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import gc
import os
import traceback
from pathlib import Path
from typing import Dict, List, Literal, Optional, Union
import numpy as np
import torch
from diffusers import AutoencoderTiny
from PIL import Image
from live2diff import StreamAnimateDiffusionDepth
from live2diff.image_utils import postprocess_image
from live2diff.pipeline_stream_animation_depth import WARMUP_FRAMES
class StreamAnimateDiffusionDepthWrapper:
def __init__(
self,
config_path: str,
few_step_model_type: str,
num_inference_steps: int,
t_index_list: Optional[List[int]] = None,
strength: Optional[float] = None,
dreambooth_path: Optional[str] = None,
lora_dict: Optional[Dict[str, float]] = None,
output_type: Literal["pil", "pt", "np", "latent"] = "pil",
vae_id: Optional[str] = None,
device: Literal["cpu", "cuda"] = "cuda",
dtype: torch.dtype = torch.float16,
frame_buffer_size: int = 1,
width: int = 512,
height: int = 512,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
do_add_noise: bool = True,
device_ids: Optional[List[int]] = None,
use_tiny_vae: bool = True,
enable_similar_image_filter: bool = False,
similar_image_filter_threshold: float = 0.98,
similar_image_filter_max_skip_frame: int = 10,
use_denoising_batch: bool = True,
cfg_type: Literal["none", "full", "self", "initialize"] = "self",
seed: int = 42,
engine_dir: Optional[Union[str, Path]] = "engines",
opt_unet: bool = False,
):
"""
Initializes the StreamAnimateDiffusionWrapper.
Parameters
----------
config_path : str
The model id or path to load.
few_step_model_type : str
The few step model type to use.
num_inference_steps : int
The number of inference steps to perform. If `t_index_list`
is passed, `num_infernce_steps` will parsed as the number
of denoising steps before apply few-step lora. Otherwise,
`num_inference_steps` will be parsed as the number of
steps after applying few-step lora.
t_index_list : List[int]
The t_index_list to use for inference.
strength : Optional[float]
The strength to use for inference.
dreambooth_path : Optional[str]
The dreambooth path to use for inference. If not passed,
will use dreambooth from config.
lora_dict : Optional[Dict[str, float]], optional
The lora_dict to load, by default None.
Keys are the LoRA names and values are the LoRA scales.
Example: {'LoRA_1' : 0.5 , 'LoRA_2' : 0.7 ,...}
output_type : Literal["pil", "pt", "np", "latent"], optional
The output type of image, by default "pil".
vae_id : Optional[str], optional
The vae_id to load, by default None.
If None, the default TinyVAE
("madebyollin/taesd") will be used.
device : Literal["cpu", "cuda"], optional
The device to use for inference, by default "cuda".
dtype : torch.dtype, optional
The dtype for inference, by default torch.float16.
frame_buffer_size : int, optional
The frame buffer size for denoising batch, by default 1.
width : int, optional
The width of the image, by default 512.
height : int, optional
The height of the image, by default 512.
acceleration : Literal["none", "xformers", "tensorrt"], optional
The acceleration method, by default "tensorrt".
do_add_noise : bool, optional
Whether to add noise for following denoising steps or not,
by default True.
device_ids : Optional[List[int]], optional
The device ids to use for DataParallel, by default None.
use_lcm_lora : bool, optional
Whether to use LCM-LoRA or not, by default True.
use_tiny_vae : bool, optional
Whether to use TinyVAE or not, by default True.
enable_similar_image_filter : bool, optional
Whether to enable similar image filter or not,
by default False.
similar_image_filter_threshold : float, optional
The threshold for similar image filter, by default 0.98.
similar_image_filter_max_skip_frame : int, optional
The max skip frame for similar image filter, by default 10.
use_denoising_batch : bool, optional
Whether to use denoising batch or not, by default True.
cfg_type : Literal["none", "full", "self", "initialize"],
optional
The cfg_type for img2img mode, by default "self".
