This model was trained on 1000 random screenshots of the TV-show Scavengers Reign (2023) on MAX. I've used the civitai default settings for training.Workflow<ol><li>I used a short Python script to grab random images from an MP4 file</li><li>Then I used czkawka (<a target="_blank" rel="ugc" href="https://github.com/qarmin/czkawka">github</a>) to get rid of any duplicate or similar images</li><li>After that, I checked all the images manually</li><li>I used the WD14 Model within kohya_ss (<a target="_blank" rel="ugc" href="https://github.com/bmaltais/kohya_ss">github</a>) for captioning</li><li>Finally I uploaded captions and images to civitai for training</li></ol>Code:<pre><code>import cv2
import random

mp4_directory = ''
output_directory = ''
frames_to_extract = 120
base_name = "Random_screenshot"
list_of_random_frames = []
frame_distance = 100
first_frame = 0 

count = 0

vidcap = cv2.VideoCapture(mp4_directory)
totalFrames = vidcap.get(cv2.CAP_PROP_FRAME_COUNT)
while count &lt; frames_to_extract:
 count += 1
 count_str = str(count)
 frames_skipped = -1
 while True:
 randomFrameNumber = random.randint(0, totalFrames)
 frames_skipped +=1
 if frames_skipped &gt; 0:
 print(f"Frame Skipped {frames_skipped}")
 if all(abs(randomFrameNumber - frame) &gt; frame_distance and randomFrameNumber&gt; first_frame for frame in list_of_random_frames):
 break
 list_of_random_frames.append(randomFrameNumber)
 photo_output = output_directory + basename + count_str + ".png"
 vidcap.set(cv2.CAP_PROP_POS_FRAMES,randomFrameNumber)
 success, image = vidcap.read()
 if success:
 cv2.imwrite(photo_output, image)
 print(f"Saving image to: {photo_output}")</code></pre>PS: If you want the dataset please contact me. I just don't want to get CivitAI in copyright trouble.

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