701 lines
32 KiB
Python
701 lines
32 KiB
Python
import inspect
|
|
from dataclasses import dataclass
|
|
from typing import Callable, Dict, List, Optional, Union
|
|
|
|
import PIL.Image
|
|
import einops
|
|
import numpy as np
|
|
import torch
|
|
from diffusers.image_processor import VaeImageProcessor, PipelineImageInput
|
|
from diffusers.models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel
|
|
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
|
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import retrieve_timesteps
|
|
from diffusers.pipelines.stable_video_diffusion.pipeline_stable_video_diffusion \
|
|
import _resize_with_antialiasing, _append_dims
|
|
from diffusers.schedulers import EulerDiscreteScheduler
|
|
from diffusers.utils import BaseOutput, logging
|
|
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
|
|
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
|
|
|
|
from ..modules.pose_net import PoseNet
|
|
|
|
from comfy.utils import ProgressBar
|
|
import comfy.model_management as mm
|
|
from comfy.clip_vision import clip_preprocess
|
|
offload_device = mm.unet_offload_device()
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
def _append_dims(x, target_dims):
|
|
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
|
|
dims_to_append = target_dims - x.ndim
|
|
if dims_to_append < 0:
|
|
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
|
|
return x[(...,) + (None,) * dims_to_append]
|
|
|
|
|
|
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
|
|
def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
|
|
batch_size, channels, num_frames, height, width = video.shape
|
|
outputs = []
|
|
for batch_idx in range(batch_size):
|
|
batch_vid = video[batch_idx].permute(1, 0, 2, 3)
|
|
batch_output = processor.postprocess(batch_vid, output_type)
|
|
|
|
outputs.append(batch_output)
|
|
|
|
if output_type == "np":
|
|
outputs = np.stack(outputs)
|
|
|
|
elif output_type == "pt":
|
|
outputs = torch.stack(outputs)
|
|
|
|
elif not output_type == "pil":
|
|
raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
|
|
|
|
return outputs
|
|
|
|
|
|
@dataclass
|
|
class MimicMotionPipelineOutput(BaseOutput):
|
|
r"""
|
|
Output class for mimicmotion pipeline.
|
|
|
|
Args:
|
|
frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):
|
|
List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_size,
|
|
num_frames, height, width, num_channels)`.
|
|
"""
|
|
|
|
frames: Union[List[List[PIL.Image.Image]], np.ndarray, torch.Tensor]
|
|
|
|
|
|
class MimicMotionPipeline(DiffusionPipeline):
|
|
r"""
|
|
Pipeline to generate video from an input image using Stable Video Diffusion.
|
|
|
|
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
|
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
|
|
|
Args:
|
|
vae ([`AutoencoderKLTemporalDecoder`]):
|
|
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
|
|
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
|
|
Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K]
|
|
(https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
|
|
unet ([`UNetSpatioTemporalConditionModel`]):
|
|
A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
|
|
scheduler ([`EulerDiscreteScheduler`]):
|
|
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
|
|
feature_extractor ([`~transformers.CLIPImageProcessor`]):
|
|
A `CLIPImageProcessor` to extract features from generated images.
|
|
pose_net ([`PoseNet`]):
|
|
A `` to inject pose signals into unet.
|
|
"""
|
|
|
|
model_cpu_offload_seq = "image_encoder->unet->vae"
|
|
_callback_tensor_inputs = ["latents"]
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKLTemporalDecoder,
|
|
image_encoder: CLIPVisionModelWithProjection,
|
|
unet: UNetSpatioTemporalConditionModel,
|
|
scheduler: EulerDiscreteScheduler,
|
|
feature_extractor: CLIPImageProcessor,
|
|
pose_net: PoseNet,
|
|
):
|
|
super().__init__()
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
image_encoder=image_encoder,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
feature_extractor=feature_extractor,
|
|
pose_net=pose_net,
|
|
)
|
|
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
|
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
|
|
|
def _encode_image(
|
|
self,
|
|
image: PipelineImageInput,
|
|
device: Union[str, torch.device],
|
|
num_videos_per_prompt: int,
|
|
do_classifier_free_guidance: bool,
|
|
image_embed_strength: float = 1.0):
