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# General
import sys
from pathlib import Path
import torch
from pytorch_lightning import LightningDataModule
# For Stage-1
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers import StableVideoDiffusionPipeline, AutoPipelineForText2Image
# For Stage-2
import tempfile
import yaml
from videogen_hub.pipelines.streamingt2v.model.video_ldm import VideoLDM
from videogen_hub.pipelines.streamingt2v.model.callbacks import SaveConfigCallback
from videogen_hub.pipelines.streamingt2v.inference_utils import (
legacy_transformation,
remove_value,
CustomCLI,
v2v_to_device,
)
# For Stage-3
import sys
sys.path.append(Path(__file__).parent / "thirdparty")
# Initialize Stage-1 model1.
def init_modelscope(device="cuda"):
pipe = DiffusionPipeline.from_pretrained(
"damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"
)
# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
# pipe.set_progress_bar_config(disable=True)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
pipe.set_progress_bar_config(disable=True)
return pipe.to(device)
def init_zeroscope(device="cuda"):
pipe = DiffusionPipeline.from_pretrained(
"cerspense/zeroscope_v2_576w", torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
return pipe.to(device)
def init_animatediff(device="cuda"):
adapter = MotionAdapter.from_pretrained(
"guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16
)
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(
model_id, motion_adapter=adapter, torch_dtype=torch.float16
)
scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
pipe.scheduler = scheduler
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()
return pipe.to(device)
def init_sdxl(device="cuda"):
pipe = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
# pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
return pipe.to(device)
def init_svd(device="cuda"):
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt",
torch_dtype=torch.float16,
variant="fp16",
)
pipe.enable_model_cpu_offload()
return pipe.to(device)
# Initialize StreamingT2V model.
def init_streamingt2v_model(ckpt_file, result_fol, device):
accelerator = "gpu" if device.startswith("cuda") else "cpu"
import os
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# print("base dir", base_dir)
config_file = f"{base_dir}/streamingt2v/configs/text_to_video/config.yaml"
print("config dir", config_file)
sys.argv = sys.argv[:1]
with tempfile.TemporaryDirectory() as tmpdirname:
storage_fol = Path(tmpdirname)
with open(config_file, "r") as yaml_handle:
yaml_obj = yaml.safe_load(yaml_handle)
yaml_obj_orig_data_cfg = legacy_transformation(yaml_obj)
yaml_obj_orig_data_cfg = remove_value(yaml_obj_orig_data_cfg, "video_dataset")
with open(storage_fol / "config.yaml", "w") as outfile:
yaml.dump(yaml_obj_orig_data_cfg, outfile, default_flow_style=False)
sys.argv.append("--config")
sys.argv.append((storage_fol / "config.yaml").as_posix())
sys.argv.append("--ckpt")
sys.argv.append(ckpt_file.as_posix())
sys.argv.append("--result_fol")
sys.argv.append(result_fol.as_posix())
sys.argv.append("--config")
sys.argv.append("configs/inference/inference_long_video.yaml")
sys.argv.append("--data.prompt_cfg.type=prompt")
sys.argv.append(f"--data.prompt_cfg.content='test prompt for initialization'")
sys.argv.append(f"--trainer.accelerator={accelerator}")
sys.argv.append("--trainer.devices=1")
sys.argv.append("--trainer.num_nodes=1")
sys.argv.append(f"--model.inference_params.num_inference_steps=50")
sys.argv.append(f"--model.inference_params.n_autoregressive_generations=4")
sys.argv.append("--model.inference_params.concat_video=True")
sys.argv.append("--model.inference_params.result_formats=[eval_mp4]")
cli = CustomCLI(
VideoLDM,
LightningDataModule,
run=False,
subclass_mode_data=True,
auto_configure_optimizers=False,
parser_kwargs={"parser_mode": "omegaconf"},
save_config_callback=SaveConfigCallback,
save_config_kwargs={"log_dir": result_fol, "overwrite": True},
)
model = cli.model
model.load_state_dict(
torch.load(cli.config["ckpt"].as_posix(), map_location=torch.device("cpu"))[
"state_dict"
]
)
return cli, model
# Initialize Stage-3 model.
def init_v2v_model(cfg, device):
from modelscope.pipelines import pipeline
model_id = cfg["model_id"]
pipe_enhance = pipeline(
task="video-to-video", model=model_id, model_revision="v1.1.0", device="cpu"
)
pipe_enhance.model.cfg.max_frames = 10000
pipe_enhance = v2v_to_device(pipe_enhance, device)
return pipe_enhance
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