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Thanks for the tutorial! I was able to run Re-Talking once with the provided example video and audio files, but since then, my PC keeps crashing (restarting) on every trial, even when running on the example files. It always crashes on the FaceDet phase (100%), or [Step 6] Lip Synthesis, as can be seen here: ruclips.net/video/eMuM4go2iXs/видео.html. What could be the reason? How do I troubleshoot? I have a very strong PC: CPU: Intel, 13900K GPUL ASUS TUF Gaming GeForce RTX 3090 OC Edition Mother board: Gigabyte Z690 AORUS Ultra RAM: Corsair VENGEANCE 64GB (2x32GB) DDR5 DRAM 5600MHz C40 4 NVME Drives Thanks, Edo

Indeed, RAG applications behave more like "searching" for pieces of content, re-ranking them, and then inserting them into a prompt to generate answers. Additionally, when generating later answers, RAG does not go back to modify the previously generated ones. Strictly speaking, RAG cannot "read" an entire document and summarize it. LLMs are also limited by the maximum context length, usually around 8K tokens. Although some closed-source models can handle over 128K tokens, it's not feasible to run such models on local machines. LangGraph operates in a way that's closer to real reading. It reads piece by piece and builds entity-relation (ER) nodes, then searches through these ER nodes to generate answers. Currently, the closest technology to human reading behavior seems to be LangGraph. Unfortunately, transformer models can only predict based on existing data and lack the ability to infer new knowledge. Recent advancements mainly involve agents predicting and executing plans to simulate reasoning, but this is still essentially prediction, and far from achieving true AGI. Even models like OpenAI’s GPT, which are claimed to be at a PhD level, fall short. For example, when I asked an LLM to "help me create a Vim editor application," it couldn't provide a complete solution.

Let’s make llama4 before llama4 🤝

As always, great job!👏🏻

Data, algorithms, and computational power are the three key elements. Why hasn't anyone added more complex connection models to Transformers? We should consider increasing the algorithmic complexity of large language models (LLMs), which can be likened to the complexity of connections in the human brain. This way, we wouldn't need to endlessly increase the number of parameters, especially since the number of artificial neurons already exceeds that of human neurons. Moreover, we haven't seen designs similar to the short-term memory neuron models from the runtime period. We should aim to design a model that can, like humans, quickly read relevant articles when faced with a problem. During the reading process, it could summarize related content into short-term memory and continuously update it. Then, based on this short-term memory, the model could verify the correctness of answers, for instance, by writing code to check the answers. Wouldn't this approach allow us to make the model smaller?

I was waiting for new video. Thanks for awesome work ❤😊

WOW! You are something else dude! No one provides content like you! Exceptional!

Awesome!

The Video that I have been waiting for !!! Thank you 🙏🏻

Woowwww awesome thanks for this ❤❤

I've followed your video to the letter, but I am getting this message and no video at all, please, help: /usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST']. warnings.warn( /usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST']. warnings.warn( Traceback (most recent call last): File "/content/drive/MyDrive/lip-sync/video-retalking-main/inference.py", line 16, in <module> from third_part.GPEN.gpen_face_enhancer import FaceEnhancement File "/content/drive/MyDrive/lip-sync/video-retalking-main/third_part/GPEN/gpen_face_enhancer.py", line 11, in <module> from utils.inference_utils import Laplacian_Pyramid_Blending_with_mask File "/content/drive/MyDrive/lip-sync/video-retalking-main/utils/inference_utils.py", line 5, in <module> from models import load_network, load_DNet File "/content/drive/MyDrive/lip-sync/video-retalking-main/models/__init__.py", line 2, in <module> from models.DNet import DNet File "/content/drive/MyDrive/lip-sync/video-retalking-main/models/DNet.py", line 10, in <module> from models.base_blocks import LayerNorm2d, ADAINHourglass, FineEncoder, FineDecoder File "/content/drive/MyDrive/lip-sync/video-retalking-main/models/base_blocks.py", line 9, in <module> from basicsr.archs.arch_util import default_init_weights File "/usr/local/lib/python3.10/dist-packages/basicsr/__init__.py", line 4, in <module> from .data import * File "/usr/local/lib/python3.10/dist-packages/basicsr/data/__init__.py", line 22, in <module> _dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] File "/usr/local/lib/python3.10/dist-packages/basicsr/data/__init__.py", line 22, in <listcomp> _dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/usr/local/lib/python3.10/dist-packages/basicsr/data/realesrgan_dataset.py", line 11, in <module> from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels File "/usr/local/lib/python3.10/dist-packages/basicsr/data/degradations.py", line 8, in <module> from torchvision.transforms.functional_tensor import rgb_to_grayscale ModuleNotFoundError: No module named 'torchvision.transforms.functional_tensor'

thanks so much

Do we need to add .UNSQUEEZE(1) to mask = img_mask[1].float().to(device) and why?Thanks!

i've trained your model through 30 epoch, but when i use model to predict It's too bad !!! then i continue to train 10 epoch more, but it's still to bad, i don't know why, i use your dataset, i use your scripts;<

Me waiting for the syntax error like: 😈🤭

Can I contact you on Gmail, please?

