ONNX

ONNX Interface

NexaTextInference

A class used for loading text models and running text generation.

Methods

• run(): Run the text generation loop.

• run_streamlit(): Run the Streamlit UI.

Arguments

• model_path (str): Path or identifier for the model in Nexa Model Hub.

• local_path (str, optional): Local path of the model.

• temperature (float): Temperature for sampling.

• min_new_tokens (int): Minimum number of new tokens to generate.

• max_new_tokens (int): Maximum number of new tokens to generate.

• top_k (int): Top-k sampling parameter.

• top_p (float): Top-p sampling parameter.

• profiling (bool): Enable timing measurements for the generation process.

• streamlit (bool): Run the inference in Streamlit UI.

Example Code

from nexa.onnx import NexaTextInference

model_path = "gemma"
inference = NexaTextInference(
    model_path=model_path,
    local_path=None,
    temperature=0.7,
    max_new_tokens=512,
    top_k=50,
    top_p=0.9,
    profiling=True
)

# run() method
inference.run()

# run_streamlit() method
inference.run_streamlit(model_path)

NexaImageInference

A class used for loading image models and running image generation.

Methods

• run(): Run the text-to-image generation loop.

• run_streamlit(): Run the Streamlit UI.

• generate_image(prompt, negative_prompt): Generate images based on the given prompt

Arguments

• model_path (str): Path or identifier for the model in Nexa Model Hub.

• local_path (str, optional): Local path of the model.

• output_path (str): Output path for the generated image. Example: "generated_images/image.png"

• num_inference_steps (int): Number of inference steps.

• num_images_per_prompt (int): Number of images to generate per prompt.

• width (int): Width of the output image.

• height (int): Height of the output image.

• guidance_scale (float): Guidance scale for diffusion.

• random_seed (int): Random seed for image generation.

• streamlit (bool): Run the inference in Streamlit UI.

Example Code

from nexa.onnx import NexaImageInference

model_path = "lcm-dreamshaper"
inference = NexaImageInference(
    model_path=model_path,
    local_path=None,
    num_inference_steps=4,
    width=512,
    height=512,
    guidance_scale=1.0,
    random_seed=0,
)

# run() method
inference.run()

# run_streamlit() method
inference.run_streamlit(model_path)

# generate_image(prompt, negative_prompt) method
inference.generate_image(prompt="a lovely cat", negative_prompt="no hair")

NexaTTSInference

A class used for loading text-to-speech models and running text-to-speech generation.

Methods

• run(): Run the text-to-speech loop.

• run_streamlit(): Run the Streamlit UI.

Arguments

• model_path (str): Path or identifier for the model in Nexa Model Hub.

• local_path (str, optional): Local path of the model.

• output_dir (str): Output directory for TTS generated audio.

• sampling_rate (int): Sampling rate for audio processing.

• streamlit (bool): Run the inference in Streamlit UI.

Example Code

from nexa.onnx import NexaTTSInference

model_path = "ljspeech"
inference = NexaTTSInference(
    model_path=model_path,
    local_path=None
)

# run() method
inference.run()

# run_streamlit() method
inference.run_streamlit()

NexaVoiceInference

A class used for loading voice models and running voice transcription.

Methods

• run(): Run the auto speech generation loop.

• run_streamlit(): Run the Streamlit UI.

Arguments

• model_path (str): Path or identifier for the model in Nexa Model Hub.

• local_path (str, optional): Local path of the model.

• output_dir (str): Output directory for transcriptions.

• sampling_rate (int): Sampling rate for audio processing.

• streamlit (bool): Run the inference in Streamlit UI.

Example Code

from nexa.onnx import NexaVoiceInference

model_path = "whisper-tiny"
inference = NexaVoiceInference(
    model_path=model_path,
    local_path=None
)

# run() method
inference.run()

# run_streamlit() method
inference.run_streamlit()