1309 lines
65 KiB
Python
1309 lines
65 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, Tuple, Union
|
|
|
|
import torch
|
|
from transformers import (
|
|
CLIPImageProcessor,
|
|
CLIPTextModel,
|
|
CLIPTextModelWithProjection,
|
|
CLIPTokenizer,
|
|
CLIPVisionModelWithProjection,
|
|
)
|
|
|
|
from ...image_processor import PipelineImageInput
|
|
from ...loaders import (
|
|
FromSingleFileMixin,
|
|
IPAdapterMixin,
|
|
StableDiffusionXLLoraLoaderMixin,
|
|
TextualInversionLoaderMixin,
|
|
)
|
|
from ...models import AutoencoderKL, ImageProjection, MotionAdapter, UNet2DConditionModel, UNetMotionModel
|
|
from ...models.attention_processor import (
|
|
AttnProcessor2_0,
|
|
FusedAttnProcessor2_0,
|
|
XFormersAttnProcessor,
|
|
)
|
|
from ...models.lora import adjust_lora_scale_text_encoder
|
|
from ...schedulers import (
|
|
DDIMScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
LMSDiscreteScheduler,
|
|
PNDMScheduler,
|
|
)
|
|
from ...utils import (
|
|
USE_PEFT_BACKEND,
|
|
is_torch_xla_available,
|
|
logging,
|
|
replace_example_docstring,
|
|
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 ..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 torch
|
|
>>> from diffusers.models import MotionAdapter
|
|
>>> from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
|
|
>>> from diffusers.utils import export_to_gif
|
|
|
|
>>> adapter = MotionAdapter.from_pretrained(
|
|
... "a-r-r-o-w/animatediff-motion-adapter-sdxl-beta", torch_dtype=torch.float16
|
|
... )
|
|
|
|
>>> model_id = "stabilityai/stable-diffusion-xl-base-1.0"
|
|
>>> scheduler = DDIMScheduler.from_pretrained(
|
|
... model_id,
|
|
... subfolder="scheduler",
|
|
... clip_sample=False,
|
|
... timestep_spacing="linspace",
|
|
... beta_schedule="linear",
|
|
... steps_offset=1,
|
|
... )
|
|
>>> pipe = AnimateDiffSDXLPipeline.from_pretrained(
|
|
... model_id,
|
|
... motion_adapter=adapter,
|
|
... scheduler=scheduler,
|
|
... torch_dtype=torch.float16,
|
|
... variant="fp16",
|
|
... ).to("cuda")
|
|
|
|
>>> # enable memory savings
|
|
>>> pipe.enable_vae_slicing()
|
|
>>> pipe.enable_vae_tiling()
|
|
|
|
>>> output = pipe(
|
|
... prompt="a panda surfing in the ocean, realistic, high quality",
|
|
... negative_prompt="low quality, worst quality",
|
|
... num_inference_steps=20,
|
|
... guidance_scale=8,
|
|
... width=1024,
|
|
... height=1024,
|
|
... num_frames=16,
|
|
... )
|
|
|
|
>>> frames = output.frames[0]
|
|
>>> export_to_gif(frames, "animation.gif")
|
|
```
|
|
"""
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
|
|
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
|
r"""
|
|
Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
|
|
Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891).
|
|
|
|
Args:
|
|
noise_cfg (`torch.Tensor`):
|
|
The predicted noise tensor for the guided diffusion process.
|
|
noise_pred_text (`torch.Tensor`):
|
|
The predicted noise tensor for the text-guided diffusion process.
