1065 lines
51 KiB
Python
1065 lines
51 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import inspect
|
|
from typing import Any, Callable, Dict, List, Optional, Union
|
|
|
|
import torch
|
|
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
|
|
|
|
from ...image_processor import PipelineImageInput
|
|
from ...loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
|
|
from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
|
|
from ...models.lora import adjust_lora_scale_text_encoder
|
|
from ...models.unets.unet_motion_model import MotionAdapter
|
|
from ...schedulers import (
|
|
DDIMScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
LMSDiscreteScheduler,
|
|
PNDMScheduler,
|
|
)
|
|
from ...utils import USE_PEFT_BACKEND, is_torch_xla_available, logging, scale_lora_layers, unscale_lora_layers
|
|
from ...utils.torch_utils import randn_tensor
|
|
from ...video_processor import VideoProcessor
|
|
from ..free_init_utils import FreeInitMixin
|
|
from ..free_noise_utils import AnimateDiffFreeNoiseMixin
|
|
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
|
|
from .pipeline_output import AnimateDiffPipelineOutput
|
|
|
|
|
|
if is_torch_xla_available():
|
|
import torch_xla.core.xla_model as xm
|
|
|
|
XLA_AVAILABLE = True
|
|
else:
|
|
XLA_AVAILABLE = False
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
EXAMPLE_DOC_STRING = """
|
|
Examples:
|
|
```py
|
|
>>> import imageio
|
|
>>> import requests
|
|
>>> import torch
|
|
>>> from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter
|
|
>>> from diffusers.utils import export_to_gif
|
|
>>> from io import BytesIO
|
|
>>> from PIL import Image
|
|
|
|
>>> adapter = MotionAdapter.from_pretrained(
|
|
... "guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16
|
|
... )
|
|
>>> pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(
|
|
... "SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter
|
|
... ).to("cuda")
|
|
>>> pipe.scheduler = DDIMScheduler(
|
|
... beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace"
|
|
... )
|
|
|
|
|
|
>>> def load_video(file_path: str):
|
|
... images = []
|
|
|
|
... if file_path.startswith(("http://", "https://")):
|
|
... # If the file_path is a URL
|
|
... response = requests.get(file_path)
|
|
... response.raise_for_status()
|
|
... content = BytesIO(response.content)
|
|
... vid = imageio.get_reader(content)
|
|
... else:
|
|
... # Assuming it's a local file path
|
|
... vid = imageio.get_reader(file_path)
|
|
|
|
... for frame in vid:
|
|
... pil_image = Image.fromarray(frame)
|
|
... images.append(pil_image)
|
|
|
|
... return images
|
|
|
|
|
|
>>> video = load_video(
|
|
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif"
|
|
... )
|
|
>>> output = pipe(
|
|
... video=video, prompt="panda playing a guitar, on a boat, in the ocean, high quality", strength=0.5
|
|
... )
|
|
>>> frames = output.frames[0]
|
|
>>> export_to_gif(frames, "animation.gif")
|
|
```
|
|
"""
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
|
|
def retrieve_latents(
|
|
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
|
|
):
|
|
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
|
|
return encoder_output.latent_dist.sample(generator)
|
|
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
|
|
return encoder_output.latent_dist.mode()
|
|
elif hasattr(encoder_output, "latents"):
|
|
return encoder_output.latents
|
|
else:
|
|
raise AttributeError("Could not access latents of provided encoder_output")
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
|
|
def retrieve_timesteps(
|
|
scheduler,
|
|
num_inference_steps: Optional[int] = None,
|
|
device: Optional[Union[str, torch.device]] = None,
|
|
timesteps: Optional[List[int]] = None,
|
|
sigmas: Optional[List[float]] = None,
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
|
|
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
|
|
|
|
Args:
|
|
scheduler (`SchedulerMixin`):
|
|
The scheduler to get timesteps from.
|
|
num_inference_steps (`int`):
|
|
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
|
|
must be `None`.
|
|
device (`str` or `torch.device`, *optional*):
|
|
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
|
|
`num_inference_steps` and `sigmas` must be `None`.
|
|
sigmas (`List[float]`, *optional*):
|
|
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
|
|
`num_inference_steps` and `timesteps` must be `None`.
|
|
|
|
Returns:
|
|
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
|
|
second element is the number of inference steps.
