Add wrapping inference within InfinityPipeline then support batch inference with multiple prompts

[nqbinhcs]

Description

This pull request adds the run_infinity_pipeline.py script for batch image generation using the Infinity model (issue #106 ). Key features include:

• Wrapping as pipeline: Loads the Infinity model, VAE, and text encoder.
• Batch Inference: Supports multiple prompts with configurable parameters (cfg_scale, tau, seed, top_k, top_p).

Example Usage

Run the script:

python tools/run_infinity_pipeline.py

Core Functionality

pipe = InfinityPipeline.from_pretrained(
    pretrained_model_name_or_path=model_path,
    vae_path=vae_path,
    text_encoder_path=text_encoder_path,
    model_type="infinity_2b",
    device="cuda",
    torch_dtype=torch.bfloat16,
    pn="1M"
)

prompts = [
    "A majestic dragon made of crystal",
    "A close-up photograph of a Corgi dog",
    "A photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest with the text 'Welcome Friends!'"
]

images = pipe(
    prompt=prompts,
    cfg_scale=3.0,
    tau=0.5,
    seed=42,
    top_k=900,
    top_p=0.97
)