|
|
dtype = next(self.image_encoder.parameters()).dtype
|
|
|
|
# if not isinstance(image, torch.Tensor):
|
|
# image = self.image_processor.pil_to_numpy(image)
|
|
# image = self.image_processor.numpy_to_pt(image)
|
|
|
|
# # We normalize the image before resizing to match with the original implementation.
|
|
# # Then we unnormalize it after resizing.
|
|
# image = image * 2.0 - 1.0
|
|
# image = _resize_with_antialiasing(image, (224, 224))
|
|
# image = (image + 1.0) / 2.0
|
|
|
|
# # Normalize the image with for CLIP input
|
|
# image = self.feature_extractor(
|
|
# images=image,
|
|
# do_normalize=True,
|
|
# do_center_crop=False,
|
|
# do_resize=False,
|
|
# do_rescale=False,
|
|
# return_tensors="pt",
|
|
# ).pixel_values
|
|
|
|
image = image.permute(0, 2, 3, 1)
|
|
image = clip_preprocess(image.clone(), 224)
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
self.image_encoder.to(device)
|
|
image_embeddings = self.image_encoder(image).image_embeds
|
|
image_embeddings = image_embeddings.unsqueeze(1)
|
|
self.image_encoder.to(offload_device)
|
|
|
|
# duplicate image embeddings for each generation per prompt, using mps friendly method
|
|
bs_embed, seq_len, _ = image_embeddings.shape
|
|
image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
|
|
image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
|
|
image_embeddings = image_embeddings * image_embed_strength
|
|
|
|
if do_classifier_free_guidance:
|
|
negative_image_embeddings = torch.zeros_like(image_embeddings)
|
|
#negative_image_embeddings = torch.randn_like(image_embeddings)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
|
|
|
|
|
|
|
|
return image_embeddings
|
|
|
|
def _encode_pose_image(
|
|
self,
|
|
pose_image: torch.Tensor,
|
|
do_classifier_free_guidance: bool,
|
|
):
|
|
# Get latents_pose
|
|
pose_latents = self.pose_net(pose_image)
|
|
print(pose_latents.shape)
|
|
|
|
if do_classifier_free_guidance:
|
|
print("doing classifier free guidance")
|
|
negative_pose_latents = torch.zeros_like(pose_latents)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
pose_latents = torch.cat([negative_pose_latents, pose_latents])
|
|
|
|
return pose_latents
|
|
|
|
def _encode_vae_image(
|
|
self,
|
|
image: torch.Tensor,
|
|
device: Union[str, torch.device],
|
|
num_videos_per_prompt: int,
|
|
do_classifier_free_guidance: bool,
|
|
):
|
|
image = image.to(device=device)
|
|
self.vae.to(device)
|
|
image_latents = self.vae.encode(image).latent_dist.mode()
|
|
self.vae.to(offload_device)
|
|
|
|
if do_classifier_free_guidance:
|
|
negative_image_latents = torch.zeros_like(image_latents)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
image_latents = torch.cat([negative_image_latents, image_latents])
|
|
|
|
# duplicate image_latents for each generation per prompt, using mps friendly method
|
|
image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)
|
|
|
|
return image_latents
|
|
|
|
def _get_add_time_ids(
|
|
self,
|
|
fps: int,
|
|
motion_bucket_id: int,
|
|
noise_aug_strength: float,
|
|
dtype: torch.dtype,
|
|
batch_size: int,
|
|
num_videos_per_prompt: int,
|
|
do_classifier_free_guidance: bool,
|
|
):
|
|
add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
|
|
|
|
passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)
|
|
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
|
|
|
|
if expected_add_embed_dim != passed_add_embed_dim:
|
|
raise ValueError(
|
|
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, " \
|
|
f"but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. " \
|
|
f"Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
|
|
)
|
|
|
|
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
|
|
add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
|
|
|
|
if do_classifier_free_guidance:
|
|
add_time_ids = torch.cat([add_time_ids, add_time_ids])
|
|
|
|
return add_time_ids
|
|
|
|
def decode_latents(
|
|
self,
|
|
latents: torch.Tensor,
|
|
num_frames: int,
|
|
decode_chunk_size: int = 8):
|
|
# [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
|
|
latents = latents.flatten(0, 1)
|
|
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
|
|
