Bubbliiiingandhkunzhe
9f34f7c8c5
* Update Flow * Update Flow * Update Flow * add image recaptioning * Fix bug in t2v * update train_reward_lora.py * update reward training * Update V5.1 and mix multi text_encoders to one pipeline * Update V5.1 training Code * Update ComfyUI * Update Comment * Delete files * update reward training * Update Readme * fix extract frames in compute_semantic_consistency * Update Readme && Remove to in prediction * Update Demo * Update Readme * Update ui * support vae gradient checkpointing in reward training * Update Training Readme --------- Co-authored-by: hkunzhe <huangkunzhe.hkz@alibaba-inc.com>
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
import base64
|
|
import json
|
|
import sys
|
|
import time
|
|
from datetime import datetime
|
|
from io import BytesIO
|
|
|
|
import cv2
|
|
import requests
|
|
|
|
|
|
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}/easyanimate/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}/easyanimate/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": "This video shows Mount saint helens, washington - the stunning scenery of a rocky mountains during golden hours - wide shot. A soaring drone footage captures the majestic beauty of a coastal cliff, its red and yellow stratified rock faces rich in color and against the vibrant turquoise of the sea.",
|
|
"negative_prompt_textbox": "Strange motion trajectory, a poor composition and deformed video, worst quality, normal quality, low quality, low resolution, duplicate and ugly, strange body structure, long and strange neck, bad teeth, bad eyes, bad limbs, bad hands, rotating camera, blurry camera, shaking camera",
|
|
"sampler_dropdown": "Euler",
|
|
"sample_step_slider": 30,
|
|
"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}/easyanimate/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
|
|
# -------------------------- #
|
|
edition = "v5.1"
|
|
outputs = post_update_edition(edition)
|
|
print('Output update edition: ', outputs)
|
|
|
|
# -------------------------- #
|
|
# Step 2: update edition
|
|
# -------------------------- #
|
|
diffusion_transformer_path = "models/Diffusion_Transformer/EasyAnimateV5.1-12b-zh-InP"
|
|
outputs = post_diffusion_transformer(diffusion_transformer_path)
|
|
print('Output update edition: ', outputs)
|
|
|
|
# -------------------------- #
|
|
# Step 3: infer
|
|
# -------------------------- #
|
|
# "Video Generation" and "Image Generation"
|
|
generation_method = "Video Generation"
|
|
length_slider = 21
|
|
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('# --------------------------------------------------------- #') |