example.py

import requests
import base64

# Important: the NVIDIA L40S will only support small resolutions, short length and no post-processing.
# If you want those features, you might need to use the NVIDIA A100.

# Use your own Inference Endpoint URL
API_URL = "https://<use your own Inference Endpoint here>.endpoints.huggingface.cloud"

# Use you own API token
API_TOKEN = "hf_<replace by your own Hugging Face token>"
def query(payload):
    response = requests.post(API_URL, headers={
        "Accept": "application/json",
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json" 
    }, json=payload)
    return response.json()

def save_video(json_response, filename):

    try:
        error = json_response["error"]
        if error:
            print(error)
            return
    except Exception as e:
        pass

    video_data_uri = ""
    try:
        # Extract the video data URI from the response
        video_data_uri = json_response["video"]
    except Exception as e:
        message = str(json_response)
        print(message)
        raise ValueError(message)
    
    # Remove the data URI prefix to get just the base64 data
    # Assumes format like "data:video/mp4;base64,<actual_base64_data>"
    base64_data = video_data_uri.split(",")[1]
    
    # Decode the base64 data
    video_data = base64.b64decode(base64_data)
    
    # Write the binary data to an MP4 file
    with open(filename, "wb") as f:
        f.write(video_data)

def encode_image(image_path):
    """
    Load and encode an image file to base64
    
    Args:
        image_path (str): Path to the image file
        
    Returns:
        str: Base64 encoded image data URI
    """

    with Image.open(image_path) as img:
        # Convert to RGB if necessary
        if img.mode != "RGB":
            img = img.convert("RGB")
        
        # Save image to bytes
        img_byte_arr = BytesIO()
        img.save(img_byte_arr, format="JPEG")

        # Encode to base64
        base64_encoded = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
        return f"data:image/jpeg;base64,{base64_encoded}"

# Example usage with image-to-video generation
image_filename = "input.jpg"
video_filename = "output.mp4"

config = {
    "inputs": {
       #"prompt": "magnificent underwater footage, clownfishes swimming around coral inside the carribean sea, real gopro footage",
       # OR
       "image": encode_image(image_filename)
    },
    
    "parameters": {

        # ------------------- settings for LTX-Video -----------------------
        
        #"negative_prompt": "saturated, highlight, overexposed, highlighted, overlit, shaking, too bright, worst quality, inconsistent motion, blurry, jittery, distorted, cropped, watermarked, watermark, logo, subtitle, subtitles, lowres",

        # note about resolution:
        # we cannot use 720 since it cannot be divided by 32
        #
        # for a cinematic look:
        "width": 768,
        "height": 480,

        # this is a hack to fool LTX-Video into believing our input image is an actual video frame with poor encoding quality
        #"input_image_quality": 70,

        # for a vertical video look:
        #"width": 480,
        #"height": 768,

        # LTX-Video requires a frame number divisible by 8, plus one frame
        # note: glitches might appear if you use more than 168 frames
        "num_frames": (8 * 16) + 1,

        # using 30 steps seems to be enough for most cases, otherwise use 50 for best quality
        # I think using a large number of steps (> 30) might create some overexposure and saturation
        "num_inference_steps": 50,

        # values between 3.0 and 4.0 are nice
        "guidance_scale": 4.0,

        #"seed": 1209877,

        # ----------------------------------------------------------------

        # ------------------- settings for Varnish -----------------------
        # This will double the number of frames.
        # You can activate this if you want:
        # - a slow motion effect (in that case use double_num_frames=True and fps=24, 25 or 30)
        # - a HD soap / video game effect (in that case use double_num_frames=True and fps=60)
        "double_num_frames": True,

        # controls the number of frames per second
        # use this in combination with the num_frames and double_num_frames settings to control the duration and "feel" of your video
        "fps": 60, # typical values are: 24, 25, 30, 60

        # upscale the video using Real-ESRGAN.
        # This upscaling algorithm is relatively fast,
        # but might create an uncanny "3D render" or "drawing" effect.
        "super_resolution": True,

        # for cosmetic purposes and get a "cinematic" feel, you can optionally add some film grain.
        # it is not recommended to add film grain if your theme doesn't match (film grain is great for black & white, retro looks)
        # and if you do, adding more than 12% will start to negatively impact file size (video codecs aren't great are compressing film grain)
        # 0% = no grain
        # 10% = a bit of grain
        "grain_amount": 12, # value between 0-100


        # The range of the CRF scale is 0–51, where:
        # 0 is lossless (for 8 bit only, for 10 bit use -qp 0)
        # 23 is the default
        # 51 is worst quality possible
        # A lower value generally leads to higher quality, and a subjectively sane range is 17–28.
        # Consider 17 or 18 to be visually lossless or nearly so;
        # it should look the same or nearly the same as the input but it isn't technically lossless.
        # The range is exponential, so increasing the CRF value +6 results in roughly half the bitrate / file size, while -6 leads to roughly twice the bitrate.
        #"quality": 18,

    }
}

# Make the API call
output = query(config)

# Save the video
save_video(output, video_filename)