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]
How to use agkavin/Avatar-Speech with llama.cpp:
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
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
# 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
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
docker model run hf.co/agkavin/Avatar-Speech:Q4_K_M
How to use agkavin/Avatar-Speech with Ollama:
ollama run hf.co/agkavin/Avatar-Speech:Q4_K_M
How to use agkavin/Avatar-Speech with Docker Model Runner:
How to use agkavin/Avatar-Speech with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull agkavin/Avatar-Speech:Q4_K_M
lemonade run user.Avatar-Speech-Q4_K_M
lemonade list
