Given that the video source used an HDR format with a unique color profile, the above command ensures a correct color representation for the extracted images. Additionally, the command aims to retrieve only distinct frames. However, using 4K resolution might have affected the extraction of distinct frames. The primary objective of the training is character training, with a focus on faces. Therefore I had to extract all faces from the initial set of 41k images. In my GitHub repository, there is a script crop_to_face.py that I used to extract all the faces into a separate folder, with a command: python3 crop_to_face.py --source_folder "/path_to_source/S01E01-03_extract/" --target_folder "/path_to_target/S01E01-03_faces/"

Multipliers: GOT subjects with a significant number of images - trained 8 images per subject per epoch, subjects with fewer images - 4/2 images per subject per epoch. Mixing Besides training faces, I wanted the model to be familiar with outfits and scenes. To achieve this, I used a subset of the frames extracted initially, without cropping them. Using the move_random_files.py script on the 41k images from the initial extraction, to move 5k random images as the foundation for scenes. I manually filtered these selected images during the captioning stage. Captioning This model is based on ❤️‍🔥 Divas model - original training, remixed recipe, and half of the dataset used for regularization. While the researchers certainly produced the lengthy study as fans, the out-of-the-ordinary simulation has important implications for the science behind climate study. [ See the Effects of Climate Change Across Earth (Video)]From this process, we extracted 41k images, which then required further filtration and adaptation for our database. Faces extraction

For faces, they were already separated into folders - folder names were used as the first tag in captions. I used my script with a graphical component to add additional names, when images included another face. Darkness - Even with my efforts to counter the dataset's dark bias by introducing random saturation, generated characters often appear slightly too dark. Using "game of thrones" in the prompt often results in darker images. However, using "game of thrones" in a negative prompt tends to produce brighter images. Training with more episodes might lessen this dark bias, but this remains to be verified. The model 👑 G ame of Thrones is based on the first three episodes of HBO's TV show Game of Thrones. As a fan of the show, I thought it would be interesting to reimagine it with a Stable Diffusion (SD) model. The main goal of the model is to replicate the show's characters with high fidelity. Given the large number of characters, interactions, and scenes it presents, it was quite a challenging endeavor. The images showcased above are the outcomes of the model. Then I used a WebUI extension with the WD14 tagger to append the rest of the captions automatically. I utilized my model evaluation test to assess various merge combinations, aiming to determine the most effective merge ratios. This step is exploratory and requires the creation and assessment of multiple merge ratios to optimize traits in the final model.

Download 3D files from Game of Thrones

Blurriness in Images: Many images extracted from the TV show displayed varying degrees of blur, which negatively impacts the training process, and possibly forces the model to be able to generate mainly blurry images. I wanted to use an algorithm to automatically filter out and discard these blurry images. My attempt can be seen in the images_filter_blurry.py script where I tried three distinct algorithms to identify and filter out face blur. Unfortunately, my tests on a sample dataset didn't establish a reliable correlation between the blur score from the algorithm and the actual perceptual blurriness upon manual inspection. Attempts at combining these algorithms didn't yield better results. While some articles point to dedicated models trained for blur detection, I wasn't able to acquire such a model for my tests. Training focus: The dataset was expanded to include half of ❤️‍🔥 Divas dataset. The primary focus was on the ❤️‍🔥 Divas dataset while also giving some attention to the preservation of GOT faces and scenes. Captioning was done in a few steps with the help of my scripts: captions_commands.py and captions_helper.py.