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
A rescale factor applied to the noise predictions.
|
|
|
|
Returns:
|
|
noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
|
|
"""
|
|
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
|
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
|
# rescale the results from guidance (fixes overexposure)
|
|
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
|
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
|
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
|
|
return noise_cfg
|
|
|
|
|
|
# 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 AnimateDiffSDXLPipeline(
|
|
DiffusionPipeline,
|
|
StableDiffusionMixin,
|
|
FromSingleFileMixin,
|
|
StableDiffusionXLLoraLoaderMixin,
|
|
TextualInversionLoaderMixin,
|
|
IPAdapterMixin,
|
|
FreeInitMixin,
|
|
):
|
|
r"""
|
|
Pipeline for text-to-video generation using Stable Diffusion XL.
|
|
|
|
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
|
library implements for all the pipelines (such as downloading or 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.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
|
|
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
|
|
- [`~loaders.StableDiffusionXLLoraLoaderMixin.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. Stable Diffusion XL uses the text portion of
|
|
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
|
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
|
text_encoder_2 ([` CLIPTextModelWithProjection`]):
|
|
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
|
|
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
|
|
specifically the
|
|
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
|
|
variant.
|
|
tokenizer (`CLIPTokenizer`):
|
|
Tokenizer of class
|
|
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
|
tokenizer_2 (`CLIPTokenizer`):
|
|
Second Tokenizer of class
|
|
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
|
unet ([`UNet2DConditionModel`]):
|
|
Conditional U-Net architecture to denoise the encoded image 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`].
|
|
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
|
|
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
|
|
`stabilityai/stable-diffusion-xl-base-1-0`.
|
|
"""
|
|
|
|
model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
|
|
_optional_components = [
|
|
"tokenizer",
|
|
"tokenizer_2",
|
|
"text_encoder",
|
|
"text_encoder_2",
|
|
"image_encoder",
|
|
"feature_extractor",
|
|
]
|
|
_callback_tensor_inputs = [
|
|
"latents",
|
|
"prompt_embeds",
|
|
"negative_prompt_embeds",
|
|
"add_text_embeds",
|
|
"add_time_ids",
|
|
"negative_pooled_prompt_embeds",
|
|
"negative_add_time_ids",
|
|
]
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKL,
|
|
text_encoder: CLIPTextModel,
|
|
text_encoder_2: CLIPTextModelWithProjection,
|
|
tokenizer: CLIPTokenizer,
|
|
tokenizer_2: CLIPTokenizer,
|
|
unet: Union[UNet2DConditionModel, UNetMotionModel],
|
|
motion_adapter: MotionAdapter,
|
|
scheduler: Union[
|
|
DDIMScheduler,
|
|
PNDMScheduler,
|
|
LMSDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
],
|
|
image_encoder: CLIPVisionModelWithProjection = None,
|
|
feature_extractor: CLIPImageProcessor = None,
|
|
force_zeros_for_empty_prompt: bool = True,
|
|
):
|
|
super().__init__()
|
|
|
|
if isinstance(unet, UNet2DConditionModel):
|
|
unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
text_encoder_2=text_encoder_2,
|
|
tokenizer=tokenizer,
|
|
tokenizer_2=tokenizer_2,
|
|
unet=unet,
|
|
motion_adapter=motion_adapter,
|
|
scheduler=scheduler,
|
|
image_encoder=image_encoder,
|
|
feature_extractor=feature_extractor,
|
|
)
|
|
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
|
|
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)
|
|
|
|
self.default_sample_size = (
|
|
self.unet.config.sample_size
|
|
if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
|
|
else 128
|
|
)
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt with num_images_per_prompt->num_videos_per_prompt
|
|
def encode_prompt(
|
|
self,
|
|
prompt: str,
|
|
prompt_2: Optional[str] = None,
|
|
device: Optional[torch.device] = None,
|
|
num_videos_per_prompt: int = 1,
|
|
do_classifier_free_guidance: bool = True,
|
|
negative_prompt: Optional[str] = None,
|
|
negative_prompt_2: Optional[str] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
pooled_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_pooled_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
|
|
prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
|
used in both text-encoders
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_videos_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`).
|
|
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
|
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
|
|
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.
|
|
pooled_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
|
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
|
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, pooled 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.