forward_vae_fn = self.vae._orig_mod.forward if is_compiled_module(self.vae) else self.vae.forward
|
|
accepts_num_frames = "num_frames" in set(inspect.signature(forward_vae_fn).parameters.keys())
|
|
|
|
# decode decode_chunk_size frames at a time to avoid OOM
|
|
pbar = ProgressBar(latents.shape[0])
|
|
frames = []
|
|
for i in range(0, latents.shape[0], decode_chunk_size):
|
|
num_frames_in = latents[i: i + decode_chunk_size].shape[0]
|
|
decode_kwargs = {}
|
|
if accepts_num_frames:
|
|
# we only pass num_frames_in if it's expected
|
|
decode_kwargs["num_frames"] = num_frames_in
|
|
|
|
self.vae.to(latents.device)
|
|
frame = self.vae.decode(latents[i: i + decode_chunk_size], **decode_kwargs).sample
|
|
self.vae.to(offload_device)
|
|
|
|
frames.append(frame.cpu())
|
|
pbar.update(decode_chunk_size)
|
|
frames = torch.cat(frames, dim=0)
|
|
|
|
# [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
|
|
frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)
|
|
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
frames = frames.float()
|
|
return frames
|
|
|
|
def check_inputs(self, image, height, width):
|
|
if (
|
|
not isinstance(image, torch.Tensor)
|
|
and not isinstance(image, PIL.Image.Image)
|
|
and not isinstance(image, list)
|
|
):
|
|
raise ValueError(
|
|
"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
|
|
f" {type(image)}"
|
|
)
|
|
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size: int,
|
|
num_frames: int,
|
|
num_channels_latents: int,
|
|
height: int,
|
|
width: int,
|
|
dtype: torch.dtype,
|
|
device: Union[str, torch.device],
|
|
generator: torch.Generator,
|
|
latents: Optional[torch.Tensor] = None,
|
|
):
|
|
shape = (
|
|
batch_size,
|
|
num_frames,
|
|
num_channels_latents // 2,
|
|
height // self.vae_scale_factor,
|
|
width // self.vae_scale_factor,
|
|
)
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
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
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return True # TODO
|
|
if isinstance(self.guidance_scale, (int, float)):
|
|
return self.guidance_scale
|
|
return self.guidance_scale.max() > 1
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
|
|
image_pose: Union[torch.FloatTensor],
|
|
pose_strength: float = 1.0,
|
|
pose_start_percent: float = 0.0,
|
|
pose_end_percent: float = 1.0,
|
|
image_embed_strength: float = 1.0,
|
|
height: int = 576,
|
|
width: int = 1024,
|
|
num_frames: Optional[int] = None,
|
|
tile_size: Optional[int] = 16,
|
|
tile_overlap: Optional[int] = 4,
|
|
num_inference_steps: int = 25,
|
|
sigmas: Optional[List[float]] = None,
|
|
min_guidance_scale: float = 1.0,
|
|
max_guidance_scale: float = 3.0,
|
|
fps: int = 7,
|
|
motion_bucket_id: int = 127,
|
|
noise_aug_strength: float = 0.02,
|
|
image_only_indicator: bool = False,
|
|
decode_chunk_size: Optional[int] = None,
|
|
num_videos_per_prompt: Optional[int] = 1,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
first_n_frames=None,
|
|
output_type: Optional[str] = "pil",
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
return_dict: bool = True,
|
|
device: Union[str, torch.device] =None,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
|
|
Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
|
|
[`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/
|
|
feature_extractor/preprocessor_config.json).
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated image.
|
|
num_frames (`int`, *optional*):
|
|
The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid`
|
|
and to 25 for `stable-video-diffusion-img2vid-xt`
|
|
num_inference_steps (`int`, *optional*, defaults to 25):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference. This parameter is modulated by `strength`.
|
|
min_guidance_scale (`float`, *optional*, defaults to 1.0):
|
|
The minimum guidance scale. Used for the classifier free guidance with first frame.
|
|
max_guidance_scale (`float`, *optional*, defaults to 3.0):
|
|
The maximum guidance scale. Used for the classifier free guidance with last frame.
|
|
fps (`int`, *optional*, defaults to 7):
|
|
Frames per second.The rate at which the generated images shall be exported to a video after generation.
