File size: 5,675 Bytes
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import json
import time
from datetime import datetime
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}/videox_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}/videox_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',
POST_TOKEN="",
timeout=5000,
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)
data = post_r.content.decode('utf-8')
return data
if __name__ == '__main__':
# initiate time
time_start = time.time()
# The Url you want to post
POST_URL = 'http://0.0.0.0:7860'
# Used in EAS. If you don't need Authorization, please set it to empty string.
TOKEN = ''
# -------------------------- #
# Step 1: update edition
# -------------------------- #
# diffusion_transformer_path = "models/Diffusion_Transformer/Wan2.1-Fun-1.3B-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"
# 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 = "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,
url=POST_URL,
POST_TOKEN=TOKEN
)
# 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)
print('# --------------------------------------------------------- #')
print(f'# Total expenditure: {time_sum}s')
print('# --------------------------------------------------------- #') |