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import os | |
import torch | |
import sys | |
import argparse | |
import random | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import numpy as np | |
from diffusers.utils import export_to_video | |
from pyramid_dit import PyramidDiTForVideoGeneration | |
from trainer_misc import init_distributed_mode, init_sequence_parallel_group | |
import PIL | |
from PIL import Image | |
def get_args(): | |
parser = argparse.ArgumentParser('Pytorch Multi-process Script', add_help=False) | |
parser.add_argument('--model_name', default='pyramid_flux', type=str, help="The model name", choices=["pyramid_flux", "pyramid_mmdit"]) | |
parser.add_argument('--model_dtype', default='bf16', type=str, help="The Model Dtype: bf16") | |
parser.add_argument('--model_path', default='/home/jinyang06/models/pyramid-flow', type=str, help='Set it to the downloaded checkpoint dir') | |
parser.add_argument('--variant', default='diffusion_transformer_768p', type=str,) | |
parser.add_argument('--task', default='t2v', type=str, choices=['i2v', 't2v']) | |
parser.add_argument('--temp', default=16, type=int, help='The generated latent num, num_frames = temp * 8 + 1') | |
parser.add_argument('--sp_group_size', default=2, type=int, help="The number of gpus used for inference, should be 2 or 4") | |
parser.add_argument('--sp_proc_num', default=-1, type=int, help="The number of process used for video training, default=-1 means using all process.") | |
return parser.parse_args() | |
def main(): | |
args = get_args() | |
# setup DDP | |
init_distributed_mode(args) | |
assert args.world_size == args.sp_group_size, "The sequence parallel size should be DDP world size" | |
# Enable sequence parallel | |
init_sequence_parallel_group(args) | |
device = torch.device('cuda') | |
rank = args.rank | |
model_dtype = args.model_dtype | |
model = PyramidDiTForVideoGeneration( | |
args.model_path, | |
model_dtype, | |
model_name=args.model_name, | |
model_variant=args.variant, | |
) | |
model.vae.to(device) | |
model.dit.to(device) | |
model.text_encoder.to(device) | |
model.vae.enable_tiling() | |
if model_dtype == "bf16": | |
torch_dtype = torch.bfloat16 | |
elif model_dtype == "fp16": | |
torch_dtype = torch.float16 | |
else: | |
torch_dtype = torch.float32 | |
# The video generation config | |
if args.variant == 'diffusion_transformer_768p': | |
width = 1280 | |
height = 768 | |
else: | |
assert args.variant == 'diffusion_transformer_384p' | |
width = 640 | |
height = 384 | |
if args.task == 't2v': | |
prompt = "A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors" | |
with torch.no_grad(), torch.cuda.amp.autocast(enabled=True if model_dtype != 'fp32' else False, dtype=torch_dtype): | |
frames = model.generate( | |
prompt=prompt, | |
num_inference_steps=[20, 20, 20], | |
video_num_inference_steps=[10, 10, 10], | |
height=height, | |
width=width, | |
temp=args.temp, | |
guidance_scale=7.0, # The guidance for the first frame, set it to 7 for 384p variant | |
video_guidance_scale=5.0, # The guidance for the other video latent | |
output_type="pil", | |
save_memory=True, # If you have enough GPU memory, set it to `False` to improve vae decoding speed | |
cpu_offloading=False, # If OOM, set it to True to reduce memory usage | |
inference_multigpu=True, | |
) | |
if rank == 0: | |
export_to_video(frames, "./text_to_video_sample.mp4", fps=24) | |
else: | |
assert args.task == 'i2v' | |
image_path = 'assets/the_great_wall.jpg' | |
image = Image.open(image_path).convert("RGB") | |
image = image.resize((width, height)) | |
prompt = "FPV flying over the Great Wall" | |
with torch.no_grad(), torch.cuda.amp.autocast(enabled=True if model_dtype != 'fp32' else False, dtype=torch_dtype): | |
frames = model.generate_i2v( | |
prompt=prompt, | |
input_image=image, | |
num_inference_steps=[10, 10, 10], | |
temp=args.temp, | |
video_guidance_scale=4.0, | |
output_type="pil", | |
save_memory=True, # If you have enough GPU memory, set it to `False` to improve vae decoding speed | |
cpu_offloading=False, # If OOM, set it to True to reduce memory usage | |
inference_multigpu=True, | |
) | |
if rank == 0: | |
export_to_video(frames, "./image_to_video_sample.mp4", fps=24) | |
torch.distributed.barrier() | |
if __name__ == "__main__": | |
main() |