I like this video , so clear , and I was able to follow and do the same thing. I understook Unet thanks to you . Thank you so much . Could you do a video on DDPM ? specially conditional or unconditional DDPM using Unet ? Thanks a million.

Shouldn't x be first in x = torch.cat([x, cls_token], dim=1) ?

Hallo, danke Ihnen für dieses Video . ich habe aber nicht verstanden,woher haben Sie die manual_test und manual_test_mask bekommen . bei der daten ordner haben wir nur die"test.zip",test_hq.zip",,"train.zip","train_hq.zip","train_mask.zip" ich habe schon immer error für die path des manual_mask und manual_test_mask beim die Inferance Teil .

The best teacher ever!

which notebook did you say you are using?

The code reads okay but RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[1, 64, 512, 512] to have 3 channels, but got 64 channels instead Can't seem to figure what's going wrong!

I am working on a project and the input videos may have frames where there is no face. How do I change the face detection code to just apply audio but no lip sync at those frames instead of throwing me a error

thank you!

Can u tell me which version of python , torch , sckit learn , and other used

That's a good one! Thank you for the video!

Uygar hocam, selamlar bir çok diğer tutorialda olduğu gibi, görseli küçük parçalara ayırmak için nn.Conv2d kullanmışız (@14:15). ancak benim bildiğim, eğer Conv2d varsa, random initialize edilen weightler de olacaktır. dolayısyla, evet resmi küçük parçalara ayırıyoruz ama aynı zamanda convolutionların weightlerini de resimlere uygulamış oluyoruz. istersen nn.Conv2d/patcher'dan sonra oluşan küçük resimleri, orjinal resim parçalarıyla karşılaştır. farklı olduklarını göreceksin. belki de ben hata yapıyorumdur. iyi çalışmalar ve başarılar

Another great video!

At last! Missed your videos😊

great video

Hi, great video! It already helped me a lot! Thanks! I'll try to train a U-Net with my own data. Can you tell me if there is anything else I need to be aware of? Does it matter what kind of data files (jpg, png, gif or tiff) I use or do they have to be jpg files + gif files with labels for the training?

Why you used random seed in training, Its generating similar samples everytime we run the generate image models. When I remove the generator=torch.manual_seed(random_seed), code from the sample_image_generation() method, The performance of the model decreases.

could you implement a DiT ? difussion transformer?

Nice work. Please, my work entails using a similar model to segment multiple parasitized cells from the uninfected cells in a microscope slide image. any hint will be appreciated, please.

Please implement CLIP if you can.....

Uygar hocam selam, ben modelinizi çalıştırdım fakat çıktıda predicted mask tamamen siyah olmaktadır. Aynı dataseti kullanıyorum. Bu datasette doğru çalıştırabilirsem başka bir datasette proje yapmama gerekiyor. sebebi ne olabilir?

Hey Uygar, Thanks a lot for the tutorial, you're like my coding sensei! I was wondering about something while coding the ViT. Why do you define hidden_dim if you're not using it later on? Or maybe you are using it and I just haven't noticed? Appreciate your help!

you are genius brooo 🤩🤩🤩

Hocam "OSError: uygarkurt/bert-restore-punctuation-turkish does not appear to have a file named tf_model.h5 but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those weights." hatası alıyorum modelinizi kullanmaya çalışırken

i got error when training : RuntimeError: Expected memory formats of X and dY are same.

Love how you explain everything

Thats cool man, your coding skills and how smooth you are coding that is even scary, maybe AI is not for me xdddd. Anyways my question is here: You are using only one layer , what If i want to use multiple layers? 22:44 after encoder_layer should I add another encoder_layer_2 with different parameters?

acaba hangi uygulamayı kullandınız?

import torch import maxvit # from .maxvit import MaxViT, max_vit_tiny_224, max_vit_small_224, max_vit_base_224, max_vit_large_224 # Tiny model network: maxvit.MaxViT = maxvit.max_vit_tiny_224(num_classes=1000) input = torch.rand(1, 3, 224, 224) output = network(input) my purpose is to do give an input as an image (1,3,224,224) and generate output as its description for that. how should i do that, what should i add more to this code?

Thanks a lot man! This helps a lot

Thank you so much, a video that difficult to find on the internet again

I have an error its No such file or directory as i put the paths of image and mask. Can you help me solving this error?

ModuleNotFoundError: No module named 'torchvision.transforms.functional_tensor' I followed your video tutorial Operation Colab started to run, prompted this, how to solve?

😊 does this new implementation include flash attention?

You are great! Adamsın :)