VideoX-Fun/examples/wan2.2_fun/post_infer_queue.py

import base64
import json
import time
import urllib.parse
import requests


def post_infer(
    generation_method, 
    length_slider, 
    url='http://127.0.0.1:7860', 
    POST_TOKEN="", 
    timeout=5,
    base_model_path="none",
    lora_model_path="none",
    lora_alpha_slider=0.55,
    prompt_textbox="A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.",
    negative_prompt_textbox="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
    sampler_dropdown="Flow",
    sample_step_slider=50,
    width_slider=672,
    height_slider=384,
    cfg_scale_slider=6,
    seed_textbox=43,
    enable_teacache = None, 
    teacache_threshold = None, 
    num_skip_start_steps = None, 
    teacache_offload = None, 
    cfg_skip_ratio = None,
    enable_riflex = None, 
    riflex_k = None, 
):
    # Prepare the data payload
    datas = json.dumps({
        "base_model_path": base_model_path,
        "lora_model_path": lora_model_path,
        "lora_alpha_slider": lora_alpha_slider,
        "prompt_textbox": prompt_textbox,
        "negative_prompt_textbox": negative_prompt_textbox,
        "sampler_dropdown": sampler_dropdown,
        "sample_step_slider": sample_step_slider,
        "width_slider": width_slider,
        "height_slider": height_slider,
        "generation_method": generation_method,
        "length_slider": length_slider,
        "cfg_scale_slider": cfg_scale_slider,
        "seed_textbox": seed_textbox,
        
        "enable_teacache": enable_teacache,
        "teacache_threshold": teacache_threshold,
        "num_skip_start_steps": num_skip_start_steps,
        "teacache_offload": teacache_offload,
        "cfg_skip_ratio": cfg_skip_ratio,
        "enable_riflex": enable_riflex,
        "riflex_k": riflex_k,
    })

    # Initialize session and set headers
    session = requests.session()
    session.headers.update({"Authorization": POST_TOKEN})

    # Send POST request
    if url[-1] == "/":
        url = url[:-1]
    post_r = session.post(f'{url}/videox_fun/infer_forward', data=datas, timeout=timeout)

    # Extract request ID from POST response headers
    request_id = post_r.headers.get("X-Eas-Queueservice-Request-Id")

    # Prepare query parameters for GET request
    query = {
        '_index_': '0',
        '_length_': '1',
        '_timeout_': str(timeout),
        '_raw_': 'false',
        '_auto_delete_': 'true',
    }
    if request_id:
        query['requestId'] = request_id

    query_str = urllib.parse.urlencode(query)

    # Polling GET request until status code is not 204
    status_code = 204
    while status_code == 204:
        if query_str:
            get_r = session.get(f'{url}/sink?{query_str}', timeout=timeout)
        else:
            get_r = session.get(f'{url}/sink', timeout=timeout)
        status_code = get_r.status_code
    # Decode and return the response content
    data = get_r.content.decode('utf-8')
    return data

if __name__ == '__main__':
    # initiate time
    time_start  = time.time()  

    # EAS队列配置
    EAS_URL = 'http://17xxxxxxxxx.pai-eas.aliyuncs.com/api/predict/xxxxxxxx'
    # Use in EAS Queue
    TOKEN   = 'xxxxxxxx'
        
    # Support TeaCache.
    enable_teacache     = True
    # Recommended to be set between 0.05 and 0.30. A larger threshold can cache more steps, speeding up the inference process, 
    # but it may cause slight differences between the generated content and the original content.
    # # --------------------------------------------------------------------------------------------------- #
    # | Model Name          | threshold | Model Name          | threshold |
    # | Wan2.2-T2V-A14B     | 0.10~0.15 | Wan2.2-I2V-A14B     | 0.15~0.20 |
    # | Wan2.2-Fun-A14B-*   | 0.15~0.20 |
    # # --------------------------------------------------------------------------------------------------- #
    teacache_threshold  = 0.10
    # The number of steps to skip TeaCache at the beginning of the inference process, which can
    # reduce the impact of TeaCache on generated video quality.
    num_skip_start_steps = 5
    # Whether to offload TeaCache tensors to cpu to save a little bit of GPU memory.
    teacache_offload    = False

    # Skip some cfg steps in inference
    # Recommended to be set between 0.00 and 0.25
    cfg_skip_ratio      = 0

    # Riflex config
    enable_riflex       = False
    # Index of intrinsic frequency
    riflex_k            = 6
        
    # "Video Generation" and "Image Generation"
    generation_method   = "Video Generation"
    # Video length
    length_slider       = 81
    # Used in Lora models
    lora_model_path     = "none"
    lora_alpha_slider   = 0.55
    # Prompts
    prompt_textbox      = "A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic."
    negative_prompt_textbox = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
    # Sampler name
    sampler_dropdown    = "Flow"
    # Sampler steps
    sample_step_slider  = 50
    # height and width 
    width_slider        = 832
    height_slider       = 480
    # cfg scale
    cfg_scale_slider    = 6
    seed_textbox        = 43

    outputs = post_infer(
        generation_method, 
        length_slider, 
        lora_model_path=lora_model_path,
        lora_alpha_slider=lora_alpha_slider,
        prompt_textbox=prompt_textbox,
        negative_prompt_textbox=negative_prompt_textbox,
        sampler_dropdown=sampler_dropdown,
        sample_step_slider=sample_step_slider,
        width_slider=width_slider,
        height_slider=height_slider,
        cfg_scale_slider=cfg_scale_slider,
        seed_textbox=seed_textbox,
        enable_teacache = enable_teacache, 
        teacache_threshold = teacache_threshold, 
        num_skip_start_steps = num_skip_start_steps, 
        teacache_offload = teacache_offload,
        cfg_skip_ratio = cfg_skip_ratio, 
        enable_riflex = enable_riflex, 
        riflex_k = riflex_k, 
        url=EAS_URL, 
        POST_TOKEN=TOKEN
    )
    # Get decoded data
    outputs = json.loads(base64.b64decode(json.loads(outputs)[0]['data']))
    base64_encoding = outputs["base64_encoding"]
    decoded_data = base64.b64decode(base64_encoding)

    is_image = True if generation_method == "Image Generation" else False
    if is_image or length_slider == 1:
        file_path = "1.png"
    else:
        file_path = "1.mp4"
    with open(file_path, "wb") as file:
        file.write(decoded_data)
        
    # End of record time
    # The calculated time difference is the execution time of the program, expressed in seconds / s
    time_end = time.time()  
    time_sum = (time_end - time_start)
    print('# --------------------------------------------------------- #')
    print(f'#   Total expenditure: {time_sum}s')
    print('# --------------------------------------------------------- #')