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import os, json, requests, random, runpod
import torch
from diffusers import AutoencoderKLCogVideoX, CogVideoXImageToVideoPipeline, CogVideoXTransformer3DModel
from cogvideox.utils.lora_utils import merge_lora, unmerge_lora
from diffusers.utils import export_to_video, load_image
from transformers import T5EncoderModel, T5Tokenizer
with torch.inference_mode():
model_id = "/content/model"
transformer = CogVideoXTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.float16)
text_encoder = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.float16)
vae = AutoencoderKLCogVideoX.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float16)
tokenizer = T5Tokenizer.from_pretrained(model_id, subfolder="tokenizer")
pipe = CogVideoXImageToVideoPipeline.from_pretrained(model_id, tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, torch_dtype=torch.float16).to("cuda")
# pipe.enable_model_cpu_offload()
def download_file(url, save_dir, file_name):
os.makedirs(save_dir, exist_ok=True)
original_file_name = url.split('/')[-1]
_, original_file_extension = os.path.splitext(original_file_name)
file_path = os.path.join(save_dir, file_name + original_file_extension)
response = requests.get(url)
response.raise_for_status()
with open(file_path, 'wb') as file:
file.write(response.content)
return file_path
@torch.inference_mode()
def generate(input):
values = input["input"]
lora_path = "/content/shirtlift.safetensors"
lora_weight = 1.0
pipe = merge_lora(pipe, lora_path, lora_weight)
input_image = values['input_image_check']
input_image = download_file(url=input_image, save_dir='/content/input', file_name='input_image_tost')
prompt = values['prompt']
# guidance_scale = values['guidance_scale']
# use_dynamic_cfg = values['use_dynamic_cfg']
# num_inference_steps = values['num_inference_steps']
# fps = values['fps']
guidance_scale = 6
use_dynamic_cfg = True
num_inference_steps = 50
fps = 8
image = load_image(input_image)
video = pipe(image=image, prompt=prompt, guidance_scale=guidance_scale, use_dynamic_cfg=use_dynamic_cfg, num_inference_steps=num_inference_steps).frames[0]
export_to_video(video, "/content/cogvideox_5b_i2v_tost.mp4", fps=fps)
result = "/content/cogvideox_5b_i2v_tost.mp4"
try:
notify_uri = values['notify_uri']
del values['notify_uri']
notify_token = values['notify_token']
del values['notify_token']
discord_id = values['discord_id']
del values['discord_id']
if(discord_id == "discord_id"):
discord_id = os.getenv('com_camenduru_discord_id')
discord_channel = values['discord_channel']
del values['discord_channel']
if(discord_channel == "discord_channel"):
discord_channel = os.getenv('com_camenduru_discord_channel')
discord_token = values['discord_token']
del values['discord_token']
if(discord_token == "discord_token"):
discord_token = os.getenv('com_camenduru_discord_token')
job_id = values['job_id']
del values['job_id']
default_filename = os.path.basename(result)
with open(result, "rb") as file:
files = {default_filename: file.read()}
payload = {"content": f"{json.dumps(values)} <@{discord_id}>"}
response = requests.post(
f"https://discord.com/api/v9/channels/{discord_channel}/messages",
data=payload,
headers={"Authorization": f"Bot {discord_token}"},
files=files
)
response.raise_for_status()
result_url = response.json()['attachments'][0]['url']
notify_payload = {"jobId": job_id, "result": result_url, "status": "DONE"}
web_notify_uri = os.getenv('com_camenduru_web_notify_uri')
web_notify_token = os.getenv('com_camenduru_web_notify_token')
if(notify_uri == "notify_uri"):
requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
else:
requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
requests.post(notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
return {"jobId": job_id, "result": result_url, "status": "DONE"}
except Exception as e:
error_payload = {"jobId": job_id, "status": "FAILED"}
try:
if(notify_uri == "notify_uri"):
requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
else:
requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
requests.post(notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
except:
pass
return {"jobId": job_id, "result": f"FAILED: {str(e)}", "status": "FAILED"}
finally:
if os.path.exists(result):
os.remove(result)
if os.path.exists(input_image):
os.remove(input_image)
runpod.serverless.start({"handler": generate})
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