|
|
"""
|
|
device = device or self._execution_device
|
|
|
|
# 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, StableDiffusionXLLoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
# dynamically adjust the LoRA scale
|
|
if self.text_encoder is not None:
|
|
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 self.text_encoder_2 is not None:
|
|
if not USE_PEFT_BACKEND:
|
|
adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
|
|
else:
|
|
scale_lora_layers(self.text_encoder_2, lora_scale)
|
|
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
|
|
|
if prompt is not None:
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
# Define tokenizers and text encoders
|
|
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
|
|
text_encoders = (
|
|
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
|
|
)
|
|
|
|
if prompt_embeds is None:
|
|
prompt_2 = prompt_2 or prompt
|
|
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
|
|
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
prompt_embeds_list = []
|
|
prompts = [prompt, prompt_2]
|
|
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, tokenizer)
|
|
|
|
text_inputs = tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, 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" {tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
|
|
|
|
# We are only ALWAYS interested in the pooled output of the final text encoder
|
|
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
|
|
pooled_prompt_embeds = prompt_embeds[0]
|
|
|
|
if clip_skip is None:
|
|
prompt_embeds = prompt_embeds.hidden_states[-2]
|
|
else:
|
|
# "2" because SDXL always indexes from the penultimate layer.
|
|
prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
|
|
|
|
prompt_embeds_list.append(prompt_embeds)
|
|
|
|
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
|
|
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
|
|
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
|
|
elif do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
negative_prompt = negative_prompt or ""
|
|
negative_prompt_2 = negative_prompt_2 or negative_prompt
|
|
|
|
# normalize str to list
|
|
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
|
|
negative_prompt_2 = (
|
|
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
|
|
)
|
|
|
|
uncond_tokens: List[str]
|
|
if 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 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, negative_prompt_2]
|
|
|
|
negative_prompt_embeds_list = []
|
|
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = tokenizer(
|
|
negative_prompt,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
negative_prompt_embeds = text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
output_hidden_states=True,
|
|
)
|
|
|
|
# We are only ALWAYS interested in the pooled output of the final text encoder
|
|
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
|
|
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
|
|
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
|
|
|
|
negative_prompt_embeds_list.append(negative_prompt_embeds)
|
|
|
|
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
|
|
|
|
if self.text_encoder_2 is not None:
|
|
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
|
else:
|
|
prompt_embeds = prompt_embeds.to(dtype=self.unet.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_videos_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1)
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
if self.text_encoder_2 is not None:
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
|
else:
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_videos_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
|
|
|
|
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_videos_per_prompt).view(
|
|
bs_embed * num_videos_per_prompt, -1
|
|
)
|
|
if do_classifier_free_guidance:
|
|
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_videos_per_prompt).view(
|
|
bs_embed * num_videos_per_prompt, -1
|
|
)
|
|
|
|
if self.text_encoder is not None:
|
|
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
if self.text_encoder_2 is not None:
|
|
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder_2, lora_scale)
|
|
|
|
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_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
|
|
|
|
# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
|
|
def decode_latents(self, latents):
|
|
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)
|
|
|
|
image = self.vae.decode(latents).sample
|
|
video = image[None, :].reshape((batch_size, num_frames, -1) + image.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,
|
|
prompt_2,
|
|
height,
|
|
width,
|
|
negative_prompt=None,
|
|
negative_prompt_2=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
pooled_prompt_embeds=None,
|
|
negative_pooled_prompt_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
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_2 is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt_2`: {prompt_2} 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) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
|
|
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
|
|
|
|
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."
