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Zero
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import torch
from diffusers import DiffusionPipeline
from typing import List, Optional, Union, Dict, Any
from diffusers import logging
from diffusers.utils.torch_utils import randn_tensor
from lvdm.models.ddpm3d import LatentDiffusion
from scheduler.t2v_turbo_scheduler import T2VTurboScheduler
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class T2VTurboVC2Pipeline(DiffusionPipeline):
def __init__(
self,
pretrained_t2v: LatentDiffusion,
scheduler: T2VTurboScheduler,
model_config: Dict[str, Any] = None,
):
super().__init__()
self.register_modules(
pretrained_t2v=pretrained_t2v,
scheduler=scheduler,
)
self.vae = pretrained_t2v.first_stage_model
self.unet = pretrained_t2v.model.diffusion_model
self.text_encoder = pretrained_t2v.cond_stage_model
self.model_config = model_config
self.vae_scale_factor = 8
def _encode_prompt(
self,
prompt,
device,
num_videos_per_prompt,
prompt_embeds: None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_videos_per_prompt (`int`):
number of images that should be generated per prompt
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
"""
if prompt_embeds is None:
prompt_embeds = self.text_encoder(prompt)
prompt_embeds = prompt_embeds.to(device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(
bs_embed * num_videos_per_prompt, seq_len, -1
)
# Don't need to get uncond prompt embedding because of LCM Guided Distillation
return prompt_embeds
def prepare_latents(
self,
batch_size,
num_channels_latents,
frames,
height,
width,
dtype,
device,
generator,
latents=None,
):
shape = (
batch_size,
num_channels_latents,
frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = randn_tensor(
shape, generator=generator, device=device, dtype=dtype
)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
"""
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps: torch.Tensor: generate embedding vectors at these timesteps
embedding_dim: int: dimension of the embeddings to generate
dtype: data type of the generated embeddings
Returns:
embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = 320,
width: Optional[int] = 512,
frames: int = 16,
fps: int = 16,
guidance_scale: float = 7.5,
motion_gs: float = 0.1,
use_motion_cond: bool = False,
percentage: float = 0.3,
num_videos_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
num_inference_steps: int = 4,
lcm_origin_steps: int = 50,
prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
):
unet_config = self.model_config["params"]["unet_config"]
# 0. Default height and width to unet
frames = self.pretrained_t2v.temporal_length if frames < 0 else frames
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# do_classifier_free_guidance = guidance_scale > 0.0 # In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_videos_per_prompt,
prompt_embeds=prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variable
num_channels_latents = unet_config["params"]["in_channels"]
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
frames,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
bs = batch_size * num_videos_per_prompt
context = {"context": torch.cat([prompt_embeds.to(self.dtype)], 1), "fps": fps}
# 6. Get Guidance Scale Embedding
w = torch.tensor(guidance_scale).repeat(bs)
w_embedding = self.get_w_embedding(w, embedding_dim=256).to(device)
context["timestep_cond"] = w_embedding.to(self.dtype)
ms_t_threshold = self.scheduler.config.num_train_timesteps * (1 - percentage)
# 7. LCM MultiStep Sampling Loop:
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
ts = torch.full((bs,), t, device=device, dtype=torch.long)
if use_motion_cond:
motion_gs_pt = torch.tensor(motion_gs).repeat(bs)
if t < ms_t_threshold:
motion_gs_pt = torch.zeros_like(motion_gs_pt)
motion_gs_embedding = self.get_w_embedding(
motion_gs_pt, embedding_dim=256, dtype=self.dtype
).to(device)
context["motion_cond"] = motion_gs_embedding
# model prediction (v-prediction, eps, x)
model_pred = self.unet(latents, ts, **context)
# compute the previous noisy sample x_t -> x_t-1
latents, denoised = self.scheduler.step(
model_pred, i, t, latents, generator=generator, return_dict=False
)
progress_bar.update()
if not output_type == "latent":
videos = self.pretrained_t2v.decode_first_stage_2DAE(denoised)
else:
videos = denoised
return videos
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