cogvideox/api/post_infer.py

import base64
import json
import sys
import time
from datetime import datetime
from io import BytesIO

import cv2
import requests
import base64


def post_diffusion_transformer(diffusion_transformer_path, url='http://127.0.0.1:7860'):
    datas = json.dumps({
        "diffusion_transformer_path": diffusion_transformer_path
    })
    r = requests.post(f'{url}/cogvideox_fun/update_diffusion_transformer', data=datas, timeout=1500)
    data = r.content.decode('utf-8')
    return data

def post_update_edition(edition, url='http://0.0.0.0:7860'):
    datas = json.dumps({
        "edition": edition
    })
    r = requests.post(f'{url}/cogvideox_fun/update_edition', data=datas, timeout=1500)
    data = r.content.decode('utf-8')
    return data

def post_infer(generation_method, length_slider, url='http://127.0.0.1:7860'):
    datas = json.dumps({
        "base_model_path": "none",
        "motion_module_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": "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. ", 
        "sampler_dropdown": "Euler", 
        "sample_step_slider": 50, 
        "width_slider": 672, 
        "height_slider": 384, 
        "generation_method": "Video Generation",
        "length_slider": length_slider,
        "cfg_scale_slider": 6,
        "seed_textbox": 43,
    })
    r = requests.post(f'{url}/cogvideox_fun/infer_forward', data=datas, timeout=1500)
    data = r.content.decode('utf-8')
    return data

if __name__ == '__main__':
    # initiate time
    now_date    = datetime.now()
    time_start  = time.time()  
    
    # -------------------------- #
    #  Step 1: update edition
    # -------------------------- #
    diffusion_transformer_path = "models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
    outputs = post_diffusion_transformer(diffusion_transformer_path)
    print('Output update edition: ', outputs)

    # -------------------------- #
    #  Step 2: infer
    # -------------------------- #
    # "Video Generation" and "Image Generation"
    generation_method = "Video Generation"
    length_slider = 49
    outputs = post_infer(generation_method, length_slider)
    
    # Get decoded data
    outputs = json.loads(outputs)
    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) % 60 
    print('# --------------------------------------------------------- #')
    print(f'#   Total expenditure: {time_sum}s')
    print('# --------------------------------------------------------- #')