| import sys |
| sys.path.append(".") |
| from causalvideovae.model import CausalVAEModel |
| from CV_VAE.models.modeling_vae import CVVAEModel |
| from opensora.models.vae.vae import VideoAutoencoderPipeline |
| from opensora.registry import DATASETS, MODELS, build_module |
| from opensora.utils.config_utils import parse_configs |
| from diffusers.models import AutoencoderKL, AutoencoderKLTemporalDecoder |
| from tats import VQGAN |
| from tats.download import load_vqgan |
| from taming.models.vqgan import VQModel, GumbelVQ |
| import torch |
| from omegaconf import OmegaConf |
| import yaml |
| import argparse |
| from einops import rearrange |
| from causalvideovae.model.modules.normalize import Normalize |
| from causalvideovae.model.modules.block import Block |
| import time |
|
|
| def total_params(model): |
| total_params = sum(p.numel() for p in model.parameters()) |
| total_params_in_millions = total_params / 1e6 |
| return total_params_in_millions |
| |
| device = torch.device('cuda') |
| data_type = torch.bfloat16 |
| video_input = torch.randn(1, 3, 33, 256, 256).to(device).to(data_type) |
| image_input = torch.randn(33, 3, 256, 256).to(device).to(data_type) |
| num = 1000 |
|
|
| """ |
| #VQGAN |
| def load_config(config_path, display=False): |
| config = OmegaConf.load(config_path) |
| if display: |
| print(yaml.dump(OmegaConf.to_container(config))) |
| return config |
| |
| def load_vqgan(config, ckpt_path=None, is_gumbel=False): |
| if is_gumbel: |
| model = GumbelVQ(**config.model.params) |
| else: |
| model = VQModel(**config.model.params) |
| if ckpt_path is not None: |
| sd = torch.load(ckpt_path, map_location="cpu")["state_dict"] |
| missing, unexpected = model.load_state_dict(sd, strict=False) |
| return model.eval() |
| |
| vqgan_ckpt='/remote-home1/clh/taming-transformers/logs/vqgan_gumbel_f8/checkpoints/last.ckpt' |
| vqgan_config='/remote-home1/clh/taming-transformers/logs/vqgan_gumbel_f8/configs/model.yaml' |
| vqgan_config = load_config(vqgan_config, display=False) |
| vqgan = load_vqgan(vqgan_config, ckpt_path=vqgan_ckpt, is_gumbel=True).to(device).to(data_type).eval() |
| vqgan.requires_grad_(False) |
| print('VQGAN') |
| print(f"Generator:\t\t{total_params(vqgan) :.2f}M") |
| print(f"\t- Encoder:\t{total_params(vqgan.encoder) :.2f}M") |
| print(f"\t- Decoder:\t{total_params(vqgan.decoder) :.2f}M") |
| # 计算程序运行时间 |
| start_time = time.time() |
| for i in range(num): |
| latents, _, [_, _, indices] = vqgan.encode(image_input) |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| video_recon = vqgan.decode(latents.to(data_type)) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents, _, [_, _, indices] = vqgan.encode(image_input) |
| video_recon = vqgan.decode(latents.to(data_type)) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
| |
| |
| |
| #TATS |
| tats_path = '/remote-home1/clh/TATS/vqgan_sky_128_488_epoch_12-step_29999-train.ckpt' |
| tats = VQGAN.load_from_checkpoint(tats_path).to(device).to(torch.float32).eval() |
| tats.requires_grad_(False) |
| print('TATS') |
| print(f"Generator:\t\t{total_params(tats) :.2f}M") |
| print(f"\t- Encoder:\t{total_params(tats.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(tats.decoder):.2f}M") |
| # 计算程序运行时间 |
| start_time = time.time() |
| for i in range(num): |
| z = tats.pre_vq_conv(tats.encoder(video_input.to(torch.float32))) |
| vq_output = tats.codebook(z) |
| latents = vq_output['embeddings'] |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| video_recon = tats.decoder(tats.post_vq_conv(latents.to(torch.float32))) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| z = tats.pre_vq_conv(tats.encoder(video_input.to(torch.float32))) |
| vq_output = tats.codebook(z) |
| latents = vq_output['embeddings'] |
| video_recon = tats.decoder(tats.post_vq_conv(latents.to(torch.float32))) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
