate = 1
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146 |
+
seed = tf.random.normal([num_examples_to_generate, noise_dim])
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147 |
+
|
148 |
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# print(seed)
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149 |
+
|
150 |
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n_samples = 1
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151 |
+
# Notice `training` is set to False.
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152 |
+
# This is so all layers run in inference mode (batchnorm).
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153 |
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examples = model(seed, training=False)
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154 |
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examples = examples * 255.0
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155 |
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size = ceil(sqrt(n_samples))
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156 |
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digit_images = np.zeros((28*size, 28*size), dtype=float)
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157 |
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n = 0
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158 |
+
for i in range(size):
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159 |
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for j in range(size):
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160 |
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if n == n_samples:
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161 |
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break
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162 |
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digit_images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = examples[n, :, :, 0]
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163 |
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n += 1
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164 |
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digit_images = (digit_images/127.5) -1
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165 |
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return digit_images
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166 |
+
|
167 |
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def TextToImage(Prompt,inference_steps, model):
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168 |
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model_id = model
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169 |
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if 'GAN' in model_id:
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170 |
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print("do something else")
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171 |
+
model = from_pretrained_keras(model)
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172 |
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image = generate_and_save_images(model)
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173 |
+
else:
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174 |
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pipe = DiffusionPipeline.from_pretrained(model_id)
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175 |
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the_randomness = int(str(time.time())[-1])
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176 |
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print('seed', the_randomness)
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177 |
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image = pipe(generator= torch.manual_seed(the_randomness), num_inference_steps=inference_steps).images[0]
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178 |
+
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179 |
+
# pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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180 |
+
# pipe = pipe.to("cpu")
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181 |
+
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182 |
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prompt = Prompt
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183 |
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print(prompt)
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184 |
+
hunting_time_limit = None
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185 |
+
if prompt.isnumeric():
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186 |
+
hunting_time_limit = abs(int(prompt))
|
187 |
+
|
188 |
+
original_image, other_images = get_other(image, hunting_time_limit)
|
189 |
+
ai_gen = Image.open(open(original_image, 'rb'))