You cannot use anything other than "none" for txt2img mode.
seed : int, optional
The seed, by default 42.
engine_dir : Optional[Union[str, Path]], optional
The directory to save TensorRT engines, by default "engines".
opt_unet : bool, optional
Whether to optimize UNet or not, by default False.
"""
self.sd_turbo = False
self.device = device
self.dtype = dtype
self.width = width
self.height = height
self.output_type = output_type
self.frame_buffer_size = frame_buffer_size
self.use_denoising_batch = use_denoising_batch
self.stream: StreamAnimateDiffusionDepth = self._load_model(
config_path=config_path,
lora_dict=lora_dict,
dreambooth_path=dreambooth_path,
few_step_model_type=few_step_model_type,
vae_id=vae_id,
num_inference_steps=num_inference_steps,
t_index_list=t_index_list,
strength=strength,
height=height,
width=width,
acceleration=acceleration,
do_add_noise=do_add_noise,
use_tiny_vae=use_tiny_vae,
cfg_type=cfg_type,
seed=seed,
engine_dir=engine_dir,
opt_unet=opt_unet,
)
self.batch_size = len(self.stream.t_list) * frame_buffer_size if use_denoising_batch else frame_buffer_size
if device_ids is not None:
self.stream.unet = torch.nn.DataParallel(self.stream.unet, device_ids=device_ids)
if enable_similar_image_filter:
self.stream.enable_similar_image_filter(
similar_image_filter_threshold, similar_image_filter_max_skip_frame
)
def prepare(
self,
warmup_frames: torch.Tensor,
prompt: str,
negative_prompt: str = "",
guidance_scale: float = 1.2,
delta: float = 1.0,
) -> torch.Tensor:
"""
Prepares the model for inference.
Parameters
----------
prompt : str
The prompt to generate images from.
num_inference_steps : int, optional
The number of inference steps to perform, by default 50.
guidance_scale : float, optional
The guidance scale to use, by default 1.2.
delta : float, optional
The delta multiplier of virtual residual noise,
by default 1.0.
Returns
----------
warmup_frames : torch.Tensor
generated warmup-frames.
"""
warmup_frames = self.stream.prepare(
warmup_frames=warmup_frames,
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
delta=delta,
)
warmup_frames = warmup_frames.permute(0, 2, 3, 1)
warmup_frames = (warmup_frames.clip(-1, 1) + 1) / 2
return warmup_frames
def __call__(
self,
image: Optional[Union[str, Image.Image, torch.Tensor]] = None,
prompt: Optional[str] = None,
) -> Union[Image.Image, List[Image.Image]]:
"""
Performs img2img or txt2img based on the mode.
Parameters
----------
image : Optional[Union[str, Image.Image, torch.Tensor]]
The image to generate from.
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
Union[Image.Image, List[Image.Image]]
The generated image.
"""
return self.img2img(image, prompt)
def img2img(
self, image: Union[str, Image.Image, torch.Tensor], prompt: Optional[str] = None
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Performs img2img.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to generate from.
Returns
-------
Image.Image
The generated image.
"""
if prompt is not None:
self.stream.update_prompt(prompt)
if isinstance(image, str) or isinstance(image, Image.Image):
image = self.preprocess_image(image)
image_tensor = self.stream(image)
image = self.postprocess_image(image_tensor, output_type=self.output_type)
return image
def preprocess_image(self, image: Union[str, Image.Image]) -> torch.Tensor:
"""
Preprocesses the image.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to preprocess.
Returns
-------
torch.Tensor
The preprocessed image.
"""
if isinstance(image, str):
image = Image.open(image).convert("RGB").resize((self.width, self.height))
if isinstance(image, Image.Image):
image = image.convert("RGB").resize((self.width, self.height))
return self.stream.image_processor.preprocess(image, self.height, self.width).to(
device=self.device, dtype=self.dtype
)
def postprocess_image(
self, image_tensor: torch.Tensor, output_type: str = "pil"
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Postprocesses the image.
Parameters
----------
image_tensor : torch.Tensor
The image tensor to postprocess.