| |
| |
| #SD2_1 |
| sd2_1_path = '/remote-home1/clh/sd2_1' |
| sd2_1 = AutoencoderKL.from_pretrained(sd2_1_path).eval().to(device).to(data_type) |
| sd2_1.requires_grad_(False) |
| print('SD2_1') |
| print(f"Generator:\t\t{total_params(sd2_1) :.2f}M") |
| print(f"\t- Encoder:\t{total_params(sd2_1.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(sd2_1.decoder):.2f}M") |
| # 计算程序运行时间 |
| start_time = time.time() |
| for i in range(num): |
| latents = sd2_1.encode(image_input)['latent_dist'].sample() |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| video_recon = sd2_1.decode(latents.to(data_type))['sample'] |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents = sd2_1.encode(image_input)['latent_dist'].sample() |
| video_recon = sd2_1.decode(latents.to(data_type))['sample'] |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
| |
| #SVD |
| svd_path = '/remote-home1/clh/svd/' |
| svd = AutoencoderKLTemporalDecoder.from_pretrained(svd_path).eval().to(device).to(data_type) |
| svd.requires_grad_(False) |
| print('SVD') |
| print(f"Generator:\t\t{total_params(svd):.2f}M") |
| print(f"\t- Encoder:\t{total_params(svd.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(svd.decoder):.2f}M") |
| # 计算程序运行时间 |
| start_time = time.time() |
| for i in range(num): |
| latents = svd.encode(image_input)['latent_dist'].sample() |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| video_recon = svd.decode(latents.to(data_type), num_frames=video_input.shape[2])['sample'] |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents = svd.encode(image_input)['latent_dist'].sample() |
| video_recon = svd.decode(latents.to(data_type), num_frames=video_input.shape[2])['sample'] |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
| |
| #CV-VAE |
| cvvae_path = '/remote-home1/clh/CV-VAE/vae3d' |
| cvvae = CVVAEModel.from_pretrained(cvvae_path).eval().to(device).to(data_type) |
| cvvae.requires_grad_(False) |
| print('CV-VAE') |
| print(f"Generator:\t\t{total_params(cvvae):.2f}M") |
| print(f"\t- Encoder:\t{total_params(cvvae.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(cvvae.decoder):.2f}M") |
| # 计算程序运行时间 |
| start_time = time.time() |
| for i in range(num): |
| latent = cvvae.encode(video_input).latent_dist.sample() |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| video_recon = cvvae.decode(latent).sample |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| latent = cvvae.encode(video_input).latent_dist.sample() |
| video_recon = cvvae.decode(latent).sample |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
| |
| |
| #NUS-VAE |
| nusvae_path = '/remote-home1/clh/CV-VAE/vae3d' |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--config", type=str, default="/remote-home1/clh/Causal-Video-VAE/opensora/video.py") |
| parser.add_argument("--ckpt", type=str, default="/remote-home1/clh/Open-Sora/OpenSora-VAE-v1.2") |
| args = parser.parse_args() |
| cfg = parse_configs(args, training=False) |
| nusvae = build_module(cfg.model, MODELS).eval().to(device).to(data_type) |
| nusvae.requires_grad_(False) |
| print('NUS-VAE') |
| print(f"Generator:\t\t{total_params(nusvae):.2f}M") |
| print(f"\t- Spatial_Encoder:\t{total_params(nusvae.spatial_vae.module.encoder):.2f}M") |
| print(f"\t- Temporal_Encoder:\t{total_params(nusvae.temporal_vae.encoder):.2f}M") |
| print(f"\t- Temporal_Decoder:\t{total_params(nusvae.temporal_vae.decoder):.2f}M") |
| print(f"\t- Spatial_Decoder:\t{total_params(nusvae.spatial_vae.module.decoder):.2f}M") |
| # 计算程序运行时间 |
| start_time = time.time() |
| for i in range(num): |
| latents, posterior, x_z = nusvae.encode(video_input) |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| video_recon, x_z_rec = nusvae.decode(latents, num_frames=video_input.size(2)) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents, posterior, x_z = nusvae.encode(video_input) |