|
|
"""
|
|
if timesteps is not None and sigmas is not None:
|
|
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
|
|
if timesteps is not None:
|
|
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
|
|
if not accepts_timesteps:
|
|
raise ValueError(
|
|
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
|
|
f" timestep schedules. Please check whether you are using the correct scheduler."
|
|
)
|
|
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
num_inference_steps = len(timesteps)
|
|
elif sigmas is not None:
|
|
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
|
|
if not accept_sigmas:
|
|
raise ValueError(
|
|
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
|
|
f" sigmas schedules. Please check whether you are using the correct scheduler."
|
|
)
|
|
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
num_inference_steps = len(timesteps)
|
|
else:
|
|
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
return timesteps, num_inference_steps
|
|
|
|
|
|
class AnimateDiffVideoToVideoPipeline(
|
|
DiffusionPipeline,
|
|
StableDiffusionMixin,
|
|
TextualInversionLoaderMixin,
|
|
IPAdapterMixin,
|
|
StableDiffusionLoraLoaderMixin,
|
|
FreeInitMixin,
|
|
AnimateDiffFreeNoiseMixin,
|
|
FromSingleFileMixin,
|
|
):
|
|
r"""
|
|
Pipeline for video-to-video generation.
|
|
|
|
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.).
|
|
|
|
The pipeline also inherits the following loading methods:
|
|
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
|
|
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
|
|
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
|
|
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
|
|
|
|
Args:
|
|
vae ([`AutoencoderKL`]):
|
|
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
|
text_encoder ([`CLIPTextModel`]):
|
|
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
|
|
tokenizer (`CLIPTokenizer`):
|
|
A [`~transformers.CLIPTokenizer`] to tokenize text.
|
|
unet ([`UNet2DConditionModel`]):
|
|
A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
|
|
motion_adapter ([`MotionAdapter`]):
|
|
A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
|
|
scheduler ([`SchedulerMixin`]):
|
|
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
|
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
|
"""
|
|
|
|
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
|
|
_optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
|
|
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKL,
|
|
text_encoder: CLIPTextModel,
|
|
tokenizer: CLIPTokenizer,
|
|
unet: Union[UNet2DConditionModel, UNetMotionModel],
|
|
motion_adapter: MotionAdapter,
|
|
scheduler: Union[
|
|
DDIMScheduler,
|
|
PNDMScheduler,
|
|
LMSDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
],
|
|
feature_extractor: CLIPImageProcessor = None,
|
|
image_encoder: CLIPVisionModelWithProjection = None,
|
|
):
|
|
super().__init__()
|
|
if isinstance(unet, UNet2DConditionModel):
|
|
unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
motion_adapter=motion_adapter,
|
|
scheduler=scheduler,
|
|
feature_extractor=feature_extractor,
|
|
image_encoder=image_encoder,
|
|
)
|
|
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
|
|
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)
|
|
|
|
def encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
lora_scale: Optional[float] = None,
|
|
clip_skip: Optional[int] = 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_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
prompt_embeds (`torch.Tensor`, *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.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
lora_scale (`float`, *optional*):
|
|
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
"""
|
|
# set lora scale so that monkey patched LoRA
|
|
# function of text encoder can correctly access it
|
|
if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
# dynamically adjust the LoRA scale
|
|
if not USE_PEFT_BACKEND:
|
|
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
|
|
else:
|
|
scale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
if prompt is not None and isinstance(prompt, (str, dict)):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if prompt_embeds is None:
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
|
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
|
|
|
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
|
text_input_ids, untruncated_ids
|
|
):
|
|
removed_text = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
if clip_skip is None:
|
|
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
|
|
prompt_embeds = prompt_embeds[0]
|
|
else:
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
|
|
)
|
|
# Access the `hidden_states` first, that contains a tuple of
|
|
# all the hidden states from the encoder layers. Then index into
|
|
# the tuple to access the hidden states from the desired layer.
|
|
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
|
# We also need to apply the final LayerNorm here to not mess with the
|
|
# representations. The `last_hidden_states` that we typically use for
|
|
# obtaining the final prompt representations passes through the LayerNorm
|
|
# layer.
|
|
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, 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_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
uncond_tokens: List[str]
|
|
if negative_prompt is None:
|
|
uncond_tokens = [""] * batch_size
|
|
elif prompt is not None and type(prompt) is not type(negative_prompt):
|
|
raise TypeError(
|
|
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
|
f" {type(prompt)}."