Returns
-------
Union[Image.Image, List[Image.Image]]
The postprocessed image.
"""
if self.frame_buffer_size > 1:
output = postprocess_image(image_tensor, output_type=output_type)
else:
output = postprocess_image(image_tensor, output_type=output_type)[0]
if output_type not in ["pil", "np"]:
return output.cpu()
else:
return output
@staticmethod
def get_model_prefix(
config_path: str,
few_step_model_type: str,
use_tiny_vae: bool,
num_denoising_steps: int,
height: int,
width: int,
dreambooth: Optional[str] = None,
lora_dict: Optional[dict] = None,
) -> str:
from omegaconf import OmegaConf
config = OmegaConf.load(config_path)
third_party = config.third_party_dict
dreambooth_path = dreambooth or third_party.dreambooth
if dreambooth_path is None:
dreambooth_name = "sd15"
else:
dreambooth_name = Path(dreambooth_path).stem
base_lora_list = third_party.get("lora_list", [])
lora_dict = lora_dict or {}
for lora_alpha in base_lora_list:
lora_name = lora_alpha["lora"]
alpha = lora_alpha["lora_alpha"]
if lora_name not in lora_dict:
lora_dict[lora_name] = alpha
prefix = f"{dreambooth_name}--{few_step_model_type}--step{num_denoising_steps}--"
for k, v in lora_dict.items():
prefix += f"{Path(k).stem}-{v}--"
prefix += f"tiny_vae-{use_tiny_vae}--h-{height}--w-{width}"
return prefix
def _load_model(
self,
config_path: str,
num_inference_steps: int,
height: int,
width: int,
t_index_list: Optional[List[int]] = None,
strength: Optional[float] = None,
dreambooth_path: Optional[str] = None,
lora_dict: Optional[Dict[str, float]] = None,
vae_id: Optional[str] = None,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
do_add_noise: bool = True,
few_step_model_type: Optional[str] = None,
use_tiny_vae: bool = True,
cfg_type: Literal["none", "full", "self", "initialize"] = "self",
seed: int = 2,
engine_dir: Optional[Union[str, Path]] = "engines",
opt_unet: bool = False,
) -> StreamAnimateDiffusionDepth:
"""
Loads the model.
This method does the following:
1. Loads the model from the model_id_or_path.
3. Loads the VAE model from the vae_id if needed.
4. Enables acceleration if needed.
6. Load the safety checker if needed.
Parameters
----------
config_path : str
The path to config, all needed checkpoints are list in config file.
t_index_list : List[int]
The t_index_list to use for inference.
dreambooth_path : Optional[str]
The dreambooth path to use for inference. If not passed,
will use dreambooth from config.
lora_dict : Optional[Dict[str, float]], optional
The lora_dict to load, by default None.
Keys are the LoRA names and values are the LoRA scales.
Example: {'LoRA_1' : 0.5 , 'LoRA_2' : 0.7 ,...}
vae_id : Optional[str], optional
The vae_id to load, by default None.
acceleration : Literal["none", "xfomers", "sfast", "tensorrt"], optional
The acceleration method, by default "tensorrt".
warmup : int, optional
The number of warmup steps to perform, by default 10.
do_add_noise : bool, optional
Whether to add noise for following denoising steps or not,
by default True.
use_lcm_lora : bool, optional
Whether to use LCM-LoRA or not, by default True.
use_tiny_vae : bool, optional
Whether to use TinyVAE or not, by default True.
cfg_type : Literal["none", "full", "self", "initialize"],
optional
The cfg_type for img2img mode, by default "self".
You cannot use anything other than "none" for txt2img mode.
seed : int, optional
The seed, by default 2.
opt_unet : bool, optional
Whether to optimize UNet or not, by default False.
Returns
-------
AnimatePipeline
The loaded pipeline.
"""
supported_few_step_model = ["LCM"]
assert (
few_step_model_type.upper() in supported_few_step_model
), f"Only support few_step_model: {supported_few_step_model}, but receive {few_step_model_type}."