| video_recon, x_z_rec = nusvae.decode(latents, num_frames=video_input.size(2)) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
| """ |
|
|
| |
| ours1_2_vae_path = '/remote-home1/clh/models/23denc_3ddec_vae_pretrained_weight' |
| ours1_2_vae = CausalVAEModel.from_pretrained(ours1_2_vae_path).eval().to(device).to(data_type) |
| ours1_2_vae.requires_grad_(False) |
| print('open_sora_plan_vae_1_2') |
| print(f"Generator:\t\t{total_params(ours1_2_vae):.2f}M") |
| print(f"\t- Encoder:\t{total_params(ours1_2_vae.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(ours1_2_vae.decoder):.2f}M") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents = ours1_2_vae.encode(video_input).sample().to(data_type) |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| video_recon = ours1_2_vae.decode(latents) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| latents = ours1_2_vae.encode(video_input).sample().to(data_type) |
| video_recon = ours1_2_vae.decode(latents) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
|
|
|
|
| |
| half3d_vae_path = '/remote-home1/clh/models/23d_vae_pretrained_weight' |
| half3d_vae = CausalVAEModel.from_pretrained(half3d_vae_path).eval().to(device).to(data_type) |
| half3d_vae.requires_grad_(False) |
| print('open_sora_plan_vae_half3d') |
| print(f"Generator:\t\t{total_params(half3d_vae):.2f}M") |
| print(f"\t- Encoder:\t{total_params(half3d_vae.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(half3d_vae.decoder):.2f}M") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents = half3d_vae.encode(video_input).sample().to(data_type) |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| video_recon = half3d_vae.decode(latents) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| latents = half3d_vae.encode(video_input).sample().to(data_type) |
| video_recon = half3d_vae.decode(latents) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
|
|
| |
| mix23d_vae_path = '/remote-home1/clh/models/mix23d_vae_pretrained_weight' |
| mix23d_vae = CausalVAEModel.from_pretrained(mix23d_vae_path).eval().to(device).to(data_type) |
| mix23d_vae.requires_grad_(False) |
| print('open_sora_plan_vae_mix23d') |
| print(f"Generator:\t\t{total_params(mix23d_vae):.2f}M") |
| print(f"\t- Encoder:\t{total_params(mix23d_vae.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(mix23d_vae.decoder):.2f}M") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents = mix23d_vae.encode(video_input).sample().to(data_type) |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| video_recon = mix23d_vae.decode(latents) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| latents = mix23d_vae.encode(video_input).sample().to(data_type) |
| video_recon = mix23d_vae.decode(latents) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
|
|
| |
| full3d_vae_path = '/remote-home1/clh/models/full3d_vae_pretrained_weight' |
| full3d_vae = CausalVAEModel.from_pretrained(full3d_vae_path).eval().to(device).to(data_type) |
| full3d_vae.requires_grad_(False) |
| print('open_sora_plan_vae_full3d') |
| print(f"Generator:\t\t{total_params(full3d_vae):.2f}M") |
| print(f"\t- Encoder:\t{total_params(full3d_vae.encoder):.2f}M") |
| print(f"\t- Decoder:\t{total_params(full3d_vae.decoder):.2f}M") |
| |
| start_time = time.time() |
| for i in range(num): |
| latents = full3d_vae.encode(video_input).sample().to(data_type) |
| end_time = time.time() |
| print(f"encode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| video_recon = full3d_vae.decode(latents) |
| end_time = time.time() |
| print(f"decode_time:{(end_time - start_time)/num :.3f}s") |
|
|
| start_time = time.time() |
| for i in range(num): |
| latents = full3d_vae.encode(video_input).sample().to(data_type) |
| video_recon = full3d_vae.decode(latents) |
| end_time = time.time() |
| print(f"rec_time:{(end_time - start_time)/num :.3f}s") |
|
|
|
|
