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agkavin
/
Avatar-Speech

Diffusers
ONNX
GGUF
conversational
Model card Files Files and versions
xet
Community

Instructions to use agkavin/Avatar-Speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use agkavin/Avatar-Speech with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("agkavin/Avatar-Speech", dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use agkavin/Avatar-Speech with llama.cpp:

    Install (macOS, Linux)
    curl -LsSf https://llama.app/install.sh | sh
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf agkavin/Avatar-Speech:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf agkavin/Avatar-Speech:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf agkavin/Avatar-Speech:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf agkavin/Avatar-Speech:Q4_K_M
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf agkavin/Avatar-Speech:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf agkavin/Avatar-Speech:Q4_K_M
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf agkavin/Avatar-Speech:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf agkavin/Avatar-Speech:Q4_K_M
    Use Docker
    docker model run hf.co/agkavin/Avatar-Speech:Q4_K_M
  • LM Studio
  • Jan
  • Ollama

    How to use agkavin/Avatar-Speech with Ollama:

    ollama run hf.co/agkavin/Avatar-Speech:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use agkavin/Avatar-Speech with Docker Model Runner:

    docker model run hf.co/agkavin/Avatar-Speech:Q4_K_M
  • Lemonade

    How to use agkavin/Avatar-Speech with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull agkavin/Avatar-Speech:Q4_K_M
    Run and chat with the model
    lemonade run user.Avatar-Speech-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
Avatar-Speech / backend
12.5 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
agkavin
avatars
9400b83 6 months ago
  • agent
    merge 6 months ago
  • api
    avatars 6 months ago
  • avatars
    avatars 6 months ago
  • e2e
    avatars 6 months ago
  • models
    Initial commit: speech_to_video project with models via LFS 6 months ago
  • musetalk
    Fix pipeline deadlocks, remove torch.compile, implement 3-queue parallel pipeline, optimize for 16fps 6 months ago
  • publisher
    Fix pipeline deadlocks, remove torch.compile, implement 3-queue parallel pipeline, optimize for 16fps 6 months ago
  • sync
    Initial commit: speech_to_video project with models via LFS 6 months ago
  • tts
    Initial commit: speech_to_video project with models via LFS 6 months ago
  • .env
    165 Bytes
    merge 6 months ago
  • __init__.py
    0 Bytes
    Initial commit: speech_to_video project with models via LFS 6 months ago
  • agent.py
    9.41 kB
    merge 6 months ago
  • config.py
    2.89 kB
    Fix pipeline deadlocks, remove torch.compile, implement 3-queue parallel pipeline, optimize for 16fps 6 months ago
  • config_wrapper.py
    534 Bytes
    Initial commit: speech_to_video project with models via LFS 6 months ago
  • requirements.txt
    767 Bytes
    Reorganize setup files and update documentation 6 months ago
  • server.py
    15 kB
    avatars 6 months ago