|
190 |
+
training_data = Image.open(open(other_images, 'rb'))
|
191 |
+
return [ai_gen, training_data]
|
192 |
+
|
193 |
+
|
194 |
+
import gradio as gr
|
195 |
+
interface = gr.Interface(fn=TextToImage,
|
196 |
+
inputs=[gr.Textbox(show_label=True, label='How many seconds to hunt for copies?',), gr.Slider(1, 1000, label='Inference Steps', value=100, step=1), gr.Dropdown(modelieo)],
|
197 |
+
outputs=gr.Gallery(label="Generated image", show_label=True, elem_id="gallery", columns=[2], rows=[1], object_fit="contain", height="auto"),
|
198 |
+
# css="#output_image{width: 256px !important; height: 256px !important;}",
|
199 |
+
title='Unconditional Image Generation')
|
200 |
+
|
201 |
+
interface.launch()
|
202 |
+
|
203 |
+
|
204 |
+
|
205 |
+
|
206 |
+
|
207 |
+
# import tensorflow as tf
|
208 |
+
# from diffusers import DiffusionPipeline
|
209 |
+
# import spaces
|
210 |
+
# # import torch
|
211 |
+
# import PIL.Image
|
212 |
+
# from PIL import Image
|
213 |
+
# from torch.autograd import Variable
|
214 |
+
# import gradio as gr
|
215 |
+
# import gradio.components as grc
|
216 |
+
# import numpy as np
|
217 |
+
# from huggingface_hub import from_pretrained_keras
|
218 |
+
# from image_similarity_measures.evaluate import evaluation
|
219 |
+
# import keras
|
220 |
+
# import time
|
221 |
+
# import requests
|
222 |
+
# import matplotlib.pyplot as plt
|
223 |
+
# import os
|
224 |
+
# from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM
|
225 |
+
# from gradio_imageslider import ImageSlider
|
226 |
+
|
227 |
+
# # os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
|
228 |
+
|
229 |
+
# # options = ['Placeholder A', 'Placeholder B', 'Placeholder C']
|
230 |
+
|
231 |
+
|
232 |
+
# # pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
|
233 |
+
# # device = "cuda" if torch.cuda.is_available() else "cpu"
|
234 |
+
# # pipeline = pipeline.to(device=device)
|
235 |
+
|
236 |
+
# # @spaces.GPU
|
237 |
+
# # def predict(steps, seed):
|
238 |
+
# # print("HI")
|
239 |
+
# # generator = torch.manual_seed(seed)
|
240 |
+
# # for i in range(1,steps):
|
241 |
+
# # yield pipeline(generator=generator, num_inference_steps=i).images[0]
|
242 |
+
|
243 |
+
# # gr.Interface(
|
244 |
+
# # predict,
|
245 |
+
# # inputs=[
|
246 |
+
# # grc.Slider(0, 1000, label='Inference Steps', value=42, step=1),
|
247 |
+
# # grc.Slider(0, 2147483647, label='Seed', value=42, step=1),
|
248 |
+
# # ],
|
249 |
+
# # outputs=gr.Image(height=28, width=28, type="pil", elem_id="output_image"),
|
250 |
+
# # css="#output_image{width: 256px !important; height: 256px !important;}",
|
251 |
+
# # title="Model Problems: Infringing on MNIST!",
|
252 |
+
# # description="Opening the black box.",
|
253 |
+
# # ).queue().launch()
|
254 |
+
|
255 |
+
|
256 |
+
# from diffusers import StableDiffusionPipeline
|
257 |
+
# import torch
|
258 |
+
|
259 |
+
|
260 |
+
# modellist=['nathanReitinger/MNIST-diffusion-oneImage',
|
261 |
+
# 'nathanReitinger/MNIST-diffusion',
|
262 |
+
# # 'nathanReitinger/MNIST-GAN',
|
263 |
+
# # 'nathanReitinger/MNIST-GAN-noDropout'
|
264 |
+
# ]
|
265 |
+
|
266 |
+
# # pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
|
267 |
+
# # device = "cuda" if torch.cuda.is_available() else "cpu"
|
268 |
+
# # pipeline = pipeline.to(device=device)
|
269 |
+
|
270 |
+
|
271 |
+
# def getModel(model):
|
272 |
+
# model_id = model
|
273 |
+
|
274 |
+
# (train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
|
275 |
+
# RANDO = str(time.time())
|
276 |
+
# file_path = 'tester/' + model_id.replace("/", "-") + "/" + RANDO + '/'
|
277 |
+
# os.makedirs(file_path)
|
278 |
+
# train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
|
279 |
+
# train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]
|
280 |
+
|
281 |
+
# print(model_id)
|
282 |
+
# image = None
|
283 |
+
# if 'diffusion' in model_id:
|
284 |
+
# pipe = DiffusionPipeline.from_pretrained(model_id)
|
285 |
+
# pipe = pipe.to("cpu")
|
286 |
+
# image = pipe(generator= torch.manual_seed(42), num_inference_steps=1).images[0]
|
287 |
+
# else:
|
288 |
+
# pipe = DiffusionPipeline.from_pretrained('nathanReitinger/MNIST-diffusion')
|
289 |
+
# pipe = pipe.to("cpu")
|
290 |
+
# test = from_pretrained_keras('nathanReitinger/MNIST-GAN')
|
291 |
+
# image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]
|
292 |
+
|
293 |
+
# ########################################### let's save this image for comparison to others
|
294 |
+
# fig = plt.figure(figsize=(1, 1))
|
295 |
+
# plt.subplot(1, 1, 0+1)
|
296 |
+
# plt.imshow(image, cmap='gray')
|
297 |
+
# plt.axis('off')
|
298 |
+
# plt.savefig(file_path + 'generated_image.png')
|
299 |
+
# plt.close()
|
300 |
+
|
301 |
+
# API_URL = "https://api-inference.huggingface.co/models/farleyknight/mnist-digit-classification-2022-09-04"
|
302 |
+
|
303 |
+
# # get a prediction on what number this is
|
304 |
+
# def query(filename):
|
305 |
+
# with open(filename, "rb") as f:
|
