Game of Thrones Official Models - King Mag the Mighty Figurine

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To obtain images from the video, I used ffmpeg, extracting four frames from each second of the video using the following command for each episode: ffmpeg -hwaccel cuda -i "/path_to_source/video_S01E01.mkv" -vf "setpts=N/FRAME_RATE/TB,fps=4,mpdecimate=hi=8960:lo=64:frac=0.33,zscale=t=linear:npl=100,format=gbrpf32le,zscale=p=bt709,tonemap=tonemap=hable:desat=0,zscale=t=bt709:m=bt709:r=tv,format=yuv420p" -pix_fmt yuv420p -q:v 3 "/path_to_target/S01E01_extract/s01_e01_%06d.jpg" This model was trained on the first three episodes of the TV show Game of Thrones. 9k images focused on characters' faces (50 subjects in total), and 4k images from different scenes. Additionally, 30k images were used as regularization images - medieval-themed images and half of the ❤️‍🔥 Divas dataset. 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.

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 I'm using the EveryDream2 trainer, which runs on a remote server from vast.ai. For this model, I've exclusively used RTX 4090 GPUs. Although there are numerous settings in the training process that can be adjusted, I'll only mention a few most important settings: the Unet learning rate 7e-7, Text Encoder (TE) learning rate 5e-8, and for the scheduler, pulsing cosine with a 0.5-2 epochs cycle. I also enabled the tag shuffling option.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 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)] This model is based on ❤️‍🔥 Divas model - original training, remixed recipe, and half of the dataset used for regularization.

Training included 9k images focused on characters' faces, 50 subjects in total, and 4k images from different scenes. Additionally, 30k images were used as regularization images - medieval-themed images as well as half of the ❤️‍🔥 Divas dataset. Ultimately, the training was stabilized with 💖 Babes 2.0 model. Overall, the model's development spanned three weeks, with GPU training on an RTX 4090 taking 3.5 days. Dataset preparation In this training, I wanted to test the theory suggesting it's more effective for the TE to be pre-trained initially, and for the Unet to be trained later with frozen and pre-trained TE. Stage 1 Automation Goal - I aspire to fully automate the entire process of converting video to an SD model. However, challenges like blurriness and the absence of a reliable face-to-name classification make it currently infeasible. The need for manual filtering and captioning makes the process both lengthy and labor-intensive. I'm optimistic that future advancements will allow for a more streamlined video-to-SD-model conversion. This would potentially speed up the creation of fast and high-quality fan fiction, visual novels, concept art, and, given advancements in image-to-video technology, even aid in creating videos, music clips, short films, and movies. 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

Captioning was done in a few steps with the help of my scripts: captions_commands.py and captions_helper.py. First, I obtained a 4K (3840 x 2160px) version of the first three episodes of the show. 4K images allow for the extraction of relatively small faces from frames that maintain a resolution higher than 768x768px, which is our base training resolution. The aspect ratio doesn't have to be 1:1, as training will automatically scale down the images to fit the target training area. Extracting images I'm uncertain if the training strategy I implemented is the best approach. My goal was to test a pre-trained TE strategy, but it remains unclear whether it's superior or inferior to the combined TE+Unet training. Moving forward, I plan to start with a TE+Unet training phase and subsequently freeze the TE while continuing Unet training - without disregarding the Unet progress from the initial phase. From this process, we extracted 41k images, which then required further filtration and adaptation for our database. Faces extraction