# NOTE: build animatediff pipeline
from live2diff.animatediff.pipeline import AnimationDepthPipeline
try:
pipe = AnimationDepthPipeline.build_pipeline(
config_path,
).to(device=self.device, dtype=self.dtype)
except Exception: # No model found
traceback.print_exc()
print("Model load has failed. Doesn't exist.")
exit()
if few_step_model_type.upper() == "LCM":
few_step_lora = "latent-consistency/lcm-lora-sdv1-5"
stream_pipeline_cls = StreamAnimateDiffusionDepth
print(f"Pipeline class: {stream_pipeline_cls}")
print(f"Few-step LoRA: {few_step_lora}")
# parse clip skip from config
from .config import load_config
cfg = load_config(config_path)
third_party_dict = cfg.third_party_dict
clip_skip = third_party_dict.get("clip_skip", 1)
stream = stream_pipeline_cls(
pipe=pipe,
num_inference_steps=num_inference_steps,
t_index_list=t_index_list,
strength=strength,
torch_dtype=self.dtype,
width=self.width,
height=self.height,
do_add_noise=do_add_noise,
frame_buffer_size=self.frame_buffer_size,
use_denoising_batch=self.use_denoising_batch,
cfg_type=cfg_type,
clip_skip=clip_skip,
)
stream.load_warmup_unet(config_path)
stream.load_lora(few_step_lora)
stream.fuse_lora()
denoising_steps_num = len(stream.t_list)
stream.prepare_cache(
height=height,
width=width,
denoising_steps_num=denoising_steps_num,
)
kv_cache_list = stream.kv_cache_list
if lora_dict is not None:
for lora_name, lora_scale in lora_dict.items():
stream.load_lora(lora_name)
stream.fuse_lora(lora_scale=lora_scale)
print(f"Use LoRA: {lora_name} in weights {lora_scale}")
if use_tiny_vae:
vae_id = "madebyollin/taesd" if vae_id is None else vae_id
stream.vae = AutoencoderTiny.from_pretrained(vae_id).to(device=pipe.device, dtype=pipe.dtype)
try:
if acceleration == "none":
stream.pipe.unet = torch.compile(stream.pipe.unet, options={"triton.cudagraphs": True}, fullgraph=True)
stream.vae = torch.compile(stream.vae, options={"triton.cudagraphs": True}, fullgraph=True)
if acceleration == "xformers":
stream.pipe.enable_xformers_memory_efficient_attention()
if acceleration == "tensorrt":
from polygraphy import cuda
from live2diff.acceleration.tensorrt import (
TorchVAEEncoder,
compile_engine,
)
from live2diff.acceleration.tensorrt.engine import (
AutoencoderKLEngine,
MidasEngine,
UNet2DConditionModelDepthEngine,
)
from live2diff.acceleration.tensorrt.models import (
VAE,
InflatedUNetDepth,
Midas,
VAEEncoder,
)
prefix = self.get_model_prefix(
config_path=config_path,
few_step_model_type=few_step_model_type,
use_tiny_vae=use_tiny_vae,
num_denoising_steps=denoising_steps_num,
height=height,
width=width,
dreambooth=dreambooth_path,
lora_dict=lora_dict,
)
engine_dir = os.path.join(Path(engine_dir), prefix)
unet_path = os.path.join(engine_dir, "unet", "unet.engine")
unet_opt_path = os.path.join(engine_dir, "unet-opt", "unet.engine.opt")
midas_path = os.path.join(engine_dir, "depth", "midas.engine")
vae_encoder_path = os.path.join(engine_dir, "vae", "vae_encoder.engine")
vae_decoder_path = os.path.join(engine_dir, "vae", "vae_decoder.engine")
if not os.path.exists(unet_path):
os.makedirs(os.path.dirname(unet_path), exist_ok=True)
os.makedirs(os.path.dirname(unet_opt_path), exist_ok=True)
unet_model = InflatedUNetDepth(
fp16=True,