|
|
)
|
|
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} 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 prompt_embeds is not None and pooled_prompt_embeds is None:
|
|
raise ValueError(
|
|
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
|
|
)
|
|
|
|
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
|
|
raise ValueError(
|
|
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
|
|
)
|
|
|
|
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
|
|
def prepare_latents(
|
|
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
|
|
):
|
|
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:
|
|
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_add_time_ids(
|
|
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
|
|
):
|
|
add_time_ids = list(original_size + crops_coords_top_left + target_size)
|
|
|
|
passed_add_embed_dim = (
|
|
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
|
|
)
|
|
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}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
|
|
)
|
|
|
|
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
|
|
return add_time_ids
|
|
|
|
def upcast_vae(self):
|
|
dtype = self.vae.dtype
|
|
self.vae.to(dtype=torch.float32)
|
|
use_torch_2_0_or_xformers = isinstance(
|
|
self.vae.decoder.mid_block.attentions[0].processor,
|
|
(
|
|
AttnProcessor2_0,
|
|
XFormersAttnProcessor,
|
|
FusedAttnProcessor2_0,
|
|
),
|
|
)
|
|
# if xformers or torch_2_0 is used attention block does not need
|
|
# to be in float32 which can save lots of memory
|
|
if use_torch_2_0_or_xformers:
|
|
self.vae.post_quant_conv.to(dtype)
|
|
self.vae.decoder.conv_in.to(dtype)
|
|
self.vae.decoder.mid_block.to(dtype)
|
|
|
|
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
|
|
def get_guidance_scale_embedding(
|
|
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
|
|
) -> torch.Tensor:
|
|
"""
|
|
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
|
|
|
|
Args:
|
|
w (`torch.Tensor`):
|
|
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
|
|
embedding_dim (`int`, *optional*, defaults to 512):
|
|
Dimension of the embeddings to generate.
|
|
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
|
|
Data type of the generated embeddings.
|
|
|
|
Returns:
|
|
`torch.Tensor`: Embedding vectors with shape `(len(w), 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
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def guidance_rescale(self):
|
|
return self._guidance_rescale
|
|
|
|
@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 and self.unet.config.time_cond_proj_dim is None
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def denoising_end(self):
|
|
return self._denoising_end
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
prompt_2: Optional[Union[str, List[str]]] = None,
|
|
num_frames: int = 16,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
timesteps: List[int] = None,
|
|
sigmas: List[float] = None,
|
|
denoising_end: Optional[float] = None,
|
|
guidance_scale: float = 5.0,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
negative_prompt_2: 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,
|
|
pooled_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_pooled_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,
|
|
guidance_rescale: float = 0.0,
|
|
original_size: Optional[Tuple[int, int]] = None,
|
|
crops_coords_top_left: Tuple[int, int] = (0, 0),
|
|
target_size: Optional[Tuple[int, int]] = None,
|
|
negative_original_size: Optional[Tuple[int, int]] = None,
|
|
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
|
|
negative_target_size: Optional[Tuple[int, int]] = 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"],
|
|
):
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
|
used in both text-encoders
|
|
num_frames:
|
|
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
|
|
amounts to 2 seconds of video.
|
|
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
|
The height in pixels of the generated video. This is set to 1024 by default for the best results.
|
|
Anything below 512 pixels won't work well for
|
|
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
|
|
and checkpoints that are not specifically fine-tuned on low resolutions.
|
|
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
|
The width in pixels of the generated video. This is set to 1024 by default for the best results.
|
|
Anything below 512 pixels won't work well for
|
|
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
|
|
and checkpoints that are not specifically fine-tuned on low resolutions.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality video 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.
|
|
denoising_end (`float`, *optional*):
|
|
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
|
|
completed before it is intentionally prematurely terminated. As a result, the returned sample will
|
|
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
|
|
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
|
|
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
|
|
Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
|
|
guidance_scale (`float`, *optional*, defaults to 5.0):
|
|
Guidance scale as defined in [Classifier-Free Diffusion
|
|
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
|
|
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
|
|
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
|
|
the text `prompt`, usually at the expense of lower video quality.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the video 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`).
|
|
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the video generation to be sent to `tokenizer_2` and
|
|
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of videos to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
|
|
applies to [`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
One or a list of [torch generator(s)](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 will ge generated by sampling using the supplied random `generator`.
|
|
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.
|
|
pooled_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
|
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
|
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, pooled negative_prompt_embeds will be generated from `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. 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
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion_xl.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 under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
|
|
[Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
|
|
using zero terminal SNR.
|
|
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
|
|
`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
|
|
explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
|
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
|
|
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
|
|
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
For most cases, `target_size` should be set to the desired height and width of the generated image. If
|
|
not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
|
|
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
|
|
micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
|
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
|
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
|
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
|
|
micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
|
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
|
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
To negatively condition the generation process based on a target image resolution. It should be as same
|
|
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
|
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
|
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.