|
|
Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
|
|
motion_bucket_id (`int`, *optional*, defaults to 127):
|
|
The motion bucket ID. Used as conditioning for the generation.
|
|
The higher the number the more motion will be in the video.
|
|
noise_aug_strength (`float`, *optional*, defaults to 0.02):
|
|
The amount of noise added to the init image,
|
|
the higher it is the less the video will look like the init image. Increase it for more motion.
|
|
image_only_indicator (`bool`, *optional*, defaults to False):
|
|
Whether to treat the inputs as batch of images instead of videos.
|
|
decode_chunk_size (`int`, *optional*):
|
|
The number of frames to decode at a time.The higher the chunk size, the higher the temporal consistency
|
|
between frames, but also the higher the memory consumption.
|
|
By default, the decoder will decode all frames at once for maximal quality.
|
|
Reduce `decode_chunk_size` to reduce memory usage.
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor is generated by sampling using the supplied random `generator`.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
callback_on_step_end (`Callable`, *optional*):
|
|
A function that calls at the end of each denoising steps during the inference. The function is called
|
|
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
|
|
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
|
|
`callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
device:
|
|
On which device the pipeline runs on.
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`,
|
|
[`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list of list with the generated frames.
|
|
|
|
Examples:
|
|
|
|
```py
|
|
from diffusers import StableVideoDiffusionPipeline
|
|
from diffusers.utils import load_image, export_to_video
|
|
|
|
pipe = StableVideoDiffusionPipeline.from_pretrained(
|
|
"stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
|
|
pipe.to("cuda")
|
|
|
|
image = load_image(
|
|
"https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
|
|
image = image.resize((1024, 576))
|
|
|
|
frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
|
|
export_to_video(frames, "generated.mp4", fps=7)
|
|
```
|
|
"""
|
|
# 0. Default height and width to unet
|
|
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
|
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
|
|
|
num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
|
|
print("num_frames: ", num_frames)
|
|
decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(image, height, width)
|
|
|
|
# 2. Define call parameters
|
|
if isinstance(image, PIL.Image.Image):
|
|
batch_size = 1
|
|
elif isinstance(image, list):
|
|
batch_size = len(image)
|
|
else:
|
|
batch_size = image.shape[0]
|
|
device = device if device is not None else self._execution_device
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
self._guidance_scale = max_guidance_scale
|
|
|
|
# 3. Encode input image
|
|
image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance, image_embed_strength=image_embed_strength)
|
|
|
|
# NOTE: Stable Diffusion Video was conditioned on fps - 1, which
|
|
# is why it is reduced here.
|
|
fps = fps - 1
|
|
|
|
# 4. Encode input image using VAE
|
|
image = self.image_processor.preprocess(image, height=height, width=width).to(device)
|
|
noise = randn_tensor(image.shape, generator=generator, device=device, dtype=image.dtype)
|
|
image = image + noise_aug_strength * noise
|
|
|
|
# needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
|
|
# if needs_upcasting:
|
|
# self.vae.to(dtype=torch.float32)
|
|
|
|
image_latents = self._encode_vae_image(
|
|
image,
|
|
device=device,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
)
|
|
image_latents = image_latents.to(image_embeddings.dtype)
|
|
|
|
ref_latent = first_n_frames[:, 0] if first_n_frames is not None else None
|
|
pose_latents = self._encode_pose_image(
|
|
image_pose, do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
)
|
|
|
|
# cast back to fp16 if needed
|
|
# if needs_upcasting:
|
|
# self.vae.to(dtype=torch.float16)
|
|
|
|
# Repeat the image latents for each frame so we can concatenate them with the noise
|
|
# image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
|
|
image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
|
|
|
|
# 5. Get Added Time IDs
|
|
added_time_ids = self._get_add_time_ids(
|
|
fps,
|
