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KDTalker is really wild 🤯👇 - Can run on single 4090 or 3090 🤩 - Uses Diffusion for generating audio-driven Talking Portraits - Preserves character identity with fine facial details - Expressions change with the speech, giving high pose diversity

84,174 просмотров • 1 год назад •via X (Twitter)

Комментарии: 7

Фото профиля Gradio
Gradio1 год назад

Play with the official gradio app: Code: Coming very soon to the @huggingface Spaces 🤗

Фото профиля TurboScribe
TurboScribe1 год назад

What REAL customers say about TurboScribe's unlimited AI transcription 👇 🇺🇸 "Not only does it transcribe with amazing accuracy, it also filters out a ton of the unnecessary noise associated with pauses in audio. Keep up the great work!" - Kevin

Фото профиля Abubakar Abid
Abubakar Abid1 год назад

cc @freddy_alfonso_

Фото профиля migero 🤌🥕🐇
migero 🤌🥕🐇1 год назад

enjoy your celebrities trying to sell you crypto in near future with this ......

Фото профиля Figure
Figure1 год назад

kdtalker never sleeps

Фото профиля Ricci👁
Ricci👁1 год назад

Looking forward to accessing Comfyui

Фото профиля stephen 🌿
stephen 🌿1 год назад

@super_bavario

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Auto regressive LLMs are officially on notice. run Gemma 4 26B diffusion gguf with llama.cpp Google just dropped DiffusionGemma-26B, and it completely flips how we generate text. instead of predicting words one by one, it generates 256 tokens in parallel using bi-directional attention. its like stable diffusion, but for language. the model starts with random text "noise" and iteratively refines and self-corrects the entire block in real-time to fix formatting and reasoning errors on the fly. since it’s a Mixture of Experts (MoE) that only activates 3.8B parameters during inference, it fits perfectly on consumer hardware. You can run the Q4_K_M quant with an 18GB VRAM budget on a single RTX 3090 or RTX 4090 with exceptional throughput. Tested on Ubuntu 22 with CUDA 13.1 using the cutting edge experimental llama.cpp branch. Here is how to compile and run it with the live terminal denoising visualizer: # 1. Clone & check out the experimental PR (#24423) - 1) git clone && cd llama.cpp -git fetch origin 2) pull/24423/head:diffusiongemma && --git checkout diffusiongemma # 2. Build with CUDA support 1) cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native 2) cmake --build build -j $(nproc) --config Release --target llama-diffusion-cli # 3. Run with live visual denoising (llama.cpp flags) ./build/bin/llama-diffusion-cli \ -m /path/to/diffusiongemma-26B-A4B-it-Q4_K_M.gguf \ -ngl 99 -cnv -n 2048 --diffusion-visual Watch the video below to see the live --diffusion-visual canvas iteratively de noising the prompt output in real time. guide and unsloth's hugging face GGUF model links are in the comments below! Is auto regressive generation officially legacy tech? Let me know what you think.

Alok

52,656 просмотров • 1 месяц назад