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# Adapted from https://github.com/luosiallen/latent-consistency-model
from __future__ import annotations
import os
import random
import numpy as np
from .pipeline.t2v_turbo_ms_pipeline import T2VTurboMSPipeline
from .scheduler.t2v_turbo_scheduler import T2VTurboScheduler
from .utils.common_utils import set_torch_2_attn
try:
import intel_extension_for_pytorch as ipex
except:
pass
from transformers import CLIPTokenizer, CLIPTextModel
from .model_scope.unet_3d_condition import UNet3DConditionModel
from .utils.lora import collapse_lora, monkeypatch_remove_lora
from .utils.lora_handler import LoraHandler
import torch
from diffusers.models import AutoencoderKL
DESCRIPTION = """# T2V-Turbo 🚀
We provide T2V-Turbo (MS) distilled from [ModelScopeT2V](https://huggingface.co/ali-vilab/text-to-video-ms-1.7b/) with the reward feedback from [HPSv2.1](https://github.com/tgxs002/HPSv2/tree/master) and [ViCLIP](https://huggingface.co/OpenGVLab/ViCLIP).
You can download the the models from [here](https://huggingface.co/jiachenli-ucsb/T2V-Turbo-MS). Check out our [Project page](https://t2v-turbo.github.io) 😄
"""
if torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CUDA 😀</p>"
elif hasattr(torch, "xpu") and torch.xpu.is_available():
DESCRIPTION += "\n<p>Running on XPU 🤓</p>"
else:
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
"""
Operation System Options:
If you are using MacOS, please set the following (device="mps") ;
If you are using Linux & Windows with Nvidia GPU, please set the device="cuda";
If you are using Linux & Windows with Intel Arc GPU, please set the device="xpu";
"""
# device = "mps" # MacOS
# device = "xpu" # Intel Arc GPU
device = "cuda" # Linux & Windows
"""
DTYPE Options:
To reduce GPU memory you can set "DTYPE=torch.float16",
but image quality might be compromised
"""
DTYPE = (
torch.float16
) # torch.float16 works as well, but pictures seem to be a bit worse
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
class T2VTurboMSPipeline1:
def __init__(self, device, unet_dir, base_model_dir):
pretrained_model_path = base_model_dir
tokenizer = CLIPTokenizer.from_pretrained(
pretrained_model_path, subfolder="tokenizer"
)
text_encoder = CLIPTextModel.from_pretrained(
pretrained_model_path, subfolder="text_encoder"
)
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
teacher_unet = UNet3DConditionModel.from_pretrained(
pretrained_model_path, subfolder="unet"
)
time_cond_proj_dim = 256
unet = UNet3DConditionModel.from_config(
teacher_unet.config, time_cond_proj_dim=time_cond_proj_dim
)
# load teacher_unet weights into unet
unet.load_state_dict(teacher_unet.state_dict(), strict=False)
del teacher_unet
set_torch_2_attn(unet)
use_unet_lora = True
lora_manager = LoraHandler(
version="cloneofsimo",
use_unet_lora=use_unet_lora,
save_for_webui=True,
)
lora_manager.add_lora_to_model(
use_unet_lora,
unet,
lora_manager.unet_replace_modules,
lora_path=unet_dir,
dropout=0.1,
r=32,
)
collapse_lora(unet, lora_manager.unet_replace_modules)
monkeypatch_remove_lora(unet)
unet.eval()
noise_scheduler = T2VTurboScheduler()
self.pipeline = T2VTurboMSPipeline(
unet=unet,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=noise_scheduler,
)
self.pipeline.to(device)
def inference(
self,
prompt: str,
height: int = 320,
width: int = 512,
seed: int = 0,
guidance_scale: float = 7.5,
num_inference_steps: int = 4,
num_frames: int = 16,
fps: int = 16,
randomize_seed: bool = False,
param_dtype="torch.float16"
):
seed = randomize_seed_fn(seed, randomize_seed)
torch.manual_seed(seed)
self.pipeline.to(
torch_device=device,
torch_dtype=torch.float16 if param_dtype == "torch.float16" else torch.float32,
)
result = self.pipeline(
prompt=prompt,
height=height,
width=width,
frames=num_frames,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_videos_per_prompt=1,
)
return result
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