276°
Posted 20 hours ago

Game of Thrones Official Models - King Mag the Mighty Figurine

£9.995£19.99Clearance
ZTS2023's avatar
Shared by
ZTS2023
Joined in 2023
82
63

About this deal

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. Overall, the model's development spanned three weeks, with GPU training on an RTX 4090 taking 3.5 days. Dataset preparation

Multipliers: GOT subjects with a significant number of images - trained 30 images per subject per epoch, subjects with fewer images - 8/4 images per subject per epoch. Stage 3 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/" 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.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 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. Data preparation presented two primary challenges: dealing with blurry images and effectively classifying face-to-name. 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.

Face Classification: the training process requires that all images of a specific individual be stored in a single directory to be able to control the number of images used for training for each subject. I tried to use automating face-to-name classification in sort_images_by_faces.py script. While it had a small success, the high rate of misclassifications meant a manual review became inevitable. Given this, I found it more efficient to manually categorize images in a single directory rather than navigate through 50 separate ones. 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. Captioning was done in a few steps with the help of my scripts: captions_commands.py and captions_helper.py. 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

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. 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. 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.

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"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. 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 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.

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 1I also added a few thousand regularization images, mainly medieval-themed and nature-only images. There are scripts in my repository that can help to obtain such images. These images were captioned automatically. Validation

Asda Great Deal

Free UK shipping. 15 day free returns.
Community Updates
*So you can easily identify outgoing links on our site, we've marked them with an "*" symbol. Links on our site are monetised, but this never affects which deals get posted. Find more info in our FAQs and About Us page.
New Comment