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from typing import *
import torch
from polygraphy import cuda
from live2diff.animatediff.models.unet_depth_streaming import UNet3DConditionStreamingOutput
from .utilities import Engine
try:
from diffusers.models.autoencoder_tiny import AutoencoderTinyOutput
except ImportError:
from dataclasses import dataclass
from diffusers.utils import BaseOutput
@dataclass
class AutoencoderTinyOutput(BaseOutput):
"""
Output of AutoencoderTiny encoding method.
Args:
latents (`torch.Tensor`): Encoded outputs of the `Encoder`.
"""
latents: torch.Tensor
try:
from diffusers.models.vae import DecoderOutput
except ImportError:
from dataclasses import dataclass
from diffusers.utils import BaseOutput
@dataclass
class DecoderOutput(BaseOutput):
r"""
Output of decoding method.
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model.
"""
sample: torch.FloatTensor
class AutoencoderKLEngine:
def __init__(
self,
encoder_path: str,
decoder_path: str,
stream: cuda.Stream,
scaling_factor: int,
use_cuda_graph: bool = False,
):
self.encoder = Engine(encoder_path)
self.decoder = Engine(decoder_path)
self.stream = stream
self.vae_scale_factor = scaling_factor
self.use_cuda_graph = use_cuda_graph
self.encoder.load()
self.decoder.load()
self.encoder.activate()
self.decoder.activate()
def encode(self, images: torch.Tensor, **kwargs):
self.encoder.allocate_buffers(
shape_dict={
"images": images.shape,
"latent": (
images.shape[0],
4,
images.shape[2] // self.vae_scale_factor,
images.shape[3] // self.vae_scale_factor,
),
},
device=images.device,
)
latents = self.encoder.infer(
{"images": images},
self.stream,
use_cuda_graph=self.use_cuda_graph,
)["latent"]
return AutoencoderTinyOutput(latents=latents)
def decode(self, latent: torch.Tensor, **kwargs):
self.decoder.allocate_buffers(
shape_dict={
"latent": latent.shape,
"images": (
latent.shape[0],
3,
latent.shape[2] * self.vae_scale_factor,
latent.shape[3] * self.vae_scale_factor,
),
},
device=latent.device,
)
images = self.decoder.infer(
{"latent": latent},
self.stream,
use_cuda_graph=self.use_cuda_graph,
)["images"]
return DecoderOutput(sample=images)
def to(self, *args, **kwargs):
pass
def forward(self, *args, **kwargs):
pass
class UNet2DConditionModelDepthEngine:
def __init__(self, filepath: str, stream: cuda.Stream, use_cuda_graph: bool = False):
self.engine = Engine(filepath)
self.stream = stream
self.use_cuda_graph = use_cuda_graph
self.init_profiler()
self.engine.load()
self.engine.activate(profiler=self.profiler)
self.has_allocated = False
def init_profiler(self):
import tensorrt
class Profiler(tensorrt.IProfiler):
def __init__(self):
tensorrt.IProfiler.__init__(self)
def report_layer_time(self, layer_name, ms):
print(f"{layer_name}: {ms} ms")
self.profiler = Profiler()
def __call__(
self,
latent_model_input: torch.Tensor,
timestep: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temporal_attention_mask: torch.Tensor,
depth_sample: torch.Tensor,
kv_cache: List[torch.Tensor],
pe_idx: torch.Tensor,
update_idx: torch.Tensor,
**kwargs,
) -> Any:
if timestep.dtype != torch.float32:
timestep = timestep.float()
feed_dict = {
"sample": latent_model_input,
"timestep": timestep,
"encoder_hidden_states": encoder_hidden_states,
"temporal_attention_mask": temporal_attention_mask,
"depth_sample": depth_sample,
"pe_idx": pe_idx,
"update_idx": update_idx,
}
for idx, cache in enumerate(kv_cache):
feed_dict[f"kv_cache_{idx}"] = cache
shape_dict = {k: v.shape for k, v in feed_dict.items()}
if not self.has_allocated:
self.engine.allocate_buffers(
shape_dict=shape_dict,
device=latent_model_input.device,
)
self.has_allocated = True
output = self.engine.infer(
feed_dict,
self.stream,
use_cuda_graph=self.use_cuda_graph,
)
noise_pred = output["latent"]
kv_cache = [output[f"kv_cache_out_{idx}"] for idx in range(len(kv_cache))]
return UNet3DConditionStreamingOutput(sample=noise_pred, kv_cache=kv_cache)
def to(self, *args, **kwargs):
pass
def forward(self, *args, **kwargs):
pass
class MidasEngine:
def __init__(self, filepath: str, stream: cuda.Stream, use_cuda_graph: bool = False):
self.engine = Engine(filepath)
self.stream = stream
self.use_cuda_graph = use_cuda_graph
self.engine.load()
self.engine.activate()
self.has_allocated = False
self.default_batch_size = 1
def __call__(
self,
images: torch.Tensor,
**kwargs,
) -> Any:
if not self.has_allocated or images.shape[0] != self.default_batch_size:
bz = images.shape[0]
self.engine.allocate_buffers(
shape_dict={
"images": (bz, 3, 384, 384),
"depth_map": (bz, 384, 384),
},
device=images.device,
)
self.has_allocated = True
self.default_batch_size = bz
depth_map = self.engine.infer(
{
"images": images,
},
self.stream,
use_cuda_graph=self.use_cuda_graph,
)["depth_map"] # (1, 384, 384)
return depth_map
def norm(self, x):
return (x - x.min()) / (x.max() - x.min())
def to(self, *args, **kwargs):
pass
def forward(self, *args, **kwargs):
pass
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