device=stream.device,
max_batch_size=stream.trt_unet_batch_size,
min_batch_size=stream.trt_unet_batch_size,
embedding_dim=stream.text_encoder.config.hidden_size,
unet_dim=stream.unet.config.in_channels,
kv_cache_list=kv_cache_list,
)
compile_engine(
torch_model=stream.unet,
model_data=unet_model,
onnx_path=unet_path + ".onnx",
onnx_opt_path=unet_opt_path, # use specific folder for external data
engine_path=unet_path,
opt_image_height=height,
opt_image_width=width,
opt_batch_size=stream.trt_unet_batch_size,
engine_build_options={"ignore_onnx_optimize": not opt_unet},
)
if not os.path.exists(vae_decoder_path):
os.makedirs(os.path.dirname(vae_decoder_path), exist_ok=True)
stream.vae.forward = stream.vae.decode
max_bz = WARMUP_FRAMES
opt_bz = min_bz = 1
vae_decoder_model = VAE(
device=stream.device,
max_batch_size=max_bz,
min_batch_size=min_bz,
)
compile_engine(
torch_model=stream.vae,
model_data=vae_decoder_model,
onnx_path=vae_decoder_path + ".onnx",
onnx_opt_path=vae_decoder_path + ".opt.onnx",
engine_path=vae_decoder_path,
opt_image_height=height,
opt_image_width=width,
opt_batch_size=opt_bz,
)
delattr(stream.vae, "forward")
if not os.path.exists(midas_path):
os.makedirs(os.path.dirname(midas_path), exist_ok=True)
max_bz = WARMUP_FRAMES
opt_bz = min_bz = 1
midas = Midas(
fp16=True,
device=stream.device,
max_batch_size=max_bz,
min_batch_size=min_bz,
)
compile_engine(
torch_model=stream.depth_detector.half(),
model_data=midas,
onnx_path=midas_path + ".onnx",
onnx_opt_path=midas_path + ".opt.onnx",
engine_path=midas_path,
opt_batch_size=opt_bz,
opt_image_height=384,
opt_image_width=384,
engine_build_options={
"auto_cast": False,
"handle_batch_norm": True,
"ignore_onnx_optimize": True,
},
)
if not os.path.exists(vae_encoder_path):
os.makedirs(os.path.dirname(vae_encoder_path), exist_ok=True)
vae_encoder = TorchVAEEncoder(stream.vae).to(torch.device("cuda"))
max_bz = WARMUP_FRAMES
opt_bz = min_bz = 1
vae_encoder_model = VAEEncoder(
device=stream.device,
max_batch_size=max_bz,
min_batch_size=min_bz,
)
compile_engine(
torch_model=vae_encoder,
model_data=vae_encoder_model,
onnx_path=vae_encoder_path + ".onnx",
onnx_opt_path=vae_encoder_path + ".opt.onnx",
engine_path=vae_encoder_path,
opt_batch_size=opt_bz,
opt_image_height=height,
opt_image_width=width,
)
cuda_stream = cuda.Stream()
vae_config = stream.vae.config
vae_dtype = stream.vae.dtype
midas_dtype = stream.depth_detector.dtype
stream.unet = UNet2DConditionModelDepthEngine(unet_path, cuda_stream, use_cuda_graph=False)
stream.depth_detector = MidasEngine(midas_path, cuda_stream, use_cuda_graph=False)
setattr(stream.depth_detector, "dtype", midas_dtype)
stream.vae = AutoencoderKLEngine(
vae_encoder_path,
vae_decoder_path,
cuda_stream,
stream.pipe.vae_scale_factor,
use_cuda_graph=False,
)
setattr(stream.vae, "config", vae_config)
setattr(stream.vae, "dtype", vae_dtype)
stream.is_tensorrt = True
gc.collect()
torch.cuda.empty_cache()
print("TensorRT acceleration enabled.")
except Exception:
traceback.print_exc()
print("Acceleration has failed. Falling back to normal mode.")
if seed < 0: # Random seed
seed = np.random.randint(0, 1000000)
return stream
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