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('# --------------------------------------------------------- #')