Usage

from raiiaf.file_encoder import file_encoder
from raiiaf.file_decoder import file_decoder
import json
from PIL import Image
import io

raiiaf.file_encoder(
    filename="encoded_img.raiiaf",  # The .raiiaf extension is required!
    latent=latent,  # initial latent noise
    chunk_records=[],
    model_name="Stable Diffusion 3",
    model_version="3",  # Model Version
    prompt="A puppy smiling, cinematic",
    tags=["puppy", "dog", "smile"],
    img_binary=binary_img_data,
    convert_float16=False,  # whether to convert input tensors to float16
    generation_settings={
        "seed": 42,
        "steps": 20,
        "sampler": "ddim",
        "cfg_scale": 7.5,
        "scheduler": "pndm",
        "eta": 0.0,
        "guidance": "classifier-free",
        "precision": "fp16",
        "deterministic": True
    },
    hardware_info={
        "machine_name": "test_machine",
        "os": "linux",
        "cpu": "Intel",
        "cpu_cores": 8,  # minimum 1
        "gpu": [{"name": "RTX 3090", "memory_gb": 24, "driver": "nvidia", "cuda_version": "12.1"}],
        "ram_gb": 64.0,
        "framework": "torch",
        "compute_lib": "cuda"
    }
)

# Decoding
decoded = raiiaf.file_decoder("encoded_img.raiiaf")
# now to save the metadata
metadata = decoded["metadata"]["raiiaf_metadata"]

# to just get specific metadata blocks
model_info = decoded["metadata"]["raiiaf_metadata"]["model_info"]

# to save decoded metadata to a json file
with open("decoded_metadata.json", "w") as f:
    json.dump(decoded["metadata"], f, indent=2)

# to save just the image_binary as png
image_bytes = decoded["chunks"].get("image")
if image_bytes is not None:
    img = Image.open(io.BytesIO(image_bytes))
    img.save("decoded_image.png")