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import torch
from omegaconf import DictConfig
from einops import rearrange
from pipeline import (
CausalInferencePipeline,
)
from utils.misc import set_seed
from demo_utils.memory import gpu
device = torch.device("cuda")
def get_video(video):
video = rearrange(video, "b t c h w -> b t h w c").cpu()
video = 255.0 * video
assert video.shape[0] == 1
video = video[0]
return video
class StreamInferenceWrapper:
def __init__(
self,
stream_model_config: DictConfig,
checkpoint_path: str,
total_generate_block_number: int,
use_ema: bool = True,
seed: int = 0,
):
torch.set_grad_enabled(False)
# Initialize pipeline
assert hasattr(stream_model_config, "denoising_step_list")
self.pipeline = CausalInferencePipeline(
stream_model_config,
device=device,
)
state_dict = torch.load(checkpoint_path, map_location="cpu")
self.pipeline.generator.load_state_dict(
state_dict[("generator" if not use_ema else "generator_ema")]
)
self.pipeline = self.pipeline.to(dtype=torch.bfloat16)
self.pipeline.text_encoder.to(device=gpu)
self.pipeline.generator.to(device=gpu)
if not stream_model_config.vae_offload_cpu:
self.pipeline.vae.to(device=gpu)
else:
self.pipeline.vae.to(device="cpu") # Offload VAE to CPU
set_seed(seed)
self.seed = seed
self.initial_noise = torch.randn(
[
1,
total_generate_block_number * self.pipeline.num_frame_per_block,
16,
60,
104,
],
device=device,
dtype=torch.bfloat16,
)
self.recorded_latents = None
self.video = None
self.stream_model_config = stream_model_config
print(f"""
{self.__class__.__name__}.__init__():
{self.initial_noise.shape = }
{self.stream_model_config = }
""")
def block_to_latent_index(self, block_index: int) -> int:
return block_index * self.pipeline.num_frame_per_block
def latent_to_video_index(self, latent_frame_index: int) -> int:
if latent_frame_index <= 0:
return 0
return (latent_frame_index - 1) * 4 + 1
def block_to_video_index(self, block_index: int) -> int:
return self.latent_to_video_index(self.block_to_latent_index(block_index))
def get_sampled_noise(
self,
start_block_index: int,
end_block_index: int,
):
current_start_latent_frame_index = self.block_to_latent_index(start_block_index)
current_end_latent_frame_index = self.block_to_latent_index(end_block_index)
print(f"{current_start_latent_frame_index = } | {current_end_latent_frame_index = }")
assert current_start_latent_frame_index < self.initial_noise.shape[1]
assert current_end_latent_frame_index <= self.initial_noise.shape[1]
sampled_noise = self.initial_noise[
:,
current_start_latent_frame_index:current_end_latent_frame_index,
...,
]
return sampled_noise
def get_initial_latents(
self,
start_block_index: int,
):
if self.recorded_latents is None:
return None
print(f"{start_block_index = }")
return self.recorded_latents[
:,
: self.block_to_latent_index(start_block_index),
]
def decode_to_pixel(
self,
latents: torch.Tensor,
):
start_decode_time = time.time()
# Move VAE to GPU if offloaded
if self.stream_model_config.vae_offload_cpu:
self.pipeline.vae.to(device=gpu)
video = self.pipeline.vae.decode_to_pixel(latents, use_cache=False)
# Optionally move VAE back to CPU after decoding
if self.stream_model_config.vae_offload_cpu:
self.pipeline.vae.to(device="cpu")
video = (video * 0.5 + 0.5).clamp(0, 1)
print(
f"{self.__class__.__name__}.decode_to_pixel() VAE decode time: {time.time() - start_decode_time:.2f} seconds"
)
return video
def update_video(
self,
video: torch.Tensor,
start_latent_frame_index: int,
):
video = get_video(video) # t, h, w, c
start_video_frame_index = self.latent_to_video_index(start_latent_frame_index)
if self.video is None:
self.video = video
else:
self.video = self.video[:start_video_frame_index]
self.video = torch.cat([self.video, video], dim=0)
def decode_and_update_video(
self,
start_block_index: int,
end_block_index: int,
):
if start_block_index == 0:
current_chunk_latent = self.recorded_latents[
:,
self.block_to_latent_index(start_block_index) : self.block_to_latent_index(
end_block_index
),
]
current_chunk_video = self.decode_to_pixel(current_chunk_latent)
else:
current_chunk_latent = self.recorded_latents[
:,
self.block_to_latent_index(start_block_index - 1) : self.block_to_latent_index(
end_block_index
),
]
current_chunk_video = self.decode_to_pixel(current_chunk_latent)
current_chunk_video = current_chunk_video[:, 9:]
self.update_video(current_chunk_video, self.block_to_latent_index(start_block_index))
def inference(
self,
start_block_index: int,
end_block_index: int,
prompt: str,
):
assert start_block_index >= 0
assert end_block_index > start_block_index
print(f"""
{self.__class__.__name__}.inference():
{start_block_index = } | {end_block_index = }
""")
sampled_noise = self.get_sampled_noise(start_block_index, end_block_index)
prompts = [prompt]
initial_latents = self.get_initial_latents(
start_block_index,
)
if initial_latents is not None:
print(f"{initial_latents.shape = }")
latents_result = self.pipeline.inference(
noise=sampled_noise,
text_prompts=prompts,
return_latents=True,
initial_latent=initial_latents,
do_not_decode_video=True,
do_not_recompute_initial_latents=True,
)
latents = latents_result
print(f"{latents.shape = }")
if self.recorded_latents is None:
self.recorded_latents = latents
else:
self.recorded_latents = torch.concat(
[
self.recorded_latents[:, :0],
latents,
],
dim=1,
)
print(f"{self.recorded_latents.shape = }")
self.decode_and_update_video(start_block_index, end_block_index)
return (
self.video,
self.video[self.block_to_video_index(start_block_index) :],
)
def reset(
self,
):
self.recorded_latents = None
self.video = None
# Clear VAE cache
self.pipeline.vae.model.clear_cache()
# Optionally move VAE back to CPU after reset if offloading
if self.stream_model_config.vae_offload_cpu:
self.pipeline.vae.to(device="cpu")
if self.pipeline.is_kv_cache_initialized():
self.pipeline._reset_kv_cache()
if self.pipeline.is_crossattn_cache_initialized():
self.pipeline._reset_crossattn_cache()
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