|
|
)
|
|
elif isinstance(negative_prompt, str):
|
|
uncond_tokens = [negative_prompt]
|
|
elif batch_size != len(negative_prompt):
|
|
raise ValueError(
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
|
" the batch size of `prompt`."
|
|
)
|
|
else:
|
|
uncond_tokens = negative_prompt
|
|
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
|
|
if self.text_encoder is not None:
|
|
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
return prompt_embeds, negative_prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
|
|
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
|
|
dtype = next(self.image_encoder.parameters()).dtype
|
|
|
|
if not isinstance(image, torch.Tensor):
|
|
image = self.feature_extractor(image, return_tensors="pt").pixel_values
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
if output_hidden_states:
|
|
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
|
|
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_enc_hidden_states = self.image_encoder(
|
|
torch.zeros_like(image), output_hidden_states=True
|
|
).hidden_states[-2]
|
|
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
|
|
num_images_per_prompt, dim=0
|
|
)
|
|
return image_enc_hidden_states, uncond_image_enc_hidden_states
|
|
else:
|
|
image_embeds = self.image_encoder(image).image_embeds
|
|
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_embeds = torch.zeros_like(image_embeds)
|
|
|
|
return image_embeds, uncond_image_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
|
|
def prepare_ip_adapter_image_embeds(
|
|
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
|
|
):
|
|
image_embeds = []
|
|
if do_classifier_free_guidance:
|
|
negative_image_embeds = []
|
|
if ip_adapter_image_embeds is None:
|
|
if not isinstance(ip_adapter_image, list):
|
|
ip_adapter_image = [ip_adapter_image]
|
|
|
|
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
|
|
raise ValueError(
|
|
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
|
|
)
|
|
|
|
for single_ip_adapter_image, image_proj_layer in zip(
|
|
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
|
|
):
|
|
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
|
|
single_image_embeds, single_negative_image_embeds = self.encode_image(
|
|
single_ip_adapter_image, device, 1, output_hidden_state
|
|
)
|
|
|
|
image_embeds.append(single_image_embeds[None, :])
|
|
if do_classifier_free_guidance:
|
|
negative_image_embeds.append(single_negative_image_embeds[None, :])
|
|
else:
|
|
for single_image_embeds in ip_adapter_image_embeds:
|
|
if do_classifier_free_guidance:
|
|
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
|
|
negative_image_embeds.append(single_negative_image_embeds)
|
|
image_embeds.append(single_image_embeds)
|
|
|
|
ip_adapter_image_embeds = []
|
|
for i, single_image_embeds in enumerate(image_embeds):
|
|
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
|
|
if do_classifier_free_guidance:
|
|
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
|
|
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
|
|
|
|
single_image_embeds = single_image_embeds.to(device=device)
|
|
ip_adapter_image_embeds.append(single_image_embeds)
|
|
|
|
return ip_adapter_image_embeds
|
|
|
|
def encode_video(self, video, generator, decode_chunk_size: int = 16) -> torch.Tensor:
|
|
latents = []
|
|
for i in range(0, len(video), decode_chunk_size):
|
|
batch_video = video[i : i + decode_chunk_size]
|
|
batch_video = retrieve_latents(self.vae.encode(batch_video), generator=generator)
|
|
latents.append(batch_video)
|
|
return torch.cat(latents)
|
|
|
|
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
|
|
def decode_latents(self, latents, decode_chunk_size: int = 16):
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
|
|
batch_size, channels, num_frames, height, width = latents.shape
|
|
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
|
|
|
|
video = []
|
|
for i in range(0, latents.shape[0], decode_chunk_size):
|
|
batch_latents = latents[i : i + decode_chunk_size]
|
|
batch_latents = self.vae.decode(batch_latents).sample
|
|
video.append(batch_latents)
|
|
|
|
video = torch.cat(video)
|
|
video = video[None, :].reshape((batch_size, num_frames, -1) + video.shape[2:]).permute(0, 2, 1, 3, 4)
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
video = video.float()
|
|
return video
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
|
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://huggingface.co/papers/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
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
strength,
|
|
height,
|
|
width,
|
|
video=None,
|
|
latents=None,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
ip_adapter_image=None,
|
|
ip_adapter_image_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
if strength < 0 or strength > 1:
|
|
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
|
|
|
|
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}.")