|
|
|
|
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.default_sample_size * self.vae_scale_factor
|
|
width = width or self.default_sample_size * self.vae_scale_factor
|
|
|
|
num_videos_per_prompt = 1
|
|
|
|
original_size = original_size or (height, width)
|
|
target_size = target_size or (height, width)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
prompt_2,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
negative_prompt_2,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
self._denoising_end = denoising_end
|
|
self._interrupt = False
|
|
|
|
# 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
|
|
|
|
# 3. Encode input prompt
|
|
lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
prompt_2=prompt_2,
|
|
device=device,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
negative_prompt_2=negative_prompt_2,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
lora_scale=lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
|
|
# 4. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, sigmas
|
|
)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 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, eta)
|
|
|
|
# 7. Prepare added time ids & embeddings
|
|
add_text_embeds = pooled_prompt_embeds
|
|
if self.text_encoder_2 is None:
|
|
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
|
|
else:
|
|
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
|
|
|
|
add_time_ids = self._get_add_time_ids(
|
|
original_size,
|
|
crops_coords_top_left,
|
|
target_size,
|
|
dtype=prompt_embeds.dtype,
|
|
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
)
|
|
if negative_original_size is not None and negative_target_size is not None:
|
|
negative_add_time_ids = self._get_add_time_ids(
|
|
negative_original_size,
|
|
negative_crops_coords_top_left,
|
|
negative_target_size,
|
|
dtype=prompt_embeds.dtype,
|
|
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
)
|
|
else:
|
|
negative_add_time_ids = add_time_ids
|
|
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
|
|
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
|
|
|
|
prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
|
|
|
|
prompt_embeds = prompt_embeds.to(device)
|
|
add_text_embeds = add_text_embeds.to(device)
|
|
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_videos_per_prompt, 1)
|
|
|
|
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,
|
|
)
|
|
|
|
# 7.1 Apply denoising_end
|
|
if (
|
|
self.denoising_end is not None
|
|
and isinstance(self.denoising_end, float)
|
|
and self.denoising_end > 0
|
|
and self.denoising_end < 1
|
|
):
|
|
discrete_timestep_cutoff = int(
|
|
round(
|
|
self.scheduler.config.num_train_timesteps
|
|
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
|
|
)
|
|
)
|
|
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
|
|
timesteps = timesteps[:num_inference_steps]
|
|
|
|
# 8. Optionally get Guidance Scale Embedding
|
|
timestep_cond = None
|
|
if self.unet.config.time_cond_proj_dim is not None:
|
|
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_videos_per_prompt)
|
|
timestep_cond = self.get_guidance_scale_embedding(
|
|
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
|
|
).to(device=device, dtype=latents.dtype)
|
|
|
|
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
|
|
)
|
|
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 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
|
|
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds:
|
|
added_cond_kwargs["image_embeds"] = image_embeds
|
|
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep_cond=timestep_cond,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://huggingface.co/papers/2305.08891
|
|
noise_pred = rescale_noise_cfg(
|
|
noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale
|
|
)
|
|
|
|
# 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)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
|
|
negative_pooled_prompt_embeds = callback_outputs.pop(
|
|
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
|
|
)
|
|
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
|
|
negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
|
|
|
|
progress_bar.update()
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
# make sure the VAE is in float32 mode, as it overflows in float16
|
|
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
|
|
|
|
if needs_upcasting:
|
|
self.upcast_vae()
|
|
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
|
|
|
# 10. Post processing
|
|
if output_type == "latent":
|
|
video = latents
|
|
else:
|
|
video_tensor = self.decode_latents(latents)
|
|
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
|
|
|
|
# cast back to fp16 if needed
|
|
if needs_upcasting:
|
|
self.vae.to(dtype=torch.float16)
|
|
|
|
# 11. Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return AnimateDiffPipelineOutput(frames=video)
|