|
motion_bucket_id,
|
|
noise_aug_strength,
|
|
image_embeddings.dtype,
|
|
batch_size,
|
|
num_videos_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
)
|
|
added_time_ids = added_time_ids.to(device)
|
|
|
|
# 6. Prepare timesteps
|
|
if sigmas is not None:
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None, sigmas)
|
|
else:
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
tile_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
image_embeddings.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
latents = latents.repeat(1, num_frames // tile_size + 1, 1, 1, 1)[:, :num_frames]
|
|
|
|
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, 0.0)
|
|
|
|
# 7. Prepare guidance scale
|
|
guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
|
|
guidance_scale = guidance_scale.to(device, latents.dtype)
|
|
guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
|
|
guidance_scale = _append_dims(guidance_scale, latents.ndim)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
|
|
# 8. Denoising loop
|
|
self.unet.to(device)
|
|
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# Calculate the actual start and end steps based on percentages
|
|
start_step_index = round(self._num_timesteps * pose_start_percent)
|
|
end_step_index = round(self._num_timesteps * pose_end_percent)
|
|
|
|
print(f"start_step_index: {start_step_index}, end_step_index: {end_step_index}")
|
|
|
|
pose_latents = einops.rearrange(pose_latents, '(b f) c h w -> b f c h w', f=num_frames)
|
|
indices = [[0, *range(i + 1, min(i + tile_size, num_frames))] for i in
|
|
range(0, num_frames - tile_size + 1, tile_size - tile_overlap)]
|
|
if indices[-1][-1] < num_frames - 1:
|
|
indices.append([0, *range(num_frames - tile_size + 1, num_frames)])
|
|
comfy_pbar = ProgressBar(len(timesteps) * len(indices))
|
|
with self.progress_bar(total=len(timesteps) * len(indices)) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# Concatenate image_latents over channels dimension
|
|
latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
|
|
|
|
# predict the noise residual
|
|
noise_pred = torch.zeros_like(image_latents)
|
|
noise_pred_cnt = image_latents.new_zeros((num_frames,))
|
|
# image_pose = pixel_values_pose[:, frame_start:frame_start + self.num_frames, ...]
|
|
weight = (torch.arange(tile_size, device=device) + 0.5) * 2. / tile_size
|
|
weight = torch.minimum(weight, 2 - weight)
|
|
|
|
for idx in indices:
|
|
# Check if the current timestep is within the start and end step range
|
|
if start_step_index <= i <= end_step_index:
|
|
# Apply pose_latents as currently done
|
|
#print(f"Applying pose on step {i}")
|
|
pose_latents_to_use = pose_latents[:, idx].flatten(0, 1)
|
|
else:
|
|
#print(f"Not applying pose on step {i}")
|
|
# Apply an alternative if pose_latents should not be used outside this range
|
|
# This could be zeros, or any other placeholder logic you define.
|
|
pose_latents_to_use = torch.zeros_like(pose_latents[:, idx].flatten(0, 1))
|
|
|
|
_noise_pred = self.unet(
|
|
latent_model_input[:, idx],
|
|
t,
|
|
encoder_hidden_states=image_embeddings,
|
|
added_time_ids=added_time_ids,
|
|
pose_latents=pose_latents_to_use,
|
|
pose_strength=pose_strength,
|
|
image_only_indicator=image_only_indicator,
|
|
return_dict=False,
|
|
)[0]
|
|
noise_pred[:, idx] += _noise_pred * weight[:, None, None, None]
|
|
noise_pred_cnt[idx] += weight
|
|
progress_bar.update()
|
|
comfy_pbar.update(1)
|
|
noise_pred.div_(noise_pred_cnt[:, None, None, None])
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
|
|
|
|
if first_n_frames is not None:
|
|
sigma = self.scheduler.sigmas[self.scheduler.step_index]
|
|
_latents = latents[:, 1:1 + first_n_frames.size(1)]
|
|
tmp = (first_n_frames - _latents / (sigma ** 2 + 1)) / (-sigma / ((sigma ** 2 + 1) ** 0.5))
|
|
noise_pred[:, 1:1 + first_n_frames.size(1)] = tmp
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
|
|
self.unet.to(offload_device)
|
|
|
|
if not output_type == "latent":
|
|
# cast back to fp16 if needed
|
|
# if needs_upcasting:
|
|
# self.vae.to(dtype=torch.float16)
|
|
frames = self.decode_latents(latents, num_frames, decode_chunk_size)
|
|
frames = tensor2vid(frames, self.image_processor, output_type=output_type)
|
|
else:
|
|
frames = latents
|
|
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return frames
|
|
|
|
return MimicMotionPipelineOutput(frames=frames)
|