|
|
|
|
if callback_on_step_end_tensor_inputs is not None and not all(
|
|
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
|
):
|
|
raise ValueError(
|
|
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
|
)
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and not isinstance(prompt, (str, list, dict)):
|
|
raise ValueError(f"`prompt` has to be of type `str`, `list` or `dict` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
if video is not None and latents is not None:
|
|
raise ValueError("Only one of `video` or `latents` should be provided")
|
|
|
|
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
|
|
raise ValueError(
|
|
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
|
|
)
|
|
|
|
if ip_adapter_image_embeds is not None:
|
|
if not isinstance(ip_adapter_image_embeds, list):
|
|
raise ValueError(
|
|
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
|
|
)
|
|
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
|
|
raise ValueError(
|
|
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
|
|
)
|
|
|
|
def get_timesteps(self, num_inference_steps, timesteps, strength, device):
|
|
# get the original timestep using init_timestep
|
|
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
|
|
|
t_start = max(num_inference_steps - init_timestep, 0)
|
|
timesteps = timesteps[t_start * self.scheduler.order :]
|
|
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
def prepare_latents(
|
|
self,
|
|
video: Optional[torch.Tensor] = None,
|
|
height: int = 64,
|
|
width: int = 64,
|
|
num_channels_latents: int = 4,
|
|
batch_size: int = 1,
|
|
timestep: Optional[int] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
device: Optional[torch.device] = None,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
decode_chunk_size: int = 16,
|
|
add_noise: bool = False,
|
|
) -> torch.Tensor:
|
|
num_frames = video.shape[1] if latents is None else latents.shape[2]
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_frames,
|
|
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:
|
|
# make sure the VAE is in float32 mode, as it overflows in float16
|
|
if self.vae.config.force_upcast:
|
|
video = video.float()
|
|
self.vae.to(dtype=torch.float32)
|
|
|
|
if isinstance(generator, list):
|
|
init_latents = [
|
|
self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
|
|
for i in range(batch_size)
|
|
]
|
|
else:
|
|
init_latents = [self.encode_video(vid, generator, decode_chunk_size).unsqueeze(0) for vid in video]
|
|
|
|
init_latents = torch.cat(init_latents, dim=0)
|
|
|
|
# restore vae to original dtype
|
|
if self.vae.config.force_upcast:
|
|
self.vae.to(dtype)
|
|
|
|
init_latents = init_latents.to(dtype)
|
|
init_latents = self.vae.config.scaling_factor * init_latents
|
|
|
|
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
|
|
# expand init_latents for batch_size
|
|
error_message = (
|
|
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
|
|
" images (`image`). Please make sure to update your script to pass as many initial images as text prompts"
|
|
)
|
|
raise ValueError(error_message)
|
|
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
|
|
raise ValueError(
|
|
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
|
|
)
|
|
else:
|
|
init_latents = torch.cat([init_latents], dim=0)
|
|
|
|
noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
|
|
latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4)
|
|
else:
|
|
if shape != latents.shape:
|
|
# [B, C, F, H, W]
|
|
raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}")
|
|
|
|
latents = latents.to(device, dtype=dtype)
|
|
|
|
if add_noise:
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
latents = self.scheduler.add_noise(latents, noise, timestep)
|
|
|
|
return latents
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def clip_skip(self):
|
|
return self._clip_skip
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
video: List[List[PipelineImageInput]] = None,
|
|
prompt: Optional[Union[str, List[str]]] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
enforce_inference_steps: bool = False,
|
|
timesteps: Optional[List[int]] = None,
|
|
sigmas: Optional[List[float]] = None,
|
|
guidance_scale: float = 7.5,
|
|
strength: float = 0.8,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_videos_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
ip_adapter_image: Optional[PipelineImageInput] = None,
|
|
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
clip_skip: Optional[int] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
decode_chunk_size: int = 16,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
video (`List[PipelineImageInput]`):
|
|
The input video to condition the generation on. Must be a list of images/frames of the video.
