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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="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="Flow",
sample_step_slider=50,
width_slider=672,
height_slider=384,
cfg_scale_slider=6,
seed_textbox=43
):
# 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,
})
# 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'
# "Video Generation" and "Image Generation"
generation_method = "Video Generation"
# Video length
length_slider = 49
# 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 = "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 name
sampler_dropdown = "Euler"
# Sampler steps
sample_step_slider = 50
# height and width
width_slider = 672
height_slider = 384
# 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,
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('# --------------------------------------------------------- #') |