306 |
+
# data = f.read()
|
307 |
+
# response = requests.post(API_URL, data=data)
|
308 |
+
# return response.json()
|
309 |
+
|
310 |
+
# # use latest model to generate a new image, return path
|
311 |
+
# ret = False
|
312 |
+
# output = None
|
313 |
+
# while ret == False:
|
314 |
+
# output = query(file_path + 'generated_image.png')
|
315 |
+
# if 'error' in output:
|
316 |
+
# time.sleep(10)
|
317 |
+
# ret = False
|
318 |
+
# else:
|
319 |
+
# ret = True
|
320 |
+
# print(output)
|
321 |
+
|
322 |
+
# low_score_log = ''
|
323 |
+
# this_label_for_this_image = int(output[0]['label'])
|
324 |
+
# low_score_log += "this image has been identified as a:" + str(this_label_for_this_image) + "\n" + str(output) + "\n"
|
325 |
+
# print("===================")
|
326 |
+
|
327 |
+
# lowest_score = 10000
|
328 |
+
# lowest_image = None
|
329 |
+
|
330 |
+
# for i in range(len(train_labels)):
|
331 |
+
# # print(i)
|
332 |
+
# if train_labels[i] == this_label_for_this_image:
|
333 |
+
|
334 |
+
# ###
|
335 |
+
# # get a real image (of correct number)
|
336 |
+
# ###
|
337 |
+
|
338 |
+
# # print(i)
|
339 |
+
# to_check = train_images[i]
|
340 |
+
# fig = plt.figure(figsize=(1, 1))
|
341 |
+
# plt.subplot(1, 1, 0+1)
|
342 |
+
# plt.imshow(to_check, cmap='gray')
|
343 |
+
# plt.axis('off')
|
344 |
+
# plt.savefig(file_path + 'real_deal.png')
|
345 |
+
# plt.close()
|
346 |
+
|
347 |
+
# # baseline = evaluation(org_img_path='results/real_deal.png', pred_img_path='results/real_deal.png', metrics=["rmse", "psnr"])
|
348 |
+
# # print("---")
|
349 |
+
|
350 |
+
# ###
|
351 |
+
# # check how close that real training data is to generated number
|
352 |
+
# ###
|
353 |
+
# results = evaluation(org_img_path=file_path + 'real_deal.png', pred_img_path=file_path+'generated_image.png', metrics=["rmse", "psnr"])
|
354 |
+
# if results['rmse'] < lowest_score:
|
355 |
+
|
356 |
+
# lowest_score = results['rmse']
|
357 |
+
# lowest_image = to_check
|
358 |
+
|
359 |
+
# # image1 = np.array(Image.open(file_path + 'real_deal.png'))
|
360 |
+
# # image2 = np.array(Image.open(file_path + 'generated_image.png'))
|
361 |
+
# # img1 = torch.from_numpy(image1).float().unsqueeze(0).unsqueeze(0)/255.0
|
362 |
+
# # img2 = torch.from_numpy(image2).float().unsqueeze(0).unsqueeze(0)/255.0
|
363 |
+
# # img1 = Variable( img1, requires_grad=False)
|
364 |
+
# # img2 = Variable( img2, requires_grad=True)
|
365 |
+
# # ssim_score = ssim(img1, img2).item()
|
366 |
+
|
367 |
+
# # # sys.exit()
|
368 |
+
# # # l2 = distance.euclidean(image1, image2)
|
369 |
+
|
370 |
+
# # low_score_log += 'rmse score:' + str(lowest_score) + "\n"
|
371 |
+
# # low_score_log += 'ssim score:' + str(ssim_score) + "\n"
|
372 |
+
# # low_score_log += 'found when:' + str(round( ((i/len(train_labels)) * 100),2 )) + '%' + "\n"
|
373 |
+
|
374 |
+
# # low_score_log += "---------\n"
|
375 |
+
|
376 |
+
# # print(lowest_score, ssim_score, str(round( ((i/len(train_labels)) * 100),2 )) + '%')
|
377 |
+
|
378 |
+
# # fig = plt.figure(figsize=(1, 1))
|
379 |
+
# # plt.subplot(1, 1, 0+1)
|
380 |
+
# # plt.imshow(to_check, cmap='gray')
|
381 |
+
# # plt.axis('off')
|
382 |
+
# # plt.savefig(file_path+str(i) + "--" + str(lowest_score) + '---most_close.png')
|
383 |
+
# # plt.close()
|
384 |
+
|
385 |
+
|
386 |
+
# # f = open(file_path + "score_log.txt", "w+")
|
387 |
+
# # f.write(low_score_log)
|
388 |
+
# # f.close()
|
389 |
+
|
390 |
+
# print("Done!")
|
391 |
+
|
392 |
+
|
393 |
+
# ############################################ return image that you just generated
|
394 |
+
# return [image, lowest_image]
|
395 |
+
|
396 |
+
|
397 |
+
# import gradio as gr
|
398 |
+
# output = "image"
|
399 |
+
# interface = gr.Interface(fn=getModel, inputs=[gr.Dropdown(modellist)], css="#output_image{width: 256px !important; height: 256px !important;}", outputs=output, title='Model Problems (infringement)') # outputs="image",
|
400 |
+
# interface.launch(debug=True)
|
flagged/log.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
Inference Steps,Seed,output,flag,username,timestamp
|
2 |
+
48,42,flagged/output/a7d3e3ed399e14f7629e/image.webp,,,2024-04-28 21:23:26.366305
|
flagged/output/a7d3e3ed399e14f7629e/image.webp
ADDED
![]() |
requirements.txt
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Automatically generated by https://github.com/damnever/pigar.
|
2 |
+
|
3 |
+
diffusers==0.27.2
|
4 |
+
gradio==4.28.3
|
5 |
+
gradio_imageslider==0.0.20
|
6 |
+
huggingface-hub==0.22.2
|
7 |
+
image-similarity-measures==0.3.6
|
8 |
+
keras==2.11.0
|
9 |
+
matplotlib==3.8.4
|
10 |
+
numpy==1.25.2
|
11 |
+
pillow==10.3.0
|
12 |
+
pytorch-msssim==1.0.0
|
13 |
+
requests==2.31.0
|
14 |
+
spaces==0.26.2
|
15 |
+
tensorflow==2.11.0
|
16 |
+
torch==2.2.2
|
tester/generation/1714450388.794121/generated_image.png
ADDED
![]() |
tester/generation/1714450388.794121/keeper.png
ADDED
![]() |
tester/generation/1714450388.794121/real_deal.png
ADDED
![]() |