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated video.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated video.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
|
|
expense of slower inference.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
|
|
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
|
|
passed will be used. Must be in descending order.
|
|
sigmas (`List[float]`, *optional*):
|
|
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
|
|
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
|
|
will be used.
|
|
strength (`float`, *optional*, defaults to 0.8):
|
|
Higher strength leads to more differences between original video and generated video.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
|
|
applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
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.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
|
|
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`. Latents should be of shape
|
|
`(batch_size, num_channel, num_frames, height, width)`.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
ip_adapter_image: (`PipelineImageInput`, *optional*):
|
|
Optional image input to work with IP Adapters.
|
|
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
|
|
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
|
|
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
|
|
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
|
|
provided, embeddings are computed from the `ip_adapter_image` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`AnimateDiffPipelineOutput`] instead of a plain tuple.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
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.
|
|
decode_chunk_size (`int`, defaults to `16`):
|
|
The number of frames to decode at a time when calling `decode_latents` method.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is
|
|
returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
|
|
"""
|
|
|
|
# 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_videos_per_prompt = 1
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt=prompt,
|
|
strength=strength,
|
|
height=height,
|
|
width=width,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
video=video,
|
|
latents=latents,
|
|
ip_adapter_image=ip_adapter_image,
|
|
ip_adapter_image_embeds=ip_adapter_image_embeds,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
self._interrupt = False
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, (str, dict)):
|
|
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
|
|
dtype = self.dtype
|
|
|
|
# 3. Prepare timesteps
|
|
if not enforce_inference_steps:
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, sigmas
|
|
)
|
|
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
|
|
else:
|
|
denoising_inference_steps = int(num_inference_steps / strength)
|
|
timesteps, denoising_inference_steps = retrieve_timesteps(
|
|
self.scheduler, denoising_inference_steps, device, timesteps, sigmas
|
|
)
|
|
timesteps = timesteps[-num_inference_steps:]
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
|
|
|
|
# 4. Prepare latent variables
|
|
if latents is None:
|
|
video = self.video_processor.preprocess_video(video, height=height, width=width)
|
|
# Move the number of frames before the number of channels.
|
|
video = video.permute(0, 2, 1, 3, 4)
|
|
video = video.to(device=device, dtype=dtype)
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
video=video,
|
|
height=height,
|
|
width=width,
|
|
num_channels_latents=num_channels_latents,
|
|
batch_size=batch_size * num_videos_per_prompt,
|
|
timestep=latent_timestep,
|
|
dtype=dtype,
|
|
device=device,
|
|
generator=generator,
|
|
latents=latents,
|
|
decode_chunk_size=decode_chunk_size,
|
|
add_noise=enforce_inference_steps,
|
|
)
|
|
|
|
# 5. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
num_frames = latents.shape[2]
|
|
if self.free_noise_enabled:
|
|
prompt_embeds, negative_prompt_embeds = self._encode_prompt_free_noise(
|
|
prompt=prompt,
|
|
num_frames=num_frames,
|
|
device=device,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
else:
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_videos_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
|
|
# 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
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
|
|
|
|
# 6. Prepare IP-Adapter embeddings
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
|
|
image_embeds = self.prepare_ip_adapter_image_embeds(
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
device,
|
|
batch_size * num_videos_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
)
|
|
|
|
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 8. Add image embeds for IP-Adapter
|
|
added_cond_kwargs = (
|
|
{"image_embeds": image_embeds}
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
|
|
else None
|
|
)
|
|
|
|
num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
|
|
for free_init_iter in range(num_free_init_iters):
|
|
if self.free_init_enabled:
|
|
latents, timesteps = self._apply_free_init(
|
|
latents, free_init_iter, num_inference_steps, device, latents.dtype, generator
|
|
)
|
|
num_inference_steps = len(timesteps)
|
|
# make sure to readjust timesteps based on strength
|
|
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
|
|
|
|
self._num_timesteps = len(timesteps)
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
|
|
# 9. Denoising loop
|
|
with self.progress_bar(total=self._num_timesteps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
# 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)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
).sample
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
|
|
|
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)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
# 10. Post-processing
|
|
if output_type == "latent":
|
|
video = latents
|
|
else:
|
|
video_tensor = self.decode_latents(latents, decode_chunk_size)
|
|
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
|
|
|
|
# 11. Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return AnimateDiffPipelineOutput(frames=video)
|